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

制药业破产的商业模式-第二部分：药物发现领域的竭泽而渔

 

 

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.

在这篇博客的第一部分，我介绍了一种简单可靠的方法去计算制药企业研发方面的内部收益率（IRR），仅仅以业内真实的历史损益表现为基础。随后，我展示了这20年来，制药企业的内部收益率一直在快速而稳定地线性下滑，这与最近BCG和德勤最近的测算一致，此现象可以被边际效益递减规律所解释，而这又是对有限的投资机会排优先级而造成的自然而然、却又不可避免的结果，而且，每一个新药都为其后继者设定了一个更高的门槛。最后，我展示了对这一稳定的线性趋势做简单外推的结果：到2020年，制药企业的内部收益率将降到0%，这意味着，现在行业已经濒临致命的衰退，因为它将进入销售负增长、研发投资负增长的死循环。

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.

在第二部分，我探索出研发效率、内部收益率（IRR）、历史和未来损益表现之间的细节的数学关系。特别是，我将展示，内部收益率的线性下降实际上是与名义投资回报率（ROI）相对应的，这是衡量研发效率的更直接的方式，而这将直接导致未来损益表现的致命衰退。随后，我将利用这个模型去做一些情景假设，以探索我们还需要对名义研发效率/名义投资回报率做多大改进，才可以保持正数的利润增长。结果显示，我们现在，即2018年，就需要一次重大突破，而且即便如此，我们仍将在复苏之前面临严重的衰退；而如果改善不够，那么，对于把行业从致命的衰退中拯救出来的任务来说，其结果仍然会太小、太迟。

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.

最后，我会确认边际效益递减规律是导致研发效率下降的唯一根本原因，并且我会解释为什么制药企业以往和当前的各种策略（持续渐进的提升、新药发现的新技术、授权引进、精准医疗、大数据和人工智能，等等）都失败了，而且在解决根本问题上还将继续失败下去。最后，我将提出一种替代策略，也许能解决此问题，但我在这个领域还有一些特殊的想法（不在这里分享，抱歉），我希望能够激发更多批判性的战略思考、自我反思和公开辩论，以便将行业的注意力重新集中在开发替代方案上，从而解决根本问题，以免为时已晚。

Nominal ROI as a direct measure of R&D productivity

名义投资回报率作为研发效率的直接测量指标

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:

在第一部分，我展示了制药企业在任意给定的年份x的内部收益率可以用如下公示计算，只需以行业真实的历史损益数据为基础：

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.

在这里c是行业内的平均投资期，即13年，从最初的研发投资开始到产生商业回报为止。

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.

这意味着，制药企业的内部收益率一直稳定地以每年0.9%的速度下滑，并且预计到2020年时，它将触及0%。这一稳定的下降趋势被德勤最近发布的报告中的新数据所再次证实，该报告称制药企业的内部收益率跌至历史新低：2017年仅仅只有3.2%。

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:

内部收益率定义了一个有效的回报率，它是在考虑时间的情况下，更加完整且精确的投资回报的测量方式，但研发效率是最好的定义方式，而且更容易理解，可被理解为简单的效率比。特别是，任意一年x的名义投资回报率（ROI）可以由平均投资期c年后的绝对名义商业回报的值除以最初投资的值来得到：

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.

如第一部分的解释，需要注意，最终的商业回报不仅包括息税前利润（EBIT），还包括未来的研发开支，因为这是以前研发投资获得的利润的可选用途。

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:

现在，通过将该方程代入上述内部收益率的原始公式，我们可以看到，内部收益率与名义投资回报率直接相关，如下所示：

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

And conversely:

然后反过来：

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:

最后，将上面的历史下降趋势代入到公式中的IRR项，把c项设定为13年的行业平均投资期，我们将得到如下公式，它显示，名义研发效率/投资回报率目前在约1.2位置（即最初投资研发的13年之后，我们仅仅能赚到20%的回报），并且以每年10%的指数级下降，并且将在2020年触及1.0（投资的净回报为零）：

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.

