GES Polypharmacology Fingerprints: A Novel Approach for Drug Repositioning

Polypharmacology is now recognized as an increasingly important aspect of drug design. We previously introduced the Gaussian ensemble screening (GES) approach to predict relationships between drug classes rapidly without requiring thousands of bootstrap comparisons as in current promiscuity prediction approaches. Here we present the GES "computational polypharmacology fingerprint" (CPF), the first target fingerprint to encode drug promiscuity information. The similarity between the 3D shapes and chemical properties of ligands is calculated using PARAFIT and our HPCC programs to give a consensus shape-plus-chemistry ligand similarity score, and ligand promiscuity for a given set of targets is quantified using the GES fingerprints. To demonstrate our approach, we calculated the CPFs for a set of ligands from DrugBank that are related to some 800 targets. The performance of the approach was measured by comparing our CPF with an in-house "experimental polypharmacology fingerprint" (EPF) built using publicly available experimental data for the targets that comprise the fingerprint. Overall, the GES CPF gives very low fall-out while still giving high precision. We present examples of polypharmacology relationships predicted by our approach that have been experimentally validated. This demonstrates that our CPF approach can successfully describe drug-target relationships and can serve as a novel drug repurposing method for proposing new targets for preclinical compounds and clinical drug candidates.

[1]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[2]  Xiaofeng Liu,et al.  SHAFTS: A Hybrid Approach for 3D Molecular Similarity Calculation. 1. Method and Assessment of Virtual Screening , 2011, J. Chem. Inf. Model..

[3]  D. Wishart,et al.  Translational biomarker discovery in clinical metabolomics: an introductory tutorial , 2012, Metabolomics.

[4]  Lazaros Mavridis,et al.  Using Spherical Harmonic Surface Property Representations for Ligand‐Based Virtual Screening , 2011, Molecular informatics.

[5]  J. Entrena,et al.  Pharmacology and Therapeutic Potential of Sigma1 Receptor Ligands , 2008, Current neuropharmacology.

[6]  Antonio Carrieri,et al.  Recent trends and future prospects in computational GPCR drug discovery: from virtual screening to polypharmacology. , 2013, Current topics in medicinal chemistry.

[7]  Zohar Yakhini,et al.  Clustering gene expression patterns , 1999, J. Comput. Biol..

[8]  Anna Vulpetti,et al.  Predicting Polypharmacology by Binding Site Similarity: From Kinases to the Protein Universe , 2010, J. Chem. Inf. Model..

[9]  G. V. Paolini,et al.  Global mapping of pharmacological space , 2006, Nature Biotechnology.

[10]  Lazaros Mavridis,et al.  Detecting Drug Promiscuity Using Gaussian Ensemble Screening , 2012, J. Chem. Inf. Model..

[11]  Ravi Iyengar,et al.  Network analyses in systems pharmacology , 2009, Bioinform..

[12]  István Bitter,et al.  Relating the shape of protein binding sites to binding affinity profiles: is there an association? , 2010, BMC Structural Biology.

[13]  Ashley M. Sherrid,et al.  Rosiglitazone Inhibits Acyl-CoA Synthetase Activity and Fatty Acid Partitioning to Diacylglycerol and Triacylglycerol via a Peroxisome Proliferator–Activated Receptor-γ–Independent Mechanism in Human Arterial Smooth Muscle Cells and Macrophages , 2007, Diabetes.

[14]  David W. Ritchie,et al.  Comparison of Ligand-Based and Receptor-Based Virtual Screening of HIV Entry Inhibitors for the CXCR4 and CCR5 Receptors Using 3D Ligand Shape Matching and Ligand-Receptor Docking , 2008, J. Chem. Inf. Model..

[15]  Iosif I Vaisman,et al.  Discrimination of thermophilic and mesophilic proteins , 2009, 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshop.

[16]  A. Nistri,et al.  Cystic fibrosis transmembrane conductance regulator modulates synaptic chloride homeostasis in motoneurons of the rat spinal cord during neonatal development , 2011, Developmental neurobiology.

[17]  Satoshi Niijima,et al.  Cross-Target View to Feature Selection: Identification of Molecular Interaction Features in Ligand-Target Space , 2011, J. Chem. Inf. Model..

