Screening of Polysorbate-80 Composition by High Resolution Mass Spectrometry with Rapid H/D Exchange.

Polysorbate (PS) is a widely used polymeric excipient in biotherapeutic formulations to stabilize and protect protein drugs. Commercial PS is a highly heterogeneous mixture of structurally related components. PS composition can impact the stabilizer performance of PS in formulated protein drugs. Characterization of PS heterogeneity is, however, analytically challenging. In this work, a high-throughput screening protocol is presented for the profiling of the PS-80 polysorbate form using high resolution mass spectrometry (HRMS) coupled with a rapid hydrogen/deuterium (H/D) exchange in deuterated methanol. The protocol takes advantage of accurate mass measurements from HRMS analysis and utilizes H/D exchange-induced mass shifts that are characteristic to structures (particularly the number of terminal hydroxyl groups) of PS molecules to definitively identify species. In particular, mass shifts caused by deuterium uptake were used (1) to confirm molecular identities assigned by accurate mass measurements (which adds an extra level of identification confidence) and (2) to differentiate isomers that have an identical mass (thus, undistinguishable by high mass accuracy), but differ in the number of terminal hydroxyls. These data were input to an automated searching algorithm against a molecular mass database covering over 17000 potential PS-80 molecular species. The identified species were then visualized with Kendrick Mass Defect plots. The analysis protocol identified and profiled over 180 species from PS-80 samples in a high-throughput fashion without requiring chromatographic separation to reduce complexity of mixtures or tandem mass spectrometric analysis to conduct structural elucidation.

Chemical Stability of the Botanical Drug Substance Crofelemer: A Model System for Comparative Characterization of Complex Mixture Drugs.

As the second of a 3-part series of articles in this issue concerning the development of a mathematical model for comparative characterization of complex mixture drugs using crofelemer (CF) as a model compound, this work focuses on the evaluation of the chemical stability profile of CF. CF is a biopolymer containing a mixture of proanthocyanidin oligomers which are primarily composed of gallocatechin with a small contribution from catechin. CF extracted from drug product was subjected to molecular weight-based fractionation and thiolysis. Temperature stress and metal-catalyzed oxidation were selected for accelerated and forced degradation studies. Stressed CF samples were size fractionated, thiolyzed, and analyzed with a combination of negative-ion electrospray ionization mass spectrometry (ESI-MS) and reversed-phase-HPLC with UV absorption and fluorescence detection. We further analyzed the chemical stability data sets for various CF samples generated from reversed-phase-HPLC-UV and ESI-MS using data-mining and machine learning approaches. In particular, calculations based on mutual information of over 800,000 data points in the ESI-MS analytical data set revealed specific CF cleavage and degradation products that were differentially generated under specific storage/degradation conditions, which were not initially identified using traditional analysis of the ESI-MS results.

The Botanical Drug Substance Crofelemer as a Model System for Comparative Characterization of Complex Mixture Drugs.

Crofelemer is a botanical polymeric proanthocyanidin that inhibits chloride channel activity and is used clinically for treating HIV-associated secretory diarrhea. Crofelemer lots may exhibit significant physicochemical variation due to the natural source of the raw material. A variety of physical, chemical, and biological assays were used to identify potential critical quality attributes (CQAs) of crofelemer, which may be useful in characterizing differently sourced and processed drug products. Crofelemer drug substance was extracted from tablets of one commercial drug product lot, fractionated, and subjected to accelerated thermal degradation studies to produce derivative lots with variations in chemical and physical composition potentially representative of manufacturing and raw material variation. Liquid chromatography, UV absorbance spectroscopy, mass spectrometry, and nuclear magnetic resonance analysis revealed substantial changes in the composition of derivative lots. A chloride channel inhibition cell-based bioassay suggested that substantial changes in crofelemer composition did not necessarily result in major changes to bioactivity. In 2 companion papers, machine learning and data mining approaches were applied to the analytical and biological data sets presented herein, along with chemical stability data sets derived from forced degradation studies, to develop an integrated mathematical model that can identify CQAs which are most relevant in distinguishing between different populations of crofelemer.

Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets.

There is growing interest in generating physicochemical and biological analytical data sets to compare complex mixture drugs, for example, products from different manufacturers. In this work, we compare various crofelemer samples prepared from a single lot by filtration with varying molecular weight cutoffs combined with incubation for different times at different temperatures. The 2 preceding articles describe experimental data sets generated from analytical characterization of fractionated and degraded crofelemer samples. In this work, we use data mining techniques such as principal component analysis and mutual information scores to help visualize the data and determine discriminatory regions within these large data sets. The mutual information score identifies chemical signatures that differentiate crofelemer samples. These signatures, in many cases, would likely be missed by traditional data analysis tools. We also found that supervised learning classifiers robustly discriminate samples with around 99% classification accuracy, indicating that mathematical models of these physicochemical data sets are capable of identifying even subtle differences in crofelemer samples. Data mining and machine learning techniques can thus identify fingerprint-type attributes of complex mixture drugs that may be used for comparative characterization of products.

Inhibition and conformational change of SERCA3b induced by Bcl-2.

An interaction of Bcl-2 with SERCA had been documented in vitro using the SERCA1a isoform isolated from rat skeletal muscle [Dremina, E. S., Sharov, V. S., Kumar, K., Azidi, A., Michaelis, E. K., Schöneich, C. (2004) Biochem. J. 383 (361-370)]. Here, we demonstrate the interaction of Bcl-2 with the SERCA3b isoform both in vitro and in cell culture. In vitro, the interaction of Bcl-2 with SERCA3b was studied using Bcl-2∆21, a truncated form of human Bcl-2, and microsomes isolated from SERCA3b-overexpressing HEK-293 cells. For these experiments, SERCA3b was quantified by a combination of amino acid analysis and Western blotting. We observed that Bcl-2∆21 both inactivates SERCA3b and co-immunoprecipitates with SERCA3b. The incubation with Bcl-2∆21 changes the distribution of SERCA3b during sucrose density gradient centrifugation, likely as the result of Bcl-2∆21-induced conformational change of SERCA3b. When SERCA3b-overexpressing HEK-293 cells were co-transfected with Bcl-2, Bcl-2-dependent SERCA3b inactivation was observed. In these cells, Bcl-2 interaction with SERCA3b was demonstrated by co-immunoprecipitation. Furthermore, overexpression of Bcl-2 reduced fluorescein isothiocyanate (FITC) labeling of SERCA3b. Together, our data provide evidence for the interaction of Bcl-2 with SERCA3b in vitro and in cell culture, and for Bcl-2-dependent conformational and functional changes of SERCA3b.