ReviewMethods in molecular biology (Clifton, N.J.)2026
Overview of Quantitative Clinical Pharmacology for T-Cell Engaging Bispecific Antibodies.
Review in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
2 authors.
Funding
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Abstract
T-cell bispecific antibodies (TCBs) are an exciting advancement in cancer immunotherapy, enabling potent and durable tumor responses by bridging CD3-positive T cells with tumor-specific antigens. By forming artificial immune synapses, TCBs activate T cells in an MHC-independent manner, initiating robust anti-tumor responses. However, these unique mechanisms present challenges, including complex pharmacokinetics (PK), target-mediated drug disposition (TMDD), and on-target toxicities such as cytokine release syndrome (CRS) and off-tumor effects. This chapter offers a comprehensive review of the clinical pharmacology considerations involved in TCB development, covering their mechanisms of action, PK and pharmacodynamic (PD) characteristics, and bioanalytical strategies. It also summarizes approaches to mitigate risks and optimize therapeutic outcomes.Advanced modeling techniques, such as quantitative systems pharmacology (QSP) and physiologically based pharmacokinetics (PBPK) modeling, are highlighted as valuable tools for addressing translational challenges. When combined with emerging applications of artificial intelligence and machine learning, these methods can provide further predictability and insights for optimizing dosing regimens, mitigating toxicities, and predicting clinical outcomes. The chapter also discusses strategies for overcoming resistance mechanisms through combination therapies and improving TCB constructs to enhance patient benefit. By integrating these insights, the chapter emphasizes the role of pharmacology and modeling advances in maximizing the therapeutic potential of TCBs for cancer treatment.
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Identifiers
42108353What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.