Two-Dimensional SEC-SEC-UV-MALS-dRI Workflow for Streamlined Analysis and Characterization of Biopharmaceuticals.

The emergence of complex biological modalities in the biopharmaceutical industry entails a significant expansion of the current analytical toolbox to address the need to deploy meaningful and reliable assays at an unprecedented pace. Size exclusion chromatography (SEC) is an industry standard technique for protein separation and analysis. Some constraints of traditional SEC stem from its restricted ability to resolve complex mixtures and notoriously long run times while also requiring multiple offline separation conditions on different pore size columns to cover a wider molecular size distribution. Two-dimensional liquid chromatography (2D-LC) is becoming an important tool not only to increase peak capacity but also to tune selectivity in a single online method. Herein, an online 2D-LC framework in which both dimensions utilize SEC columns with different pore sizes is introduced with a goal to increase throughput for biomolecule separation and characterization. In addition to improving the separation of closely related species, this online 2D SEC-SEC approach also facilitated the rapid analysis of protein-based mixtures of a wide molecular size range in a single online experimental run bypassing time-consuming deployment of different offline SEC methods. By coupling the second dimension with multiangle light scattering (MALS) and differential refractive index (dRI) detectors, absolute molecular weights of the separated species were obtained without the use of calibration curves. As illustrated in this report for protein mixtures and vaccine processes, this workflow can be used in scenarios where rapid development and deployment of SEC assays are warranted, enabling bioprocess monitoring, purity assessment, and characterization.

Does the choice of platinum doublet matter? A study to evaluate the impact of platinum doublet choice for treatment of platinum-sensitive ovarian cancer recurrence on the development of future PARP inhibitor and platinum resistance.

OBJECTIVES: The use of a platinum doublet for the treatment of platinum-sensitive epithelial ovarian cancer (EOC) recurrence is well established. The impact of the non­platinum chemotherapy used as part of a platinum doublet on PARP inhibitor (PARPi) and platinum sensitivity it not known. We aimed to describe oncologic outcomes in cases of recurrent EOC receiving PARPi as maintenance therapy based on preceding platinum doublet. METHODS: Retrospective study of patients with platinum-sensitive recurrent ovarian, fallopian tube or primary peritoneal cancer treated with platinum doublet followed by maintenance PARPi from 1/1/2015 and 1/1/2022. Comparisons were made between patients receiving carboplatin + pegylated liposomal doxorubicin (CD) versus other platinum doublets (OPDs). Descriptive statistics, Kaplan-Meier and univariate survival analyses were performed. RESULTS: 100 patients received PARPi maintenance following a platinum doublet chemotherapy regimen for platinum-sensitive recurrence. 25/100 (25%) received CD and 75/100 (75%) received OPDs. Comparing CD and OPDs, median progression-free survival was 8 versus 7 months (p = 0.26), median time to platinum resistance was 15 versus 13 months (p = 0.54), median OS was 64 versus 90 months (p = 0.28), and median OS from starting PARPi was 25 versus 26 months (p = 0.90), respectively. CONCLUSIONS: Using pegylated liposomal doxorubicin as part of a platinum doublet preceding maintenance PARPi for platinum-sensitive recurrence does not seem to hasten PARPi resistance or platinum resistance compared to OPDs. Although there was a non-significant trend towards increased OS among patients who received a platinum doublet other than CD prior to PARPi, the OS from PARPi start was similar between groups. Given the retrospective nature of this study and small study population, further research is needed to evaluate if the choice of platinum doublet preceding PARPi maintenance impacts PARPi resistance, platinum resistance and survival.

The microbiome and gynecologic cancer: cellular mechanisms and clinical applications.

