Investigating screw-agitator speed ratio impact on feeding performance in pharmaceutical manufacturing using discrete element method.

In continuous powder handling processes, precise and consistent feeding is crucial for ensuring the quality of the final product. The intermixing effect caused by agitators, which alters the powder's bulk density, flow rate, and flow patterns, plays a significant role in this process, yet it is often overlooked. This study combines discrete element method (DEM) modeling and experiments using a commercial-scale feeder to propose a Digital Twin (DT) framework. The DEM model accurately captures key flow features, such as bypass trajectories, stagnant zones, and preferential flow patterns, while providing quantitative predictions for the feed factor and zones prone to material accumulation. Scenario analysis is performed to identify the most favorable operating ranges of the screw-agitator ratio and screw speed, considering the cohesive properties of the powder. The study demonstrates that powders with poor flow characteristics require tighter operational constraints, as the screw-agitator ratio is susceptible to variations in mass feed rate. This contribution highlights the importance of selecting an appropriate screw-agitator ratio instead of maintaining a fixed value. Properly choosing this ratio helps determine an optimal operation window, which aims to achieve a minimum agitation level needed to induce unhindered flow and reduce variability in the mass flow rate.

Automated particle inspection of continuously freeze-dried products using computer vision.

The pharmaceutical industry is progressing towards more continuous manufacturing techniques. To dry biopharmaceuticals, continuous freeze drying has several advantages on manufacturing and process analytical control compared to batch freeze-drying, including better visual inspection potential. Visual inspection of every freeze-dried product is a key quality assessment after the lyophilization process to ensure that freeze-dried products are free from foreign particles and defects. This quality assessment is labor-intensive for operators who need to assess thousands of samples for an extensive amount of time leading to certain drawbacks. Applying Artificial Intelligence, specifically computer vision, on high-resolution images from every freeze-dried product can quantitatively and qualitatively outperform human visual inspection. For this study, continuously freeze-dried samples were prepared based on a real-world pharmaceutical product using manually induced particles of different sizes and subsequently imaged using a tailor-made setup to develop an image dataset (with particle sizes from 50µm to 1 mm) used to train multiple object detection models. You Only Look Once version 7 (YOLOv7) outperforms human inspection by a large margin, obtaining particle detection precision of up to 88.9% while controlling the recall at 81.2%, thus detecting most of the object present in the images, with an inference time of less than 1 s per vial.

The influence of thermal radiation during microwave-assisted freeze-drying of pharmaceutical unit doses.

New drying technologies for biologicals have recently been developed to accelerate the time-consuming batch freeze-drying (BFD) process. Among others, microwave-assisted freeze-drying (MFD) has been suggested as a faster and more effective drying technology. In this study, MFD cycles with the microwave radiation switched on and off were performed to assess the contribution of the microwave radiation to drying acceleration. It was found that thermal radiation emitted by the drying chamber walls was predominantly accelerating the drying of monodose placebos rather than microwave radiation. The combination of ultra-low chamber pressure, high thermal heat transfer and a short primary-to-secondary phase transition reduces drying times by more than 80 % compared to conventional BFD. In a second step, a design of experiment approach was used to assess the effect of thermal radiation versus microwave radiation and their combination, together with dosage properties such as fill volume and excipient concentration upon drying rate. The outcome showed the importance of high fill volume and high excipient concentration for an effective microwave contribution to the drying rate. Nevertheless, the drying acceleration for small pharmaceutical dosages with restricted solutes was mainly driven by thermal radiation rather than 2.45 GHz microwave radiation. The inability of ice to convert microwave energy into heat hampers the potential use of microwave freeze-drying for single-dose vaccines.

Optimizing the secondary drying phase of a continuous Spin-Freeze Drying process: A semi-mechanistic modelling approach.

