Drug discovery 2.0: Rise of the biologics? Not so fast

Along with ominous drumbeats heralding the decline of the pharmaceutical industry, another recurring lament that we hear these days is about the decline of small molecules and the rise of biologics like antibodies and other proteins. The last decade has seen several successful antibody drugs, especially against cancer. Based on this success the prevailing wisdom (which does not tire of making its presence known) seems to suggest that traditional small molecule drugs are being shown the door even as biologics with their much higher specificities and lack of side effects are being welcomed in.

Not so fast. A rather refreshing opinion piece from a B. Meunier from CNRS in this week's Angewandte Chemie makes a strong case that rumors of small molecules' deaths are grossly exaggerated. Along the way Meunier also takes a swipe at what he considers to be Europe's stifling innovation culture; his point is that since the "new" model of pharmaceutical research is going to entail "Big" Pharma licensing compounds in from small companies, the model can only thrive in countries where a healthy startup culture already exists. And Meunier does not see European countries steering that boat. Of course he does not discuss the rather baleful VC funding environment in the US, but that's a tale of woe for another article.

Meunier points out some rather obvious but important problems with biologics that their upbeat proponents sometimes cheerfully dismiss. Foremost is the price tag, with the minimum cost of a monoclonal antibody being in the tens of thousands of dollars, and many costing hundreds of thousands. Secondly, antibodies have had their biggest success against cancer where the high cost has to be balanced against the terminal nature of the disease, the little time that patients have and the possibility of complete remission. Contrast that with chronic diseases like diabetes, heart disease and arthritis where people have to be on therapies for years. It would be prohibitively expensive to consider an antibody treatment in such cases unless there's some new revolutionary way to lower costs of production; needless to say such breakthroughs are not visible around the corner. 

Thirdly, antibodies and protein therapeutics have obvious clinical limitations; they cannot cross the blood brain barrier and are therefore of little use against neurodegenerative disorders like Alzheimer's, and they can mostly attach only to extracellular targets and therefore may be useless for targeting intracellular proteins. There's also the perpetually inconvenient fact that they have to be injected, another stumbling block on the way to using them against chronic diseases. Finally, Meunier thinks that the whole economic basis for biologics, namely the belief that generics will take a very long time to hit the market because of the production challenges, may well be refuted sooner than we think. If there's one lesson we have learnt from the history of innovation, from the development of paper to the atomic bomb, it's that other countries don't lack smart innovators, and given enough time they will always come up with novel ideas to speed up production and lower costs. Putting all your eggs in the "It will be decades before India and China can come up with generic versions of biologics" basket is a dangerous strategy.

Ultimately small molecules have one thing going for them; they have worked ever since the dawn of drug discovery and they continue to be robust, versatile and easy to make. As Meunier points out, occasional competitors like oligonucleotide therapies have largely bit the dust. And of course the sheer number of ways in which you can combine six elements to build a drug is literally astronomical; we have barely scratched the surface of chemical diversity and there's still myriad scaffolds to plumb. Meunier's message is thus to keep up the good work with small molecules, employing the whole toolkit of chemistry comprising not just synthesis but analysis and modeling. Biologics may stridently stake out certain areas of the drug kingdom, but small molecules will continue to rule for the foreseeable future.

Latest Scientific American posts

Not to beat a dead Sprague Dawley rat, but I have another post on Sci Am comparing airplane and drug design, this time discussing some of the actual complexity of protein-drug binding.

On occasion of a special "Beginnings" blog festival on multiple sites, I make a case for why the Origin of Life is chemistry's grand question.

And here's some thoughts on the chemistry of Curiosity.

Again, drug design and airplane design are not the same

A while back I had a post about an article that compared airplane design to drug design. I discussed the challenges in drug design compared to airplane design and why the former is much less predictable than the latter, the short answer being "biological complexity".

Now the analogy surfaces again in a different context. C & EN has an interview with Kiran Mazumdar-Shaw, CEO of India's largest biopharmaceutical company Biocon. Shaw is an accomplished woman who does not hold back when she laments the current depressing state of drug development. I think many of us would commiserate with her disappointment at the increasing regulatory hurdles that new drugs have to face. But at one point she says something that I don't quite agree with:

Mazumdar-Shaw dismisses the argument that drugs create a public safety imperative mandating stricter oversight than many other regulated products. “So you think passenger safety is any less important than patient safety?” she asked. Yet aircraft makers don’t face a 12-year, all-or-nothing proposition when designing, developing, and commercializing an airplane. Nor, she added, does Boeing have to prove that it is making something fundamentally different than what Airbus already has on the market.

