Improving the quality of decision analysis in early stage drug development, by design

Transcription

1 Improving the quality of decision analysis in early stage drug development, by design Itay Perlstein, PhD Clinical Pharmacology and Drug Development Consulting Clinical PK Services 1 Quality by Design in Pre-Clinical and Clinical Research 13-Dec-2015 Tel Aviv University

2 Agenda Introduction to multiple attribute analysis QbD aspects in clinical pharmacology: closing the loop between CMC and clinical performance TPP/QTPP/CQA QbR & Label Strategic considerations in pharmaceutical portfolio management Drug level assessment Portfolio level assessment Real options applications in generic drug portfolio management 2

3 Multiple Attribute Analysis in Clinical Development Multiple-criteria decision analysis: in our daily life we make decisions after weighting multiple criteria based on our intuition and we may be comfortable with the consequences when stakes are high, it is important to properly structure the problem and evaluate multiple criteria In most of such cases multiple parties have input and will be deeply affected from the consequences 3

4 Goal: Reaching more informed and better decisions Structuring complex problems well and considering multiple criteria explicitly leads to more informed and better decisions Generally, easy to explain and defend Allows transparency Input providers & decision makers turnover Allows updating when new data comes in If different weight are considered 4

5 Methodology List all relevant attributes Identify the critical attributes Develop a model for each critical attribute Define weight for each critical attribute Combine all models into one utility model Identify the sweet spot of the utility model 5 Simplified CUI example with only two attributes (Khan, Krishna & Perlstein; 2009)

6 TPP,QbD, and QbR: Beginning with the Goal in Mind Target Product Profile (TPP) Quality Target Product Profile (QTPP) Critical Quality Attributes (CQA) Label END Question Based Review (QBR) 6

7 Target Product Profile Quality Target Product Profile (21) Critical Quality Attributes (22) The Question Based Review (QBR) The TPP serves as part of a Briefing Document It provides a format for discussions between a sponsor and the FDA The FDA positioned its draft guidance on the TPP as facilitating better communication between the sponsor and the regulatory body because it summarizes the drug development program in terms of intended labeling content and claims forms the basis of design for the development of the product Considerations could include: Intended use in clinical setting, route of administration, dosage form, delivery systems Dosage strength(s) Container closure system Therapeutic moiety release or delivery Attributes affecting pharmacokinetic characteristics Drug product quality criteria (eg, sterility, purity, stability, and drug release) a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality CQAs are generally associated with the drug substance, excipients, intermediates (inprocess materials), and drug product Purity Strength Particle size distribution Bulk density Drug release stability aerodynamic properties (for inhaled products) Sterility (for parenterals) Adhesion (for transdermal patches) The template (document) should be used by all reviewers for NDA and snda reviewes The document should not exceed Pages The QBR focuses on key questions pertinent to the review, and integrates information across studies Typical questions posed during the review of NDAs and sndas are provided The specific questions depend on the characteristics of the drug, drug product, patient population, and indication Reviewers should answer the questions using a deductive approach (ie, starting with the conclusion and following with supportive details) 7

8 Clinical Utility Index A mathematical function that can be helpful in choosing the best dose/regimen for a compound, comparing it to competitors, etc The CUI process reduces a multidimensional problem to a single number, called utility, that can be used to compare various doses, regimens, and/or compounds 8 Khan, Krishna & Perlstein AAPS J 2009 Lee Hodge, Introduction to the CUI Tool TM (Originally Developed by Pharsight for Merck)

9 CUI example: Which insomnia compound should move forward? (Ouellet, Lalonde, et al) Final Agreed 5 Attributes: Effect and Weight Lead compound: slightly lower CUI 30 mg Backup compound: higher CUI but unfeasible dose 800 mg 9 A separate model is developed for each attribute The CUI integrates the models according to the defined effect and weight

10 Integrating pre-clinical data from multiple sources: Multispecies visualization 10 Allows integrating data from different sources: In-vitro studies Animal models Toxicology models Healthy subjects (predicted/observed ) Patients Easy to update when new data is generated Commercial Tool (not presented): incas TM : Interactive Non-Clinical Assessment of Safety (originally developed by Mango Solutions for GSK)

11 Early Clinical Portfolio Level Assessment: From the gut feeling to a transparent set of criteria Therapeutic Area Compound Commercial (06) CNS (10) Clinical (07) CMC (04) Total NME MNE NME High POS Cardiovascular (08) Oncology (09) 11 NME NME MNE NME NME NME MNE NME NME Mid POS LowP OS

12 The Generic Drug Company Model The GDC Model OBJECTIVES: Determine priorities of R&D Efforts in order to optimize Management s attention BASIC ELEMENTS: Build on real options concepts and Coefficient of Variation of Gross Profits in order to derive Six classification categories 12 GDC=Generic drug company; NPV= Net Present Value; ROV=Real option valuation; DTA=Decision tree analysis Source: Capturing Value from Optionality in Pharma R&D, Konig et al

13 The Generic Drug Company Model Status: Generic drug company has 39 projects in different stages of development Portfolio management is required (reassessment) for attention & budgeting prioritization Project stage Advanced Products (limited/medium uncertainty) Products in development (first and second tier) Candidate products or products on hold Number of Projects Total number of project 39 13

