JCS Volume 15 Issue 3

Volume 15 Issue 3

Journal for Clinical Studies PEER REVIEWED

AI for Medical Writers Friend or Foe?

Automation in Clinical Trials:

Increasing Sophistication Requires Strategies for Adoption

Advancing NIPT Workflows:

Using Size Selection to Enrich Fetal Fraction, Enabling Extended Blood Storage in EDTA tubes

Unlock Clinical Research Site Potential

by Addressing Resource Challenges and Trial Complexity

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Contents 4

FOREWORD

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Journal for Clinical Studies MANAGING DIRECTOR Mark A. Barker BUSINESS DEVELOPMENT info@senglobalcoms.com EDITORIAL MANAGER Beatriz Romao beatriz@senglobalcoms.com DESIGNER Jana Sukenikova www.fanahshapeless.com RESEARCH & CIRCULATION MANAGER Jessica Chapman jessica@senglobalcoms.com ADMINISTRATOR Barbara Lasco barbara@senglobalcoms.com FRONT COVER istockphoto PUBLISHED BY Senglobal Ltd. Unite 5.02, E1 Studios, 7 Whitechapel Road, E1 1DU, United Kingdom Tel: +44 (0) 2045417569 Email: info@senglobalcoms.com www.journalforclinicalstudies.com Journal for Clinical Studies – ISSN 1758-5678 is published quarterly by Senglobal Ltd.

The opinions and views expressed by the authors in this journal are not necessarily those of the Editor or the Publisher. Please note that although care is taken in the preparation of this publication, the Editor and the Publisher are not responsible for opinions, views, and inaccuracies in the articles. Great care is taken concerning artwork supplied, but the Publisher cannot be held responsible for any loss or damage incurred.

This publication is protected by copyright.

Volume 15 Issue 3 Autumn 2023 Senglobal Ltd.

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Automation in Clinical Trials: Increasing Sophistication Requires Strategies for Adoption

As clinical trials have become more complex with more moving parts, so too have the requirements on technology and specifically on automation. Automation comes in various forms; new capabilities, integrations, and Robotic Process Automation (RPA) are examples.

These technologies provide a consistent, deterministic means of automating repetitive and predictable tasks. Ronan Fox at ICON underlines how putting quality at the forefront, combined with risk and stakeholder management, ensures that we deliver fit-for-use business enabling automation in a repeatable and consistent way.

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Addressing Missing Data in Clinical Trials – The Data Science approach.

Digital Health Technologies (DHTs) have revolutionised clinical trial data collection, while also promising to make research more efficient and more patient centric. However, shifting the power to input data from clinicians to participants increases the risk of missed datapoints.

This can compromise the ability to draw inferences or lead to incomplete submissions, threatening the success of otherwise highly promising clinical trials. Pamela Adede at Phastar explains how to address missing data in clinical trials.

10 FDA’s New Draft Guidance for Psychedelic Drugs Opens Doors for Drug Developers in the US In July 2023, Australia became the first country to permit psychiatrists to prescribe 3,4-methyl​ enedioxy​ methamphetamine (MDMA) and psilocybin for use in psychedelic-a ssisted psychotherapy to treat certain mental health conditions (MDMA for post-traumatic stress disorder [PTSD] and psilocybin for treatment-resistant depression [TRD]). Jaime Gavazzi at Clarivate discusses FDA’s new draft guidance for psychedelic drugs.

REGULATORY 12 EU CTR Pushes Sponsors and CROs to Clean Up Their Act The Clinical Trials Regulation represents a milestone on the journey to a more competitive European R&D environment, particularly for multinational studies. Improved trial transparency – and EMA has recently opened a public consultation in this area – will make it easier for patients to participate in research. A harmonised approach to clinical trial applications across Europe should lead to faster approvals. Stephan Ohnmacht at R&D Business Consulting and Werner Engelbrecht at Veeva Vault Clinical Operations discuss how The European Medicines Agency’s plan to harmonise all clinical trial information requires a significant change in how companies collect and store trial data and records.

14 Focus on End-to-end Innovation and Efficiency Drives Life Sciences Service Provider Consolidation The world has changed in Life Sciences and innovation and efficiency have become key to survival. Pharma manufacturers and marketing authorisation holders in the Life Sciences sector are under relentless pressure to adapt to changing strategic priorities against a backdrop of globalisation and increasing regulatory complexity across the whole value chain. Xavier Duburcq, Chairman & CEO of ProductLife Journal for Clinical Studies 1

Contents Group, and Denis Gross, CSO, highlight the impact this is having on regulatory service companies as they, too, race to reinvent themselves to help their pharma and medtech clients navigate increasing regulatory complexity.

LOGISTICS AND SUPPLY CHAIN

RESEARCH & DEVELOPMENT

As clinical trials become more complex in design, seek to include varying populations and reach patients around the world, having a robust end-to-end supply chain solution is vital to ensure investigational drugs are readily available at clinical sites when needed. To help build a robust clinical supply chain that supports global trials effectively and efficiently, PCI Pharm Services outlines the key areas to consider.

16 Advancing NIPT Workflows: Using Size Selection to Enrich Fetal Fraction, Enabling Extended Blood Storage in EDTA Tubes Prenatal screening for Down’s Syndrome and other chromosomal imbalances by the analysis of a blood sample has been available since the 1980s. The original methods utilised maternal pregnancy markers, such as alpha-fetoprotein (AFP) and human chorionic gonadotropin (hCG), which lacked specificity (too many false positives) and sensitivity (too many false negatives). In this article, Thomas Lyons, Dr. Rachel Shelmerdine, Rebecca Underwood and Dr. Joanne Mason at Yourgene Health explain how to make NIPT more accessible whilst ensuring patients receive highly accurate prenatal screening results.

34 Building Robust Clinical Supply Chains to Support Global Trials

20 Unlock Clinical Research Site Potential by Addressing Resource Challenges and Trial Complexity Staffing and trial enrolment remain the most pressing issues research sites face, according to the 2023 WCG Clinical Research Site Challenges survey. As trial complexity increases and sites continue to struggle with resourcing, sites are having to do more with less. Despite these challenges, sites are finding innovative ways to move forward with help from sponsors, clinical research organisations (CROs), and other partners. All of this has the potential to reshape the future of clinical research. Dawn Sauro at WCG outlines how to unblock Clinical Research Site Potential by Addressing Resource Challenges and Trial Complexity.

TECHNOLOGY 24 AI for Medical Writers – Friend or Foe? Artificial Intelligence (AI) is beginning to affect almost every industry, and medical writing is no different. But how does this relate to our industry? How will AI affect medical writers? What’s already available and what is in the pipeline? Should medical writers be happy and embrace the technology, or should we resist as much as we can, assuming that we will all be replaced by machines? This article Jamie Norman at TriloDocs Ltd., and Lisa Chamberlain James at Trilogy Writing & Consulting Ltd discuss the current state of the art of AI in medical writing and asks the question: AI for medical writers – friend or foe?

THERAPEUTICS 28 Using QSP Modeling to Advance Knowledge and Therapeutics for Alzheimer’s and Parkinson’s Disease This article explores the use of quantitative systems pharmacology (QSP) modeling to provide further insights into the mechanism of action of neurodegenerative diseases and the likelihood of success of new drugs in development. This unique approach allows for the prediction of not only biomarkers, but more importantly, clinical outcomes. Piet van der Graaf and Hugo Geerts at Certara elaborate on the prospective prediction of lecanemab’s Phase 3 CLARITY AD clinical trial using Certara’s QSP Alzheimer’s disease platform. This prediction was based on the use of a so called “virtual biomarker” and that concept can be applied to other neurodegenerative and rare diseases.

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Foreword Artificial intelligence (AI) is a branch of computer science that allows machines to work efficiently, can analyse complex data.

The research focused on AI has increased tremendously, and its role in healthcare service and research is emerging at a greater pace.

Artificial intelligence is now affecting every area of our lives. From the self-driving cars that increasingly populate our roads, to the virtual assistants that live in our phones (and have spelt the end of anyone naming their child Siri or Alexa ever again). Nobody can question that, so far, AI has yielded a positive and far less apocalyptic effect on humanity than the killerrobots of Hollywood initially had us believe.

In this journal, we will also explore more about prenatal screening for Down’s Syndrome and other chromosomal imbalances by the analysis of a blood sample has been available since the 1980s.

The original methods utilised maternal pregnancy markers, such as alpha-fetoprotein (AFP) and human chorionic gonadotropin (hCG), which lacked specificity (too many false positives) and sensitivity (too many false negatives). In this article, Thomas Lyons, Dr. Rachel Shelmerdine, Rebecca Underwood and Dr. Joanne Mason at Yourgene Health explain how to make NIPT more accessible whilst ensuring patients receive highly accurate prenatal screening results.

I would like to thank all our authors and contributors for making this issue an exciting one. We are working relentlessly to bring you the most exciting and relevant topics through our journals.

One industry looking to capitalise on the benefits of AI is pharmaceuticals, where its uses and possible implementation almost seem unlimited. Healthcare in general has already unearthed a cavernous depth of application for AI within the sector, from CT scans that can be read by deep learning algorithms, to natural language programming that can analyse vast quantities of unstructured data in electronic health records for quicker patientdiagnosis.

Beatriz Romao, Editorial Manager, Journal for Clinical Studies

Artificial Intelligence (AI) is beginning to affect almost every industry, and medical writing is no different. But how does this relate to our industry? How will AI affect medical writers? What’s already available and what is in the pipeline? Should medical writers be happy and embrace the technology, or should we resist as much as we can, assuming that we will all be replaced by machines? This article Jamie Norman at TriloDocs Ltd., and Lisa Chamberlain James at Trilogy Writing & Consulting Ltd discuss the current state of the art of AI in medical writing and asks the question: AI for medical writers – friend or foe?

Digital Health Technologies (DHTs) have revolutionised clinical trial data collection, while also promising to make research more efficient and more patient centric. However, shifting the power to input data from clinicians to participants increases the risk of missed datapoints. This can compromise the ability to draw inferences or lead to incomplete submissions, threatening the success of otherwise highly promising clinical trials. Pamela Adede at Phastar explains how to address missing data in clinical trials.

JCS – Editorial Advisory Board

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Hermann Schulz, MD, Founder, PresseKontext

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Ashok K. Ghone, PhD, VP, Global Services MakroCare, USA

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Jeffrey W. Sherman, Chief Medical Officer and Senior Vice President, IDM Pharma.

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Bakhyt Sarymsakova – Head of Department of International Cooperation, National Research Center of MCH, Astana, Kazakhstan

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Jim James DeSantihas, Chief Executive Officer, PharmaVigilant

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Catherine Lund, Vice Chairman, OnQ Consulting

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Mark Goldberg, Chief Operating Officer, PAREXEL International Corporation

Cellia K. Habita, President & CEO, Arianne Corporation

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Maha Al-Farhan, Chair of the GCC Chapter of the ACRP

Chris Tait, Life Science Account Manager, CHUBB Insurance Company of Europe

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Deborah A. Komlos, Principal Content Editor, Clarivate

Rick Turner, Senior Scientific Director, Quintiles Cardiac Safety Services & Affiliate Clinical Associate Professor, University of Florida College of Pharmacy

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Elizabeth Moench, President and CEO of Bioclinica – Patient Recruitment & Retention

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Robert Reekie, Snr. Executive Vice President Operations, Europe, AsiaPacific at PharmaNet Development Group

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Francis Crawley, Executive Director of the Good Clinical Practice Alliance – Europe (GCPA) and a World Health Organization (WHO) Expert in ethics

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Stanley Tam, General Manager, Eurofins MEDINET (Singapore, Shanghai)

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Stefan Astrom, Founder and CEO of Astrom Research International HB

Georg Mathis, Founder and Managing Director, Appletree AG

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Steve Heath, Head of EMEA – Medidata Solutions, Inc

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4 Journal for Clinical Studies

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Automation in Clinical Trials

Increasing Sophistication Requires Strategies for Adoption As clinical trials have become more complex with more moving parts, so too have the requirements on technology and specifically on automation. Automation comes in various forms; new capabilities, integrations, and Robotic Process Automation (RPA) are examples. These technologies provide a consistent, deterministic means of automating repetitive and predictable tasks. As automation needs become more complex, the sophistication required of automation has also increased. Artificial Intelligence (AI) and Machine Learning (ML) have aided in managing this complexity through “learning while automating”.

RPA enables us to quickly automate processes to the extent that the initial automation can be iterated upon, continually improving and extending the positive impact. For example, at ICON we have enabled automation using RPA across a wide range of disciplines: finance, Trial Master File loading, locking of clinical databases, and managing help desk tickets, to name a few. These processes are both clinical and administrative in nature, enabling consistency, timeliness and quality to remain as the focus.

re-validation may need to be executed based on updates to inputs, processes/algorithms and intended use.

What can individual organisations do to ensure they are compliant with regulation?

1. a. b.

2.

Embed a culture of quality into organisations where everyone is responsible for quality in application lifecycle management by asking questions at the conception stage: Can the requirements be specified in such a way as to be directly measurable and testable?

Where is the data and how robust is the integration plan and design to allow for high quality including performance?

Expand the use of automated test tools to cover more of the application ecosystem. This enables quality to be continually verified and baked into the core of the application/solution.

This will give a baseline for the software and the ability to continuously monitor the performance of the software in realworld circumstances.

A key challenge to the adoption of automation in clinical trials is the preservation of compliance with regulatory requirements, particularly as RPA can be continually updated as business needs change and new requirements are discovered.

Some key components of the approach described by the FDA in their Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning document, published in 2018, are: 1.

2.

3.

4.

The establishment of a quality system and best practice for development.

Demonstration of safety and effectiveness through a pre-market review so that patient risk can be managed throughout the lifecycle.

