Skip to main content
NCBI home page
Biophysical Reviews logoLink to Biophysical Reviews
. 2025 Jul 30;17(5):1401–1413. doi: 10.1007/s12551-025-01335-5

The interaction of small molecules with phospholipid membranes studied by solid-state NMR and molecular dynamics simulation

Ilya A Khodov 1,✉, Daniel Huster 2, Holger A Scheidt 2
PMCID: PMC12847621  PMID: 41613886

Abstract

The investigation of the interactions of small lipophilic molecules (e.g., drugs) with lipid membranes represents an active field of contemporary biophysical research. The determination of their membrane insertion, their distribution within the lipid bilayer, and their effect on lipid membranes themselves hold significant pharmacological importance since the plasma membrane often represents the first contact site for the interaction of a drug with the cell. In this work, we review recent applications of solid-state NMR spectroscopy that have been conducted to study the interaction of lipid membranes with a large variety of small drug-like molecules (e.g., local anesthetics, statins, NSAIDs, kinase inhibitors). We aim to briefly highlight previous research while outlining the promising prospects using experimental and computational methods, including 2H, 31P, 1H magic-angle spinning (MAS) NOESY NMR, and molecular dynamics (MD) simulations, which provide highly useful tools for a comprehensive understanding of these interactions.

Keywords: Small molecules, Lipid membranes, Drug interactions, Solid-state NMR, 1H NMR, 31P NMR, MAS NOESY NMR, Molecular dynamics, Biophysical research

Introduction

The partitioning of small drugs and other bioactive molecules into lipid bilayers is a pivotal factor influencing pharmacokinetics, efficacy, and toxicity. Small-molecule interactions with biological membranes play a crucial role in drug uptake, distribution, and the manifestation of side effects (Peetla et al. 2009; Migliorati et al. 2022; Li and Jusko 2023). A significant proportion of drugs are lipophilic, which leads to non-specific membrane binding, concentrating them at the site of action and enhancing their effective local concentration (Sargent and Schwyzer 1986; Scheidt and Huster 2008; Szlenk et al. 2019, 2021). Translocation from the extracellular fluid to the membrane increases the effective concentration of a drug by at least 1000-fold due to the reduction of the dimensionality of distribution from three to two (Sargent and Schwyzer 1986; Murray et al. 1997). Model membrane studies are very useful to understand pharmacokinetic processes, including drug transport, accumulation, and efficacy, while also providing insights into the underlying mechanisms of drug delivery and potential off-target toxicity (Peetla et al. 2009; Centi et al. 2020; Bortolotti et al. 2023; Ge et al. 2025). Recent studies have highlighted the dynamic and multifaceted nature of drug-membrane interactions (Andrade et al. 2021; Le-Deygen et al. 2022; Zambrano et al. 2024). Advanced biophysical techniques and molecular dynamics simulations have revealed that several factors, including membrane composition, fluidity, the organization of microdomains, and leaflet asymmetry (Semeraro et al. 2024; Pabst and Keller 2024; Engberg et al. 2025a; Heberle and Doktorova 2025; Varma and Deserno 2025), significantly influence the partitioning and biological activity of drugs (Galiullina et al. 2019; Engberg et al. 2020; Bochicchio et al. 2020; Dey et al. 2021; Musabirova et al. 2022; Khodov et al. 2023; Huster et al. 2024; Saha Roy et al. 2024). For example, non-selective membrane interactions often underlie toxic effects; the harmful actions of amphotericin B have been linked to its non-specific pore formation in cholesterol-rich membranes (Peetla et al. 2009). Recent studies have also demonstrated that the aggregation state of amphotericin B within lipid bilayers modulates its release rate and toxicity (Svirkin et al. 2022). Likewise, cationic amphiphilic drugs can induce membrane remodeling and disruption, which contributes to adverse effects and off-target toxicity (Palermo et al. 2011).

The extent of membrane partitioning is related to drug resistance mechanisms, where altered lipid compositions in cancer or microbial cells can influence drug uptake and retention (Peetla et al. 2010, 2013; Shi et al. 2020; Vinarov et al. 2021). The role of cholesterol in modulating drug partitioning and membrane structure has been emphasized, showing that elevated cholesterol levels can reduce drug affinity for membranes by altering bilayer order (Yang et al. 2021; Szomek et al. 2024). Understanding these intricate molecular interactions remains crucial for rational drug design, aiming to optimize therapeutic efficacy while minimizing off-target effects.

Drug-membrane binding is a highly complex, molecule-specific process involving not only the depth of insertion but also the orientation, tilt angle, and induced perturbations within lipid bilayers (Szlenk et al. 2019; Shahoei and Tajkhorshid 2020; Sanver et al. 2020; Phromchaloem et al. 2025). The precise location of drug embedding can range from the polar headgroup region to deep within the hydrophobic acyl core, often determining its pharmacological efficacy and membrane-modulating effects (Molugu et al. 2022). Standard solid-state NMR (ssNMR) techniques (McCalpin et al. 2022), such as 1H MAS NOESY (Scheidt and Huster 2008) and 31P or 2H NMR (Seelig 1977, 1978; Davis 1983) provide detailed atomic-level insights into these interactions under near-physiological conditions, revealing both drug positioning and the resulting biophysical changes in membrane order and dynamics.

Work over the last decades has highlighted the utility of 1H MAS NOESY for examining the interactions of specific drugs with lipid membranes, as reviewed in Scheidt and Huster (2008). For example, 1H MAS NOESY has been employed to explore the interaction of fenamates (nonsteroidal anti-inflammatory drugs) with phospholipid membranes, revealing their dynamic distribution and penetration into the lipid bilayer (Khodov et al. 2022, 2023). Similarly, research has demonstrated that small molecules such as NSAIDs (e.g., ibuprofen) predominantly localize in the upper acyl chain/glycerol region of phosphatidylcholine bilayers, with functional groups orienting towards the aqueous phase, a behavior that is consistent across cholesterol-containing membranes (Kremkow et al. 2020; Wood et al. 2021; Kashnik et al. 2023). Additionally, kinase inhibitors exhibit distinct membrane embedding modes: aromatic moieties penetrate the hydrophobic core, while polar substituents remain near the surface, modulating membrane stiffness or fluidity, as evidenced by 2H order parameters (Haralampiev et al. 2020; Luck et al. 2021; Fischer et al. 2023). Furthermore, the perturbation of lateral pressure profiles and lipid dynamics by amphiphilic peptides and drugs can be quantified using 19F and 31P NMR, highlighting the delicate balance between membrane stabilization and disruption (Bouraoui et al. 2020; Grage et al. 2022). 1H MAS NOESY, especially when used in conjunction with molecular dynamics (MD) simulations, creates a detailed, residue-specific portrait of how small solutes interact with and perturb the membrane matrix (Scheidt and Huster 2008).

Static 31P NMR of phospholipid headgroups (Seelig 1978) provides insights into surface binding and drug-induced headgroup distortion, which are critical for understanding membrane activity of the compound. Furthermore, 2H NMR of perdeuterated lipid acyl chains of the phospholipid molecules (Davis 1983) yields segmental order parameters (SCD), which quantify changes in chain mobility and lipid packing. These solid-state NMR experiments can be performed on fully hydrated bilayers, such as multilamellar vesicles or aligned lipid stacks, at physiological temperatures, pH, and ion concentration, preserving native-like lipid phases and dynamics.

Methodological approaches

Solid-state NMR techniques

Solid-state nuclear magnetic resonance (ssNMR) offers a comprehensive array of methods that facilitate the investigation of drug-membrane interactions at the molecular level. Advancements in ssNMR have significantly improved the sensitivity and adaptability of these methods. In particular, 1H magic-angle spinning (MAS) nuclear Overhauser enhancement spectroscopy (NOESY), 31P NMR, and 2H NMR have proven to be invaluable tools for probing these interactions in greater detail. For example, two-dimensional 1H NOESY spectra under MAS conditions reveal through-space proton-proton cross-relaxation rates between the molecular segments of drugs and lipids. Due to the intermolecular nature and the high dynamics in lipid membranes, this cross-relaxation is determined not by a fixed distance and can therefore be interpreted as the probability of contact between the individual molecular segments within the membrane. High 1H-1H NOE cross-relaxation rates indicate that the drug molecule often approaches a given lipid segment, such as choline headgroups, glycerol, and acyl chains (Khodov et al. 2022)(Gopinath et al. 2021). Quantitative analysis of NOESY cross-relaxation rates enables mapping of the drug’s transmembrane distribution and orientation (Scheidt and Huster 2008). For instance, plots of drug-lipid cross-relaxation rates versus lipid segment position reveal the drug’s localization along the membrane normal (Engberg et al. 2025b). Larger NOE values to terminal methyls and headgroups suggest deep insertion, while stronger NOEs to headgroups indicate surface localization.

