Modeling the permeability of drug-like molecules through the cell wall of Mycobacterium tuberculosis: an analogue based approach.

The emergence of drug resistant strains of Mycobacterium Tuberculosis (Mtb) accentuates the urgent need for the development of novel antitubercular drugs. The major causes of drug resistance are genetic mutations, the influx-efflux transporter system, and the complex cell wall system of Mtb, which can function as permeability barriers. The driving force for permeability of small molecules through a biological system depends on various physicochemical factors. To understand the permeability of small molecules and subsequent cell inhibition, we have developed predictive QSAR models based on reported enzyme-based (IC50) and cell-based (MIC) Mtb inhibitory data. The compounds that are highly active in enzyme-based assays and have significant variation in cell-based assays are assumed to possess the required permeability through the Mtb cell wall. The obtained models suggest the importance of molecular connectivity, lipophilicity (log P, size, shape), electrotopology (relative atomic mass, polarizability, electronegativity, ionization potential, atomic charges, van der Waals volume, hybridization, hydrogen bond acceptors/donors, number of fused rings) and functional groups (hydroxyl groups, primary and secondary amines) of a molecule in determining both its inhibitory potency and Mtb cell permeability. The models were validated with known Mtb inhibitors (9804) collected from the ChEMBL database, Mtb drugs (27) and clinical candidates (5). Further, these validated models were used to screen and prioritize a large database of compounds, including Zinc (152 128), Asinex (435 215), DrugBank (6531) and antimicrobial compounds (1324). The results obtained from 2D-QSAR analysis could improve our understanding towards Mtb cell permeability, which may aid in the rational design of novel potent molecules for tuberculosis (TB).

[1]  A K Nandedkar Comparative study of the lipid composition of particular pathogenic and nonpathogenic species of Mycobacterium. , 1983, Journal of the National Medical Association.

[2]  V. Viswanadhan,et al.  Pyrid-2-yl and 2-CyanoPhenyl fused heterocyclic compounds as human P2X$$_{3}$$3 inhibitors: a combined approach based on homology modelling, docking and QSAR analysis , 2014, Molecular Diversity.

[3]  George Papadatos,et al.  The ChEMBL bioactivity database: an update , 2013, Nucleic Acids Res..

[4]  G. Madhavi Sastry,et al.  Homology modeling of membrane proteins: A critical assessment , 2006, Comput. Biol. Chem..

[5]  Christoph A. Sotriffer,et al.  MycPermCheck: the Mycobacterium tuberculosis permeability prediction tool for small molecules , 2013, Bioinform..

[6]  E. Rubin,et al.  Bacterial Growth and Cell Division: a Mycobacterial Perspective , 2008, Microbiology and Molecular Biology Reviews.

[7]  R. Todeschini,et al.  Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing / Volume II: Appendices, References , 2009 .

[8]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[9]  G. N. Sastry,et al.  Dipeptidyl peptidase IV inhibitors: a new paradigm in type 2 diabetes treatment. , 2014, Current drug targets.

[10]  G Narahari Sastry,et al.  The efficacy of conceptual DFT descriptors and docking scores on the QSAR models of HIV protease inhibitors. , 2012, Medicinal chemistry (Shariqah (United Arab Emirates)).

[11]  P. Brennan Structure, function, and biogenesis of the cell wall of Mycobacterium tuberculosis. , 2003, Tuberculosis.

[12]  Lemont B. Kier,et al.  Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information , 1995, J. Chem. Inf. Comput. Sci..

[13]  A. Kastaniotis,et al.  Function of Heterologous Mycobacterium tuberculosis InhA, a Type 2 Fatty Acid Synthase Enzyme Involved in Extending C20 Fatty Acids to C60-to-C90 Mycolic Acids, during De Novo Lipoic Acid Synthesis in Saccharomyces cerevisiae , 2008, Applied and Environmental Microbiology.

[14]  Mohammed H Bohari,et al.  FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols , 2012, Journal of Molecular Modeling.

[15]  P J Goodford,et al.  Physicochemical-activity relationship in practice. 2. Rational selection of benzenoid substituents. , 1975, Journal of medicinal chemistry.

[16]  Mohammed H Bohari,et al.  Modeling anti-HIV compounds: the role of analogue-based approaches. , 2012, Current computer-aided drug design.

[17]  G Narahari Sastry,et al.  Virtual high throughput screening in new lead identification. , 2011, Combinatorial chemistry & high throughput screening.

[18]  G. N. Sastry,et al.  2D and 3D quantitative structure-activity relationship studies on a series of bis- pyridinium compounds as choline kinase inhibitors , 2006 .

[19]  G. N. Sastry,et al.  Choline kinase: an important target for cancer. , 2006, Current medicinal chemistry.

[20]  Gregory A. Reichard SARVision Plus by ChemApps , 2008, J. Chem. Inf. Model..

[21]  H. Nikaido,et al.  Mycobacterial cell wall: structure and role in natural resistance to antibiotics. , 1994, FEMS microbiology letters.

[22]  N. Devaraj,et al.  Membrane assembly driven by a biomimetic coupling reaction. , 2012, Journal of the American Chemical Society.

[23]  I. Smith,et al.  Mycobacterium tuberculosis Pathogenesis and Molecular Determinants of Virulence , 2003, Clinical Microbiology Reviews.

[24]  J. Liu,et al.  Mycolic Acid Structure Determines the Fluidity of the Mycobacterial Cell Wall* , 1996, The Journal of Biological Chemistry.

[25]  Liem Nguyen,et al.  Molecular biology of drug resistance in Mycobacterium tuberculosis. , 2013, Current topics in microbiology and immunology.

[26]  Gordon M. Crippen,et al.  Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions , 1987, J. Chem. Inf. Comput. Sci..

[27]  C. Choudhury,et al.  Structural and Functional Diversities of the Hexadecahydro‐1H‐cyclopenta[a]phenanthrene Framework, a Ubiquitous Scaffold in Steroidal Hormones , 2016, Molecular informatics.

[28]  Gibson S. Kibiki,et al.  New Drugs against Tuberculosis: Problems, Progress, and Evaluation of Agents in Clinical Development , 2008, Antimicrobial Agents and Chemotherapy.

[29]  G. Narahari Sastry,et al.  Sequence, Structure, and Active Site Analyses of p38 MAP Kinase: Exploiting DFG-out Conformation as a Strategy to Design New Type II Leads , 2011, J. Chem. Inf. Model..

[30]  A Srinivas Reddy,et al.  Virtual screening in drug discovery -- a computational perspective. , 2007, Current protein & peptide science.

[31]  Richard Svensson,et al.  Impact of Stereospecific Intramolecular Hydrogen Bonding on Cell Permeability and Physicochemical Properties , 2014, Journal of medicinal chemistry.

[32]  CHUN WEI YAP,et al.  PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints , 2011, J. Comput. Chem..

[33]  S. Ajmani,et al.  Toward a general predictive QSAR model for gamma-secretase inhibitors , 2013, Molecular Diversity.