A government center for drug discovery?

The New York Times has an article about a new center for drug discovery that the government is going to set up in the face of the declining pace of new drug discovery in the pharmaceutical industry. We all know what's wrong with the industry, with the numero uno factor being the pursuit of short-term profit goals at the expense of basic science and long-term benefit. The question is, can the government make up for this shortfall?

Perhaps, if it's done right. But the vision for the new center does not inspire me with much confidence. The center seems to mainly be a result of NIH director Francis Collins's conviction that gene-based drug discovery is the wave of the future. Collins is disappointed with the fact that Big Pharma has been unsuccessful with "translational genomics" in spite of spending millions of man hours and dollars. He thinks that if done right, this kind of translational approach will result in new drugs. As he makes it clear in his book "The Language of Life", Collins is a longstanding proponent of genomics-based medicine.

But isn't this what everyone has been saying ever since genome sequencing became possible? We all remember the hype about genomics enabling a new generation of 'rational' drugs based on intimate knowledge of genes and their protein products. The fact that this vision has not panned out could partly be ascribed to the lackluster efforts and the constant waves of lay-offs in industry, but maybe there's also a deeper reason why the optimism has hit a wall. Maybe, and experts have been saying this for a while now, it's simply because taking a genomics-based approach to drug discovery ignores all the other complexities of biological systems like signal-transduction and epigenetics. Discovering the gene and protein is one thing, understanding the intricate interactions of the protein as part of a multi-layered cellular communication network is quite another. We are still struggling to understand the very basics of how proteins and genes link up in cells to enable complex physiological and behavioral responses, let alone rationally design drugs to block specific parts of those responses. In the absence of such understanding, simply pinning your hopes on the promise of 'translational genomics' seems to me to be another big sink for money and personnel.

So what else can a government center for drug discovery do which could be more concrete and fruitful? Ironically, the article contains part of the answer when it highlights the complexity of biological systems stated above.

Consider this remarkable fact; in the last century, only two breakthrough treatments for mental illness have been developed, lithium for bipolar illness and chlorpromazine (Thorazine) for schizophrenia. Many other successful antipsychotic drugs were spinoffs of thorazine. Also consider that even today we have little clue about how these drugs work, let alone how to rationally design them. Thorazine likely affects multiple neurotransmitter pathways involving serotonin, norepinephrine, dopamine etc. While we have made great advances in the last fifty years, brain chemistry remains as complex as ever. At a molecular level, the main problem is in understanding the basic mechanisms and specificity through which a molecule as simple as dopamine binds to only certain subtypes of a neurotransmitter receptor, stimulates multiple second-messenger pathways to different extents and elicits a complex behavioral response. In this case we know most of the genes and we know most of the protein products, yet we are light years away from understanding how Thorazine works. Lithium is an even more mysterious substance whose workings are almost akin to black magic. Instead of chasing the genes, thoroughly understand the action of a few of these drugs on a biochemical level and we can make significant inroads into understanding brain chemistry.

So if there's one thing a new government center for drug discovery can do, it should be to focus on these specific problems in the most general way rather than pool together resources for "translational genomics". Doing translational genomics will simply mean advancing work which industry is already involved in; it will largely be more of the same and there's good reasons why it may not work.

Here's what I think instead. If the center truly wants to do something productive that pharmaceutical companies cannot, let it put together a mini Manhattan Project type team focused on understanding a few specific problems, like how lithium works in the brain. The problems should be picked based both on their medical importance and their potential impact in enabling general understanding of the field. Just like the Manhattan Project did, get together the best people in the country from several disciplines who are experts at multidisciplinary thinking and problem solving. If you want to attack the lithium problem for instance, get together chemists, biologists, neuroscientists, pharmacologists, doctors and perhaps even a few physicists, mathematicians and computer scientists. Put them in a couple of large rooms (and maybe even seclude them in the majestic mountains of New Mexico) and give them enough funds. And then most importantly, give them almost complete freedom to brainstorm about specific problems. Let them consider every possible approach, from running basic biophysical experiments to the most advanced neural imaging techniques. Don't limit yourself to any one philosophy like translational genomics or any other currently fashionable mantra. Genomics can certainly be part of the mix but it should not be put on a pedestal. Combine the oldest tools of classical pharmacology with the newest tools of molecular biology. If the industry has been missing one thing, it's been the presence of bright young people who are given complete freedom to focus on their diverse ideas without strings attached and constant fear of unemployment. The government can give these men and women what industry has taken away from them in recent years.

