Critical product parameters, like the residual moisture content and product temperature (Tp), must be optimized and controlled when designing a freeze-drying process. The regulation of other process parameters is used to control these parameters, including chamber pressure, shelf temperature and drying times.
The utilization of accurate process analytical technology to relate critical product parameters to controllable process parameters is vital to supply the best cycle conditions for each product, some of which can be more difficult than others.
Ms. Emily Gong, Senior Research Scientist, Physical Sciences Inc., USA, recently gave a webinar that outlined how SMART TDLAS-based technology can be used with any product formulation, including highly concentrated amorphous systems, to control critical product parameters like Tp accurately. This article outlines the webinar.
SMART freeze-drying technology
Changing the process parameters controls the critical product parameters needed for the optimization of freeze-drying cycle development. The relationship between these parameters is complex and is affected by the freeze dryer because of heat transfer to the vial and equipment limitations and the type of product because of product resistance to mass flow.
Models can predict the freeze-drying cycles if quantifications of vial heat transfer coefficients, product resistance and equipment capability limits are made. Yet, models are not always utilized because of the level of expertise that is needed to execute them.
Process development more commonly happens using an iterative trial-and-error method, which can be costly and time consuming. SMART freeze-drying technology was designed as a lyophilization cycle development tool based on a model of steady state heat and mass transfer in vials and decades of empirical observations.1,2
The first product commercially available was based on Manometric Temperature Measurement (MTM) to determine the Tp at the sublimation interface during the freeze-drying cycle and was created in collaboration with leading academics at Purdue University and the University of Connecticut.
Periodic ascertainment of Tp together with the model allows real-time prediction of how altering process conditions (Ts, shelf temperature and drying chamber pressure, Pc) influence future product temperatures.
This stops the empirical nature of freeze-drying and allows scientists who have minimal knowledge of lyophilization to develop efficient process cycles successfully.
The SMART algorithm is a series of steps that accelerates and optimizes the development of lyophilization cycles. It starts by determining the target Tp as a function of Tc or eutectic temperature (Teu) and setting the chamber pressure to make sure that there is a driving force for sublimation.
Until a steady state of sublimation is determined, an initial conservative Ts is utilized. A heat and mass transfer model of freeze-drying to predict the resulting Tp is utilized to establish Further Ts set-points.
Throughout the process of determining the Ts set-point, the Tp is established by utilizing the heat and mass transfer model of drying together with measurements of the batch average water vapor mass flow rate. A comparison is made between the measured Tp and the target Tp.
If the calculated Tp strays too far away from the target, an updated quantification of Ts is utilized as the shelf temperature set-point. In order to re-establish steady-state after process conditions are altered, this process only happens after an appropriate equilibration time (~1 hour).
This process is repeated until around two-thirds of the product cake has dried. There is a risk of edge vials completing drying after that point, which could result in incorrect knowledge of the ice surface area undertaking sublimation and an inaccurate model predictions of Tp.
Ts stays the same after that time-point. The webinar compared two techniques utilized for establishing Tp and the resulting SMART freeze-drying cycles for drying two different formulations.
MTM is a pressure rise-based measurement method that is utilized mainly to establish product temperature and cake resistance at the ice sublimation interface. The pressure rise comes from the fast closure of the isolation valve between the freeze dryer condenser and product chamber.
Figure 1. SMART Algorithm Overview. Image Credit: SP Scientific Products
The algorithm utilized to calculate Tp uses the vapor pressure of ice at the sublimation interface (Pice) and the product resistance (Rp) to guide the choice of Ts. There are a number of limitations of this technique, one of which is the need for a fast-closing valve.
This usually limits the application to smaller lyophilizers that use butterfly valves. To seal the large spool pieces that connect the chamber and condenser, larger dryers typically use mushroom valves. These valves cannot achieve the <1 second closing times.
A further limitation is the need for a lyophilizer leak rate of <30 mTorr/hour. The closing of the isolation valve also disrupts the drying process, leading to a rise in the Tp, which may be a risk for cycles with the Tp running close to Tc or Teu.
Product formulation is another vital factor to consider. Common for many biopharmaceuticals, amorphous formulations are able to reabsorb water in the dry layer when the valve closes.
This leads to an under prediction of the Tp because of a failure of the pressure rise data processing algorithm. The MTM SMART algorithm can then set the Ts too high and cause a runaway process with eutectic melting or collapse of the product which is being dried.
Another method that avoids these MTM limitations is the utilization 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 allows continuous, real-time measurements of near-IR absorption by water vapor at ~1.4 um, stopping the requirement for the pressure rise measurement. In the dryer, the optical measurement is taken in the spool connecting the lyophilizer chamber and condenser.