Ongoing monitoring of the device to incorporate risk management in the development, validation, and execution of any changes, managed through tightly controlled change management practices.

Use of post market real-world performance reporting to maintain the continued assurance of safety and effectiveness.

The discussion document from the FDA focuses on Software as a Medical Device. The same concepts could be applied to a validated process for the implementation of sophisticated RPA automation.

Given the speed of development associated with RPA, how can it be controlled, and the evidence gathered in such a way to prove the efficacy/benefit associated with the specific automation?

Similar decision points could be used to determine what level of 6 Journal for Clinical Studies

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3.

4.

Use a standard method of specifying requirements and their regulatory impact to identify and then focus on the critical features of applications that may present the highest risk to the patient. This will enable a risk managed approach.

Use technology to curate and manage product and data lifecycles. Utilising a combination of fully integrated Application Lifecycle Management tools and individual technology tools to help manage agile processes within our regulated environment has enabled rapid compliance within delivered solutions.

Central to the approach in utilising automation technologies such as RPA, AI and ML is identifying when and where we can get the most benefit and ensuring those technologies are fit for the business intended use. Successful adoption of automation relies on business stakeholders’ buy-in and awareness of the associated benefits and limitations. To gain the maximum impact from any automation requires that business stakeholders are driven by the need to automate and enabled to make decisions quickly for optimal delivery.

Much of this organisational/management effort builds trust, both in the underlying technology and the capability to deliver to a high level of quality. Quality is about much more than bugs or errors – it is a measure of how well a solution matches the need of the business, which incorporates fitness to requirements, robustness, security, performance, sustainability, compliance, and of course, errors. www.journalforclinicalstudies.com

Engaging with key stakeholders early and often ensures that risk is minimised, identified and governed in an appropriate way, and in the context of the patient associated with the use of the AI/ML augmented automation.

Putting quality at the forefront, combined with risk and stakeholder management, ensures that we deliver fit-for-use business enabling automation in a repeatable and consistent way.

Ronan Fox Ronan Fox is Senior Vice President of IT R&D at ICON. Ronan's role encompasses the complete software product life cycle – product management, design, test, validation and delivery. He directs and defines product roadmaps and agile approaches to maximise efficiency and effectiveness. Ronan drives intellectual property identification and protection for clinical and financial enterprise systems. To ensure products meet stakeholders' needs, Ronan creates strategies and operating models to ensure connectivity between IT and customers for robust, sustainable and scalable products.

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Addressing Missing Data in Clinical Trials – The Data Science Approach Digital Health Technologies (DHTs) have revolutionized clinical trial data collection, while also promising to make research more efficient and more patient centric. However, shifting the power to input data from clinicians to participants increases the risk of missed datapoints. This can compromise the ability to draw inferences or lead to incomplete submissions, threatening the success of otherwise highly promising clinical trials.

Digital Data Sources and Missed Datapoints Recent years have seen adoption of a wide range of digital health technologies in clinical trials, from wearables and sensors to electronic patient reported outcomes (ePROs) and diaries. The potential benefits of this shift are well documented and include expanded patient accessibility and inclusivity to increased real-world data.

However, there are also potential pitfalls. It moves control of data generation and entry from highly trained staff to clinical trial participants who may not have the same precision focus or pay the same attention to detail as their professional counterparts. This is compounded by the fact that decentralized trials (DCTs), where the frequency of data collection may be hourly or even daily, vastly increase the number of data points being collected. In fact, phase III clinical trials currently generate an average of 3.6 million data points – three times the data collected by late-stage trials 10 years ago.1 The ability to capture data remotely without supervision can all add up to missing data or values within records or time series.

A complete sample size and variability per study protocol, with complete datapoints, is required for efficient analysis. Missed data points can lead to diminished statistical power and affect analysis,

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thus negatively impacting a sponsor’s ability to demonstrate product efficacy. If participants with missing values are omitted from analysis, misleading results might be obtained regarding the effect of treatment, unreliable P values may be obtained, and assessments of the importance of prognostic factors may be inaccurate.2 It follows, then, that it is of utmost importance to have complete data as much as possible.

Power of Data Science Data science, which combines the power of statistics, advanced analytics, and artificial intelligence (AI) techniques such as machine learning (ML) to uncover actionable insights in large datasets, is at the forefront of many innovations in clinical research.

It is being used in the collection, management, and analysis of clinical data, automating the processes and reducing error rates. This is important because securing the overall quality of clinical data is paramount to ensuring quality care and appropriate decision-making in the medical and healthcare fields. Importantly, it also offers possible solutions to the missing data problem.

Risk-based Monitoring through Real-time Metrics The first option is risk-based monitoring, a form of centralized monitoring in which sponsors can review the study-wide data in near real time as it accumulates, allowing early identification of predefined risks. Data science tools build visualizations and provide an oversight of the trial data. These data driven insights optimize subject and site monitoring by enabling early notice of trends such as missing data, outliers, and unusual behaviors. Monitors are then able to quickly spot missing datapoints and untimely data entry and therefore take timely action.

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The approach also allows data scientists to compare the data being collected by individual sites, as it accumulates, to identify outliers.

With each site following the same protocol, all datasets should be roughly equivalent. As such, those logging fewer changes than their counterparts could be at risk of missing datapoints. Using analytics and visualization, researchers can quickly spot such outliers then use additional visualizations, such as form-by-form comparisons, to investigate the root of the anomaly. They can then take any necessary corrective action, such as providing additional site training, before the issue is able to impact on data quality.

Other Solutions This is not the only way data science tools are being used to address the missing data issue in clinical trials.

ML algorithms, for example, can recognize patterns from previous trials, and use that information to highlight areas in which missing data may occur in the future. Likewise, predictive analysis can be used to analyze results from historical studies, and forecast potential future issues. It can also lead to the formation of new hypotheses to be validated by trial data. In addition, predictive modelling can be used to identify patients who will respond favorably to treatments, based on their clinical history, thus reducing occurrences of missing data due to patient drop out.

Big data analytics is particularly suited to DHT-driven trials, which generate a high volume, velocity and variety of data. The technology can collect these large amounts of unstructured, real-world data, organize, analyze, and visualize the results, to explore them for unexpected patterns. This can result in more accurate missing data predictions and insights than traditional data management solutions.

Another potential solution to missing data is data augmentation, or artificially increasing the amount of data by generating new data points from the existing data. In simple terms, it extends the data and generates more records, thereby making up for the missing information. However, this approach should be used with caution in clinical research. As it extrapolates from existing data, it therefore runs the risk of inducing bias.

Some approaches are best suited to particular types of missing data. Estimation equations, which replace missing data with averages www.journalforclinicalstudies.com

from across the data set, for example, can be useful when dealing with a minimal number of empty fields.

Conclusion Missing data can undermine the scientific credibility of conclusions and threaten the success of drug development efforts. When it comes to solutions, prevention is better than cure. Along with the usual validation procedures of ensuring accuracy and completeness of data, efforts should be targeted to minimizing missing data during the design, planning, conduct, and analytic stages. The regular monitoring of real-time metrics throughout the duration of the study, is a highly effective preventive measure.

In circumstances where missing data does occur, possible corrective actions include use of estimation equations and data augmentation, though both have their limitations. As data science continues to develop, the industry expects these solutions to evolve, helping researchers to produce the high-quality clinical evidence needed to make reliable clinical trial inferences, and give studies the very best chance of success.

REFERENCES 1.

2.

https://www.globenewswire.com/news-release/2021/01/12/2157143/0/ en/Rising-Protocol-Design-Complexity-Is-Driving-Rapid-Growth-inClinical-Trial-Data-Volume-According-to-Tufts-Center-for-the-Studyof-Drug-Development.html Ibrahim JG, Chu H, Chen MH. Missing data in clinical studies: issues and methods. J Clin Oncol. 2012 Sep 10;30(26):3297-303. doi: 10.1200/JCO.2011.38.7589.

Epub 2012 May 29. PMID: 22649133; PMCID: PMC3948388. https://www.ncbi. nlm.nih.gov/pmc/articles/PMC3948388/#B5

Pamela Adede Data Operations Programmer at Phastar where her core work entails programming databases for data capture, validation, and extraction for clinical trials. Pamela holds a BSc degree in Computer Science from Kabarak University in Kenya.

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FDA’s New Draft Guidance for Psychedelic Drugs Opens Doors for Drug Developers in the US In July 2023, Australia became the first country to permit psychiatrists to prescribe 3,4-methyl​enedioxy​methamphetamine (MDMA) and psilocybin for use in psychedelic-assisted psychotherapy to treat certain mental health conditions (MDMA for post-traumatic stress disorder [PTSD] and psilocybin for treatment-resistant depression [TRD]).1 This change in regulation directly followed an action taken by the US Food and Drug Administration (FDA) that could accelerate similar changes in the US. It also came on the heels of positive published results from a clinical program evaluating MDMA as a treatment for PTSD that is close to seeking regulatory action.

In June 2023, the FDA published the draft guidance for industry, Psychedelic Drugs: Considerations for Clinical Investigations, to provide a resource for investigations of psychedelic drugs to treat specific medical conditions (eg, depression, PTSD, substance use disorders).2 It is the first FDA guidance document created to aid industry in designing clinical trials for these products. In the agency’s announcement3 of the availability of the guidance, Tiffany Farchione, MD, director, Division of Psychiatry, Center for Drug Evaluation and Research, FDA, noted that “sponsors evaluating the therapeutic potential of these drugs should consider their unique characteristics when designing clinical studies” as they are “still investigational products.” The guidance provides developers with information about clinical trial conduct, data collection, safety for trial participants, and requirements for new drug applications (NDAs).

The agency clarified in the guidance that the term “psychedelics” refers to “classic psychedelics,” which are generally understood to be drugs such as psilocybin (a hallucinogenic chemical found in specific mushrooms) and lysergic acid diethylamide (LSD) that influence the brain’s serotonin system, and “entactogens” or “empathogens” (eg, MDMA).

When conducting clinical studies for these agents, specific safety considerations include psychoactive effects (eg, mood and cognitive changes, hallucinations), which create an environment for abuse potential and require safety measures to prevent misuse throughout development. The FDA’s 2017 guidance for industry, Assessment of Abuse Potential of Drugs,4 provides information about how sponsors can assess the abuse potential of their drugs through study investigations of chemistry, pharmacology, pharmacokinetics, animal and human behaviour, abuse-related adverse events (AEs) in human studies, and abuse reports from other sources.

Investigations occurring under an investigational new drug application (IND) for products that the Drug Enforcement Administration (DEA) has classified as schedule I controlled substances under the Controlled Substances Act (CSA) must comply with applicable DEA regulatory requirements, the FDA noted. MDMA and psilocybin are both schedule I substances.

Potential Groundbreaking Regulatory Action At the forefront of the psychedelic-drugs-as-therapeutics movement in the US is the Multidisciplinary Association for Psychedelic 10 Journal for Clinical Studies

Studies (MAPS), which has been evaluating psychedelic-assisted psychotherapy since 1992. In 1985, the DEA criminalised the use and possession of MDMA; in response, MAPS was founded in 1986 to facilitate research and education about this treatment option.

Several MAPS studies have evaluated the safety and effectiveness of MDMA-assisted psychotherapy for the treatment of PTSD, and the FDA granted MAPS breakthrough therapy designation to MDMA for this indication in 2017. Recent reports from MAPS suggest that MDMA could face a regulatory decision as early as 2024 after a planned submission of an NDA later in 2023.5 The most recent published data6 supporting the safety and effectiveness of MAPS’s MDMA-assisted psychotherapy to treat PTSD comes from a second, confirmatory phase III study completed in November 2022. The randomised, double-blind, placebo-controlled, multisite study (MAPP2) evaluated manualised MDMA-assisted psychotherapy (MDMA-AT) for the treatment of PTSD of moderate or greater severity in >100 participants aged ≥18 years. The primary endpoint was change from baseline in Clinician-Administered PTSD Scale for DSM 5 (CAPS-5), and the key secondary endpoint was change from baseline in Sheehan Disability Scale (adapted SDS) total score. Participants were randomised to receive a flexible dose of MDMA 80 or 120 mg or placebo, followed by a supplemental halfdose of 40 or 60 mg MDMA or placebo with manualised MDMAassisted psychotherapy in 3 monthly experimental sessions.

In September 2023, MAPS announced publication of the MAPP2 study results.7 The least squares (LS) mean change in CAPS-5 score (95% confidence interval [CI]) was -23.7 (-26.94, -20.44) for MDMA-AT versus -14.8 (-18.28, -11.28) for placebo with therapy (p-value = <0.001), demonstrating statistically significant improvement in PTSD after 3 sessions. The LS mean change in SDS score (95% CI) was 3.3 (-4.03, -2.60) for MDMA-AT versus -2.1 (-2.89, -1.33) for placebo with therapy (p-value = 0.03). In the MDMA-AT group, five participants had a severe treatment-emergent adverse event (TEAE) compared to two in the placebo group. There were no deaths or serious TEAEs in the study.

Overall, approximately 1,700 participants have received MDMA in the clinical program with only one serious adverse reaction. MAPS is also enrolling 400 participants by invitation for a long-term study (MPLONG) assessing the safety and effectiveness of MDMA-AT for PTSD, which began in March 2021 and is projected to complete in September 2024.

Additional Psychedelic Agents in Clinical Development As of February 2023, COMP-360, a synthesised formulation of psilocybin developed by COMPASS Pathways plc that received breakthrough therapy designation by the FDA in 2018, has advanced to phase 3 as a candidate for TRD. A multicenter, randomized, double-blind, controlled study is recruiting >560 participants to investigate the efficacy, safety, and tolerability of two administrations of COMP360 administered with psychological support in adults with TRD. Participants are randomized 2:1:1 to receive COMP360 25 mg, 10 mg, or 1 mg. The primary outcome measure is the change from baseline in Montgomery-Åsberg Depression Rating Scale (MADRS) total score at week 6. The study is estimated to complete in May 2025.

