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Journal of pharmaceutical sciences · 2026

Verification of novel CFD-based scale-down devices against 5 L bottles for freezing and thawing of protein drug substance: Thermal histories, concentration shifts, ice-liquid interfacial area, and stability prediction.

СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Nagel Ricarda, Hauptmann Astrid, Tober Reinhard, Neves Rafaela, Rodrigues Miguel Â, Bechtold-Peters Karoline, Friess Wolfgang

Аннотация

Large-scale freezing and thawing of biopharmaceutical drug substance creates spatially heterogeneous stresses that are challenging to replicate and predict during process- and formulation development using traditional scale-down methods. In this study, we assessed the ability of two novel computational fluid dynamics-based scale-down devices (SDDs) against the 5 L PharmaTainer® bottle at-scale counterpart to replicate the key stress parameters during freeze-thaw (F/T) processes. We followed a multimodal comparison approach, including 3D temperature mapping, quantification of cryoconcentration during freezing, stratification during thawing, and predictions of protein aggregation (high molecular weight species, subvisible particles, and turbidity) after F/T cycling. Ice-liquid interfacial stress was quantitatively assessed through spatially resolved specific surface area analysis, mapping both SDD and 5 L bottle, and for the first time, this was directly supported by spatially resolved in-ice micro-computed tomography (μCT) imaging. A box SDD most accurately reproduced the spatial patterns and parameter magnitudes observed at scale in the 5 L PharmaTainer®. A wedge SDD captured qualitative trends but had limitations in quantitative accuracy. Overall, the results demonstrate that CFD-based SDDs provide a predictive, mechanistically supported approach for scaling down large-scale F/T processes.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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