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`ELSEVIER
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`Available online at www.5ciencedirectcom
`acmuc:
`urn-c1-
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`JOURNAL OF
`mm
`ECONOMICS
`
`Joumal of Health Economics 22 (2003) IS] —l85
`
`www.elsevier.enm/locate/econbasc
`
`The price of innovation: new estimates
`of drug development costs
`
`Joseph A. DiMasi“, Ronald W. Hansenb, Henry G. Grabowskic
`“ Tufts Centerfor the Study ofDmg Deielopment. Tufts University. 193 South Street.
`Suite 550. Boston. MA 021]]. l/Sl'l
`
`b William E. Simon Graduate School QfBusmers' Adminirlmlinn. lx'nivemityQfRochexter. Rochester: NY. USA
`c Department ofEconamics. Duke University Durham. NC US»!
`
`Received l7 January 2002‘. received in revised lbnn 24 May 2002: accepted 28 October 2002
`
`Abstract
`
`The research and development costs 0168 randomly selected new drugs were obtained from a sur-
`vey 01' l 0 pliannaceutical Iinns. These data were used to estimate the average pre-tax cost ofnew drug
`development. The costs of compounds abandoned during testing were linked to the costs of com-
`pounds thut obtained marketing approval. The estimated average out-of-pocket cost per new drug is
`US$ 403 million (2000 dollars). Capitalizing out-of-poeket costs to the point of marketing approval
`at a real discount rate OH 1% yields a total pre-approval cost estimate ofUS$ 802 million (2000 dol-
`lars). When compared to the results ol'an earlier study mm a similar methodology, total capitalized
`costs were shovm to have increased at an annual rate ot’7.4% above general price inflation.
`C 2003 Elsevier Science B.V. All rights reserved.
`
`JEL classification: L65; 031
`
`Kgmiords: Innovation: R&D cost: Pharmaceutical industry: Discount rate. Technical success rates
`
`
`l. lntmduction
`
`Innovations in the health sciences have resulted in dramatic changes in the ability to treat
`disease and improve the quality of life. Expenditures on phamiaceuticals have grown faster
`than other major components of the health care system since the late 19905. Consequently.
`the debates on rising health care costs and thc dcvclopmcnt of new medical technologies
`have focused increasingly on the pharmaceutical industry. which is botha major participant
`in the health care industry and a major source of advances in health care technologies.
`
`‘ Corresponding author. 'l‘el.: +1-617-636-2l 16.
`E-mail address;josepll.dirnasi@tulls.edu (IA. DiMasi).
`
`see front matter £3“ 2003 Elsevier Science B.\’. All rights reserved.
`0167-6296503/5
`doi: 10. IO] 6v’SOl67~6296(02)00 l 26-]
`
`Exhbit 2067
`Slayback v. Sumitomo
`IPR2020—01053
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`l52
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`in. Dnllari e! at. Journal ofllealrh Economics 22 2003) 1517 185
`
`One of the key components of the discussion is the role of private sector phamtaeeutical
`industry investments in R&D and an understanding of the factors that affect this process.
`Although the industry engages in many forms of innovation. in general the most significant
`is the discovery and development of new chemical and biopharrnaceutical entities that
`become new therapies. Our prior research (DiMasi et al.. I991) found that the discovery
`and development of new drugs is a very lengthy and costly process. In the research-based
`drug industry. R&D decisions have very long-term ramifications. and the impact of market
`or public policy changes may not be fully realized for many years. From both a policy
`perspective. as well as an industrial perspective. it is therefore important to continue to
`analyve the components of and trends in the costs of pharmaceutical innovation.
`In this paper we will build on research conducted by the cunent authors (DiMasi et al..
`1991) and others on the economics of pharmaceutical R&D. As we described in our prior
`study. "Empirical analyses of the cost to discover and develop NCEs are interesting on
`several counts. First. knowledge of R&D costs is important for analyzing issues such as
`the returns on R&D investment. Second. the cost of a new drug has direct bearing on
`the organizational structure of innovation in pharmaceuticals. In this regard. higher real
`R&D costs have been cited as one of the main factors underlying the recent trend toward
`more mergers and industry consolidation. Third. R&D costs also influence the pattern
`of international resource allocation. Finally. the cost of R&D has become an important
`issue in its own right in the recent policy deliberations involving regulatory requirements
`and the economic performance of the pharmaceutical industry". In the decade that has
`followed the publication of our earlier study. these issues remain paramount. In addition.
