HEALTH ECONOMICS
`Health Econ. (2009)
`Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hec.1454
`
`SPENDING ON NEW DRUG DEVELOPMENT1
`
`CHRISTOPHER PAUL ADAMSa, and VAN VU BRANTNERb
`aBureau of Economics, Federal Trade Commission, Washington, DC, USA
`bGlobal Consumer & Small Business Banking, Bank of America, Charlotte, NC, USA
`
`SUMMARY
`This paper replicates DiMasi et al. (J. Health Econ. 2003; 22: 151–185; Drug Inf. J. 2004; 38: 211–223) estimates of
`expenditure on new drug development using publicly available data. The paper estimates that average expenditure
`on drugs in human clinical trials is around $27m per year, with $17m per year on drugs in Phase I, $34m on drugs in
`Phase II and $27m per year on drugs in Phase III of the human clinical trials. The paper’s estimated expenditure on
`new drug development is somewhat greater than suggested by the survey results presented in DiMasi et al.
`(J. Health Econ. 2003; 22: 151–185; Drug Inf. J. 2004; 38: 211–223). The paper combines a 12-year panel of research
`and development expenditure for 183 publicly traded firms in the pharmaceutical industry with panel of drugs in
`human clinical trials for each firm over the same period. The paper estimates drug expenditure by estimating the
`relationship between research and development expenditure and the number of drugs in development for 1682
`company/years (183 firms multiplied by the number of years for which we have financial and drug development
`information). The paper also estimates expenditure on drugs in various therapeutic categories. Copyright r 2009
`John Wiley & Sons, Ltd.
`
`Received 14 May 2007; Revised 24 November 2008; Accepted 5 January 2009
`
`KEY WORDS: pharmaceuticals; drug development
`
`1. INTRODUCTION
`
`DiMasi et al. (2003, 2004) estimate the cost of new drug development for all drugs and for drugs in
`certain therapeutic categories, respectively. The authors estimate the average cost of new drug
`development to be $802m per new drug. This number has become a central part of the policy debates on
`numerous issues regarding the pharmaceutical industry including the Medicare Prescription Drug Act,
`drug importation, generic entry and vaccine development. Drug companies argue the high cost of drug
`development justifies the high prices paid by governments,
`insurers and customers. Given the
`importance of the $802m number to the debate it is important to know whether it is correct and what it
`means.
`DiMasi et al. (2003) calculate the cost of new drug development with data from two sources. The
`authors survey 10 large pharmaceutical firms and ask those firms to report the expenditure in human
`clinical trials for 68 drugs chosen at random from the Tuft’s drug development database called the
`CSDD. The authors then use information on average success rates and successful durations from the
`CSDD data to calculate the cost of bringing a new drug to market. Recently, Light and Warburton
`(2005) point out numerous problems with DiMasi et al. (2003). In particular, because ‘cost data used
`
`*Correspondence to: Bureau of Economics, Federal Trade Commission, 601 New Jersey Avenue NW, Washington, DC 20580,
`USA. E-mail: cadams@ftc.gov
`1The authors are not aware of any potential conflicts that may bias their work. As far as the authors are aware, the study raises no
`ethical issues.
`
`Copyright r 2009 John Wiley & Sons, Ltd.
`
`Petitioner Mylan Pharmaceuticals Inc. - Exhibit 1068 - Page 1
`
`

`
`C. P. ADAMS AND V. V. BRANTNER
`
`was proprietary and confidential, readers cannot know how each company collected its data, or what
`was counted as research costs, and no independent verification of the accuracy of the information is
`possible’ (p. 1031). This paper provides an independent verification of the survey cost data by using an
`alternative publicly available data source on research and development expenditure. Adams and
`Brantner (2006) verify the second part of DiMasi et al. (2003) paper by using publicly available data to
`estimate success rates and average successful durations.