Advanced pulse sequences, such as 1H MAS radiofrequency-driven dipolar recoupling (RFDR) (Bennett et al. 1992) and 1H-detected dynamic nuclear polarization (DNP)-enhanced fast MAS (Badoni et al. 2024) are now being used to accelerate magnetization transfer, significantly reducing mixing times compared to conventional NOESY methods (Alam and Holland 2006; Aucoin et al. 2009; Ramamoorthy and Xu 2013; Trzeciak et al. 2020). These innovations have enabled more accurate modeling of membrane fluidity and the effects of drugs on membrane properties. For example, a recent study has employed ssNMR to study the impact of drugs on lipid bilayer dynamics, providing insights into how compounds like salcaprozate sodium modulate lipid motion and enhance membrane fluidity (Ling et al. 2024).

31P NMR spectroscopy (Seelig 1978; Ellena et al. 1986) is an essential tool in investigating the interactions between small drug molecules and lipid membranes as it offers insights into the dynamics of lipid headgroups and bilayer organization. The phosphorus nucleus in the lipid headgroup exhibits high sensitivity to its environment, enabling the detection of subtle alterations in membrane structure upon drug binding (Scherer and Seelig 1989). This offers essential insights into the conformational alterations and structural reorganizations within the membrane, thereby advancing a more comprehensive understanding of the molecular mechanisms that govern drug-membrane interactions. In bilayers, the 31P chemical shift tensor exhibits axial symmetry about the bilayer normal due to rotation of the lipid molecules along their long axis, which is reflected in the characteristic powder pattern observed in the NMR spectrum. The shape of the powder pattern is a key indicator of the bilayer’s phase state. The chemical shift anisotropy (CSA), defined as the difference between the 0° and 90° edges (Akutsu 2020; Fischer et al. 2023), indicates headgroup orientation, providing a direct measure of how drug binding affects lipid headgroup dynamics and tilt.

Recent studies have also investigated how drug molecules, particularly amphiphilic and cationic drugs, affect lipid bilayer dynamics. For instance, squalamine, a cationic aminosterol, interacts with lipid membranes by shifting phase transition temperatures and disrupting lipid headgroup packing, as observed through changes in the 31P NMR spectra. Electrostatic forces primarily drive this interaction without causing significant membrane thinning (Grage et al. 2025).

The binding of drugs to lipid membranes often induces significant changes in membrane structure, including the perturbation of lipid bilayers. Certain kinase inhibitors, such as sunitinib, have a pronounced impact on membrane integrity, as observed by changes in 31P NMR spectra. This suggests that drug-membrane interactions can disrupt the bilayer even without altering its phase state (Luck et al. 2021). Studies focusing on 2H NMR and the determination of lipid chain order parameters highlight significant improvements in methodology and understanding of lipid-drug interactions. 2H NMR provides further insight into membrane dynamics, particularly by monitoring acyl chain order and dynamics. This method measures the quadrupolar splitting (ΔνQ) of C–2H bonds, which reflects the order of lipid chains (Seelig 1978; Ellena et al. 1986). The determination of order parameter values serves as a reliable and reproducible method (Petrache et al. 2000) for characterizing multilamellar and unilamellar vesicles (Renault et al. 2006), bicelles (Aussenac et al. 2003), and lipid bilayers (Marsan et al. 1999). It is related to lipid packing properties and membrane elasticity (Abboud 1987). The interaction of drugs with lipid membranes often influences this order, with different drugs having varying effects based on their physicochemical properties. For example, rigid molecules like steroids or peptides tend to increase chain order, which is reflected in a larger ΔνQ values, while molecules that fluidize the membrane, such as anesthetics, reduce chain order (Luck et al. 2021; Fischer et al. 2022). This modeling approach provides insights into the permeability of drug molecules and their effects on lipid bilayer structure. In addition to 31P and 2H NMR, quadrupolar nuclei such as 14N and 17O can offer complementary structural information on drug-lipid interactions, particularly regarding headgroup orientation and bilayer perturbations (Santos et al. 2004; McCalpin et al. 2023). Further studies have emphasized the integration of 2H NMR relaxation data to study lipid bilayer fluctuations and their relationship to material properties that affect collective lipid dynamics (Molugu et al. 2018). A notable recent development is the use of graph neural networks (GNNs) to predict 2H NMR order parameters for various drugs across different lipid membranes. This approach enables faster screening of drug effects on membrane dynamics (Fischer et al. 2022).

Furthermore, antimicrobial peptides have been shown to significantly disrupt membrane order, an effect observable through both 31P and 2H NMR (Kumar et al. 2022). These and other effects have also been explored by Prof. Weingarth’s research group, which used solid-state NMR to reveal how antimicrobial peptides such as Teixobactin, Clovibactin, and Brevibacillin 2 V influence membrane structure (Zhao et al. 2021; Shukla et al. 2022, 2023). Recent studies focusing on serotonin and its metabolites have revealed how small changes in molecular structure can significantly impact lipid dynamics. For example, serotonin and its derivatives, including N-acetylserotonin, significantly alter lipid chain order in model membranes. These interactions help explain the biological effects of serotonin and its metabolites in modulating membrane properties, which could be leveraged for designing membrane-modulating drugs (Engberg et al. 2025b). These findings offer valuable insights into how drug molecules, such as serotonin and its analogs, affect membrane properties, thereby enhancing our understanding of their pharmacological actions (Engberg et al. 2020).

Molecular dynamics simulations

All-atom MD simulations have advanced over the last 30 years to provide realistic descriptions on the time evolution of large biological systems for up to ~ 100 µs (Venable et al. 2015; Friedman et al. 2018; Pezeshkian and Marrink 2021; Brown and Marrink 2024). All intra- and intermolecular interactions between the atoms are modeled with empirical force fields. Newton’s equation of motion is solved with a typical time step of 1 fs, providing new coordinates as a result of the individual forces. The obtained trajectories yield time-resolved positions and orientations for each individual atom, providing a wealth of structural and dynamic information. MD studies of drug-membrane systems provide an atomic-scale framework allowing for the study of the dynamics, penetration depth, and the distribution of a drug molecule in the bilayer (Martinotti et al. 2020; Bunker and Róg 2020; Róg et al. 2021). In contrast to NMR, which is an ensemble technique, MD can describe the properties of individual lipids in interaction with a drug molecule. In MD, all atoms of the drugs and lipids are explicitly modeled with atomic coordinates.

With that, MD can also provide direct insight into the interaction of small-molecule drugs with membranes (Martinotti et al. 2020; Bunker and Róg 2020; Róg et al. 2021). Trajectories yield density profiles of the drug along the bilayer normal and the time-averaged orientation of the drug’s molecular axis. The density distributions in a POPC bilayer confirmed the NMR-derived insertion depth, demonstrating that simulations can reproduce the experimental membrane localization (Engberg et al. 2025b). More generally, simulation-derived radial distribution functions between drug atoms and lipid atoms can be correlated with NOESY contact propensities to cross-validate the predicted drug depth. Another synergy is the use of NMR restraints in MD structure refinement. Methods have been developed to impose orientational or distance restraints derived from ssNMR onto simulated structures. For membrane peptides and proteins, for example, measured 15N chemical shifts and 15N–1H dipolar coupling tensors can be translated into orientational restraint potentials in an MD or torsion‐angle dynamics framework (Jeong et al. 2014). This approach has been applied mainly to proteins. However, the principle extends to drug-liquid systems: one could, in theory, restrain simulated drug orientation or interatomic distances to match measured NOEs or CSAs, yielding ensemble models consistent with the NMR data. In many recent studies, MD and NMR are used iteratively, with simulations first predicting NMR observables and then refining the model based on experimental results (Vermeer et al. 2007). More recently, Kang et al. performed integrated ssNMR and MD simulations on a β-defensin analog (Kang et al. 2019). Extensive NMR (including 13C, 1H, and 31P NMR data) defined residue-specific insertion depths and lipid interactions, which were reproduced and interpreted in atomic detail by the MD simulations (Hakobyan and Heuer 2019).

Combining NMR and MD simulations

The powerful synergy between MD and NMR is the use of computational “NMR observables” (Case 2002; Pastor et al. 2002; Vermeer et al. 2007; Künze et al. 2021; Stenström et al. 2022). The ensemble of MD-derived coordinates can be used to calculate order parameters, densities, and correlation functions that directly correspond to parameters determined from ssNMR measurements (Fig. 1). For example, segmental order parameters (SCD) can be readily computed from MD simulations and compared to 2H NMR data (Feller et al. 2002b; Huber et al. 2002; Antila et al. 2021). A good agreement validates the lipid and drug force fields, whereas systematic deviations (often at lipid-water interfaces) highlight force field limitations (Yu and Klauda 2020). Indeed, calibrating lipid force fields against 2H NMR order parameters has become standard practice (Lee et al. 2016). Modern force fields, such as CHARMM36, Slipid, and Amber Lipid17, incorporate such validation, and recent reparameterizations (e.g., CHARMM36/LJ-PME) explicitly include bilayer compression isotherms and NMR order data as targets (Li et al. 2024). Using the morphogenic opioid pentapeptide leucine-enkephalin as an example, the flexibility of peptide molecular structures is demonstrated through the combined application of MD and NMR data (Chandrasekhar et al. 2006). A combination of nuclear magnetic resonance (NMR) measurements and molecular dynamics (MD) simulations has revealed atomic-resolution behavior patterns of small molecules in lipid bilayers (Feller et al. 2002a).

