The beginning of behavioral pharmacology is most often associated with the work of Peter Dews and the psychopharmacology lab at Harvard. He, along with collaborators such as William Morse, Roger Kelleher, Joseph Brady, and Charles Schuster and many others at National Institutes of Health, helped to train and mentor generations of pharmacologists who later went on to start behavioral labs in BioPharma. From identification of antianxiety and antipsychotic medications to application in neurological disease models, behavioral procedures have demonstrated their continuing worth. The present review summarizes a small fraction of the work exemplifying the utility of behavior in detecting a novel treatment for schizophrenia, the potential use of calpain inhibitors in the treatment of alcohol use disorder, N-methyl-d-aspartate receptor antagonist development, identification of cannabinoid receptors, the relatively recently appearing field of safety pharmacology, and the utility of a timed motor response with applicability across multiple neurological disease models. We believe that behavioral pharmacology methods continue to maintain great importance in drug discovery and development and a failure to recognize this, places much higher risk on advancing novel treatments for central nervous system diseases. Significance statement Behavioral pharmacology methods have proven quite useful in identifying drug mechanism of action, potential therapeutic efficacy and safety, and as methods representing neurological and psychiatric disease symptoms. Given a recent renewal of interest in these under-served disease areas, the present review summarizes some behavioral pharmacology from past drug discovery campaigns that have provided and should continue to provide value to the biotech and pharma industries.

The debate about the continued use of animals in research has intensified in recent years, with few signs that consensus can be achieved in the near future. Animal activist groups and their supporters call for an immediate halt to all animal research. The counterargument from research scientists and many others across various facets of society is that suitable alternatives have not been sufficiently established and validated to enable all animal research to cease without halting scientific progress. We suggest a compromise rooted in the existing regulations and based on the consensus that not all research efforts contribute enough benefit to ethically justify the use of animals in research.More specifically, we describe a Benefit Assessment Matrix that can provide a streamlined and practical guide for both scientists and Animal Ethics Committees or equivalent bodies such as Institutional Animal Care and Use Committees to assess the benefit of proposed research. The organizational premise is that rigorous high-quality research is more likely to produce tangible benefit than poor quality, low rigor research. Implementation of the Benefit Assessment Matrix will enable more rapid phasing out of poor-quality research and support the objective of promoting rigorous high-quality research, meeting expectations of both sides of the debate.

The metabotropic glutamate receptor 2 (mGlu2) is a heavily studied therapeutic target in neuropsychiatry for which we anticipate a renewed interest in the near future. We review the rationale and the outcome of clinical trials with mGlu2/3 receptor agonists in schizophrenia, a field of intense research since a seminal publication by Patel and colleagues (2007). We summarize evidence about selective, potent and safe agents with quantifiable CNS penetration that can be used to test hypotheses of mGlu2 receptors involvement in neuropsychiatric diseases. We summarize lessons learned from previous programs that should be considered to maximize the probability of success when targeting orthosteric and allosteric enhancement of mGlu2 receptor function in schizophrenia and beyond. First, we propose expanding our focus beyond presynaptic mGlu2 receptor stimulation in schizophrenia to novel hypotheses and that the choice of a therapeutic indication no longer be dictated by commercial opportunity but following science as a driver. Second, evidence on internal validity of preclinical studies supporting efficacy claims in the mGlu2 field is very limited. This gap will need to be closed when reviewing the rationale to re-initiate efforts in this field. Third, the pomaglumetad program was halted due to insufficient clinical efficacy, partly because of the inability to identify a treatment responder population. In preclinical studies, effects of mGlu2/3 receptor stimulation also seemed to vary significantly between laboratories. Definition of the responsive subject population and development of response-predicting biomarkers is therefore one of the main avenues of further research in the mGlu2 field.

