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Analysis of host-cell proteins in biotherapeutic proteins by comprehensive online two-dimensional liquid chromatography/mass spectrometry.

ABSTRACT: Assays for identification and quantification of host-cell proteins (HCPs) in biotherapeutic proteins over 5 orders of magnitude in concentration are presented. The HCP assays consist of two types: HCP identification using comprehensive online two-dimensional liquid chromatography coupled with high resolution mass spectrometry (2D-LC/MS), followed by high-throughput HCP quantification by liquid chromatography, multiple reaction monitoring (LC-MRM). The former is described as a "discovery" assay, the latter as a "monitoring" assay. Purified biotherapeutic proteins (e.g., monoclonal antibodies) were digested with trypsin after reduction and alkylation, and the digests were fractionated using reversed-phase (RP) chromatography at high pH (pH 10) by a step gradient in the first dimension, followed by a high-resolution separation at low pH (pH 2.5) in the second dimension. As peptides eluted from the second dimension, a quadrupole time-of-flight mass spectrometer was used to detect the peptides and their fragments simultaneously by alternating the collision cell energy between a low and an elevated energy (MSE methodology). The MSE data was used to identify and quantify the proteins in the mixture using a proven label-free quantification technique ("Hi3" method). The same data set was mined to subsequently develop target peptides and transitions for monitoring the concentration of selected HCPs on a triple quadrupole mass spectrometer in a high-throughput manner (20 min LC-MRM analysis). This analytical methodology was applied to the identification and quantification of low-abundance HCPs in six samples of PTG1, a recombinant chimeric anti-phosphotyrosine monoclonal antibody (mAb). Thirty three HCPs were identified in total from the PTG1 samples among which 21 HCP isoforms were selected for MRM monitoring. The absolute quantification of three selected HCPs was undertaken on two different LC-MRM platforms after spiking isotopically labeled peptides in the samples. Finally, the MRM quantitation results were compared with TOF-based quantification based on the Hi3 peptides, and the TOF and MRM data sets correlated reasonably well. The results show that the assays provide detailed valuable information to understand the relative contributions of purification schemes to the nature and concentrations of HCP impurities in biopharmaceutical samples, and the assays can be used as generic methods for HCP analysis in the biopharmaceutical industry.

SUBMITTER: Doneanu CE

PROVIDER: S-EPMC3338939 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundParkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use.MethodsWe presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts.ResultsA list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD.ConclusionsThis study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.

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Analysis of host-cell proteins in biotherapeutic proteins by comprehensive online two-dimensional liquid chromatography/mass spectrometry.

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