Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets

Drug discovery is a long and difficult process that benefits from the integration of virtual screening methods in experimental screening campaigns such as to generate testable hypotheses, accelerate and/or reduce the cost of drug development. Current drug attrition rate is still a major issue in all therapeutic areas and especially in the field of cancer. Drug repositioning as well as the screening of natural compounds constitute promising approaches to accelerate and improve the success rate of drug discovery. We developed three compounds libraries of purchasable compounds: Drugs-lib, FOOD-lib and NP-lib that contain approved drugs, food constituents and natural products, respectively, that are optimized for structure-based virtual screening studies. The three compounds libraries are implemented in the MTiOpenScreen web server that allows users to perform structure-based virtual screening computations on their selected protein targets. The server outputs a list of 1,500 molecules with predicted binding scores that can then be processed further by the users and purchased for experimental validation. To illustrate the potential of our service for drug repositioning endeavours, we selected five recently published drugs that have been repositioned in vitro and/or in vivo on cancer targets. For each drug, we used the MTiOpenScreen service to screen the Drugs-lib collection against the corresponding anti-cancer target and we show that our protocol is able to rank these drugs within the top ranked compounds. This web server should assist the discovery of promising molecules that could benefit patients, with faster development times, and reduced costs and risk.

[1]  Thomas Sander,et al.  DataWarrior: An Open-Source Program For Chemistry Aware Data Visualization And Analysis , 2015, J. Chem. Inf. Model..

[2]  Tudor I. Oprea,et al.  DrugCentral: online drug compendium , 2016, Nucleic Acids Res..

[3]  Dik-Lung Ma,et al.  Drug repositioning by structure-based virtual screening. , 2013, Chemical Society reviews.

[4]  Andrew P. Turnbull,et al.  Crystal Structures of Three Classes of Non-Steroidal Anti-Inflammatory Drugs in Complex with Aldo-Keto Reductase 1C3 , 2012, PloS one.

[5]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[6]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[7]  Rosane Minghim,et al.  InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams , 2015, BMC Bioinformatics.

[8]  Lionel Colliandre,et al.  e-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design , 2012, Bioinform..

[9]  Steven J. M. Jones,et al.  Drug repositioning for personalized medicine , 2012, Genome Medicine.

[10]  L. Hutchinson,et al.  High drug attrition rates—where are we going wrong? , 2011, Nature Reviews Clinical Oncology.

[11]  Ruben Abagyan,et al.  Recipes for the Selection of Experimental Protein Conformations for Virtual Screening , 2010, J. Chem. Inf. Model..

[12]  K. Śmietana,et al.  Trends in clinical success rates , 2016, Nature Reviews Drug Discovery.

[13]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[14]  P. Fischer,et al.  Protein structures in virtual screening: a case study with CDK2. , 2006, Journal of medicinal chemistry.

[15]  Matthieu Montes,et al.  Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query , 2013, J. Chem. Inf. Model..

[16]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[17]  Xiaomin Luo,et al.  TarFisDock: a web server for identifying drug targets with docking approach , 2006, Nucleic Acids Res..

[18]  R. Prichard,et al.  Interaction of mebendazole with tubulin from body wall muscle, intestine, and reproductive system of Ascaris suum. , 1994, The Journal of parasitology.

[19]  David Jou,et al.  Drug design targeting protein-protein interactions (PPIs) using multiple ligand simultaneous docking (MLSD) and drug repositioning: discovery of raloxifene and bazedoxifene as novel inhibitors of IL-6/GP130 interface. , 2014, Journal of medicinal chemistry.

[20]  T. Ashburn,et al.  Drug repositioning: identifying and developing new uses for existing drugs , 2004, Nature Reviews Drug Discovery.

[21]  B. Shoichet,et al.  Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes. , 2003, Journal of medicinal chemistry.

[22]  G. Maggiora,et al.  Molecular similarity in medicinal chemistry. , 2014, Journal of medicinal chemistry.

