Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening.

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

[1]  G. Tew,et al.  Role of Amphiphilicity in the Design of Synthetic Mimics of Antimicrobial Peptides with Gram-negative Activity. , 2013, ACS medicinal chemistry letters.

[2]  P. Brachman,et al.  Bacterial infections of humans: epidemiology and control. , 1993 .

[3]  Carl J. Huberty,et al.  Applied MANOVA and discriminant analysis , 2006 .

[4]  Charles C. Persinger,et al.  How to improve R&D productivity: the pharmaceutical industry's grand challenge , 2010, Nature Reviews Drug Discovery.

[5]  Feng Luan,et al.  TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases. , 2013, Bioorganic & medicinal chemistry.

[6]  Amanda M. Fenner,et al.  Selective inhibition of bacterial and human topoisomerases by N-arylacyl O-sulfonated aminoglycoside derivatives. , 2013, ACS medicinal chemistry letters.

[7]  M. Cordeiro,et al.  Abelson tyrosine-protein kinase 1 as principal target for drug discovery against leukemias. Role of the current computer-aided drug design methodologies. , 2013, Current topics in medicinal chemistry.

[8]  Yovani Marrero-Ponce,et al.  Linear Indices of the "Molecular Pseudograph's Atom Adjacency Matrix": Definition, Significance-Interpretation, and Application to QSAR Analysis of Flavone Derivatives as HIV-1 Integrase Inhibitors , 2004, J. Chem. Inf. Model..

[9]  Pawel Lewicki,et al.  Statistics : methods and applications : a comprehensive reference for science, industry, and data mining , 2006 .

[10]  H. Khavasi,et al.  Simple three-component method for the synthesis of spiroindeno[1,2-b]pyrido[2,3-d]pyrimidine-5,3'-indolines. , 2011, ACS combinatorial science.

[11]  Humberto González-Díaz,et al.  Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity. , 2013, Current topics in medicinal chemistry.

[12]  V. V. Kleandrova,et al.  Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. , 2012, Bioorganic & medicinal chemistry.

[13]  Wolfgang Jahnke and Daniel A. Erlanson.  Fragment-based approaches in drug discovery , 2013 .

[14]  Cees Neef,et al.  Efficacy, nephrotoxicity and ototoxicity of aminoglycosides, mathematically modelled for modelling-supported therapeutic drug monitoring. , 2012, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[15]  Feng Luan,et al.  A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection. , 2012, Molecular bioSystems.

[16]  Humberto González Díaz,et al.  Unified QSAR and network‐based computational chemistry approach to antimicrobials, part 1: Multispecies activity models for antifungals , 2008, J. Comput. Chem..

[17]  K. Bush,et al.  In Vitro Antibacterial Activities of JNJ-Q2, a New Broad-Spectrum Fluoroquinolone , 2010, Antimicrobial Agents and Chemotherapy.

[18]  Humberto González-Díaz,et al.  First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines , 2011, Molecular Diversity.

[19]  Yovani Marrero Ponce.  Total and Local Quadratic Indices of the Molecular Pseudograph’s Atom Adjacency Matrix: Applications to the Prediction of Physical Properties of Organic Compounds , 2003, Molecules : A Journal of Synthetic Chemistry and Natural Product Chemistry.

[20]  W. Jahnke,et al.  Fragment-Based Approaches , 2007 .

[21]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[22]  V. V. Kleandrova,et al.  Chemoinformatics in anti-cancer chemotherapy: multi-target QSAR model for the in silico discovery of anti-breast cancer agents. , 2012, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[23]  Alejandro Speck-Planche,et al.  Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry. , 2013, Current topics in medicinal chemistry.

[24]  P. Nordmann,et al.  Emergence of Metallo-β-Lactamase NDM-1-Producing Multidrug-Resistant Escherichia coli in Australia , 2010, Antimicrobial Agents and Chemotherapy.

[25]  Humberto González-Díaz,et al.  ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. , 2012, Bioorganic & medicinal chemistry.

[26]  Eduardo A. Castro,et al.  Tomocomd-Cardd, a novel approach for computer-aided ‘ rational’ drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds , 2004, J. Comput. Aided Mol. Des..

[27]  Yovani Marrero-Ponce,et al.  Ligand-Based Virtual Screening and in Silico Design of New Antimalarial Compounds Using Nonstochastic and Stochastic Total and Atom-Type Quadratic Maps. , 2005 .

[28]  Francisco Torrens,et al.  Atom-based 3D-chiral quadratic indices. Part 2: prediction of the corticosteroid-binding globulinbinding affinity of the 31 benchmark steroids data set. , 2006, Bioorganic & medicinal chemistry.

[29]  Jürgen Bajorath,et al.  Integration of virtual and high-throughput screening , 2002, Nature Reviews Drug Discovery.

[30]  Yovani Marrero-Ponce,et al.  3D-chiral Atom, Atom-type, and Total Non-stochastic and Stochastic Molecular Linear Indices and their Applications to Central Chirality Codification , 2005, J. Comput. Aided Mol. Des..

[31]  P. Barrow,et al.  Enterobacteriaceae , 2008, Poultry Diseases.

[32]  Francisco Torrens,et al.  TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices. , 2007, Bioorganic & medicinal chemistry.

[33]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[34]  R. Borchardt.  Optimizing the "drug-like" properties of leads in drug discovery , 2006 .

[35]  Blaise Hanczar,et al.  Small-sample precision of ROC-related estimates , 2010, Bioinform..

[36]  D. Pei,et al.  High-throughput screening of one-bead-one-compound libraries: identification of cyclic peptidyl inhibitors against calcineurin/NFAT interaction. , 2011, ACS combinatorial science.

[37]  K. Chan,et al.  Library synthesis and antibacterial investigation of cationic anthraquinone analogs. , 2012, ACS combinatorial science.

[38]  Eugenio Uriarte,et al.  Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. , 2009, Molecular pharmaceutics.

[39]  B. Narasimhan,et al.  Synthesis, characterization, biological evaluation and QSAR studies of 11-p-substituted phenyl-12-phenyl-11a,12-dihydro-11H-indeno[2,1-c][1,5]benzothiazepines as potential antimicrobial agents. , 2012, European journal of medicinal chemistry.

[40]  Paola Brun,et al.  Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. , 2011, European journal of medicinal chemistry.

[41]  Hans-Joachim Böhm,et al.  A guide to drug discovery: Hit and lead generation: beyond high-throughput screening , 2003, Nature Reviews Drug Discovery.

[42]  L. Turner,et al.  Summary of Pharmacokinetics and Tissue Distribution of a Broad‐Spectrum Fluoroquinolone, JNJ‐Q2 , 2012, Clinical pharmacology in drug development.

[43]  B. Prasad,et al.  Inhibition of 1-deoxy-D-xylulose-5-phosphate reductoisomerase by lipophilic phosphonates: SAR, QSAR, and crystallographic studies. , 2011, Journal of medicinal chemistry.

[44]  Humberto González-Díaz,et al.  Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. , 2011, European journal of medicinal chemistry.