To which my counter-question would be, "What do you think is the probability of unforeseen problems showing up in aircraft design compared to drug design"? I see a rather clear flaw in the analogy; aircraft design is not as tightly regulated because most aircraft work as designed and the attrition rate in their development is quite low. The number of failures in aircraft development pale in comparison with the number of Phase II failures in drug development. In fact as the article quoted in my previous post described, these days you can almost completely simulate an aircraft on a computer. Regulatory agencies can thus be much more confident and insouciant about approving a new airplane.

This is far from the case for drugs. First of all there is no clear path to a drug's development and in the initial stages most people don't have a clue as to what the final product is going to look like. But more importantly, designing drugs is just so much riskier than designing aircraft that regulatory agencies have to be more circumspect. How many times do drugs show all kinds of side-effects which would never have been predicted at the beginning? How many times do drugs show side-effects that would not even have been imagined at the beginning? It's this almost complete lack of prediction driven by the sheer complexities of biology that distinguishes drugs from airplanes.

In another part of the interview Mazumdar-Shaw voices her impatience with regulators' recalcitrance to adopt new technologies. The example she gives is of Hawk-Eye, a computer that tracks a tennis ball's movement and makes it easier to call out the result of a disputed bounce. Just like sports authorities are reluctant to use these technologies to override the flaws in human judgement, Mazumdar-Shaw thinks regulators are reluctant to use new technologies to overcome the limitations of human judgement. The point is not irrelevant but the truth is that decision making in drug development is far more complex than decision making in tennis tournaments. For Hawk-Eye to track tennis balls is a simple matter of physics, and it can do this with high accuracy. Contrast this with drug development where the "event" to be analyzed is not the bounce of a ball but the efficacy of a drug in a large clinical trial as assessed by a variety of complex statistical measures. In addition, approving a drug is inherently more subjective, being based on efficacies of existing therapies, the exact numerical superiority of the extra benefits, cost and patient populations. Good luck writing a computer problem that could possibly assess this morass of sometimes conflicting information and reach an informed judgement.

I think many of us are frustrated with the increasing regulatory hurdles that new drugs face and we all wish that the process was smoother. Personally I don't think that the FDA's systems for assessing risks is as finely attuned to potential benefits as it should be. But I don't find myself following Mazumdar-Shaw in advocating for drug approvals that are as easy as aircraft approvals. The former is science and engineering. The latter is science with a healthy dose of intuition and art. And some black magic.

Protein-ligand crystal structures: WYSI(N)WYG

Crystal structures of proteins bound to small molecules have become ubiquitous in drug discovery. These structures are routinely used for docking, homology modeling and lead optimization. Yet as several authors have shown over the years, many of these structures can hide flaws that don't become apparent until they are actually revealed through analysis. Worse still, there may be flaws that never become apparent because nobody takes the trouble to look at the original data.

A recent paper from the group at OpenEye has a pretty useful analysis of flaws in crystal structures. They carry on a tradition most prominently exemplified by Gerard Kleywegt at Uppsala. The authors describe common metrics used for picking crystal structures from the PDB and demonstrate their limitations. They propose new metrics to remedy the problems with these parameters. And they use these metrics to analyze about 700 crystal structures used in structure-based drug design and software validation and find out that only about 120 structures or so pass the rigorous criteria used for distinguishing good structures from bad ones.

The most important general message in the article is about the difference between accuracy and completeness of data, and the caveat that any structure on a computer screen is a model and not reality. Even very accurate looking data may be incomplete and this flaw is often neglected by modelers and medicinal chemists when picking crystal structures. For instance, the resolution of a protein structure is often used as a criterion for selecting one among many structures of the same protein from the PDB. Yet the resolution only tells you how far apart atoms can be distinguished from each other. It does not tell you if the data is complete to begin with. So for instance, a crystallographer can acquire only 80% of the theoretically maximum possible data and present it with a resolution of 1.5 A, in which case the structure is clearly incomplete and possibly flawed for use in structure-based drug design. Another important metric is the R-free factor which is obtained by omitting certain parts of the data and refitting the rest to the model. A difference between R-free and the R-factor (a factor denoting the original difference between the full set of data and the model) of more than 0.45 for a structure with resolution 3.5 A or more is a red flag.