[23]  John J. Irwin,et al.  ZINC 15 – Ligand Discovery for Everyone , 2015, J. Chem. Inf. Model..

[24]  B. Barlogie,et al.  Antitumor activity of thalidomide in refractory multiple myeloma. , 1999, The New England journal of medicine.

[25]  Saskia Preissner,et al.  SuperDRUG2: a one stop resource for approved/marketed drugs , 2017, Nucleic Acids Res..

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

[27]  N. Vargesson Thalidomide‐induced teratogenesis: History and mechanisms , 2015, Birth defects research. Part C, Embryo today : reviews.

[28]  A. Olğaç,et al.  The potential role of in silico approaches to identify novel bioactive molecules from natural resources. , 2017, Future medicinal chemistry.

[29]  A. Bender,et al.  In silico target fishing: Predicting biological targets from chemical structure , 2006 .

[30]  Jonathan W. Essex,et al.  Ensemble Docking into Multiple Crystallographically Derived Protein Structures: An Evaluation Based on the Statistical Analysis of Enrichments , 2010, J. Chem. Inf. Model..

[31]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[32]  Su-Jane Wang,et al.  Inhibition of glutamate release by fluspirilene in cerebrocortical nerve terminals (synaptosomes) , 2002, Synapse.

[33]  Ann Cranney,et al.  Benefit-Risk Assessment of Raloxifene in Postmenopausal Osteoporosis , 2005, Drug safety.

[34]  R. Harrison,et al.  Phase II and phase III failures: 2013–2015 , 2016, Nature Reviews Drug Discovery.

[35]  John Buckingham,et al.  Dictionary of natural products , 2014 .

[36]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[37]  M. Wong,et al.  In Silico Identification and In Vitro and In Vivo Validation of Anti-Psychotic Drug Fluspirilene as a Potential CDK2 Inhibitor and a Candidate Anti-Cancer Drug , 2015, PloS one.

[38]  Jennifer R. Grandis,et al.  Targeting the IL-6/JAK/STAT3 signalling axis in cancer , 2018, Nature Reviews Clinical Oncology.

[39]  Brian K. Shoichet,et al.  Virtual screening of chemical libraries , 2004, Nature.

[40]  Oakland J. Peters,et al.  Predicting new indications for approved drugs using a proteochemometric method. , 2012, Journal of medicinal chemistry.

[41]  J. Bajorath,et al.  Quo vadis, virtual screening? A comprehensive survey of prospective applications. , 2010, Journal of medicinal chemistry.

[42]  Lirong Chen,et al.  Use of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology , 2013, PloS one.

[43]  C. Rudin,et al.  Effective treatment of diverse medulloblastoma models with mebendazole and its impact on tumor angiogenesis. , 2015, Neuro-oncology.

[44]  Agnieszka K. Witkiewicz,et al.  The history and future of targeting cyclin-dependent kinases in cancer therapy , 2015, Nature Reviews Drug Discovery.

[45]  Yongsheng Wang,et al.  Sulindac, a non-steroidal anti-inflammatory drug, mediates breast cancer inhibition as an immune modulator , 2016, Scientific Reports.

[46]  Tao Jiang,et al.  ChemmineR: a compound mining framework for R , 2008, Bioinform..

[47]  Olivier Sperandio,et al.  FAF-Drugs3: a web server for compound property calculation and chemical library design , 2015, Nucleic Acids Res..

[48]  Olivier Sperandio,et al.  Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis. , 2015, Future medicinal chemistry.

[49]  J. Vane,et al.  Mechanism of action of anti-inflammatory drugs. , 1996, Scandinavian journal of rheumatology. Supplement.

[50]  David Lagorce,et al.  FAF‐Drugs4: free ADME‐tox filtering computations for chemical biology and early stages drug discovery , 2017, Bioinform..

[51]  A D Dayan,et al.  Albendazole, mebendazole and praziquantel. Review of non-clinical toxicity and pharmacokinetics. , 2003, Acta tropica.

[52]  H. Bryant,et al.  Mechanism of action and preclinical profile of raloxifene, a selective estrogen receptor modulation. , 2001, Reviews in endocrine & metabolic disorders.

