An Update on Current Trend in Sample Preparation Automation in Bioanalysis: strategies, Challenges and Future Direction

Automation in sample preparation improves accuracy, productivity, and precision in bioanalysis. Moreover, it reduces resource consumption for repetitive procedures. Automated sample analysis allows uninterrupted handling of large volumes of biological samples originating from preclinical and clinical studies. Automation significantly helps in management of complex testing methods where generation of large volumes of data is required for process monitoring. Compared to traditional sample preparation processes, automated procedures reduce associated expenses and manual error, facilitate laboratory transfers, enhance data quality, and better protect the health of analysts. Automated sample preparation techniques based on robotics potentially increase the throughput of bioanalytical laboratories. Robotic liquid handler, an automated sample preparation system built on a robotic technique ensures optimal laboratory output while saving expensive solvents, manpower, and time. Nowadays, most of the traditional extraction processes are being automated using several formats of online techniques. This review covered most of the automated sample preparation techniques reported till date, which accelerated and simplified the sample preparation procedure for bioanalytical sample analysis. This article critically analyzed different developmental aspects of automated sample preparation techniques based on robotics as well as conventional sample preparation methods that are accelerated using automated technologies.

Piston-driven automated liquid handlers

Laboratory capacities are often limited by time-consuming manual repetitive procedures rather than analysis time itself. While modern instruments are typically equipped with an autosampler, sample preparation often follows manual procedures including many labor-intensive, monotonous tasks. Particularly, for a high number of samples, well plates, and low microliter pipetting, manual preparation is error-prone often requiring repeated experiments. Sampling and sample preparation can account for greater analytical variability than instrument analysis. Repetitive tasks such as liquid handling benefit strongly from technological advances and led to the increasing applications of various automated liquid handlers (ALHs). In this review, we discuss the considerations for ALHs in the microliter range and highlight advantages and challenges when transforming from manual to automated workflows. We strongly focused on differences in liquid handling and outlined advantages due to sensor-controlled pipetting. ALHs can substantially improve costs-effectiveness and laboratory capacity. This is a consequence of increased efficiency, and throughput of laboratories while simultaneously raising data quality. Additionally, ALHs can improve safety, documentation of data, and sustainability. While automation requires careful consideration and resource demanding implementation, we believe it offers numerous advantages and can help to transform modern laboratories.

Automation With Intelligence in Drug Research

The industry has adopted Clinical Data Interchange Standards Consortium standards for clinical trial data and the Food and Drug Administration electronic common technical document standard for documents for many years but still faces many challenges. The solutions based on these standards enable integration among solo systems, but the integration needs to be based on business requirements and provides the end-to-end intelligence for the business. The more standards are adopted, the more meaningful and timely metadata are needed to manage the change of the standards and need to be applied in the process. Automation that uses artificial intelligence and machine learning will be the next game changer in the industry to provide data with higher quality and more efficiency. This article discusses the challenges in managing standards adoption, potential approaches for automation through using robotic processes, artificial intelligence, and the maturity model for metadata-driven automation in clinical research.

Small-molecule bioanalytical sample preparation method development starting from the BASICS

Aim: To develop a robust small-molecule bioanalytical sample preparation method, systematic investigations of the bioanalytical handling conditions of analytes and IS of interest are preferred. So far, such investigations are done manually and are labor-intensive and error-prone. Result: An automation-assisted system has been developed to facilitate such systematic investigations. The system takes experimental design and automates the majority of the wet laboratory work. In addition, the system also automates the data extraction, recovery/loss computation, graphing and reporting. Conclusion: The automation-assisted system greatly reduces errors and labor involved in the systematic investigation of analytes and IS both for wet chemistry experiments and for data extraction and processing, enhances data processing efficiency and overall sample preparation method development productivity.

Current advances and strategies towards fully automated sample preparation for regulated LC–MS/MS bioanalysis

Robotic liquid handlers (RLHs) have been widely used in automated sample preparation for liquid chromatography–tandem mass spectrometry (LC–MS/MS) bioanalysis. Automated sample preparation for regulated bioanalysis offers significantly higher assay efficiency, better data quality and potential bioanalytical cost-savings. For RLHs that are used for regulated bioanalysis, there are additional requirements, including 21 CFR Part 11 compliance, software validation, system qualification, calibration verification and proper maintenance. This article reviews recent advances in automated sample preparation for regulated bioanalysis in the last 5 years. Specifically, it covers the following aspects: regulated bioanalysis requirements, recent advances in automation hardware and software development, sample extraction workflow simplification, strategies towards fully automated sample extraction, and best practices in automated sample preparation for regulated bioanalysis.