Drug discovery on the grid: the WISDOM initiative and beyond

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1 Drug discovery on the grid: the WISDOM initiative and beyond V. Breton CNRS-IN2P3, LPC Clermont-Ferrand Credit: A. Da Costa, V. Kasam

2 Goals Table of content Materials and methods Results Issues encountered Perspectives OGF22 PharmaGrid workshop V. Breton 2

3 Introduction WISDOM stands for World-wide In Silico Docking On Malaria Main objective: use grids to foster R&D on neglected diseases Driving idea: grid-enabled drug discovery should enable non for profit public private partnership at reduced cost OGF22 PharmaGrid workshop V. Breton 3

3M Molecular docking (FlexX, Autodock) ~1 to 15 minutes Chembridge: 500 000 Targets: Data challenge on EGEE ~ 2 to 30 days on ~5000

4 Grid-enabled virtual screening Millions of potential drugs to test against interesting proteins! High Throughput Screening 1-10$/compound, several hours Too costly for neglected disease! Compounds: ZINC: 4.3M Molecular docking (FlexX, Autodock) ~1 to 15 minutes Chembridge: Targets: Data challenge on EGEE ~ 2 to 30 days on ~5000 computers PDB: 3D structures Cheap and fast! Selection of the best hits Hits screening using assays performed on living cells Leads Clinical testing Drug OGF22 PharmaGrid workshop V. Breton 4

Molecular docking Molecular dynamics AMBER

visualization WET LABORATORY 30 In vitro

5 FLEXX AUTODOCK Materials and methods: virtual screening workflow Millions Molecular docking Molecular dynamics AMBER 5000 Re-ranking MMPBSA-GBSA CHIMERA Ligand Amber Ligand Ligand 2 Hydrogen Bonds Catalytic aspartic residues 180 Complex visualization WET LABORATORY 30 In vitro tests Credit: A. Da Costa 10 In vivo tests OGF22 PharmaGrid workshop V. Breton 5

6 Docking Targets : structures publicly available on Protein Data Base Re-refinement of the structures on the Grid (Embrace - EGEE) Docking software packages Open source: Autodock Licensed: FlexX Public database of drug-like ligands Chembridge (~ compounds) Zinc (> 4 Millions) OGF22 PharmaGrid workshop V. Breton 6

7 Grid performances for docking 1% 2% 2% 3% 3% Significant contributions from several International grid infrastructures 38% 15% 12% 3% 3% 5% 6% 7% EGEE Germany Switzerland EGEE Asia Pacific EGEE Russia Auvergrid EuChinaGrid EELA EGEE South Western Europe EGEE Central Europe EGEE Northern Europe EGEE Italy EGEE South Eastern Europe EGEE France EGEE UKI Over 420 CPU years in 10 weeks to dock 4 malaria targets in 2006 A record throughput of docked compounds per hour OGF22 PharmaGrid workshop V. Breton 7

8 Best hits from docking step based on: Docking energy Docked pose Preparatory Phase MD refinement using Amber For one complete simulation, all necessary steps are embedded in one single script. Simulation Phase Re-ranking and Analysis Phase Mol2 Target +prepi top, crd min.rst top pdb, top Antechamber Leap Sander Min Sander MD Ptraj MMPBSA prepi top, crd min.rst md.rst Output Output Final output appended into a file A. Ferrari, G. Degliesposti, M. Sgobba, G. Rastelli. Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors. Bioorganic & Medicinal Chemistry In Press. OGF22 PharmaGrid workshop V. Breton 8

9 Grid Performances for MD 25, 000 compounds: Plasmepsin: 5000 compounds Pf-DHFR: 15,000 compounds Pf-GST: 5000 compounds Number of Jobs 500 Total Number of compounds simulated Estimated duration on 1 CPU Duration on the grid Maximum number of concurrent 347 days 25 days 90 running jobs Number of computing elements used 1 Average duration of a job 16.6 hours OGF22 PharmaGrid workshop V. Breton 9

10 Virtual screening on the grid: deployment status Dates Target (s) CPU consumed Data Specific features Status EGEE AuverGrid produced Summer Malaria: 80 years 1TB First data In vitro tests 2005 plasmepines challenge In vivo tests Spring 2006 Winter 2006 Avian flu: Neuraminidase N1 Malaria: GST, DHFR, Tubulin 100 years* 400 years 800 GB 1,6TB <45 days needed for preparation > dockings / hr In vitro tests Under analysis Fall 2007 Avian flu: Estimated 100 CPU Estimated Joint deployment First stage Neuraminidase N1 years* 800 GB* on CNGrid under analysis Spring 2008 Diabetes: Estimated 120 CPU Estimated New production Under way amylase years 800 GB environment Spring 2008 Malaria: To be estimated To be Joint deployment In preparation DHPS estimated on desktop grid OGF22 PharmaGrid workshop V. Breton 10

Post-MD: in vitro biological validation Inhibitor only 30 min reaction S C P 1 2 3 4 5 6 7 8 9

154.91 2 146.03 3 209.90 4 101.32 5 174.97 6 91.32 7 86.61 8 98.72 9 248.84 10 241.51 11 107.

