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Adaptive Licensing: a useful approach for drug licensing in ... - TOPRA

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<strong>Adaptive</strong> <strong>Licens<strong>in</strong>g</strong>:<br />

a <strong>useful</strong> <strong>approach</strong> <strong>for</strong><br />

<strong>drug</strong> <strong>licens<strong>in</strong>g</strong> <strong>in</strong> the EU?<br />

London, <strong>TOPRA</strong>,<br />

November 2012<br />

Hans-Georg Eichler<br />

An agency of the European Union


‘Eroom’s Law’: The number of<br />

new <strong>drug</strong>s approved by the FDA<br />

per billion dollars (<strong>in</strong>flationadjusted)<br />

spent on R&D has<br />

halved roughly every 9 years<br />

Scannell JW et al. Nature Rev Drug Disc, March 2012<br />

2


Trend <strong>in</strong> actual cl<strong>in</strong>ical development time <strong>for</strong> new development projects<br />

approved between 2000-2009<br />

Median<br />

12<br />

11<br />

10<br />

9<br />

8<br />

7<br />

6<br />

5<br />

4<br />

3<br />

2<br />

1<br />

n of <strong>in</strong>terventions/trial patient<br />

n of patients/trial<br />

n of trials/dossier<br />

0<br />

2000(15)<br />

2001(20)<br />

2002(17)<br />

2003(10)<br />

2004(8)<br />

2005(11)<br />

2006(12)<br />

2007(7)<br />

2008(11)<br />

2009(12)<br />

Year of approval<br />

Actual cl<strong>in</strong>ical development time is calculated <strong>for</strong> new development projects as the time between ‘First human dose’ (T-1-1) and ‘First approval’ (T-4-2). Data<br />

represent all new development projects that reached ‘First approval’ (T-4-2) between 2000-2009, where the start and end milestone dates <strong>for</strong> the <strong>in</strong>terval are<br />

available. (n) = number of projects analysed <strong>in</strong> each year. This analysis is based on data from a consistent cohort of 17 companies participat<strong>in</strong>g each year<br />

between 2001 and 2010.<br />

3<br />

© CMR International, a Thomson Reuters bus<strong>in</strong>ess


Evolution of life<br />

Mutation<br />

Selection<br />

Evolution of <strong>drug</strong> regulation<br />

Catastrophe<br />

Clamp Down<br />

4


Knowledge, <strong>in</strong>vestment<br />

The b<strong>in</strong>ary nature of<br />

<strong>drug</strong> regulation<br />

Current model of <strong>licens<strong>in</strong>g</strong><br />

“The Magic Moment”<br />

Evidence vs. access trade-off<br />

Time (years)<br />

5


The regulator’s dilemma<br />

“…it has been said that the FDA has<br />

just two speeds of [<strong>drug</strong>] approval –<br />

too fast and too slow.”<br />

Hamburg MA & Sharfste<strong>in</strong> JM. NEJM 360;24: 2493-5; 2009<br />

6


EMA Road map to 2015<br />

[…] a key issue <strong>for</strong> regulators will be whether a<br />

more ‘staggered‘ approval (or progressive<br />

<strong>licens<strong>in</strong>g</strong>) concept should be envisaged <strong>for</strong><br />

situations not covered by conditional<br />

market<strong>in</strong>g authorisations […]<br />

The Agency would like to launch a debate with<br />

all stakeholders on the appropriateness of<br />

<strong>in</strong>troduc<strong>in</strong>g such a concept, <strong>in</strong>clud<strong>in</strong>g a<br />

consideration of appropriate <strong>in</strong>centives to<br />

support new medic<strong>in</strong>es development.<br />

7


Different names, same ideas<br />

• EMA: staggered approval<br />

• FDA: progressive reduction of uncerta<strong>in</strong>ty<br />

• Health Canada: progressive authorization<br />

• HSA S<strong>in</strong>gapore: test bed <strong>for</strong> adaptive regulation<br />

• Payers (HTAi): managed entry<br />

• MIT/NEWDIGS: adaptive <strong>licens<strong>in</strong>g</strong> project<br />

8


NEWDIGS - Collaborators<br />

(NEW Drug Development ParaDIGmS)<br />

Pharma/Biotech Regulators Academia Payers<br />

AstraZeneca<br />

BMS<br />

GSK<br />

J&J<br />

Novartis<br />

Pfizer<br />

Qu<strong>in</strong>tiles<br />

Sanofi-aventis<br />

FDA<br />

EMA<br />

HSA (S<strong>in</strong>gapore)<br />

HealthCanada<br />

SwissMedic<br />

MIT<br />

Harvard<br />

Sloan –Ketter<strong>in</strong>g<br />

MGH/Partners<br />

Dana Farber<br />

Natl U of S<strong>in</strong>gapore<br />

Aetna<br />

Medco<br />

Wellpo<strong>in</strong>t<br />

NICE<br />

9


Cl<strong>in</strong>ical Pharmacology & Therapeutics (2012); 91 3, 426–437<br />

10


Millions<br />

Gilenya Case Study Results<br />

11<br />

• <strong>Adaptive</strong> <strong>approach</strong> <strong>in</strong>creases NPV<br />

