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Correlating Drug-Target Residence Time and Post-antibiotic Effect: Insight into Target Vulnerability.

ABSTRACT: Target vulnerability correlates the level of drug-target engagement required to generate a pharmacological response. High vulnerability targets are those that require only a relatively small fraction of occupancy to achieve the desired pharmacological outcome, whereas low vulnerability targets require high levels of engagement. Here, we demonstrate that the slope of the correlation between drug-target residence time and the post-antibiotic effect (PAE) can be used to define the vulnerability of bacterial targets. For macrolides, a steep slope is observed between residence time on the E. coli ribosome and the PAE, indicating that the ribosome is a highly vulnerable drug target. The analysis of the residence time-PAE data for erythromycin, azithromycin, spiramycin, and telithromycin using a mechanistic pharmacokinetic-pharmacodynamic model that integrates drug-target kinetics into predictions of drug activity lead to the successful prediction of the cellular PAE for tylosin, which has the longest residence time (7.1 h) and PAE (5.8 h). Although the macrolide data support a connection between residence time, PAE, and bactericidality, many bactericidal β-lactam antibiotics do not give a PAE, illustrating the role of factors such as protein resynthesis in the expression of target vulnerability.

SUBMITTER: Davoodi S

PROVIDER: S-EPMC7150629 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.

Dataset Information

Correlating Drug-Target Residence Time and Post-antibiotic Effect: Insight into Target Vulnerability.

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