Mechanistic Modeling of Intra‐Tumor Spatial Distribution of Antibody‐Drug Conjugates: Insights into Dosing Strategies in Oncology. Issue 1 (19th October 2020)

• Record Type: Journal Article
• Authors: Weddell, Jared; Chiney, Manoj S.; Bhatnagar, Sumit; Gibbs, John P.; Shebley, Mohamad
• Is Part Of: Clinical and translational science. Volume 14:Issue 1(2021)
• Page Start: 395
• Page End: 404
• Publication Date: 2020-10-19
• Subjects: Medicine, Experimental -- Periodicals; Medical innovations -- Periodicals; 616.027
• DOI: 10.1111/cts.12892
• Languages: English
• ISSNs: 1752-8054

Abstract

Abstract : Antibody drug conjugates (ADCs) provide targeted delivery of cytotoxic agents directly inside tumor cells. However, many ADCs targeting solid tumors have exhibited limited clinical efficacy, in part, due to insufficient penetration within tumors. To better understand the relationship between ADC tumor penetration and efficacy, previously applied Krogh cylinder models that explore tumor growth dynamics following ADC administration in preclinical species were expanded to a clinical framework by integrating clinical pharmacokinetics, tumor penetration, and tumor growth inhibition. The objective of this framework is to link ADC tumor penetration and distribution to clinical efficacy. The model was validated by comparing virtual patient population simulations to observed overall response rates from trastuzumab‐DM1 treated patients with metastatic breast cancer. To capture clinical outcomes, we expanded upon previous Krogh cylinder models to include the additional mechanism of heterogeneous tumor growth inhibition spatially across the tumor. This expansion mechanistically captures clinical response rates by describing heterogeneous ADC binding and tumor cell killing; high binding and tumor cell death close to capillaries vs. low binding, and high tumor cell proliferation far from capillaries. Sensitivity analyses suggest that clinical efficacy could be optimized through dose fractionation, and that clinical efficacy is primarily dependent on the ADC‐target affinity, payload potency, and tumor growth rate. This work offers a mechanistic basis to predict and optimize ADC clinical efficacy for solid tumors, allowing dosing strategy optimization to improve patient outcomes.