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SUMMARYThere is an urgent need for the discovery and development of new antitubercular agents that target novel biochemical pathways and treat drug-resistant forms of the disease. One approach to addressing this need is through high-throughput screening of drug-like small molecule libraries against the whole bacterium in order to identify a variety of new, active scaffolds that will stimulate additional biological research and drug discovery. Through the Molecular Libraries Screening Center Network, the NIAID Tuberculosis Antimicrobial Acquisition and Coordinating Facility tested a 215,110-compound library against M. tuberculosis strain H37Rv. A medicinal chemistry survey of the results from the screening campaign is reported herein. CONFLICT OF INTEREST STATEMENTCompeting interests: Dr. Goldman is a NIAID staff member who either in the past or currently provides oversight for the project that generated the data used as the basis for this work.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public AccessAuthor Manuscript Tuberculosis (Edinb) The MLSCN was established in 2005 as a pilot program to assemble a large library of biologically relevant small molecules and make them available through a network of HTS laboratories to researchers worldwide through a competitive assay submission process. Acceptance of the TAACF assay into the MLSCN program made available the unique resources of the NIH Small Molecule Repository (SMR), significantly expanding the spectrum of molecules tested for activity against TB. For this screen, a 215,110-compound library from the SMR was examined for anti-TB activity using the assay described previously, 7 with the only change to the screening protocol being the elimination of the polyethylene incubator bags, resulting in the identification of a number of novel chemical scaffolds. Moreover, even for classes of compounds identified earlier during testing of the NIAID ChemBridge library, 7 additional examples emerged that further clarified the structure-activity picture. Since the compounds in the SMR have been examined in scores of diverse assays undertaken by the MLSCN, and the results published on the NIH PubChem website, 8 another motivation for conducting the MLSCN campaign is the ability to correlate antituberculosis activity of the hits with other biological activities that these compounds may possess, potentially providing information about possible mechanisms of action or toxicity. The raw screening results upon which the structural analysis below is based are now publicly available on PubChem (assay AIDs 1332 and 1626). MATERIALS ...

The authors have developed a high-throughput screen (HTS) that allows for the identification of potential inhibitors of the severe acute respiratory syndrome coronavirus (SARS CoV) from large compound libraries. The luminescent-based assay measures the inhibition of SARS CoV-induced cytopathic effect (CPE) in Vero E6 cells. The assay was validated in 96-well plates in a BSL3 containment facility. The assay is sensitive and robust, with Z values > 0.6, signal to background (S/B) > 16, and signal to noise (S/N) > 3. The assay was further validated with 2 different diversity sets of compounds against the SARS CoV. The "hit" rate for both libraries was approximately 0.01%. The validated HTS assay was then employed to screen a 100,000-compound library against SARS CoV. The hit rate for the library in a single-dose format was determined to be approximately 0.8%. Screening of the 3 libraries resulted in the identification of several novel compounds that effectively inhibited the CPE of SARS CoV in vitro-compounds which will serve as excellent lead candidates for further evaluation. At a 10-µM concentration, 3 compounds with selective indexes (SI 50 ) of > 53 were discovered. (Journal of Biomolecular Screening 2007:33-40)

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Purpose Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested. Methods A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate. Results In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models. Conclusion Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.