Mass‐spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom‐up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

Identification and characterization of N-glycopeptides from complex samples are usually based on tandem mass spectrometric measurements. Experimental settings, especially the collision energy selection method, fundamentally influence the obtained fragmentation pattern and hence the confidence of the database search results (“score”). Using standards of naturally occurring glycoproteins, we mapped the Byonic and pGlyco search engine scores of almost 200 individual N-glycopeptides as a function of collision energy settings on a quadrupole time of flight instrument. The resulting unprecedented amount of peptide-level information on such a large and diverse set of N-glycopeptides revealed that the peptide sequence heavily influences the energy for the highest score on top of an expected general linear trend with m/z. Search engine dependence may also be noteworthy. Based on the trends, we designed an experimental method and tested it on HeLa, blood plasma, and monoclonal antibody samples. As compared to the literature, these notably lower collision energies in our workflow led to 10–50% more identified N-glycopeptides, with higher scores. We recommend a simple approach based on a small set of reference N-glycopeptides easily accessible from glycoprotein standards to ease the precise determination of optimal methods on other instruments. Data sets can be accessed via the MassIVE repository (MSV000089657 and MSV000090218).

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The integration of physiological knowledge into process control strategies is a cornerstone for the improvement of biopharmaceutical cell culture technologies. The present contribution investigates the applicability of specific productivity as a physiological control parameter in a cell culture process producing a monoclonal antibody (mAb) in CHO cells. In order to characterize cell physiology, the on-line oxygen uptake rate (OUR) was monitored and the time-resolved specific productivity was calculated as physiological parameters. This characterization enabled to identify the tight link between the deprivation of tyrosine and the decrease in cell respiration and in specific productivity. Subsequently, this link was used to control specific productivity by applying different feeding profiles. The maintenance of specific productivity at various levels enabled to identify a correlation between the rate of product formation and the relative abundance of high-mannose glycoforms. An increase in high mannose content was assumed to be the result of high specific productivity. Furthermore, the high mannose content as a function of cultivation pH and specific productivity was investigated in a design of experiment approach. This study demonstrated how physiological parameters could be used to understand interactions between process parameters, physiological parameters, and product quality attributes.Electronic supplementary materialThe online version of this article (doi:10.1007/s00253-016-7380-4) contains supplementary material, which is available to authorized users.

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Background In January 2017, the European Commission approved Terrosa ® (company code RGB-10) as one of the first biosimilar medicinal products of teriparatide for the same indications as for the reference medicinal product Forsteo ® (Lilly France S.A.S.), which has been on the market in the European Union since 2003. The active pharmaceutical ingredient of the reference medicinal product is the biologically active 1-34 fragment of the endogenous human parathyroid hormone [PTH(1-34)]. It is one of the three bone anabolic agents used in the treatment of osteoporosis promoting bone formation and preventing fragility fractures. Objective The objective of this paper is to summarise the results of the comparative analysis of representative batches of both the RGB-10 drug product and the reference medicinal product performed by physicochemical and in vitro biological methods. Methods A series of state-of-the-art analytical methods were applied in a comparative head-to-head manner for testing the similarity in respect to purity, content, structure and potency. Results Based on the results of the comprehensive physicochemical and biological characterisation, RGB-10 proved to be highly similar to the reference medicinal product with respect to the critical quality attributes investigated. Conclusion The results of the quality comparability study demonstrated similarity of RGB-10 to the reference medicinal product, providing the scientific basis for conducting a specifically designed clinical programme, and supported registration of the Marketing Authorisation Application of RGB-10 in the EU.

SummarySignal intensities in long series of HPLC-MS experiments often vary, which decrease reproducibility and may cause bias in the results. It was found that the sensitivity of various components change differently; in our case variability is in the order of 20-40%; and it is most likely due to changing conditions in ESI ionization. The most often used intensity correction methods do not take this effect into account. The change in signal intensities (peak areas) can be well described by a polynomial function; we found that a 4 th order polynomial is most often suitable. We suggest a simple correction algorithm based on polynomial fitting. When the experiments were inherently well reproducible, this correction improved reproducibility from 12% to 3% (on average for various components). When random errors were larger, this improvement was less significant (15% to 12% in nano-ESI), but nevertheless essential in order to avoid possible bias in the results.