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Build and Optimize a BioTech Portfolio:Target Selection and
Disease Indications
Pankaj AgarwalComputational Biologist
[email protected]
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Acknowledgments & Conflicts of Interest
GSK
Colleagues @ GSK
BioAge
Bill & Melinda Gates Foundation
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Observations
1. Most (expensive) failures are due to lack of efficacy i.e.
poor target selection
2. Causes of clinical trial result of p>0.05 a. Lack of
pharmacology
b. Poor target selectionc. Poor selection of
patients/indication/sub-population
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Outline: Data-Driven Methods for1. Build Portfolio
a. Target Identificationb. Assessing Data Sources for Target
Identification using ML and DL
2. Systematic Drug Repurposing (SyDR) a. Experience b. Methods:
Genetics and Cmapc. Example: Optimize Portfolio
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Strategy
Starting Point
Target Id: Practical View
ValidationPlan
● Disease● Technology/Modality● Data Source
○ Mendelian○ GWAS○ CRISPR○ Paper
● Decision Maker? Budget? ● Criteria? ● Robust?● Competitive
Advantage?
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Assessments of Target Data Sources Using SoTA Methods:Deep
Learning, Network Diffusion &Tensor Factorization
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Target outcomes from Pharmaprojects and Features from
Harmonizome
https://pharmaintelligence.informa.com/
http://amp.pharm.mssm.edu/Harmonizome/
Phase IIIOutcome
Targ
ets
ParsingFilteringMappingSummarization
Omic Features
Systematic interrogation of diverse Omic data reveals
interpretable, robust and generalizable transcriptomic features of
clinically successful therapeutic targets. Rouillard AD, Hurle MR,
Agarwal P. PLoS Comput Biol. (2018)
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Systematic interrogation of diverse Omic data reveals
interpretable, robust and generalizable transcriptomic features of
clinically successful therapeutic targets. Rouillard AD, Hurle MR,
Agarwal P. PLoS Comput Biol. (2018)
Standard ML Classifier for Clinical Targets
– Logistic Regression– Random Forest
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SDAE discovers hidden target features from transcriptomicHidden
variables explain important patterns of expression across normal
human tissues
input corruptedinputhidden, explanatory
variablesreconstructed
input
EncoderNetwork
DecoderNetwork
mRNA expression in normal human tissues from theGenotype-Tissue
Expression (GTEx) Project
~20,000 Genes
30 T
issu
es
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SDAEs may discover features of clinically successful targets
+ phase III successx phase III failure
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Non-specific
TissueSpecific
Conventional Supervised Feature Selection
PP
V
TPR
----- model: 0.810±0.032 AUPRC- - - random: 0.78 AUPRC
GTEx SDAE outperformed conventional ML approach using 1000s of
features
brain,pituitary
blood,spleennon-specific
liver, kidney,small intestine,pancreas,bladder, skin
muscle,adipose
Deep Unsupervised Feature Learning
PP
V
TPR
----- model: 0.833±0.018 AUPRC- - - random: 0.78 AUPRC
SDAEs may discover features of clinically successful targets
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Using DTINetNetwork Diffusion
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DTInet from A network integration approach for drug-target
interaction prediction and computational drug repositioning from
heterogeneous information, Luo et al Nature Communication
(2017)
1. PPI:InWeb: 625K2. MGI3. HPN
– 25K
HSDN: 148K
Integrating Biological Networks for Drug Target Prediction and
Prioritization.Ji X, Freudenberg JM, Agarwal P.Methods Mol Biol.
2019;1903:203-218.
https://www.ncbi.nlm.nih.gov/pubmed/?term=Ji%20X%5bAuthor%5d&cauthor=true&cauthor_uid=30547444https://www.ncbi.nlm.nih.gov/pubmed/?term=Freudenberg%20JM%5bAuthor%5d&cauthor=true&cauthor_uid=30547444https://www.ncbi.nlm.nih.gov/pubmed/?term=Agarwal%20P%5bAuthor%5d&cauthor=true&cauthor_uid=30547444https://www.ncbi.nlm.nih.gov/pubmed/30547444
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Proper negative examples from rewiring bipartite graph reduce
accuracyRepurposing is harder than target identification?
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Integrating Biological Networks for Drug Target Prediction and
Prioritization.Ji X, Freudenberg JM, Agarwal P.Methods Mol Biol.
2019;1903:203-218.
Also see: Predicting clinically promising therapeutic hypotheses
using tensor factorization. Jin Yao, Mark R. Hurle, Matthew R.
Nelson and Pankaj Agarwal. BMC Bioinformatics (2019) 20:69.
https://www.ncbi.nlm.nih.gov/pubmed/?term=Ji%20X%5bAuthor%5d&cauthor=true&cauthor_uid=30547444https://www.ncbi.nlm.nih.gov/pubmed/?term=Freudenberg%20JM%5bAuthor%5d&cauthor=true&cauthor_uid=30547444https://www.ncbi.nlm.nih.gov/pubmed/?term=Agarwal%20P%5bAuthor%5d&cauthor=true&cauthor_uid=30547444https://www.ncbi.nlm.nih.gov/pubmed/30547444https://doi.org/10.1186/s12859-019-2664-1https://doi.org/10.1186/s12859-019-2664-1https://doi.org/10.1186/s12859-019-2664-1
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10 years go by and you have a thriving clinical portfolio
Can you expand the indications and help more patients?
