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Dual-Event Machine Learning Models to Accelerate Drug Discovery

Sean Ekins1,2*, Robert C. Reynolds3,4*, Hiyun Kim5, Mi-Sun Koo5, Marilyn

Ekonomidis5, Meliza Talaue5, Steve D. Paget5, Lisa K. Woolhiser6, Anne J.

Lenaerts6, Barry A. Bunin1, Nancy Connell5 and Joel S. Freundlich5,7* 1Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA. 2Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA. 3Southern Research Institute, 2000 Ninth Avenue South, Birmingham, AL 35205, USA. 4Current address: University of Alabama at Birmingham, College of Arts and Sciences , Department of Chemistry, 1530 3rd

Avenue South, Birmingham, Alabama 35294-1240, USA. 5Department of Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ – New Jersey Medical School, 185 South

Orange Avenue Newark, NJ 07103, USA. 6Department of Microbiology, Immunology and Pathology, Colorado State University, 200 West Lake Street, CO 80523, USA. 7Department of Pharmacology & Physiology, UMDNJ – New Jersey Medical School, 185 South Orange Avenue Newark, NJ

07103, USA.

.

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

one new drug (bedaquiline) in 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

TB facts

streptomycin (1943)

para-aminosalicyclic acid (1949)

isoniazid (1952) (Bayer, Roche, Squibb)

pyrazinamide (1954)

cycloserine (1955)

ethambutol (1962)

rifampicin (1967)

~ 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

Phenotypic screening HTS Hit rates

SRI papers

Usually less than 1%

Bayesian Model Construction: Mtb Whole-Cell HTS

• Learning from 3,779 compounds from an NIAID library

- active: MIC < 5 mM

- inactive: MIC ≥ 5 mM

Bayesian machine learning

Ekins, Williams and Xu, Drug Metab Dispos 38: 2302-2308, 2010

Bayesian classification is a simple probabilistic classification model. It is based on

Bayes’ theorem

h is the hypothesis or model

d is the observed data

p(h) is the prior belief (probability of hypothesis h before observing any data)

p(d) is the data evidence (marginal probability of the data)

p(d|h) is the likelihood (probability of data d if hypothesis h is true)

p(h|d) is the posterior probability (probability of hypothesis h being true given the

observed data d)

A weight is calculated for each feature using a Laplacian-adjusted probability

estimate to account for the different sampling frequencies of different features.

The weights are summed to provide a probability estimate

Novel Bayesian Models for Mtb Whole-Cell Efficacy

SRI MLSMR 220K single point model

active: ≥90% inhibition @ 10 mM; inactive <90% inhibition @ 10 mM

SRI MLSMR 2.5K dose reponse model

active: IC50 ≤ 5 mM; inactive: IC50 > 5 mM

Ekins, S. et al., Mol. Biosyst. 2010, 6, 840-51; Ekins, S. et al., Mol. Biosyst. 2010, 6, 2316-2324.

• Laplacian-corrected Bayesian classifier models (Accelrys Discovery Studio)

• Molecular function class fingerprints of maximum diameter 6 (FCFP_6)

• Simple molecular descriptors chosen including AlogP, molecular weight,

# rotatable bonds, # rings, # hydrogen bond acceptors, # hydrogen bond

donors, and polar surface area

• Validated w/ leave-one-out cross-validation & leave-50%-out cross-validation

Model Building and Validation

Bayesian Classification TB Models

Dateset

(number of

molecules)

External

ROC Score

Internal

ROC

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

We can use the public data for machine learning model building

Using Discovery Studio Bayesian model

Leave out 50% x 100

Ekins et al., Mol BioSyst, 6: 840-851, 2010

Bayesian Classification Models for TB

Good

Bad

active compounds with MIC < 5uM

Laplacian-corrected Bayesian classifier models were generated using FCFP-6 and

simple descriptors. 2 models 220,000 and >2000 compounds


Bayesian Classification Dose response

Good

Bad


100K library Novartis Data FDA drugs

Additional test sets

Suggests models can predict data from the same and independent labs

Enrichments 4-10 fold

Initial enrichment – enables screening few compounds to find actives

21 hits in 2108 cpds 34 hits in 248 cpds 1702 hits in >100K cpds

Ekins and Freundlich, Pharm Res, 28, 1859-1869, 2011. Ekins et al., Mol BioSyst, 6: 840-851, 2010

Testing to date has been retrospective

Can we use our models to select compounds and influence

design?

Prospective prediction

Do it enough times to show robustness

Testing prospectively

Ranked Asinex 25K library with MLSMR dose response model –

Bayesian score range -28.4 – 15.3

99 compounds screened (Bayesian score 9.4 – 15.3).

12 cpds were identified with IC90 < 30 ug/mL

~12% hit rate

Most active SYN 22269076

Pyrazolo[1,5-a]pyrimidine

IC50 1.1ug/ml (3.2uM)

Bayesian Machine Learning Models – testing

Bayesian

Score 14.9 10.6 9.8 Bob Reynolds (SRI)

Some follow up compounds for the Asinex hit

Principal component analysis (PCA) of all SRI data sets to

illustrate overlap of chemistry space using the datasets

from this study (red TAACF-CB2, green = MLSMR, black =

kinase dataset), 3PCs explain 72% of the variance.

