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      Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis

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          Abstract

          The rise of multi-drug resistant (MDR) and extensively drug resistant (XDR) tuberculosis around the world, including in industrialized nations, poses a great threat to human health and defines a need to develop new, effective and inexpensive anti-tubercular agents. Previously we developed a chemical systems biology approach to identify off-targets of major pharmaceuticals on a proteome-wide scale. In this paper we further demonstrate the value of this approach through the discovery that existing commercially available drugs, prescribed for the treatment of Parkinson's disease, have the potential to treat MDR and XDR tuberculosis. These drugs, entacapone and tolcapone, are predicted to bind to the enzyme InhA and directly inhibit substrate binding. The prediction is validated by in vitro and InhA kinetic assays using tablets of Comtan, whose active component is entacapone. The minimal inhibition concentration (MIC 99) of entacapone for Mycobacterium tuberculosis ( M.tuberculosis) is approximately 260.0 µM, well below the toxicity concentration determined by an in vitro cytotoxicity model using a human neuroblastoma cell line. Moreover, kinetic assays indicate that Comtan inhibits InhA activity by 47.0% at an entacapone concentration of approximately 80 µM. Thus the active component in Comtan represents a promising lead compound for developing a new class of anti-tubercular therapeutics with excellent safety profiles. More generally, the protocol described in this paper can be included in a drug discovery pipeline in an effort to discover novel drug leads with desired safety profiles, and therefore accelerate the development of new drugs.

          Author Summary

          The rise of multi-drug resistant (MDR) and extensively drug resistant (XDR) tuberculosis around the world, including in industrialized nations, poses a great threat to human health. This resistance highlights the need to develop new, effective and inexpensive anti-tubercular agents. Unfortunately, conventional approaches have yielded very few successes in the field of anti-infective drug discovery. It is a challenge to design drugs with both efficacy and safety. These challenges are reflected in the high costs involved in bringing new drugs to market. It has been estimated that the cost to launch a successful new drug is in excess of US$800 million. We have developed a novel computational strategy to systematically identify cross-reactivity between different drug target families. In this paper we demonstrate the strength of this approach through the discovery that existing commercially available drugs prescribed for the treatment of Parkinson's disease have the potential to treat MDR and XDR tuberculosis. The protocol described herein can be included in a drug discovery pipeline in an effort to accelerate the development of new drugs with reduced side effects.

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          Most cited references37

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          Multi-target therapeutics: when the whole is greater than the sum of the parts.

          Drugs designed to act against individual molecular targets cannot usually combat multigenic diseases such as cancer, or diseases that affect multiple tissues or cell types such as diabetes and immunoinflammatory disorders. Combination drugs that impact multiple targets simultaneously are better at controlling complex disease systems, are less prone to drug resistance and are the standard of care in many important therapeutic areas. The combination drugs currently employed are primarily of rational design, but the increased efficacy they provide justifies in vitro discovery efforts for identifying novel multi-target mechanisms. In this review, we discuss the biological rationale for combination therapeutics, review some existing combination drugs and present a systematic approach to identify interactions between molecular pathways that could be leveraged for therapeutic benefit.
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            Global mapping of pharmacological space.

            We present the global mapping of pharmacological space by the integration of several vast sources of medicinal chemistry structure-activity relationships (SAR) data. Our comprehensive mapping of pharmacological space enables us to identify confidently the human targets for which chemical tools and drugs have been discovered to date. The integration of SAR data from diverse sources by unique canonical chemical structure, protein sequence and disease indication enables the construction of a ligand-target matrix to explore the global relationships between chemical structure and biological targets. Using the data matrix, we are able to catalog the links between proteins in chemical space as a polypharmacology interaction network. We demonstrate that probabilistic models can be used to predict pharmacology from a large knowledge base. The relationships between proteins, chemical structures and drug-like properties provide a framework for developing a probabilistic approach to drug discovery that can be exploited to increase research productivity.
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              inhA, a gene encoding a target for isoniazid and ethionamide in Mycobacterium tuberculosis.

              Isoniazid (isonicotinic acid hydrazide, INH) is one of the most widely used antituberculosis drugs, yet its precise target of action on Mycobacterium tuberculosis is unknown. A missense mutation within the mycobacterial inhA gene was shown to confer resistance to both INH and ethionamide (ETH) in M. smegmatis and in M. bovis. The wild-type inhA gene also conferred INH and ETH resistance when transferred on a multicopy plasmid vector to M. smegmatis and M. bovis BCG. The InhA protein shows significant sequence conservation with the Escherichia coli enzyme EnvM, and cell-free assays indicate that it may be involved in mycolic acid biosynthesis. These results suggest that InhA is likely a primary target of action for INH and ETH.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                July 2009
                July 2009
                3 July 2009
                : 5
                : 7
                : e1000423
                Affiliations
                [1 ]Department of Biology, University of York, York, United Kingdom
                [2 ]Institute of Chemical Biology & Drug Discovery, Department of Chemistry, Stony Brook University, Stony Brook, New York, United States of America
                [3 ]Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California, United States of America
                [4 ]San Diego Supercomputer Center, University of California San Diego, La Jolla, California, United States of America
                [5 ]Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
                University of Houston, United States of America
                Author notes
                [¤]

                Current address: Institute of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom

                Conceived and designed the experiments: LX PEB. Performed the experiments: SLK NL NB LX. Analyzed the data: SLK NL NB PJT LX. Contributed reagents/materials/analysis tools: NB PJT LX. Wrote the paper: SLK NL NB PJT LX PEB.

                Article
                09-PLCB-RA-0040R2
                10.1371/journal.pcbi.1000423
                2699117
                19578428
                5cb88614-86d7-41d7-9fa6-753b10e71d10
                Kinnings et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 14 January 2009
                : 28 May 2009
                Page count
                Pages: 10
                Categories
                Research Article
                Biotechnology/Small Molecule Chemistry
                Computational Biology/Macromolecular Structure Analysis
                Computational Biology/Systems Biology
                Infectious Diseases/Bacterial Infections
                Pharmacology/Adverse Reactions
                Pharmacology/Drug Development
                Pharmacology/Drug Resistance

                Quantitative & Systems biology
                Quantitative & Systems biology

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