Over the past decade, continuous spin freeze-drying technology has emerged as a promising alternative to conventional batch freeze-drying, effectively addressing many of the latter's inherent disadvantages. Much of the focus during this period has been on controlling and optimizing the primary drying phase of this process. However, optimizing the secondary drying step is equally critical for the overall efficiency of the process. The primary aim of this study was to develop a comprehensive semi-mechanistic model for the secondary drying phase in continuous spin freeze-drying, accounting for the effects of process settings such as freezing rate and product temperature on desorption kinetics. Additionally, the study aimed to address discrepancies between conventional desorption models, typically applied in batch freeze-drying, and the observed data in this research. To achieve this, a residual moisture-dependent activation energy was introduced to improve the accuracy of the desorption model. Using NIR spectroscopy and IR-thermography, unknown model parameters could reliably be estimated using a simple and fast procedure. The calibrated model successfully predicted the final moisture content with an accuracy within 0.11% of the measured value under previously untested process conditions. Ultimately, the proposed semi-mechanistic model demonstrated its reliability in predicting the impact of new process conditions on both product temperature and residual moisture over time, enabling the development of a practical design space.

Hybrid modeling of T-shaped partial least squares regression and transfer learning for formulation and manufacturing process development of new drug products.

T-shaped partial least squares regression (T-PLSR) is a valuable machine learning technique for the formulation and manufacturing process development of new drug products. An accurate T-PLSR model requires experimental data with multiple formulations and process conditions. However, it is usually challenging to collect comprehensive experimental data using large-scale manufacturing equipment because of the cost, time, and large consumption of raw materials. This study proposes a hybrid modeling of T-PLSR and transfer learning (TL) to enhance the prediction performance of a T-PLSR model for large-scale manufacturing data by exploiting a large amount of small-scale manufacturing data for model building. The proposed method of T-PLSR+TL was applied to a practical case study focusing on scaling up the tableting process from an experienced compaction simulator to a less-experienced rotary tablet press. The T-PLSR+TL models achieved significantly better prediction performance for tablet quality attributes of new drug products than T-PLSR models without using large-scale manufacturing data with new drug products. The results demonstrated that T-PLSR+TL is more capable of addressing new drug products than T-PLSR by using small-scale manufacturing data to cover a scarcity of large-scale manufacturing data. Furthermore, T-PLSR+TL holds the potential to streamline formulation and manufacturing process development activities for new drug products using an extensive database.

Evaluating the Improvement of Blend Potency Measurements in the Feed Frame of a Rotary Tablet Press Using Combined NIR and Raman Spectroscopy.

This study investigated the added value of combining both near-infrared (NIR) and Raman spectroscopy into a single NIRaman Combi Fiber Probe for in-line blend potency determination in the feed frame of a rotary tablet press. A five-component platform formulation was used, containing acetylsalicylic acid as the Active Pharmaceutical Ingredient (API). Calibration models for the determination of 1 and 5%w/w label claim tablets were developed using NIR and Raman spectra of powder blends ranging from 0.75 to 1.25%w/w and 3.75 to 6.25%w/w API, respectively. Step-change experiments with deliberate 10% deviation steps from the label claims were performed, from which the collected spectra were used for model validation. For model development and validation, low-level data fusion was explored through concatenation of preprocessed NIR and Raman spectra. Mid-level data fusion was also evaluated, based on extracted features of the preprocessed data. Herewith, score vectors were extracted by transforming preprocessed spectra through Principal Component Analysis, followed by critical feature selection through Elastic Net Regression. Partial Least Squares regression was applied to regress singular, low-level or mid-level fused data versus blend potency. It could be concluded that irrespective of the data fusion technique, an increase in Step-Change Sensitivity (SCS) and decrease in Root Mean Squared Error (RMSE) was observed when predicting the 5%w/w step-change experiment. For the prediction of the 1%w/w step-change experiment, no added benefit with regard to SCS and RMSE was observed due to the addition of the noisy NIR spectra.

Relevance of controlled cooling and freezing phases in T-cell cryopreservation.

