Combining Metabolite - Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery Sean Ekins 1,2* , Peter B. Madrid 3* , Malabika Sarker 3 , Shao - Gang Li 4 , Nisha Mittal 4 , Pradeep Kumar 5 , Xin Wang 4 , Thomas P. Stratton 4 , Matthew Zimmerman, 6 Carolyn Talcott 3 , Pauline Bourbon 3 , Mike Travers 1 , Maneesh Yadav 3 and Joel S. Freundlich 4* 1 Collaborative Drug Discovery Inc., 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA. 2 Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay - Varina, NC 27526, USA. 3 SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA. 4 Departments 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. 5 Department of Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University – New Jersey Medical School, 185 South Orange Avenue, Newark, NJ 07103, USA. .
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Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery
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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.
.
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
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
~ 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
Over 8000 molecules with dose
response data for Mtb in CDD
Public from NIAID/SRI
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).
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.
TB molecules and target information database connects
molecule, gene, pathway and literature
Sarker et al., Pharm Res 2012, 29, 2115-2127.
Pharmacophore developed (using AccelrysDiscovery 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.
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
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
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
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
NH
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