Sara Peribañez-dominguez1, Lindsay Clegg2, Weifeng Tang2, Pradeep Sharma3
1Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, 2Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, 3Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca
Introduction: Meta-analysis of clinical studies has demonstrated that pharmacokinetics of drugs may be altered in cancer patients compared to healthy volunteers due to possible change in drug metabolism [1]. Also, post hoc analysis from clinical studies have shown that renal clearance of creatinine is altered in cancer population compared to healthy subjects [2]. Metformin exposure differs between healthy individuals and the cancer population. Metformin is eliminated roughly 80% through renal clearance [3], we propose using metformin as a case study drug and employing the Simcyp PBPK platform to evaluate whether we can accurately predict pharmacokinetic differences between healthy and cancer populations due to changes in renal clearance. Methods: Literature survey was conducted to collect clinical data in healthy volunteers and cancer subjects [4-10]. Descriptive meta-analysis was performed to evaluate the differences in PK of metformin in two populations. Subsequently, PBPK analysis was performed in healthy and cancer population using Simcyp software (Version 23.2). The metformin model from the Simcyp library was employed, which included a first-order absorption model, a full PBPK model, and an elimination model that accounted for renal, biliary, and metabolic clearance via CYP3A4. Simulations were conducted for metformin doses of 500 mg and 1000 mg once daily (QD), with 10 trials comprising 10 individuals each, both in healthy subjects and the default cancer population available in Simcyp. The accuracy of these models was assessed by comparing the simulation results with the observed data for each population group. Additionally, the precision of the models was evaluated by calculating the Average Fold Error (AFE). Furthermore, key pharmacokinetic parameters, including the area under the concentration-time curve (AUC), peak plasma concentration (Cmax), and drug clearance rate (CL), were analyzed and compared between the healthy subjects and cancer patients to better understand the differences in drug exposure and elimination across these groups. Results: Meta-analysis from literature studies have demonstrated that metformin exposure is in general higher in cancer compared to healthy individuals. The validated metformin model from the Simcyp library accurately predicted pharmacokinetic parameters in healthy and cancer populations. For healthy subjects, a 500 mg dose yielded predicted Cmax and AUCinf values of 1133 ng/mL and 8037.5 ng·h/mL, closely matching observed values of 1169.5 ng/mL and 7736.17 ng·h/mL. At 1000 mg, predictions were 2265.8 ng/mL and 16075 ng·h/mL, compared to observed means of 1518.5 ng/mL and 11214 ng·h/mL. In cancer patients, predictions for a 500 mg dose were 912 ng/mL and 8320 ng·h/mL, aligning with observed values of 912 ng/mL and 8320 ng·h/mL. For a 1000 mg dose, predicted values were 2420 ng/mL and 17603 ng·h/mL, versus observed 2465 ng/mL and 22917 ng·h/mL. The mean eGFR is 148.21 and 114.8 mL/min in healthy subjects and cancer patients respectively. A notable 10% increase in AUC was shown in cancer patients compared to healthy subjects, demonstrating the capability of the model to capture population-specific pharmacokinetic variations. Conclusion: Using metformin as a case study, we demonstrated that PBPK modeling can reasonably well predict drug clearance in cancer patients by considering changes in renal clearance due to the disease. Future work with more drugs which are cleared by renal clearance will further strengthen our conclusions. This approach highlights the importance of incorporating disease-specific physiological alterations for better pharmacokinetic predictions in clinical settings.
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Reference: PAGE 33 (2025) Abstr 11623 [www.page-meeting.org/?abstract=11623]
Poster: Drug/Disease Modelling - Absorption & PBPK