Pharma / Biotech is one of the most M&A intensive industries. Over a period of approximately 20 years, the industry has spent over USD 1.4 trillion in pharma / biotech acquisitions (excl. generics, OTC, Animal Health, CROs and CMOs acquisitions).
The aim of this analysis is to assess which of the acquirers have really gained from those deals based on a simplified DCF analysis of the acquired R&D and marketed products (i.e. this excludes acquisitions of technology platforms that can enhance future drug discovery efforts, as this is extremely hard to assess).
I have outlined below the steps and criteria in my analysis:
– Identified M&A deals performed by big Pharma and Biotech between 1994 and 2012. Brought forward (future value) of M&A expenditure prior to 2015 to 2015 (Valuation was done as of 31/12/2015) using an estimated WACC on a firm-by-firm basis. M&A deals post-2012 were excluded from the analysis because it was assumed that those deals cannot have an immediate or visible effect 3 years later.
– Identified the marketed products acquired as well as the products that were still under the R&D phase at the time of acquisition.
– Found the historical sales of marketed products (as well as forecasts if they are still under patent protection).
– In regards to R&D products, if those products entered the market the historical and forecasted sales of these products were used.
– Multiplied the 6-year (2010 – 2015) average Free Cash Flow (FCF) to Sales ratio of each firm by the sales of products to find FCF of each product. It should be noted that the R&D expenditure was added back prior to calculating each firm’s FCF to Sales ratio. The assumption is that, in the future, there will be no R&D expense on acquired marketed products (unless the acquirer decides to spend cash on R&D to assess if these marketed products can benefit other patient populations or be used in other indications). In the calculation of FCF for acquired R&D products, R&D expense was again added back to FCF. R&D expenses of an acquired R&D product were estimated based on three parameters: therapeutic category of each product, probability of success by phase, risk of the R&D project and time. Then the value of R&D expense was subtracted from the overall DCF value of each product.
– Brought forward (future value) the calculated FCF of the products prior to 2015 to 2015 and discounted the calculated FCF sales post-2015 to 2015 (this was done by multiplying the forecasted sales by the FCF to Sales ratio using the same WACC used to bring forward the M&A expenditure).
– Divided the total DCF value (acquired marketed + acquired R&D) by the total M&A Expenditure (by firm) resulting in the M&A ROI ratio.
The acquirers included in the analysis were: Pfizer, Roche, Sanofi, Merck & Co, Gilead Sciences, Amgen, Bristol Myers Squibb, Shire, Biogen, Takeda, Merck KGaA, Eli Lilly, Celgene, Novartis, AstraZeneca, GlaxoSmithKline and Abbott Laboratories.
The results of these analyses are presented below:
Figure 1: DCF Value of Acquired R&D and Marketed Products by Company (As of 31/12/2015)
Figure 2: Total, Time-Adjusted M&A Expenditure by Company (As of 31/12/2015)
Figure 3: Ratio of DCF Value to Total, Time-Adjusted M&A Expenditure by Company (As of 31/12/2015)
The clear winner is Biogen. Biogen is the lowest spender (USD 2.8 bn.) and the value generated by its acquired product is USD 26.2 bn. resulting in an M&A Effectiveness Ratio of 9.28. What is really impressive is the fact that 98% of that value is attributed to acquired R&D products. This implies that Biogen has the ability to spot the “hottest” products that fit to the company’s existing R&D strategy and product portfolio. It also means that Biogen acquires biotech companies with potentially successful R&D projects in their pipelines ensuring the company’s long-term growth.
Gilead and BMS have a much lower M&A effectiveness ratio than Biogen, but still relativey high considering the huge risk of R&D focused acquisitions. Both Gilead’s and BMS’s high ratio are attributed to the DCF value of acquired R&D products.
Instead, Merck and Roche have generated value mostly through targeting companies with marketed products. These buyers will potentially face pipeline gaps in the future when the patents of the marketed products acquired expire. As a result, they would need either to keep making acquisitions of firms with established products in the market or focus on building a stronger internal R&D capabilities.
Eli Lilly and Sanofi are breakeven, which means (at least in theory), M&A does not make any difference for them in terms of generating value.
The clear loser is Pfizer, which is the biggest spender (over USD 500 bn.) while the value of its M&A product portfolio is close to USD 300 bn., meaning it has “lost” USD 200 bn.
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It should be noted that the analysis above has the following limitations:
– WACC of the acquirer at the time of acquisition and after the acquisition changes. Also, the discount rate used in discounting FCF of a specific product (project) might differ from the discount rate of the business. In this case, WACC has been assumed constant and equal to the discount rate used to discount the FCF of each product.
– A similar reasoning applies to the FCF / Sales ratio (which might differ between the products and companies), but for the purpose of this analysis it has been assumed that the ratio is the same across all products (under the same firm).
– Sales were projected up to patent expiry. After the patent expiry, sales were assumed to be equal to zero, which might not be the case in real life. Usually there is a sharp drop due to pressure from generics / biosimilars, however the present value of the FCF during that period is likely to be very low.
– R&D expenses of R&D products were estimated based on documented success rates by therapeutic category (see here) and out-of-pocket R&D expenditure as well as clinical development time frames (see here). The discount rate (or forward rate) used to account for time value of money was the firm-specific WACC except that the beta was changed each time a product entered to the next phase. It has been assumed that a beta of 2.5 is relevant for a research project, which was reduced by 0.25 for each of the next phases, reaching a beta of 1.25 when the product hits the market.
