WMZJWJ: 11—29
`ilmrmmunammsmzo
`ORIGINAL RESEARCH ARTICLE
`QMWWMWM
`
`Returns on Research and Development
`for 19905 New Drug Introductions
`
`Henry Grabowslci,1 Iohn Vernon1 and Ioseph A. DiMasi2
`
`1 Department of Economics, Duke University, Durham, North Carolina, USA
`
`2 Tufts Center for the Study of Drug Development, Tufts University, Boston, USA
`
`Abstract
`
`Background: Previously published research by the authors found that returns on
`research and development (R&D) for drugs introduced into the US mar-
`ket in the I970s and l980s were highly skewed and that the top decile of
`new drugs accounted for close to half the overall market value. In the l9905,
`however, the R&D environment for new medicines underwent a number of
`
`the rapid growth of managed-care or-
`changes including the following:
`ganisations; indications that R&D costs were rising at a rate faster than that of
`overall inflation; new market strategies of major firms aimed at simultaneous
`launches across world markets; and the increased attention focused on the
`
`pharmaceutical industry in the political arena.
`
`Objective: The aim of this study was to examine the worldwide retums on R&D
`for drugs introduced into the US market in the first half of the 1990s, given that
`there have been significant changes to the R&D environment for new medicines
`over the past decade or so.
`
`Results: Analysis of new drugs entering the market from 19.90 to 1994 resulted
`in findings similar to those of the earlier research — pharmaceutical R&D is
`characterised by a highly skewed distribution of returns and a mean industry
`internal rate of return modestly in excess of the cost of capital.
`
`Conclusions: Although the distribution of retums on R&D for new drugs con-
`tinues to be highly skewed, the analysis reveals that a number of dynamic forces
`are currently at work in the industry. In particular, R&D costs as well as new drug
`introductions, sales and contribution margins increased significantly compared
`with their 19805 values.
`
`Competition in the research-based pharma-
`
`Our prior analyses indicate that this industry has
`
`ceutical industry centres on the introduction of new
`
`exhibited very skewed distributions of returns. In
`
`drug therapies. In this paper, we examine the re-
`
`this regard, several significant new classes of drug
`
`turns on research and development (R&D) for new
`
`therapies have been introduced since the late
`
`drug entities introduced into the US market in
`the first half of the 19905. This research work
`
`1970s. Early movers in these classes have obtained
`
`the highest returns on R&D. We found that the top
`
`builds directly on earlier analyses of returns on
`
`decile of new drugs accounted for close to half of the
`
`R&D for the 19?0s and 19805 introductions per-
`formed by Grabowski and Vemonlml
`
`overall market value associated with all the new drug
`
`introductions in our 19705 and 19805’ samples.
`
`Exhibit 1075
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`12
`Gmbowski et at.
`
`
`The results of our prior analysis are also consis-
`tent with an economic model of rivalrous R&D
`
`consider the effects on R&D of the proposed
`Clinton Health Care Reform Act and the Waxman-
`
`competition. In particular, the promise of above-
`
`Hatch Act of 1984.53]
`
`average expected returns produces rapid increases
`
`In the next section of this paper, we describe the
`
`in industry R&D expenditures, as firms compete to
`
`exploit these opportunities until the returns be-
`
`come unattractive. From an industry perspective,
`our results indicate that mean returns on R&D are
`
`relatively close in value to the risk-adjusted cost of
`
`capital for drug industry investments. This rent-
`
`seeking model is also supported by a recent empir-
`
`ical analysis by Scherer, who finds a strong rela-
`
`tionship between industry R&D outlays and profits
`over the period 1962 to 1996.31
`
`An investigation into the drug returns in the
`
`1990s is timely on a number of grounds. First, this
`
`decade has been characterised by the rapid growth
`
`of managed-care organisations on the demand side
`of the market for pharmaceuticals.[4] This has led
`to greater access to and utilisation of pharmaceuti-
`
`cals, but also greater generic competition in the
`
`post-patent period. Second, a new study of R&D
`
`costs by DiMasi and colleagues indicates that the
`
`R&D costs for new drugs have continued to rise
`much faster than the rate of general inflation.[5]
`This reflects, among other factors, the increased
`
`size of clinical trials compared with those for ear-
`
`lier new drug introductions. Third, many firms are
`
`changing their market strategies and attempting to
`
`launch their products simultaneously across world
`
`markets, reflecting the higher R&D investment
`
`costs and more intensive competition from new
`
`molecules in the same product class.
