Lessons From 60 Years of Pharmaceutical Innovation Nature Munos

7/30/2019 Lessons From 60 Years of Pharmaceutical Innovation Nature Munos

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From 1950 to 2008, the US Food and Drug Administration(FDA) approed 1,222 new drugs (new molecular entities(NMEs) or new ioogis). Howeer, athough the ee ofinestment in pharmaeutia researh and deeopment(R&D) has inreased dramatiay during this period to US$50 iion per year at present1 the numer ofnew drugs that are approed annuay is no greater nowthan it was 50 years ago. Indeed, in 2008, ony 21 newdrugs were approed for marketing in the United States,whih is we eow the ee required to seure the futureof the pharmaeutia industry.

With the aim of inestigating this issue, this artieanayses the output of new drugs NMEs or new io-ogis approed y the FDA from the ompaniesresponsie during the past 60 years (see BOX 1 for detaisof the methodoogy). This anaysis shows that the rate ofprodution of new drugs y these ompanies has eenonstant (athough the rates differ for eah ompany)

sine they egan produing drugs. Surprisingy, nothingthat ompanies hae done in the past 60 years hasaffeted their rates of new-drug prodution: whetherarge or sma, foused on sma moeues or ioogis,operating in the twenty-first entury or in the 1950s,ompanies hae produed NMEs at steady rates, usu-ay we eow one per year. This harateristi raisesquestions aout the sustainaiity of the industrys R&Dmode, as osts per NME hae soared into iions ofdoars. It aso haenges the rationae for major mergersand aquisitions (M&A), as none has had a detet-ae effet on new-drug output. Finay, it suggests thatdrug ompanies need to e oder in redesigning their

researh organizations if they are to esape the inreasingpressures reated y inear new-drug output and rapidyrising R&D osts.

Rate of new drug introduction

Of the 1,222 NMEs that hae een approed sine 1950,1,103 are sma moeues and 119 are ioogis. FIGURE 1ashows the timeine of these approas. Athough at firstgane there are no oious patterns, on oser oser-

ation sute trends emerge. For the 30 years etween1950 and 1980, the trend ine is asiay fat. Thenfor the next 15 years, the ure sopes genty upwards,uminating in 51 approas in 1996, 4 years after theenatment of the Prescription Drug User Fee Act (PDUFA).Sine 1996, approas hae returned to their histori-a range. There has een speuation that the peak in1996 was aused y the FDA proessing a akog ofappiations with the hep of the reenty approed user

fees. Athough this may hae payed a part, other fa-tors were inoed, as disussed eow. A seond trendis that approas of ioogis are not taking off as mighte expeted from a new tehnoogy.

Many players, but few winners

At present, there are more than 4,300 ompanies that areengaged in drug innoation2, yet ony 261 organizations(6%) hae registered at east one NME sine 1950. Ofthese, ony 32 (12%) hae een in existene for the entire59-year period. The remaining 229 (88%) organizationshae faied, merged, een aquired, or were reated ysuh M&A deas, resuting in sustantia turnoer in the

Lilly Corporate Center,

Indianapolis,

Indiana 46285, USA.

e-mails: [email protected];

[email protected]

doi:10.1038/nrd2961

New molecular entity

(NME). A medication

containing an active ingredient

that has not been previously

approved for marketing in any

form in the United States. NMEis conventionally used to refer

only to small-molecule drugs,

but in this article the term

includes biologics as a

shorthand for both types of

new drug.

Prescription Drug User

Fee Act

A US law passed in 1992 that

allows the US Food and Drug

Administration to collect fees

from drug manufacturers to

fund the new-drug approval

process.

Lessons from 60 years ofpharmaceutical innovationBernard Munos

Abstract | Despte uprecedeted vestmet pharmaceutca research ad deveopmet

(R&D), the umber of ew drugs approved b the US Food ad Drug Admstrato (FDA)

remas ow. To hep uderstad ths coudrum, ths artce vestgates the record of

pharmaceutca ovato b aasg data o the compaes that troduced the

~1,200 ew drugs that have bee approved b the FDA sce 1950. Ths aass shows thatthe ew-drug output from pharmaceutca compaes ths perod has esseta bee

costat, ad remas so despte the attempts to crease t. Ths suggests that, cotrar to

commo percepto, the ew-drug output s ot depressed, but ma smp refect the

mtatos of the curret R&D mode. The mpcatos of these fdgs ad optos to

acheve sustaabt for the pharmaceutca dustr are dscussed.

AnAlySiS

NATURE REvIEWS |Drug Discovery vOlUME 8 | DEcEMbER 2009 |959

2009 Macmillan Publishers Limited. All rights reserved
mailto:[email protected]:[email protected]


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Box 1 | Data collection and analysis

Dntn

For the purpose of this study, an innovation is a new molecular entity (NME) or a new biologic approved by the US Foodand Drug Administration (FDA), and excludes non-drug compounds such as imaging agents and cosmetics. This articleuses NME as shorthand for both types of drugs.

Biologics are all therapeutic proteins, regardless of their approval route.

A blockbuster is defined here as an NME the peak sales of which exceed $1 billion, expressed in year-2000 dollars

equivalent. Figures have been adjusted for inflation using the US Bureau of Labor Statistics Drug Inflation Index.

A large pharmaceutical company is one of the top 15 drug companies, or their predecessors and joint ventures(for example, Ciba, SmithKline and DuPontMerck). All other companies, including biotechnology companies, arecategorized as small pharmaceutical companies.

Data

NME data were obtained from the FDA under a freedom-of-information request, and were cross-checked against Lillysown record, as well as lists of FDA approvals that are routinely published in the press. When several companies havecollaborated on the development of a drug, or the ownership of the compound has changed before approval, thecompany receiving FDA approval has been credited with the innovation.

Sales and patent data were obtained from the EvaluatePharma Database (see Further information).

Inflation data were obtained from the US Bureau of Labor Statistics.

T

All statistical calculations were done with the JMP software, version 5.1.1, or Excel 2007.

exn

11 organizations out of 261 (accounting for 13 NMEs out of 1,222) are non-commercial entities (non-profit andgovernmental organizations) or companies that no longer exist and have been omitted from most of the analysis.Of the remaining 250 companies, 26 (accounting for 63 NMEs) had fewer than 10 data points and were also excludedfrom most of the analysis.

Ptnta mtatn

It could be argued that defining innovation as NMEs approved by the FDA does not give due credit to innovationoriginating outside the United States. However, because the pharmaceutical industry is global, and the United Statesis by far its largest market, most NMEs are eventually submitted to the FDA for approval.

