Consideration of a Credibility Assessment Framework in Model-Informed Drug Development: Potential Application to Physiologically-Based Pharmacokinetic Modeling and Simulation.

The use of computational models in drug development has grown during the past decade. These model-informed drug development (MIDD) approaches can inform a variety of drug development and regulatory decisions. When used for regulatory decision making, it is important to establish that the model is credible for its intended use. Currently, there is no consensus on how to establish and assess model credibility, including the selection of appropriate verification and validation activities. In this article, we apply a risk-informed credibility assessment framework to physiologically-based pharmacokinetic modeling and simulation and hypothesize this evidentiary framework may also be useful for evaluating other MIDD approaches. We seek to stimulate a scientific discussion around this framework as a potential starting point for uniform assessment of model credibility across MIDD. Ultimately, an overarching framework may help to standardize regulatory evaluation across therapeutic products (i.e., drugs and medical devices).

Using a physiologically based pharmacokinetic model to link urinary biomarker concentrations to dietary exposure of perchlorate.

Exposure to perchlorate is widespread in the United States and many studies have attempted to character the perchlorate exposure by estimating the average daily intakes of perchlorate. These approaches provided population-based estimates, but did not provide individual-level exposure estimates. Until recently, exposure activity database such as CSFII, TDS and NHANES become available and provide opportunities to evaluate the individual-level exposure to chemical using exposure surveillance dataset. In this study, we use perchlorate as an example to investigate the usefulness of urinary biomarker data for predicting exposures at the individual level. Specifically, two analyses were conducted: (1) using data from a controlled human study to examine the ability of a physiologically based pharmacokinetic (PBPK) model to predict perchlorate concentrations in single-spot and cumulative urine samples; and (2) using biomarker data from a population-based study and a PBPK model to demonstrate the challenges in linking urinary biomarker concentrations to intake doses for individuals. Results showed that the modeling approach was able to characterize the distribution of biomarker concentrations at the population level, but predicting the exposure-biomarker relationship for individuals was much more difficult. The type of information needed to reduce the uncertainty in estimating intake doses, for individuals, based on biomarker measurements is discussed.

Bayesian analysis of a rat formaldehyde DNA-protein cross-link model.

As the initial effort in a multi-step uncertainty analysis of a biologically based cancer model for formaldehyde, a Markov chain Monte Carlo (MCMC) analysis was performed for a compartmental model that predicts DNA-protein cross-links (DPX) produced by formaldehyde exposure. The Bayesian approach represented by the MCMC analysis integrates existing knowledge of the model parameters with observed, formaldehyde-DPX-specific data, providing a statistically sound basis for estimating model output uncertainty. Uncertainty and variability were evaluated through a hierarchical structure, where interindividual variability was considered for all model parameters and that variability was assumed to be uncertain on population levels. The uncertainty of the population mean and that of the population variance were significantly reduced through the MCMC analysis. Our investigation highlights several issues that must be dealt with in many real-world analyses (e.g., issues of parameters' nonidentifiability due to limited data) while demonstrating the feasibility of conducting a comprehensive quantitative uncertainty evaluation. The current analysis can be viewed as a case study, for a relatively simple model, illustrating some of the constraints that analysts will face when applying Bayesian approaches to biologically or physiologically based models of increasing complexity.