Another look at the Law of Diminishing Returns


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


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.


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.


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.


A diminishing pool of viable new drug targets


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:


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.


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.


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.


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


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?


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.


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.


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.


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:


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?


Limited potential impact of Pharma’s current strategies


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


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


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.


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.


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.


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


An alternative approach to improve R&D productivity


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?


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.

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?


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?


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?


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.


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.


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



译者:汤诗语 转载请注明

@Stevevai1983 @黑暗时代 @an小安 @只买医药股 @平峰 @xuelangren @梁宏 @不明真相的群众




多多多岛2018-12-25 16:32


沧浪之水V_V2018-11-17 20:21


还未懂得2018-06-03 17:19

我刚打赏了这篇帖子 ¥6.00,也推荐给你。

林先生2018-05-27 12:05

@汤诗语 这3篇文章翻译得很流畅,谢谢。原作者在第1篇末端曾提到制药工业将被生物制药业自然衍生替代,这是一个增加复杂性的历史过程。第2、第3篇的内容似乎没有就此展开进一步的讨论。他提到的“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”,也只是从数量/时间上延缓衰落的过程。

张小丰2018-05-23 09:52