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Personalized dosing approaches play important roles in clinical practices to improve benefit: risk profiles. Whereas this is also important for drug development, especially in the context of drugs with narrow therapeutic windows, such approaches have not been fully evaluated during clinical development. Fazpilodemab (BFKB8488A) is an agonistic bispecific antibody which was being developed for the treatment of nonalcoholic steatohepatitis. The objective of this study was to characterize the exposure‐response relationships of fazpilodemab with the purpose of guiding dose selection for a phase II study, as well as to evaluate various personalized dosing strategies to optimize the treatment benefit. Fazpilodemab exhibited clear exposure‐response relationships for a pharmacodynamic (PD) biomarker and gastrointestinal adverse events (GIAEs), such as nausea and vomiting. Static exposure‐response analysis, as well as longitudinal adverse event (AE) analysis using discrete‐time Markov model, were performed to characterize the observations. Clinical trial simulations were performed based on the developed exposure‐response models to evaluate probability of achieving target PD response and the frequency of GIAEs to inform phase II dose selection. Dynamic simulation of personalized dosing strategies demonstrated that the AE‐based personalized dosing is the most effective approach for optimizing the benefit–risk profiles. The approach presented here can be a useful framework for quantifying the benefit of personalized dosing for drugs with narrow therapeutic windows. [ABSTRACT FROM AUTHOR]

Fenebrutinib is a CYP3A substrate and time‐dependent inhibitor, as well as a BCRP and OATP1B transporter inhibitor in vitro. Physiologically‐based pharmacokinetic (PBPK) modeling strategies with the ultimate goal of understanding complex drug‐drug interactions (DDIs) and proposing doses for untested scenarios were developed. The consistency in the results of two independent approaches, PBPK simulation and endogenous biomarker measurement, supported that the observed transporter DDI is primarily due to fenebrutinib inhibition of intestinal BCRP, rather than hepatic OATP1B. A mechanistic‐absorption model accounting for the effects of excipient complexation with fenebrutinib was used to rationalize the unexpected observation of itraconazole‐fenebrutinib DDI (maximum plasma concentration (Cmax) decreased, and area under the curve (AUC) increased). The totality of the evidence from sensitivity analysis and clinical and nonclinical data suggested that fenebrutinib is likely a sensitive CYP3A substrate. This advanced PBPK application allowed the use of model‐informed approach to facilitate the development of concomitant medication recommendations for fenebrutinib without requiring additional clinical DDI studies. [ABSTRACT FROM AUTHOR]

Purpose: Fenebrutinib (GDC-0853), a Bruton's tyrosine kinase (BTK) inhibitor was investigated in a Phase 2 clinical trial in patients with rheumatoid arthritis (RA). Our aim was to apply a model-informed drug development (MIDD) approach to examine the totality of available clinical efficacy data. Methods: Population pharmacokinetics (popPK) modeling, exposure-response (E-R) analysis, and model-based meta-analysis (MBMA) of fenebrutinib were performed based on the Phase 2 data. Results: PopPK of fenebrutinib after oral administration was described using a 3-compartment model with linear elimination and a flexible absorption transit compartment model. Healthy subjects had a 52% higher apparent clearance than patients. E-R analyses based on longitudinal ACR20, ACR50, and ACR70 and DAS28 (CRP) data modeled fenebrutinib effect with an Emax function, and an efficacy plateau was achieved within the exposure range obtained in the Phase 2 clinical trial. Based on literature data, a summary-level clinical efficacy database was constructed, and MBMA determined ACR20, ACR50, and ACR70 responder rates in the placebo and adalimumab arms of the Phase 2 clinical trial were found to be consistent with historical data for these treatments. Conclusions: Our multi-pronged approach applied MIDD to maximize knowledge extraction of efficacy data and enabled robust interpretation from a Phase 2 clinical trial. [ABSTRACT FROM AUTHOR]