CPV of the Future: AI-Powered Continued Process Verification for Bioreactor Processes - Ondracka, Andrej; Gasset, Arnau; García-Ortega, Xavier; Hubmayr, David; van Wijngaarden, Joeri; Montesinos-Seguí, José Luis; Valero, Francisco; Manzano, Toni (PDA journal of pharmaceutical science and technology, 2023)
According to the standard guidelines by the FDA, process validation in biopharma manufacturing encompasses a life cycle consisting of three stages: process design (PD), process qualification (PQ), and continued process verification (CPV). The validity and efficiency of the analytics methods employed during the CPV require extensive knowledge of the process. However, for new processes and new drugs, such knowledge is often not available from Process performance qualification and Validation (PPQV). In this work, the suitability of methods based on machine learning/artificial intelligence (ML/AI) for the CPV applied in bioprocess monitoring and cell physiological control of the yeast was studied with limited historical data. In particular, the production of recombinant lipase 1 (Crl1) under hypoxic conditions in fed-batch cultures was considered as a case study. Supervised and unsupervised machine learning models using data from fed-batch bioprocesses with different gene dosage clones under normoxic and hypoxic conditions were evaluated. Firstly, a multivariate anomaly detection (isolation forest) model was applied to the batch phase of the bioprocess. Secondly, a supervised random forest model for prediction of required operator's control actions during the semiautomated fed-batch phase under hypoxic conditions was assessed to maintain the respiratory quotient (RQ) within the desired range for maximizing the specific production rate ( ). The performance of these models was tested on historical data using independent evaluation of the process by the process control engineer (subject matter expert-SME), and on real-time data in the case of manual action prediction, where the model was implemented to guide the control of the bioprocess. The work presented here constitutes a proof-of-concept that multivariate analytics methods, based on machine learning, can be a valuable tool for real-time monitoring and control of biopharma manufacturing bioprocesses to improve its efficiency and to assure product quality.