Epidemiology in the era of digital data. Andrew Roddam September 2017

Transcription

1 Epidemiology in the era of digital data Andrew Roddam September 2017

2 There are four big decisions in making medicines Molecule Target Clinical biological experiment Demonstration of clinical effect and value...and it costs over $1bn and takes more than a decade

3 Broad uses for Real World Evidence Current uses of real world evidence derived from complex and diverse sources Real-World Evidence Generation and Evaluation of Therapeutics: Proceedings of a Workshop: National Academy of Sciences

4 However data depth and linkage are not equal JAMA. 2014;311(24): doi: /jama

5 Growing external pressure from regulators, payers and healthcare providers

6 There are four big decisions in making medicines Molecule Target Clinical biological experiment Demonstration of clinical effect and value

7 Genetic evidence for drug indications more than doubles the success rate Drugs with human genetic information >2x more likely to be successful Those that succeed are more likely to be genetically validated or Failures at each stage are more likely to be those without genetic validation

8 Genome Scan vs Phenome Scan Genome scan (GWAS): Screen of genetic variants for a particular trait of interest Asthma Phenome Scan (PheWAS): Screen of phenotypic associations for a particular variant of interest

9 Real World Data advances our understanding of drug and disease Target Preclinical Clinical Launch Target Function Response to Drugs Use of electronic health and medical record data can address critical disease factors: Characterizing disease Lack of understanding of the biology: target identification and validation, indication selection Identifying factors that contribute to disease progression Identifying patients for clinical trials Predicting drug response Identifying factors associated with drug response

10 There are four big decisions in making medicines Molecule Target Clinical biological experiment Demonstration of clinical effect and value

11 Evidence needed for medicines in today s world From efficacy to effectiveness

12 New approaches were required to demonstrate the effectiveness of medicines in usual care settings Efficacy and safety of medicines typically assessed in randomised clinical trails Need to understand the true value of a medicine in a usual care setting 1 just 3% of asthma patients and 7% Of COPD patients would be considered eligible for a standard Randomised Clinical Trials 2 1 Treweek S & Zwarenstein M. Trials, 10:37 (2009) 2 Herland, K., et al Respir Med 99, (2005)

13 RCT in real world setting conducted pre-approval: COPD Salford Lung Study with Relvar/Breo Patients in primary care Aged 40+ years GP diagnosis of COPD Taking ICS and/or LABA and/or LAMA Exacerbation in last 3 yrs Consented Constant real-time data collection of all interventions/safety monitoring Randomisation visit FF/VI open label 12 months of usual care 3 phone calls Usual treatment End of study visit 2,802 COPD patients 1 electronic medical record system 80 GP practices 130 pharmacies 3,000 people trained as part of study 265 million rows of data Bakerly N et al. Respir Res 2015;16:101

14 Where do the novel/emerging data sources fit? Molecule Target Clinical biological experiment Demonstration of clinical effect and value

15 Google search terms can help infectious disease dynamics and prediction From: Bakker etal, PNAS, June 2016, 113:24:

16 What might a healthcare data footprint look like?

17 Contextualizing patients and drug use through social listening Publically available f data is collected from multiple sources (Facebook, Twitter, Internet chat rooms) System filters out noise, highlights important information, and supplemental data is added (spontaneous data, etc.) Users can interactively explore the information using an easy to use interface that helps facilitate the review process

18 What questions can social media address? Sharing of social data now provides insights into underlying disease pathology and response to treatment Social media data can inform clinical trials as well as early research. Examples of Social Media Applications Pharmacovigilance: Early detection of adverse events Alternate indications: Detection of positive side effects suggesting alternate indications for a compound or a target/pathway Clinical trials: Recruitment, site selection, and listening to patients during the trial (e.g., characterize B:R, new endpoints) Understanding disease: Patient preferences, unmet need, subpopulations, and symptom burden Understanding therapeutics: Patient preferences, response, benefits and side effects, subpopulations, off-label use

19 Listening to Patients via Remote Data Capture Examples of Remote Data Capture Applications Patient reported outcomes: Smartphone apps facilitate patient reported outcomes such as pain direct to providers/clinical trial server according to schedule or ad hoc which can reduce missing data and improve data quality. Biological measurements: Devices can be worn or used by patients outside of a provider visit for continuous and/or real-time measurement, e.g. body fat scale, thermometer, accelerometer, heart rate monitor, pulse oximeter, blood pressure monitor, spirometer. Adherence: Devices and apps can capture when and if a patient took a medicine, e.g. e-inhalers, smart pills, smart dispensers, mobile apps, sensors. Audio/Imaging of patient symptoms: Smartphone apps enable remote images (e.g., rash), video (e.g., how a patient is taking an inhaler), audio (e.g., cough) of patient state direct to providers/clinical trial server.

20 PARADE Study Industry 1 st Apple Research Kit Study Patient Rheumatoid Arthritis Data From Real World Study Walk Test Wrist Test Successfully used Apple ResearchKit and social media to recruit 399 subjects self-consented (within 1 month of launch) Subject retention: 160 completed 1-month 103 completed 2 months 45 completed 3 months Social media and mhealth approaches may provide a means to enroll more diverse populations in the future. mhealth approaches may be of value for exploring the use of sensor technology in patients with mobility disorders.

21 Digitally enabled clinical studies of the future Agnostic to data source, integrated with analytics engine Informed recruitment Digital site selection and patient recruitment Data Integration Real time analytics Data management tools connect and share data Direct from patient Data captured digitally, remotely and reliably. Integrated external data Flexible input and transfer of data from patients and care providers

22 Accelerating the discovery and development of new medicines through capturing the full patient experience in real-time Did the patient take the medicine? Did the markers of the patient s disease improve? Did the patient feel better? What else was impacting the patient s health? Was it the right medicine for the right patient? 22

23 Thank you