Acknowledgements • GARP has been funded by a grant to CDDEP from the Bill & Melinda Gates Foundation • Ramanan Laxminarayan, PI • Modeling: § Itamar Megiddo (CDDEP) § David L Smith Ø Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health
§ Eili Klein Ø Princeton University
Outline • Introduction • Pneumococcal Modeling • PneuMOD
Source: World Health Organization, 2002
Source: World Health Organization, 2002
Interventions to prevent & control pneumococcal disease
Prevention
Treatment
Protection
Policy Questions • How can we best (most cost-effectively) reduce pneumococcal disease burden through a combination of tools (vaccination, treatment)? • Can increased treatment access reduce the burden of disease in the long term when drug resistance is likely? • What is the value of treatment access improvements in countries with pneumo vaccination? • What population level strategies (like multiple first line treatments or subsidized high quality antibiotics) can most address the challenge of resistance?
Outline • Introduction • Pneumococcal Models • PneuMOD
Why model pneumococci? • How many lives could be saved? • At what cost? § Purchase cost of interventions § Opportunity cost
• How certain are the answers? • How could uncertainty be reduced?
What is a model?
How does a model of pneumococci work?
From Individuals
To Populations
Colonization / Infection
Uncolonized
Colonized
Infected
Transmission
Shedding/ Mixing / Exposure
Serotypes Pneumococcal Global Serotype Project
Immunity
Age-Specific Incidence of Invasive Pneumococcal Disease in the United States by Serogroup Lipsitch, M. et al. PLoS Med 2, e15 (2005).
Drug Treatment & Resistance
EPAR-694; No of Pages 8
eview
Trends in Parasitology Vol.xxx No.x
ure 1. The pharmacokinetic determinants of outcome in uncomplicated malaria. Drug concentrations must exceed minimum parasiticidal concentrations (MPCs) until all asites are eliminated; this usually takes at least four 48-h asexual life cycles in P. falciparum. The area under the blood or plasma concentrationFigure time curve (AUC) predicts between dose and treatment response. The ideal 3. The association therapeutic response. The maximum concentration (CMAX) is considered less important in the treatment of uncomplicated malaria. For slowly eliminated antimalarial treatment dose changes over time with the spread of resistance. (a) Initially, gs, the blood or plasma concentration on day 7 is a good correlate of the AUC and thus treatment response. because all parasites are drug sensitive, lower drug levels might not increase the
at are often of greater interest because the lower blood ncentrations are associated with treatment failure or a lection of resistant parasites, whereas the highest conntrations are more likely to be associated with toxicity 8,19]. It is therefore important to complete pharmacokitic sampling when patients are withdrawn from a study lowing adverse events or treatment failure. The greater e inter-individual variability and the narrower the erapeutic range, the greater will be the proportion of tients with sub-therapeutic or toxic drug levels. Low oral oavailability is the main cause of large inter-individual riability in blood concentrations (e.g. lumefantrine) [9]. ter-individual variability can be exacerbated by the use use of concomitant medications that interact with the
risk of treatment failure. (b) With the development of resistance, higher drug
define therapeutic drug-concentration ranges in non4 immune populations; in stable malaria-transmission areas, this means young children. Pharmacokinetic studies have usually not been designed to describe adequately the relationship between drug concentrations over time [pharmacokinetics (PK)] and therapeutic response and drug safety [pharmacodynamics (PD)] (Box 2). As a result, most antimalarials have been introduced with dosage regimens derived from dose-finding studies in non-pregnant adult patients. These dosage regimens have often been too close to the minimum effective dose required, both for cost-saving and because of concerns over drug safety. The dosage regimens of amodiaquine, primaquine, mefloquine, halofantrine,
associated with increase lier reinfections [9,27]. I drug disposition is comp and clearance of chlorp with dapsone, is lower i whereas clearance of its is higher in young childr ent volume of distributio weight [28]. However, no chlorproguanil-dapsone dren and adults when do Concentrations of pipera dihydroartemisinin, are terminal elimination ph low plasma piperaquin associated with treatme Young children with susceptible to quinine c [32]. There is no evidenc misinin derivatives is si dren, although marked the power of small studie the pharmacokinetic stud young children concern r it has been developed volume of distribution age, it appears unlikely t [33]. Small studies did n concentrations of deseth olite of amodiaquine) or absorption and dispositi infants and children, the larial pharmacokinetics
Pregnant women Pregnant women are at from malaria than no
concentrations are required to ach With higher levels of resistance, th effective dose and that associated
Biology, Ecology, Epidemiology, Economics… • Colonization § Duration
• Transmission § Shedding § Contact § Establishment
• Microbial Competition § Exclusion § Dominance
• Infection
• Immunity § Inhibit Colonization § Prevent Disease § Strain Specificity
• Interventions § Drugs § Vaccines
Outline • Introduction • Pneumococcal Models • PneuMOD
Markov-Chain Carriage Model Colonized Population growth
!
b(...)
b(...)
b(...)
d (...)
d (...)
d (...)
d (...)
