Stratification of cephalosporins based on physicochemical and pharmacokinetic variables using multivariate statistical tools

Carlos Alberto Escobar Angulo, Antistio Alviz Amador, Julián Javier Martínez Zambrano
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Abstract

Introduction

Cephalosporins, a class of beta-lactam antibiotics, are commonly used in medical practice. However, their potential advantages, based on physicochemical and pharmacokinetic variables, are often overlooked. This research, proposing strategies based on multivariate statistics to stratify different cephalosporins, is a significant step towards providing the prescribing team with more rational and effective options. The potential benefits of this research are promising, as it has the potential to significantly improve the efficacy and safety of cephalosporin therapy.

Method

Exploratory study and review of pharmacokinetic parameters of cephalosporins. Data were extracted from DrugBank go.drugbank.com, and multivariate statistical techniques such as Pearson correlation and cluster analysis were applied. This approach allowed the identification of groupings of cephalosporins with similar characteristics, thus facilitating their rational selection in clinical practice.

Results

The results reveal that cefazolin, cefotetan, cefoperazone, and ceftriaxone form the conglomerate with the most favorable properties for reaching effective concentrations at the site of action due to their high solubility, high percentage of binding to plasma proteins, and adequate residence times in the organism. Solubility, protein binding, half-life, MRT, molecular weight, volume of distribution, number of interactions, and pKa are all critical factors that influence the efficacy and safety of cephalosporin therapy.

Conclusions

It is relevant to highlight the use of multivariate statistics as a tool for drug selection and rational use. In the present study, cefazolin, cefotetan, cefoperazone, and ceftriaxone were highlighted as the best therapeutic alternatives according to the variables selected for the study.
基于物理化学和药代动力学变量使用多变量统计工具的头孢菌素分层
头孢菌素是一类β -内酰胺类抗生素,在医疗实践中广泛使用。然而,基于物理化学和药代动力学变量,它们的潜在优势往往被忽视。本研究提出基于多元统计对不同头孢菌素进行分层的策略,为处方团队提供更合理有效的选择迈出了重要的一步。这项研究的潜在益处是有希望的,因为它有可能显著提高头孢菌素治疗的疗效和安全性。方法对头孢菌素类药物的药动学参数进行探索性研究和综述。数据提取自DrugBank go.drugbank.com,采用Pearson相关、聚类分析等多元统计技术。这种方法可以识别具有相似特征的头孢菌素的分组,从而促进临床实践中合理选择头孢菌素。结果头孢唑林、头孢替坦、头孢哌酮和头孢曲松形成的复合物质具有较高的溶解度、与血浆蛋白的结合率高、在体内停留时间长等特点,最有利于在作用部位达到有效浓度。溶解度、蛋白结合、半衰期、MRT、分子量、分布体积、相互作用次数和pKa都是影响头孢菌素治疗疗效和安全性的关键因素。结论强调多变量统计作为药物选择和合理使用的工具具有重要意义。在本研究中,根据研究选择的变量,头孢唑林、头孢替坦、头孢哌酮和头孢曲松被强调为最佳治疗方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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