The OpenEye authors instead talk about a variety of measures that provide much better information about the fidelity of the data than resolution. The true measure of the data is of course the actual electron density. Any structure that is seen on the computer screen results from the fitting of a model to this electron density. While proteins are often fit fairly well to the density, the placement of ligands is often more ambiguous, partly because protein crystallographers are not always interested in the small molecules. The paper documents several examples of electron density that was either not fit or incorrectly fit by ligand atoms. In some cases sparse or even non-existent density was fit by guesswork, as in the illustration above. All these badly fit atoms show up in the final structure but only an expert would know this. The only way to overcome these problems is to take a look at the original electron density, which is unfortunately a task that most medicinal chemists and modelers are ill equipped for.

The worst problem with these crystal structures is that because they look so accurate and nice on a computer screen, they may fall into Donald Rumsfeld's category of "unknown unknowns". But even with "known unknowns" the picture is a disturbing one. When the authors apply all their filters and metrics to a set of 728 protein-ligand structures frequently used in benchmarking docking programs and in actual structure-based drug design projects, they find that only 17% or so (121) of the structures make it through. They collect these structures together and call the resulting database Iridium which can be downloaded from their website. But the failure of the 580 or so common structures used by leading software vendors and pharmaceutical companies to pass important filters leaves you wondering how many resources we may have wasted by using them and how much uncertainty is out there. 

Something to think about, especially when considering the 50,000 or so protein structures in the PDB. What you see may not be what you get.

Gene duplication and molecular promiscuity

Head over to the Scientific American blog for a post that discusses a recent article suggesting a link between gene duplication and the promiscuity of enzymes involved in secondary metabolism in plants. Since gene duplication frees up one copy of the gene to "experiment", it can potentially accumulate mutations that confer the ability to bind and process more than one substrate. We should partly thank gene duplication for giving us many secondary metabolites which are used as drugs (both recreational and non-recreational), flavors and food products.

The Higgs boson and the future of science

My latest post on the Scientific American blog network ties together several threads about reductionism, emergence and the nature of scientific problems which I have explored on this blog.

Philip Anderson: Anderson first described the so-called Higgs mechanism and also fired the first modern salvo against strong reductionism (Image: Celeblist)

The discovery of the Higgs boson (or the "Higgs-like particle" if you prefer) is without a doubt one of the signal scientific achievements of our time. It illustrates what sheer thought - aided by data of course - can reveal about the workings of the universe and it continues a trend that lists Descartes, Hume, Galileo and Newton among its illustrious forebears. From sliding objects down an incline to smashing atoms at almost the speed of light in a 27 kilometer tunnel, we have come a long way. Dissecting our origins and the universe around us scarcely gets any better than this.

Yet even as the exciting discovery was being announced, I could not help but think about what the Higgs does not do for us. It does not speed up the time needed to discover a new cancer drug. It does not help us understand consciousness. It does not tell us how life began or whether it exists elsewhere in the universe. It does not explain romantic love, how to design the best solar cell, why people have certain political preferences and how exactly to predict the effects of climate change. In fact we can safely predict that the discovery of the Higgs boson, as consciousness-elevating as it is, does not impact the daily work of 99% of all pure and applied scientists in the world.

I do not say all this to downplay the discovery of the particle which is an unparalleled triumph of human thought, hard work and experimental ingenuity. I also do not say this to make the obvious point that a discovery in one field of science does not automatically solve problems in other fields. Rather, I say this to probe the deeper reality beyond that point, to highlight the multifaceted nature of science and the sheer diversity of problems and phenomena that it presents to us at every level of inquiry. And I say this with a suspicion that the Higgs boson may be the most fitting tribute to the limitations of what has been the most potent philosophical instrument of scientific discovery - reductionism.

In one sense the discovery of this fundamental component of matter can be seen as the culmination of reductionist thinking, accounting as it does for the very existence of mass. Reductionism is the great legacy of the twentieth century, a philosophy whose seeds were sown when Greek philosophers started mulling the nature of matter. The method is in fact quite intuitive; ever since they stepped down from the trees, human beings have tried to solve problems by breaking them down into simpler parts. In the twentieth century the fruits of reductionism have been nothing short of awe-inspiring. Reductionism is what told us that molecules are made of atoms, that the universe is expanding, that DNA is a double helix and that you can build lasers and computers. The reductionist ethic has given us quantum mechanics, relativity, quantum chemistry and molecular biology. Over the centuries it has been used by its countless practitioners as a fine scalpel which has laid bare the secrets of nature. In fact many of the questions answered using the reductionist method were construed as being amenable to this method even before their answers were provided; for instance, how do atoms combine to form molecules? What is the basic nature of the gene? What are atoms themselves made up of?

