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.