这个结果以之前Scannell等人的报告一致，它显示出，制药行业的研发效率（以每10亿美元研发开支创造出的新分子实体（NME）的数量的形式）一直在以指数级下滑，从1950年起每年约下滑7.4%（60年累计下滑99%）。注意，年下降率2.6%的差异可以由每个新分子实体的平均商业价值的下降所解释，其原因很可能是每个新药都为其后继者设定了更高的门槛，也缩小了后继者可以提升的空间，因而后继者的增量价值也减少了，同时，来自仿制药和me-too药物的竞争也更加激烈。

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.

内部收益率、名义研发效率/投资回报率之间的直接数学关联，以及过去和未来的损益表现可以由下面3张图来展示。注意，每张图上由红色的点表示的趋势与根据上述公式所计算出的趋势完全一致，而且也非常符合历史损益数据及德勤最近测算的内部收益率。

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

现在我们可以清晰地看到，真实情况中，研发效率究竟下滑得有多么快。

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.

不仅如此，现在我们可以使用这些公式去预测研发效率或名义投资回报率的提升对未来损益表现的影响，包括持续渐进式的提升或重大的技术突破，以计算出究竟需要多少提升才能保持正数的利润增长，以避免致命的衰退。

Impact of continuous improvement in R&D productivity

研发效率的持续渐进式的提升的影响

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.

持续渐进式的改进的最终目标是在较长时期内提高整体研发效率，这要么来自提升新批准药物的数量及其商业价值，要么来自降低每个新药所需的研发开支，或者更可能是这两者的结合。在任何状况下，改变都是缓慢的，效率的提升来自年复一年的小幅渐进提升的积累。

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?

那么，我们必须每年提升多少名义研发效率/投资回报率，才能保持正数的利润增长，以避免致命衰退？5%、10%、15%，甚至20%？我们必须从什么时候启动这个逐年提升的过程？有人之前问过这些问题吗？

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:

在我们使用上述这些公式去计算持续渐进式的提升对未来损益表现的影响之前，请考虑到，所有的改进都必须适用于当前基线。也就是说，我们必须先抵消掉当前年度研发效率的下降，随后我们才可以提升整体的研发效率。在此基础上，从2018年起，每年持续提高5％、10％、15％或20％的名义研发效率/投资回报率的预期影响如下图所示：

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%.

我们能看到的是，从2018年起每年提升研发效率5%、甚至10%可以减缓、但不能扭转目前投资回报率和内部收益率的下降趋势。此外，它对损益表现上的致命衰退几乎毫无影响。即便每年15%的研发效率提升也不足以避免糟糕的衰退，行业的销售和利润仍将下降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.

事实上，我们需要提升名义研发效率/投资回报率至少每年20%才能逆转损益表现的下滑，而且即便如此，行业的销售和利润仍然会下滑大约三分之一，在2030年见底后才会重新上升。这是因为，将研发效率的提升转化为销售和利润的增长需要多年的投资期。换句话说，未来10年的损益表现已经由以往和当前低水平的研发效率所决定，我们也做不了什么。

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:

那么，我们什么时候需要开始逐年提升呢？下面这些图分别显示了从2018、2020、2022或2024年起，名义研发效率/投资回报率每年提升20%造成的影响：

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.

简而言之，我们需要从现在、即2018年开始，每年提升20%的名义研发效率/投资回报率，因为等得越久，影响就越小，就更难避免致命的衰退。

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?

研发效率每年持续20％的增长确实是个非常高的目标，要求将新批准的药物数量或平均商业价值每年提高20％，或将开发每种新药所需的研发投入每年减少20％。这可以实现吗？是否有一家、或全部的制药企业的持续改进策略可以产生如此大的影响？请考虑到，现在没有哪一家制药企业在持续改进方面的努力对这60年来研发效率的快速稳定的下降趋势产生了多大影响。事实上，制药公司过去努力的影响已经包含在当前下降的基线中，再预期制药公司目前持续改进的各种策略能使研发效率每年再增加20％，这有多大合理性？我们过去为什么没有实现？

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.

我将这个问题留给读者反馈，同时让我们再考虑一下研发效率的重大突破造成的潜在影响。

Impact of a major breakthrough in R&D productivity

研发效率的重大突破的影响

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?