`the congressional debates on Medicare prescription drug coverage and various new state
`initiatives to fill gaps in coverage for the elderly and the uninsured have intensified the
`interest in the performance of the pharmaceutical industry.
`In the current study we are not attempting to directly answer the policy debates men-
`tioned above. Rather. our focus is on providing new estimates of economic parameters
`associated with the drug development process. In particular. we concentrate on estimates
`of the costs of phamraceutical innovation. Our prior estimates have been used by the Office
`of Technology assessment (OTA). the Congressional Budget Office (CBO). and various
`researchers to analyze policy questions such as the effects on R&D activities of health care
`financing reform or changes in intellectual property legislation related to the phamiaceutical
`industry.
`The approach used in this paper follows our previous study (DiMasi et al.. 1991) and
`the earlier work by Hansen (1979). Given the similarity in methodologies. we are able
`to compare our results in the current study with the estimates in the earlier studies to
`illustrate trends in development costs. All three studies used micro-level data on the cost
`and timing of development obtained thrOugh confidential surveys of plannaceutical firms
`for a random sample of new drugs first investigated in humans by these firms. In the current
`study. the new drugs were first tested in humans anywhere in the world between 1983 and
`1994. The reported development costs ran through 2000.Ultimately. we are interested in
`the expected cost of development per approved new drug. The uncertainties in the research
`and development process result in expenditures on many development projects that are not
`successful in producing a marketed product. However. to produce an estimate of expected
`cost for a marketed product. we must allocate the costs of the unsuccessful projects to those
`
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`J11. Dill-last et al. "Journal ofHealth Economics 22 (2003) I51 I85
`
`153
`
`that result in a marketed new product. The R&D process is lengthy. and as such it is important
`to know at what stage of development expenses occur. Viewed as an investment project. it
`is necessary to know both the amountof expenditures and the timing of these expenditures.
`since funds committed to R&D in advance of any returns from sales have both a direct
`and an opportunity cost. We used a unique database to estimate various cost parameters
`in the development process. Of particular concern is the estimation of the average pre-tay
`cost of new drug development. since we are interested in the resource costs of new drug
`development and how they have changed over time.
`
`I. 1. Previous studies ofthe cost ofpharmaceutical innovation
`
`A summary of early studies of the cost of drug development can be found in the authors’
`previous study miMasi ct al.. 1991) and in OTA (1993). In brief. the early studies were
`either based on a case study of a specific drug (usually ignoring the cost of failed projects)
`or relied on aggregate data. Since the R&D process often extends for a decade or more
`and the new drug development process often changes. it is difficult to estimate the cost
`of development from aggregated annual data. In contrast. the study by Hansen (1979)
`and the current authors’ previous study (DiMasi et al.. 1991) estimated development cost
`based on data supplied by firms for a representative sample of drug development
`efforts.
`
`DiMasi ct al. (1991) used data on self-originated new drugs to estimate the average cost
`of developing a new drug. They obtained data from 12 pharmaceutical firms on the research
`and development costs of 93 randomly selected new drugs that entered clinical trials be-
`tween l970 and 1982. From these data they estimated the average pre-tav out-of-pocket
`cost per approved drug to be US$ 114 million (1987 dollars). Since these expenditures
`were spread out over nearly a dozen years. they capitalized these expenditures to the date
`of marketing approval using a 9% discount rate. This yielded an estimate of US$ 231
`million (1987 dollars). Measured in constant dollars. this value is more than double that
`obtained by Hansen for an earlier sample. DiMasi ct al. (1991) also found that the average
`cost of the first two phases of clinical trials doubled between the first and second half of
`their sample. This led to the expectation that development costs would be higher in future
`samples.