Yet as we enter the second decade of the twenty-first century, it is clear that reductionism as a principal weapon in our arsenal of discovery tools is no longer sufficient. Consider some of the most important questions facing modern science, almost all of which deal with complex, multifactorial systems. How did life on earth begin? How does biological matter evolve consciousness? What are dark matter and dark energy? How do societies cooperate to solve their most pressing problems? What are the properties of the global climate system? It is interesting to note at least one common feature among many of these problems; they result from the buildup rather than the breakdown of their operational entities. Their signature is collective emergence, the creation of attributes which are greater than the sum of their constituent parts. Whatever consciousness is for instance, it is definitely a result of neurons acting together in ways that are not obvious from their individual structures. Similarly, the origin of life can be traced back to molecular entities undergoing self-assembly and then replication and metabolism, a process that supersedes the chemical behavior of the isolated components. The puzzle of dark matter and dark energy also have as their salient feature the behavior of matter at large length and time scales. Studying cooperation in societies essentially involves studying group dynamics and evolutionary conflict. The key processes that operate in the existence of all these problems seem to almost intuitively involve the opposite of reduction; they all result from the agglomeration of molecules, matter, cells, bodies and human beings across a hierarchy of unique levels. In addition, and this is key, they involve the manifestation of unique principles emerging at every level that cannot be merely reduced to those at the underlying level.

The traditional picture of science asserts that X can be reduced to Y. Reality is more complicated (Image: P. W. Anderson, Science, 1972)

A classic example of emergence: The exact shape of a termite mound is not reducible to the actions of individual termites (Image: Wikipedia Commons)

This kind of emergence has long since been seen as key to the continued unraveling of scientific mysteries. While emergence had been implicitly appreciated by scientists for a long time, its modern salvo was undoubtedly a 1972 paper in Science by the Nobel Prize winning physicist Philip Anderson titled "More is Different", a title that has turned into a kind of clarion call for emergence enthusiasts. In his paper Anderson (who incidentally first came up with the so-called Higgs mechanism) argued that emergence was nothing exotic; for instance, a lump of salt has properties very different from those of its highly reactive components sodium and chlorine. A lump of gold evidences properties like color that don't exist at the level of individual atoms. Anderson also appealed to the process of broken symmetry, invoked in all kinds of fundamental events - including the existence of the Higgs boson - as being instrumental for emergence. Since then, emergent phenomena have been invoked in hundreds of diverse cases, ranging from the construction of termite hills to the flight of birds. The development of chaos theory beginning in the 60s further illustrated how very simple systems could give rise to very complicated and counterintuitive patterns and behavior that are not obvious from the identities of the individual components.

Many scientists and philosophers have contributed to considered critiques of reductionism and an appreciation of emergence since Anderson wrote his paper. These thinkers make the point that not only does reductionism fail in practice (because of the sheer complexity of the systems it purports to explain), but it also fails in principle on a deeper level. In his book "The Fabric of Reality" for instance, the Oxford physicist David Deutsch has made the compelling point that reductionism can never explain purpose; to drive home this point he asks us if it can account for the existence of a particular atom of copper on the tip of the nose of a statue of Winston Churchill in London. Deutsch's answer is a clear no, since the fate of that atom was based on contingent, emergent phenomena, including war, leadership and adulation. Nothing about the structure of copper atoms allows us to directly predict that a particular atom will someday end up on the tip of that nose. Chance plays an outsized role in these developments and reductionism offers us little solace to understand such historical accidents.

Complexity theorist Stuart Kauffman who has written about the role of contingency as a powerful argument against strong reductionism (Image: Wikipedia Commons)