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Usona Institute (Usona) is planning8 a new study to evaluate the safety and efficacy of PSIL201, a chemically synthesized form of psilocybin, to treat major depressive disorder (MDD) after promising results from its randomised, double-blind, placebo-controlled phase 2 study evaluating a single oral dose of PSIL201, which was conducted in collaboration with The Emmes Company, LLC. Participants were randomised 1:1 to receive PSIL201 25 mg or oral niacin 100 mg, which served as an active placebo. In August 2023, Usona announced9 the publication of results from the study, which showed that treatment with PSIL201 resulted in a mean difference of 12.3 (95% CI: -17.5, -7.2; p-value <0.001) from baseline to day 43. Compared with niacin, treatment with PSIL201 resulted in a mean difference of -2.31 (95% CI: 3.50, 1.11; p-value <0.001) in SDS scores from baseline to day 43.

No serious TEAEs were reported, although participants who received PSIL201 had a higher rate of overall AEs and severe AEs. The First Step While US regulatory complications seem inevitable, the potential approval of a psychedelic in the next year presents an opportunity for much more research in this space and provides a novel approach to treating people with mental health conditions. The FDA will not regulate the psychotherapy element of the treatment; however, it will be possible to note the requirement in the labelling. The approval of a psychedelic as a medical treatment would signal the possibility of overcoming decades of legislative hurdles and the ever-present war on drugs to meet patients’ needs, especially amid an ongoing acceptance of the medical and recreational use of cannabis in many US states – arguably the first battle the medical community fought to destigmatize scheduled substances.

REFERENCES 1.

2.

Update on MDMA and psilocybin access and safeguards from 1 July 2023.

Australian Government Website. https://www.tga.gov.au/news/news/ update-mdma-and-psilocybin-access-and-safeguards-1-july-2023 Psychedelic Drugs: Considerations for Clinical Investigations Guidance for Industry. Food and Drug Administration. https://www.fda.gov/ media/169694/download

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3.

4.

5.

6.

7.

8.

9.

FDA Issues First Draft Guidance on Clinical Trials with Psychedelic Drugs.

Food and Drug Administration Website. https://www.fda.gov/newsevents/press-announcements/fda-issues-first-draft-guidance-clinicaltrials-psychedelic-drugs Assessment of Abuse Potential of Drugs Guidance for Industry. Food and Drug Administration https://www.fda.gov/media/116739/download MAPS PBC Publishes Results of Successful Confirmatory Phase 3 Trial of MDMA-Assisted Therapy for PTSD. Multidisciplinary Association for Psychedelic Studies Website. https://maps.org/2023/09/13/maps-pbcpublishes-results-of-successful-confirmatory-phase-3-trial-of-mdmaassisted-therapy-for-ptsd/ Mitchell J M, Ot’alora G M, van der Kolk B, et al. MDMA-assisted therapy for moderate to severe PTSD: a randomized, placebo-controlled phase 3 trial. Nat Med. 2023. https://doi.org/10.1038/s41591-02302565-4 MAPS PBC Publishes Results of Successful Confirmatory Phase 3 Trial of MDMA-Assisted Therapy for PTSD. Multidisciplinary Association for Psychedelic Studies Website. https://maps.org/2023/09/13/maps-pbcpublishes-results-of-successful-confirmatory-phase-3-trial-of-mdmaassisted-therapy-for-ptsd/ Usona statement on JAMA publication of PSIL201 data. Usona Institute Website. https://www.usonainstitute.org/updates/usona-statement-onjama-publication-of-psil201-data Ibid.

Jaime Gavazzi Jaime Gavazzi is a Principal Content Editor for the Cortellis suite of life science intelligence solutions at Clarivate. Her previous roles include writing and editing for books, online magazines, educational coursework, government proposals, and government regulatory publications.

Her primary assignments at Clarivate include reporting on FDA drug/device advisory committee meetings and drug approvals.

Email: jaime.polychrones@clarivate.com

Journal for Clinical Studies 11

Regulatory

EU CTR Pushes Sponsors and CROs to Clean Up Their Act The European Medicines Agency’s plan to harmonise all clinical trial information requires a significant change in how companies collect and store trial data and records.

On 31 January 2022, European Union pharmaceutical legislation known as the Clinical Trials Regulation1 entered into application – hoping to harmonise the processes for assessment and supervision of clinical trials throughout the EU. The regulation aims to make it more efficient to carry out multinational trials by enabling sponsors to submit one application via a single online platform – known as the Clinical Trials Information System (CTIS) – that would grant approval to run a clinical trial across several European countries. The regulation also intends to make it more efficient for EU Member States to evaluate and authorise such applications together, via the Clinical Trials Information System.

Prior to the new set of regulations, clinical trial sponsors had to submit clinical trial applications separately to national competent authorities and ethics committees in each country to gain regulatory approval to run a clinical trial.

The Clinical Trials Regulation1 represents a milestone on the journey to a more competitive European R&D environment, particularly for multinational studies. Improved trial transparency – and EMA has recently opened a public consultation in this area – will make it easier for patients to participate in research. A harmonised approach to clinical trial applications across Europe should lead to faster approvals.

However, despite being given a year transition period to adapt to the incoming regulations, companies have been facing challenges in several areas since EU CTR became mandatory for all new starting trials in January earlier this year. Sponsors and contract research organisations (CROs) find it difficult to coordinate submissions crossfunctionally and meet tight deadlines, partly due to fragmented and time-consuming data collection. Redactions are another sticking point.

Disclosures must be fully integrated within the standard clinical trial process yet occur more frequently across the trial lifecycle than before.

Some issues are outside companies’ control and will only ease once the Clinical Trials Information System (CTIS) reaches a steady state. Still, companies don’t need to wait for the next phase of CTIS to improve their oversight of the complete submission file. Changing the resourcing of their regulatory and clinical teams and adapting their data collection, request for information (RFI), and redaction processes will provide much-needed visibility sooner. The benefits are not limited to EU CTR. Clarifying ownership and creating a single source of trial information would also make rest-of-world studies more efficient.

One Crew at the Helm Many small, single-country studies are well-suited to being pilot 12 Journal for Clinical Studies

submissions to CTIS, and have been used to identify the limitations of existing processes and systems. Insights from these first submissions have helped sponsors decide on an effective resourcing model for subsequent studies, including whether or not to outsource to CROs. Sponsors that insource have improved alignment by creating a single EU CTR cross-functional process and team structure, spanning from regulatory and clinical to quality, safety, and trial disclosure. In the past, these teams had independent responsibilities.

Regulatory managed the submission of health authority approvals while clinical teams handled ethics committee approvals at the site or country level. Condensing regulatory and ethics committee approvals into a single process requires adjustments to team structure and responsibilities.

Companies have set up their centralised submission teams for success by clarifying ownership of the end-to-end process and confirming new responsibilities. Among Veeva customers, there isn’t a consensus on which team should be in the driver’s seat: roughly half point to regulatory while the remainder opts for clinical operations. Irrespective of which team leads, its submission responsibilities are broader than before, extending to regulatory, ethics, and disclosures.

As the nominated team is pivotal in collecting trial-related information, its remit should be clearly communicated to the broader organisation. Understanding who is accountable for gathering information from different stakeholders (e.g., quality, regulatory affairs, clinical) is critical, particularly during RFIs from EMA, which require a response within 14 days. A tracker specifying ownership for each task can make it easier to collect RFI answers across the organisation.

Customers are also exploring new ways of working to counter the challenges of fragmented data collection and dynamic redactions. Some sponsors have delegated data entry into CTIS to CROs for outsourced studies, eliminating a few extraneous steps. Similarly, centralising responsibility (whether internally or outsourced) for redactions helps to manage this process more effectively because training, guidelines, and SOPs can be implemented in just one dedicated team.

Reaping the Benefits of a Streamlined Approach Already, some customers have become familiar enough with the regulation to submit Parts I and II together. Leading companies go further during the initial submission by including many countries under Part II. Submitting multi-country studies in parallel decreases the number of evaluation cycles, reduces the risk to patient recruitment, and shortens the overall approval timeline. But it’s a heavier upfront lift, for which easy access to trial-related information is essential.

While it may be tempting to navigate a submission with pre-existing data collection processes, this could prove shortsighted. Currently, CTIS lacks an API capable of receiving data and documents from either Volume 15 Issue 3

Regulatory sponsor systems or technology partners (including Veeva). However, the next phase of CTIS will probably involve API capabilities, which could make company integrations more straightforward. Doing the hard work now to create a robust data foundation will pay dividends if (and when) the API is introduced by EMA. Sponsors and CROs that have centralised data entry into one team (resourced with up to 10 people in enterprise biopharma) are already seeing the benefits of the new model. Having one part of the organisation accountable for data entry and upload to CTIS streamlines user access management and reduces the training effort required.

However, data entry teams often need external support to create a repository of all the data points for each CTIS submission and then scale this structured approach across the company. This CTIS data collection tracker2 should be a single source of truth of all reportable data from different systems.

It can also be helpful to reflect on the spirit of the regulation when setting up a process to manage redactions. Companies need to balance disclosure risk with the benefits to patients of greater trial transparency. Usually, the two main types of data anonymisation activities (commercially confidential information and protected personal data) take place using a hybrid approach. Redactions of company information are often centralised because they require legal knowledge (e.g., information relating to patents), and personal data redactions are decentralised so local teams can apply their national legal understanding.

Modifying redactions can quickly become messy in a hybrid model. Information that is confidential today might not be in a few months when the trial ends. Nor do all redactions involve obscuring text. Some content may need to change or be rephrased. The complexity of managing redactions could hinder patients and sites from enjoying easy access to study-related information envisioned in the regulation. To address these areas, sponsors should work in a system that maintains a close relationship between source and redacted documents so that teams can easily manage changes to content before public disclosure.

Centralisation is the New Benchmark The main aim of the Clinical Trial Regulation was to ensure the EU offers an attractive and favourable environment for carrying out clinical research on a large scale, with high standards of public transparency, robustness of data generated and safety for clinical trial participants.

A single point of access to European clinical trial information will benefit patients, as participation in ongoing trials becomes more straightforward.

Mandatory use of the same system should enhance stakeholder access to trial information without compromising robust data privacy standards.

However, EU CTR also challenges sponsors and CROs to reconsider how they collect and store trial data and documents for EU and nonEU studies. The European Medicines Agency has thrown down the gauntlet – and for some companies, this provides a welcome opportunity to get their houses in order.

REFERENCES 1.

2.

www.ema.europa.eu/en/human-regulatory/research-development/ clinical-trials/clinical-trials-regulation https://www.ema.europa.eu/documents/template-form/ctis-structureddata-form-initial-application-additional-member-state-concernedsubstantial_en.xlsx

www.journalforclinicalstudies.com

Stephan Ohnmacht Vice President, R&D Business Consulting.

Pamela Adede is a seasoned computer scientist and data professional known for providing high quality service whilst employing her multi talents which include efficiency, analytical thinking, and fast learning.

Werner Engelbrecht Senior Director, Strategy, Veeva Vault Clinical Operations. Werner has extensive experience in the pharmaceuticals and life sciences industry, across a range of roles, with his career spanning over twenty years. For the last twelve years, he has brought his in-depth industry knowledge to operational, and sales and account management teams at CROs (contract research organisations). In his current role as Senior Director Strategy at Veeva Systems, Werner heads up a team that is dedicated to using digital transformation to navigate the complexities of clinical trials and speed up development of new medicines.

Journal for Clinical Studies 13

Regulatory

Focus on End-to-end Innovation and Efficiency Drives Life Sciences Service Provider Consolidation The world has changed in Life Sciences and innovation and efficiency have become key to survival. Pharma manufacturers and marketing authorisation holders in the Life Sciences sector are under relentless pressure to adapt to changing strategic priorities against a backdrop of globalisation and increasing regulatory complexity across the whole value chain. Xavier Duburcq, Chairman & CEO of ProductLife Group, and Denis Gross, CSO, highlight the impact this is having on regulatory service companies as they, too, race to reinvent themselves to help their pharma and medtech clients navigate increasing regulatory complexity.

In Life Sciences, the world order has changed forever now, as the golden years of traditional small-molecule drugs draw to a close and the expiry of associated patents renews the call for innovation.

All of the momentum now, even among regulators and pricing and reimbursement authorities, is towards promoting new technologies and therapies that address unmet needs; biologics; health-tech and medical technology (medtech). Traditional pharma companies, which up to now have led on their legacy products, must now focus their strategies and resources on high-expertise, high-value activities, and infuse their more routine operational activities with new economies and efficiencies.

From the surge in global drug consumption and increased research and development (R&D) activity, with greater clinical trial complexity, to continuously evolving regulatory requirements globally, and a growing need for global transparency and standardisation, the Life Sciences industry is under enormous pressure to reinvent itself. This is not least as pharmaceutical companies start to develop more complex products and deliver in more countries, in an era that is seeing growing regulatory complexity across the whole value chain – from pre-clinical to commercialisation stages.

Shift to Global Partners All of this is having an impact on the Life Sciences service provider market, which must now support pharma, biotech and medical device companies with a different blend of services and delivery models. Large pharma companies, under new price and profit pressure, must find new ways to source the support they need at lower cost, while innovative startups need specific expertise without necessarily having the budget for tailored solutions. That required support could extend from an early development phase right through to post-launch lifecycle maintenance, with differing priorities across that spectrum.

