Preclinical Data Forum Network
|“Our Network has supported the development of six project proposals, of which three were funded. The most significant project originated by the Network members is the EQIPD collaboration dedicated to developing a quality system for non-regulated drug discovery research.”
|Anton Bespalov and Thomas Steckler
The ECNP Preclinical Data Forum Network provides communication (and technological) platform for the dialog related to the modern issues in preclinical research and for preclinical data sharing with the goal of enhancing the data utility for clinical R&D.
The goal is to systematically advance the status of preclinical research through identification of the best research practices, development and implementation of novel data quality standards and providing recommendations to the neuroscience community.
To achieve this goal, the plan is to:
- Engage the committed community of preclinical scientists from academia and industry
- Establish a community forum which enables exchanges, collaborations, development and dissemination of best preclinical research practices
- Focus on interplay of preclinical data robustness and methodology (e.g. study design and statistical methods)
- Contribute to formalizing the science of translatability
- Build a Data Exchange and Information Repository
Over the last 10-15 years, hundreds of compounds were advanced into clinical testing based on promising preclinical data. Vast majority of these compounds were later attritioned due to the lack of clinical efficacy. The efficacy-related attrition rates are especially high in neuroscience areas. For example, out of more than 160 compounds tested in trials for stroke, only one compound had demonstrated clinical efficacy. There are multiple reasons for the clinical failure of preclinically efficacious compounds including, but not limited to, the strong placebo effects, deficiencies in preclinical study design and conduct, poor pharmacokinetic properties and side-effects of the investigational drugs. Insufficient brain penetration and target engagement create an extra challenge in the area of neuroscience. Moreover, the predictive (and construct) validity of preclinical models that are used to generate efficacy data supporting the advancement of compounds into clinical studies is often questioned.
Failure to translate is an issue for both the pharmaceutical industry and for the academic groups involved in psychopharmacological research and in development of novel therapies for psychiatric and neurological patients, leading to serious consequences. First, many major pharma companies significantly reduced their efforts in the neuroscience area. This reduction has been especially significant for psychiatry drug discovery. The number of major pharmaceutical companies with the active drug discovery programs for novel antidepressants, antipsychotics or anxiolytics is gradually decreasing. This tendency is worrisome considering a high unmet medical need for the patients continuing to suffer from depression, schizophrenia, anxiety disorders or many other neuropsychiatric disorders which are inadequately and/or only partially treated at present.
Second, there seem to be a genuine dissatisfaction with the predictability, reliability and quality of preclinical data, leading to organizational unwillingness to invest further in the preclinical science. While understandable, this view is quite dangerous, as there is no a priori reason to suggest that forsaking the preclinical tests will render drug development in the neuroscience area more successful. At best, it may maintain the status quo at somewhat reduced costs (with the costs incurred for preclinical drug development being a fraction of the costs of clinical trials).
The funding agencies, publishers, patient advocacy groups and the general public are also concerned with the pharmaceutical productivity decline and the data quality contribution to this decline, and are looking for ways to improve the replicability and reliability of preclinical data. Thus, there is an urgent need to improve the predictability and reliability of preclinical data to provide better guidance and to aid decision making in CNS drug development, which hopefully will lead to the enhanced success rates desperately needed in this area of drug development, and to regain trust at various levels in the work we do.
Behavioural pharmacologists at several companies in Europe and pre-clinical and clinical scientists from academia have held discussions on preclinical data reproducibility and translatability. Particularly, the impact of false positive preclinical data was determined to be significant. It was acknowledged that there are various sources of false positive results and that there is an urgent need to exchange information that could help to reduce false positives and to avoid redundant efforts. During the course of these discussions, it also became clear that the problem with the quality of preclinical data is not limited to behavioural studies but extends to in vitro, ex vivo and in vivo studies in various fields (e.g. electrophysiology). In 2013, the decision was made to elevate the group’s efforts by engaging the additional discussion partners and addressing the practical issues such as development of certain guidelines / standards for preclinical research.
In August 2014, ECNP recognized the proposal to form a new Network that will focus on robustness, reproducibility, translatability and reporting transparency of preclinical data. Network’s founding members have published on the preclinical data reproducibility, are recognized as the leaders in this field and/or are leading such efforts in their organizations