Stuart W Hughes
Lilly Research Laboratories, Erl Wood Manor, Windlesham

Mark O Cunningham
Institute of Neuroscience, The Medical School, University of Newcastle upon Tyne

https://doi.org/10.36866/pn.92.30

It is currently no great secret that the drug discovery industry is facing major challenges. Over the last 30 years the cost of developing a new drug has been increasing rapidly, whereas the number of new agents that are approved on an annual basis has remained roughly constant. As such, the manner in which the cost per molecule is steadily rising is seen by many as unsustainable (Scannell et al. 2012). Alongside this situation the drug discovery industry is also being impacted by a glut of patent expiries of some its best-selling agents leading to significant decreases in revenue. Although these challenging circumstances are having a significant bearing on R&D efforts in a range of therapeutic areas, probably the greatest impact has been felt in neuroscience. It has been estimated that only around 8% of CNS agents entering clinical trials end up as successfully marketed drugs, lagged only by oncology and women’s health at around 5% and 4%, respectively (Kola & Landis, 2004). As a result of this, over the last few years several large pharmaceutical companies have reduced their interest in developing new drugs for CNS ailments, with psychiatric disorders being particularly hard hit.

Given the huge and increasing expenditure on drug discovery efforts, it is important to ask why more drugs do not successfully make it through clinical trials. Twenty years ago the main reason for this was unpredictable pharmacokinetics (PK), with around 40% of molecules failing for this reason (Frank & Hargreaves, 2003). Through the use of a variety of predictive of assays this has now largely been addressed. However, what used to be a close second behind PK as the reason for failing to make it through clinical trials is now firmly established as the number one reason, namely lack of efficacy. In fact, more than half of all drugs fail because they essentially do not have the desired effect on the disease state of interest in Phase II proof-of-concept (POC) trials (Arrowsmith, 2011). Why this is the case essentially then comes down to two, not necessarily exclusive, possibilities. The first is that the exposure of unbound drug in the target organ and/or binding to the proposed molecular target were insufficient to test the therapeutic hypothesis. The second is simply that the therapeutic hypothesis was incorrect. Whilst the first of these possibilities can be addressed through the use of improved biomarkers, the second speaks more fundamentally to a lack of knowledge of human disease processes at their most basic, molecular level.

It is against this backdrop that a renewed interest in collaborations between industry and academia has emerged. Such collaborations are of course nothing new but in the last 5–10 years the nature of these, the scale on which they are occurring, and the expectations placed on them have certainly taken on a new appearance. Possibly, one of the main differences is that in earlier years such collaborations might have taken on the form of more focused, small-scale studies with a particular question in mind. For example, this might have been an academic laboratory investigating the mechanism of action of a new molecule using certain techniques and preparations that were particular areas of expertise. In contrast, it appears that collaborations now often involve multi-centred consortia focused on more fundamental and broad-reaching issues in drug discovery centred on the two possibilities for clinical failure outlined above. In many cases these collaborations comprise public–private partnerships as in the case of the EU Innovative Medicines Initiative (IMI) whereas in other instances they are supported wholly by individual companies. An example of the latter is Eli Lilly’s Centre for Cognitive Neuroscience (CCN), which is a virtual grouping of leading academics who work alongside Lilly scientists to find improved ways for developing new drugs to treat cognitive disorders. Ultimately, many of these collaborations come under the broader heading of open innovation and are based on the premise that casting the net wider and bringing more expertise and intellect to the table will accelerate the delivery of solutions to what is arguably the most significant current problem in drug discovery, that is, providing better translation between preclinical in vitro animal models and human disease.