`Based on an analysis by Myers and Shyam-Sunder performed for the OTA. the OTA
`(1993) report noted that the cost-of-capital for the industry was roughly 10% in the early
`[9805. This is moderately higher than the 9% used by DiMasi et al. (1991). The OTA also
`recalculated the DiMasi et al. (1991) numbers using an interest rate that varied over the life
`of the R&D cycle thereby raising the cost estimate by US$ 100 million in 1990 dollars.1
`The OTA presented both pre- and post-tax cost estinmes.
`
`
`' The OTA applied a range of discount rates that varied with the time to marketing approval. They chose 14%
`for the earliest stage R&D and 10% for development just prior to approval. with rates in between that declined
`linearly With time in development. 'lhrs approach was meant to capture the essence of the risk-return staircase
`perspective expressed by Myers and others. and discussed below. The methodology described in Myers and liowe
`(1997) is actually quite different. but the OTA technique yielded results that would not be much different (for the
`same distribution of costs) than what one would have obtained with the correct methodology (Myers and Howe.
`1997. p. 33).
`
`

`

`154
`
`JA. Dbl-last et (11. "Journal ofHeaIth Economics 22 (2003) I51 .185
`
`
`
`1963 1967 1971 1975 1979 1983 1987 1991 1995 1999
`
`- NCEs +Real R&D (20003) ——~crs Trend
`
`28
`
`243
`g
`
`165
`3
`122M
`
`8 §
`4 Q
`
`0
`
`Fig. 1. Inflation-adjusted industry R&D expenditures (2000 dollars) and US new chemical entity (N'CE) approvals
`from 1963 to 2000. Source ol'data: l’hRMA (2001) and 'l'ut‘Ls CS DI) Approved NCF. Database.
`
`1.2. Aggregate dam wraivses
`
`There have been no recent comprehensive studies of the cost of developing new pharma-
`ceuticals from synthesis to marketing approval based on actual project-level data. However.
`aggregate data and data on parameters of the drug development process suggest that R&D
`costs have increased substantially since our earlier study. For example. the Pharmaceutical
`Research and Manufacturers of America (PhRMA. 2000) publishes an annual report on the
`R&D expenditures of its member firms that shows a continuous increase in outlays well in
`excess of inflation. Reports on specific components of the R&D process. such as the number
`of subjects in clinical trials (OTA. 1993: The Boston Consulting Group [ECG]. 1993). also
`suggest an increase in the real cost of pharmaceutical innovation.
`1
`Published aggregate industry data suggest that R&D costs have been increasing. Fig.
`shows reported aggregate annual domestic prescription drug R&D expenditures for mem-
`bers of the US pharmaceutical industry since 1963. The chart also shows the number of
`US new drug approvals by year. Given the much faster rate of growth of R&D expendi-
`tures. data such as these suggest that R&D costs have increased over time. However. they
`cannot be conclusive or precise. For one matter. the drug development process is known
`to be very lengthy. Thus. new drug approvals today are associated with R&D expenditures
`that were incurred many years prior. Ignoring the inherent lag structure underlying these
`data and simply dividing current R&D expenditures by the number of new drug approvals
`will in general yield inaccurate estimates? Given a substantial increasing trend in R&D
`
`2 lhe estimates would also vary widely from year-to-year. For example. if we divrded each year's real R&D
`expenditures by that year‘s nlunber of NC}: apprmals. we would obtain [83 1 billion for 2000, U83 743 million
`for I999. USS 839 million for 1998. USS 568 million for l997. USS 400 million for I996. USS 635 million for
`I995. and [SS R78 million for I994. While there is a general upward trend in such calculations. the year-to-year
`variability is not credible.
`
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`in. Dill-last et at. "Journal ofHealth Economics 22 (2003) I51 I85
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`l55
`
`expenditures. such calculations will result in greatly exaggerated estimates of out-of-pocket
`cost per approval.
`Secondly. even properly lagged time series would tend to be imprecise if aggregate in-
`dustry data were used as reported. The industry data include expenditures on improvements
`to existing products. Thus. they would overestimte pro-approval development costs. On
`the other hand. they also do not incorporate all of the R&D on licensed-in drugs since
`firms or other organi7ations that are not members of the US trade association would have
`conducted some of the work. On that account the data would tend to underestimate costs.
