Distinguishing statistical significance from clinical significance

In the past post I was talking about the difference between statistical and clinical significance and how many reported studies have apparently mixed up the two. Now here's a nice case where people seem to be aware of the difference. The article is also interesting in its own right. It deals with AstraZeneca's cholesterol lowering statin drug Crestor being approved by the FDA as a preventive measure for heart attacks and stroke. If this works out Crestor could be a real cash cow for the company since its patent does not expire till 2016 (unlike Lipitor which is going to hit Pfizer hard next year).

The problem seems to be that prescription of the drug would be based on high levels not of cholesterol but of C-Reactive Protein which is an inflammatory marker of high cholesterol. The CRP-inflammation-cholesterol connection is widely believed to hold but there is no consensus in the medical community about the exact causative link (many factors can lead to high CRP levels).

The more important recent issue seems to be a study published in The Lancet which indicates a 9% increased risk of Type 2 diabetes associated with Crestor. As usual the question is whether these risks outweigh the benefits. The Crestor trial was typical of heart disease trials and involved a large population of 18,000 subjects. As the article notes, statistical significance in the reduction of heart attacks in this population does not necessarily translate to clinical significance:

Critics said the claim of cutting heart disease risk in half — repeated in news reports nationwide — may have misled some doctors and consumers because the patients were so healthy that they had little risk to begin with.

The rate of heart attacks, for example, was 0.37 percent, or 68 patients out of 8,901 who took a sugar pill. Among the Crestor patients it was 0.17 percent, or 31 patients. That 55 percent relative difference between the two groups translates to only 0.2 percentage points in absolute terms — or 2 people out of 1,000.

Stated another way, 500 people would need to be treated with Crestor for a year to avoid one usually survivable heart attack. Stroke numbers were similar.

“That’s statistically significant but not clinically significant,” said Dr. Steven W. Seiden, a cardiologist in Rockville Centre, N.Y., who is one of many practicing cardiologists closely following the issue. At $3.50 a pill, the cost of prescribing Crestor to 500 people for a year would be $638,000 to prevent one heart attack.

Is it worth it? AstraZeneca and the F.D.A. have concluded it is.

Others disagree.

“The benefit is vanishingly small,” Dr. Seiden said. “It just turns a lot of healthy people into patients and commits them to a lifetime of medication.”

To some this may seem indeed like a drug of the affluent. Only time will tell.

How useful is cheminformatics in drug discovery?

Just like bioinformatics, cheminformatics has come into its own an independent framework and tool for drug design. As a measure of the field's independence and importance, consider that at least five journals primarily dedicated to it have emerged in the last couple of years, and 15000 articles on it have been published since 2003.

But how useful is it in drug discovery? The answer, just like for other approaches and technologies, is that it depends. For calculating and analyzing some properties it is more useful than for others. A group at Abbott summarizes the current knowledge of cheminformatics approaches as applied to various parameters.

To do this the group utilizes a useful classification scheme made (in)famous by ex-Secretary of Defense Donald Rumsfeld (Rumsfeld et al., J. Improb. Res. 2002). This is the classification of knowledge and facts into 'known knowns', 'unknown knowns', 'known unknowns' and 'unknown unknowns'.

The 'known known' category of properties calculated by cheminformatics consists of those that we think we have a very accurate handle on and that are easy to calculate. These include molecular weight, substructure (SS) searches, and ligand efficiency. Molecular weight can be easily calculated, and a variety of studies have indicated that high MW generally impacts drug discovery negatively. Thus the general thrust is on keeping your compounds small. Ligand efficiency is calculated as the free energy of binding per heavy atom. Medicinal chemists are more familiar with IC50 than free energy. Since IC50s are usually easy to measure and the number of heavy atoms are of course known, ligand efficiency would be a 'known known'. However the caveat is that IC50 is not the same as in vivo activity, so biochemical potency in terms of ligand efficiency might be a very different kettle of fish. Lastly, substructure searches can be carried out easily by many computer programs. Such searches are usually used when a compound having a substructure similar to one that is known is sought. The problem with SS searches arises when the decision on which SS to search becomes subjective. SS is also valuably used when certain SSs are to be avoided. In general SS searching belongs with the 'known known' category because of its ease, but subjective interpretations can render this more fuzzy.

In the 'unknown knowns' category lie an interesting set of properties; those which we think we know how to calculate but which sometimes look deceptively simple and are often subject to overconfidence in calculation. The first property in this category is one of the most important ones in drug design; logP, which is regarded to be a measure of the lipophilicity of the compound, a key parameter dictating absorption, bioavailability, and partitioning of drugs between membranes and body fluids. Several programs can calculate logP. However, as the article notes, a recent study of no less than 30 such programs located a mean error in logP calculation of 1 log unit, which means that some programs would do much worse. Thus calculation of logP, just like other computational techniques, crucially depends on the method used to calculate it. The caveat is that absolute cutoffs for logP values in library design of compounds searches might mislead, but many programs seem to do a fairly good job in producing trends. Solubility is another parameter that is notoriously difficult to calculate, especially since its calculation hinges on calculation of pKa values. pKa values in turn again are very method-dependent, and trouble arises especially for charged compounds, which include most drugs. Medicinal chemists are understandably suspicious of theoretical solubility prediction, but as for logP, such calculations may at least be used for quick estimation of qualitative trends. Another parameter in this category is plasma protein binding. Although we know a fair amount about the effect of this parameter on drug ADME, good luck trying to calculate this. Lastly, in vivo ADME is a minefield of complications. I personally would not have placed this in the 'unknown knowns' category, but at least some of the properties in this category can be calculated on the basis of specialized fragment-based models.