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, as water vapor flows through this duct.
The calculations that are taken at angles to the flow path result in Doppler shifts of the absorption peaks as a function of flow velocity (to higher and lower wavelengths). Analysis of the resulting low-pressure absorption peaks allows the measurement of both flow velocity (m/s) and the water vapor density (molecules/ cm3).
Together with the knowledge of the spool cross-sectional area, this information allows the determination of the water vapor mass flow rate, dm/dt (g/s). To allow the calculation of the batch average Tp, the dm/dt values are combined with the heat and mass transfer model of vial-based freeze-drying, replacing the need for the MTM pressure rise measurements.
Comparison of MTM And TDLAS method to determine Tp
Previous evidence indicated that the MTM technique supplied temperature determinations which were inaccurate compared to thermocouple based measurements for hygroscopic formulations, like PVP and highly concentrated amorphous formulations.
It was also previously demonstrated that the TDLAS based sensor supplied accurate calculations for all of the product formulations that were tested.
A number of case studies were set up during this R&D effort, in two different freeze dryers and utilizing two excipients, to establish whether the development of the TDLAS SMART freeze dryer algorithm could be utilized successfully to develop drying cycles for all product formulations and could be applied to any size lyophilizer.
To create optimized recipes as the cycle progresses, the SMART freeze-drying model incorporates data from MTM or TDLAS to permit automated adjustments of Ts and vacuum set-points.
Ms. Gong discussed the comparison of utilizing SMART-TDLAS or MTM to establish Tp during primary drying of 5% sucrose or 10% sucrose/10% BSA in either a pilot-scale freeze dryer (LyoConstellation, S20, 20 ft2 shelf area, SP Scientific Products, USA) or in a small lab-scale freeze dryer (LyoStar 3, 4.6 ft2 shelf area, SP Scientific Products, USA).
During all of the studies, after around two-thirds of primary drying, the mass and heat transfer model broke down when the edge vials were nearly dry, leading to incorrect knowledge of the total ice surface area having sublimation. The shelf temperature stayed constant to the end of primary drying following this time-point.
Both lab- and pilot-scale TDLAS- and MTM-SMART kept the Tp below the Tc and quantified the product temperature at the bottom of the vial (Tb) accurately during primary drying of 5% sucrose.
For the more concentrated amorphous formulation (10% BSA, 10% sucrose), the TDLAS method predicted the Tb accurately again, but the MTM technique did not, as seen in Figure 2.
Figure 2. Lab Scale TDLAS-based SMART Freeze-Drying Cycle for 10% BSA/10% Sucrose showing the first two-thirds of primary drying. Image Credit: SP Scientific Products
There was a noticeable difference between TDLAS and MTM established value of the batch average Tb and resultant calculation of Tp after as little as 4 hours of primary drying. This is probably because of water reabsorption by the dried product, resulting in an overly aggressive cycle and the potential for product collapse.
More experiments supplied evidence that TDLAS-SMART gave highly repeatable cycles with data output, like heat transfer coefficient and product resistance, which can guide future process development and help in creating a robust design space for a product.
The TDLAS-based SMART freeze dryer can quantify Tp accurately, producing cycles that keep a Tp lower than the Tc for all vials. It is of particular interest that TDLAS can be applied to any formulation, including high solid content formulations that fail when utilizing MTM SMART techniques of calculation and to any freeze dryer.
An economic benefit is that TDLAS-SMART allows cycle development in a single experiment, reducing time and money.
- Tang, X. C., Nail, S. L., & Pikal, M. J. (2005). Freeze-drying process design by manometric temperature measurement: design of a smart freeze dryer. Pharmaceutical research, 22(4), 685-700.
- Gieseler, H., Kramer, T., & Pikal, M. J. (2007). Use of manometric temperature measurement (MTM) and SMART™ freeze dryer technology for development of an optimized freeze-drying cycle. Journal of Pharmaceutical Sciences, 96(12), 3402-3418.
Produced from materials originally authored by Emily Gong from Physical Sciences Inc
About SP Scientific Products
SP is a synergistic collection of well-known, well-established, and highly regarded scientific equipment brands — SP VirTis, SP FTS, SP Hotpack, SP Hull, SP Genevac, SP PennTech, and most recently SP i-Dositecno — joined to create one of the largest and most experienced companies in freeze-drying/lyophilization, complete aseptic fill-finish production lines, centrifugal evaporation and concentration, temperature control/thermal management, glassware washers and controlled environments.
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