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Target Drug Discovery (TDD)

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Target Drug Discovery (TDD) Criblages et Méthodologies In silico Dominique Douguet Ecole Thématique de Criblage 2-4 Octobre 2018 Carry-Le-Rouet Pharmacology & Neurosciences Genomic platform, ion channels, small G proteins, vesicular transport, immunology Institut de Pharmacologie Moléculaire et Cellulaire, UMR 7275 CNRS - Université Nice Sophia Antipolis, France ChemInfoScreen Chimiothèque Nationale ChemBioScreen • HTS assay optimization • Hit Identification ChemInfoScreen ADME-Tox Evaluation • Structure-Activity Relationships (SAR) • Pharmacokinetics Hit-to-Lead optimization programs to Explore and Cure living systems ChemInfoScreen: Cheminformat ics Plat form To date: 8 sites + Coordinating site at UGCN LIT (Didier Rognan) CMC (Alexandre Varnek) Paris Orléans Strasbourg BFA (Pierre Tufféry) Institut Pasteur (Olivier Sperandio) ICOA (Pascal Bonnet) Montpellier Marseille Nice IPMC (Dominique Douguet) UGCN (Philippe Jauffret) CRCM (Xavier Morelli) CBS (Gilles Labesse) Target Drug Discovery (TDD) Pathology Identified Target Protein Sequence 3D Structure Known No Yes Ligands Identified Hits Lead Optimisation Clinical trials Approved drug ~ 10-14 years/~1 Billion $ Target Drug Discovery (TDD) Pathology ChemInfoScreen Identified Target ChemBioScreen Protein Sequence IR FRISBI 3D Structure Chimiothèque Nationale Known No Yes Ligands Identified Hits Medicinal Chemistry ADME-Tox Lead Optimisation Clinical trials Approved drug .~ 10-14 years/~1 Billion $ Target Drug Discovery (TDD) Pathology ChemInfoScreen Identified Target ChemBioScreen Virtual ligand-based Protein Sequence Screenings structure-based IR FRISBI CN subset Libraries 3D Structure approved drugs Design Chimiothèque Nationale commercial cpds Known No Yes Raw data PAINS/reactivity alert Ligands analysis analogs in catalogs and properties prediction Identified Hits SAR building LogP, Sol.,Kd, Kon/Koff Medicinal Chemistry prioritizing cpds ADME-Tox Lead Optimisation Synthesis prioritizing reactants scaffold hopping Clinical trials Bio-Profiling Metabolism (site, & ADME-Tox CYP450s…) Prediction Off-targets Approved drug .~ 10-14 years/~1 Billion $ Target Drug Discovery (TDD) Pathology ChemInfoScreen Identified Target ChemBioScreen Virtual ligand-based Protein Sequence Screenings structure-based IR FRISBI 3D Structure Chimiothèque Nationale Known No Yes Ligands Identified Hits Medicinal Chemistry ADME-Tox Lead Optimisation Clinical trials Approved drug .~ 10-14 years/~1 Billion $ Ligand- and Structure-based Screenings Ligand-based Structure-based Known ligands 2D 3D experimental structure/model • Graph/substructure of the target • Fingerprint (eg: ECFP4) bit set if the feature is present 3D O d1 = 9-10 Å • Pharmacophore d3 = 6-7 Å N Ar d2 = 3-4 Å • Shape Docking 2D/3D QSAR model (requires a large dataset) Target Drug Discovery (TDD) Pathology ChemInfoScreen Identified Target ChemBioScreen Virtual ligand-based Protein Sequence Screenings structure-based IR FRISBI CN subset Libraries 3D Structure approved drugs Design Chimiothèque Nationale commercial cpds Known No Yes Ligands Identified Hits Medicinal Chemistry ADME-Tox Lead Optimisation Clinical trials Approved drug .