Metabolic Stability Screen in Drug Discovery

Abstract Objectives The discovery and development of TB drugs has met limited success, with two new drugs approved over the last 40 years. Part of the difficulty resides in the lack of well-established in vitro or in vivo targets of potency and physicochemical and pharmacokinetic parameters. In an attempt to benchmark and compare such properties for anti-TB agents, we have experimentally determined and compiled these parameters for 36 anti-TB compounds, using standardized and centralized assays, thus ensuring direct comparability across drugs and drug classes. Methods Potency parameters included growth inhibition, cidal activity against growing and non-growing bacteria and activity against intracellular mycobacteria. Pharmacokinetic parameters included basic physicochemical properties, solubility, permeability and metabolic stability. We then attempted to establish correlations between physicochemical, in vitro and in vivo pharmacokinetic and pharmacodynamic indices to tentatively inform future drug discovery efforts. Results Two-thirds of the compounds tested showed bactericidal and intramacrophage activity. Most compounds exhibited favourable solubility, permeability and metabolic stability in standard in vitro pharmacokinetic assays. An analysis of human pharmacokinetic parameters revealed associations between lipophilicity and volume of distribution, clearance, plasma protein binding and oral bioavailability. Not surprisingly, most compounds with favourable pharmacokinetic properties complied with Lipinski's rule of five. Conclusions However, most attempts to detect in vitro–in vivo correlations were unsuccessful, emphasizing the challenges of anti-TB drug discovery. The objective of this work is to provide a reference dataset for the TB drug discovery community with a focus on comparative in vitro potency and pharmacokinetics.

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Metabolic Stability for Drug Discovery and Development

Clinical Pharmacokinetics ◽

10.2165/00003088-200342060-00002 ◽

2003 ◽

Vol 42 (6) ◽

pp. 515-528 ◽

Cited By ~ 106

Author(s):

Collen M Masimirembwa ◽

Ulf Bredberg ◽

Tommy B Andersson

Keyword(s):

Drug Discovery ◽

Metabolic Stability ◽

Drug Discovery And Development

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PredMS: a random Forest model for predicting metabolic stability of drug candidates in human liver microsomes

Bioinformatics ◽

10.1093/bioinformatics/btab547 ◽

2021 ◽

Author(s):

Jae Yong Ryu ◽

Jeong Hyun Lee ◽

Byung Ho Lee ◽

Jin Sook Song ◽

Sunjoo Ahn ◽

...

Keyword(s):

Drug Discovery ◽

Random Forest ◽

Predictive Value ◽

Human Liver ◽

Liver Microsomes ◽

Metabolic Stability ◽

Drug Discovery And Development ◽

Forest Model ◽

Stability Data ◽

External Test

Abstract Motivation Poor metabolic stability leads to drug development failure. Therefore, it is essential to evaluate the metabolic stability of small compounds for successful drug discovery and development. However, evaluating metabolic stability in vitro and in vivo is expensive, time-consuming, and laborious. Additionally, only a few free software programs are available for metabolic stability data and prediction. Therefore, in this study, we aimed to develop a prediction model that predicts the metabolic stability of small compounds. Results We developed a computational model, PredMS, which predicts the metabolic stability of small compounds as stable or unstable in human liver microsomes. PredMS is based on a random forest model using an in-house database of metabolic stability data of 1,917 compounds. To validate the prediction performance of PredMS, we generated external test data of 61 compounds. PredMS achieved an accuracy of 0.74, Matthew’s correlation coefficient of 0.48, sensitivity of 0.70, specificity of 0.86, positive predictive value of 0.94, and negative predictive value of 0.46 on the external test dataset. PredMS will be a useful tool to predict the metabolic stability of small compounds in the early stages of drug discovery and development. Availability and implementation The source code for PredMS is available at https://bitbucket.org/krictai/predms, and the PredMS web server is available at https://predms.netlify.app. Supplementary information Supplementary data are available at Bioinformatics online.

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