Drug developers often apply for IND, NDA, and ANDA submissions to the US FDA. These regulatory submissions should have data on bioavailability, bioequivalence, pharmacokinetics, toxicology, clinical pharmacology, and preclinical evaluation. All these studies require a fully developed and validated bioanalytical method. Numerous bioanalytical techniques are developed following regulatory guidelines to quantitate drugs and their metabolites in biological samples. Besides, regulatory bodies have specific guidelines to help drug developers develop robust bioanalytical methods for supporting pharmaceutical studies.
Bioanalytical method development and validation are essential for the success of drug development studies. These bioanalytical methods provide crucial quantitative and qualitative data on analytes, including biomarkers, drugs, and biological products. These validated bioanalytical methods deliver critical data on drug efficiency and safety. Validation parameters such as accuracy, sensitivity, and selectivity are vital in drug development assessments such as GLP toxicity studies. However, accelerating bioanalytical method development and validation remains critical in saving resources and experimental time. Therefore, the current article explores the role of automation in accelerating bioanalytical method development and validation.
Accelerating bioanalytical method development and validation
Today, industrial processes have effectively incorporated automation into work environments. Similarly, Life Science research has also started integrating automation into different ranges of experimental tasks. Academic experimental setup often includes a high level of manual labor where multiple studies solely depend on a single researcher manually experimenting.
On the other hand, industrial environments have heavily invested in automation to increase their outputs and profits. A similar level of benefit has been seen in clinical laboratories employing automation to increase speed and reliability. Hence, automation has several advantages in accelerating bioanalytical method development and validation.
Automation plays a crucial factor in increasing the reproducibility of a bioanalytical method. Automated approaches can improve reproducibility by reducing human errors, increasing data generation, and decreasing contamination. However, the extent of improvement will depend on individual protocols. Data variability due to human errors is a common issue in clinical research. This variation in experimental data can arise knowingly or unknowingly during the experiment. Automation can effectively replace many human-based steps and minimize the risk of variability.
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Efficiency is of utmost importance within manufacturing and production. Drug developers can adopt automated tools and thereby increase the rate of production and decrease their dependency on manual resources. Automatic tools can help scientists improve their efficiency by generating a higher rate of experimental output compared to manual setups. Besides, automated workflows help scientists focus on more critical tasks and dedicate time to brainstorming ideas.
Automation has a special space in applied research where scientists are focused on developing novel therapies such as tissue engineering and cell-based therapeutics. These research fields heavily focus on transitioning from pure research to clinical settings. Here, automation can help accelerate the transition from early-stage drug development to later stages of clinical setting, eventually producing large volumes of drug products.
In Conclusion
Drug discovery and development is a demanding endeavor. Therefore, supporting bioanalytical method development and validation through automated protocols can accelerate drug development timelines.