What about 'known unknowns'? This includes polar surface area (PSA)'. PSA is a known unknown because we know that is not exactly a real, measurable quantity. However it is still a useful parameter since PSA has been shown to relate to membrane permeability and hence is especially useful for guessing blood-brain barrier penetration for CNS drugs. A set of rules similar to the Lipinski rules says that compounds with a PSA of less than 120 A^2 are more likely to penetrate membranes. As for other properties though, calculation of PSA depends on method and usually involves calculating the 3D structure of a molecule and then calculating the PSA by assigning some kind of a 'surface area' associated with polar atoms. As the article notes, one can get wildly different results depending on which atoms are assigned as 'polar'. Plus compounds containing sulfur or phosphorus can result in big discrepancies. Clearly PSA is a useful parameter, but we have to go some way in calculating it reliably. Finally, similarity searching is all the rage these days and promises a windfall of potential discoveries. In its simplest incarnation, similarity searching aims to discover compounds similar in some structural metric to a given compound, with the assumption that similarity in structure would correspond to similarity in function. But similarity, just like beauty, is notoriously in the eye of the beholder. As the authors quip from a quaint piece of literature-related controversy:

Deciding whether two molecules are similar is much like trying to decide whether something is beautiful. There are no concrete definitions, and most chemists take an “I know it when I see it” attitude (attributed to United States Justice Potter Stewart, concurring opinion in Jacobellis v. Ohio 378 U.S. 184 (1964), regarding possible obscenity in The Lovers)."

As again illustrated, different similarity searching methods give very different hits. One of the most successful metrics for measuring similarities has turned out to be the 'Tanimoto coefficient', but other metrics also abound. Thus it is quite remarkable that similarity is already being used widely in every endeavor from virtual screening to finding new protein targets for old drugs based on drug similarity. One of the most valuable applications of similarity is in finding bioisosteres (chemically similar fragments with improved properties), something which medicinal chemists try to do all the time. A recent review of similarity-based methods in JMC summarizes the utility of such techniques. Nonetheless, similarity based methods are still among the 'known unknowns' because we don't have an objective handle on what constitutes similarity, and we may never have such a handle even if such methods are widely and successfully adopted.

Finally we come to the dreaded 'unknown unknowns'. As the authors ask, can we even list properties which we have no idea about? We can take a shot. This category includes flight of fancy which may never be achieved but which are worth striving for. One such holy grail is the large-scale, high-throughput computation of binding free energies. The binding free energy for a protein ligand complex includes contributions from an enormous number of complicated factors, but the mere attempt to calculate all these factors has valuably increased our understanding of biological systems. Thus we should continue in this endeavor. Another fancy endeavor which is the talk of the town these days is systems biology, where construction of biological networks and applications of graph theory are supposed to shed valuable light on molecular interactions. Such approaches may well be successful, but they always run the risk of becoming too abstract and divorced from reality to be truly understandable. QSAR models can suffer from the same shortcomings.

In the end, only a robust collaboration between informaticians, computer scientists, medicinal chemists and biologists can make sense of the jungle of data uncovered by cheminformatics approaches. It is key for one group of scientists to keep reality checks on others. In the end it's all about reality checks. We all know what happened when these were not applied to the original classification scheme.

Muchmore, S., Edmunds, J., Stewart, K., & Hajduk, P. (2010). Cheminformatic Tools for Medicinal Chemists Journal of Medicinal Chemistry DOI: 10.1021/jm100164z

How pretty is your (im)perfect p?

In my first year of grad school we were once sitting in a seminar in which someone had put up a graph with a p-value in it. My advisor asked all the students what a p-value was. When no one answered, he severely admonished us and said that any kind of scientist engaged in any kind of endeavor should know what a p-value is. It is after all paramount in most statistical studies, and especially in judging the consequences of clinical trials.

So do we really know what it is? Apparently not, not even scientists who routinely use statistics in their work. In spite of this, the use and overuse of the p-value and the constant invoking of "statistical significance" are universal phenomena. When a very low p-value is cited for a study, it is taken for granted that the results of the study must be true. My dad, who is a statistician turned economist, often says that one of the big laments about modern science is that too many scientists are never formally trained in statistics. This may be true, especially with regard to the p-value. An article in ScienceNews drives home this point and I think should be read by every scientist or engineer who remotely uses statistics in his or her work. The article builds on concerns raised recently by several statisticians about the misuse and over-interpretation of these concepts in clinical trials. In fact there seems to be an entire book which is devoted to criticizing the use of statistics; "The Cult of Statistical Significance".

So what's the p-value? A reasonable accurate definition is that it is the probability of getting a result at least extreme as the one you get under the assumption that the null hypothesis is true". The null hypothesis is the hypothesis that your sample is a completely unbiased sample. The p-value is basically related to the probability that you will get a result by chance alone in a completely unbiased sample; thus, the lower it is, the more "confident" you can be that chance did not produce the result. It is usually calculated as a cutoff value. Ronald Fisher, the great statistician who pioneered its use, rather arbitrarily decided this value to be 0.05. Thus generally if one gets a p-value of 0.05, he or she is "confident" that the result which is being observed is one that is "statistically significant".

But this is where misuses and misunderstandings about the p-value just begin to manifest themselves. Firstly, a p-value of 0.05 does not immediately point to a definitive conclusion:

But in fact, there’s no logical basis for using a P value from a single study to draw any conclusion. If the chance of a fluke is less than 5 percent, two possible conclusions remain: There is a real effect, or the result is an improbable fluke. Fisher’s method offers no way to know which is which. On the other hand, if a study finds no statistically significant effect, that doesn’t prove anything, either. Perhaps the effect doesn’t exist, or maybe the statistical test wasn’t powerful enough to detect a small but real effect.

