Quantitative structure–activity relationship study on the inhibitors of fatty acid amide hydrolase

Lü, Peng; Zhang, Ruisheng; Yuan, Yongna; Gong, Zhiguo

doi:10.1002/cem.1314

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…There are numerous other chemometric methods in the literature including partial least squares, principal component analysis, artificial neural network, support vector machines, and multiple linear regression . The MVRF methodology was chosen over these other options in part because of the ease in which it can handle data nonlinearities as well as complex response vectors.…”

Section: Introductionmentioning

confidence: 99%

Accounting for host cell protein behavior in anion‐exchange chromatography

Swanson

Nettleton

et al. 2016

Biotechnology Progress

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Host cell proteins (HCP) are a problematic set of impurities in downstream processing (DSP) as they behave most similarly to the target protein during separation. Approaching DSP with the knowledge of HCP separation behavior would be beneficial for the production of high purity recombinant biologics. Therefore, this work was aimed at characterizing the separation behavior of complex mixtures of HCP during a commonly used method: anion-exchange chromatography (AEX). An additional goal was to evaluate the performance of a statistical methodology, based on the characterization data, as a tool for predicting protein separation behavior. Aqueous two-phase partitioning followed by two-dimensional electrophoresis provided data on the three physicochemical properties most commonly exploited during DSP for each HCP: pI (isoelectric point), molecular weight, and surface hydrophobicity. The protein separation behaviors of two alternative expression host extracts (corn germ and E. coli) were characterized. A multivariate random forest (MVRF) statistical methodology was then applied to the database of characterized proteins creating a tool for predicting the AEX behavior of a mixture of proteins. The accuracy of the MVRF method was determined by calculating a root mean squared error value for each database. This measure never exceeded a value of 0. (DSP) as they behave most similarly to the target protein during separation. Approaching DSP with the knowledge of HCP separation behavior would be beneficial for the production of high purity recombinant biologics. Therefore, this work was aimed at characterizing the separation behavior of complex mixtures of HCP during a commonly used method: anion-exchange chromatography (AEX). An additional goal was to evaluate the performance of a statistical methodology, based on the characterization data, as a tool for predicting protein separation behavior. Aqueous two-phase partitioning (ATPS) followed by two-dimensional electrophoresis (2DE) provided data on the three physicochemical properties most commonly exploited during DSP for each HCP: pI (isoelectric point), molecular weight, and surface hydrophobicity. The protein separation behaviors of two alternative expression host extracts (corn germ and E. coli) were characterized. A multivariate random forest (MVRF) statistical methodology was then applied to the database of characterized proteins creating a tool for predicting the AEX behavior of a mixture of proteins. The accuracy of the MVRF method was determined by calculating a root mean squared error (RMSE) value for each database. This measure never exceeded a value of 0.045 (fraction of protein populating each of the multiple separation fractions) for AEX.

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Section: Introductionmentioning

confidence: 99%

Accounting for host cell protein behavior in anion‐exchange chromatography

Swanson

Nettleton

et al. 2016

Biotechnology Progress

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“…A review of these software has been presented in Reference 6. Descriptor selection can be done by variable selection methods such as heuristic method 7, 8, factor analysis (FA) 9, 10 and genetic algorithm (GA) 10, 11. GA‐based variable‐selection approaches are more convenient than other variable selection approaches.…”

Section: Introductionmentioning

confidence: 99%

Quantitative structure–property relationship for surface tension of some common alcohols

Khajeh

Modarress

2011

Journal of Chemometrics

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Several types of descriptors were used to derive quantitative structure-property relationship (QSPR) for prediction of surface tension of 142 alcohols at 298.15 K. Genetic function approximation (GFA) was employed to select optimal subset of descriptors that have significant contribution to the surface tension of the training set. By using GFA, a statistically significant QSPR model with the squared correlation coefficient values of 0.9784 for training set and 0.9802 for testing set was developed. On the same data set, another QSPR model was developed based on adaptive neuro-fuzzy inference system (ANFIS) with the squared correlation coefficient values of 0.9882 for training set and 0.985 for testing set. The obtained results in this paper suggest that with the proposed methods, it is possible to obtain a good estimation of surface tension of alcohol compounds.

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Quantitative structure–activity relationship study on the inhibitors of fatty acid amide hydrolase

Cited by 2 publications

References 39 publications

Accounting for host cell protein behavior in anion‐exchange chromatography

Accounting for host cell protein behavior in anion‐exchange chromatography

Quantitative structure–property relationship for surface tension of some common alcohols

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