Where large pharma companies once favoured local boutique services to fulfil specific requirements, these companies are now looking for global partners that can combine the right blend of specialist expertise with international capability and right-shoring options to keep pricing competitive, where the kind of support required is more routine and/or more readily industrialised/ automated. Realising they cannot fulfil that spectrum of needs, many boutique service providers are joining forces with or being 14 Journal for Clinical Studies

subsumed into international consultancies and regulatory/ compliance providers, to become a vital part of a wider offering that can be managed end to end for the client.

Agility is Key So what is it that Life Sciences companies are looking for in the 2020s?

Strategically, traditional players are branching out into biotech and more personalised patient treatments, where demand and future profitability will be concentrated. Other priorities include becoming more agile and responsive to global market opportunities (including improved speed to market), enhancing patient safety, and eliminating supply chain delays and product shortages.

All of these evolving scenarios require an evolving blend of service-based support, because of the difficulties of meeting these diverse needs internally, and because of the impact on agility if manufacturers and MAHs try to spread themselves too thinly by trying to control everything themselves.

Ideally, they want to source everything from one supplier – but knowing that they will have access to the right blend of expertise, experience and capacity, with the right geographical coverage, and an optimised delivery model according to the type of service/stage of the product lifecycle.

Broadening Regulatory and Quality Related Support At the product development/pre-marketing authorisation stage, the emphasis for regulatory and quality related support is relevant expertise (e.g. in the context of biotech/medtech) via world-class experts in the target countries, and the promise of reduced time to market. Requirements are likely to span pure regulatory support to more strategic advice around building optimal development plans, from clinical and non-clinical strategy (including early market access considerations to best position the product in terms of indication and target patient population) to actual pharmaceutical development/CMC activity (via the optimal pathway for both the active substance and the finished product), and full consideration of all aspects of quality assurance across the supply chain.

At a post approval lifecycle maintenance level, meanwhile, the requirement is for an end-to-end service delivered with maximum efficiency and at a competitive price. A global capability with the flexibility to draw on ‘right-shore’ resources as appropriate, along with technology to automate routine, labour-intensive work (e.g. use of AI in some activities), becomes important here, to deliver the right support cost-efficiently.

Service Provider Consolidation Over the next five years we can expect to see service provider consolidation continue, as manufacturer and marketing authorisation holder requirements grow and as the broadening impact of new development technologies and process digitalisation become more embedded. Merger and acquisition activity is expected to continue across the broader life sciences industry, as buyers compete for innovative assets. PwC expects that as major Volume 15 Issue 3

Regulatory pharma companies look for merger and acquisition opportunities to achieve their growth plans, midsize biotech companies that can fill in pipeline gaps in the back half of the decade will receive significant attention throughout the rest of this year.

“While macroeconomic conditions may remain challenging in 2023, a resetting of valuations and the need for health industries companies to innovate and transform their businesses to achieve their growth goals and stay ahead of competitors will create a compelling case for M&A.” says Christian Moldt, Global Health Industries Deals Leader, Partner, PwC Germany.

PwC also notes: “However, only a limited number of truly innovative assets are available, and valuations and competition for these businesses will remain high. We expect more small to midrange deals and the formation of joint ventures, rather than takeovers of larger companies, to continue to dominate M&A activity.”

There is no doubt that the Life Sciences service provider community is rising to the challenge of providing the support that both traditional pharma and younger biotech and medtech companies need. There are a number of routes to achieving this – including easing supply chain issues and focusing on patient outcomes and value creation. Reinvention of services, provider consolidation and new models of service delivery will continue to be major trends into the mid-2020s. Agility will be key to service providers adapting to new market realities, surviving and flourishing. Service providers who fail to rise to the challenge may find themselves targets for takeover.

Just some of the recent consolidation activity in Life Sciences – in the industry itself and among the service provider community Industry tie-ups • Germany-based Ariceum’s purchase of British biotech Theragnostics, for THG-008, a radio-labelled, cancerimaging PARP inhibitor currently in a phase 1 trial at Memorial Sloan Kettering Hospital in New York; as well as the FDA-approved Nephroscan, used to identify kidney disorders; and a Ga-68 kit technology IP currently licensed to Novartis.

• • • • • •

Ironwood’s purchase of VectivBio, for rights to phase 3 drug apraglutide, currently in development to treat short bowel syndrome with intestinal failure.

Oncology company Biodexa’s planned purchase of Varian Biopharmaceuticals, a private U.S. precision oncology company, targeting three rare brain cancers.

GSK’s acquisition of Bellus Health for its phase 3 chronic cough treatment, camlipixant, a rival to Merck’s anticipated gefapixant.

Sun Pharma’s acquisition of Concert Pharmaceuticals for the latter’s JAK inhibitor/alopecia treatment.

Leap Therapeutics’ acquisition of Flame Biosciences for the latter’s clinical-stage anti-Claudine18.2 antibody, FL301, and two pre-clinical candidates, targeting cancers.

The merger of hC Bioscience and 4SR Biosciences, towards identifying a preliminary tRNA-based therapy, based on 4SR’s sequencing technology. (The acquisition of 4SR comes just more than a year after the company launched.)

www.journalforclinicalstudies.com

•

Elicio Therpeutics’ merger with Angion Biomedica.

Cancer-focused Elicio expects to launch a phase 1–2 trial of its cancer vaccine therapy in the coming months.

PriceWaterhouseCoopers (PwC) predicts buoyant M&A activity in expected areas for future pharma/biotech growth throughout 2023, with a particular focus on oncology and immunology, as well as treatments linked to central nervous system and cardiovascular diseases, plus vaccines, as the market continues to place a premium on therapeutic area leadership.

Examples of recent Life Sciences service providers consolidation • •

•

Healthcare company AmerisourceBergen’s acquisition of PharmaLex A raft of strategic acquisitions by ProductLife Group, including DS InPharmatics (DSI), Zwiers Regulatory Consultancy in 2022, then Cilatus and Pharma D&S Group in 2023, to build a comprehensive, fully integrated set of global biopharmaceutical development and regulatory consulting activities.

ProPharma Group’s acquisition of OneSource Regulatory to boost its regulatory consultancy services.

Despite global macroeconomic challenges, PwC has seen companies with capital flexibility become more willing to deploy the resources needed to acquire assets with significant upside potential. PwC expects the pharma services sector to see continued healthy consolidation activity throughout 2023, as achieving scale becomes critical in the various subsectors – from differentiated contract development management organisations (CDMOs) to contract research organisations (CROs).

Xavier Duburcq Xavier Duburcq is CEO at ProductLife Group, a leading provider of global Regulatory and compliance outsourcing and consulting services for Life Sciences. Prior to joining ProductLife in 2020, Xavier was Group Vice President and Head of Life Sciences & Chemicals at Altran. He holds a degree in Pharmacy and a PhD in Immunology, both gained at Lille University of Health and Law, as well as a Masters in Executive Leadership & Strategic Marketing from Solvay Business School, in France.

Denis Gross Denis Gross is a global R&D leader and regulatory affairs expert with over 30 years of experience in Life Sciences. He has a proven track record of successful medical solutions development, and worldwide regulatory approvals. Denis joined ProductLife Group in September 2018, initially implementing the group’s business development strategy, managing the global delivery operations for Regulatory Affairs, Pharmacovigilance & Safety, and Quality Compliance businesses.

In 2022, he became Chief Solutions Officer, with global oversight of all services and competencies offered by ProductLife Group.

Journal for Clinical Studies 15

Research & Development

Advancing NIPT Workflows:

Using Size Selection to Enrich Fetal Fraction, Enabling Extended Blood Storage in EDTA tubes Prenatal screening for Down’s Syndrome and other chromosomal imbalances by the analysis of a blood sample has been available since the 1980s. The original methods utilised maternal pregnancy markers, such as alpha-fetoprotein (AFP) and human chorionic gonadotropin (hCG), which lacked specificity (too many false positives) and sensitivity (too many false negatives).1 In 2011, the field was revolutionised by the introduction of noninvasive prenatal testing (NIPT), which directly measures the presence of placental-derived DNA, representing the fetus, circulating in the mother’s blood.2 This method is used today to screen pregnancies, minimising the number of people who needlessly undergo invasive testing, which has a higher risk of spontaneous miscarriage and has greatly improved sensitivity and specificity over the maternal markers.3,4 Cell-free DNA and Non-invasive Prenatal Testing The technology we know today as NIPT was made possible by the first discovery of circulating cell-free fetal DNA (cffDNA) in the mother’s blood by Dennis Lo in 1997.5,6 The proportion of cffDNA in the mother’s blood that is derived from the fetus is referred to as the fetal fraction. It was immediately apparent that this had the potential to open the door to improved prenatal screening; however, at that time, no one could identify a method to analyse the fetal DNA amongst the maternal DNA circulating in the blood because of the relatively low fetal fraction, typically around 10% of cell-free (cfDNA) is cffDNA at 12 weeks gestation.

The insight that solved this problem was made, again, by Dennis Lo in 2008, who realised that it wasn’t necessary to separate the fetal and maternal DNA – instead he developed a method based on counting chromosomal markers.7 Down’s Syndrome is caused by an extra copy of chromosome 21; therefore, a woman pregnant with a fetus affected

by Down’s Syndrome will show a small increase in the amount of chromosome-21 DNA in circulation. Chromosome 21 represents about 2.1% of the human genome but in a pregnancy affected by Down’s Syndrome, this is increased to around 2.2% (depending on the fetal fraction). If it was feasible to measure this small but significant increase, then it would allow prenatal screening of trisomy 21. Technical advances in DNA sequencing that allowed the analysis and chromosomal mapping of millions of cffDNA fragments made it possible to accurately count and calculate the proportion of chromosome-21-derived DNA. A proportion of 2.1% was good evidence of an unaffected pregnancy; a higher proportion indicated the presence of trisomy 21. This insight led to the establishment of NIPT as we know it today, meaning fetal trisomy can be screened for from a maternal blood sample without the need for an invasive procedure which carries a risk of spontaneous miscarriage of healthy pregnancies.

NIPT methodologies, greatly improving the performance of prenatal screening; however, some challenges remain. Fetal fraction is highly variable but is broadly accepted as lower in lower gestational age and mothers with a higher BMI. As fetal fraction decreases, the amount of chromosome-21 DNA derived from the fetus also decreases, meaning the difference between affected and unaffected results becomes more difficult to measure reliably. Many NIPT providers have a minimum fetal fraction requirement and below this level the test is deemed to have failed.

Increasing the depth of DNA analysis can partially help to overcome low fetal fraction by generating more data points to analyse, but this substantially increases the cost of testing and doesn’t overcome the issue that the relationship between count density (the number of individual measurements of chromosomes) and accuracy is not linear: at very low fetal fractions even a significant increase in sequencing levels will not provide a reliable result. At fetal fractions below 5%, there is a significant increase in the required depth of sequencing. A better alternative to increasing sequencing depth would be to increase fetal fraction, which would have the combined benefits of improved accuracy at lower cost.

Enriching Fetal Fraction with Size-selection Methodologies Cell-free fetal DNA fragments are generally shorter than those of cell-free maternal DNA and, therefore, offer a means to select and enrich for fetal DNA using size-selection methodologies. However, this approach has been limited by the unavailability of a sizeselection method which has both the accuracy and precision to separate maternal and fetal DNA, and with a high enough DNA recovery to ensure that the sample is preserved for downstream

Challenges with Fetal Fraction Over the years this theory has been translated into a plethora of 16 Journal for Clinical Studies

Volume 15 Issue 3

Research & Development testing. Recent advances in DNA size-selection technology have seen the emergence of instruments that can more accurately and precisely separate DNA fragments, giving reproducible, high yielding size-selected DNA with enriched fetal fraction.8 An example is Ranger® Technology from Yourgene Health which is

reduced fetal fraction. Use of clinical-grade technologies offering scalable and precise DNA size selection with >97% reproducibility and 80% recovery, helps to ensure consistent enrichment of the fetal fraction in NIPT. These can provide call results from as little as 2% fetal fraction, compared to the industry standard of 4%. Having the ability to increase the fetal component of the cfDNA by reducing the maternal DNA content within a sample will help to lower failure rates and deliver more reliable NIPT results, meaning more pregnant women will have the opportunity to benefit from NIPT early in their pregnancies.

A recent collaborative study with Tommy’s charity and St Mary’s NHS Hospital (Manchester, UK) assessed the ability of Ranger Technology to recover the fetal fraction from whole blood stored in EDTA BCTs over a prolonged period using the IONA® Nx NIPT Workflow (Yourgene Health). The ability to detect trisomy samples was unchanged across the extended time periods, up to 7 days, and the study concluded that utilising size selection in NIPT workflows allows the use of EDTA BCTs for prolonged periods over 8 hours.9

Figure 1: Histogram profiles for size-selected (blue) and non-size-selected (red) DNA libraries used in NIPT applications. Size-selection methodologies enrich for fetal DNA fragments as the fragments are shorter than those of maternal origin.

Est. Fetal Fraction of Size Selected cfDNA (Ranger Technology)

Fetal Fraction Estimations of Maternal cfDNA Samples

by Trisomy and X/Y Chromosome Ratios

40

30

20

10

Cost Savings and Avoidance of IP Litigation Streck BCTs are expensive, with a single tube costing $8–10 USD. In contrast, EDTA BCTs cost around $0.55. We conducted a cost-benefit analysis which demonstrated that for mid- to high-throughput laboratories, the actual value attached to that cost-saving could be significant from as early as year one. An NIPT laboratory running 8,000 samples per year could expect to see savings of over $55,000 by year three from switching to an EDTA/Ranger Technology workflow.

A lower-throughput laboratory processing 2,000 samples per year could expect to see nearly $29,000 saved per year by choosing EDTA over Streck BCTs. For ultra-high-throughput laboratories looking to run >100,000 samples per year, the overall saving in year one alone is estimated to be just over $700,000 (Table 1). Further cost savings would also be expected due to a reduction in failure rates, although this cannot be quantified at this time until further planned studies are completed.

