Presentation on Thursday 18 march 2016 a ACS National meeting. Session on BIG DATA. My last ACS presentation a the national meeting !

2. Using Machine Learning Models Based on
Phenotypic Data to Discover New
Molecules for Neglected Diseases
Sean Ekins
Collaborative Drug Discovery, Inc., Burlingame, CA.
Collaborations Pharmaceuticals, Inc. Fuquay Varina, NC.
Collaborations in Chemistry, Inc. Fuquay Varina, NC.
Wikipedia

3. Machine Learning Examples
• Data is BIG for neglected diseases
• To discover new leads
• Tuberculosis – from public data to open models to create IP
• Chagas Disease - from public data to create new IP
• Ebola virus – from little data to create open data and IP
• Other diseases, emerging diseases?

4. Neglected Disease Drug Discovery
An urgent need for new therapeutics
http://www.mm4tb.org/

5. Tuberculosis kills 1.6-1.7m/yr (~1 every 8 seconds)
1/3rd of worlds population infected!!!!
streptomycin (1943)
para-aminosalicyclic acid (1949)
isoniazid (1952)
pyrazinamide (1954)
cycloserine (1955)
ethambutol (1962)
rifampicin (1967)
Multi drug resistance in 4.3% of cases
Extensively drug resistant increasing
incidence
one new drug (bedaquiline) in 40 yrs
Tuberculosis

6. Tested >350,000 molecules Tested ~2M 2M >300,000
>1500 active and non toxic Published 177 100s 800
Bigger Open Data: Screening for New Tuberculosis Treatments
~350,000 accessible
TBDA screened over 1 million, 1 million
more to go
TB Alliance + Japanese pharma screens
R43 LM011152-01

7. Over 8 years analyzed in vitro data and built 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,
R43 LM011152-01

8. 5 active compounds vs Mtb in a few months
7 tested, 5 active (70% hit rate)
Ekins et al.,Chem
Biol 20, 370–378,
2013
1. Virtually screen
13,533-member GSK
antimalarial hit library
2. Bayesian Model = SRI
TAACF-CB2 dose
response + cytotoxicity
model
3. Top 46 commercially
available compounds
visually inspected
4. 7 compounds chosen
for Mtb testing based
on
- drug-likeness
- chemotype diversity
GSK #
Bayesian
Score Chemical Structure
Mtb H37Rv
MIC
(mg/mL)
GSK
Reported
% Inhibition
HepG2 @ 10
mM cmpd
TCMDC-
123868 5.73 >32 40
TCMDC-
125802 5.63 0.0625 5
TCMDC-
124192 5.27 2.0 4
TCMDC-
124334 5.20 2.0 4
TCMDC-
123856 5.09 1.0 83
TCMDC-
123640 4.66 >32 10
TCMDC-
124922 4.55 1.0 9
R43 LM011152-01

9. • BAS00521003/ TCMDC-125802 reported to be a P.
falciparum lactate dehydrogenase inhibitor
• Only one report of antitubercular activity from 1969
- solid agar MIC = 1 mg/mL (“wild strain”)
- “no activity” in mouse model up to 400 mg/kg
- however, activity was solely judged by
extension of survival!
Bruhin, H. et al., J. Pharm. Pharmac. 1969, 21, 423-433.
.
MIC of 0.0625 ug/mL
• 64X MIC affords 6 logs of
kill
• Resistance and/or drug
instability beyond 14 d
Vero cells : CC50 = 4.0
mg/mL
Selectivity Index SI =
CC50/MICMtb = 16 – 64
In mouse no toxicity but
also no efficacy in GKO
model – probably
metabolized.
Ekins et al.,Chem Biol 20, 370–378, 2013
Taking a compound in vivo identifies issues
R43 LM011152-01

10. Modeling and mapping Mouse in vivo data
Mouse TB model data over 70 yrs
784 training and 60 test set
Extended earlier study
J Chem Inf Model. 2014 Apr 28;54(4):1070-82