Secondly, as the article says, the most common and erroneous conclusion drawn from a p-value of 0.05 is that there is 95% confidence about the null hypotheses being false, that is, there is 95% confidence that the result being observed is statistically significant and not by chance alone. But as the article notes, this is a rather blasphemous logical fallacy:

Correctly phrased, experimental data yielding a P value of 0.05 means that there is only a 5 percent chance of obtaining the observed (or more extreme) result if no real effect exists (that is, if the no-difference hypothesis is correct). But many explanations mangle the subtleties in that definition. A recent popular book on issues involving science, for example, states a commonly held misperception about the meaning of statistical significance at the 0.05 level: “This means that it is 95 percent certain that the observed difference between groups, or sets of samples, is real and could not have arisen by chance.”
That interpretation commits an egregious logical error (technical term: “transposed conditional”): confusing the odds of getting a result (if a hypothesis is true) with the odds favoring the hypothesis if you observe that result. A well-fed dog may seldom bark, but observing the rare bark does not imply that the dog is hungry. A dog may bark 5 percent of the time even if it is well-fed all of the time

This underscores the above point, that a low p-value may also mean that the result is indeed a fluke result. An especially striking problem with p-values emerged in studies of the controversial link between antidepressants and suicide. The placebo is the golden standard in clinical trials. But it turns out that not enough attention is always paid to the fact that the frequency of incidents even in two different placebo groups might be different. Thus, when calculating p-values, one must consider not their absolute values but the difference in values with reference to the placebo. Thus, a study investigating possible links between Prozac and Paxil reached a conclusion that was opposite to the real one. Consider this:

“Comparisons of the sort, ‘X is statistically significant but Y is not,’ can be misleading,” statisticians Andrew Gelman of Columbia University and Hal Stern of the University of California, Irvine, noted in an article discussing this issue in 2006 in the American Statistician. “Students and practitioners [should] be made more aware that the difference between ‘significant’ and ‘not significant’ is not itself statistically significant.”

A similar real-life example arises in studies suggesting that children and adolescents taking antidepressants face an increased risk of suicidal thoughts or behavior. Most such studies show no statistically significant increase in such risk, but some show a small (possibly due to chance) excess of suicidal behavior in groups receiving the drug rather than a placebo. One set of such studies, for instance, found that with the antidepressant Paxil, trials recorded more than twice the rate of suicidal incidents for participants given the drug compared with those given the placebo. For another antidepressant, Prozac, trials found fewer suicidal incidents with the drug than with the placebo. So it appeared that Paxil might be more dangerous than Prozac.

But actually, the rate of suicidal incidents was higher with Prozac than with Paxil. The apparent safety advantage of Prozac was due not to the behavior of kids on the drug, but to kids on placebo — in the Paxil trials, fewer kids on placebo reported incidents than those on placebo in the Prozac trials. So the original evidence for showing a possible danger signal from Paxil but not from Prozac was based on data from people in two placebo groups, none of whom received either drug. Consequently it can be misleading to use statistical significance results alone when comparing the benefits (or dangers) of two drugs.

As a general and simple example of how p-values can mislead, consider two drugs X and Y, one of whose effects seem more significant than the other when compared to placebo. In this case Drug X has a p-value of 0.04 while the second one Y has a p-value of 0.06. Thus Drug X has more "significant" effects than Drug Y, right? Well, but what happens when you just compare the two drugs to each other rather than to placebo? As the article says:

If both drugs were tested on the same disease, a conundrum arises. For even though Drug X appeared to work at a statistically significant level and Drug Y did not, the difference between the performance of Drug X and Drug Y might very well NOT be statistically significant. Had they been tested against each other, rather than separately against placebos, there may have been no statistical evidence to suggest that one was better than the other (even if their cure rates had been precisely the same as in the separate tests)

Nor does, as some would think, the hallowed "statistical significance" equate to "importance". Whether the result is really important or not depends on the exact field and study under consideration. Thus, a new drug that is statistically significant may only lead to one or two extra benefits per thousand people, which may not be clinically signicant. It is flippant to publish a statistically significant result as a generally significant result for the field.

The article has valuable criticisms of many other common practices, including the popular practice of meta-analyses (combining and analyzing different analyses), where differences between various trials may be obscured. These issues also point to a perpetual problem in statistics which is frequently ignored; that of ensuring that variations in your sample are uniformly distributed. Usually this is an assumption, but it's an assumption that may be flawed, especially in genetic studies of disease inheritance. This is part of an even larger and most general situation in any kind of statistical modeling; that of determining the distribution of your data. In fact a family friend who is an academic statistician once told me that about 90% of his graduate students' time is spent in determining the form of the data distribution. Computer programs have made the job easier, but the problem is not going to go away. The normal distribution may be one of the most remarkable aspects of her identity that nature reveals to us, but assuming such a distribution is also a slap in our face since it indicates that we don't really know the shape of the distribution.

A meta-analyses study with the best-selling anti-diabetic drug Avandia locks in on the problems:

Meta-analyses have produced many controversial conclusions. Common claims that antidepressants work no better than placebos, for example, are based on meta-analyses that do not conform to the criteria that would confer validity. Similar problems afflicted a 2007 meta-analysis, published in the New England Journal of Medicine, that attributed increased heart attack risk to the diabetes drug Avandia. Raw data from the combined trials showed that only 55 people in 10,000 had heart attacks when using Avandia, compared with 59 people per 10,000 in comparison groups. But after a series of statistical manipulations, Avandia appeared to confer an increased risk.

In principle, a proper statistical analysis can suggest an actual risk even though the raw numbers show a benefit. But in this case the criteria justifying such statistical manipulations were not met. In some of the trials, Avandia was given along with other drugs. Sometimes the non-Avandia group got placebo pills, while in other trials that group received another drug. And there were no common definitions.

