pharmaceutical evolution: the case for user led innovation · 2019-10-03 · pharmaceutical...

Pharmaceutical evolution: The case for user led innovation

Jack Scannell*

Findacure Scientific Conference

Drug Repurposing for Rare Diseases

February 29th 2016

* J W Scannell Analytics LTD, CASMI (Oxford) & Innogen (Edinburgh). [email protected] , [email protected]

mailto:[email protected]

mailto:[email protected]

Pharmaceutical Innovation as “Intelligent Design”

The aspiration that has been progressively industrialized:

• Molecular component x misbehaves in a way that causes phenotypic disease trait y

• Molecular component x can be drugged with d in a way that causes an improvement in y without an unacceptable decline in other traits

• There still exist many identifiable, exploitable, instances of x, d, and y

• Therefore the industrial process set out above will deliver, at low cost, a large number of high POS drug candidates into clinical trials

Targetidentification

Target validation

Screendevelopment

ScreeningLead

optimizationPreclinical

developmentClinical trials

Phase I-III

2

Jack Scannell, Feb. 2016, Findacure Conference

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Scientific Triumph – Research Inputs Have Got Much Better

1010x change in DNA sequencing efficiency? 104x change in protein X-ray crystallography efficiency?

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1969 1990 2003 2005 2010

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100

1000

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1940 1960 1980 2000 2020

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3Source: Bernstein analysis, Hogan 1997, Geysent et al, 2003, Dolle 2011, Sanger 1988, Meldrum et al, 2011, Joachimiak 2009, Van Brunt 1986, Mayr & Fuerst 2008.

3


Scientific Paradox: Irreproducible Results Despite Technological PowerMuch Basic Research is Irreproducible and/or False (e.g. Begley & Ellis, 2012)

53 “landmark” academic studies revisited at Amgen

6

47

"Landmark" studies

Irreproducible

Reproducible

Irreproducible studies were more widely cited in academic literature

0

50

100

150

200

250

Irreproducible Reproducible

Cit

atio

ns

4

Sources: Begley, C. G., & Ellis, L. M. (2012). Drug development: Raise standards for preclinical cancer research. Nature, 483, 531–533. doi:10.1038/483531a; See also Prinz, F., Schlange, T., & Asadullah, K. (2011). Believe it or not: how much can we rely on published data on potential drug targets? Nature Reviews. Drug Discovery, 10, 712. doi:10.1038/nrd3439-c1 & Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine. doi:10.1371/journal.pmed.0020124


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1950 1960 1970 1980 1990 2000 2010

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Note: R&D costs are based on the PhRMA Annual Survey 2011 and reference Munos 2009. PhRMA is a trade association that does not include all drug and biotechnology companiesso the PhRMA figure understates R&D spending at an industry level. Total industry expenditure since 2004 has been 30% to 40% higher than the PhRMA members' total spend, whichformed the basis of this figure. The NME count, on the other hand, is the total number of small molecule and biologic approvals by the FDA from all sources, not just PhRMAmembers. The overall picture seems fairly robust to the precise details of cost and inflation calculations. Source: Munos 2009, PhRMA annual surveys, FDA, and Bernstein analysis.

Structureof DNA

Restrictionenzymes

RecombinantDNA

DNAsequencing

Humaninsulin

First wave of biotecha-interferon, b-interferong-interferon, HGH, Transplasminogen activator, EPO, G-SCF, GM-CSF, CeredaseHep B vaccine, DNAse, Interleukin-2, Factor VIII, etc.

Dolly theSheep

Humangenome v1

FDA tightens regulationpost thalidomide

FDA clears backlogfollowing PDUFA

regulations & perhapsrelaxes on HIV drugs

Increase in‘orphans’ plus ‘targeted’

cancer drugs

5

Scientific Paradox – Output Efficiency Has Declined 100xNumber of new molecules approved by FDA (pharma and biotech) per $bn of global R&D spending


Two Possible, Non-Exclusive, Explanations For Contrast Between R&D Input and Output Trends

Progressive adoption of worse discovery methods

Progressive depletion of tractable opportunities

6Source: Wiki Commons/public domain

* Actually, this alchemist is working with phosphorus, but it is a better picture than I could find of lead to gold transmutation


Why People Tend of Over-Estimate The Power of Intelligent DesignSurvivor Bias and Post-Hoc Rationalisation

• Things look as if the have been rationally designed, even if they follow from random variation and selection (e.g., the human eye)

• People forget the rational-sounding drug failures and invent post-hoc stories for successes

• Claims of rational design make investors write cheques and doctors write prescriptions

• Here are some brute facts:

– R&D costs per approved drug roughly 100x higher than in the 1950s

– Small molecule drugs seem more likely to fail in the clinic today than in the 1960s and1970s

7

Source: Wiki Commons/public domain


Charles Darwin: “… as more individuals are produced than can possibly survive, there must in every case be a struggle for existence...”

24.3

19.4

14.6

12.4

8.6

4.6

1.6

0

5

10

15

20

25

30

How many projects per approved drug?

1662

353

153

70

200

400

600

800

1000

1200

1400

1600

1800

All trials Companycollab.

Companyspons.

FDAindications

How many trials for 7 FDA-approved bevacizumab indications?