11. Optimizing the triazine series as part of this project, improve solubility and show in
vivo efficacy
1U19AI109713-01

12. Chagas Disease
• About 7 million to 8 million people
estimated to be infected worldwide
• Vector-borne transmission occurs in the
Americas.
• A triatomine bug carries the
parasite Trypanosoma cruzi which causes
the disease.
• The disease is curable if treatment is
initiated soon after infection.
• No FDA approved drug, pipe line sparse
Hotez et al., PLoS Negl Trop Dis. 2013 Oct
31;7(10):e2300
R41-AI108003-01

13. • Modeled data with over 300,000 cpds but focused on smaller set
• Dataset from PubChem AID 2044 – Broad Institute data
• Dose response data (1853 actives and 2203 inactives)
• Dose response and cytotoxicity (1698 actives and 2363 inactives)
• EC50 values less than 1 mM were selected as actives.
• For cytotoxicity greater than 10 fold difference compared with EC50
• Models generated using : molecular function class fingerprints of maximum
diameter 6 (FCFP_6), AlogP, molecular weight, number of rotatable bonds,
number of rings, number of aromatic rings, number of hydrogen bond
acceptors, number of hydrogen bond donors, and molecular fractional polar
surface area.
• 5-fold cross validation or leave out 50% x 100 fold cross validation was used
to calculate the ROC for the models generated
T. cruzi Machine Learning models
R41-AI108003-01
Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878

14. Model
Best
cutoff
Leave-one out
ROC
5-fold cross
validation ROC
5-fold cross
validation sensitivity
(%)
5-fold cross
validation
specificity (%)
5-fold cross
validation
concordance (%)
Dose response
(1853 actives,
2203 inactives)
-0.676 0.81 0.78 77 89 84
Dose response
and cytotoxicity
(1698 actives,
2363 inactives)
-0.337 0.82 0.80 80 88 84
External ROC Internal ROC
Concordance
(%)
Specificity
(%) Sensitivity (%)
0.79 ± 0.01 0.80 ± 0.01 73.48 ± 1.05 79.08 ± 3.73 65.68 ± 3.89
5 fold cross validation
Dual event 50% x 100 fold cross validation
R41-AI108003-01
Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878

15. Good Bad
Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878
T. cruzi Dose Response and cytotoxicity Machine Learning model features
Tertiary amines, piperidines and aromatic
fragments with basic Nitrogen
Cyclic hydrazines and electron poor
chlorinated aromatics
R41-AI108003-01

16. Bayesian Machine Learning Models
- Selleck Chemicals natural product lib. (139 molecules);
- GSK kinase library (367 molecules);
- Malaria box (400 molecules);
- Microsource Spectrum (2320 molecules);
- CDD FDA drugs (2690 molecules);
- Prestwick Chemical library (1280 molecules);
- Traditional Chinese Medicine components (373 molecules)
7569 molecules
99 molecules
R41-AI108003-01 Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878

17. Synonyms Infection Ratio EC50 (µM) EC90 (µM) Hill slope
Cytotoxicity CC50
(µM)
Chagas mouse model (4
days treatment,
luciferase): In vivo
efficacy at 50 mg/kg bid
(IP) (%)
(±)-Verapamil
hydrochloride, 715730,
SC-0011762
0.02, 0.02 0.0383 0.143 1.67 >10.0 55.1
29781612,
Pyronaridine 0.00, 0.00 0.225 0.665 2.03 3.0 85.2
511176, Furazolidone 0.00, 0.00 0.257 0.563 2.81 >10.0 100.5
501337,
SC-0011777,
Tetrandrine
0.00, 0.00 0.508 1.57 1.95 1.3 43.6
SC-0011754,
Nitrofural 0.01, 0.01 0.775 6.98 1.00 >10.0 78.5*
* Used hydroxymethylnitrofurazone for in vivo study (nitrofural pro-drug)
Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878
H3C
O
N
CH3
N
CH3
H3C
O
CH3
O
H3C
O
H3C
N
N
HN
N
N
OH
Cl
O
CH 3
O
N
N
+
N
O
O
–
O
O
O
N
+
O
O
–
N
H
N
NH2
O
In vitro and in vivo data for compounds selected
R41-AI108003-01