~ 10-14 years/~1 Billion $ Chemical space Chemical universe 1020-1060 ‘druglike’ molecules Barnaby Roper « The chemist as astronaut: Searching for biologically useful space in the chemical universe » D. Triggle, Biochem.Pharmacol., 2009. Weininger D., Encyclopedia of Computational Chemistry, Vol 8,p1056; Bohacek RS. et al., Med. Res. Rev., 1996; Ertl P., J.Chem.Inf.Comput. Sci., 2003. ‘Druglike’: C, N, O, S, P, H, Cl, Br, F, I and MW ≤ 500 (Dobson C.M., Nature, 2004); Walters W.P., J. Med. Chem., 2018. Chemical Space & Screening 1080 1060 1020 1017 108 atoms seconds isolated molecules ‘druglike’ molecules2 sand grains in the Universe1 age of the Earth ● CAS: ~100.106 (organics/inorganics) ● Dedicated to Pharmacology: Commercial: 106 (screening libraries) Naturals: 106 (theoretically) < 0.1.106 (isolated (10%)3) Toxins: 20.106 (theoretically) ~0.2.106 (UniProtKB (1%)4) Dune of Pilat Drugs: < 2000 FDA approved small-molecule drug structures (MW ≤ 2000) 2 ‘Druglike’: C, N, O, S, P, H, Cl, Br, F, I and MW ≤ 500 (Dobson C.M., Nature, 2004) 3 Harvey A., Drug Discovery Today, 2000 1 Source: C. Magnan, Collège de France, http://www.lacosmo.com/dixpuissance80.html 4 Zhang Y, Dongwuxue Yanjiu, 2015 ChemicalChemical Space space - What is the usable size of a chemical library? - Experimental High Throughput Screening (HTS) * A screening campaign may assay up to 500 000 compounds / week a low cost estimate ~ 0.40 $ / compound 1 (1 million compounds = 400 000 $) (includes cost of the chemical synthesis, high-throughput-screening disposables, capital costs and human resources) several side issues: molecule re-supply, solubility, chemical stability, presence of PAINS (false positives)… as well as the management of waste products ! * It is commonly accepted that the suitable size of a library is ~250 000 to optimize the 2,3 likelihood of finding a hit 1 Lipinski C. and Hopkins A., Nature, 2004, 432, 855-860. 3 Baell J, ACS Med Chem Lett, 2018. 2 Hibert M. and Haiech J., médecine/sciences, 2000, 16, 1332-9. ChemicalChemical Space space - What is the usable size of a chemical library? - Virtual Screening * Building chemical structures 1 Example of the GDB-13 database: (13 atoms [C, N, O, S, Cl]) (<< mean drug size) - Combinatorial enumeration of structures - 3D Building, minimizing and validating structures Results: Pyridoxine 910 111 673 structures (13 atoms) 39 882 (h) CPU time (= 1661 days of computation on 1 processor) ~0.16s /molecule (540 000 molecules / day / processor) * Evaluating properties and/or interactions (e.g.: calculating the binding free energy ∆G of a ligand-protein complex) - Using empiric method (docking method): ~20s to 3 min /molecule followed by visual inspection - Using Molecular Dynamic (MD): hours to few days of calculation /molecule 1 Blum LC, Reymond JL.., J Am Chem Soc. 2009, 31(25), 8732-3. Target Drug Discovery (TDD) Pathology ChemInfoScreen Identified Target ChemBioScreen Virtual ligand-based Protein Sequence Screenings structure-based IR FRISBI CN subset Libraries 3D Structure approved drugs Design Chimiothèque Nationale commercial cpds Known No Yes Raw data PAINS/reactivity alert Ligands analysis analogs in catalogs and properties prediction Identified Hits SAR building LogP, Sol.,Kd, Kon/Koff Medicinal Chemistry ADME-Tox Lead Optimisation Clinical trials Approved drug .