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More than 50 bispecific antibodies are in oncology clinical development with a large diversity in formats, directed at a range of immune and tumor targets. Bispecific have demonstrated the potential for enhanced efficacy and reduced systemic toxicity. However, they are complex modalities with challenges to overcome in early clinical trials, including selection of relevant starting doses and dose escalation strategy due to non-intuitive exposure-response relationships. In addition, prediction and management of cytokine-release syndrome (CRS) is also important. Multiple factors need to be considered for preclinical to clinical translation, including tumor growth rate, receptor expression for both targets, binding affinities, effector-to-target-cell ratio, presence of soluble target and PK differences. Mechanistic, quantitative systems pharmacology (QSP) models are increasingly being used in the design of bispecifics and to guide translation research, clinical trial design, dose regimen optimization, and patient selection.
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Scientists at pharmaceutical companies who are interested in how QSP models can be used to design novel bispecific antibody drug and to optimize the discovery and development of existing bispecific antibody drug.
