Acs combining cheminformatics methods and pathway analysis to identify molecules with whole final
2 likes563 views
This document describes combining cheminformatics and pathway analysis methods to identify molecules with activity against Mycobacterium tuberculosis (M. tuberculosis), the bacteria that causes tuberculosis. Specifically, it discusses analyzing over 300,000 compounds tested against M. tuberculosis to identify molecule descriptors associated with activity. It also discusses building models to predict activity using public M. tuberculosis screening data. The goal is to identify new targets for tuberculosis drugs by focusing on essential bacterial enzymes that have no human homologs and integrating target and compound data into pathway models.
![Targets, metabolites and pathways pursued in this study
Essential Gene Pathway Essential Substrate/s
bioB (Rv1589) Biotin biosynthesis dethiobiotin
thiE (Rv0414c) Thiamine biosynthesis 2-(4-methylthiazol-5-yl)ethyl phosphate and
[(4-amino-2-methyl-pyrimidin-5-yl)methoxy-
oxido-phosphoryl] phosphate
cysE (Rv2335) Cysteine biosynthesis L-serine and acetyl-CoA
cobC (Rv2231c) No pathway assigned L-threonine O-3-phosphate
glpX (Rv1099c) glycolysis and gluconeogenesis D-fructose 1,6-bisphosphate
ppgK (Rv2702) Amino sugar and nucleotide sugar metabolism β-D-glucose
Gluconeogenesis
arcA (Rv1001) arginine degradation V (arginine deiminase pathway) L-arginine
panD (Rv3601c) β-alanine biosynthesis IV L-aspartate
otsA (Rv3490) trehalose biosynthesis I UDP-D-glucose and α-D-glucose 6-
phosphate
Sarker et al., Pharm Res 2012, in press](https://image.slidesharecdn.com/acscombiningcheminformaticsmethodsandpathwayanalysistoidentifymoleculeswithwhole-final-120329123554-phpapp01/85/Acs-combining-cheminformatics-methods-and-pathway-analysis-to-identify-molecules-with-whole-final-21-320.jpg)
Acs combining cheminformatics methods and pathway analysis to identify molecules with whole final
- 1. Combining Cheminformatics Methods and Pathway Analysis to Identify Molecules with Whole Cell Activity Against Mycobacterium Tuberculosis Malabika Sarker1, Carolyn Talcott1, Peter Madrid1, Sidharth Chopra1, Barry A. Bunin2 Gyanu Lamichhane3, Joel S. Freundlich4 and Sean Ekins2, 5, 1SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA. 2Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA. 3Johns Hopkins School of Medicine, Department of Medicine, 1550 Orleans St, Room 103, Baltimore, MD 21287, USA. 4Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ – New Jersey Medical School, 185 South Orange Avenue Newark, NJ 07103, USA. 5Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA. .
- 2. Applying CDD to Build a disease community for TB Tuberculosis Kills 1.6-1.7m/yr (~1 every 8 seconds) 1/3rd of worlds population infected!!!! Multi drug resistance in 4.3% of cases Extensively drug resistant increasing incidence No new drugs in over 40 yrs Drug-drug interactions and Co-morbidity with HIV Collaboration between groups is rare These groups may work on existing or new targets Use of computational methods with TB is rare Literature TB data is not well collated (SAR) Funded by Bill and Melinda Gates Foundation
- 4. Molecules with activity against ~ 20 public datasets for TB Including Novartis data on TB hits >300,000 cpds Patents, Papers Annotated by CDD Open to browse by anyone http://www.collaborativedrug. com/register
- 5. Simple descriptor analysis on > 300,000 compounds tested vs TB Atom Dataset MWT logP HBD HBA RO 5 count PSA RBN MLSMR Active ≥ 90% inhibition at 10uM 357.10 3.58 1.16 4.89 0.20 42.99 83.46 4.85 (N = 4096) (84.70) (1.39) (0.93) (1.94) (0.48) (12.70) (34.31) (2.43) Inactive < 90% inhibition at 10uM (N = 350.15 2.82 1.14 4.86 0.09 43.38 85.06 4.91 216367) (77.98)** (1.44)** (0.88) (1.77) (0.31)** (10.73) (32.08)* (2.35) TAACF- NIAID CB2 Active ≥ 90% inhibition at 10uM 349.58 4.04 0.98 4.18 0.19 41.88 70.28 4.76 (N =1702) (63.82) (1.02) (0.84) (1.66) (0.40) (9.44) (29.55) (1.99) Inactive < 90% inhibition at 10uM 77.75 (N 352.59 3.38 1.11 4.24 0.12 42.43 (30.17)* 4.72 =100,931) (70.87) (1.36)** (0.82)** (1.58) (0.34)** (8.94)* * (1.99)
- 6. Fitting into the drug discovery process Ekins et al, Trends in Microbiology 19: 65-74, 2011
- 7. BMGF 3 Academia/ Govt lab – Industry screening partnerships CDD used for data sharing / collaboration – along with cheminformatics expertise Previously supported larger groups of labs – many continued as customers
- 8. More Medicines for Tuberculosis CDD is a partner on a 5 year project supporting >20 labs and providing cheminformatics support Already found hits for a TB target using docking www.mm4tb.org
- 9. Bayesian Classification TB Models We can use the public data for machine learning model building Using Discovery Studio Bayesian model Leave out 50% x 100 Dateset Internal (number of External ROC molecules) ROC Score Score Concordance Specificity Sensitivity MLSMR All single point screen (N = 220463) 0.86 ± 0 0.86 ± 0 78.56 ± 1.86 78.59 ± 1.94 77.13 ± 2.26 MLSMR dose response set (N = 2273) 0.73 ± 0.01 0.75 ± 0.01 66.85 ± 4.06 67.21 ± 7.05 65.47 ± 7.96 Ekins et al., Mol BioSyst, 6: 840-851, 2010
- 10. Additional test sets 1702 hits in >100K cpds 34 hits in 248 cpds 21 hits in 2108 cpds 100K library Novartis Data FDA drugs Suggests models can predict data from the same and independent labs Initial enrichment – enables screening few compounds to find actives Ekins et al., Mol BioSyst, 6: 840-851, 2010 Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011.
