March 1, 2013 | 13
I minored in physics in college, and ever since then I have had a lively interest in the subject and its history. Although initially trained as an organic chemist, part of the reason I decided to study computational and theoretical chemistry is because of their connections to physics by way of quantum chemistry, electrostatics and statistical thermodynamics. No other science can boast the kind of fundamental insights into the most basic workings of the universe that physics has provided in the twentieth century. Even today when we think of the purest, most exalted science we think of physics.
Not surprisingly, I have several physicist friends with whom I often talk shop. It’s always interesting to hear about their work which ranges from cosmology to solid-state physics. Yet I find that I sometimes I have trouble explaining my own work to them. And this is certainly not because they lack the capacity to understand it. It’s because the nature of drug discovery is sometimes rather alien to physicists and especially to theoretical physicists. The physicists have trouble understanding drug discovery not because it’s hard but because it seems too messy, unrigorous, haphazard, subject to serendipity.
But drug discovery and design is indeed all this and more, and that’s precisely why it works. Success in drug discovery demands a diverse mix of skills that range from highly rigorous analysis to statistical extrapolation, gut feeling and intuition, and of course, a healthy dose of good luck. All of these are an essential part of the cocktail (to borrow a drug metaphor). A good drug not only binds to a defective protein in the body with high affinity and regulates its activity but it also has optimal properties like minimum side effects and the right rate of absorption, distribution, metabolism and excretion. In addition it has to be made from cheap raw materials and amenable to large-scale production with low environmental impact. Designing a drug is thus the quintessential multi-objective optimization problem; it basically boils down to engineering a small organic molecule that’s going to interact with a mind-bogglingly intricate system which intimately interacts with that molecule.
No wonder that models play an integral role in the discovery of new drugs. Complex systems yield themselves to modeling much more than simple systems, since the much greater number of moving parts often makes them refractory to rigorous theorizing. In this sense drug discovery is very much like chemistry which the Nobel Prize winning chemist Roald Hoffmann has trouble explaining to physicists for similar reasons. Provocatively Hoffmann says:
“When I am trying to explain complex chemical concepts, I have three kinds of audiences in mind: my fellow academics in the humanities, intelligent laymen and physicists. Out of these I find that it’s hardest to explain chemistry to physicists, because they think they understand, but they don’t.”
Hoffmann makes a point which was made more explicit by another Nobel Laureate, the chemist William Lipscomb. Lipscomb lamented the difficulty that physicists present in assuming that chemistry is “physics without rigor”. They think that chemistry is not as useful as physics because it can’t always be approached from a first-principles viewpoint. For a theoretical physicist, anything that cannot be accurately expressed as a differential equation subject to numerical if not analytical solution is suspect. True success in physics is exemplified by quantum electrodynamics, the most accurate theory that we know which agrees with experiment to 12 decimal places. While not as stunningly accurate as QED, most of theoretical physics in the twentieth century consisted of rigorously solving equations and getting answers that agreed with experiment to an unprecedented degree. The tremendous success that physics enjoyed in predicting phenomena spread over 24 orders of magnitude made physicists fall in love with precise measurement and calculation. The goal of many physicists was, and still is, to find three laws that account for at least 99% of the universe. But the situation in drug discovery is more akin to the situation in finance described by the physicist-turned-financial modeler Emanuel Derman; we drug hunters would consider ourselves lucky to find 99 laws that describe 3% of the drug discovery universe.
Physics strives to find universal laws, drug discovery like chemistry thrives on exceptions. While there certainly are general principles dictating the binding of a drug to its target protein, every protein-drug system is like a human being, presenting its own quirky personality and peculiar traits that we have to deconvolute by using every tool at our disposal, whether rigorous or not. In fact as anyone in the field would know, drug discovery scientists take great satisfaction in understanding these unique details, knowing what makes that particular molecule and that particular protein tick. Try to convince any scientist working in drug discovery that you have found an equation that would allow you to predict the potency, selectivity and side-effects of a drug starting from its chemical structure and which would be universally applicable to any drug and any protein, and you will be met with ridicule.