Fig. 1.

Fig. 1

Open in a new tab

Overview of the interplay between NMR spectroscopy and molecular dynamics simulations. NMR parameters can be calculated from the MD ensemble and from the MD trajectory by analyzing correlation functions. NMR input is important for MD force field calibration

Calculating lipid chain order parameters from MD data is just one particularly useful application. More generally, the complementarity of NMR and MD simulation is based on a more general foundation (Fig. 1). MD can provide the correlation function of any intra- or intermolecular bond vector of the system. A Fourier transform of the correlation function provides the spectral density function that describes numerous NMR parameters, such as relaxation rates and the NOE. Studies have shown that 2H relaxation rates (Vogel et al. 2007), 13C relaxation rates (Klauda et al. 2008; Vogel et al. 2010), 1H-1H cross-relaxation rates, and even lateral lipid diffusion (Feller et al. 1999) can be precisely calculated from MD data. Combining extensive simulation data, numerous NMR relaxation rates, and a sophisticated analysis, the whole dynamic landscape of a phospholipid membrane has recently been described (Smith et al. 2022).

Applications of ssNMR and MD to study the interaction of drugs with membranes

Statins

Statins, also known as HMG-CoA reductase inhibitors, are widely used to manage hypercholesterolemia and reduce cardiovascular risk by inhibiting the biosynthesis of cholesterol (Lefer et al. 2001). Their primary role is the reduction of low-density lipoprotein cholesterol (LDL-C), a well-established risk factor for atherosclerosis. However, statins exert a broad spectrum of biological effects beyond their canonical function, known as “pleiotropic” effects, which influence various biological systems through their interactions with cellular membranes and membrane-bound proteins (Schachter 2005; Sodero and Barrantes 2020). These effects extend beyond lipid-lowering, encompassing anti-inflammatory, anti-oxidative, and anti-proliferative activities that offer therapeutic potential in multiple disease contexts. Statins may also inhibit tumor growth and promote cancer cell apoptosis, making them potential adjuncts in cancer therapies. This is attributed to their modulation of lipid biosynthesis and Ras family GTPase signaling (Ahmadi et al. 2020).

The therapeutic impact of statins is not limited to cardiovascular diseases, as they also regulate the differentiation and function of bone cells, thereby influencing osteosynthesis factors, which holds promise for improving bone health and treating osteoporosis (Chamani et al. 2021). Moreover, statins have garnered significant attention in the context of viral infections, particularly in their potential to reduce COVID-19-related morbidity through immunomodulatory effects (Li et al. 2021).

A defining characteristic of statins is their amphiphilic nature, which allows them to interact with lipid bilayers and modify membrane properties. Through solid-state NMR studies, Galiullina et al. (2019) examined the interactions of five statins – atorvastatin, cerivastatin, fluvastatin, rosuvastatin, and pravastatin – with lipid bilayers composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC). A separate study (Shurshalova et al. 2021) focused on pitavastatin, further characterizing its influence on membrane structure. Their findings revealed that all statins predominantly localize at the lipid-water interface, with varying extents depending on their hydrophobicity. Atorvastatin, cerivastatin, fluvastatin, and rosuvastatin intercalate into the upper chain and glycerol region of the bilayer, while pravastatin, the most hydrophilic statin, partitions less deeply. The lipophilic statins accumulate deeper in the bilayer compared to their hydrophilic counterparts, reflecting their differential interactions with membrane components (Galiullina et al. 2019).

In agreement with these findings, an earlier study by Redondo-Morata et al. (2016) demonstrated that different statins have specific effects on the mechanical properties of supported lipid bilayers by increasing their elastic moduli. This effect was found to be concentration-dependent and varied with the lipid bilayer order, suggesting that statins induce structural changes to lipid bilayers, thereby influencing their mechanical stability.

MD simulations had already suggested such a membrane interaction of statins, including lovastatin, cerivastatin, and pravastatin (Kuba et al. 2021). MD simulations demonstrated that the depth at which statins embed into the bilayer correlates with their hydrophobicity, with lovastatin inserting deepest into the core of the membrane. At the same time, pravastatin remains largely at the interface (Sarr et al. 2008). Additionally, Sanford et al. (2013) demonstrated that various statins, including atorvastatin, fluvastatin, and simvastatin, increased bilayer elasticity, suggesting a common mechanism of action that alters lipid bilayer properties. This study also indicated that statins influence membrane protein function by altering bilayer properties, such as elasticity and order, with some statins, like fluvastatin, exerting more potent effects than others.

Penkauskas et al. (2020) further explored the cholesterol-independent effects of lipophilic statins on lipid bilayers, particularly their interaction with membrane proteins. Their findings suggest that these effects contribute to the pleiotropic actions of statins, highlighting their potential therapeutic roles beyond merely lowering cholesterol. The interaction of statins with membrane structures is nuanced, with significant variations in their effects. Studies have shown that statins, such as lovastatin and fluvastatin, induce minimal changes to the bilayer’s packing. In contrast, pravastatin induces more substantial disorder in lipid chains, particularly in the central regions of the bilayer. These findings suggest that lipophilic statins maintain a more stable lipid packing, while more hydrophilic statins like pravastatin induce greater fluidity and disorder within the bilayer (Al’Aref et al. 2011).

This altered fluidity has profound implications for membrane protein function, as statins can influence the lateral pressure and elasticity of lipid membranes, potentially impacting membrane-bound receptors and signaling pathways. Further supporting this, Sanford et al. demonstrated, using the gramicidin-based fluorescence assay and single-channel electrophysiology, that statins alter the mechanical properties of lipid bilayers by shifting the equilibrium of gramicidin channels towards a more conductive state. Fluvastatin, in particular, was found to have the most pronounced effect, whereas rosuvastatin exhibited the least influence on bilayer properties (Sanford et al. 2013). These findings suggest that the ability of statins to modulate bilayer properties plays a crucial role in their pleiotropic effects, influencing a range of membrane-associated processes independent of their cholesterol-lowering capabilities.

The interactions of statins with lipid bilayers are complex and depend on the hydrophobicity of these drugs. Their ability to alter bilayer structure and mechanical properties likely contributes to their wide-ranging biological effects, extending beyond cholesterol reduction. Hydrophilic statins like pravastatin tend to induce more pronounced changes in bilayer fluidity, while lipophilic statins exhibit a more substantial, stabilizing effect on membrane packing. These differences in membrane interactions help explain the varied pharmacological profiles of individual statins and their pleiotropic effects. These findings underscore the critical role of ssNMR investigations in providing deep insights into the interactions between statins and lipid membranes, which contribute to their diverse therapeutic potentials.

Nonsteroidal anti-inflammatory drugs (NSAIDs)

Nonsteroidal anti-inflammatory drugs (NSAIDs), including ibuprofen, naproxen, and diclofenac, are commonly used for their anti-inflammatory and analgesic properties. These drugs, classified as weak acids, interact with lipid membranes in a manner that affects their structural integrity and fluidity. Understanding these interactions is crucial not only for comprehending the therapeutic actions of NSAIDs but also for recognizing the side effects they may cause, particularly concerning the gastrointestinal tract.

SsNMR studies have played a pivotal role in elucidating the membrane interactions of NSAIDs. For instance, when ibuprofen is incorporated into POPC/cholesterol membranes, ssNMR reveals that the drug induces disorder in the lipid bilayer, especially within the glycerol and upper acyl chain regions. These structural alterations increase membrane fluidity while the overall lamellar structure remains intact. The 1H MAS NOESY spectra further demonstrate that ibuprofen’s aromatic ring and methyl groups primarily reside near the upper acyl chain/glycerol interface, with its carboxylate group facing the aqueous phase (Gaede and Gawrisch 2004; Kremkow et al. 2020). These findings highlight a stable yet disordered membrane, an observation confirmed by Boggara and Krishnamoorti (2010) and Kashnik et al. (2023).

Moreover, the role of cholesterol in modifying the membrane positioning of ibuprofen has been explored. While cholesterol appears to reduce the disordering effect of ibuprofen by rigidifying adjacent lipids, it does not significantly alter the overall position of ibuprofen within the bilayer. This finding is in contrast with earlier studies, such as that by Alsop et al. (2015) using X-ray diffraction, which suggested that cholesterol might expel ibuprofen from the membrane interface. In line with the NMR results, MD simulations revealed that cholesterol has only a minimal effect on ibuprofen’s membrane position, although it does influence local membrane rigidity (Lichtenberger et al. 2012; Khajeh and Modarress 2014). Interestingly, curcumin displays membrane binding behavior analogous to cholesterol, including transbilayer insertion and hydrogen bonding with phosphate headgroups, leading to segmental lipid ordering (Barry et al. 2009; Ravula et al. 2017). These similarities suggest that curcumin, like cholesterol, may modulate membrane structure and dynamics in a functionally relevant manner.

