The Effect of R&D Scale on the Probability of Long-term Financial Success in the Research-Based Pharmaceutical Industry

Transcription

1 The Effect of R&D Scale on the Probability of Long-term Financial Success in the Research-Based Pharmaceutical Industry Darren Filson * Claremont Graduate University and Neal Masia Pfizer Inc June 9, 2004 * Send correspondence to Darren Filson, Associate Professor of Economics, Claremont Graduate University, 160 E. Tenth St., Claremont, CA Phone: (909) Fax: (909) Darren.Filson@cgu.edu. Neal Masia is Director, Economic Policy, Pfizer Inc, New York, NY. Phone: (212) Neal.Masia@pfizer.com. We thank Michael Maher and the participants at the January, 2004 meeting of the Pharmaceutical Economics and Policy Council for their comments. The views expressed herein are solely those of the authors.

2 The Effect of R&D Scale on the Probability of Long-term Financial Success in the Research-Based Pharmaceutical Industry Abstract: We introduce a computational model of the evolution of a research-based pharmaceutical firm and parameterize it using existing estimates of R&D costs, profit distributions, and candidate attrition rates. We use the model to estimate how the probability of continuously covering the capitalized costs of R&D from new product revenues depends on R&D scale. In the model, even small reductions in profitability have substantial impacts on firm success and innovation, but it may take over ten years for such effects to become visible to consumers. Smaller and newer firms are most vulnerable to reductions in the rewards for innovation. JEL Codes: C41: Duration Analysis; C61: Dynamic Computational Models; G31: Investment Policy; L65: Pharmaceutical Industry; O32: Management of R&D Keywords: innovation, survival, selection, firm size, technological change 2

3 The largest research-based pharmaceutical firms are among the largest firms in the economy. At the same time, many relatively small biotechnology firms pursue pharmaceutical innovations. The range of firm size in the research-based pharmaceutical industry is enormous, equaled perhaps only in the software market, where many small firms attempt to innovate alongside giants like Microsoft. Of course, small biotechnology firms cannot exist without extensive external support, and a key objective of a small firm is to either form an alliance with or be acquired by a large pharmaceutical firm who can effectively fund its R&D operation. 1 The main goal of this paper is to estimate the probability that a pharmaceutical innovator large or small can cover its capitalized costs of R&D from its internally generated revenues from successful innovations on an ongoing basis. This probability is time-dependent, and we consider durations ranging up to one hundred years. We develop a computational model of an R&D intensive firm to examine how success probabilities depend on the scale of the firm s R&D and the maturity of its research program. In the model, the firm has a given scale at the initial (discovery) stage of the R&D process. Over time, successes at the discovery stage enter later stages and may eventually be approved for marketing. We define failure of a firm as the point at which the expected net present value of the firm s R&D operation does not exceed its accumulated capitalized R&D expenses. Defined in this way, failure would not necessarily lead to immediate exit; a firm in such a position might pursue reorganization, alliances, mergers, or other strategies in an attempt to survive. Instead, our goal is to clarify which types of firms can expect to survive in the long run without extensive external support or reorganization. In order to survive in the long run, a firm must cover its capitalized costs on an ongoing basis. Our analysis is useful for firms planning the scale of their R&D operations and for government policy makers who wish to assess how their policy decisions might affect the rate of innovation and which firms will be viable on a stand-alone basis under a variety of potential scenarios. We parameterize the model using estimates of R&D costs, profit distributions, and candidate attrition rates from the existing literature (DiMasi, Hansen, and Grabowski 2003 and Grabowski, Vernon, and DiMasi 2002), but we also consider how costs and profits may have changed in more recent times. We explore the impact of various policy initiatives. Recently, the issue of importation 3

4 from Canada as a means to lower U.S. prices has received much attention. Direct price caps in the U.S. market could also be imposed. We consider the impact of such policies on the prospects for success for established firms and new entrants in different R&D size categories. We reach three general conclusions. First, new firms have much lower financial success probabilities than established firms, holding the scale of the discovery research program fixed. Second, within the groupings of established and new firms, small firms have lower success probabilities than large firms. Third, new and small firms are more vulnerable to policy changes that lower returns to pharmaceutical discoveries. Taken together, these results suggest that if policy changes or changes in technological opportunities have a generally negative impact on the research-based pharmaceutical industry, large established firms are in a much better position to withstand any adverse effects than smaller newer firms. Our results suggest that the impact of adverse policy changes on the pace of innovation could be dramatic. Although we do not explicitly model interactions between firms, our results suggest the opportunities for large firms to partner with or adopt early-stage research projects from smaller firms would diminish significantly. Our results also seem consistent with the pattern of large firm mergers in recent years. In addition to our general conclusions, we provide specific estimates of how financial success probabilities relate to the size of a firm s R&D budget, the average number of projects it has in human clinical trials, and the number of discovery research program it initiates each month. Our estimates suggest that in order to virtually guarantee success for even a relatively short duration such as 20 years, a firm must operate a very large R&D program. For example, we estimate that even an established firm with an average of 31 projects in clinical trials on an ongoing basis cannot be assured of success over a 20-year period. However, by recent cost estimates (Gilbert, Henske, and Singh 2003) such a firm might have an R&D budget for internal projects of over $1.7 billion. Only the very largest pharmaceutical firms fund R&D on this scale. The model also provides estimates of when the impacts of policy changes would be noticeable to consumers. Given the long time lag between investment in early-stage research and the introduction of new products, policymakers would often be out of office by the time the adverse impacts of their policy decisions would be visible to the public. 1 The biotechnology industry is characterized by alliances, and the number of alliances has grown dramatically over time (see Filson and Morales 2003). Small firms in other high-tech industries also often have licensing or selling technology as their primary goal (see Filson and Gretz 2004). 4

