cheminfo2016 In memory of 22/09/2016 UNIVERSIDADE DE SÃO PAULO - USP Instituto de Química de São Carlos - IQSC

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1 UNIVERSIDADE DE SÃO PAULO - USP Instituto de Química de São Carlos - IQSC Grupo de Química Medicinal do IQSC/USP Carlos Montanari (Carlos.Montanari@usp.br) cheminfo In memory of Ex-Pfizer Sandwich, UK 1

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3 Bibliografia [1] Carlos A. Montanari. Química Medicinal: Métodos e Fundamentos em Planejamento de Fármacos. 1a. ed. São Paulo - SP: Editora da Universidade de São Paulo, [2] Kowalski, B.R. Chemometrics: Mathematics and Statistics in Chemistry. Ednato Asi Series, D. Reidel Publ. Co., Dordrecht, 1984 [3] Denis Fourches, Eugene Muratov & Alexander Tropsha. Curation of chemogenomics data. Nature Chemical Biology 2015, 11, 535 [4] Johannes Kirchmair, Andreas H. Göller, [ ], Gisbert Schneider Predicting drug metabolism: experiment and/or computation? Nature Reviews Drug Discovery 2015, 14, [5] Jayme L. Dahlin, James Inglese & Michael A. Walters. Mitigating risk in academic preclinical drug discovery. Nature Reviews Drug Discovery 2015, 14, [6] Johann Gasteiger. Cheminformatics: Computing target complexity. Nature Chemistry 2015, 7, [7] Benício B. Neto, Ieda S. Scarminio, Roy, E. Bruns Como fazer experimentos, Ed. da UNICAMP, Campinas, 2001 [8] (a) David B. Searls. Data integration: challenges for drug discovery. Nature Reviews Drug Discovery, , (b) Douglas B. Kitchen, Hélène Decornez, [ ].Jürgen Bajorath. Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Reviews Drug Discovery 2004, 3, [9] Jason G Lomnitz & Michael A Savageau. Elucidating the genotype phenotype map by automatic enumeration and analysis of the phenotypic repertoire. Systems Biology and Applications 2015, 1, [10] Yi Sun, Zhen Sheng, [ ], Zhiwei Cao. Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer. Nature Communications 2015, 6,

4 Desafios da descoberta de fármacos: Encontrar uma substância com valores múltiplos agregados Fármaco New drugs? (Zingales et al. Mem Inst Oswaldo Cruz 2014) 4

5 Fexinidazole Interação Ligante-receptor Estereoeletrônica Estereodinâmica Coligativas 3D 2D 1D Benznidazole Data continues to grow 5

6 Combinatorial paradigm All living organisms: CHONPS 99% + How many substances can be synthesized? (Kihlberg et al. J. Med. Chem ) (Reymond et al. J. Chem. Info. Model. 2012) 6

7 How to discover a new drug? Sir James Black the most fruitful basis for the discovery of a new drug is to start with an old drug 7

8 Integrating Technologies... Interação Ligante-receptor Estereoeletrônica Estereodinâmica Coligativas 3D 2D 1D DB Social molecules Chemical space navigation Virtual Screening X ray (PDB) Cloning. Expression. Isolation. Purification Docking Receptor mapping Pharmacophore hypothesis Ro3 (Montanari et al. Eur. J. Med. Chem. 2008) (Gaudio e Montanari J. Comput.-Aided Mol. Des Montanari et al. Bioorg. Med. Chem. 2008) MM SAR QSAR 2D and 3D Synthesis Target Validation Ligands Leads Pre- and clinical phases Drug approval Drug Calorimetry (MOA/TD signature) Enzyme kinectics (MOA) TD (MOB) Fragsimilar Drugsimilar Ligandsimilar Drug Chem-Bio space Optimized PD & PK (Montanari. et al. J. Med. Chem. 2000; Montanari. et al. Burger s MedChem vol ) 8

