Practical Advice on Scientific Design of Freeze-Drying Process: 2023 Update.

OBJECTIVE: The purpose of this paper is to re-visit the design of three steps in the freeze-drying process, namely freezing, primary drying, and secondary drying steps. Specifically, up-to-date recommendations for selecting freeze-drying conditions are provided based on the physical-chemical properties of formulations and engineering considerations. METHODS AND RESULTS: This paper discusses the fundamental factors to consider when selecting freezing, primary drying, and secondary drying conditions, and offers mathematical models for predicting the duration of each segment and product temperature during primary drying. Three simple heat/mass transfer primary drying (PD) models were tested, and their ability to predict product temperature and sublimation time showed good agreement. The PD models were validated based on the experimental data and utilized to tabulate the primary drying conditions for common pharmaceutical formulations, including amorphous and partially crystalline products. Examples of calculated drying cycles, including all steps, for typical amorphous and crystalline formulations are provided. CONCLUSIONS: The authors revisited advice from a seminal paper by Tang and Pikal (Pharm Res. 21(2):191-200, 2004) on selecting freeze-drying process conditions and found that the majority of recommendations are still applicable today. There have been a number of advancements, including methods to promote ice nucleation and computer modeling for all steps of freeze-drying process. The authors created a database for primary drying and provided examples of complete freeze-drying cycles design. The paper may supplement the knowledge of scientists and formulators and serve as a user-friendly tool for quickly estimating the design space.

Best Practices and Guidelines (2022) for Scale-up and Technology Transfer in Freeze Drying Based on Case Studies. Part 2: Past Practices, Current Best Practices, and Recommendations.

Scale-up and transfer of lyophilization processes remain very challenging tasks considering the technical challenges and the high cost of the process itself. The challenges in scale-up and transfer were discussed in the first part of this paper and include vial breakage during freezing at commercial scale, cake resistance differences between scales, impact of differences in refrigeration capacities, and geometry on the performance of dryers. The second part of this work discusses successful and unsuccessful practices in scale-up and transfer based on the experience of the authors. Regulatory aspects of scale-up and transfer of lyophilization processes were also outlined including a topic on the equivalency of dryers. Based on an analysis of challenges and a summary of best practices, recommendations on scale-up and transfer of lyophilization processes are given including projections on future directions in this area of the freeze drying field. Recommendations on the choice of residual vacuum in the vials were also provided for a wide range of vial capacities.

Recommended Best Practices in Freeze Dryer Equipment Performance Qualification: 2022.

Best practices for performing freeze dryer equipment qualification are recommended, focusing on identifying methods to quantify shelf thermal uniformity (also known as "shelf surface uniformity"), equipment capability, and performance metrics of the freeze dryer essential to the pharmaceutical Quality by Design paradigm. Specific guidelines for performing shelf temperature mapping, freeze dryer equipment limit testing (the capability curve), and condenser performance metrics have been provided. Concerning shelf temperature mapping and equipment capability measurements, the importance of paying attention to the test setup and the use of appropriate testing tools are stressed. In all the guidelines provided, much attention has been paid to identifying the balance between obtaining useful process knowledge, logistical challenges associated with testing in the production environment vs that at laboratory scale, and the frequency of the testing necessary to obtain such useful information. Furthermore, merits and demerits of thermal conditions maintained on the cooled surfaces of the freeze dryer condenser have been discussed identifying the specific influence of the condenser surface temperature on the process conditions using experimental data to support the guidelines. Finally, guidelines for systematic leak rate testing criteria for a freeze dryer are presented. These specific procedural recommendations are based on calculations, measurements, and experience to provide useful process and equipment knowledge.

Best Practices and Guidelines (2022) for Scale-Up and Tech Transfer in Freeze-Drying Based on Case Studies. Part 1: Challenges during Scale Up and Transfer.

The freeze-drying process scale-up and transfer remain a complicated and non-uniform practice. We summarized inefficient and good practices in these papers and provided some practical advice. It was demonstrated that using the same process set points/times in laboratory and commercial scale dryers may lead to loss of product quality (collapse or vial breakage). The emerging modeling approach demonstrated practical advantages. However, the upfront generation of some input parameters (vial heat transfer coefficient, minimum controllable pressure, and maximum sublimation rate) is essential for model utilization. While the primary drying step can be transferred with a high degree of confidence (e.g., using modeling), and secondary drying is usually fairly straightforward, predicting potential changes in product behavior during freezing remains challenging.