Clearly it is extremely messy to compare the effects of a drug across different trials with different populations and parameters. So what is the way out of this tortuous statistical jungle? One simple remedy would be to simply insist on a lower p-value for significance, typically 0.0001. But there's another way. The article says that Bayesian methods which were neglected for a while in such studies are now being preferred to address the shortcomings of such interpretations since they include the need to acquire previous knowledge in drawing new conclusions. Bayesian or "conditional probability" has a long history and it can very valuably provide counter-intuitive but correct answers. The problem with prior information is that it itself might not be available and may need to be guessed, but an educated guess about it is better than not using it at all. The article ends with a simple example that demonstrates this:

For a simplified example, consider the use of drug tests to detect cheaters in sports. Suppose the test for steroid use among baseball players is 95 percent accurate — that is, it correctly identifies actual steroid users 95 percent of the time, and misidentifies non-users as users 5 percent of the time.

Suppose an anonymous player tests positive. What is the probability that he really is using steroids? Since the test really is accurate 95 percent of the time, the naïve answer would be that probability of guilt is 95 percent. But a Bayesian knows that such a conclusion cannot be drawn from the test alone. You would need to know some additional facts not included in this evidence. In this case, you need to know how many baseball players use steroids to begin with — that would be what a Bayesian would call the prior probability.

Now suppose, based on previous testing, that experts have established that about 5 percent of professional baseball players use steroids. Now suppose you test 400 players. How many would test positive?

• Out of the 400 players, 20 are users (5 percent) and 380 are not users.

• Of the 20 users, 19 (95 percent) would be identified correctly as users.

• Of the 380 nonusers, 19 (5 percent) would incorrectly be indicated as users.


So if you tested 400 players, 38 would test positive. Of those, 19 would be guilty users and 19 would be innocent nonusers. So if any single player’s test is positive, the chances that he really is a user are 50 percent, since an equal number of users and nonusers test positive.

In the end though, the problem is certainly related to what my dad was talking about. I know almost no scientists around me, whether chemists, biologists or computer scientists (these mainly being the ones I deal with) who were formally trained in statistics. A lot of statistical concepts that I know were hammered into my brain by my dad who knew better. Sure, most scientists including myself were taught how to derive means and medians by plugging numbers into formulas, but very few were exposed to the conceptual landscape, very few were taught how tricky the concept of statistical significance can be, how easily p-values can mislead. The concepts have been widely used and abused even in premier scientific journals and people who have pressed home this point have done all of us a valuable service. In other cases it might simply be a nuisance, but when it comes to clinical trials, simple misinterpretations of p-values and significance could translate to differences between life and death.

Statistics is not just another way of obtaining useful numbers; like mathematics it's a way of looking at the world and understanding its hidden, counterintuitive aspects that our senses don't reveal. In fact isn't that what science is precisely supposed to be about?

Some books on statistics I have found illuminating:

1. The Cartoon Guide to Statistics- Larry Gonick and Woollcott Smith. This actually does a pretty good job of explaining serious statistical concepts.
2. The Lady Tasting Tea- David Salzburg. A wonderful account of the history and significance of the science.
3. How to Lie with Statistics- Darrell Huff. How statistics can disguise facts, and how you can use this fact to your sly advantage.
4. The Cult of Statistical Significance. I haven't read it yet but it sure looks useful.
5. A Mathematician Reads The Newspaper- John Allen Paulos. A best-selling author's highly readable exposition on using math and statistics to make sense of daily news.

Chips worth their salt

This from the WSJ caught my eye today:

"PepsiCo Develops 'Designer Salt' to Chip Away at Sodium Intake"

Later this month, at a pilot manufacturing plant here, PepsiCo Inc. plans to start churning out batches of a secret new ingredient to make its Lay's potato chips healthier.

The ingredient is a new "designer salt" whose crystals are shaped and sized in a way that reduces the amount of sodium consumers ingest when they munch. PepsiCo hopes the powdery salt, which it is still studying and testing with consumers, will cut sodium levels 25% in its Lay's Classic potato chips. The new salt could help reduce sodium levels even further in seasoned Lay's chips like Sour Cream & Onion, PepsiCo said, and it could be used in other products like Cheetos and Quaker bars...working with scientists at about a dozen academic institutions and companies in Europe and the U.S., PepsiCo studied different shapes of salt crystals to try to find one that would dissolve more efficiently on the tongue. Normally, only about 20% of the salt on a chip actually dissolves on the tongue before the chip is chewed and swallowed, and the remaining 80% is swallowed without contributing to the taste, said Dr. Khan, who oversees PepsiCo's long-term research.

PepsiCo wanted a salt that would replicate the traditional "salt curve," delivering an initial spike of saltiness, then a body of flavor and lingering sensation, said Dr. Yep, who joined the company in June 2009 from Swiss flavor company Givaudan SA.

"We have to think of the whole eating experience—not just the physical product, but what's actually happening when the consumer eats the product," Dr. Yep explained.

The result was a slightly powdery ingredient that tastes like regular salt.

"Secret new ingredient"?! Perhaps not. The first thing that popped into my mind after reading this was "polymorph". People in drug discovery face the polymorph beast all the time. Polymorphs are different crystal packing arrangements of a molecule that can have dramatically different dissolution rates. Often a drug which otherwise has impeccable properties crystallizes in a form that renders it as "brick dust" which is unable to dissolve. Polymorphs also serve as a way to get around patents; you can actually patent a different polymorph of an existing drug. And of course, due to their unpredictable nature (computational prediction of crystal packing is still in a rather primitive stage) polymorphs cause extremely serious problems; the HIV protease inhibitor Ritonavir had to actually be withdrawn from the market because of the appearance of an unexpected polymorph.

To me it sounds like PepsiCo has hit on the right polymorph for common salt that has enhanced dissolution rates. I hope they have done adequate stability studies on it, because polymorphs can actually interconvert into each other based on environmental conditions. But it's a neat idea that can potentially be used for other food ingredients.

The details do matter

Consider a protein-ligand binding model. How easy is it to predict the best and worst binders in terms of affinity? Now, how hard is it to quantitatively rank these binders in terms of free energy of binding?

The former, while not an easy problem, has been solved in various ways multiple times for individual problems. In fact a new docking program is expected to at least achieve the minimal goal of ranking the most active ligands at the top and the least active at the bottom.