The government has always been good at this kind of free-for-all interaction among talented scientists who are unencumbered by research funds and job insecurity. A new government center for drug discovery could be a great idea, but only if it provides the kind of freedom to operate that brings out the best in creative minds. Focusing on translational genomics to me seems to be another way to repeat what has been done and to waste more funds, time and personnel. Instead, do what the government does best; let them think, and let their minds soar.

From bull horns to under the lens of Anton

In 1988, a young computer scientist named David Shaw was working at Morgan Stanley, one of the first Wall Street firms interested in using computer algorithms for trading. Shaw was an expert in parallel processing, speeding up calculations by executing them in a parallel process over multiple processors. Previously he had been a computer science professor at Columbia University had tried to sell his computer skills to a number of companies, but only Morgan Stanley was genuinely interested. As Shaw started working at the company, he began to think not just of programming strategies but of creative ways in which they could be applied to trading. In a meeting where he was supposed to talk only about his algorithms, he went one step beyond and described better methods for trading using these algorithms. Eyebrows went up in the room. Shaw was essentially seen as overstepping his bounds as a programmer. The higher-ups told him clearly that his job was simply building the computer architecture. He could leave the trading to them. Shaw quit and started his own company. Ten years later, it was one of the most successful hedge funds in the world and Shaw was a billionaire. One can only speculative how much the Morgan Stanley executives cried over the loss they had suffered when Shaw left.

But now D E Shaw is a totally different animal.

One of the most anticipated talks at the American Chemical Society meeting in Boston which I just came back from was by this Wall Street mover turned pure scientist. He is a remarkable and brilliant man. What other Wall Street hedge fund manager who made billions using mathematical algorithms for trading (and was known as “King Quant” at one point) basically retires from the dizzying world of finance to fully engage himself with computer simulations of proteins and drug discovery research? Well, Shaw has done this, and is blazing his way toward some potentially revolutionary research. In a nutshell, molecular dynamics (MD) is a technique that simulates the motions of systems at the molecular and atomic levels. It is especially useful for proteins since it can accurately capture their real-life motion in living organisms. MD is based on Newton's laws of motions and essentially involves solving the equations of motions to calculate the forces and velocities of all the atoms in the system. In practice, due to the very large number of atoms in a typical protein (usually thousands, surrounded by tens of thousands of water molecules) MD is quite computationally intensive and challenging to implement; until now, most simulations have been restricted to nanosecond time frames (which can easily correspond to days of actual computer time).

Shaw heads D E Shaw Research, a company totally separate from the financial powerhouse that has as its long-term goal, a fundamental transformation in the process of drug discovery. As the story goes, Shaw got somewhat bored of making millions and wanted to attack scientific problems that could benefit from the application of advanced computer algorithms. He got his old job as computer science professor at Columbia University and started looking around for the right problem. Fortunately for the field of biochemistry, Shaw started having discussions with a friend of his, the well-known physical chemist Richard Friesner at Columbia who is also the chief scientific advisor for the computational chemistry company Schrodinger. Friesner piqued Shaw’s interest and started giving him little problems in computational chemistry and biology which Shaw solved during his spare time. Finally he realized that MD simulations of proteins which had previously been typically restricted to the nanosecond time range stood a chance of being truly and very significantly useful if they could be expanded to the 10 microsecond-millisecond range, since this is the time scale on which most interesting biological motions occur.