`
`In addition to these economic developments,
`
`the industry continues to be the subject of consid-
`
`erable attention by policy makers. Recent policy
`
`initiatives in the US include a Medicare prescrip-
`
`tion drug benefit, the parallel importation of drugs
`
`from Canada and Mexico, and various state pro-
`
`grammes affecting drug costs and utilisation by the
`
`poor and elderly populations. The potential effects
`
`of these policy initiatives on R&D returns remain
`
`data samples and methodology for our analysis of
`the returns to 1990 to 1994 new chemical entities
`
`(NCEs). ‘Empirical Results’ presents the empirical
`
`findings on the distribution of returns and a sensitiv-
`
`ity analysis involving the main economic parameters.
`
`‘Drug Innovation and Industry Evolution Since 1970’
`
`provides a discussion of the results and compari-
`
`sons with the historical findings from our prior work,
`
`which is based on the same methodology. The final
`
`section provides a brief summary and conclusions.
`
`Methodology and Data Inputs
`
`Overview
`
`This section explains the methodology and key
`
`data inputs used in estimating the returns to 1990
`
`to 1994 NCEs. Our sample includes ‘large-mole-
`
`cule’ biologics, in addition to traditional ‘small
`
`molecule’ chemical drugs. A detailed discussion of
`
`the general methodology is provided in our earlier
`papers on R&D returns.[1*2] Our focus here is on
`the similarities and differences of the 19905 sample
`
`compared with our analysis of prior NCE cohorts.
`
`The basic sample comprises 118 NCEs intro-
`duced into the US between 1990 and 1994. This is a
`
`comprehensive sample of the NCEs originating from
`
`and developed by the pharmaceutical industry that
`were introduced into the US in the 1990 to 1994 time
`
`period. However, three drugs were omitted from our
`
`sample because they failed to appear in any year in
`the IMS sales data audits. These drugs were distrib-
`uted outside of normal sales channels and were
`
`likely to have nonrepresentative R&D costs be-
`
`cause of their special indications.
`The number of NCE introductions increased
`
`significantly in the early [9905 compared with the
`1980s. The corresponding 1980 to 1984 sample
`was 64 NCEs. This increase in NCEs reflects the
`
`an important issue for research. Our past work on
`
`increased R&D expenditures for new entities by
`
`R&D returns has provided a framework for the
`
`the traditional pharmaceutical industry as well as
`
`Congressional Budget Office and other groups to
`
`the growth of the independent biopharmaceutical
`
`© MES Intemotlonol Lh'fled. All rights reserved.
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`13
`R&D Returns for 19905 New Drug Inlroduclions
`
`
`industry.[3] The latter industry was in its infancy
`in the early 1980s, but by the early 1990s it had
`
`mean cost of capital for pharmaceuticals over this
`
`period was just over 1 1%. Consequently, I 1% was
`
`become a significant source of new drug introduc-
`tions. There is also a significant increase in the
`
`selected as the baseline value for the cost of capital
`
`in this analysis of 1990 NCEs. This represents a
`
`number of new drugs approved for orphan drug
`indications. As we have discussed elsewhere, there
`
`small increase from the 10.5% cost of capital
`utilised for the 1980 NCEs.
`
`is a high degree of overlap between the biopharma-
`ceutical and orphan drug sub-samples.[3]
`
`Our basic procedure is as follows: for each new
`drug in our sample, worldwide sales profiles are
`
`constructed over the drug’s product life cycle.
`
`These sales values are converted to after-tax prof-
`
`its and cash-flow values using industry data on
`
`profit margins and other economic parameters.
`These data are combined with R&D investment in-
`
`formation, based on the recent analysis by DiMasi et
`al.[51 Mean net present values (NPVs) and internal
`rate of return (lRRs) are then computed for this port-
`
`folio of new drug introductions. The distribution
`of returns is another major focus of our analysis.