持续渐进式的提升需要持续多年、年复一年的研发效率提升，与之不同的是重大突破，一些新技术可以在非常短的时间内、很可能仅1年内，就能一次性地大幅提升研发效率。这些年里，我们看到了一些药物发现方面的技术突破，不过没有哪一次突破能对研发效率的快速稳定的下滑产生什么影响，但我们仍然可以设想一下：如果现在，即2018年，我们就可以将研发效率提升100%、200%、300%，甚至400%呢？这会对预测的损益表现产生多大冲击？

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:

在我们计算之前，我们必须考虑到，重大突破可以让研发效率在当前基线上产生一次性的跳增，但研发效率仍然会继续以每年10%的速度下降，因为这种突破不可持续，长期看边际效益递减规律仍会起作用。以这些假设为基础，提升名义研发效率/投资回报率100%、200%、300%或400%的影响如下图所示：

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.

我看可以看出，研发效率100%的增长（即2倍于此前）仅仅可以将下滑延缓5年，200%的增长（即3倍于此前）也仅仅可以延缓10年，它们都不能避免下降，而且行业的销售和利润仍将从高峰期开始长年下降。即便研发效率400%的增长（即5倍于此前）也只能延缓下滑20年，不过最起码行业销售可以在回调之后创下新高。

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?

我将会在后面探讨我们如何做才有望在研发效率上取得突破，但现在假设，如果我们真的能将研发效率提升400%，那么我们什么时候需要它？我们还剩多少时间来研发和实现这样的突破？

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

下面这些图分别展示了在2018、2020、2022或2024年时，名义研发效率/投资回报率提升400%有望造成的影响：

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.

这些图又一次显示，底线是，我们必须就在现在，即2018年，创造出一次重大突破，因为等得越久，我们能产生的影响就越小，就更难以将行业从致命衰退中拯救出来。

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.

现在，为了评估我们如何才能实现重大突破，我们需要再次审视边际效益递减规律，以准确理解什么导致了这个趋势，这样我们才能找到如何解决根本问题的办法。

Another look at the Law of Diminishing Returns

重新审视边际效益递减规律

 

In Part 1 of this blog, I showed that the linear decline in IRR can be fully explained by the Law of Diminishing Returns as a natural and unavoidable consequence of prioritizing a limited set of investment opportunities. In particular, I demonstrated that prioritizing a limited set of random investment opportunities by their IRR over time produces a perfect linear decline in IRR, which passes right through 0%, exactly as we have seen with Pharma

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s R&D productivity. Moreover, the IRR plot of prioritized investment opportunities follows a perfect linear decline regardless of their initial distribution.

在这篇博客的第一部分，我指出，内部收益率的线性下滑能由边际效益递减规律所充分解释，而这又是对有限的投资机会排优先级而造成的自然而然、却又不可避免的结果。特别是，我说明了对一个由随机的、以内部收益率为特征的投资机会的有限集合排优先级，将会造成内部收益率的完美线性下降，而且还会击穿0%，正如我们在制药企业的研发效率上已经看到的那样。不仅如此，无论初始分布如何，对内部收益率排优先级而生成的曲线都呈现完美的线性下降趋势。

In fact, the only condition required to guarantee that a sequence of investments follows the Law of Diminishing Returns in this way, is that the total number and/or potential value of investment opportunities is ultimately limited. In essence, there must be some critical limiting factor, which is both exhaustible and in short supply.

实际上，令边际效益递减规律在投资方面奏效的唯一限制性因素是投资机会的总数量和/或潜在价值最终是有限的。从本质上讲，必须有一些关键的限制因素，它们既可被用尽，又供不应求。

So what could be the ultimate limiting factor in Pharma R&D? It is certainly not the number of potential new drugs itself, since the number of possible drug-like molecules has been estimated to exceed the number of atoms in the entire solar system.

所以，什么是制药企业研发的最根本限制因素？这当然不是潜在的新药分子的数量问题，因为类似药物的分子的数量估计超过了整个太阳系中的原子数量。

And it is not the unmet clinical need or potential value of new drugs, since we spend more each year on healthcare for our growing and ageing population. Indeed, there appears to be no end to human suffering, and we will always get sick and die at least once in our lives, despite medical progress.

这也不是未满足的临床需求或新药的潜在价值的问题，因为老龄化日趋严重，我们每年花在医疗上的钱都在增长。确实，人类的痛苦永远不会消失，无论医疗如何发展，我们都将生病和死亡至少一次。

The real answer, as I explain below, is that we are rapidly running out of viable new drug targets that could possibly be addressed with existing approaches and technologies.