No effect’ reference (x1) x2 fetal enrichment

0 0

10

20

30

40

Est. Fetal Fraction of Non-Size Selected cfDNA

Figure 2: Comparison of fetal fraction between samples size selected as opposed to those that are processed without size selection.

able to accurately and reliably prepare a specific size fraction of the cfDNA. Size-selection technologies help to capture the shorter fetal DNA fragments, reducing the proportion of maternal DNA content (Figure 1). Applying size-selection technologies to NIPT samples can achieve up to two-fold fetal fraction increases compared with nonsize-selected samples (Figure 2).

Fetal Fraction Recovery Following Storage in EDTA Blood Collection Tubes An additional benefit of size selection on cfDNA is that it is possible to consider the use of standard EDTA tubes rather than expensive specialised preservation blood collection tubes (BCTs) for NIPT. Size selection can reduce the maternally derived cfDNA, maintaining, and even enriching, the fetal fraction of these samples stored in EDTA gel tubes, as demonstrated by the Centre de recherche du CHU de Quebéc-Université Laval using Ranger Technology.8 When using EDTA tubes, cell lysis due to sample instability can result in the release of maternally derived genomic DNA, the presence of which can cause dilution of the cffDNA, leading to www.journalforclinicalstudies.com

Table 1: Predicted cost savings years 1–3 for a high-throughput (100,000 samples/year) laboratory utilising Ranger Technology and EDTA BCTs. The calculation is based on internal data and considers licensing from Yourgene Health, technology implementation costs, as well as BCTs used per patient per sample run. The cost of EDTA BCTs was set at $0.55 per tube, and Streck at $9.64. These values represent an average from publicly available list prices and have been verified using external customer data.

There are also annual cost-benefits derived from a reduction in test failures due to insufficient fetal fraction. Enrichment by size selection can salvage up to 50% of samples which would otherwise be failed due to low fetal fraction. The average laboratory might expect around 3–4% of all samples tested to have a fetal fraction below the analytical sensitivity. By using size selection to salvage these low fetal fraction samples, laboratories stand to make significant cost savings in addition to those achieved by switching to EDTA BCTs. Another consideration is the fragility of glass Streck BCTs compared to plastic EDTA BCTs; using plastic tubes for sample transport will reduce the number of lost samples due to breakages and is more compatible with high-throughput automation.

In the US, an additional financial concern surrounds future IP licensing payments for use of the cell stabilisation BCT, adding significant costs to these tests. This is a huge financial burden for all Journal for Clinical Studies 17

Research & Development molecular diagnostic tests performed by US labs if they continue to use cell stabilisation blood tubes.

The Next Generation of NIPT Workflows The results of the studies referenced in this article show that utilising size selection in NIPT workflows allows the use of EDTA BCTs for prolonged periods over 8 hours. Data indicate that not only is fetal fraction maintained, but that the results of NIPT are concordant across the 7 days of the study.

Many NIPT clinics and hospitals take a maternal blood sample with an expensive BCT, such as a Streck tube, and the sample is stable for up to 14 days. However, the data show the capability to use EDTA tubes, which are commonly used and a significantly lower cost, with proven stability up to 7 days. The use of EDTA blood tubes in NIPT could offer a considerable saving in the cost of the stabilising BCTs, and in the US where there is IP in the use of stabilising BCTs, avoid costly licensing fees.

By enabling EDTA BCTs to be used for collection, transport and analysis of samples, these technologies provide clinicians and laboratories with reduced costs and improved flexibility, making NIPT more accessible whilst ensuring patients receive highly accurate prenatal screening results.

REFERENCES 1.

2.

3.

4.

5.

6.

7.

8.

9.

Sillence, K.A., Madgett, T.E., Roberts, L.A, et al. Non-Invasive Screening Tools for Down’s Syndrome: A Review. Diagnostics 3, 291–314 (2013).

Chiu, R.W. & Lo, Y.M. Non-invasive Prenatal Diagnosis by Fetal Nucleic Acid Analysis in Maternal Plasma: The Coming of Age. Semin. in Fetal and Neonatal Med. 16, 88–93 (2011).

Gadsbøll, K., Petersen, O.B., Gatinois, V., et al. Current Use of Noninvasive Prenatal Testing in Europe, Australia and the USA: A Graphical Presentation. Acta Obst. Gynecol. Scand. 99, 722–730 (2020).

Minear, M.A., Lewis, C., Pradhan, S., et al. Global Perspectives on Clinical Adoption of NIPT. Prenat. Diagn. 35, 959–967 (2015).

Lo, Y.M., Corbetta, N., Chamberlain, P.F., et al. Presence of Fetal DNA in Maternal Plasma and Serum. Lancet 350, 485–487 (1997).

Romero, R. A Profile of Dennis Lo, DM, DPhil, FRCP, FRCPath, FRS. Am. J. Obstet. and Gynecol. 218, 371–378 (2018).

Chiu, R.W., Cantor, C.R., & Lo, Y.M. Non-invasive Prenatal Diagnosis by Single Molecule Counting Technologies. Trends in Genet. 25, 324–331 (2009).

Daryabari, S.S., Giroux, S., Caron, A., et al. Improving Fetal Fraction of Noninvasive Prenatal Screening Samples Collected in EDTA-Gel Tubes Using Gel Size Selection: A Head-To-Head Comparison of Methods. J. Mol. Diagn.

24, 955–962 (2022).

Jefferies, J., Hutchinson, V., Ryan, D., et al. Poster: A Study to Demonstrate that Prolonged Storage of Blood in EDTA tubes is Compatible with NIPT when Using Size Selection to Enrich Fetal Fraction. International Society for Prenatal Diagnosis, 19–21 June 2023, Edinburgh International Conference Centre.

Thomas Lyons Thomas is a senior scientist at Yourgene Health. He has 10 years’ experience as a scientist in the molecular diagnostics sector across oncology, infectious disease, and reproductive health fields. Prior to working in the molecular diagnostics sector, Thomas worked in an experimental flow cytometry laboratory looking at reproductive innovation in the livestock sector. He has an MSc in Genomic Medicine from Queen Mary University of London and a BSc in Genetics from the University of Liverpool.

Email: thomas.lyons@yourgenehealth.com

Dr. Rachel Shelmerdine Rachel has held lead roles in the diagnostic industry within product management, R&D, product development and innovation/ medical affairs. Rachel is currently a senior product manager at Yourgene Health and has a proven track record of taking innovative, proof-ofprinciple technologies through regulated product development processes to the successful launch of clinical IVD genetic products. Rachel holds a PhD in Molecular Immunology from the University of Sheffield, Executive MBA and an MSc in Statistics from Lancaster University and completed an undergraduate degree in Medical Biochemistry with the University of Manchester.

Email: rachel.shelmerdine@yourgenehealth.com

Rebecca Underwood Rebecca is a product manager at Yourgene Health. She has 14 years’ experience in product management working in a variety of industries involving scientific applications, specialising in new product development, and taking products to market. She has an BSc in Medical Materials Science from Nottingham University.

Email: rebecca.underwood@yourgenehealth.com

Dr. Joanne Mason Joanne is the Chief Scientific Officer at Yourgene Health and has been a champion of modernising diagnostics having previously held positions as VP Biodiscovery with Cambridge Epigenetix (now biomodal) and Director of Sequencing and Sample Acquisition for Genomics England. She has acted as an advisor on the DOH Rare Disease Policy Board, MHRA Genomics for Diagnosis Forum and UK NEQAS – Genomics England Steering Committee and Genomics England Sequencing Advisory Board.

Joanne holds a PhD from Cambridge in Molecular and Cellular Biology.

Email: jo.mason@yourgenehealth.com

18 Journal for Clinical Studies

Volume 15 Issue 3

Corporate Profile

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Journal Journalfor forClinical ClinicalStudies Studies1919

Research & Development

Unlock Clinical Research Site Potential by Addressing Resource Challenges and Trial Complexity Staffing and trial enrolment remain the most pressing issues research sites face, according to the 2023 WCG Clinical Research Site Challenges survey. As trial complexity increases and sites continue to struggle with resourcing, sites are having to do more with less. Despite these challenges, sites are finding innovative ways to move forward with help from sponsors, clinical research organisations (CROs), and other partners. All of this has the potential to reshape the future of clinical research.

About the Survey and the Results In February-March 2023, WCG surveyed more than 500 US-based clinical research sites. The survey asked site leadership/owners, research administration staff, clinical research coordinators/ clinical research nurses, institutional review board/regulatory representatives, physicians/principal investigators, and others about their perspectives on the major challenges they are facing, solutions they have implemented to overcome those obstacles, and more. Thirty nine percent of survey respondents represented academic medical centres (AMCs), and the remaining 61% represented non-academic research centres (non-AMCs) including community hospitals, health systems, physician practices, and independent research sites.

A Threat to Opening New Trials According to the survey results, 63% of sites identified staffing and retention as their most significant concern, recruitment and enrolment was second at 48%, and trial complexity and study startup tied for third at 36%.

To explore the implications of the survey findings, WCG convened representatives from a sponsor, an AMC, and a non-AMC for a discussion on its podcast, WCG Talks Trials. The episode featured: • • • •

Lisa Richman Ballance – Associate Vice President for Research Strategy and Regulatory Affairs at Virginia Commonwealth University (VCU) John Musser – Senior Director, Clinical Research Administration at Florida Cancer Specialists & Research Institute Dan Otap – Principal, Alliance and Partnerships Lead at Genentech Sandy Smith, RN, MSN, AOCN – Senior Vice President, Clinical Solutions and Strategic Partnerships at WCG

Smith pointed out that 52% of the respondents indicated that challenges they identified in the survey have had a direct impact on their site’s ability to open trials. WCG’s proprietary data supports this finding: in 2022, more than 3,300 US sites opened only one clinical trial, and fewer than 50 sites opened 50 or more trials.

“What surprised me most about that is that even running one trial…requires a lot of infrastructure,” Ballance said, expressing concern that this trend could lead to a decline in smaller sites.

Staffing and Start-up Are Inexorably Linked Many clinical research sites continue to struggle with staffing and 20 Journal for Clinical Studies

retention, which is affecting the number of trials they can handle.

Staffing is a more significant issue for non-AMCs (74%) than for AMCs (56%).

Study start-up challenges relate directly to the shortage of research professionals – not just the clinical research coordinator role but positions in other areas, including coverage analysis, budgets, contracts, and regulatory submissions. This can often lead to longer activation timelines at sites and costly trial delays.

Volume 15 Issue 3

Research & Development Meanwhile, the number of US investigators continues to decline. This prompted the inclusion of a question in the survey on physician engagement. Nearly a fifth (19%) of respondents cited physician engagement and interest as a concern. It was a much bigger concern for AMCs (22%), ranking fourth. It came in ninth for non-AMCs, with 11% citing it.

“We are at an inflection point in this industry where we have to have new investigators developed, we have to have staff developed or we’re going to have a dearth of opportunity,” Otap said.

Ballance agreed: “Let me just say that I have often seen some ebbs and flows over time, but this has actually gotten to a point where if we don’t have more physician researchers, we will be in crisis.” To that end, VCU has been investing in pipeline programs to bring more people to the table as clinical and translational scientists, including PhD nurses.

Trial Complexity: The Burden Keeps Growing Over the past several years, clinical trials have become increasingly complex, putting additional burdens on clinical research sites. When the survey inquired about trial complexity, 36% of respondents named it as a top concern. Research sites frequently cite trial and protocol complexity as a reason to avoid clinical trials with multiple arms or complex trial designs.

It was the third-highest concern for AMCs (38%) and the fourth for non-AMCs (36%).

“We have seen more data points, and we have seen lengthier trials with more procedures—and this has impacted our start-up times,” Ballance reported. “Part of the issue is we do a lot of trials that are standard of care, plus procedures that are research only.

Even just adding complexity to the coverage analysis process can create delays.”

Trial complexity and acuity have been top of mind for Otap, and they relate to everything from resource management to feasibility reviews. He points to the Tufts Center for the Study of Drug Development (CSDD) data showing that complexity continues to climb. Phase II and III trials have an average of 20 endpoints or more per study, and phase III trials collect an average of 3.6 million data points.1 “That’s mind boggling to me,” he said. “This clearly has

www.journalforclinicalstudies.com

downstream effects on a site’s ability to both conduct their ongoing studies, as well as open any newer studies.”

As Technology Advances, Challenges Increase Technology continues to be a concern for sites, with 18% of all sites saying it is a top concern, and a significant split between AMCs and non-AMCs. Technology is tied at 22% with physician engagement/ interest at AMCs (ranked 4th), while technology ranks 7th at nonAMCs (13%).

From a community practice perspective, John Musser emphasizes the importance of integrating technology solutions, particularly considering the diverse and sometimes conflicting solutions provided by sponsors and CROs. Otap attributed the fact that technology advancements are sometimes a burden to “innovation outpacing the end user informed experience and feedback from users.” He pointed to the “low-hanging fruit” of single sign-on methodologies. Tech’s biggest selling point is end-user ease, he explained. “We need to keep that top of mind.”

“The take home here, in my eyes, is that as sponsors we have a responsibility to take site staff and participant experience and operationality into consideration when vetting new vendors and deploying new tech software solutions. We need to be intentional about both the upstream planning, as well as the downstream process review so that we can continue to improve in this space.”

Focusing on Solutions Although technology may be a challenge for some sites, it can also be a solution if used efficiently. Both AMCs (29%) and non-AMCs (21%) are leveraging it for workflow efficiency.

Sites are putting solutions in place to solve the challenges they face, especially related to staffing. For example, 57% of non-AMCs report hiring additional staff (39% at AMCs) and 45% of non-AMCs identify training as a solution (41% of AMCs) – particularly with hiring new staff.