There is some debate about the number of NMEs that were approved prior to the US KefauverHarris amendment of1962, which requires proof of effectiveness and safety before a drug can be registered. Data that were compiled at thetime by de Haen and used in a congressional testimony by FDA Commissioner Alexander Schmidt in 1974 show that 360NMEs were approved between 1951 and 1962, or ~30 per year. It seems that this figure was subsequently revised by

the FDA, and reduced to 227. As this occurred a long time ago, it is difficult to reconstruct what happened. However, theediting seems to have been careful, as the data provided by the FDA meticulously listed each drug by its brand name andactive ingredient, as well as its dosage form, sponsor and exact date of approval. The 133 missing potential NMEs wereevidently excluded for a reason and, lacking evidence to challenge it, the data were used as provided.

It might be argued that NMEs are an inappropriate measure of innovation because a molecule with little therapeuticvalue can conceivably be approved, provided it meets FDA requirements. However, given the costs of drug researchand development, this is unlikely to occur in practice. A drug that is innovative may nevertheless fail to generatesubstantial sales revenues, raising questions about the value of its innovation (for example, as with Pfizers inhaledinsulin product Exubera). This article considers that these molecules represent genuine advances and should countas innovation, as their market failure can be explained by several factors that are unrelated to innovation, such asmispricing, reluctance on the part of physicians or patients to change from established drugs, and competition.

The decision to credit the company that secures a drug approval with the corresponding innovation could bequestioned, because that molecule may have been licensed from another company that receives no credit.As licensees are often thought to be large companies, and licensors smaller ones, this could potentially bias theanalysis by giving certain companies more, or less, credit than warranted. However, this concern does not seemto be justified. Over the past 20 years, a thriving market for innovation has developed, involving thousands ofcollaborations each year including licensing, cross-licensing, sublicensing, joint discovery, co-development,buy-back options, loans, equity stakes, outsourcing, warrants and joint ventures which often makes it impossibleto assess the precise contribution made by a company to the approval of a new drug. In addition, small companiesare eager participants in this market, in which they collaborate with other small companies more frequently thanwith large ones. Between 1980 and 2004, small companies had a slightly greater share of discovery projects than didlarger companies (47% versus 38%), but both shared equally in the number of development projects (45% versus46%)42. The view that the division of labour might allow large companies to capture a greater share of developmentprojects and FDA approvals is therefore not supported by the data. In addition, as innovation networks spread, thelocus of innovation tends to shift from individual companies to the network43. So, the decision to credit the companyat the centre of that network with the innovation that it creates seems to be justified, especially given that, byorganizing and managing the network to gain FDA approval, that company often makes the greatest contributionto the process.

AnAlysis

960 | DEcEMbER 2009 | vOlUME 8 www.nat.m/w/dd

http://www.evaluatepharma.com/Default.aspxhttp://www.evaluatepharma.com/Default.aspx


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industry(FIG. 1b). Of the 261 organizations, ony 105 existtoday, whereas 137 hae disappeared through M&A and19 were iquidated.

Despite this intense turnoer, the fat that 32 om-panies hae suried the entire period suggests thatthere are ways to innoate that are sustainae. Thisgroup inudes 23 ompanies that hae found uniqueways to thrie despite their smaer size. Some are highyfoused on a partiuar disease area or therapeutistrategy (Noo Nordisk, Ferring, Grifos, Ucb, Endoand Purdue); some se produts and series in addi-tion to drugs (baush and lom, and Aergan); someare entrenhed in their home-ountry market (Takeda,Santen, Eisai, Angeini and Orion); some are ongomer-ates (boehringerIngeheim, Soay, baxter and carterWaae); and some onentrate on generis (Tea andMission Pharmaa).

At the high end of the innoation sae, 21 om-panies hae produed haf of a the NMEs that haeeen approed sine 1950, ut haf of these ompaniesno onger exist. FIGURE 1c shows that Merk has een

the most produtie firm, with 56 approas, oseyfoowed y liy and Rohe, with 51 and 50 appro-as, respetiey. Gien that many arge pharmaeutiaompanies estimate they need to produe an aerage of23 NMEs per year to meet their growth ojeties, thefat that none of them has eer approahed this ee ofoutput is onerning.

The dynamics of drug innovation

The timeines of umuatie NME approas for the threemost produtie ompanies in the industry are shown inFIG. 2a. Surprisingy, the pots are amost straight ines,indiating that these ompanies hae deiered innoa-tion at a onstant rate for amost 60 years. The outputsfrom ess produtie ompanies, some of whih arepotted in FIG. 2b, show a simiar inear pattern, athoughit is more errati and with smaer sopes. The staerates of output that are apparent in FIGS 2a,b suggest thatNME prodution at a pharmaeutia ompany foowsa Poisson distriution. This hypothesis is onfirmedy the statistia anaysis desried in Suppementaryinformation S1 (ox)).

Importanty, as Poisson distriutions are harater-ized y a onstant ut stohastiay ariae rate ofourrene, this impies that the aerage annua NMEoutput of drug ompanies is onstant, and has een sofor neary 60 years. This is onsistent with the fat that

the drug industry produes no more NMEs today than60 years ago, whih has important impiations. If noth-ing that drug ompanies hae done in the past 60 yearshas sueeded in raising their mean annua NME output,there is not a high proaiity that estaished strategieswi hange this now. FIGURE 2c shows that the industrysNME output has traked its expeted aues. This sug-gests that the output may not e depressed, as ommonythought, ut may simpy refet the innoatie apaityof the estaished R&D mode. As the integrated orpo-rate aoratory is one of the few features shared y om-panies during the 60-year period, it is possie that theonstant NME output is a fixture of that mode. If this is

true, the industrys efforts to emrae new approahes toinnoation, suh as open innoation3, are of partiuarimportane.

Another surprising finding is that ompanies that doessentiay the same thingan hae rates of NME outputthat differ widey. This suggests there are sustantia dif-ferenes in the aiity of different ompanies to fosterinnoation. In this respet, the fat that the ompaniesthat hae reied heaiy on M&A (FIG. 2b) tend to agehind those that hae not (FIG. 2a) suggests that M&Aare not an effetie way to promote an innoation utureor remedy a defiit of innoation.

If the NME output of drug ompanies is onstant,the ony way to inrease the oera industry output is toinrease the numer of ompanies, whih runs ounterto the surge of M&A atiity of the past 12 years. Indeed,FIG. 2c suggests that there may e a orreation etweenthe expeted NME output for the industry (on the asis ofthe anaysis desried in Suppementary information S1(ox)) and the numer of ompanies inoed. A oserexamination of this reationship (FIG. 2d) onfirms that the

expeted NME output and the numer of ompanies areosey orreated in a noninear reationship that expains95% of the hanges in expeted NME output y hangesin the numer of ompanies. As the numer of ompa-nies inreases, the expeted NME output inreases morethan proportionay. One possie interpretation is that aarger numer of ompanies aeerates the aquisitionof knowedge, reating what eonomists a a spioer an industry-wide enefit that enaes a ompanies toe more produtie. This has important impiations forthe design of new R&D modes.

