- Email: [email protected]
Do large mergers increase or decrease the productivity of pharmaceutical R&D?
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feature Do large mergers increase or decrease the productivity of pharmaceutical R&D? There is current uncertainty regarding the effects of mergers on pharmaceutical R&D productivity, with various mechanisms reported by which mergers could either help or harm R&D, and mixed empirical findings in prior analyses. Here, we present an analysis that is novel in several ways: we use downstream measures of R&D productivity, account for both inputs and outputs in our calculations, and use a selfcontrolled design. We find that recent large pharmaceutical mergers are associated with statistically significant increases in R&D productivity. These results are perhaps not surprising in light of the broader literature on R&D productivity that points to two factors as instrumental in driving higher R&D productivity (depth of scientific information, and objectivity of decision-making based on that information), both of which could be expected to increase because of a merger.
Introduction There are differing opinions as to the impact of mergers on R&D productivity, with some studies finding positive impacts and others negative ones. On the one hand, mergers involve some level of disruption, loss of R&D talent [1–8], and funding reduction [2,5,8], as well as elimination of parallel paths of inquiry [10,11]. On the other hand, mergers can involve applying the best R&D processes of each company to the combined entity, creating knowledge and other synergies [2,3,5–8,11–13], and potentially enabling more objective decisions regarding the portfolio and other drivers of R&D success [2,3,11]. Supporting this view, leading research regarding R&D productivity points to two factors that are critical, each of which might be expected to increase following a merger: (i) the
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ability to bring the best science to bear on a disease area; and (ii) the ability to take a ‘fresh look’ at the assets in the portfolio, objectively prioritizing those most likely to drive advances in treatment [14,15]. To assess which hypothesis is more likely to be correct (i.e., whether mergers reduce or augment R&D productivity), we compare R&D productivity in the years following a merger to R&D productivity in non-merger years.
Approach We consider R&D productivity in the period from 2001 to 2011. We look back to 1998 for mergers given that we assume a 3-year window for merger impact (see below for details). We include the top 25 pharmaceutical companies by end-of-period revenue (AbbVie, Allergan,
Amgen, Astellas Pharma, AstraZeneca, Bayer, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Daiichi Sankyo, Eisai, Eli Lilly, Gilead Sciences, GlaxoSmithKline, Johnson & Johnson, Merck & Co, Novartis, Novo Nordisk, Pfizer, Roche, Sanofi, Shire, Takeda, and UCB) and use EvaluatePharma data to identify mergers with a nominal deal value greater than US$20B. We restrict our sample to these large mergers, because they are the ones most likely to affect industry R&D productivity [5]. This screening resulted in 13 merger events in our sample (Table 1). It should be noted that this is a small sample size and, as such, the results should be interpreted with caution. This is an intrinsic limitation of analyses of large mergers in biopharma, and other researchers who have investigated this topic have also been restricted to
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Q1 Michael S. Ringel1, [email protected] and Michael K. Choy2
Q2
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TABLE 1
Mergers in our samplea Acquiring/current company
Target company
Deal completion
Deal value (US$B)
GlaxoSmithKline Pfizer AstraZeneca Sanofi Pfizer Pfizer Roche Merck & Co Sanofi Novartis Bayer Astellas Pharma Sanofi
Glaxo Wellcome + SmithKline Beecham Warner-Lambert Astra + Zeneca Aventis Wyeth Pharmacia Genentech Schering-Plough Sanofi + Synthélabo Alcon Schering AG Yamanouchi + Fujisawa Genzyme
12/27/2000 6/19/2000 5/21/1999 8/20/2004 10/16/2009 4/16/2003 3/26/2009 11/3/2009 5/25/1999 8/26/2010 8/29/2006 4/1/2005 4/8/2011
244 116 89 75 74 73 51 45 39 30 24 24 21
a
Data are from EvaluatePharma, adjusted to constant 2015$ using an OECD deflator provided by EIU.
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small sample sizes (N = 1 [7], several [1], 4 [8], 7 [13], 11 [16], 15 [17], and 19 [12]). Selecting a control group is not trivial with observational data; for instance, confounding factors, such as a weak R&D enterprise, might drive both poor performance and a propensity to merge, creating a false impression of causality between mergers and poor R&D performance [2,8,9,11,12,17,18]. Conversely, it is possible that acquiring companies deliberately target companies with impending launches as the focus of their merger and acquisition, driving a false correlation of mergers and improved R&D performance. In our sample, we do not see obvious evidence of this: despite some anecdotal examples (e.g., Wyeth’s Prevnar-13 launched a year after the Pfizer acquisition), there is no statistically significant difference in the value of launches from the acquired entity in the year of merger or year following versus equivalent 2year periods before or after mergers (ANOVA P = 0.97, P = 0.81, both nonsignificant). Nevertheless, there remains a risk of confounding in observational studies. Several researchers have attempted to correct for confounding factors by using a matched control group of companies and adjusting for these influences [2,5,13], but the usage of such matching in observational studies presumes that the investigator understands, can observe, and can find suitable matches for all relevant variables, presumptions that are rarely, if ever, fully valid [19]. To ascertain the best method to address confounding in observational studies, the US Food and Drug Administration (FDA), the Foundation for the National Institutes of Health (NIH), and other parties created a joint institute, called the Observational Medical Outcomes Partnership (OMOP), to undertake a comprehensive, systematic evaluation of
2