24 22 163.50 23 99.56 24 115.62 25 88.23 26 94.42 27 75.62 28 100.40 29 114.84 30 246.

concentration Fluorescence measurement -> IC50 100% 80% 60% 40% 20% 0% 0 100 200 300 400 500

11 Post-MD: in vitro biological validation Inhibitor only 30 min reaction S C P Compound IC 50 FRET (nm) a Plasmepsin FRET substrate 30 compounds All 30 compounds show inhibition activity for nm concentration Fluorescence measurement -> IC50 100% 80% 60% 40% 20% 0% No.14 inhibitor (nm) 6 compounds have IC50 under 80 nm OGF22 PharmaGrid workshop V. Breton 11

Post-MD: in vivo biological validation Impact on Plasmodium falciparum growth Compound IC 50 (M) 1 >8,3E-06 2 >>2,5E-05 3 >8,3E-06 4 >8,3E-06 5 4,63E-06 6 >8,3E-06 7 >>2,5E-05 8 3,76E-06 9 >8,3E-06

12 Post-MD: in vivo biological validation Impact on Plasmodium falciparum growth Compound IC 50 (M) 1 >8,3E-06 2 >>2,5E-05 3 >8,3E-06 4 >8,3E ,63E-06 6 >8,3E-06 7 >>2,5E ,76E-06 9 >8,3E >>2,5E-05 P. falciparum infected red blood cells Radioactivity incorporation in parasite % of control 10 compounds 10 compou,n > IC [Molecule], M 2 compounds show IC50 in micromolar range OGF22 PharmaGrid workshop V. Breton 12

13 Post-MD: in vivo biological validation Cytotoxicity on Human Cell model Human cells 10 compounds 10 compou,n Results not yet known Radioactivity incorporation on Human Cells -> IC50 % of control [Molecule], M OGF22 PharmaGrid workshop V. Breton 13

14 Issue: deployment of licensed software on the grid Most docking and Molecular Dynamics software packages are distributed under commercial licenses Solutions found on a case by case basis FlexX: licenses made freely available by BioSolveIT License server deployed at Fraunhofer Institute One token per grid node running FlexX Up to 5000 concurrent tokens used Amber: gentleman agreement Users: Amber available only to grid users from institutes owning an Amber license Resources First step: Amber deployed only on clusters owning an Amber license Second step: Amber freely deployed on the grid OGF22 PharmaGrid workshop V. Breton 14

15 Issue: private - public collaboration model Disease Knowledge GRID Credit: R. Ziegler (Novartis) Disease Research Resource Private labs Public labs Disease Computing Resource Public labs Pharmaceutical lab Disease Medical Resource Public labs Private labs International agencies Private Domain Disease Computing Resource Pharmaceutical lab Successfully implemented by NOVARTIS on dengue in collaboration with Univ. Basel OGF22 PharmaGrid workshop V. Breton 15

16 Issue: intellectual property Molecules selected in silico belong to public databases Molecules patented for their antimalarial activity Who claims IP? Many actors: computing centres, WISDOM collaboration partners, wet labs Agreed statement all information including analysis of potential hits are made publicly available. If a group takes screening information and synthesizes the physical compound and tests it extensively in the wet-lab, it might establish IP on their side and can establish claims on the physical compound and its behavior in biological assays as long as they cite the source for their initial analysis correctly. OGF22 PharmaGrid workshop V. Breton 16

17 Issue: access to resources Docking one biological target on 1 million drug-like compounds requires between 2 and 30 CPU years depending on the software Requests received from research groups around the globe for docking targets related to numerous diseases AIDS, Avian Flu, Diabetes, Malaria, Schistosomiasis, Tuberculosis Need for more resources Interoperability -> Access to multiple grids Interoperability of desktop grids and EGEE for molecular docking (EDGES) Implementation of open standards to access EGEE and DEISA for molecular dynamics (OMII-Europe) OGF22 PharmaGrid workshop V. Breton 17

18 Deployment of MD computations on DEISA Credit: M. Riedel S. Brewer (OMII Europe) OGF22 PharmaGrid workshop V. Breton 18

19 Issue: security All biomedical data (input, output) are stored in a metadata catalogue with fine grain access control WISDOM Production Environment OGF22 PharmaGrid workshop V. Breton 19

20 Perspectives Innovative Medicines Initiative (European Commission) Goal: revitalize the biopharmaceutical research and development (R&D) environment for Europe Strategic Research Agenda Identification of bottlenecks in the pharmaceutical R&D process Recommendations to improve competitiveness Key action: improve knowledge management Develop enhanced knowledge representation models and data exchange standards for complex systems, Build a core reference database of validated experimental data extracted from the literature, Design standards for and build an expert tool to allow the federation of local databases in a secured environment. OGF22 PharmaGrid workshop V. Breton 20

