Accelerating the Development and Transfer of Freeze-Drying Operations for the Manufacturing of Biopharmaceuticals by Model-Based Design of Experiments

A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

Practical use of primary drying models in an industrial environment with limited availability of equipment sensors

In the pharmaceutical industry, lyophilization is typically adopted to extend long-time stability of valuable thermolabile medicines and vaccines. Primary drying is the most time-consuming and energy-intensive step of the entire process; thus, accelerating and optimizing the primary drying recipe is a key process development goal. To that purpose, mathematical models have been proposed and successfully validated. However, models typically require invasive experiments and/or sensors (e.g. product temperatures) for parameter estimation, which are rarely available in good manufacturing practice (GMP) environment. This represents a severe limitation when leveraging the model to transfer operation recipes across different facilities and for scale-up. In this study, we assess the possibility to exploit limited industrial data for model parameter estimation, namely pressure measurements and gravimetric tests, by defining a calibration protocol that is tested on two different pieces of equipment. Results are verified on a recently proposed model, and show that statistically meaningful estimates can be obtained without the need of product temperature measurements, and that model predictions and optimal inputs trajectories are comparable to those obtained from the model calibrated using the full set of temperature and pressure data.

Streamlining tablet lubrication design via model-based design of experiments

In oral solid dosage production through direct compression powder lubrication must be carefully selected to facilitate the manufacturing of tablets without degrading product manufacturability and quality (e.g. dissolution). To do so, several semi-empirical models relating compression performance to process operating conditions have been developed. Among them, we consider an extension of the Kushner and Moore model (Kushner and Moore, 2010, International Journal Pharmaceutics, 399:19) that is useful for the purpose, but requires an extensive experimental campaign for parameters identification. This implies the preparation and compression of multiple powder blends, each one with a different lubrication extent. In turn, this translates into a considerable consumption of Active Pharmaceutical Ingredient (API), and into time-consuming experiments. We tackled this issue by proposing a novel model-based design of experiments (MBDoE) approach, which minimizes the number of optimal blends for model calibration, while obtaining statistically sound parameters estimates and model predictions. Both sequential and parallel MBDoE configurations were compared. Experimental results involving two placebo blends with different lubrication sensitivity showed that this methodology is able to reduce the experimental effort by 60-70% with respect to the standard industrial practice independently of the formulation considered and configuration (i.e. parallel vs. sequential) adopted.

Towards in silico Process Modeling for Vaccines

Chemical, manufacturing, and control development timelines occupy a significant part of vaccine end-to-end development. In the on-going race for accelerating timelines, in silico process development constitutes a viable strategy that can be achieved through an artificial intelligence (AI)-driven or a mechanistically oriented approach. In this opinion, we focus on the mechanistic option and report on the modeling competencies required to achieve it. By inspecting the most frequent vaccine process units, we identify fluid mechanics, thermodynamics and transport phenomena, intracellular modeling, hybrid modeling and data science, and model-based design of experiments as the pillars for vaccine development. In addition, we craft a generic pathway for accommodating the modeling competencies into an in silico process development strategy.