Understanding pathways leads to drug discovery. Really?

One of the most often heard rationales for studying the genetics of disease is that increased understanding of gene pathways, or the products of gene pathways, will lead to new drug discoveries.  The idea is that even if mapping studies, like GWAS (see many of our earlier posts) don't discover 'the'  or 'blockbuster' genes 'for' a trait, they reveal genetic pathways in the biology of the trait that drugs can target.

However, while there have been some successes or hopeful trials, overall there is only limited evidence yet that this is as yet happening -- in fact, after a burst of initial enthusiasm for the new doors opened by the sequencing of the human genome, pharmaceutical companies have been cutting back on research and development, and few new drugs are currently in the pipeline -- drug discovery is risky, and payoffs are few.  This constriction of the new drug pipeline is why Francis Collins, director of the National Institutes of Health, has pushed "translational medicine" and NIH's involvement in drug development.

Well, as the special report on allergies in the Nov 24 issue of Nature notes, immunologists and allergists have long thought they understood what causes asthma, but the disease has increased dramatically in the last 3 decades or so, and new treatments are few and far between.  So much for the benefits of understanding pathway components. 

Since the discovery of immunoglobulin E (IgE) almost half a century ago, there has been a massive expansion in knowledge about how IgE antibodies work. Research has unravelled IgE's role in a myriad of cellular and molecular targets driving inflammatory responses and underlying complex allergic disorders. This knowledge might have been expected to lead to novel preventative and therapeutic pathways — unfortunately, this has not been the case.

The dramatic rise in allergy and asthma worldwide has increased the clinical need for treatment, but research focusing heavily on IgE as the main malefactor in allergies has not been translated into widespread patient benefit.

One problem, according to a piece by Stephen Holgate on why this is so, has been the pharmaceutical industry's reliance on animal models to both better understand the disease as well as to test new treatments.  But, humans are not mice.  In addition, allergic disease is complex, and involves not only biological pathways, but their interactions with environmental triggers as well as, presumably, an underlying genetic susceptibility.

Traditional therapy of allergic disease has in large part relied on the abatement of symptoms with H₁-antihistamines (rhinoconjunctivitis, food allergy, urticaria), adrenaline (anaphylaxis) or β2–adrenoceptor agonists (asthma), and the suppression of inflammation with corticosteroids. Besides improving the pharmacology of known drugs, the only novel asthma therapies to emerge are leukotriene inhibitors (for example, montelukast) and the non-anaphylactogenic anti-IgE, omalizumab, both of which are directed at targets identified well over 40 years ago.

There have been disappointments with a wide range of biologics targeting activating receptors on T cells, cytokines, chemokines, adhesion molecules and inflammatory mediators. Having shown convincing efficacy in in-vitro cell systems and animal models, and possibly some level of efficacy in acute allergen challenge in mild asthma, all of these have fallen short of expectations when trialled in human asthma. In moderate–severe asthma, where the unmet therapeutic need is greatest, trials of novel biologics have revealed only small subgroups in which efficacy has been shown or is suggestive.

And, it has long been assumed that asthma is primarily an allergic response, but this is no longer thought to be so.  The idea now is that perhaps impaired innate immunity comes first, and leads to allergy.  So, much is known about allergies and asthma, but nowhere near enough.

The asthma question is one that highlights many of the current problems in epidemiology, genetics, and the understanding of causation.  Asthma prevalence has been climbing in the US and other industrialized countries since the 1980s.  Given its precipitous rise, it would seem that the problem is quintessentially one for environmental epidemiology, but even when looked at from that perspective, no convincing environmental cause has yet been identified, and in fact studies have produced a frustrating litany of contradictions -- it's cleanliness or dirt, breast feeding or bottle, this gene or that.  Yes, epidemiologists have turned to genetics to try to understand the disease, but clearly there's no genetic explanation for such a recent epidemic. 

Like most other common chronic conditions, asthma is a complex disease, with multiple causes and multiple pathways.  As Holgate concludes:

In the future, it is essential that asthma is not treated as a single disorder, but rather defined by causative pathways. We need new diagnostic biomarkers to identify patients most likely to respond to highly selective biologics, such as anti-IL-5 biologic (mepolizumab) and anti-IL-13 (lebrikizumab). These therapies are only active in particular subtypes of asthma, when the molecules they target lie on a causative disease pathway. 

Studies of large numbers of people with a common chronic disease like asthma, or heart disease or hypertension, are necessarily pooling cases with different causes, pathways, genetic backgrounds, and outcomes.  This limits the potential for successful findings.

Biological traits are the result of interactions among many different factors, genetic and otherwise.  Such interactions, and the way that evolution works, leads to redundancy, alternative pathways, overlapping pathways, and the like.  This was a major theme of our book The Mermaid's Tale.  Complexity is an easy word to say, and perhaps it's easy to use it to excuse failure to find blockbuster findings.  But the last couple of decades have systematically shown causal complexity to be real.

Besides complexity itself, a major problem is not simply that humans aren't mice.  It's also that we are all unique in our environmental exposures, genomics, and how our bodies respond.  Identifying single genes that may be involved in complex diseases, or even biophysiological pathways, may be a rationale for sticking with the genetic approach to understanding disease, but it's a far cry from prevention, treatment or cure.