5 II. The Model The model considers a single firm with a given scale of discovery research and a portfolio of R&D projects at various stages of development. 2 The unit of time is a month, and the firm can live forever. The scale of discovery research remains fixed over time; this allows us to compute success probabilities as a function of the scale of the discovery research program. Discovery research stochastically yields R&D projects, which correspond to candidates that have entered human clinical trials. The portfolio of R&D projects evolves over time; some projects progress to subsequent stages and others fail and are abandoned. A small minority of projects eventually yield marketable products. Marketable products yield positive cash flows that are stochastically determined. The model is designed to isolate the effect of R&D scale on financial viability; there are no systematic differences between the projects pursued by large and small firms. Further, there are no economies of scope, and all shocks are independent. 3 Each month, the firm might fail. We define failure as when the net present value of the firm s research program does not exceed its accumulated capitalized costs of R&D. When new products are introduced, the cash generated is applied against the accumulated capitalized costs of R&D. The exact calculations involved are discussed below. If the firm fails, the analysis stops. Thus, our model is essentially a duration model that estimates how likely it is that a firm with a given scale of discovery research can cover its opportunity costs of R&D from its profits from successful innovations for different lengths of time. In order to describe the model more fully, it is necessary to introduce notation. The most basic ingredient of the model is a discovery research program. A discovery research program must exist for T d months before having a chance of becoming an R&D project. Each month, each discovery research program that has existed for T d months becomes an R&D project with probability λ d ; those that do not become projects at that point are abandoned. If a 2 While we believe our model is general enough to be applicable to many other R&D intensive industries, we use some terms specific to the pharmaceutical industry below. In the application to the pharmaceutical industry, discovery research in the model corresponds to the discovery and pre-clinical stages of pharmaceutical R&D. R&D projects in the model correspond to candidates that have entered human clinical trials. 3 Thus, our model suppresses any intrinsic productivity advantages that large established firms might have over smaller and newer firms in the conduct of R&D, and focuses instead on financial advantages. Henderson and Cockburn (1996) and Cockburn and Henderson (2001) analyze how scale, scope, and spillovers affect the productivity of both early stage discovery research and later development. In contrast, in our model every research program is equally productive, but large established firms have two financial advantages. First, large firms have more research programs and projects, and this diversification allows bad shocks on some to be cancelled out by good ones on others. Second, large firms introduce new products more frequently, and because of this capitalized costs have less time to accumulate before they can be covered by profits from new drugs. Profits from successful new products can cover the capitalized costs of ongoing programs and projects. 5

6 discovery research program becomes an R&D project this month, no project-related costs are incurred until the following period, when the project is one month old. We impose stationarity on the scale of discovery research; this allows us to compute success probabilities as functions of the firm s scale of discovery research. Each month, the firm initiates n discovery research programs. The firm also maintains every discovery research program it had in the previous month that was less than T d months old. An established firm has n discovery research programs in each age category, from 1 to T d. Each month, each discovery research program costs c d. In our analysis below, we compare established firms, who begin with an ongoing discovery research program and possibly several R&D projects, to new entrants, who begin by initiating n discovery research programs and have no other programs or projects. In the model, R&D projects are discovery research programs that have successfully yielded a compound that can begin human clinical trials. Each R&D project is characterized by how many months it has been in development. It takes T months to complete an R&D project. Each month, each R&D project costs c(t) and fails with probability 1 λ(t), where t = 1, T. If an R&D project fails at any point it is abandoned. If it succeeds at every point, it becomes a product that is introduced into the market the following month. When a product i is introduced into the market, the net present value of its profits, π i, is determined randomly according to the distribution f(π i ). We assume that the firm earns the net present value of every product s profits immediately when the product is introduced into the market. This assumption is made solely for simplicity. The timing of the firm s earnings does not affect firm failure or anything else in the model because all decisions are made using net-present-value criteria. Investors in the firm require an expected rate of return each month of r. Therater represents the opportunity cost of capital; it is the rate of return investors expect to earn on investments with risk comparable to the firm s risk. This opportunity cost of capital affects how R&D costs are valued. Essentially, the firm acts as though it has obtained its R&D funds from its investors. Interest on those funds accumulates at the rate r, which compensates the investors for giving up their funds and bearing risk. The firm also discounts future payoffs at the rate r. 6

