Medicine makers need to get better at math. Improved mathematical modeling would accelerate the move toward automated processes, cut production times, and reduce costs.

Researchers Francesco Destro, PhD, and Massimiliano Barolo, PhD, at the University of Padova’s computer-aided process engineering laboratory, made the case for investment in modeling in a new study, arguing that drug makers lag behind other industries.

“Mathematical modeling can support the implementation of Quality-by-Design and Quality-by-Control across all stages of the pharmaceutical life cycle. They can help increase process understanding,” Destro says. “Models are key to arriving at a description of the design space; they are at the heart of most advanced PATs [process analytical technologies]; they allow building soft sensors, through which key process variables can be reliably estimated when they cannot be measured.”

Models are also vital for the design and implementation of advanced process control systems, according to Destro, who adds, “They can be used to build a virtual environment through which one can simulate the operation of a real plant.”

And there is a clear business case for investment in modeling, notes Barolo, who cites process control as a major benefit.

“Active quality control systems for pharmaceutical process development and manufacturing could reduce the product launch time by more than 30%, increase manufacturing and supply chain capacity and responsiveness by 20 to 30%, and prevent major compliance issues by reducing manual errors and variability,” Barolo adds.

Model behavior

And the good news for the biopharmaceutical sector is that other industries have already done the math, Barolo says.

“Much can be borrowed from other industrial sectors, for example, the chemical process industry, where the use of mathematical models is consolidated,” he explains. “There is room for transferring to the pharma sector research on optimal model-based design of experiments, both for model discrimination and for parameter identification; this could drastically reduce the costs associated to expensive experimentation.”

For Destro, model use in biopharmaceutical process development could be improved, citing a 2020 study.

“Pharmaceutical process synthesis, using mathematical models, is still under-explored. More systematic procedures to quantify mathematical model uncertainty and to assess model credibility, across the entire life cycle of a product, would be most welcome and can support regulatory filing,” Destro maintains. “The development of benchmark simulators of pharmaceutical processes would help gain confidence in advanced process monitoring, active process control, and process optimization; new benchmark simulators can also support the transition to continuous manufacturing.”

Another positive for biopharmaceutical companies is that key regulators support manufacturing innovation and are receptive to emerging ideas, Destro says.

“The Emerging Technology initiatives of the FDA and of the EMA are great enablers: regulators help industry to bring new technologies to plant assisting practitioners in every stage,” he tells GEN. “At the same time, with this procedure, regulators become aware of novel technology and can prepare standard procedures for future applications involving a given technology.”