R IN PHARMACOMETRICS, MODELING AND SIMULATION SHARVARI BHAGWAT

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1 R IN PHARMACOMETRICS, MODELING AND SIMULATION SHARVARI BHAGWAT SCIENTIST CLINICAL PHARMACOLOGY, MODELING AND SIMULATION

2 PRESENTATION OUTLINE Introduction to pharmacometrics and model based drug development What is pharmacometrics Model based drug development Use of R as a simulation tool Use of R as a model assessment tool Conclusion 2

3 INTRODUCTION What is pharmacometrics Pharmacokinetics - What the body does to the drug Pharmacodynamics - What the drug does to the body Pharmacometrics the science of developing and applying mathematical and statistical methods to characterize, understand and predict a drug s pharmacokinetic, pharmacodynamics and biomarkeroutcomes behavior Ette E., Williams P., Pharmacometrics: The Science of Quantitative Pharmacology, Wiley Interscience,

4 INTRODUCTION Model based drug development Bonate P., Pharmacokinetic-Pharmacodynamic Modeling and Simulation, Springer,

5 MODEL BASED DRUG DEVELOPMENT (MBDD) Pharmacostatistical models of drug efficacy and safety are developed from preclinical and available clinical data MBDD based approaches can be used to facilitate quantitative decision making Models can be used to summarize essential information in an efficient way, so that knowledge from different studies and external sources can be integrated. Miller, R., Ewy, W., Corrigan, B. W., Ouellet, D., Hermann, D., Kowalski, K. G., Lalonde, R. L. (2005). How Modeling and Simulation Have Enhanced Decision Making in New Drug Development. Journal of Pharmacokinetics and Pharmacodynamics, 32(2), Kimko, H., & Pinheiro, J. (2014). Model-based clinical drug development in the past, present and 5future : a commentary. British Journal of Clinical Pharmacology.

6 USE OF R AS A SIMULATION TOOL

7 STRUCTURAL PK MODEL Step 1 - Describing the structural PK model In this example, a two compartment model with zero order input and first order elimination was used a closed form equation was used to describe the structural model C t = D Tinf A α 1 e α(t td) + B β 1 e β(t td) if t-td Tinf C t = D Tinf A 1 α e_αtinf α(t td Tinf) )e + B 1 if not β e_βtinf )e β(t td Tinf) td = time of infusion Tinf = infusion length D = total dose Kimko, H., & Pinheiro, J. (2014). Model-based clinical drug development in the past, present and future : a commentary. British Journal of Clinical Pharmacology. 7

8 STRUCTURAL PK MODEL The deterministic structural PK model can be used to get an estimate of exposures and compare exposures between different scenarios 100 mg of drug given over different infusion lengths Hypothetical data for representation 8

9 STRUCTURAL PK MODEL How would the maximum exposure change with doses? Hypothetical data for representation 9

10 STATISTICAL MODEL Step 2 - Describing the statistical model There are generally two sources of variability Between subject variability (BSV) Most parameters are distributed log-normally since they cannot be negative Residual variability This is the unexplained variability in the concentrations across subjects If the mean and variance is known from previous studies/literature, a distribution of the variability can be simulated 10

11 COVARIATE MODEL Step 3 - Describing the covariate effects Effects of covariates such as weight, age etc. on pharmacokinetic parameters can be incorporated in the parameter definitions when writing the model in R 11

12 SIMULATING POPULATION PK USING THE COMPLETE MODEL Structural model + statistical model + covariate model = population pharmacokinetics Can be used to predict the variability in clinical trial exposures Can be used to support decisions regarding clinical trial design. Hypothetical data for representation 12

13 SIMULATING LARGE CLINICAL TRIALS There are scenarios where multiple doses need to be simulated using complex models over a long period of time. Number of subjects in the trial could be very large and/or simulation of multiple trials may be needed If simulations are coded with differential equations, they can take several hours to complete Functions can be written in C++ and loaded into R 13

14 SIMULATING POPULATION PHARMACOKINETICS USING THE TARGET MEDIATED DRUG DISPOSITION MODEL This model is described using the differential equations shown below in the white box Since it would be tedious to integrate these equations and obtain a close form equations, simulations are done using the differential equations TMDD model schematic 14

15 SIMULATING POPULATION PHARMACOKINETICS USING THE TARGET MEDIATED DRUG DISPOSITION MODEL Simulation conditions Hypothetical drug showing TMDD Dosing 100 mg every 8 weeks for 8 cycles Simulate a trial with 500 subjects 15

16 COMPILING SIMULATIONS IN R USING C++ (UBIQUITY) Ubiquity A model development and deployment tool (ubiquity.grok.tv) Developed by John Harrold and Anson Abraham Uses C++ to compile simulations in a fraction of the time as compared to R Can be customized to simulate using any PK model Can be used to conveniently simulate complex scenarios 16

17 GENERATING AN R-SHINY GUI USING UBIQUITY Hypothetical data for representation 17

18 GENERATING AN R-SHINY GUI USING UBIQUITY Simulation time - ~56 seconds with Ubiquity >30 min if done using only R Hypothetical data for representation 18

19 GENERATING AN R-SHINY GUI USING UBIQUITY Hypothetical data for representation 19

20 USE OF R AS A MODEL ASSESSMENT TOOL

21 USE OF R AS A MODEL ASSESSMENT TOOL While simulations are commonly done in R, modeling of clinical data is mostly done using dedicated modeling software such as NONMEM However, R is used for generating diagnostic plots when assessing model fits 21

22 DIAGNOSTIC PLOTS Observed concentrations vs population predicted and individual predicted concentrations Hypothetical data for representation 22

23 DIAGNOSTIC PLOTS Conditional weighted residuals vs population predicted concentration and time Hypothetical data for representation 23

24 DIAGNOSTIC PLOTS Covariate analysis Hypothetical data for representation 24

25 Dose normalized concentration VISUAL PREDICTIVE CHECK Time Data for representation 25

26 CONCLUSION R can be used in the model based drug development process at various stages It can be used as a versatile clinical trial simulation tool It is commonly used for model assessment and for generating diagnostic plots using NONMEM output User-friendly Shiny interfaces can be created for ease of use 26