`
`Cos’r of Capital
`
`In our earlier analysis of 1980 NCEs, we
`
`utilised a 10.5% real cost of capital for the phar-
`
`maceutical firms. This was based on an analysis of
`
`the industry using the capital asset pricing model
`
`that was performed by Myers and
`(CAPM)
`Shyum—Sunder.[9] Their study was commissioned by
`the Office of Technology Assessment as part of a
`larger study on R&D costs, risk and rewardsJID]
`They found that the real after-tax cost of capital on
`
`equity plus debt varied between 10 and 11% during
`the 1980s.
`
`For our sample of 1990 to 1994 introductions,
`
`the relevant investment period spans the mid-
`
`1980s through the late 1990s. In their original ar-
`
`ticle, Myers and Shyum-Sunder provided esti-
`
`As Myers and Shyum—Sunder indicated in their
`
`original article,
`
`the CAPM approach provides
`
`somewhat conservative cost-of—capital values with
`
`respect to investment in new prescription drugs.
`
`One reason is that the equity market data on which
`
`the CAPM analysis is based pertain to all the dif-
`ferent functional areas and commercial activities
`
`of drug firms (which can include over-the-counter
`
`drugs, animal health, basic chemicals, etc.). An-
`
`other reason why the cost of capital may be under-
`
`stated is the fact that many pharmaceutical firms
`
`carry significant cash balances. Indeed, Myers and
`
`Shyum-Sunder found that many pharmaceutical
`
`firms have large positive cash balances and are ac-
`
`tually net lenders rather than net borrowers. Con-
`
`sequently, these firms have a negative debt ratio.
`
`Myers and Shyum-Sunder did a sensitivity analy-
`
`sis to gauge how this factor would affect their 1990
`
`value and they found it causes the nominal (and
`
`real cost) of capital to increase by almost a full
`percentage point.[91
`Several surveys have been performed of the
`
`hurdle rates used by US companies. A general
`
`finding is that hurdle rates are typically greater
`
`than the weighted cost of capital computed by a
`CAPM analysism] One of the authors undertook
`an informal survey of six pharmaceutical firms in
`
`mid-2001 with respect to the hurdle rates that drug
`firms utilise in their R&D investment decisions.
`
`The survey of these firms yielded (nominal) hurdle
`rates from 13.5% to over 20%. If one takes 3% as
`
`mates of the cost of capital for 1985 and 1990.
`
`the long-run expected rate of inflation, then an
`
`Myers and Howe have subsequently provided a
`related analysis for 1994111] We also performed a
`comparable CAPM for analysis for January 2000.
`The results of these CAPM-based studies are
`
`summarised in DiMasi et al.[5]
`
`1 1% real rate of return corresponds to a nominal
`
`rate of 14%. This 14% rate is within the range of
`
`hurdle rates utilised by the drug firms in their R&D
`investment decisions, but it is at the lower end of
`
`the range. This is consistent with the view that a
`
`Using these four CAPM-based analyses, occur-
`
`CAPM analysis provides conservative estimates
`
`ring at roughly 5-year intervals, we found that the
`
`on the industry’s cost of capital.
`
`© MES Intemufloml Lh'fled. All rights reserved.
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`14
`Gmbowski et at.
`
`
`Myers and Howe further indicate that the R&D
`
`Using this extrapolation procedure, we esti-
`
`decision process can be modelled as a compound
`option pricing model.[“] Under this model, at any
`point in the R&D decision-making process, future
`
`R&D serves as a form of leverage, or debt, assum-
`
`mated the mean out—of-pocket R&D expenditures for
`the drugs in our sample to be $US308.4 million. This
`is approximately double the estimated R&D expen-
`ditures (in $US, 2000 values) for the 1980 to 1984
`
`ing the firm decides to undertake further develop-
`
`samples of NCEs. DiMasi also estimated a repre-
`
`ment and marketing. Since this ‘debt’ or leverage
`
`sentative investment period of 12 years from initial
`
`declines over the subsequent stages of the R&D
`
`drug synthesis to Food and Drug Administration
`
`process, so will the firm’s cost of capital. lmple-
`
`(FDA) approval. We were able to allocate the out-
`
`mentation of this model requires unobservable
`
`informational inputs compared with the standard
`
`CAPM approach using a weighted cost of capital.