methodologies for control selection. After review of potential designs across multiple data sets, these researchers concluded that, at least for medical outcomes studies, the most robust methods are self-controlled designs, where outcomes for a single case (e.g., company) during an exposure period (e.g., during the period of merger impact) are compared with outcomes during windows before and potentially after the exposure period [19]. We follow this approach, and use as our control the R&D productivity in non-merger years from the same companies, both before and after the ‘postmerger window’. Similar to several other analysts [2,4–6,16,20], we use the 3 years following deal close as the post-merger window. We use the pro forma R&D activity from all companies that contribute to the final entity; for example, in looking at the R&D productivity impact of the Pfizer-Wyeth merger that closed in 2009, our post-merger window comprises the 3 years following the year of deal close (in this case, 2010–2012), whereas our non-merger control comprises Pfizer R&D productivity post-2012 and the combined R&D productivity of Pfizer and Wyeth before 2010. We measure performance using R&D productivity, which is the total amount of innovation created for a given amount of R&D investment (i.e., the ratio between value delivered and R&D spend committed). This is a standard metric used in other (non-merger) assessments of the performance of R&D [21,22], but it has not been used in the literature assessing mergers. Below, we discuss the choice of this metric: the numerator, denominator, and use of a ratio. First, consider the numerator. Other analysts have used R&D spending as the metric of innovation (in this case, without a
denominator) [2,20]. It is clear that the total amount of R&D spent is a poor correlate of value creation, given the large variation possible in how well that spend might be deployed, and indeed, the hypothesis that mergers create disruptive churn, during which period much spend can be wasted [23]. Similarly, other analysts have used patents created [4,5,12] or pipeline counts [6,11,13,16] (often per dollar of R&D spend) as the measure of innovation. However, there is a significant body of literature showing that there can be orders of magnitude in variation between companies in translating upstream activity, such as patents or pipeline counts, to actual value for patients, meaning this measure is also likely to be a poor indicator of actual innovation delivered [14,15,21,22]. The ideal numerator is a measure of total value created for patients. However, systematic, objective measures of drug value are currently not available [24]. Although there is reason for optimism that these will be developed over time, in the meantime we believe that the best proxy for innovation delivered to patients is the closest available downstream measure. Pharmaceutical sales figures are downstream of R&D spending, patents, and pipeline counts; thus, in this analysis, as in these other more recent assessments of R&D productivity, we use the total peak sales generated from new molecular entities (NMEs, new drugs approved by the FDA) per dollar of R&D spend as the measure of success. There is reason to believe that this proxy tracks patient value reasonably closely: both depend on the quantity of patients prescribed the drug (Q) and, where they differ (price, P, versus value per patient, V), there should be a correlation given that payers, physicians, and patients have influence over access, reimbursement, and payment for products
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TABLE 2
Industry R&D spend during our sample period (2001–2011) plus 3-year tailsa Year
Industry R&D spend (US$M)
Annual growth in R&D spend
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
51 655 57 400 66 163 70 113 76 930 86 030 96 366 105 060 114 238 124 852 134 968 142 727 139 609 139 885 145 723 142 879 142 202 144 373
11.1% 15.3% 6.0% 9.7% 11.8% 12.0% 9.0% 8.7% 9.3% 8.1% 5.7% 2.2% 0.2% 4.2% 2.0% 0.5% 1.5%
a Shaded cells denote years of a major merger. There is no significant difference in the growth of R&D spend at the industry level during merger versus non-merger years. R&D spend data are from EvaluatePharma, adjusted to constant 2015$ using an OECD deflator provided by EIU.
NMEs in any given year trace some portion of their return back to investments made in many different prior years. However, by far the heaviest portion of spend occurs during the period shortly before launch during Phase III/ Regulatory filing, with average costs of this phase more than ten times those of even Phase I clinical testing (which in turn is much higher than the per-project discovery and preclinical costs) [26]. Given this heavy weighting of total investment to the period shortly before launch, and because the data are not available to make specific tracings of investment to return, several prior analyses [22,27] have used the approximation of standard 4-year lags between spend and launch to compare investment and return, and we follow this approach. That is, continuing with the Pfizer-Wyeth example above, for a merger closing in 2009, the R&D productivity is the R&D spend in the 3-year post-merger window (2010–2012) versus the expected peak sales of NMEs launched 4 years later (2014–2016). It is unclear whether to treat separate mergers involving the same company as independent events for the purposes of statistical analyses. Such an approach could well be justified because, at different time points, these companies have different management teams, projects in the pipeline, and R&D technologies involved. However, a more conservative analysis would treat these mergers as non-independent events. Below, we analyze our merger sample both ways, and find equivalent results. Finally, as with any comparison where the treatment and control arms are evaluated post
hoc and not randomly assigned a priori, it is important to remember that any correlation observed does not necessarily imply causation. Any observed difference could be the result of the impact of mergers on R&D productivity, but it could likewise be the other way around: that differences in merger proclivity are driven by underlying differences in R&D productivity premerger [2,17], or that another, third variable drives differences in both R&D productivity and merger rates. We have attempted to control for these factors by using within-company matched comparisons, a relatively long time window, and a control period that covers both before and after the merger. However, there is no way completely to eliminate alternative interpretations of any observed correlations, given the nonexperimental set-up of this comparison. This same caveat applies to all prior analyses on this topic, given that no mergers have been experimentally forced.