18. 7,569 cpds => 99 cpds => 17 hits (5 in nM range)
Infection Treatment Reading
0 1 2 3 4 5 6 7
Pyronaridine Furazolidone Verapamil
Nitrofural Tetrandrine Benznidazole
In vivo efficacy of the 5 tested compounds
Vehicle
Ekins et al., PLoS Negl Trop Dis. 2015 Jun 26;9(6):e0003878R41-AI108003-01

19. Pyronaridine: New anti-Chagas and known anti-Malarial
EMA approved in combination with
artesunate
The IC50 value 2 nM against the growth
of KT1 and KT3 P. falciparum
Known P-gp inhibitor
Active against Babesia and Theileria
Parasites tick-transmitted
R41-AI108003-01
Work provided starting point for a phase II and phase I grant (submitted)
N
N
HN
N
N
OH
Cl
O
CH 3
Broad group
missed this cpd

20. 2014-2015 Ebola outbreak
March 2014, the
World Health
Organization (WHO)
reported a major
Ebola outbreak in
Guinea, a western
African nation
8 August 2014, the
WHO declared the
epidemic to be an
international public
health emergency
I urge everyone involved in all aspects of this epidemic to openly and rapidly report their experiences and
findings. Information will be one of our key weapons in defeating the Ebola epidemic. Peter Piot
Wikipedia
Wikipedia

21. Madrid PB, et al. (2013) A Systematic Screen of FDA-Approved Drugs for Inhibitors of Biological
Threat Agents. PLoS ONE 8(4): e60579. doi:10.1371/journal.pone.0060579
Chloroquine in mouse

22. Machine Learning for EBOV
• 868 molecules from the viral pseudotype entry assay and the EBOV replication assay
• Salts were stripped and duplicates removed using Discovery Studio 4.1 (Biovia, San
Diego, CA)
• IC50 values less than 50 mM were selected as actives.
• Models generated using : molecular function class fingerprints of maximum diameter 6
(FCFP_6), AlogP, molecular weight, number of rotatable bonds, number of rings,
number of aromatic rings, number of hydrogen bond acceptors, number of hydrogen
bond donors, and molecular fractional polar surface area.
• Models were validated using five-fold cross validation (leave out 20% of the database).
• Bayesian, Support Vector Machine and Recursive Partitioning Forest and single tree
models built.
• RP Forest and RP Single Tree models used the standard protocol in Discovery Studio.
• 5-fold cross validation or leave out 50% x 100 fold cross validation was used to
calculate the ROC for the models generated

23. Models
(training set 868 compounds)
RP Forest
(Out of bag
ROC)
RP Single Tree
(With 5 fold
cross validation
ROC)
SVM
(with 5 fold
cross validation
ROC)
Bayesian
(with 5 fold
cross validation
ROC)
Bayesian
(leave out
50% x 100
ROC)
Open
Bayesian
(with 5 fold
cross
validation
ROC)
Ebola replication (actives = 20)
0.70 0.78 0.73 0.86 0.86 0.82
Ebola Pseudotype (actives = 41)
0.85 0.81 0.76 0.85 0.82 0.82
Ebola HTS Machine learning model cross validation
Receiver Operator Curve Statistics.
Ekins et al., F1000Res 4:1091, (2015)

24. Discovery Studio pseudotype Bayesian model
B
Discovery Studio EBOV replication model
Good Bad
Good Bad
Ekins et al., F1000Res 4:1091, (2015)