As an e seen in FIG. 2c, atua NME output for19961997 eary ies outside the 95% onfideneand of its expeted aue, suggesting that an externafator temporariy oosted the numer of NMEs thatwere approed. Seera expanations hae een offered,most of whih entre on the impat of the PDUFA of1992. They inude a earing of the akog of new drugappiations that were sumitted efore 1992; the set-ting of performane goas that required swift ation onpost-1992 sumissions; a surge of post-1992 sumissionsto take adantage of PDUFA efore it might expire; and atemporary aeeration of drug R&D aross the industryto try to inrease NME output. These hypotheses are notamenae to statistia testing, ut the ast two an ereadiy dismissed, as they are not onsistent with the waythe industry works. The fator reating to performane

goas may hae payed a part, ut muh of the surge anproay e asried to the earing of the akog ofnew drug appiations.

As the output of new ioogis aso foows a Poissondistriution, its pattern is simiar to that for NMEs, inwhih approas futuate around a onstant, ow ee(FIG. 1a). This has ed to the suggestion that iotehnoogy isnot deiering on its promise to inrease the rate of innoa-tioneause it has o-opted the pharmaeutia industrysageing usiness mode instead of rafting its own4.

lasty, further statistia anaysis (see Suppementaryinformation S2 (ox)) an e used to auate the pro-aiity that a ompanys NME output wi exeed 2 or

AnAlysis


http://www.nature.com/nrd/journal/v8/n12/suppinfo/nrd2961.htmlhttp://www.nature.com/nrd/journal/v8/n12/suppinfo/nrd2961.htmlhttp://www.nature.com/nrd/journal/v8/n12/suppinfo/nrd2961.htmlhttp://www.nature.com/nrd/journal/v8/n12/suppinfo/nrd2961.htmlhttp://www.nature.com/nrd/journal/v8/n12/suppinfo/nrd2961.htmlhttp://www.nature.com/nrd/journal/v8/n12/suppinfo/nrd2961.htmlhttp://www.nature.com/nrd/journal/v8/n12/suppinfo/nrd2961.htmlhttp://www.nature.com/nrd/journal/v8/n12/suppinfo/nrd2961.html


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NumberofNMEsorNBEs

60

50

40

30

20

10

0

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Small molecules (NMEs)Biopharmaceuticals (NBEs)Total

a

b

Numberof

NMEs

Percen

tag

e(%)

60

50

40

30

20

10

0

50

40

30

20

10

0

Merck

Lilly

Ho

ffmann

LaRoche

Pfizer

Wye

th

Abbo

tt

Up

john

Johnson

&Johnson

Schering

Plough

Burroughs

Wel

lcome

Warner

Lam

ber

t

Bristo

lMyers

Bristo

lMyers

Squ

ibb

Lederle

San

doz

Bayer

Novart

is

SmithKline

&French

Squ

ibb

Sterl

ing

Ciba

Number of NMEsCumulative percentage of NMEs

c

360 NMEs came from 9 bigpharmas that have existedfor the whole period

79 NMEs came from 23 smallpharmas that have existedfor the whole period

105 NMEs came from 66 smallpharmas created by M&A

60 NMEs came from 7 bigpharmas created by M&A

25 NMEs came from19 small pharmasthat were liquidated

193 NMEs came from103 small pharmasthat disappearedthrough M&A

400 NMEs camefrom 34 big pharmasthat disappearedthrough M&A

In existenceNo longer in existence

3 per year, whih are 0.06% and 0.003%, respetiey. Itis therefore unikey that most ompanies wi sueed inraising their NME output aoe what they onsider to e

their threshod of sustainaiity. It is equay unikey thatthe industry wi ahiee an oera output that is muhgreater than the urrent one.

The cost of NMEs

The ost per NME has een inreasing for deades.FIGURE 3a dispays 12 independent NME ost esti-mates514 spanning 48 years, and FIG. 3b pots the samedata on a ogarithmi sae. both harts show that NMEosts hae een growing exponentiay at an annua rateof 13.4% sine the 1950s.

Aording to the Pharmaeutia Researh andManufaturers of Ameria (PhRMA), the memers of

whih are mosty arge drug ompanies, R&D spend-ing has een growing at an aerage ompounded rate of12.3% sine 1970 (REF. 1). Athough the oera output

of NMEs has therefore stagnated, the industry is pro-duing them more effiienty as it has een ae to meetthe inrease in the ost per NME with a ess than om-mensurate inrease in R&D spending. In other words,the industry is etter at what it does than it was prei-ousy, muh of whih is to generate data to meet FDArequirements. Howeer, this inreased effiieny has nottransated into a sustained inrease in the disoery ofnew treatments.

DiMasi has estimated that the aerage ost per NMEwas $802 miion in 2000 for sma moeues8, and$1,318 miion in 2005 for ioogis11. These aerages,howeer, do not inude post-approa osts for Phase Iv

Fgure 1 | on nw d. a | Tmee of approvas of ew moecuar ettes (nMEs) ad ew boogca

ettes (nBEs) b the US Food ad Drug Admstrato (FDA) betwee 1950 ad 2008. b | Characterstcs of the 261

orgazatos that have produced the 1,222 nMEs approved sce 1950. | 21 compaes have produced haf of a the

nMEs that have bee approved sce 1950, athough haf of these compaes o oger exst . i parts b ad , both

ew sma moecues ad ew boogcs are grouped as nMEs for smpct. M&A, mergers ad acqustos. For detas

of the aass, see BOX 1.

AnAlysis




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Merck

RocheLilly

CumulativenumberofNMEs

60

50

40

30

20

10

0

1950

1953

1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

2001

2004

2007

a

Lilly (for comparison)Johnson & JohnsonWyethPfizerBristolMyers Squibb

CumulativenumberofNMEs

60

50

40

30

20

10

0

1950

1953

1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

2001

2004

2007

b

NMEs approvedNumber of companies

NumberofNM

Esapproved

Numberofc

ompanies

60

50

40

30

20

10

0

200

180

160

140

120

100

80

60

40

20

0

1950

1954

1958

1962

1966

1970

1974

1978

1982

1986

1990

1994

1998

2002

2006

c

ExpectedNMEoutput

29

27

25

23

21

19

15

17

180160140120100

d

Number of companies

ActualNMEs

ExpectedNMEs

studies that might e required y the FDA; they aso omitosts to gain reguatory approa in non-US markets orotain additiona ae aims for new indiations. Mostimportanty, they assume that the proaiity that amoeue wi suessfuy emerge from inia trias isaout 21.5%, whereas reent industry data suggest thatthis aue is 11.5%. When DiMasis figures are adjustedfor these items as we as for infation and other ostinreases (for exampe, owing to more stringent regu-

atory requirements), the ost per NME inreases on-sideray, as summarized in FIG. 3c. These aerages asoonea potentiay arge differenes etween ompanies.For exampe, etween 2000 and 2008, Pfizer spent a totaof $60 iion on R&D, and reeied FDA approa fornine NMEs. by ontrast, Progenis, whih reeied FDAapproa for methynatrexone romide (Reistor) in2008, spent $400 miion on R&D oer the same period,suggesting a ost per NME that is sustantiay owerthan Pfizers.