7 Given r, f(π i ), and the c(t)andλ(t) functions, we can compute the expected net present value of any R&D project at any stage using a simple backwards induction procedure. First, immediately prior to introduction, the expected net present value of a product is W = 0 πf ( π ) dπ (1) From equation (1), it follows that an R&D project at the beginning of its T th month in development has the value 1 V ( T ) = c( T ) + λ( T) 1+ W r (2) That is, the project costs c(t), succeeds with probability λ(t), and earns W one period from now if it succeeds. Now, given the value of a project that has been in development t months, we can compute the value of a project that has been in development t 1 months as follows: 1 V ( t 1) = c( t 1) + λ( t 1) V ( t) 1+ r (3) Once we have obtained V(1), we can use it to compute the value of each discovery research program. The value of a discovery research program in month T d is 1 U ( Td ) = cd + λd V (1) 1+ r (4) Given U(t+1), we can compute the value of a discovery research project in t 1, T ) : [ d 1 U ( t) = cd + U ( t + 1) 1+ r (5) Given W, V(.), and U(.), along with the scale of the firm s discovery research and the numbers and stages of the firm s various research projects, we can compute the expected net present value of the firm s research activities. This value is then compared to the accumulated capitalized costs of past R&D. If the expected net present value of the firm s current research activities does not exceed the accumulated capitalized costs of past R&D, the firm fails. The failure rule we use may seem unusual because it incorporates costs that are sunk. For example, the accumulated capitalized costs of past R&D (out-of-pocket expenses capitalized at the rate r up to the present) cannot be wholly recovered if the firm exits because even if it sells off all of its current projects for their NPVs, the value 7

8 obtained cannot compensate the investors for their sunk costs. Therefore, we cannot necessarily associate failure as we have defined it with exit. However, we do think it likely that a firm that could not compensate investors at the rate r on an ongoing basis would eventually be involved in some type of restructuring or exit. Parameterization To parameterize the model, we use information on costs, lengths, and probabilities of success associated with various stages of the pharmaceutical R&D process provided by DiMasi, Hansen, and Grabowski (2003) (hereafter referred to as DHG). Their estimates are for new chemical entities (NCEs) first tested in humans anywhere in the world between 1983 and There are good reasons to believe that the cost estimates provided by DHG are lower than the current costs. The estimates are based on data from ten to twenty years before the time we write, and evidence from earlier studies suggests that the costs of drug discovery have been rising substantially over time (see DiMasi et al and DHG). Several recent observers of the industry suggest that average capitalized costs per NCE (costs capitalized up to the point of marketing approval, taking into account the many failures required to obtain a single approval) are substantially higher than the $802 million (year 2000 dollars) that DHG estimate (for example, see Gilbert, Henske, and Singh 2003). Thus, while we begin by using the estimates provided by DHG, we also explore the implications of cost increases on the size of R&D budget needed to ensure success. Pharmaceutical R&D involves an initial discovery stage where a large number of compounds are considered. A small fraction of the compounds investigated in the discovery stage eventually end up being tested in humans. In reality, the discovery stage is followed by a pre-clinical stage, but the data DHG use does not allow them to distinguish between these two stages. They also do not observe failure rates within the discovery-pre-clinical stage. In their calculations, they estimate that the out-of-pocket cost associated with the discovery-pre-clinical stage required to produce one approved NCE is $121 million (year 2000 dollars), and they assume that this cost is evenly distributed over a 52-month period. Given this, we set T d = 52. To compute c d, we must convert the $121 million into per program costs. The Food and Drug Administration (FDA) reports that it takes between 5,000 and 10,000 discovery research programs to yield an average of one new approved drug. For simplicity we assume that exactly 4 As DHG discuss in detail, not all new drugs are new chemical entities. Some are minor extensions or modifications of existing chemical entities. New chemical entities are the most innovative new drugs. We discuss below how much of a typical firm s R&D budget goes toward generating new chemical entities. The profit estimates discussed below (from Grabowski, Vernon, and DiMasi 8

9 7500 discovery research programs have to be operated over T d periods to yield an average of one new approved drug. Given this, c d = *52 (6) where c d is measured in millions of year 2000 dollars. DHG estimate that the probability that a drug that begins human clinical trials is eventually approved is.215. Given this, λ d 1/(.215) = 7500 (7) These estimates are combined with the DHG opportunity cost of capital estimate of 0.11 to construct U(.). To construct V(.), we must use information about human clinical trials. Human clinical trials begin with Phase 1, where the safety of the compound is explored in a small sample of healthy volunteers. Phase 2 tests for effectiveness in patients with the target disease. Phase 3 involves long-term testing in large diverse patient samples to demonstrate the specific efficacy and safety attributes to be claimed in the product label. Phases may overlap. Long-term animal testing may be conducted concurrently with human clinical trials. If Phase 3 is successfully completed, an application for a new drug approval is submitted to the FDA. If the application filing is successful, the new drug is approved for marketing. Failure can occur at any point along this pipeline, and as noted above, DHG find that only 21.5% of the compounds that enter Phase 1 are eventually approved for marketing. DHG provide several results that allow us to parameterize the R&D project stage of the model. DHG estimate that, on average, it takes 90 months for a drug to proceed from beginning Phase 1 to marketing approval. Thus,inthemodel,T = 90. Most other information is also provided in terms of averages, and for simplicity we assume that every drug is average in terms of its R&D costs at each stage, the length of the phases, and probabilities of success (all R&D projects have the same T and c(t) andλ(t) functions). Table 1 presents the summary of information reported by DHG that we use to compute c(t) andλ(t). Following DHG, we assume that the out-ofpocket costs associated with each development phase are spread evenly throughout the phase. We further assume that λ(t) is constant within the following mutually exclusive and exhaustive stages: Phase 1 before Phase 2 begins; 2002) are also for new chemical entities, and they take into account the additional costs and profits of products derived from the new chemical entities. 9