However, in spite of impressive advances, the latter problem is still regarded as a holy grail.

Now consider molecular dynamics simulations of proteins. Coarse-grained MD approximates atomistic details by subsuming them into a broader framework; for instance, "united atom" force fields will sometimes treat the hydrogen atoms attached to carbons implicitly without explicitly representing them. Coarse-grained MD has been indispensable for simulating large systems where explicit representation of fine details would be prohibitively time-consuming. But coarse grained MD would not always be able to shed light on cases where the fine details do matter, such as proton transfer in enzyme active sites and the general detailed modeling of enzymatic reactions.

Finally, consider solvation models in molecular simulations, a topic of perpetual development and high interest. Implicit models where the solvent and solute are considered as mean dielectrics and their interaction is modeled as a sum of electrostatic and non-electrostatic interactions are all the rage. They frequently work very well and I have myself used them numerous times. But consider cases where the detailed thermodynamics of individual water molecules in protein active sites need to be modeled. Implicit solvation can be of scant use in such circumstances. The use of implicit solvation often makes general predictions about qualitative differences between protein-ligand interactions possible, but it can mask the detailed reasons for those differences and indeed cannot even account for such differences many times.

Something similar seems to be happening for climate change. It is relatively easy to make general statements about extreme events occurring. It is generally true that putting all that buried CO2 back into the atmosphere as a high entropy substance is probably a bad idea, and that cutting emissions is probably a good idea. My problem is not so much about politicians suggesting such general solutions as it is about them sounding crystal clear about all the scientific details. It's much harder to predict the details about individual effects and 'rank' them in terms of their severity, nor is it easier to rank individual solutions to the problem in terms of relative impact. That is something that is an inherent limitation of the science at this point, and any good scientist worth his salt should acknowledge this. Nonetheless, the science has been declared 'settled' and politicians seem to suggest implementing concrete policies in spite of the coarse-grained nature of the problem. As I mentioned before, the fathers of empirical inquiry Newton, Bacon, Locke, Boyle and Hume would have been rather chagrined with this state of affairs.

It's even more disconcerting to realize that activists propose solar and wind power which could be useful in limited amounts but have by no means proven to be robust, as global high energy density solutions. On the other hand there is nuclear power, a proven existing technology that packs more energy than any other, is clean, decidedly CO2-free and highly efficient and deployable. Yet the same politicians who condemn fossil fuels and talk about climate change constantly fail to tout the one solution that could solve the problem they are trying to address. What can you say when someone ignores a solution to a problem that's staring them in the face?

Freeman Dyson, who has been duly and gratuitously vilified for his skepticism about climate models, said that he lost interest in climate change when the issue turned from scientific to political. One can understand why he said that. The exemplar of tentative scientific understanding was Niels Bohr, and Einstein's quip about him captures the perfect attitude we should all have about complex scientific issues, an attitude that is sadly lost on many climate modelers; Einstein said of Bohr that "He looks like someone who never behaves as if he is in possession of the truth, but one who is perpetually groping".

Gropers are especially encouraged to apply to The Academy.

Remote control of peptide screw sense

As is well-known, peptides helices can be right or left handed. Many details of structure, amino acid identity and orientation can control this screw sense, and sometimes the controlling factors can be quite subtle. In a JACS communication, Jonathan Clayden (yes, the co-author of the amazing organic chemistry textbook) and his group uncover a surprising factor that controls the helical screw sense and also incorporate a neat "reporter group" to monitor the screw sense.

But this reporter group is nothing fancy and is simply a gycine installed in the middle of a long sequence of amino acids which consists of alpha-aminoisobutyric acid or Aib. Aib is simply alanine with an extra methyl at the alpha carbon. It is well known to impart helical propensities to peptides and has been used several times as a helical 'lock'.

In this case the Gly is in the center of a 20 amino acid peptide where all other residues are Aib. The peptide is clearly helical, but what's the screw sense? That's where the power of NMR spectroscopy comes in. The two protons in Gly are diastereotopic which means that in principle they could have different chemical shifts and signals in the NMR spectrum. In practice though, rapid interconversion between the left and right handed helices leads to an average and gives a single signal in the spectrum.

However if interconversion between the two screw senses could be 'biased' by making the equilibrium constant favor one of them, then one could presumably observe two separate signals for the two Gly protons even if the transition is fast on the NMR time scale. To accomplish this, Clayden et al. do something peculiar; they incorporate a L-Phe residue at the N-terminal of the helix. This group, even if far away from the central Gly, somehow seems to remotely interact differently with each of the two Gly protons. The incorporation of this terminal group leads to a considerable splitting in the signals of the two protons (up to 100 ppm), easily distinguishing them apart. Also for some reason, N-terminal groups seem to work better than C-terminal groups.

The reasons for the transmission of this effect over no less than 27 bonds are not clear, but they probably have something to do with the subtle change in conformational behavior that dictate helix folding. The authors even observe small differences for amide vs ester bonds as capping groups. Finally, they obtain an x-ray structure of this helix which turns out to be a 3/10 helix and confirm their observations.

These days there is a drive to 'tether' certain parts of oligopeptides to lock the resulting conformation in a helical form. Sometimes, even constraining end groups covalently (by metathesis for instance) seems to ensure a critical 'nucleation' structure that then zips up the rest of the helix. The exact percentage of the helix in solution could be a matter for discussion, but this study seems to indicate similiar end-group influenced conformational organization. I thought it was neat and points to further challenges and questions in our understanding of the deceptively simple question, "Why are helices stable in solution"?