- 11. Searching for TB molecular mimics; collaboration Azaserine exhibited a good fit for this pharmacophore, as judged by its quantitative FitValue (= 2.1) and visual inspection. Modeling – CDD Biology – Johns Hopkins Chemistry – Texas A&M Lamichhane G, et al Mbio, 2: e00301-10, 2011
- 12. CDD and SRI STTR collaboration CDD SRI Literature data on Aim 1 Pathway data (targets) molecules Develop API to and their targets link CDD and Species differences in pathways SRI databases Similarity search with a mimic enables target Where to intervene fishing Aim 2 Target and compound data added to pathway model Combine the knowledge Aim 3 Select new targets Identify new Take mimic strategy targets for drugs
- 13. Mimic strategy 1. The enzymes around these metabolites are "in vivo essential". 2. These enzymes have no human homolog. 3. These enzyme targets are not yet explored though some enzymes from the same pathways are drug targets (experimental or predicted).
- 14. Leverages work of Identification of essential in vivo enzymes of Mtb SRI Lamichhane et al., Sassetti et al., Analysis of metabolic pathway and reaction information for the essential enzymes SRI Approach taken Comparison of non-human-homologues enzymes with Mtb in similar to that of vivo essential gene set SRI Lamichhane et al Mbio paper 2011 -Instead mimic the Selection of targets – in vivo essential, not homologous to human and not known as TB drug-targets SRI substrate Uses data from SRI In silico design of small molecule inhibitors or pharmacophores for selected enzyme targets CDD and CDD databases to select targets that have not been In vitro testing of selected pharmacophores SRI exploited with small molecules
- 15. The cellular overview diagram for M. tuberculosis H37Rv, from the TBCyc database (http://tbcyc.tbdb.org/index.shtml) TBCyc gave a total of 53 non-redundant pathways for the set of 314 essential in vivo genes. Sarker et al., Pharm Res 2012, in press
- 16. Venn diagram shows the degree of association between the in vivo mutants of Mtb in different animal models Sarker et al., Pharm Res 2012, in press
- 17. Anishetty et al 185 proteins from Mtb absent in human S. Anishetty. et al., Comput Biol Chem. 29:368-378 (2005). Sassetti et al 49 proteins unique to Mtb C.M. Sassetti, et.al., Molecular microbiology. 48:77-84 (2003). Among 314 essential in vivo proteins of Mtb 66 proteins were non- human homolgs
- 18. TB target database for in vivo essential genes. https://www.collaborativedrug.com/buzz/2011/05/02/new-tb-targets-and- molecules-data-available-for-public-access-use/
- 19. TB molecules with activity in vitro and target information (from CDD) - now added external links to pathways, literature etc. 14 known gene targets and 31 predicted gene targets for already known 35 approved TB drugs
- 20. TB molecules and target information database connects molecule, gene, pathway and literature
- 21. Targets, metabolites and pathways pursued in this study Essential Gene Pathway Essential Substrate/s bioB (Rv1589) Biotin biosynthesis dethiobiotin thiE (Rv0414c) Thiamine biosynthesis 2-(4-methylthiazol-5-yl)ethyl phosphate and [(4-amino-2-methyl-pyrimidin-5-yl)methoxy- oxido-phosphoryl] phosphate cysE (Rv2335) Cysteine biosynthesis L-serine and acetyl-CoA cobC (Rv2231c) No pathway assigned L-threonine O-3-phosphate glpX (Rv1099c) glycolysis and gluconeogenesis D-fructose 1,6-bisphosphate ppgK (Rv2702) Amino sugar and nucleotide sugar metabolism β-D-glucose Gluconeogenesis arcA (Rv1001) arginine degradation V (arginine deiminase pathway) L-arginine panD (Rv3601c) β-alanine biosynthesis IV L-aspartate otsA (Rv3490) trehalose biosynthesis I UDP-D-glucose and α-D-glucose 6- phosphate Sarker et al., Pharm Res 2012, in press
- 22. Pharmacophore developed (using Accelrys Discovery Studio) from 3D conformations of the substrate van der Waals surface for the metabolite mapped onto it pharmacophore plus shape searched in 3D compound databases from vendors In silico hits collated Filtered for TB whole cell activity and reactivity Compounds filtered based on Bayesian score using models derived from NIAID / Southern Research Inst data to retrieve ideal molecular properties for in vitro TB activity
- 23. Example of mimic strategy for bioB Rv1589 Biotin biosynthesis dethiobiotin Take substrate and generate 3D conformers and build a pharmacophore Pharmacophore Use the pharmacophore to search vendor libraries in 3D Searching Maybridge (57K) Buy and test gives 72 molecules – many of compounds them hydrophobic so they stand a chance of in vitro activity