Uncolonized
Population decay
Shedding Increases
Markov-Chain Carriage Model
Infection
Increasing Risk
Immune Stages
Waning
Waxing
ê Duration of Carriage ê Prob. Colonization after Exposure ê Shedding ê Risk of Infection ê Severity of Disease
Competition (Serotypes & Strains) Meets Strong “Neutrality” Conditions
Etc.
Drug Treatment / Compliance
TREPAR-694; No of Pages 8
Review
Trends in Parasitology Vol.xxx No.x
Figure 1. The pharmacokinetic determinants of outcome in uncomplicated malaria. Drug concentrations must exceed minimum parasiticidal concentrations (MPCs) until all parasites are eliminated; this usually takes at least four 48-h asexual life cycles in P. falciparum. The area under the blood or plasma concentration time curve (AUC) predicts Figure 3. The association between dose and treatment response. The ideal the therapeutic response. The maximum concentration (CMAX) is considered less important in the treatment of uncomplicated malaria. For slowly eliminated antimalarial treatment dose changes over time with the spread of resistance. (a) Initially, drugs, the blood or plasma concentration on day 7 is a good correlate of the AUC and thus treatment response. because all parasites are drug sensitive, lower drug levels might not increase the risk of treatment failure. (b) With the development of resistance, higher drug
that are often of greater interest because the lower blood concentrations are associated with treatment failure or a selection of resistant parasites, whereas the highest concentrations are more likely to be associated with toxicity [18,19]. It is therefore important to complete pharmacokinetic sampling when patients are withdrawn from a study following adverse events or treatment failure. The greater the inter-individual variability and the narrower the
define therapeutic drug-concentration ranges in nonimmune populations; in stable malaria-transmission 4 areas, this means young children. Pharmacokinetic studies have usually not been designed to describe adequately the relationship between drug concentrations over time [pharmacokinetics (PK)] and therapeutic response and drug safety [pharmacodynamics (PD)] (Box 2). As a result, most antimalarials have been intro-
mg kg!1 doses o lower plasma dr in older patien trations achieve significantly low decrease in the associated with lier reinfections drug disposition and clearance with dapsone, i whereas clearan is higher in youn ent volume of di weight [28]. How chlorproguanil-d dren and adults Concentrations dihydroartemisi terminal elimin low plasma pip associated with Young childr susceptible to q [32]. There is no misinin derivati dren, although the power of sma the pharmacokin young children c it has been de volume of distr age, it appears u [33]. Small stud concentrations o olite of amodiaq absorption and infants and chil larial pharmaco
Pregnant wome Pregnant wome from malaria t
concentrations are req With higher levels of r effective dose and tha
Ordinary Differential Equations…. …Individual-Based Simulation With maternal immunity
!
•
u 0 = d1, 0 (x1, 0 + y1, 0 ) − Λu0 + µ (1 − u0 ) !mu0 if k > 1 in m.i. case
m.i. if k = 1 ! u k = d1,k (x1,k + y1,k ) − ωk Λuk − γ k uk + γ k +1uk +1 − µ N uk +mu0 •
s.t.
k ≥1
•
x i ,k = d i +1,k xi +1,k + bi −1,k xi −1,k − (d i ,k + bi ,k )xi ,k + (α i ,k yi ,k − σ i ,k xi ,k ) − θ x ,i ,k xi ,k + θ x ,i ,k −1 xi ,k −1
(
)
+ ci ,k ωk Λ (1 − pi ,k ) uk + ∑w≤i − 2 xw,k + ci −1,k ωk Λ (1 − pi −1,k )xi −1,k − ωk Λ ∑w≥i −1 cw,k xi ,k − µ N xi ,k + mxk
! !" # # $
m.i. k = 0 (-) ; k > 0 (+)
•
y i ,k = d i +1,k yi +1,k + bi −1,k yi −1,k − (d i ,k + bi ,k )yi ,k + (σ i ,k xi ,k − α i ,k yi ,k ) − θ y ,i ,k yi ,k + θ y ,i ,k −1 yi ,k −1
( [
]
)
+ ci ,k ωk Λ pi ,k uk + ∑w≤i −1 xw,k + ∑w≤i −1 y w,k + ci −1,k ωk Λ ( pi −1,k xi −1,k + yi −1,k ) − ωk Λ ∑w≥i −1 cw,k yi ,k − (µ N + µ I )yi ,k + mxk
! !" # # $
m.i. k = 0 (-) ; k > 0 (+)
2000
4000
Vaccination and Antibiotics Campaigns Starting on Year 10 Campaign 20% coverage 40% coverage 20% coverage & antibiotics control 40% coverage & antibiotics control
0
Colonization prevalence per 10,000
Simulated Vaccination
5
10
15
60 40
333.94 204.97 439.15 110.30
20
Discounted Infections
0
ction incidence per 10,000
Year
20
The model produces important policy relevant results Output: Calibrate model parameters
Run/solve model implementing interventions
- Mortality - Infection - Colonization - ‌.
Simulated for Kenya
Need better data for… • • • • •
Duration of carriage Complexity of colonies Risk of infection Competitive dominance Immunity § § § §
Colonization Infection Cross-immunity Age
• • • • • •
Local strains Health seeking Treatment compliance Non-sterile tissue PK/PD Underlying populations Human mobility
Burden of Pneumococci - I
Source: World Bank, 2000; World Health Organization, 2000