A rebinding-assay for measuring extreme kinetics using label-free biosensors

Abstract

In vitro kinetic measurements allow mechanistic characterization of binding interactions and are particularly valuable throughout drug discovery, from confirmation of on-target binding in early discovery to fine-tuning of drug-binding properties in pre-clinical development. Early chemical matter often exhibits transient kinetics, which remain challenging to measure in a routine drug discovery setting. For example, characterization of irreversible inhibitors has classically relied on the alkylation rate constant, yet this metric fails to resolve its fundamental constituent rate constants, which drive reversible binding kinetics and affinity complex inactivation. In other cases, extremely rapid association processes, which can approach the diffusion limit, also remain challenging to measure. To address these limitations, a practical kinetic rebinding assay is introduced that may be applied for kinetic screening and characterization of compounds. The new capabilities afforded by this probe-based assay emerge from mixed-phase partitioning in a flow-injection configuration and have been implemented using label-free biosensing. A finite element analysis-based biosensor model, simulating inhibition of rebinding within a crowded hydrogel milieu, provided surrogate test data that enabled development and validation of an algebraic model for estimation of kinetic interaction constants. An experimental proof-of-principle demonstrating estimation of the association rate constant, decoupled from the dissociation process, provided further validation.

High-Throughput Kinetic Characterization of Irreversible Covalent Inhibitors of KRASG12C by Intact Protein MS and Targeted MRM

“…While the current analytic bimolecular model was derived for irreversible covalent inhibitors, the entire approach may be replicated without change for reversible covalent inhibitors with the exception of the analytic bimolecular model, which can be replaced by a set of coupled ordinary differential equations (ODE) that are fit to the data through a combination of numerical integration and nonlinear regression curve fitting. This ODE-based approach to fitting more complex interactions has been the standard in SPR analysis for over three decades 30 and can be readily applied here.…”