Solà, J., Helliwell, M., & Clayden, J. (2010). N- versus C-Terminal Control over the Screw-Sense Preference of the Configurationally Achiral, Conformationally Helical Peptide Motif Aib-Gly-AibJournal of the American Chemical Society DOI: 10.1021/ja100662d

Screening probes and probing screens

High Throughput Screening (HTS), with all its strengths and limitations, is still the single-best way to discover novel interesting molecules in drug discovery. Thomas Kodadek of Scripps Florida has an interesting article on screening in the latest issue of Nat. Chem. Biol which is a special issue on chemical probes.

Kodadek talks about the very different properties required for drugs and probes and the limitations and unmet needs in current HTS strategies. He focuses on mainly two kinds of screening; functional assays and binding assays. The former can consist of phenotypic screening wherein one is only interested in a particular cellular response. This is more useful for drugs. However for probes, target selectivity is important and one must have knowledge of the target. HTS hits can hit all kinds of protein targets, thus making it hard to find out if your compounds are being selective. Mutagenesis and siRNA studies can shed light on target selectivity but this is not easy to do.

One of the possible solutions Kodadek suggests to circumvent the problem of gauging selectivity is to use binding assays instead of functional assays. He notes a pretty clever idea used in binding assays; that of throwing in cell extracts with miscellaneous proteins that could mop up greasy, non-selective compounds. This strategy cannot be easily used in functional assays. Binding assays are also typically less expensive than functional assays.

There is also a discussion of some of the very practical problems associated with screening. Screening typically has low hit rates and more importantly, hits from screening are not leads. You usually need a dedicated team of synthetic chemists to make systematic SAR modifications to these hits to optimize them further. As the author says, few synthetic chemists wish to serve as SAR facilities for their biologist colleagues. Plus it is not easy to lure industrial chemists to serve this function in academia (although the present economic climate may have made this easier). Thus, biologists with no synthetic background need to be able to make at least some modifications to their hits. For this purpose Kodadek suggests the use of modular molecules with easily available building blocks which can be cheaply and easily connected together by relatively inexperienced chemists; foremost in his recommendations are peptoids, N-substituted oligoglycines which are biologically active and easy to synthesize. Thus, if libraries for screening are enriched in such kinds of molecules, it could make it easy for biologists without access to sophisticated synthetic chemists and facilities to cobble together leads. Of course this would lead to a loss of diversity in the libraries, but that's the tradeoff necessary for going down the long road from hit to lead.

Lastly, Kodadek briefly talks about prospects for screening in academia. Academic drug discovery is gradually becoming more attractive with the recent long lull in industry. However academic scientists are typically not very familir with the post-synthesis optimization of drugs including optimization of metabolic properties, bioavailability and PK. Academic scientists who can pursue such studies or partner with DMPK contracting companies may be paid back their dues.

One topic which Kodadek does not mention is virtual screening (VS). VS can complement HTS and at least some studies indicate that the rate of success in VS can match, if not exceed, that in HTS. In addition, new ligand-based methods which use properties such as molecular shape to screen for compounds similar to given hits can also valuably complement HTS follow up studies.

Screening is still the best bet for discovering new drugs, but hit rates are typically still very low (1% would be a godsend). Only a concerted effort at designing libraries, ensuring selectivity and synthetic accessibility will make it easier.

Kodadek, T. (2010). Rethinking screening Nature Chemical Biology, 6 (3), 162-165 DOI: 10.1038/nchembio.303

Middle Ages March

As far as possible I try to avoid writing about the teaching of evolution and opposition to climate change in this country because of their overly politicized nature, but this piece in the NYT is one that no one can wisely ignore. It details a growing movement to conflate rejection of evolution with rejection of climate change that many people, and sadly especially conservatives, are spearheading. States are trying to introduce bills encouraging the teaching of “all sides” of scientific issues. Conservative politicians are advocating for students to know “all the facts”. But nobody is fooled by these thinly veiled promotions of ignorance. These developments should appear ominous to anyone since they indicate a resourceful war against science and all it stands for.

A couple of years ago, journalist Chris Mooney wrote "The Republican War on Science", a laundry list of instances of systematic negligence and subversion by the Bush administration when it came to scientific matters. When Obama became President, those of us like Mooney heaved a sigh of relief, since the new President seemed to have a genuine appreciation for science and its funding and strove to "restore science to its rightful place". Sadly, what we did not fully realize is that the War on Science is not really fought in the corridors of Washington but rather on the streets and churches of states all over the country. No Presidential mandate can quell the intensity with which the foot soldiers in these quarters fight the war.

The main goal of these foot soldiers is to seed doubts about the foundational nature of scientific facts in the minds of the gullible. They want to misrepresent the tentative nature of scientific understanding as equivalent to complete lack of understanding. They don't understand or willfully neglect the simple fact that some things in science are more certain than others, and many things are so well-understood so as to be virtually certain. But by pitching the very nature of science as some kind of loose, tentative theorizing disguised as facts, these eager evangelists are destroying the very fabric of scientific inquiry and indeed, one of the essential bedrocks on which modern civilization is founded. To me their ultimate objective seems clear; convince people that most if not all of science and not just climate change and evolution consists of "just theories". Once that basic groundwork has been established, they are free to play fast and loose with each and every aspect of science that bears on public policy, which in the modern world encompasses most important spheres of political and public activity.

The anti-science crowd is too clever to call for downright subversion of science and embrace of religious dogma. Consider Tim Moore, a politician from Kentucky who claims that his motivation is not religious but it is to oppose the “distortion of scientific knowledge”. Surely Moore is intelligent enough to understand the number of religious votes he would garner if his suggestions are implemented. Moore and others are too clever to directly call for an indictment of science. Hence they are resorting to the gradual mobilization of doubt. Start with eager young minds first. The relentless movement to include "intelligent" design in textbooks as a valid "alternative" to evolution is well-known. Now they are also calling for textbooks to teach "both sides" of climate change. The time will come when they would insist that every scientific topic with which they have an issue should be accompanied by its opposite in school textbooks, simply because scientists are engaging in healthy debate about that topic. Stem cells and alternative energy are two prominent issues that come to mind. Scientists are still not sure what kind of technologies would make solar and wind power a reality? Good! Make sure you include every bit of opposition to these technologies as part of your textbook lessons. Scientists are still trying to understand how exactly stem cells would make it possible to cure or contain life-threatening disorders? Fantastic! Make that a case for including every bit of opposition to stem cell research so that you could argue against it; the religious aspects could always be smuggled in later through the back door. Lively technical disagreements taking place in the pages of scientific journals would be held up as resounding evidence that the soul of science itself is an amorphous blob devoid of certain existence. This is nothing less than the rape and rabid hijacking of the normal scientific process to portray it as some kind of fundamental structural flaw in the whole enterprise.