`By comparing aggregate annual expenditure on research and development across firms and over time
`to the number of drugs in human clinical trials for each firm and each year, we can determine the
`‘marginal expenditure’ on an additional drug in development. If Drug Firm A spends an additional
`$50m in 1992 relative to 1991 but in 1992 Drug Firm A has two additional drugs in development we
`argue this provides an estimate of average annual expenditure by Drug Firm A, i.e. $25m per drug per
`year. Similarly, if Drug Firm B spends $100m more than Drug Firm A in 1992 but Drug Firm B has an
`additional four drugs in development in 1992, then we estimate drug expenditure to be $25m per drug
`per year. Note that this is an estimate of the correlation between expenditure and the number of drugs in
`development. We are not attempting to estimate the impact of an additional dollar of expenditure on
`the number of drugs in development or the impact of additional drug on the amount of expenditure.
`There are a number of advantages to this approach. First, we are using publicly available data so
`our results can be verified by other researchers. Second, we are using data from 183 publicly traded
`firms rather than 10 firms selected by the study’s authors. Our selection criteria is that the firms
`have research and development expenditure information in the CompuStat data base, be in the
`pharmaceutical industry (see Danzon et al., 2004) and have drugs in the Pharmaprojects data set
`(see Adams and Brantner, 2006). These firms range in size from 100 employees to almost 180 000
`employees with sales ranging from $2m annually to almost $45b annually. Third, we are using
`contemporaneous reports of research and development expenditure where the reports are scrutinized by
`both the market and the SEC. In their comment on DiMasi et al. (2003), Light and Warburton (2005)
`argue that
`
`considering the clear interest of pharmaceutical companies in higher (rather than lower) estimates of
`drug development costs, and sampled firms’ likely awareness of the intended use of the survey data, it
`is not unlikely that companies would deliberately and systematically overstate costs in their survey
`responses (p. 1031).
`
`We argue that such biases are less likely here given the large number of firms and the checks on the
`reports including audits.
`Of course there are also serious concerns about the approach we use here. First, the data are
`aggregate research and development expenditure. Those not only include expenditure on drugs in
`human clinical trials but also include development expenditure on drugs yet to reach trials. To identify
`the amount spent in human clinical trials we must infer the information from cross sectional and time-
`series variation in expenditure that is associated with variation in the number of drugs in development.
`Such variation may lead to spurious estimates. For example, if one firm specializes in anti-infective
`drugs and we compare the specialty firm’s expenditure on anti-infective drugs to that of a firm that has
`just one or two anti-infective drugs, we may estimate that expenditure on the extra drug as being small.
`This low estimate may be due savings from specialization rather than an accurate measure of the cost of
`adding another anti-infective drug.
`Second, we are estimating changes for the ‘marginal drug’, which may be more expensive than the
`average drug.2 The relationship between expenditure on the marginal drug and expenditure on the
`average drug depends on what assumption the reader is willing to make regarding how expenditure per
`
`2Thanks to Eric Durbin for pointing this out.
`
`Copyright r 2009 John Wiley & Sons, Ltd.
`
`Health Econ. (2009)
`DOI: 10.1002/hec
`
`Petitioner Mylan Pharmaceuticals Inc. - Exhibit 1068 - Page 2
`
`

`
`SPENDING ON NEW DRUG DEVELOPMENT
`
`drug changes with the number of drugs. If expenditure per drug is constant then the marginal and the
`average are the same. On the other hand, if expenditure per drug is increasing with the number of drugs
`in development then marginal expenditure will be higher than average expenditure. A number of papers
`suggest that there may be economies of scale or scope in drug development (Cockburn and Henderson,
`1996, 2001; Danzon et al., 2004). If there are economies of scale then we would expect marginal
`expenditure to be less than average expenditure.3 Note that marginal expenditure may be a more useful
`measure for determining the incentive effects of policy changes.
