On October 30, 2023 Applied BioMath (www.appliedbiomath.com), the industry-leader in providing model-informed drug discovery and development (MID3) support to help accelerate and de-risk therapeutic research and development (R&D), reported their participation at the 38th Annual Meeting of the Society for Immunotherapy of Cancer (SITC) (Free SITC Whitepaper) occurring November 1-5, 2023 in San Diego, CA (Press release, Applied BioMath, OCT 30, 2023, View Source [SID1234636464]).
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During the conference, Fei Hua, PhD, Vice President of Modeling and Simulation Services at Applied BioMath, will present the poster entitled, "Overcoming the dose-response prediction limitation from bench to clinic for T-cell engagers: Using Quantitative Systems Pharmacology (QSP) modeling in the development of CDR404 for solid tumors."
While T-cell Engagers (TCEs) offer a potentially very potent approach to treat cancer, they also have complex relationships between binding potency, exposure, and efficacy. These complex relationships bring challenges to drug discovery and development such as identifying optimal Kd for the drug candidate, predicting efficacious doses, and selecting dosing regimens for clinical study.
Applied BioMath’s poster describes a QSP model developed to support the first-in-human trial design for CDR404, a highly potent and highly specific antibody-based T-cell engager that binds bivalently to a MAGE-A4 peptide displayed on HLA-A*02:01 on cancer cells and monovalently to CD3 on T-cells. The QSP model will become an integrated part of the clinical development program and can be updated with emerging clinical data to facilitate discovery of a safe and therapeutic dose range for CDR404.
"For therapeutics with complex mechanisms of action (MoA), QSP modeling can be integral for progressing through the R&D pipeline," said John Burke, PhD, Co-founder, President and CEO. "Particularly for TCEs, we’ve had experience where QSP modeling has tested target feasibility, optimized molecule design, predicted therapeutic index, and guided clinical trial design."
To learn more about Applied BioMath, visit www.appliedbiomath.com.