真正的答案，如我下面所述，是我们正在快速地消耗已经所剩无几的可行新药靶点，这些靶点是通过现有方法和技术所能利用的。

A diminishing pool of viable new drug targets

可用的新药靶点日渐减少

 

Ultimately, all drugs work by interacting with at least one specific molecule or 

“

drug target” in the body. Furthermore, all such drug targets must satisfy all of the following criteria in order to provide a viable source of effective new drugs:

最终，所有药物都与体内至少一种特定的分子、或称为“靶点”的相互作用而起效。此外，所有靶点必须同时满足所有下面这些条款，才能提供研发有效新药时可以使用的原型：

1. Clear correlation or relationship with human disease

2. Can be targeted with small molecules or large proteins

3. Not already exploited by existing approved drugs

4. Not already tested and failed due to mechanism of action

5. Commercially viable, linked to a clear unmet need

1. 与人类疾病的清晰关系或联系

2. 能被小分子或大的蛋白质所靶向

3. 尚未被已获批准的药物所使用

4. 作用机制未被此前的测试证明为失败

5. 商业上可行，与清晰的未满足的需求相对应

According to the Human Protein Atlas, there are 19,613 proteins encoded by the human genome. Of these, 14,545 (74%) have no known link or relationship with disease, which rules them out as potential new drug targets because they fail to meet criterion 1 above. Perhaps these proteins are non-essential, as any deficiencies can be compensated by other proteins or pathways; or perhaps they are essential, however any deficiencies are lethal before birth so they never have the chance to cause any disease. In any case, we have no reason to believe that targeting these proteins will do anything for any known human disease.

根据《人类蛋白质图谱》，人类基因组编码了19,613种蛋白质。其中，14,545种（74%）与疾病没有已知的关联或联系，这让它们无法作为潜在的新药靶点，从而出局了，因为它们无法满足上述条款1。可能这些蛋白质并不是必须的，因为任何的缺少都能被其他蛋白质或途径来弥补；也可能它们是必须的，但缺乏它们中的任何一种都会导致无法产生新生命，所以它们从来没有机会引起任何疾病。无论如何，我们没有任何理由认为，以这些蛋白质为靶点会对任何已知的人类疾病有疗效。

Now of the 5,068 proteins that have any link to disease, 3,131 (16% of all human proteins) are considered to be “undruggable”, either because they have no obvious pocket capable of binding small molecule drugs, or because they are intracellular and thus inaccessible to large proteins that cannot penetrate the cell membrane. We must rule out these proteins as potential new drug targets because we currently have no way to target them, so they fail to meet criterion 2 above.

这样，其中5,068种蛋白质与疾病有关联，3,131种（人类蛋白质总数中的16%）被认为是“不可成药”，要么它们明显的不可能与小分子药物相结合，要么它们在细胞内部，而大的蛋白质无法穿透细胞膜。我们必须排除这些潜在可能作为新药靶点的蛋白质，因为我们当前找不到靶向它们的办法，所以它们无法满足上述的条款2。

This leaves only 1,937 potential drug targets (10% of all human proteins), but 672 of these have already been fully exploited as proven drug targets by current approved drugs. Once a new drug target is first identified and exploited by an original first-in-class drug, any “me-too” drugs that follow tend to provide little, if any incremental benefit or value to patients, and profit mostly by taking market share from the original drug. In essence, drug targets are an exhaustible resource rather like oil: once we have tapped its potential value, it’s gone; we can’t have our cake and eat it. Therefore, we must also rule out these proteins as potential new drug targets, simply because they are no longer new, and they fail to meet criterion 3 above.

