Gareth J. Lewis

A physiologically‐based pharmacokinetic (PBPK) model of niraparib and its primary metabolite using a relevant virtual cancer population is reported here. A series of in vitro experiments using liver S9, microsomes, and hepatocytes with various inhibitors and recombinant supersomes demonstrated that niraparib is specifically metabolized by carboxylesterase 1 via amide hydrolysis to an acid metabolite (M1). Available virtual cancer populations, along with reference populations, were applied to modeling simulations using fixed trial designs with demographic and clinical chemistry parameters from patients receiving niraparib in clinical studies. Simulations of niraparib and its metabolite M1 were verified across numerous available clinical studies and repeat dose ranges in cancer patients within 2‐fold. The PBPK model was used to simulate exposures in moderately hepatic impaired, healthy Chinese and Japanese virtual populations as a surrogate of cancer comorbidity. The PBPK model confirmed minimal DDI liability with niraparib as a precipitant for most in vitro tested drug metabolizing enzymes and transporters. In vitro, niraparib lacks any CYP inhibition, induces CYP1A2 but not CYP3A4, and is not a CYP substrate, unlike some other PARPi's, which inhibit and induce numerous enzymes/transporters and are objects of CYP metabolism. At clinically relevant doses of niraparib ≥ 200 mg, a weak induction risk is predicted with sensitive CYP1A2 substrates, such as caffeine, and both niraparib and olaparib clinically increase serum creatinine in cancer patients, with up to a moderate inhibition risk predicted with MATE‐1/‐2K substrates, such as metformin, using a PBPK model of niraparib in the absence of a dedicated DDI study.

Physiologically‐based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug–drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well‐characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, f_m,CYP3A4, and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of f_m,CYP3A4 (0.086–1.0), Fg (0.11–1.0) and hepatic availability (0.09–0.96) were included. Predictions were most often accurate for compounds that are not P‐gp substrates or that are P‐gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first‐order absorption model might be more appropriate for a P‐gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P‐gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.

Considerable interest remains across the pharmaceutical industry and regulatory landscape in capabilities to model oral contraceptives (OCs), whether combined (COCs) with ethinyl estradiol (EE) or progestin‐only pill. Acceptance of COC drug–drug interaction (DDI) assessment using physiologically‐based pharmacokinetic (PBPK) is often limited to the estrogen component (EE), requiring further verification, with extrapolation from EE to progestins discouraged. There is a paucity of published progestin component PBPK models to support the regulatory DDI guidance for industry to evaluate a new chemical entity's (NCE's) DDI potential with COCs. Guidance recommends a clinical interaction study to be considered if an investigational drug is a weak or moderate inducer, or a moderate/strong inhibitor, of CYP3A4. Therefore, availability of validated OC PBPK models within one software platform, will be useful in predicting the DDI potential with NCEs earlier in the clinical development. Thus, this work was focused on developing and validating PBPK models for progestins, DNG, DRSP, LNG, and NET, within Simcyp, and assessing the DDI potential with known CYP3A4 inhibitors (e.g., ketoconazole) and inducers (e.g., rifampicin) with published clinical data. In addition, this work demonstrated confidence in the Simcyp EE model for regulatory and clinical applications by extensive verification in 70+ clinical PK and CYP3A4 interaction studies. The results provide greater capability to prospectively model clinical CYP3A4 DDI with COCs using Simcyp PBPK to interrogate the regulatory decision‐tree to contextualize the potential interaction by known perpetrators and NCEs, enabling model‐informed decision making, clinical study designs, and delivering potential alternative COC options for women of childbearing potential.