`
`Therefore. R&D cost estimates based on project-level data are needed to assure a reasonable
`level of confidence in the accuracy of the results. We present results based on such data in
`this study.
`The remainder of this paper is organized as follows. Section 2 describes the standard
`drug development paradigm. which serves as the structure through which the results are
`reported. Section 3 contains a description ofthe survey sample data and the population from
`which it was drawn. Section 4 describes the methodology used to derive R&D cost esti-
`mates. We present our base case pre-marketing approval R&D cost estimates in Section 5.
`as well as a comparison of our results with those of earlier studies to examine R&D cost
`trends. Section 6 provides sensitivity analyses for key parameters. Section 7 focrses on
`some extensions of the base case analyses: estimates of clinical development costs for ap-
`proved drugs by therapeutic significance. estimates of post-approval R&D costs. and a tax
`analysis. Section 8 contains data and analyses that corroborate our results. Finally. we offer
`some conclusions in Section 9.
`
`2. The new drug development process
`
`New dmg development can proceed along varied pathways for different compounds. but
`a development paradigm has been articulated that has long served well as a general model.
`The paradigm is explained in some detail elsewhere (DiMasi ct al.. 1991: US Food and Drug
`Administration [FDA]. 1999). In outline form, the paradigm portrays new drug discovery
`and development as proceeding in a sequence of (possibly overlapping) phases. Discovery
`programs result in the synthesis of compounds that are tested in assays and animal models.
`It was not possible to disaggregate our data into discovery and preclinical development
`testing costs.3 so for the purposes of this study discovery and preclinical development costs
`are grouped and referred to as preclinical costs.
`Clinical (human) testing typically proceeds through three successive phases. In phase I.
`a small number of usually healthy volunteers4 are tested to establish safe dosages and to
`gatherinforrnation on the absorption. distribution. metabolic effects. excretion. and toxicity
`of the compound. To conduct clinical testing in the United States. a manufacturer must first
`
`3 The reported basic research expenditures by firm were highly variable. and suggest that different firms may
`categorize their pre-human testing expenditures somewhat difl‘ererrtly. Thus. we report pre-human testing costs in
`one figure.
`4 in some therapeutic areas, testing is initially done on patients who have the disease or condition for which the
`compound is intended to be a treatment. This is ordinarily true in the cancer and AIDS areas.
`
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`

`156
`
`JA. Dill-last et at. "Journal ofHealth Economics 22 (2003) I51 185
`
`file an investigational new drug application (IND) with the FDA. However. initiation of
`human testing can. and ofien does. occur first outside the United States.
`Phase II trials are conducted with subjects who lave the targeted disease or condition
`and are designed to obtain evidence on safety and preliminary data on efficacy. The number
`of subjects tested in this phase is larger than in phase land may number in the hundreds.
`The final pre-approval clinical testing phase. phase III, typically consists of a number of
`large-scale (often multi-center) trials that are designed to firmly establish efficacy and to
`uncover side-effects that occur infrequently. The number of subjects in phase III trials for
`a compound can total in the thousands.
`Once drug developers believe that they have enough evidence of safety and efficacy.
`they will compile the results of their testing in an application to regulatory authorities
`for marketing approval. In the United States. manufacturers submit a new drug appli-
`cation (NDA) or a biological lieeme application (BLA) to the FDA for review and
`approval.
`
`3. Data
`
`Ten multinational pharmaceutical firms. including both foreign and US-owned firms.
`provided data through a confidential survey of their new dnig R&D costs.S Data were
`collected on clinical phase costs for a randomly selected sample of the investigational drugs
`of the fimis participating in the survey.6 The sample was taken from a Tufts Center for the
`Study of Drug Development (CSDD) database of investigational compounds. Cost and time
`data were also collected for expenditures on the kind of animal testing that often occurs
`concunently with clinical trials.7 The compounds chosen were all self-originated; that is.
`their development up to initial regulatory marketing approval was conducted under the
`auspices of the surveyed firm.8 Licensed-in compounds were excluded becarrse non-survey
`firms would have conducted portions of the R&D.9
`We also collected data from the cost survey participants on their aggregate annual phar-
`maceutical R&D expenditures for the period 1980—1999. The firms reported on total an-
`nual R&D expenditures broken down by expenditures on self-originated new dnigs. on
`licensed-in or otherwise acquired new drugs. and on already-approved dnigs. Annual ex-
`penditures on self-originated new drugs were further decomposed into expenditures during
`the pre-human and clinical periods.