这样排除后，只剩下1,937种潜在的药物靶点（人类蛋白质总数中的10%），但其中672种已经被证明为靶点，并被当前已获批准的药物所充分利用。一旦一个新药靶点被确认，并被一个原创的first-in-class药物所利用，任何跟随的me-too药物往往只能为患者带来微乎其微的益处或价值，但它们却能抢夺最初上市的药物的市场份额，而这是它们的主要利润来源。本质上，药物靶点是可被用尽的资源，就像原油：一旦我们把其潜在价值开采出来，它就消失了；我们不可能去吃已经在肚子里的蛋糕。因此，我们必须指出，这些蛋白质之所以被标记为潜在的靶点，仅仅是因为它们已经不再是新的了，它们无法满足上述的条款3。

So now we are left with only 1,265 potential new drug targets:

算到现在，我们只剩下1,265个潜在的新药靶点：

At first glance, it seems that we have more than twice as many potential new drug targets left to find and exploit as those we have already exploited, so we should not be overly concerned about running out any time soon. But what about the other two criteria, 4 and 5? How many of these potential drug targets have already been tested but failed to yield any drugs due to mechanism of action? How many have not yet been tested, but are still unlikely to yield any drugs? And how many will yield only drugs that are not commercially viable in any case?

初看上去，我们似乎有两倍于已被采用的靶点尚未被探索和采用过，所以我们不应该特别担心靶点被耗尽的时刻会很快到来。但，如果考虑另外两个条款、条款4和条款5呢？有多少潜在靶点的作用机制被测试为失败？有多少靶点尚未被测试，但仍然不太可能形成药物？有多少靶点可以形成药物，但在商业上无论如何都不可行？

Now this is where the numbers get a bit fuzzy because they are not widely reported (or at least I could not easily find them), but we can make some very rough estimates.

现在这个数字比较模糊，因为相关报告并不多（至少我不能轻松地找到），但是我们可以做一些粗略的估算。

First, let’s say that about 50% of all drug targets we have ever fully tested produced at least one approved drug, while the other 50% failed to deliver any drug at all, due to fundamental reasons (e.g., safety) based on mechanism of action. Given that we now have approved drugs for 672 drug targets, this would imply that we have already fully tested a similar number of drug targets without ever producing any drug, so we can rule these out as potential new drug targets because they are not new, and fail to meet criterion 4 above. Furthermore, we can rule out another 50% (297) of the remaining 593 untested drug targets because they are unlikely to deliver new drugs for the same fundamental reasons.

首先，假设我们测试过的所有靶点中的50%可以形成至少一种获批的药物，而另外50%则无法形成任何药物，因为作用机制方面的根本性的原因（例如，安全性）。鉴于我们已经针对672个靶点开发出已获批的药物，可以推测，相似数量的靶点也已被我们完全测试过，但却没有形成任何药物，所以，我们可以把它们排除掉，不再作为潜在的新药靶点，因为它们既不新，又无法满足上述的条款4。不仅如此，我们还可以另外再把剩余的593个靶点中的50%（297）个排除掉，因为它们也不太可能去形成新药，理由同上。

Now we are left with only 296 potential drug targets, but how many of these will produce drugs that are commercially viable? It has been estimated that only about 25% of new approved drugs manage to fully recover their own R&D costs and make any commercial return. Many of those that fail commercially are me-too drugs that compete for the same drug target, but many are also novel first-in-class drugs that compete with other drugs acting by different mechanisms to target the same disease, or that target diseases with insufficient clinical need.

现在，我们只剩下296个潜在的靶点，但是，它们中有多少能够形成商业上可行的新药？据估计，只有25%的获批新药的收入能够完全覆盖其研发开支，从而获得商业上的回报。商业上失败的药物中，有很多是me-too药物，它们在同一个靶点的市场里相互竞争；但是，也有很多是创新的first-in-class药物，它们面临着和它们的作用机制不同、但适应症相同的药物的竞争；还有一些药物的适应症的临床需求不足。

So let’s assume that 50% (148) of the remaining 296 potential drug targets are not commercially viable (i.e., do not meet criterion 5 above), and we are now left with only 148 potential new drug targets, compared with 672 that we have already exploited with existing approved drugs:

所以，我们假定，剩余的296个潜在靶点中的50%（148）没有商业上的可行性（即无法满足上述的条款5），这样，我们只剩下148个潜在的新药靶点，作为对比，我们已经有672个靶点被已存在且已获批的药物所采用：

Again, this is just a rough estimate based on some crude assumptions, but still it is clear that we are rapidly running out of viable new drug targets that meet all 5 criteria above. We are literally scraping the barrel for the last remaining drug targets, and chances are we are already working on all these remaining targets in direct competition with each other. Now is it really any wonder that R&D productivity has been declining so rapidly by the Law of Diminishing Returns?

