Extending the small molecule similarity principle to all levels of biology

Transcription

1 Extending the small molecule similarity principle to all levels of biology Patrick Aloy Challenges within and between Omics data integration November 9, 08

2 The omics revolution is not yielding better drugs M molecules DISEASE Disease-gene correlation Disease models Complexity M ole c ule s Pharmacodynamics Pharmacokinetics k molecules THERAPY High attrition rates in pre-clinical and clinical stages of drug discovery More investment did not translate into more drugs approved Expensive experiments are needed to elucidate the bioactivity of small molecules Data are sparse and dirty Chemistry and biology are disconnected

3 Bioinformatics is heaven (cheminformatics is hell) Proteins Small molecules Public data Building blocks (0 aa) Domain architecture Sequence - Structure - Function Optimized by evolution 0k proteins in Human Beautiful databases Proprietary data Monolithic entities No architecture Structure? Function Optimized by people 80M commercial molecules Chaotic databases

4 The Chemical Checker Networks Targets Chemistry D Mechanism of action roles D Metabolic genes Scaffolds Crystals Signaling Structural keys Binding Biological processes Physicochemistry HTS M 500k 00k Interactome 0k M bioactive molecules 5 data types, from chemistry to the clinics Major small molecule databases are integrated State-of-the-art machine learning Possibly applicable to any molecule (i.e. 80M) Automated and flexible Cells Gene expression Cancer cell lines Chemical genetics Morphology Cell k Clinics Therapeutic areas Indications Side effects Diseases and toxicology Drug-drug interactions

5 5 Chemistry Chemistry Targets Networks D Mechanism of action roles D Metabolic genes Scaffolds Crystals Signaling Structural keys Binding Biological processes Physicochemistry HTS Interactome What is the D structure of the molecule? And its D structure? What scaffolds (chemotypes, synthetic families)? Size, molecular weight, charge, lipophilicity, drug-likeness Cells Gene expression Cancer cell lines Chemical genetics Morphology Cell Clinics Therapeutic areas Indications Side effects Diseases and toxicology Drug-drug interactions

6 6 Communicating chemistry to the computer What the computer scientist does What the organic chemist does What the taxonomist does What the physicist does

7 7 Targets Clinics Cells Networks Targets Chemistry D Mechanism of action roles Gene expression D Metabolic genes Cancer cell lines Scaffolds Crystals Signaling Chemical genetics Therapeutic areas Indications Side effects Structural keys Binding Biological processes Morphology Diseases and toxicology Physicochemistry HTS Interactome Cell Drug-drug interactions Mode of action (when available), inhibition/ activation Drug metabolizing enzymes, transporters and carriers Crystalized small molecules in the PDB, structural family of receptors Binding assays in the literature HTS (chemogenomics) binding and functional assays

8 8 Networks Cells Networks Targets Chemistry D Mechanism of action roles Gene expression D Metabolic genes Cancer cell lines Scaffolds Crystals Signaling Chemical genetics Structural keys Binding Biological processes Morphology Physicochemistry HTS Interactome Cell Popular bioactivity ontology Metabolic (metabolites + drugs) Signaling cascades of the targets Biological processes of the targets Neighbors of the targets in large biological networks (interactomes) (Tissues where the targets are expressed) Clinics Therapeutic areas Indications Side effects Diseases and toxicology Drug-drug interactions

9 9 Cells Cells Networks Targets Chemistry D Mechanism of action roles Gene expression D Metabolic genes Cancer cell lines Scaffolds Crystals Signaling Chemical genetics Structural keys Binding Biological processes Morphology Physicochemistry HTS Interactome Cell Transcriptional response in cell lines (LINCS) Growth inhibition in cancer cell lines (NCI60) Growth inhibition in a panel of yeast mutants (equivalent to genetic interactions) Changes in morphology, measured with a cellpainting assay Clinics Therapeutic areas Indications Side effects Diseases and toxicology Drug-drug interactions

10 0 Clinics Chemistry Targets Networks D Mechanism of action roles D Metabolic genes Scaffolds Crystals Signaling Structural keys Binding Biological processes Physicochemistry HTS Interactome Therapeutic areas (ATC codes) Indications (disease terms) Side effects in drug package labels Drug-drug interactions (Pharmacogenomics) (General toxicology) Cells Gene expression Cancer cell lines Chemical genetics Morphology Cell Clinics Therapeutic areas Indications Side effects Diseases and toxicology Drug-drug interactions

11 A few (ahem) straight applications to biopharma

12 Characterization of chemical collections A front-end to rapidly characterize chemicals Global view of the chemical and biological space Popularity, singularity and mappability scores help contextualize compounds

13 Complex queries to compound libraries Similar MoA/indication, diverse chemistry and pharmacogenomics Scaffolds Mechs. of action Pathways Cancer cell lines Indications Erlotinib Vandetanib Gefitinib Ponatinib 5 Semaxanib 6 Dasatinib Different therapeutic areas, similar gene expression and side effects D Mechs. of action Gene expression Therap. areas Side effects Propantheline Rimantadine Proparacaine Tetracaine

14 Organizing bioactivity data Large-scale target prediction Based on chemical and biological similarities Target > Target > Target Chemistry Binding Networks Cells Clinics Ranked list of similar molecules in different chemical and biological spaces Chemical prediction Based on chemistry Bromperidol Benperidol Adding phenotypic data Prediction (08) Crosspharmacology DRD HDAC HDAC8 HDAC Tacedinaline Knowledge (bef. 06) O Adding chemogenomics data Chemogenomic prediction F Ch N em ical si m. Spiperone HDAC5 HDAC6 Phenotypic prediction AC06JE Yeast chemical genetics Nortryptiline HRH

15 5 Clinical trial toxicity failures Flexible knowledge-based clinical predictors Black-box predictor Based on toxicity panels Based on liable targets

16 6 Clinical trial toxicity failures Flexible knowledge-based clinical predictors Black-box predictor Based on toxicity panels Based on liable targets O Tozadenant HO H C F N OH N S N N O CH VX-75 F S N N N Cl O Cl BIA0-7 O N N N N + O - CH

17 7 Concluding remarks (wrap-up) The Omics revolution Drug discovery pipeline The accumulation of omics data is not yielding better drugs Computational tools are focused on curating and organizing the data Duran-Frigola et al, Curr Opinion Syst Biol (08) Drug discovery is highly inefficient and secretive, and current computational tools are insufficient and isolated Miscellanea of Nat Rev Drug Discov articles

18 8 Structural Bioinformatics & Network Biology group Miquel Duran-Frigola Carles Pons Eduard Pauls Sergi Bayod Francesco Sirci Martino Bertoni Lídia Mateo Csaba Fehér Adrià Fernández-Torras Víctor Alcalde Oriol Guitart We are looking for postdocs!!

19 Extending the small molecule similarity principle to all levels of biology Patrick Aloy Challenges within and between Omics data integration November 9, 08