A non-invasive multipoint product temperature measurement for pharmaceutical lyophilization.

Monitoring product temperature during lyophilization is critical, especially during the process development stage, as the final product may be jeopardized if its process temperature exceeds a threshold value. Also, in-situ temperature monitoring of the product gives the capability of creating an optimized closed-loop lyophilization process. While conventional thermocouples can track product temperature, they are invasive, limited to a single-point measurement, and can significantly alter the freezing and drying behavior of the product in the monitored vial. This work has developed a new methodology that combines non-invasive temperature monitoring and comprehensive modeling. It allows the accurate reconstruction of the complete temperature profile of the product inside the vial during the lyophilization process. The proposed methodology is experimentally validated by combining the sensors' wirelessly collected data with the advanced multiphysics simulations. The flexible wireless multi-point temperature sensing probe is produced using micro-manufacturing techniques and attached outside the vial, allowing for accurate extraction of the product temperature.

Statistical electromagnetics for industrial pharmaceutical lyophilization.

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 ± 1 dB. The resulting heating uniformity measured through residual moisture analysis shows 12.7% of normalized SD 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.

A Compact Model for Lyophilizer Equipment Capability Estimation.

This work presents a compact model for the equipment capability limit of a common configuration of pharmaceutical lyophilizers, a product chamber separated from the condenser by a duct and isolation valve, at a wide range of design parameters. The equipment capability limit is one of the most important characteristics determining the lyophilization design space for a particular product, container, and equipment combination. Experimental measurements of equipment capability are time-consuming and expensive, especially at the production scale. Numerical modeling using computational fluid dynamics may reduce the number of experiments and provide insights into the physics of the process with high resolution. The computational fluid dynamics (CFD) modeling has been used in this work to develop a compact model for lyophilizer equipment capability. This eliminates the need for end users to create a full CFD model of the equipment and process. Full CFD and compact model simulations for laboratory and pilot-scale lyophilizers have been compared with tunable diode laser absorption spectroscopy measurements of the water vapor mass flow during ice slab tests. The compact model results average deviation from the experimental data is within 10%, whereas the full CFD simulations are within 5%. The compact model is based on several key parameters which are the main characteristics of a lyophilizer affecting the equipment capability curve. These parameters are discussed, and their effect on the modeling results is shown.

Recommended Best Practices for Lyophilization Validation 2021 Part II: Process Qualification and Continued Process Verification.

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.

A Software Tool for Lyophilization Primary Drying Process Development and Scale-up Including Process Heterogeneity, I: Laboratory-Scale Model Testing.

Freeze-drying is a deceptively complex operation requiring sophisticated design of a robust and efficient process that includes understanding and planning for heterogeneity across the batch and shifts in parameters due to vial or lyophilizer changes. A software tool has been designed to assist in process development and scale-up based on a model that includes consideration of the process heterogeneity. Two drug formulations were used to test the ability of the new tool to develop a freeze-drying cycle and correctly predict product temperatures and drying times. Model inputs were determined experimentally, and the primary drying heterogeneous freeze-drying model was used to design drying cycles that provided data to verify the accuracy of model-predicted product temperature and primary drying time. When model inputs were accurate, model-predicted primary drying times were within 0.1 to 15.9% of experimentally measured values, and product temperature accuracy was between 0.2 and 1.2°C for three vial locations, center, inner edge, and outer edge. However, for some drying cycles, differences in vial heat transfer coefficients due to changes in shelf and product temperature as well as altered product resistance due to product temperature-dependent microcollapse increased inaccuracy (up to 28.6% difference in primary drying time and 5.1°C difference in product temperature). This highlights the need for careful determination of experimental conditions used to calculate model inputs. In future efforts, full characterization of location- and shelf temperature-dependentKv as well as location- and product temperature-dependentRp will enhance the accuracy of the predictions by the model within the user-friendly software.

Recommended Best Practices for Lyophilization Validation-2021 Part I: Process Design and Modeling.

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.

LyoPRONTO: an Open-Source Lyophilization Process Optimization Tool.

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.