Other NSAIDs, such as diclofenac, exhibit similar membrane-localizing behavior as ibuprofen, resulting in comparable changes in lipid chain order and headgroup reorientation. However, larger and more lipophilic NSAIDs, such as indomethacin, penetrate deeper into the bilayer, inducing more significant disorder in the lipid chains (Pereira-Leite et al. 2018).

In addition to these structural changes, the interactions between NSAIDs and lipid membranes have important pharmacological implications. By altering membrane properties, including fluidity and permeability, NSAIDs may contribute to gastrointestinal toxicity, particularly by disrupting the phospholipid layers that protect the gastric mucosa (Sharma et al. 2019). Furthermore, these interactions can influence the drugs’ ability to enter cells, potentially affecting drug delivery and systemic effects. MD simulations have shown that NSAIDs, such as ibuprofen, exhibit a relatively flat free-energy profile across the membrane’s headgroup region, facilitating their penetration into the bilayer (Nunes et al. 2011).

Fenamates, a subclass of NSAIDs, have also been extensively studied for their interaction with lipid membranes. These include mefenamic acid, flufenamic acid, and tolfenamic acid, all of which modify the physical properties of lipid bilayers. Studies utilizing NMR have demonstrated that fenamates dynamically distribute within the bilayer, with mefenamic and tolfenamic acids increasing lipid chain order, while flufenamic acid has the opposite effect, slightly reducing chain order (Khodov et al. 2022). Additionally, 1H MAS NOESY NMR studies have shown how these fenamates interact with the lipid-water interface, particularly affecting the conformational equilibria within the bilayer (Khodov et al. 2023).

Further research has demonstrated that fenamates also influence the monomer–dimer equilibrium of bilayer-spanning channels such as gramicidin, enhancing membrane fluidity. This effect has been linked to their ability to alter ion channel function, suggesting a membrane-mediated mechanism that contributes to their pharmacological actions (Sanford et al. 2011).

In addition to their structural impact, fenamates activate specific ion channels, such as TRPA1, which play a role in nociception and inflammation. Fenamates, such as flufenamic acid and mefenamic acid, act as potent, reversible agonists of TRPA1, contributing to their analgesic effects. This mechanism is distinct from their action on cyclooxygenases (COX), thus revealing an additional therapeutic pathway (Herz et al. 2010).

Moreover, fenamates such as mefenamic acid have demonstrated neuroprotective properties, particularly in stroke models. These effects appear unrelated to their anti-inflammatory actions, offering promising therapeutic potential for neurodegenerative diseases (Khansari and Halliwell 2009).

Thus, the study of NSAID interactions with lipid bilayers through ssNMR, MD simulations, and other biophysical techniques offers invaluable insights into their pharmacodynamics and potential side effects. These findings not only enhance our understanding of NSAIDs’ therapeutic and toxic effects but also contribute to the design of safer, more effective drugs with fewer off-target impacts.

Kinase inhibitors and their membrane interactions

Kinase inhibitors (KIs) have become a cornerstone in cancer treatment, primarily due to their selective inhibition of protein kinases, which are integral to cellular signaling and tumor progression. Emerging research has highlighted that KIs not only engage with intracellular proteins but also interact with lipid membranes. These interactions significantly influence the therapeutic efficacy and side effect profile of KIs. The localization of these inhibitors within membrane structures is crucial for understanding their comprehensive pharmacological actions, as it can affect drug bioavailability, distribution, and the development of resistance mechanisms.

A pivotal study by Luck et al. (2021) utilized solid-state nuclear magnetic resonance (NMR) techniques, specifically 1H MAS NMR, to investigate the interactions of four KIs—sunitinib, erlotinib, idelalisib, and lenvatinib with—lipid vesicles composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and POPC/cholesterol mixtures. This study demonstrated that all four KIs partition into the lipid bilayer, indicating their significant interaction with the membrane. The 1H MAS NOESY spectra revealed proton signals from the inhibitors, predominantly interacting with lipid glycerol and methylene regions, suggesting their localization within the glycerol/interfacial region of the bilayer. In their study (Fischer et al. 2020), the authors characterized ruxolitinib-lipid membrane interactions using multiple biophysical techniques. Their findings demonstrate that while ruxolitinib inserts into the membrane and localizes near the glycerol backbone, it exerts minimal effects on membrane structure and integrity.

Interestingly, the study also revealed distinct differences in the membrane effects of the KIs. Sunitinib and idelalisib, being more hydrophobic, caused a decrease in membrane acyl chain order, as evidenced by a reduction in deuterium order parameters and motion-sensitive probe assays. In contrast, erlotinib and lenvatinib enhanced membrane lipid packing. These findings suggest that sunitinib and idelalisib, by inserting deeper into the membrane, disrupt lipid organization, whereas erlotinib and lenvatinib potentially stabilize the lipid structure. This differential membrane effect could explain the variation in side effect profiles observed for these drugs (Luck et al. 2021). Haralampiev et al. (2016, 2020) examined the interactions of small-molecule kinase inhibitors, including lapatinib and tofacitinib, with lipid membranes. Their results indicated that these inhibitors penetrated the lipid bilayer, with lapatinib embedding more deeply and disrupting the membrane structure, while tofacitinib had minimal effects. This study highlights the significance of drug-membrane interactions in understanding cellular uptake and potential side effects, providing valuable context for the development of kinase inhibitors.

Further analysis of membrane interactions across various KIs has been provided in other studies. For example, by a combination of NMR spectroscopy and MD simulations (as discussed above) Fischer et al. (2023) compared ceritinib and imatinib, two kinase inhibitors with distinct effects on the conformation and dynamics of lipid membranes. While imatinib caused minimal membrane perturbation, ceritinib significantly disturbed membrane integrity, particularly in lipid bilayers containing cholesterol. This study highlights the impact of kinase inhibitors (KIs) on their membrane interaction profiles, specifically their cytotoxicity and associated side effects, demonstrating that ceritinib’s strong affinity for POPC is linked to alterations in membrane structure. Prakash (Prakash 2021) investigated how membranes can directly modulate kinase activity, suggesting that kinase-membrane interactions can lead to changes in the catalytic activity of the kinase domain. This review discussed how membrane interactions influence kinase function, particularly in multi-domain kinases. Studies on protein kinase C (PKC), a key kinase in cell signaling, have further demonstrated how membrane interactions affect its activation. Igumenova (Igumenova 2015) highlighted the importance of membrane recruitment for PKC activation, where conformational rearrangement upon membrane binding relieves the enzyme’s autoinhibition, enabling its catalytic function. The process of activation is significantly influenced by membrane composition, especially the presence of specific lipids.

Conclusion

The study of small molecules in interactions with lipid membranes is a key area of biophysical research with significant implications for pharmacology. The partitioning of drugs, especially lipophilic ones, into lipid bilayers is critical for their pharmacokinetics, influencing drug absorption, distribution, and toxicity. The lipid composition, fluidity, and organization of membrane microdomains significantly affect drug behavior, suggesting that the heterogeneity of biological membranes also affects drug efficacy and side effect profiles. SsNMR techniques, such as 1H MAS NOESY, 31P NMR, and 2H NMR, provide detailed atomic-level insights into how drugs interact with lipid bilayers. At the same time, MD simulations offer a dynamic, time-resolved representation of these processes. This combination provides a comprehensive understanding of how small molecules such as NSAIDs, statins, and kinase inhibitors interact with and alter membrane properties.

Acknowledgements

DH would like to thank Kai Zumpfe for providing a snapshot picture of a MD simulation for Fig. 1.

Author Contribution

I.K. and H.A.S. wrote the main manuscript text. D.H. contributed conceptual input and provided Fig. 1. All authors contributed to the planning and structure of the review, participated in editing the manuscript, and approved the final version.