Shaw started D E Shaw research and collected a team of highly talented chemists, biologists and computer scientists to tackle the problem. After a decade or so, these efforts have manifested themselves as Desmond, a protein MD program that has vastly accelerated computer simulations of proteins. Desmond essentially relies on many ingenious methods to simplify the calculation of forces and velocities involved in a typical MD computation. What is even more remarkable is that Shaw’s group has designed ‘Anton’, a 512 node state-of-the-art machine, a special purpose machine explicitly designed for protein MD and named after Anton van Leeuwenhoek, the legendary 17th century Dutch scientist who trained the microscope on the microbial world and unearthed a wondrous universe teeming with life. Just like the 17th century Anton probed the events of the bacterial world, the 21st century Anton seeks to probe the molecular-level events of the protein world, The machine does only MD, and it does this using a razor sharp scalpel.

To give an idea of the kind of quantum leap Anton provides for MD simulations, Shaw gave some numbers, and I can swear I saw some people who were almost nodding off suddenly become wide awake. According to Shaw, the fastest supercomputer which does parallel processing today can crunch about 200 nanoseconds per day for a typical sized protein. Anton surpasses this number by two orders of magnitudes and spews out 17,400 ns or 17 microseconds per day. Such numbers would have been unthinkable a decade ago; until Desmond appeared on the scene, the world record for long protein MD simulations had been held by a group from the University of Illinois, with a total time of 10 microseconds.

So what’s the significance of being able to simulate in this time scale? Tremendous. It’s like the difference between nuclear weapons and the biggest conventional bombs previously available. When nukes arrived on the scene, some politicians like Winston Churchill shrugged them off by thinking that they were “just bigger bombs”. But as the old saying goes, quantity can have a quality all of its own. Nuclear weapons heralded a completely new era of warfare because of the ability of a single weapon to raze a whole city. The basic unit of destruction changed from a human being to entire cities. Desmond and Anton promise such conceptual transformations. As mentioned before, breaking the 10 microsecond barrier is a real turning point since most interesting physiological events happen on time scales of microseconds-milliseconds.

Entering the world of millisecond simulations is like unlocking the door to a rainforest with millions of exotic species that you suspected existed, but which you had no way of viewing and studying. In the last few years, Desmond has been used to study highly significant conduction events in ion channels proteins which conduct sodium and potassium, has been used to reconcile experimental and conceptual contradictions in the structure of proteins called G-Protein Coupled Receptors which are absolutely crucial in both basic physiological processes and in the action of drugs, and has been used to study proteins called kinases whose misregulation in involved in cancer. All these events are very slow with respect to conventional MD and were until now mostly inaccessible. Shaw showed some spectacular examples of proteins actually folding and unfolding multiple times. In some cases his group has obtained quantitative agreement with experiments.

I think it was the end of the talk which made a few jaws drop. When you have a protein structure and want to find out a small drug molecule which can modulate its activity, one of the key goals is to first find out where the drug binds. Almost all drugs regulate the activity of proteins- and therefore treat diseases in which these proteins are misregulated- by binding into very specific pockets on the proteins. With the kinds of time scales available, Shaw can achieve this with a devastatingly straightforward simulation. In a video that appeared a little surreal, he simply let the drug roam all around the protein surface and find the binding pocket. Like a curious dog sniffing around for the buried bone, the little guy went in and out of crevices and gullies, lingered for some time outside the binding site, and then, with a little hesitation, finally ensconced himself firmly in his cozy home.

Molecular dynamics by itself is not going to revolutionize drug discovery. But it can provide unprecedented insight into the behavior of biological systems at the molecular level. What we witnessed in that room on Thursday was a different ball game. One in which the ball had been hit out of the park. More surprises should follow.

VYTORIN: NEW PROBLEMS FOR A NEW ERA

The NYT has a piece today in which it describes the problems riddling Vytorin, a combination of ezetimibe and simvastatin, which is taken by 3 million around the world and makes 5 billion for its owners, Schering-Plough and Merck. The piece says that in spite of such widespread usage, there is apparently no clinical trial data that demonstrates that the second piece of the cocktail- ezetimibe- is efficacious. Much more concerning is the fact that there may be a link between ezetimibe and cancer, although the link looks fragile at best right now.