Analysis and discussion The results of the analysis are shown in Table 3. The average R&D productivity observed during post-merger windows is 1.83 times the average R&D productivity observed in the comparison non-merger years. There is considerable variability in year-to-year R&D productivity of each company. However, despite this variability, there is enough of an association between mergers and R&D productivity that a signal is detectable and statistically significant (P = 0.016 when treating each merger as an independent event,
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based on whether they deliver value above alternatives [24,25]. Second, consider the use of a ratio that includes a denominator. Most analysts have focused on productivity (the ratio of output to input) rather than on total output, but a few have not, focusing on total output instead [2,4,16,20]. There are two reasons to measure productivity of the merged entity rather than simply assess total output. First, productivity itself is a relevant metric, capturing the ability of the enterprise to persist. If productivity is so low that the returns to the company are below the cost of capital, such investment cannot be sustained. Second, even when focused on total output, an analysis conducted on the output of just one company presumes the employees lost and capital removed completely disappear and are not reactivated or reinvested elsewhere. However, of course, this is not true. Many employees lost to one company redeploy to other companies, including biotechnology startups, and the capital returned to shareholders remains to be invested in other ways [26], often in the same sector, given the investment patterns of investors active in pharmaceuticals. To assess the importance of this latter point it is worth understanding which effect predominates at the sector level: whether mergers remove total industry R&D activity, or instead result in the capacity redeployment. An analysis of industry-level growth of R&D spend during merger versus non-merger years shows no significant impact of merger activity on R&D totals when analysed in-year or with 1, 2, or 3year lags (P = 0.35, 0.50, 0.62, and 0.66, respectively, all nonsignificant). Results from combinations of contiguous years are similarly nonsignificant, supporting the view that capacity is primarily shifted to R&D investments elsewhere in the industry, rather than eliminated entirely (Table 2). Given that total patient value created depends on industry-level not company-level output, considering company-level output and not productivity misses the point that this capacity appears to be redeployed elsewhere in the industry, presumably to opportunities with higher productivity. A further methodology consideration is the time lag between the investments made in R&D and the actual launch of NMEs into the market. Launches in any given year are the result of multiple years of investment, weighted in a complex way based on the particular history of each product. Given that it typically takes more than a decade (and sometimes several decades) to proceed from initial idea to launch of a drug,
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TABLE 3
Comparison of R&D productivity in post-merger windows versus controlsa Company
Astellas Pharma AstraZeneca Bayer GlaxoSmithKline
Merck & Co Novartis Pfizer
Roche Sanofi
Average
Merger
Yamanouchi + Fujisawa Astra + Zeneca Schering AG Glaxo Wellcome + SmithKline Beecham Schering-Plough Alcon Warner-Lambert Pharmacia Wyeth Genentech Synthélabo Aventis Genzyme
Annual spend (US$B)
Annual launches (US$B)
Productivity
Ratio
Post-merger window
Other years
Post-merger window + 4-year lag
Other years
Post merger
Other years
1.72
1.94
2.01
0.10
1.17
0.05
23.63
3.93 2.38 5.50
5.07 2.54 6.74
2.52 5.24 1.08
1.33 0.16 2.02
0.64 2.20 0.20
0.26 0.06 0.30
2.44 34.64 0.66
8.78 9.34 10.4
7.83 6.29 11.4
3.78 8.54 1.86
1.95 1.74 1.23
0.43 0.91 0.18
0.25 0.28 0.11
1.73 3.32 1.65
5.48 7.27
4.26 1.75
1.60 0.95
0.50 0.28
0.29 0.13
1.70 2.09
5.57
2.90
1.25
0.41
0.22
1.83
8.54 6.34
7.04
11.2 11.4 8.74 5.11 7.17 6.35
1.10 2.98 1.52 1.20 0.28 7.22
0.10 0.26 0.17 0.24 0.04 1.14
0.91 2.43 1.60 1.79 0.30 8.62
a
The table compares performance during post-merger windows and outside these windows for the merger events in our sample. Data are shown for each merger and for each company, as described in the main text. R&D spend and peak sales data are from EvaluatePharma, adjusted to constant 2015$ using an OECD deflator provided by EIU. Where peak sales have not yet been achieved, the EvaluatePharma projected estimate is used.
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P = 0.027 when performing the analysis at the company level). This result appears to be robust across time periods, in that our comparison set includes mergers from the start of our window to the end with no clear trendline over time (regression P = 0.79, nonsignificant), as well as across deal sizes (regression P = 0.22, nonsignificant). The largest merger (Glaxo Wellcome + SmithKlineBeecham) is one of the few showing a negative correlation with R&D productivity. It might be that, at this deal size, the negative effects of merging outweigh the beneficial ones. However, it is not possible to determine this, given the small sample size of the current analysis. There are conflicting analyses in the literature regarding the impact of mergers on R&D innovation, with some studies showing improved performance following mergers [6,7,11–13] and others suggesting decreased innovation [1,2,5,16,20]. The differences might be in part the result of different methodologies, with negative impacts found by studies typically assessing intermediate output metrics, such as patents or even input metrics, such as spending, and totals rather than productivity ratios. What matters for patients is not intermediate metrics, but actual creation of quality medicines. Given the large literature on the disconnect between such intermediate metrics and actual value creation, and indeed that mergers might be an event improving the connection by stopping activity on ultimately nonvalue-adding projects (‘truth seeking’ over ‘progression
4
seeking’) [14,15,28], the use of such metrics has particular potential to be misleading regarding value creation during mergers. Furthermore, patient benefit is served by the system-level R&D productivity of biopharma, not the output from any one company. As such, the relevant metric at the company level is productivity (used here, and by most other analysts), not company output, given the evidence that capacity and investment appear to be redeployed elsewhere in the industry following a merger. This analysis shows that, in contrast to the analyses that omit these considerations, mergers are associated with improved value creation for patients. Admittedly, there remain limitations in this analysis. The sample size is small at just 13 events. Peak sales are used as a proxy for patient value creation, and are not an exact correlate with the desired metric. R&D spending is not directly traced to the associated NMEs; instead a standard 4-year time lag is used. A self-controlled design is used and, while such designs have outperformed case-control and other methods in ascertaining whether an effect is present, they might be limited in their ability to estimate the magnitude of effects [19]. As such, we would caution against using this study to estimate the magnitude of the relationship between mergers and R&D productivity. Finally, as with any observational study, conclusions are limited to associations and not inferences of causality. All of these limitations are important areas where follow-up research would be valuable.