An even more forceful proponent of this contingency-based critique of reductionism is the complexity theorist Stuart Kauffman (supposedly an inspiration for the Jeff Goldblum character in "Jurassic Park") who has laid out his thoughts in two books. Just like Anderson, Kauffman does not deny the great value of reductionism in illuminating our world, but he also points out the factors that greatly limit its application. One of his favorite examples is the role of contingency in evolution and the object of his attention is the mammalian heart. Kauffman makes the case that no amount of reductionist analysis could explain tell you that the main function of the heart is to pump blood. Even in the unlikely case that you could predict the structure of hearts and the bodies that house them starting from the Higgs boson, such a deductive process could never tell you that of all the possible functions of the heart, the most important one is to pump blood. This is because the blood-pumping action of the heart is as much a result of historical contingency and the countless chance events that led to the evolution of the biosphere as it is of its bottom-up construction from atoms, molecules, cells and tissues. As another example, consider the alpha amino acids which make up all proteins on earth. These amino acids come in two potential varieties, left-handed and right-handed. With very few exceptions, all the functional amino acids that we know of are left handed, but there's no reason to think that right handed amino acids wouldn't have served life equally well. The question then is, why left-handed amino acids? Again, reductionism is silent on this question mainly because the original use of left-handed amino acids during the origin of life was to the best of our knowledge a matter of contingency. Now some form of reductionism may still explain the subsequent propagation of left-handed amino acids and their dominance in biological processes by resorting to molecular level arguments regarding chemical bonding and energetics, but this description will still leave the origins issue unresolved. Even something as fundamental as the structure and function of DNA - which by all accounts was a triumph of reductionism - is much better explained by principles of chemistry like electrostatic attraction and hydrogen bonding.

Life as we know it is based on left-handed amino acids. But there is no reason why right-handed amino acids could not sustain life (Image: Islamickorner)

Reductionism then falls woefully short when trying to explain two things; origins and purpose. And one can see that if it has problems even when dealing with left-handed amino acids and human hearts, it would be in much more dire straits when attempting to account for say kin selection or geopolitical conflict. The fact is that each of these phenomena are better explained by fundamental principles operating at their own levels. Chemistry has its covalent bonds and steric effects, geology has its weathering and tectonic shifts, neurology has its memory potentiation and plasticity and sociology has its conflict theory. And as far as we can tell, these sciences will continue to progress without needing the help of Higgs bosons and neutrinos. This also seems to make it unlikely that the discovery of a single elegant equation linking the four fundamental forces (the purported "theory of everything"), while undoubtedly representing one of the greatest intellectual achievements of humanity, will give sociologists and economists little pause for thought, even as they continue to study the stock market and democracies using their own special toolkit of bedrock principles.

This rather gloomy view of reductionism may sound like science is at a dead end or at the very least has started collapsing under the weight of its own success. But such a view would be as misplaced as announcements about the "end of science" which have surfaced every couple of years for the last two hundred years. Every time the end of science has been announced, science itself proved that claims of its demise were vastly exaggerated. Firstly, reductionism will always be alive and kicking since the general approach of studying anything by breaking it down into its constituents will continue to be enormously fruitful. But more importantly, it's not so much the end of reductionism as the beginning of a more general paradigm that combines reductionism with new ways of thinking. The limitations of reductionism should be seen as a cause not for despair but for celebration since it means that we are now entering new, uncharted territory. There are still an untold number of deep mysteries that science has to solve, ranging from dark energy, consciousness and the origin of life to more supposedly pedestrian concerns like superconductivity, cancer drug discovery and the behavior of glasses. Many of these questions require interdisciplinary approaches which result in the crafting of fundamental principles that are unique to the problem statement. Such a meld will inherently involve reductionism only as one component.

Now there are some who may not consider these problems as "fundamental" enough but that is because they would be peering through the lens of traditional twentieth century science. One of the sad casualties of the reductionist undertaking is a small group of people who think that cosmology and particle physics constitute the only things truly worth doing and the epitome of fundamental science; the rest is all detail that can be filled in by second-rate minds. This is in spite of the inconvenient fact that perhaps 80% of physicists are not concerned at all with fundamental questions. But you would be deluding yourself if you are thinking that turbulence in fluids is a second-rate problem (still unsolved) for second-rate minds, especially if you remember that Heisenberg thought that God would will be able to provide an explanation for quantum mechanics but not for turbulence. The fact is that "pedestrian" concerns like superconductivity have engaged some of the best minds of the last fifty years without fully succumbing to them, and at their own levels they are as hard as the discovery of the Higgs boson or the accelerating universe. Exploring these worthy conundrums is every bit as exciting, deep and satisfying as any other endeavor in science. Those who are wondering what's next should not worry; a sparkling journey lies ahead.

To guide us on this journey all we have to remember are the words of one of the twentieth century's great reductionists and one of Peter Higgs's heroes. Paul Dirac closed his famous text on quantum theory with stirrings that will hopefully be as great a portent for the emergent twenty-first century as they were for the reductionist twentieth: "Some new principles are here needed".

