A Physics-Constrained Marcus-Theory Optimization Framework Restores Parameter Identifiability in Deep-Pocket Oncogenic Drug Kinetics: A KRAS G12C and STAT3 Case Study

Water exclusion in deep pockets for mutant KRAS G12C and STAT3 SH2 lowers lambda_ext by 90% and yields identifiability when K_d is anchored.

• Why it matters: Empirical Hill-type dose-response models can miss microenvironmental effects in deep binding pockets, leaving key kinetic parameters poorly identifiable. The framework targets how local dielectric changes affect pocket kinetics and which constraints recover identifiability.
• What they did: They coupled non-adiabatic Marcus kinetics to a Hill-type steady-state scheme, modeling epsilon_local collapse from 80.4 to 2.0 using pocket radii and reference constants for PDB 6UT0 and 6NJS. They proved three propositions with synthetic data, then stress-tested identifiability on napabucasin viability curves from 701 cancer cell lines.
• The result: Water exclusion lowered lambda_ext by 90%, and at dG = -0.10 eV increased k_C by 8.0-fold (KRAS) and 3.6-fold (STAT3). The model is identifiable only after anchoring K_d, with SSE change < 10^-7% while fitted k_off,q shifts by 3.5 log units, matching predictions.

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