The Value of Intelligent Early Safety Pharmacology Intervention. Derek Leishman PhD Senior Fellow, Global Head of Safety Pharmacology

Transcription

1 The Value of Intelligent Early Safety Pharmacology Intervention Senior Fellow, Global Head of Safety Pharmacology

2 Outline Part 1: Why do we front-load safety assessment? Part 2: Where does attrition come from? Part 3: Early testing strategy Part 4: Some proposals Conclusions 2

3 Disclaimer I don t normally include this slide but. Maybe its time for a provocative proposal or 6 The views expressed in this presentation are my own and do not necessarily reflect those of Eli Lilly, nor do they always reflect current practice at Eli Lilly 3

4 Part 1: Why Do We Front-Load Safety Assessment? Think as you work, for in the final analysis, your worth to your company comes not only in solving problems, but also in anticipating them. Tom Lehrer The wise man avoids evil by anticipating it. Publilius Sirus (Roman author; 1 st C B.C.) "Worry not about the possible troubles of the future; for if they come, you are but anticipating and adding to their weight; and if they do not come, your worry is useless..." Hugh Blair 4

5 Lessons from 60 Years of Pharmaceutical Innovation Bernard Munos, Corporate Strategy Eli Lilly Nature Reviews Drug Discovery Vol. 8, Dec 2009, Exponentially escalating costs of drug development 5 5

6 Biggest Proportion of Costs Lies in Failure Drug discovery and development is a case of trial and error It follows that a large proportion of what is spent on R&D ends up being spent on failed compounds We need to fail more cheaply We need to learn from the trials and especially the errors Hank McKinnell (former CEO of Pfizer) once said we re failing 98% of the time, if we could fail 96% of the time we d double productivity 6

7 Success Rates Getting Better? Sadly success rates continue to decline Success in Phase 3 to Approval is 65% Success from Phase 2 to Phase 3 continues to fall now 22% The ratio of Phase 1 starts to Approval now around 30:1 The ratio of new target project starts to Phase 1 starts remains difficult to determine 7

8 Early Attrition is High but Cheap 8

9 Part 2: Where Does Attrition Come From? 9

10 Attrition Comes from Different Sources Attrition comes in different flavours Its about the compound! Its about the target! Its about the project execution! Managing attrition due to these different flavours suggests different tactics and timing Safety pharmacology concerned with target-based effects, off-target effects and how these should be brought together 10

11 Target Assessments Can Happen Before The Project is Initiated Target assessments can be built from publicly available information 11 More mature targets have more data available Balance of novelty or being first, over following and needing to differentiate Maintaining a perspective on hypothetical risks is very important Need to identify the key experiments around effects you believe are important and/or likely Target assessments can result in attrition of whole project not just compounds Solutions likely involve other functions (e.g. ADME and PK/PD) and not iterations in functional studies

12 On-Target vs. Off-Target It s about the molecule! = an expression of off-target effects herg is the poster child of off-target effects Where do you best address these off-target effects? WHILE YOU RE MAKING MOLECULES! What value is your first herg data if you gather it immediately prior to first-in-human studies? You are characterizing the candidate and working out how to address potential QT issues around fixed properties of the molecule 12

13 Is it Off- or On-Target? What does a series of compounds need to look like to settle the on- vs. off-target question? Poor selectivity doesn t help Testing high exposures doesn t help Highly similar structures don t help Low bioavailability is potentially surmountable Antagonists when developing agonists help, also doing experiments in knockout animals Another company s experience may imply different structure with same effect implicates target 13

14 Bringing it All Together Good Execution Experiments should form a learning map Not a flow chart or filter funnel Don t continually need to repeat the experiment if we ve learned what we need Identify the key questions Doing no safety pharmacology before the GLP studies is low think Screening all compounds across many assays is also low think The catch how do you know what you don t know 14

15 Part 3: Early Testing Strategy 15

16 Predict What? What does your early study need to predict? All the early assessments should be predictive of effects in man? Or The early assessment should be predictive of the next study e.g. the GLP study How predictive are the assays? How does prevalence impact my strategy? Integrative risk assessments are important, how do you do those? 16

17 Babies and Bath Water False positives are the babies and represent lost opportunity cost False negatives are expensive and potentially very damaging Regulators clearly have to have low toleration of false negatives Pharma seeks to minimize the false positives If you have to choose false negatives must be minimized 17

18 Case 1 Early assessment assay is 80% sensitive and 80% specific for effects in man 20% false positives, 20% false negatives GLP study is similar 80% sensitivity and specificity They re different and the false +ve, false ve don t overlap Can be advantageous in combination a minimum number of positives will make it to man Disadvantage positive in GLP study following months of compound optimization 18

