Physiologically‐based pharmacokinetic (PBPK) modeling is being increasingly used in drug development to avoid unnecessary clinical drug–drug interaction (DDI) studies and inform drug labels. Thus, regulatory agencies are recommending, or indeed requesting, more rigorous demonstration of the prediction accuracy of PBPK platforms in the area of their intended use. We describe a framework for qualification of the Simcyp Simulator with respect to competitive and mechanism‐based inhibition (MBI) of CYP1A2, CYP2D6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4/5. Initially, a DDI matrix, consisting of a range of weak, moderate, and strong inhibitors and substrates with varying fraction metabolized by specific CYP enzymes that were susceptible to different degrees of inhibition, were identified. Simulations were run with 123 clinical DDI studies involving competitive inhibition and 78 clinical DDI studies involving MBI. For competitive inhibition, the overall prediction accuracy was good with an average fold error (AFE) of 0.91 and 0.92 for changes in the maximum plasma concentration (C_max) and area under the plasma concentration (AUC) time profile, respectively, as a consequence of the DDI. For MBI, an AFE of 1.03 was determined for both C_max and AUC. The prediction accuracy was generally comparable across all CYP enzymes, irrespective of the isozyme and mechanism of inhibition. These findings provide confidence in application of the Simcyp Simulator (V19 R1) for assessment of the DDI potential of drugs in development either as inhibitors or victim drugs of CYP‐mediated interactions. The approach described herein and the identified DDI matrix can be used to qualify subsequent versions of the platform.

As part of a collaboration between Medicines for Malaria Venture (MMV), Certara UK and Monash University, physiologically‐based pharmacokinetic (PBPK) models were developed for 20 antimalarials, using data obtained from standardized in vitro assays and clinical studies within the literature. The models have been applied within antimalarial drug development at MMV for more than 5 years. During this time, a strategy for their impactful use has evolved. All models are described in the supplementary material and are available to researchers. Case studies are also presented, demonstrating real‐world development and clinical applications, including the assessment of the drug–drug interaction liability between combination partners or with co‐administered drugs. This work emphasizes the benefit of PBPK modeling for antimalarial drug development and decision making, and presents a strategy to integrate it into the research and development process. It also provides a repository of shared information to benefit the global health research community.

Sparsentan is a dual endothelin/angiotensin II receptor antagonist indicated to reduce proteinuria in patients with primary IgA nephropathy at high risk of disease progression. In vitro data indicate that sparsentan is likely to inhibit or induce various CYP enzymes at therapeutic concentrations. Sparsentan as a victim and perpetrator of CYP3A4 mediated drug–drug interactions (DDIs) has been assessed clinically. A mechanistic, bottom‐up, physiologically‐based pharmacokinetic (PK) model for sparsentan was developed based on in vitro data of drug solubility, formulation dissolution and particle size, drug permeability, inhibition and induction of metabolic enzymes, and P‐glycoprotein (P‐gp) driven efflux. The model was verified using clinical PK data from healthy adult volunteers administered single and multiple doses in the fasted and fed states for a wide range of sparsentan doses. The model was also verified by simulation of clinically observed DDIs. The verified model was then used to test various DDI simulations of sparsentan as a perpetrator and victim of CYP3A4 using an expanded set of inducers and inhibitors with varying potency. Additional perpetrator and victim DDI simulations were performed using probes for CYP2C9 and CYP2C19. Simulations were conducted to predict the effect of complete inhibition of P‐gp inhibition on sparsentan absorption and clearance. The predictive simulations indicated that exposure of sparsentan could increase greater than two‐fold if co‐administered with a strong CYP3A4 inhibitor, such as itraconazole. Other potential DDI interactions as victim or perpetrator were all within two‐fold of control. The effect of complete P‐gp inhibition on sparsentan PK was negligible.

The topoisomerase I inhibitor payload (DXd) released from the human epidermal growth factor receptor 2‐targeted antibody–drug conjugate (ADC) trastuzumab deruxtecan (T‐DXd) is eliminated by hepatic uptake via OATP1B1/3, metabolism by CYP3A, biliary excretion via P‐glycoprotein and breast cancer resistance protein, and urinary excretion. In a clinical drug–drug interaction (DDI) study between T‐DXd and ritonavir (a strong cytochrome P450 [CYP] 3A/organic anion transporting polypeptide 1B inhibitor) or itraconazole (a strong CYP3A inhibitor), a 1.2‐fold increase in DXd exposure was observed. In this study, a physiologically based pharmacokinetic (PBPK) model of T‐DXd was constructed to quantitatively describe the pharmacokinetics (PK) of DXd and the DDI of DXd released from T‐DXd. The minimal ADC PBPK model was developed to describe both intact ADC and payload PK using in vitro data of DXd and clinical PK data after T‐DXd administration. The developed model reasonably described the PK of T‐DXd and DXd after intravenous administration of T‐DXd. The main elimination pathway of DXd was estimated to be biliary excretion, followed by urinary excretion, with metabolism contributing to a lesser extent. DDIs with ritonavir and itraconazole simulated by the PBPK model reproduced the clinical DDI study results, indicating that the impact of concomitant medications was not clinically meaningful. These findings suggest that this model may potentially be used for DDI evaluation of future DXd‐containing ADCs, aiding in the prediction of DDIs under different scenarios and in specific populations.