`DiMasi et al.[51 performed a sensitivity analysis us-
`ing this option value approach, and showed that for
`
`reasonable values of the forward looking discount
`
`rates, the CAPM and option value models yield
`
`comparable results.
`
`Research and Development (R&D)
`
`Investment Expenditures
`
`To obtain representative R&D investment ex-
`
`penditures for the new drug entities in our sample,
`
`we relied on the recently completed study by
`DiMasi et al.[51 This study obtained R&D cost
`data for a randomly constructed sample of 68
`
`of-pocket R&D costs over this 12-year period us-
`ing weights derived from the DiMasi et al. study.[5]
`Capitalising these costs to the date of marketing,
`at a real cost of capital of l 1%, yields $U8613
`
`million as the average (pre-tax) capitalised R&D
`
`investment per 1990 to 1994 NCE introduction.
`
`Our analysis is performed on an after-tax basis.
`
`For the time period under study, we estimated a
`
`30% average effective tax rate for the pharmaceu-
`
`tical industry (see ‘Effective Tax Rates’). Since
`
`R&D expenditures can be expensed for tax pur-
`
`poses, we multiplied the pre-tax values by 0.7 to
`
`get an after-tax value. This is shown in the first row
`
`of table I. Utilising the 30% effective tax rate,
`$U3613 million pre-tax capitalised corresponds to
`an after-tax value of $U S429 million.
`
`In addition to these pre-launch R&D expendi-
`
`drugs first tested clinically between 1983 and
`
`tures, firms also undertake R&D outlays in the
`
`1994. The DiMasi study is designed to measure
`
`post-approval period for product extensions such
`
`the average cost of a new drug introduction and
`
`as new indications, formulations and dosage lev-
`
`includes discovery costs as well as the costs as-
`sociated with failed candidates.
`
`The mean introduction of our sample NCEs is
`
`1992 while the mean introduction of drug candi-
`
`dates analysed in the DiMasi study is 1997. DiMasi
`and colleagues had previously undertaken an anal-
`
`ysis of the costs of 19805 introductions using
`
`the same methodology employed in their new
`study.[131 That study was centred on 1984. Given
`the availability of these two R&D cost studies
`centred around 1984 and 1997, we can utilise a
`
`linear extrapolation procedure to estimate the mean
`R&D costs for our sample cohort.l
`
`1 Since our sample is centred around 1992, we utilise the
`following linear extrapolation equation to derive R&D costs:
`R&D92 = R&Dg4 + (8113) R&D9'}.
`
`els. Since these activities can be viewed as spillo-
`
`vers from the original N CE introduction, these on-
`
`going R&D investment expenditures, as well as
`
`any extra revenues that they generate, are appro-
`
`priately incorporated into the analysis. On the
`basis of the DiMasi et al. study,[5] we estimated
`
`TdJle L Capilalised research and development (H&D) costs torthe
`meannewd'lemiml entityinlt'le1990to 1994 san'ple
`H&D costs
`Pretax
`After tax
`
`($US millions: 2000 values)“
`Discovery and development
`Product extensions after Iamch
`Total
`
`$613
`$73
`$886
`
`$429
`$51
`$480
`
`a
`
`FED costs include expenditures on product tailum as wel as
`successes.
`
`b MDccstsarempitalisedtomefirstyearctmalkefing using
`an11%costofmpital.
`
`© Adls lntemotloml Lh'lled. All rights reserved.
`
`Phamocoeconomlcs 2002: 20 SLppl. 3
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`15
`R&D Returns for 19905 New Drug lntroduclions
`
`
`the average post-approval R&D costs per NCE in
`our sample period to be $USlO? million (before
`tax).2 We allocated these costs equally over the
`first 8 years of an NCE’s market life, using a dis-
`
`count rate of 1 1% from the date of marketing. This
`yields a present value of $US73 million (before
`tax) and $USSl million dollars (after tax).
