Dysbiosis represents changes in composition and structure of the gut microbiome community (microbiome), which may dictate the physiological phenotype (health or disease). Recent technological advances and efforts in metagenomic and metabolomic analyses have led to a dramatical growth in our understanding of microbiome, but still, the mechanisms underlying gut microbiome–host interactions in healthy or diseased state remain elusive and their elucidation is in infancy. Disruption of the normal gut microbiota may lead to intestinal dysbiosis, intestinal barrier dysfunction, and bacterial translocation. Excessive uremic toxins are produced as a result of gut microbiota alteration, including indoxyl sulphate, p-cresyl sulphate, and trimethylamine-N-oxide, all implicated in the variant processes of kidney diseases development. This review focuses on the pathogenic association between gut microbiota and kidney diseases (the gut–kidney axis), covering CKD, IgA nephropathy, nephrolithiasis, hypertension, acute kidney injury, hemodialysis and peritoneal dialysis in clinic. Targeted interventions including probiotic, prebiotic and symbiotic measures are discussed for their potential of re-establishing symbiosis, and more effective strategies for the treatment of kidney diseases patients are suggested. The novel insights into the dysbiosis of the gut microbiota in kidney diseases are helpful to develop novel therapeutic strategies for preventing or attenuating kidney diseases and complications.

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Microbiome–metabolome reveals the contribution of gut–kidney axis on kidney disease

The prevalence of many chronic diseases has increased over the last decades. It has been postulated that dysbiosis driven by environmental factors such as antibiotic use is shifting the microbiome in ways that increase inflammation and the onset of chronic disease. Dysbiosis can be defined through the loss or gain of bacteria that either promote health or disease, respectively. Here we use multiple independent datasets to determine the nature of dysbiosis for a cluster of chronic diseases that includes urinary stone disease (USD), obesity, diabetes, cardiovascular disease, and kidney disease, which often exist as co-morbidities. For all disease states, individuals exhibited a statistically significant association with antibiotics in the last year compared to healthy counterparts. There was also a statistically significant association between antibiotic use and gut microbiota composition. Furthermore, each disease state was associated with a loss of microbial diversity in the gut. Three genera, Bacteroides, Prevotella, and Ruminococcus, were the most common dysbiotic taxa in terms of being enriched or depleted in disease populations and was driven in part by the diversity of operational taxonomic units (OTUs) within these genera. Results of the cross-sectional analysis suggest that antibiotic-driven loss of microbial diversity may increase the risk for chronic disease. However, longitudinal studies are needed to confirm the causative effect of diversity loss for chronic disease risk.

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Defining Dysbiosis for a Cluster of Chronic Diseases.

Human body is inhabited by vast number of microorganisms which form a complex ecological community and influence the human physiology, in the aspect of both health and diseases. These microbes show a relationship with the human immune system based on coevolution and, therefore, have a tremendous potential to contribute to the metabolic function, protection against the pathogen and in providing nutrients and energy. However, of these microbes, many carry out some functions that play a crucial role in the host physiology and may even cause diseases. The introduction of new molecular technologies such as transcriptomics, metagenomics and metabolomics has contributed to the upliftment on the findings of the microbiome linked to the humans in the recent past. These rapidly developing technologies are boosting our capacity to understand about the human body-associated microbiome and its association with the human health. The highlights of this review are inclusion of how to derive microbiome data and the interaction between human and associated microbiome to provide an insight on the role played by the microbiome in biological processes of the human body as well as the development of major human diseases.

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Human microbiome: an academic update on human body site specific surveillance and its possible role.

