Literature DB >> 31120755

Microcavity-Supported Lipid Bilayers; Evaluation of Drug-Lipid Membrane Interactions by Electrochemical Impedance and Fluorescence Correlation Spectroscopy.

Sivaramakrishnan Ramadurai1, Nirod Kumar Sarangi1, Sean Maher1, Nicola MacConnell1, Alan M Bond2, Dennis McDaid3, Damien Flynn3, Tia E Keyes1.   

Abstract

Many drugs have intracellular or membrane-associated targets, thus understanding their interaction with the cell membrane is of value in drug development. Cell-free tools used to predict membrane interactions should replicate the molecular organization of the membrane. Microcavity array-supported lipid bilayer (MSLB) platforms are versatile biophysical models of the cell membrane that combine liposome-like membrane fluidity with stability and addressability. We used an MSLB herein to interrogate drug-membrane interactions across seven drugs from different classes, including nonsteroidal anti-inflammatories: ibuprofen (Ibu) and diclofenac (Dic); antibiotics: rifampicin (Rif), levofloxacin (Levo), and pefloxacin (Pef); and bisphosphonates: alendronate (Ale) and clodronate (Clo). Fluorescence lifetime correlation spectroscopy (FLCS) and electrochemical impedance spectroscopy (EIS) were used to evaluate the impact of drug on 1,2-dioleyl- sn-glycerophosphocholine and binary bilayers over physiologically relevant drug concentrations. Although FLCS data revealed Ibu, Levo, Pef, Ale, and Clo had no impact on lipid lateral mobility, EIS, which is more sensitive to membrane structural change, indicated modest but significant decreases to membrane resistivity consistent with adsorption but weak penetration of drugs at the membrane. Ale and Clo, evaluated at pH 5.25, did not impact the impedance of the membrane except at concentrations exceeding 4 mM. Conversely, Dic and Rif dramatically altered bilayer fluidity, suggesting their translocation through the bilayer, and EIS data showed that resistivity of the membrane decreased substantially with increasing drug concentration. Capacitance changes to the bilayer in most cases were insignificant. Using a Langmuir-Freundlich model to fit the EIS data, we propose Rsat as an empirical value that reflects permeation. Overall, the data indicate that Ibu, Levo, and Pef adsorb at the interface of the lipid membrane but Dic and Rif interact strongly, permeating the membrane core modifying the water/ion permeability of the bilayer structure. These observations are discussed in the context of previously reported data on drug permeability and log P.

Entities:  

Year:  2019        PMID: 31120755     DOI: 10.1021/acs.langmuir.9b01028

Source DB:  PubMed          Journal:  Langmuir        ISSN: 0743-7463            Impact factor:   3.882


  6 in total

1.  The novel potential biomarkers for multidrug-resistance tuberculosis using UPLC-Q-TOF-MS.

Authors:  Huai Huang; Yu-Shuai Han; Jing Chen; Li-Ying Shi; Li-Liang Wei; Ting-Ting Jiang; Wen-Jing Yi; Yi Yu; Zhi-Bin Li; Ji-Cheng Li
Journal:  Exp Biol Med (Maywood)       Date:  2020-02-11

Review 2.  Combined Second Harmonic Generation and Fluorescence Analyses of the Structures and Dynamics of Molecules on Lipids Using Dual-Probes: A Review.

Authors:  Yi Hou; Jianhui Li; Bifei Li; Qunhui Yuan; Wei Gan
Journal:  Molecules       Date:  2022-06-11       Impact factor: 4.927

3.  Synthesis of Spin-Labeled Ibuprofen and Its Interaction with Lipid Membranes.

Authors:  Denis S Baranov; Anna S Smorygina; Sergei A Dzuba
Journal:  Molecules       Date:  2022-06-27       Impact factor: 4.927

4.  Multimodal Investigation into the Interaction of Quinacrine with Microcavity-Supported Lipid Bilayers.

Authors:  Nirod Kumar Sarangi; Amrutha Prabhakaran; Tia E Keyes
Journal:  Langmuir       Date:  2022-05-13       Impact factor: 4.331

5.  Robust Photoelectric Biomolecular Switch at a Microcavity-Supported Lipid Bilayer.

Authors:  Guilherme B Berselli; Aurélien V Gimenez; Alexandra O'Connor; Tia E Keyes
Journal:  ACS Appl Mater Interfaces       Date:  2021-06-14       Impact factor: 9.229

6.  Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms.

Authors:  Nicole Pearcy; Yue Hu; Michelle Baker; Alexandre Maciel-Guerra; Ning Xue; Wei Wang; Jasmeet Kaler; Zixin Peng; Fengqin Li; Tania Dottorini
Journal:  mSystems       Date:  2021-08-03       Impact factor: 6.496

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.