Statins have been the wonder drugs of our time, with Atorvastatin (Lipitor) being the best-selling drug in the world. Their efficacy in reducing heart attacks has been demonstrated in large-scale trials. Ezetimibe which blocks cholesterol absorption in the intestine and reduces LDL has much more tenuous effects. Recent large-scale studies found that while ezetimibe does reduce LDL, it's effect on the variables that actually matter are far less certain; there was no evidence that it actually reduces heart attacks. Yet it continues to be prescribed by thousands of doctors around the country, with patients shelling out considerable amounts for it.

However, the article raises the much bigger issue of knowing exactly when a drug is efficacious. According to the article, utility of a drug is usually gauged by "surrogate endpoints", that is, endpoints which indicate reductionist type effects rather than an actual increase in life-span or quality of lives. Take cancer for example. For most cancer drugs, tumor shrinkage is a convincing endpoint, not an actual increase in life-span. Or take cholesterol medications. The causal link between LDL cholesterol lowering and reduced heart attacks is apparently well-proven. Yet the body is complex enough for this causal link to be questioned; such questions arise most often in truly large sample studies, when the drug is on the market. Clearly the only true indication of side-effects or efficacy will come from such large-scale studies.

But there's a dilemma here as far as I can see it. The reason why surrogate endpoints are used seems clear to me; it's simply much more easy to look for such effects and ascribe them to drug action than effects like "increase in life-span" which can be controlled by multiple factors. Let's say two men who are the same age have been prescribed the same cancer medication. Both show shrinkage in tumors. Apparently the medicine works. But can this be translated into an observation about the difference in their life-spans, which can be attributed to so many different factors. Especially if the general health of one of the patients is worse than the other, then the cancer medicine can basically be an adjuvant and not the primary cause for his prolonged survival. How do we know that it's the cancer medicine and not his general health condition that actually increased his life-span? Naturally a reduced incidence of heart attacks is much easier to analyze than an increase in life-span. But even there it seems that so many factors can be responsible for a heart attack that it would be a problem trying to attribute specific effects to the medication, or especially the lack thereof of its effects.

The article also says that the FDA has become stringent about medications that address chronic problems like heart disease. The stringent bar set for a true study of efficacy seems to be about 10,000 patients over about four or five years. If every pharmaceutical company needs to do such a study to get approval, they might as well start digging their grave right away. Drug development is already so risky and expensive that putting a drug through 10,000 patients over five years and having the FDA almost certainly reject it after that would spell doom for all drug makers. Yet it is clearly unethical for doctors to keep on prescribing medication whose efficacy has not been demonstrated.

There does not seem to be an easy way out of this problem. To me right now it seems that, with all the compromises and problems it entails, the best bet might be to set such a stringent bar for medications for which good alternatives exist (and medications for chronic heart disease seem to fall into that category now) but relax the need for such large-scale trials for unmet and critical needs for which no good drugs exist. I am pretty sure the FDA is not going to set the bar for cancer so high.

So what's the way out for companies? To me the soundest scheme for now seems to be for doctors to provide information on the label saying that the drug did not show efficacy in a fair number of patients, and let the patient decide for himself or herself. It would be ridiculous in my opinion for the FDA to demand that the company withdraw the drug. Also, as a general thought, I think that both the FDA and public opinion need to get over their obsession of approving drugs only if they show efficacy in 100% or even 90% patients. What if a drug shows efficacy in 50% of patients? The rational thing would be for doctors and companies to explicitly say this on their label and let the consumer decide. That's the kind of thing that should happen in a liberal society.

Note: Over at The Pipeline, Derek has quite a few posts on this

THE COLOUR OF YELLOW

Curcumin, the active yellow constituent of turmeric, has been a mainstay of Indian medicine, food, and culture for centuries. But now, after several years of neglect, it is becoming one of the big success stories of how Western medicine can draw on ancient Eastern knowledge...

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