~ 10-14 years/~1 Billion $ Target Drug Discovery (TDD) Pathology Identified Target Protein Sequence 3D Structure Known No Yes Ligands A hit ~ a molecule with Identified Hits µM range of activity MW [1-200] Hit-to-Lead Lead Optimisation LogP [0.5-4] Lead Clinical trials Optimization MW < 500 LogP < 5 Approved nbHA<5, Drug drug nbHD<10 .~ 10-14 years/~1 Billion $ Target Drug Discovery (TDD) Pathology Identified Target Protein Sequence Teague et al., Angew. Chem. Int. Ed., 1999 3D Structure Drug-like hits > 0.1 µM MW > 350 unfavored Known LogP > 3 No Yes Ligands Lead-like hits A hit ~ a molecule with Identified Hits > 0.1 µM µM range of activity MW < 350 MW LogP < 3 (polar) LogP MW [1-200] Hit-to-Lead Lead Optimisation LogP [0.5-4] High affinity hits << 0.1 µM Lead MW >> 350 MW Clinical trials Optimization LogP < 3 LogP MW < 500 LogP < 5 Approved nbHA<5, Drug drug nbHD<10 .~ 10-14 years/~1 Billion $ Target Drug Discovery (TDD) Pathology Identified Target LE > 0.35 ; LLE > 5 ; PFI < 7 LE = pX50*1.37 /#heavy atoms (kcal/mol/atom) LipE = LLE = pX50 - cLogP PFI = Chrom LogDpH7.4 + #Ar rings iPFI = Chrom LogP + #Ar rings Protein Sequence Teague et al., Angew. Chem. Int. Ed., 1999 Leeson and Springthorpe, Nat Rev Drug Discov, 2007. 3D Structure Drug-like hits Leeson and Young, ACS Med. Chem. Lett., 2015. > 0.1 µM Young and Leeson, J. med. Chem., 2018. MW > 350 unfavored Known LogP > 3 No Yes Ligands Lead-like hits A hit ~ a molecule with Identified Hits > 0.1 µM µM range of activity MW < 350 MW LogP < 3 (polar) LogP MW [1-200] Hit-to-Lead Lead Optimisation LogP [0.5-4] High affinity hits << 0.1 µM Lead MW >> 350 MW Clinical trials Optimization LogP < 3 LogP MW < 500 LogP < 5 Approved nbHA<5, Drug drug nbHD<10 .~ 10-14 years/~1 Billion $ Target Drug Discovery (TDD) Pathology Identified Target LE > 0.35 ; LLE > 5 ; PFI < 7 LE = pX50*1.37 /#heavy atoms (kcal/mol/atom) LipE = LLE = pX50 - cLogP PFI = Chrom LogDpH7.4 + #Ar rings iPFI = Chrom LogP + #Ar rings Protein Sequence Teague et al., Angew. Chem. Int. Ed., 1999 Leeson and Springthorpe, Nat Rev Drug Discov, 2007. 3D Structure Drug-like hits Leeson and Young, ACS Med. Chem. Lett., 2015. > 0.1 µM Young and Leeson, J. med. Chem., 2018. MW > 350 unfavored Known LogP > 3 Identifying goodNo –Yes progressable - Hits Ligands Lead-like hits A hit ~ a molecule with Identified Hits > 0.1 µM µM range of activity MW < 350 MW LogP < 3 (polar) LogP MW [1-200] Hit-to-Lead Lead Optimisation LogP [0.5-4] High affinity hits << 0.1 µM Lead MW >> 350 MW Clinical trials Optimization LogP < 3 LogP MW < 500 LogP < 5 Approved nbHA<5, Drug drug nbHD<10 .~ 10-14 years/~1 Billion $ e Drug 3D Pharmacokinetic data set Searches by: Names Substructures keywords Target name… http://chemoinfo.ipmc.cnrs.fr Drug-like Fragments and Frameworks rings , fused rings and acyclics ( linkers and substituants) X : anchoring point for substituents (Bemis & Murcko definition) Drug frameworks Most populated frameworks in approved drugs 47% structures are represented by only 24 “frameworks” 1939 1939 1946 1942 1940 framework 1949 the
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