Sources: Paul, S et al., (2010). How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nature Reviews. Drug Discovery, 9(3), 203–14. doi:10.1038/nrd3078 (left chart) and Scannell, JW. (2015). Pharmaceutical evolution: Clinical selection versus intelligent design. ABPI UK Biopharma R&D Sourcebook 2015, reprinted as Innogen working paper no. 115 (right chart)

Nobody Knows Which Drugs Will Sell WellAnalysts Pre-Launch Forecasts Wildly Inaccurate, But Better Than Industry Forecasts

9

0

5

10

15

20

25

30

35

402

0%

30

%

50

%

70

%

90

%

11

0%

13

0%

15

0%

17

0%

19

0%

21

0%

23

0%

25

0%

27

0%

29

0%

31

0%

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0%

35

0%

37

0%

39

0%

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0%

43

0%

Nu

mb

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of

mo

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Consensus pre-launch peak sales forecast / actual peak sales

Source: adapted (i.e., smoothed data and including estimated curve for outliers) from Cha, Rifai, Sarraf in NRDD 12, 737-738 (2013). Analysis based on 260 drugs, with consensus forecasts taken one year before launch

analystsdisappointed

analystspleasantlysurprised


Field-based Discovery is Important, Yet Under-Researched and Under-Appreciated

22

7

85

59

0

20

40

60

80

100

120

140

160

1998 FDA approvals Subsequent new uses

Lab-led discovery

Field-based discovery

No new uses

60%

11%

29%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Basis of field discovery

Other

Serendipity

Understanding ofmechanism andpathophysiology

10Source: DeMonaco HJ, Ayfer A, Von Hippel, E. (2006). The Major Role of Clinicians in the Discovery of Off-Label Drug Therapies. Pharmacotherapy, 26(3), 323–332.


Drug Repurposing and User-Led Innovation in Common DiseasesThe Most Transformative Drugs of the Last 25 Years

• Anaesthetics: propofol & remifentanil

• Cardiology: lovastatin & ACE inhibitors

• Dermatology: TNF blockers & onabotulinum toxin

• Endocrinology: bisphosphonates & metformin

• Gastroenterology: omeprazole & TNF blockers

• Genetic disease: alglucerase & nitisinone

• Nephrology: ACE inhibitors & epoetin alfa

• Neurology: sumatriptan & interferon beta

• Oncology: imatinib & rituximab

• Ophthalmology: anti-VEGF agents & latanoprost

• Psychiatry: fluoxetine & clozapine

• Pulmonary: epoprostenol & fluticasone + salmeterol

• Rheumatology: TNF blockers & bisphosphonates

• Urology: sildenafil & tamsulosin

11Source: Kesselheim, A. S., & Avorn, J. (2013). The most transformative drugs of the past 25 years: a survey of physicians. Nature Reviews. Drug Discovery, 12(6), 425–31. doi:10.1038/nrd3977


To Summarise the Problem….

Targetidentification

Target validation

Screendevelopment

ScreeningLead

optimizationPreclinical

developmentClinical trials

Phase I-III

Phase I Phase II Phase IIIFDA,EMA,HTA

Basic science “push” has proven insufficiently predictive of clinical

activity in man*

Expensive bottleneck with questionable real-world

relevance

insufficient variationon which real-worldselection should act

Drugs for sale

12* Target-based drug discovery does appear to work better in genetically simply – often rare – diseases where its underlying assumptionsare more likely to be true. This may help explain the recent uptick in drug approval rates. For more information here, see Scannell et al. Nature Reviews Drug Discovery, 2012.


“Letting a Hundred Flowers Blossom and a Hundred Schools of Thought Contend is the Policy for Promoting Progress*”

• Incentivize user-led innovation

• Get the maximum amount of acceptably safe chemical diversity into the real world at minimum cost

• Invest to improve health systems’ ability to track and manage real-world efficacy and safety

• Reduce demands for PIII evidence that frequently fails to predict real-world utility but which adds cost

• Reduce the time it takes lessons learnt in the clinic to influence discovery

13* Mao Tse Tung circa 1956


Comments, Questions?(or Further Reading)

• For a reference list -> Scannell, JW. (2015). Pharmaceutical evolution: Clinical selection versus intelligent design. ABPI UK Biopharma R&D Sourcebook 2015, reprinted as Innogen working paper no. 115

• DeMonaco, J., Ali, A. & von Hippel, E. (2006). The major role of clinicians in the discovery of off-label drug therapies. Pharmacotherapy, vol. 26, pp. 323-332

• von Hippel, E., DeMonaco. J. & de Jong, J. (2014) Market failure in the peer-to-peer diffusion of user innovations: The case of “off-label” discoveries by medical clinicians. http://ssrn.com/abstract=2275562

• Roin, B. (2014) Solving The Problem of New Uses. http://nrs.harvard.edu/urn-3:HUL.InstRepos:11189865

• Scannell, JW., Blanckley, A., Boldon, H., & Warrington, B. (2012). Diagnosing the decline in pharmaceutical R&D efficiency. Nature Reviews Drug Discovery, 11(3), 191–200. doi:10.1038/nrd3681

• Scannell, JW., & Bosley, J. (2016). When quality beats quantity: Decision theory, drug discovery, and the reproducibility crisis. PLOS ONE. doi: 10.1371/journal.pone.0147215

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pharmaceutical evolution: the case for user led innovation · 2019-10-03 · pharmaceutical...

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