`Third, we use Pharmaprojects’ definition of a ‘drug development project’ and assign the drug to the
`‘originator’. In general, this definition corresponds to a new patented molecular entity. In the main
`part of the analysis we drop drugs that are new formulations of existing drugs (i.e. an extended
`release version of an existing drug). The analysis does not account for the fact that the drug
`development project is part of a joint venture (and thus expenditure is spread across multiple firms) or is
`being developed by an altogether different firm (and our method is assigning the drug project to the
`wrong firm).4 Such mis-measurement may bias our estimates downward. It should be noted that our
`counts of drugs in the different phases are measuring the development associated with the originating
`firm.
`In order to have a number that is comparable to DiMasi et al.’s (2003) average expenditure over the
`sample period, we control for differences between firms and differences over time. We attempt to
`control for some cross-sectional variation by conditioning on net sales. If for example, larger firms
`spend more on drug development projects than smaller firms then net sales should control for this
`variation. Similarly, if firms are spending more on drug development projects at the end of the period
`than at the beginning then our controls for time will provide a better sense of the average expenditure
`per project during the period. Note that identification of spending per drugs is coming to some extent
`from the fact that larger firms have more drugs and that there are more drugs over time in the database.
`The controls attempt to separately identify the effect of having another drug in human clinical trials
`from the effect of being large or later in time.
`DiMasi et al. (2003) uses a similar approach to verify their own estimates. The authors use firm level
`R&D expenditure reported by PhRMA and estimate lagged expenditure on firm level counts of
`approved drugs. The authors estimate average expenditure per approved drug to be between $354m and
`$558m. These numbers are similar to their estimate of $403m using the survey data. Other researchers
`have simply divided aggregate R&D expenditure by the total number of approvals per year. The
`concern with these approaches is that less than one in four drugs in human clinical trials actually make it
`to the market and the process can take between 6 and 12 years with substantial variation across drugs
`(Adams and Brantner, 2003).
`The rest of the paper proceeds as follows. Section 2 discusses the data used in this study and provides
`some background information on new drug development. Section 3 presents the results. Section 4
`concludes.
`
`2. DATA AND BACKGROUND
`
`This paper combines data from two data sources. Information on each firm’s research and development
`expenditure comes from the Standard Poor’s CompuStat Industrial file and Global Vantage Industrial
`Commercial file used by Danzon et al. (2004).5 This data set provides financial information on publicly
`traded drug companies including net sales, employment and expenditure on research and development.
`
`3To the extent one is concerned that large firms may have lower (or higher) expenditure per drug than smaller firms, some of this
`variation is accounted for in the analysis through conditioning on sales revenue.
`4Danzon et al. (2005) analyze joint ventures.
`5All monetary values are in 1999 dollars using the domestic manufacturing Producer Price Index.
`
`Copyright r 2009 John Wiley & Sons, Ltd.
`
`Health Econ. (2009)
`DOI: 10.1002/hec
`
`Petitioner Mylan Pharmaceuticals Inc. - Exhibit 1068 - Page 3
`
`

`
`C. P. ADAMS AND V. V. BRANTNER
`
`Table I. Firm/year summary statistics
`
`Variable
`
`Number of drugs
`R&D expenditure ($m)
`Net sales ($m)
`Employees (’000)
`
`Obs
`
`2245
`1682
`1701
`1537
`
`Mean
`
`4
`264
`2355
`11
`
`Median
`
`Std. Dev.