If this kind of descent into ignorance is terrible for schools and students, it's not at all helped by declining standards of science and math education in this country and by global competition in science and technology. What may be even more tragic is that such efforts, which started during the Reagan era but were much milder back then, would form such an ungodly and impenetrable meld of science, conservative politics and religion that it may well become impossible to ever separate the three. Sadly, one consistently finds mainly Republicans being opposed to climate change and the teaching of evolution. Those few Republicans who do support either or both of these are already keeping their mouths shut for fear of being alienated from the party. At the same time, evangelical Christians are convincing their brothers and sisters to add climate change to their list of enemies which long includes evolution. Since the Reagan era conservatism has already become synonymous with evangelical religion. Now they are also trying to make the two synonymous with anti-scientism. The effect of all this would be to downright intimidate any person with conservative sentiments who dares to have respect for the scientific process. It would also mean an exponential decline in members of the conservative coalition with any appreciation of science; after all, if evolution and climate change deniers are going to be the main recruits to the movement, the probability that these people will have any appreciation for the scientific method would already be very low to begin with.

Accompanying this active propaganda against science is a slick publicity campaign that pits scientific issues as not really being scientific but being political dogfights between liberals and conservatives, and declares science and especially academic science to be a political liberal enterprise. It extols the folksy, down to earth demeanor of grass roots politicians and encourages derision towards "elitist", high-brow scientists educated at respectable schools along with the politicians of the Eastern Establishment who nurture them. The two-time election of George W Bush (ironically a failed member of the Eastern Establishment) demonstrated that many citizens of this country are indeed suckers for such stereotypes and are ready to fundamentally mistrust any educated intellectual or scientist. Whether we like it or not, conservatives have turned this confluence of mutually reinforcing strategies and stereotypes into a well-oiled PR machine that is set to pay its own way into hell.

Is there any silver lining at all to this precipitous slide into the Middle Ages? The article does talk about conservative Christians who seem to display a refreshing acceptance of both evolution and climate change. Their numbers are low, but their convictions seem strong. They think that earth and everything that it encompasses are God's creations and need to be taken care of. Atheists may vehemently disagree with this interpretation, but as E O Wilson says in his book ”Creation”, at least they can leave aside differences and try to find common ground for this most important of causes. No matter how powerful and influential the leaders of the war against science seem, they critically depend on the citizenry to make their voice known. They speak because their constituencies listen. They prey and thrive on the nods of their audience. Educate the audience, and the tables turn; now it’s they who decide whether the magician on stage lives or dies.

We don't know yet whether this citizenry can wake up to the wisdom of recognizing science as a value-neutral, apolitical, open-minded, independent and freedom-loving framework to improve their lives. But it is clear that to have any chance of rescuing this country from the divisive forces of ignorance which are gradually making their way from coast to coast, one must use every tactic at his or her disposal to drive home the importance of science and to try to reinforce its separation from politics and religion.

These days one regularly comes across opposite and polarized factions of "New Atheists" who are up in arms against "Accommodationists". The former faction believes that only a highly vocal effort to weed out religion from the masses can turn enough people toward science, even if it permanently alienates the hardest of the fundamentalists. The latter faction believes that a more moderate approach will work better. Both factions believe that fundamentalists will largely remain unmoved.

To me the arguments between them mainly seem to be based on degree, since many from the latter also call themselves atheists. I have never understood why the approach needs to be either/or. It is clear that insiders from the religious establishment still stand the best chance of convincing their own flock. These promising young insiders are going to be persuaded only when they are repeatedly convinced and in turn convince others that yes, they can safely practice their faith and still believe in science as a candle in the dark. Whether atheists like it or not, their support is crucial. People come in all kinds of shades, and the best bet for us to convince them about the value of science is to pitch it to them at all levels, in all forms and guises, vocally and mildly, through every possible channel. Human society is a complex organism, and it needs a complex mix of ideas to cause fundamental changes. Just like in my field of computational chemistry, when you don't know the composition of this mix, you simply try out all combinations.

It seems to be the least we can do to stop a straight downhill crash into dark ignorant oblivion.

A promising book falls apart

The atomic bombings of Hiroshima and Nagasaki were such horrific and singular historical events that any new retelling of them deserves to be read seriously. It was with such thoughts that I picked up Charles Pellegrino's "The Last Train From Hiroshima". The first few pages were enough to glue me to my chair. In an almost poetically clinical manner Mr. Pellegrino describes the effects of the bomb on human beings in the first few seconds after the detonation. His accounts of people evaporating and the "iron in their blood separating" while their friends who were protected in "shock bubbles" that were mere feet away were absolutely riveting.

Yet in spite of this promising start I could not shake off the gnawing feeling that something was wrong. For instance I have read my fair share of atomic history and so I was astonished to not find absolutely any mention of William "Deke" Parsons in the book. Parsons was a physicist and naval captain who played a part in designing the 'gun type' Little Boy and was instrumental in arming the Hiroshima bomb on flight. Earlier his hands had almost bled from practicing the arming, which had to occur at a precise given time twenty five thousand feet up in the air on the 'Enola Gay'. There is a superb account of him in Stephen Walker's "Shockwave: Countdown to Hiroshima". In response to a comment I made on Amazon, Mr. Pellegrino replied that he did not mention Parsons simply because he has already been part of so many accounts, which to me does not seem a good enough reason for the exclusion. Apart from this omission I also noted Mr. Pellegrino's statement that Stanley Miller and his advisor Harold Urey won a Nobel Prize for their classic experiment pioneering origin of life research. Urey won a Nobel, but for his discovery of deuterium. Miller was nominated for the prize a few times, and in my opinion should have won it.

