TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases.

The interest on computational techniques for the discovery of neuroprotective drugs has increased due to recent fail of important clinical trials. In fact, there is a huge amount of data accumulated in public databases like CHEMBL with respect to structurally heterogeneous series of drugs, multiple assays, drug targets, and model organisms. However, there are no reports of multi-target or multiplexing Quantitative Structure-Property Relationships (mt-QSAR/mx-QSAR) models of these multiplexing assay outcomes reported in CHEMBL for neurotoxicity/neuroprotective effects of drugs. Accordingly, in this paper we develop the first mx-QSAR model for multiplexing assays of neurotoxicity/neuroprotective effects of drugs. We used the method TOPS-MODE to calculate the structural parameters of drugs. The best model found correctly classified 4393 out of 4915 total cases in both training and validation. This is representative of overall train and validation Accuracy, Sensitivity, and Specificity values near to 90%, 98%, and 80%, respectively. This dataset includes multiplexing assay endpoints of 2217 compounds. Every one compound was assayed in at least one out of 338 assays, which involved 148 molecular or cellular targets and 35 standard type measures in 11 model organisms (including human). The second aim of this work is the exemplification of the use of the new mx-QSAR model with a practical case of study. To this end, we obtained again by organic synthesis and reported, by the first time, experimental assays of the new 1,3-rasagiline derivatives 3 different tests: assay (1) in absence of neurotoxic agents, (2) in the presence of glutamate, and (3) in the presence of H2O2. The higher neuroprotective effects found for each one of these assays were for the stereoisomers of compound 7: compound 7b with protection=23.4% in assay (1) and protection=15.2% in assay (2); and for compound 7a with protection=46.2% in assay (3). Interestingly, almost all compounds show protection values >10% in assay (3) but not in the other 2 assays. After that, we used the mx-QSAR model to predict the more probable response of the new compounds in 559 unique pharmacological tests not carried out experimentally. The results obtained are very significant because they complement the pharmacological studies of these promising rasagiline derivatives. This work paves the way for further developments in the multi-target/multiplexing screening of large libraries of compounds potentially useful in the treatment of neurodegenerative diseases.

[1]  Feng Luan,et al.  Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach. , 2012, Ecotoxicology and environmental safety.

[2]  Francisco Torrens,et al.  Bond‐Based 2D Quadratic Fingerprints in QSAR Studies: Virtual and In vitro Tyrosinase Inhibitory Activity Elucidation , 2010, Chemical biology & drug design.

[3]  Feng Luan,et al.  In silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosis. , 2012, Combinatorial chemistry & high throughput screening.

[4]  F. Prado-Prado,et al.  Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. , 2008, Current topics in medicinal chemistry.

[5]  F. Atienzar,et al.  Multiplexing cell viability assays. , 2011, Methods in molecular biology.

[6]  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.

[7]  Shao-Hua Yang,et al.  Neuroprotection targeting ischemic penumbra and beyond for the treatment of ischemic stroke , 2012, Neurological research.

[8]  Santiago Vilar,et al.  Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. , 2008, Current topics in medicinal chemistry.

[9]  J. Matías‐Guiu,et al.  CSF from amyotrophic lateral sclerosis patients produces glutamate independent death of rat motor brain cortical neurons: Protection by resveratrol but not riluzole , 2011, Brain Research.

[10]  Ramón García-Domenech,et al.  New agents active against Mycobacterium avium complex selected by molecular topology: a virtual screening method. , 2003, The Journal of antimicrobial chemotherapy.

[11]  N. Stergiopulos,et al.  Update on the Role of Cannabinoid Receptors after Ischemic Stroke , 2012, Mediators of inflammation.

[12]  A. Guekht,et al.  Cerebrolysin improves symptoms and delays progression in patients with Alzheimer's disease and vascular dementia. , 2012, Drugs of today.

[13]  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.

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

[15]  David Lerner,et al.  Hydroxy-1-aminoindans and derivatives: preparation, stability, and reactivity. , 2006, The Journal of organic chemistry.

[16]  Ken Arai,et al.  Pathophysiologic Cascades in Ischemic Stroke , 2012, International journal of stroke : official journal of the International Stroke Society.

[17]  E. Uriarte,et al.  3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites. , 2009, Biochimica et biophysica acta.

[18]  Ana B. Porto-Pazos,et al.  Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network. , 2010, Current drug metabolism.

[19]  Feng Luan,et al.  Multi-target drug discovery in anti-cancer therapy: fragment-based approach toward the design of potent and versatile anti-prostate cancer agents. , 2011, Bioorganic & medicinal chemistry.

