ReciBioPharm’s Edita Botonjic-Sehic on analytics, AI, and continuous RNA manufacturing

2024-07-12

ReciBioPharm’s head of process analytics and data science, Edita Botonjic-Sehic, takes us through how analytics and artificial intelligence are spurring the adoption of more efficient continuous manufacturing processes.

The development of RNA therapeutics has seen significant expansion in recent years. Recent approvals, including Dicerna Pharmaceuticals’ Rivfloza for primary hyperoxaluria and Ionis Pharmaceuticals’ Wainua for transthyretin-related hereditary amyloidosis transthyretin-related hereditary amyloidosis, highlight the rapid progress in this field. With over 1,072 therapies in the pipeline, there is a growing need to enhance manufacturing processes for speed and quality.

Transition to continuous manufacturing

Edita Botonjic-Sehic explains, “While batch manufacturing is well-established and many biopharma companies have regulatory approval for their therapies using this technique, it’s increasingly recognized as a bottleneck in production processes. Batch manufacturing can potentially take weeks to complete, and scaling production is limited to bioreactor size and how quickly batches can be turned over.”

Continuous manufacturing, in contrast, reduces manufacturing costs, speeds up production times, lowers the risk of human error, reduces scale-up investment, and improves product quality. Despite these advantages, its adoption for commercial biologics production has been slow. However, regulatory bodies like the International Council for Harmonisation and the FDA are now encouraging continuous manufacturing, recognizing its potential benefits.

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Role of process analytic technologies

“Data collection plays a pivotal role throughout drug production, serving as evidence of the product’s quality,” says Botonjic-Sehic.

Regulatory bodies require this data to ensure the drug is effective and safe for patients. In traditional batch manufacturing, assays are often conducted offline, taking days to complete. Continuous manufacturing, however, benefits from real-time monitoring and measurement of critical quality attributes (CQAs) through process analytic technologies.

“These technologies reduce manual labor, eliminate time-consuming steps, and remove the need for sample transport,” Botonjic-Sehic adds. “What previously took two weeks of offline analyses can now be completed in a matter of days.”

Enhancing continuous manufacturing

Adopting inline or online alternatives reduces the risk of human error and ensures an uninterrupted manufacturing process. It also minimizes the number of employees needed by reducing the time spent analyzing samples, thus reducing operator needs. “Drug developers using process analytic technologies can minimize batch failures, reduce the need for samples pulled from the process for analysis, and eliminate material waste,” Botonjic-Sehic explains.

Adapting to future demand

As the industry moves towards ‘Industry 5.0,’ RNA therapy producers need robust implementation strategies to leverage inline process analytic technologies, automation, and real-time data analytics. Machine learning and AI can create models for real-time monitoring and control, enabling proactive identification of risks and trend analysis. This approach allows drug developers to minimize failure risks, reduce waste, and ensure higher quality products.

Future of RNA manufacturing

“Implementing continuous manufacturing for monoclonal antibodies often requires substantial investment due to necessary infrastructure changes,” says Botonjic-Sehic. “However, in the relatively new field of RNA therapeutics, applying continuous manufacturing is potentially less complex.” Developers can design and construct continuous manufacturing lines from the ground up, bringing greater flexibility and potentially reducing investment requirements.

The future of RNA manufacturing holds tremendous promise as the biopharmaceutical industry embraces digital transformation. By leveraging process analytic technologies, automation, and real-time data analytics, drug developers can optimize their continuous manufacturing processes, achieving higher efficiency, improved product quality, and reduced costs. The integration of machine learning and AI will further enhance process understanding and control, enabling predictive and proactive decision-making.

As the RNA therapeutic landscape evolves, continuous manufacturing is poised to become the gold standard, offering greater agility and flexibility in meeting the growing demand for innovative and life-saving RNA therapies.