再次申明，这只是在非常粗糙的假设下做出的非常粗略的估计，但很显然，符合上述所有5个条款的新药靶点已经稀少，而我们正在高速消耗它们。我们只能在最后剩下的这些靶点里逐个地寻找机会，而且，我们也确实正在竭泽而渔，相互竞争，短兵相接。现在，还有谁仍旧怀疑研发效率因边际效益递减规律而正在高速下滑这一事实？

Limited potential impact of Pharma’s current strategies

有限的潜在机会对制药企业当前策略的影响

 

Given that we are rapidly running out of viable new drug targets, it is easy to see why Pharma

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s R&D productivity has been declining so rapidly by the Law of Diminishing Returns. Moreover, it is easy to see why none of Pharma’s past efforts has made any difference, and why none of its current strategies will make any difference, either: They do not address the underlying issue.

鉴于我们正在高速消耗所剩无几的新药靶点，很容易看出，制药企业的研发效率正在因边际效益递减规律而高速下滑。而且，非常容易看出为什么不同药企以往的努力并没有带来什么差别，也非常容易看出为什么它们当前的策略在未来得到的结果并不会有什么差异：它们都无法解决根本问题。

Almost all of Pharma’s past and current strategies are designed to improve R&D productivity in one or more of the following ways:

几乎所有的制药企业以往及当前的策略都是用下述方法中的一种或多种来提升研发效率：

1. Increase the efficiency by which we identify viable new drug targets that meet all 5 key criteria listed earlier

2. Increase the efficiency by which we identify safe and effective new drugs against those targets identified in 1 above

3. Increase the quality and expected commercial value of those drugs identified in 2 above

1. 通过筛选出同时满足上述所有5项条款的新药靶点来提升效率

2. 针对符合第1条的靶点，通过识别以此为靶点的新药的安全性和有效性来提升效率

3. 提升符合第2条的药物的质量及其期望商业回报

For example, molecular biology, genomics, proteomics and bioinformatics have been developed to increase the efficiency of target discovery by improving our understanding of human biology and disease, while other technologies like rational drug design, cheminformatics, combinatorial chemistry and high throughput screening have been developed to increase the efficiency of drug discovery by exploring new chemical space. Meanwhile, open innovation and in-licensing have been developed to source new drugs and technologies more efficiently than internal innovation. Precision medicine with biomarkers and real-world evidence has been developed to increase the clinical benefit and commercial value of new drugs in specific patient populations. Now there is a big push with big data, machine learning and AI to make significant improvements in all these areas. And of course, continuous improvement has been Pharma’s favorite long-term strategy to improve overall efficiency.

例如，分子生物学、基因组学、蛋白质组学和生物信息学已经发展起来，通过提高我们对人类生物学和疾病的理解来提高靶点发现的效率，而其他技术，例如合理药物设计、化学信息学、组合化学和高通量筛选也已经发展起来，通过探索新的化学空间来提高药物发现的效率。同时，开放式创新和授权引进也已发展起来，相较内部创新，它们有助于提升获得新药和新技术的效率。基于生物标志物和现实证据的精准医疗也已经发展起来，这可以为特定患者群提供临床效益，从而提升新药的商业价值。现在，在所有这些领域里，大数据、机器学习和人工智能都对提升效率有巨大的推动作用。当然，持续渐进的提升也一直是制药公司所偏好的提高整体效率的长期策略。

Note that none of these strategies can increase the overall number of viable new drug targets that meet the 5 key criteria above. Instead, they are simply designed to exploit the remaining pool of viable new drug targets more efficiently, which ironically, will only accelerate its depletion.

注意，没有哪一项策略能提升满足前述5项关键条款的可行新药靶点的数量。相反，它们的目标却是提升挖掘剩余可行新药靶点的资源的效率，具有讽刺意味的是，这只会加快其枯竭的速度。

These strategies have not worked, and will not work, because they do not address the underlying issue: We are rapidly running out of viable new drug targets that can be targeted by classic small molecule drugs or large therapeutic proteins.

这些策略并没有奏效，也不会奏效，因为它们无法解决根本问题：我们正在高速消耗所剩无几的新药靶点，这些靶点可以被经典的小分子药物或大的治疗性蛋白质所靶向。

So how can we address this problem to improve R&D productivity?

