In an article published in Science Translation Medicine, Disciplined approach to drug discovery and early development, Robert Plenge of Merck Research Laboratories writes on the best practices for drug discovery. He outlines what is necessary for the highest chances of success. This is important as diminished productivity in therapeutics research and development (R&D) has driven drug costs up while delivering value to patients some consider insufficient.
Plenge’s Argument
Plenge outlines four areas—causal human biology, therapeutic modality, biomarkers of target modulation, and proof of concept (PoC) clinical trials—that have received ample attention. But it is important to link the concepts to test therapies in human trials. He acknowledges this will not end all late-stage R&D failures as drug discovery is an inherently risky venture. But he outlines examples that suggest feasibility. He notes that his principles will improve the R&D portfolio’s chances of success in developing much needed therapies to patients.
He notes that it is important to have evidence from human biology before beginning the process. With careful study of human genetics, you can learn a lot. Using animal models without human tissue or cells is problematic. While this is hard in some disease areas, it underscores his point that it will inherently be challenging to develop therapies in some areas. Furthermore, the target goal is going need to be pragmatic. For example, gain-of-function on an enzyme is rare. So you have to accept the limitations of what the science can do for your ultimate goal.
Plenge calls for insight into biomarkers and stresses they should be as human-related as possible. It may be easier in some cases, like diabetes, but others will be difficult. He describes human genetic studies as “Nature’s randomized clinical trial,” and they offer an increased opportunity to know what a researcher should be looking for to use in a clinical trial.
Finally, in a clinical trial, it should be small, fast, and meaningful as possible to test proof-of-concept. This means that in some cases, there may be diseases that researchers may not wish to consider looking into. It also means picking patients carefully to find those most likely to show a response. If this can be done, and there is a trustworthy biomarker in place, the research is in good shape.
Limitations?
Derek Lowe writes about some of the limitations in this paper. “First and foremost,” he says, “there is an underlying assumption that we have sufficient data from humans to enable the discovery of new therapeutic targets and biomarkers. Validation of this assumption requires an ecosystem to define which sources of human data establish causality; members of the ecosystem must then work systematically toward building such databases that are accessible to all.”
He outlines the example that no resource enables systemic identification of human genetic variants linked to clinical outcomes in large patient populations in a setting appropriate for recall. However, there are efforts to generate these databases. Another problem outlined by Lowe is that one may not have sufficient “experiments of nature,” by mining human genetic diversity. Even if you do, it may not provide adequate information of how to modulate a proposed target.
Furthermore, “clinical trials technologies and designs may not (or not yet) allow for the sort of data collection and biomarkers that you’d like. Even with these complications, I’d say that these criteria are definitely worth aiming for. But even if you have all of them going for you, you can still easily fail in the clinic. Note also that even though many infectious diseases score high by these standards, targets for them can be hard to come by – those projects tend to have high preclinical failure rates, which aren’t addressed here.”
While outlining other problems, Lowe describes the biggest issue as that it is hard to meet all of the rules proposed by Plenge at the same time. “Something’s going to come up short, and judgment calls are going to have to be made about which of those are deal breakers and which aren’t. I’d say that lack of any human biology connection is a deal-breaker, for example, while lack of a really good fast-readout biomarker is just a sign that you’re going to be spending a lot more time and money in the clinic than you want to (not that that’s not a big consideration in itself). But I think that overall, Plenge is correct, that the closer you come to these ideals, the better off you’re going to be. They’re something to shoot for.”