Information Technology Supported Risk Assessment Framework for Scenario Analysis in Drug Discovery and Development

1. Information Technology Supported Risk Assessment Framework for Scenario Analysis in Drug Discovery and Development Zhengru Tang Email: cmpztang@livjm.ac.uk Postgraduate Conference School of CMS, LJMU, 17-18 Mar 2004

2. Outline of Presentation • Background • Problem • Aim of the project • Methodology • Proposed Model • Input/output parameters • Algorithm • Sample results • Future Work PG Conference, CMS, LJMU

3. Drug Development Commercialisation Producing New Medicines Drug Discovery PG Conference, CMS, LJMU

4. Discovery Early Development Late Development Commercialisation Therapeutic Target Identification Chemical Lead Discovery Scale up Synthesis Pre- Clinical Studies in Animals Clinical Trial Phase I: Safety Clinical Trial Phase II: Dosage Clinical Trial Phase III: Efficacy Regulatory Submission Manufacture Marketing & Sales Post Marketing Surveillance Drug Discovery and Development Process PG Conference, CMS, LJMU

5. Time of the process PG Conference, CMS, LJMU

6. Drug Discovery & Early Development Source Selection vary Screening Model Selection vary Disease Target Identification vary Chemical Structure hard to be determined Compound Management Full automation vs. Partial automation Compound purity vs. toxicity Lab technology scale up New toxicity found No obvious advantage compare with known drugs Difficulty in obtaining rare or expensive raw material Clinical Trails Inappropriate disease selected Too small volunteer number selected Too more or too less dosage given Side effect or allergy even death No advantage compare with known drug Clinical trial aborted cause of lack of samples Patient corporation Registration Registration materials not sufficient or standard Delay because of unfamiliar to the procedures Other organisations already had it registered DNA(New Drug Application) not approved by FDA Manufacturing Technology not mature cause the poor product quality Accidents because of the not-meet-the-need equipment Wrong operation Poor products quality and quantity Insufficient energy or raw materials provided Poor management Poor market Marketing Unpredictable adverse reactions Human mistakes while use (i.e. wrong dose, wrong drug, wrong concentration prescribed) Price's change by government Price too high compare with similar drugs Poor market information Not-nice-looking package, inconvenient use Risksin Drug Development Process PG Conference, CMS, LJMU

7. Drug Discovery Drug Development Problem GO? NO GO? Decision Making PG Conference, CMS, LJMU

8. Aim of the Project • To construct a framework to help the decision-making by constructing a model with an algorithm that can be used to objectively assess the probability of success of a compound being developed as a pharmaceutical agent. • To generate an output, such as cost/benefit ratio that allows comparison of one compound against another, thereby informing a judgment about which compounds to invest in and ranking their priority. PG Conference, CMS, LJMU

9. Financial Management Risk Assessment Drug Discovery Decision Making Drug Development Mathematic Tool Knowledge Elicitation Methodology PG Conference, CMS, LJMU

10. Making Molecule Phase I Pre- Clinical Phase I Pre- Clinical Scale up Synthesis B2 B2 A2 A1 B1 A2 Intellectual Property01 Intellectual Property01 P1 IP01 A2.1 A2.2 Part of the Model Target Identification A1: In vitro activity? B1: Scale up? PG Conference, CMS, LJMU

11. Chemistry/Manufacturing Clinical/Marketing Financial Returns Proposed Model PG Conference, CMS, LJMU

12. Required Inputs • Duration of each stage • Cost of each stage • Probability of successful completion of each stage (Transitional Probability) • Revenue collected of each stage PG Conference, CMS, LJMU

13. Required Outputs • Total Cost of the candidate drug • Time to launch the market • Probability to success PG Conference, CMS, LJMU

14. Algorithm - Time Key parameters: Duration, starting time, finishing time For stage Pre-Clinical Trial (PC): Finish(PC) = duration(PC) + time of completing earlier stages = D_PC + Max(finish(TI),finish(SS)) = D_PC+MAX(Finish_TI, D_SS+Finish(TI) PG Conference, CMS, LJMU

15. Algorithm - Cost Key parameters: Total cost, discount rate, total revenue For stage Intellectual Property01: Total Cost of IP01 = Cost of IP01 * Transitional Probability A2.2 + Cost of TI + Cost of SS+ Cost of FM + Cost of PC NPV Cost of IP01 = total Cost of IP01 /(1+discount rate) ^ Starting time of IP01 NPV Profit of IP01 = NPV Revenue – NPV Cost PG Conference, CMS, LJMU

16. Algorithm - Probability Key parameters: Transitional probability For stage Target Identification (TI): Prob(Starting TI|MM=1) = 1 Prob(Completing TI|MM=1) = A1 so P_TI = P(TI=1|MM=1) = A1*1 = A1 For stage Scale up Synthesis (SS): Prob(Starting SS|TI=1) = P_TI = A1 Prob(completing SS|TI=1) = B1 So P_SS = P(SS=1|TI=1) and P(TI=1|MM=1) = B1 * A1 * 1 = B1*A1 PG Conference, CMS, LJMU

17. Sample Input Values PG Conference, CMS, LJMU

18. Excel Spreadsheet Model Time to Complete Finish_SS=D_SS+Finish_TI NPV Cost C_NPV_CP=Cost_CP/(1+H6)^Start_CP Probability of Success Pro_PhI=TransProb_B2*TransProb_A2.1*(Pro_FM*Pro_PC) PG Conference, CMS, LJMU

19. Excel Spreadsheet Model (cont.) PG Conference, CMS, LJMU

20. Risk Analysis Software Crystal Ball • Simulation model based on Excel Spreadsheet model • Define the cells with a range or a set of valueswith a probability distribution (assumption) • Perform Monte Carlo analysis • Show the results in different values (forecast) PG Conference, CMS, LJMU

21. Define Assumptions PG Conference, CMS, LJMU

22. Define Input Parameter - Cost • Features: • It is a positive number • It has no up limitation • The value of it is the most likely • It is more likely to be in the vicinity of the mean than far away • Values: • Minimum • Mean • Maximum PG Conference, CMS, LJMU

23. Target Identification TI Distribution Type Distribution Parameters Cost of Target Identification C_TI Min 0 Mean 1.00 Max +Infinite Duration of Target Identification D_TI Min 360 Likeliest 400 Max 440 Transitional Probability of Target Identification A1 Min 0.81 N/A Max 0.97 Sample Input Distributions PG Conference, CMS, LJMU

24. Sample Forecast – Time to Launch PG Conference, CMS, LJMU

25. Sample Scenario 1 PG Conference, CMS, LJMU

26. Sample Scenario 2 PG Conference, CMS, LJMU

27. Sample Scenario 3 PG Conference, CMS, LJMU

28. Four different Compounds PG Conference, CMS, LJMU

29. IP01 – Sell Compound after Pre-Clinical trial IP03 – Sell Compound after PhaseII Clinical Trials Compare 4 different Compounds Probability regarding Time/Cost PG Conference, CMS, LJMU

30. Compare 4 different compounds (cont.) PG Conference, CMS, LJMU

31. Future Research • Rank more compounds different ways considering different scenarios • Validate the model, populate with real data • Write up thesis PG Conference, CMS, LJMU

32. Thank You! PG Conference, CMS, LJMU