ACS talk wed 28 2012

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