9 Eficiência atômica e Lipofílica do ligantre 22/09/2016 Descoberta de novos agentes quimioterápicos por integração de tecnologias Coleção de substâncias ZINC, (in-home DB) F i l t e r 1. MM 2. Lig.-H 3. Rotação 4. NHOH 5. ClogP Inibidores conhecidos F L E x - X 30-60% melhores pontuados Alvo 3D (Glide) A - d o c k Análise pós-docagem Coleção de substâncias (DOCAGEM, PCA, SIMCA, ANN, RANDOM FOREST) Agrupamento (SIMILARIDADE, HCA) Modelos DMPK: Solubilidade, Absorção, SELEÇÃO Metabolismo, BBB, Toxidez (GRID/VolSurf) QSAR 2D e 3D (CoMFA/CoMSIA/HQSAR/ROCS) (nosso sistema) Assinatura molecular Fragmento-similar Ligante-similar Banco de dados filtrado M1 Fármaco-similar O m e g a 1. 2D 3D 2. Ad H/Carga 3. Conformeros P2 P1 P3 P4 P F r e d Conformeros Docagem de corpo rígido 30-60% melhores pontuados Seletividade Ensaio Bioquímico Ensaio Celular Candidatos a fármacos 20 Propriedades fármacos administrados por via oral Nossa meta Candidatos a fármacos: (Yusof & Segall, DDT 2013) T. cruzi pec 50 > 5 SI > 10 PFI < 8 # anéis Ar < 5 MW < 500 Da 9

10 Estratégias de planejamento de fármacos Sem 3D, sem ligantes 3D, sem ligantes Combichem, VS, HTS de novo L B V S Sem 3D, ligantes Farmacóforos, similaridade 3D QSAR 3D 3D, ligantes Planejamento baseado na estrutura T B V S Hypothesis-driven Molecular Design (Plowright et al. DDT ) 10

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12 Informações contidas na proteína Estrutura terciária Sequência primária Sequência de assinaturas Localização Genôma/cromossomo Substratos Co-fatores Compartimento celular Tipo de célula Tecido Organismos Navegação de espaço químico 12

13 Similaridade molecular Descritores físico-químicos e similaridade molecular 13

14 Estratégias quimiométricas Literatura. Exploração de Dados & Informação Hipótese s Problema Planejamento experimental Experimentos Objetivo s Análise de agrupamentos Dados Exploração de dados Classificação Regressão Otimização Modelo qualitativo Modelo quantitativo Modelo empírico Chemometrics & cheminformatics techniques Four building blocks o Methods Experimental design Pattern Recognition Calibration o Software o Instrumentation (LC-MS) o Applications MOTIVATIONS FOR DESIGN Screening Saving time Quantitative modelling Optimisation PATTERN RECOGNITION PCA, Discriminant analysis and Cluster analysis Exploratory Data Analysis Unsupervised Pattern Recognition Supervised Pattern Recognition 14

15 Chemometrics & cheminformatics techniques Decision Trees Chemometrics & cheminformatics techniques ANN R: A neural network plot created using functions from the neuralnet package. ( 15

16 Physicochemical musings & ChEMBL 14% of drugs have ADME/Tox scores > 2 compared with 39% of ChEMBL molecules 1791 oral drugs colour-coded according to their ADMET score Same drugs compared with the scores for the total content of the ChEMBL database (approximately 200k compounds) MedChemBuzz 1. Average oral drug potency is approx 50 nm 2. 8% of oral drugs have both MM > 400 and AlogP > 4 (41% of ChEMBL molecules with nanomolar potency) 3. The majority of oral drugs have off-target pharmacological activities (392 oral drugs, N = number of off-target hits with reported potencies 1 µm): 4. There was no clear relationship between in vitro potency and therapeutic dose MedChemBuzz 16

17 Structural moieties known to form reactive metabolites MedChemBuzz Improved PK profile through fluorine and deuterium incorporation MedChemBuzz 17