`
`2
`37
`110
`1
`
`6
`551
`5438
`25
`
`Max
`
`45
`4678
`44 611
`179
`
`Information on drugs in development comes from a Pharmaprojects data set used by Adams and
`Brantner (2006) and Abrantes-Metz et al. (2005). This data set uses public information to track drugs
`through the development process, providing information on the length of time in different phase as well
`as when and if drugs completed a development phase. The two data sets overlap for the years
`1989–2001. The data sets are matched using the name of the pharmaceutical firm.6 Pharmaprojects
`updates its information on the firms developing each drug after a merger, so we used text searches of the
`database and searches of a related data set called the Manufacturing Index to determine the ownership
`of drugs over time.7
`According to Danzon et al. (2004) there are 383 firms in their original data. Once we match these
`firms to firms in the Pharmaprojects data we are left with 183 firms. It is not clear exactly why there are
`firms that do not match. The two data sets do not exactly overlap in time and that may explain some of
`it. Another explanation is that the Pharmaprojects does not capture name changes or mergers among
`smaller firms (see footnote 7). Table I presents some basic summary statistics for this sample of firm/
`year combinations. Table I shows there are an average of four drugs in development for each firm for
`each year 1989–2001. Note this measure is not a very good measure of the stock of drugs in development
`because we only observe drugs entering one of the stages of human clinical trials after 1989. In the
`average firm/year $264m is spent on research and development, $2355m is made in sales and there are
`11 000 employees. Note that medians are substantially lower than the means suggesting that the
`distributions are all skewed toward zero.
`Figures 1–3 present the distribution of the number of drugs in human clinical trials per firm/year, the
`amount of R&D expenditure per firm/year, and a scatter plot of the two, respectively. The first two
`figures show that the distributions of drugs and expenditures are heavily skewed to zero. The third
`figure seems to show a positive correlation between the amount of R&D expenditure per firm per year
`and the number of drugs in development per firm per year.
`Figure 4 presents a summary of the research and development process for new drugs. The first
`stage of drug discovery is commonly called ‘preclinical development’. In this stage pharmaceutical
`firms analyze thousands of drugs to determine whether one may have an affect on a disease or
`condition. As candidates are discovered these drugs are tested on animals to determine whether the
`drug may be safe and effective in human beings. It is estimated that drugs spend over 4 years in
`preclinical testing. DiMasi et al. (2003) do not have direct survey information on preclinical expenditure
`because pharmaceutical firms do not track preclinical expenditure by particular drug candidates.
`Given this and given that the Pharmaprojects data are based on public information and are not very
`reliable regarding drugs in preclinical development, we do not estimate expenditure on preclinical
`development.
`After preclinical development the sponsoring firm applies for an investigation new drug application
`(IND) with the FDA in order to test the candidate in humans.8 There are three steps to human clinical
`
`6This matching was done by hand in order for it to be as accurate as possible.
`7This was done for all mergers involving firms in the Forbes’ top 20 of pharmaceutical industry over the period as well as any other
`major mergers in the pharmaceutical industry.
`8If the firm wants to eventually market the drug in the US the firm must apply for an IND prior to undertaking human trials. That
`said, there are exceptions.
`
`Copyright r 2009 John Wiley & Sons, Ltd.
`
`Health Econ. (2009)
`DOI: 10.1002/hec
`
`Petitioner Mylan Pharmaceuticals Inc. - Exhibit 1068 - Page 4
`
`

`
`SPENDING ON NEW DRUG DEVELOPMENT
`
`Figure 1. Drugs in development
`
`Figure 2. Annual R&D expenditure
`
`trials. In Phase I, the drug is tested for safety on a small group (e.g. 20) of healthy volunteers. Phase II
`tests concentrate on safety but the test is on a larger group of patients with the condition (e.g. 200).
`Phase III are the large efficacy trials with upwards of 3,000 patients participating. Once the trials are
`completed the results of all three stages are presented to the FDA in the form of a new drug application
`(NDA).
`Table II presents some basic summary statistics on the drugs owned by the firms in the sample. The
`first set of three rows show the mean length in months of successful durations. The second set of three
`
`Copyright r 2009 John Wiley & Sons, Ltd.