Nephrolithiasis is a common urological disease with high prevalence and recurrence rates. Characterizing gut microbiome profiles of nephrolithiasis patients may provide valuable insights and potential biomarkers for the disease. Therefore, we explored the relation between gut microbiome and nephrolithiasis using 16S ribosomal RNA (rRNA) gene sequencing. 13 patients with multiple kidney stones and 13 matched healthy controls were recruited. A decreasing trend in number of observed species in nephrolithiasis patients was detected, although statistical significance was not reached (p = 0.086). The inter-group variability in community structure by beta diversity analysis showed a clear separation between nephrolithiasis patients and healthy controls. Twenty genera differentiated significantly in relative abundance between nephrolithiasis patients and healthy controls (all p < 0.05). Among the 20 genera, Phascolarctobacterium, Parasutterella, Ruminiclostridium_5, Erysipelatoclostridium, Fusicatenibacter and Dorea were correlated with the concentration of the trace elements in blood, including potassium, sodium, calcium and chlorinum. Characteristic microbiome in nephrolithiasis patients was also identified by linear discriminant analysis effect size (LEfSe). These findings may provide novel and non-invasive potential diagnostic biomarkers for nephrolithiasis, and contribute to prevention and treatment of nephrolithiasis from the perspective of maintaining micro-ecological equilibrium in gut.

16S rRNA gene sequencing reveals altered composition of gut microbiota in individuals with kidney stones.

Editorial:

In the present editorial, Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) equations were derived using the algorithm of the partition coefficient for the cancer risk prediction and assessment in human cells under synchrotron radiations. Further mixed surfactant systems of an anionic surfactant and several non-ionic surfactant of different hydrophobic chain lengths were taken together to calculate the parameters [1-22]. Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) equations under synchrotron radiations were developed from the surface tension data to predict cancer risk assessment in human cells and compared with Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) derived values to understand probable mechanisms of action of the mixed surfactants blends for aquatic toxicity. The established Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) equations for mixed surfactants indicate the given blends of surfactant act as polar narcotic. As mentioned before, surfactants are used in personal hygiene products, laundry and dishwashing detergents which are discharged into domestic wastewater after use and thus enter the aquatic environment. Because of the increasing use of surfactants, the identification of Nano compounds with low toxicity and good surface activity properties are of great interest. Also, development Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) models provide a possible tool for this research. Quantitative Structure Activity Relationships (QSARs) relate biological, physiochemical, biochemical, pharmaceutical, clinical, medical and medicinal data such as molecular weight and solubility to biological responses. In addition, the biological, physiochemical, biochemical, pharmaceutical, clinical, medical and medicinal data describe properties such as toxicity, pharmacological effects and carcinogenicity. Using this technique only a relatively small number of experiments will be needed to construct a model which will give indications about Nano compounds that are expected to be harmful without any further biological, physiochemical, biochemical, pharmaceutical, clinical, medical and medicinal testing. Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) studies are indubitably of great importance in modern pharmaceutical, medical and medicinal chemistry and also biochemistry. 

To obtain a significant correlation, it is essential that appropriate descriptors are employed, whether they are theoretical, computational, numerical, mathematical, physical and empirical or derived from readily available experimental characteristics of molecular and chemical structures. Many descriptors reflect simple molecular and chemical properties and can thus provide insight into the biological, physiochemical, biochemical, pharmaceutical, clinical, medical and medicinal nature of the activity/property under synchrotron radiations consideration. Furthermore, using combinations of topological, physiochemical, biological, biochemical, pharmaceutical, clinical, medical and medicinal parameters along with the indicator parameters, a tremendous improvement in the statistics has been observed. Activity constants can be a key parameter for understanding and quantifying chemical and physical phenomena such as reaction rates, biological uptake, biological transport and environmental fate. 

It has been shown that acid–base properties affect the toxicity. The research for quantitative relations between the chemical and molecular structure i.e., biological, physiochemical, biochemical, pharmaceutical, clinical, medical, medicinal, structural, molecular and conformational properties and the biological, physiochemical, biochemical, pharmaceutical, clinical, medical, medicinal, structural, molecular and conformational responses is the subject of Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) studies. These relationships will hopefully help to understand and explain the driving forces behind the Nano drugs action; ultimately supporting the development of new Nano compounds exhibiting describes biological, physiochemical, biochemical, pharmaceutical, clinical, medical, medicinal, structural, molecular and conformational properties. 