“Many sites are filling vacant positions with earlier-career professionals,” Smith reported. “So training may be taking a little bit longer.” Some sites are designing novel training programs to increase the number of available research professionals.

Journal for Clinical Studies 21

Research & Development participant recruitment, enrollment, and retention processes.

Document, simplify, and standardize workflows and processes that are conducted regularly.

6. Identify where the gaps exist in your research workflows; determine what must be done on site versus what can be outsourced.

7. Establish clear communication at your site and ensure that site staff members are aware of their specific roles and responsibilities for each trial.

8. Invest in ongoing training, education, and networking opportunities for site staff.

9. Build strong relationships with sponsors and CROs to enhance communication and transparency.

10. Adopt a quality management system and identify best practices to ensure regulatory requirements are met.

5.

That’s what’s happening at Florida Cancer Specialists. “We’re having to revamp our entire training regimen to make sure we can find really good candidates and then keep them here and train them,” Musser said. “You also have to make sure people feel that they want to stay at that location, especially in community practice settings.”

“Creating that person-to-person relationship will go a long way,” he added. “And for those scenarios where you’re unable to find those people, partner with the right research service to augment [your current staff].” When you can find or train the right people for a role – data management, financial management, budget, contract negotiations, etc. – he recommends working with a partner.

Ballance agreed, stressing the importance of sites prioritizing their investments and focusing on what they do best. “One of the things that we have found incredibly beneficial is to turn to outside expert partners like WCG, especially when it comes to staffing for peak times.” VCU has turned to WCG for coverage analysis, contract negotiation, and budget development. The survey finds that 20% of AMCs and 11% of non-AMCs have taken the same approach.

“Sites must speak up about what they need when they need it,” Musser said. “Don’t wait for a monitor visit. You should be able to pick up the phone and have that conversation with them right then, because if you’re having that issue, more than likely another site’s having that issue.” During all this, research participants need to be the primary focus.

VCU has redoubled its focus on participant engagement. “We should be asking our participants, ‘Where are the pain points that really limit the progress of clinical trials?” Ballance said. “It would be a shame to fix technology issues and to find out that parking was the main problem.”

Otap agreed: “As simplistic as it sounds to me, the best solution is consistently putting the other person in that scenario face forward and trying to think on their behalf what works best for them.”

WCG’s survey report concluded with recommendations for both sponsors/CROs and sites.

10 ways research sites can address their key challenges: 1.

2.

3.

4.

Focus on the trial participant experience throughout the recruitment, enrollment, and retention process.

Use and invest in technology systems that can improve existing workflows and streamline research operations.

Leverage data and technology to optimize participant recruitment, enrollment, and retention.

Implement diversity, equity, and inclusion strategies in your

22 Journal for Clinical Studies

10 ways sponsors and CROs can help address key site challenges: 1.

Develop protocols with the participant and site experience in mind to address trial complexity concerns and obtain site input on protocol designs before the trial begins.

2. Release the final protocol in a complete state to minimize the site having to address amendments during trial activation or shortly after trial initiation.

3. Set realistic study start-up timelines for the sites conducting your trials.

4. Work to simplify and streamline the feasibility process, budgets, contracts, site training, and safety reporting.

5. Create robust diversity action plans for the sites conducting your trials.

6. Identify site-specific needs and provide personalized support for each site, whether that be through people, process, or technology.

7. Evaluate the technology needs for your trials and provide training for the platforms that sites will be frequently using.

8. Support new and less experienced sites and investigators to improve diversity.

9. Collaborate with sites to evaluate how existing site technology can be leveraged to identify, enroll, and consent potential study participants more effectively.

10. Facilitate open lines of communication and collaboration throughout the trial.

REFERENCES 1.

2.

Tufts CSDD Impact Report Volume 23 Number 1 January/February 2021 2023 WCG Clinical Research Site Challenges Report www.wcgclinical. com/insights/2023-clinical-research-site-challenges-survey-report

Dawn Sauro Dawn Sauro is Chief Growth Officer at WCG and is responsible for the company's enterprise growth and commercialization strategy.

Dawn has more than 30 years' experience as a drug development and clinical research expert, strategist, and leader and represents the voice of the customer throughout WCG. Prior to joining WCG, Dawn served as Chief Operating Officer at Elligo, Chief Development Officer at Clinipace Clinical Research (now Caidya), President, Development Innovations at Sarah Cannon, and Senior Vice President and General Manager, Hematology/Oncology at inVentiv Health (now Syneos).

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Journal for Clinical Studies 23

Technology

AI for Medical Writers – Friend or Foe? Artificial Intelligence (AI) is beginning to affect almost every industry, and medical writing is no different. But how does this relate to our industry? How will AI affect medical writers? What’s already available and what is in the pipeline? Should medical writers be happy and embrace the technology, or should we resist as much as we can, assuming that we will all be replaced by machines? This article discusses the current state of the art of AI in medical writing and asks the question: AI for medical writers – friend or foe?

How Did We Get Here?

What a year it’s been for artificial intelligence (AI) already! The pace at which the conversation around AI has accelerated in just a few short months is unprecedented. However, AI is certainly not new.

As a term, AI was coined back in the 1950s,1 and ever since then, the technology, models, and processing power have advanced. With ChatGPT leading the way, along with Google, Meta, and a host of other tech companies, the paradigm is shifting so rapidly that in the time between writing this article and publishing it, there could be something new to discuss in the world of AI. But what led us to this point? What triggered this explosion?

AI is not new nor are language models such as those employed by ChatGPT. As we enter the age of AI, and with ChatGPT competing with the behemoth of Google, the success is best explained by Google’s own history.

In the early days of the internet, conducting a “search” seemed like something of a dark art. Companies would invest their marketing budgets in promoting their URL because the idea of just being able to search for the company seemed to be a pipe dream. Even with the advent of the first search engines, if you did not know how to write queries using Boolean logic, getting any meaningful results felt like a lottery.

And then Google came along: no pop-up ads, no confusing page layout, just a simple search box. And it worked. Effortlessly. The beauty was in how they made something so complex incredibly simple and accessible. And the rest, as they say, is search history.

And now history repeats itself: AI is not new, but a simple, well designed interface such as ChatGPT makes it appear effortless and provides powerful results. This has captured the imagination of the world. It is certainly impressive and has prompted a flood of examples demonstrating its power. As Arthur C. Clarke famously said, “Any sufficiently advanced technology is indistinguishable from magic”.2 What was once a niche domain for data scientists and AI technologists has suddenly become widely accessible. We now see everyone leveraging its power for everything from drafting emails to answering exam questions. This explosion has been so large and rapid that it has outpaced working practices and even legislation. This 24 Journal for Clinical Studies

has led to the kind of concerns that triggered the open letter from tech leaders in which they urged a pause in development of AI to allow some checks and regulations to be put in place.3 What AI is and How It Works in a Writing Context In a rapidly changing sector, what is already available and for what purpose?

The term AI is very broad. Different branches of it often get conflated, but there are disciplines within the discipline. At its highest level, AI is a catch-all term for any computational technique that enables machines to mimic human behaviour. This could be as simple as a macro in excel that automatically performs a set of calculations or procedures or as advanced as a facial recognition algorithm.

The next layer of detail is referred to as “machine learning”, which is a subset of AI that uses statistical methods to improve a model based on experience. For example, for image recognition, this could be a system that improves the accuracy of recognising a certain animal under increasingly ambiguous scenarios.

The next deeper level is so-called “deep learning”. It is a subset within machine learning, where a neural network is used to make connections. Incredibly large, multi-layered networks create computational systems that work more like the human brain. Many deep learning algorithms are actually closer to “black box systems”, in which the outcomes may be incredibly accurate but difficult to explain. This is one of the areas that makes some groups pause because they often show emergent behaviours that were not predicted by humans and can be unsettling, adding to concerns that AI is out of control.

This is where the notion of “explainable AI” comes in.4 Being able to reverse-engineer outcomes and explain the results of AI models creates a more comforting outcome, although this may mean sacrificing some of the computational power provided by deep learning models.

Where Does ChatGPT Fit in?

ChatGPT uses neural nets to support the computation power of its outcomes. As a large language model, it retains a degree of “explainability”.5 Large language models generally use statistical models. In simple terms, a language model uses a set of training data to create a probability of the next word or series of words in a sentence. ChatGPT’s power comes from access to perhaps the largest corpus of training data of any language mode. However, even ChatGPT has shown emergent behaviours. For example, it can be used to solve maths problems, which it was not specifically designed for, and although it can “solve” maths problems, it cannot interpret statistics.

Language modelling also cannot assign probabilities to linguistically valid sequences that may not have been in the training Volume 15 Issue 3

Technology data. This is a positive in the sense that in can create novel texts, but it also can produce results that are grammatically correct but factually incorrect. That is, it can assess the probability of word sequences but cannot understand their meaning. In this way, language models differ from cognitive models, which, as their name suggests, are closer to our own abilities to solve problems.

The challenge of interpreting new concepts is an important consideration for AI. This has been illustrated using the “Monty Hall” problem from the medium of gameshows.6 The Monty Hall problem is a brain teaser, in the form of a probability puzzle, loosely based on the American television gameshow “Let's Make a Deal” and named for its original host, Monty Hall.

Imagine that you are given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say number 1, and the host, who knows what's behind the doors, opens another door, say number 3, which reveals a goat. He then says to you, “Do you want to pick door number 2?” Is it to your advantage to switch your choice?

Most people’s intuition is to stick with their original choice. However, the correct response is, counterintuitively, to switch.

Switching gives a two in three probability of winning a car, while sticking with your original choice gives only a one in three chance. If you do not believe it, there are plenty of referenceable articles on this topic that can be found on Google.

If you pose this question to ChatGPT, you will receive the correct response, suggesting that you switch. This is due to the training data, which most likely included a written reference to how this problem is solved. However, what if we made this a “dumb” problem, where the answer is much more obvious? In this case, we pose the same problem but with a small change: this time the doors are made of clear glass so that you can see behind every door. Under these conditions, you can easily pick the door with the car behind because you can see it, and when asked to switch this time, you would clearly stick with your choice. However, when posing this challenge to ChatGPT, it always suggests switching choice (Figure 1).

Figure 1. GPT prompt and response to the “dumb” Monty Hall Problem. With thanks to Colin Fraser, Data Scientist at Meta.6

This is a reflection of the language model’s inability to reason in the same way as a human – to make deductions from premises or to process insights rather than to make probabilistic inferences from word frequencies. This explains why making new inferences from data can be challenging, and it is exactly the kind of challenge www.journalforclinicalstudies.com

we face in interpreting statistical data from new drugs. The margins for error in this context are significantly smaller so we cannot rely on language models alone.

Like any technology, ChatGPT is just a tool. As with any tool, it is only as good as the person using it. ChatGPT is incredibly powerful, but to build products around it, its underlying working models, nuances, and other details need to be understood.

How Could AI Help Medical Writers?

Many generic language models are able to create authentic content, but they do not always perform well when the content is novel or its frame of reference is new, as was the case with the dumb Monty Hall problem previously mentioned.6 This is simply a result of the training data used because language models can only produce content related to the data they have been trained on. A welldocumented downside of generic language models is “computer hallucinations”, where a language model “makes up” information or cites references when it has no information. This is obviously a major concern for the field of scientific writing.

To address this, some niche tools have been specifically trained to produce content relating to scientific information. An example is Ferma AI,7 which searches the abstracts of papers to answer specific text-based questions and can support research scientists. Another is BioGPT,8 which is a spin off from ChatGPT designed specifically for life sciences and produces more relevant biological text. Our own tool, TriloDocs,9 combines a sectorspecific language model with a core of expert rules to provide a set of “guiderails” and only interprets relevant information from clinical trial data in relation to specific best practice criteria.

It seems that the future of AI in the medical writing sphere may not be as stand-alone tools but rather within platforms that use it in the context of wider rules and other elements. Using AI tools in the medical writing space as more of a “walled garden” makes sense because of reluctance to upload intellectual property, personal data, or other sensitive information to open platforms, where data ownership and data protection are currently being debated.

Regulatory Authorities need to be confident in the accountability and traceability of raw data and documents supporting any claims. GDPR (General Data Protection Regulation), protection of commercially sensitive information, and “AI hallucinations”, not to mention the specific context of medical writing remain major concerns.

Nonetheless, language models are undoubtably powerful tools for creating authentic-looking texts from certain prompts, rewriting texts for different audiences (e.g., in other languages), and producing simplified summaries. Most medical writers would be delighted to pass on routine, mundane, and repetitive tasks to a computer, which can do them more efficiently, accurately, and quickly. This could liberate writers to concentrate on the highly skilled tasks of contextualising and interpreting clinical data and allow them to have meaningful data discussions with clinical teams much earlier than is currently possible. In the medcomms and medical journalism worlds, AI tools can help writers more quickly and accurately create time-sensitive documents and sift through huge amounts of literature.

What Are the Risks of AI? We have already touched on some of the key risks involved in using AI. Data privacy is often the main risk that springs to mind.

However, this is an inherent risk of any technology and not specific to AI. Some AI platforms present a risk of being internet-based.

Also, “open” systems present a risk even in a non-AI context.

Journal for Clinical Studies 25

Technology Some emerging options allow developers to build a language model within a secure environment (although the training data are publicly available). How this develops in the medical writing arena will be interesting.