Background: The term Behavioral and Psychological Symptoms of Dementia (BPSD) covers a group of phenomenologically and medically distinct symptoms that rarely occur in isolation. Their therapy represents a major unmet medical need across dementias of different types, including Alzheimer’s disease. Understanding of the symptom occurrence and their clusterization can inform clinical drug development and use of existing and future BPSD treatments. Objective: The primary aim of the present study was to investigate the ability of a commonly used principal component analysis to identify BPSD patterns as assessed by Neuropsychiatric Inventory (NPI). Methods: NPI scores from the Aging, Demographics, and Memory Study (ADAMS) were used to characterize reported occurrence of individual symptoms and their combinations. Based on this information, we have designed and conducted a simulation experiment to compare Principal Component analysis (PCA) and zero-inflated PCA (ZI PCA) by their ability to reveal true symptom associations. Results: Exploratory analysis of the ADAMS database revealed overlapping multivariate distributions of NPI symptom scores. Simulation experiments have indicated that PCA and ZI PCA cannot handle data with multiple overlapping patterns. Although the principal component analysis approach is commonly applied to NPI scores, it is at risk to reveal BPSD clusters that are a statistical phenomenon rather than symptom associations occurring in clinical practice. Conclusions: We recommend the thorough characterization of multivariate distributions before subjecting any dataset to Principal Component Analysis.

The EQIPD Quality System was designed with the ultimate mission to provide a framework to ensure the quality and integrity of non-regulated preclinical biomedical research. For research quality to be sustained over time, it is crucial to have continuous improvement mechanisms that routinely monitor the research-related processes and enable solutions for identified issues. The present article is focused on these monitoring and assessment procedures that make the EQIPD Quality System a fully functional 'system' (as opposed to a mere collection of guidelines, work instructions and policies). In this context, a critical instrument are the internal and external assessments of the EQIPD Quality System performance described in detail. The assessment procedures emphasize the unique nature of the EQIPD Quality System being user-friendly, flexible and fit-for-purpose. By undergoing the (voluntary) external EQIPD assessment (leading to the EQIPD certification after all EQIPD core requirements have been implemented), a research unit: (i) secures confidence in the quality of data generated, (ii) ensures continuous improvement of research processes, and (iii) obtains an independent seal of quality communicating commitment to best research practices to the research community.

Recently, many funding agencies have released guidelines on the importance of considering sex as a biological variable (SABV) as an experimental factor, aiming to address sex differences and avoid possible sex biases to enhance the reproducibility and translational relevance of preclinical research. In neuroscience and pharmacology, the female sex is often omitted from experimental designs, with researchers generalizing male-driven outcomes to both sexes, risking a biased or limited understanding of disease mechanisms and thus potentially ineffective therapeutics. Herein, we describe key methodological aspects that should be considered when sex is factored into in vitro and in vivo experiments and provide practical knowledge for researchers to incorporate SABV into preclinical research. Both age and sex significantly influence biological and behavioral processes due to critical changes at different timepoints of development for males and females and due to hormonal fluctuations across the rodent lifespan. We show that including both sexes does not require larger sample sizes, and even if sex is included as an independent variable in the study design, a moderate increase in sample size is sufficient. Moreover, the importance of tracking hormone levels in both sexes and the differentiation between sex differences and sex-related strategy in behaviors are explained. Finally, the lack of robust data on how biological sex influences the pharmacokinetic (PK), pharmacodynamic (PD), or toxicological effects of various preclinically administered drugs to animals due to the exclusion of female animals is discussed, and methodological strategies to enhance the rigor and translational relevance of preclinical research are proposed.

Limited reproducibility of preclinical data is increasingly discussed in the literature. Failure of drug development programs due to lack of clinical efficacy is also of growing concern. The two phenomena may share an important root cause — a lack of robustness in preclinical research. Such a lack of robustness can be a relevant cause of failure in translating preclinical findings into clinical efficacy and hence attrition, and exaggerated cost in drug development. Apart from the study design and data analysis factors (e.g., insufficient sample sizes, failure to implement blinding, and randomization), heterogeneity among experimental models (e.g., animal strains) and the conditions of the studyused between different laboratories is a major contributor to the lacking of robustness of research findings. The flipsideof this coin is that the understanding of the causes of heterogeneity across experimental models may lead to the identification of relevant factors for defining the responder populations. Thus, this heterogeneity within preclinical findings could be an asset, rather than an obstacle, for precision medicine. To enable this paradigm shift, several steps need to be taken to identify conditions under which drugs do not work. An improved granularity in the reporting of preclinical studies is central among them (i.e., details about the study design, experimental conditions, quality of tools and reagents, validation of assay conditions, etc.). These actions need to be discussed jointly by the research communities interested in preclinical data robustness and precision medicine. Thus, we propose that a lack of robustness due to the heterogeneity across models and conditions of the study is not necessarily a liability for biomedical research but can be transformed into an asset of precision medicine.