[20]  Igor V. Tetko,et al.  Virtual Computational Chemistry Laboratory – Design and Description , 2005, J. Comput. Aided Mol. Des..

[21]  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.

[22]  B. Dudas,et al.  Glycosaminoglycans and neuroprotection. , 2012, Handbook of experimental pharmacology.

[23]  Francisco Torrens,et al.  Atom- and Bond-Based 2D TOMOCOMD-CARDD Approach and Ligand-Based Virtual Screening for the Drug Discovery of New Tyrosinase Inhibitors , 2008, Journal of biomolecular screening.

[24]  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..

[25]  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.

[26]  Y. Marrero-Ponce,et al.  A computer-based approach to the rational discovery of new trichomonacidal drugs by atom-type linear indices. , 2005, Current drug discovery technologies.

[27]  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.

[28]  L. G. Pérez-Montoto,et al.  Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. , 2010, Current pharmaceutical design.

[29]  Alan R. Katritzky,et al.  CODESSA-Based Theoretical QSPR Model for Hydantoin HPLC-RT Lipophilicities , 2001, J. Chem. Inf. Comput. Sci..

[30]  Patrick Dallemagne,et al.  Synthesis and Initial Results for MAO-B Inhibition by New N-Propargyl-3-pyrrol-1-ylindanamine Derivatives, Analogues of Rasagiline , 2003, Journal of enzyme inhibition and medicinal chemistry.

[31]  Peter C. Jurs,et al.  Classification of Inhibitors of Protein Tyrosine Phosphatase 1B Using Molecular Structure Based Descriptors , 2003, J. Chem. Inf. Comput. Sci..

[32]  Humberto González-Díaz,et al.  2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins. , 2011, European journal of medicinal chemistry.

[33]  D. Howells,et al.  Improving the Efficiency of the Development of Drugs for Stroke , 2012, International journal of stroke : official journal of the International Stroke Society.

[34]  Cristian R. Munteanu,et al.  MIND-BEST: Web server for drugs and target discovery; design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from Trichomonas gallinae. , 2011, Journal of proteome research.

[35]  Maykel Pérez González,et al.  Applications of 2D descriptors in drug design: a DRAGON tale. , 2008, Current topics in medicinal chemistry.

[36]  J. Dorado,et al.  Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions. , 2010, Journal of proteome research.

[37]  Mati Karelson,et al.  Six-membered cyclic ureas as HIV-1 protease inhibitors: a QSAR study based on CODESSA PRO approach. Quantitative structure-activity relationships. , 2002, Bioorganic & medicinal chemistry letters.

[38]  Francisco Torrens,et al.  Atom, atom-type and total molecular linear indices as a promising approach for bioorganic and medicinal chemistry: theoretical and experimental assessment of a novel method for virtual screening and rational design of new lead anthelmintic. , 2005, Bioorganic & medicinal chemistry.

[39]  Satoshi Ichikawa,et al.  The first radical method for the introduction of an ethynyl group using a silicon tether and its application to the synthesis of 2'-deoxy-2'-C-ethynylnucleosides. , 2003, The Journal of organic chemistry.

[40]  Amnon Hoffman,et al.  Rasagiline – a novel MAO B inhibitor in Parkinson’s disease therapy , 2007, Therapeutics and clinical risk management.

[41]  J. Gras.  Mirabegron for the treatment of overactive bladder. , 2012, Drugs of today.

[42]  B. Frenguelli,et al.  Putative depolarisation-induced retrograde signalling accelerates the repeated hypoxic depression of excitatory synaptic transmission in area CA1 of rat hippocampus via group I metabotropic glutamate receptors , 2012, Neuroscience.

[43]  W Tatton,et al.  The Anti‐Parkinson Drug Rasagiline and Its Cholinesterase Inhibitor Derivatives Exert Neuroprotection Unrelated to MAO Inhibition in Cell Culture and in Vivo , 2001, Annals of the New York Academy of Sciences.

[44]  Mati Karelson,et al.  Antimalarial activity: a QSAR modeling using CODESSA PRO software. , 2006, Bioorganic & medicinal chemistry.

[45]  L. G. Pérez-Montoto,et al.  Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. , 2010, Current drug metabolism.

[46]  Ruth Brenk,et al.  Mining the ChEMBL Database: An Efficient Chemoinformatics Workflow for Assembling an Ion Channel-Focused Screening Library , 2011, J. Chem. Inf. Model..

[47]  Mahmud Tareq Hassan Khan,et al.  Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds. , 2010, Current pharmaceutical design.

[48]  K. Arai,et al.  Injury and repair in the neurovascular unit , 2012, Neurological research.

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