Estimating the ost of NMEs is ompex eause themoney spent on R&D is returned in reenue oer seerayears. R&D expenses shoud therefore e depreiated

oer that period. Howeer, the duration of this periodis unear, and has proay hanged oer time, as si-ene and reguations hae transformed drug researh.Pratiay, there is itte onsensus among experts onhow to apitaize and depreiate drug R&D. Puishedstudies hae used periods ranging from 4 to 12 years.

Howeer, the finding that drug ompanies produeNMEs at a onstant rate makes it possie to deeopsimpe estimates of NME osts at a ompany ee y

diiding eah ompanys annua R&D spending y its rateof NME prodution. FIGURE 3d shows the distriution ofNME osts aross the industry for 2008. Ony 27% ofompanies hae osts per NME eow $1.0 iion. Themagnitude of these figures is worrying and as for moreresearh to fuy understand the impiations.

Does regulation hinder innovation?

The growth in R&D spending is needed to offset infa-tion and the inreasing urden of reguation, as we asother fators that oud e ontriuting to greater osts,suh as higher faiure rates. As infation has een ~3.7%sine 1950 and the annua growth in R&D spending has

Fgure 2 | T dnam d nnatn. a | The cumuatve umber of ew moecuar ettes (nMEs) orgatg

from the three most productve compaes over the perod studed: Merck, l ad Roche.b | The cumuatve umber

of nMEs from seected compaes that have bee heav voved mergers ad acqustos, wth l cuded for

comparso. | The nME output of the dustr cose tracks the expected vaue o the bass of the aass descrbed

Suppemetar formato S1 (box), suggestg that output s ot depressed at preset, but smp refects the ovatve

capact of the estabshed research ad deveopmet mode. d | The expected nME output ad the umber of compaes

are cose correated a oear reatoshp that expas 95% of the chages expected nME output b chages

the umber of compaes.

AnAlysis




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|

c

NMEcost(US$billions)

2.0

1.5

1.0

0

0.5

2010199019701950

a

Estimate Value of item (US$ millions)

y = 1E113e0.1335x

R2 = 0.98

Log(NMEcost)

8.0

6.0

4.0

0

2.0

2010199019701950

by = 0.1335x260R2 = 0.98

Numberofcompanies

14

10

12

8

6

4

2

0

5

00

1,5

00

2,5

00

3,5

00

4,5

00

5,5

00

6,5

00

7,5

00

8,5

00

9,5

00

10,5

00

11,5

00

12,5

00

13,5

00

14,5

00

15,5

00

16,5

00

17,5

00

d

Cost per NME

DiMasi estimate in 2000 dollars Adjustment for post-approval R&D Adjustment for new indications

and non-US approvals (20%) Adjustment for success rate (11.5%

versus 21.5%)

Adjusted DiMasi estimate in 2000 dollars

80295160

697

1,754

3,911

Adjustment for inflation (3.7% per year)

Adjustment for other cost increases,

such as regulation (8.3% per year)

Adjusted DiMasi estimate in 2008 dollars

592

1,565

een 12.3%, one an infer that reguatory and otherosts hae een growing at ~8.3% annuay, whihtransates into a douing eery 8.5 years. This inreasehas often een attriuted to the inreasing prudeneof reguatory odies foowing the high-profie with-drawas of drugs suh as rofeoxi (vioxx; Merk),eriastatin (bayo; bayer), trogitazone (Rezuin;Warner-lamert) and isapride (Propusid; JanssenPharmaeutia).

The eidene aaiae on the effet of reguation oninnoation is more nuaned. It is interesting that a 1983study of the drug industry y the Nationa Aademy ofEngineering 15 had aready noted the inreasing regu-atory urden, and oied onern that the resutinghigher ost of innoation in the United States was under-mining the ompetitieness of the US drug industry andputting it at a disadantage ompared with its Europeanand Japanese ompetitors. In fat, the opposite hap-

pened: sine that report was puished, US pharmaeu-tia ompanies hae outperformed their internationaompetitors and emerged as the dominant fore in theindustry.

A possie reason for this paradox an e found inother researh puished at aout the same time16. Itshows that ountries with a more demanding regua-tory apparatus, suh as the United States and the UK,hae fostered a more innoatie and ompetitie phar-maeutia industry. This is eause exating regua-tory requirements fore ompanies to e more seetiein the ompounds that they aim to ring to market.conersey, ountries with more permissie systems

tend to produe drugs that may e suessfu in theirhome market, ut are generay not suffiienty innoa-tie to gain widespread approa and market aeptaneesewhere. This is onsistent with studies indiating that,y making researh more risky, stringent reguatoryrequirements atuay stimuate R&D inestment andpromote the emergene of an industry that is researhintensie, innoatie, dominated y few ompaniesandprofitae17,18.

Is bigger better?

A puzzing trend of reent years has een the graduaerosion in the share of innoation that is aptured yNMEs sponsored y arge pharmaeutia ompanies(see BOX 1 for definitions). Sine the eary 1980s, theirshare of NMEs has deined from ~75%, a ee that hadeen onstant sine 1950, to ~35% (FIG. 4a). At the sametime, the share of NMEs that is attriutae to sma io-

tehnoogy and pharmaeutia ompanies has amosttreed, from ~23% to neary 70%. Sine 2004, smaompanies hae onsistenty mathed or outperformedtheir arger ompetitors. The expeted share of NMEsgeneray foows these trends unti 2004, when theystaiize at aout 50% eah.

The inrease in the NME output from sma ompa-nies has een drien y two fators. The first is a rise inthe numer of sma ompanies that hae produed anNME, whih neary doued from 78 to 145 during the1980s and 1990s. This was faiitated y the growth of en-ture apita that has funded muh of the ioteh oom.Seond, the mean annua NME output of sma ompanies

Fgure 3 | T t nw d. a | A pot of tweve depedet estmates of the cost of a ew moecuar ett (nME)

spag 48 ears514. b | The same data potted o a ogarthmc scae. The expoet the e equato part a ad the

gradet of the e part b show that the cost per nME has grow at a aua compoud rate of 13.35% sce the ate

1950s. | Adjusted costs per nME: 2000 versus 2008. d | Dstrbuto of costs per nME for the dustr 2008.