14 The Generic Drug Company Model Initial screening: 1 Compute Expected Net Present Value (ENPV) of all projects 2 Compute NPV of Best Case Scenario 3 If NPV of Best Case Scenario below 5M$ classify project as If CV < 001 and ENPV>30M$ classify project as 1 5 Priority Management applies to all other projects ENPV>$30 CV< Full investment Routinely Monitor Apply Priority Management Active Involvement to enhance success scenario Active involvement to create success Scenario Last chance actions Best case NPV< $5M Consider Termination

15 The Generic Drug Company Model 1 Compute Gross Profit under six alternative scenaria 2 Compute Variance of Gross Profit (GP) 3 Compute Coefficient of Variation of GP: CV = Standard deviation(gp)/ ENPV 15

16 The GDP: Example of One Project Technology Scenario Products Competing/ Our position Annual Summary expected gross contribution table R&D failure branch 500% Clinical trial failure branch 1900% Submission failure branch 380% Successful launch branch 7220% Aggregate Accumulative Aggregate discounted Year Expected Gross Profit $18,769 $14,769 $13,489 $13,489 $13,489 Variance of GP E+13 14E+13 14E+13 Total Variance 11E+14 CV of ENPV 1897%

17 The GDP: The same process is done with the entire portfolio Project Expected NPV CV Advanced Products - high certainty Advanced Products - medium uncertainty First year of marketing Best case scenario Probability of best case scenario Pessimistic case scenario Probability of pessimistic case scenario maximum cash investment in case of failure to launch $24,312, % 2003 $51,217,686 5% $7,083,057 5% -$100,000 $22,084, % 2002 $38,760,691 38% $5,283,882 0% -$100,000 $13,517, % 2003 $38,199,169 23% $5,542,545 5% -$1,450,000 $11,729, % 2004 $27,697,493 27% $8,587,238 18% -$2,290,000 $12,866, % 2003 $33,991,904 27% $2,557,917 0% -$250,000 $8,590, % 2003 $16,749,272 48% $4,228,198 19% -$100,000 $8,291, % 2002 $19,341,209 10% $2,692,711 Not relevant Not relevant $1,813, % 2003 $3,174,128 48% $909,252 10% -$100,000 $12,378, % 2003 $50,702,993 5% $4,163,472 14% -$800,000 Entire Portfolio Products in development (first tier) Products in development (second tier) $36,297, % 2004 $151,453,917 4% $21,206,189 11% -$2,400,000 $12,463, % 2006 $71,096,030 18% -$2,056,399 0% -$5,120,000 $10,721, % 2005 $67,842,660 15% $8,550,055 2% -$3,700,000 $8,613, % 2005 $86,377,342 8% $4,133,684 4% -$3,360,000 $7,660, % 2005 $44,582,570 19% $688,802 1% -$2,230,000 $6,950, % 2005 $37,389,520 14% $1,153,400 3% -$2,500,000 $4,757, % 2004 $44,080,791 4% $6,876,437 11% -$1,100,000 $4,548, % 2005 $27,262,864 2% $3,606,670 10% -$1,010,000 $3,535, % 2004 $17,772,634 12% $3,904,972 46% -$1,037,500 $3,469, % 2005 $37,881,209 2% $2,644,012 10% -$1,010,000 $1,788, % 2004 $13,013,140 15% $38,225 0% -$1,080,000 $1,586, % 2005 $11,659,335 7% -$824,894 3% -$2,820,000 $1,453, % 2005 $6,759,295 47% -$424,719 7% -$2,900,000 $1,368, % 2004 $16,033,840 3% $459,828 6% -$1,010,000 $531, % 2004 $3,453,351 19% $584,902 8% -$650,000 $37, % 2006 $9,879,064 12% -$2,434,108 6% -$3,400,000 $52, % 2004 $4,429,711 11% -$659,656 3% -$1,010,000 -$540, % 2004 $1,105,388 17% -$682,395 15% -$1,140, $16,115, % 2006 $105,355,896 13% $330,250 6% -$3,216,667 $2,796, % 2004 $19,062,820 3% $2,047,995 13% -$1,214,000 $2,320, % 2006 $23,537,158 3% $2,048,558 21% -$2,620,000

18 Project Priority Management CV ENPV -,,,,,,,,,,,,,, Class 2: Routinely Monitor Class 3: Active Involvement to enhance success scenario Class 4: Active involvement to create success Scenario Class 5: Last chance actions 18

19 Project Expected NPV CV Class Advanced Products - high certainty ENPV -,,,,,,,,,,,,,, $24,312, $22,084, $13,517, $11,729, $12,866, $8,590, $8,291, $1,813, Advanced Products - medium uncertainty $12,378, CV Products in development (first tier) Products in development (second tier) $36,297, $12,463, $10,721, $7,660, $8,613, $6,950, $4,757, $4,548, $3,535, $3,469, $1,788, $1,586, $1,453, $1,368, $531, $37, $52, $540, Candidate products or products on hold 19 $16,115, $2,796,

20 Summary Quantifying methods for better informing decision making were discussed Portfolio management tools for Internal decision making Translational development decision making Early phase of clinical development Regulatory interaction Competitor analysis Mature (generic) development phase of products 20

21 Acknowledgments: Duane Boyle, GSK Rajesh Krishna & John Wagner, Merck Frank LaCreta, BMS Ron Kenett, KPA 21

22 Questions? Contact info: Itay Perlstein, PhD Clinical Pharmacology and Drug Development Consulting Clinical PK Services 22