`When the after-tax values (see column two of
`
`table I) are added, the mean capitalised value for
`
`both pre- and post-approval R&D for the drugs in
`our sample is estimated to be $US480 million. This
`is the baseline value that we compare with the pres-
`ent value of net revenues for the mean NCE in our
`
`sample.
`
`Global Sales
`
`In our prior analysis, we obtained US sales data
`on each NCE in the sample. We then estimated
`
`worldwide sales for these compounds using a
`
`worldwide sales multiplier common to all NCEs.
`
`ally than US compounds with smaller domestic
`sales.[141
`
`To obtain worldwide sales data, we collected
`
`sales data that firms provide in their annual reports,
`
`in the reports of financial analysts, and in publi-
`cations such as MedAdNews. The last-mentioned
`
`source has compiled an annual survey of world-
`
`wide drug sales, by product, since 1990 on an ex-
`
`panding basis over time. The compilation for 2000
`
`includes information on the 500 top—selling pre-
`scription drugs worldwide.[15]
`A complementary source of data that we also
`relied on was lMS data on worldwide sales, which
`
`is based on audit data sources from a large number
`of countries. The lMS data source was available to
`
`us (from a prior project) for a sub-sample of drugs
`
`consisting of the largest selling global drugs in our
`
`sample. It provided a check on the sales informa-
`
`tion provided by the company sources. ln most
`cases, the IMS sales values were less than the com-
`
`One limitation of this approach is that the ratio
`
`pany values. This reflected the fact that the IMS
`
`of worldwide sales to domestic sales varies signif-
`
`does not capture all the sales channels available
`
`icantly, both over time and across drugs in our
`
`across countries, while the company data do in-
`
`sample.
`
`clude every channel.
`
`In the current analysis, our approach was to ob-
`
`In about 25% of the overlapping observations,
`
`tain worldwide sales data directly on as large a
`
`however, the IMS sales were greater than the com-
`
`group of the drugs as possible. We were generally
`successful in this endeavour, in that we were able
`
`pany-reported values. An analysis into why this
`
`was the case revealed that the sub-sample of drugs
`
`to obtain worldwide sales data for a majority of the
`
`with higher lMS sales was marketed intemation-
`
`NCEs in our sample (66 N CEs) using several com-
`
`ally under multiple names and by several differ-
`
`plementary data sources. These 66 drugs ac-
`counted for more than 90% of total US sales
`
`realised by our sample of NCEs and presumably a
`
`similar, or even larger, share of its realised world-
`
`wide sales. With respect to the latter point, there is
`
`evidence that the larger selling US drugs diffuse
`
`across more countries and have larger sales glob-
`
`2 DiMasi et al.[5] obtained data from all the firms partici—
`pating in his survey on pre—approva] and post—approval RELD
`expenditures. On the basis of an analysis of these data, they
`estimated that out—of—pocket R&D expenditures for product
`extensions in the past—approval period were 34.8% of pre—
`approval R&D expenditures. Applying this percentage to our
`estimate of $U8308.4 million for pre—approval R&D yields
`an estimate of $USIU? million (in $US, 2000 values) as the
`R&D cost for post—launch product improvements.
`
`ent companies. Consequently, sources such as
`
`MedAdNews didn’t capture all of the sales that
`
`were licensed to different companies for a partic-
`
`ular molecule. For the sub-sample of drugs for
`
`which this was an issue, we utilised the larger IMS
`
`worldwide sales values because they better cap-
`tured the worldwide market.
`
`Using this approach and these complementary
`data sources, we assembled worldwide sales data
`
`for 66 of the NCEs over the period of 1990 to 2000.
`
`For the remaining (very small selling) drugs in our
`
`sample, we multiplied their US sales values by a
`
`representative global sales multiplier to obtain es-
`timates of their worldwide sales. The value of the
`
`global sales multiplier was 2.19. As discussed, this
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`16
`Gmbowski et a1.
`
`
`latter sub-sample of drugs accounts for a very
`
`years of sales information and its US patent expires
`
`small share of overall sales for the full sample.
