Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

In a multi-target complex network, the links (Lij) represent the interactions between the drug (di) and the target (tj), characterized by different experimental measures (Ki, Km, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (cj). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%–90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

[1]  Feng Luan,et al.  Chemoinformatics in multi-target drug discovery for anti-cancer therapy: in silico design of potent and versatile anti-brain tumor agents. , 2012, Anti-cancer agents in medicinal chemistry.

[2]  T. Mosmann Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. , 1983, Journal of immunological methods.

[3]  Ruth Nussinov,et al.  From allosteric drugs to allo-network drugs: state of the art and trends of design, synthesis and computational methods. , 2013, Current topics in medicinal chemistry.

[4]  R. Nussinov,et al.  Send Orders of Reprints at Reprints@benthamscience.net Allo-network Drugs: Extension of the Allosteric Drug Concept to Protein- Protein Interaction and Signaling Networks , 2022 .

[5]  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.

[6]  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..

[7]  Jürgen Bajorath,et al.  Classification of Compounds with Distinct or Overlapping Multi-Target Activities and Diverse Molecular Mechanisms Using Emerging Chemical Patterns , 2013, J. Chem. Inf. Model..

[8]  Jürgen Bajorath,et al.  Differential Shannon Entropy Analysis Identifies Molecular Property Descriptors that Predict Aqueous Solubility of Synthetic Compounds with High Accuracy in Binary QSAR Calculations , 2002, J. Chem. Inf. Comput. Sci..

[9]  Ruth Nussinov,et al.  Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review , 2012, Pharmacology & therapeutics.

[10]  N. Trinajstic,et al.  Information theory, distance matrix, and molecular branching , 1977 .

[11]  Edward D Rothman,et al.  Statistics, methods and applications , 1987 .

[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]  P Botella-Rocamora,et al.  Spatial moving average risk smoothing , 2013, Statistics in medicine.

[14]  Nenad Trinajstić,et al.  Chemical graph theory: Modeling the thermodynamic properties of molecules , 1980 .

[15]  Jens Meiler,et al.  Identification of Metabotropic Glutamate Receptor Subtype 5 Potentiators Using Virtual High-Throughput Screening , 2010, ACS chemical neuroscience.

[16]  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.

[17]  Daniel J. Graham,et al.  Information Content in Organic Molecules: Quantification and Statistical Structure via Brownian Processing , 2004, J. Chem. Inf. Model..

[18]  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.

[19]  Jürgen Bajorath,et al.  Molecular Scaffolds with High Propensity to Form Multi-Target Activity Cliffs , 2010, J. Chem. Inf. Model..

[20]  David Loewenstern,et al.  Significantly lower entropy estimates for natural DNA sequences , 1997, Proceedings DCC '97. Data Compression Conference.

[21]  N. Williams,et al.  Recent Advances in the Genetics of the ALS-FTLD Complex , 2012, Current Neurology and Neuroscience Reports.

[22]  Wei Huang,et al.  Novel dual inhibitors of AChE and MAO derived from hydroxy aminoindan and phenethylamine as potential treatment for Alzheimer's disease. , 2002, Journal of medicinal chemistry.

[23]  Humberto González-Díaz,et al.  Markov entropy backbone electrostatic descriptors for predicting proteins biological activity. , 2004, Bioorganic & medicinal chemistry letters.

[24]  Feng Luan,et al.  Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. , 2013, ACS chemical neuroscience.

[25]  Humberto González Díaz,et al.  Markovian negentropies in bioinformatics. 1. A picture of footprints after the interaction of the HIV-1 -RNA packaging region with drugs , 2003, Bioinform..

[26]  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.

[27]  H. Sharma A combination of tumor necrosis factor‐α and neuronal nitric oxide synthase antibodies applied topically over the traumatized spinal cord enhances neuroprotection and functional recovery in the rat , 2010, Annals of the New York Academy of Sciences.

[28]  M. Naoi,et al.  Revelation in the neuroprotective functions of rasagiline and selegiline: the induction of distinct genes by different mechanisms , 2013, Expert review of neurotherapeutics.

[29]  Danail Bonchev,et al.  Symmetry and information content of chemical structures , 1976 .

[30]  Falk Schreiber,et al.  Exploration of biological network centralities with CentiBiN , 2006, BMC Bioinformatics.

[31]  C. Linn,et al.  Neuroprotection of rat retinal ganglion cells mediated through alpha7 nicotinic acetylcholine receptors , 2013, Neuroscience.

[32]  L. G. Pérez-Montoto,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.

[33]  Jürgen Bajorath,et al.  Distinguishing between Natural Products and Synthetic Molecules by Descriptor Shannon Entropy Analysis and Binary QSAR Calculations , 2000, J. Chem. Inf. Comput. Sci..