AnAlysis




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Large companies Small companies

ShareofN

MEsapproved(%)

100

80

60

40

20

0

1950

1954

1958

1962

1966

1970

1974

1978

1982

1986

1990

1994

1998

2002

2006

aActual

Actual

Expected

Expected

MeanannualNMEoutputofs

mallcompanies

0.18

0.16

0.14

0.12

0.02

0.04

0.06

0.08

0.10

0.00

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

b

Orphan drug

A drug that is specifically

developed for a disease that

affects a patient population of

fewer than 200,000 people in

the United States. The Orphan

Drug Act provides financial

incentives to develop such

drugs, including marketing

exclusivity for that indication

for 7 years after approval.

has inreased from ~0.04 to ~0.12 sine 1995, owing to theemergene of new, more produtie ompanies (FIG. 4b).conersey, the deine in the output of arge ompanieshas een drien y the dwinding numer of arge phar-maeutia ompanies, whih has dereased y 50% oerthe past 20 years.

It is too eary to te whether the trends of the past10 years are artefats or eidene of a more fundamen-ta transformation of the drug innoation dynamis thathae preaied sine 1950. Hypotheses to expain these

trends, whih oud e tested in the future, inude: first,that the NME output of sma ompanies has inreasedas they hae eome more enmeshed in innoation net-works; seond, that arge ompanies are making moredetaied inestigations into fundamenta siene, whihstreth researh and reguatory timeines; and third,that the heightened safety onerns of reguators affetarge and sma ompanies differenty, perhaps eause asustantia numer of sma firms are deeoping orphandrugs and/or drugs that are ikey to gain priority reiewfrom the FDA owing to unmet media needs.

Aording to a reent report2, the 4,300 iotehno-ogy ompanies spend ~$28 iion annuay on R&D,

ompared with $50 iion for arge pharmaeutiaompanies1. by irtue of their numer, sma firms o-etiey an expore far more diretions, and inestigateareas that their arger, more onseratie ompetitorsaoid. Howeer, ony a sma fration of these smaompanies wi e rewarded with an FDA approa.Indiiduay, they are a muh ess reiae soure ofNMEs than arge ompanies, ut oetiey, they pro-due more, for ess. In this strange equation ies perhapsone potentia aenue for renewing the pharmaeutiaR&D mode. The innoation risis of the pharmaeutiaindustry is ourring in the midst of a new goden ageof sientifi disoery. If arge ompanies oud organizeinnoation networks to harness the sientifi diersity ofiotehnoogy ompanies and aademi institutions, andomine it with their own deeopment expertise, theymight e ae to reerse the fores that are underminingtheir researh mode; that is, they might e ae to owertheir osts and inrease their output.

Is consolidation good for innovation?

M&A atiity is often seen as a strategy to take a thin-ning pipeine. Using the data oeted for this study, thisstrategy an e tested y measuring the efore and afterPoisson parameters of ompanies that hae engaged inthese transations (as the expeted NME output of agroup of ompanies is equa to the sum of their Poissonparameters). The popuation in this study has 24 aquisi-tions and 6 mergers with a minimum of 10 years of dataefore and after eah transation. FIGURE 5a summarizesthe oetie expeted annua NME output of the om-panies inoed. Ony sma ompanies show a sight,ut signifiant, inrease in NME output at the 95% on-fidene ee. For arge ompanies, M&A do not seem toreate or destroy aue. In fat, one an summarize theimpat of M&A in the pharmaeutia industry on R&Das 1+1=1. This is onsistent with a reent anaysis19.

A more detaied anaysis adds interesting nuanes.FIGURE 5b ooks separatey at M&A inoing arge andsma ompanies. For arge ompanies, haf of the sixmergers anaysed inreased NME output (y 44%),whereas the other haf redued NME output (y 36%).For sma ompanies, neary 80% of aquisitions inreasedNME output (y 118%), whereas the rest redued NMEoutput (y 33%). For arge ompanies, the proportionswere reersed: 70% of aquisitions redued NME output(y 20%), whereas 30% of aquisitions inreased NMEoutput (y 41%). caution shoud e taken in interpreting

these numers eause of the sma sampe size. In addi-tion, many ompanies inoed in M&A sine 2000 werenot inuded in the anaysis eause they did not meetthe inusion riteria of 20 years of data. This anaysiswi need to e repeated as more data eome aaiae.For now, the eidene suggests that M&A an hep smaompanies, ut are not an effetie means to oost NMEoutput among arger ompanies.

What next?

Scaling patent cliffs. Not ony is the disoery of NMEseusie, ut their saes prospets are highy skewedtowards zero, further reduing the ikeihood of otaining

Fgure 4 | i b btt? a | Actua versus expected

shares of ew moecuar ettes (nMEs) for arge ad sma

pharmaceutca compaes. b | Mea aua nME output

for sma compaes. See BOX 1 for deftos.

AnAlysis




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ExpectedannualNMEoutput

16

14

12

10

8

6

0

2

4

Small companiesn = 15

a

Large companiesn = 15

Total samplen = 30

1.6

2.6

12.0 11.8

13.614.4

Before M&AAfter M&A

b Mergers(largecompanies)

Acquisition involvingSmallcompanies

Largecompanies

Number of companies

Resulted in greaterNME output

NME increase (%)

Resulted in lowerNME output

NME decrease (%)

6

50%

44%

50%

36%

14

79%

118%

21%

33%

10

30%

41%

70%

20%

Open-source R&DA broad-based participatory

research model in which a

virtual network of volunteers

use online tools to address a

problem in which they share

an interest.

Disruptive innovation

A process to turn cutting-edge

science into novel products

with such superior features

that they create vast new

markets, which unsettles

established products and

technology.

a return on inestment in R&D. FIGURE 6a pots the fre-queny distriution of peak saes for NMEs. It is asedon 329 of the more reent NMEs for whih suh data areaaiae. The underying data show that the proaiitythat an NME wi ahiee okuster status is ~21%, asuess rate that has not hanged in 20 years despite on-siderae inestment to improe the hanes of suess.This ow proaiity is seen een though arge pharma-eutia ompanies and enture apitaists wi sedomproeed with the deeopment of a moeue uness ithas okuster potentia, supported y sophistiatedforeasts and reiews y experiened exeuties.

More worryingy, it aso suggests that the industrysmost haowed ompetenies ustomer knowedge,disease expertise and deades of experiene do not

seem to e of muh hep in prediting suess20. Thisfores the industry to naigate without a reiae roadmap, whih is a haenge it shares with other ok-uster-dominated usinesses suh as motion pitures oroi and gas exporation. This has important impiationsfor the management of innoation, whih is disussed inthe next setion.