Physicists also have to understand that in drug discovery, understanding is much more important than accuracy, another principle applicable to chemistry in general. In chemistry there are a lot of rather unrigorous, semi-quantitative concepts that are nonetheless part of the chemist’s everyday vocabulary. In fact trying to make them more precise will sometimes diminish their utility. For instance there’s little point in calculating or measuring the absolute value of the energy of interaction of a protein with a drug to four decimal places, but calculating differences in this quantity could be very useful, even if there are errors in the individual numbers. Far more important than calculation however is in explaining why; why a small change in a drug causes a large change in its activity, why one enantiomer causes side-effects while another does not, why making a molecule mimicking the natural substrate of a protein failed, why adding a fluorine to a compound adversely affected solubility. “Why” in turn can lead to “what I should make next”, which is really what a drug hunter wants to know. In most of these cases the number of variables is so large that calculation would be hopelessly impossible in any case, but even if it were possible, dissecting every factor quantitatively is not half as important as explanation. And here’s the key point; the explanation can come from any quarter and from any method of inquiry, from calculation to intuition.
This brings us to reductionism which we have discussed on this blog before. Part of the reason drug discovery can be challenging to physicists is because they are steeped in a culture of reductionism. Reductionism is the great legacy of twentieth-century physics, but while it worked spectacularly well for particle physics it doesn’t quite work for drug design. A physicist may see the human body or even a protein-drug system as a complex machine whose understandings we can completely understand once we break it down into its constituent parts. But the chemical and biological systems that drug discoverers deal with are classic examples of emergent phenomena. A network of proteins displays properties that are not obvious from the behavior of the individual proteins. An aggregate of neurons displays behavior that completely belies the apparent simplicity of neuronal structure and firing. At every level there are fundamental laws governing a particular system which we have to understand. Reductionism certainly doesn’t work in drug discovery in practice since the systems are so horrendously complicated, but it may not even work in principle. Physicists need to understand that drug discovery presents reductionism in a straitjacket; it can help you a little bit at every level, but it has very little wiggle room beyond that level.
Physicists may also sometimes find themselves bewildered by the inherently multidisciplinary nature of pharmaceutical research. It is impossible to discover a new drug without the contribution of people from a variety of different fields, and no one scientist can usually claim the credit for a novel therapeutic. This concept is somewhat alien especially to theoretical physicists who are used to sitting in a room with pencil and paper and uncovering the great mysteries of the universe. To be sure, there are areas of physics like experimental particle physics which now require enormous team effort (with the LHC being the ultimate incarnation of such teamwork), but even in those cases the scientists involved have been mostly physicists.
So are physicists doomed to look at drug discoverers with a jaundiced eye? I don’t think so. The nature of physics itself has significantly changed in the last thirty years or so. New fields of inquiry have presented physicists with the kind of complex systems opaque to first-principles approaches that chemists and biologists are familiar with. This is apparent in disciplines like biophysics, nonlinear dynamics, atmospheric physics, and the physics of large disordered systems. Many phenomena that physicists study today, from clouds to strange new materials are complex phenomena that don’t succumb to reductionist approaches. In fact, as the physicist Philip Anderson reminds us, reductionism does not even help us fully understand well-known properties like superconductivity.
The new fields demand new approaches and their complexity means that physicists have to abandon strictly first-principles approaches and indulge in the kind of modeling familiar to chemists and biologists. Even cosmology is now steeped in model-building due to the sheer complexity of the events it studies. In addition, physicists are now often required to build bridges with other disciplines. Fields like biophysics are often as interdisciplinary as anything found in drug discovery. And just like in drug discovery, physicists now have to accept the fact that a novel solution to their problem may come from a non-physicist.
All this can only be a good augury if it means that more physicists are going to join the working ranks of drug discoverers. And it will all work out splendidly as long as they are willing to occasionally hang their reductionist hats at the door, supply pragmatic solutions and not insist on getting answers right to twelve decimal places.
This is a revised and updated version of a previous post on The Curious Wavefunction.
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