10 while Phase 1 and Phase 2 overlap; Phase 2 before Phase 3 begins; Phase 3; and final review. Table 2 reports our estimated out-of-pocket R&D project costs and probabilities of continuation for every month from 1 to T.We discuss the exact formulas for generating the Table 2 data from the Table 1 data in the Appendix. 5 For marketable products, Grabowski, Vernon, and DiMasi (2002) provide estimates of the distribution of the net present value of profits for NCEs at the point of marketing approval. They report ten net present value deciles. The distribution is very skewed. Only 30% of new drugs cover their accumulated capitalized costs of R&D; most new drugs fall far short of this mark. Rare blockbusters are essential for ensuring profitability. We use these estimates to construct f(π). Each new drug falls into a random decile (the probability of falling into any decile is 1/10). The estimates Grabowski, Vernon, and DiMasi (2002) report are based on drugs introduced to the market in the 1990 to 1994 period. Because DHG s cost estimates are for drugs first tested in humans between 1983 and 1994, the profits data must be rescaled to reflect introduction dates well after Grabowski, Vernon, and DiMasi (2002) estimate that the expected excess profit of a new drug is $45 million. We rescale every decile in the profit distribution by a common factor so that when the DHG cost estimates are used, each drug has an expected excess profit of $45 million. Given that the capitalized cost is $802 million, this implies that the expected net present value of profits at the point of marketing approval is $847 million. Note that we do not consider the impact that taxes would have on the analysis. Grabowski, Vernon, and DiMasi (2002) assume that there is an effective corporate income tax of 30% during the period they analyze. We do not include taxes because we want to compare the results from our analysis with reported R&D expenses, which are reportedonapre-taxbasis. Simulation To run the model simulation, we first fix the scale of discovery research, n. Then we simulate the evolution of 100 firms R&D project portfolios over a one hundred year period. To determine the established firms initial R&D project portfolio, we first run the model assuming that the firms begin with no projects. Then we compute the average number of projects in each of eight stages each month over the life of the firms to construct the average 5 As we discuss in the appendix, our model s average capitalized cost per new drug is slightly less than the DHG estimate of $802 million because the unit of time we use is a month. DHG allow for fractions of a month and use continuous compounding. However, 10

11 portfolio. 6 Then we rerun the simulation assuming that each firm s initial portfolio is the average portfolio, where each project in a given stage is assumed to be at the midpoint of that stage. Then we re-compute the average portfolio and rerun the simulation again until the average portfolio does not change. In this sense, the results reported for established firms are for firms that begin in month 1 of year 1 as average firms given their scale of discovery research. For new firms, we assume the firm begins with no projects or programs, and initiates n programs in the first month. 7 The main goal of the analysis is to compute financial success probabilities. To this end, we keep track of the failure period of each of the 100 firms. If a firm succeeds for the full 100 years, we note that as well. We compute success rates at five and ten year intervals. To allocate the revenues from new drugs, each firm in the simulation has a cash account and an R&D expenditure account. These keep track of stock variables; the amount of cash the firm has and the accumulated R&D expenses, capitalized at the rate r up to the present. Each period the firm remains active, it takes money out of its cash account to pay for its discovery research programs and research projects. Money that remains in the cash account does not appreciate or depreciate. We assume cash earns its opportunity cost of capital each period and thus does not generate any additional value for the firm; it plays no role in the analysis. The accumulated R&D expenses from the previous period are capitalized to the current period by multiplying by (1 + r) and then the current R&D expenses are added to this stock. When the firm introduces a new product to the market, the profits associated with the product are first applied against the accumulated costs in the R&D expenditure account. If the profits exceed the accumulated capitalized R&D costs, the excess is placed in the cash account. We assume that each firm initially has the difference is very slight (approximately $2 million, which is about 1/3 of 1% of $802 million) and for convenience we continue to refer to DHG s figure of $802 million below. 6 The eight mutually exclusive and exhaustive stages are: Phase 1 before long-term animal tests begin, Phase 1 after long-term animal tests begin but before Phase 2 begins, Phase 1 after Phase 2 begins, Phase 2 after Phase 1 ends, Phase 3 before long-term animal tests end, Phase 3 after long-term animal tests end but before Phase 2 ends, Phase 3 after Phase 2 ends, and the final review period. Typically the average values are fractions. We round off to the nearest integer, but we also round up or down as necessary to ensure that we specify the correct total number of projects in development in an average month. 7 Clearly, a firm could use a model like ours to assess its own prospects for success. All that is needed is the firm s initial scale of discovery research (the scale could evolve over time) and the firm s initial number of R&D projects at each stage. The firm could use its own estimates of c(t) andλ(t) orrelyonours. 11

