Self-Driving Lab for Solid-Phase Extraction Process Optimization and Application to Nucleic Acid Purification

Abstract

Sorptive bioprocesses are the basis for numerous biotechnological applications such as enzyme immobilization, biosensors, controlled drug delivery, water treatment, and molecular purification. Yet due to the complexity of these processes, their optimization is still time, labor, and cost-intensive. This research presents a flexible self-driving laboratory (SDL) designed for the accelerated development and optimization of solid-phase extraction processes. As a use case, the SDL was used to optimize a DNA purification process using silica magnetic beads. Through the integration of robotics, machine learning, and data-driven experimentation, the SDL demonstrates a highly accelerated process optimization with minimal human intervention. In the multistep purification approach, the system is able to optimize buffer compositions for DNA extraction from complex samples, demonstrating effectiveness in both conventional chaotropic salt-based methods and innovative chaotropic salt-free buffers. The study highlights the SDL's capability to autonomously refine process parameters, achieving significant enhancements in yield and purity of the product. This blueprint for future self-driving optimization of bioprocess parameters showcases the potential of autonomous systems to revolutionize biochemical process development, offering insights into scalable, environmentally sustainable, and cost-effective solutions.