Given the widespread presence of such large-scale industry–academic collaborations and consortia it is tempting to assume that bigger is indeed better and that many heads must surely be better than one or two. However, before jumping to this conclusion it is worthwhile acknowledging some of the challenges and characteristics associated with such arrangements to assess their true value to drug discovery. The first and most obvious challenge is that such collaborations are only as good as the question or questions they seek to address. Whilst this may seem a self-evident statement and one that is easy to deal with through appropriate planning, the complexity of some of the disorders being tackled in this way means that this is not the case. A case in point is the area of complex psychiatric disorders. Despite huge advances in neuroscience over the last few decades, our basic understanding of psychiatric disease at the cellular and molecular level remains scant at best. As such, understanding how to frame research to genuinely unravel the mechanistic basis of human psychiatric disease is far from straightforward. For example, developing better animal models for these most human of conditions is fraught with complications whereas using existing animal models, potentially alongside drugs previously discovered phenotypically by serendipitous means, to try and dissect the essential pathways that mediate human psychiatric disorders is equally laden with difficulties. As such, given that in such areas we know little or nothing definite about basic mechanisms, it is far from an easy to task to effectively design large collaborative research efforts that may deliver such information. Of course, one way around this is to concentrate solely on diseases where we know something very definite about their underpinnings, e.g. diseases with a known genetic cause. However, this would exclude a host of large unmet medical needs and lead to an increased focus on rare disorders which may or may not lead to more broadly applicable treatments further down the line. A second potential issue is that whilst large-scale collaborations undoubtedly open up new technical and experimental possibilities, it is important that this does not become simply a ‘data binge’ which may well deliver a wealth of new information about particular animal models and assay formats, but not lead to genuine new insights into human disease or robustly validated new drug targets. The key point here is that although collaborations may facilitate the conducting of many studies that would not otherwise have occurred, there is the danger that this simply constitutes ‘more of the same‘ rather than a fundamental shift in the way that drug discovery is carried out. For example, continuing to operate on the assumption that certain disorders can be addressed by a straightforward target-based discovery effort and then simply generating additional data and assays to support that idea may be, at its core, a flawed, or at least fruitless, strategy (Swinney & Anthony, 2011). A third issue with large collaborations is their inherent organisational complexity. Ensuring that all parties work effectively in partnership to achieve the main objectives of the collaboration can be far from straightforward, meaning that it is easy for the scientific endeavour to become fragmented and lack cohesion. These challenges aside, it is our view that the coming together of academia and industry in large numbers to try to solve some of the most pressing medical needs of the 21st century provides a unique opportunity to further science in a way that may not be possible by other means. As long as such collaborations are directed at the right goals, are aimed at tackling well-defined problems related to human disease, and provide genuine novelty and unity in the way they try to achieve these ends, they would appear to offer a powerful route to innovation.

Despite the emergence of large consortia-based collaborations, the importance of more small-scale traditional collaborations should not be overlooked or discarded. Indeed, such collaborations can have immense value in addressing very specific scientific issues. In terms of a successful outcome, a key characteristic of these collaborations is the coming together of different parties with a clearly shared goal to solve a particular problem. In contrast, the practice of pharmaceutical companies funding PhD students and postdocs in academic laboratories with few questions asked and minimal expectations imposed should be considered a thing of the past. A positive example of a successful small-scale collaboration is one that recently took place between one of the authors (S W Hughes) and Richard Horner at the University of Toronto. These two parties came together with the aim of understanding the cellular mechanisms that cause the loss of muscle tone (i.e. atonia) in the tongue musculature during rapid eye movement (REM) sleep, a process inextricably linked with the sleep-disordered breathing condition, obstructive sleep apnoea (OSA). Through the use of a unique in vivo rodent assay developed in Horner’s lab, which allows the monitoring of genioglossus muscle tone during natural sleep and wake states whilst providing the capacity to introduce pharmacological agents directly to the corresponding motor neuron pool, i.e. the hypoglossal motor nucleus, this led to the development of a new framework for explaining REM sleep atonia based on the opening of a certain class of K+ channels in this sleep state and thereby challenged the dogmatic view that this phenomenon depends on glycinergic inhibition (Grace et al. 2013).

In summary, it is evident that collaborations between academia and industry have taken on a new form in recent years. This has largely been driven by the declining productivity and increasing costs associated with delivering new drugs to the market, which in turn is the result of a high failure rate in Phase II clinical POC studies. As such, large scale consortia have emerged with the main goal of improving translatability between preclinical models and human disease states. Whilst this is undoubtedly a positive development, it is clear that certain challenges exist with such arrangements which should be considered carefully in order to maximise their output in terms of providing definitive new insights into human disease and potential routes to treating them. Alongside these larger efforts, the undoubted value of smaller, traditional collaborations should not be underestimated. Indeed, the manner in which such collaborations naturally facilitate the focused investigation of very specific problems allied to the practical ease and efficiency with which they can operate means that although they may not provide the impressive bandwidth of large consortia they may ultimately offer the best value for money.

References

Arrowsmith J (2011). Trial watch: Phase II failures: 2008–2010. Nat Rev Drug Discov 10, 328–332.

Frank R & Hargreaves R (2003). Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov 2, 566–580.

Grace KP, Hughes SW & Horner RL (2013). Identification of the mechanism mediating genioglossus muscle suppression in REM sleep. Am J Respir Crit Care Med 187, 311–319.

Kola I & Landis J (2004). Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3, 711–715.

Swinney DC & Anthony J (2011). How were new medicines discovered? Nat Rev Drug Discov 10, 507–519.

Scannell JW, Blanckley A, Boldon H & Warrington B (2012). Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 11,
191–200.