When cells are cryopreserved, they go through a freezing process with several distinct phases (i.e., cooling until nucleation, ice nucleation, ice crystal growth and cooling to a final temperature). Conventional cell freezing approaches often employ a single cooling rate to describe and optimize the entire freezing process, which neglects its complexity and does not provide insight into the effects of the different freezing phases. The aim of this work was to elucidate the impact of each freezing phase by varying different process parameters per phase. Hereto, spin freezing was used to freeze Jurkat T cells in either a Me2SO-based or Me2SO-free formulation. The cooling rates before ice nucleation and after total ice crystallization impacted cell viability, resulting in viability ranging from 26.7% to 52.8% for the Me2SO-free formulation, and 22.5%-42.6% for the Me2SO-based formulation. Interestingly, the degree of supercooling upon nucleation did not exhibit a significant effect on cell viability in this work. However, the rate of ice crystal formation emerged as a crucial factor, with viability ranging from 2.4% to 53.2% for the Me2SO-free formulation, and 0.3%-53.2% for the Me2SO-based formulation, depending on the freezing rate. A morphological study of the cells post-cryopreservation was performed using confocal microscopy, and it was found that cytoskeleton integrity and cell volume were impacted, depending on the formulation-process parameter combination. These findings underscore the importance of scrutinizing all cooling and freezing phases, as each phase impacted post-thaw viability in a distinct way, depending of the specific formulation used.

A framework for the in silico assessment of the robustness of an MPC in a CDC line in function of process variability.

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding-blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis.

Cracking the code: Spatial heterogeneity as the missing piece for modeling granular fluidized bed drying.

Pharmaceutical twin-screw wet granulation is a multifaceted and intricate process pivotal to drug product development. Accurate modeling of this process is indispensable for optimizing manufacturing parameters and ensuring product quality. The fluid bed dryer, an integral component of this granulation process, significantly influences the granular critical quality attributes. This study builds upon prior research by integrating experimental findings on granule segregation during fluid bed drying into an existing compartmental model, enhancing its predictive capabilities. An additional model layer on granule segregation behavior is composed and integrated into the existing model structure in this study. The added model compartment describes probability distributions on the vertical position of granules within each granule size class considered. To beware of overfitting, predictions of both the moisture content after drying and the granule bed temperature throughout drying are discussed in this study relative to experimental data from earlier published studies. These independent analyses demonstrated a marked improvement in prediction accuracy compared to earlier published model structures. The refined model accurately predicts the residual moisture content after drying for an untrained formulation. Moreover, it simultaneously makes accurate predictions of the granular bed temperature, which emboldens its structural correctness. This advancement makes it a powerful tool for predicting the behavior of the pharmaceutical fluid bed drying, which holds significant promise to facilitate pharmaceutical product development.

Analysis of the effect of formulation properties and process parameters on granule formation in twin-screw wet granulation.

In the last few years, twin-screw wet granulation (TSWG) has become one of the key continuous pharmaceutical unit operations. Despite the many studies that have been performed, only little is known about the effect of the starting material properties on the stepwise granule formation along the length of the twin-screw granulator (TSG) barrel. Hence, this study obtained a detailed understanding of the effect of formulation properties (i.e., Active Pharmaceutical Ingredient (API) properties, formulation blend particle size distribution and formulation drug load) and process settings on granule formation in TSWG. An experimental set-up was used allowing the collection of granules at the different TSG compartments. Granules were characterized in terms of granule size, shape, binder liquid and API distributions. Liquid-to-solid (L/S) ratio was the only TSG process parameter impacting the granule size and shape evolution. Particle size and flow properties (e.g., flow rate index) had an important effect on the granule size and shape changes whereas water-related properties (e.g., water binding capacity and solubility) became influential at the last TSG compartments. The API solubility and L/S ratio were found to have a major impact on the distribution of binder liquid over the different granule size fractions. In the first TSG compartment (i.e., wetting compartment), the distribution of the API in the granules was influenced by its solubility in the granulation liquid.

Validation of model-based design of experiments for continuous wet granulation and drying.

This paper presents an application case of model-based design of experiments for the continuous twin-screw wet granulation and fluid-bed drying sequence. The proposed framework consists of three previously developed models. Here, we are testing the applicability of previously published unit operation models in this specific part of the production line to a new active pharmaceutical ingredient. Firstly, a T-shaped partial least squares regression model predicts d-values of granules after wet granulation with different process settings. Then, a high-resolution full granule size distribution is computed by a hybrid population balance and partial least squares regression model. Lastly, a mechanistic model of fluid-bed drying simulates drying time and energy efficiency, using the outputs of the first two models as a part of the inputs. In the application case, good operating conditions were calculated based on material and formulation properties as well as the developed process models. The framework was validated by comparing the simulation results with three experimental results. Overall, the proposed framework enables a process designer to find appropriate process settings with a less experimental workload. The framework combined with process knowledge reduced 73.2% of material consumption and 72.3% of time, especially in the early process development phase.

