Modeling, Simulation and Data Analysis of Pharmaceutical Manufacturing Processes

Dear Colleagues,

Pharmaceutical processing models, whether linear or non-linear, discrete or continuous, static or dynamic, explicit or implicit, deterministic or probabilistic, portray mathematical representations of correlations between manufacturing variables. Pharmaceutical processing models simulate drug and excipient syntheses and their formulations, amongst various other important product lifecycle steps such as analytical testing, packaging, and supply chain management. These correlations underly "ab initio" physio-chemical phenomena such as Mass and Heat Transfer, Mass Flows, Reaction Kinetics and Thermodynamics, setting up "First Principle" models. As real-world situations increase the complexity of the interplay between variables, the system’s behavior often deviates from this "knowledge-based" description. Empirical models are therefore recruited to analyze arbitrary correlations of available datasets, creating hybrid optimization threads. These threads may also be delegated by Artificial Intelligence through Neuronal Networks, Machine Learning algorithms, and Data Mining functions.

The outcome must be the creation of models computed through simulations that put out readable, meaningful, and repeatable results. At the macro level of magnification, these simulations reveal how pharmaceutical systems operate together to create an entire process line or a production facility.  Such intelligent in silico analogues offer a blend of stochastic, empirical, and mechanistic wisdom now invaluable in terms of technical, economic, and environmental risk-eliminating factors. Where these analogues evolve to provide the conditions of mirrored functional space models capable of responding to physical state changes of a simulated object, "Digital Twins" servicing process surveillance, optimization, and prediction are manifested.

The application of the mentioned enabling tools on pharmaceutical manufacturing processes positively impacts their efficiency, standardization, flexibility, logistics, budgeting and, therefore, their overall robustness. Under this lens, Expert Agents for rational and stepwise Workflow Design, Piloting and Scaling, Life Cycle Management, What If Data-driven Decisions, and Process Control concepts are utilized. Interestingly, all of these applications, which are empowered by Big Data Analytics, slowly yet consistently materialize the vision of individualized therapeutic approaches for special patients and/or populations.

Henceforward, through their algorithmic models, advanced process simulations will embellish the interface of singularity between humans and machines to operate alongside each other towards successful, efficient, autonomous, streamlined, and flexible pharmaceutical development. The theme of this Special Issue of the MDPI journal Pharmaceutics is  "Modeling, Simulation and Data Analysis of Pharmaceutical Manufacturing Processes"; it attempts to curate the methods and protocols for how any of the above-mentioned novelties will play out on the scientific odyssey from model Process Design and Statistical Data Analysis to the hallmark of harmonized human–machine operations. This is one of the many steps in the journey of the pharmaceutics field to potentialize the advent of a more anthropocentric epoch of abundancy and sustainability, thus embracing the promised "Industry 5.0" sociotechnical renaissance.

Prof. Dr. Milos Kojic
Guest Editor

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