Air travel constitutes the safest mode of travel in the world today. What is even more impressive is the way airplanes are designed by modeling and simulation, sometimes before the actual prototype is built. In fact simulation has been a mainstay in the aeronautical industry for a long time and what seems like a tremendously complex interaction of metal, plastic and the unpredictable movements of air flow can now be reasonably captured in a computer model.
In a recent paper, Walter Woltosz of Simulations Plus Inc. asks an interesting question: compared to the aeronautical industry where modeling has been applied to airplane design for decades, why has it taken so long for modeling to catch on in the pharmaceutical industry? In contrast to airplane design which is now a well-accepted and widely used tool, why is simulation of drugs and proteins still (relatively) in the doldrums? Much progress has surely been made in the field during the last thirty years or so, but modeling is nowhere as integrated in the drug discovery process as computational fluid dynamics is in the airplane design process.
Woltosz has an interesting perspective on the topic since he himself was involved in modeling the early Space Shuttles. As he recounts, what's interesting about modeling in the aeronautical field is that NASA was extensively using primitive 70s computers to do it even before they built the real thing. A lot of modeling in aeronautics involves figuring out the right sequence of movements an aircraft should take in order to keep itself from breaking apart. Some of it involves solving the Navier-Stokes equations that dictate the complicated air flow around the plane, some of it involves studying the structural and directional effects of different kinds of loads on materials used for construction. The system may seem complicated but as Woltosz tells it, simulation is now used ubiquitously in the industry to discard bad models and tweak good ones.
Compare that to the drug discovery field. The first simulations of pharmaceutically relevant systems started in the early 80s. Since then the field has progressed in fits and starts and while many advances have come in the last two decades, modeling approaches are not a seamless part of the process. Why the difference? Woltosz comes up with some intriguing reasons, some obvious and others more thought-provoking.
1. First and foremost of course, biological systems are vastly more complicated than aeronautical systems. Derek has already written at length about the fallacy of applying engineering analogies to drug discovery and I would definitely recommend his thoughts on the topic. In case of modeling, I have already mentioned that the modeling community is getting ahead of itself by trying to chew on more complexity than it can bite. Firstly you need to have a list of parts to simulate and we are still very much in the process of putting together this list. Secondly, having the list will tell us little about how the parts interact. Biological systems display complex feedback loops, non-linear signal-response features and functional "cliffs" where a small change in the input can lead to a big change in the output. As Woltosz notes, while aeronautical systems can also be complex, their inputs are much more well-defined.
But the real difference is that we can actually build an airplane to test our theories and simulations. The chemical analogy would be the synthesis of a complex molecule like a natural product to test the principles that went into planning its construction. In the golden age of organic synthesis, synthetic feats were undertaken for structure confirmation but also to validate our understanding of the principles of physical organic chemistry, conformational analysis and molecular reactivity. Even if we get to a point where we think we have a sound grounding of the principles governing the construction and workings of a cell, it's going to be a while before we can truly confirm those principles by building a working cell from scratch.
2. Another interesting point concerns the training of drug discovery researchers. Woltosz is probably right that engineers are much more of generalists than pharmaceutical scientists who are usually rigidly divided into synthetic chemists, biologists, pharmacologists, modelers, process engineers etc. The drawback of this compartmentalization is something I have experienced myself as a modeler; scientists from different disciplines can mistrust each other and downplay the value of other disciplines in the discovery of a new drug. This is in spite of the fact that drug discovery is an inherently complex and multidisciplinary process which can only benefit from an eclectic mix of backgrounds and approaches. A related problem is that some bench chemists, even those who respect modeling, want modeling to provide answers, but they don't want to run experiments (such as negative controls) which can advance the state of the field. They are reluctant to carry out the kind of basic measurements (such as measuring solvation energies of simple organic molecules) which would be enormously valuable in benchmarking modeling techniques. A lot of this is unfortunate since it's experimentalists themselves who are going to ultimately benefit from highly validated computational approaches.
There's another point which Woltosz does not mention but which I think is quite important. Unlike chemists, engineers are usually more naturally inclined to learn programming and mathematical modeling. Most engineers I know know at least some programming. Even if they don't extensively write code they can still use Matlab or Mathematica, and this is independent of their specialty (mechanical, civil, electrical etc.). But you would be hard-pressed to find a synthetic organic chemist with programming skills. Also, since engineering is inherently a more mathematically oriented discipline, you would expect an engineer to be more open to exploring simulation even if he doesn't do it himself. It's more about the culture than anything else. That might explain the enthusiasm of early NASA engineers to plunge readily into simulation. The closest chemical analog to a NASA engineer would be a physical chemist, especially a mathematically inclined quantum chemist who may have used computational techniques even in the 70s, but how many quantum chemists (as compared to synthetic chemists for instance) work in the pharmaceutical industry? The lesson to be drawn here is that programming, simulation and better mathematical grounding need to be more widely integrated in the traditional education of chemists of all stripes, especially those inclined toward the life sciences.
3. The third point that Woltosz makes concerns the existence of a comprehensive knowledge base for validating modeling techniques and he thinks that a pretty good knowledge base exists today upon which we can build useful modeling tools. I am not so sure. Woltosz is mainly talking about physiological data and while that's certainly valuable, the problem exists even at much simpler levels. I would like to stress again that even simple physicochemical measurements of parameters such as solvation energies which can contribute to benchmarking modeling algorithms are largely missing, mainly because they are unglamorous and underfunded. On the bright side, there have been at least some areas like virtual screening where researchers have judiciously put together robust datasets for testing their methods. But there's a long way to go and much robust basic scientific experimental data needs to be gathered. Again, this can come about only if scientists from other fields recognize the potential long-term value that modeling can bring to drug discovery and contribute to its advancement.
Woltosz's analogy of drug design and airplane design also reminds me of something that Freeman Dyson once wrote about the history of flight. In "Imagined Worlds", Dyson described the whole history of flight as a process of Darwinian evolution in which many designs (and lives) were destroyed in the service of better ones. Perhaps we also need a merciless process of Darwinian evaluation in modeling. Some of this is already taking place in the field of protein modeling field with CASP and in protein-ligand modeling with SAMPL, but the fact remains that the drug discovery community as a whole (and not just modelers) will have to descend on the existing armamentarium of modeling tools and efficiently and ruthlessly evaluate them to pick out the ones that work. This has not happened yet.
Ultimately I like the fact that Woltosz is upbeat, and while the real benefits coming out of the process are uncertain, I definitely do agree with him that that we will know the answer only if the pharmaceutical industry makes a concerted effort to test, refine, retain and discard modeling approaches to drug design at all levels. That's the only way we will know what works. Sadly, one of the problems is that it will necessarily be a slow, long-term validation and development effort that will need the constant engagement of the global drug discovery community as a whole. It may be too much to ask in this era of quick profits and five-year exit strategies. On the other hand, we are all in this together, and we do want to have our chance at the drug discovery equivalent of the moon shot.
Woltosz, W. (2011). If we designed airplanes like we design drugs… Journal of Computer-Aided Molecular Design DOI: 10.1007/s10822-011-9490-5
Idiosyncratic Thinking: a computer heuristics lecture
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