T-shaped partial least squares for high-dosed new active pharmaceutical ingredients in continuous twin-screw wet granulation: Granule size prediction with limited material information.

This work presents a granule size prediction approach applicable to diverse formulations containing new active pharmaceutical ingredients (APIs) in continuous twin-screw wet granulation. The approach consists of a surrogate selection method to identify similar materials with new APIs and a T-shaped partial least squares (T-PLS) model for granule size prediction across varying formulations and process conditions. We devised a surrogate material selection method, employing a combination of linear pre-processing and nonlinear classification algorithms, which effectively identified suitable surrogates for new materials. Using only material properties obtained through four characterization methods, our approach demonstrated its predictive prowess. The selected surrogate methods were seamlessly integrated with our developed T-PLS model, which was meticulously validated for high-dose formulations involving three new APIs. When surrogating new APIs based on Gaussian process classification, we achieved the lowest prediction errors, signifying the method's robustness. The predicted d-values were within the range of uncertainty bounds for all cases, except for d90 of API C. Notably, the approach offers a direct and efficient solution for early-phase formulation and process development, considerably reducing the need for extensive experimental work. By relying on just four material characterization methods, it streamlines the research process while maintaining a high degree of accuracy.

Mechanistic modeling of semicontinuous fluidized bed drying of pharmaceutical granules by incorporating single particle and bulk drying kinetics.

In this work, a mechanistic fluidized bed drying model computing the granule moisture content in function of granule size, drying time, process settings and formulation properties is developed. Modeling the moisture content distribution concerning the granule size is essential for tabletability and drug product quality. This work combines a mechanistic bulk model and a single-particle drying kinetics model in a semicontinuous mode. The added model complexity allows physical approximations of drying phenomena at both the drying system level and the granular level. This includes quantifying the variations in moisture content by taking into account the specific dryer design and the variations in granule size. The model performance was quantified through industrially relevant case studies. It was revealed that the proposed model structure accurately predicts the drying behavior of the yield fraction. However, systematic model biases were observed for the fine and coarse fractions of the granule size distribution. In addition, discrepancies in the predicted outgoing air properties (relative air humidity and air temperature) were obtained. Further enhancement of the model complexity, e.g. complete incorporation of fluidization and segregation phenomena, is likely to improve the model performance. Notwithstanding, the developed model forms a step towards a formulation-generic fluidized bed drying model as interacting mechanisms on different levels of the drying system are considered.

Exploring the effect of raw material properties on continuous twin-screw wet granulation manufacturability.

Twin-screw wet granulation (TSWG) stands out as a promising continuous alternative to conventional batch fluid bed- and high shear wet granulation techniques. Despite its potential, the impact of raw material properties on TSWG processability remains inadequately explored. Furthermore, the absence of supportive models for TSWG process development with new active pharmaceutical ingredients (APIs) adds to the challenge. This study tackles these gaps by introducing four partial least squares (PLS) models that approximate both the applicable liquid-to-solid (L/S) ratio range and resulting granule attributes (i.e., granule size and friability) based on initial material properties. The first two PLS models link the lowest and highest applicable L/S ratio for TSWG, respectively, with the formulation blend properties. The third and fourth PLS models predict the granule size and friability, respectively, from the starting API properties and applied L/S ratio for twin-screw wet granulation. By analysing the developed PLS models, water-related material properties (e.g., solubility, wettability, dissolution rate), as well as density and flow-related properties (e.g., flow function coefficient), were found to be impacting the TSWG processability. In addition, the applicability of the developed PLS models was evaluated by using them to propose suitable L/S ratio ranges (i.e., resulting in granules with the desired properties) for three new APIs and related formulations followed by an experimental validation thereof. Overall, this study helped to better understand the effect of raw material properties upon TSWG processability. Moreover, the developed PLS models can be used to propose suitable TSWG process settings for new APIs and hence reduce the experimental effort during process development.