Our findings are consistent with the mechanistic interpretation that mergers create an event during which the leadership of the new company takes a ‘fresh look’ at its approach to R&D. This process allows it (at least with respect to the target company, and in well-managed cases, on both sides of the transaction) to be more objective in the assessment of its scientific hypotheses in each disease area and in particular the likelihood of assets in the portfolio to drive advances in treatment, shedding projects that have been maintained despite limited approval likelihood or value creation potential [14,15]. Such reassessments are a boon to patients by objectively prioritizing those assets most likely to drive advances in treatment. This is not to say mergers are the only way to achieve these goals. It is valuable for companies to have ongoing processes that ensure objective decisions on assets and other aspects of R&D based on unbiased decision-makers with access to full information. There is considerable opportunity in the design of R&D operations to achieve this goal, as discussed in a growing body of literature on the topic [14,15,28]. However, our experience is that mergers provide a forcing function that can stimulate companies to undertake such changes if they have otherwise been deferred. Neither is this to say all mergers are good. Even from an R&D productivity perspective, there is variability in outcomes, with some mergers in our sample appearing to have depressed the flow of medication to patients. This
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Acknowledgments The authors gratefully acknowledge the contributions of Ulrik Schulze and Mathias Baedeker, as well as several anonymous reviewers, to this work. References 1 LaMattina, J.L. (2011) The impact of mergers on pharmaceutical R&D. Nat. Rev. Drug Discov. 10, 559–560 2 Danzon, P.M. et al. (2004) Mergers and Acquisitions in the Pharmaceutical and Biotech Industries. National Bureau of Economic Research
3 Cassiman, B. et al. (2005) The impact of M&A on the R&D process: an empirical analysis of the role of technological- and market-relatedness. Res. Policy 34, 195–220 4 Cloodt, M. et al. (2006) Mergers and acquisitions: their effect on the innovative performance of companies in high-tech industries. Res. Policy 35, 642–654 5 Ornaghi, C. (2009) Mergers and innovation in big pharma. Int. J. Ind. Org. 27, 70–79 6 Prabhu, J.C. et al. (2005) The impact of acquisitions on innovation: poison pill, placebo, or tonic? J. Market. 69, 114–130 7 Shibayama, S. et al. (2008) Effect of mergers and acquisitions on drug discovery: perspective from a case study of a Japanese pharmaceutical company. Drug Discov. Today 13, 86–93 8 Ravenscraft D.J., Long W.F. (2000) Paths to creating value in pharmaceutical mergers. In Mergers and Productivity (Kaplan, S.N., ed.), pp. XXX–YYY, publisher 9 Comanor, W.S. and Scherer, F.M. (2013) Mergers and innovation in the pharmaceutical industry. J. Health Econ. 32, 106–113 10 Munos, B. (2009) Lessons from 60 years of pharmaceutical innovation. Nat. Rev. Drug Discov. 8, 959–968 11 Grabowski H., Kyle M. (2008) Mergers and alliances in pharmaceuticals: effects on innovation and R&D productivity. In: The Economics of Corporate Governance and Mergers (Gugler, K. and Yurtoglu, B.B., eds), pp. XXX–YYY, publisher 12 Bruegman, E. (2007) Pharmaceutical Mergers and the Organization and Productivity of Innovation. Harvard University Press 13 Koenig, M.E.D. and Mezick, E.M. (2004) Impact of mergers & acquisitions on research productivity within the pharmaceutical industry. Scientometrics 59, 157–169 14 Ringel, M. et al. (2013) Does size matter in R&D productivity? If not, what does?. Nat. Rev. Drug Discov. 12, 901–902 15 Peck, R.W. et al. (2015) Why is it hard to terminate failing projects in pharmaceutical R&D? Nat. Rev. Drug Discov. 14, 663–664 16 CenterWatch Inc (2000) Some troubling numbers for big pharma consolidation. In Vivo 1, 2000 17 Shimura, H. et al. (2014) A lesson from Japan: research and development efficiency is a key element of pharmaceutical industry consolidation process. Drug Discov. Today 8, 57–63
18 Higgins, M.J. and Rodriguez, D. (2006) The outsourcing of R&D through acquisitions in the pharmaceutical industry. J. Fin. Econ. 80, 351–383 19 Ryan, P.B. et al. (2013) A comparison of the empirical performance of methods for a risk identification system. Drug Saf. 36, S143–S158 20 Haucap J., Stiebale J. (2016) Research: innovation suffers when drug companies merge. Harvard Bus. Rev. XX, XXX–YYY. 21 Paul, S.M. et al. (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214 22 Schulze, U. et al. (2014) R&D productivity: on the comeback trail. Nat. Rev. Drug Discov. 13, 331–332 23 Knott, A.M. (2012) The trillion-dollar R&D fix. Harvard Bus. Rev. 76, XXX–YYY. 24 Eichler, H.-G. et al. (2015) Assessing the relative efficacy of new drugs: an emerging opportunity. Nat. Rev. Drug Discov. 14, 443–444 25 Blumenthal, D. and McGinnis, J.M. (2015) Measuring vital signs: an IOM report on core metrics for health and health care progress. JAMA 313, 1901–1902 26 DiMasi, J.A. et al. (2016) Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ. 47, 20–33 27 Scannell, J.W. et al. (2012) Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11, 191–200 28 Owens, P.K. et al. (2015) A decade of innovation in pharmaceutical R&D: the Chorus model. Nat. Rev. Drug Discov. 14, 17–28 29 Hansell, G. et al. (2014) Unlocking Acquisitive Growth: Lessons from Successful Serial Acquirers. The Boston Consulting Group 30 Lubkeman, M. et al. (2014) Creating Value from Midsize Biopharma Acquisitions. The Boston Consulting Group
Michael S. Ringel1,* Michael K. Choy2 1
The Boston Consulting Group, Exchange Place, 31st Floor, Boston, MA 02109, USA 2 The Boston Consulting Group, 466 Springfield Avenue, Summit, NJ 07901, USA *Corresponding author.
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Q5
Q4
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variability is consistent with the broader (nonR&D) literature that has found a range of outcomes from mergers and acquisitions, with many ill-conceived or poorly executed mergers Q3 destroying value [37,38]. These studies have pointed to capabilities in sourcing deal opportunities, selectivity in pursuing only those deals with clear value-creation theses, and usage of well-run post-merger integration processes as differentiating factors driving success [29,30]. Lastly, there are considerations outside the scope of the current analysis that could be important factors for policy-makers or executives involved in merger decisions. We have not evaluated the impact of mergers on employment either within or beyond national borders, on investors, or on consumers (e.g., via drug prices). Ultimately, merger decisions might depend on these or other issues. What this analysis does show is that some prior analyses suggesting mergers harm the flow of medication to patients have been in error because of the omission of the considerations covered in this analysis. When considering downstream measures and accounting for both inputs and outputs, mergers appear associated with increased R&D productivity in delivering medicines to patients.
