Practical solutions for including sex as a biological variable (SABV) in preclinical neuropsychopharmacological research.

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.

PEERS - An Open Science "Platform for the Exchange of Experimental Research Standards" in Biomedicine.

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.

Roadmap for Advancing Pre-Clinical Science in Traumatic Brain Injury.

Pre-clinical models of disease have long played important roles in the advancement of new treatments. However, in traumatic brain injury (TBI), despite the availability of numerous model systems, translation from bench to bedside remains elusive. Integrating clinical relevance into pre-clinical model development is a critical step toward advancing therapies for TBI patients across the spectrum of injury severity. Pre-clinical models include in vivo and ex vivo animal work-both small and large-and in vitro modeling. The wide range of pre-clinical models reflect substantial attempts to replicate multiple aspects of TBI sequelae in humans. Although these models reveal multiple putative mechanisms underlying TBI pathophysiology, failures to translate these findings into successful clinical trials call into question the clinical relevance and applicability of the models. Here, we address the promises and pitfalls of pre-clinical models with the goal of evolving frameworks that will advance translational TBI research across models, injury types, and the heterogenous etiology of pathology.

Systematic Review and Methodological Considerations for the Use of Single Prolonged Stress and Fear Extinction Retention in Rodents.

Posttraumatic stress disorder (PTSD) is a mental health condition triggered by experiencing or witnessing a terrifying event that can lead to lifelong burden that increases mortality and adverse health outcomes. Yet, no new treatments have reached the market in two decades. Thus, screening potential interventions for PTSD is of high priority. Animal models often serve as a critical translational tool to bring new therapeutics from bench to bedside. However, the lack of concordance of some human clinical trial outcomes with preclinical animal efficacy findings has led to a questioning of the methods of how animal studies are conducted and translational validity established. Thus, we conducted a systematic review to determine methodological variability in studies that applied a prominent animal model of trauma-like stress, single prolonged stress (SPS). The SPS model has been utilized to evaluate a myriad of PTSD-relevant outcomes including extinction retention. Rodents exposed to SPS express an extinction retention deficit, a phenotype identified in humans with PTSD, in which fear memory is aberrantly retained after fear memory extinction. The current systematic review examines methodological variation across all phases of the SPS paradigm, as well as strategies for behavioral coding, data processing, statistical approach, and the depiction of data. Solutions for key challenges and sources of variation within these domains are discussed. In response to methodological variation in SPS studies, an expert panel was convened to generate methodological considerations to guide researchers in the application of SPS and the evaluation of extinction retention as a test for a PTSD-like phenotype. Many of these guidelines are applicable to all rodent paradigms developed to model trauma effects or learned fear processes relevant to PTSD, and not limited to SPS. Efforts toward optimizing preclinical model application are essential for enhancing the reproducibility and translational validity of preclinical findings, and should be conducted for all preclinical psychiatric research models.

Introduction to the EQIPD quality system.

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.

Pre-Clinical Common Data Elements for Traumatic Brain Injury Research: Progress and Use Cases.

Traumatic brain injury (TBI) is an extremely complex condition due to heterogeneity in injury mechanism, underlying conditions, and secondary injury. Pre-clinical and clinical researchers face challenges with reproducibility that negatively impact translation and therapeutic development for improved TBI patient outcomes. To address this challenge, TBI Pre-clinical Working Groups expanded upon previous efforts and developed common data elements (CDEs) to describe the most frequently used experimental parameters. The working groups created 913 CDEs to describe study metadata, animal characteristics, animal history, injury models, and behavioral tests. Use cases applied a set of commonly used CDEs to address and evaluate the degree of missing data resulting from combining legacy data from different laboratories for two different outcome measures (Morris water maze [MWM]; RotorRod/Rotarod). Data were cleaned and harmonized to Form Structures containing the relevant CDEs and subjected to missing value analysis. For the MWM dataset (358 animals from five studies, 44 CDEs), 50% of the CDEs contained at least one missing value, while for the Rotarod dataset (97 animals from three studies, 48 CDEs), over 60% of CDEs contained at least one missing value. Overall, 35% of values were missing across the MWM dataset, and 33% of values were missing for the Rotarod dataset, demonstrating both the feasibility and the challenge of combining legacy datasets using CDEs. The CDEs and the associated forms created here are available to the broader pre-clinical research community to promote consistent and comprehensive data acquisition, as well as to facilitate data sharing and formation of data repositories. In addition to addressing the challenge of standardization in TBI pre-clinical studies, this effort is intended to bring attention to the discrepancies in assessment and outcome metrics among pre-clinical laboratories and ultimately accelerate translation to clinical research.