19 GLP Study Early Study True Incidence Scenario 1 Positive TP FP Negative FN TN total Positive 8 18 Negative 2 72 total Positive 2 14 Negative total 2 72 High lost opportunity cost come with high reload costs Effectively excluded false negatives All these examples based on first promising compound in each of the 100 projects being tested. Response to a positive will be make and test a new molecule. Eventually 90 projects may succeed but will take iterations. Can assume time spent in lead optimization for a project team is expensive $1M/month. 19

20 Case 2 Early assessment and GLP study are 80% sensitive and 80% specific for effects in man They re very similar and the false +ve, false ve overlap entirely Can be advantageous no reload cost with unexpected positive Easier to create the predictivity database when comparing two short term animal studies Disadvantage Haven t closed the false negative gap 20

21 GLP Study Early Study True Incidence Scenario 2 Positive TP FP Negative FN TN total Positive 8 18 Negative 2 72 total Positive 0 0 Negative total 2 72 Lost opportunity cost modest and taken early 18 candidates Risk false negatives Rely on early clinical studies to capture false negative These false negative projects are an expensive reload cost depending on where true effect can be observed Could attempt to increase predictivity of both assays 21

22 FIH Study In Vivo Dog herg Real QT Scenario? TQT +ve TQT -ve total Positive Negative 5 53 total TQT +ve TQT -ve total Positive 4 7 Negative 1 46 Total TQT +ve TQT -ve total Positive 1 0 Negative total 1 46 herg effect common but relatively predictable Estimate likelihood of positive TQT was at one time as high as 30% Sensitivity and specificity of assays as described in Wallis, 2010 Nonclinical false negative FIH study believed 100% predictive of TQT Matches current experience with very few +ve TQT studies now 22

23 GLP Study Early Study 2 Early Study 1 Improved Combination Scenario Positive 8 18 Negative 2 72 total Positive 2 14 Negative 0 58 total Positive 0 0 Negative total 0 58 Do two different early assays 2 nd study equivalent to GLP study Lost opportunity cost still relatively high Minimize reload cost as conducted early No false negatives Still largely testing negatives which you d prefer to predict The first assay example here could be in silico or in cerebro 23

24 GLP Study Early Study 2 Early Study 1 Predict The Trouble Compounds Scenario Positive 8 8 Negative 2 32 total Positive 2 6 Negative 0 26 total Positive 0 0 Negative total 0 76 Confidently decide 50 of the 100 molecules unlikely to be positive and exclude from testing Do the two different early assays Lost opportunity cost is now minimized Minimize reload cost Still no false negatives This is effectively the scenario you have when half the portfolio is large molecules 24

25 Focused Early Testing Delivers Number of Candidate Compounds with the Problem Number of Candidate Compounds without the Problem Number of Projects Reaching Man (negative in assays) Best Case Scenario 1 (different predictivity for man) Scenario 2 (same predictivity for man) Scenario 3 (different predictivity for man applied early) No false negatives 14 false positive projects delayed >6 months false negatives (+14, +18?) No false negatives 14 false positive projects minimally delayed Scenario 4 (Focused Testing) (+6, +8?) No false negatives and 7 projects minimally delayed 25

26 GLP Study Early Study 2 Early Study 1 Scenario Tricky Finding #1 Positive 1 49 Negative 1 49 total Positive 1 9 Negative 0 40 total Positive 0 0 Negative total 0 40 Rare event present in 2 out of 100 candidates First assay not at all predictive; 50% sensitivity and 50% specificity Second study remains 80% specific and sensitive Third (GLP) study very similar to 2 nd study High lost opportunity cost 1 st Study must be very early and cheap if want to avoid losing projects 26

27 GLP Study Early Study 2 Early Study 1 Combination Scenario Tricky Finding #2 Positive Negative 5 45 total Positive 4 9 Negative 1 36 total Positive 0 0 Negative total 1 36 Common event present in 25 out of 100 candidates First assay 80% sensitivity and 60% specificty Second study remains 80% specific and sensitive Third (GLP) study very similar to 2 nd study High lost opportunity cost False negative 27

28 GLP Study Early Study 2 Early Study 1 Combination Scenario Tricky Finding #2b Swap Order of Assays Positive Negative 5 60 total Positive 4 24 Negative 1 36 total Positive 0 0 Negative total 1 36 Common event present in 25 out of 100 candidates First assay 80% sensitivity and 80% specificty Second study remains 80% sensitive but 60% specific Effectively reversing the order of the first two studies Third (GLP) study very similar to 2 nd study Same ultimate outcome but tested 15 more compounds in 2 assays 28

