Webinar: Knowledge-based approaches to decreasing clinical attrition rates

Transcription

1 Webinar: Knowledge-based approaches to decreasing clinical attrition rates Speakers: Dr. Richard K. Harrison Gavin Coney Teresa Fishburne May 2018

2 2 Clarivate Analytics is the global leader in providing trusted insights and analytics to accelerate the pace of innovation Xtalks Partner for this Event Delivering intelligence for Life Science professionals in discovery, preclinical, clinical, commercialization and generics Powering life science data and trusted content with analytics Clarivate Professional Services offer unrivalled data science expertise, evidence-based consulting and independent advice across the pharmaceutical R&D value chain

3 3 Clarivate Analytics speakers Dr. Richard K. Harrison Chief Scientific Officer Gavin Coney Head of Clinical Products Teresa Fishburne Head of Clinical & Regulatory Professional Services Over 30 years of experience in the life sciences industry Career has focused on all aspects of pre-clinical drug discovery Has held positions of increasing responsibility at Aventis, Merck, DuPont and Wyeth Pharmaceuticals Supports decision making by professionals within Life Science organizations who are interested in gaining intelligence relating to: Clinical Development Clinical Operations Competitive Intelligence Has worked within informatics for 18 years and within the Life Sciences for the last 8 years Over 20 years of industry experience in Clinical Operations, Regulatory Affairs and Quality management Manages a team of clinical and regulatory professionals that create high-value solutions to address customers ever-evolving needs for strategic intelligence.

4 Industry Overview Dr. Richard K Harrison

5 Probability of success to market 5 What is the current rate of clinical trial attrition? 100% 90% 80% 91% 70% 60% 65% 50% 40% 30% 20% 17% 10% 5% 7% 0% Animal model to market Phase I to market Phase II to market Phase III to market Submission to market According to the Centers for Medical Research (CMR) the probability of success moving from Phase 1 to market is less than 10% over all therapy areas Ref: CMR Factbook, 2016

6 6 What are the main causes of attrition? Reason for failure Percentage failure by therapeutic area 6% 13% 15% 6% 32% 7% 52% 7% 24% 3% 5% 13% 17% Efficacy Operational Safety Strategy Commercial Efficacy is the reason for failure in more than half of Phase II and Phase III trials Failure is across all therapy areas with Oncology being the greatest Nature Reviews Drug Discovery 15, (2016) Oncology Musculoskeletal Cardiovascular Metabolic Central nervous system Infectious disease Alimentary Other

7 7 What are the main causes of attrition? According to a study by Astra Zeneca 40% of their projects failed in clinical trials because no clear link was made between the target and the disease An additional 29% failed because the compound chosen did not have the correct physical properties or did not reach the target tissue Nature Reviews Drug Discovery (2014)

8 8 Clinical failure rates and reasons for failure According to the Centers for Medical Research (CMR) in the year 2015 the probability of success moving from Phase 1 to market is less than 10% over all therapy areas Efficacy is the reason for failure in more than half of Phase II and Phase III trials According to a study by Astra Zeneca 40% of their projects failed in clinical trials because no clear link was made between the target and the disease An additional 29% failed because the compound chosen did not have the correct physical properties or did not reach the target tissue Nature Reviews Drug Discovery 15, (2016)

9 9 Knowledge based approaches to decrease attrition Insights into trial endpoint success Increased use of biomarkers to decrease attrition Correlation to trial success Optimum number of biomarkers The impact of protocol amendments on trial success Impact budget planning, Impact on patient enrollment Impact on cycle times. Incorporating strategies to reduce the number of amendments When are amendments required Making amendments more effective. Source: Clarivate Analytics, Cortellis Clinical Trials Intelligence

10 Insights Into Trial Endpoint Success Gavin Coney

11 11 Trial protocol design: rising data / intelligence challenge Granularity of disease understanding: more datapoints and greater specificity Increasing data collection (Datapoints collected per patient visit) Increased data volumes from multiple cohorts Phase 1 Phase 2 Phase Medidata: Analysis of hosted trial data Rising volumes of published results Real World Evidence Digital Health Cap Gemini, Healthcare survey