那么，我们该如何解决此问题来提高研发效率呢？

An alternative approach to improve R&D productivity

一条提升研发效率的替代路径

 

Ultimately, the only way we can break free from the Law of Diminishing Returns is to increase the number of viable new drug targets; and the only way we can do this is to remove or relax at least one of the 5 key criteria listed earlier.

最终

，

我们摆脱边际效益递减规律的唯一途径是增加可行的新药靶点数量; 我们实现这一点的唯一方法是去掉或放松前述的5项关键条款中的至少一项。

At first, it seems that all these criteria are absolute critical requirements for any new drug target. For example, if there is no clear link with human disease, or if there is no clear unmet need, then there is no viable drug target. Furthermore, if we have already tested a drug target and it failed for safety reasons, or if we have already fully exploited it with existing approved drugs, then we cannot exploit it further. And finally, if we can’t hit a specific drug target with small molecules or large proteins, then we can’t develop an effective drug against that target.

初看上去，所有这些条款似乎都是任何新药靶点都需要满足的绝对关键的要求。例如，如果与人类疾病没有明确的联系，或者如果没有明显的未满足的需求，那么这就不是可行的靶点。此外，如果我们已经测试了某个靶点，并且由于安全性而失败，或者如果现有已批准的药物已经充分利用了该靶点，那么我们就无法继续开发它。最后，如果我们不能用小分子或大的蛋白质靶向特定的靶点，那么我们就无法开发靶向该靶点的有效药物。

Or can we? Are we really limited to using small molecules and large proteins as drugs to target specific proteins and treat diseases more generally?

我们真的不能吗？我们真的仅限于用小分子和大的蛋白质作为药物去靶向特定的蛋白质来治疗疾病？

Small molecules have the great benefit that they can penetrate cell membranes to reach potential drug targets within the cell, but on the other hand, they require a clear binding pocket within the target protein, otherwise they have the wrong size and shape to bind effectively and specifically to flat protein surfaces. Meanwhile, large therapeutic proteins such as antibodies can form much stronger, more specific interactions with such flat protein surfaces, but they are generally unable to penetrate cell membranes and get into the cell. Thus by limiting our potential drug repertoire to small molecules and large proteins, we are effectively limiting our pool of potential new drug targets to extracellular proteins, or intracellular proteins that have a clear binding pocket. At the moment, we have no means to target intracellular proteins that have no clear binding pocket, yet there are thousands of these “undruggable” proteins encoded by the human genome.
小分子可以穿透细胞膜以进入细胞内潜在的靶点，但另一方面，它们也需要在靶蛋白内有一个清晰的、可以结合它的“口袋”，否则，它们的大小和形状就与“口袋”不匹配，从而无法有效地结合，特别是扁平的蛋白质表面。同时，诸如抗体等大型的治疗性蛋白质可以与这种扁平蛋白质的表面形成更强烈、更特异的相互作用，但是它们通常不能穿透细胞膜并进入细胞。因此，这把我们的潜在药物库限制在小分子和大的蛋白质的范围内，潜在的新药靶点要么是细胞外蛋白质，要么是具有清晰结合“口袋”的细胞内蛋白质。目前，我们没有办法去靶向没有明确结合“口袋”的细胞内蛋白质，但人类基因组编码了数以千计的此类“不可成药”的蛋白质。

According to the Human Protein Atlas, 3,131 (about 16%) of all proteins encoded by the human genome are “undruggable” proteins that have a clear link with disease, but can’t be targeted with either small molecules or large proteins because they are intracellular and have no clear binding pocket. This compares with only 1,937 druggable targets, of which 672 have already been fully exploited with existing approved drugs, and perhaps only 148 remain viable as explained above. Therefore, we could potentially increase the total number of viable new drug targets by as much as 20 fold, if only we could find an effective way to target them. So how can we do this?

