Establishing column batch repeatability according to Quality by Design (QbD) principles using modeling software

Rácz, Norbert; Kormány, Róbert; Fekete, Jenő; Molnár, Imre

doi:10.1016/j.jpba.2015.01.037

Cited by 19 publications

(3 citation statements)

References 31 publications

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“…In tools like Chromsword 7 and ACD/ChromGenius, 6 the prediction is based on the molecular structure of the compound itself and the method development is aided by an internal database of similar compounds of known RT. Other tools, such as Drylab, 8 assist method development starting from a small subset of preliminary experiments performed at different temperature, pH values, and gradient conditions. Method optimization is then guided by accurate RT prediction down to minor, but important, details, such as the expected peak width.…”

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confidence: 99%

Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification

et al. 2016

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We propose a new QSRR model based on a Kernel-based partial least-squares method for predicting UPLC retention times in reversed phase mode. The model was built using a combination of classical (physicochemical and topological) and nonclassical (fingerprints) molecular descriptors of 1383 compounds, encompassing different chemical classes and structures and their accurately measured retention time values. Following a random splitting of the data set into a training and a test set, we tested the ability of the model to predict the retention time of all the compounds. The best predicted/experimental R value was higher than 0.86, while the best Q value we observed was close to 0.84. A comparison of our model with traditional and simpler MLR and PLS regression models shows that KPLS better performs in term of correlation (R), prediction (Q), and support to MetID peak assignment. The KPLS model succeeded in two real-life MetID tasks by correctly predicting elution order of Phase I metabolites, including isomeric monohydroxylated compounds. We also show in this paper that the model's predictive power can be extended to different gradient profiles, by simple mathematical extrapolation using a known equation, thus offering very broad flexibility. Moreover, the current study includes a deep investigation of different types of chemical descriptors used to build the structure-retention relationship.

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confidence: 99%

Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification

et al. 2016

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“…Todaythanks to new developments of modelling software -it is also possible to simultaneously optimize three method variables. Beside temperature and gradient steepness, ternary mobile phase composition or mobile phase pH are often selected as third variable [32][33][34]. Since neither ternary composition nor mobile phase pH impact retention in a linear fashion, their effect have to be measured at least at three levels.…”

Section: Systematic Methods Optimizationmentioning

confidence: 99%

Separation of antibody drug conjugate species by RPLC: A generic method development approach

Fekete

Molnár²,

Guillarme

2017

Journal of Pharmaceutical and Biomedical Analysis

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This study reports the use of modelling software for the successful method development of IgG1 cysteine conjugated antibody drug conjugate (ADC) in RPLC. The goal of such a method is to be able to calculate the average drug to antibody ratio (DAR) of and ADC product. A generic method development strategy was proposed including the optimization of mobile phase temperature, gradient profile and mobile phase ternary composition. For the first time, a 3D retention modelling was presented for large therapeutic protein. Based on a limited number of preliminary experiments, a fast and efficient separation of the DAR species of a commercial ADC sample, namely brentuximab vedotin, was achieved. The prediction offered by the retention model was found to be highly reliable, with an average error of retention time prediction always lower than 0.5% using a 2D or 3D retention models. For routine purpose, four to six initial experiments were required to build the 2D retention models, while 12 experiments were recommended to create the 3D model. At the end, RPLC can therefore be considered as a good method for estimating the average DAR of an ADC, based on the observed peak area ratios of RPLC chromatogram of the reduced ADC sample.

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“…The flow characteristics of the blender used in production were predicted without performing lab-scale experiments. Rácz et al [56] used model predictions to form a prognosis on the deviation of 12 chromatography columns that came from different lots of the same manufacturer. In Ref.…”

Section: Model-based Design Of Experimentsmentioning

confidence: 99%

Model-Based Design of Process Strategies for Cell Culture Bioprocesses: State of the Art and New Perspectives

Pörtner¹

2017

New Insights Into Cell Culture Technology

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Production processes for biopharmaceuticals with mammalian cells have to provide a nearly optimal environment to promote cell growth and product formation. Design and operation of a bioreactor are complex tasks, not only with respect to reactor configuration and size but also with respect to the mode of operation. New concepts for the design and layout of process strategies are required to meet regulatory demands and to guarantee efficient, safe, and reproducible biopharmaceutical production. Key elements are critical process parameters (CPPs), which affect critical quality attributes (CQAs), quality by design (QbD), process analytical tools (PAT), and design of experiment (DoE). In this chapter, some fundamentals including process and control strategies as well as concepts for process development are discussed. Examples for novel model-based concepts for the design of experiments to identify suitable fed-batch-feeding strategies are shown.

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Establishing column batch repeatability according to Quality by Design (QbD) principles using modeling software

Cited by 19 publications

References 31 publications

Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification

Kernel-Based, Partial Least Squares Quantitative Structure-Retention Relationship Model for UPLC Retention Time Prediction: A Useful Tool for Metabolite Identification

Separation of antibody drug conjugate species by RPLC: A generic method development approach

Model-Based Design of Process Strategies for Cell Culture Bioprocesses: State of the Art and New Perspectives

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