It is now possie to omine knowedge of druginnoation and new-produt saes with patent expira-tions to mode how drug ompanies might surie thearge upoming reenue osses aused y the expira-tion of patents on key okuster drugs, somethingoften referred to as patent iffs (see Suppementary

information S3 (ox) for a desription of this simua-tion too). FIGURES 6b,c summarize the resuts for the13 argest pharmaeutia ompanies, reated y usingthis too to generate proaiity distriutions for saesand net inome that refet the stohasti nature of druginnoation at the ompany ee. The resuts indiatethat ontinuing with the urrent usiness mode mayresut in a redution of 510% in saes and 2030% innet inome in 20122015. Saes shoud susequentyreoer to their 2011 peak, ut net inome may remaindown y 15% a performane that is unikey to peasestakehoders.

Choosing a course.If the performane of the urrentusiness mode annot satisfy stakehoders, M&A arenot a soution, and the proess improements and ost-utting measures that are ommony used annot makea suffiient differene, perhaps the industry ought toemrae more radia hange and seize the opportunityto redesign the mode. Four points to onsider in disus-sions on suh a redesign an e put forward on the asis

of the anaysis in this artie.First, the industry needs to hange its innoation

dynamis to moe eyond onstant NME output. This isa daunting task. As nothing that the industry has done inthe past 60 years has sustantiay affeted mean output,it must enture further away from its omfort zone as itreuids its R&D mode. As was noted y the preiouschief Exeutie Offier of GaxoSmithKine, Jean-PierreGarnier21: R&D produtiity is the numer one issue.If it is not fixed, nothing ese an work.

Seond, there are radia and suessfu experimentsthat an e used as uiding oks or for inspiration; forexampe, Innoentie22,23, chorus24, puipriate part-nerships2527, open-source R&D2830, X Prize31, innoationnetworks32, FIPNet33, onsortia and arious omina-tions of these and other initiaties34. These efforts aimto harness the goa rain to aess the est sieneand ideas whereer they may e. Suh open arhiteturefor R&D has key adantages: it heightens ompetition,redues osts and inreases agiity y making it easierto initiate and terminate projets. More importanty, itmakes it easier to manage disruptive innovation y oat-ing it outside the orporate was, where it an thrieunenumered.

Third, in many organizations, short-term prioritiesenourage margina innoation, whih proides morereiae returns on inestment, at the expense of major

hange. Therefore, organizations that depend on reak-through disoeries need a separate, proteted areathe soe purpose of whih is disruptie innoation. Inthe past, this was proided y independent aorato-ries, suh as bristoMyers Squis PharmaeutiaResearh Institute or Merk Researh las. Howeer,these units were neer quite ae to free themseesfrom orporate attempts to inrease the numer ofokusters y making sientists more responsie tomarket needs. The resut has usuay een a greateremphasis on imitatie researh, fewer reakthroughsand drugs that miss the okuster mark 80% ofthe time.

Fgure 5 | impat ndt ndatn. a | impact

of mergers ad acqustos (M&A) o ew moecuar

ett (nME) output. For sma compaes, there s a 95%

probabt that M&A has creased nME output sght.

However, for arge compaes, ad for the tota sampe,

there s a 95% probabt that M&A dd ot crease nME

output. b | Vaue created b M&A. See BOX 1 for deftos.

AnAlysis


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Frequency(%)

30

25

20

0

15

10

5

70 1 2 3 4 5 6

an = 329p (blockbuster) = 21%

Peak sales (US$ billions)

Salesoftop13companies

(US$billions)

450

400

350

0

300

250

200

150

100

50

2008 2010 2012 2014 2016 2018

b

Years

25% quartileMedian75% quartile

Netincomeoftop13companie

s

(US$billions)

90

80

70

0

60

50

40

30

20

10

2008 2010 2012 2014 2016 2018

c

Years

25% quartileMedian75% quartile

Black swan

A metaphor that designates

rare random events of key

importance that reshape

markets, industries and

societies.

Fourth, the industry must rethink its proess u-ture. Suess in the pharmaeutia industry depends onthe random ourrene of a few black swan35 produts.common proesses that are standard pratie in mostompanies reate itte aue in an industry dominated yokusters36. These inude deeoping saes foreasts fornew produts, whih are inaurate neary 80% of the time.Another exampe is portfoio management, whih has

een widey adopted y the industry as a risk managementtoo, ut has faied to protet it from patent iffs. Duringthe past oupe of deades, there has een a methodiaattempt to odify eery faet of the drug usiness intosophistiated proesses, in an effort to redue the ari-anes and inrease the preditaiity. This has produed afase sense of ontro oer a aspets of the pharmaeutiaenterprise, inuding innoation. As Jean-Pierre Garnierputs it: The eaders of major orporations inudingpharmaeutias hae inorrety assumed that R&D wassaae, oud e industriaized and oud e drien ydetaied metris and automation. The grand resut: a ossof persona aountaiity, transpareny and the passion ofsientists in disoery and deeopment37.

Conclusion

In the past 60 years, the pharmaeutia industry hasdeiered oer 1,220 new drugs that hae payed animportant part in improing pui heath and extend-ing ife expetany y an aerage of 2 months eah year38.The R&D mode that has powered that suess, howeer,

is showing signs of fatigue: osts are skyroketing, reak-through innoation is eing, ompetition is intense andsaes growth is fattening. This uster of symptoms hasoften foretod major disruption in other industries39,40.Their experienes show that industries an surie suhupheaas; someone aways finds a way to redesignthe usiness mode, ut that someone, ominousy, hassedom een an inument41.

coud pharmaeutias e different? Drug researhtoday is the ous of many interesting experiments thathae the potentia to rejuenate the R&D mode. Many ofthem are taking pae in areas that hae traditionay eenoerooked y the arge ompanies, suh as negeteddiseases and iodefene, whih is onsistent with thepreditions of cayton christensen41. Neertheess,arge ompanies hae aso sponsored some highy inno-

atie onepts, some of whih are highighted in thepreious setion. Howeer, athough these experimentsare proeeding, the industry is inreasingy aught in apiner etween an NME output that is essentiay inear,and ikey to remain so, and a ost of produing NMEsthat is inreasing exponentiay. At some point, the situ-ation wi eome untenae. This oud tempt inestorsto fore whoesae hange onto the industry, uness theindustry pre-empts them with radia initiaties.

Fgure 6 | sa nw ma ntt (NMe).

a | Frequec dstrbuto of peak saes for nMEs.

b | Predcted saes for the top 13 pharmaceutca

compaes from 20092018. | Predcted et come forthe top 13 pharmaceutca compaes from 20092018.