Risk of errors. In our experience with TriloDocs, the risk of human error has been significantly reduced, if not eliminated. Important data that humans may miss are identified by the tool, and we have not yet found an issue raised during quality assurance that was not already identified by the technology. The problem of AI hallucination is a cause for real concern because there is no room for false data, inferences, or references when dealing with clinical and scientific data. The more niche platforms will have to specifically eliminate this risk, which may pose a significant challenge. From a medical writing perspective, a conservative approach is always best. Our experience is that it is better for the tool to highlight where something is missing or interpretations cannot be made, flagging data points for the medical writer to investigate rather than having a tool that produces a “complete” but misleading draft.

Other considerations include the ethical debate about AI, which is far outside the scope of this article. Jamie Bartlett,10 a journalist and author specialising in technology and a regular speaker on the topic of futurism has warned that only three things can be guaranteed about the future of technology: firstly, that data storage capabilities and demand will continue to grow at an exponential scale; secondly, that the processing power of computing will also continue to grow, which along with the ability to store huge amounts of data, has powered this latest AI revolution; and thirdly and most importantly, that human drives and behaviours will not change.

The limiting factor to AI is how we implement these tools and how ethically we can introduce checks and balances to manage them. There is almost an AI paradox playing out in front of us: we all want AI to help us to do our jobs better or at least take away the more menial parts of our work without replacing us altogether.

Unfortunately for some, that choice will not be theirs to make.

What Does All This Mean for Medical Writers?

One thing we always stress when talking about our own platform, TriloDocs, is that it does not replace the medical writer. TriloDocs simply accelerates and enhances the writer’s ability to have meaningful data discussions with the clinical team and speeds crafting of the report. We have not yet met anyone who actually enjoys trawling trough data with a highlighter pen and interrogating tables for information; crafting a strong narrative around the data, however, is an entirely different proposition.

Highly skilled medical writers bring value as critical thinkers as they create study reports and related documentation. We are still some way off from the ultimate goal of AGI (Artificial General Intelligence), which moves AI into the realm of human-like thought.

Until that point, critical thinking can only be done by humans. In the short time that tools like ChatGPT have captured our imagination, there is already an adage that describes where things could be going in the short term: AI might not take your job, but someone who uses AI will.11 AI is not going away – medical writers cannot influence that – but we can influence how we approach and use AI. If we view AI as a tool that can supplement our work, make us more efficient and accurate, and relieve us of some of the heavy lifting, then it can become a powerful resource, freeing us to focus on the more valuable work of critical thinking and crafting a strong narrative in our highly complex and vital work.

26 Journal for Clinical Studies

Acknowledgements The authors gratefully acknowledge Dr. Barry Drees’ input on this article.

REFERENCES 1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

McCarthy J, Minsky ML, Rochester LBM, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence. 1955 [cited 2023 Jul 10] https://web.archive.org/web/20070826230310/http:/ www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html.

Clarke AC. Profiles of the future: an inquiry into the limits of the possible.1st ed. New York: Harper & Row; 1973.

Future of Life Institute. Pause giant AI experiments: an open letter. 2023 [cited 2023 Jul 10]. Available from: https://futureoflife.org/open-letter/ pause-giant-ai-experiments/.

Google. Explainable AI in industry (tutorial). 2023 [cited Jul 11]. https:// sites.google.com/view/explainable-ai-tutorial.

Potts C. Stanford Webinar: GPT-3 & beyond. 2023 [cited 2023 Jul 10]. https://www.youtube.com/watch?v=-lnHHWRCDGk. Fraser C. ChatGPT: automatic expensive BS at scale. Medium. 2023 [cited 2023 Jul 10]. https://medium.com/@colin.fraser/chatgpt-automaticexpensive-bs-at-scale-a113692b13d5.

Ferma. The quickest path to your next eureka. 2023 [cited 2023 Jul 10]. https://www.ferma.ai/.

Microsoft/BioGPT. BioGPT: requirements and installation. 2023 [cited 2023 Jul 10]. https://github.com/microsoft/BioGPT. Trilogy Writing & Consulting Ltd. TriloDocs AI-enhanced medical writing: the AI tool for clinical study reports. 2023 [cited 2023 Jul 10]. https://trilogywriting.com/trilodocs.

Bartlett J. The People vs Tech: how the internet is killing democracy. 1st ed. India: Ebury Press; 2018.

Confino P, Burton A. AI might not replace you, but a person who uses A.I. could. Fortune. 2023 Apr 25 [cited 2023 Jul 10].https://fortune. com/2023/04/25/artificial-intelligence-ai-replace-humans-promptengineers-chatgpt/.

First published in Medical Writing, September 2023, Volume 32, Number 3.

Jamie Norman Jamie Norman is the Chief Product Officer of TriloDocs GmbH. Jamie began his career in marketing, working with several life sciences brands. In 2010, he made the transition to technical Product Management and has since worked in product leadership roles for several high-growth and enterprise software brands specialising in data science and AI-led products.

Lisa Chamberlain James Lisa is a CEO of Trilogy Writing & Consulting Ltd. Following a PhD and postdoctorate at the University of Cambridge, in 2000, she began a medical writing career. She has since been involved in EMWA as a member of the Educational Committee, mentor, leader, and assessor of workshops, and she teaches and reviews workshops for the American Medical Writers Association. Lisa holds an EMWA Professional Development Certificate, is a visiting lecturer for Kings College, London, initiated and chaired the EMWA Pharmacovigilance and Communicating with the Public Special Interest Groups, is Chair of EMWA’s Geoff Hall Scholarship Committee, Section Editor for Medical Writing, and a Fellow of the Royal Society of Medicine.

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Journal for Clinical Studies 27

Therapeutics

Using QSP Modeling to Advance Knowledge and Therapeutics for Alzheimer’s and Parkinson’s Disease This article explores the use of quantitative systems pharmacology (QSP) modeling to provide further insights into the mechanism of action of neurodegenerative diseases and the likelihood of success of new drugs in development. This unique approach allows for the prediction of not only biomarkers, but more importantly, clinical outcomes. Here we elaborate on the prospective prediction of lecanemab’s Phase 3 CLARITY AD clinical trial using Certara’s QSP Alzheimer’s disease platform. This prediction was based on the use of a so called “virtual biomarker” and that concept can be applied to other neurodegenerative and rare diseases.

Alzheimer’s disease is an irreversible, progressive brain disorder affecting more than 6.5 million Americans.1 It is characterised by the formation of amyloid beta plaques and neurofibrillary, or tau, tangles in the brain, which result in loss of neurons and their connections.1 On July 6, 2023, the U.S. Food and Drug Administration (FDA) granted traditional approval for Eisai’s Leqembi (lecanemab-irmb), an amyloid beta-directed antibody indicated to treat patients with Alzheimer’s disease.1 Leqembi was initially approved by the FDA in January 2023 under the Accelerated Approval pathway based on clinical data demonstrating the drug’s effect on a surrogate endpoint – reducing amyloid plaques in the brain – that was reasonably likely to predict a clinical benefit to patients. The decision to grant Leqembi traditional approval was made after the CLARITY AD confirmatory trial verified the drug’s efficacy.1 Leqembi became the first approved treatment shown to reduce the rate of disease progression and to slow cognitive and functional decline in adults with Alzheimer’s disease.2 Early Response It is widely agreed that the key to success in Alzheimer’s disease treatment is early intervention. It is important to identify at-risk patients and start meaningful treatment before they develop symptoms because at that point it is very difficult to reverse the disease.

Therefore, it is especially significant that Certara’s quantitative systems pharmacology (QSP) Alzheimer’s disease platform was able to predict the successful outcome of Eisai’s lecanemab CLARITY AD clinical trial one year before the data became available, when all the previous monoclonal antibodies had failed. Certara’s results were presented by BioArctic at AD/PD 2022 the International Conference on Alzheimer’s and Parkinson’s disease in Barcelona, Spain in March 2022.3 Eisai licensed lecanemab from BioArctic.

QSP combines computational modelling and experimental data to examine the relationships between a drug, the biological system, and the disease process.

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Certara’s QSP platform is particularly well suited to studying Alzheimer’s disease because it reflects the underlying biology of the amyloid aggregation pathway from the monomeric form to the plaque form. It is a mechanistic, realistic platform that integrates relevant biology and clinical data, and strikes a good balance between dataand mechanism-driven approaches. As this tool helps to replace animal studies, it also enables drug developers to follow the guidance in the FDA Modernization Act 2.0.4 In addition, it allows the identification of “virtual biomarkers,” which are (currently) inaccessible biomarkers driving the pathology that help to make the link to functional clinical outcomes for amyloid therapeutic agents.

Building Confidence Eisai’s initial goal was to use Certara’s QSP Alzheimer’s disease platform to create a mechanistic model that would help to ensure it chose the optimal dose for its Phase 3 trial because it had only limited information from its Phase 2 trials on which to base that decision.

Eisai also wanted to gain mechanistic understanding of the biomarker results it could expect to see during that trial and endeavour to identify differentiating factors that would give it confidence that its compound would be successful in the clinic.

Certara and Eisai published the results of their successful collaboration in a paper entitled “A Combined PBPK and QSP Model for Modeling Amyloid Aggregation in Alzheimer's Disease” in CPT: Pharmacometrics & Systems Pharmacology in January 2023.5 Important Differentiator At the beginning of the collaboration, a number of amyloid antibodies, all with seemingly similar properties, had failed. Certara integrated its QSP Alzheimer’s disease platform with its physiologically based pharmacokinetic (PBPK) platform to investigate the target engagement of the antibodies and explore any differentiators.

Postmortem biospecimens were used to determine the absolute levels and concentrations of different amyloid species, especially intermediate fractions of the pathological beta species, which cannot be evaluated in a living patient. Positron emission tomography (PET) imaging of amyloid load and fluid biomarker data from historical observational studies were all used as input to calibrate the platform.

Lecanemab did not look different from the other antibodies in terms of the measured biomarkers, especially brain amyloid load as reported by Standard-Uptake Value Ratio (SUVR) PET imaging.

But this particular biomarker didn’t tell the whole story. As the QSP platform demonstrated, the antibodies bind to different subspecies of amyloid, each with their own contribution to the Alzheimer’s disease pathology. The slightly different binding profile of lecanemab is key to its success.

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Therapeutics QSP Amyloid Aggregation Model

Many of the failed antibodies did not sufficiently reduce the concentration of beta amyloid protofibrils, a key intermediate species, which the scientific community believes is important for functional outcomes in Alzheimer’s disease. Even though the effect on amyloid plaque biomarkers were similar for all antibodies, lecanemab had the greatest reduction of those protofibrils.

While it is currently not possible to measure protofibrils in a living human brain, their dynamics after treatment with a candidate compound can now be predicted based on its pharmacology.

This QSP platform is unique in that it involves both the explicit modelling of the intermediate beta amyloid species in the brain and fluid biomarkers of proteins that are generated inside the brain and migrate into the cerebrospinal fluid (CSF) and plasma.

It can also simulate disease progression from birth on, allowing researchers to study the longitudinal and historical progression of the disease, enabling them to explore the impact of different antibodies at different stages of the disease.

Virtual Biomarker Interestingly, clinical trials reported changes in plasma p-tau levels after amyloid antibodies removed beta amyloid from the human brain. However, there was only a weak correlation between changes in brain amyloid load, measured with SUVR and changes in plasma phospho-tau levels, suggesting a more complex relationship.

The QSP platform identified the brain Abeta monomer level as a virtual biomarker that had a better correlation with the changes in plasma phospho-tau. The model provided a mechanistic and biological rationale as to why the Aβ42 monomer level dynamics inside the human brain drive this plasma phospho-tau biomarker.

That has important implications for the drug’s clinical rollout, because a plasma sample is much easier to obtain than CSF samples or a PET scan.

Minimising Side Effects The biggest challenge in clinical practice is to manage the delicate balance between efficacy and side effects. A major side effect with all amyloid antibodies is amyloid-related imaging abnormalities with edema (ARIA-E), a very serious and potential lethal side effect. It is hypothesized to be due to microbleeds in the brain vasculature and is also often accompanied by haemorrhage (ARIA-H).

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Certara’s QSP Alzheimer’s disease platform can predict both the compound’s efficacy and the likelihood of it producing this type of side effect. Therefore, it is possible to optimise the drug’s outcome by adjusting the titration dosing regimen.

These advances echo some of the legacy modelling work that Certara conducted with COVID-19 vaccines during the pandemic, studying different dosing intervals to optimise the immune response.

While the COVID-19 vaccine modelling focused mainly on efficacy, the focus here is on how to best balance efficacy and side effects. The Alzheimer’s disease modelling suggests that it is possible to minimise but not eliminate the side effects.

The Alzheimer’s disease platform has now been applied to several customers’ projects.

Clinical Repository A long-term monitoring initiative that is currently underway is the National Institutes of Health Alzheimer's Disease Preclinical Efficacy Database (AlzPED),6 which is a repository of the clinical trajectory of patients who are treated with anti-amyloid antibodies. It was initiated by the Alzheimer’s Association to look at the impact of anti-amyloid antibodies in real-world clinical practice.

The goal of this database is to better understand why specific patient populations respond better or do not respond at all to certain anti-amyloid therapies, and to streamline, optimise and personalise treatment.

For instance, Roche’s gantenerumab only failed due to a lack of clinical efficacy in females, whereas the treatment shows clear benefit in the male population, despite a similar reduction in the amyloid biomarker. Nobody understands yet why females and males respond so differently in this particular trial. The hope is that the Alzheimer’s disease QSP platform might be able to generate hypotheses about this and other observations.

Regulatory Interest The FDA takes an active interest in QSP modelling advances. The Certara team was invited to present its Alzheimer’s disease work to the FDA a few months ago and the Agency is in the process of obtaining a license. It also gave webinars to the Agency on its immunogenicity, vaccine, and immuno-oncology simulators earlier this year.

