Optimizing the Drug Development Process using Pharmacometric tools:
A case study on Type 2 diabetes patients

Serge Guzy, Ph.D., Professor of Phamacometrics, VP Pharmacometrics, Bioforum

Pharmacometrics can be defined as the analysis and interpretation of data produced in pre-clinical and clinical trials. It includes Population Pharmacokinetic/Pharmacodynamic modeling, Disease progression modeling and Clinical Trial Simulation.

Population pharmacokinetic and Pharmacodynamic modeling involves the analysis of data from a group (population) of individuals, with all their data analyzed simultaneously to provide information about the variability of the model's parameters.

Disease progression models are mathematical models to describe, explain, investigate and predict the changes in disease status as a function of time. It incorporates the functions of natural disease progression as well as Drug action which reflects the effect of a drug on disease status.

Clinical Trial simulation provides a data set that will resemble the results of an actual trial that has not been conducted yet. Multiple replications of a clinical trial simulation can be used to make statistical inferences like estimate the power of the trial (Predicting p-value) and/or estimate the expected % of the population that should fall within a predefined therapeutic range.

A sponsor has a proprietary antibody that was engineered as a broad anti-inflammatory agent, with the potential as a treatment for many diseases, including diabetes (Type 2 Diabetes, T2D), rheumatoid arthritis, acute gout, systemic juvenile idiopathic arthritis (sJIA), and perhaps for the treatment of cardiovascular disease. This study concentrates on T2D patients where a Pharmacometric analysis was performed to Select the right Pharmacokinetic (PK) model based on Preclinical PK data, Characterize the PK correlation with a Pharmacodynamic (PD) biomarker, based on Phase 1 data, Propose the optimal trial conditions for the upcoming Phase 2 trial.

Based on Preclinical Pharmacokinetic study on cynomolgus monkeys, first time in man trial (Phase 1) was proposed using combined Pharmacokinetic/allometric scaling modeling.

Phase 1 data included both IV and SC route as well as single and multiple dose regimens. The PD marker was a surrogate of the disease progression with the drug inhibiting the PD marker production. A Population PK/PD modeling was performed and revealed that maximum inhibition effect is itself reduced by the drug (feedback inhibition).

Being aware of that feedback inhibition phenomena, the design of the Phase 2 trial was assessed using simulations. The goal of the simulation procedure was to quantitatively assess the dose response relationship for the monthly SC dosing and decide the different arm conditions for Phase 2.

The results of this complex PK/PD analysis lead to an optimal study design of the upcoming Phase 2 trial. The trial would include Five arms (Four treatments groups and 1 Placebo Group). The simulations predict enough separation between the different Dosing arms and the ability to show there is indeed a Dose Response relationship, key objective in order to optimize the probability of success in having the drug approved in Phase 3.

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Abstract Reference & Short Personal Biography of Presenting Author

Serge Guzy acquired his Chemical Engineering Degree in 1982 from the University of Brussels and received his Master’s Degree in Chemistry and Biophysics in 1985 from the Weizmann Institute. Serge obtained his Doctorate in Biomedical Engineering in 1990 from Technion and, one year later, got his Post-doctorate Degree from UC Berkeley in Chemical Engineering.Serge held a faculty position at UCSF School of Pharmacy between 1991-1996.

Serge expertise includes services includes mathematical modelling, statistical modelling and simulation of clinical trials, Population Pharmacokinetics, Pharmacodynamics, compartment analysis, design of experiments and optimization algorithms development. Serge founded POP-PHARM in 2004, with the goal of providing consulting and software development in support of drug development. With more than 30 years of experience with modeling and simulation, Serge Guzy established new methods for statistical population approaches in drug development, based on Monte Carlo simulation algorithms. The resulting MC-PEM methodology and population software development made him internationally recognized. These new tools have already been well utilized in drug development.

Serge brings vast professional experience. Serge is currenlty associated with Bioforum as VP of Pharmacometrics, Serge serves also as President and CEO of Pop Pharm. He is also Affiliate Professor at the University of Maryland, Adjunct Professor at the University of Minnesota, Courtesy Professor at the College of Pharmacy, Florida and Adjunct Professor at the University of Denver.