Better Portfolio Decisions through Predictive Analytics

Transcription

1 Better Portfolio Decisions through Predictive Analytics Speakers Jamie Munro, Global Practice Leader, Portfolio & Licensing, Clarivate Analytics Karthik Subramanian, Competitive Intelligence & Clinical Product Director, Life Sciences, Clarivate Analytics

2 The funding challenge (illustrative) 2

3 CMR, a Clarivate Analytics business, is a leading benchmarking company that has been working with biopharmaceutical companies for the last quarter of a century to benchmark R&D performance 3 Consortium A pre-competitive consortium working directly with biopharma Working with many well-known biopharma CMR Rich history 25 years working with clients to assess: R&D & Clinical productivity and provide actionable KPIs, data and insights Comprehensive coverage CMR works closely with: 80% of Top 20 & Three-quarters of Top 50 pharmaceutical companies (measured by R&D expenditure)

4 Indexed to 2006 The number of new medicines (NMEs) & Sales Revenues have outpaced R&D Costs R&D expenditure Development times NME output Sales * Year Source: CMR Factbook 2017

5 New drugs address fewer patients 5 US FDA CDER NME approvals Patients covered by approved indications (M) Source: FDA, IPD, Clarivate Analytics analysis

6 Based on new approvals in the US, the industry is focused on the science and targeting unmet needs 6 Approvals Rare indications 33% (9) 41% (17) 47% (21) 41% (9) 39% (18) Enhanced regulatory designation (Fast track, Breakthrough and accelerated) Breakthrough therapy designation 48% (13) 66% (27) 60% (27) 73% (16) 61% (28) 11% (3) 20% (8) 22% (10) 32% (7) 37% (17) First in class 33% (9) 41% (17) 36% (16) 36% (8) 33%(15) Source: FDA, Clarivate Analytics analysis

7 Sales from products approved in last five years as % total pharma sales Many large pharma revenues are driven by older products 7 Sales from products approved in last five years ( ) Established products driving sales Sales from products approved ($M) 80% 70% Off-patent sales as % total sales (2017) 60% 50% 40% Sources: Clarivate Analytics 30% 20% 10% 0%

8 Probability of success to market Typically, less than one in ten new drugs entering clinical trials makes it to market 8 100% 90% 91% 80% 70% 60% 60% 50% 40% 30% 20% 16% 10% 0% 5% 7% First toxicity dose to market First human dose to market First patient dose to market First pivotal dose to market Submission to market Source: CMR Factbook 2017

9 The vast majority of costs are associated with drugs that will never treat patients $3.2 Bn $2.6 Bn $1.9 Bn Cost of failure* Source: CMR *Approximate and illustrative Year

10 10 The largest number of failures occurs in Phase 2 Discontinuation Number of Projects Data extracted from Cortellis Competitive Intelligence from Mar 2015 till 15 Sep 2018 Termination Phase US Market Global Market Phase A Drug Project is defined here as a Drug/Indication/Company/Country combination in the drug development for Cortellis Phase Phase Pre-Registration Source: Cortellis

11 Reasons for Failure 11 Global - Phase 1 Projects - Reasons for Discontinuation Global - Phase 2 Projects - Reasons for Discontinuation Pipeline Prioritization Other Pipeline Prioritization Other Not Reported Not Reported Lack of Activity or Efficacy Lack of Activity or Efficacy Deal Termination/Company Bankruptcy Adverse Events Deal Termination/Company Bankruptcy Adverse Events Global - Phase 3 Projects - Reasons for Discontinuation Global - Pre-Registration Projects - Reasons for Discontinuation Pipeline Prioritization Other Pipeline Prioritization Other Not Reported Not Reported Lack of Activity or Efficacy Lack of Activity or Efficacy Deal Termination/Company Bankruptcy Adverse Events Deal Termination/Company Bankruptcy Adverse Events Source: Cortellis

12 12 Many companies are already employing a range of initiatives to improve likelihood of success and predicting likelihood of success Examples include: Learning reviews Statistical and qualitative assessments Team assessments versus central assessments But there is also the need for predictive analytics which come into their own at the portfolio level

13 Let s recap the Clarivate Analytics Maturity Curve

14 Competitive Intelligence maturity curve 14 Market dynamics New Innovation Predictive Tools CI Staff Information Tactical Strategic Visionary Budget Capable Reactive Emerging Ad Hoc

15 Predictive Modeling Harnessing machine learning and mathematical analysis

16 * US / International Patent Pending 16

17 Benchmarks only tell you the history 17 Each of the 15 qualitative traits have different weights applied based on the stage of the drug project. No two drugs carry these traits with the same weights Characteristics of the development program In which therapeutic area does this indication reside? Does the drug have a special regulatory designation (Fast Track, etc.)? Are biomarkers in use to define the patient population? Characteristics of the drug Is drug a reformulation or combination of approved drugs? Do we know the Reason for past discontinuation? Characteristics of the development sponsor How big is the development sponsor (L/M/S)? And improving.

