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C-Path Data Integration and Analytics Approaches

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C-Path Data Integration and Analytics Approaches C-Path’s Data Science Transforming data into actionable knowledge for drug development Klaus Romero MD MS FCP Director of Clinical Pharmacology and Quantitative Medicine The Critical Path Institute Impact on Regulatory Science ‐ 15 global, pre-competitive, public-private partnerships with ‐ Participation from industry, academia, advocacy groups, and regulators 2 How C-Path Works: A Public-Private Partnership • Act as a trusted, neutral third party • Convene scientific consortia of industry, academia, and government for sharing of data/expertise ✓ The best science ✓ Active consensus building ✓ The broadest experience ✓ Shared risk and costs • Enable iterative FDA/EMA/PMDA participation in developing new methods to assess the safety and efficacy of medical products Official regulatory endorsement of novel methodologies and drug development tools 3 What is Model-Informed Drug Development? ▪ Development and application of pharmaco-statistical models of drug efficacy and safety from preclinical and clinical data to improve drug development knowledge management and decision-making1 ▪ Quantitative framework for prediction and extrapolation, centered on knowledge and inference generated from integrated models of compound-, mechanism-, and disease-level data and aimed at improving the quality, efficiency and cost effectiveness of decision making2 4 1 Lalonde 2007 [PMID 17522597] | 2 Marshall 2016 [PMID 27069774] Critical questions for trial design • How many patients should be recruited to properly power the trial? • What should be the inclusion criteria? • Can the control arm be optimized? • What types of progression rates are expected for different subpopulations? • What measures of progression are most adequate, at which stages of the disease continuum? • How long should the trial duration be? • How often should I assess? • What is the time-varying probability of dropouts, and what are their predictors? How should one go about providing sound quantitative answers to these questions?5 Answer 1: Quantifying variability Quantifying multiple sources of variability simultaneously within the patient population reduces overall unexplained variability patient patient patient feature 1 feature 3 feature 1 patient feature 2 Unexplained Unexplained Unexplained variability in variability in variability in patients with patients with patients with rare diseases rare diseases rare diseases Result: The ability to predict more accurate progression rates for heterogeneous subpopulations of patients in clinical trials 6 Answer 2: Multiple data sources Understanding the ‘universe’ of a given disease’s heterogeneity patient patient feature 1 feature 1 Unexplained Unexplained variability in variability in patient patient feature 2 feature 2 patients with ALL patients rare diseases with rare from a single diseases study Result: The ability to more accurately account for the heterogeneity in rare diseases and avoid biased conclusions on few data sources 7 Answer 3: Drug-trial-disease modeling Disease Disease progression and Longitudinal characterization model observational, registries, RWD & clinical trial data Clinical Trial Drug Simulator Trial Drug effects model / Placebo effects model, Disease modifying dropout model effect RWD & clinical trial data 8 Putting it altogether • Start with an understanding of what sponsors can practically use to design clinical trials, and reverse engineer 푆 푆 푡 = 0 1/훽 훽 훽 −훽푟푡 푆0 + 1 − 푆0 푒 푐표푛푡 휃푝표푤푒푟 푇푉푃 = 휃 푖 휃푐푎푡푖 푖 푖 푟푒푓 푐표푒푓푓 푓 푆; 훼,훽 훤 훼 + 훽 = ∙ 푆 훼−1 ∙ 1 − 푆 훽−1 훤 훼 + 훤 훽 Execution 9 Clinical Data Shared with C-Path Clinical data: 146 studies: 77,054 subjects 90000 80000 70000 TTC Transplant Therapeutics Consortium PRO Patient Reporting Outcomes Consortium 60000 Huntington's Disease Friedreich’s Ataxia Database 50000 Fredric's Ataxia Type 1 Diabetes Subjects 40000 Duchenne Muscular Dystrophy Healthy Kidney Study 30000 Polycystic kidney disease Multiple sclerosis 20000 Tuberculosis Parkinson's disease 10000 Alzheimer's disease 0 Jun 2014 Jan 2016 Jun 2017 Jan 2019 Jan 2015 Jun 2015 Jun 2016 Jan 2017 Jan 2018 Jun 2018 May 2019 Nonclinical: 179 studies; 11775 subjects. ReSeqTB: 9215 Individual Isolates 10 RDCA-DAP: A resource for the future of drug development in rare diseases Actionable rare disease drug development solutions 11 RDCA-DAP: A resource for the future of drug development in rare diseases Interface level I: Data interrogator to help