Disappointments in translating preclinical findings into clinical efficacy have triggered a number of changes in neuroscience drug discovery ranging from investments diverted to other therapeutic areas to reduced reliance on efficacy claims derived from preclinical models. In this chapter, we argue that there are several existing examples that teach us on what needs to be done to improve the success rate. We advocate the reverse engineering approach that shifts the focus from preclinical efforts to "model" human disease states to pharmacodynamic activity as a common denominator in the journey to translate clinically validated phenomena to preclinical level and then back to humans. Combined with the research rigor, openness, and transparency, this reverse engineering approach is well set to bring new effective and safe medications to patients in need.

Significant efforts have been channeled into developing antibodies for the treatment of CNS indications. Disappointment with the first generation of clinical Tau antibodies in Alzheimer's disease has highlighted the challenges in understanding whether an antibody can reach or affect the target in the compartment where it is involved in pathological processes. Here, we highlight different aspects essential for improving translatability of Tau-based immunotherapy.

Laboratory workflows and preclinical models have become increasingly diverse and complex. Confronted with the dilemma of a multitude of information with ambiguous relevance for their specific experiments, scientists run the risk of overlooking critical factors that can influence the planning, conduct and results of studies and that should have been considered a priori. To address this problem, we developed “PEERS” (Platform for the Exchange of Experimental Research Standards), an open-access online platform that is built to aid scientists in determining which experimental factors and variables are most likely to affect the outcome of a specific test, model or assay and therefore ought to be considered during the design, execution and reporting stages. The PEERS database is categorized into in vivo and in vitro experiments and provides lists of factors derived from scientific literature that have been deemed critical for experimentation. The platform is based on a structured and transparent system for rating the strength of evidence related to each identified factor and its relevance for a specific method/model. In this context, the rating procedure will not solely be limited to the PEERS working group but will also allow for a community-based grading of evidence. We here describe a working prototype using the Open Field paradigm in rodents and present the selection of factors specific to each experimental setup and the rating system. PEERS not only offers users the possibility to search for information to facilitate experimental rigor, but also draws on the engagement of the scientific community to actively expand the information contained within the platform. Collectively, by helping scientists search for specific factors relevant to their experiments, and to share experimental knowledge in a standardized manner, PEERS will serve as a collaborative exchange and analysis tool to enhance data validity and robustness as well as the reproducibility of preclinical research. PEERS offers a vetted, independent tool by which to judge the quality of information available on a certain test or model, identifies knowledge gaps and provides guidance on the key methodological considerations that should be prioritized to ensure that preclinical research is conducted to the highest standards and best practice.

Academic Core Facilities are optimally situated to improve the quality of preclinical research by implementing quality control measures and offering these to their users.

While high risk of failure is an inherent part of developing innovative therapies, it can be reduced by adherence to evidence-based rigorous research practices. Supported through the European Union’s Innovative Medicines Initiative, the EQIPD consortium has developed a novel preclinical research quality system that can be applied in both public and private sectors and is free for anyone to use. The EQIPD Quality System was designed to be suited to boost innovation by ensuring the generation of robust and reliable preclinical data while being lean, effective and not becoming a burden that could negatively impact the freedom to explore scientific questions. EQIPD defines research quality as the extent to which research data are fit for their intended use. Fitness, in this context, is defined by the stakeholders, who are the scientists directly involved in the research, but also their funders, sponsors, publishers, research tool manufacturers, and collaboration partners such as peers in a multi-site research project. The essence of the EQIPD Quality System is the set of 18 core requirements that can be addressed flexibly, according to user-specific needs and following a user-defined trajectory. The EQIPD Quality System proposes guidance on expectations for quality-related measures, defines criteria for adequate processes (i.e. performance standards) and provides examples of how such measures can be developed and implemented. However, it does not prescribe any pre-determined solutions. EQIPD has also developed tools (for optional use) to support users in implementing the system and assessment services for those research units that successfully implement the quality system and seek formal accreditation. Building upon the feedback from users and continuous improvement, a sustainable EQIPD Quality System will ultimately serve the entire community of scientists conducting non-regulated preclinical research, by helping them generate reliable data that are fit for their intended use.

