前文:制药业破产的商业模式-第一部分:行业已濒临致命的衰退 网页链接

Pharma’s broken business model — Part 2: Scraping the barrel in drug discovery


by kelvin stott — on May 2, 2018 01:13 PM EDT

(作者Kelvin Stott,系诺华制药公司的研发组合投资管理经理)

In Part 1 of this blog, I introduced a simple robust method to calculate Pharma’s Internal Rate of Return (IRR) in R&D, based only on the industry’s actual historic P&L performance. Further, I showed that Pharma’s IRR has followed a rapid and steady linear decline over 20 years, which is consistent with recent estimates from BCG and Deloitte, and can be fully explained by the Law of Diminishing Returns as a natural and unavoidable consequence of prioritizing a limited set of investment opportunities while each new drug raises the bar for the next. Finally, I showed that a simple extrapolation of this robust linear trend means that Pharma’s IRR will hit 0% by 2020, which implies that the industry is now on the brink of terminal decline as it enters a vicious cycle of negative growth with diminishing sales and investment into R&D.


Here in Part 2, I explore the mathematical relationship between R&D productivity, IRR and past and future P&L performance in more detail. In particular, I show how the linear decline in IRR actually corresponds to an exponential decline in nominal Return on Investment (ROI) as a more direct measure of R&D productivity, which then leads directly to terminal decline in future P&L performance. I then use this model to run some what-if scenarios, to explore how much we will need to improve nominal R&D productivity/ROI in order to maintain positive P&L growth. The results show that we need a major breakthrough right now, in 2018, and even then we will face a period of significant contraction before any recovery, while anything less would be too little, too late to save the industry from terminal decline.


Finally, I identify the single limiting factor that is ultimately responsible for driving the decline in R&D productivity by the Law of Diminishing Returns, and I explain why many of Pharma’s past and current strategies (continuous improvement, new discovery technologies, in-licensing, precision medicine, big data and AI, etc.) have all failed and will continue failing to address the underlying issue. Moreover, I propose an alternative strategy that might just solve the problem, but while I have my own specific ideas in this area (not shared here, sorry), I hope to stimulate more critical strategic thinking, self-reflection and open debate in order to refocus the industry’s attention on developing alternative solutions to tackle the underlying issue before it is too late.


Nominal ROI as a direct measure of R&D productivity


In Part 1, I showed that Pharma’s Internal Rate of Return in any given year x can be calculated by the following formula, based only on the industry’s actual historic P&L performance:


IRR(x) = [(EBIT(x+c) + R&D(x+c)) / R&D(x)]^(1/c) - 1

Where c is the industry average investment period of 13 years, from an initial R&D investment to the resulting commercial returns.


Moreover, I showed that Pharma’s historic IRR has followed a rapid and steady decline over 20 years, which fits the following linear equation almost perfectly (R^2 = 0.9916):

不仅如此,我还展示了制药企业的历史内部收益率持续快速且稳定地下降了超过20年,这几乎完美地符合下面的线性方程式(R^2 = 0.9916):

IRR(x) = -0.00912*(x-2020)

This means that Pharma’s IRR has been declining at a steady rate of about 0.9% per year and is projected to hit 0% by 2020. This robust downward trend has recently been confirmed by yet another data point from Deloitte, which reported that Pharma’s IRR fell to a new record low of just 3.2% in 2017.


The IRR defines an effective interest rate that provides a more complete and accurate measure of return on investment over time, but R&D productivity is best defined and more easily understood as a simple efficiency ratio. In particular, the nominal Return on Investment (ROI) in any year x measures the absolute nominal value of commercial returns vs original R&D investment over the average investment period c:


ROI(x) = (EBIT(x+c) + R&D(x+c)) / R&D(x)

As explained in Part 1, note that the ultimate commercial returns include not only EBIT, but alsofuture R&D spending as an optional use of profits that result from the original R&D investment.


Now, by substituting this equation into the original formula for IRR above, we can see that IRR is directly related to the nominal ROI as follows:


IRR(x) = ROI(x)^(1/c) - 1

And conversely:


ROI(x) = [1 + IRR(x)]^c

Finally, by substituting the historic linear trend above into the IRR term of this equation, and the industry average investment period of 13 years into the cterm, we get the following formula, which shows that nominal R&D productivity/ROI currently stands at about 1.2 (i.e., we get only 20% back on top of our original R&D investment after 13 years), is declining exponentially by about 10% per year, and will hit 1.0 (zero net return on investment) by 2020:


ROI(x) = [1 - 0.00912*(x-2020)]^13 ≈ 0.899^(x-2020)

This result is consistent with an earlier report by Scannell et al., which shows that Pharma R&D productivity (in terms of NMEs per $Bn R&D spend) has been declining exponentially by about 7.4% per year since 1950 (99% over 60 years). Note that the 2.6% difference in the annual rate of decline must be explained by a decline in the average commercial value per NME, most likely due to diminishing incremental benefit as each new drug raises the bar and reduces the scope for improvement by the next, as well as increasing competition from generics and me-too drugs.


The direct mathematical relationship between IRR, nominal R&D productivity/ROI, and both past and future P&L performance is illustrated in the following 3 charts. Note that the trends represented by red dotted lines in each chart are all fully consistent with each other according to the formulae above, and fit closely with the historic P&L data as well as recent IRR estimates from Deloitte.