18 Reducing bioactivation potential by chemical design MedChemBuzz Bioisosteres in Drug Design 18

19 Softwares required for solving it, and learning through them, can be free, or is available through free web services, or is commercial. : ChemAxon Marvin Beans and JCHEM software for molecular editing and visualization, conversion of molecular formats, canonicalization, and generation of hashed fingerprints. : R statistical and machine learning software. The VCCLAB web service includes an interface to the DRAGON program for the generation of molecular descriptors, and to the CORINA program for the generation of 3D molecular models. RDKit: Open-Source Cheminformatics Software complex data with the agility and freedom that only an open platform can bring molecular modeling and cheminformatics (NEQUIMED/IQSC/USP license) The free OpenOffice package includes a spreadsheet application. 19

20 Online DB ogs-medchem/banco-dedados/ Structural Bioinformatics 20

21 Brenda, the comprehensive enzyme information system a free database of commercially-available compounds for virtual screening A Structural View of Biology a pictorial database that provides an at-a-glance overview of the contents of each 3D structure deposited in the a database of measured binding affinities, focusing chiefly on the interactions of protein considered to be drug-targets with small, drug-like molecules a unique bioinformatics and cheminformatics resource that combines detailed drug data (chemical, pharmacological and pharmaceutical) a manually curated chemical database of bioactive molecules with drug-like properties offers broad range of cheminformatics software tools supporting molecule manipulation and processing an overview of recent topics, interesting reactions, and information on important chemicals for organic chemists datasets UC Irvine Machine Learning Repository 21

22 Team-Based Learning Pre-class reading Chemoinformatics: A view of the field and current trends in method development Por: Vogt, Martin; Bajorath, Juergen BIOORGANIC & MEDICINAL CHEMISTRY Volume: 20 Edição: 18 Páginas: Publicado: SEP Open PHACTS: semantic interoperability for drug discovery Por: Williams, Antony J.; Harland, Lee; Groth, Paul; et al. DRUG DISCOVERY TODAY Volume: 17 Edição: Páginas: Publicado: NOV

23 ZINC A Free Database of Commercially Available Compounds for Virtual Screening John J. Irwin and Brian K. Shoichet* J Chem Inf Model. 2005; 45(1): doi: /ci ChEMBL: a large-scale bioactivity database for drug discovery Anna Gaulton,1 Louisa J. Bellis,1 A. Patricia Bento,1 Jon Chambers,1 Mark Davies,1 Anne Hersey,1 Yvonne Light,1 Shaun McGlinchey,1 David Michalovich,2 Bissan Al-Lazikani,3 and John P. Overington1* Nucleic Acids Res Jan; 40(Database issue): D1100 D1107. doi: /nar/gkr777 Open Babel: An open chemical toolbox Por: O'Boyle, Noel M.; Banck, Michael; James, Craig A.; et al. JOURNAL OF CHEMINFORMATICS Volume: 3 Número do artigo: 33 Publicado: OCT DrugBank: a comprehensive resource for in silico drug discovery and exploration Por: Wishart, David S.; Knox, Craig; Guo, An Chi; et al. NUCLEIC ACIDS RESEARCH Volume: 34 Edição especial: SI Páginas: D668- D672 Publicado: JAN

24 Virtual screening strategies in drug discovery Por: McInnes, Campbell CURRENT OPINION IN CHEMICAL BIOLOGY Volume: 11 Edição: 5 Páginas: Publicado: OCT 2007 How were new medicines discovered? Por: Swinney, David C.; Anthony, Jason NATURE REVIEWS DRUG DISCOVERY Volume: 10 Edição: 7 Páginas: Publicado: JUL 2011 Integrating Everything: The Molecule Selection Toolkit, a System for Compound Prioritization in Drug Discovery David J. Cummins* and Michael A. Bell Eli Lilly and Company, 893 South Delaware Street, Indianapolis, Indiana 46285, United States J. Med. Chem., 2016, 59 (15), pp DOI: /acs.jmedchem.5b01338 Drug-like properties and the causes of poor solubility and poor permeability Por: Lipinski, CA JOURNAL OF PHARMACOLOGICAL AND TOXICOLOGICAL METHODS Volume: 44 Edição: 1 Páginas: Publicado: JUL-AUG 2000 Molecular properties that influence the oral bioavailability of drug candidates Por: Veber, DF; Johnson, SR; Cheng, HY; et al. JOURNAL OF MEDICINAL CHEMISTRY Volume: 45 Edição: 12 Páginas: Publicado: JUN