Funding

This research was funded by the grant of the Russian Science Foundation (project no. 24–23-00257-П).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Abboud S (1987) Subtle alterations in the high-frequency QRS potentials during myocardial ischemia in dogs. Comput Biomed Res 20:384–395. 10.1016/0010-4809(87)90052-8 [DOI] [PubMed] [Google Scholar]
  2. Ahmadi M, Amiri S, Pecic S et al (2020) Pleiotropic effects of statins: a focus on cancer. Biochim Biophys Acta - Mol Basis Dis 1866:165968. 10.1016/J.BBADIS.2020.165968 [DOI] [PubMed] [Google Scholar]
  3. Akutsu H (2020) Structure and dynamics of phospholipids in membranes elucidated by combined use of NMR and vibrational spectroscopies. Biochim Biophys Acta - Biomembr 1862:183352. 10.1016/J.BBAMEM.2020.183352 [DOI] [PubMed] [Google Scholar]
  4. Al’Aref SJ, Sanford RL, Koeppe RE, Andersen OS (2011) Statins Modify Bilayer Mechanical Properties. Biophys J 100:500a. 10.1016/J.BPJ.2010.12.2928
  5. Alam TM, Holland GP (2006) 1H-13C INEPT MAS NMR correlation experiments with 1H-1H mediated magnetization exchange to probe organization in lipid biomembranes. J Magn Reson 180:210–221. 10.1016/J.JMR.2006.02.013 [DOI] [PubMed] [Google Scholar]
  6. Alsop RJ, Armstrong CL, Maqbool A et al (2015) Cholesterol expels ibuprofen from the hydrophobic membrane core and stabilizes lamellar phases in lipid membranes containing ibuprofen. Soft Matter 11:4756–4767. 10.1039/C5SM00597C [DOI] [PubMed] [Google Scholar]
  7. Andrade S, Ramalho MJ, Loureiro JA, Pereira MC (2021) Liposomes as biomembrane models: biophysical techniques for drug-membrane interaction studies. J Mol Liq 334:116141. 10.1016/J.MOLLIQ.2021.116141 [Google Scholar]
  8. Antila HS, Ferreira MT, Ollila OHS, Miettinen MS (2021) Using open data to rapidly benchmark biomolecular simulations: Phospholipid conformational dynamics. J Chem Inf Model 61:938–949. 10.1021/ACS.JCIM.0C01299 [DOI] [PMC free article] [PubMed]
  9. Aucoin D, Camenares D, Zhao X et al (2009) High-resolution 1H MAS RFDR NMR of biological membranes. J Magn Reson 197:77–86. 10.1016/J.JMR.2008.12.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Aussenac F, Laguerre M, Schmitter JM, Dufourc EJ (2003) Detailed structure and dynamics of bicelle phospholipids using selectively deuterated and perdeuterated labels. 2H NMR and molecular mechanics study. Langmuir 19:10468–10479. 10.1021/LA034268Q [Google Scholar]
  11. Badoni S, Berruyer P, Niccoli L et al (2024) Maximizing relayed 1H hyperpolarization transfer by slow-fast MAS NMR Spectroscopy. J Phys Chem A 128:7005–7012. 10.1021/ACS.JPCA.4C02452 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Barry J, Fritz M, Brender JR et al (2009) Determining the effects of lipophilic drugs on membrane structure by solid-state NMR spectroscopy: the case of the antioxidant curcumin. J Am Chem Soc 131:4490–4498. 10.1021/JA809217U [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Bennett AE, Ok JH, Griffin RG, Vega S (1992) Chemical shift correlation spectroscopy in rotating solids: radio frequenc-driven dipolar recoupling and longitudinal exchange. J Chem Phys 96:8624–8627. 10.1063/1.462267 [Google Scholar]
  14. Bochicchio A, Brandner AF, Engberg O et al (2020) Spontaneous membrane nanodomain formation in the absence or presence of the neurotransmitter serotonin. Front cell Dev Biol 8:601145. 10.3389/FCELL.2020.601145 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Boggara MB, Krishnamoorti R (2010) Small-angle neutron scattering studies of phospholipid−NSAID adducts. Langmuir 26:5734–5745. 10.1021/LA903854S [DOI] [PubMed] [Google Scholar]
  16. Bortolotti A, Troiano C, Bobone S et al (2023) Mechanism of lipid bilayer perturbation by bactericidal membrane-active small molecules. Biochim Biophys Acta - Biomembr 1865:184079. 10.1016/J.BBAMEM.2022.184079 [DOI] [PubMed] [Google Scholar]
  17. Bouraoui A, Ghanem R, Berchel M et al (2020) Branched lipid chains to prepare cationic amphiphiles producing hexagonal aggregates: supramolecular behavior and application to gene delivery. Org Biomol Chem 18:337–345. 10.1039/C9OB02381J [DOI] [PubMed] [Google Scholar]
  18. Brown CM, Marrink SJ (2024) Modeling membranes in situ. Curr Opin Struct Biol 87:102837. 10.1016/J.SBI.2024.102837 [DOI] [PubMed] [Google Scholar]
  19. Bunker A, Róg T (2020) Mechanistic understanding from molecular dynamics simulation in pharmaceutical research 1: drug delivery. Front Mol Biosci 7:604770. 10.3389/FMOLB.2020.604770 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Case DA (2002) Molecular dynamics and NMR spin relaxation in proteins. Acc Chem Res 35:325–331. 10.1021/AR010020L [DOI] [PubMed] [Google Scholar]
  21. Centi A, Dutta A, Parekh SH, Bereau T (2020) Inserting small molecules across membrane mixtures: insight from the potential of mean force. Biophys J 118:1321–1332. 10.1016/J.BPJ.2020.01.039 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Chamani S, Liberale L, Mobasheri L et al (2021) The role of statins in the differentiation and function of bone cells. Eur J Clin Invest 51:e13534. 10.1111/ECI.13534 [DOI] [PubMed] [Google Scholar]
  23. Chandrasekhar I, Van Gunsteren WF, Zandomeneghi G et al (2006) Orientation and conformational preference of leucine-enkephalin at the surface of a hydrated dimyristoylphosphatidylcholine bilayer: NMR and MD simulation. J Am Chem Soc 128:159–170. 10.1021/JA054785Q [DOI] [PubMed] [Google Scholar]
  24. Davis JH (1983) The description of membrane lipid conformation, order and dynamics by 2H-NMR. Biochim Biophys Acta 737:117–171. 10.1016/0304-4157(83)90015-1 [DOI] [PubMed] [Google Scholar]
  25. Dey S, Surendran D, Engberg O et al (2021) Altered membrane mechanics provides a receptor-independent pathway for serotonin action. Chemistry 27:7533–7541. 10.1002/CHEM.202100328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Ellena JF, Pates RD, Brown MF (1986) 31P NMR spectra of rod outer segment and sarcoplasmic reticulum membranes show no evidence of immobilized components due to lipid-protein interactions. Biochemistry 25:3742–3748. 10.1021/BI00361A002 [DOI] [PubMed] [Google Scholar]
  27. Engberg O, Bochicchio A, Brandner AF et al (2020) Serotonin Alters the Phase Equilibrium of a Ternary Mixture of Phospholipids and Cholesterol. Front Physiol11:578868. 10.3389/FPHYS.2020.578868 [DOI] [PMC free article] [PubMed]
  28. Engberg O, Döbel V, Engel KM, Huster D (2025) Characterization of lipid chain order and dynamics in asymmetric membranes by solid-state NMR spectroscopy. Faraday Discuss. 10.1039/D4FD00192C [DOI] [PubMed] [Google Scholar]
  29. Engberg O, Saha Roy D, Krupa P et al (2025) Molecules in the serotonin-melatonin synthesis pathway have distinct interactions with lipid membranes. J Phys Chem B 129(10):2687–2700 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Feller SE, Huster D, Gawrisch K (1999) Interpretation of NOESY cross-relaxation rates from molecular dynamics simulation of a lipid bilayer. J Am Chem Soc 121:8963–8964. 10.1021/JA991456N [Google Scholar]
  31. Feller SE, Brown CA, Nizza DT, Gawrisch K (2002a) Nuclear Overhauser Enhancement Spectroscopy Cross-Relaxation Rates and Ethanol Distribution across Membranes. Biophys J 82:1396–1404. 10.1016/S0006-3495(02)75494-5 [DOI] [PMC free article] [PubMed]
  32. Feller SE, Gawrisch K, MacKerell AD (2002) Polyunsaturated fatty acids in lipid bilayers: intrinsic and environmental contributions to their unique physical properties. J Am Chem Soc 124:318–326. 10.1021/JA0118340 [DOI] [PubMed] [Google Scholar]
  33. Fischer M, Luck M, Werle M et al (2020) Binding of the small-molecule kinase inhibitor ruxolitinib to membranes does not disturb membrane integrity. Biochem Biophys Reports 24:100838. 10.1016/J.BBREP.2020.100838 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Fischer M, Schwarze B, Ristic N, Scheidt HA (2022) Predicting 2H NMR acyl chain order parameters with graph neural networks. Comput Biol Chem 100:107750. 10.1016/J.COMPBIOLCHEM.2022.107750 [DOI] [PubMed] [Google Scholar]
