The transport of drugs or drug delivery systems across the cell membrane is a complex biological process, often difficult to understand because of its dynamic nature. In this regard, model lipid membranes, which mimic many aspects of cell-membrane lipids, have been very useful in helping investigators to discern the roles of lipids in cellular interactions. One can use drug-lipid interactions to predict pharmacokinetic properties of drugs, such as their transport, biodistribution, accumulation, and hence efficacy. These interactions can also be used to study the mechanisms of transport, based on the structure and hydrophilicity/hydrophobicity of drug molecules. In recent years, model lipid membranes have also been explored to understand their mechanisms of interactions with peptides, polymers, and nanocarriers. These interaction studies can be used to design and develop efficient drug delivery systems. Changes in the lipid composition of cells and tissue in certain disease conditions may alter biophysical interactions, which could be explored to develop target-specific drugs and drug delivery systems. In this review, we discuss different model membranes, drug-lipid interactions and their significance, studies of model membrane interactions with nanocarriers, and how biophysical interaction studies with lipid model membranes could play an important role in drug discovery and drug delivery.

Understanding the role of lipids in drug transport is critical in cancer chemotherapy to overcome drug resistance. In this study, we isolated lipids from doxorubicin-sensitive (MCF-7) and -resistant (MCF-7/ADR) breast cancer cells to characterize the biophysical properties of membrane lipids (particularly lipid packing and membrane fluidity) and to understand the role of the interaction of cell membrane lipids with drug/nanocarrier on drug uptake and efficacy. Resistant cell membrane lipids showed significantly different composition and formed more condensed, less fluid monolayers than did lipids from sensitive cells. Doxorubicin, used as a model anticancer agent, showed a strong hydrophobic interaction with resistant cell membrane lipids but significantly less interaction, as well as a different pattern of interaction (i.e., ionic), with sensitive ones. The threshold intracellular doxorubicin concentration required to produce an antiproliferative effect was similar for both sensitive and resistant cell lines, suggesting that drug transport is a major barrier in determining drug efficacy in resistant cells. In addition to the biophysical characteristics of resistant cell membrane lipids, lipid-doxorubicin interactions appear to decrease intracellular drug transport via diffusion as the drug is trapped in the lipid bilayer. The rigid nature of resistant cell membranes also seems to influence endosomal functions that inhibit drug uptake when a liposomal formulation of doxorubicin is used. In conclusion, biophysical properties of resistant cell membrane lipids significantly influence drug transport, and hence drug efficacy. A better understanding of the mechanisms of cancer drug resistance is vital to developing more effective therapeutic interventions. In this regard, biophysical interaction studies with cell membrane lipids might be helpful to improve drug transport and efficacy through drug discovery and/or drug delivery approaches by overcoming the lipid barrier in resistant cells.

Supplementary data are available at Bioinformatics online.

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Industrial biotechnology provides an efficient, sustainable solution for chemical production. However, designing biochemical pathways based solely on known reactions does not exploit its full potential. Enzymes are known to accept non-native substrates, which may allow novel, advantageous reactions. We have previously developed a computational program named Biological Network Integrated Computational Explorer (BNICE) to predict promiscuous enzyme activities and design synthetic pathways, using generalized reaction rules curated from biochemical reaction databases. Here, we use BNICE to design pathways synthesizing propionic acid from pyruvate. The currently known natural pathways produce undesirable by-products lactic acid and succinic acid, reducing their economic viability. BNICE predicted seven pathways containing four reaction steps or less, five of which avoid these by-products. Among the 16 biochemical reactions comprising these pathways, 44% were validated by literature references. More than 28% of these known reactions were not in the BNICE training dataset, showing that BNICE was able to predict novel enzyme substrates. Most of the pathways included the intermediate acrylic acid. As acrylic acid bioproduction has been well advanced, we focused on the critical step of reducing acrylic acid to propionic acid. We experimentally validated that Oye2p from Saccharomyces cerevisiae can catalyze this reaction at a slow turnover rate (10(-3) s(-1) ), which was unknown to occur with this enzyme, and is an important finding for further propionic acid metabolic engineering. These results validate BNICE as a pathway-searching tool that can predict previously unknown promiscuous enzyme activities and show that computational methods can elucidate novel biochemical pathways for industrial applications. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:303-311, 2016.