[53]  A. Keith Stewart,et al.  How Thalidomide Works Against Cancer , 2014, Science.

[54]  Heng Luo,et al.  DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome , 2016, Scientific Reports.

[55]  Mitsutoshi Nakada,et al.  Identification of antipsychotic drug fluspirilene as a potential anti-glioma stem cell drug , 2017, Oncotarget.

[56]  Björn Krüger,et al.  The holistic integration of virtual screening in drug discovery. , 2013, Drug discovery today.

[57]  Kenneth C Anderson,et al.  Thalidomide in multiple myeloma--clinical trials and aspects of drug metabolism and toxicity. , 2008, Expert opinion on drug metabolism & toxicology.

[58]  Pierre Tufféry,et al.  MTiOpenScreen: a web server for structure-based virtual screening , 2015, Nucleic Acids Res..

[59]  Jack A Roth,et al.  Mebendazole elicits a potent antitumor effect on human cancer cell lines both in vitro and in vivo. , 2002, Clinical cancer research : an official journal of the American Association for Cancer Research.

[60]  Dan Yan,et al.  Growth-suppressive activity of raloxifene on liver cancer cells by targeting IL-6/GP130 signaling , 2017, Oncotarget.

[61]  Jürgen Bajorath,et al.  Virtual screening methods that complement HTS. , 2004, Combinatorial chemistry & high throughput screening.

[62]  D. Newman,et al.  Natural Products as Sources of New Drugs from 1981 to 2014. , 2016, Journal of natural products.

[63]  Douglas B Kell,et al.  Analysing and Navigating Natural Products Space for Generating Small, Diverse, But Representative Chemical Libraries , 2018, Biotechnology journal.

[64]  A. Harvey,et al.  The re-emergence of natural products for drug discovery in the genomics era , 2015, Nature Reviews Drug Discovery.

[65]  Jack A Roth,et al.  The anthelmintic drug mebendazole induces mitotic arrest and apoptosis by depolymerizing tubulin in non-small cell lung cancer cells. , 2002, Molecular cancer therapeutics.

[66]  Michael Nilges,et al.  Comparative Evaluation of 3D Virtual Ligand Screening Methods: Impact of the Molecular Alignment on Enrichment , 2010, J. Chem. Inf. Model..

[67]  P Hassel,et al.  Experimental Comparison of Low Doses of 1.5 mg Fluspirilene and Bromazepam in Out-patients with Psychovegetative Disturbances , 1985, Pharmacopsychiatry.

[68]  M. Mazzanti,et al.  Drug-repositioning opportunities for cancer therapy: novel molecular targets for known compounds. , 2016, Drug discovery today.

[69]  Jung-Hsin Lin,et al.  idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach , 2012, Nucleic Acids Res..

[70]  J P Laclette,et al.  Inhibition of tubulin polymerization by mebendazole. , 1980, Biochemical and biophysical research communications.

[71]  Napoleone Ferrara,et al.  VEGF and the quest for tumour angiogenesis factors , 2002, Nature Reviews Cancer.

[72]  Gary L Gallia,et al.  Antiparasitic mebendazole shows survival benefit in 2 preclinical models of glioblastoma multiforme. , 2011, Neuro-oncology.

[73]  Yi Jin,et al.  Inhibitors of type 5 17β-hydroxysteroid dehydrogenase (AKR1C3): Overview and structural insights , 2011, The Journal of Steroid Biochemistry and Molecular Biology.

[74]  C J Niemegeers,et al.  The pharmacology of penfluridol (R 16341) a new potent and orally long-acting neuroleptic drug. , 1970, European journal of pharmacology.

[75]  Giovanni Mazzoni,et al.  Mebendazole inhibits growth of human adrenocortical carcinoma cell lines implanted in nude mice , 2008, Cancer Chemotherapy and Pharmacology.

[76]  Liang Ouyang,et al.  Review of natural product databases , 2015, Cell proliferation.