Innovating cell culture process development with deep learning‐powered robotic experimentation using the first <scp>I</scp> ndustrial <scp>S</scp> mart <scp>L</scp> ab <scp>F</scp> ramework

Traditional biologics process development, including antibody and recombinant protein production, typically relies on labor-intensive, iterative cell culture optimization to determine optimal process parameters. To address this inefficiency, we introduced the Industrial Smart Lab Framework for Cell Culture (ISLFCC), an autonomous laboratory that combines deep learning and robotic experimentation to enhance cell culture processes. In this system, robotic arms sample various bioreactors for analysis, and the IoT system transmits these analysis results to decoder-only transformer deep learning models. Based on these analysis results, these models predict future cell states and recommend optimal actions, which are then executed by automation devices through the IoT system, such as adjusting nutrient feeds and temperature shifts. In a comparative case study, our AI-driven process development for three different cell clones resulted in an average titer increase of 26.8% and maintained lactate levels below 1 g/L without rebound in the late phase within just a single batch, surpassing traditional three-stage empirical process development methods. Moreover, our approach has greatly automated cell culture to ensure enhanced reproducibility, data accuracy, adaptability to various cell lines, and seamless scalability across production scales, marking the first implementation of high-throughput automated cell culture in 3 and 15 L bioreactors. By merging AI with robotic execution, ISLFCC provides a transformative framework that accelerates biologics development, representing a paradigm shift towards autonomous, data-driven biomanufacturing.