25 Intramolecular Hydrogen Bonding in Medicinal Chemistry Por: Kuhn, Bernd; Mohr, Peter; Stahl, Martin JOURNAL OF MEDICINAL CHEMISTRY Volume: 53 Edição: 6 Páginas: Publicado: MAR When barriers ignore the "rule-of-five" Por: Kramer, Stefanie D.; Aschmann, Helene E.; Hatibovic, Maja; et al. ADVANCED DRUG DELIVERY REVIEWS Volume: 101 Páginas: Publicado: JUN Cell permeability beyond the rule of 5 Por: Matsson, Par; Doak, Bradley C.; Over, Bjorn; et al. ADVANCED DRUG DELIVERY REVIEWS Volume: 101 Páginas: Publicado: JUN How Beyond Rule of 5 Drugs and Clinical Candidates Bind to Their Targets Por: Doak, Bradley C.; Zheng, Jie; Dobritzsch, Doreen; et al. JOURNAL OF MEDICINAL CHEMISTRY Volume: 59 Edição: 6 Páginas: Publicado: MAR

26 Ligand efficiency: a useful metric for lead selection Por: Hopkins, AL; Groom, CR; Alex, A DRUG DISCOVERY TODAY Volume: 9 Edição: 10 Páginas: Número do artigo: PII S (04)03106-X Publicado: MAY Ligand efficiency metrics considered harmful Por: Kenny, Peter W.; Leitao, Andrei; Montanari, Carlos A. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN Volume: 28 Edição: 7 Páginas: Publicado: JUL 2014 Improving the Plausibility of Success with Inefficient Metrics Por: Shultz, Michael D. ACS MEDICINAL CHEMISTRY LETTERS Volume: 5 Edição: 1 Páginas: 2-5 Publicado: JAN

27 An integrated approach to fragmentbased lead generation: Philosophy, strategy and case studies from AstraZeneca's drug discovery programmes Por: Albert, Jeffrey S.; Blomberg, Niklas; Breeze, Alexander L.; et al. CURRENT TOPICS IN MEDICINAL CHEMISTRY Volume: 7 Edição: 16 Páginas: Publicado: 2007 ADMET in silico modelling: Towards prediction paradise? Por: van de Waterbeemd, H; Gifford, E NATURE REVIEWS DRUG DISCOVERY Volume: 2 Edição: 3 Páginas: Publicado: MAR 2003 Hypothesis driven drug design: improving quality and effectiveness of the design-make-test-analyse cycle Por: Plowright, Alleyn T.; Johnstone, Craig; Kihlberg, Jan; et al. DRUG DISCOVERY TODAY Volume: 17 Edição: 1-2 Páginas: Publicado: JAN 2012 Virtual screening of chemical libraries Por: Shoichet, BK NATURE Volume: 432 Edição: 7019 Páginas: Publicado: DEC

28 BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities Por: Liu, Tiqing; Lin, Yuhmei; Wen, Xin; et al. NUCLEIC ACIDS RESEARCH Volume: 35 Edição especial: SI Páginas: D198- D201 Publicado: JAN 2007 The Cambridge Structural Database in Retrospect and Prospect Por: Groom, Colin R.; Allen, Frank H. ANGEWANDTE CHEMIE- INTERNATIONAL EDITION Volume: 53 Edição: 3 Páginas: Publicado: JAN