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?
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
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?
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.
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.
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.
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