12 12 Dataset: Granular trial design components indexed for every trial Trial / Intervention Trial milestones Trial Protocol Insights Patient Enrollment Condition Phase Recruitment Status Interventions Combination? Action Class Intervention Category Trial Design Active Control Start Date Primary Endpoint Completion Date Enrolment End Date End Date Patient Segment Biomarker Biomarker Type Biomarker Role Age Race Inclusion Criteria Exclusion Criteria Endpoints Endpoint Types Endpoints Met? Results Adverse Events Sponsor / Collaborator Commercially Relevant? Site Name Contact Name City State Country Region Enrolment Count Enrolment Rate Over 300,000 Trials Focus on drugs, biomarkers and devices All therapy areas; Global coverage Source: Cortellis Clinical Trials Intelligence, Clarivate Analytics, April 2018

13 % of Trials Applying Biomarkers By Role % Growth In Application Of Biomarker Roles Over 5 years 13 Rising use of biomarkers Toxic Biomarker Role marker: The biomarker indicates if a disease already exists (diagnostic biomarker), or how such a disease may develop in an individual case regardless of the type of treatment (prognostic biomarker). effect marker: The biomarker gives an indication of the probable effect of treatment on the patient Toxic effect marker: The biomarker indicates a treatment-related adverse reaction Source: Biomarker Roles Within Clinical Trials. An Analysis Of Clinical Trials from and Clarivate Analytics. Coney, Gavin. Clarivate Analytics, Cortellis Clinical Trials Intelligence.

14 Number of Trials 14 Growth in trial volume by phase Phase 2 Phase 3 End Year All trials within the time period, irrespective of whether the trial endpoints were met Source: Cortellis Clinical Trials Intelligence, March 2018

15 Number of Trials 15 Trial endpoint success: Reporting bias Endpoints Met Yes Endpoints Achieved No Phase 1 4 4,550 trials with known outcomes with start date 2005 or later Trial endpoint success determined by explicit statement made by sponsor referencing the endpoint Source: Cortellis Clinical Trials Intelligence, March 2018

16 Number of Trials 16 Are phase II trials failing quietly? Focus on phase 3 successes Phase 2 Phase 3 Endpoints Met Phase 2 Phase / End Year Phase 3 Commercially Relevant: Is the primary intervention being studied owned by the trial sponsor? Source: Cortellis Clinical Trials Intelligence, March 2018

17 Proportion Of Trials 17 Biomarkers and trial endpoint success Endpoints Met Yes Are Biomarkers Used? No Is at least one biomarker applied within the trial design. Could be biomarker for efficacy, toxicity or disease Source: Cortellis Clinical Trials Intelligence, March 2018

18 Phase 18 Number of biomarkers vs endpoint success by phase Phase 4 Endpoints Met Phase 3 Phase 2 Phase Average number of biomarkers All trials with known outcome; Segmented By Phase and then trial endpoint status Mean of Number of Biomarkers within each of the 8 groups Source: Cortellis Clinical Trials Intelligence, March 2018

19 % of category that did reach endpoint Number Of Trials Endpoint Not Met Number of Trials Endpoint Met 20 Segmenting biomarker role and endpoint success Toxic Toxic Toxic Toxic Toxic Toxic Toxic Toxic Toxic Biomarker Role Combinations All trials categorized according to the combination of biomarkers Ranked by descending number of trials that were reported to have reached their endpoint (green = yes; red = no) Yellow bar displays the % of trials in each category that were reported as successful Source: Clarivate Analytics, Cortellis Clinical Trials Intelligence

20 Number of Trials Endpoint Not Met Number of Trials Endpoint Met 21 Endpoint success and application of biomarker roles % of Trials = Yes % of Trials using this combination that were successful Endpoints Met Endpoints Not Met Endpoints Met % of endpoints met in category Toxic Toxic Toxic Toxic Toxic Toxic Toxic Toxic Toxic Toxic=Yes All None =Yes Biomarker Role Combinations Rank order now selected as % of trials in category that were reported as reaching their endpoint Source: Clarivate Analytics, Cortellis Clinical Trials Intelligence

21 22 Summary A biomarker strategy has been seen to positively impact likelihood of meeting trial endpoint That effect is not uniform:- Positive Phase 2-4; Negative Phase 1 Toxic markers strongly associated markers not strongly correlated Need for the best intelligence to inform protocol design