  35. Fischer M, Luck M, Werle M et al (2023) The small-molecule kinase inhibitor ceritinib, unlike imatinib, causes a significant disturbance of lipid membrane integrity: a combined experimental and MD study. Chem Phys Lipids 257:105351. 10.1016/J.CHEMPHYSLIP.2023.105351 [DOI] [PubMed] [Google Scholar]
  36. Friedman R, Khalid S, Aponte-Santamaría C et al (2018) Understanding conformational dynamics of complex lipid mixtures relevant to biology. J Membr Biol 251:609–631. 10.1007/S00232-018-0050-Y [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Gaede HC, Gawrisch K (2004) Multi-dimensional pulsed field gradient magic angle spinning NMR experiments on membranes. Magn Reson Chem 42:115–122. 10.1002/MRC.1329 [DOI] [PubMed] [Google Scholar]
  38. Galiullina LF, Scheidt HA, Huster D et al (2019) Interaction of statins with phospholipid bilayers studied by solid-state NMR spectroscopy. Biochim Biophys Acta - Biomembr 1861:584–593. 10.1016/j.bbamem.2018.12.013 [DOI] [PubMed] [Google Scholar]
  39. Ge Y, Lu H, Martí J (2025) Influence of local ordering in the permeation of temozolomide through the brain plasmatic membrane. Biophys Chem 324:107457. 10.1016/J.BPC.2025.107457 [DOI] [PubMed] [Google Scholar]
  40. Gopinath T, Weber D, Wang S et al (2021) Solid-state NMR of membrane proteins in lipid bilayers: to spin or not to spin? Acc Chem Res 54:1430–1439. 10.1021/ACS.ACCOUNTS.0C00670 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Grage SL, Afonin S, Ieronimo M et al (2022) Probing and manipulating the lateral pressure profile in lipid bilayers using Membrane-active peptides—a solid-state 19F NMR study. Int J Mol Sci 23:4544. 10.3390/IJMS23094544 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Grage SL, Guschtschin-Schmidt N, Meng B et al (2025) Interaction of squalamine with lipid membranes. J Phys Chem B 129:1760–1773. 10.1021/ACS.JPCB.4C06576 [DOI] [PubMed] [Google Scholar]
  43. Hakobyan D, Heuer A (2019) Comparing an all-atom and a coarse-grained description of lipid bilayers in terms of enthalpies and entropies: from MD simulations to 2D lattice models. J Chem Theory Comput 15:6393–6402. 10.1021/ACS.JCTC.9B00390 [DOI] [PubMed] [Google Scholar]
  44. Haralampiev I, Scheidt HA, Abel T et al (2016) The interaction of sorafenib and regorafenib with membranes is modulated by their lipid composition. Biochim Biophys Acta - Biomembr 1858:2871–2881. 10.1016/J.BBAMEM.2016.08.014 [DOI] [PubMed] [Google Scholar]
  45. Haralampiev I, Alonso de Armiño DJ, Luck M et al (2020) Interaction of the small-molecule kinase inhibitors tofacitinib and lapatinib with membranes. Biochim Biophys Acta - Biomembr 1862:183414. 10.1016/J.BBAMEM.2020.183414 [DOI] [PubMed] [Google Scholar]
  46. Heberle FA, Doktorova M (2025) Exploring the sensitivities of experimental techniques to various types of membrane asymmetry using atomistic simulations. Faraday Discuss. 10.1039/D4FD00200H [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Herz JM, Hu H, Tian J et al (2010) Activation of TRPA1 channels by Fenamate NSAIDs. FASEB J 24:583.3–583.3. 10.1096/FASEBJ.24.1_SUPPLEMENT.583.3
  48. Huber T, Rajamoorthi K, Kurze VF et al (2002) Structure of docosahexaenoic acid-containing phospholipid bilayers as studied by 2H NMR and molecular dynamics simulations. J Am Chem Soc 124:298–309. 10.1021/JA011383J [DOI] [PubMed] [Google Scholar]
  49. Huster D, Maiti S, Herrmann A (2024) Phospholipid membranes as chemically and functionally tunable materials. Adv Mater 36:2312898. 10.1002/ADMA.202312898 [DOI] [PubMed] [Google Scholar]
  50. Igumenova TI (2015) Dynamics and membrane interactions of protein Kinase C. Biochemistry 54:4953–4968. 10.1021/ACS.BIOCHEM.5B00565 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Jeong JC, Jo S, Wu EL et al (2014) ST-analyzer: a web-based user interface for simulation trajectory analysis. J Comput Chem 35:957–963. 10.1002/JCC.23584 [DOI] [PubMed] [Google Scholar]
  52. Kang X, Elson C, Penfield J et al (2019) Integrated solid-state NMR and molecular dynamics modeling determines membrane insertion of human β-defensin analog. Commun Biol 21(2):1–10. 10.1038/s42003-019-0653-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Kashnik AS, Selyutina OY, Baranov DS et al (2023) Localization of the ibuprofen molecule in model lipid membranes revealed by spin-label-enhanced NMR relaxation. Biochim Biophys Acta - Biomembr 1865:184215. 10.1016/J.BBAMEM.2023.184215 [DOI] [PubMed] [Google Scholar]
  54. Khajeh A, Modarress H (2014) The influence of cholesterol on interactions and dynamics of ibuprofen in a lipid bilayer. Biochim Biophys Acta - Biomembr 1838:2431–2438. 10.1016/J.BBAMEM.2014.05.029 [DOI] [PubMed] [Google Scholar]
  55. Khansari PS, Halliwell RF (2009) Evidence for neuroprotection by the fenamate NSAID, mefenamic acid. Neurochem Int 55:683–688. 10.1016/J.NEUINT.2009.06.014 [DOI] [PubMed] [Google Scholar]
  56. Khodov IA, Musabirova GS, Klochkov VV et al (2022) Structural details on the interaction of fenamates with lipid membranes. J Mol Liq 367:120502. 10.1016/J.MOLLIQ.2022.120502 [Google Scholar]
  57. Khodov IA, Belov KV, Huster D, Scheidt HA (2023) Conformational State of Fenamates at the Membrane Interface: A MAS NOESY Study. Membranes (Basel) 13:607. 10.3390/membranes13060607 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Klauda JB, Eldho NV, Gawrisch K et al (2008) Collective and noncollective models of NMR relaxation in lipid vesicles and multilayers. J Phys Chem B 112:5924–5929. 10.1021/JP075641W [DOI] [PubMed] [Google Scholar]
  59. Kremkow J, Luck M, Huster D et al (2020) Membrane interaction of ibuprofen with cholesterol-containing lipid membranes. Biomol 10(10):1384. 10.3390/BIOM10101384 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Kuba JO, Yu Y, Klauda JB (2021) Estimating localization of various statins within a POPC bilayer. Chem Phys Lipids 236:105074. 10.1016/J.CHEMPHYSLIP.2021.105074 [DOI] [PubMed] [Google Scholar]
  61. Kumar K, Sebastiao M, Arnold AA et al (2022) In situ solid-state NMR study of antimicrobial peptide interactions with erythrocyte membranes. Biophys J 121:1512–1524. 10.1016/J.BPJ.2022.03.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Künze G, Huster D, Samsonov SA (2021) Investigation of the structure of regulatory proteins interacting with glycosaminoglycans by combining NMR spectroscopy and molecular modeling - the beginning of a wonderful friendship. Biol Chem 402:1337–1355. 10.1515/HSZ-2021-0119 [DOI] [PubMed] [Google Scholar]
  63. Le-Deygen IM, Safronova AS, Mamaeva PV et al (2022) Drug–Membrane Interaction as Revealed by Spectroscopic Methods: the Role of Drug Structure in the Example of Rifampicin, Levofloxacin and Rapamycin. Biophysica 2:353–365. 10.3390/BIOPHYSICA2040032
  64. Lee J, Cheng X, Swails JM et al (2016) CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J Chem Theory Comput 12:405–413. 10.1021/ACS.JCTC.5B00935 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Lefer AM, Scalia R, Lefer DJ (2001) Vascular effects of HMG CoA-reductase inhibitors (statins) unrelated to cholesterol lowering: new concepts for cardiovascular disease. Cardiovasc Res 49:281–287. 10.1016/S0008-6363(00)00247-9 [DOI] [PubMed] [Google Scholar]
  66. Li X, Jusko WJ (2023) Utility of minimal physiologically based pharmacokinetic models for assessing fractional distribution, oral absorption, and series-compartment models of hepatic clearance. Drug Metab Dispos 51:1403–1418. 10.1124/DMD.123.001403 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Li SD, Zhang SY, Peng B (2021) Statins as candidate therapeutic agents for coronavirus disease 2019 (COVID-19). Chin Med J (Engl) 134:407–409. 10.1097/CM9.0000000000001205 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Li X, He Y, Wang Y et al (2024) CHARMM36 all-atom gas model for lipid nanobubble simulation. J Chem Inf Model. 10.1021/ACS.JCIM.4C01027 [DOI] [PubMed] [Google Scholar]
