The measurement and control of high-risk host cell proteins for polysorbate degradation in biologics formulation

Non-ionic surfactant polysorbates (PS), including PS-80 and PS-20, are commonly used in the formulation of biotherapeutic products for both preventing surface adsorption and acting as stabilizer against protein aggregation. Trace levels of residual host cell proteins (HCPs) with lipase or esterase enzymatic activity have been shown to degrade polysorbates in biologics formulation. The measurement and control of these low-abundance, high-risk HCPs for polysorbate degradation is an industry-wide challenge to achieve desired shelf-life of biopharmaceuticals in liquid formulation, especially for high-concentration formulation product development. Here, we reviewed the challenges, recent advances and future opportunities of analytical method development, risk assessment and control strategies for polysorbate degradation during formulation development with a focus on enzymatic degradation. Continued efforts to advance our understanding of polysorbate degradation in biologics formulation will help develop high-quality medicines for patients.

Explainable t-SNE for single-cell RNA-seq data analysis

Single-cell RNA (scRNA-seq) sequencing technologies trigger the study of individual cell gene expression and reveal the diversity within cell populations. To measure cell-to-cell similarity based on their transcription and gene expression, many dimension reduction methods are employed to retrieve the corresponding low-dimensional embeddings of input scRNA-seq data to conduct clustering. However, the methods lack explainability and may not perform well with scRNA-seq data because they are often migrated from other fields and not customized for high-dimensional sparse scRNA-seq data. In this study, we propose an explainable t-SNE: cell-driven t-SNE (c-TSNE) that fuses the cell differences reflected from biologically meaningful distance metrics for input scRNA-seq data. Our study shows that the proposed method not only enhances the interpretation of the original t-SNE visualization for scRNA-seq data but also demonstrates favorable single cell segregation performance on benchmark datasets compared to the state-of-the-art peers. The robustness analysis shows that the proposed cell-driven t-SNE demonstrates robustness to dropout and noise in dimension reduction and clustering. It provides a novel and practical way to investigate the interpretability of t-SNE in scRNA-seq data analysis. Unlike the general assumption that the explainanbility of a machine learning method needs to compromise with the learning efficiency, the proposed explainable t-SNE improves both clustering efficiency and explainanbility in scRNA-seq analysis. More importantly, our work suggests that widely used t-SNE can be easily misused in the existing scRNA-seq analysis, because its default Euclidean distance can bring biases or meaningless results in cell difference evaluation for high-dimensional sparse scRNA-seq data. To the best of our knowledge, it is the first explainable t-SNE proposed in scRNA-seq analysis and will inspire other explainable machine learning method development in the field.

Tracking a Circular Economy Transition Through Jobs: Method Development and Application in Two Cities

The methodology presented in this paper produces a circular economy jobs (CE jobs) measure. Using jobs as a proxy indicator, these measure gives cities a robust number to indicate progress toward the circular economy and is designed to serve as a first step in developing a circular economy strategy. The CE jobs measure tracks the inputs and outputs of goods in a city's “boundaries” through the material import dependency of the city's economic sectors. At the same time, tracking and assessing the circularity of the local jobs in these economic sectors will also provide city leaders with an indication of which sectors circularity is happening and could potentially happen. This paper also concludes that the process of coming to the CE jobs has two parts, the first more relevant to the local government and the second better influenced by the national government. Both need to come together for a truly circular local economy to happen.

Quality risk assessment and DoE – Practiced validated stability-indicating chromatographic method for quantification of Rivaroxaban in bulk and tablet dosage form

A systematic DoE and Analytical Quality by Design (AQbD) approach was utilized for the development and validation of a novel stability indicating high-performance thin–layer chromatographic (HPTLC) method for Rivaroxaban (RBN) estimation in bulk and marketed formulation. A D-optimal design was used to screen the effect of solvents, volume of solvents, time from spotting to development and time for development to scanning. ANOVA results and Pareto chart revealed that toluene, methanol, water and saturation time had an impact on retention time. The critical method and material attributes were further screened by Box-Behnken design (BBD) to achieve optimal chromatographic condition. A stress degradation study was carried out and structure of major alkaline degradant was elaborated. According to the design space, a control strategy was used with toluene: methanol: water (6:2:2) and the saturation time was 15 min. A retention factor (RF) of 0.59 ± 0.05 was achieved for RBN using chromatographic plate precoated with silica gel at detection wavelength 282 nm with optimized conditions. The linear calibration curve was achieved in the concentration range of 200–1,200 ng/band with r 2 > 0.998 suggesting good coordination between analyte concentration and peak areas. The quadratic model was demonstrated as the best fit model and no interaction was noted between CMAs. The optimized HPTLC method was validated critically as stated in International Conference on Harmonization (ICH) Q2 (R1) guideline and implemented successfully for stress degradation study of RBN. The developed HPTLC method obtained through AQbD application was potentially able to resolve all degradants of RBN achieved through forced degradation study. The obtained results demonstrate that a scientific AQbD approach implementation in HPTLC method development and stress degradation study drastically minimizes the number of trials in experiments, ultimately time and cost of analysis could be minimized.

