Slides from SERMACS 2015 meeting in Memphis 2015 describing a collaborative project with SRI International and Rutgers. The work was published in PLOS ONE http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141076

1. Combining Metabolite-Based Pharmacophores
with Bayesian Machine Learning Models for
Mycobacterium tuberculosis Drug Discovery
Sean Ekins1,2*, Peter B. Madrid3*
, Malabika Sarker3, Shao-Gang Li4,
Nisha Mittal4, Pradeep Kumar5, Xin Wang4, Thomas P. Stratton4,
Matthew Zimmerman,6 Carolyn Talcott3
, Pauline Bourbon3, Mike
Travers1, Maneesh Yadav3 and Joel S. Freundlich4*
1Collaborative Drug Discovery Inc., 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA.
2Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA.
3SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA.
4Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens,
Rutgers University – New Jersey Medical School, 185 South Orange Avenue, Newark, NJ 07103, USA.
5Department of Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University – New Jersey Medical
School, 185 South Orange Avenue, Newark, NJ 07103, USA.
.

2. streptomycin (1943)
para-aminosalicyclic acid (1949)
isoniazid (1952)
pyrazinamide (1954)
cycloserine (1955)
ethambutol (1962)
rifampicin (1967)
Globally ~$500M in R&D /yr
Multi drug resistance in 4.3%
of cases
Extensively drug resistant
increasing incidence
one new drug (bedaquiline) in
40 yrs
TB key points

3. Tested >350,000 molecules Tested ~2M 2M >300,000
>1500 active and non toxic Published 177 100s 800
Big Data: Screening for New Tuberculosis Treatments
How many will become a new drug?
How do we learn from this big data?
TBDA screened over 1 million, 1 million
more to go
TB Alliance + Japanese pharma screens

4. ~ 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
Molecules with activity
against

5. Over 8000 molecules with dose
response data for Mtb in CDD
Public from NIAID/SRI

6. Phase I - 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).

7. Multi-step process
1. Identification of essential in vivo enzymes of Mtb involved intensive
literature mining and manual curation, to extract all the genes essential
for Mtb growth in vivo across species.
2. Homolog information was collated from other studies.
3. Collection of metabolic pathway information involved using TBDB.
4. Identifying molecules and drugs with known or predicted targets
involved searching the CDD databases for manually curated data. The
structures and data were exported for combination with the other data.
5. All data were combined with URL links to literature and TBDB and
deposited in the CDD database.
Initially over 700 molecules in dataset
Dataset Curation: TB molecules and target information
database connects molecule, gene, pathway and literature
Sarker et al., Pharm Res 2012, 29, 2115-2127.

8. TB molecules and target information database connects
molecule, gene, pathway and literature
Sarker et al., Pharm Res 2012, 29, 2115-2127.

9. 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
Sarker et al., Pharm Res 2012, 29, 2115-2127.

10. Two Proposed Mimics of D-fructose 1,6 bisphosphate
Computationally searched >80,000 molecules – and used bayesian
models for filter - 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
Sarker et al., Pharm Res 2012, 29: 2115-2127
a.
b.
1R41AI088893-01

11. Phase II – Mimic approach expanded
 Specific Aim 1: Develop molecular mimics of at least 20 additional
substrates of in vivo essential enzymes.
 SPECIFIC AIM 2: Progress molecules discovered in phase I and
identify the putative target/s.
 SPECIFIC AIM 3: Develop the approach into a commercial product

12. 66 Pharmacophores of substrates and metabolites
Developed for Mtb Enzymes
Green = Hydrogen bond acceptor, Purple = hydrogen bond donor, cyan = hydrophobe
Grey – van der Waals surface

13. Filter hits with Bayesian Models
Top scoring molecules
assayed for
Mtb growth inhibition
Mtb screening
molecule
database/s
High-throughput
phenotypic
Mtb screening
Descriptors + Bioactivity (+Cytotoxicity)
Bayesian Machine Learning classification Mtb Model
Molecule Database
(e.g. GSK malaria
actives)
virtually scored
using Bayesian
Models
New bioactivity data
may enhance models
Identify in vitro hits and test models3 x published prospective tests ~750
molecules were tested in vitro
198 actives were identified
>20 % hit rate
Multiple retrospective tests 3-10 fold
enrichment
N
H
S
N
Ekins et al., Pharm Res 31: 414-435, 2014
Ekins, et al., Tuberculosis 94; 162-169, 2014
Ekins, et al., PLOSONE 8; e63240, 2013
Ekins, et al., Chem Biol 20: 370-378, 2013
Ekins, et al., JCIM, 53: 3054−3063, 2013
Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011
Ekins et al., Mol BioSyst, 6: 840-851, 2010
Ekins, et al., Mol. Biosyst. 6, 2316-2324, 2010,