Alas, the riveting start of the book and the author's accounts have now virtually fallen apart. In two New York Times articles it has been reported that the most egregious error in the book consists of the story of one Joseph Fuoco who was supposed to be on one of the planes. Mr. Fuoco makes several appearances in the book, and I had found myself scratching my head when I read his accounts, having never heard of him before. The New York Times and other resources discovered that Mr. Fuoco never took part in the bombing missions. Instead the relevant man is one Charles Corliss who has not been mentioned in the book. Astonishingly, Mr. Fuoco seems to have completely duped the author as Mr. Pellegrino himself admitted; he submitted several photographs and letters to Mr. Pellegrino as proof of his role in the mission, including a letter of commendation from President Truman. Clearly Mr. Fuoco proved to be a remarkably facile con man.

But sadly, this and many other errors have cast serious doubt on the validity of the book. This is a pity since Mr. Pellegrino is an interesting writer who has written books on diverse topics ranging from Jesus's tomb to Atlantis . As of now the publisher (Henry Holt) has a blurb on the Amazon page saying that further printing and shipping of the book has been halted (which makes me cherish my first printing copy). Even Mr. Pellegrino's PhD. from Victoria University in New Zealand is being questioned. As usual, an otherwise fine author seems to have sullied his name by sloppy writing on an important topic.

Marshall Nirenberg (1927-2010)

In the latest issue of Cell, Edward Scolnick offers a moving and insightful tribute to his former mentor, Marshall Nirenberg. Nirenberg won the Nobel Prize for Medicine along with Har Gobind Khorana and Robert Holley for one of the most important scientific discoveries, the cracking of the genetic code. Scolnick pinpoints the essential nature of the man and his work.

The world of science knows the substance of his work: He opened up the mystery of the genetic code with the poly-U-polyphenylalanine experiment, then with the triplet binding assay with Phil Leder, and finally with the triplet termination assay with Tom Caskey. From UUU to all 64 triplets: the “periodic table” for molecular biology. But how many have read the poly-U paper—I mean, really read it? Marshall’s approach to science comes through, loud and clear. Every possible control is tested. Every method is described so that anyone else can reproduce it. Every piece of data, even imperfect data, is included in the tables and figures. The incorporation of phenylalanine into the product is more than a thousand-fold over background without the poly-U template, and the incorporation of this amino acid is selective for this homopolymer. How many of us would take the care to characterize the actual product to be sure it had the characteristics of polyphenylalanine? Marshall believed deeply in being meticulous and in being certain that one’s data were true, that the results were due to the variables one had manipulated and were not caused by some vagary that had not been thought of or controlled for. He believed that the methods in one’s paper should be so clearly described that any investigator trying to reproduce the results could do so on the very first attempt. Supplementary sections could never be a part of a Nirenberg paper...

Scolnick also ends with an admonition and a wise message that every member of the government should read.

Members of Congress—guardians of taxpayer dollars and decision-makers for NIH funding—often ask themselves whether the funding for science provided to NIH really matters to the health of the nation. We should tell them to think for a moment about the impact of the cracking of the genetic code. If it were not for Marshall Nirenberg’s work, there would be no recombinant DNA technology, which changed life science and medicine. The sequence of the human genome and the era of modern genomics could not have come into being. Protein therapeutics for cancer and autoimmune diseases, drugs for HIV, statins for atherosclerosis, and modern vaccines all ultimately owe their origin to the knowledge of the code. The genetic code is the periodic table for biological science...How can we honor Marshall Nirenberg’s memory? We can remember all the things he stood for: complete truth in science, meticulous attention to detail, passionate love for discovery, thorough training of students, deep values about how science should be carried out. Marshall will never be dead, because what he stood for in science is timeless—bridging generations of scientists, and bridging millennia

How easy is it to forget that the greatest applied scientific breakthroughs and the greatest fundamental discoveries in science have mainly come out of government-funded basic research? Those who forget this will only condemn their own grandchildren to paucity of knowledge and bereave them of that magical horizon of understanding that science reveals. It is up to all of us to ensure that Nirenberg and others live on through a commitment to such basic scientific research.

Book review: The Dale Carnegie of Silicon-29 NMR

Chemical Shifts and Coupling Constants for Silicon-29 (Landolt-Börnstein: Numerical Data and Functional Relationships in Science and Technology - New Series / Condensed Matter)

By Radha Raman Gupta (Contributor), Manfred Dieter Lechner (Contributor), Heinrich Marsmann (Contributor), Bozhana Mikhova (Contributor), Frank Uhlig (Contributor)

If you read this book, you cannot help but be instantly transformed into a silicon-29 NMR lover and specialist, purveyor of a wonderful, deep and satisfying field of inquiry. This book whose publication was a watershed did for silicon-29 NMR what Dale Carnegie's book did for the average shy person and what Ayn Rand did for capitalism. It finally brought silicon-29 NMR lovers out of the shadows of their self-imposed silence into the mainstream community. As for the price, I can say only one thing; this is one of the very very few products, whether in the shoe store, car store or pet store, whose price is completely justified by its quality. It's a clarion call to all silicon-29 NMR specialists who can finally reclaim the dream that the original topaz and mica lovers only dared to contemplate. I am pretty sure that even after several decades it will remain the most referenced and readily available book on the market, and will continue to be a godsend for those hesitant men and women who are uncertain about their future in the silicon-29 NMR field. Three words; buy it now.