Spatial Variation of Pressure in the Lyophilization Product Chamber Part 2: Experimental Measurements and Implications for Scale-up and Batch Uniformity
Abstract:Product temperature during the primary drying step of freeze-drying is controlled by a set point chamber pressure and shelf temperature. However, recent computational modeling suggests a possible variation in local chamber pressure. The current work presents an experimental verification of the local chamber pressure gradients in a lab-scale freeze-dryer. Pressure differences between the center and the edges of a lab-scale freeze-dryer shelf were measured as a function of sublimation flux and clearance between … Show more
“…As proven by Trelea et al, this assumption works reasonably well when the condenser is separated from the drying chamber by a pipe, as for the equipment used for this study. Moreover, as confirmed by previous studies based on computational fluid dynamics simulations, , it is assumed that the variation of total pressure between the chamber and the condenser is negligible compared to the variation of water vapor partial pressure and that there are no significant pressure leaks in the chamber.…”
Primary
drying is the most time-consuming and energy-intensive
step in pharmaceutical freeze-drying. Minimizing the duration of this
stage is of paramount importance to speed up process development and
product manufacturing. In this study, we propose a stochastic modeling
framework that can help to reach this target. The framework is composed
of five sequential steps: model development, sensitivity analysis,
model calibration, model validation, and dynamic optimization. Three
critical issues are addressed and accounted for in the model structure,
namely, (i) the effect of time-varying operating conditions on the
process key performance indicators (KPIs); (ii) the dynamic evolution
of the water vapor partial pressure inside the drying chamber; and
(iii) the impact of drying heterogeneity on the primary drying duration.
We cope with the first two issues by introducing macroscopic energy
and mass balances within the model formulation. The third issue is
addressed by allocating intralot variability as a parametric uncertainty
in the model parameter with the strongest sensitivity toward the process
KPIs. The proposed stochastic model is calibrated and validated with
data generated from industrial experiments. Nonlinear dynamic optimization
is then exploited to minimize the duration of primary drying while
simultaneously guaranteeing the fulfillment of tight constraints on
the product temperature and sublimation rate. Experimental results
show a reduction of ∼20% of the primary drying duration with
the optimized protocol when compared to standard (i.e., at constant
shelf temperature and chamber pressure) protocols, while ensuring
the same product quality.
“…As proven by Trelea et al, this assumption works reasonably well when the condenser is separated from the drying chamber by a pipe, as for the equipment used for this study. Moreover, as confirmed by previous studies based on computational fluid dynamics simulations, , it is assumed that the variation of total pressure between the chamber and the condenser is negligible compared to the variation of water vapor partial pressure and that there are no significant pressure leaks in the chamber.…”
Primary
drying is the most time-consuming and energy-intensive
step in pharmaceutical freeze-drying. Minimizing the duration of this
stage is of paramount importance to speed up process development and
product manufacturing. In this study, we propose a stochastic modeling
framework that can help to reach this target. The framework is composed
of five sequential steps: model development, sensitivity analysis,
model calibration, model validation, and dynamic optimization. Three
critical issues are addressed and accounted for in the model structure,
namely, (i) the effect of time-varying operating conditions on the
process key performance indicators (KPIs); (ii) the dynamic evolution
of the water vapor partial pressure inside the drying chamber; and
(iii) the impact of drying heterogeneity on the primary drying duration.
We cope with the first two issues by introducing macroscopic energy
and mass balances within the model formulation. The third issue is
addressed by allocating intralot variability as a parametric uncertainty
in the model parameter with the strongest sensitivity toward the process
KPIs. The proposed stochastic model is calibrated and validated with
data generated from industrial experiments. Nonlinear dynamic optimization
is then exploited to minimize the duration of primary drying while
simultaneously guaranteeing the fulfillment of tight constraints on
the product temperature and sublimation rate. Experimental results
show a reduction of ∼20% of the primary drying duration with
the optimized protocol when compared to standard (i.e., at constant
shelf temperature and chamber pressure) protocols, while ensuring
the same product quality.
“…In this section, we compare traditional single setpoint cycles for chamber pressure and shelf temperature to optimized cycles with variable chamber pressure and/or shelf temperature. For the 2 mL of 5% mannitol solution in Schott 6R vials used in the experiments presented earlier, a typical cycle is simulated at the chamber pressure and shelf temperature setpoint values of 150 mTorr and 30°C respectively, as provided by Sane et al (35) for 5% mannitol. Figure 15a shows the results for a partial load cycle with 398 vials on a single shelf of Millrock REVO.Fig.…”
Section: Process Optimizationmentioning
confidence: 99%
“…Figure 16 compares two different single setpoint cycles with an optimized cycle with variable chamber pressure and shelf temperature for 2 mL of 5% sucrose in Schott 6R vials. The maximum product temperature limit in this case is set to − 35°C so as to be 3°C below the glass transition temperature for 5% sucrose (35). The product resistance for freeze drying 5% sucrose is obtained from the Cake Resistance Library in the Excel-based Lyocycle Design and Transfer Template (22) developed by Dr. Serguei Tchessalov at Pfizer Inc.…”
Section: Cycle Optimization With Variable Inputsmentioning
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.
“…However, a longer time is required to achieve ideal vacuum conditions, and the pressure and temperature are less uniform across the chamber, which can impact product quality. 22,23 The drying process requires huge energy consumption due to the low temperature and the vacuum system. 24 In this work, a freezing-dissolving technology was for the first time developed as a fast, simple, and sustainable method as an alternative to the freeze-drying technology.…”
A new technology,
a freeze-dissolving method, has been developed
to isolate nanoparticles or ultrafine powder and is a more efficient
and sustainable method than the traditional freeze-drying method.
In this work, frozen spherical ice particles were produced with an
aqueous solution of sodium bicarbonate or ammonium dihydrogen phosphate
at various concentrations to generate nanoparticles of NaHCO
3
or (NH
4
)(H
2
PO
4
). The freeze-drying
method sublimates ice, and nanoparticles of NaHCO
3
or (NH
4
)(H
2
PO
4
) in the ice templates remain.
The freeze-dissolving method dissolves ice particles in a low freezing
point solvent at temperatures below 0 °C, and then, nanoparticles
of NaHCO
3
or (NH
4
)(H
2
PO
4
) can be isolated after filtration. The freeze-dissolving method
is 100 times faster with about 100 times less energy consumption than
the freeze-drying method as demonstrated in this work with a much
smaller facility footprint and produces the same quantity of nanoparticles
with a more uniform size distribution.
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