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feature Do large mergers increase or decrease the productivity of pharmaceutical R&D? There is current uncertainty regarding the effects of mergers on pharmaceutical R&D productivity, with various mechanisms reported by which mergers could either help or harm R&D, and mixed empirical findings in prior analyses. Here, we present an analysis that is novel in several ways: we use downstream measures of R&D productivity, account for both inputs and outputs in our calculations, and use a selfcontrolled design. We find that recent large pharmaceutical mergers are associated with statistically significant increases in R&D productivity. These results are perhaps not surprising in light of the broader literature on R&D productivity that points to two factors as instrumental in driving higher R&D productivity (depth of scientific information, and objectivity of decision-making based on that information), both of which could be expected to increase because of a merger.
Introduction There are differing opinions as to the impact of mergers on R&D productivity, with some studies finding positive impacts and others negative ones. On the one hand, mergers involve some level of disruption, loss of R&D talent [1–8], and funding reduction [2,5,8], as well as elimination of parallel paths of inquiry [10,11]. On the other hand, mergers can involve applying the best R&D processes of each company to the combined entity, creating knowledge and other synergies [2,3,5–8,11–13], and potentially enabling more objective decisions regarding the portfolio and other drivers of R&D success [2,3,11]. Supporting this view, leading research regarding R&D productivity points to two factors that are critical, each of which might be expected to increase following a merger: (i) the
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ability to bring the best science to bear on a disease area; and (ii) the ability to take a ‘fresh look’ at the assets in the portfolio, objectively prioritizing those most likely to drive advances in treatment [14,15]. To assess which hypothesis is more likely to be correct (i.e., whether mergers reduce or augment R&D productivity), we compare R&D productivity in the years following a merger to R&D productivity in non-merger years.
Approach We consider R&D productivity in the period from 2001 to 2011. We look back to 1998 for mergers given that we assume a 3-year window for merger impact (see below for details). We include the top 25 pharmaceutical companies by end-of-period revenue (AbbVie, Allergan,
Amgen, Astellas Pharma, AstraZeneca, Bayer, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Daiichi Sankyo, Eisai, Eli Lilly, Gilead Sciences, GlaxoSmithKline, Johnson & Johnson, Merck & Co, Novartis, Novo Nordisk, Pfizer, Roche, Sanofi, Shire, Takeda, and UCB) and use EvaluatePharma data to identify mergers with a nominal deal value greater than US$20B. We restrict our sample to these large mergers, because they are the ones most likely to affect industry R&D productivity [5]. This screening resulted in 13 merger events in our sample (Table 1). It should be noted that this is a small sample size and, as such, the results should be interpreted with caution. This is an intrinsic limitation of analyses of large mergers in biopharma, and other researchers who have investigated this topic have also been restricted to
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Q1 Michael S. Ringel1, [email protected] and Michael K. Choy2
Q2
DRUDIS 2037 1–5 Drug Discovery Today Volume 00, Number 00 June 2017
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TABLE 1
Mergers in our samplea Acquiring/current company
Target company
Deal completion
Deal value (US$B)
GlaxoSmithKline Pfizer AstraZeneca Sanofi Pfizer Pfizer Roche Merck & Co Sanofi Novartis Bayer Astellas Pharma Sanofi
Glaxo Wellcome + SmithKline Beecham Warner-Lambert Astra + Zeneca Aventis Wyeth Pharmacia Genentech Schering-Plough Sanofi + Synthélabo Alcon Schering AG Yamanouchi + Fujisawa Genzyme
12/27/2000 6/19/2000 5/21/1999 8/20/2004 10/16/2009 4/16/2003 3/26/2009 11/3/2009 5/25/1999 8/26/2010 8/29/2006 4/1/2005 4/8/2011
244 116 89 75 74 73 51 45 39 30 24 24 21
a
Data are from EvaluatePharma, adjusted to constant 2015$ using an OECD deflator provided by EIU.
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small sample sizes (N = 1 [7], several [1], 4 [8], 7 [13], 11 [16], 15 [17], and 19 [12]). Selecting a control group is not trivial with observational data; for instance, confounding factors, such as a weak R&D enterprise, might drive both poor performance and a propensity to merge, creating a false impression of causality between mergers and poor R&D performance [2,8,9,11,12,17,18]. Conversely, it is possible that acquiring companies deliberately target companies with impending launches as the focus of their merger and acquisition, driving a false correlation of mergers and improved R&D performance. In our sample, we do not see obvious evidence of this: despite some anecdotal examples (e.g., Wyeth’s Prevnar-13 launched a year after the Pfizer acquisition), there is no statistically significant difference in the value of launches from the acquired entity in the year of merger or year following versus equivalent 2year periods before or after mergers (ANOVA P = 0.97, P = 0.81, both nonsignificant). Nevertheless, there remains a risk of confounding in observational studies. Several researchers have attempted to correct for confounding factors by using a matched control group of companies and adjusting for these influences [2,5,13], but the usage of such matching in observational studies presumes that the investigator understands, can observe, and can find suitable matches for all relevant variables, presumptions that are rarely, if ever, fully valid [19]. To ascertain the best method to address confounding in observational studies, the US Food and Drug Administration (FDA), the Foundation for the National Institutes of Health (NIH), and other parties created a joint institute, called the Observational Medical Outcomes Partnership (OMOP), to undertake a comprehensive, systematic evaluation of
2