25. Effect of drug treatment on infection with Ebola-GFP
3 Molecules selected from MicroSource Spectrum virtual screen and tested in vitro
All of them nM activity
-8 -7 -6 -5 -4
-10
0
10
20
30
40
50
60
70
80
90
100
110
Chloroquine
Pyronaridine
Quinacrine
Tilorone
Untreated control
Log Conc. (M)%EbolaInfection
Compound EC50 (mM) [95% CI] Cytotoxicity CC50 (µM)
Chloroquine 4.0 [1.0 – 15] 250
Pyronaridine 0.42 [0.31 – 0.56] 3.1
Quinacrine 0.35 [0.28 – 0.44] 6.2
Tilorone 0.23 [0.09 – 0.62] 6.2
Duplicate experiments
control
Ekins et al., F1000Res 4:1091, (2015)

26. Making Ebola models available
• From data published by others …to proposing target
• Collaborated with lab to open up their screening data, build models,
identified more active inhibitors
• To date the most potent drugs and drug-like molecules
• Still a need for a drug that could be used ASAP
• Models in MMDS http://molsync.com/ebola/
More data continues to be published
• We collated 55 molecules from the literature
• A second review lists 60 hits
– Picazo, E. and F. Giordanetto, Drug Discovery Today. 2015 Feb;20(2):277-86
• Additional screens have identified 53 hits and 80 hits respectively
– Kouznetsova, J., et al., Emerg Microbes Infect, 2014. 3(12): p. e84.
– Johansen, L.M., et al., Sci Transl Med, 2015. 7(290): p. 290ra89.
 Litterman N, Lipinski C and Ekins S 2015 F1000Research 2015, 4:38

27. 1000’s of models from
• Skipped targets with > 100,000 assays and sets
with < 100 measurements
• Converted data to –log
• Dealt with duplicates
• 2152 datasets
• Cutoff determination
• Balance active/ inactive ratio
• Favor structural diversity and activity distribution
Clark and Ekins, J Chem Inf Model. 2015 Jun 22;55(6):1246-60
http://molsync.com/bayesian2

28. What do 2000 ChEMBL models
look like
Folding bit size
Average
ROC
http://molsync.com/bayesian2 Clark and Ekins, J Chem Inf Model. 2015 Jun 22;55(6):1246-60

29. PolyPharma a new free app for drug discovery

30. #ZikaOpen
Image by John Liebler

31. Proposed workflow for rapid drug discovery against Zika virus
Ekins S, Mietchen D, Coffee M et al. F1000Research 2016, 5:150
(doi: 10.12688/f1000research.8013.1)

32. HOMOLOGY MODELS FOR ZIKA
Models developed with SWISS-MODEL
Will dock millions of compounds
vs these models
Ekins et al., F1000Research 5:275 (2016)

33. Ekins S, Mietchen D, Coffee M et al. 2016 F1000Research 2016,
5:150 (doi: 10.12688/f1000research.8013.1)
Compounds and chemical libraries suggested for testing against Zika virus

34. • Data is out there to produce models for neglected diseases
• Also modeled Marburg, Lassa, Dengue..
• Computational and experimental collaborations with open data have lead to :
– New hits and leads
– New IP
– New grants for collaborators
• Even Ebola had enough data to build models and suggest compounds to test
in 2014
• Zika = starting from scratch – no data – need to use other approaches
• Make findings open and published immediately
• Need for easier facilities to test compounds
• Challenges still – sharing and accessing information / knowledge
• How do we prepare for the next BIG ONE
Conclusions

35. Alex Clark
Jair Lage de Siqueira-Neto
Joel Freundlich
Peter Madrid
Robert Davey
Megan Coffee
Robert Reynolds
Nadia Litterman
Christopher Lipinski
Christopher Southan
Antony Williams
Carolyn Talcott
Malabika Sarker
Steven Wright
Mike Pollastri
Ni Ai
Barry Bunin and all colleagues at CDD
Acknowledgments and contact info
ekinssean@yahoo.com
collabchem

36. ZIKAOPEN ACKNOWLEDGMENTS
Tom Stratton
Priscilla L. Yang

37. Software on github
Models can be accessed at
• http://molsync.com/bayesian1
• http://molsync.com/bayesian2
• http://molsync.com/transporters
• http://molsync.com/ebola/