Proteomics-based, multivariate random forest method for prediction of protein separation behavior during cation-exchange chromatography

Swanson, Ryan K.; Xu, Ruo; Nettleton, Dan; Glatz, Charles E.

doi:10.1016/j.chroma.2012.06.009

Cited by 15 publications

(29 citation statements)

References 78 publications

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“…The goal of the current study was to develop a statistical method to predict separation behavior while using far fewer properties of a large number of proteins, thereby making it applicable to the large mix of HCP. When applied to IEX elution of a set of model proteins, an earlier statistical method based on only the three properties obtained by the 3D characterization method, provided accuracy comparable to the QSRR approach …”

Section: Introductionmentioning

confidence: 99%

“…Log K has been correlated with other measures of SH that are not applicable to complex mixtures . A polymer‐salt aqueous two‐phase partitioning (ATPS) system was employed where the top phase is rich in polymer and is more hydrophobic than the bottom, salt‐rich phase . The combination of pI, MW, and Log K is our 3D characterization.…”

Section: Introductionmentioning

confidence: 99%

“…Anion‐exchange chromatography (AEX) offers mild and nondenaturing operating conditions and can achieve an attractive degree of resolution . The mechanism by which proteins are retained during ion‐exchange chromatography is primarily electrostatic interactions with the positively charged functional group (for AEX applications) immobilized on the surface of the resin.…”

Section: Introductionmentioning

confidence: 99%

“…The entire decision structure is a full grown, univariate response tree. A prediction is then obtained by routing a test case (i.e., a data point or protein that was withheld from the set used to grow the tree) through the tree based on the three predictor variables until a terminal node is reached …”

Section: Introductionmentioning

confidence: 99%

“…One of the advantages of the MVRF method is the ability to handle multiple predictor and response variables while computing predictions in a straightforward manner once the forest has been completed. There is no explicit prediction equation that can be reported, rather there is a computerized decision tree into which any number of proteins (each represented by its own set of molecular properties) can be entered to generate a predicted elution profile …”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accounting for host cell protein behavior in anion‐exchange chromatography

Swanson

Nettleton

et al. 2016

Biotechnology Progress

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An impurity characterization based approach for the rapid development of integrated downstream purification processes

Timmick

Vecchiarello

Goodwine

et al. 2018

Biotech & Bioengineering

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In this study, we describe a new approach for the characterization of process-related impurities along with an in silico tool to generate orthogonal, integrated downstream purification processes for biological products. A one-time characterization of process-related impurities from product expression in Pichia pastoris was first carried out using linear salt and pH gradients on a library of multimodal, salt-tolerant, and hydrophobic charge induction chromatographic resins. The Reversed-phase ultra-performance liquid chromatography (UPLC) analysis of the fractions from these gradients was then used to generate large data sets of impurity profiles. A retention database of the biological product was also generated using the same linear salt and pH gradients on these resins, without fraction collection. The resulting two data sets were then analyzed using an in silico tool, which incorporated integrated manufacturing constraints to generate and rank potential three-step purification sequences based on their predicted purification performance as well as whole-process "orthogonality" for impurity removal. Highly ranked sequences were further examined to identify templates for process development. The efficacy of this approach was successfully demonstrated for the rapid development of robust integrated processes for human growth hormone and granulocyte-colony stimulating factor.

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A combined screening and in silico strategy for the rapid design of integrated downstream processes for process and product‐related impurity removal

Vecchiarello

Timmick

Goodwine

et al. 2019

Biotech & Bioengineering

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Integrated designs of chromatographic processes for purification of biopharmaceuticals provides potential gains in operational efficiency and reductions of costs and material requirements. We describe a combined method using screening and in silico algorithms for ranking chromatographic steps to rapidly design orthogonally selective integrated processes for purifying protein therapeutics from both process-and product-related impurities. IFN-α2b produced in Pichia pastoris containing a significant product variant challenge was used as a case study. The product and product-related variants were screened on a set of 14 multimodal, ion exchange, and hydrophobic charge induction chromatography resins under various pH and salt linear gradient conditions. Data generated from reversed-phase chromatography of the fractions collected were used to generate a retention database for IFN-α2b and its variants. These data, in combination with a previously constructed process-related impurity database for P. pastoris, were input into an in silico process development tool that generated and ranked all possible integrated chromatographic sequences for their ability to remove both process and product-related impurities. Top-ranking outputs guided the experimental refinement of two successful three step purification processes, one comprising all bind-elute steps and the other having two bind-elute steps and a flowthrough operation. This approach suggests a new platform-like approach for rapidly designing purification processes for a range of proteins where separations of both process-and product-related impurities are needed. K E Y W O R D Shigh throughput process development, integrated downstream processing, mixed mode chromatography, process development tool, protein chromatography, straight-through processing

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Proteomics-based, multivariate random forest method for prediction of protein separation behavior during cation-exchange chromatography

Cited by 15 publications

References 78 publications

Accounting for host cell protein behavior in anion‐exchange chromatography

Accounting for host cell protein behavior in anion‐exchange chromatography

An impurity characterization based approach for the rapid development of integrated downstream purification processes

A combined screening and in silico strategy for the rapid design of integrated downstream processes for process and product‐related impurity removal

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