  69. Lichtenberger LM, Zhou Y, Jayaraman V et al (2012) Insight into NSAID-induced membrane alterations, pathogenesis and therapeutics: characterization of interaction of NSAIDs with phosphatidylcholine. Biochim Biophys Acta - Mol Cell Biol Lipids 1821:994–1002. 10.1016/J.BBALIP.2012.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Ling J, Schroder R, Wuelfing WP et al (2024) Molecular Investigation of SNAC as an oral peptide permeation enhancer in lipid membranes via solid-state NMR. Mol Pharm. 10.1021/ACS.MOLPHARMACEUT.4C01061 [DOI] [PubMed] [Google Scholar]
  71. Luck M, Fischer M, Werle M et al (2021) Impact of selected small-molecule kinase inhibitors on lipid membranes. Pharmaceuticals 14:746. 10.3390/PH14080746 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Marsan MP, Muller I, Ramos C et al (1999) Cholesterol orientation and dynamics in dimyristoylphosphatidylcholine bilayers: a solid state deuterium NMR analysis. Biophys J 76:351–359. 10.1016/S0006-3495(99)77202-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Martinotti C, Ruiz-Perez L, Deplazes E, Mancera RL (2020) Molecular dynamics simulation of small molecules interacting with biological membranes. ChemPhysChem 21:1486–1514. 10.1002/CPHC.202000219 [DOI] [PubMed] [Google Scholar]
  74. McCalpin SD, Ravula T, Ramamoorthy A (2022) Saponins form nonionic lipid nanodiscs for protein structural studies by nuclear magnetic resonance spectroscopy. J Phys Chem Lett 13:1705–1712. 10.1021/ACS.JPCLETT.1C04185 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. McCalpin SD, Fu R, Ravula T et al (2023) Magnetically aligned nanodiscs enable direct measurement of 17O residual quadrupolar coupling for small molecules. J Magn Reson 346:107341. 10.1016/J.JMR.2022.107341 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Migliorati JM, Liu S, Liu A et al (2022) Absorption, distribution, metabolism, and excretion of us food and drug administration-approved antisense oligonucleotide drugs. Drug Metab Dispos 50:888–897. 10.1124/DMD.121.000417 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Molugu TR, Xu X, Lee S et al (2018) Solid-state 2H NMR studies of water- mediated lipid membrane deformation. Mod Magn Reson 1225–1250. 10.1007/978-3-319-28388-3_143
  78. Molugu TR, Thurmond RL, Alam TM et al (2022) Phospholipid headgroups govern area per lipid and emergent elastic properties of bilayers. Biophys J 121:4205–4220. 10.1016/J.BPJ.2022.09.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Murray D, Ben-Tal N, Honig B, McLaughlin S (1997) Electrostatic interaction of myristoylated proteins with membranes: Simple physics, complicated biology. Struct 5(8):985–989. 10.1016/s0969-2126(97)00251-7 [DOI] [PubMed]
  80. Musabirova G, Engberg O, Gupta A et al (2022) Serotonergic drugs modulate the phase behavior of complex lipid bilayers. Biochimie 203:40–50. 10.1016/j.biochi.2022.04.006 [DOI] [PubMed] [Google Scholar]
  81. Nunes C, Brezesinski G, Lima JLFC et al (2011) Synchrotron SAXS and WAXS study of the interactions of NSAIDS with lipid membranes. J Phys Chem B 115:8024–8032. 10.1021/JP2025158 [DOI] [PubMed] [Google Scholar]
  82. Pabst G, Keller S (2024) Exploring membrane asymmetry and its effects on membrane proteins. Trends Biochem Sci 49:333–345. 10.1016/J.TIBS.2024.01.007 [DOI] [PubMed] [Google Scholar]
  83. Palermo EF, Lee DK, Ramamoorthy A, Kuroda K (2011) Role of cationic group structure in membrane binding and disruption by amphiphilic copolymers. J Phys Chem B 115:366–375. 10.1021/JP1083357 [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Pastor RW, Venable RM, Feller SE (2002) Lipid bilayers, NMR relaxation, and computer simulations. Acc Chem Res 35:438–446. 10.1021/AR0100529 [DOI] [PubMed] [Google Scholar]
  85. Peetla C, Stine A, Labhasetwar V (2009) Biophysical interactions with model lipid membranes: applications in drug discovery and drug delivery. Mol Pharm 6:1264–1276. 10.1021/MP9000662 [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Peetla C, Bhave R, Vijayaraghavalu S et al (2010) Drug resistance in breast cancer cells: biophysical characterization of and doxorubicin interactions with membrane lipids. Mol Pharm 7:2334–2348. 10.1021/MP100308N [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Peetla C, Vijayaraghavalu S, Labhasetwar V (2013) Biophysics of cell membrane lipids in cancer drug resistance: implications for drug transport and drug delivery with nanoparticles. Adv Drug Deliv Rev 65:1686–1698. 10.1016/J.ADDR.2013.09.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Penkauskas T, Zentelyte A, Ganpule S et al (2020) Pleiotropic effects of statins via interaction with the lipid bilayer: a combined approach. Biochim Biophys Acta - Biomembr 1862:183306. 10.1016/J.BBAMEM.2020.183306 [DOI] [PubMed] [Google Scholar]
  89. Pereira-Leite C, Nunes C, Bozelli JC et al (2018) Can NO-indomethacin counteract the topical gastric toxicity induced by indomethacin interactions with phospholipid bilayers? Colloids Surf B Biointerfaces 169:375–383. 10.1016/J.COLSURFB.2018.05.019 [DOI] [PubMed] [Google Scholar]
  90. Petrache HI, Dodd SW, Brown MF (2000) Area per lipid and acyl length distributions in fluid phosphatidylcholines determined by 2H NMR spectroscopy. Biophys J 79:3172–3192. 10.1016/S0006-3495(00)76551-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Pezeshkian W, Marrink SJ (2021) Simulating realistic membrane shapes. Curr Opin Cell Biol 71:103–111. 10.1016/J.CEB.2021.02.009 [DOI] [PubMed] [Google Scholar]
  92. Phromchaloem C, Na Nakorn N, Muensritharam L, Boonyarat W (2025) Absorption study of fat-soluble vitamins into dipalmitoylphosphatidylcholine (DPPC) bilayer by MD simulations. J Mol Graph Model 135:108929. 10.1016/J.JMGM.2024.108929 [DOI] [PubMed] [Google Scholar]
  93. Prakash P (2021) A regulatory role of membrane by direct modulation of the catalytic kinase domain. Small GTPases 1–11:1. 10.1080/21541248.2020.1788886 [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Ramamoorthy A, Xu J (2013) 2D H/ H RFDR and NOESY NMR experiments on a membrane-bound antimicrobial peptide under magic angle spinning. J Phys Chem B 117:6693–6700. 10.1021/JP4034003 [DOI] [PubMed]
  95. Ravula T, Hardin NZ, Ramadugu SK, Ramamoorthy A (2017) PH tunable and divalent metal ion tolerant polymer lipid nanodiscs. Langmuir 33:10655–10662. 10.1021/ACS.LANGMUIR.7B02887 [DOI] [PubMed] [Google Scholar]
  96. Redondo-Morata L, Lea Sanford R, Andersen OS, Scheuring S (2016) Effect of statins on the nanomechanical properties of supported lipid bilayers. Biophys J 111:363–372. 10.1016/J.BPJ.2016.06.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Renault M, Réat V, Sugawara M et al (2006) Giant vesicles as an efficient intermediate for H NMR analyses of proteoliposomes in water suspension and in oriented lipid bilayers. Comptes Rendus Chim 9:401–407. 10.1016/J.CRCI.2005.06.009/ 
  98. Róg T, Girych M, Bunker A (2021) Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design. Pharm 14(14):1062. 10.3390/PH14101062 [DOI] [PMC free article] [PubMed]
  99. Saha Roy D, Singh A, Vaidya VA et al (2024) Effects of a serotonergic psychedelic on the lipid bilayer. ACS Chem Neurosci 15:4066. 10.1021/ACSCHEMNEURO.4C00484 [DOI] [PubMed] [Google Scholar]
  100. Sanford RL, Al’Aref SJ, Koeppe RE, Andersen OS (2011) Fenamates Alter Bilayer Properties. Biophys J 100:500a. 10.1016/J.BPJ.2010.12.2927
  101. Sanford RL, Al’Aref SJ, Koeppe RE, Andersen OS (2013) Statins alter lipid bilayer properties. Biophys J 104:86a. 10.1016/J.BPJ.2012.11.515 [Google Scholar]
  102. Santos JS, Lee DK, Ramamoorthy A (2004) Effects of antidepressants on the conformation of phospholipid headgroups studied by solid-state NMR. Magn Reson Chem 42:105–114. 10.1002/MRC.1327 [DOI] [PubMed] [Google Scholar]
  103. Sanver D, Sadeghpour A, Rappolt M et al (2020) Structure and dynamics of dioleoyl-phosphatidylcholine bilayers under the Influence of Quercetin and Rutin. Langmuir 36:11776–11786. 10.1021/ACS.LANGMUIR.0C01484 [DOI] [PubMed] [Google Scholar]