References:
1. P. W. Anderson, More is Different, Science, 1972, 177, 393
2. David Deutsch, "The Fabric of Reality", 2004
3. Stuart Kauffman, "Reinventing the Sacred", 2009; "At Home in the Universe", 1996
Other reading:
1. Terrence Deacon, "Incomplete Nature", 2011
2. John Horgan, "The End of Science", 1997
3. Robert Laughlin, "A Different Universe", 2006

Change is here

As they say, the essence of chemistry is change, so it seems only fitting that I will be moving part of my blogging to a new blog (with the same name) on the Scientific American blogging network. My first post talks about what gets me out of bed every morning (it's not breakfast). Many thanks to Bora Zivkovic for this great opportunity. At SciAm I will be joining a first-rate cast of writers who between them seem to have every field of human inquiry covered.

Am I leaving FoS then? Far from it. FoS has introduced me to some great bloggers and has also provided me with a fine platform. The way I intend to do this is to save the more technical and drug discovery related posts for this site while holding forth on other topics on SciAm. That should ensure that both sites have something different to offer. I will of course be linking to posts on the other site; you could comment on those posts either there or here.

As always, what makes a blog successful are the readers and commentators. Through Twitter, Facebook and mentions on other blogs, many of my posts have gotten more attention than they deserved and for that I am thankful; you should believe me when I say that I have learnt much more from you than you could ever learn from my posts. Hopefully you will continue to hang around both this site and the other one, and I hope to continue benefiting from the interactions.

Zn(III)? Not so fast

Chemists love rogues, oddballs which seem to defy the rules and bond, react, and exist on their own terms. The rogues are valuable because they push the boundaries, teach us about new principles of structure and reactivity and challenge coveted preconceptions. One of the most striking rogues in the history of chemistry was the compound xenon hexafluoroplatinate which shattered belief in the non-reactivity of noble gases. Another immensely productive rogue was the first stable carbocation, a species that was considered too unstable to isolate until George Olah surmounted the energy barrier. Some rogues are literal rogues in the sense that they need to be incarcerated in order to prevent their unruly bonds from going haywire; witness the classic taming of cyclobutadiene by Donald Cram. There's no doubt about it; rogues' galleries are shining gems in the chemical establishment.

A couple of months ago it seemed that another minor rogue had made its appearance. Students around the world know that the most stable oxidation state of zinc is +2. The rationale is simple; unlike transition metals like iron and nickel, zinc contains a fully filled d orbital with an electronic configuration of d¹⁰s². It is therefore quite happy to lose its outermost 2s electrons and remain stable. Thus it came as a surprise when a paper detailing theoretical calculations on zinc compounds predicted the existence of a complex with Zn as Zn(III). That fact would have raised the eyebrows of a million freshmen memorizing transition metals trends for their final exams. The existence of the unusual zinc was based on quantum chemical calculations done by Puru Jena's group at VCU and their basic rationale was that a ligand that was oxidizing and electronegative enough would essentially force the metal to donate extra electrons. They seemed to find such a complex in Zn(AuF₆)₃. On the face of it that would make sense, but it's worth keeping in mind that nature loves filled orbitals; for instance, even a ligand as oxidizing and electronegative as CF3 does not force copper to adopt the +2 state (copper's most stable oxidation state being +1, resulting from its d¹⁰s¹ configuration).

Now another paper suggests, with ample evidence it seems, that the suspected Zn(III) rogue might be an upstanding chemical citizen after all. Sebastian Riedel's group in Berlin has examined the purported Zn(III) complex and found that not only does zinc adopt its good old +2 oxidation state in the compound, but that the compound would not be thermodynamically stable. They use a quantum chemical method more sophisticated than the previous one and include more relativistic effects. Where does relativity enter the picture, you ask. It turns out that for heavy metals, the speed (in a crude sense) of the d s-electrons can be fast enough to warrant relativistic calculations; indeed, relativistic quantum chemistry has shed light on many commonplace and yet unusual phenomena, like the color of gold and the liquid state of mercury at room temperature. Relativity is not the exclusive domain of physicists.

I am not enough of an expert in quantum chemistry, but it clearly seems from the latest paper that the more accurate calculations which account for finer details of electron correlation and relativistic effects indicate something striking; the purported complex with Zn in the +3 oxidation state would undergo an exothermic reaction. Put simply, it would not be stable and would decompose to a compound with good old Zn(II). For me, a more troubling fact was the structure of the compound published in the original paper. The structure was supposed to be a low-energy minimum but it has two fluorines practically colliding with each other in an unholy, 197 pm, sub-Van der Waals radius squeeze (illustrated above). The present calculations find another low-energy structure existing only 10 kJ/mol above the previous structure that is consistent with a Zn(II) state. Other calculations indicate that the fluorines in the previously proposed structure are best described as radical anions bridging two AuF₅ units.

