Dataset Information

How Does Glycation Affect Binding Parameters of the Albumin-Gliclazide System in the Presence of Drugs Commonly Used in Diabetes? In Vitro Spectroscopic Study.

ABSTRACT: In this research, the selected drugs commonly used in diabetes and its comorbidities (gliclazide, cilazapril, atorvastatin, and acetylsalicylic acid) were studied for their interactions with bovine serum albumin-native and glycated. Two different spectroscopic methods, fluorescence quenching and circular dichroism, were utilized to elucidate the binding interactions of the investigational drugs. The glycation process was induced in BSA by glucose and was confirmed by the presence of advanced glycosylation end products (AGEs). The interaction between albumin and gliclazide, with the presence of another drug, was confirmed by calculation of association constants (0.11-1.07 × 104 M-1). The nature of changes in the secondary structure of a protein depends on the drug used and the degree of glycation. Therefore, these interactions may have an influence on pharmacokinetic parameters.

SUBMITTER: Wiglusz K

PROVIDER: S-EPMC8270297 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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How Does Glycation Affect Binding Parameters of the Albumin-Gliclazide System in the Presence of Drugs Commonly Used in Diabetes? In Vitro Spectroscopic Study.

Wiglusz Katarzyna K Żurawska-Płaksej Ewa E Rorbach-Dolata Anna A Piwowar Agnieszka A

Molecules (Basel, Switzerland) 20210624 13

In this research, the selected drugs commonly used in diabetes and its comorbidities (gliclazide, cilazapril, atorvastatin, and acetylsalicylic acid) were studied for their interactions with bovine serum albumin-native and glycated. Two different spectroscopic methods, fluorescence quenching and circular dichroism, were utilized to elucidate the binding interactions of the investigational drugs. The glycation process was induced in BSA by glucose and was confirmed by the presence of advanced gly ...[more]

PMID: 34202801

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Project description:BackgroundEcological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs.MethodsWe simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure.ResultsOverall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA's error rates under almost all scenarios.ConclusionWe encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.

Dataset Information

How Does Glycation Affect Binding Parameters of the Albumin-Gliclazide System in the Presence of Drugs Commonly Used in Diabetes? In Vitro Spectroscopic Study.

Publications

How Does Glycation Affect Binding Parameters of the Albumin-Gliclazide System in the Presence of Drugs Commonly Used in Diabetes? In Vitro Spectroscopic Study.

Similar Datasets

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