The microbiome plays a vital function in maintaining human health and homeostasis. Each microbiota has unique characteristics, including those of the gastrointestinal and female reproductive tract. Dysbiosis, or alterations to the composition of the microbial communities, impacts the microbiota-host relationship and is linked to diseases, including cancer. In addition, studies have demonstrated that the microbiota can contribute to a pro-carcinogenic state through altered host immunologic response, modulation of cell proliferation, signaling, gene expression, and dysregulated metabolism of nutrients and hormones.In recent years, the microbiota of the gut and female reproductive tracts have been linked to many diseases, including gynecologic cancers. Numerous pre-clinical and clinical studies have demonstrated that specific bacteria or microbial communities may contribute to the development of gynecologic cancers. Further, the microbiota may also impact the toxicity and efficacy of cancer therapies, including chemotherapy, immunotherapy, and radiation therapy in women with gynecologic malignancies. The microbiota is highly dynamic and may be altered through various mechanisms, including diet, exercise, medications, and fecal microbiota transplantation. This review provides an overview of the current literature detailing the relationship between gynecologic cancers and the microbiota of the female reproductive and gastrointestinal tracts, focusing on mechanisms of carcinogenesis and strategies for modulating the microbiota for cancer prevention and treatment. Advancing our understanding of the complex relationship between the microbiota and gynecologic cancer will provide a novel approach for prevention and therapeutic modulation in the future.

Inequities in Inpatient Obstetrics Pain Management and Evaluation: Age, Race, Mental Health, and Obesity.

OBJECTIVE: To evaluate disparities of pain management among patients giving birth in inpatient Obstetrics units based on age, race, BMI, and mental health diagnoses. METHODS: A retrospective cohort study was performed and included all individuals giving birth at a tertiary-care institution in 2019. Patient-reported pain scores, and inpatient narcotic administration and dosing for pain control were collected. Models were adjusted for race, age, BMI, and diagnoses of anxiety, depression, opioid use disorder, and/or schizophrenia. RESULTS: 4788 Individuals met the inclusion criteria. A higher proportion of African American patients reported severe pain (n = 233/607, 38.4%) and received narcotics (n = 653/1141, 57.2%) compared to patients of other races. Despite controlling for several possible confounders, African American patients (OR 1.55, 95% CI 1.08-2.22), patients with increased BMI (OR 1.02, 95% CI 1.01-1.03), and patients with a mental health diagnosis (OR 2.33, 95% CI 1.32-4.12) were more likely to have worse pain at rest. Older patients were more likely to be administered narcotics (n = 447/757, 59.0%) compared to younger patients (patients aged 18-26: n = 577/1257, 52.3%; patients aged 27-35: n = 1451/2774, 52.3%; p < 0.001), despite younger patients being more likely to have severe pain (OR 1.50; 95% CI 1.20-1.86; p = 0.001). CONCLUSIONS: Patients who are Non-Hispanic African American and patients with obesity and mental health diagnoses experience inequities in postpartum pain management. Pain is complex and multifactorial and can be impacted by cultural, social, environmental factors and more. Further studies on factors that influence pain perception and management in inpatient obstetrics units are needed.

In Silico Method Development of Achiral and Chiral Tandem Column Reversed-phase Liquid Chromatography for Multicomponent Pharmaceutical Mixtures.

Tandem column liquid chromatography (LC) is a convenient, cost-effective approach to resolve multicomponent mixtures by serially coupling columns on readily available one-dimensional separation systems without specialized user training. Yet, adoption of this technique remains limited, mainly due to the difficulty in identifying optimal selectivity out of many possible tandem column combinations. At this point, method development and optimization require laborious "hit-or-miss" experimentation and "blind" screening when investigating different column selectivity without standard analytes. As a result, many chromatography practitioners end up combining two columns of similar selectivity, limiting the scope and potential of tandem column LC as a mainstay for industrial applications. To circumvent this challenge, we herein introduce a straightforward in silico multifactorial approach as a framework to expediently map the separation landscape across multiple tandem columns (achiral and chiral) and eluent combinations (isocratic and gradient elution) under reversed-phase LC conditions. Retention models were built using commercially available LC simulator software showcasing less than 2% difference between experimental and simulated retention times for analytes of interest in multicomponent pharmaceutical mixtures (e.g., metabolites and cyclic peptides).

Selective Plate-Based Assay for Trace EDTA Analysis via Boron Trifluoride-methanol Derivatization UHPLC-QqQ-MS/MS Enabling Biologic and Vaccine Processes.