[34]  D. Thirumalai,et al.  Proteins associated with diseases show enhanced sequence correlation between charged residues , 2004, Bioinform..

[35]  Ritu Jain,et al.  QSPR Correlation of the Melting Point for Pyridinium Bromides, Potential Ionic Liquids , 2002, J. Chem. Inf. Comput. Sci..

[36]  Daniel J. Graham,et al.  Information Content in Organic Molecules: Brownian Processing at Low Levels , 2007, J. Chem. Inf. Model..

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

[38]  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.

[39]  Feng Luan,et al.  Multi-target inhibitors for proteins associated with Alzheimer: in silico discovery using fragment-based descriptors. , 2013, Current Alzheimer research.

[40]  W. Khan,et al.  Impact of 5-HT₃ receptor antagonists on peripheral and central diseases. , 2012, Drug discovery today.

[41]  J. Juillard,et al.  [Parkinson disease]. , 1985, Revue de l'infirmiere.

[42]  Martin Vingron,et al.  Lethality and entropy of protein interaction networks. , 2005, Genome informatics. International Conference on Genome Informatics.

[43]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[44]  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.

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

[46]  S. Mandel,et al.  Mechanism of neuroprotective action of the anti-Parkinson drug rasagiline and its derivatives , 2005, Brain Research Reviews.

[47]  Daniel J. Graham,et al.  Information Content in Organic Molecules: Aggregation States and Solvent Effects , 2005, J. Chem. Inf. Model..

[48]  Kunal Roy,et al.  Comparative QSPR studies with molecular connectivity, molecular negentropy and TAU indices , 2003, Journal of molecular modeling.

[49]  P. Camps,et al.  Undifferentiated and Differentiated PC12 Cells Protected by Huprines Against Injury Induced by Hydrogen Peroxide , 2013, PloS one.

[50]  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.

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

[52]  C. McMurray,et al.  Oxidative stress and mitochondrial dysfunction in neurodegenerative diseases , 2007, Neuroscience.

[53]  T G Dewey,et al.  The Shannon information entropy of protein sequences. , 1996, Biophysical journal.

[54]  Alejandro Speck-Planche,et al.  Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals. , 2013, Bioorganic & medicinal chemistry.

[55]  Jan Vyhnánek,et al.  Analysis of fMRI time-series by entropy measures. , 2012, Neuro endocrinology letters.

[56]  A. Gunn,et al.  Partial neuroprotection by nNOS inhibition during profound asphyxia in preterm fetal sheep , 2013, Experimental Neurology.

[57]  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.

[58]  David R. Cox,et al.  Time Series Analysis , 2012 .

[59]  L. Martin Biology of mitochondria in neurodegenerative diseases. , 2012, Progress in molecular biology and translational science.

[60]  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.

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

[62]  Daniel J. Graham,et al.  Base Information Content in Organic Formulas , 2000, J. Chem. Inf. Comput. Sci..

[63]  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.

[64]  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.

[65]  P. Reddy,et al.  Rasagiline‐induced serotonin syndrome , 2011, Movement disorders : official journal of the Movement Disorder Society.

[66]  Kathrin Heikamp,et al.  Large-Scale Similarity Search Profiling of ChEMBL Compound Data Sets , 2011, J. Chem. Inf. Model..

[67]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[68]  Daniel J. Graham,et al.  Information Content in Organic Molecules: Reaction Pathway Analysis via Brownian Processing , 2004, J. Chem. Inf. Model..

[69]  M N D S Cordeiro,et al.  Role of ligand-based drug design methodologies toward the discovery of new anti- Alzheimer agents: futures perspectives in Fragment-Based Ligand Design. , 2012, Current medicinal chemistry.

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

[71]  Bhyravabhotla Jayaram,et al.  NeuroDNet - an open source platform for constructing and analyzing neurodegenerative disease networks , 2013, BMC Neuroscience.

[72]  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.

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

[74]  Yoshua Bengio,et al.  Collaborative Filtering on a Family of Biological Targets , 2006, J. Chem. Inf. Model..

[75]  Daniel J. Graham,et al.  Information and Organic Molecules: Structure Considerations via Integer Statistics , 2002, J. Chem. Inf. Comput. Sci..

[76]  P. Khadikar,et al.  Modelling of carbonic anhydrase inhibitory activity of sulfonamides using molecular negentropy. , 2003, Bioorganic & medicinal chemistry letters.

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

[78]  L B Kier,et al.  Use of molecular negentropy to encode structure governing biological activity. , 1980, Journal of pharmaceutical sciences.

[79]  Cristian R. Munteanu,et al.  New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks. , 2012, Journal of theoretical biology.