The Path to Biomedical Progress

Throughout the post, “we” refers to GiveWell and Good Ventures, who work as partners on the Open Philanthropy Project.

We’ve continued to look into scientific research funding for the purposes of the Open Philanthropy Project. This hasn’t been a high priority for the last year, and our investigation remains preliminary, but I plan to write several posts about what we’ve found so far. Our early focus has been on biomedical research specifically.

Most useful new technologies are the product of many different lines of research, which progress in different ways and on different time frames. I think that when most people think about scientific research, they tend to instinctively picture only a subset of it. For example, people hoping for better cancer treatment tend instinctively to think about “studying cancer” as opposed to “studying the general behavior of cells” or “studying microscopy techniques,” even though all three can be essential for making progress on cancer treatment. Picturing only a particular kind of research can affect the way people choose what science to support.

I’m planning to write a fair amount about what I see as promising approaches to biomedical sciences philanthropy. Much of what I’m interested in will be hard to explain without some basic background and vocabulary around different types of research, and I’ve been unable to find an existing guide that provides this background. (Indeed, many of what I consider “overlooked opportunities to do good” may be overlooked because of donors’ tendencies to focus on the easiest-to-understand types of science.)

This post will:

Basic guide to the roles of different types of biomedical research


Below are some distinctions I’ve found it helpful to draw between different kinds of research. This picture is highly simplified: many types of research don’t fit neatly into one category, and the relationships between the different categories can be complex: any type of research can influence any other kind. In this diagram, I’ve highlighted the directions of influence I believe are generally most salient.

(A) Improving tools and techniques. Biomedical researchers rely on a variety of tools and techniques that were largely developed for the general purpose of measuring and understanding biological processes, rather than with any particular treatment or disease/condition in mind. Well-known examples include microscopes and DNA sequencing, both of which have been essential for developing more specific knowledge about particular diseases and conditions. More recent examples include CRISPR-related gene editing techniquesRNA interference, and using embryonic stem cells to genetically modify mice. All three of these provide ways of experimenting with changes in the genetic code and seeing what results. The former two may have direct applications for treatment approaches in addition to their value in research; the latter two were both relatively recently honored with Nobel Prizes. Improvements in tools and techniques can be a key factor in improving most kinds of research on this list. Sometimes improvements in tools and techniques (e.g., faster/cheaper DNA sequencing; more precise microscopes) can be as important as the development of new ones.

(B) Studying healthy biological processes. Basic knowledge about how cells function, how the immune system works, the nature of DNA, etc. has been essential to much progress in biomedical research. Many of the recent Nobel Prizes in Physiology or Medicine were for work in this category, some of which led directly to the development of new tools and techniques (as in the case of CRISPR-based gene editing, which is drawn from insights about bacterial immune systems).

(C) Studying diseases and conditions of interest. Much research focuses on understanding exactly what causes a particular disease and condition, as specifically and mechanistically as possible. Determining that a disease is caused by bacteria, a virus, or by a particular overactive gene or protein can have major implications for how to treat it; for example, the cancer drug Gleevec was developed by looking for a drug that would bind to a particular protein, which researchers had identified as key to a particular cancer. Note that (C) and (B) can often be tightly intertwined, as studying differences between healthy and diseased organisms can tell us a great deal both about the disease of interest and about the general ways in which healthy organisms function. However, (B) may have more trouble attracting support from non-scientists, since the applications can be less predictable and clear.

(D) Generating possible treatments. No matter how much we know about the causes of a particular disease/condition, this doesn’t guarantee that we’ll be able to find an effective treatment. Sometimes (as with Herceptin – more below) treatments will suggest themselves based on prior knowledge; other times the process comes down largely to trial and error. For example, malaria researchers know a fair amount about the parasite that causes malaria, but have only identified a limited number of chemicals that can kill it; because of the ongoing threat of drug resistance developing, they continue to go through many thousands of chemicals per year in a trial-and-error process, checking whether each shows potential for killing the relevant parasite. (Source)

(E) Preliminarily evaluating possible treatments (sometimes called “preclinical” work). Possible treatments are often first tested “in vitro” – in a simplified environment, where researchers can isolate how they work. (For example, seeing whether a chemical can kill isolated parasites in a dish.) But ultimately, a treatment’s value depends on how it interacts with the complex biology of the human body, and whether its benefits outweigh its side effects. Since clinical trials (next paragraph) are extremely expensive and time-consuming, it can be valuable to first test and refine possible treatments in other ways. This can include animal testing, as well as other methods for predicting a treatment’s performance.

(F) Clinical trials. Before a treatment comes to market, it usually goes through clinical trials: studies (often highly rigorous experiments) in which the treatment is given to humans and the results are assessed. Clinical trials typically involve four different phases: early phases focused on safety and preliminary information, and later phases with larger trials focused on definitively understanding the drug’s effects. Many people instinctively picture clinical trials when they think about biomedical research, and clinical trials account for a great deal of research spending (one estimate, which I haven’t vetted, is that clinical trials cost tens of billions of dollars a year, over half of industry R&D spending). However, the number of clinical trials going on generally is – or should be – a function of the promising leads that are generated by other types of research, and the most important leverage points for improving treatment are often within these other types of research.

(A) – (C) are generally associated with academia, while (D) – (F) are generally associated with industry. There are a variety of more detailed guides to (D) – (F), often referred to as the “drug discovery process” (example).

Example: Herceptin

Herceptin is a drug used for certain breast cancers, first approved in 1998. Its development relied on relatively recent insights and techniques, and it is notable for its relative lack of toxicity and side effects compared to other cancer drugs. I perceive it as one of the major recent success stories of biomedical research (in terms of improving treatment, as opposed to gaining knowledge) – it was one of the best-selling drugs of 2013 – and it’s an unusually easy drug to trace the development of because there is a book about it, Her-2: The Making of Herceptin (which I recommend).

Here I list, in chronological order, some of the developments which seem to have been crucial for developing Herceptin. My knowledge of this topic is quite limited, and I don’t mean this as an exhaustive list. I also wish to emphasize that many of the items on this list were the result of general inquiries into biology and cancer – they weren’t necessarily aimed at developing something like Herceptin, but they ended up being crucial to it. Throughout this summary, I note which of the above types of research were particularly relevant, using the same letters in parentheses that I used above.

As detailed above, many essential insights necessary for Herceptin’s development came out very long before the idea of Herceptin had been established. My impression is that most major biomedical breakthroughs of the last few decades have a similar degree of reliance on a large number of previous insights, many of them fundamentally concerning tools and techniques (A) or the functioning of healthy organisms (B) rather than just disease-specific discoveries.

General misperceptions that can arise from over-focusing on certain types of research

I believe that science supporters often have misperceptions about the promising paths to progress, stemming from picturing only certain types of research. Below, I informally list some of these misperceptions, as informal non-attributed quotes.