Functional Group Distributions, Partition Coefficients, and Resistance Factors in Lipid Bilayers Using Site Identification by Ligand Competitive Saturation

Abstract

Small molecules such as metabolites and drugs must pass through the membrane of the cell, a barrier primarily comprising phospholipid bilayers and embedded proteins. To better understand the process of passive diffusion, knowledge of the ability of various functional groups to partition across bilayers and the associated energetics would be of utility. In the present study, the site identification by ligand competitive saturation (SILCS) methodology has been applied to sample the distributions of a diverse set of chemical solutes representing the functional groups of small molecules across phospholipid bilayers composed of 0.9:0.1 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine/ cholesterol and a mixture of 0.52:0.18:0.3 1,2-dioleoyl-sn-glycero-3phospho-L-serine/1,2-dioleoyl-sn-glycero-3-phosphocholine/cholesterol used in parallel artificial membrane permeability assay experiments. A combination of oscillating chemical potential grand canonical Monte Carlo and molecular dynamics in the SILCS simulations was applied to achieve solute sampling through the bilayers and surrounding aqueous environment from which the distribution of solutes and the functional groups they represent were obtained. Results show differential distribution of aliphatic versus aromatic groups with the former having increased sampling in the center of the bilayers versus in the region of the glycerol linker for the latter. Variations in the distribution of different polar groups are evident, with large differences between negative acetate and positive methylammonium with accumulation of the polar-neutral and acetate solutes above the bilayer head groups. Conversion of the distributions to absolute free energies allows for a detailed understanding of energetics of functional groups in different regions of the bilayers and for calculation of absolute free-energy profiles of multifunctional drug-like molecules across the bilayers from which partition coefficients and resistance factors suitable for insertion into the homogenous solubility−diffusion equation for calculation of permeability were obtained. Comparisons of the calculated bilayer/solution partition coefficients with 1-octanol/water experimental data for both drug-like molecules and the solutes show overall good agreement, validating the calculated distributions and associated absolute free-energy profiles.

Modeling the partitioning of amphiphilic molecules and co-solvents in biomembranes

“…The model will be generally applicable to bilayer structures which reasonably match the parsing scheme, namely, a solvent-excluding core slab and two symmetric outer slabs which contain a portion of the lamella-forming amphiphile and solvent. This includes block copolymer structures (Lee & Feijen, 2012) in aqueous or organic solvent and other lipid membrane applications, such as pharmacokinetic modeling (Levin, 1980;Meylan et al, 1999;Lind et al, 2021;Anderson et al, 1988) and lipid nanoparticle drug loading (Mu ¨ller et al, 2000;Wissing et al, 2004). The model may also be appropriate to study the interplay of structure changes and partitioning in anesthesia (Vemparala et al, 2006;Himbert et al, 2020).…”

“…One can then approximate the lipid phase as the hydrophobic octanol phase to estimate the amount of amphiphile located in the lipid phase. This leads to wide use of the octanol-water partition coefficient in pharmacokinetic modeling (Levin, 1980;Meylan et al, 1999;Lind et al, 2021;Anderson et al, 1988) and estimating the efficacy of anesthetics (Seeman, 1972;Heimburg & Jackson, 2007). However, log P does not capture the structural details of the distribution of an amphiphilic co-solvent between the hydrophobic core and water-accessible headgroup region of the bilayer.…”

Characterizing the Access of Cholinergic Antagonists to Efferent Synapses in the Inner Ear

“…In a limited survey of charged compounds that access the inner ear, those that gain access (i.e., glycopyrrolate, methscopolamine, and IEM1460) are aliphatic quaternary ammonium analogs while the α9α10nAChR antagonists Cmpd7a, 10c, and 11e are aromatic quaternary ammonium analogs. There are key differences in how aliphatic and aromatic compounds interact with lipid membranes as well as a variety of transporter and efflux mechanisms that might facilitate drug permeability ( Metzner et al, 2006 ; Geldenhuys et al, 2010 ; Lind et al, 2021 ). Whether some of these differences contribute to the entry or efflux of these cholinergic drugs in or out of the inner ear and brain remains to be determined, but some cholinergic drugs are substrates for choline transporter uptake and P -glycoprotein-mediated efflux which may heavily influence drug accumulation in a particular compartment ( Daneman et al, 2010 ; Geldenhuys et al, 2010 ; Wakuda et al, 2019 ).…”

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of Drug Molecule Passive Permeability

“…SILCS simulations were carried out using the hybrid oscillating chemical potential (µex) GCMC-MD approach 49 to facilitate solute and water sampling in the target system. A detailed description of the SILCS simulation protocol can be accessed in our previous work 48 . In brief, the simulation system consists of POPC/cholesterol bilayer and randomly distributed solutes (benzene, propane, dimethylether, methanol, formamide, imidazole, acetate, and methylammonium), each at a concentration of 0.25 M immersed in an aqueous solution of 55 M water.…”

“…A step towards obtaining atomic details in the context of physics-based methods while achieving a high level of computational efficiency was the application of the site identification by ligand competitive saturation (SILCS) method to lipid bilayers 48 . In that study it was shown that the SILCS methods, which applies hybrid oscillating excess chemical potential (µex) Grand Canonical Monte Carlo (GCMC)/MD simulations to sample the distribution of chemical solutes and water throughout heterogeneous molecular system, 49 was effective in mapping the functional group affinity pattern, termed FragMaps, across two bilayers (0.9:0.1 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC)/cholesterol and a mixture of 0.52:0.18:0.3 1,2dioleoyl-sn-glycero-3-phospho-L-serine (DOPS)/1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC)/cholesterol used in PAMPA experiments).…”