Drug-Target Kinetics in Drug Discovery

Abstract

The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.

Keywords: CNS cancer; Kinetic selectivity; PK/PD modeling; blood brain barrier; drug-target residence time; target occupancy; target vulnerability; therapeutic window.

Figure 1

Reaction coordinate for a one-step binding event. Target (E) and drug (I) binding leads to the drug–target complex (E-I). The driving force for binding is given by the difference in free energy between E+I and E-I (ΔG_Kd). Experimental measurements of the equilibrium dissociation constant K_d, or parameters such as IC₅₀ values, provide a quantitative estimate of the thermodynamics for binding. The rate at which the drug-target complex forms (k_on) and dissociates (k_off) is given by the difference in free energy between the respective ground states (E+I or E-I) and the rate-limiting transition state (ΔG_kon and ΔG_koff). ΔG_Kd is related to K_d by the relationship ΔG° = −RT ln K. Assuming that parameters such as the transmission coefficient are the same for two drug molecules, then the difference in free energy for the rate of complex dissociation of the two molecules can be given by ΔΔG_koff = −RT ln(k_off¹/k_off²). The lifetime of the drug–target complex is often quantified by the residence time, t_R, where t_R = 1/k_off. The figure shows a simple one-step mechanism, although in many cases slow-binding inhibitors operate through a two-step induced-fit mechanism.^,,−

Figure 2

Time-dependent target occupancy: kinetic selectivity. A compound is assumed to bind reversibly to three targets with the same thermodynamic affinity (10 nM) but have different residence times on the three targets: Target 1, 1 s; Target 2, 10 h; and Target 3, 50 h). In addition, the compound is assumed to bind covalently to a fourth target (Target 4). Target occupancy has been simulated using Kintek,^, assuming either a 1.5 μM (A and B) or 0.5 μM (C and D) dose of compound that is absorbed with k_a 3 h^–1 but eliminated with two different rates, k_e 0.139 h^–1 (t_1/2 5 h) (A and C) or k_e 0.69 h^–1 (t_1/2 1 h) (B and D). Reversible binding is assumed to occur via a one-step mechanism with the following on and off-rates. Target 1: k_on 100 μM^–1 s^–1, k_off 1 s^–1. Target 2: k_on 2.78 × 10^–3 μM^–1 s^–1, k_off 2.78 × 10^–5 s^–1. Target 3: k_on 5.56 × 10^–4 μM^–1 s^–1, k_off 5.56 × 10^–6 s^–1. For Target 4, it is assumed that the compound binds in a two-step mechanism in which the initial rapid binding of the compound to the target, defined by k_on 100 μM^–1 s^–1 and k_off 1 s^–1 is followed by a second step with k_inact 5.56 × 10^–4 s^–1 leading to the covalent drug-target complex. In each case the target concentration is fixed at 1 nM (i.e., no target turnover).

Figure 3

Target vulnerability plots. Vulnerability functions are shown for low (red) and high (blue) vulnerability targets. The vulnerability function is defined by the minimum level of engagement required for any effect to be observed (TO_min) and the level of engagement that leads to the maximal efficacy (TO_max). The third parameter required to define the function is the Hill coefficient or slope factor that determines the steepness of the effect response between TO_min and TO_max. For the low vulnerability target, the full physiological effect of the drug requires close to 100% target engagement, whereas only ∼35% engagement is needed for the high vulnerability target. The Hill coefficients for the two functions are 4.6 (high) and 16.4 (low).

Figure 4

Vulnerability of two antibacterial targets: LpxC and FabI. (A) Correlation between residence time (t_R) and postantibiotic effect (PAE) for two series of antibacterial compounds that target UDP-3-O-acyl-N-acetylglucosamine deacetylase from Pseudomonas aeruginosa (paLpxC) and the enoyl-ACP reductase from Staphylococcus aureus (saFabI). (B) Vulnerability functions after global fitting of the PAE data to a PK/PD model that integrates drug-target kinetics into predictions of drug activity. Both plots support the conclusion that paLpxC is more vulnerable than saFabI.^, Adapted from ref. (24) with permission from the Royal Society of Chemistry.

Figure 5

Mechanistic PK/PD model. (A) The kinetic mechanism for two-step time-dependent inhibition replaced the Hill equation in a standard antibacterial pharmacodynamic model. (B) The model was used to successfully predict the efficacy of a paLpxC inhibitor in an animal model of infection (solid line). PK/PD modeling assuming rapid equilibrium between drug and target significantly underestimates the observed efficacy (dashed line). Figure adapted from Walkup et al.

Figure 6

PK/PD model predicts the efficacy of CC-292, a covalent inhibitor of Btk (A) The kinetic scheme for inhibition of Btk by CC-292. Since CC-292 is an irreversible inhibitor, k₆ = 0. (B) Structure of CC-292. (C) Fluorescent analogue of CC-292 used to quantify Btk engagement. (D) Predicted and observed efficacy of CC-292 in a rat model of collagen induced arthritis. (E) Vulnerability function for engagement of Btk. Adapted from ref. (26) with permission from the Royal Society of Chemistry.