Clinical Pharmacology Strategies for Bispecific Antibody Development: Learnings from FDA-Approved Bispecific Antibodies in Oncology.

Bispecific antibodies, by enabling the targeting of more than one disease-associated antigen or engaging immune effector cells, have both advantages and challenges compared with a combination of two different biological products. As of December 2023, there are 11 U.S. Food and Drug Administration-approved BsAb products on the market. Among these, 9 have been approved for oncology indications, and 8 of these are CD3 T-cell engagers. Clinical pharmacology strategies, including dose-related strategies, are critical for bispecific antibody development. This analysis reviewed clinical studies of all approved bispecific antibodies in oncology and identified dose-related perspectives to support clinical dose optimization and regulatory approvals, particularly in the context of the Food and Drug Administration's Project Optimus: (1) starting doses and dose ranges in first-in-human studies; (2) dose strategies including step-up doses or full doses for recommended phase 2 doses or dose level(s) used for registrational intent; (3) restarting therapy after dose delay; (4) considerations for the introduction of subcutaneous doses; (5) body weight vs. flat dosing strategy; and (6) management of immunogenicity. The learnings arising from this review are intended to inform successful strategies for future bispecific antibody development.

Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model.

Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.