orient researchers Interface level II: Data analysis subset generator Interface level III: Advanced data analytics workbench 12 Clinical Study Simulations Evaluate different design options Drug-Trial-Diosease Simulated results Models 푑퐹푉퐶 Trial optimization = 푘푖푛 × 1 + 퐴푔푒퐼푛 − 푑퐴푔푒 through 1 + 퐴푔푒푂푢푡 × 퐹푉퐶 × 푘표푢푡 simulations exp(푙표푔푖푡01) 푃01 = (1 + exp(푙표푔푖푡01)) Statistical Analysis Study Execution •X dose •N •Frequency of observations Design selection •Inclusion/exclusion criteria 13 Disease Progression Model Input Modeling Output 14 Disease Progression Model Input Modeling Output Patient-level data 15 Disease Progression Model Input Modeling Output Patient-level Baseline data severity Age Sex Demographics Genetics Medications Baseline biomarkers Dropouts Longitudinal biomarkers Longitudinal endpoints 16 Disease Progression Model Input Modeling Output Baseline Clinical severity Understanding studies Age Sex of disease worsening Demographics Genetics Trajectory Rate Baseline Medications biomarkers Predictors Web Clinical Dropout Longitudinal biomarkers Trial Longitudinal Simulator endpoints 17 Disease Progression Model Input Modeling Output TRANSFORMATION DATA KNOWLEDGE 18 Disease Progression Model Input Modeling Output Use TRANSFORMATION OPTIMIZE Trial Design DATA KNOWLEDGE 19 C-Path’s impact in MIDD ▪ Survival model to predict T1D diagnosis, based on islet AA positivity. ▪ A model that changes the landscape for RCTs for T1D prevention. 20 C-Path’s impact in MIDD ▪ Multiple endpoints over time in Duchenne Muscular Dystrophy (DMD). Speed Speed Score Age Age Age Score Speed Volume 21 Age Age Age Preliminary findings from the FA database Biphasic pattern 25ft walk velocity walk velocity 25fttest Years of Age 22 So… Models, trials and endpoints? • Quantitatively understanding sources of variability, multiple measures and markers across a disease continuum can streamline the pathway towards regulatory acceptance of quantitative solutions that can inform clinical trial design questions. 23 AD CTS: n=50 24 AD CTS: n=50, with genetic enrichment 25 AD CTS: n=50, with genetic enrichment, and baseline severity characterization 26 So… Models, trials and biomarkers? • It’s all about the sources of variability • Unless dealing with safety or diagnosis, biomarkers are either: • Covariates in a model • One of many endpoints in a model • Quantitatively understanding disease progression helps improve the understanding of biomarkers and other relevant sources of variability, and can streamline the pathway towards regulatory acceptance of quantitative solutions to improve clinical trial design efficiency 27 RDCA-DAP: A resource for the future of drug development in rare diseases 28 29 Thank you! Rare Disease Cures Accelerator Platform Reference architecture with key components/services/tools User authentication service User accounts/Roles User Portal to access functionalities Admin Access management Search and data provisioning Self Service Analytics Engine Sharing and Collaboration space Modeling and Simulation Platform Container config library Data Catalog, Models, Metadata Ontology Management Results library Compute governance Data Modeling Service Analysis Data Sets/Data Marts Cloud/Elastic Compute infrastructure Ontology Database Data Preparation/Presentation Audit Audit tracking/Versioning Workflow schedulers and engine Workflow Data Staging/Datarepositories Data Data Ingestion/Integration Data Sources NORD db Patient Registries Pharma X Clinical Studies Academic etc. 31 Rare Disease Cures Accelerator Platform Reference architecture with key components/services/tools: Some proposed examples Okta, LDAP User authentication service IAM, Active Dir User accounts/Roles User Portal to access functionalities Admin Access management Search and data provisioning Self Service Analytics Engine Sharing and Collaboration space Modeling and Simulation Platform Elastic search Rshiny, Tableau.. Wiki, Sharepoint, Slack Rstudio/Jupyter notebooks Matlab, nonmem, R/Python, simcyp Kubernetes, docker Git WDL wiki CKAN S3, Redshift, RDS Container config library Protege Data Catalog, Models, Metadata Ontology Management Results library Turbot, cyclecloud Compute governance Talend AWS, Azure.. Data Modeling Service Analysis Data Sets/Data Marts Cloud/Elastic Compute infrastructure Ontology Database Data Preparation/Presentation Audit Audit tracking/Versioning Workflow schedulers and engine Workflow Data Staging/Datarepositories Data Data Ingestion/Integration Data Sources NORD db Patient Registries Pharma X Clinical Studies Academic etc. 32.
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