Structure–Kinetic Relationships of Passive Membrane Permeation from Multiscale Modeling

Passive Transport across Cell Membranes beyond the Overton Rule: Insights from Solute Exchange in Vesicles and Molecular Dynamics of Atropisomers

Bioavailability of a drug is critically dependent on its cell membrane permeability. Empirical rules guiding drug design consolidated the dogma that large molecules cannot cross cell membranes by passive diffusion. However, the more amphiphilic atropisomers of redaporfin, an 1135 Da bacteriochlorin photosensitizer used in photodynamic therapy, exhibited fast cell uptake and high photodynamic activity in vitro. This motivated detailed studies of redaporfin atropisomers and their interactions with cell membrane models. Experimental studies on membrane affinity, permeation rates, and exchange dynamics were complemented by molecular dynamics simulations, to reveal the nature of the interactions between the atropisomers and lipid bilayers, the orientation and location of the membrane-bound atropisomers, free energy profiles, and mechanisms governing membrane permeation. Our results indicate that the asymmetric distribution of the meso-phenyl sulfonamide groups (atropisomer α4) generates a large amphiphilic moment. This enhances its membrane affinity and positions the bacteriochlorin ring deeper in the membrane. However, these strong membrane interactions result in a slow exchange of α4 between lipid membranes, restricting its distribution in complex, membrane-rich environments. In contrast, the more symmetrical atropisomer αβαβ exhibits approximately 10-fold lower membrane affinity and localizes closer to the membrane-water interface. This weaker interaction facilitates rapid exchange between membranes, occurring within minutes at 37 °C. Molecular dynamics simulations reveal relatively low energy barriers for membrane translocation, consistent with experimentally estimated fast translocation. Distinct permeation mechanisms were observed for the two atropisomers, providing insights into their differential behavior in passive membrane transport. In particular, the fast cell uptake of the α4 atropisomer is properly described by the bind-flip mechanism, where the sulfonamide groups first approach the bilayer in a "binding" mode, and then the molecule "flips" to place the macrocycle in a more internal position. Our results show how amphiphilicity and conformation flexibility are critical determinants in the cellular internalization of large molecules.

Biased Equilibrium Drives Cyclosporine Membrane Permeability: The Goldilocks Energy Barriers

Conformational flexibility allows macrocyclic peptides like cyclosporine A (CycA) to cross membranes, yet drug design leveraging this property has largely failed. A key challenge is linking specific conformers to function, as different conformers govern permeability versus target binding. We reveal a mechanism that enhances CycA and alisporivir (ALI) permeability: trans-to-cis isomerization at MeVal11-MeBmt1 creates conformers that remain "soluble" in both membrane-like and aqueous environments. A biased equilibrium favors this conformer in protic environments, while a lipophilic conformer with cis MeLeu9-MeLeu10 dominates in aprotic conditions. This mechanism explains why CycH, Valspodar (VALSPO), and O-acetyl CycA (OAc-CycA) fail to cross membranes─they adopt similar states but lack this biased equilibrium. Our findings provide a new strategy for designing membrane-permeable N-methylated macrocycles and underscore the role of high-energy conformers as transition states between membrane permeability and target engagement─offering critical insights for drug development.

Predicting Distribution Coefficients (LogD) of Cyclic Peptides Using Molecular Dynamics Simulations

PURPOSE

The distribution coefficient (LogD) is a critical property for oral peptide drug design. In this study, we focused on cyclic peptides (octreotide and its analogs) and aimed to determine their LogD values at four pHs using both the simulation and experimental approaches.

METHODS

For the experimental approach, the shake-flask method with LCMS quantification was employed to determine LogD values. For the simulation approach, the partition coefficient (LogP) was obtained from the solvation free energy calculations using molecular dynamics (MD) simulation. The LogD values were then calculated from the obtained LogP values considering the predicted pKa and ionization states of each peptide residue. More peptide properties such as polar surface area (PSA), number of intramolecular hydrogen bonds, solvent accessible surface area (SASA), and radius of gyration (Rg) were also calculated with the aid of MD simulation.

