Abstract


A necessary requirement for a pharmacological effect is that a drug molecule tightly interacts with its disease relevant target molecule in the patient. Kinases are regulatory, signal transmitting enzymes and are a large protein family that belongs to the most frequent targets of pharmaceutical industry, as deregulation of kinases has been associated with the development of a variety of diseases, including cancer. In drug discovery, equilibrium binding metrics such as the affinity (Ki, KD) or potency (IC50, EC50) are usually applied for the systematic profiling for potent and selective drug candidates. In recent years, dynamic binding parameters, the drugs association (kon) and dissociation (koff) rates for desired primary-targets and undesired off-targets, were discussed to be better predictors than steady-state affinity per se (KD = koff / kon) for the onset and duration of the drug-target complex in the open in vivo environment and thereby for the therapeutic effect and safety of the drug. It is yet unclear whether and when the binding kinetics parameters can influence drug action in the complex context of pharmacokinetics and pharmacodynamics and how the kinetic rate constants can be optimized rationally. One major obstacle for providing proof for the hypothesis that drug binding kinetics is of importance for drug action is the generation of large and comparable binding kinetic datasets.

The aim of this thesis was the comprehensive analysis of the binding kinetic and affinity parameters of a diverse spectrum of 270 small-molecule kinase inhibitors against a panel of pharmacologically relevant kinases to study the role played by binding kinetics for drug discovery: The generated dataset was utilized to assess the effect of chemical properties on drug binding kinetics, and to evaluate the impact of kinetic rate constants on the success of compounds in the drug discovery pipeline.

Large scale profiling was made possible by a recently developed “kinetic Probe Competition Assay” (kPCA), whose evaluation is based on Motulsky’s and Mahan’s “kinetics of competitive binding” theory. Monte Carlo analyses performed in this dissertation widened the theoretical knowledge of this theory, provided new insights into its limitations and allowed to derive recommendations about how to best design assays. It was demonstrated that kPCA is indeed high-throughput compatible and that it is comparable to other biochemical and biophysical assay formats in terms of precision and accuracy.

Multivariable linear regression for the description of the determined kinase inhibitors’ target binding characteristics (kon or koff or KD) using molecular properties and/or particular kinase-inhibitor interactions as descriptors supported the assumption that molecular properties of compounds might affect binding kinetics, generated new hypothesis about molecular determinants influencing binding kinetic parameters and provided a rational basis for following structure-kinetic relationship studies. Remarkably, the binding kinetic rate constants were better described by the established models than binding affinities.

Interestingly, the systematic, quantitative analysis of kinase inhibitors’ target binding kinetics indicated that a slow dissociation rate for the main target is a feature which is more frequently observed in inhibitors that reached approval or late stage clinical testing than in earlier phases of clinical development. In addition, it was demonstrated that binding kinetics of kinase inhibitors is a better predictor for the time course of target engagement in cells as compared to affinity per se. Furthermore, in some study cases simulations using a standard pharmacokinetics model and a modified model considering the inhibitors binding kinetics lead to different in vivo kinase occupancy time profiles. It was illustrated by simulations how the concept of kinetic selectivity can be applied to turn an unselective compound in equilibrium conditions into a more selective compound in the open in vivo situation, where the thermodynamic equilibrium of drug-target binding is not necessarily reached.

Thus the generated data and models provide evidence for the importance of binding kinetics in drug discovery and represent a valuable resource for future studies in this field.