Development of Diclofenac Sodium 3D Printed Cylindrical and Tubular-Shaped Tablets through Hot Melt Extrusion and Fused Deposition Modelling Techniques.

The present study aimed to develop 3D printed dosage forms, using custom-made filaments loaded with diclofenac sodium (DS). The printed tablets were developed by implementing a quality by design (QbD) approach. Filaments with adequate FDM 3D printing characteristics were produced via hot melt extrusion (HME). Their formulation included DS as active substance, polyvinyl alcohol (PVA) as a polymer, different types of plasticisers (mannitol, erythritol, isomalt, maltodextrin and PEG) and superdisintegrants (crospovidone and croscarmellose sodium). The physicochemical and mechanical properties of the extruded filaments were investigated through differential scanning calorimetry (DSC), X-ray diffraction (XRD) and tensile measurements. In addition, cylindrical-shaped and tubular-shaped 3D dosage forms were printed, and their dissolution behaviour was assessed via various drug release kinetic models. DSC and XRD results demonstrated the amorphous dispersion of DS into the polymeric filaments. Moreover, the 3D printed tablets, regardless of their composition, exhibited a DS release of nearly 90% after 45 min at pH 6.8, while their release behaviour was effectively described by the Korsmeyer-Peppas model. Notably, the novel tube design, which was anticipated to increase the drug release rate, proved the opposite based on the in vitro dissolution study results. Additionally, the use of crospovidone increased DS release rate, whereas croscarmellose sodium decreased it.

Effect of process parameters and formulation properties on the lead-lag between in-line NIR tablet press feed frame and off-line NIR tablet measurements.

The use of in-line near-infrared (NIR) measurements for tablet potency monitoring and diversion was studied. First, the optimal sample size for in-line NIR measurements inside the feed chute and the dosing and filling chamber of the tablet press feed frame was determined to allow proper comparison between these different measurement positions. Because of the considerably longer measurement time needed to obtain the same sample size inside the feed chute compared to the feed frame, the possibility of powder segregation inside the feed chute and the additional powder mixing inside the feed frame, the latter is preferred over the feed chute for in-line blend potency monitoring. Next, a design of experiments (DoE) was performed to evaluate the effect of paddle speed, turret speed, overfill level and formulation properties upon the lead-lag and the time it takes before the powder blend that is expelled at the dosing station is measured by the NIR inside the dosing chamber. Lead-lag is defined as the difference in time and API concentration between the measured in-line NIR response inside the filling chamber of the feed frame and the off-line NIR tablet response. Paddle speed and turret speed were the only compression parameters affecting lead-lag. Lead-lag decreased with increasing paddle speed for the first formulation. For the second formulation, lead-lag decreased with decreasing paddle speed and/or increasing turret speed. Formulation properties did not have an effect on the lead-lag. The in-line NIR response inside the dosing chamber of the feed frame was found to be closely following the tablet NIR response. Therefore, the dosing chamber could be used as an additional in-line NIR position for tablet potency monitoring and diversion. It can provide an extra layer of confidence about the final tablet quality. To demonstrate this potential benefit of simultaneous in-line NIR measurements inside the filling and dosing chamber of the feed frame, a tableting experiment was performed where a surrogate API spike was introduced into the product stream to mimic a potential process disturbance. The in-line NIR measurements inside the filling chamber allow diverting tablets in-time when the blend potency crosses the predefined control limits. And because the NIR response inside the dosing chamber closely follows the tablet NIR response, tablet diversion can discontinue when the blend potency inside the dosing chamber is again within the control limits. This could increase the yield of the tableting process by avoiding a longer than needed wash-out period and rejecting tablets that meet the release limits.

Partial least squares regression to calculate population balance model parameters from material properties in continuous twin-screw wet granulation.