RESULTS

For a total of 28 LogD values across four pHs, the predicted values from the simulation under the OPLS-AA forcefield agreed with the experimental values, with an average deviation of 1.39 ± 0.86 log units, displaying better predictions compared to the data generated under the CHARMM forcefield or using commercial software. In addition, the analysis of PSA, SASA, and Rg data suggested the peptides exhibited some conformational flexibility in both aqueous and organic phases.

CONCLUSIONS

The method developed in this study can predict the LogD values at a wide pH range covering multiple formulation/physiological conditions and therefore can provide insights into designing oral peptide drugs, especially for early-stage projects.

Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood-Brain Barrier

The discovery and development of new pharmaceutical drugs is a costly, time-consuming, and highly manual process, with significant challenges in ensuring drug bioavailability at target sites. Computational techniques are highly employed in drug design, particularly to predict the pharmacokinetic properties of molecules. One major kinetic challenge in central nervous system drug development is the permeation through the blood-brain barrier (BBB). Several different computational techniques are used to evaluate both BBB permeability and target delivery. Methods such as quantitative structure-activity relationships, machine learning models, molecular dynamics simulations, end-point free energy calculations, or transporter models have pros and cons for drug development, all contributing to a better understanding of a specific characteristic. Additionally, the design (assisted or not by computers) of prodrug and nanoparticle-based drug delivery systems can enhance BBB permeability by leveraging enzymatic activation and transporter-mediated uptake. Neuroactive peptide computational development is also a relevant field in drug design, since biopharmaceuticals are on the edge of drug discovery. By integrating these computational and formulation-based strategies, researchers can enhance the rational design of BBB-permeable drugs while minimizing off-target effects. This review is valuable for understanding BBB selectivity principles and the latest in silico and nanotechnological approaches for improving CNS drug delivery.

Protein-free domains in native and ferroptosis-driven oxidized cell membranes: a molecular dynamics study of biophysical properties and doxorubicin uptake

Ferroptosis is a regulated form of cell death characterized by iron-dependent lipid peroxidation of polyunsaturated fatty acids (PUFAs). Despite its significance, the precise molecular mechanisms underlying ferroptosis remain elusive, particularly concerning their impact on membrane properties. This study aimed to investigate the biophysical changes in plasma membranes due to lipid peroxidation during ferroptosis and their impact on the uptake of doxorubicin (DOX), a potent anticancer agent linked to ferroptosis. Using all-atom molecular dynamics simulations, we compared native red blood cell membranes (protein-free domains) with a ferroptosis model, in which PUFAs were replaced with hydroperoxide derivatives. Our findings reveal that the ferroptotic membrane exhibits decreased thickness and increased lipid area while maintaining overall integrity. The hydroperoxide groups localized in the disordered tail regions, enhancing tail mobility and facilitating hydrogen bonding. Lipid lateral diffusion was significantly altered, both layers of the ferroptotic membrane exhibited slower diffusion rates compared to the native membrane. Furthermore, lipid oxidation affected diffusion activation energies. Importantly, we found that DOX could penetrate the oxidized ferroptosis membrane with a lower free-energy barrier (∆GPB) of approximately 38 kJ.mol-1. Consequently, DOX's permeability was approximately seven orders of magnitude higher than that of the native membrane. In summary, lipid peroxidation during ferroptosis induces extensive structural and dynamic changes, influencing membrane behavior and potentially offering insights that could inform future therapeutic strategies.

