Regulatory Demands for More Process Control Data Fueling Innovation
Advances in technology are making it easier for biopharma companies to embrace continuous manufacturing. However, they are also changing what regulators expect from drug firms in terms of the process control strategies they use. The post Regulatory Demands for More Process Control Data Fueling Innovation appeared first on GEN - Genetic Engineering and Biotechnology News .
Regulators are encouraging the use of continuous manufacturing processes for drug production, but with the condition that production is tightly controlled. This expectation is driving innovation in technology, which is reshaping the manufacturing industry. A recent study by Korean researchers highlights the significant impact of technological advancements and regulation on this evolving landscape.
Professor Moo Sun Hong, a chemical and biological engineering professor from Seoul National University, explains to GEN that several technological breakthroughs, including process analytical technologies (PAT), mechanistic models, and digital twins, have made continuous biomanufacturing more viable. These technologies enable real-time monitoring, prediction of process behavior, optimization of operating conditions, and early detection of deviations, allowing manufacturers to shift from reactive quality testing to proactive, model-informed process control.
Regulators' expectations are shifting from focusing on end-product testing to emphasizing a science- and risk-based understanding of processes throughout the product lifecycle. ICH Q13 guidance places greater importance on validated process models, real-time monitoring, and robust control strategies to ensure consistent product quality during continuous operation.
As technology advances, regulators may demand even more data regarding the models developers use during production. Professor Hong anticipates that regulators will become more receptive to advanced model-informed control strategies, provided there is solid evidence supporting the reliability of the underlying models. He also notes that continuous manufacturing's growing adoption is likely to result in regulators placing more emphasis on demonstrating that process models remain accurate over time through performance monitoring and model updates supported by new data.
Artificial intelligence (AI) is expected to play a more significant role in controlling continuous biopharmaceutical manufacturing processes, according to Hong. AI will primarily be used alongside mechanistic modeling, not as a replacement for it. In the near term, AI is likely to impact soft sensing, anomaly detection, process optimization, and the development and maintenance of digital twins.
Hybrid approaches that combine AI with first-principles models are particularly promising, as they can enhance predictive performance while retaining interpretability and physical consistency required for industrial deployment and regulatory acceptance.
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