脂质体
替考拉宁
万古霉素
抗生素
化学
色谱法
材料科学
纳米技术
金黄色葡萄球菌
细菌
生物化学
生物
遗传学
作者
Azucena González Gómez,Saifuddin Syed,Kenji Marshall,Zeinab Hosseinidoust
出处
期刊:ACS omega
[American Chemical Society]
日期:2019-06-21
卷期号:4 (6): 10866-10876
被引量:88
标识
DOI:10.1021/acsomega.9b00825
摘要
Liposomes are attractive vehicles for localized delivery of antibiotics. There exists, however, a gap in knowledge when it comes to achieving high liposomal loading efficiencies for antibiotics. To address this issue, we investigated three antibiotics of clinical relevance against staphylococcal infections with different hydrophilicity and chemical structure, namely, vancomycin hydrochloride, teicoplanin, and rifampin. We categorized the suitability of different encapsulation techniques on the basis of encapsulation efficiency, lipid requirement (important for avoiding lipid toxicity), and mass yield (percentage of mass retained during the preparation process). The moderately hydrophobic (teicoplanin) and highly hydrophobic (rifampin) antibiotics varied significantly in their encapsulation load (max 23.4 and 15.5%, respectively) and mass yield (max 74.1 and 71.8%, respectively), favoring techniques that maximized partition between the aqueous core and the lipid bilayer or those that produce oligolamellar vesicles, whereas vancomycin hydrochloride, a highly hydrophilic molecule, showed little preference to any of the protocols. In addition, we report significant bias introduced by the choice of analytical method adopted to quantify the encapsulation efficiency (underestimation of up to 24% or overestimation by up to 57.9% for vancomycin and underestimation of up to 61.1% for rifampin) and further propose ultrafiltration and bursting by methanol as the method with minimal bias for quantification of encapsulation efficiency in liposomes. The knowledge generated in this work provides critical insight into the more practical, albeit less investigated, aspects of designing vesicles for localized antibiotic delivery and can be extended to other nanovehicles that may suffer from the same biases in analytical protocols.
科研通智能强力驱动
Strongly Powered by AbleSci AI