Academic Core Facilities are optimally situated to improve the quality of preclinical research by implementing quality control measures and offering these to their users.

A recent report by DeGroot et al. ( 1 DeGroot S.R. Zhao-Shea R. Chung L. Klenowski P.M. Sun F. Molas S. et al. Midbrain dopamine controls anxiety-like behavior by engaging unique interpeduncular nucleus microcircuitry. Biol Psychiatry. 2020; 88: 855-866 Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar ) described how ventral tegmental area dopamine engages dopamine D1 receptor–expressing neurons in the caudal interpeduncular nucleus that innervate ventral neuronal populations of this nucleus, thereby amplifying the ventral tegmental area signal to modulate certain aspects of behavior in mice in the commonly used elevated plus maze and open field tests. Reported experiments employed cutting-edge technologies to deliver a convincing set of results.

While high risk of failure is an inherent part of developing innovative therapies, it can be reduced by adherence to evidence-based rigorous research practices. Numerous analyses conducted to date have clearly identified measures that need to be taken to improve research rigor. Supported through the European Union’s Innovative Medicines Initiative, the EQIPD consortium has developed a novel preclinical research quality system that can be applied in both public and private sectors and is free for anyone to use. The EQIPD Quality System was designed to be suited to boost innovation by ensuring the generation of robust and reliable preclinical data while being lean, effective and not becoming a burden that could negatively impact the freedom to explore scientific questions. EQIPD defines research quality as the extent to which research data are fit for their intended use. Fitness, in this context, is defined by the stakeholders, who are the scientists directly involved in the research, but also their funders, sponsors, publishers, research tool manufacturers and collaboration partners such as peers in a multi-site research project. The essence of the EQIPD Quality System is the set of 18 core requirements that can be addressed flexibly, according to user-specific needs and following a user-defined trajectory. The EQIPD Quality System proposes guidance on expectations for quality-related measures, defines criteria for adequate processes (i.e., performance standards) and provides examples of how such measures can be developed and implemented. However, it does not prescribe any pre-determined solutions. EQIPD has also developed tools (for optional use) to support users in implementing the system. Further, EQIPD is preparing training support and assessment services for those research units that successfully implement the quality system and would like to seek formal accreditation. Building upon the feedback from users and continuous improvement, a sustainable EQIPD Quality System will ultimately serve the entire community of scientists conducting non-regulated preclinical research, by helping them generate reliable data that are fit for their intended use.

Over the last two decades, awareness of the negative repercussions of flaws in the planning, conduct and reporting of preclinical research involving experimental animals has been growing. Several initiatives have set out to increase transparency and internal validity of preclinical studies, mostly publishing expert consensus and experience. While many of the points raised in these various guidelines are identical or similar, they differ in detail and rigour. Most of them focus on reporting, only few of them cover the planning and conduct of studies. The aim of this systematic review is to identify existing experimental design, conduct, analysis and reporting guidelines relating to preclinical animal research. A systematic search in PubMed, Embase and Web of Science retrieved 13 863 unique results. After screening these on title and abstract, 613 papers entered the full-text assessment stage, from which 60 papers were retained. From these, we extracted unique 58 recommendations on the planning, conduct and reporting of preclinical animal studies. Sample size calculations, adequate statistical methods, concealed and randomised allocation of animals to treatment, blinded outcome assessment and recording of animal flow through the experiment were recommended in more than half of the publications. While we consider these recommendations to be valuable, there is a striking lack of experimental evidence on their importance and relative effect on experiments and effect sizes.

Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.