`The National Institutes of Health (NIH) support through their own labs and through grants
`to researchers in academic and other non-profit institutions a substantial amount of research
`
`
`5 Using phannaceutical sales to measure lirrn size, four of the survey firms are top 10 companies. mother four
`are among the next IO largest fimts. and the remaining two are outside the top 20 (P513. 2000).
`‘5 A copy of the survey instrument is available upon request.
`7 long—tenn teratogenicity and carcinogenicity testing may he conducted aiter the initiation ofclinical trials.
`8 lhis does not preclude situations in which the firm sponsors trials that are conducted by or in collaboration
`with a govemment agency. an individual or group in academia. a non-profit institute. or another firm.
`9 Large pharmaceutical finns much more otten license-in than license-out new drug candidates. Firms that
`license-in compounds for further development pay a price for that right through up-front fees. milestone payments.
`and royalty arrangements.
`
`

`

`Jxl. Dill/last et (.11. «Journal ofHeaIrh Economics 22 (2003) I51 ~l85
`
`l 57
`
`that expands fundamental knowledge about human biology (NIH. 2000: Scherer. 2000).
`This basic research sometimes results in leads that industrial researchers can capitaliye
`on to assist them in discovering new therapeutic compounds.10 Some new compounds
`investigated by phamiaceutical fimis, however. originated in goverrunent or academic labs.
`It is unclear whether the discovery and early development costs for such compounds are
`similar to those for compounds originating in industrial labs. These drugs. though. represent
`a very small portion of the total number developed. For example. NIH (2000) found that
`of 47 FDA-approved drugs that had reached at least USS 500 million in US sales in 1999.
`the government had direct or indirect use or ownership patent rights to only four of them.“
`In addition. we used a Tufts CSDD database supplemented by commercial databases to
`determine that of the 284 new drugs approved in the United States from 1990 to 1999.12
`3.3% originated from industrial sources (either from the sponsoring firm or from another
`firm from which the compound was licensed or otherwise acquired). Government sources
`accounted for 3.2% of these approvals and academia and other non-profits accounted for
`the other 3.5%. ‘3
`The survey firms accounted for 42% of pharmaceutical industry R&D expenditures.”
`The survey compounds were selected at random from data contained in the Tufts CSDD
`database of imv'estigational compounds for the firms that agreed to participate in the R&D
`cost survey. Of the 68 compounds chosen. 61 are small molecule chemical entities. fourare
`recombinant proteins, two are monoclonal antibodies, and one is a vaccine. Initial human
`testing anywhere in the world for these compounds occurred during the period 1983—1994.
`Development costs were obtained through 2000.15
`
`'0 The NIH also supports the development ol'research tools that drug developers find useful. In addition. it funds
`training for many scientists. some of whom eventually are employed in the industrial sector.
`” lhe four dnrgs were developed in part through the use of NIH-funded patented technologies. Three of the
`four products are recombinant proteins. with two being the same drug produced by two dillerent companies. Each
`of the relevant patented teclurologies was developed at academic or non-profit institutions with financial suppon
`from the NIH.
`
`'2 The definition ot‘a new drug used for this analysis is a therapeutic new molecular entity approved by the F DA‘s
`Center for Dmg Evaluation and Research.
`'3 lhe proportion of investigational drugs that derive from industrial sources is likely to be even higher. since
`acquired drugs have higher clinical approval success rates than do self-originated drugs (Dr-Mam. 2001b). Our
`cost sm‘vcy linns were less reliant on licensing-in drugs from non-industrial sources than were firms as a whole;
`98.8% oftheir new drug approvals during 1990— 1999 were from industrial sources. DiMasi (2000) found markedly
`greater market entry of small niche pharmaceutical firms in the 19905 relative to earlier periods as measured by
`sponsorship of new chemical entity (NCE) approvals. A disproportionate share ofthe approvals obtained by these
`new entrants was for drugs that originated in academia.