22 Impact and Strategies Relating To Trial Amendments Teresa Fishburne

23 Median Duration (in Days) 24 Amendments 600 Internal Protocol Review - Protocol synopsis developed to Protocol approved internally Protocol Approved to End of Enrolment Protocol approved internally to last subject enrolled days days days 62 days 0 2 or more amendments 2 or more amendments no or 1 amendment no or 1 amendment Source: CMR Global Clinical Performance Metrics Programme. CMR International, a Clarivate Analytics business Source: Getz, et al Tufts CSDD

24 Budget impact and key facts 25 $60,000,000 $50,000,000 $50M $40,000,000 $35M $30,000,000 Phase II $20,000,000 $10,000,000 $12M $20M $13.2M $14.4M Phase III $0 Average Cost of Trial Average Cost of Trial After 1 Amendment Average Cost of Trial After 2 Amendments $20 Billion annual spend in direct and indirect costs 45% of all amendments are avoidable 2/3 Phase III trials > 1 global amendment 74% of all Phase II trials > 1 global amendment Rare diseases amendments > non-rare Source: Source: CMR Global Clinical Performance Metrics Programme. CMR International, a Clarivate Analytics business Source: Getz, et al Tufts CSDD

25 Enrollment Enrollment and cycle times 26 Cycle time Trial Budget 70% 60% 50% Proportion of protocol amendments by timing interval 60% 40% 30% 30% Amendments 20% 10% 0% 10% Start-up Enrolment Treatment Duration Strategies Biomarkers Inclusion / Exclusion Criteria Data are shown for Phase Ip, Phase II and Phase III studies that completed enrolment (Last Patient Enrolled milestone) between 2011 and 2015 and had one or more protocol amendment. This analysis includes ongoing and terminated studies. Source: CMR Global Clinical Performance Metrics Programme. CMR International, a Clarivate Analytics business Source: Getz, et al Tufts CSDD

26 27 Strategy to reduce amendments use of biomarkers Probability of Success With and Without Biomarkers 80% 70% 60% 50% 40% 30% Without Biomarkers With Selection Biomarkers 20% 10% 0% Ph 1 - Ph 2 Ph 2 - Ph 3 Ph 3 - NDA/BLA Ph 1- Approval Rare disease programs and programs that utilized selection biomarkers had higher success rates at each phase of development vs. the overall dataset. A three-fold higher likelihood of approval from Phase 1 was calculated for programs that utilized selection biomarkers (25.9%, n=512) vs. programs that did not (8.4%, n=9,012). Bio article concluded that the enrichment of patient enrollment at the molecular level is a more successful strategy than heterogeneous enrollment. ~150 biomarkers are included on FDA approved drug labels. Such a strategy is associated with improved approval success and reduced approval timelines Source: Clarivate Analytics, Cortellis Clinical Trials Intelligence Source: Hay et al; Clinical development success rates for investigational drugs. Nature Biotechnology (2014) Source: BIO, Biomedtracker (2016)

27 28 Strategy to reduce amendments Inclusion/exclusion criteria Newly emerging medical knowledge Regular violations of entry criteria Low recruitment rates Build off approved/launched competing drugs with like MOA/indications Build off internal Phase 1/2 Literature search New or novel criteria Incorporating Biomarkers: By roles ( effect, disease marker, toxic effect) By type (genomic, proteomic, physiological, biochemical, cellular, structural, anthropormorphic) Biomarkers to address responders vs nonresponders Source: Clarivate Analytics, Cortellis Clinical Trials Intelligence 28

28 29 Overall strategy to reduce amendments Key points Protocol Review Biomarkers Inclusion / Exclusion Criteria 19 additional days of internal review = 0 or 1 amendments Longer internal review time = shorter trials Incorporate more internal review approaches Bundle non-urgent amendments, where applicable programs that utilized selection biomarkers had higher success rates at each phase of development vs. the overall dataset. enrollment at the molecular level is a more successful strategy than heterogeneous enrollment. ~150 biomarkers are included on FDA approved drug labels. Using internal data to build criteria Build off approved/launched competing drugs with like MOA/indications Assess feasibility / appropriateness Use of predictive biomarkers to identify responders vs nonresponders

29 30 Questions Cortellis images, CTI, CCI To learn more go to: Clarivate.com/Cortellis

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