Designing a freeze-drying process requires optimization and control of critical product parameters, such as the product temperature (Tp) and residual moisture content. These parameters are controlled by the regulation of other process parameters including shelf temperature, chamber pressure, and drying times. The use of accurate process analytical technology to relate critical product parameters to controllable process parameters is essential to provide optimal cycle conditions for each product, some of which can be more challenging than others.
SMART Freeze-Drying Technology
Altering the process parameters controls the critical product parameters required for the optimization of freeze-drying cycle development. The relationship between these parameters is complex and is impacted by the type of product (due to product resistance to mass flow) and the freeze dryer (due to heat transfer
to the vial and equipment limitations). If measurements of product resistance, vial heat transfer coefficients, and equipment capability limits are made, models can predict the freeze-drying cycles. However, models are not often used due to the level of expertise required to implement them. More commonly, process development occurs in an iterative trial-and-error approach which can be time consuming and costly.
SMART freeze-drying technology was created as a lyophilization cycle development tool based on a model of steady state heat and mass transfer in vials and decades of empirical observations. In collaboration with leading academics at the University of Connecticut and Purdue University, the first commercial product available was based on Manometric Temperature Measurement (MTM) to determine the Tp at the sublimation interface, during the freeze-drying cycle. Periodic determination of Tp combined with the model enables real-time prediction of how changing process conditions (shelf temperature, Ts, and drying chamber pressure, Pc) affect future product temperatures. This eliminates the empirical nature of freeze-drying and enables scientists, possessing minimal knowledge of lyophilization, to successfully develop efficient process cycles.
The SMART algorithm works as a series of steps accelerating and streamlining the development of lyophilization cycles. It begins with determining the target Tp as a function of Tc or eutectic temperature (Teu), and setting the chamber pressure to ensure there is a driving force for sublimation. An initial conservative Ts is used until a steady state of sublimation is established. Further Ts set-points are determined using a heat and mass transfer model of freeze-drying to predict the resulting Tp. During the process of establishing the Ts set-point, the Tp is determined using measurements of the batch average water vapor mass flow rate in combination with the heat and mass transfer model of drying.
A comparison is made between the measured Tp and the target Tp. If the measured Tp deviates too far from the target, an updated calculation of Ts is used as the shelf temperature set‑point. This process only occurs after an appropriate equilibration time (~1 hour) in order to re-establish steady-state after process conditions are changed. This process is repeated until approximately twothirds of the product cake has dried. After that point in drying, there is a risk of edge vials completing drying, leading to incorrect knowledge of the ice surface area undergoing sublimation and an inaccurate model predictions of Tp. Following that time-point the Ts remains constant.
MTM is a pressure rise-based measurement technique mainly used to determine cake resistance and product temperature at the ice sublimation interface. The pressure rise results from the quick closure of the isolation valve between the product chamber and freeze dryer condenser. The algorithm used to calculate Tp utilizes the vapor pressure of ice at the sublimation interface (Pice) and the product resistance (Rp) to guide the choice of Ts. There are several limitations of this method, one of which is the requirement for a fast-closing valve. This typically limits the application to smaller lyophilizers that utilize butterfly valves. Larger dryers tend to use mushroom valves to seal the large spool pieces that connect the chamber and condenser. These valves are not capable of achieving the <1 second closing times.
Another limitation is the requirement for a lyophilizer leak rate of <30 mTorr/hour. In addition, the closing of the isolation valve disrupts the drying process resulting in a rise in the Tp which may be a risk for cycles with the Tp running close to Tc or Teu. Finally, another important factor to consider is that of product formulation. Amorphous formulations, common for many biopharmaceuticals, can reabsorb water in the dry layer when the valve closes. This results in a failure of the pressure rise data processing algorithm and an under prediction of the Tp. The MTM SMART algorithm can then set the Ts too high and lead to a runaway process with collapse or eutectic melting of the product undergoing drying.
An alternative approach that circumvents these MTM limitations is the use of a Tunable Diode Laser Absorption Spectroscopy (TDLAS) water vapor mass flow rate monitor in combination with the freeze-drying SMART algorithm. The TDLAS sensor enables continuous, real-time measurements of near-IR absorption by water vapor at ~1.4 um, eliminating the need for the pressure rise measurement. In the dryer, the optical measurement is made in the spool connecting the lyophilizer chamber and condenser. Water vapor flows through this duct and the light absorption along two line-of-sight measurement angles (45 and 135 degrees with respect to the gas flow) is detected using two laser beams originating from the same laser. The measurements made at angles to the flow path result in Doppler shifts of the absorption peaks (to higher and lower wavelengths) as a function of flow velocity.
Analysis of the resulting low-pressure absorption peaks enables the measurement of both the water vapor density (molecules/ cm3) and flow velocity (m/s). This information is combined with the knowledge of the spool cross-sectional area enabling the determination of the water vapor mass flow rate, dm/dt (g/s).
The dm/dt values are combined with the heat and mass transfer model of vial-based freeze-drying to enable the calculation of the batch average Tp, replacing the need for the MTM pressure rise measurements.
Recently, Ms. Emily Gong, Senior Research Scientist, Physical Sciences Inc., USA, presented a webinar describing how SMART TDLAS-based technology can be applied to any product formulation, including highly concentrated amorphous systems, to accurately control critical product parameters, such as Tp. This tech note summarizes the webinar and includes a selection of questions from the Q&A sessions.