Use of a multi-vial mathematical model to design freeze-drying cycles for pharmaceuticals at known risk of failure

[EN] Freeze-drying is a dehydration method suitable for the stabilization of heat-labile pharmaceutical products, such as vaccines. Due to the vial-to-vial variability of heat and mass transfer during the process, the value of the critical process parameters (e.g., product temperature, sublimation rate) may be different between vials and batches often present significant product quality heterogeneity. The aim of this work was the development of a dynamic, multi-vial mathematical model making it possible to predict risk of failure of the process, defined as the percentage of vials potentially rejected by quality inspection. This tool could assist the design of freeze-drying cycle.This work was sponsored by GlaxoSmithKline Biologicals SA which was involved in all stages of the study conduct and analysisScutellà, B.; Trelea, IC.; Bourlés, E.; Fonseca, F.; Passot, S. (2018). Use of a multi-vial mathematical model to design freeze-drying cycles for pharmaceuticals at known risk of failure. En IDS 2018. 21st International Drying Symposium Proceedings. Editorial Universitat Politècnica de València. 315-322. https://doi.org/10.4995/IDS2018.2018.7421OCS31532

Model for Heat and Mass Transfer in Freeze-Drying of Pellets

International audienceLyophilizing frozen pellets, and especially spray freeze-drying, have been receiving growing interest. To design efficient and safe freeze-drying cycles, local temperature and moisture content in the product bed have to be known, but both are difficult to measure in the industry. Mathematical modeling of heat and mass transfer helps to determine local freeze-drying conditions and predict effects of operation policy, and equipment and recipe changes on drying time and product quality. Representative pellets situated at different positions in the product slab were considered. One-dimensional transfer in the slab and radial transfer in the pellets were assumed. Coupled heat and vapor transfer equations between the temperature-controlled shelf, the product bulk, the sub-limationfront inside the pellets, and the chamber were established and solved numerically. The model was validated based on bulk temperature measurement performed at two different locations in the product slab and on partial vapor pressure measurement in the freeze-drying chamber. Fair agreement between measured and calculated values was found. In contrast, a previously developed model for compact product layer was found inadequate in describing freeze-drying of pellets. The developed model represents a good starting basis for studying freeze-drying of pellets. It has to be further improved and validated for a variety ofproduct types and freeze-drying conditions (shelf temperature, total chamber pressure, pellet size, slab thickness, etc.). It could be used to develop freeze-drying cycles based on product quality criteria such as local moisture content and glass transition temperature

Dynamic Modeling of <i>Carnobacterium maltaromaticum</i> CNCM I-3298 Growth and Metabolite Production and Model-Based Process Optimization

Carnobacterium maltaromaticum is a species of lactic acid bacteria found in dairy, meat, and fish, with technological properties useful in food biopreservation and flavor development. In more recent years, it has also proven to be a key element of biological time–temperature integrators for tracking temperature variations experienced by perishable foods along the cold-chain. A dynamic model for the growth of C. maltaromaticum CNCM I-3298 and production of four metabolites (formic acid, acetic acid, lactic acid, and ethanol) from trehalose in batch culture was developed using the reaction scheme formalism. The dependence of the specific growth and production rates as well as the product inhibition parameters on the operating conditions were described by the response surface method. The parameters of the model were calibrated from eight experiments, covering a broad spectrum of culture conditions (temperatures between 20 and 37 °C; pH between 6.0 and 9.5). The model was validated against another set of eight independent experiments performed under different conditions selected in the same range. The model correctly predicted the growth kinetics of C. maltaromaticum CNCM I-3298 as well as the dynamics of the carbon source conversion, with a mean relative error of 10% for biomass and 14% for trehalose and the metabolites. The paper illustrates that the proposed model is a valuable tool for optimizing the culture of C. maltaromaticum CNCM I-3298 by determining operating conditions that favor the production of biomass or selected metabolites. Model-based optimization may thus reduce the number of experiments and substantially speed up the process development, with potential applications in food technology for producing starters and improving the yield and productivity of the fermentation of sugars into metabolites of industrial interest

The underlying inflammatory chronic disease influences infliximab pharmacokinetics

International audienceInfliximab is an anti-tumor necrosis factor monoclonal antibody approved in chronic inflammatory diseases such as rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), Crohn's disease (CD) and ulcerative colitis (UC). Infliximab pharmacokinetics is variable between patients, but influence of the underlying disease was never assessed. This study aimed at assessing this influence using a cohort of patients monitored in a single center and with the same assay. Infliximab trough concentrations were determined on samples collected between weeks 0 and 22 after treatment initiation in 218 patients treated for RA, PsA, AS, CD or UC. Infliximab pharmacokinetics was analyzed by a one-compartment population model with first-order elimination rate constant. In AS patients, volume of distribution (V) and elimination clearance (CL) were 5.4 L and 0.24 L/day, respectively. In CD and UC patients, V was 49% and 52% higher than in AS, respectively, and CL was 47% and 60% higher than in AS, respectively. In RA patients, CL was 49% higher than in AS patients. Simulations showed that without methotrexate, a 3 mg/kg dosing regimen would lead only 16% of RA patients to reach the target concentration (2.5 mg/L) at week 22, whereas target concentrations would be reached in approximately half of RA patients cotreated with methotrexate, as well as half of CD (3.5 mg/L) and UC (3.7 mg/L) patients. The suboptimality of approved dosing regimens supports the development of dosing optimization based on concentration measurements