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Systematic Drug Repurposing Mandate: Systematically Identify New
Indications for Active and Terminated Assets
Primarily evaluated internal compounds, but used others as
comparators
Considerations
1. What is the Goal? End Point: PoC, Approval, Reimbursed,
Accolades?2. Decision-making and budget3. Alignment of
disease/phenotype with company strategy4. Quality, IP, targets of
assets 5. Validation strategy and critical path6. Evidence
connecting asset/target to disease: Key computational focus
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Prescriptions(Adverse Events, EHRs,
Social Media)
Inte
rnal
Dat
abas
es
Targ
et
Iden
tific
atio
n
Genetic Targets
Animal Models(Safety, Efficacy, Markers)Phenotypic Assays
(Screens, Expression)
Clinical Trials(Safety, Efficacy, Markers)
Scie
ntifi
c Li
tera
ture
Hypotheses Sources Hurle, .. Agarwal (2013) Clin Pharm Therap 93
(4), 335-341Fast
Follower
Novelty
Launch
Clinical
Pre-Clinical
http://www.nature.com/clpt/journal/v93/n4/full/clpt20131a.htmlhttp://www.nature.com/clpt/journal/v93/n4/full/clpt20131a.html
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Repurposing from Genetics (GWAS)
Genome Wide Association Studies
(GWAS)
Industry pipeline155 GWAS genes targeted by drugs/biologics
actively pursued by
the industry (2.5X expected)
GWAS trait similar to drug
indication?
Increased confidence in the indication (63 targets)
(ex., PPARG)
Potential drug repositioning opportunities (92 targets)
No
Yes
Functional Annotations
ENCODE (Nature, Sep 2012)
FANTOM5 (Nature, Mar, 2014)
New Functional Annotations
Jhamb D, Magid-Slav M, Hurle MR, Agarwal P. Pathway analysis of
GWAS loci identifies novel drug targets and repurposing
opportunities. Drug Discov Today. 2019.
Sanseau P, Agarwal P, ... Cardon L, Mooser V. Nature
Biotechnology 33,317-20 (2012)
https://www.ncbi.nlm.nih.gov/pubmed/?term=Jhamb%20D%5BAuthor%5D&cauthor=true&cauthor_uid=30935985https://www.ncbi.nlm.nih.gov/pubmed/?term=Magid-Slav%20M%5BAuthor%5D&cauthor=true&cauthor_uid=30935985https://www.ncbi.nlm.nih.gov/pubmed/?term=Hurle%20MR%5BAuthor%5D&cauthor=true&cauthor_uid=30935985https://www.ncbi.nlm.nih.gov/pubmed/?term=Agarwal%20P%5BAuthor%5D&cauthor=true&cauthor_uid=30935985
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External and internal expression dataConnectivity Map:
Drug-Disease mapping
● Numerous tools on Lincsproject.org
● Lamb, …, Golub: Science. 2006 Sep 29;313(5795):1929-35. The
Connectivity Map: using gene-expression signatures to connect small
molecules, genes, and disease.
● Profiled entire preclinical and clinical pipeline (400+
compounds) in 6+ cellular systems
Systematic evaluation of connectivity map for disease
indications. Jie Cheng, Lun Yang, Vinod Kumar & Pankaj Agarwal.
Genome Medicine 6, 95 (2014)
http://www.genometry.com/open-innovation/screening.png?attredirects=0
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Discovered using Connectivity MapsEH: EPHX2 repurposing for
IBD
Reisdorf, Xie,.., Agarwal (2019). Preclinical evaluation of
EPHX2 inhibition as a novel treatment for inflammatory bowel
disease. PLoS One.
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EPHX2 experimental data
Reisdorf, Xie,.., Agarwal (2019). Preclinical evaluation of
EPHX2 inhibition as a novel treatment for inflammatory bowel
disease. PLoS One.
Mouse DSS-induced colitis model Ex-vivo human samples
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SyDR Repurposing Examples
1. EPHX2 for IBD & wound healing (published)2. BET for NASH
& Liver Fibrosis (published)3. Immune: RA, Psoriasis, Atopic
Dermatitis, UC, Crohn’s, IBD4. Neuro: ALS, MS5. Respiratory:
Asthma, COPD6. Metabolic: HF7. Cancer
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Model for Data-Driven “Public” Drug Discovery
Reisdorf, Chhugani, Sanseau, Agarwal: Expert Opinions in Drug
Discovery, 2017 May..
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Learnings1. Fantastic Opportunity: we were making medicines!2.
People: Diverse Team & Decision-Making3. Data & Computation
Pedigree
a. Be computationally humble, open, agnostic4. Faster
experimental-computational cycle5. Always test multiple
hypotheses
a. Avoid experimental results dumpster diving -- Independent
statisticianb. Do not to be hard on yourself -- Internal
comparators
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Conclusions1. Build and Optimize Portfolios through target
selection and indication
expansion 2. AI/ML promising, but easy to make mistakes: limited
# of training examples,
unbalanced sets, and domain of applicability3. Happy to
brainstorm and collaborate
a. Open to scientific & business ideasb. Please find me here
or [email protected]
mailto:[email protected]
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Conclusions1. Build and Optimize Portfolios through target
selection and indication
expansion 2. AI/ML promising, but easy to make mistakes: limited
# of training examples,
unbalanced sets, and domain of applicability3. Happy to
brainstorm and collaborate
a. Open to scientific & business ideasb. Please find me here
or [email protected]
10,000+ disease ÷ 10 new treatments/year
= 1,000 years
mailto:[email protected]