1. PhRMA. Pharmaceutical Industry Profile 2009.PhRMA website [online] (2009).

2. Burrill & Company. Biotech 2008: a 20/20 vision to

2020. Available from the BayBio website [online]

(2008).

3. Stoeffels, P. Collaborative innovation for the post-crisis

world. Boston Globe (2 Feb 2009).

4. Pisano, G. Science Business: The Promise, The Reality,

and The Future of Biotech (Harvard Business School

Press, Boston, Massachusetts, 2006).

5. Hansen, R. W. in Issues in Pharmaceutical Economics

(ed. Chien, R. I.) 151187 (Lexington Books,

Lexington, 1979).

6. DiMasi, J. A., Hansen, R. W., Grabowski, H. G.

& Lasagna, L. Cost of innovation in the

pharmaceutical industry.J. Health Econ. 10,

107142 (1991).

7. Wiggins, S. N. The Cost of Developing a New Drug(Pharmaceutical Manufacturers Association,

Washington, 1987).

8. DiMasi, J. A., Hansen, R. W. & Grabowski, H. G.

The price of innovation: new estimates of drug

development costs.J. Health Econ.22, 151185

(2003).

9. US Congress, Office of Technology Assessment.

Pharmaceutical R&D: Costs, Risks, and Rewards,

OTA-H-522 (US Government Printing Office,

Washington, 1993).

10. Lehman Brothers. Drug R&D Costs, Success

Rates, and Emerging Technologies (1997) in

PAREXELs Pharmaceutical R&D Statistical

Sourcebook 2000 (Parexel International Corp,

Waltham, 2000).

This document also includes another estimate

of NME costs from the US Office of Technology

Assessment for the year 1990.

11. DiMasi, J. A., Grabowski, H. G. The cost ofbiopharmaceutical R&D. Manage. Decis. Econ.28,

469479 (2007).

12. Schwartzman, D. Innovations in the Pharmaceutical

Industry (Johns Hopkins University Press, Baltimore,

1976).

13. Clymer, H. A. in The Economics of Drug Innovation

(ed. Cooper, J. D.) 109124 (The American University,

Washington, 1970).

14. Bailey, M. Research and development costs and

returns: the U. S. pharmaceutical industry.J. Polit.

Econ.80, 7085 (1972).

This article contains estimates of NME costs

before and after the 1962 KefauverHarris

amendment.

15. National Academy of Engineering. The

Competitive Status of the U.S. Pharmaceutical

Industry (National Academies Press, Washington,

1983).

AnAlysis


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16. Grabowski, H. G., Vernon, J. M. & Thomas, L. G.

Estimating the effects of regulation on

international innovation.J. Law Econ.21,

133163 (1978).

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Kennedy School of Government, Harvard University,

Cambridge, Massachusetts, 2007).

18. Scherer, F. M. Time-cost trade-offs in uncertain

empirical research projects. Nav. Res. Logistics Q.13,

7182 (1966).

19. Grabowski, H. G. & Kyle, M. in The Economics of

Corporate Governance and Mergers (eds Gugler, K.& Yurtoglu, B.) 262287 (Edward Elgar, Cheltenham,

2008).

20. Jack, A. An acute talent for innovation. Financial

Times (2 Feb 2009).

21. Iglehart, J. K. Good science and the marketplace for

drugs: a conversation with Jean-Pierre Garnier. Health

Aff.22, 119127 (2003).

22. Wilan, K. Profile: Alpheus Bingham. Nature Biotech.

25, 1072 (2007).

23. Travis, J. Science by the masses. Science319,

17501752 (2008).

24. Bonabeau, E., Bodick, N. & Armstrong R. A more

rational approach to new product development.

Harvard Bus. Rev. (March 2008).

25. Moran, M. et al.The new landscape of neglected

disease drug development(The Wellcome Trust,

London, 2006).

26. Moran, M. et al.The malaria product pipeline (The

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2007).

27. Berkley S. Ending an epidemic: the international AIDS

vaccine initiative pioneers a publicprivate

partnership. Innovations1, 5266 (2006).

28. Munos, B. Can open-source R&D reinvigorate drug

research? Nature Rev. Drug Discov.5, 723729 (2006).

29. Singh, S. India takes an open source approach to drug

discovery. Cell133, 201203 (2008).

30. Scott, W. & ODonnell, M. Distributed drug discovery,

Part 1: linking academia and combinatorial chemistry

to find drug leads for developing world diseases.

J. Comb. Chem. 11, 313 (2009).

31. Singer, E. The X Prizes new frontier: genomics.

Technology Review(5 Oct 2006).

32. Huston, L. (interview) Innovation networks: looking for

ideas outside the company. Knowledge@Wharton(14 Nov 2007).

33. Maurer, S. Choosing the right incentive strategy for

research and development in neglected diseases.

Bull. World Health Organ.84, 376381 (2006).

34. Hughes, B. An audience with Steven Paul. Nature Rev.

Drug Discov.8, 14 (2009).

35. Taleb, N. The Black Swan: The Impact of the Highly

Improbable (Random House, London, 2007).

36. De Vany, A. Hollywood Economics: How Extreme

Uncertainty Shapes the Film Industry (Routledge,

London, 2003).

An important book about the dynamics of

blockbuster economics, and its first rigorous

mathematical treatment.

37. Garnier, J.-P. Rebuilding the R&D engine in big

pharma. Harvard Bus. Rev.86, 6876 (2008).

A candid assessment by the former CEO of GSK of

the process culture that dominates pharmaceutical

R&D.

38. Arias, E. United States Life Tables, 2004. National

Center for Health Statistics website [online]

(2004).

39. Hartung, A. Create Marketplace Disruption (FT Press,

Upper Saddle River, 2008).

40. Anthony, S. D. Can you spot the early warnings?

Strategy & Innovation3, no. 2 (March/April 2005).

41. Christensen, C. Seeing Whats Next: Using Theories

of Innovation to Predict Industry Change (Harvard

Business School Press, Boston, Massachusetts,

2004).

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Leadership: Lessons from Pharmaceuticals (Center for

Transatlantic Relations, Washington, 2008).

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innovation.Admin. Sci. Q.41, 116145 (1996).

AcknowledgementsI thank the late Armen Tashjian (Harvard School of Public

Health and Harvard Medical School) for his unrelenting sup-

port, and M. Munos (Johns Hopkins School of Public Health)

for her extensive feedback on previous versions of the

manuscript.

Competing interests statementThe author declares competing financial interests: see web

version for details.

FURTHER INFORMATIONEvauatePharma databae:

http://www.evauatepharma.com/Defaut.aspx

SUPPLEMENTARY INFORMATIONsee oe artce: S1(box) | S2(box) | S3(box)

All liNks Are AcTive iN The oNliNe PDf

AnAlysis


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I fom poi y Mo (deceMber 2009)

Box S1 | What is a Poisson distribution?