- 24. Substrate Pharmacophores Developed for Mtb Enzymes a. b. g. h. c. d. i. j. k. l. e. f. Green = Hydrogen bond acceptor, Purple = hydrogen bond donor, cyan = hydrophobe Grey – van der Waals surface Sarker et al., Pharm Res 2012, in press
- 25. Two Proposed Mimics of D-fructose 1,6 bisphosphate Computationally searched >80,000 molecules – narrowed to 842 hits -tested 23 compounds in vitro (3 picked as inactives), lead to 2 proposed as mimics of D-fructose 1,6 bisphosphate DFP000133SC MIC 40μg/ml a. DFP000134SC MIC 20μg/ml b. Sarker et al., Pharm Res 2012, in press
- 26. Proposed generalized workflow for molecule discovery 1. Find candidate genes 2. Prioritize target candidate 3. For each candidate 4. Submit top mimics for coding potential targets. list. molecule develop preliminary experimental pharmacophore model that validation and lead suggests mimics. optimization 1. choose pathogen 1. Annotate (choose properties: 1. Develop pharmacophore 1. select molecules from 3 pathways, reactions, EC#, models from metabolites GO characterization) 2. order from vendor 2. Search known drug 2. Filtering (choose thresholds) databases for compounds 3. test in vitro / ex vivo 2. search for genes mapping to 3. Sort (choose criteria: number pharmacophore, 4. add results to CDD choose source-- of pathways, number of database experimental in vitro/ex vivo reactions, ...) 3. Filter based on ADME/Tox data, in silico (single/double) properties 5. prioritize compounds for knockout (choose nutrient set, 4. Annotate reaction substrates lead optimization / in vivo survival conditions) with structure information. 4. Filter based on other studies models for target choose filter (no human bioactivity 6. partnering with 3rd party for ortholog, ..., user edit) preclinical/ clinical studies Output: Prioritized target list 5. Sorting or Pareto annotated with prioritizing optimization of results properties and associated Output: Experimental results Output: target candidate reactions with their substrates to be fed into the CDD list--gene names associated annotated with structure (these database with reference identifier. are the candidate molecules to Output: Pharmacophores and mimic). candidate mimics for substrates of target enzymes Metabolites (and metadata, required as sdf file for software) Molecule id, source
- 27. Summary POC took < 6mths - - Submitted phase II STTR, Still need to test vs target - verify it hits suggested target – optimize cpds. Need to link SRI and CDD databases via API – new product • Computational models based on Whole cell TB data could improve efficiency of screening • Collaborations get us to interesting compounds quickly • Additional prospective validation ongoing with IDRI, Southern Research Institute and UMDNJ using machine learning models - testing small numbers of compounds • UMDNJ – mined GSK malaria public data, scored with bayesian models – ordered from vendors
- 28. Bayesian Machine Learning Models – Improve Hit Rates Example 1. Kinase library Example 2. Asinex library Ranked Asinex 25K library with dose response model - 99 screened.16 cpds were identified with IC50<100uM Compare with HTS screening below Library Number Hit rate Notes size of hits (%) Reference Diverse 100997 1782 1.76 library Ananthan Diverse 215110 3817 1.77 library Maddry Human kinase focussed 25671 1329 5.18 library Reynolds Example 3. IDRI: 3 models - 48 compounds tested, 11 activity < or equal to MIC 10uM (22.9% hit rate) Example 4. UMDNJ 1 model – 4 tested, 3 active (1 MIC < 0.125ug/ml)
- 29. What next - Apps for collaboration ODDT – Open drug discovery teams Flipboard-like app for aggregating social media for diseases etc Alex Clark, Molecular Materials Informatics, Inc Williams et al DDT 16:928-939, 2011 Clark et al submitted 2012 Ekins et al submitted 2012
- 30. Acknowledgments collaborators (Allen Casey, Robert Reynolds etc..) Alex Clark (Molecular Materials Informatics, Inc) Accelrys CDD Funding BMGF Award Number R41AI088893 from the National Institute Of Allergy And Infectious Diseases. Email: ekinssean@yahoo.com Slideshare: http://www.slideshare.net/ekinssean Twitter: collabchem Blog: http://www.collabchem.com/ Website: http://www.collaborations.com/CHEMISTRY.HTM