BENJAMIN GLICKSBERG
DATA SCIENCE PLATFORMS
FOR PATIENT-CENTRED DRUG DISCOVERY BENJAMIN GLICKSBERG, PH.D. CURRENTLY SERVES ON THE LEADERSHIP TEAM OF CHARACTER BIOSCIENCES AS THE VP AND HEAD OF DATA SCIENCE AND MACHINE LEARNING. Dr Glicksberg was an Assistant Professor in AI for Human Health and Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, where he led a team using machine learning on multi-modal and multi-omic patient data for personalising medicine. His research applications range from predictive modelling to drug discovery. Dr Glicksberg received his Ph.D. from the Icahn School of Medicine at Mount Sinai and completed post-doctoral work at the University of California, San Francisco.
CHALLENGES OF TRADITIONAL DRUG DEVELOPMENT PROCESS Bringing a new drug to market is a complex, lengthy endeavour and is beset with challenges. The traditional drug development process can span over a decade and requires substantial investment, on the order of 100s of millions to billions of dollars. Furthermore, there is a high failure rate for novel drugs across the stages of development. Lastly, even if a drug gets approved, there is no guarantee that it will be equally beneficial to patients with different demographic and clinical characteristics. Despite the numerous successful treatments available, it is clear that new strategies should be developed to overcome these challenges to generate a higher likelihood of success and more treatment options for patients. With recent
advancements in machine learning techniques and computing power, data science is poised to not only streamline drug discovery but also identify more personalised medicine applications.
PRECISION MEDICINE AND PERSONALISED TREATMENT APPROACHES Precision medicine aims to provide the right therapy for the right patient at the right time. It is becoming increasingly apparent that medicines do not work the same way for everyone. They can have varying levels of safety and efficacy for individuals with different characteristics. Some of this variability can be linked to genetics and is, therefore, especially relevant in diseases with high levels of genetic contribution. In certain cancers, for instance, there may be particular causal
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