The employment of ethylenediaminetetraacetic acid (EDTA) across several fields in chemistry and biology has required the creation of a high number of quantitative assays. Nonetheless, the determination of trace EDTA, especially in biologics and vaccines, remains challenging. Herein, we introduce an automated high-throughput approach based on EDTA esterification in 96-well plates using boron trifluoride-methanol combined with rapid analysis by ultra-high-performance liquid chromatography-triple quadrupole tandem mass spectrometry (UHPLC-QqQ-MS/MS). Derivatization of EDTA to its methyl ester (Me-EDTA) serves to significantly improve chromatographic performance (retention, peak shape, and selectivity), while also delivering a tremendous enhancement of sensitivity in the positive ion mode electrospray ionization (ESI+). This procedure, in contrast to previous EDTA methods based on complexation with metal ions, is not affected by high concentration of other metals, buffers, and related salts abundantly present in biopharmaceutical processes (e.g., iron, copper, citrate, etc.). Validation of this assay for the determination of ng·mL-1 level EDTA in monoclonal antibody and vaccine products demonstrated excellent performance (repeatability, precision, and linear range) with high recovery from small sample volumes while also providing an advantageous automation-friendly workflow for high-throughput analysis.

Enantioselective UHPLC Screening Combined with In Silico Modeling for Streamlined Development of Ultrafast Enantiopurity Assays.

Enantioselective chromatography has been the preferred technique for the determination of enantiomeric excess across academia and industry. Although sequential multicolumn enantioselective supercritical fluid chromatography screenings are widespread, access to automated ultra-high-performance liquid chromatography (UHPLC) platforms using state-of-the-art small particle size chiral stationary phases (CSPs) is an underdeveloped area. Herein, we introduce a multicolumn UHPLC screening workflow capable of combining 14 columns (packed with sub-2 µm fully porous and sub-3 µm superficially porous particles) with nine mobile phase eluent choices. This automated setup operates under a vast selection of reversed-phase liquid chromatography, hydrophilic interaction liquid chromatography, polar-organic mode, and polar-ionic mode conditions with minimal manual intervention and high success rate. Examples of highly efficient enantioseparations are illustrated from the integration of chiral screening conditions and computer-assisted modeling. Furthermore, we describe the nuances of in silico method development for chiral separations via second-degree polynomial regression fit using LC simulator (ACD/Labs) software. The retention models were found to be very accurate for chiral resolution of single and multicomponent mixtures of enantiomeric species across different types of CSPs, with differences between experimental and simulated retention times of less than 0.5%. Finally, we illustrate how this approach lays the foundation for a streamlined development of ultrafast enantioseparations applied to high-throughput enantiopurity analysis and its use in the second dimension of two-dimensional liquid chromatography experiments.

Automated Hydrophobic Interaction Chromatography Screening Combined with In Silico Optimization as a Framework for Nondenaturing Analysis and Purification of Biopharmaceuticals.

The mounting complexity of new modalities in the biopharmaceutical industry entails a commensurate level of analytical innovations to enable the rapid discovery and development of novel therapeutics and vaccines. Hydrophobic interaction chromatography (HIC) has become one of the widely preferred separation techniques for the analysis and purification of biopharmaceuticals under nondenaturing conditions. Inarguably, HIC method development remains very challenging and labor-intensive owing to the numerous factors that are typically optimized by a "hit-or-miss" strategy (e.g., the nature of the salt, stationary phase chemistry, temperature, mobile phase additive, and ionic strength). Herein, we introduce a new HIC method development framework composed of a fully automated multicolumn and multieluent platform coupled with in silico multifactorial simulation and integrated fraction collection for streamlined method screening, optimization, and analytical-scale purification of biopharmaceutical targets. The power and versatility of this workflow are showcased by a wide range of applications including trivial proteins, monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), oxidation variants, and denatured proteins. We also illustrate convenient and rapid HIC method development outcomes from the effective combination of this screening setup with computer-assisted simulations. HIC retention models were built using readily available LC simulator software outlining less than a 5% difference between experimental and simulated retention times with a correlation coefficient of >0.99 for pharmaceutically relevant multicomponent mixtures. In addition, we demonstrate how this approach paves the path for a straightforward identification of first-dimension HIC conditions that are combined with mass spectrometry (MS)-friendly reversed-phase liquid chromatography (RPLC) detection in the second dimension (heart-cutting two-dimensional (2D)-HIC-RPLC-diode array detector (DAD)-MS), enabling the analysis and purification of biopharmaceutical targets.

