Next Generation Healthcare Technology

Transcription

1 Next Generation Healthcare Technology Predictive Analytics: From Deep Data to Knowledge (in collaboration with Qlaym Healthcare) Dr. Kerstin Bode-Greuel Senior Manager, Marketing & Offering Development 28. April

2 A wealth of information is hidden in Deep Data How can the information be discovered? 2

3 Predictive analytics enables the discovery of as yet unknown information in Deep Data In real world healthcare data, predictive analytics discloses a much broader variety of outcomes compared to the observations made in controlled clinical trials, thereby highlighting new areas of benefit for patients 3

4 Predictive analytics can extract knowledge from Healthcare Data Self-learning Artificial Intelligence alrithms yield patterns that are further analyzed for their relevance Data Groups Relevant Information anonymized data predictive analytics statistical analytics Medical Expertise 4

5 Predictive analytics delivers four core benefits that are relevant for R&D, market access, and marketing IMS Health Qlaym joined approach Core benefits Predictive Analytics Medical Event Tracer Medical Expertise Experience in Evidence Generation Longitudinal Data Assets Identification and characterization of patient groups which respond best / worst to therapy Hypothesis generation regarding unexpected drug effects Earlier detection of (rare) expected drug effects Simulation of clinical trials in Real World Data 5

6 Taking advantage of the four benefits, predictive analytics can enhance pharma s performance in its major areas of challenge More successful launch More successful development Launch/Marketing Market Access Life Cycle Management Drug Development Registration Better and safer use of medicines Drug Discovery 6

7 Predictive Analytics Use Case 1: Application to marketing and life cycle management More successful launch More successful development Market Access Launch/Marketing Life Cycle Management Drug Discovery Drug Development Registration Better and safer use of medicines Detect rare events earlier Identify as yet unknown therapeutic benefits (new IP) Identify patients with high/low benefit target your drug to patients with highest benefit and lowest risk 7

8 Detection of rare (e.g., safety-relevant) events is more effective with Predictive Analytics After market launch, it takes a while until the number of observed cases is sufficient to assess statistical significance Patients treated per month Market Launch Early adopters Market uptake time Side effect rate No data before market launch fluctuating values Stable and statistically significant values time 8

9 The bleeding risk associated with novel oral anticoagulants (NOACs) could reliably have been evaluated two years earlier Frequency ratio after/before Conventional analytics Frequency ratio after/before K92.1 Melena Medical expertise based Tracer analytics Time Any Bleeding Event (ABE) Tracer D62 Acute posthemorrhagic anemia R04 Hemorrhage from respiratory passages I60 Nontraumatic subarachnoid hemorrhage I61 Nontraumatic intracerebral hemorrhage D68.3 Hemorrhagic disorder due to extrinsic circulating anticoagulants B03 Antianemic preparations e.t.c. fluctuating values Stable and statistically significant values 9

10 Predictive analytics automatically identifies patient groups that share certain properties without a priori hypothesis In RWD, Predictive Analytics identified a group of patients with a history of gastritis that proved to be more susceptible to bleeding upon NOAC therapy Average improvement per subgroup Gastritis, reflux Average aggravation per subgroup 35 bleeding events per 100 py 10

11 The relevance of unexpected findings is statistically validated in a second analytical step The higher risk of NOAC related bleeding events (per 100 py) in patients with a history of gastritis is statistically significant Subgroup analytics may lead to new evidence for a better positioning of drugs, based on unexpected new findings, co-morbidities and co-medications 11

12 Predictive Analytics Use Case 2: Application to market access More successful development More successful launch Strengthen the evidence for your drug s benefit even if data are limited Simulate your clinical trial in real world data to expand your comparator analysis Market Access Launch/Marketing Life Cycle Management Drug Development Registration Better and safer use of medicines Drug Discovery 12

13 Heterogeneity in comparative efficacy requirements across HTA bodies leads to complex development programs Predictive Analytics can generate information on all requested reference drugs Dealing with heterogeneity of regulatory demands for Comparator drugs Placebo Indirect comparison Target population Endpoints Source: Weber, S., Jain, M., Nallagangula, T. K., Jawla, S., Rai, N., Dev, D., & Cook, N. (2015, November). Heterogeneity in Relative Efficacy Assessments (REA) across European HTA Bodies: Opportunity for Improving Efficiency and Speed of Access to Patients? Poster presented at ISPOR 18th Annual European Congress, 7 11 November 2015, MiCo - Milano Congressi, Milan, Italy 13

14 Outcomes from clinical trials can be connected with the real world Bridging comparative efficacy assessment with comparative effectiveness assessment Clinical Trial Data Real World Data Innovative Drug in Development Comparator A ( bridging comparator ) Comparator B is requested by HTA 14

15 Reconstruct clinical trials based on RW data to facilitate (indirect) comparisons among drugs Taking advantage of the stronger explanatory power of predictive analytics Clinical Trial Data Real World Data Innovative Drug in Development Comparator A ( bridging comparator ) Prediction how innovative drug would compare to B Comparator B is requested by HTA Direct comparison of A and B 15

16 Predictive Analytics can also enhance R&D More successful development Optimize your trial design based on patient groups with highest unmet need Rescue your drug after failed trials by discovering strong responders and/or new clinical benefits in your trial data Drug Development More successful launch Registration Market Access Launch/Marketing Life Cycle Management Better and safer use of medicines Drug Discovery 16