根据《人类蛋白质图谱》，人类基因组编码的所有蛋白质中有3,131种（约16％）与疾病有明确关联，但却“不可成药”，因为不能用小分子或大的蛋白质去靶向，因为它们在细胞内，又没有清晰的结合“口袋”。相比之下，只有1,937个“可成药”的靶点，其中672个已经被现有获批的药物所充分利用，并且可能只剩148个可行的靶点尚未被利用，如上所述。因此，如果我们能够找到有效的方法来靶向它们，我们可能会将可行的新药靶点的总数量增加20倍。那么，我们该如何做到？

First, it is clear that small molecules do not have the size and shape required to bind effectively and specifically to large and flat protein surfaces. They are simply unable to compete with the tight and specific binding that occurs between different protein molecules within the cell, which is why we have never been able to develop an effective small molecule inhibitor of any known protein-protein interaction. Therefore, we are forced to use large molecules in order to compete effectively with these strong interactions, but this leaves us with the other problem: How to get such large molecules into cells in the first place?

首先，很明显，小分子不具备有效且特异的结合大而平坦的蛋白质的表面所需的大小和形状。它们也竞争不过细胞内不同蛋白质之间的紧密而特异的结合，这就是为什么我们从未开发出任何对已知的蛋白质-蛋白质之间的相互作用有效的小分子抑制剂。因此，我们不得不使用大分子来与这些强烈的相互作用有效竞争，但这留给我们另一个问题：首先如何让这些大分子先进入细胞？

If only we could find a reliable way to get large molecules into cells, then we could potentially target thousands of different proteins and protein-protein interactions that are currently beyond reach within the cell. So again, how to achieve this?

如果我们能找到一种可靠的办法来让这些大分子进入细胞，那么我们就潜在地有数以千计的不同蛋白质去靶向这些蛋白质-蛋白质之间的相互作用，但现实却是我们还无法进入细胞内。那么又是这个问题，如何达到这一目标？

The cell membrane is notoriously difficult to penetrate, especially by large molecules, but nature has shown that it can be done. For example, several large macrocyclic antibiotics and bacterial toxin proteins are known to cross the cell membrane. So can we adapt these molecules to act as drugs once they get into the cell? Or better still, can we understand how they get into cells in the first place and apply these principles to design a whole new class of cell-penetrating therapeutic proteins that could be adapted to bind tightly and specifically to any target protein in the cell? I have my own specific ideas that I would like to pursue in this regard, but hopefully it is clear by now that getting large molecules into cells is perhaps the only way to address the real underlying issue of declining R&D productivity. This problem is too important to rely on just one idea, so we need to pursue as many potential solutions as possible, in order to reverse the decline in R&D productivity and save the industry from terminal decline, before it is too late.

细胞膜非常难以穿透，尤其是大分子，但大自然表明这可以完成。例如，我们已知有几种大型的大环抗生素和细菌毒蛋白可以穿过细胞膜。那么，我们是否可以改变这些分子，让它们进入细胞后产生药效？或者更好，我们是否可以先理解它们是如何进入细胞的、然后应用其原理去设计出一类全新的穿透细胞的治疗性蛋白质、它们能与细胞内的任意靶蛋白紧密且特异地结合？在这方面，我有自己的具体想法，也在此领域有所追求，但希望现在可以明确，让大分子进入细胞可能是解决研发效率下降这一真正根本问题的唯一途径。这个问题太重要了，不能仅仅依靠这一个想法，所以我们需要尽可能多地寻求解决方案，以扭转研发效率的下降，并在致命的衰退之前挽救行业，以免为时已晚。

 

In summary, Pharma R&D productivity is declining by the Law of Diminishing Returns because we are rapidly running out of viable new drug targets that can be intercepted by small molecules or large proteins. None of Pharma’s past or current strategies to improve R&D productivity has worked because they do not address the underlying issue, and the only way to solve this problem is to develop completely new modalities that can address currently “undruggable” targets within the cell.

总之，制药企业的研发效率正遵循着边际效益递减规律而下降，因为我们正在快速消耗为数不多的、能通过与小分子或大的蛋白质相互作用而起效的可行新药靶点。制药企业以往或当前的策略都无法提高研发效率，因为它们没有解决根本问题，解决这个问题的唯一方法是开发全新的模式，以利用目前细胞内“不可成药”的靶点。

It is still not too late, but time is running out very fast.

现在仍然还不算太晚，但时间正在快速流逝。

 

原文链接：https://endpts.com/pharmas-broken-business-model-part-2-scraping-the-barrel-in-drug-discovery/

译者：汤诗语 转载请注明