Automated ion exchange chromatography screening combined with in silico multifactorial simulation for efficient method development and purification of biopharmaceutical targets.

Bioprocess development of increasingly challenging therapeutics and vaccines requires a commensurate level of analytical innovation to deliver critical assays across functional areas. Chromatography hyphenated to numerous choices of detection has undeniably been the preferred analytical tool in the pharmaceutical industry for decades to analyze and isolate targets (e.g., APIs, intermediates, and byproducts) from multicomponent mixtures. Among many techniques, ion exchange chromatography (IEX) is widely used for the analysis and purification of biopharmaceuticals due to its unique selectivity that delivers distinctive chromatographic profiles compared to other separation modes (e.g., RPLC, HILIC, and SFC) without denaturing protein targets upon isolation process. However, IEX method development is still considered one of the most challenging and laborious approaches due to the many variables involved such as elution mechanism (via salt, pH, or salt-mediated-pH gradients), stationary phase's properties (positively or negatively charged; strong or weak ion exchanger), buffer type and ionic strength as well as pH choices. Herein, we introduce a new framework consisting of a multicolumn IEX screening in conjunction with computer-assisted simulation for efficient method development and purification of biopharmaceuticals. The screening component integrates a total of 12 different columns and 24 mobile phases that are sequentially operated in a straightforward automated fashion for both cation and anion exchange modes (CEX and AEX, respectively). Optimal and robust operating conditions are achieved via computer-assisted simulation using readily available software (ACD Laboratories/LC Simulator), showcasing differences between experimental and simulated retention times of less than 0.5%. In addition, automated fraction collection is also incorporated into this framework, illustrating the practicality and ease of use in the context of separation, analysis, and purification of nucleotides, peptides, and proteins. Finally, we provide examples of the use of this IEX screening as a framework to identify efficient first dimension (1D) conditions that are combined with MS-friendly RPLC conditions in the second dimension (2D) for two-dimensional liquid chromatography experiments enabling purity analysis and identification of pharmaceutical targets.

Trapping-Enrichment Multi-dimensional Liquid Chromatography with On-Line Deuterated Solvent Exchange for Streamlined Structure Elucidation at the Microgram Scale.

At the forefront of chemistry and biology research, development timelines are fast-paced and large quantities of pure targets are rarely available. Herein, we introduce a new framework, which is built upon an automated, online trapping-enrichment multi-dimensional liquid chromatography platform (TE-Dt-mDLC) that enables: 1) highly efficient separation of complex mixtures in a first dimension (1 D-UV); 2) automated peak trapping-enrichment and buffer removal achieved through a sequence of H2 O and D2 O washes using an independent pump setup; and 3) a second dimension separation (2 D-UV-MS) with fully deuterated mobile phases and fraction collection to minimize protic residues for immediate NMR analysis while bypassing tedious drying processes and minimizing analyte degradation. Diverse examples of target isolation and characterization from organic synthesis and natural product chemistry laboratories are illustrated, demonstrating recoveries above 90 % using as little as a few micrograms of material.

In Silico Multifactorial Modeling for Streamlined Development and Optimization of Two-Dimensional Liquid Chromatography.

Continued adoption of two-dimensional liquid chromatography (2D-LC) in industrial laboratories will depend on the development of approaches to make method development for 2D-LC more systematic, less tedious, and less reliant on user expertise. In this paper, we build on previous efforts in these directions by describing the use of multifactorial modeling software that can help streamline and simplify the method development process for 2D-LC. Specifically, we have focused on building retention models for second dimension (2D) separations involving variables including gradient time, temperature, organic modifier blending, and buffer concentration using LC simulator (ACD/Labs) software. Multifactorial retention modeling outcomes are illustrated as resolution map planes or cubes that enable straightforward location of 2D conditions that maximize resolution while minimizing analysis time. We also illustrate the practicality of this approach by identifying conditions that yield baseline separation of all compounds co-eluting from a first dimension (1D) separation using a single combination of 2D stationary phase and elution conditions. The multifactorial retention models were found to be very accurate for both the 1D and 2D separations, with differences between experimental and simulated retention times of less than 0.5%. Pharmaceutical applications of this approach for multiple heartcutting 2D-LC were demonstrated using IEC-IEC or achiral RPLC-chiral RPLC for 2D separations of multicomponent mixtures. The framework outlined here should help make 2D-LC method development more systematic and streamline development and optimization for a variety of 2D-LC applications in both industry and academia.

