A greener pharmaceutical industry

Process safety

Safety is a fundamental aspect of green chemistry, and it is emphasised in two of the twelve principles of green chemistry, which focus on minimising hazardous syntheses and promoting inherently benign chemistry to prevent accidents. Preventing chemical accidents is essential, not only to safeguard the well-being of workers but also to protect the surrounding population and the environment. Key steps towards safe chemistry are detailed here.

Risk assessment

A risk assessment is an essential step in evaluating the potential hazards associated with chemical processes prior to implementation. It systematically identifies, evaluates, and mitigates risks to ensure that both employee safety and environmental protection are maintained throughout the process (ACS, 2024). Key characteristics of effective risk assessments include:

• Hazard identification: Determining what could go wrong in the process, such as chemical reactivity, toxicity, or equipment failure.

• Consequence analysis: Evaluating the potential impact of identified hazards on human health, the environment, and production.

• Mitigation strategies: Implementing measures to reduce the likelihood or severity of accidents, such as engineering controls, personal protective equipment, and emergency response plans.

Process Analytical Technology (PAT)

Process Analytical Technology (PAT) is a framework advanced by regulatory agencies, notably the U.S. Food and Drug Administration (FDA, 2024), aimed at enhancing the understanding and control of manufacturing processes (Chew and Sharratt, 2010). It represents a system designed to design, analyse, and control pharmaceutical manufacturing processes through the measurement of critical process parameters that affect critical quality attributes. The primary goal of PAT is to ensure the quality of the final product by controlling the manufacturing process in real-time, reducing reliance on end-product sampling and testing (Simon et al., 2015). 

PAT employs a variety of analytical tools and techniques to monitor and control the physical, chemical, and biological processes involved in pharmaceutical manufacturing. This approach enables manufacturers to detect variations in the process as they occur, allowing for immediate adjustments to maintain product quality.

Digital twins

A digital twin is a virtual model of a process that can be used to understand and improve the function of the real-world application through simulation (Stark et al., 2019). The advent of digital twin technology represents a transformative shift across various industries, including the pharmaceutical sector (Chen et al., 2020). This innovation, emerging from the confluence of Industry 4.0 technologies, can mirror the behaviour and dynamics of systems in real-time. 

Digital twin simulations can predict aspects of product quality, productivity, and specific process characteristics, substantially reducing the need for time-consuming and costly physical testing. Continuous analysis facilitates ongoing control and optimisation of the process, ensuring that manufacturing operations are both efficient and adaptable to changing conditions. Furthermore, digital twins serve an educational purpose, acting as a dynamic training platform for operators and engineers. Through simulations that mimic real-world scenarios and provide instant feedback, digital twins create an immersive learning environment that prepares operators for a wide range of operational situations. 

Beyond the direct manufacturing process, digital twins extend their utility to pre- and post-manufacturing activities, such as material tracking, serialisation, and quality assurance. This wide-ranging application of digital twin technology underscores its value in not only optimising manufacturing processes but also in enhancing the overall quality and efficiency of pharmaceutical production.