`
`Health Econ. (2009)
`DOI: 10.1002/hec
`
`Petitioner Mylan Pharmaceuticals Inc. - Exhibit 1068 - Page 5
`
`

`
`C. P. ADAMS AND V. V. BRANTNER
`
`Figure 3. R&D expenditure by drugs in development
`
`Figure 4. CDER chart of the development process
`
`Table II. Summary statistics for drugs
`
`Duration (months)
`Phase I
`Phase II
`Phase III
`Success (frequency)
`Phase I
`Phase II
`Phase III
`
`Obs
`
`235
`144
`130
`
`314
`302
`184
`
`Mean
`
`16.58
`30.65
`27.15
`
`0.75
`0.48
`0.71
`
`Copyright r 2009 John Wiley & Sons, Ltd.
`
`Health Econ. (2009)
`DOI: 10.1002/hec
`
`Petitioner Mylan Pharmaceuticals Inc. - Exhibit 1068 - Page 6
`
`

`
`SPENDING ON NEW DRUG DEVELOPMENT
`
`rows shows the frequency with which drugs successfully complete the phase. The table shows that these
`drugs seem to be fairly representative (see Adams and Brantner, 2006; DiMasi et al., 2003). Successful
`durations vary by a month or two and success rates vary by a few percentage points of those reported in
`Adams and Brantner (2006).
`
`3.1. Mean expenditure estimates
`
`3. RESULTS
`
`Table III presents regression results for the amount of research and development expenditure on the
`number of drugs in human clinical trials. There are six regressions reported in the table. First are the
`basic regressions on the number of drugs in human clinical trials then on the number of drugs in each of
`the three phases of development. These regressions are then repeated adding measures of time and firm
`characteristics. All results report robust standard errors clustering on firm name. The number of drugs
`is the number of drugs in development for each firm/year combination. Note that ‘new formulations’ of
`existing drugs are not included in the count variable.9 This is done in order to make the estimates closer
`to DiMasi et al. (2003) estimate for new molecular entities.10 The variable time is simply the number of
`years from 1988. The variable ‘sales’ is the amount of net sales for each firm/year. The time and sales
`variables allow the analysis to capture changes in expenditure over the time period and across firms,
`where ‘sales’ is probably best thought of a measure of firm size.
`Table III shows that average expenditure per drug in human clinical trials is between $74m and $27m
`per year.11 Once we include controls for time and firm characteristics, the results suggest that the
`average expenditure on drugs across all three phases of development is approximately $27m per year.
`This estimate is quite precise and is statistically different from zero at traditional levels. If sales are not
`accounted for then Phase I expenditure is estimated to be $81m per year, $68m for drugs in Phase II and
`$77m for drugs in Phase III. Once time and sales are accounted for, these estimates fall to $16m, $34m
`and $27m respectively.12 The Phase II and III expenditures are estimated precisely and are statistically
`different from zero. The Phase I estimate is less precisely estimated and 0 lies within the traditional
`confidence interval.
`How do these results compare to the estimates of expenditure in DiMasi et al. (2003)? We estimate
`the average annual expenditure on drugs in all three phases of human clinical trials is $27m. If we take
`DiMasi et al. (2003) estimates of expenditure for each phase of $15m, $24m and $86m for Phases I, II
`and III, respectively, and weight them proportionally to the time spent in development and the
`probability of being in each of the phases then we have the appropriate comparison.13 This
`transformation gives an estimate of annual expenditure of $21m.14 Our estimate is higher than this
`transformed estimate from DiMasi et al. (2003) although $21m lies within the 95% confidence
`interval.15 To compare the expenditure by phase it is necessary to do another transformation. The
`numbers presented in DiMasi et al. (2003) are for the average drug over the length of the phase, while
`
`9A new formulation may, for example, be an extended release version of an existing approved drug.
`10Thanks to an anonymous referee for this suggestion. The estimates of expenditure per drug including formulations are lower
`than the estimates presented here. The last section suggests that this occurs because expenditure on new formulations is
`substantially lower than for other drugs.
`11Most of the decrease seems to come from including the sales variable.
`12Note that these results are most properly thought of as correlations between the number of drugs and the amount of expenditure.