A Minireview of Microfluidic Scaffold Materials in Tissue Engineering

In 2020, nearly 107,000 people in the U.S needed a lifesaving organ transplant, but due to a limited number of donors, only ∼35% of them have actually received it. Thus, successful bio-manufacturing of artificial tissues and organs is central to satisfying the ever-growing demand for transplants. However, despite decades of tremendous investments in regenerative medicine research and development conventional scaffold technologies have failed to yield viable tissues and organs. Luckily, microfluidic scaffolds hold the promise of overcoming the major challenges associated with generating complex 3D cultures: 1) cell death due to poor metabolite distribution/clearing of waste in thick cultures; 2) sacrificial analysis due to inability to sample the culture non-invasively; 3) product variability due to lack of control over the cell action post-seeding, and 4) adoption barriers associated with having to learn a different culturing protocol for each new product. Namely, their active pore networks provide the ability to perform automated fluid and cell manipulations (e.g., seeding, feeding, probing, clearing waste, delivering drugs, etc.) at targeted locations in-situ. However, challenges remain in developing a biomaterial that would have the appropriate characteristics for such scaffolds. Specifically, it should ideally be: 1) biocompatible—to support cell attachment and growth, 2) biodegradable—to give way to newly formed tissue, 3) flexible—to create microfluidic valves, 4) photo-crosslinkable—to manufacture using light-based 3D printing and 5) transparent—for optical microscopy validation. To that end, this minireview summarizes the latest progress of the biomaterial design, and of the corresponding fabrication method development, for making the microfluidic scaffolds.

Detection of Benzo[a]pyrene Diol Epoxide Adducts to Histidine and Lysine in Serum Albumin In Vivo by High-Resolution-Tandem Mass Spectrometry

Electrophilic diol epoxide metabolites are involved in the carcinogenicity of benzo[a]pyrene, one of the widely studied polycyclic aromatic hydrocarbons (PAHs). The exposure of humans to this PAH can be assessed by measuring stable blood protein adducts, such as to histidine and lysine in serum albumin, from their reactive metabolites. In this respect, measurement of the adducts originating from the genotoxic (+)-anti-benzo[a]pyrene diol epoxide is of interest. However, these are difficult to measure at such low levels as are expected in humans generally exposed to benzo[a]pyrene from air pollution and the diet. The analytical methods detecting PAH-biomarkers still suffer from low selectivity and/or detectability to enable generation of data for calculation of in vivo doses of specific stereoisomers, for evaluation of risk factors and assessing risk from exposures to PAH. Here, we suggest an analytical methodology based on high-pressure liquid chromatography (HPLC) coupled to high-resolution tandem mass spectrometry (MS) to lower the detection limits as well as to increase the selectivity with improvements in both chromatographic separation and mass determination. Method development was performed using serum albumin alkylated in vitro by benzo[a]pyrene diol epoxide isomers. The (+)-anti-benzo[a]pyrene diol epoxide adducts could be chromatographically resolved by using an HPLC column with a pentafluorophenyl stationary phase. Interferences were further diminished by the high mass accuracy and resolving power of Orbitrap MS. The achieved method detection limit for the (+)-anti-benzo[a]pyrene diol epoxide adduct to histidine was approximately 4 amol/mg serum albumin. This adduct as well as the adducts to histidine from (−)-anti- and (+/−)-syn-benzo[a]pyrene diol epoxide were quantified in the samples from benzo[a]pyrene-exposed mice. Corresponding adducts to lysine were also quantified. In human serum albumin, the anti-benzo[a]pyrene diol epoxide adducts to histidine were detected in only two out of twelve samples and at a level of approximately 0.1 fmol/mg.