Now we can see clearly, in real terms, just how fast R&D productivity has been declining.


Furthermore, we can now use these formulae to predict the impact of improving nominal R&D productivity/ROI on future P&L performance, either by continuous improvement or by making major technology breakthroughs, in order to determine just how much improvement is required to maintain positive P&L growth and avoid terminal decline.


Impact of continuous improvement in R&D productivity


The ultimate goal of continuous improvement is to improve overall R&D productivity over an extended period of time, either by increasing the number or commercial value of new approved drugs, or by decreasing the R&D investment required to develop each new drug, or possibly a combination of both. In any case, change is slow and efficiency is improved only gradually by small amounts each year over many years.


So by how much do we need to increase nominal R&D productivity/ROI each year in order to maintain positive P&L growth and avoid terminal decline? Is it 5%, 10%, 15% or even 20%? And by when do we need to start making these annual improvements? Has anyone even asked these questions before?


Before we use the formulae above to calculate the impact of continuous improvement on future P&L performance, consider that any improvements must be applied to the current baseline. In other words, we must counteract the current annual decline in R&D productivity before we can start increasing overall R&D productivity in absolute terms. On that basis, the expected impact of consistently improving nominal R&D productivity/ROI by 5%, 10%, 15% or 20% each year from 2018 is shown in the following charts:


What we can see is that improving R&D productivity by 5% or even 10% each year from 2018 would slow, but not reverse the current decline in nominal ROI and IRR. Moreover, it would make virtually no difference to the projected terminal decline in P&L performance. Even a 15% annual increase in R&D productivity would barely be enough to avoid terminal decline, and the industry’s sales and profits would still fall by almost 50%.


In fact, we would need to increase nominal R&D productivity/ROI by at least 20% each year to reverse the projected decline in P&L performance, and even then, the industry’s sales and profits would fall by about one third before they begin to pick up again in 2030. This is because it will take several years for any improvement in R&D productivity to translate into increased sales and profits due to the long investment period. In other words, the next 10 years of P&L performance are already largely determined by the past and current low levels of R&D productivity, and there is now very little we can do about this.


So by when do we need to start making these annual improvements? The charts below show the impact of improving nominal R&D productivity/ROI by 20% per year from 2018, 2020, 2022 or 2024:


In short, we need to start improving nominal R&D productivity/ROI by 20% per year right now, from 2018, because the longer we wait the less impact it will have to avoid terminal decline.


A 20% sustained annual increase in R&D productivity is a very high target indeed, which would require increasing the number or average commercial value of new approved drugs by 20% each year, or decreasing the R&D investment required to develop each new drug by 20% per year. So is it achievable? Could any, or even all of Pharma’s strategies for continuous improvement ever make this much impact? Consider that none of Pharma’s past efforts at continuous improvement has made any difference at all to the rapid and steady decline in R&D productivity over the last 60 years. In fact, the impact of Pharma’s past efforts is already included in the current declining baseline, so how reasonable is it to expect that any of Pharma’s current strategies for continuous improvement will increase R&D productivity by an additional 20% each year, on top of what we have been able to achieve in the past?


I will leave this question open for readers to reflect, meanwhile let us now consider the potential impact of a major breakthrough in R&D productivity.


Impact of a major breakthrough in R&D productivity


Unlike continuous improvement, which requires making incremental annual improvements in R&D productivity over many years, new technologies have the potential to make a significant impact on R&D productivity within a short timeframe, and possibly even within a single year. Now, we have seen many breakthrough technologies in drug discovery over the years, and not one of these has made any difference to the rapid and steady decline in R&D productivity, but still let us consider: What if we could improve R&D productivity now in 2018 by 100%, 200%, 300%, or even 400%? What would be the impact on projected P&L performance?


Before we run the calculations, we must consider that a major breakthrough may provide a one-time jump in R&D productivity from the current baseline, but R&D productivity would then continue to decline at the current rate of 10% per year because no improvement is sustainable in the long term due to the Law of Diminishing Returns. On that basis, the impact of increasing nominal R&D productivity/ROI by 100%, 200%, 300% or 400% is shown in the charts below:


Here we can see that a 100% increase (two-fold improvement) in R&D productivity would delay the tail end of terminal decline by only 5 years, while a 200% increase (three-fold improvement) would delay terminal decline by about 10 years, but would not avoid it, and the industry’s sales and profits would still decline from their peak in the next couple of years. Even a 400% increase (five-fold improvement) in R&D productivity would only delay terminal decline by 20 years, but at least the industry’s sales could reach a new higher peak after a short dip.


I will discuss below how we might be able to achieve such a breakthrough in R&D productivity, but assuming we could increase R&D productivity by 400%, by when would we need to achieve it? How much time do we have left to develop and implement such a breakthrough?


The following charts show the expected impact of increasing nominal R&D productivity/ROI by 400% in 2018, 2020, 2022, or in 2024:


Here again, the bottom line is that we need a major breakthrough right now, in 2018, because the longer we wait the less impact it will have to save the industry from terminal decline.


Now, in order to evaluate how we might achieve this, we need to take another look at the Law of Diminishing Returns to understand exactly what is driving this trend so that we can finally figure out how to address the underlying issue.




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