18 18 Benchmarks only tell you the history cont. Clarivate predictive model doesn t just look at benchmarks and qualitative traits, but also adjusts itself to future milestones based on their outcome for each of the drug programs ANDA Approval ANDA Filing Availability For Outlicensing Expected Approval Expected Clinical Trial Outcome Announcements Expected Clinical Trial Start Date Expected Clinical Trial End Date IND Filing Expected NDA/sNDA/BLA/sBLA Filing Interim/Topline/Initial Trial Results Reported PDUFA/BsUFA Date Predicted Launch Date Regulatory Committee Meetings Regulatory Response Safety/Black Box Warnings Trial Completion/Final Data Reported

19 Each drug project is treated unique and hence Clarivate predictive model applies predictions individually based on various factors

20 Accuracy of the predictive modeling

21 21 Available drug development pipeline, 2000-present Training set used to develop the algorithm: Test set held back, used to validate the model: 2015-present

22 22 Accuracy of the Predictions Phase No. of Phase Transitions (Success/Failure) Prediction Rates Ph 1 -> Ph % Ph 2 -> Ph % Ph 3 -> Pre-Reg % Pre-Reg -> Reg % Applying the model on each pipeline history taken from Cortellis Competitive Intelligence from Mar 2015 till 15 Sep 2018 Accuracy is defined by success when the prediction was 50% and above on a successful phase transition

23 23 Precision of the model Naïve Model (Flipping a coin): Ph 1 to Ph 2: 50% Ph 2 to Ph 3: 50% Ph 3 to Pre-Reg: 50% Pre-Reg to Reg: 50% So when a drug project successfully transitions from Ph1 to Ph2, its 50% less precise When it fails, its again 50% less precise Defined Wisdom (Flipping a weighted coin): Success Rates from CMR: : Ph 1 to Ph 2: 61% Ph 2 to Ph 3: 36% Ph 3 to Pre-Reg: 75% Pre-Reg to Reg: 88% So when a drug project successfully transitions from Ph2 to Ph3, its 64% less precise When it fails, its 36% less precise Now, lets calculate precision % (delta) for each pipeline history taken from Cortellis Competitive Intelligence from Mar 2015 till 15 Sep 2018 using Naïve model, Defined Wisdom and Clarivate predictive model (prediction for each transition) Lesser the delta, more precise the model is

24 Precision of the model 24 Clarivate predictive model outperforms Defined Wisdom and Naïve models Clarivate Predictive Model Defined Wisdom Naïve Model Delta % Defined wisdom is a fifth better than naïve model Predictive model is a quarter better than Defined wisdom

25 25 Accuracy and Precision of the model Between Mar 2015 and Sep 2018, 92% of the drugs for which the model predicted a 90% or greater probability of successful phase transition did indeed successfully transition 88% of all Phase 1 drugs for which the model predicted an 80% or greater probability of entering Phase 2 did successfully transition 77% of all Phase 2 drugs for which the model predicted a 70% or greater probability of entering Phase 3 did in fact successfully transition More than 3/4 of the drugs with a >=70% predicted chance of failure did in fact fail

26 26 How futuristic can the algorithm be? More qualitative data elements and Milestones can be added in the future based on trends Doesn t follow a template (simple rules of thumb) to generate probabilities of success at each stage Predictions are based on quantifiable, data-driven evidence Benchmarks from gold standard Cortellis platform content so model is based on rich and varied source of information Data refreshed daily, hence algorithm fine tunes itself No predefined weights on each data element mean the algorithm doesn t fall into the same pattern of predictions Machine Learning/Data Science algorithm helps continuously improve

27 27 Summary Clarivate proprietary model will be available in Cortellis platform for use late Q It helps Identify timeline and success rates at each phase of drugs in Cortellis Competitive Intelligence Calculate a tailored prospective portfolio view for client Help client prioritize indications for portfolio Plug client data and tools into the maturity curve Apply custom model to improve client portfolio success rates

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