computational drug discovery: present and future jonny wray€¦ · digital (and other) biomarkers...
TRANSCRIPT
Computational Drug Discovery: Present and
Future
Jonny Wray Head of Discovery Informatics
Motivations
Neurodegeneration and Dementia Growing and Aging Population
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Source: World Alzheimer Report 2015: The Global Impact of Dementia
Drug Discovery and Development
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Process Overview
Drug Discovery Process Analysis
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An Industry Ripe for Innovation
Eroom’s law
Source: Cook et. al., Nature Reviews Drug Discovery 13, 419-431 (2014) Source: DiMasi et. al., Journal of Health Economics 47, 20-33 (2016)
Industry productivity is decreasing Costs are massive and increasing Reasons for failures
Science Fiction or Future of Medicine
Instant diagnosis and instant treatment Ongoing diagnosis and ongoing treatment • Catch early then cure or stop progression
• Early diagnosis is best indicator of cancer survival • Neurodegeneration – treat early as symptomatic disease can’t be reversed
Requirements • Indicators of pre-symptomatic disease
• Ideally based on cheap and simple tests • Continuous screening of population, or at risk population.
• Issues with false positives • Determination of what treatment will work for the individual • Continued monitoring for recurrence
• Centralized vs distributed diagnosis and treatment
• Tests performed routinely by patient without doctor involvement • Treatment plans formulated for each individual • Drugs synthesized locally and targeted at specific individual • Localized biopsy, analysis, and drug delivery by nano-robotics 5
How Drugs Work
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Drugs Need to Alter Phenotype
DNA RNA Protein Protein-Protein Interaction
Pathway Pathway-Pathway Interaction
Network Networks of Networks
Higher Order Networks
Trait
GENOTYPE PHENOTYPE PROTEOME
… to change this Intervening here…
• Phenotype is an emergent property of cellular networks • Both drug discovery and molecular biomarker discovery are multilevel problems • Laboratory techniques are:
• Inherently reductionist • Do not bridge levels well • Hampered by combinatorics
• Computational approaches can address these issues
Computational Approaches in Drug Discovery
Preclinical Discovery • Target identification
• Statistical analysis of gene mutations to disease relationships
• Computational chemistry • Predict docking of compounds to protein targets
• Chemical optimization
• Change chemical structure to optimize multiple parameters
• Predictive toxicology • Lhasa is example of UK company providing such tools
• Predictive ADME
• Systems pharmacology: compartmental modelling
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Future • Computational chemistry mainly limited by availability of data (e.g. good
crystal structures)
• Predictive ADME also mainly limited by experimental data providing model parameters
• Biology approaches are mainly statistical, not mechanistic
• Computational modelling enables mechanistic insight, bridging the levels between molecular and phenotypic
• Future trends will be away from population based towards predictions on the individual (personalized medicine).
e-Therapeutics: Network-Driven Drug Discovery (NDD)
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Molecular Networks are Surrogates of Disease Phenotype
Dysregulated network module identification Source: Schadt, E., et al. Nature Reviews Drug Discovery (2009)
Pathological interaction identification in Huntington’s disease Source: Tourette, C., et al. Journal Biological Chemistry (2014)
Network Analysis
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Error vs Attack Tolerance: Biological Networks are Robust
• Albert, R., H. Jeong, and A. L. Barabasi. 2000. “Error and Attack Tolerance of Complex Networks.” Nature 406 (6794): 378–82.
𝐼𝑚𝑝𝑎𝑐𝑡 = ∆ 𝐴𝑣𝑔. 𝑆ℎ𝑜𝑟𝑡𝑒𝑠𝑡 𝑃𝑎𝑡ℎ
Attack: Targeted by Degree Error: Targeted Randomly
vs
Network-Driven Drug Discovery
- Statistical network analysis - Noisy and incomplete biological data to constrain network construction - FANTASI and POETS: allowing us to think of analysis not currently possible due to computational constraints
- Genes to Proteins - Current cellular interaction data is measured at a gene level (22k in human): massive simplification - Protein level interaction data is starting to be produced (100k – 1M) - Other molecular players
- Dynamics - All analysis is performed on structure of static networks - Signal transduction (e.g. as a result of a drug) is a dynamic process operating on those networks
- Traditional systems biology: coupled differential equations - Under constrained with experimental data
- Networks rewire as a result of perturbations - Continuously with environmental triggers – e.g. cancer development - Drug perturbations – development of drug resistance and possible temporal combinations
- Networks of networks - Intracellular interactions, and higher system interactions
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Limitations and Computational Issues
Future of Personalized Medicine
Cancer biopsy -> Molecular Profiling -> Specific treatment Breast cancer -> HER2+ve -> Perjeta or Herceptin -> HER2-ve HR+ve -> Palbociclib or Kisqali …. Issues:
• HER2+ve breast cancer: 20% • Overall response rate to Herceptin treatment1: 26% • Triple negative: 10-15% of all cases: no targeted treatment
• Diagnostic tests are based on expression of 3 proteins • PAM50 test, based on 50 genes: prognostic test only, not for treatment (at the moment)
• Cancer, even in one tissue, is caused by multiple mechanisms, different in each individual • Heterogeneity is a leading cause of failure during discovery • Future
• Everyone (potentially) has a different mechanism uniquely defined by the molecular profile of their cancer cells • Drug response progression will be tracked and treatment changed depending on result of that progression
11 1. Trastuzumab for HER2-Positive Metastatic Breast Cancer: Clinical and Economic Considerations. Jeyakumar and Younis. (2012)
Current State of the Art
Future of Personalized Medicine
Biopsy Markers • Whole genome sequencing based markers. Plus other system wide measurements (not just genes). • Limited tissues available for biopsy. Not (currently) suitable for early detection.
Blood Markers • Early detection. Distributed testing model. • Theranos: blood testing technology requiring very small amounts of blood. Deal with Walgreens to install direct to consumer units in drug stores.
Digital (and other) Biomarkers • Parkinson’s disease: mobile devices and smell.
Development Costs
• Everything becomes an orphan disease: not viable for development with current costs • Drug development productivity will have to rise to enable true advances in personalized medicine
Computational contributions • Inverse problems
• multivariate measurements to infer state of the diseased system. • Therapy predictions
• Given state of diseased system, what drugs will perturb it towards a healthy state? • Temporal combinations: what secondary treatment to use after changes due to initial (e.g. cancer drug resistance)?
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Future Advances
Translatability of Preclinical Models
“Conditions with solid, highly predictive phenotypic animal models (pain, infection, diabetes, etc) would be the lowest hanging fruit. Conditions where the best assays are less predictive (they are noisy, off target, or measure a proxy rather than the condition itself) would be higher up. Conditions where creating the assay required new technology would be higher as well, and conditions with no good assay yet (Alzheimer’s) would be mostly out of reach.” Prediction: preclinical animal models will be complemented (replaced?) by computer models Simulations as preclinical models
• Ability to explain the relationship between neural changes and behavioral symptoms • Simulate the effect of medications on both neural and behavioral processes • Ability to predict and explain the interaction of other biological systems and the brain • Predict and explain early stage biomarkers of disease as well as biomarkers of disease progression
• Acceptance by FDA and other authorities
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