IDEA Collider | David Grainger
IDEA Collider
English - November 25, 2019 12:23 - 53 minutes - 49.1 MB - ★★★★★ - 2 ratingsLife Sciences Science Business pharmaceutical innovation author interviews book club book reviews pharmaceuticals innovation pharma Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
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?