17 and would facilitate Adaptive Pathways Anticipating pivotal role of RWE for EMA vision for Adaptive Pathways Identify patient group with highest medical need from RWD Tailor inclusion and exclusion criteria for study using RW study simulation Select clinical endpoints and quantify outcomes for comparators in RW simulation Identify best performing subgroups Facilitate indirect comparisons through bridging comparators Get an updated understanding of the competitors benefits and disadvantages in preparation of product launch Consolidate safety information Discover unknown therapeutic effects Create options for new IP Study 2 Study 3 Expansion Studies Phase I/IIa Study 1 Register Launch HTA Expansion Launches Decision points Market Access Enhance the explanatory and statistical power of your trial data Analysis includes data from own product Analysis based on reference/comparator data Market uptake Market uptake Life cycle Drug benefit & market time gain Adaptive vs Best practice Life cycle 17

18 Predictive Analytics creates patient-centric information that drives Pharma to become a more patient benefit-driven organization More successful launch More successful development Market Access Launch/Marketing Life Cycle Management Drug Development Registration Better and safer use of medicines Drug Discovery 18

19 and this creates tangible value! Expected Net Present Value model, including real options analysis Launch Scenario NPV(1) Launch Scenario (includes Sales Forecast) x P 1 pnpv(1) Launch Scenario (includes Sales Forecast) 54% enpv 46% FV 1 65% 35% FV 2 90% 10% (e)npv = (expected) Net Present Value FV x = future value after successful completion of milestone x FV 3 31 % 4 % 19 % 46 % NPV(2) Up to Non-approval. NPV(3) Up to Failure of Ph III NPV(4) Up to Failure of Ph II x P 2 x P 3 x P 4 pnpv(2) Stop after Non-approval pnpv(3) Stop after Failure of Ph III pnpv(4) Stop after Failure of Ph II enpv (sum of pnpvs) NPVs are calculated according to state-of-the-art methodology, cash flows are modelled up to expiry of market exclusivity (ME) A terminal value is calculated based on assumed residual cash flows after expiry of market exclusivity. Discount rate: cost of capital reflects systematic risk, while unsystematic risk is reflected by judgmental probabilities. K.M. Bode-Greuel & J.M. Greuel (2005): Determining the value of drug development candidates and technology platforms. J. Comm. Biotechn., Vol. 11 No. 2, pp

20 Project example: innovative therapy for Parkinson s disease, in Phase I Base case planning Expected NPV (4), Future Value (48, ff.) million 4 Investment per Milestone million 1,5 years Phase I 50% 50% 48 2 years Phase II CMC 35% 65% Phase III L.T. Tox 338 3,3 years 62% 38% Registration 931 1,3 years 80% 20% ,361 LAUNCH Probability of launch: 9% Peak sales: EUR 560 million 20

21 Scenario 1: increase likelihood of approval by improving your understanding about your competitors performance and by planning your program for optimum patient benefit with reduced uncertainty. Expected NPV (11), Future Value (64, ff.) million 11 Investment per Milestone million 1,5 years Phase I 50% 50% 64 2 years Phase II CMC 35% 65% Phase III L.T. Tox 389 3,3 years 62% 38% 1,3 years Registration 90% 1,053 10% ,361 LAUNCH Probability of launch: 10% Peak sales: EUR 560 million 21

22 this may also increase your confidence in completing the phase III program successfully Expected NPV (19), Future Value (82, ff.) million 19 Investment per Milestone million 1,5 years Phase I 50% 50% 82 2 years Phase II CMC 35% 65% Phase III L.T. Tox 448 3,3 years 70% 30% 1,3 years Registration 90% 1,053 10% ,361 LAUNCH Probability of launch: 11% Peak sales: EUR 560 million 22

23 Scenario 2: focus on patient group with high expected benefit Patient share decrease by 25%, price increase by 30%, probability increase Expected NPV (13), Future Value (69, ff.) million 13 Investment per Milestone million 1,5 years Phase I 50% 50% 69 2 years Phase II CMC 45% 55% Phase III L.T. Tox 320 3,3 years 75% 25% Registration 744 1,3 years 90% 10% LAUNCH Probability of launch: 15% Peak sales: EUR 409 million 23

24 Aligning the development strategy with RWE may reduce risk and enhance value Action Innovative Therapy for Parkinson's Disease Scenario 1 Gain a better understanding of your competitor's performance ->select comparators to demonstrate relevant benefits of your product Effect Value enpv, EUR million Base case plan 4 Increase likelihood of approval (80% -> 90%) 11 Scenario 2 Learn from your competitor's RW data which unmet needs remain, position your drug with the right (secondary) endpoints to demonstrate added value, create more convincing data for regulators Scenario 3 Focus development on a patient subgroup for which you can assume strong benefits (75% of base case population), expect a higher price (130% of base case) Increase likelihood of approval and of Ph III success (80% -> 90%, 62% -> 70%) Increase likelihood of approval, Ph II and Ph III success (80% -> 90%, 35% -> 45%, 62% -> 75%)

25 Summary and conclusions Predictive analytics helps disclosing the value of medicines for patients In the real world, there is a much greater diversity of patients, treatment patterns, and outcomes than in clinical trials Understanding this diversity by taking advantage of predictive analytics will create opportunities for innovation and marketing Based on four key capabilities, predictive analytics helps to focus on patient benefit in drug development, facilitates market access, and enables a safer and better use of medicines in the real life. The learnings from predictive analytics improve planning and decision making towards a more patient- and value-driven organization 25

26 Für weitere Informationen stehe ich Ihnen gerne zur Verfügung: Dr. med. Kerstin Bode-Greuel Senior Manager Marketing & Offering Development Telefon: 069/

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