There's other theoretical evidence in there too, along with citations of experimental facts that point to the recalcitrance of Zn to adopt a +3 state. The study seems to be as solid a piece of evidence against Zn(III) as can be obtained using current levels of theory. But the real reason I want to point out this paper is because it illustrates one of the key roles of theory and computation in chemistry; as an invitation to experiment. Just because the complex is thermodynamically unstable does not mean it cannot be isolated under any conditions. One of the great lessons of chemical science in the last forty years has been the fact that given the right conditions almost anything can be synthesized, stored and isolated (Cram's cyclobutadiene again being a case in point) and that the words stable and unstable are highly relative constructions created by an impoverished chemical lexicon.

This tussle between two predictions illustrates the function of theory in chemistry as the ultimate teaser; if I were an experimentalist I would be rubbing my hands in glee, sending my armies of postdocs and graduate students into the lab to try to synthesize Zn(AuF₆)₃. As Excimer commented on another site, "Enough theory! Someone make the damn thing already".
Image source: ACS publications.

"Arsenic bacteria": Coffin, meet nails

For those dogged souls still following the whole debacle of arsenic-eating bacteria, it seems that Science has published what should be close to the death knell for "arsenic life". I already mentioned the report by Rosy Redfield and there's another one by Tobias Erb's group at ETH. The title of the paper is "GFAJ-1 is an arsenate-resistant, phosphate-dependent organism".

It's worth reflecting on that title again; "arsenate-resistant, phosphate-dependent". Yes, that description applies to GFAJ-1. It also applies to me, Shamu the killer whale, E. coli 0157 and Francis Bacon. In fact it applies to all the normal life forms that we know. So basically the title says that GFAJ-1 is not much different in this respect from any other bacterium that you may happen to find in a thimbleful of mud scooped up from your backyard.

The paper goes on to analyze the behavior of the bacterium in the presence and absence of phosphorus and arsenate. The bacterium seems to survive in tiny concentrations of phosphate, a concentration that was interestingly deemed as an "impurity" in the original Wolfe-Simon studies. It also does not survive on arsenate but starts dividing as soon as trace amounts of phosphorus are added. The authors' conclusion is clear: "We conclude that cultures in the previous study might have grown on trace amounts of phosphate rather than arsenate". This is what several experts had suspected since the beginning. Their suspicion was based on life's extraordinarily resilience and its ability to zealously guard and use every single atom of precious growth nutrients.

The authors also analyze the composition of the biomolecules (nucleotides, sugars etc.) in GFAJ-1 in the presence and absence of arsenate. They find only phosphate incorporated in the organism's essential machinery. While this does not necessarily argue against the use of arsenate, it demonstrates that when given a choice GFAJ-1 clearly prefers phosphate.

That observation is however not as striking as the next one where they find some metabolites containing arsenate, specifically sugars with arsenate appended to them. The question then is, are these metabolites formed biogenically or abiotically? To try to distinguish between these possibilities, the authors ran mock experiments where they treated glucose medium with arsenates. The purported metabolites showed up in the products and their formation is also supported by simple thermodynamic arguments which favor the attachment of arsenates to sugars. Thus it seems that simple chemistry rather than complex biology is sufficient for explaining the small amounts of arsenated metabolites. The scientists further resort to careful experiments to rule out the existence of other arsenated biomolecules.

The sum total of these experiments says that GFAJ-1 can grow in the presence of phosphate, that it cannot grown in arsenate, and that it can grow in high concentrations of arsenate only when supplemented with limiting concentrations of phosphate. Taken together with the other paper by Rosy Redfield, this is as good a case against arsenic-based life that we can make right now.

The papers are good examples of the conservative yet decisive style that scientists are accustomed to pitching their results in. Unfortunately the original authors have not reacted as conservatively. If anything their responses are transparently shallow and unconvincing. When asked about the results, Felisa-Wolfe Simon said that:

"There is nothing in the data of these new papers that contradicts our published data."

That reply almost convinces me that denial is the most sincere form of self-deception.

A current collaborator of Wolfe-Simon had even more remarkable things to say:

“There are many reasons not to find things — I don’t find my keys some mornings,” he said. “That doesn’t mean they don’t exist. The absence of a finding is not definitive.”