`
`in year 12. By year 9, this product was in the mature
`
`Ute—Cycle Sales Profiles
`
`portion of its product life cycle. By using a refer-
`
`ence life-cycle curve, the product was projected to
`
`have relatively stable sales (in constant dollar
`
`Since data were available for the years 1990 to
`
`terms) until year 12. A significant decline is then
`
`2000, 7 to l 1 years of worldwide sales values for
`
`projected in the period after US patent expiry be-
`
`the NCEs in our sample were provided, depending
`on their date of introduction into the US market.
`
`cause of the entry of generic competitors and re-
`lated economic factors.
`
`The next task was to estimate future sales over the
`
`complete market life of these products. Twenty
`
`years was chosen as the expected market life. This
`
`is the same assumption that we utilised for l980s
`
`new drug introductions. We believe this to be a
`
`reasonable time horizon for an IRR analysis. Any
`
`sales remaining after 20 years of market life are
`likely to be very small, given the sales erosion ex-
`
`perienced by most products from generic competi-
`
`tion and product obsolescence. Furthermore, these
`
`sales will also be severely discounted by the cost
`
`of capital in an IRR analysis.
`
`We utilised a two-step procedure to project fu-
`
`ture sales values. These steps involve forecasting
`
`sales to the point of US patent expiry and then pro-
`
`jecting sales in the post-patent period. The two-
`
`step approach is illustrated in figure I for one of
`
`the products in our sample. This product was intro-
`duced into the US market in 1992. There are 9
`
`The estimated sales decline after patent expiry
`
`is based on the experience of major commercial
`
`products coming off patent in the 1994 to 1997
`
`period. In particular, we examined worldwide sales
`
`losses for a sample of NCEs for a 4-year period
`
`following their US patent expiry. The average per-
`
`centage declines observed were 31, 28, 20 and
`
`20%, respectively. We utilised these percentages
`
`to project sales in the first 4 years after patent ex-
`
`piry and, thereafter, a 20% decline until the prod-
`
`uct’s market life is completed in year 20. In our
`
`prior work, we found that generic competition is
`
`focused on products with significant sales at the
`
`time of US patent expiry. Consequently, for the
`
`drugs concentrated in the bottom four deciles of
`
`our sample (with worldwide sales of less than
`$US40 million in year 10 of their market life), we
`
`assume that the probability of generic competition
`
`is very low. For these drugs we assume that sales
`
`losses in the mature phase of cycle will proceed at
`
`a more moderately declining rate based on the ref-
`
`erence curve used for the pre-patent expiry period.
`
`We should note that the percentage declines in
`
`sales from generic competition in the US market
`
`observed in prior studies are much greater than the
`
`worldwide losses in sales for major commercial
`products observed here.[15] Hence, the decline in
`
`worldwide sales in the post-patent period is amel-
`
`iorated by the lower incidence of generic competi-
`
`tion and sales losses outside the US. This may
`
`change by the time this cohort actually reaches
`
`patent expiry during the current decade, because
`
`reference pricing and generic competition are on
`the rise in many European countries.[”]
`
`Figure 2 provides a plot of the sales life-cycle
`profile (in $US, 2000 values) for the top two dec-
`
`Proieded values
`(dashed lines)
`
`8(1) -
`
`700 -
`
`
`
`
`
`
`MeansalesIn$USmillions(2000values}
`
`
`
`
`
`01 2 3 4 5 B 1‘ 8 9101112131415181?181920
`
`Salasyear
`
`Fig. 1. Actual and projected worldwide sales values for a rep-
`resentative sample product.
`
`© MES Intemo’rloml Lh'fled. All rights reserved.
`
`thnocoeconomlcs 2032: 20 SLppl. 3
`
`000006
`
`000006
`
`

`

`17
`R&D Returns for 19905 New Drug Introduclions
`
`
`
`
`
`
`g 1stDecile
`A 2nd Decile
`0 Mean
`
`g
`
`‘ I Medan
`
`0 'l_l_l_l—l—l—l_l_l_l_l_l—l—l—l—l_l_l_l_l
`12 3 4 5 B 'r’ 8 91011121314151817181920
`
`Sales year
`
`Fig. 3. Comparison of mean worldwide sales curves for new
`drug introductions in the 1990 to 1994 and 1980 to 1984
`samples.
`
`© Adls lntemufloml Lh'fled. All rights reserved.
