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

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.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

Mapping strategies towards improved external validity in preclinical translational research

INTRODUCTION

Translation is about successfully bringing findings from preclinical contexts into the clinic. This transfer is challenging as clinical trials frequently fail despite positive preclinical results. Limited robustness of preclinical research has been marked as one of the drivers of such failures. One suggested solution is to improve the external validity of in vitro and in vivo experiments via a suite of complementary strategies.

AREAS COVERED

In this review, the authors summarize the literature available on different strategies to improve external validity in in vivo, in vitro, or ex vivo experiments; systematic heterogenization; generalizability tests; and multi-batch and multicenter experiments. Articles that tested or discussed sources of variability in systematically heterogenized experiments were identified, and the most prevalent sources of variability are reviewed further. Special considerations in sample size planning, analysis options, and practical feasibility associated with each strategy are also reviewed.

EXPERT OPINION

The strategies reviewed differentially influence variation in experiments. Different research projects, with their unique goals, can leverage the strengths and limitations of each strategy. Applying a combination of these approaches in confirmatory stages of preclinical research putatively increases the chances of success in clinical studies.