29 GLP Study Early Study 2 Early Study 1 Combination Scenario Tricky Finding #2c, Eliminate False -Ve Positive Negative 1 45 total Positive 1 9 Negative 0 36 total Positive 0 0 Negative 0 36 total Common event present in 25 out of 100 candidates Increase first assay to 95% sensitivity and 60% specificty Second study remains 80% specific and sensitive Third (GLP) study very similar to 2 nd study High lost opportunity cost Eliminates the false negative 29

30 Multiple Effects Now imagine 2% incidence of CNS effects 10% incidence of haemodynamic effects 25% incidence of QTc effects Liver effect, PK suboptimal Different predictivity in the now multiple early assays Some assays 85% sensitive, 85% specific Some assays less predictive Do the math It takes a lot of resource It takes much longer than you d like 30

31 Learning? Speed and efficiency are best served with early and GLP studies which predict each other very well, but risk missing something Combinations of assays with different spectrums of predictivity can minimize late surprises Must both be applied early to add to speed and efficiency Effectively identifying problem series then applying two tests would be very effective Need to match assay to prevalence where effects are rare raise specificity, where effects common increase sensitivity Lost opportunity through false positives are inevitable minimize reload cost 31

32 Screening vs Focused Evaluation Early studies are attractive to tackle attrition but how many do you want to pay for? More effective when exclude testing predicted negatives This reduces cost If it takes more than 50 lead optimization projects to produce a drug how many compounds need to be made? How many do you test and in how many screening studies? 250 compounds? 5 assays? Worst case 1250 studies Even at $1000 each = $1.25M They may be cheap but be careful of volume 32

33 So Why Screen? How do you know where to focus your efforts? Prevalent effects (limited proportion of negative assays make screening more cost effective) If you have to screen Make it cheap Consider sentinel philosophy Build in silico models may mean generating IC 50 rather than single concentration In silico models ultimately save even more since you don t make as many positive compounds 33

34 <1 >1 & <3 >3 & <10 >10 & <30 >30 & <100 >100 & <300 >300 & <1000 >1000 & <3000 >3000 & <10000 >10000 & <30000 >30000 & < > Percentage of Compounds How Common Is herg Block? % of Compounds (n=1686) % of Compounds 5 0 [3H] Dofetilide Binding herg IC50 (nm) 34

35 SHARING IN EACH OTHER S FAILURES TO HELP US ALL BE SUCCESSFUL 35

36 Proposal 1 Facilitate Target-Safety Assessment Create a database of target-related safety e.g. Wiki Start with key systems and relatively common targets Use an independent group to host the database e.g. SPS Grows from pilot exercise (Gul Erdmeli, Steve Jenkinson) Partner with academia? 36

37 Proposal 2 Share Early Assay Experience Share in silico models with smaller companies who don t have the depth of experimental experience to build the models Retain no structural information Use an independent group to host the models e.g. SPS Have additional tutorial information to follow if in silico models suggest potential effects Fund in itunes-like manner, small cost per structure tested 37

38 Proposal 3 Facilitate Understanding of Predictivity 1. Share translation information to facilitate assessment of the predictivity of nonclinical studies Increased numbers increases confidence in predictivity 2. Use an independent group to host the sharing Already good examples e.g. ILSI-HESI QT and CV, ABPI & AMF, TI Pharma Current examples are static no facility to constantly add new information and thus assess impact of new methods and technology 5. Maximize funds by avoiding duplication and by ensuring complimentary efforts 38

39 Proposal 4 Share Regulatory Studies Facilitate understanding of on- vs. off-target Example in your GLP studies you observe an effect. You know another company has also been developing compounds against this target. They have also conducted a GLP study. Question: did they see the same thing? Share outcomes of GLP studies in a manner similar to clinicaltrials.gov 39

40 Proposal 5 Increase Quality and Consistency Cross company consistency for the same assays becomes very important Need to understand that similar studies have similar sensitivity and quality Best Practice Need to make quality data affordable for all Need an incentive to quality needn t be a regulator, cross pharma group such as IQ & SPS consortium? 40

41 Proposal 6 Focus vs. Blinkered Stop worrying about ICH S7 Guideline has some elements pointing to long term patient toleration, it is however primarily intended to facilitate FIH Safety pharmacology effects can be important whether or not they are in core studies Increase sharing in non ICH S7- related information ICH S7 41

42 Conclusions We need to make what we do now efficient so we have the resources for the new challenges Early assessment is the direction to take but it needs to be focused to minimize resource burn Sharing knowledge and data are critical to our overall success A treasure trove of proprietary information may be useless if the ship sinks SHARE! 42