Physiologically-based pharmacokinetic (PBPK) modeling and simulation is increasingly accepted due to the enormous cost and time saving benefits that can be realized through its ability to address regulatory concerns without always defaulting to clinical study — particularly relating to assessing complex drug-drug interactions (DDIs). Independent validation of simulations against clinical data provides confidence in the results and guides users in adopting best practices. In this blog post, I’ll discuss a paper— authored by scientists working at the US Food and Drug Administration (FDA)— describing the development and validation of PBPK models to assess the impact of pharmacogenetics and poly-pharmacy on drug disposition.
Is a clinical study necessary?
Although dedicated clinical pharmacology studies can quantify the impact of certain intrinsic or extrinsic factors on drug exposure, investigating every possible scenario is not feasible, especially when there is complex interplay between multiple factors. Demonstration of PBPK modeling capabilities to assess these risks requires robust evidence that simulations can accurately predict the impact of multiple factors and provide meaningful data for both drug developers and regulators.
FDA scientists leverage the Simcyp Simulator
Using the Simcyp Simulator, researchers from the US FDA created PBPK models for four substrates of the key drug metabolism enzymes, CYP3A4 and CYP2D6. The models were assessed on the potential to predict the effects of decreased enzyme activities on drug concentrations as a consequence of co-administered inhibitors and/or genetic variation. The team reported high predictive accuracy against the clinical data, concluding that models can be used to understand the effects of individual or combined factors and might be used to support decisions on whether, when and how to conduct a clinical trial.
PBPK brings value to drug programs
This study provides further validation of the value of PBPK modeling and simulation in prospectively assessing the effects of genetic polymorphisms or DDIs on pharmacokinetics.
Optimizing clinical trials improves patient safety and reduces the risk of late-stage “surprises” due to unanticipated DDIs. By identifying those less informative trials that can be avoided altogether, pharmaceutical companies could potentially save around $1-2 million per study and accelerate the time to market by as much as two years.
All information presented derive from public source materials.
Further reading
Vieira MD, Kim MJ, Apparaju S, Sinha V, Zineh I, Huang SM, Zhao P. PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways. Clinical Pharmacology and Therapeutics. 2014; 95(5):550-7.
To learn how PBPK modeling and simulation has impacted key label elements in more than a dozen cases, driving down R&D costs and timelines, and allowing for greater population inclusion within the label, watch this webinar by Dr. Steve Toon. Let me know what you think in the comments section!