To which I might add that there is a possibility that disgruntled unicorns with chemistry PhDs looking for jobs may well exist, since we haven't found any yet.


Update: Paul@Chembark nicely weighs in.

The Uncertainty Principle for climate (and chemical) models

A recent issue of Nature had an interesting article on what seems to be a wholly paradoxical feature of models used in climate science; as the models are becoming increasingly realistic, they are also becoming less accurate and predictive because of growing uncertainties. I can only imagine this to be an excruciatingly painful fact for climate modelers who seem to be facing the equivalent of the Heisenberg uncertainty principle for their field. It's an especially worrisome time to deal with such issues since the modelers need to include their predictions in the next IPCC report on climate change which is due to be published next year.

A closer look at the models reveals that this behavior is not as paradoxical as it sounds, although it's still not clear how you would get around it. The article especially struck a chord with me I see similar problems bedeviling models used in chemical and biological research. In case of climate change, the fact is that earlier models were crude and did not account for many fine-grained factors that are now being included (such as the rate at which ice falls through clouds). In principle and even in practice there's a bewildering number of such factors (partly exemplified by the picture on top). Fortuitously, the crudeness of the models also prevented the uncertainties associated with these factors from being included in the modeling. The uncertainty remained hidden. Now that more real-world factors are being included, the uncertainties endemic in these factors reveal themselves and get tacked on to the models. You thus face an ironic tradeoff; as your models strive to mirror the real world better, they also become more uncertain. It's like swimming in quicksand; the harder you try to get out of it, the deeper you get sucked in.

This dilemma is not unheard of in the world of computational chemistry and biology. A lot of the models we currently use for predicting protein-drug interactions for instance are remarkably simple and yet accurate enough to be useful. Several reasons account for this unexpected accuracy; among them cancellation of errors (the Fermi principle), similarities of training sets to test sets and sometimes just plain luck. Error analysis is unfortunately not a priority in most of these studies, since the whole point is to publish correct results. Unless this culture changes our road to accurate prediction will be painfully slow.

But here's an example of how "more can be worse". For the last few weeks I have been using a very simple model to try to predict the diffusion of druglike molecules through cell membranes. This is an important problem in drug development since even your most stellar test-tube candidate will be worthless until it makes its way into cells. Cell membranes are hydrophobic while the water surrounding them is hydrophilic. The ease with which a potential drug transfers from the surrounding water into the membrane depends among other factors on its solvation energy, on how readily the drug can shed water molecules; the smaller the solvation energy, the easier it is for drugs to get across. This simple model which calculates the solvation energy seems to do unusually well in predicting the diffusion of drugs across real cell membranes, a process that's much more complex than just solvation-desolvation. 

One of the fundamental assumptions in the model is that the molecule exists in just one conformation in both water and the membrane. This assumption is fundamentally false since in reality, molecules are highly flexible creatures that interconvert between several conformations both in water and inside the membrane. To overcome this assumption, a recent paper explicitly calculated the conformations of the molecule in water and included this factor in the diffusion predictions. This was certainly more realistic. To their surprise, the authors found that making the calculation more realistic made the predictions worse. While the exact mix of factors responsible for this failure can be complicated to tease apart, what's likely happening is that the more realistic factors also bring more noise and uncertainty with them. This uncertainty piles up, errors which were likely canceling before no longer cancel, and the whole prediction becomes fuzzier and less useful.

I believe that this is what is partly happening in climate models. Including more real-life factors in the models does not mean that all those factors are well-understood. You are inevitably introducing some known unknowns. Ill-understood factors will introduce more uncertainty. Well-understood factors will introduce less uncertainty. Ultimately the accuracy of the models will depend on the interplay between these two kinds of factors, and currently it seems that the rate of inclusion of new factors is higher than the rate at which those factors can be accurately calculated.

The article goes on to note that in spite of this growing uncertainty the basic predictions of climate models are broadly consistent. However it also acknowledges the difficulty in explaining the growing uncertainty to a public which has become more skeptical of climate change since 2007 (when the last IPCC report was published). As a chemical modeler I can sympathize with the climate modelers. 

But the lesson to take away from this dilemma is that crude models sometimes work better than more realistic ones. Perhaps the climate modelers should remember George Box's quote that "all models are wrong, but some are useful". It is a worthy endeavor to try to make models more realistic, but it is even more important to make them useful.
Image source