12 a zero cash balance and zero in its R&D expenditure account. 8 This accounting method allows us to keep track of the extent to which the firm is covering its accumulated capitalized R&D expenses from its profits on new drugs. Cases The goal is to compute success probabilities for real pharmaceutical firms. To generate a realistic range of initial discovery scales and R&D project portfolios, we rely on the extensive data on pharmaceutical R&D provided by PAREXEL (2003). Unless otherwise noted, all of the statistics presented in this subsection are from PAREXEL (2003). R&D budget sizes vary substantially. The top 45 pharmaceutical R&D spenders in 2001 had R&D budgets ranging from $100 million to almost $5 billion. The top 100 public biotechnology firms in 2002 had R&D budgets ranging from $33 million to over $1 billion. Of course, not all R&D is for discovery and new product development. On average, approximately 80% of pharmaceutical R&D goes toward advancing scientific knowledge and developing new products and services; the remainder goes toward improving and modifying existing products and services. For our study, it would be useful to break new product R&D expenses down further according to the amounts spent on in-house projects vs. licensed or acquired projects, but recent data on this breakdown is not available. DiMasi et al. (1991) report that during the period , 73.7%, 16.3%, and 10.0% of total pharmaceutical R&D was spent on self-originated new chemical entities, existing approved products, and licensed and acquired compounds, respectively. PAREXEL (2003) reports that of the 671 NCEs that had their first investigational new drug application (an IND, which is filed at the beginning of human clinical trials to request permission to begin testing in humans) filed during the period 1981 to 1992, 508 were self-originated. However, it seems likely that the share spent on licensed and acquired compounds and the proportion of such compounds in the development pipeline have risen substantially since then; as 8 Alternatively, we could assume that established firms begin with some accumulated capitalized costs of R&D. If we set the initial amount in the R&D expenditure account using an iterative process like the one described above for setting the initial number of R&D projects (so that each firm begins with an average amount of accumulated capitalized costs given its scale), our qualitative conclusions do not change. For large established firms, our quantitative results do not change. These firms start with several R&D projects near completion, and they easily cover their initial capitalized costs with revenues from products introduced to the market soon after the simulation begins. As a result, their success rates are identical to those reported below. However, smaller established firms become much less likely to succeed when they begin with accumulated costs. They have fewer projects near completion when the simulation begins, and a few bad shocks can be devastating. 12

13 Filson and Morales (2003) report, the number of alliances between biotechnology firms and pharmaceutical firms has risen dramatically in the past two decades. 9 Given the problems with isolating the share of pharmaceutical R&D budgets that can be attributed to inhouse NCEs, and given the fact that the R&D budgets PAREXEL (2003) provides are for recent years whereas the cost estimates provided by DHG concern R&D from decades ago, we also consider evidence on the number of NCEs firms have in their R&D pipelines and how many NCEs they introduce each year, on average. This information can be used to estimate what the size of the discovery research budget must be in order to generate such a distribution of R&D projects. PAREXEL (2003) provides a considerable amount of evidence on R&D pipelines, particularly for large firms. In the period , the number of new drug applications (NDAs, which are filed after human clinical trials are complete and the drug is ready for final FDA review) for new molecular entities (an NME, which is similar to an NCE) filed with the FDA s Center for Drug Evaluation and Research (CDER) has ranged from 22 to 48. These figures include inhouse and licensed in or acquired drugs, but they clearly indicate that on average, NME production is at the rate of much less than one per firm per year. Following firms over the period , the number of NMEs introduced to the market by each firm each year ranges from 0 to 6. Figures are also provided for the number of new molecular entities and novel biologics (excluding line extensions) in Phase 3 by firm for the 18 firms with the most drugs in Phase 3 in February The number ranges from 1 to 11; the average is 4.7. The majority of these involve partnerships; the number of inhouse projects ranges from 0 to 4, and the average is 1.9. Some information is provided for NCEs by phase for particular firms. GlaxoSmithKline, one of the firms with the largest R&D project portfolios, had from 14 to 25 NCEs enter Phase 1 and from 4 to 10 NCEs enter Phase 2 each year during the period 1999 to 2002 (1999 and 2000 numbers represent the pre-merger combination of Glaxo Wellcome and SmithKline Beecham). In March, 2003, GlaxoSmithKline had 61 NCEs in all phases of clinical development. Again, some of these were licensed in or acquired, but the figures show that even the largest firms R&D pipelines are not overwhelmingly large in terms of the number of NCEs in development that the model must 9 In the mid-1980s, less than 100 alliances between drug companies and biotechnology companies were being initiated each year. By the late-1990s, over 400 such alliances were being initiated each year. 13