`
`Phumocoeconomlcs 2002: 20 SLppl. 3
`
`000007
`
`billion. This may be compared with peak sales of
`near $USl .8 billion for the 1980s cohort. The peak
`sales for the 1990s cohort also occur later than for
`the 1980s cohort.
`
`PreTox Contribufions and Other
`
`Economic Parameters
`
`The next step in the analysis was to obtain rev-
`
`enues net of production and distribution costs (of-
`
`ten categorised in the economic literature as
`
`‘quasi-rents’). For this purpose, we analysed pre-
`
`tax contribution margins in pharmaceuticals dur-
`
`ing the 1990s. As in prior work, we utilised data
`derived from the income statements of the pharma-
`
`ceutical divisions of a number of major multina-
`
`tional drug companies to obtain representative
`values on contribution margins over timelLZ]
`
`Our analysis of the data on these firms indicated
`
`that average contribution margins gradually in-
`
`creased from 42% in the early part of the 1980s to
`
`approximately 45% at the end of the decade. 0n
`the basis of these data, we constructed a linear con-
`
`tribution margin schedule over time. In particular,
`
`the contribution margin is 42% in the first year of
`
`the product life and grows by increments of 0.3%
`
`
`
`
`
`500 -
`
`1990-1994
`0 1900-1984
`
`é
`
`§
`
`§
`
`é
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`Meansalesin$USmillions(2000values}
`
`
`
`
`
`values} 78‘ O
` 3SalesIn$USmllllons(2000
`
`
` I'I l' I I l' II I I I I I I I I'
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`1 2 3 4 5 6 'r’ 8 9101112131415161?181920
`
`Fig. 2. Worldwide sales profiles or 1990 to 1994 new drug in-
`lroduclions.
`
`Salmyear
`
`iles as well as the mean and median drug com-
`
`pounds in our 1990 to [994 sample. The sales
`
`curves illustrate the highly skewed distribution of
`
`sales in pharmaceuticals that was observed for
`
`early cohorts. The peak sales of the top decile com-
`
`pounds are several times the peak sales of the
`
`second decile compounds. The mean sales curve
`
`is also significantly above the median.
`
`Figure 3 provides a plot of mean worldwide
`
`sales for the 1990s sample compared with that for
`the 1980s cohort (in $US, 2000 values). Mean
`
`sales have increased significantly in real terms,
`with peak sales increasing from $US345 million
`for the 1980s cohort to $US458 million for the
`
`1990s cohort. There is also the suggestion that
`
`sales curves have become somewhat steeper in the
`
`ascending sales growth stages of the life cycle,
`
`with a longer plateau before generic competition
`
`and product obsolescence take hold.
`
`Figure 4 shows a corresponding plot of the
`
`mean worldwide sales for the top decile com-
`
`pounds in the 1990 to 1994 and 1980 to 1984 pe-
`
`riods. This is instructive, given that the prospective
`
`returns for top decile compounds are primary driv-
`
`ers of R&D investment activities in pharmaceuti-
`
`cals. For the l990s cohort, the top decile com-
`pounds reached peak sales of more than $US2.5
`
`000007
`
`

`

`Gmbowski st of.
`
`
`
`
`
`
`é 1993-1994
`C) 1980-1984
`
`
`
`
`
`ii
`
`
`
`
`
`
`
`
`
`
`
`Salesin$USmillions(2000values}
`
`g
`
`3
`
`S
`
`§
`
`0
`
`
`
`
`
`
`
`
`
`
`
`
`1 2 3 4 5 B 3' 8 9101112131415181?181920
`
`Salesyear
`
`shifts over this period. The direct-to-consumer ad-
`
`vertisin g to sales ratio increased from 1 .2% to 2.2%
`
`between 1996 and 2000, at the expense of physi-
`
`cian detailing and hospital medical journal adver-
`tising.[13]
`For the current analysis, we did make one rela-
`
`tively minor change in the allocation and timing of
`
`marketing expenditures related to launch. In par-
`
`ticular, we estimated pre-marketing launch expen-
`
`ditures in the order of 5 and 10% of first year sales
`
`in the 2 years immediately prior to launch. These
`
`marketing expenditures are for activities such as
`
`pre-launch meetings and symposiums, pricing and
`
`focus group studies, and sales force training. Our
`
`assumptions concerning the size and timing of
`
`these expenditures were guided by a recent survey
`
`report on pre-launch marketing expenditures by in-
`
`dustry consultants as well as interviews with some
`of the participating companiesllgl
`As indicated above, our model is structured so
`
`Fig. 4. Comparison of mean worldwide sales cum for top
`decile drugs in the 1990 to 1994 and 198010 1984 samples.
