Separation of biological proteins by liquid chromatography

Separation and purification of short-, medium-, and long-stranded RNAs by RP-HPLC using different mobile phases and C18 columns with various pore sizes

Nucleic acids, which have been employed in medicines for various diseases, are attracting attention as a new pharmaceutical model. Depending on the target substances, nucleic acid medicines with various nucleic acid chain lengths (several tens of nucleotides [nt] to several thousands of nt) exist. The purification of synthesized nucleic acids is crucial as various impurities remain in the crude product after synthesis. Presently, reversed-phase high-performance liquid chromatography (RP-HPLC) represents an effective purification method for nucleic acids. However, the information regarding the HPLC conditions for separating and purifying nucleic acids of various chain lengths is insufficient. Thus, this technical note describes the separation and purification of short-, medium-, and long-stranded nucleic acids (several tens of nt to thousands of nt) by RP-HPLC with various mobile phases and octadecyl-based columns with various pore sizes, such as normal (9-12 nm), wide (30 nm), and super wide (>30 nm) pores.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Precision medicine and metabolic syndrome

Metabolic syndrome (MetS) is one of the most important health issues around the world and a major risk factor for both type 2 diabetes mellitus (T2DM) and cardiovascular diseases. The etiology of MetS is determined by the interaction between genetic and environmental factors. Effective prevention and treatment of MetS notably decreases the risk of its complications such as diabetes, obesity, hypertension, and dyslipidemia. According to recent genome-wide association studies, multiple genes are involved in the incidence and development of MetS. The presence of particular genes which are responsible for obesity and lipid metabolism, affecting insulin sensitivity and blood pressure, as well as genes associated with inflammation, can increase the risk of MetS. These molecular markers, together with clinical data and findings from proteomic, metabolomic, pharmacokinetic, and other methods, would clarify the etiology and pathophysiology of MetS and facilitate the development of personalized approaches to the management of MetS. The application of personalized medicinebased on susceptibility identified genomes would help physicians recommend healthier lifestyles and prescribe medications to improve various aspects of health in patients with MetS. In recent years, personalized medicine by genetic testing has helped physicians determine genetic predisposition to MetS, prevent the disease by behavioral, lifestyle-related, or therapeutic interventions, and detect, diagnose, treat, and manage the disease. Clinically, personalized medicine is providing effective strategies for the prevention and treatment of MetS by reducing the time, cost, and failure rate of pharmaceutical clinical trials. It is also eliminating trial-and-error inefficiencies that inflate health care costs and undermine patient care.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Finding a Needle in a Haystack: Proteomics in Heart Failure

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Proteomics has aided HF biomarker discovery, which allows for greater disease insights.

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Experiment design can be tailored to HF research to discover novel biomarkers.

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Primary methods include MS, protein microarray, aptamer, and PEA-based technologies.

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Proteomics can detect unique low abundance proteins and detect protein modifications.

Circulating protein biomarkers provide information regarding pathways in heart failure (HF) and can add important value to clinicians. Advancements in proteomics allow researchers to measure a multitude of proteins simultaneously with excellent sensitivity and selectivity to detect low abundance proteins. This helps identify previously unrecognized pathways in HF and discover biomarkers and potential targets for HF therapies. Although several proteomic methods exist, including mass spectrometry, protein microarray, aptamer, and proximity extension assay−based techniques, each have their unique advantages. This paper provides an overview of the various proteomic methods, with examples of how each has contributed to understanding the pathways in HF.

Protein-Based Fingerprint Analysis for the Identification of Ranae Oviductus Using RP-HPLC

This work demonstrated a method combining reversed-phase high-performance liquid chromatography (RP-HPLC) with chemometrics analysis to identify the authenticity of Ranae Oviductus. The fingerprint chromatograms of the Ranae Oviductus protein were established through an Agilent Zorbax 300SB-C8 column and diode array detection at 215 nm, using 0.085% TFA (v/v) in acetonitrile (A) and 0.1% TFA in ultrapure water (B) as mobile phase. The similarity was in the range of 0.779-0.980. The fingerprint chromatogram of Ranae Oviductus showed a significant difference with counterfeit products. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) successfully identified Ranae Oviductus from the samples. These results indicated that the method established in this work was reliable.

HPLC-MS/MS method optimisation for matrix metalloproteinase 3 and matrix metalloproteinase 9 determination in human blood serum using target analysis

A quantitative analysis of zinc endopeptidases matrix metalloproteinase 9 (MMP9) and matrix metalloproteinase 3 (MMP3) from human blood serum are presented. Both matrix metalloproteinases (MMP) are present in human blood serum and can be used as biomarkers for different diseases. The analysis was performed using LC-MS/MS with a triple quadrupole mass spectrometer, based on two specific peptides of each MMP in comparison with an enzyme-linked immunosorbent assay (ELISA). While the conditions for the LC-MS/MS analysis of MMP9 peptides were previously reported for bronchoalveolar lavage fluid, the analysis of MMP3 peptides was newly quantified for human blood serum herein for the first time. For MMP3, the linear behaviour was determined in the concentration range from 1.0-200.0ng/mL (R²=0.997) with an LLOD of 0.5ng/mL. For MMP9, linearity was determined in the concentration range from 6.5-65.0ng/mL (R²=0.995) with an LLOD of 2.0ng/mL.

Bromelain: from production to commercialisation

Bromelain is a mixture of proteolytic enzymes found in pineapple (Ananas comosus) plants. It can be found in several parts of the pineapple plant, including the stem, fruit, leaves and peel. High demand for bromelain has resulted in gradual increases in bromelain production. These increases have led to the need for a bromelain production strategy that yields more purified bromelain at a lower cost and with fewer production steps. Previously, bromelain was purified by conventional centrifugation, ultrafiltration and lyophilisation. Recently, the development of more modern purification techniques such as gel filtration, ion exchange chromatography, affinity chromatography, aqueous two-phase extraction and reverse micelle chromatography has resulted in increased industrial bromelain production worldwide. In addition, recombinant DNA technology has emerged as an alternative strategy for producing large amounts of ultrapure bromelain. An up-to-date compilation of data regarding the commercialisation of bromelain in the clinical, pharmaceutical and industrial fields is provided in this review. © 2016 Society of Chemical Industry.

Development and Validation of a Micellar Capillary Electrophoresis Method for Determination of IFNβ-1b in Lyophilized Formulation of a Biosimilar Product

Human interferons (IFNs) are key cytokines secreted by immune system. They have several effects such as antiviral and anti tumors activity, activating immune cells and healing of multiple sclerosis. The type IFNs present in humans are α ,β and Υ. IFN β is a polypeptide, normally produced by fibroblasts and seems to be more species-specific than IFN. Structural properties of IFNs are important for their biologic effects. There are a few analytical techniques for separation, identification and determination of IFNs in its formulations such as mass spectroscopy, RP-HPLC and capillary electrophoresis (CE). In this study we used Micellar Electrokinetic Chromatography (MEKC) as a unique mode of CE because of its capability to separate neutral as well as charged solutes. We used sodium tetraborate (Borax) as buffer without any modifier and sodium dodecyl sulfate (SDS) as surfactant. The optimum MECK running buffer consisted of Borate 50 Mm; SDS 20 mM pH =9.6. The validated method was used for determination of the IFN β-1b formulation which is manufactured in Iran. From 9 collected different batches, all of them had acceptable potency as claimed on their label with average 102.25 ±10.030 %. This is the first time that a MEKC method is introduced for quantification of IFN β-1b in its pharmaceutical dosage forms. The method is reliable safe, rapid and accurate.