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Antibody‐drug conjugates are important molecular entities in the treatment of cancer, with 8 antibody‐drug conjugates approved by the US Food and Drug Administration since 2000 and many more in early‐ and late‐stage clinical development. These conjugates combine the target specificity of monoclonal antibodies with the potent anticancer activity of small‐molecule therapeutics. The complex structure of antibody‐drug conjugates poses unique challenges to pharmacokinetic (PK) and pharmacodynamic (PD) characterization because it requires a quantitative understanding of the PK and PD properties of multiple different molecular species (eg, conjugate, total antibody, and unconjugated payload) in different tissues. Quantitative clinical pharmacology using mathematical modeling and simulation provides an excellent approach to overcome these challenges, as it can simultaneously integrate the disposition, PK, and PD of antibody‐drug conjugates and their components in a quantitative manner. In this review, we highlight diverse quantitative clinical pharmacology approaches, ranging from system models (eg, physiologically based pharmacokinetic [PBPK] modeling) to mechanistic and empirical models (eg, population PK/PD modeling for single or multiple analytes, exposure‐response modeling, platform modeling by pooling data across multiple antibody‐drug conjugates). The impact of these PBPK and PK/PD models to provide insights into clinical dosing justification and inform drug development decisions is also highlighted. [ABSTRACT FROM AUTHOR]

Purpose: The phase Ib/II open-label study (NCT01992653) evaluated the antibody-drug conjugate polatuzumab vedotin (pola) plus rituximab/obinutuzumab, cyclophosphamide, doxorubicin, and prednisone (R/G-CHP) as first-line therapy for B-cell non-Hodgkin lymphoma (B-NHL). We report the pharmacokinetics (PK) and drug-drug interaction (DDI) for pola.Methods: Six or eight cycles of pola 1.0-1.8 mg/kg were administered intravenously every 3 weeks (q3w) with R/G-CHP. Exposures of pola [including antibody-conjugated monomethyl auristatin E (acMMAE) and unconjugated MMAE] and R/G-CHP were assessed by non-compartmental analysis and/or descriptive statistics with cross-cycle comparisons to cycle 1 and/or after multiple cycles. Pola was evaluated as a potential victim and perpetrator of a PK drug-drug interaction with R/G-CHP. Population PK (popPK) analysis assessed the impact of prior treatment status (naïve vs. relapsed/refractory) on pola PK.Results: Pola PK was similar between treatment arms and independent of line of therapy. Pola PK was dose proportional from 1.0 to 1.8 mg/kg with R/G-CHP. Geometric mean volume of distribution and clearance of acMMAE ranged from 57.3 to 95.6 mL/kg and 12.7 to 18.2 mL/kg/day, respectively. acMMAE exhibited multi-exponential decay (elimination half-life ~ 1 week). Unconjugated MMAE exhibited formation rate-limited kinetics. Exposures of pola with R/G-CHP were similar to those in the absence of CHP; exposures of R/G-CHP in the presence of pola were comparable to those in the absence of pola.Conclusions: Pola PK was well characterized with no clinically meaningful DDIs with R/G-CHP. Findings are consistent with previous studies of pola + R/G, and support pola + R/G-CHP use in previously untreated diffuse large B-cell lymphoma. [ABSTRACT FROM AUTHOR]

Background and Objectives: Monomethyl auristatin E (MMAE, a cytotoxic agent), upon releasing from valine-citrulline-MMAE (vc-MMAE) antibody-drug conjugates (ADCs), is expected to behave like small molecules. Therefore, evaluating the drug-drug interaction (DDI) potential associated with MMAE is important in the clinical development of ADCs. The objective of this work was to build a physiologically based pharmacokinetic (PBPK) model to assess MMAE-drug interactions for vc-MMAE ADCs. Methods: A PBPK model linking antibody-conjugated MMAE (acMMAE) to its catabolite unconjugated MMAE associated with vc-MMAE ADCs was developed using a mixed 'bottom-up' and 'top-down' approach. The model was developed using in silico and in vitro data and in vivo pharmacokinetic data from anti-CD22-vc-MMAE ADC. Subsequently, the model was validated using clinical pharmacokinetic data from another vc-MMAE ADC, brentuximab vedotin. Finally, the verified model was used to simulate the results of clinical DDI studies between brentuximab vedotin and midazolam, ketoconazole, and rifampicin. Results: The pharmacokinetic profile of acMMAE and unconjugated MMAE following administration of anti-CD22-vc-MMAE was well described by simulations using the developed PBPK model. The model's performance in predicting unconjugated MMAE pharmacokinetics was verified by successful simulation of the pharmacokinetic profile following brentuximab vedotin administration. The model simulated DDIs, expressed as area under the concentration-time curve (AUC) and maximum concentration ( C) ratios, were well within the two-fold of the observed data from clinical DDI studies. Conclusions: This work is the first demonstration of the use of PBPK modelling to predict MMAE-based DDI potential. The described model can be extended to assess the DDI potential of other vc-MMAE ADCs. [ABSTRACT FROM AUTHOR]