Failed trials for central nervous system disorders do not necessarily invalidate preclinical models and drug targets

Abstract

A recent article identified five key technical determinants that make substantial contributions to the outcome of drug R&D projects (Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat. Rev. Drug Discov. 13, 419-431 (2014)) 1 . Careful consideration of such determinants might be particularly valuable in the fields of neurology and psychiatry, in which successful drug development has declined precipitously over the past decade. This decline has largely been fuelled by a high failure rate in the translation of preclinical efficacy findings, caused by multiple factors (see Supplementary information S1 (table)), including limited training and poor protocol design, inadequate animal models, insufficiently validated therapeutic targets and problems with data handling and reporting.Here, we focus on three factors that can be addressed immediately in order to re-evaluate the therapeutic potential of older drugs and targets and to increase the probability of success for future preclinical-to-clinical translation: data robustness, data generalizability and target engagement data, a factor that was also highlighted in the recent article 1 . We argue that the many failed clinical trials in neuropsychiatry do not necessarily invalidate the potential of a drug target or an animal model. Rather, these failures indicate a need for improved experimental designs and a robust translational strategy to better inform compound and dose selection for clinical trials. We conclude that many of the drugs and targets in neuropsychiatry that have been discarded because of negative clinical trial outcomes may deserve re-evaluation using contemporary knowledge, methodology and tools.

Sex differences in the hypothalamic–pituitary–adrenal axis: An obstacle to antidepressant drug development?

“…Therefore, failed clinical trials do not necessarily invalidate animal models but generate important questions about the translational process. Obviously, other issues such as the lack of data robustness, data generalizability, and target engagement when designing and evaluating preclinical studies should also be carefully taken into consideration (Bespalov et al, ). However, the academic community, as well as the industry, should take advantage of preclinical knowledge and thoroughly evaluate and interpret preclinical experiments before implementing clinical trials (Kokras & Dalla, ).…”

Aligning physiology with psychology: Translational neuroscience in neuropsychiatric drug discovery

“…Considering the number of animals used in subsequent attempts to replicate findings from other laboratories, however, due care and optimal experimental design is crucial not only to reduce animal use, but to improve the replication of results. Lack of replicability associated with biased publication of positive data is not only a serious issue undermining the value and credibility of industrial and academic preclinical research (Bespalov et al, 2016;Frye et al, 2015;Peers et al, 2012), but also represents a weakness in behavioural modelling in animals as a whole (Jarvis and Williams, 2016;Landis et al, 2012). The lack of reproducibility and robustness has led to the creation of initiatives by scientific publishing companies [ARRIVE, (McGrath et al, 2010;McNutt, 2014)], and endorsed by scientific associations such as the European College of Neuropsychopharmacology, academic and industrial consortia (http:// addconsortium.org/) as well as reviewers (http://f1000research.…”

“…Bespalov and colleagues have recently focused on factors which may underlie apparent lack of validity of animal models (Bespalov et al, 2016); some of which are reiterated in this review. These factors include the robustness and generalizability of preclinical data by which it is sometimes difficult to replicate preclinical studies, even from within the same laboratory (Lindner, 2007), let alone across different laboratories [e.g., (Crabbe et al, 1999;Scott et al, 2008;Wahlsten et al, 2003)].…”

Kinoscope: An Open-Source Computer Program for Behavioral Pharmacologists

“…Moreover, experienced staff members using the Kinoscope can streamline and audit the training of new members, by making use primarily of the visual maps, thus improving the consistency and reproducibility of scoring by novice researchers. Recently several concerns have been raised with regards to the validity of experimental data (Steckler, 2015; Bespalov et al, 2016). Many factors should be taken into account in improving the quality of experimental studies (Kilkenny et al, 2009; McNutt, 2014; Macleod et al, 2015) and perhaps another overlooked factor is the quality of manual scoring of behavioral experiments, which in turn may result in poor inter-rater agreement and inevitably low reproducibility.…”