Recent advances in Rational Drug Repositioning & Translational Bioinformatics Shameer Khader, PhD Director of Bioinformatics, Data Science and Precision Medicine Philips Healthcare, Cambridge, MA Adjunct member: Institute of Next Generation Healthcare & Dudley Laboratory Mount Sinai Health System, New York, NY
[email protected] | @kshameer Novel Approaches to Understanding the Mechanisms of the Neuropsychiatric Symptoms in Alzheimer’s and Advancing Therapy Development Workshop Sponsored by the National Institute of Mental Health (NIMH) May 8-9, 2017
Acknowledgements
Design, development and analytics of RepurposeDB is supported by the following NIH grants: R01-DK09824203 and U54-CA189201-02
Public health relevance • Implementing data-driven methods that use real-time clinical variables in a hypothesis-free approach could help us to find new features • Designing predictive and prescriptive models would help to accelerate stratification of patients at risk for improved care • Major implications in quality of healthcare delivery and impact on patient outcomes • Precision medicine is poised to optimize care and reduce cost http://www.nejm.org/doi/pdf/10.1056/NEJMp1401111 http://jama.jamanetwork.com/article.aspx?articleid=1674245 http://www.thelancet.com/journals/langlo/article/PIIS2214-109X%2814%2970356-0/abstract Figures courtesy: The Lancet, JAMA and NEJM
Objective •
To discuss recent advances in translational bioinformatics
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CTSA mission to design, develop and democratize biomedical and healthcare data science •
Rational Drug Repositioning
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EMR-driven phenotyping and data harvesting
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Phenotyping (Computational and algorithmic phenomics)
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Data-driven disease subtyping
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GWAS to PheWAS using EMR-linked biorepositories
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Large-scale mining of disease and therapeutic trajectories Figure courtesy: B. Glicksberg
We live in a world of growing population, new diseases and resurgence of old diseases
Mental, behavioral and neurological combined = 10.4 DALY
Drug development is costly, market-driven and not scalable to address the population or patient-level challenge
Data is cheap, (almost readily) available and reusable
http://www.genome.jp/Fig/stats/db_growth.gif Nat Rev Mol Cell Biol 2009, 10(11):791-803.
Data-driven methods that combine computational and experimental approaches could complement, improve or reduce the cost of drug discovery Patient population that need better therapies
Drug repositioning
Druggable targets EHR-based validation
Molecular profiles
Biological networks
Experimental validation
Improved therapies to patients Families with rare disease
Disease modules
Figure from: Shameer et.al Current Topics in Medicinal Chemistry
Why drug repositioning? Bring therapies to market in approximately half the budget & time required by traditional drug development cycle.
Figure from: Shameer et.al Current Topics in Medicinal Chemistry
Systematic or Target based drug repositioning? How to select a drug repositioning strategy?
Figure from: Shameer et.al Current Topics in Medicinal Chemistry
Icahn School of Medicine at Mount Sinai, New York, USA. +Currently at Philips Healthcare, Cambridge, USA
Introduction
Gene Signature-based Repurposing
Multiple factors such as pharmacogenetics, comorbidities, and side effect tolerance influence patients’ clinical responses to statin therapy. Furthermore, it has been demonstrated that statins are biologically pleiotropic agents which effect a variety of cellular functions in addition to their characteristic effect of HMG-CoA reductase inhibition. Previous work has demonstrated an unexpected pravastatin-paroxetine adverse interaction of increased blood glucose. 1
Rational drug repositioning and validation using RWE from EMR
Gene Signature-based Repurposing The recent Yellow-II trial by Kini et al.2 used a combination of optical coherence tomography (OCT), near infrared spectroscopy (NIRS), and intravascular ultrasound (IVUS) intravascular imaging with network biology to characterize changes in plaque morphology and transcriptomics in response to high-dose rosuvastatin therapy. In this study, 117 differentially expressed genes were detected from baseline to follow up, with a variety of roles including cholesterol metabolism, collagen catabolism, platelet function, and especially the immune system. Here, we explore their potential utility for drug target discovery and drug repurposing.