14 keep track of. In the simulations below, we consider a range of initial R&D scales from very small new biotechnology entrants up to the largest incumbents. III. Results Figure 1 graphs the percentage of established firms that have not failed by year; Figure 2 graphs the percentage of new firms that have not failed by year. The model generates three natural measures of the size of the firm s R&D operation, and we report all three. The first is the firm s estimated average annual R&D budget, in millions of year 2000 dollars. The second is the average number of projects the firm has in clinical development each month. The third is the number of discovery research programs the firm initiates each month. We set the third variable and the model then generates the other two from the constructed sample of one hundred firms. The results in Figure 1 are intuitive. Larger firms have higher probabilities of covering their accumulated capitalized costs from internal revenues. In the model, firms that are sufficiently large are essentially assured of success, while relatively small firms cannot achieve success in the long run. For example, none of the firms with an R&D budget of $162 million per year and an average of 6 projects in clinical trials each month succeed for the full one hundred year period. In reality, there are examples of large firms that appear to have failed (or would have, absent a merger) in recent years and tiny firms that were able to succeed and grow; the model suggests that both of these events would be rare. The results suggest that the largest firms who have proven successful in generating new products in the past are much more likely to continue forward than smaller, untested firms. Further, the model does not account for the possibility that two successful firms will merge. How successful firms act is not the object of the model; rather, the model predicts how likely a firm is to succeed on its own. With those caveats, comparing Figures 1 and 2 we reach two main conclusions. First, new firms have much lower success probabilities than established firms, holding the scale of the discovery research program fixed. Second, within the groupings of established and new firms, small firms have lower success probabilities than large firms. Figures 3 and 4 provide a snapshot of a particular duration, 20 years, and yield the same conclusion. New firms in the model have trouble achieving success because they begin with no projects at advanced stages. They must wait the full 142 months before having any chance of introducing a new drug and obtaining some profits that can be applied against the accumulated capitalized costs of R&D. In contrast, established firms with projects near 14

15 completion obtain profits relatively early; capitalized costs have less time to accumulate. The effect of firm size is mainly due to diversification. Even when small established firms have projects at advanced stages, the number of such projects is small. Given this, a few bad shocks can be devastating. Rising Costs At the base case parameter values, the capitalized cost of a new approved drug (which includes the costs of many failed attempts) is $802 million 2000 dollars. As we noted above, this estimate is based on DHG s study of drugs that entered human clinical trials between 1983 and Some recent estimates from Gilbert, Henske, and Singh (2003) suggest that the current capitalized cost is approximately double this amount: $1.7 billion 2003 dollars. Regardless of what the current level of costs is, the easiest way to incorporate higher cost estimates into our analysis is as follows. Assume that costs at every stage (discovery, clinical trials, etc.) have risen in the same proportion, and that the durations of each stage, the probabilities of success, and the opportunity cost of capital have remained the same. Further, assume that the profit distribution has risen in the same proportion as costs. In this scenario, the only aspect of our results that would change would be the size of the R&D budget required to sustain a given success probability. To match the $1.7 billion 2003 dollar figure, the R&D budgets we report would have to roughly double in size. Importantly, our estimates of the average number of projects in clinical trials and the scale of the discovery research program required to sustain a given success probability would not change at all. In this sense, the average number of projects in clinical trials and the scale of the discovery research program are real measures that firms can use to gauge their prospects for success. Of course, if costs at every stage have not risen in proportion or if the durations of stages or probabilities of success have changed, then our model can accommodate such changes with changes in the relevant parameter values. Policy Effects Given the results presented in Figures 1-4, our main findings in this subsection may be anticipated: it is perhaps not surprising that new and small firms are more vulnerable to policy changes that hurt pharmaceutical firms. However, because policy-makers often seem eager to support new and small firms in other contexts, it is worth emphasizing this policy effect. Here we provide a detailed discussion of how policy changes that reduce the profitability of new chemical entities affect different types of firms. We do the analysis for the base case parameter values (based on the 15