methodologies for control selection. After review of potential designs across multiple data sets, these researchers concluded that, at least for medical outcomes studies, the most robust methods are self-controlled designs, where outcomes for a single case (e.g., company) during an exposure period (e.g., during the period of merger impact) are compared with outcomes during windows before and potentially after the exposure period [19]. We follow this approach, and use as our control the R&D productivity in non-merger years from the same companies, both before and after the ‘postmerger window’. Similar to several other analysts [2,4–6,16,20], we use the 3 years following deal close as the post-merger window. We use the pro forma R&D activity from all companies that contribute to the final entity; for example, in looking at the R&D productivity impact of the Pfizer-Wyeth merger that closed in 2009, our post-merger window comprises the 3 years following the year of deal close (in this case, 2010–2012), whereas our non-merger control comprises Pfizer R&D productivity post-2012 and the combined R&D productivity of Pfizer and Wyeth before 2010. We measure performance using R&D productivity, which is the total amount of innovation created for a given amount of R&D investment (i.e., the ratio between value delivered and R&D spend committed). This is a standard metric used in other (non-merger) assessments of the performance of R&D [21,22], but it has not been used in the literature assessing mergers. Below, we discuss the choice of this metric: the numerator, denominator, and use of a ratio. First, consider the numerator. Other analysts have used R&D spending as the metric of innovation (in this case, without a
denominator) [2,20]. It is clear that the total amount of R&D spent is a poor correlate of value creation, given the large variation possible in how well that spend might be deployed, and indeed, the hypothesis that mergers create disruptive churn, during which period much spend can be wasted [23]. Similarly, other analysts have used patents created [4,5,12] or pipeline counts [6,11,13,16] (often per dollar of R&D spend) as the measure of innovation. However, there is a significant body of literature showing that there can be orders of magnitude in variation between companies in translating upstream activity, such as patents or pipeline counts, to actual value for patients, meaning this measure is also likely to be a poor indicator of actual innovation delivered [14,15,21,22]. The ideal numerator is a measure of total value created for patients. However, systematic, objective measures of drug value are currently not available [24]. Although there is reason for optimism that these will be developed over time, in the meantime we believe that the best proxy for innovation delivered to patients is the closest available downstream measure. Pharmaceutical sales figures are downstream of R&D spending, patents, and pipeline counts; thus, in this analysis, as in these other more recent assessments of R&D productivity, we use the total peak sales generated from new molecular entities (NMEs, new drugs approved by the FDA) per dollar of R&D spend as the measure of success. There is reason to believe that this proxy tracks patient value reasonably closely: both depend on the quantity of patients prescribed the drug (Q) and, where they differ (price, P, versus value per patient, V), there should be a correlation given that payers, physicians, and patients have influence over access, reimbursement, and payment for products
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DRUDIS 2037 1–5 Drug Discovery Today Volume 00, Number 00 June 2017
TABLE 2
Industry R&D spend during our sample period (2001–2011) plus 3-year tailsa Year
Industry R&D spend (US$M)
Annual growth in R&D spend
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
51 655 57 400 66 163 70 113 76 930 86 030 96 366 105 060 114 238 124 852 134 968 142 727 139 609 139 885 145 723 142 879 142 202 144 373
11.1% 15.3% 6.0% 9.7% 11.8% 12.0% 9.0% 8.7% 9.3% 8.1% 5.7% 2.2% 0.2% 4.2% 2.0% 0.5% 1.5%
a Shaded cells denote years of a major merger. There is no significant difference in the growth of R&D spend at the industry level during merger versus non-merger years. R&D spend data are from EvaluatePharma, adjusted to constant 2015$ using an OECD deflator provided by EIU.
NMEs in any given year trace some portion of their return back to investments made in many different prior years. However, by far the heaviest portion of spend occurs during the period shortly before launch during Phase III/ Regulatory filing, with average costs of this phase more than ten times those of even Phase I clinical testing (which in turn is much higher than the per-project discovery and preclinical costs) [26]. Given this heavy weighting of total investment to the period shortly before launch, and because the data are not available to make specific tracings of investment to return, several prior analyses [22,27] have used the approximation of standard 4-year lags between spend and launch to compare investment and return, and we follow this approach. That is, continuing with the Pfizer-Wyeth example above, for a merger closing in 2009, the R&D productivity is the R&D spend in the 3-year post-merger window (2010–2012) versus the expected peak sales of NMEs launched 4 years later (2014–2016). It is unclear whether to treat separate mergers involving the same company as independent events for the purposes of statistical analyses. Such an approach could well be justified because, at different time points, these companies have different management teams, projects in the pipeline, and R&D technologies involved. However, a more conservative analysis would treat these mergers as non-independent events. Below, we analyze our merger sample both ways, and find equivalent results. Finally, as with any comparison where the treatment and control arms are evaluated post
hoc and not randomly assigned a priori, it is important to remember that any correlation observed does not necessarily imply causation. Any observed difference could be the result of the impact of mergers on R&D productivity, but it could likewise be the other way around: that differences in merger proclivity are driven by underlying differences in R&D productivity premerger [2,17], or that another, third variable drives differences in both R&D productivity and merger rates. We have attempted to control for these factors by using within-company matched comparisons, a relatively long time window, and a control period that covers both before and after the merger. However, there is no way completely to eliminate alternative interpretations of any observed correlations, given the nonexperimental set-up of this comparison. This same caveat applies to all prior analyses on this topic, given that no mergers have been experimentally forced.