Drug Repurposing
R
Postive Correlation
Yellow II Gene Expression Gene 1
Original Scale
Negative Correlation
Gene n
Drug Activity
• Drug repurposing is the application of known, previously approved drugs for new disease indications
Negative Correlation with Transcriptomic Signature Positive Correlation with Transcriptomic Signature
• Drug repurposing discovery is generally categorized as: a) Disease-signature based
Y = Xβ + Ζγ + ε • Fixed effects for demographic covariates and drug administration • Random intercept per subject • Lab values evaluated before and after drug administration
Postive Correlation
b) Target based Acta Crystallogr D Biol Crystallogr 63(Pt 1): 80-93 Jan 2007
Results
J Mol Bio 426(4):843852 Feb. 2014
Yellow II Gene Expression Gene 1
• Evaluation of our #2 predicted drug in conjunction with any statin Drug
Imatinib (small molecule)
Negative Correlation
PCSK9 inhibitor (mAb)
Aims Drug Activity
Gene n
Negative Correlation with Transcriptomic Signature
Here, we aimed to use Yellow-II transcriptomic results to: 1. Explore five detected novel genes which have limited biochemical evidence, functional annotations, or previous associations with disease 2. Use transcriptomic results for the discovery of potential drug targets and candidates drugs which may be repurposed for adjuvant statin therapy
Positive Correlation with Transcriptomic Signature
Transcriptome Analysis
Keyword Cluster Cholesterol Metabolism SREBP Lipid Synthesis Control Innate Immune Response SH3 Domain Platelet Activation
Gene Count 7/105 3/17 7/105 6/105 5/97
47,258
31,026
30,452
Discovered Drug
6,354
2,818
2,710
New Drug + Statin
1,801
1,182
1160
Demographics Age (years) Sex (%) Ethnicity (%)
Fold Enrichment 24.1 35.8 2.4 5.5 4.8
Mean Std. Dev. Male Female Caucasian African American Asian Hispanic/Latino Other
All 64.35 12.6 58.25 41.75 16.01 10.61 1.55 13.21 58.63
Statin 64.67 12.35 58.74 41.26 16.11 10.19 1.59 12.88 59.24
New Drug 62.54 14.25 50.48 49.52 15.02 16.75 0.74 20.26 47.23
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Both 62.57 13.67 48.80 51.20 13.20 9.6 0.0 19.20 58.0
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Laboratory Test
• 5 of 117 genes had limited previous functional annotation Protein Observed Experimental Network Relationships3 TMEM135 Co-localization; (18) Genetic interactions; (5) Physical interactions; (1) TMEM51 Co-localization; (19) Physical interactions; (1) SCIMP Co-expression; (24) Physical interactions; (2) KIAA1551 Co-localization; (18) Genetic interactions; (1) Physical interactions; (2) C15orf39 Co-localization; (19) Genetic interactions; (2) Physical interactions; (1)
Prescribed Drug With Lipid Labs With Labs & Demographics
Any statin
Y = Xβ + Ζγ + ε
Physically Interacting Genes3 CDC23 MRPL12 LYN, RGS16 TBC1D4, EXOC1 RPLP1
• Fixed effects for demographic covariates and drug administration • Random intercept per subject • Lab values evaluated before and after drug administration
Total Cholesterol LDL HDL
1. 2. 3.
Triglycerides LDL:HDL Cholesterol:HDL
Laboratory Testet.al Statin Unpublished data Kipp Total Cholesterol
n School of Medicine at Mount Sinai, New York, USA USA. +Currently at Philips Healthcare, Cambridge, USA
Effect of the drug and in conjunction with statin on various CVD biomarkers
ed Repurposing
Results Original Scale
Log Transformed
orrelation with omic Signature
Y = Xβ + Ζγ + ε • Fixed effects for demographic covariates and drug administration • Random intercept per subject • Lab values evaluated before and after drug administration
s
ion with any statin
abs With Labs & Demographics 30,452
Laboratory Test
Drug Effect
β Coefficient P Val
Total Cholesterol LDL
-3.82 mg/dL -3.81 mg/dL
0.0030 0.0004
HDL
+1.87 mg/dL
80% 95,373 drug pairs were enriched for coadministration Drug A
&
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AA individuals had more prescription instances and unique medications compared to all other racial groups
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HL individuals had the fewest prescription instances and unique medications compared to all other groups
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Polypharmacy (4+ simultaneous drug prescriptions) varied according to race (χ2 p