16 DHG estimates); rescaling costs and profits as described in the previous subsection would not affect our conclusions. 10 At the base case parameter values, the cost of a new approved drug capitalized up to the point of marketing approval is $802 million and the expected net present value of profits of the drug at the point of marketing approval is $847 million. Thus, the excess profit at the time of marketing approval is $45 million. If policy changes (such as price caps, profit limitations, or weakened intellectual property protections) reduce the expected net present value of profits of a new approved drug to even slightly less than $802 million, the firm cannot justify beginning any discovery research programs. In this case, new firms will not enter and existing ones will not initiate any new discovery research programs. If no adjustment to restore profitability or lower costs occurs, then firms will survive only long enough to complete their currently viable discovery research programs and projects still in development. The notion that policy changes could reduce average profits by this amount (a bit more than 5%) is not fanciful; policymakers advocating the adoption of Canadian prices in the U.S. routinely suggest that prices in the U.S. market should be 40-50% lower than current levels. Given that approximately half of industry profits come from the U.S. market (PAREXEL 2003) and that marginal costs are approximately 1/6 of the market price (according to Hughes et al. 2003), it would only take a price cut of 8.34% in the U.S. market to cause a 5% reduction in profits absent any adjustment to restore profitability. The negative effects of the resulting reductions in innovation on consumer welfare could be substantial (see Hughes et al for estimates of how price reductions for branded pharmaceuticals impact consumer welfare). The order in which projects and programs are shut down is critical for any policy analysis, because it implies that the effects of a given adverse policy change on established firms might not be very noticeable for many years. For our base case parameters, Figure 5 graphs the expected net present value of a new drug as it proceeds from the discovery stage through the R&D project stages to completion. Clearly, the value of a single discovery research program is negligible (near zero initially, and never over $23,000); a major hurdle is passed when human clinical trials can begin (at 90 months from completion). In general, the value of a new drug being tested rises as it advances 10 Our policy analysis emphasizes price caps and other sources of reduced profitability, but evidence suggests that other types of policies also have the (perhaps unanticipated) impact of harming small firms relatively more than large ones. Thomas (1990) provides 16

17 toward completion. This occurs because the probability of obtaining final approval rises (more hurdles having already been completed), expected future profits are discounted less, and there are fewer future costs to bear. 11 Given this, when an adverse event occurs, it is optimal for the firm to abandon early stage discovery research programs first, then later stage discovery research programs, then early stage R&D projects, and then later stage R&D projects. The model allows us to investigate timeframes for how companies would react to reductions in expected profits. For example, the model indicates that a policy that reduces profits by 10% causes established firms to shut down only discovery research programs with six months of maturity or less. All more mature programs and all drugs in clinical trials remain viable. Thus, the effects on new drug development would not be observed until those discovery research programs with six months of maturity or less would have borne fruit had they not been shut down. At the base case parameter values, this would require, at a minimum, 46 months of further discovery research and 90 months of clinical trials and review, for a total of 136 months, which is over eleven years. Figure 6 demonstrates the relationship between potential reductions in profits and the length of time before such reductions would be visible to consumers in the form of reduced product introductions. Recall that at the base case parameter values, a new drug must pass through 52 months of discovery research and 90 months of clinical trials and review. Table 2 shows that the average drug enters Phase 2 of clinical trials 13 months after clinical trials begin and enters Phase 3 26 months after that. Given this, Figure 6 shows that it would require a profit reduction of approximately 46% before the firm began shutting down R&D projects entering Phase 1 clinical trials, 55% before the firm began shutting down R&D projects entering Phase 2 clinical trials, and 79% before the firm began shutting down R&D projects entering Phase 3. The industry would have to suffer a truly devastating blow in order for the effects on new product introductions to be observed within a few years. Phase 3 takes an average of 34 months to complete, and it is followed by a further period of 18 months that includes FDA final review prior to approval. Thus, even if early stage Phase 3 projects were abandoned, the effects on new drug introductions would not be observed for over four years. Importantly, the model suggests that any reduction in profits over about 5% will eventually cause all firms to stop producing new drugs. However, the estimates suggest that exit may take a very long time to occur. an analysis of the impacts of FDA regulatory changes in 1962 and finds that the resulting increase in development costs harmed large firms much less than small ones. 17

18 Of course, it is possible that major changes in productivity, changes in the underlying structure of demand for pharmaceuticals, or changes in the policy environment in other parts of the world could improve the profitability of new drug returns and offset the reductions in the US market. While demand will likely increase over time and improve profit outlooks, other factors such as technological opportunities have tended in recent years to move in the opposite direction. As we discuss below, the most likely event that would restore industry profitability (if any) would be the exit of smaller and newer firms, which would result in reduced innovation that would reduce competition among branded products in most therapeutic categories. It is worthwhile to consider less dramatic profit reductions in order to explore the impacts of policy changes that would not cause firms to shut down any discovery research programs. As an example to illustrate the effects, consider an across-the-board profit reduction of exactly 5%. That is, we re-run the model using.95*π rather than π. This allows returns to remain marginally positive (the excess profit is $2.79 million). The 5% across-the-board profit reduction is for illustrative purposes only, and if anything it is a low estimate of the reduction many industry critics would like to see. The size of the profit reduction is not critical for the analysis, as long as the reduction is low enough that it does not cause all discovery programs to cease as in the previous example. Comparable effects would be observed with smaller profit reductions, but they would be smaller. A summary of the main conclusions from this exercise is presented in Figure 7. Figure 7 shows that, in general, new firms regardless of size are much more adversely affected by the profit reduction. The reason for this is that they begin without any high-value projects in clinical trials. As we noted above, a firm s clinical trials program is much more resilient to adverse shocks than its discovery research program. All firms discovery research programs fall in value to near zero when profits fall. This means that a firm cannot easily cover its accumulated capitalized costs solely with the value of its discovery research program; it must have high-expected-value clinical trials projects. A new firm that does not experience large numbers of positive shocks early on cannot succeed. Figure 7 also shows that small firms, regardless of whether they are new or established, are more vulnerable to the profit reduction. However, even large established firms do not completely escape the adverse effects of the profit reduction. Combining the results on size and firm age, we see that the most vulnerable firms are not the 11 In reality, changing preliminary signals about the profit potential of a new drug as it moves through clinical trials might cause the 18