Analysis and discussion The results of the analysis are shown in Table 3. The average R&D productivity observed during post-merger windows is 1.83 times the average R&D productivity observed in the comparison non-merger years. There is considerable variability in year-to-year R&D productivity of each company. However, despite this variability, there is enough of an association between mergers and R&D productivity that a signal is detectable and statistically significant (P = 0.016 when treating each merger as an independent event,
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based on whether they deliver value above alternatives [24,25]. Second, consider the use of a ratio that includes a denominator. Most analysts have focused on productivity (the ratio of output to input) rather than on total output, but a few have not, focusing on total output instead [2,4,16,20]. There are two reasons to measure productivity of the merged entity rather than simply assess total output. First, productivity itself is a relevant metric, capturing the ability of the enterprise to persist. If productivity is so low that the returns to the company are below the cost of capital, such investment cannot be sustained. Second, even when focused on total output, an analysis conducted on the output of just one company presumes the employees lost and capital removed completely disappear and are not reactivated or reinvested elsewhere. However, of course, this is not true. Many employees lost to one company redeploy to other companies, including biotechnology startups, and the capital returned to shareholders remains to be invested in other ways [26], often in the same sector, given the investment patterns of investors active in pharmaceuticals. To assess the importance of this latter point it is worth understanding which effect predominates at the sector level: whether mergers remove total industry R&D activity, or instead result in the capacity redeployment. An analysis of industry-level growth of R&D spend during merger versus non-merger years shows no significant impact of merger activity on R&D totals when analysed in-year or with 1, 2, or 3year lags (P = 0.35, 0.50, 0.62, and 0.66, respectively, all nonsignificant). Results from combinations of contiguous years are similarly nonsignificant, supporting the view that capacity is primarily shifted to R&D investments elsewhere in the industry, rather than eliminated entirely (Table 2). Given that total patient value created depends on industry-level not company-level output, considering company-level output and not productivity misses the point that this capacity appears to be redeployed elsewhere in the industry, presumably to opportunities with higher productivity. A further methodology consideration is the time lag between the investments made in R&D and the actual launch of NMEs into the market. Launches in any given year are the result of multiple years of investment, weighted in a complex way based on the particular history of each product. Given that it typically takes more than a decade (and sometimes several decades) to proceed from initial idea to launch of a drug,
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DRUDIS 2037 1–5 Drug Discovery Today Volume 00, Number 00 June 2017
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TABLE 3
Comparison of R&D productivity in post-merger windows versus controlsa Company
Astellas Pharma AstraZeneca Bayer GlaxoSmithKline
Merck & Co Novartis Pfizer
Roche Sanofi
Average
Merger
Yamanouchi + Fujisawa Astra + Zeneca Schering AG Glaxo Wellcome + SmithKline Beecham Schering-Plough Alcon Warner-Lambert Pharmacia Wyeth Genentech Synthélabo Aventis Genzyme
Annual spend (US$B)
Annual launches (US$B)
Productivity
Ratio
Post-merger window
Other years
Post-merger window + 4-year lag
Other years
Post merger
Other years
1.72
1.94
2.01
0.10
1.17
0.05
23.63
3.93 2.38 5.50
5.07 2.54 6.74
2.52 5.24 1.08
1.33 0.16 2.02
0.64 2.20 0.20
0.26 0.06 0.30
2.44 34.64 0.66
8.78 9.34 10.4
7.83 6.29 11.4
3.78 8.54 1.86
1.95 1.74 1.23
0.43 0.91 0.18
0.25 0.28 0.11
1.73 3.32 1.65
5.48 7.27
4.26 1.75
1.60 0.95
0.50 0.28
0.29 0.13
1.70 2.09
5.57
2.90
1.25
0.41
0.22
1.83
8.54 6.34
7.04
11.2 11.4 8.74 5.11 7.17 6.35
1.10 2.98 1.52 1.20 0.28 7.22
0.10 0.26 0.17 0.24 0.04 1.14
0.91 2.43 1.60 1.79 0.30 8.62
a
The table compares performance during post-merger windows and outside these windows for the merger events in our sample. Data are shown for each merger and for each company, as described in the main text. R&D spend and peak sales data are from EvaluatePharma, adjusted to constant 2015$ using an OECD deflator provided by EIU. Where peak sales have not yet been achieved, the EvaluatePharma projected estimate is used.
Features PERSPECTIVE
P = 0.027 when performing the analysis at the company level). This result appears to be robust across time periods, in that our comparison set includes mergers from the start of our window to the end with no clear trendline over time (regression P = 0.79, nonsignificant), as well as across deal sizes (regression P = 0.22, nonsignificant). The largest merger (Glaxo Wellcome + SmithKlineBeecham) is one of the few showing a negative correlation with R&D productivity. It might be that, at this deal size, the negative effects of merging outweigh the beneficial ones. However, it is not possible to determine this, given the small sample size of the current analysis. There are conflicting analyses in the literature regarding the impact of mergers on R&D innovation, with some studies showing improved performance following mergers [6,7,11–13] and others suggesting decreased innovation [1,2,5,16,20]. The differences might be in part the result of different methodologies, with negative impacts found by studies typically assessing intermediate output metrics, such as patents or even input metrics, such as spending, and totals rather than productivity ratios. What matters for patients is not intermediate metrics, but actual creation of quality medicines. Given the large literature on the disconnect between such intermediate metrics and actual value creation, and indeed that mergers might be an event improving the connection by stopping activity on ultimately nonvalue-adding projects (‘truth seeking’ over ‘progression
4
seeking’) [14,15,28], the use of such metrics has particular potential to be misleading regarding value creation during mergers. Furthermore, patient benefit is served by the system-level R&D productivity of biopharma, not the output from any one company. As such, the relevant metric at the company level is productivity (used here, and by most other analysts), not company output, given the evidence that capacity and investment appear to be redeployed elsewhere in the industry following a merger. This analysis shows that, in contrast to the analyses that omit these considerations, mergers are associated with improved value creation for patients. Admittedly, there remain limitations in this analysis. The sample size is small at just 13 events. Peak sales are used as a proxy for patient value creation, and are not an exact correlate with the desired metric. R&D spending is not directly traced to the associated NMEs; instead a standard 4-year time lag is used. A self-controlled design is used and, while such designs have outperformed case-control and other methods in ascertaining whether an effect is present, they might be limited in their ability to estimate the magnitude of effects [19]. As such, we would caution against using this study to estimate the magnitude of the relationship between mergers and R&D productivity. Finally, as with any observational study, conclusions are limited to associations and not inferences of causality. All of these limitations are important areas where follow-up research would be valuable.