The Poisson statistical distribution can used to model independent low-probability events arising at a constant but stochas-tic rate of occurrence. Typical applications include the number of defective parts in a batch, or the number of mutations ona stretch of DNA after a given irradiation treatment. Because of their constant rate of occurrence, the cumulative number ofPoisson-distributed events plots as a wavy straight line (as in figures 2a and 2b), which is a signature of this statistical dis-tribution.

The annual NME output of a firm is a discrete random variable N that can take the values 0, 1, 2, 3, etc. Since each yearsoutput (or observation) can be reasonably assumed to be independent of the output in prior and subsequent years, the observa-tions are said to be independent and identically distributed. If they follow a Poisson distribution, the probability that a com-

pany will produce exactly k NMEs during time t is:

P(k) =where: = annual rate of NME production for that company

t = time in years

To test whetherN follows a Poisson distribution, one measures its distance to a true Poisson distribution with a similarparameter. If the two distributions are identical, which is the null hypothesis, that distance converges to zero as the number

of observations increases. In practice, this is done with the help of the Kolmogorov-Smirnov test, which is included in manystatistical software packages.

Poisson random variables also have the unusual property that the mean and variance are both equal to the parameter. Thiseasily-tested necessary condition for Poisson distributions can provide additional evidence in support of the null hypothesis.

To test the null hypothesis, the Kolmogorov-Smirnov statistic was calculated for every random variable Ni

that had at least 10observations. Based upon the value of that test, the software estimated the probability that cis Poisson-distributed. Figure A1summarizes the results.

For companies older than 14 years, the probability that their NME output follows a Poisson distribution is greater than 90%. Forcompanies older than 27 years, it is 100%. There is no significant outlier, and the ranges of values are very tightly clustered.

Figure A2 shows the scatter plot of means (y axis) and variances (x axis) along with the 95% confidence intervals around themean for companies older than 27 years.

As can be seen, the slope of the regression line is very close to 1, and well within the confidence intervals of the means. Thet statistic testing the equality of the mean and variance is 0.0066, which is well within the critical values for rejecting the nullhypothesis at the 5% level.(2.27).

(t)k e

k!

Figure A1 | As the number of observations increases, the probability that

the null hypothesis is true quickly rises to 100%.

Figure A2 | Plot showing the equality of the means

and variances of the random variables Ni.

nature revIews |drug discovery www.nat.m/w/

SUPPLEMENTARY INFORMATION


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The fact that the random variables Ni are Poisson distributed implies that their sum is also Poisson dis-

tributed because the Poisson distribution is stable to addition. Consequently, the NME output of the drugindustry (or any subset of companies) follows a Poisson distribution whose parameter is equal to the sumof the parameters

iof the companies in the industry (or in that subset): =

i. Because the number of

active companies varies slightly from year-to-year due to mergers, acquisitions, and other business factors,the expected annual NME output for the industry will experience small year-to-year changes.




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Box S2 | Probability that the NME output of a company will exceed 2 or 3 per year

Using further statistical analysis, it is possible to calculate the probability that the NME output of a company will exceed 2 or3 per year.

Plotting the Poisson parameters is for our population (see supplementary information S1 (box)), rank-ordered by size, reveals

that they are not randomly distributed, but fall along a curve that recalls an exponential function (Figure B2). This in turnsuggests that their distribution might be modelled with a probability distribution from the exponential family. The gamma

distribution is a commonly-used and versatile member of this family, whose properties are driven by a shape parameter, a, anda scale parameter, b. When the shape parameter is between 0 and 1, the gamma distribution plots as an exponential curve.Figure B1 shows such plots for various combinations of a and b.

The mean, variance and skewness are simple expressions of a and b:

Mean = abVariance = ab2Skewness = 2a-

Estimating the mean, variance and skewness from the data for our population yields values of 0.17, 0.058 and 2.84 respec-tively. Therefore, if the

is are gamma-distributed, their distributions shape and scale parameters must satisfy the equations:

0.17 = ab0.058 = ab2

Solving them yields a solution for a = 0.50 and b = 0.34. These in turn give a value for skewness of 2.83, which matches theestimate of 2.84. It is therefore possible to fit a gamma distribution to the

is. Figure B2 compares the actual and the fitted

distributions. They overlap closely, as expected.

The fact that Poisson parameters are gamma-distributed makes it possible to calculate the probability that a companys NMEoutput will exceed 2 or 3 per year. IfF

(

i, 0.50, 0.34) is the cumulative distribution function:

F(

i>2, 0.50, 0.34) = 0.06%

F(

i>3, 0.50, 0.34) = 0.003%

These odds make it unlikely that most companies will succeed in raising their NME output above what they see as their

threshold of sustainability. It is equally unlikely that the industry will achieve a combined output that is much greater than thecurrent one.

Figure B3 shows the scatter plot of actual (x axis) versus simulated values (y axis).

The null hypothesis, which posits that Poisson parameters across drug companies are gamma-distributed, implies that theslope of the regression line in Figure B3 equals 1. The t-statistic testing the equality of the actual and fitted values is -2.16,which is within the critical values for rejecting the null hypothesis at the 5% level (2.27).

Figure B1 | Examples of gamma probability density function for various combinations of shape (a) and scale (b) parameters.




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Figure B3 | s plo of l iml ipm.

Figure B2 | compio of l iml ipm.




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Box S3 | The Monte Carlo simulation tool

The simulation tool calculates a companys income statements for the years 2009-2018. It starts with2008 actual numbers, and calculates the figures for the following years using the following assumptions:

Year-end sales are calculated by inflating beginning-of-the-year sales by 4% to reflect the price andvolume growth of existing products, then adding sales from the years NMEs, and subtracting saleslost to generics.The number of NMEs in a given year is drawn from a Poisson distribution that uses that companys

i parameter. For each NME, a peak sales is randomly generated using the frequency distributionshown in figure 6a. Sales are ramped up to reach 25% of peak the first year (pro-rated), 63% thesecond, and 100% the third.Sales lost to generics amount to 80% of an NMEs peak sales. The loss takes effect immediatelyupon patent expiration, and is pro-rated for the first year.Cost-of-goods-sold is a constant percentage of sales equal to the 2008 figure.R&D expenses are a constant percentage of sales equal to the 2008 figure.Selling, General and Administrative Expenses are equal to the 2008 actual figure, inflated by 2%each year to reflect wage inflation. Since the model aims at simulating the impact of business-as-usual, it makes no allowance for restructuring charges or headcount reduction.Other Income and Deductions are a constant percentage of sales equal to an average of the last 3years.Taxes are a constant percentage of sales equal to the 2008 figure.


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