Top scoring molecules assayed for

Mtb growth inhibition

Mtb screening

molecule database

High-throughput

phenotypic

Mtb screening

Descriptors + Bioactivity (+Cytotoxicity)

Bayesian Machine Learning Mtb Model

Molecule Database

(e.g. GSK malaria actives)

virtually scored using Bayesian Models

New bioactivity data

may enhance models

Identify in vitro hits

Increased hit/lead discovery efficiency

NH

S

N

NH

S

N

Dual-Event models

Dual-Event models

Become more stringent in what we call an ACTIVE

IC90 < 10 ug/ml (CB2) or <10uM (MLSMR) and a selectivity index (SI)

greater than ten.

SI was calculated as SI = CC50/IC90 where CC50 is the concentration that

resulted in 50% inhibition of Vero cells (CC50).

Bayesian Classification TB Models

Dateset

(number of

molecules)

External

ROC

Score

Internal

ROC


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

NEW Dose resp and

cytotoxicity (N =

2273) 0.82 ± 0.02 0.84 ± 0.02 82.61 ± 4.68 83.91 ± 5.48 65.99 ± 7.47

Single pt ROC XV AUC = 0.88

Dose resp = 0.78

Dose resp + cyto = 0.86

Ekins et al., PLOSONE, in press 2013

A new dataset to use as a test set for models

Bayesian Machine Learning Models – blind testing

Dual event model shows increased enrichment

Ekins et al.,Chem Biol 20, 370–378, 2013

1. Virtually screen 13,533-member GSK antimalarial hit library

2. Model = SRI TAACF-CB 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

Prospective prediction of antimalarial compounds vs Mtb

Dateset

(number of molecules)

External

ROC Score

Internal ROC


TAACF-CB2 IC90 and

cytotoxicity (1783) 0.64 0.59 ± 0.01 0.63 ± 0.02 55.74 ±1.31 61.61 ± 8.96

Prospective prediction of antimalarial compounds vs Mtb

7 tested, 5 active (70% hit rate)


Bayesian Model Follow-up: Do we have a lead?

• BAS00521003/ TCMDC-125802 reported to be a

P. falciparum lactate dehydrogenase inhibitor

• Only one report (that we were unaware of when

picking the compound) 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.

SRI MLSMR 220K library contains:

107 hits with this substructure

- 3 nitrofuryl hydrazones

- 10 furyl hydrazones

- 19 nitrophenyl hydrazones

32 inactives with this substructure

Maddry et al., Tuberculosis 2009, 89, 354.

MIC of 0.0625 ug/mL

Efficacy Profiling of TCMDC-125802

• 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 vivo Evaluation of TCMDC-125802

Goal: Evaluate the in vivo safety and efficacy of JSF-2019 in mouse

models of TB infection

Step #2: 7-day Maximum Tolerated Dose study in mice

- formulated in 0.5% methyl cellulose

- single dose p.o. @ 30, 100, and 300 mg/kg in B6D2F1 mice

- no overt toxicity

Lisa Woolhiser and Anne Lenaerts (CSU)

Step #3: evaluation in GKO mouse model of TB infection

- Five 12 week-old female C57BL/6 mice infected with Mtb Erdman via

low-dose aerosol exposure

- Days 16 – 23 : dosed w/ 300 mg/kg JSF-2019 p.o. OR 25 mg/kg INH

OR untreated

- Sacrificed day 24 and lung and spleen homogenates were cultured

- no difference in lungs and spleens vs. control

http://goo.gl/UujRX Ballel et al., Fueling Open-Source drug discovery: 177 small-

molecule leads against tuberculosis ChemMedChem 2013.

GSK screened 2M compounds – 3 yrs ago

Bayesian predictions for 14,000 cpds exposed 11 / 15 (73%)

correct when paper was published

Further prospective validation example

Why screen cpds?

Conclusions

>38,000 molecules screened through Bayesian models

106 molecules were tested in vitro

17 actives were identified (22.5 % hit rate)

Identified several novel potent lead series with good cytotoxicity & selectivity

Some series have been missed in SRI screening data

Took a non toxic molecule quickly in vivo – Have made analogs in attempt to

overcome in vivo efficacy failure

All Bayesian models shared with Abbott and Merck in TB Accelerator project

All Bayesian models are freely available to researchers


Acknowledgments

The project described was supported by Award Number R43 LM011152-01

“Biocomputation across distributed private datasets to enhance drug

discovery” from the National Library of Medicine (PI: S. Ekins)

Accelrys

The CDD TB has been developed thanks to funding from the Bill and

Melinda Gates Foundation (Grant#49852 “Collaborative drug discovery for

TB through a novel database of SAR data optimized to promote data

archiving and sharing”)

Allen Casey (IDRI)

Joel Freundlich Lab

You can find me @... CDD Booth 205

PAPER ID: 13433

PAPER TITLE: “Dispensing processes profoundly impact biological assays and computational and statistical

analyses”

April 8th 8.35am Room 349

PAPER ID: 14750

PAPER TITLE: “Enhancing High Throughput Screening For Mycobacterium tuberculosis Drug Discovery

Using Bayesian Models”

April 9th 1.30pm Room 353

PAPER ID: 21524

PAPER TITLE: “Navigating between patents, papers, abstracts and databases using public sources and

tools”

April 9th 3.50pm Room 350

PAPER ID: 13358

PAPER TITLE: “TB Mobile: Appifying Data on Anti-tuberculosis Molecule Targets”


PAPER ID: 13382

PAPER TITLE: “Challenges and recommendations for obtaining chemical structures of industry-provided

repurposing candidates”


PAPER ID: 13438

PAPER TITLE: “Dual-event machine learning models to accelerate drug discovery”

April 10th 3.05 pm Room 350

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