[18]  David W. Ritchie,et al.  Clustering and Classifying Diverse HIV Entry Inhibitors Using a Novel Consensus Shape-Based Virtual Screening Approach: Further Evidence for Multiple Binding Sites within the CCR5 Extracellular Pocket , 2008, J. Chem. Inf. Model..

[19]  Nathanael Weill,et al.  Development and Validation of a Novel Protein-Ligand Fingerprint To Mine Chemogenomic Space: Application to G Protein-Coupled Receptors and Their Ligands , 2009, J. Chem. Inf. Model..

[20]  H. Meltzer,et al.  5‐HT2A and D2 receptor blockade increases cortical DA release via 5‐HT1A receptor activation: a possible mechanism of atypical antipsychotic‐induced cortical dopamine release , 2001, Journal of neurochemistry.

[21]  T. Branchek,et al.  Characterization of LY344864 as a pharmacological tool to study 5-HT1F receptors: binding affinities, brain penetration and activity in the neurogenic dural inflammation model of migraine. , 1997, Life sciences.

[22]  Didier Rognan,et al.  Protein-Ligand-Based Pharmacophores: Generation and Utility Assessment in Computational Ligand Profiling , 2012, J. Chem. Inf. Model..

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

[24]  Z. Deng,et al.  Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors. , 2006, Journal of medicinal chemistry.

[25]  Jürgen Bajorath,et al.  High-resolution view of compound promiscuity. , 2013, F1000Research.

[26]  D. Ritchie,et al.  Protein docking using spherical polar Fourier correlations , 2000, Proteins.

[27]  A. Bender,et al.  Modeling Promiscuity Based on in vitro Safety Pharmacology Profiling Data , 2007, ChemMedChem.

[28]  Timothy Clark,et al.  An analytical, variable resolution, complete description of static molecules and their intermolecular binding properties. , 2005, Journal of chemical information and modeling.

[29]  J. Mestres,et al.  In Silico Receptorome Screening of Antipsychotic Drugs , 2010, Molecular informatics.

[30]  C. Chong,et al.  New uses for old drugs , 2007, Nature.

[31]  Didier Rognan,et al.  Computational Profiling of Bioactive Compounds Using a Target-Dependent Composite Workflow , 2013, J. Chem. Inf. Model..

[32]  Jordi Mestres,et al.  SHED: Shannon Entropy Descriptors from Topological Feature Distributions , 2006, J. Chem. Inf. Model..

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

[34]  K. Tsuda,et al.  Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network , 2011, PloS one.

[35]  Chuang Liu,et al.  Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..

[36]  Graham J. L. Kemp,et al.  Fast computation, rotation, and comparison of low resolution spherical harmonic molecular surfaces , 1999, J. Comput. Chem..

[37]  Lazaros Mavridis,et al.  Predicting drug promiscuity using spherical harmonic surface shape-based similarity comparisons , 2011 .

[38]  G. Klebe,et al.  Merging chemical and biological space: Structural mapping of enzyme binding pocket space , 2009, Proteins.

[39]  Yichuan Zhao,et al.  Semi-empirical likelihood inference for the ROC curve with missing data , 2012 .

[40]  E. Scott,et al.  CYTOCHROME P450 17A1 STRUCTURES WITH PROSTATE CANCER DRUGS ABIRATERONE AND TOK-001 , 2011, Nature.

[41]  P. Aloy,et al.  Unveiling the role of network and systems biology in drug discovery. , 2010, Trends in pharmacological sciences.

[42]  Gergely Zahoránszky-Köhalmi,et al.  Drug Effect Prediction by Polypharmacology-Based Interaction Profiling , 2012, J. Chem. Inf. Model..

[43]  R. Solé,et al.  The topology of drug-target interaction networks: implicit dependence on drug properties and target families. , 2009, Molecular bioSystems.

[44]  Pablo Martínez-Camblor,et al.  Area under the ROC curve comparison in the presence of missing data , 2013 .

[45]  Anders Wallqvist,et al.  Exploring Polypharmacology Using a ROCS-Based Target Fishing Approach , 2012, J. Chem. Inf. Model..

[46]  Andreas Bender,et al.  Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics , 2008, J. Chem. Inf. Model..

[47]  Bernard Maigret,et al.  Benchmarking of HPCC: A novel 3D molecular representation combining shape and pharmacophoric descriptors for efficient molecular similarity assessments. , 2013, Journal of molecular graphics & modelling.

[48]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

[49]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.