Our findings are consistent with the mechanistic interpretation that mergers create an event during which the leadership of the new company takes a ‘fresh look’ at its approach to R&D. This process allows it (at least with respect to the target company, and in well-managed cases, on both sides of the transaction) to be more objective in the assessment of its scientific hypotheses in each disease area and in particular the likelihood of assets in the portfolio to drive advances in treatment, shedding projects that have been maintained despite limited approval likelihood or value creation potential [14,15]. Such reassessments are a boon to patients by objectively prioritizing those assets most likely to drive advances in treatment. This is not to say mergers are the only way to achieve these goals. It is valuable for companies to have ongoing processes that ensure objective decisions on assets and other aspects of R&D based on unbiased decision-makers with access to full information. There is considerable opportunity in the design of R&D operations to achieve this goal, as discussed in a growing body of literature on the topic [14,15,28]. However, our experience is that mergers provide a forcing function that can stimulate companies to undertake such changes if they have otherwise been deferred. Neither is this to say all mergers are good. Even from an R&D productivity perspective, there is variability in outcomes, with some mergers in our sample appearing to have depressed the flow of medication to patients. This
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Acknowledgments The authors gratefully acknowledge the contributions of Ulrik Schulze and Mathias Baedeker, as well as several anonymous reviewers, to this work. References 1 LaMattina, J.L. (2011) The impact of mergers on pharmaceutical R&D. Nat. Rev. Drug Discov. 10, 559–560 2 Danzon, P.M. et al. (2004) Mergers and Acquisitions in the Pharmaceutical and Biotech Industries. National Bureau of Economic Research
3 Cassiman, B. et al. (2005) The impact of M&A on the R&D process: an empirical analysis of the role of technological- and market-relatedness. Res. Policy 34, 195–220 4 Cloodt, M. et al. (2006) Mergers and acquisitions: their effect on the innovative performance of companies in high-tech industries. Res. Policy 35, 642–654 5 Ornaghi, C. (2009) Mergers and innovation in big pharma. Int. J. Ind. Org. 27, 70–79 6 Prabhu, J.C. et al. (2005) The impact of acquisitions on innovation: poison pill, placebo, or tonic? J. Market. 69, 114–130 7 Shibayama, S. et al. (2008) Effect of mergers and acquisitions on drug discovery: perspective from a case study of a Japanese pharmaceutical company. Drug Discov. Today 13, 86–93 8 Ravenscraft D.J., Long W.F. (2000) Paths to creating value in pharmaceutical mergers. In Mergers and Productivity (Kaplan, S.N., ed.), pp. XXX–YYY, publisher 9 Comanor, W.S. and Scherer, F.M. (2013) Mergers and innovation in the pharmaceutical industry. J. Health Econ. 32, 106–113 10 Munos, B. (2009) Lessons from 60 years of pharmaceutical innovation. Nat. Rev. Drug Discov. 8, 959–968 11 Grabowski H., Kyle M. (2008) Mergers and alliances in pharmaceuticals: effects on innovation and R&D productivity. In: The Economics of Corporate Governance and Mergers (Gugler, K. and Yurtoglu, B.B., eds), pp. XXX–YYY, publisher 12 Bruegman, E. (2007) Pharmaceutical Mergers and the Organization and Productivity of Innovation. Harvard University Press 13 Koenig, M.E.D. and Mezick, E.M. (2004) Impact of mergers & acquisitions on research productivity within the pharmaceutical industry. Scientometrics 59, 157–169 14 Ringel, M. et al. (2013) Does size matter in R&D productivity? If not, what does?. Nat. Rev. Drug Discov. 12, 901–902 15 Peck, R.W. et al. (2015) Why is it hard to terminate failing projects in pharmaceutical R&D? Nat. Rev. Drug Discov. 14, 663–664 16 CenterWatch Inc (2000) Some troubling numbers for big pharma consolidation. In Vivo 1, 2000 17 Shimura, H. et al. (2014) A lesson from Japan: research and development efficiency is a key element of pharmaceutical industry consolidation process. Drug Discov. Today 8, 57–63
18 Higgins, M.J. and Rodriguez, D. (2006) The outsourcing of R&D through acquisitions in the pharmaceutical industry. J. Fin. Econ. 80, 351–383 19 Ryan, P.B. et al. (2013) A comparison of the empirical performance of methods for a risk identification system. Drug Saf. 36, S143–S158 20 Haucap J., Stiebale J. (2016) Research: innovation suffers when drug companies merge. Harvard Bus. Rev. XX, XXX–YYY. 21 Paul, S.M. et al. (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214 22 Schulze, U. et al. (2014) R&D productivity: on the comeback trail. Nat. Rev. Drug Discov. 13, 331–332 23 Knott, A.M. (2012) The trillion-dollar R&D fix. Harvard Bus. Rev. 76, XXX–YYY. 24 Eichler, H.-G. et al. (2015) Assessing the relative efficacy of new drugs: an emerging opportunity. Nat. Rev. Drug Discov. 14, 443–444 25 Blumenthal, D. and McGinnis, J.M. (2015) Measuring vital signs: an IOM report on core metrics for health and health care progress. JAMA 313, 1901–1902 26 DiMasi, J.A. et al. (2016) Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ. 47, 20–33 27 Scannell, J.W. et al. (2012) Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov. 11, 191–200 28 Owens, P.K. et al. (2015) A decade of innovation in pharmaceutical R&D: the Chorus model. Nat. Rev. Drug Discov. 14, 17–28 29 Hansell, G. et al. (2014) Unlocking Acquisitive Growth: Lessons from Successful Serial Acquirers. The Boston Consulting Group 30 Lubkeman, M. et al. (2014) Creating Value from Midsize Biopharma Acquisitions. The Boston Consulting Group
Michael S. Ringel1,* Michael K. Choy2 1
The Boston Consulting Group, Exchange Place, 31st Floor, Boston, MA 02109, USA 2 The Boston Consulting Group, 466 Springfield Avenue, Summit, NJ 07901, USA *Corresponding author.
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Q5
Q4
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variability is consistent with the broader (nonR&D) literature that has found a range of outcomes from mergers and acquisitions, with many ill-conceived or poorly executed mergers Q3 destroying value [37,38]. These studies have pointed to capabilities in sourcing deal opportunities, selectivity in pursuing only those deals with clear value-creation theses, and usage of well-run post-merger integration processes as differentiating factors driving success [29,30]. Lastly, there are considerations outside the scope of the current analysis that could be important factors for policy-makers or executives involved in merger decisions. We have not evaluated the impact of mergers on employment either within or beyond national borders, on investors, or on consumers (e.g., via drug prices). Ultimately, merger decisions might depend on these or other issues. What this analysis does show is that some prior analyses suggesting mergers harm the flow of medication to patients have been in error because of the omission of the considerations covered in this analysis. When considering downstream measures and accounting for both inputs and outputs, mergers appear associated with increased R&D productivity in delivering medicines to patients.
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