A blog article by Ashlie Reker Ph.D.
At its most basic interpretation, in vivo means “in living”. This term is sometimes confused with in vitro, which means “in glass”. Pertaining to biomedical research, in vitro utilizes cells that are kept alive in a glass dish or tube to collect data on how diseases and/or treatments impact living organisms at the cellular level.
This data is then often used to inform on the details of study design and execution for in vivo research, which utilizes live animal testing to collect data on how diseases and/or treatments impact an entire living organism. In vivo studies in biomedicine are considered “pre-clinical” as, like in vitro informs on in vivo, in vivo informs on clinical studies, which utilize human testing to collect data on how diseases and/or treatments impact patients.
Animal research is important for drug development as it focuses on understanding the biology and genetics of living organisms with a high degree of accuracy and translatability. In biomedical studies, this type of analysis aims to understand disease etiology and progression. It is used to identify potential biochemical pathways that might serve as targets for drugs to cure or mitigate diseases. Without animal research, our medical science progress would not be nearly as advanced as it is today.
In vivo research achieves this by executing scientific studies in progressively higher order animal models, for example, rodents to swine to non-human primates. Early phase pharmaceutical and biomedical research strongly focus on drug metabolism, safety, and efficacy, using pharmacokinetic and pharmacodynamic techniques. Because the ultimate goal is to develop approved treatments for humans, there are strict requirements in pre-clinical in vivo research surrounding data collection, tracking, and integrity. Operational consistency and data management of pre-clinical in vivo studies are key to achieving IND approved research. In vivo protocols must be designed and conducted with a high degree of precision and recorded for future replication and data validation experiments. Time and process-sensitive tasks must be carefully tracked with complete chain of custody and audit trails covering all operations. Metadata must also be recorded for future reference and can be critical to identify disparities between validation studies.
In order to meet the stringent requirements for in vivo studies, modern labs rely on at least one laboratory information management system (LIMS). Drug discovery LIMS software is a vital investment for any biomedical or pharmaceutical organization conducting in vivo testing. Cloud-based LIMS are increasingly preferred as they promote collaboration between specialized teams throughout the development pipeline.
Climb 2.0 meets and exceeds the needs for drug discovery labs to undergo a digital transformation. This azure cloud-based platform is a fully auditable repository for experimental design, study execution, data capture, and animal and sample metadata recording. Climb 2.0 software also provides other key features to optimize in vivo based R&D such as exceptionally comprehensive animal colony management, API compatibility and permission-based access.