`There has been no effort made to account for endogeniety in the joint decisions to increase expenditure and take more drugs into
`clinical trials.
`13From DiMasi et al. (2003) the average durations are 12, 26 and 34 months, respectively.
`14The average expenditure is (12*1510.71*26*2410.31*34*86)/(12126134) 5 (18014431906)/72 5 21.
`15$21m does not lie within the 90% confidence interval. Although, this does not account for sampling error with the original
`DiMasi et al. (2003) estimate.
`
`Copyright r 2009 John Wiley & Sons, Ltd.
`
`Health Econ. (2009)
`DOI: 10.1002/hec
`
`Petitioner Mylan Pharmaceuticals Inc. - Exhibit 1068 - Page 7
`
`

`
`C. P. ADAMS AND V. V. BRANTNER
`
`Table III. R&D expenses OLS (robust standard errors)
`
`1
`74.31
`(5.87)
`
`All phases
`
`Phase I
`
`Phase II
`
`Phase III
`
`2
`
`80.72
`(18.61)
`68.01
`(12.72)
`76.94
`(23.90)
`
`Time
`
`Time2
`
`Sales
`
`Constant
`
`23.75
`(5.91)
`2.09
`(0.46)
`0.07
`(0.01)
`147.19
`147.97
`22.64
`21.49
`19.87
`(20.88)
`(21.00)
`(31.04)
`(36.18)
`(17.20)
`Observations
`1682
`1682
`1682
`1682
`1682
`R2
`0.59
`0.59
`0.60
`0.60
`0.89
`Standard errors are clustered on firm names. Is statisically different from 0 at 1% level and is at 5% level.
`
`5
`26.85
`(3.44)
`
`3
`74.86
`(5.91)
`
`62.34
`(10.88)
`4.32
`(0.83)
`
`4
`
`78.05
`(18.61)
`69.07
`(12.67)
`80.08
`(24.07)
`62.27
`(13.08)
`4.31
`(0.97)
`
`6
`
`16.78
`(10.35)
`33.59
`(6.80)
`26.78
`(11.08)
`25.31
`(6.76)
`2.19
`(0.53)
`0.07
`(0.01)
`23.16
`(17.69)
`1682
`0.89
`
`we have estimated expenditure for 1 year. If we use the phase durations presented in Table II we can
`estimate expenditure for the whole phase. This procedure gives 1.38 17m 5 $24m for Phase I, which is
`more than DiMasi et al. (2003) estimate of $15m for Phase I. For Phase II the same method produces an
`estimate of $86m, which is much higher than the DiMasi et al. (2003) estimate of $24m. Finally, for
`Phase III this method gives an estimate of $61m, which is less than the DiMasi et al. (2003) estimate of
`$86m. For Phases I and III, the DiMasi et al. (2003) estimates lie within the 95% confidence interval
`around our estimates. However, there is no overlap between the confidence interval around the DiMasi
`et al. (2003) estimate of Phase II expenditure and the confidence interval around our estimate.
`It is not clear what explains such a large discrepancy between our estimate of Phase II expenditure
`and DiMasi et al. (2003) estimate. One possibility and a more general concern is that our method may
`be misallocating expenditure to drugs in different stages of development. This may occur for two
`reasons. First, we assume that if a drug moves into a new phase in a particular year then the drug has
`been in that phase for the whole year. Still, given the expected difference in Phases II and III expenditure
`this assumption is more likely to lead to an underestimate of Phase III expenditure than an overestimate
`of Phase II expenditure. Second, the relationship between financial years and the years assigned in the
`data. Again, this may reduce the accuracy of the estimates but it is unlikely to bias the estimates.