Mapping the Separation Landscape in Two-Dimensional Liquid Chromatography: Blueprints for Efficient Analysis and Purification of Pharmaceuticals Enabled by Computer-Assisted Modeling.

Recent developments in two-dimensional liquid chromatography (2D-LC) now make separation and analysis of very complex mixtures achievable. Despite being such a powerful chromatographic tool, current 2D-LC technology requires a series of arduous method development activities poorly suited for a fast-paced industrial environment. Recent introductions of new technologies including active solvent modulation and a support for multicolumn 2D-LC are helping to overcome this stigma. However, many chromatography practitioners believe that the lack of a systematic way to effectively optimize 2D-LC separations is a missing link in securing the viability of 2D-LC as a mainstay for industrial applications. In this work, a computer-assisted modeling approach that dramatically simplifies both offline and online 2D-LC method developments is introduced. Our methodology is based on mapping the separation landscape of pharmaceutically relevant mixtures across both first (1D) and second (2D) dimensions using LC Simulator (ACD/Labs) software. Retention models for 1D and 2D conditions were built using a minimal number of multifactorial modeling experiments (2 × 2 or 3 × 3 parameters: gradient slope, column temperature, and different column and mobile phase combinations). The approach was first applied to online 2D-LC analysis involving achiral and chiral separations of complex mixtures of enantiomeric species. In these experiments, the retention models proved to be quite accurate for both the 1D and 2D separations, with retention time differences between experiments and simulations of less than 3.5%. This software-based concept was also demonstrated for offline 2D-LC purification of drug substances.

Two distinct E3 ligases, SCFFBXL19 and HECW1, degrade thyroid transcription factor 1 in normal thyroid epithelial and follicular thyroid carcinoma cells, respectively.

Thyroid transcription factor 1 (TTF1) regulates the tissue-specific expression of genes. However, the molecular regulation of TTF1 in thyroid normal and carcinoma cells has not been revealed. Here we identify 2 distinct ubiquitin E3 ligases that are responsible for TTF1 degradation in normal thyroid cells and carcinoma cells, respectively. Phorbol myristate acetate induced TTF1 protein degradation in the ubiquitin-proteasome system in both HTori3 thyroid follicular epithelial cells and follicular thyroid carcinoma 133 (FTC133) cells. Lysine 151 residue was identified as a ubiquitin acceptor site within TTF1 in both cell types. Overexpression of E3 ubiquitin protein ligase 1 containing HECT, C2, and WW domain (HECW1) induced TTF1 degradation and ubiquitination in Htori3 cells but not in FTC133 cells. Overexpression of ubiquitin E3 ligase subunit FBXL19 increased TTF1 ubiquitination and degradation in FTC133 cells, but it had no effect on TTF1 levels in Htori3 cells. Overexpression of TTF1 increased thyroglobulin and sodium/iodide symporter mRNA levels, cell migration, and proliferation in HTori3 cells, whereas the effects were reversed by the overexpression of HECW1. This study reveals an undiscovered molecular mechanism by which TTF1 ubiquitination and degradation is regulated by different E3 ligases in thyroid normal and tumor cells.-Liu, J., Dong, S., Wang, H., Li, L., Ye, Q., Li, Y., Miao, J., Jhiang, S., Zhao, J., Zhao, Y. Two distinct E3 ligases, SCFFBXL19 and HECW1, degrade thyroid transcription factor 1 in normal thyroid epithelial and follicular thyroid carcinoma cells, respectively.

The HECT ubiquitin E3 ligase Smurf2 degrades µ-opioid receptor 1 in the ubiquitin-proteasome system in lung epithelial cells.