Rather than promising simplistic causation and consequent intervention miracles, we feel that students and young investigators, and the funding mechanisms, should be geared towards coming to grips with complexity, rather than just spinning out ever more details.  Major practical, and we also believe conceptual challenges lie ahead.  Asthma is a good case in point.

GWAS revisited: vanishing returns at expanding costs?

We've now had a chance to read the 4 papers on genomewide association studies (GWAS) in the New England Journal of Medicine last week, and we'd like to make a few additional comments. Basically, we think the impression left by the science commentary in the New York Times that GWAS are being seriously questioned by heretofore strong adherents was misleading. Yes, the authors do suggest that all the answers are not in yet, but they are still believers in the genetic approach to common, complex disease.

David Goldstein (whose paper can be found here) makes the point that SNPs (single nucleotide polymorphisms, or genetic variants) with major disease effects have probably been found by now, and it's true that they don't explain much of the heritability (evidence of overall inherited risk) of most diseases or traits. He believes that further discoveries using GWAS will generally be of very minor effects. He concludes that GWAS have been very successful in detecting the most common variants, but now have reached the point of diminishing returns. He says that "rarer variants will explain missing heritability", and these can't be identified by GWAS, so human genetics now needs to turn to sequencing whole genomes to find these.

Joel Hirschhorn (you can find his paper here) states that the main goal of GWAS has never been disease prediction, which indeed they've only had modest success with, but rather the discovery of biologic pathways underlying polygenic disease or traits. GWAS have been very successful at this--that is, they've confirmed that drugs already in use are, as was basically also known, targeting pathways that are indeed related to the relevant disease, although he says that further discoveries are underway. Unlike Goldstein, he believes that larger GWAS will find significant rare variants associated with disease.

Peter Kraft and David Hunter (here) tout the "wave of discoveries" that have resulted from GWAS. They do say that by and large these discoveries have low discriminatory ability and predictive power, but believe that further studies of the same type (only much bigger) will find additional risk loci that will help explain polygenic disease and yield good estimates of risk. They suggest that, because of findings from GWAS, physicians will be able to predict individual risk for their patients in 2 to 3 years.

John Hardy and Andrew Singleton (here) describe the GWAS method and point out that people are surprised to learn that it's often just chromosome regions that this method finds, not specific genes, and that some of these are probably not protein-coding regions but rather have to do with regulating gene expression. Notably, unlike the other authors who all state that the "skeptics were wrong", but somehow don't bother to cite their work so that the reader could check that claim (they do cite the Terwilliger and Hiekkalinna paper we mentioned here last week, but that is on a specialized technical issue, not the basic issues related to health effects).

They also state that the idea of gene by environment interaction is a cliche, and has never been demonstrated. Whether they mean by this that there is no environmental effect on risk or simply that it's difficult to quantify (or, a technical point, that environmental effects are additive) is not clear. If the former, that's patently false--even risk of breast cancer in women who do carry the known and undoubted BRCA1 or BRCA2 risk alleles, varies significantly by decade of birth. Or the huge rise in diabetes, obesity, asthma, autism, ADHD, various cancers, and many other diseases just during the memory of at least some living scientists who care to pay attention. And, see our post of 4/18.

So, we find none of the supposed general skepticism here. Yes, these papers do acknowledge that risk explained by GWAS has been low, but they claim this as 'victory' for the method, and dismiss, minimize, or (worse) misrepresent problems that were raised long ago, and instead say either that risk will be explained with bigger studies, or GWAS weren't meant to explain risk in the first place (it's not clear that the non-skeptics agree with each other about the aim of GWAS, or about whether they have now served their purpose and it's time to move on--to methods that apparently actually do or also do explain heritability and predict risk.)

The 'skeptics' never said that GWAS would find nothing. What at least some of us said was that what would be found would be some risk factors, but that complex traits could not by and large be dissected into the set of their genetic causes in this way.

Rather than face these realities, we feel that what is being done now is to turn defeat into victory by claiming that ever-larger efforts will finally solve the problem. We think that is mythical. Unstable and hardly-estimable small, probabilistic relative risks will not lead to revolutionary 'personalized medicine', and there are other and better ways to find pathways. If a pathway (network of interacting genes) is so important, it should have at least some alleles that are common and major enough that they should already be known (or could easily be known from, say, mouse experiments); once one member is known, experimental methods exist to find its interacting gene partners.

In a way it's also a sad consequence of ethnocentric thinking to suppose that because we can't find major risk alleles in mainstream samples from industrialized populations, that such undiscovered alleles might not exist in, or even be largely confined to, smaller or more isolated populations, where they could be quite important to public health. They do and, ironically, mapping methods (a technical point: especially linkage mapping) can be a good way to find them.

But if we're in an era of diminishing, if not vanishing, returns, we're also in an era in which we think we will not only get less, but will have to spend and hence sequester much more research resources to get it. So there are societal as well as scientific issues at stake.

In any case, we already have strong, pathway-based personalized medicine! By far the major risk factor for most diabetes, for example, involves energy and fat metabolic pathways. Individuals at risk can already target those pathways in simple, direct ways: walk rather than taking the elevator, and don't over-eat!

If those pathways were addressed in this way, there would actually be major public impact, and ironically, what would remain would be a residuum of cases that really are genetic in the meaningful sense, and they would be more isolated and easier to study in appropriate genetic, genomic, and experimental ways.