Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis

Abstract

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Machine learning models identify molecules active against the Ebola virus in vitro

“…Specifically we have previously analyzed large datasets for Mycobacterium tuberculosis to build machine learning models that use single point data, dose-response data 43, 45 , combine bioactivity and cytotoxicity data (e.g. Vero, HepG2 or other model mammalian cells) 28, 29, 46 or combinations of these sets 47, 48 .…”

Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets

“…We have also extended this approach and validated the models with a set of 102,000 compounds from the same laboratory containing 1702 molecules with ≥90% inhibition at 10 μM, representing a hit rate of 1.66% (10). We were able to demonstrate 10-fold enrichments in finding active compounds in the top ranked 600 molecules (10).…”

“…Filters can enable removal or flagging of undesirable molecules (thiol traps and redox-active compounds, epoxides, anhydrides, and Michael acceptors that can covalently modify a cysteine moiety in a surrogate protein (13)(14)(15)), false positives and frequent hitters (16). For example, we have previously compared the filtering of malaria hits and datasets screened against Mtb, and three antimalarial datasets had very high failures with the Abbott Alerts (11), while a similar pattern was seen for Mtb (> 81% failure) versus known Mtb drugs (> 50% failure) (10).…”

“…Critically, we have sought to utilize cheminformatics to not only predict small molecules with activity against Mtb, but to leverage methods to select for chemical entities, either as library inputs or screening hits, with a desired set of physiochemical properties (10)(11)(12). Filters can enable removal or flagging of undesirable molecules (thiol traps and redox-active compounds, epoxides, anhydrides, and Michael acceptors that can covalently modify a cysteine moiety in a surrogate protein (13)(14)(15)), false positives and frequent hitters (16).…”

Genetic Strategies for Identifying New Drug Targets

“…In a recent screen for antimycobacterial compounds (65), testing in excess of 100,000 compounds led to the identification of 1,549 hits. The prioritization of these for further development is currently based almost entirely on chemoinformatic approaches (66,67). However, knowledge of a compound's specific target accelerates the medicinal chemistry required to improve lead compounds.…”