In modern pharmaceutical, medical and medicinal chemistry, computer – aided Nano drugs design method such as QuantitativeStructure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) studies have greatly accelerated the pace of Nano drugs discovery in recent decade. The underlying assumption behind Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) analysis is that the variation of biologic activity within a group of Nano compounds can be correlated with the variation of their respective structural, molecular and chemical feature. It should be noted that many machine – learning methods have been extensively applied to the process of Nano drugs discovery for building Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) models.

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Cancer Risk Prediction and Assessment in Human Cells under Synchrotron Radiations Using Quantitative Structure Activity Relationship (QSAR) and Quantitative Structure Properties Relationship (QSPR) Studies

Hyperoxaluria due to endogenously synthesized and exogenously ingested oxalates is a leading cause of recurrent oxalate stone formations. Even though, humans largely rely on gut microbiota for oxalate homeostasis, hyperoxaluria associated gut microbiota features remain largely unknown. Based on 16S rRNA gene amplicons, targeted metagenomic sequencing of formyl-CoA transferase (frc) gene and qPCR assay, we demonstrate a selective enrichment of Oxalate Metabolizing Bacterial Species (OMBS) in hyperoxaluria condition. Interestingly, higher than usual concentration of oxalate was found inhibitory to many gut microbes, including Oxalobacter formigenes, a well-characterized OMBS. In addition a concomitant enrichment of acid tolerant pathobionts in recurrent stone sufferers is observed. Further, specific enzymes participating in oxalate metabolism are found augmented in stone endures. Additionally, hyperoxaluria driven dysbiosis was found to be associated with oxalate content, stone episodes and colonization pattern of Oxalobacter formigenes. Thus, we rationalize the first in-depth surveillance of OMBS in the human gut and their association with hyperoxaluria. Our findings can be utilized in the treatment of hyperoxaluria associated recurrent stone episodes.

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Hyperoxaluria leads to dysbiosis and drives selective enrichment of oxalate metabolizing bacterial species in recurrent kidney stone endures

A rapid, simple, sensitive, and selective chromatographic method has been developed for the simultaneous estimation of atorvastatin calcium, losartan potassium, atenolol, and aspirin in marketed formulation. The separation was carried out on a KYA TECH HiQ Sil C18HS (250 × 4.6 mm i.d., 5 µm particle size) column using a mobile phase of acetonitrile: 0.02 M potassium dihydrogen phosphate buffer (pH 3.4) (70:30% v/v). The chromatographic analysis has been carried out using flow rate of 1 mL/min and detection at 236 nm. The proposed method was applied for analysis of ATN, ASP, LOS and ATO from plasma. ATO showed a linear response in the concentration range of 4–24 µg/mL, LOS and ATN showed a linear response in the concentration range of 20–120 µg/mL and ASP showed a linear response in the concentration range of 30–180 µg/mL in tablet formulation analysis. ATO, LOS, ASP, and ATN showed a linear response in the concentration range of 25–150, 50–300, 100–600, and 50–300 ng/mL in plasma analysis, respectively. T...

Rp-hplc method for simultaneous estimation of atorvastatin calcium, losartan potassium, atenolol, and aspirin from tablet dosage form and plasma

