Lyophilization is a common unit operation in pharmaceutical manufacturing but is a prolonged vacuum drying process with poor energy utilization. Microwave-assisted vacuum drying has been investigated to accelerate the lyophilization process. However, the literature lacks methodical approaches that consider the lyophilizer, the lyophilizate, the microwave power uniformity, the resulting heat uniformity, and the scalability. We present a microwave-vacuum drying method based on the statistical electromagnetics theory. The method offers an optimum frequency selection procedure that accounts for the lyophilizer and the lyophilizate. The 2.45 GHz frequency conventionally utilized is proven to be far from optimum. The method is applied in a microwave-assisted heating configuration to pharmaceutical excipients (Sucrose and Mannitol) and different myoglobin formulations in a lab-scale lyophilizer. At 18 GHz frequency and  60 W microwave power, the method shows nearly three times speed-up in the primary drying stage of sucrose relative to the conventional lyophilization cycle for typical laboratory batches. The uniformity of the microwave power inside the chamber is controlled within ±1dB. The resulting heating uniformity measured through residual moisture analysis shows 12.7% of normalized standard deviation of moisture level across the batch in a microwave-assisted cycle as opposed to 15.3% in the conventional cycle. Conventional and microwave lyophilized formulations are characterized using solid-state hydrogen-deuterium exchange-mass spectrometry (ssHDX-MS), solid-state Fourier transform infrared spectroscopy (ssFTIR), circular dichroism (CD), and accelerated stability testing (AST). Characterization shows comparable protein structure and stability. Heat and mass transfer simulations quantify further effects of optimal volumetric heating via the high-frequency statistical microwave heating.

This work presents a new user-friendly lyophilization simulation and process optimization tool, freely available under the name LyoPRONTO. This tool comprises freezing and primary drying calculators, a design-space generator, and a primary drying optimizer. The freezing calculator performs 0D lumped capacitance modeling to predict the product temperature variation with time which shows reasonably good agreement with experimental measurements. The primary drying calculator performs 1D heat and mass transfer analysis in a vial and predicts the drying time with an average deviation of 3% from experiments. The calculator is also extended to generate a design space over a range of chamber pressures and shelf temperatures to predict the most optimal setpoints for operation. This optimal setpoint varies with time due to the continuously varying product resistance and is taken into account by the optimizer which provides varying chamber pressure and shelf temperature profiles as a function of time to minimize the primary drying time and thereby, the operational cost. The optimization results in 62% faster primary drying for 5% mannitol and 50% faster primary drying for 5% sucrose solutions when compared with typical cycle conditions. This optimization paves the way for the design of the next generation of lyophilizers which when coupled with accurate sensor networks and control systems can result in self-driving freeze dryers.

This work describes lyophilization process validation and consists of two parts. Part I focuses on the process design and is described in the current paper, while part II is devoted to process qualification and continued process verification. The intent of these articles is to provide readers with recent updates on lyophilization validation in the light of community-based combined opinion on the process and reflect the industrial prospective. In this paper, the design space approach for process design is described in details, and examples from practice are provided. The approach shows the relationship between the process inputs; it is based on first principles and gives a thorough scientific understanding of process and product. The lyophilization process modeling and scale-up are also presented showing the impact of facility, equipment, and vial heat transfer coefficient. The case studies demonstrating the effect of batch sizes, fill volume, and dose strength to show the importance of modeling as well as the effect of controlled nucleation on product resistance are discussed.

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This work describes the lyophilization process validation and consists of two parts. Part one (Part I: Process Design and Modeling) focuses on the process design and is described in the previous paper, while the current paper is devoted to process qualification and continued process verification. The goal of the study is to show the cutting edge of lyophilization validation based on the integrated community-based opinion and the industrial perspective. This study presents best practices for batch size determination and includes the effect of batch size on drying time, process parameters selection strategies, and batch size overage to compensate for losses during production. It also includes sampling strategies to demonstrate batch uniformity as well as the use of statistical models to ensure adequate sampling. Based on the LyoHUB member organizations survey, the best practices in determining the number of PPQ runs are developed including the bracketing approach with minimum and maximum loads. Standard practice around CQA and CPP selection is outlined and shows the advantages of using control charts and run charts for process trending and quality control. The case studies demonstrating the validation strategy for monoclonal antibody and the impact of the loading process on the lyophilization cycle and product quality as well as the special case of lyophilization for dual-chamber cartridge system are chosen to illustrate the process validation. The standard practices in the validation of the lyophilization process, special lyophilization processes, and their impact on the validation strategy are discussed. Graphical Abstract