`"' The data used were aggregate firm pharmaceutical R&D expenditures for the cost survey firms. as reported
`on our questiorurainr. in comparison to I’hRMA member firm R&D expenditures (1994 1997) on ethical phanna-
`ceuticals, adjusted to global expenditure levels (PhRMA. 2001).
`'5 Surveys were sent to 24 firms (some of. whom have since merged). Twelve finns responded that they would
`panicipate in some form. The data that two firms ultimately provided were not useable. The 10 firms from which
`we used data provided information on 76 compounds. However. the data for eight of these compounds were not
`sufficiently comprehensive to use. The firms that did not participate in the survey cited a number of reasons for
`not doing so. The reasons included the extra demands that the transition ell‘ects oI‘a relatively recent merger were
`placing on their relevant personnel. the time and expense of retrieving archival records in the manner required by
`the study, and difiiculties in gathering the relevant data in a uniform manner because their accounting systems had
`changed sigiificantly over the study period
`
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`158
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`JA. DIA-las/ et (11. «Journal ofHealth Economics 22 (2003) 151 185
`
`We selected a stratified random sample of investigational compounds. Stratification was
`based on the time elapsed since the origination of clinical trials and the current status of
`that testing. Reported costs were weighted to reflect the characteristics of the population.
`so that knowledge of the population from which the sample was drawn was needed. The
`population is composed of all investigational compounds in the Tufts CSDD investigational
`drug database that met study criteria: the compounds were self-originated and first tested in
`humans anywhere in the world from [983 to 1994. and we had the information necessary
`to classify them according our strata. We found 538 imrestigational drugs that met these
`criteria. Of these compounds. 82 (15.2%) have been approved for marketing. 9 (1.7%) had
`NDAs or BLAs that were submitted and are still active. 5 (0.9%) had NDAs or BLAs
`submitted but abandoned. 227 (42.2%) were terminated in 4 years or less from the initiation
`of clinical trials. 172 (32.0%) were terminated more than 4 years after the start of clinical
`testing. and 43 (8.0%) were still in active testing as of the most recent check (31 March
`2001).
`Some firms were not able to provide full phase cost data for every new dnig sampled.
`For example. phase I cost data were available for 66 of the 68 new drugs. However. we
`lmd some phase cost data for every drug in the sample. In addition. five compounds were
`still active at the time of the study. For these drugs it is possible that there will be some
`future costs for the drug’s most recent phase. Thus. for this reason our cost estimates may
`be somewhat conservative. However. given the small number of drugs in this category and
`the fact that the impact would be on only one phase for each of these drugs, our overall cost
`estimates are not likely to be materially alTectcd.
`
`4. Methodology for estimating new drug development costs
`
`The approach that we use to estimate development costs is similar to that described in
`our earlier work (DiMasi et al.. 1991). We will outline here the general methodology for
`developing an overall cost estimate. In describing the approach. it will be clear that cost
`estimates for important components of the drug development process will also be derived
`along the way.
`The survey sample was stratified to reduce sampling error. Results from previous anal-
`yses suggested that the variability of drug costs tends to increase with the development
`phase or the amount of time that a drug spends in testing (Hansen. 1979: DiMasi ct al..
`1991). Costs for successful drugs (ie. those that achieve regulatory approval) also tend to
`be higher and more variable than those for drug failures. Thus. we based our strata on the
`length of time that failed compounds were in clinical testing and whether or not a compound
`had reached the stage in which an application for marketing approval had been filed with
`the FDA.
`
`'° Specifically. we used four strata: compounds that failed in 4 years or less of clinical testing: corrpomds that
`failed alter more than 4 years had elapsed from initial human testing: compounds for which an NDA or a BLA had
`been submitted to the FDA; and compounds that were still in active testing (as of 30 March 2001 ). Compounds
`for which an application for marketing approval had been submitted or which had been abandoned afier lengthy
`testing were deliberately oversampled. The reported sample values were then weighted. where the weights were
`determined so that the sample perfectly reflects the population in terns of the four strata.
