Compound biological signatures facilitate phenotypic screening and target elucidation

We developed a statistical method, BIOSEA, able to identify molecules that are capable of reproducing a desired cellular phenotype, by scanning a large compound collection based on biological similarity. Our method leverages highly incomplete and noisy compound bioactivity signatures from historical high-throughput screening campaigns. We applied our approach in a phenotypic screening workflow and found novel nanomolar inhibitors of cell division that reproduce the mode of action of reference natural products. In a drug discovery setting, our biological hit expansion protocol revealed new inhibitors of the NKCC1 co-transporter for autism spectrum disorders. Furthermore, we demonstrate BIOSEA’s capabilities to predict novel targets for old compounds. We report new activities for the drugs nimedipine, fluspirilene and pimozide applicable for compound repurposing and rationalizing drug side effects. Our results highlight the opportunities of reusing public bioactivity data for prospective drug discovery applications where the target or mode of action is not known.

[1]  Garrett M Morris,et al.  Comparison of ultra‐fast 2D and 3D ligand and target descriptors for side effect prediction and network analysis in polypharmacology , 2013, British journal of pharmacology.

[2]  I. Barasoain,et al.  Overcoming Tumor Drug Resistance with High‐Affinity Taxanes: A SAR Study of C2‐Modified 7‐Acyl‐10‐Deacetyl Cephalomannines , 2007, ChemMedChem.

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

[4]  Nicole E. Bodycombe,et al.  Connecting Small Molecules with Similar Assay Performance Profiles Leads to New Biological Hypotheses , 2014, Journal of biomolecular screening.

[5]  R. Khazipov,et al.  Effects of oxytocin on GABA signalling in the foetal brain during delivery. , 2008, Progress in brain research.

[6]  Benno Roozendaal,et al.  Stress and Memory: Opposing Effects of Glucocorticoids on Memory Consolidation and Memory Retrieval , 2002, Neurobiology of Learning and Memory.

[7]  A. Hopkins.  Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[8]  Scott M. Williams,et al.  Variation in the &agr;2B-adrenergic receptor gene (ADRA2B) and its relationship to vascular response in vivo , 2005, Pharmacogenetics and genomics.

[9]  L. Alvares,et al.  Reconsolidation Allows Fear Memory to Be Updated to a Less Aversive Level through the Incorporation of Appetitive Information , 2015, Neuropsychopharmacology.

[10]  S. N. Timasheff,et al.  Role of the colchicine ring A and its methoxy groups in the binding to tubulin and microtubule inhibition. , 1998, Biochemistry.

[11]  Anne Mai Wassermann,et al.  A screening pattern recognition method finds new and divergent targets for drugs and natural products. , 2014, ACS chemical biology.

[12]  C. R. Ingram,et al.  Cerebral arterial spasm--a controlled trial of nimodipine in patients with subarachnoid hemorrhage. , 1983, The New England journal of medicine.

[13]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[14]  Monica Campillos,et al.  HitPick: a web server for hit identification and target prediction of chemical screenings , 2013, Bioinform..

[15]  Anna Ivana Scovassi,et al.  Arylthioindole inhibitors of tubulin polymerization. 3. Biological evaluation, structure-activity relationships and molecular modeling studies. , 2007, Journal of medicinal chemistry.

[16]  T. Fitzgerald.  Molecular features of colchicine associated with antimitotic activity and inhibition of tubulin polymerization. , 1976, Biochemical pharmacology.

[17]  Álvaro Cortés Cabrera,et al.  CRDOCK: An Ultrafast Multipurpose Protein-Ligand Docking Tool , 2012, J. Chem. Inf. Model..

[18]  David S. Wishart,et al.  DrugBank 4.0: shedding new light on drug metabolism , 2013, Nucleic Acids Res..

[19]  P. Nordlund,et al.  Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift Assay , 2013, Science.

[20]  Michael J. Keiser,et al.  Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.

[21]  Michael J. Keiser,et al.  Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets , 2012, Nature.

[22]  Anne Mai Wassermann,et al.  Public Domain HTS Fingerprints: Design and Evaluation of Compound Bioactivity Profiles from PubChem's Bioassay Repository , 2016, J. Chem. Inf. Model..