`Another possibility is that there is under reporting of drugs in human clinical trials particularly in the
`earlier phases.16
`As other work has shown, expenditure on research and development is increasing at a substantial
`rate. In fact, here we have it increasing at a parabolic rate although this is due to the particular
`functional form that is used in the estimation. The results also show that there is a strong relationship
`between sales and research and development expenditure with every $1 in sales associated with an extra
`$0.07 in R&D expenditure. Note also, adding sales to the regression substantially improves the model’s
`ability to explain the data. The constant in this estimation cannot really be interpreted as we do not have
`a measure of the stock of drugs in development as of 1989. We are only able to observe new
`development starts in 1989 and later.
`The baseline regression measures the average expenditure on new drugs by firm and year. It does not
`account for whether that average may be driven up by the large increase in R&D expenditure observed
`
`16It is not clear exactly how such under reporting would bias the results and in what direction.
`
`Copyright r 2009 John Wiley & Sons, Ltd.
`
`Health Econ. (2009)
`DOI: 10.1002/hec
`
`Petitioner Mylan Pharmaceuticals Inc. - Exhibit 1068 - Page 8
`
`

`
`SPENDING ON NEW DRUG DEVELOPMENT
`
`during the period or by large expenditures by the larger pharmaceutical firms. Latter regressions add a
`parabolic time trend and a parameter (‘sales’) to capture variation across firms. The time trend estimates
`capture the large increase in expenditure that occurred during the period. The sales coefficient suggests
`that large firms, at least large firm/years, are associated with large expenditures per drug.17 This
`variation across firms is also captured to some extent via the quantile regression analysis presented in
`the next section.18 This analysis suggests larger firms spend more on clinical trials.
`Note that the measured positive relationship between sales and expenditure may not be causal.
`Larger pharmaceutical firms may have different R&D strategies than smaller firms. For example, Big
`Pharma may run substantially more trials over different
`treatments, comparison groups, and
`populations compared with smaller firms. Such trials may put the firm in a better position to sell the
`drug internationally and in multiple domestic markets for different indications. Note also that these
`numbers do not measure expenditure by smaller non-publicly traded firms such as those funded by
`venture capital firms.
`
`3.2. Quartile expenditure estimates
`
`Table IV presents results from the 25, 50 and 75% quartile regressions. Comparing these results to the
`results from columns 5 and 6 in Table III we see that expenditure per drug per year is substantially less
`at the lower quartiles. At the bottom quartile, expenditure per new drug in development is around $9m
`per year, with $15m at the median and $18m at the top quartile. All these numbers are estimated fairly
`precisely. These numbers compare to $27m per year for the mean. Similarly, estimated expenditures per
`phase of development are substantially lower at the 25, 50 and 75% quartiles relative to the mean. The
`results suggest that the distribution of expenditure on drug development is quite skewed. These results
`suggest that a number of firms spend very large sums on drug development.
`As before, we can transform our median estimates to compare with the median estimates presented in
`DiMasi et al. (2003). This procedure gives 1.38 14.60 5 $20m for Phase I, which is more than DiMasi
`et al. (2003) estimate of $14m for Phase I. For Phase II the same method produces an estimate of $36m,
`which is much higher than the DiMasi et al. (2003) estimate of $17m. Finally, for Phase III this method
`gives an estimate of $30m, which is much less than the DiMasi et al. (2003) estimate of $62m.
`
`3.3. Implications for cost estimates
`
`If we use the mean estimates for expenditure on drugs in development in place of the survey estimates
`used by DiMasi et al. (2003) we can recalculate the over all ‘cost of drug development’ or more
`accurately the net revenue needed to make investment in drug development profitable. Doing this
`calculation using the same durations and success rates as reported in Adams and Brantner (2006) we
`estimate new drug development cost to be $1214m, which is much higher than the original estimate of
`$802m or even the Adams and Brantner (2006) estimate of $867m. These high estimates may be due to
`measurement of expenditure on the marginal drug rather than the average drug.19 However, such an
`estimate may be more useful to policy makers as it is more likely to measure the impact of changes in
`
`17The baseline analysis is measured at the firm/year level. This means that we are measuring the expenditure of the average firm/
`year on a drug rather than the expenditure on the average drug. If the average firm/year is large then our measure may be high
`because large firms happen to spend more on drugs. By adding a coefficient for firm size the measure can be adjusted to account
`for the variation in expenditure across firms. Note however, that if the average drug is developed in a large firm then these results
`may need to be adjusted either by adding back in the sales coefficient multiplied by the sales of the firm, which produces the
`average drug or by looking at the quartile estimates in the next section.