19 established firms that would suffer immediate (but manageable) profit reductions, but rather the newer, smaller biotechnology and niche/high-tech pharmaceutical firms that policymakers seem eager to support in other contexts. The growing reliance and enthusiasm of large-scale pharmaceutical firms for partnerships with smaller biotechnology firms suggests that large-scale reductions in success rates for smaller, newer firms may have important second-order effects on larger firms as well. Policy Changes and Practices that Could Cause Profit Reductions Several policies or practices could cause an across-the-board profit reduction of 5% or more. One example is U.S. consumers importing drugs from Canada. Canada imposes price caps on prescription drugs, whereas the U.S. does not. Thus, U.S. consumers can obtain drugs for lower prices by buying them in Canada or ordering them through the Internet from Canadian firms. 12 Price differences for branded drugs between the two countries sometimes approach 50%. Another example is that the U.S. government, or individual state governments, could increase their use of mandatory rebates to extract additional price concessions from manufacturers these rebates already are mandated to be at least 15.1% and are often higher. The government could impose reference pricing, which would peg drug prices at the lowest price for a given therapeutic class or to prices in other countries; see Danzon and Ketcham (2003) for a useful analysis of the relative effectiveness of such proposals. More indirectly, the Congress could weaken patent protections for pharmaceutical innovation or strengthen the incentives for generics manufacturers to risk infringement, as proposed in the last Congress. All of these proposals, and others, continue to be actively pursued at the federal and state levels. Discussion/Conclusion To an extent, the model s results overstate the effects of policies such as price caps because the model does not allow for the kinds of equilibrating adjustments that would occur in the industry. For example, if price caps in the U.S. were imposed, the first effect is that the expected profitability of new drugs would fall. In reaction to falling profits, some firms would abandon discovery research programs and others might exit the industry entirely. Both of these events would have the effect of reducing future competition in therapeutic categories, which would cause expected expected net present value to move in a non-monotonic way. However, on average, the pattern is as depicted in Figure Canadian importation is not an insurmountable problem. It seems likely that policy changes could eliminate the cross-border arbitrage opportunity while preserving national sovereignty. For example, pharmaceuticals could be sold at U.S. prices within Canada but with non- 19

20 profits of the survivors to rise again. Roughly, if price caps cause industry profits to fall by x%, in the long run the number of branded competitors in the average therapeutic category would also fall by approximately x%. 13 Hughes et al (2003) estimate a model quite different from ours that calculates the consumer welfare implications of a reduction in innovation and find that consumers are much worse off in the long run when short-run price caps are imposed. 14 The government could attempt to make up for the loss by investing in drug-related research more aggressively or making changes to the regulatory and approval process that would lower the cost of discovering new drugs in the first place though the NIH invests in such research today and has had a very limited record of stand-alone success in discovering new drugs (see DHG for exact figures). Assuming policies affecting patent lengths and generic entry were not changed, the patent-protected life of a new drug would not change. The only equilibrating variable would be the number of competing patented products in different therapeutic categories. Put differently, if profitability was reduced through price caps, it would be (at least partially) restored in the long run through less innovation and competition. Consumers in the future would therefore be faced with fewer and worse therapeutic options than they would be under the current policy regime. Who would survive? Who would exit? Although our model considers a single firm rather than an industry equilibrium, we think our results provide some insight into these questions. The firms that are most vulnerable to policies that raise costs or reduce profits are first, new entrants, and second, small established firms. Thus, the model suggests that, in response to rising costs and falling profits, the industry would evolve toward an environment with fewer and larger firms where smaller and newer firms would be even more dependent on the large established firms than they are now. transferable rebates that Canadian consumers (consumers with Canadian addresses) could collect. The effective Canadian price would stay at the current capped level, but arbitragers would not be able to benefit from the difference in prices. 13 In the long run, we expect that the equilibrium average profit per new drug will be just high enough to encourage innovation, as it is at present. Given that the average profit per new drug cannot fall, when industry profits fall the number of new drugs must fall. Of course, the reduction may not be exactly in proportion to the reduction in industry profits. When competitors exit, the remaining firms may be able to exercise greater market power, which means that fewer than x% would have to exit to restore profitability on a per new drug basis. On the other hand, many branded drugs are imperfect substitutes because they work in different ways and have different side effects and so on. When goods are imperfect substitutes, the exit of one competitor may do little to enhance the profitability of the remaining firms; the consumers who would have purchased the exiting good simply go untreated or are treated with nonpharmaceutical means. In this case, more than x% of the firms would have to exit to restore profitability on a per new drug basis. 14 Other relevant prior work includes Grabowski and Vernon (1987), who analyze the impacts of policy changes on innovation in a model where firms either innovate or imitate. 20