21 Pharmaceutical R&D: requirements on knowledge management Capacity to search, query, extract, integrate and share data in a scientifically and semantically consistent manner across heterogeneous sources (public and proprietary) ranging from chemical structures and omics to clinical trial data, Capacity to integrate and share scientific tools (e.g., modelling, simulation) as modules in a generic framework and apply them to relevant dynamic data sets, Expressive data representation and exchange standards, Dynamic and customizable configuration of applications, Encapsulation of validated physiological models, when applicable, Flexible, secure (covering all aspects of data protection encountered in a biomedical context), and scalable IT infrastructure. Innovative Medicines Initiative (IMI) Strategic Research Agenda OGF22 PharmaGrid workshop V. Breton 21

22 Recommended actions to address technical gaps Develop a strategy to identify the areas of interest to all stakeholders, Provide mechanisms for data federation across heterogeneous data sources, Provide a flexible and secure collaborative environment serving all stakeholders, Provide standards and mechanisms for consistent data integration and data sharing, Provide standards and mechanisms for consistent integration of complex scientific tools and computational models, Insure interoperability of computing services across organizations, Develop broad and generic research projects for bridging gaps in current technologies. Innovative Medicines Initiative (IMI) Strategic Research Agenda OGF22 PharmaGrid workshop V. Breton 22

23 Roadmap to knowledge management SHARE roadmap Complexity Quality of Domain Specific Knowledge Service Management and Ontologies User Knowledge Management Friendliness Tools and standards On Demand Access Infrastructure Interoperability Data Integration Tools and Standards Distributed Data Models Improved Distributed Data Management Time Visionaries EarlyAdopters EarlyMajority Late Majority OGF22 PharmaGrid workshop V. Breton 23

24 Conclusion In silico drug discovery routinely deployed on grid infrastructures Number of issues already addressed Next step: improve knowledge management to enable pharmaceutical R&D environment LPC Clermont-Ferrand: Biomedical grid CEA, Acamba project: Biological targets, Chemogenomics HealthGrid: Biomedical grid, Dissemination Univ. Los Andes: Biological targets, Malaria biology ITB CNR: Bioinformatics, Molecular modelling Univ. Pretoria / CSIR: Bioinformatics, Malaria biology SCAI Fraunhofer: Knowledge extraction, Chemoinformatics Univ. Modena: Biological targets, Molecular Dynamics Chonnam nat. univ.: In vitro testing KISTI: Grid technology Academica Sinica: Grid user interface Biological targets In vitro testing Mahidol Univ.: Biochemistry, in vitro testing OGF22 PharmaGrid workshop V. Breton 24

25 Acknowledgements Academia Sinica, Taiwan Hurng-Chun LEE, Simon C. LIN (Grid Computing Center) Ying-Ta WU, Chon-Chen LEE (Genomic Research Center) HealthGrid Nicolas SPALINGER, Nicolas JACQ, SCAI-Fraunhofer Institute, Germany Martin HOFMANN, Vinod KASAM Modena University, Italy Giulio RASTELLI, Gianluca DEGLIESPOSTI Chonnam National University, Korea Doman KIM, Young-Min KIM (Neuraminidases), Hee-Kyoung KANG (Plasmepsin) ITB-CNR Luciano MILANESI, Pasqualina D URSI, Gabrielle TROMBETTI CNRS-IN2P3-LPC, Clermont-Fd, France Jean SALZEMANN, Ana DA COSTA, Vincent BLOCH, Yannick LEGRE OGF22 PharmaGrid workshop V. Breton 25

26 Point mutations do impact inhibitory effectiveness Variation of docking score on wild type (T06) and mutated targets Orig. E119A E119D H275F R293K E119A_o Y344_o potential hits T01:E119A T05:R293K T01 E119A OGF22 PharmaGrid workshop V. Breton 26

Neuraminidase Second screening (2 nmol) Spectrofluorometric detector RF-551 362 nm

27 In vitro tests at Chonnam National University 4-Methylumbeliferyl-N-acetyl-α-D-neuramininic acid ammonium salt [4MU-NANA]; Substrate First screening (200 nmol) Recombinant Neuraminidase Second screening (2 nmol) Spectrofluorometric detector RF nm excitation and 448 nm emission wavelengths Red Kinetic study Blue Inhibition OGF22 PharmaGrid workshop V. Breton 27

emission at 448 nm Rank Compounds Relative activity of Neu1 1 113 67 2

28 Results on 308 compounds tested in vitro 4MU-NANA : 20 µm/rm Neuraminidase : 10 mu/reaction Measure at excitation 362 nm and emission at 448 nm Rank Compounds Relative activity of Neu On UV 63 Tamiflu 100 OGF22 PharmaGrid workshop V. Breton

Grid computing in Europe and neighbouring countries. Vincent Breton Institut des Grilles du CNRS LPC Clermont-Ferrand

Grid computing in Europe and neighbouring countries Vincent Breton Institut des Grilles du CNRS LPC Clermont-Ferrand 1 From a single PC to a grid Farm of PCs Entreprise grid: mutualization of ressources