`18Note that the coefficient estimate on sales is larger for the 75% quartile compared with the 50% quartile and the 25% quartile.
`That is, for drugs with larger expenditures there is a stronger relationship between the size of the firm and the size of the
`expenditures.
`19Note also that these estimates are based on the very high Phase II expenditure estimates.
`
`Copyright r 2009 John Wiley & Sons, Ltd.
`
`Health Econ. (2009)
`DOI: 10.1002/hec
`
`Petitioner Mylan Pharmaceuticals Inc. - Exhibit 1068 - Page 9
`
`

`
`C. P. ADAMS AND V. V. BRANTNER
`
`Table IV. R&D expenses quantiles (standard errors)
`
`25%
`
`50%
`
`50%
`14.60
`(0.19)
`
`75%
`18.68
`(0.15)
`
`75%
`
`18.31
`(0.44)
`22.59
`(0.39)
`15.45
`(0.50)
`4.09
`(0.85)
`0.36
`(0.06)
`0.10
`(0.00)
`13.81
`(2.69)
`1682
`0.80
`
`25%
`8.88
`(0.12)
`
`All phases
`
`Phase I
`
`Phase II
`
`Phase III
`
`Time
`
`Time2
`
`Sales
`
`Constant
`
`7.65
`18.35
`(0.42)
`(0.55)
`10.73
`13.98
`(0.36)
`(0.46)
`7.84
`13.05
`(0.44)
`(0.58)
`2.48
`2.56
`3.58
`3.52
`4.46
`(0.69)
`(0.80)
`(1.01)
`(1.00)
`(0.80)
`0.19
`0.19
`0.29
`0.29
`0.39
`(0.05)
`(0.05)
`(0.07)
`(0.07)
`(0.05)
`0.07
`0.07
`0.08
`0.08
`0.10
`(0.00)
`(0.00)
`(0.00)
`(0.00)
`(0.00)
`6.82
`6.54
`14.85
`2.15
`2.16
`(2.27)
`(2.61)
`(3.31)
`(3.26)
`(2.52)
`1682
`1682
`1682
`1682
`1682
`Observations
`Pseudo R2
`0.60
`0.60
`0.70
`0.70
`0.80
`Standard errors are in parenthesis. Refers to statistical significance from 0 at the 1% level, at the 5% level.
`
`policy on the development of new drugs. We could interpret these estimates as stating a firm would need
`expected net revenue of over $1 billion to develop one more drug for the market.20
`
`3.4. Expenditure by therapy
`
`DiMasi et al. (2004) presents estimates of drug development costs for a small number of major
`therapies. In attempt to replicate this work, Table V presents results similar to those presented in
`Table III but where the drug counts are by major therapeutic category. Table V presents the marginal
`cost of a drug by major therapy grouping. This number is estimated by counting the number of drugs in
`human clinical trials for each of the major categories presented.21 Note, that we would not expect these
`numbers to be negative.22 The table shows cardiovascular, dermatological, genitourinary, anticancer
`and neurological drugs all have more expenditure per drug in human clinical trials than the average
`drug. Note, however, only genitourinary drugs are estimated to be statistically different from the
`average at traditional levels. New formulations of existing drugs have substantially smaller expenditure
`in human clinical trials than the average drug. In fact, expenditure on new formulation is not estimated
`to be statistically different from zero, but is statistically different from the average. The reader may be
`surprised that biotech drugs are estimated to have less than average expenditure (although the estimate
`i