In the pharmaceutical industry, twin-screw wet granulation has become a realistic option for the continuous manufacturing of solid drug products. Towards the efficient design, population balance models (PBMs) have been recognized as a tool to compute granule size distribution and understand physical phenomena. However, the missing link between material properties and the model parameters limits the swift applicability and generalization of new active pharmaceutical ingredients (APIs). This paper proposes partial least squares (PLS) regression models to assess the impact of material properties on PBM parameters. The parameters of the compartmental one-dimensional PBMs were derived for ten formulations with varying liquid-to-solid ratios and connected with material properties and liquid-to-solid ratios by PLS models. As a result, key material properties were identified in order to calculate it with the necessary accuracy. Size- and moisture-related properties were influential in the wetting zone whereas density-related properties were more dominant in the kneading zones.

Evaluation of the influence of material properties and process parameters on granule porosity in twin-screw wet granulation.

In recent years, continuous twin-screw wet granulation (TSWG) is gaining increasing interest from the pharmaceutical industry. Despite the many publications on TSWG, only a limited number of studies focused on granule porosity, which was found to be an important granule property affecting the final tablet quality attributes, e.g. dissolution. In current study, the granule porosity along the length of the twin-screw granulator (TSG) barrel was evaluated. An experimental set-up was used allowing the collection of granules at the different TSG compartments. The effect of active pharmaceutical ingredient (API) properties on granule porosity was evaluated by using six formulations with a fixed composition but containing APIs with different physical-chemical properties. Furthermore, the importance of TSWG process parameters liquid-to-solid (L/S) ratio, mass feed rate and screw speed for the granule porosity was evaluated. Several water-related properties as well as particle size, density and flow properties of the API were found to have an important effect on granule porosity. While the L/S ratio was confirmed to be the dictating TSWG process parameter, granulator screw speed was also found to be an important process variable affecting granule porosity. This study obtained crucial information on the effect of material properties and process parameters on granule porosity (and granule formation) which can be used to accelerate TSWG process and formulation development.

Evaluation of a PAT-based in-line control system for a continuous spin freeze-drying process.

Continuous spin freeze-drying provides a range of opportunities regarding the implementation of several in-line process analytical technologies (PAT) to control and optimize the freeze-drying process at the individual vial level. In this work, two methods were developed to (1) control the freezing phase by separately controlling the cooling and freezing rate and (2) control the drying phase by controlling the vial temperature (and hence the product temperature) to setpoint values and monitoring the residual moisture content. During the freezing phase, the vial temperature closely followed the decreasing setpoint temperature during the cooling phases, and the crystallization phase was reproducibly controlled by regulating the freezing rate. During both primary and secondary drying, vial temperature could be maintained on the setpoint temperature which resulted in an elegant cake structure after every run. By being able to accurately control the freezing rate and the vial temperature, a homogeneous drying time (SD = 0.07-0.09 h) between replicates was obtained. Applying a higher freezing rate significantly increased primary drying time. On the other hand, fast freezing rates increased the desorption rate. Finally, the residual moisture of the freeze-dried formulation could be monitored in-line with a high accuracy providing insight on the required length of the secondary drying phase.

Successful batch and continuous lyophilization of mRNA LNP formulations depend on cryoprotectants and ionizable lipids.

The limited thermostability and need for ultracold storage conditions are the major drawbacks of the currently used nucleoside-modified lipid nanoparticle (LNP)-formulated messenger RNA (mRNA) vaccines, which hamper the distribution of these vaccines in low-resource regions. The LNP core contains, besides mRNA and lipids, a large fraction of water. Therefore, encapsulated mRNA, or at least a part of it, is subjected to hydrolysis mechanisms similar to unformulated mRNA in an aqueous solution. It is likely that the hydrolysis of mRNA and colloidal destabilization are critical factors that decrease the biological activity of mRNA LNPs upon storage under ambient conditions. Hence, lyophilization as a drying technique is a logical and appealing method to improve the thermostability of these vaccines. In this study, we demonstrate that mRNA LNP formulations comprising a reduction-sensitive ionizable lipid can be successfully lyophilized, in the presence of 20% w/v sucrose, both by conventional batch freeze-drying and by an innovative continuous spin lyophilization process. While the chemical structure of the ionizable lipid did not affect the colloidal stability of the LNP after lyophilization and redispersion in an aqueous medium, we found that the ability of LNPs to retain the mRNA payload stably encapsulated, and mediate in vivo and in vitro mRNA translation into protein, post lyophilization strongly depended on the ionizable lipid in the LNP formulation.