[23]  J. Medina-Franco,et al.  Shifting from the single to the multitarget paradigm in drug discovery. , 2013, Drug discovery today.

[24]  M. Jordan,et al.  Effects of vinblastine, podophyllotoxin and nocodazole on mitotic spindles. Implications for the role of microtubule dynamics in mitosis. , 1992, Journal of cell science.

[25]  R. V. Magnus,et al.  Fluspirilene in Schizophrenia , 1973 .

[26]  G. Superti-Furga,et al.  Target profiling of small molecules by chemical proteomics. , 2009, Nature chemical biology.

[27]  D C Swinney,et al.  Phenotypic vs. Target‐Based Drug Discovery for First‐in‐Class Medicines , 2013, Clinical pharmacology and therapeutics.

[28]  John P. Overington,et al.  How many drug targets are there? , 2006, Nature Reviews Drug Discovery.

[29]  A. Hopkins.  Network pharmacology , 2007, Nature Biotechnology.

[30]  J. A. Grant,et al.  A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. , 2005, Journal of medicinal chemistry.

[31]  I. Barasoain,et al.  Inhibition of microtubules and cell cycle arrest by a new 1-deaza-7,8-dihydropteridine antitumor drug, CI 980, and by its chiral isomer, NSC 613863. , 1994, Cancer research.

[32]  Anne Mai Wassermann,et al.  Bioturbo Similarity Searching: Combining Chemical and Biological Similarity To Discover Structurally Diverse Bioactive Molecules , 2013, J. Chem. Inf. Model..

[33]  P. Clemons,et al.  Target identification and mechanism of action in chemical biology and drug discovery. , 2013, Nature chemical biology.

[34]  Xiaofeng Liu,et al.  ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method , 2013, Bioinform..

[35]  S. Noppen,et al.  Novel colchicine-site binders with a cyclohexanedione scaffold identified through a ligand-based virtual screening approach. , 2014, Journal of medicinal chemistry.

[36]  E. Mcwhinnie,et al.  DNA sequencing and CRISPR-Cas9 gene editing for target validation in mammalian cells. , 2014, Nature chemical biology.

[37]  R. N. Brogden,et al.  Pimozide: A Review of Its Pharmacological Properties and Therapeutic Uses in Psychiatry , 2012, Drugs.

[38]  P. Clemons,et al.  Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. , 2007, Combinatorial chemistry & high throughput screening.

[39]  O. Arslan,et al.  Differential in vitro inhibition effects of some antibiotics on tumor associated carbonic anhydrase isozymes of hCA-IX and hCA-XII , 2008 .

[40]  Lirong Wang,et al.  TargetHunter: An In Silico Target Identification Tool for Predicting Therapeutic Potential of Small Organic Molecules Based on Chemogenomic Database , 2013, The AAPS Journal.

[41]  Y. Ben-Ari.  Excitatory actions of gaba during development: the nature of the nurture , 2002, Nature Reviews Neuroscience.

[42]  J. Peters.  Polypharmacology - foe or friend? , 2013, Journal of medicinal chemistry.

[43]  G. Turecki,et al.  Epigenetic modulation of glucocorticoid receptors in posttraumatic stress disorder , 2014, Translational Psychiatry.

[44]  Yuan Wang,et al.  Using Information from Historical High-Throughput Screens to Predict Active Compounds , 2014, J. Chem. Inf. Model..

[45]  B. Shukitt-Hale,et al.  Nimodipine improves spatial working memory and elevates hippocampal acetylcholine in young rats , 1991, Pharmacology Biochemistry and Behavior.

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

[47]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Patrick A. Curmi,et al.  Insight into tubulin regulation from a complex with colchicine and a stathmin-like domain , 2004, Nature.

[49]  Peter S. Kutchukian,et al.  Rethinking molecular similarity: comparing compounds on the basis of biological activity. , 2012, ACS chemical biology.

[50]  J. Peters,et al.  Pharmacological Promiscuity: Dependence on Compound Properties and Target Specificity in a Set of Recent Roche Compounds , 2009, ChemMedChem.

[51]  W. Pearson.  Empirical statistical estimates for sequence similarity searches. , 1998, Journal of molecular biology.

[52]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .