Errors, Contaminations, and Confounding Factors in in vivo Studies: Challenges and Practical Solutions

In vivo studies remain a cornerstone of biomedical, pharmacological, and toxicological studies, providing critical insights into the safety and efficacy of novel interventions. However, the reliability and translational significance of these experiments are often compromised by methodological mistakes, hidden contaminations, and uncontrolled confounding factors. By identifying threats to validity and offering practical solutions, the current review aimed to enhance reproducibility, reduce unnecessary expenditure of time and resources, and improve the ethical and scientific integrity of in vivo studies. Poor study design, insufficient randomization, and operator-related inconsistencies introduce variability that may obscure true biological effects. Similarly, viral or microbial infections, environmental contaminants in feed or bedding, and cross-contamination between animals can profoundly alter immune, metabolic, or behavioral outcomes, often without being detected until results prove inconsistent. Furthermore, factors such as temperature, light cycles, handling stress, circadian rhythms, and biological characteristics of the animals introduce additional layers of complexity, leading to irreproducible or contradictory findings. The present study consolidated the existing evidence on the primary sources of errors, contamination, and confounding factors in in vivo studies, supported by practical case examples. Additionally, the present study emphasized best practices for mitigation, such as standardizing protocols, following animal research guidelines in in vivo experiments, utilizing specific-pathogen-free animals, continuously monitoring environmental and health parameters, and providing comprehensive staff training. Thus, emerging solutions such as automation, artificial intelligence, and the increasing incorporation of in vitro and in silico alternatives were explored as methods to decrease reliance on animal testing models.

https://jlar.rovedar.com/index.php/JLAR/article/view/78/109