`
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`

`in. Dbl-last et (11. "Journal ofHeaItir Economics 22 (2003) I51 .185
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`l59
`
`4.]. Expected costs in the clinical period
`
`Since new drug development is a risky process. with many compounds failing for every
`one that succeeds. it is necessary to analyze costs in expected value terms. The total clinical
`period cost for an individual drug can be viewed as the realization of a random variable. 6.
`Given that it is not certainthat development ofa randomly selected investigational compound
`will proceed to a given phase. we may define expected clinical costs for a randomly selected
`investigational drug to be C = E (c) = PWIIc + pu/tmc-l- put/11mg + pAuAlc. where p1. Pu.
`and pin. are the probabilities that a randomly selected investigational compound will enter
`phases I—III. respectively. pA the probability that long-term animal testing will be conducted
`during the clinical trial period. and the ,u’s are conditional expectations. Specifically. it“...
`ttulc. urine. and [LAlc are the population mean costs for drugs that enter phases H“. and
`clinical period long-term animal testing, respectively.
`Weighted mean phase costs derived from the cost survey data were used to estimate the
`conditional expectations. A description of how the probabilities were estimated is presented
`in the next section. Assuming that the estimated mean phase costs and success probabilities
`are stochastically independent. the estimated expected value is an unbiased estimate of the
`population expected value.
`
`4.2. Clinical success and phase attrition rates
`
`An overall clinical approval success rate is the probability that a compound that enters the
`clinical testing pipeline will eventually be approved for marketing. Attrition rates describe
`the rate at which investigational dmgs fall out of testing in the various clinical phases. A
`phase success rate is the probability that a dmg will attain marketing approval if it enters the
`given phase. A phase transition probability is the likelihood that an investigational drug will
`proceed in testing front one phase to the next. All of these probabilities can be estimated
`from data in the Tufts CSDD database of investigational drugs from which our survey
`sample was drawn.
`The clinical approval success rate was estimated using a two-stage statistical estimation
`process that has been described in detail elsewhere (DiMasi etal.. 1991: DiMasi. 200 lb). The
`data used here consist of the investigatioml drugs in the Tufts CSDD database that were first
`tested in humans anywhere in the world from 1983 to 1994. with information on their status
`(approval or research abandonment) obtained through early 2001. Given that some of these
`investigational drugs were still in active testing at the end of the study period. some of the
`data are right-censored. Survival analysis can be applied in such a situation. where survival
`indicates that a drug has not reached its ultimate fate (either approval or abandonment).
`The Tufts CSDD database of investigational compounds contains infomiation on the
`latest phase that an abandoned compound was in when it was tenninated. These data were
`used to determine the distribution of research terminations by phases.” These results.
`
`'7 A small proportion of the compounds in the database were either still in clinical development (8.0%) orhad
`an NDA or BIA filed but not yet approved ( l .796). For those drugs in these groups that will eventually fail. their
`abandonment will tend to occur in later testing phases. To deal with the potential bias in the estimated distribution
`of research terminations that would result from usingjust these compounds that had been abandoned by the end of
`
`

`

`[60
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`JA. Dill-last et al. «Journal ofHealth Economics 22 (2003) I51 18.5
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`together with the estimated overall clinical approval success rate were used to provide
`estimates of the probability that an investigational drug will enter a given phase. phase
`attrition rates. and phase transition probabilities. The estimated overall clinical approval
`success rate and the probabilities of entering various phases provide results with which
`estimates can be derived that include the cost of dnigs that fail to make it through the
`development process. Specifically, we use the probabilities of entering a phase to estimate
`the expected out-of-pocket clinical cost per investigational drug. Adding the out-of-pocket
`preclinical cost estimate described below yields an estimate of total out-of-pocket cost per
`investigational drug. Dividing this estimate by the overall clinical success rate yields our
`estimate of out-of-pocket cost per approved drug.
`
`4. 3. Out-of—pocket discovery and preclinical development costs
`
`Many costs incurred prior to clinical testing cannot be attributed to specific compounds.
`Thus. aggregate level data at the firm level were used to impute costs per drug for R&D
`incurred prior to human testing. Specifically. time series data for each surveyed firm on
`spending on pre-human R&D and on human testing for 1980—1999 were o