0:00 Defining innovation

01:00 Incremental innovation vs big changes

01:45 On designing back from the unmet need, and introducing innovation

(Interruption by a phone call �)

02:54 Problem backwards vs solution forwards

03:55 On the ‘guided random walk’ and adoption of agility/ serendipity (low validity environments in pharma)

04:45 Prediction, hubris and certainty in process

06:50 The stopping rule in drug development (07:30 interruption by a phone call �)

07:50 Zombie projects

08:00 The ‘Keytruda story’ as ‘the biggest poison in our industry’

08:50 On ‘busters’ vs blockbusters

09:20 On breadth of exploration in Discovery/ ‘pick the winners’/ ‘kill the losers’

11:05 The misaligned incentives that lead to decisions to continue - the ‘legions of zombies’

11:50 Spreading resource too broadly without good filters

12:20 On the development of better filters, and too much resource in the ecosystem

13:00 Does constraining resource lead to better outcomes?

15:00 On ‘Follow the Science’

16:30 On giving people the benefit of the doubt… Now what…?

(17:40 One more phone interruption - sorry! � Leads to some audio spiking from here…)

19:00 On a disease like Alzheimer’s - pinning a tail on a large donkey

19:50 On ‘value signals’ in development

20:45 On hubris in selection of models

22:15 On allowing ‘the whole market’ to distort clinical development

25:00 How important are measures of innovation? The role of the incentive structure

25:30 On decision quality (and the distraction of ‘resources’)

26:30 How does more data improve decision quality?

27:00 On being successful or not being blamed for failure

29:00 On the feedback loop and its utility in pharma

30:20 What would a better incentive structure look like?

31:00 What do we mean by failure?

32:00 On the misattribution of error

33:30 The way we misuse language, biases, and the impact of language on ‘failure’

34:40 What are the most important lessons you’ve learned over time?

34:55 On the power of dissociating asset from infrastucture, idea from process

37:20 On the ‘organisation’ problem - separate nodes with a ‘project pilot’

38:20 On the translation of success in one therapeutic area into another - ‘process structures are not transferable’

39:15 On ‘retrenchment’ in major pharma, into fewer therapeutic areas

40:50 On the ‘nonsense’ of product profiling too early

42:30 ‘Instead of recognising you’re pinning the tail on a donkey, you think you’re aiming’

42:50 What drives David Grainger?

44:30 What is the role of tech and AI in early development?

45:00 What problem is AI solving?

45:30 Better predictions in a low validity environment

46:30 What kind of ‘training data’ would we use?

47:00 Unknown vs unknowable data

48:30 On which books David would recommend

50:30 What are David’s ambitions?

52:30 Does 2019 look very different than 1999?