Equilibrium studies of protein aggregates and homogeneous nucleation in protein formulation

Kiese, Sylvia; Pappenberger, Astrid; Frieß, Wolfgang; Mahler, Hanns‐Christian

doi:10.1002/jps.21858

Cited by 48 publications

(27 citation statements)

References 29 publications

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“…Surface tension and rheology experiments [89,90] provide evidence that the prevention of adsorption of the protein (LDH in the present case) at the interface is more related to surface energy and surface pressure than to the CMC. Our own studies with mAbs found that surfactants stabilize antibodies against mechanical stress, and found polysorbates and poloxamers have the ability to prevent aggregation and/or particle formation against shaking at concentrations well below their CMC [25,101]. …”

Section: Discussionmentioning

confidence: 98%

Key interactions of surfactants in therapeutic protein formulations: A review

Khan

Mahler

Kishore

2015

European Journal of Pharmaceutics and Biopharmaceutics

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221

129

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Section: Discussionmentioning

confidence: 98%

Key interactions of surfactants in therapeutic protein formulations: A review

Khan

Mahler

Kishore

2015

European Journal of Pharmaceutics and Biopharmaceutics

Self Cite

221

129

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“…However, a clear seasonal dependency of skill can be expected in Europe for the European Flood Awareness System (Kiese et al, 2010;Pappenberger and Buizza, 2009;Pappenberger et al, 2011b). In Fig.…”

Section: Seasonalitymentioning

confidence: 95%

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

Pappenberger

Ramos²,

Cloke

et al. 2015

Journal of Hydrology

164

152

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s u m m a r yThe skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are 'toughest to beat' and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon.Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naïve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better.

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“…Kiese et al (2010) showed that shaking vials cause IgG1 aggregation but that the insoluble aggregates caused by the former could reversibly dissociate into soluble aggregates. This aggregation was probably due to the air-liquid interface in the vials.…”

Section: Freeze Thaw and Storagementioning

confidence: 98%

Aggregates in monoclonal antibody manufacturing processes

Vázquez-Rey¹,

Lang

2011

Biotech & Bioengineering

407

304

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Monoclonal antibodies have proved to be a highly successful class of therapeutic products. Large-scale manufacturing of pharmaceutical antibodies is a complex activity that requires considerable effort in both process and analytical development. If a therapeutic protein cannot be stabilized adequately, it will lose partially or totally its therapeutic properties or even cause immunogenic reactions thus potentially further endangering the patients' health. The phenomenon of protein aggregation is a common issue that compromises the quality, safety, and efficacy of antibodies and can happen at different steps of the manufacturing process, including fermentation, purification, final formulation, and storage. Aggregate levels in drug substance and final drug product are a key factor when assessing quality attributes of the molecule, since aggregation might impact biological activity of the biopharmaceutical. In this review it is analyzed how aggregates are formed during monoclonal antibody industrial production, why they have to be removed and the manufacturing process steps that are designed to either minimize or remove aggregates in the final product.

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Equilibrium studies of protein aggregates and homogeneous nucleation in protein formulation

Cited by 48 publications

References 29 publications

Key interactions of surfactants in therapeutic protein formulations: A review

Key interactions of surfactants in therapeutic protein formulations: A review

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

Aggregates in monoclonal antibody manufacturing processes

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