BioPharm International - March 2021

BioPharm International - Regulatory Sourcebook - March 2021

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30 BioPharm International eBook March 2021 www.biopharminternational.com working group used PBPK models in Simcyp to compare observed and predicted outcomes for renal and hepatic impairment studies for 26 dr ugs eliminated princi- pally by metabolism (15). For renal impairment, all predictions were considered acceptable using the predefined two-fold criteria. For hepatic impairment, the same con- clusion was reached with excep- tion of three cases for which a modest over prediction was noted. Moving forward, these models are f inding utilit y in rationalizing drug development in these special populations and contributing to a totality of evidence approach to safety assessment. IMPACT OF PBPK MODELING ON ASSESSING FOOD EFFECT Over the past 10 years, approxi- mately 40% of approved dr ugs intended for oral administration report some change in pharmaco- kinetics when dosed with a meal. T hese cha nges in pha r macok i- netics are commonly referred to as food effects and can be large enough where they warrant special instructions—such as taking the dose on an empty stomach—for physicians and patients to ensure safe and effective dosing. Accordingly, the study of food ef fec t in clinica l development is expected by major regulatory agencies around the world and represents an area where virtu- al-human approaches could have an impact in streamlining new drug development. The physical, chemical, and biological processes involved in drug absorption and food effects have been integrated into such an approach, namely PBPK biopharmaceutics models, and were evaluated by scientists work- ing with the IQ Consortium (7). The authors generated de novo mechanistic absorption models for 30 drugs, using a pre-specified modeling approach to prospectively evaluate the predictive performance of the PBPK models and to establish best practices intended to help drive consistency, rigor, and acceptance of the approach. Notably, the authors had human pharmacokinetic data following intravenous adminis- tration, in addition to oral dosing with and without food, for all drugs in the study, which allowed them to better study the changes in PK that arise from changes in absorp- tion, as opposed to any potential effect of food on either elimination or distribution. A thorough anal- ysis of the data revealed no clear trend in prediction accuracy with Biopharmaceutics Classification System (BCS) designation. However, the authors concluded that when the effect of food administration involves changes in gastrointestinal physiology such as fluid volume, motility, pH, and bile salts, the food effects were generally predictable. A decision tree was formulated based upon the analysis to guide the cat- egorization of application of PBPK models of food effects into those of high, medium, and low confi- dence (7). Such categorization will be helpful in the interpretation of PBPK food effect models as well as in identifying areas where addi- tional research is needed (i.e., those models considered moderate and low confidence). CONCLUSION PBPK modeling has been estab- lished as an important predictive science in pharmaceutical dr ug development over the past t wo decades; the aforementioned case studies from the IQ Consortium demonstrate the significant prog- ress that the industry is making toward enabling 'virtual' human e x per i ments a nd st rea m l i n i ng drug development. REFERENCES 1. C. Perry, et al., Curr. Pharmacol. Rep., 6 71–84 (2020). 2. M. Jamei, G. L. Dickinson, A. Rostami- Hodjegan, Drug Metab. Pharmacokinet., 24 (1) 53–75 (2009). 3. L. Kuepfer L, et al., CPT Pharmacometrics Syst. Pharmacol., 5 (10) 516–531 (2016). 4. M. Shebley, et al., Clin. Pharmacol. Ther., 104 (1) 88-110 (2018). 5. M. Jamei, et al., Expert Opinion on Drug Metabolism & Toxicology. 5 (2) 211–223 (2009). 6. N. Miller, et al., Clinical Pharmaco- kinetics 58 (6) 727–746 (June 2019). 7. A.E. Riedmaier, et al., AAPS J. 22(6) 123 (2020). 8. H.M. Jones, et al., Clin. Pharmacol. Ther. 97 (3) 247–262 (2015). 9. H.J. Einolf, et al., Clin. Pharmacol. Ther. 95 (2) 179–188 (2014). 10. M.L. Vieira, et al., Clin. Pharmacol. Ther. 95 (2) 189–198 (2014). 11. FDA, In Vitro Drug Interaction Studies — Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry (CDER, January 2020). 12. I.E. Templeton, et al., CPT Pharmacometrics Syst. Pharmacol. 5 (10) 505–515 (2016). 13. Y. Chen, et al., CPT Pharmacometrics Syst. Pharmacol. 8 (9) 685–695 (2019). 14. O.A. Fahmi, et al., Drug Metab. Dispos. 44 (10) 1720–1730 (2016). 15. T. Heimbach, et al., Clin. Pharmacol. Ther. in press. BP ABOUT THE AUTHORS Heidi J. Einolf is director, mod- eling and simulation, phar ma- c o k i n e t i c s c i e n c e s , N o v a r t i s ; Stephen D. Hall is senior research fe l low, d r ug d i s p o sit ion, a nd Tracy Williams is senior direc- t o r A D M E / To x i c o l o g y/ P K P D, both with Eli Lilly and Co; Aarti Patel is director, DMPK modeling, Gla xoSm it hK line; Chr istopher Gibson is d ist ing uished scien- tist and Nancy G.B. Agrawal is v ice-president, pha r macok inet- ics, pharmacodynamics, and drug metabolism, bot h w it h Merck; and Jens Sydor,* jens.x.sydor @ gsk.com, is vice-president, DMPK, GlaxoSmithKline; all authors are members of the IQ Consortium. *To whom all correspondence should be addressed Regulatory Sourcebook Quality Collaboration

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