The aim of present research is to investigate the effect of various carbopols (934 and 940) on drug release from fluconazole gel formulation developed for topical application. Fluconazole (FCZ) is the first of a new subclass of synthetic triazole antifungal agent and it is commonly used in treatment of most fungal infections. It is available as topical powder, tablets and sterile solutions for intravenous use. In present research, full factorial design has been applied to study the effect of type of carbopols on quality attributes of FCZ gel formulation. FCZ topical gel formulation batches (F1 to F9) have been formulated as per runs obtained in factorial design and further evaluated for pH, drug content, viscosity, invitro drug release kinetics, etc. Fourier transform infrared spectroscopy (FTIR) study indicated no any chemical or structural changes in FCZ during formulation studies. Drug diffusion studies have shown time required for 90% of total drug release (t90%) from all formulations in between 28.7 ± 2.3 to 208.4 ± 3.9 min. Batch F9 showed maximum t90% attributed to highest viscosity resulted in slower drug release amongst all batches (F1-F11), however, vice versa results have been observed with batch F1. These results were in accordance with concept of inverse relationship between drug release and viscosity of formulation. It has been observed that drug release from all gel formulation batches obeyed korsmeyer-peppas model. Permeation of drug through formed gel depends on viscosity and pH of gel. From present study it can be concluded that topical gel formulations of FCZ with desirable drug diffusion pattern can be successfully prepared by using carbopol 934 and carbopol 940, where both carbopols have extended the drug release at their respective higher concentrations.

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Effect of Carbopol 934 and 940 on Fluconazole Release from Topical Gel Formulation: A Factorial Approach

Atorvastatin Calcium and Ramipril are used in combination for treatment of hypertension. The present work deals with simple spectrophotometric method development for simultaneous estimation of Atorvastatin Calcium (ATR) and Ramipril (RAM) in two component capsule formulation. The method employed first order derivative spectroscopy1. For determination of sampling wavelength 10 μg/ml of each of ATR and RAM were scanned in 200–350 nm range and sampling wavelengths were 294 nm for ATR were RAM showed zero crossing point and 229 nm for RAM were ATR showed zero crossing point in first order derivative spectroscopy. For this method linearity observed in 5–40 μg/ml for ATR and 2–20 μg/ml for RAM. The recovery studies confirmed accuracy of proposed method and low values of standard deviation confirmed precision of method. The method is validated as per ICH guidelines.

Development and Validation of Method for Simultaneous Estimation of Atorvastatin Calcium and Ramipril from Capsule Dosage Form by First Order Derivative Spectroscopy.

Atorvastatin calcium (AT) is hypolipidemic agent and Amlodipine besylate (AM) is calcium channel blocker. The combination of both drugs is used in treatment of hypertension. The present communication deals with development of simple, sensitive, rapid and economical method for simultaneous estimation of atorvastatin calcium and amlodipine besylate in combined dosage form. The AT and AM stock solutions are prepared in (50:50 V/V) methanol: water. The method of analysis is derivative spectroscopy to eliminate spectral interference by measuring absorbances at 241nm and 250 nm for AM and AT respectively. The AT and AM are linear in concentration range of 0-14 µg/ml and 0-7 µg/ml respectively. The limit of detection (LOD) and limit of quantitation (LOQ) of AM was 0.29 and 0.75 µg respectively. The limit of detection (LOD) and limit of quantitation (LOQ) of AT was 0.21 and 0.60 µg respectively. The results of analysis were validated by ICH Q2B (R1). The results of recovery studies and precision were found to be within limits.

DEVELOPMENT AND VALIDATION OF DERIVATIVE SPECTROPHOTOMETRIC METHOD FOR ESTIMATION OF ATORVASTATIN CALCIUM AND AMLODIPINE BESYLATE IN TABLET DOSAGE FORM Research Article

Swapnil D. Jadhav

Papers

Hyperoxaluria leads to dysbiosis and drives selective enrichment of oxalate metabolizing bacterial species in recurrent kidney stone endures

Rp-hplc method for simultaneous estimation of atorvastatin calcium, losartan potassium, atenolol, and aspirin from tablet dosage form and plasma

Effect of Carbopol 934 and 940 on Fluconazole Release from Topical Gel Formulation: A Factorial Approach

Development and Validation of Method for Simultaneous Estimation of Atorvastatin Calcium and Ramipril from Capsule Dosage Form by First Order Derivative Spectroscopy.

DEVELOPMENT AND VALIDATION OF DERIVATIVE SPECTROPHOTOMETRIC METHOD FOR ESTIMATION OF ATORVASTATIN CALCIUM AND AMLODIPINE BESYLATE IN TABLET DOSAGE FORM Research Article