14. Hits found in Phase II
• Screened > 200,000 compounds, 14,733 retrieved, tested 110
• 3 actives based on standard Alamar Blue assay
• Most promising – quinoxaline di-N-oxides
Menadione Menadione Indole-3-acetamide Lipoamide
Ekins et al.,PLOS ONE, 10: e0141076, 2015

15. Synthetic routes to the A) arylamide and B) quinoxaline di-
N-oxide families
Arylamides MIC ≥50µM – not pursued further
• quinoxaline di-N-oxide initial hit previously shown to have MIC
3.13ug/ml, no cytotox (Villar et al., J Antimicrobial Chemotherapy, 62, 547-54,
2008)
Ekins et al.,PLOS ONE, 10: e0141076, 2015

16. Structures of quinoxaline di-N-oxides with the most
promising antitubercular activities and selectivities
Ekins et al.,PLOS ONE, 10: e0141076, 2015

17. Physiochemical and ADME data
Mouse liver microsomal
stability
Kinetic
Solubility
Caco-2 Cell Permeability
Molecule Compound
remaining
after 1h in
the presence
of NADPH
(%)
Compound
remaining
after 1h in the
absence of
NADPH (%)
Solubility
Limit at 2 h
(µM)
Mean A-
>B Papp
(10-6 cm
s-1)
Mean B->A
Papp (10-6
cm s-1)
Efflux
ratioPapp
(B->A)/Papp
(A->B)
SRI50 0.06 77.5 125 0.0 0.0 N/A
SRI54 0 79.1 15.6 0.66 0.10 0.15
SRI58 63.6 110 125 2.3 0.57 0.25
SRI58 did not exhibit quantifiable
blood levels in mice

18. Activity of SRI50 against wild type and clinical MDR-TB
strains
Strain Drug Resistancea Strain Type
SRI50
(µg/mL)
H37Rv None Laboratory 0.16
210 None Clinical 0.31
692 pan-susceptible Clinical 0.16
91 RIF, EMB Clinical 0.16
36 INH, RIF, EMB Clinical 0.16
116 INH, EMB, PAS Clinical 0.16
31 INH, RIF, EMB, KAN, SM, CAP Clinical 0.31
a RIF = rifampicin; EMB = ethambutol; INH = isoniazid; PAS = p-aminosalicyclic acid;
KAN = kanamycin; SM = streptomycin; CAP = capreomycin
Ekins et al.,PLOS ONE, 10: e0141076, 2015

19. Mtb transcriptional response to SRI54 as compared to other
small molecule antituberculars and environmental stresses
131 genes up-regulated, 184 down-regulated
Ekins et al.,PLOS ONE, 10: e0141076, 2015

20. TB Mobile Vers.2
Ekins et al., J Cheminform 5:13, 2013
Clark et al., J Cheminform 6:38 2014
Predict targets
Cluster molecules
http://goo.gl/vPOKS
http://goo.gl/iDJFR

21. Target prediction with TB Mobile
FtsZ, CysH, DprE1 and Rv1885c
CysS
Ekins et al.,PLOS ONE, 10: e0141076, 2015

22. Summary
• Combining bioinformatics and cheminformatics leads to synergies
• In this study we computationally searched >206,000 Asinex Gold
molecules with over 60 pharmacophores of Mtb essential substrates or
metabolites and ultimately tested ~110 compounds in vitro
• We identified 3 compounds possessing whole cell activity against Mtb
(MIC 2.5 – 40 microg/mL)
• More stringent hit criteria than for phase I
• Our strategy identified a series that previously led to in vivo actives
• Current work confounded by poor in vivo PK

23. All at CDD, SRI, and many others …Funding: 2R42AI088893-02 NIAID, CDD TB has been
developed thanks to funding from the Bill and Melinda Gates Foundation (Grant#49852)