Opioids are widely used for relieving clinical acute or chronic pain. The biological effects of opioids are through activating µ-opioid receptor 1 (MOR1). Most studies have focused on the consequences of agonist-induced MOR1 phosphorylation, ubiquitination, and internalization. Agonist-mediated MOR1 degradation, which is crucial for receptor stability and responsiveness, has not been well studied. E3 ubiquitin-protein ligase SMURF2 (Smurf2), a homolog to E6AP carboxy terminus (HECT) ubiquitin E3 ligase, has been shown to regulate MOR1 ubiquitination and internalization; however, its role in MOR1 degradation has not been studied. Here, we demonstrate that Smurf2 mediates [d-Ala2,N-MePhe4,Gly5-ol]-enkephalin (DAMGO, an agonist of MOR1)-induced MOR1 ubiquitination and degradation. DAMGO decreased MOR1 levels in the ubiquitin-proteasome system. MOR1 was modified by a Lys48-linked polyubiquitin chain. Overexpression of Smurf2 induced MOR1 ubiquitination and accelerated DAMGO-induced MOR1 degradation, whereas downregulation of Smurf2 attenuated MOR1 degradation. Furthermore, DAMGO increased lung epithelial cell migration and proliferation, and the effect was attenuated by overexpressing Smurf2. Collectively, these data unveil that Smurf2 negatively regulates MOR1 activity by reducing its stability. We also demonstrate an unrevealed biological function of MOR1 in lung epithelial cells. DAMGO-MOR1 promote cell migration and proliferation in lung epithelial cells, suggesting a potential effect of DAMGO in lung repair and remodeling after lung injury.

pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments.

Determining the net charge and protonation states populated by a small molecule in an environment of interest or the cost of altering those protonation states upon transfer to another environment is a prerequisite for predicting its physicochemical and pharmaceutical properties. The environment of interest can be aqueous, an organic solvent, a protein binding site, or a lipid bilayer. Predicting the protonation state of a small molecule is essential to predicting its interactions with biological macromolecules using computational models. Incorrectly modeling the dominant protonation state, shifts in dominant protonation state, or the population of significant mixtures of protonation states can lead to large modeling errors that degrade the accuracy of physical modeling. Low accuracy hinders the use of physical modeling approaches for molecular design. For small molecules, the acid dissociation constant (pKa) is the primary quantity needed to determine the ionic states populated by a molecule in an aqueous solution at a given pH. As a part of SAMPL6 community challenge, we organized a blind pKa prediction component to assess the accuracy with which contemporary pKa prediction methods can predict this quantity, with the ultimate aim of assessing the expected impact on modeling errors this would induce. While a multitude of approaches for predicting pKa values currently exist, predicting the pKas of drug-like molecules can be difficult due to challenging properties such as multiple titratable sites, heterocycles, and tautomerization. For this challenge, we focused on set of 24 small molecules selected to resemble selective kinase inhibitors-an important class of therapeutics replete with titratable moieties. Using a Sirius T3 instrument that performs automated acid-base titrations, we used UV absorbance-based pKa measurements to construct a high-quality experimental reference dataset of macroscopic pKas for the evaluation of computational pKa prediction methodologies that was utilized in the SAMPL6 pKa challenge. For several compounds in which the microscopic protonation states associated with macroscopic pKas were ambiguous, we performed follow-up NMR experiments to disambiguate the microstates involved in the transition. This dataset provides a useful standard benchmark dataset for the evaluation of pKa prediction methodologies on kinase inhibitor-like compounds.

Use of MALDI-MS Combined with Differential Hydrogen-Deuterium Exchange for Semiautomated Protein Global Conformational Screening.

Matrix-assisted laser desorption/ionization (MALDI) coupled with a time-of-flight (TOF) mass-spectrometry (MS) detector is acknowledged to be very useful for analysis of biological molecules. At the same time, hydrogen-deuterium exchange (HDX) is a well-known technique for studying protein higher-order structure. However, coupling MALDI with HDX has been challenging because of undesired back-exchange reactions during analysis. In this report, we survey an approach that utilizes MALDI coupled with an automated sample preparation to compare global conformational changes of proteins under different solution conditions using differential HDX. A nonaqueous matrix was proposed for MALDI sample preparation to minimize undesirable back-exchange. An automated experimental setup based on the use of a liquid-handling robot and automated data acquisition allowed for tracking protein conformational changes as a difference in the number of protons exchanged to deuterons at specified solution conditions. Experimental time points to study the deuteration-labeling kinetics were obtained in a fully automated manner. The use of a nonaqueous matrix solution allowed experimental error to be minimized to within 1% RSD. We applied this newly developed MALDI-HDX workflow to study the effect of several common excipients on insulin folding stability. The observed results were corroborated by literature data and were obtained in a high-throughput and automated manner. The proposed MALDI-HDX approach can also be applied in a high-throughput manner for batch-to-batch higher-order structure comparison, as well as for the optimization of protein chemical modification reactions.