  104. Sargent DF, Schwyzer R (1986) Membrane lipid phase as catalyst for peptide-receptor interactions. Proc Natl Acad Sci USA 83:5774–5778. 10.1073/PNAS.83.16.5774 [DOI] [PMC free article] [PubMed]
  105. Sarr FS, Guillaume YC, André C (2008) Magnesium cation effect on passive diffusion of statin molecules: molecular chromatography approach. J Pharm Biomed Anal 47:651–657. 10.1016/J.JPBA.2008.02.008 [DOI] [PubMed] [Google Scholar]
  106. Schachter M (2005) Chemical, pharmacokinetic and pharmacodynamic properties of statins: an update. Fundam Clin Pharmacol 19:117–125. 10.1111/J.1472-8206.2004.00299.X [DOI] [PubMed] [Google Scholar]
  107. Scheidt HA, Huster D (2008) The interaction of small molecules with phospholipid membranes studied by 1H NOESY NMR under magic-angle spinning. Acta Pharmacol Sin 29:35–49. 10.1111/j.1745-7254.2008.00726.x [DOI] [PubMed] [Google Scholar]
  108. Scherer PG, Seelig J (1989) Electric charge effects on phospholipid headgroups. Phosphatidylcholine in mixtures with cationic and anionic amphiphiles. Biochemistry 28:7720–7728. 10.1021/BI00445A030 [DOI] [PubMed] [Google Scholar]
  109. Seelig J (1977) Deuterium magnetic resonance: theory and application to lipid membranes. Q Rev Biophys 10:353–418. 10.1017/S0033583500002948 [DOI] [PubMed] [Google Scholar]
  110. Seelig J (1978) 31P nuclear magnetic resonance and the head group structure of phospholipids in membranes. Biochim Biophys Acta - Rev Biomembr 515:105–140. 10.1016/0304-4157(78)90001-1 [DOI] [PubMed] [Google Scholar]
  111. Semeraro EF, Frewein MPK, Pabst G (2024) Structure of symmetric and asymmetric lipid membranes from joint SAXS/SANS. Methods Enzymol 700:349–383. 10.1016/BS.MIE.2024.02.017 [DOI] [PubMed] [Google Scholar]
  112. Shahoei R, Tajkhorshid E (2020) Menthol binding to the human α4β2 nicotinic acetylcholine receptor facilitated by its strong partitioning in the membrane. J Phys Chem B 124:1866–1880. 10.1021/ACS.JPCB.9B10092 [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Sharma VK, Nagao M, Rai DK, Mamontov E (2019) Membrane softening by nonsteroidal anti-inflammatory drugs investigated by neutron spin echo. Phys Chem Chem Phys 21:20211–20218. 10.1039/C9CP03767E [DOI] [PubMed] [Google Scholar]
  114. Shi L, Wang Y, Wang Q et al (2020) Transforming a toxic drug into an efficacious nanomedicine using a lipoprodrug strategy for the treatment of patient-derived melanoma xenografts. J Control Release 324:289–302. 10.1016/J.JCONREL.2020.05.025 [DOI] [PubMed] [Google Scholar]
  115. Shukla R, Lavore F, Maity S, et al (2022) Teixobactin kills bacteria by a two-pronged attack on the cell envelope. Nat 608(7922):390–396. 10.1038/s41586-022-05019-y [DOI] [PMC free article] [PubMed]
  116. Shukla R, Peoples AJ, Ludwig KC et al (2023) An antibiotic from an uncultured bacterium binds to an immutable target. Cell 186:4059-4073.e27. 10.1016/J.CELL.2023.07.038 [DOI] [PubMed] [Google Scholar]
  117. Shurshalova GS, Scheidt HA, Fischer M et al (2021) Interaction of the pitavastatin with model membranes. Biochem Biophys Reports 28:101143. 10.1016/J.BBREP.2021.101143 [DOI] [PMC free article] [PubMed] [Google Scholar]
  118. Smith AA, Vogel A, Engberg O et al (2022) A method to construct the dynamic landscape of a bio-membrane with experiment and simulation. Nat Commun 13:1–13. 10.1038/s41467-021-27417-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  119. Sodero AO, Barrantes FJ (2020) Pleiotropic effects of statins on brain cells. Biochim Biophys Acta - Biomembr 1862:183340. 10.1016/J.BBAMEM.2020.183340 [DOI] [PubMed] [Google Scholar]
  120. Stenström O, Champion C, Lehner M et al (2022) How does it really move? Recent progress in the investigation of protein nanosecond dynamics by NMR and simulation. Curr Opin Struct Biol 77:102459. 10.1016/J.SBI.2022.102459 [DOI] [PubMed] [Google Scholar]
  121. Svirkin Y, Lee J, Marx R et al (2022) Amphotericin B release rate is the link between drug status in the liposomal bilayer and toxicity. Asian J Pharm Sci 17:544–556. 10.1016/J.AJPS.2022.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  122. Szlenk CT, Gc JB, Natesan S (2019) Does the Lipid bilayer orchestrate access and binding of ligands to transmembrane orthosteric/allosteric sites of g protein-coupled receptors? Mol Pharmacol 96:527–541. 10.1124/MOL.118.115113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  123. Szlenk CT, GC JB, Natesan S (2021) Membrane-facilitated receptor access and binding mechanisms of long-acting β2-Adrenergic receptor agonists. Mol Pharmacol 100:406–427. 10.1124/MOLPHARM.121.000285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  124. Szomek M, Akkerman V, Lauritsen L et al (2024) Ergosterol promotes aggregation of natamycin in the yeast plasma membrane. Biochim Biophys Acta - Biomembr 1866:184350. 10.1016/J.BBAMEM.2024.184350 [DOI] [PubMed] [Google Scholar]
  125. Trzeciak K, Paluch P, Pawlak T et al (2020) The influence of the stereochemistry and C-end chemical modification of dermorphin derivatives on the peptide-phospholipid interactions. Biochim Biophys acta Biomembr 1862:183066. 10.1016/J.BBAMEM.2019.183066 [DOI] [PubMed] [Google Scholar]
  126. Varma M, Deserno M (2025) The interplay of composition and mechanics in the thermodynamics of asymmetric ternary lipid membranes. Faraday Discuss. 10.1039/D4FD00196F [DOI] [PubMed] [Google Scholar]
  127. Venable RM, Brown FLH, Pastor RW (2015) Mechanical properties of lipid bilayers from molecular dynamics simulation. Chem Phys Lipids 192:60–74. 10.1016/J.CHEMPHYSLIP.2015.07.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  128. Vermeer LS, De Groot BL, Réat V et al (2007) Acyl chain order parameter profiles in phospholipid bilayers: computation from molecular dynamics simulations and comparison with 2H NMR experiments. Eur Biophys J 36:919–931. 10.1007/S00249-007-0192-9 [DOI] [PubMed] [Google Scholar]
  129. Vinarov Z, Abrahamsson B, Artursson P et al (2021) Current challenges and future perspectives in oral absorption research: An opinion of the UNGAP network. Adv Drug Deliv Rev 171:289–331. 10.1016/J.ADDR.2021.02.001 [DOI] [PubMed] [Google Scholar]
  130. Vogel A, Tan KT, Waldmann H et al (2007) Flexibility of ras lipid modifications studied by 2H solid-state NMR and molecular dynamics simulations. Biophys J 93:2697–2712. 10.1529/BIOPHYSJ.107.104562 [DOI] [PMC free article] [PubMed] [Google Scholar]
  131. Vogel A, Reuther G, Roark MB et al (2010) Backbone conformational flexibility of the lipid modified membrane anchor of the human N-Ras protein investigated by solid-state NMR and molecular dynamics simulation. Biochim Biophys Acta 1798:275–285. 10.1016/J.BBAMEM.2009.09.023 [DOI] [PubMed] [Google Scholar]
  132. Wood M, Morales M, Miller E et al (2021) Ibuprofen and the Phosphatidylcholine Bilayer: membrane water permeability in the presence and absence of cholesterol. Langmuir 37:4468–4480. 10.1021/ACS.LANGMUIR.0C03638 [DOI] [PubMed] [Google Scholar]
  133. Yang Y, Dong H, Zhou HX (2021) Effects of cholesterol on the partitioning of a drug molecule in lipid bilayers. J Phys Chem B 125:5338–5345. 10.1021/ACS.JPCB.1C02436 [DOI] [PMC free article] [PubMed] [Google Scholar]
  134. Yu Y, Klauda JB (2020) Update of the CHARMM36 united atom chain model for hydrocarbons and phospholipids. J Phys Chem B 124:6797–6812. 10.1021/ACS.JPCB.0C04795 [DOI] [PubMed] [Google Scholar]
  135. Zambrano P, Manrique-Moreno M, Petit K et al (2024) Differential scanning calorimetry in drug-membrane interactions. Biochem Biophys Res Commun 709:149806. 10.1016/J.BBRC.2024.149806 [DOI] [PubMed] [Google Scholar]
  136. Zhao X, Wang X, Shukla R et al (2021) Brevibacillin 2V, a novel antimicrobial lipopeptide with an exceptionally low hemolytic activity. Front Microbiol 12:693725. 10.3389/FMICB.2021.693725 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

No datasets were generated or analysed during the current study.

Articles from Biophysical Reviews are provided here courtesy of Springer

RESOURCES