Pharmaceutical Engineering Magazine

Stakeholders across industries are becoming accustomed to using information technology (IT) systems, applications, and business solutions that feature artificial intelligence (AI) and machine learning (ML). Even though some of these uses show phenomenal performance, thorough risk management is required to ensure quality and regulatory compliance are met within the life sciences industry. By leveraging specialized frameworks and methods, we compiled a holistic framework to dynamically identify, assess, and mitigate risks when AI and ML features are in use.

There is much that large-scale commercial stem cell therapy processes can adopt from the existing bioprocessing industry. This article addresses some of the unique challenges posed by large-scale stem cell and stem cell–derived product manufacturing processes, and what should be considered while designing a manufacturing facility.

Regulatory Trends & Quality Initiatives - Shortages of essential medicines around the world have been an ongoing concern for patients, caregivers, and regulators and have been exacerbated by the COVID-19 pandemic. Many regulators have instituted requirements for reporting potential or actual drug shortages. To further minimize drug shortages, regulators in the United States and France recently established requirements for risk management on drug shortages avoidance. Such requirements could spread beyond these two countries, especially because risk management for product availability is included in the revision of the ICH Q9(R1) guideline “Quality Risk Management.” Shortages of essential medicines around the world have been an ongoing concern for patients, caregivers, and regulators and have been exacerbated by the COVID-19 pandemic. Many regulators have instituted requirements for reporting potential or actual drug shortages. To further minimize drug shortages, regulators in the United States and France recently established requirements for risk management on drug shortages avoidance. Such requirements could spread beyond these two countries, especially because risk management for product availability is included in the revision of the ICH Q9(R1) guideline “Quality Risk Management.”

Continuous manufacturing (CM) challenged regulators’ expectations and regulatory frameworks. This article discusses how US regulators addressed the regulatory hurdles related to CM to broaden its adoption through engagement, regulatory science, guidance, and international harmonization.

North Carolina’s Research Triangle is the largest of its kind in the US. Thanks to years of effort from industry, pharmaceutical professionals, and education institutions, it is synonymous with pharmaceutical and biotechnology excellence.

As the pharmaceutical industry faces ever-changing global challenges and market forces, it must review and revise product design to ensure that quality products remain available in the marketplace while moving toward zero pollution for air, water, and soil. This article provides an introduction on how quality products can integrate sustainability by design.

This article explores life-cycle activities for machine learning (ML) within regulated life sciences. It positions and contextualizes the life cycle and management of the machine learning subsystem or components within a wider system life cycle. It also gives general descriptions and guidance illustrated by a case study demonstrating a machine learning application to medical image recognition, or software as a medical device (SaMD).

Many organizations are evaluating how advanced therapy medicinal products (ATMPs) and other traditional modalities may be combined within the same facility or within a newly constructed agnostic building. This article outlines a broad framework to evaluate different types of modalities that may be accommodated concurrently and then uses a case study to explain how the approach may be applied to an existing facility.