The Added Value of Assessing Ligand–Receptor Binding Kinetics in Drug Discovery

Guo, Dong; Heitman, Laura H.; IJzerman, Adriaan P.

doi:10.1021/acsmedchemlett.6b00273

Cited by 29 publications

(24 citation statements)

References 13 publications

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“…However, thermodynamic selectivity may not map with kinetic selectivity, and indeed as noted above, kinetic selectivity can still exist even in the absence of thermodynamic selectivity. 21,23,38−41 …”

Section: Kinetic Selectivitymentioning

confidence: 99%

Drug–Target Kinetics in Drug Discovery

Tonge

2017

ACS Chem. Neurosci.

222

235

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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.

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Section: Kinetic Selectivitymentioning

confidence: 99%

Drug–Target Kinetics in Drug Discovery

Tonge

2017

ACS Chem. Neurosci.

222

235

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“…For instance, alternative poses with slightly higher free energies can be stabilized during ligand design (4). Also, the connectivity between states can be taken into account to design inhibitors with long residence times (5, 6), increasingly seen as a desirable objective in drug design (7, 8).…”

Section: Introductionmentioning

confidence: 99%

Mapping the Ligand Binding Landscape

Dickson

2018

Biophysical Journal

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The interaction between a ligand and a protein involves a multitude of conformational states. To achieve a particular deeply bound pose, the ligand must search across a rough free-energy landscape with many metastable minima. Creating maps of the ligand binding landscape is a great challenge, as binding and release events typically occur on timescales that are beyond the reach of molecular simulation. The WExplore enhanced sampling method is well suited to build these maps because it is designed to broadly explore free-energy landscapes and is capable of simulating ligand release pathways that occur on timescales as long as minutes. WExplore also uses only unbiased trajectory segments, allowing for the construction of Markov state models (MSMs) and conformation space networks that combine the results of multiple simulations. Here, we use WExplore to study two bromodomain-inhibitor systems using multiple docked starting poses (Brd4-MS436 and Baz2B-ICR7) and synthesize our results using a series of MSMs using time-lagged independent component analysis. Ranking the starting poses by exit rate agrees with the crystal structure pose in both cases. We also predict the most stable pose using the equilibrium populations from the MSM but find that the prediction is not robust as a function of MSM parameters. The simulated trajectories are synthesized into network models that visualize the entire binding landscape for each system, and we examine transition paths between deeply bound stable states. We find that, on average, transitions between deeply bound states convert through the unbound state 81% of the time, implying a trial-and-error approach to ligand binding. We conclude with a discussion of the implications of this result for both kinetics-based drug discovery and virtual screening pipelines that incorporate molecular dynamics.

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“…For instance, alternative poses with slightly higher free energies can be stabilized during ligand design [4]. Also, the connectivity between states can be taken into account to design inhibitors with long residence times [5,6], increasingly seen as a desirable objective in drug design [7,8].…”

Section: Introductionmentioning

confidence: 99%

Mapping the ligand binding landscape

Dickson

2018

Preprint

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The interaction between a ligand and a protein involves a multitude of conformational states. To achieve a particular deeply-bound pose the ligand must search across a rough free energy landscape, with many metastable minima. Creating maps of the ligand binding landscape is a great challenge, as binding and release events typically occur on timescales that are beyond the reach of molecular simulation. The WExplore enhanced sampling method is well-suited to build these maps, as it is designed to broadly explore free-energy landscapes, and is capable of simulating ligand release pathways that occur on timescales as long as minutes. WExplore also uses only unbiased trajectory segments, allowing for the construction of Markov state models (MSM) and conformation space networks that combine the results of multiple simulations. Here we use WExplore to study two bromodomain-inhibitor systems using multiple docked starting poses (Brd4-MS436 and Baz2B-ICR7), and synthesize our results using a series of MSMs using time-lagged independent component analysis. Ranking the starting poses by exit rate agrees with the crystal structure pose in both cases. We also predict the most stable pose using the equilibrium populations from the MSM, but find that the prediction is not robust as a function of MSM parameters. The simulated trajectories are synthesized into network models that visualize the entire binding landscape for each system, and we examine transition paths between deeply-bound stable states. We find that, on average, transitions between deeply bound states convert through the unbound state 81% of the time, implying a trialand-error approach to ligand binding. We conclude with a discussion of the implications of this result for both kinetics-based drug discovery and virtual screening pipelines that incorporate molecular dynamics.

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The Added Value of Assessing Ligand–Receptor Binding Kinetics in Drug Discovery

Cited by 29 publications

References 13 publications

Drug–Target Kinetics in Drug Discovery

Drug–Target Kinetics in Drug Discovery

Mapping the Ligand Binding Landscape

Mapping the ligand binding landscape

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