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Therapeutics Tackling Tau Tau is the next major target in Alzheimer’s disease. The Certara team is combining its amyloid and tau QSP models to facilitate investigation of combination therapy in living patients. It is also starting to examine neuro-inflammation in Alzheimer’s disease.7 A QSP Model for Parkinson’s Disease The tau progression model, which can be used as a template for an a-synuclein (α-Syn) progression model after appropriate parameter modifications, can be applied to candidate drugs for Parkinson’s disease. Aggregation of α-Syn is associated with the dysfunctionality and degeneration of dopamine neurons in Parkinson’s disease.8 Certara’s CNS platform also allows it to estimate the functional clinical outcomes for Parkinson’s disease patients in terms of the clinical scales of rigidity (stiffness), tremor and bradykinesia (slow movement), which are well calibrated using the symptomatic treatments that are available.

This is important because even disease-modifying therapies in Parkinson’s disease need to be administered on top of the existing standard of care, (various formulations of L-dopa), and different dopamine modulators often mask to a different degree some of the functional benefits of the disease modifying therapy.

Creating Virtual Twins This highlights the need for Certara’s concept of virtual twins. Here an individualised QSP model for each patient in the active treatment arm is created, complete with baseline genotypes, comedications and disease state, allowing it to simulate the untreated (placebo) trajectory and subsequently compare with the actual treatment outcome on a per-patient basis. This will reduce the variability and increase the clinical signal of any novel therapeutic intervention.

Such an approach can increase the probability of success and reduce the number of placebo patients required in clinical trials for common neurodegenerative diseases.

Other applications include rare neurological diseases, where significant challenges for developing new therapies include the limited number of patients with the condition and sometimes the invasive nature of the intervention. Consequently, it is close to impossible to conduct a double-blind, placebo-controlled study.

In most cases, sponsors need to demonstrate the benefits of their candidate drug in a single-arm trial comparing it with the standard of care or historical controls which are hypothesized to be well matched with the treatment population. However, because of the limited number of patients and the variability of the functional trajectories due to baseline conditions, not only is such a selection somewhat arbitrary, but it is difficult to achieve clear statistical significance.

The use of external controls or synthetic controls is an important area for regulators. For example, of course no parent will sign up their child with a rare disease for a placebo study. Those children need treatment and regulatory agencies are exploring new guidelines for these single-arm trials.

Simulating the clinical trajectory of untreated computer-generated virtual twins allows one to compare the outcome for each individual patient with and without the active treatment, enabling each patient to serve as their own external placebo control. By taking this virtual twin QSP approach, a sponsor can additionally generate evidence of a positive difference between their active treatment and the standard of care.

Future Applications New developments to Certara’s CNS QSP platforms will enable the 32 Journal for Clinical Studies

investigation of frontotemporal dementia9 and rare diseases, such as progressive supranuclear palsy10 or corticobasal degeneration.11 Conclusion QSP modelling can be used to predict a candidate drug’s efficacy both in biomarker and clinical readouts and optimise its side effects profile.

These advances are showing potential to improve new therapies not only for Alzheimer’s and Parkinson’s disease but also for other forms of dementia and rare neurological diseases.

REFERENCES 1.

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FDA lecanemab approval press release: https://www.fda.gov/newsevents/press-announcements/fda-converts-novel-alzheimers-diseasetreatment-traditional-approval Eisai lecanemab approval press release: https://www.eisai.com/ news/2023/news202349.html BioArctic press release: “Latest Lecanemab Data to be Presented at the AD/PD™ Congress.” March 11, 2022. https://www.bioarctic.se/en/latestlecanemab-data-to-be-presented-at-the-ad-pd-congress-2/ Han JJ. FDA Modernization Act 2.0 allows for alternatives to animal testing.

Artif Organs. 2023 Mar;47(3):449-450. doi: 10.1111/aor.14503. Epub 2023 Feb 10. PMID: 36762462. https://pubmed.ncbi.nlm.nih.gov/36762462.

Geerts, H, Walker, M, Rose, R, et al. A combined physiologicallybased pharmacokinetic and quantitative systems pharmacology model for modeling amyloid aggregation in Alzheimer’s disease. CPT Pharmacometrics Syst Pharmacol. 2023; 12: 444-461. Doi:10.1002/ psp4.12912.

NIH: AlzPED https://alzped.nia.nih.gov/ Hugo Geerts, Silke Bergeler, Mike Walker, Piet van der Graaf, Jean-Philippe Courade, “Analyzing the clinical trial failures of anti-tau and anti-asyn antibodies using a quantitative systems pharmacology model.” Scientific Reports. 2023 Sep 1;13(1):1434.

Gómez-Benito M, Granado N, García-Sanz P, Michel A, Dumoulin M, Moratalla R. Modeling Parkinson's Disease With the AlphaSynuclein Protein. Front Pharmacol. 2020 Apr 23;11:356. doi: 10.3389/ fphar.2020.00356. PMID: 32390826; PMCID: PMC7191035.

Alzheimer’s Association: Frontotemporal dementia https://www.alz. org/alzheimers-dementia/what-is-dementia/types-of-dementia/ frontotemporal-dementia National Organization for Rare Disorders (NORD): Progressive Supranuclear Palsy https://rarediseases.org/rare-diseases/progressivesupranuclear-palsy/ NORD: Corticobasal Degeneration https://rarediseases.org/rare-diseases/ corticobasal-degeneration

Piet van der Graaf Piet van der Graaf, PharmD, PhD, is Senior Vice President and Head of QSP at Certara and Professor of Systems Pharmacology at Leiden University. Before joining Certara, Piet was the CSO of the Leiden Academic Centre for Drug Research and held various research leadership positions at Pfizer across discovery and clinical development.

Hugo Geerts Hugo Geerts, PhD, is Head of QSP Neurosciences at Certara. As the former Co-founder of In Silico Biosciences, Hugo has 21 years of mechanism based QSP modeling experience in neurology and psychiatry. He also has 20 years of drug discovery and development experience while a Research Fellow at the Janssen Research Foundation laboratoria in Beerse, Belgium.

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Journal for Clinical Studies 33

Logistics & Supply Chain

Application Note

Building Robust Clinical Supply Chains to Support Global Trials As clinical trials become more complex in design, seek to include varying populations and reach patients around the world, having a robust end-to-end supply chain solution is vital to ensure investigational drugs are readily available at clinical sites when needed.

To help build a robust clinical supply chain that supports global trials effectively and efficiently, key areas to consider include: 1. Demand Forecasting and Planning A crucial starting point is to have an understanding of the trial protocol and patient enrolment projections to estimate the demand for the investigational drug. Factors such as trial duration, patient recruitment rates, dosing schedules, and geographical distribution should be considered. In addition, factors related to the study drug such as manufacturing schedule and quantities, dating period and stability program, and kit design should also be considered. Working closely with internal clinical teams and external partners such as contract research organisations (CROs) and your partner contract development and manufacturing organisations (CDMOs) are important for gathering accurate information for demand forecasting.

2. Global Sourcing and Procurement With the major upheaval in supply chains over the past number of years, global sourcing and procurement have been in the spotlight.

Disruptions to the pharmaceutical supply chain vary in severity, frequency and lead time – and they happen regularly. Identifying reliable suppliers for raw materials/APIs, packaging materials etc and working with global CDMOs with a local presence in your clinical trial territories will help secure your supply chain. The criteria for evaluating suppliers should be based on their quality, reliability, regulatory compliance, and ability to scale-up to meet global clinical demand and ultimately commercialisation.

3. Manufacturing and Packaging Factor in your clinical supply strategy. Whether you are manufacturing and packing your clinical supplies in-house or partnering with a global integrated CDMO you should ensure your processes have the flexibility to quickly respond to unplanned events that will occur during the trial. Variations from planned enrolment rates or geographies, catastrophic loss of inventory, or interruptions in the supply chain will necessitate creative solutions. Packaging strategies should also be flexible to accommodate issues that arise.

4. Distribution and Logistics Develop a distribution strategy that not only ensures timely and compliant delivery of investigational drugs to trial sites worldwide, but maintains the integrity and safety of your drug products throughout the supply chain. Your chosen distribution partners should have expertise in global shipping, customs regulations, and 34 Journal for Clinical Studies

cold chain management if applicable for temperature sensitive drug products.

It is also important to think about global logistics and the countries where you will launch your new compound. For instance, assessing international regulations and the importation and exportation of a clinical trial medication in terms of time and cost, which will eventually be part of the commercialisation plan.

5. Inventory Management Implementing an efficient inventory management system to monitor stock levels at different sites enables the optimisation of production and distribution strategies for efficient and robust clinical supply management. Utilising advanced inventory technology facilitates data driven planning to help prevent stockouts and overstock situations, ensuring clinical supply matches patient demand.

Application of technologies such as Late Stage Customisation (LSC) and Just-in-Time (JIT) supply models provide a streamlined approach, minimising waste and loss, optimising inventory, and furthermore enabling a more nimble supply capable of adapting to changing clinical demands. Given the ever-increasing high value of medicines, particularly for rare diseases and indications such as oncology, this is important in getting vital medicines to patients around the world.

6. Regulatory Compliance Stay informed about the regulatory requirements and guidelines for clinical supply chain management in different regions. Ensure that requirements for the supply such as label text and release as well as all activities from sourcing to distribution comply with the relevant regulations (e.g., Good Manufacturing Practices, Good Distribution Practices, local regulations).

7. Risk Management Proactive supply chain analysis and scenario planning is needed to address questions such as; what would happen if an unplanned event was to occur at an existing supplier? What impact would a natural disaster have on supply chains? If there was a surge in demand, could the existing supply chain model absorb it? What risk control and mitigation plans are in place to prevent drug supply disruptions and shortages of critical medicines?

In line with regulatory authority guidance to help prevent drug shortages and as good practice, potential risks should be identified and contingency plans proportional to the impact should be established to address supply chain disruptions. Risk management plans should be reviewed and updated regularly based on changing circumstances.

8. Communication and Collaboration Fostering open communication between different internal Volume 15 Issue 3

Application Note

Logistics & Supply Chain We provide a global service with localised focus, delivering over 200 protocols a year in over 100 countries, utilising best-inclass technologies combined with our experienced and dedicated teams. Our global network of innovative centers of excellence across Asia Pacific, Europe, North America and Canada provide a seamless service, supporting the global supply of investigational medicines with pharmaceutical development, clinical drug product manufacturing, packaging, labelling, storage and distribution and full returns services.

PCI Clinical SMART (Supply Management And Readiness Team) offers the expertise to foresee and plan ahead to overcome hurdles experienced during the clinical lifecycle. SMART Clinical Supply Managers have the expertise to manage single trials or entire clinical programs with support that can be customised to meet specific study requirements and adapted as the trial progresses. Providing dedicated experienced project management with end-to-end capabilities from protocol development, establishing processes and SOPs to study close adds robustness to your supply chain.

At PCI, we embrace a digital-first mindset and leverage industryleading technology. Our digital supply chain management platform pci | bridgeTM, is built to deliver an industry-leading customer experience, unlocking productivity with access to real-time supply chain information and digital workflows. The innovative technology is designed to provide accessibility to production and distribution status, open invoicing and inventory information, documentation approvals and easy reporting features. Providing real-time supply chain visibility using cross-functional workflows and data-driven insights ensures milestones are met throughout the product lifecycle, supporting clinical and commercial supply chains alike.

departments, including clinical operations, regulatory, supply chain, and quality assurance will assist in optimising the clinical supply management process as drug products progress through the development lifecycle and will ultimately aid planning and preparation for launch. Collaborating closely with CROs, clinical sites, and CDMOs will also ensure responsiveness and alignment on supply chain needs. Creating communication plans that document the individuals involved from the various departments and companies with escalation routes and blinding levels will help in creating and maintaining open communications.

9. Technology and Data Integration Implementing advanced technologies such as supply chain management software, IXRS systems, data analytics, and AI will optimise supply chain operations. Integrating data from various sources will enhance operational visibility, aid decision-making and bring efficiencies. The more you can refine your future plan based on actual real-time data, the quicker you can react to changes and the more predictable your supply chain can become.

10. Continuous Improvement In the fast paced world of clinical trials nothing stands still. To help build a robust supply chain that supports your global trial you should regularly assess the performance of your clinical supply chain and identify areas for improvement.

PCI Pharma Services – Your Clinical Trial Destination. Our World.

PCI is a leading global CDMO, truly spanning the cycle, connecting development and commercialisation, de-risking the supply chain providing clients with integrated end-to-end drug development, manufacturing and packaging capabilities that increase their products’ speed to market and opportunities for commercial success. www.journalforclinicalstudies.com

Although supply-chain risks are unavoidable, biopharmaceutical companies can minimise their disruptive effects through greater visibility, rigorous risk management, and utilising newer technologies that help companies better anticipate and respond to shocks.

At PCI, our consultative and flexible approach combined with scalable integrated clinical supply solutions across drug product manufacturing and packaging, ensures optimisation and security of clinical trial supply chains helping to mitigate risk and deliver overall trial efficiencies.

Edward Groleau Ed Groleau has over 30 years of experience in the Pharmaceutical industry. He joined PCI Pharma Services in 2018 and became Sr. Director of PCI’s Supply Management And Readiness Team (SMART) in 2022. This group partners with clinical trial sponsors to provide any clinical supply management services needed from protocol development through destruction. Prior to PCI, Ed worked in numerous departments at Eli Lilly. He spent 15 years in various pharmaceutical development and analytical laboratories and in 2003 moved to Lilly’s Clinical Trial Supplies group with increasing responsibilities as the department adjusted to changes to the industry and regulatory requirements. In 2011 he became part of a highly integrated CM&C team responsible for overseeing the development of compounds from discovery through the proof-of-concept stage. In 2016 Ed moved to Elanco, Lilly’s animal health division, where he established a global clinical trial supplies group for developing companion and food animal projects.

Journal for Clinical Studies 35

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