NPV<br />

eNPV<br />

Real $ 1,440,469,095 $ 325,180,180<br />

<strong>Adaptive</strong> $ 2,116,101,362 $ 378,517,356<br />

Gilenya NPVs by Scenario<br />

$2,000<br />

$1,800<br />

$1,600<br />

$1,400<br />

$1,200<br />

$1,000<br />

$800<br />

$600<br />

$400<br />

$200<br />

$-<br />

$1,440<br />

NPV<br />

$1,794<br />

$325<br />

eNPV<br />

$379<br />

Historic<br />

<strong>Adaptive</strong><br />

<strong>Adaptive</strong> <strong>Licens<strong>in</strong>g</strong> Economics<br />

December 2011


Knowledge, <strong>in</strong>vestment<br />

A better model <strong>for</strong> evolution?<br />

Current model of <strong>licens<strong>in</strong>g</strong><br />

“The Magic Moment”<br />

<strong>Adaptive</strong><br />

<strong>Licens<strong>in</strong>g</strong><br />

Time (years)<br />

12


<strong>Adaptive</strong> <strong>Licens<strong>in</strong>g</strong><br />

AL is a prospectively planned, adaptive <strong>approach</strong><br />

to regulation of <strong>drug</strong>s.<br />

Through iterative phases of evidence gather<strong>in</strong>g<br />

followed by regulatory evaluation and license<br />

adaptation,<br />

AL seeks to maximize the positive impact of new<br />

<strong>drug</strong>s on public health<br />

by balanc<strong>in</strong>g timely access <strong>for</strong> patients with the<br />

need to provide adequate evolv<strong>in</strong>g <strong>in</strong><strong>for</strong>mation<br />

on benefits and harms.<br />

13


<strong>Adaptive</strong> <strong>Licens<strong>in</strong>g</strong><br />

AL builds on exist<strong>in</strong>g regulatory processes,<br />

<strong>in</strong>clud<strong>in</strong>g Conditional Authorization and<br />

exist<strong>in</strong>g PhV tools.<br />

To achieve the full potential of AL, <strong>licens<strong>in</strong>g</strong><br />

decisions should ideally be aligned with<br />

coverage and prescribers’ decisions.<br />

14


“Precursors” to<br />

<strong>Adaptive</strong> <strong>Licens<strong>in</strong>g</strong><br />

• Conditional Market<strong>in</strong>g Authorization<br />

• New Pharmacovigilance legislation<br />

• Risk Management Plans<br />

• Periodic Safety Update Reports<br />

• Five-year renewal of market<strong>in</strong>g authorization<br />

• (Compassionate use programs)<br />

15


<strong>Adaptive</strong> <strong>Licens<strong>in</strong>g</strong><br />

AL builds on exist<strong>in</strong>g regulatory processes,<br />

<strong>in</strong>clud<strong>in</strong>g Conditional Authorization and<br />

exist<strong>in</strong>g PhV tools.<br />

To achieve the full potential of AL, <strong>licens<strong>in</strong>g</strong><br />

decisions should ideally be aligned with<br />

coverage and prescribers’ decisions.<br />

16


<strong>Adaptive</strong> <strong>Licens<strong>in</strong>g</strong> – a ‘systems <strong>approach</strong>’<br />

Regulatory<br />

Agency<br />

Payer / HTA<br />

body<br />

Prescriber<br />

Patient as<br />

payer<br />

Drug<br />

candidates<br />

Market and<br />

patient<br />

access<br />

Does the <strong>drug</strong><br />

do more good<br />

than harm <strong>in</strong> a<br />

def<strong>in</strong>ed group<br />

of patients?<br />

What are the<br />

health and cost<br />

consequences<br />

associated with<br />

this <strong>drug</strong><br />

relative to other<br />

<strong>in</strong>terventions?<br />

How does the<br />

<strong>drug</strong> per<strong>for</strong>m<br />

relative to other<br />

<strong>in</strong>terventions <strong>in</strong><br />

this patient?<br />

Am I will<strong>in</strong>g and<br />

able to pay <strong>for</strong><br />

this treatment<br />

out-of-pocket?<br />

Eichler et al, Nature Rev Drug Disc, 2010<br />

17


AL scenarios – “design factors”<br />

Incremental vs. trans<strong>for</strong>mative <strong>approach</strong>es<br />

• broaden treatment-eligible population<br />

• reduce uncerta<strong>in</strong>ty around endpo<strong>in</strong>t<br />

• reduce uncerta<strong>in</strong>ty around study design<br />

• reduce statistical uncerta<strong>in</strong>ty<br />

• enable new-new combo development<br />

• ensure effectiveness<br />

• address rare Adverse Events<br />

18


The O2 problem<br />

Obstacles and Opportunities<br />

• Not just focus on front end (<strong>in</strong>itial <strong>licens<strong>in</strong>g</strong>)<br />

• Make the ’systems <strong>approach</strong>’ work<br />

• Rapid learn<strong>in</strong>g systems<br />

• Cont<strong>in</strong>ual optimisation of <strong>drug</strong> use<br />

19


Conclusions?<br />

• Not a panacea, not necessarily a route <strong>for</strong> all<br />

<strong>drug</strong>s, one size doesn't fit all,<br />

• but might help regulators avoid the<br />

reputation trap (“too fast and too slow”),<br />

• if properly managed and communicated,<br />

might be the best (or only?) option to<br />

balance the access and evidence.<br />

20


Thank you!<br />

21

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