Multiple-injection high-throughput gas chromatography analysis.

Multiple-injection techniques have been shown to be a simple way to perform high-throughput analysis where the entire experiment resides in a single chromatogram, simplifying the data analysis and interpretation. In this study, multiple-injection techniques are applied to gas chromatography with flame ionization detection and mass detection to significantly increase sample throughput. The unique issues of implementing a traditional "Fast" injection mode of multiple-injection techniques with gas chromatography and mass spectrometry are discussed. Stacked injections are also discussed as means to increase the throughput of longer methods where mass detection is unable to distinguish between analytes of the same mass and longer retentions are required to resolve components of interest. Multiple-injection techniques are shown to increase instrument throughput by up to 70% and to simplify data analysis, allowing hits in multiple parallel experiments to be identified easily.

HIPK2 C-terminal domain inhibits NF-κB signaling and renal inflammation in kidney injury.

HIPK2 is a multifunctional kinase that acts as a key pathogenic mediator of chronic kidney disease and fibrosis. It acts as a central effector of multiple signaling pathways implicated in kidney injury, such as TGF-ß/Smad3-mediated extracellular matrix accumulation, NF-κB-mediated inflammation, and p53-mediated apoptosis. Thus, a better understanding of the specific HIPK2 regions necessary for distinct downstream pathway activation is critical for optimal drug development for CKD. Our study now shows that caspase-6-mediated removal of the C-terminal region of HIPK2 (HIPK2-CT) lead to hyperactive p65 NF-κB transcriptional response in kidney cells. In contrast, the expression of cleaved HIPK2-CT fragment could restrain the NF-κB transcriptional activity by cytoplasmic sequestration of p65 and the attenuation of IκBα degradation. Therefore, we examined whether HIPK2-CT expression can be exploited to restrain renal inflammation in vivo. The induction of HIPK2-CT overexpression in kidney tubular cells attenuated p65 nuclear translocation, expression of inflammatory cytokines, and macrophage infiltration in the kidneys of mice with unilateral ureteral obstruction and LPS-induced acute kidney injury. Collectively, our findings indicate that the HIPK2-CT is involved in the regulation of nuclear NF-κB transcriptional activity and that HIPK2-CT or its analogs could be further exploited as potential antiinflammatory agents to treat kidney disease.

Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization.

Development of meaningful and reliable analytical assays in the (bio)pharmaceutical industry can often be challenging, involving tedious trial and error experimentation. In this work, an automated analytical workflow using an AI-based algorithm for streamlined method development and optimization is presented. Chromatographic methods are developed and optimized from start to finish by a feedback-controlled modeling approach using readily available LC instrumentation and software technologies, bypassing manual user intervention. With the use of such tools, the time requirement of the analyst is drastically minimized in the development of a method. Herein key insights on chromatography system control, automatic optimization of mobile phase conditions, and final separation landscape for challenging multicomponent mixtures are presented (e.g., small molecules drug, peptides, proteins, and vaccine products) showcased by a detailed comparison of a chiral method development process. The work presented here illustrates the power of modern chromatography instrumentation and AI-based software to accelerate the development and deployment of new separation assays across (bio)pharmaceutical modalities while yielding substantial cost-savings, method robustness, and fast analytical turnaround.

Biocatalytic cascade process of islatravir: Analytical and regulatory control strategy of minor enantiomer.

Commercial process of islatravir (MK-8591, EFdA) utilizes biocatalytic cascade reactions to construct the ribose moiety of the molecule which bears three chiral centers. However, this biocatalytic process also brought analytical challenges where all stereoisomers and process related compounds are controlled in one isolated intermediate, the final drug substance. A chiral LC method was developed to resolve all those compounds from islatravir and its minor enantiomer by thorough column screening and careful optimization. Detail of designing key method validation components such as method linearity, precision and robustness is discussed, and their results were presented. The method was successfully validated to fulfill various expectation from each individual health authority including FDA, EMA, PMDA, and ANVISA.