Background and Objective Proprotein convertase subtilisin/kexin type 9 inhibition with monoclonal antibodies such as alirocumab significantly reduces low-density lipoprotein-cholesterol levels ± other lipid-lowering therapies. We aimed to develop and qualify a population pharmacokinetics (PopPK) model for alirocumab in healthy subjects and patients, taking into account the mechanistic target-mediated drug disposition (TMDD) process. Methods This TMDD model was developed using a subset of the alirocumab clinical trial database, including nine phase I/II/III studies (n = 527); the model was subsequently expanded to a larger data set of 13 studies (n = 2870). Potential model parameters and covariate relationships were explored, and predictive ability was qualified using a visual predictive check. Results The TMDD model was built using the quasi-steady-state approximation. The final TMDD–quasi-steady-state model included a significant relationship between distribution volume of the central compartment and disease state: distribution volume of the central compartment was 1.56-fold higher in patients vs. healthy subjects. Separately, application of the model to the expanded data set revealed a significant relationship between linear clearance and statin co-administration: linear clearance was 1.27-fold higher with statins. The good predictive performance of the TMDD model was assessed based on graphical and numerical quality criteria, together with the visual predictive check and comparison of the predictions to those from a PopPK model with parallel linear and Michaelis–Menten clearances (i.e., simplification of the TMDD PopPK model). Conclusions This mechanistic TMDD PopPK model integrates the interaction of alirocumab with its target and accurately predicts both alirocumab and total proprotein convertase subtilisin/kexin type 9 concentrations in healthy subjects and patients.

Glofitamab is a novel T cell bispecific antibody developed for treatment of relapsed‐refractory diffuse large B cell lymphoma and other non‐Hodgkin's lymphoma indications. By simultaneously binding human CD20‐expressing tumor cells and CD3 on T cells, glofitamab induces tumor cell lysis, in addition to T‐cell activation, proliferation, and cytokine release. Here, we describe physiologically‐based pharmacokinetic (PBPK) modeling performed to assess the impact of glofitamab‐associated transient increases in interleukin 6 (IL‐6) on the pharmacokinetics of several cytochrome P450 (CYP) substrates. By refinement of a previously described IL‐6 model and inclusion of in vitro CYP suppression data for CYP3A4, CYP1A2, and 2C9, a PBPK model was established in Simcyp to capture the induced IL‐6 levels seen when glofitamab is administered at the intended dose and dosing regimen. Following model qualification, the PBPK model was used to predict the potential impact of CYP suppression on exposures of various CYP probe substrates. PBPK analysis predicted that, in the worst‐case, the transient elevation of IL‐6 would increase exposures of CYP3A4, CYP2C9, and CYP1A2 substrates by less than or equal to twofold. Increases for CYP3A4, CYP2C9, and CYP1A2 substrates were projected to be 1.75, 1.19, and 1.09‐fold following the first administration and 2.08, 1.28, and 1.49‐fold following repeated administrations. It is recommended that there are no restrictions on concomitant treatment with any other drugs. Consideration may be given for potential drug–drug interaction during the first cycle in patients who are receiving concomitant CYP substrates with a narrow therapeutic index via monitoring for toxicity or for drug concentrations.

Antibody‐drug conjugates (ADCs) represent a promising anticancer approach. Although physiologically based pharmacokinetics (PBPK) modeling became essential in Pharmacometrics to characterize exposure in different tissues, very few PBPK models have been published for ADCs, none within the PK‐Sim/MoBi software. To capture the pharmacokinetics (PK) of an anti‐Claudin 18.2 ADC, a PBPK model was built in PK‐Sim and MoBi and compared to observations from three clinical studies after intravenous (IV) administration in 109 patients with cancer. The PK parameters were considered inaccurate if the predicted error ratios were outside the two‐fold error range (0.5–2). In PK‐Sim, we defined one PBPK model comprising three compounds (ADC, payload, and naked antibody), which were mechanistically linked. This model captured the ADC PK profile. However, additional clearance mechanisms were essential to improve the fit of the ADC elimination phase. After integration of target‐mediated drug disposition (TMDD) and deconjugation of the payload in MoBi, 3 parameters were optimized for each of the ADC and the payload (degradation rate constant and reference concentration of the target, deconjugation rate constant, lipophilicity, nonspecific hepatic clearance rate constant and passive renal clearance of the payload). The PK data were adequately captured for both observed compounds, with a predicted error ratio within the two‐fold range: C_max__ADC (1.07–1.50), C_{max_Payload} (0.56–1.18), AUC_{0−504h_ADC} (0.73–1.23) and AUC_{0‐504h_payload} (0.77–1.37). The “parameter optimization” of different parameters allowed accurately capturing the observed data for both ADC and payload in cancer patients for an anti‐Claudin 18.2 ADC. This analysis paves the way for PBPK modeling of other ADCs currently in development.