Aggregated Compound Biological Signatures Facilitate Phenotypic Drug Discovery and Target Elucidation.

Predicting the cellular response of compounds is a challenge central to the discovery of new drugs. Compound biological signatures have risen as a way of representing the perturbation produced by a compound in the cell. However, their ability to encode specific phenotypic information and generating tangible predictions remains unknown, mainly because of the inherent noise in such data sets. In this work, we statistically aggregate signals from several compound biological signatures to find compounds that produce a desired phenotype in the cell. We exploit this method in two applications relevant for phenotypic screening in drug discovery programs: target-independent hit expansion and target identification. As a result, we present here (i) novel nanomolar inhibitors of cellular division that reproduce the phenotype and the mode of action of reference natural products and (ii) blockers of the NKCC1 cotransporter for autism spectrum disorders. Our results were confirmed in both cellular and biochemical assays of the respective projects. In addition, these examples provided novel insights on the information content and biological significance of compound biological signatures from HTS, and their applicability to drug discovery in general. For target identification, we show that novel targets can be predicted successfully for drugs by reporting new activities for nimedipine, fluspirilene, and pimozide and providing a rationale for repurposing and side effects. Our results highlight the opportunities of reusing public bioactivity data for prospective drug discovery, including scenarios where the effective target or mode of action of a particular molecule is not known, such as in phenotypic screening campaigns.

[1]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

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

[3]  Thomas J. Fltzgerald.  Molecular features of colchicine associated with antimitotic activity and inhibition of tubulin polymerization , 1976 .

[4]  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.

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

[6]  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.

[7]  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.

[8]  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.

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

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

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

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

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

[14]  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.

[15]  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.

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

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

[18]  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.

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

[20]  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.

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

[22]  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 .

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

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

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

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

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

[28]  J. Baell,et al.  New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. , 2010, Journal of medicinal chemistry.

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

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

[31]  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.

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

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

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

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

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

[37]  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.

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

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

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

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

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

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

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

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

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

[47]  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.

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

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

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

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

[52]  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..