As anyone who has worked in the pharmaceutical industry knows, there is a constant push to accelerate and optimize development processes. Physiologically based biopharmaceutics modeling (PBBM) is an ally in informing formulation design, mitigating development risks, and reducing reliance on clinical trials through virtual assessment.

In this blog post, I will highlight two case studies that showcase the utility of PBBM in evaluating food effects and guiding the development of dissolution specifications.

What is PBBM?

PBBM is a computational modeling approach used to predict the pharmacokinetics and pharmacodynamics of drugs within the body. It builds upon the established physiologically based pharmacokinetic (PBPK) modeling by integrating physiological characteristics with drug product properties to mechanistically capture drug absorption and simulate the full spectrum of absorption, distribution, metabolism, and excretion (ADME).

GastroPlus® exemplifies this modeling framework by providing a robust mathematical tool for tracking drug transit through various gastrointestinal compartments. The software accounts for critical factors such as dissolution, precipitation, permeation, and metabolism under varied dosing conditions. It enables modeling across diverse physiologies, including age, weight, dietary habits, and disease states, and can be applied across multiple preclinical species. This versatility facilitates understanding of complex drug formulation-physiological interactions, enhancing mechanistic insights and informing effective drug development strategies.

Selumetinib Case Study

This case study highlights how PBBM was used to understand the food effect mechanisms of two unique formulations of selumetinib and establish a safe space for capsule dissolutioni.

Selumetinib is a weakly basic drug formulated as a hydrogen sulfate salt to improve solubility. It was investigated in two main dosage forms: an immediate-release capsule and enteric-coated granules. The capsule is designed for rapid dissolution and displays a negative food effect, while the granules delay release until after stomach emptying, reducing variability caused by food.

The PBBM incorporated in vitro data, such as solubility, dissolution rates, and precipitation kinetics alongside in vivo clinical pharmacokinetic data from both intravenous and oral studies. It accounted for factors such as gut metabolism, P-glycoprotein efflux, and variations in gastrointestinal physiology between fasted and fed states.

After validation across multiple clinical scenarios, the model was used to assess how slow the dissolution rate could be without negatively impacting clinical performance. Simulations of a virtual batch with slower dissolution than the reference capsule revealed only minor differences in pharmacokinetic profiling, suggesting that the virtual formulation would remain effective, providing a safe space and sound regulatory basis for dissolution specifications.

In assessing food effects, the model clarified mechanisms behind the negative food effect for the capsule and the minimal food effect for the granules. For capsules, the model displayed reduced dissolution and increased precipitation in the higher-pH stomach environment after eating. The granules exhibited resilience to food effects due to the enteric coating which dissolves after gastric emptying. For both formulations, first-pass gut extraction was higher in the fed state due to delayed gastric emptying and dilution in the stomach chyme, but this was compensated by a lower liver extraction due to the higher blood flow.

Importantly, the PBBM was instrumental in integrating physiological processes to quantitatively describe selumetinib pharmacokinetics. This systems-based approach linked in vitro dissolution data with biological processes, allowing separation of the relative contributions of dissolution, absorption, and first-pass metabolism. Without the PBBM, these interdependent mechanisms would have remained obscured, potentially leading to misinterpretation of the food effect as purely dissolution-driven and resulting in less effective formulation design, dosing guidance, or regulatory justification.

Overall, the PBBM highlighted the importance of formulation characteristics and physiological factors on selumetinib absorption. Future applications of this model could be to predict food impacts for alternative formulations and analyze pharmacokinetics in different populations, such as pediatric patients.

Omaveloxolone Case Study

This case study highlights how PBBM was used to understand food effect mechanisms and critical performance factors of an omaveloxolone capsule formulation.

Omaveloxolone is a highly lipophilic drug formulated as an amorphous solid to improve solubility. Omaveloxolone demonstrates a marked food effect, exhibiting a significant increase in Cmax—350% with a high-fat breakfast—while showing only a 15% increase in AUC. This result is atypical, as a correlation between changes in AUC and Cmax is generally expected.

A PBBM was developed by integrating pharmacokinetic data, enzyme kinetic parameters, and in vivo drug-drug interaction studies to establish clearance and distribution. The model included absorption kinetics derived from in vitro dissolution testing, passive permeability predictions validated with in vivo data, and calculations for first-pass extraction based on drug-enzyme interactions.

Parameter sensitivity analyses (PSAs) using the validated PBBM revealed critical factors that influence omaveloxolone pharmacokinetics. The most impactful parameters are bile salt solubilization, particle radius, and enzyme kinetics. These analyses highlighted critical parameters to focus on for optimizing formulation and understanding inter- and intra-subject variability in drug performance.

The PBBM also provided understanding of the positive food effect mechanism. In the fed state, faster and greater dissolution occurred due to higher bile salt solubilization. This resulted in greater absorption primarily in the upper small intestine, where metabolizing enzyme expression is highest, leading to increased first-pass metabolism and only modest changes in the amount of drug reaching systemic circulation. This resulted in a transient surge in Cmax without a corresponding rise in AUC – and hence the uncommon food effect characteristics. As with the selumetinib case study, this phenomenon could not be described using in vitro data alone but required PBBM to explain both the physicochemical and biological changes associated with food. While in vitro solubility and dissolution testing could showcase an increase in drug release due to higher bile salt solubilization, it could not account for differences in regional absorption and first pass gut metabolism occurring in the fed state.

Overall, these findings provide valuable knowledge for optimizing omaveloxolone formulation strategies and understanding sources of variability in drug performance.

Conclusion

The studies on selumetinib and omaveloxolone highlight the critical importance of employing PBBM to enhance our understanding of drug behavior, particularly in relation to formulation and food effects. Both cases exemplify how advanced modeling techniques can simulate the dynamic, interconnected processes that occur in vivo after dosing, providing a solid foundation for designing formulations and dosing strategies.

Together, these examples showcase how PBBM can de-risk development decisions and reduce reliance on costly clinical trials through virtual assessments. By bridging the gap between in vitro and in vivo understandings, PBBM enhances confidence in product performance and supports informed, science-based strategies.

[1] Pepin XJH, Hammarberg M, Mattinson A, Moir A. Physiologically Based Biopharmaceutics Model for Selumetinib Food Effect Investigation and Capsule Dissolution Safe Space – Part I: Adults. Pharm Res. 2023 Feb;40(2):387-403. doi: 10.1007/s11095-022-03339-2

[1] Pepin XJH, Hynes SM, Zahir H, Walker D, Semmens LQ, Suarez-Sharp S. Understanding the mechanisms of food effect on omaveloxolone pharmacokinetics through physiologically based biopharmaceutics modeling. CPT Pharmacometrics Syst Pharmacol. 2024 Oct;13(10):1771-1783. doi: 10.1002/psp4.13221