`
`per year. We also assume that contribution margins
`
`will continue to rise at this same rate during the
`
`that margins average 45% over the full product life
`
`current decade. Hence, over the full 20-year life
`
`cycle. Given the assumed pattern of launch expen-
`
`cycle, target contribution margins are expected to
`
`ditures, contribution margins for each product
`
`rise from 42% in year one, to 48% by year 20, with
`
`are below representative industry values in the
`
`a mean contribution margin of 45% over the full
`
`first 3 years of marketing. However, as a product
`
`life cycle.
`
`While we constrained margins to average 45%
`
`matures, both promotional and administrative
`costs decline in relative terms, and contribution
`
`over the life cycle, we also recognise, as in our
`
`margins increase over average industry values in
`
`earlier analyses, that promotion and marketing ex-
`
`the later years of the life cycle.
`
`penditures are concentrated in the launch phases of
`
`The model is also structured to provide for cap-
`
`the life cycle. In our prior analysis, we developed
`
`the following allocation rule based on a regression
`
`ital expenditures on plant and equipment (P&E).
`As in our model for the 1980s cohort, we assumed
`
`analysis of promotional and marketing outlays:
`
`overall capital expenditures for P&E to be equal to
`
`promotion and marketing is equal to sales in year
`
`40% of tenth year sales. Half of these outlays are
`
`1, declines to 50% in year 2, and falls to 25% in
`
`assumed to occur in the first 2 years before market-
`
`year 3. We retained this assumed pattern on mar-
`
`in g and the other half during the initial 10 years of
`
`keting outlays in the present analysis. Interviews
`
`the product’s market life. These assumptions imply
`
`with industry participants indicated that the initial
`
`an average capital investment to sales ratio of 3.3%
`
`post-launch years continue to be the primary focus
`
`over the full product life cycle. This is generally
`
`of marketing and promotion activities.
`An analysis performed by Rosenthal et al.[13]
`
`consistent with data from pharmaceutical industry
`income statements.
`
`indicates that the drug industry’s marketing ex-
`
`In particular, we checked the reasonableness of
`
`penses to sales ratios have remained relatively
`
`our assumptions by comparing this implied 3.3%
`
`stable around 14% in the 1996 to 2000 period. How-
`
`capital investment to sales ratio with the corre-
`
`ever,
`
`there were some important compositional
`
`sponding ratios observed on industry income state-
`
`© MES lntemutloml Lh'fled. All rights reserved.
`
`P'hmnocoeconomlcs 21302: 20 SLppl. 3
`
`000008
`
`000008
`
`

`

`19
`R&D Returns for 19905 New Drug Introductions
`
`
`ments during the 19903. We found that the drug
`
`and positive cash flow in the early years of a prod-
`
`industry capital investment to sales ratio averaged
`
`uct’s market life. This reverses in the latter years
`
`about 7.0% during the 19903. However, the latter
`value includes investment for R&D as well as
`
`of a product’s life.
`
`production, marketing and administrative facil-
`
`ities. In our model, provisions for capital invest-
`ment in R&D facilities are included in the cost
`
`estimates provided by DiMasi et al.[51 Accord-
`ingly, we asked some industry members involved
`
`with strategic planning for information on what
`
`percentage of their P&E expenditures was devoted
`to R&D, versus other firm activities. We obtained
`
`a range of 40 to 50% of total capital expenditures
`devoted to R&D. Given this range, the capital in-
`vestments to sales ratio for non-R&D activities
`
`implied by our model is consistent with the ob-
`
`served data from company income statements.
`
`For working capital, it was assumed that ac-
`
`counts receivables are equal to 2 mo