Elucidating Antibiotic Permeation through the Escherichia coli Outer Membrane: Insights from Molecular Dynamics

Antibiotic resistance represents a critical public health threat, with an increasing number of Gram-negative pathogens demonstrating resistance to a broad range of clinical drugs. A primary challenge in enhancing antibiotic efficacy is overcoming the robust barrier presented by the bacterial outer membrane. Our research addresses a longstanding question: What is the rate of antibiotic permeation across the outer membrane (OM) of Gram-negative bacteria? Utilizing molecular dynamics (MD) simulations, we assess the passive permeability profiles of four commercially available antibiotics─gentamicin, novobiocin, rifampicin, and tetracycline across an asymmetric atomistic model of the Escherichia coli (E. coli) OM, employing the inhomogeneous solubility-diffusion model. Our examination of the interactions between these drugs and their environmental context during OM permeation reveals that extended hydrogen bond formation and drug-cation interactions significantly hinder the energetics of passive permeation, notably affecting novobiocin. Our MD simulations corroborate well with experimental data and reveal new implications of solvation on drug permeability, overall advancing the possible use of computational prediction of membrane permeability in future antibiotic discovery.

Membrane Composition Allows the Optimization of Berberine Encapsulation in Liposomes

Berberine (BBR) is a natural molecule with noteworthy pharmacological properties, including the prevention of antibiotic resistance in Gram-negative bacteria. However, its oral bioavailability is poor, thus resulting in an impaired absorption and efficacy in humans. In combination with other drugs, liposomes have been shown to enhance the availability of the drug, representing a smart delivery system to target tissues and reduce negative side effects. To date, there is a lack of studies on BBR and liposomes that enable the rationalization and molecular-based design of such formulations for future use in humans. In this work, the encapsulation of BBR into liposomes is proposed to overcome current limitations using a combination of experimental and computational assays to rationalize the membrane composition of liposomes that maximizes BBR encapsulation. First, the encapsulation efficiency was measured for several membrane compositions, revealing that it is enhanced by cholesteryl hemisuccinate and, to a lesser extent, by cholesterol. The physical basis of the BBR encapsulation efficiency and permeability was clarified using molecular dynamics simulation: using the lipid composition, one can tune the capability of membranes to attract, i.e., to adsorb, the molecules onto their surface. Overall, these findings suggest a rational strategy to maximize the encapsulation efficiency of liposomes by using negatively charged lipids, thus representing the basis for designing delivery systems for BBR, useful to treat, e.g., antibiotic resistance.

Statistical Mechanical Theories of Membrane Permeability

A popular theoretical framework to compute the permeability coefficient of a molecule is provided by the classic Smoluchowski-Kramers treatment of the steady-state diffusive flux across a free-energy barrier. Within this framework, commonly termed "inhomogeneous solubility-diffusion" (ISD), the permeability, P, is expressed in closed form in terms of the potential of mean force and position-dependent diffusivity of the molecule of interest along the membrane normal. In principle, both quantities can be calculated from all-atom MD simulations. Although several methods exist for calculating the position-dependent diffusivity, each of these is at best an estimate. In addition, the ISD model does not account for memory effects along the chosen reaction coordinate. For these reasons, it is important to seek alternative theoretical formulations to determine the permeability coefficient that are able to account for the factors ignored by the ISD approximation. Using Green-Kubo linear response theory, we establish the familiar constitutive relation between the flux density across the membrane and the difference in the concentration of a permeant molecule, j = PΔC. On this basis, we derive a time-correlation function expression for the nonequilibrium flux across a membrane that is reminiscent of the transmission coefficient in the reactive flux formalism treatment of transition rates. An analysis based on the transition path theory framework is exploited to derive alternative expressions for the permeability coefficient. The different strategies are illustrated with stochastic simulations based on the generalized Langevin equation in addition to unbiased molecular dynamics simulations of water permeation of a lipid bilayer.

Molecular Mechanism of P53 Peptide Permeation through Lipid Membranes from Solid-State NMR Spectroscopy and Molecular Dynamics Simulations

Macrocyclic peptides show promise in targeting high-value therapeutically relevant binding sites due to their high affinity and specificity. However, their clinical application is often hindered by low membrane permeability, which limits their effectiveness against intracellular targets. Previous studies focused on peptide conformations in various solvents, leaving a gap in understanding their interactions with and translocation through lipid bilayers. Addressing this, our study explores the membrane interactions of stapled peptides, a subclass of macrocyclic peptides, using solid-state nuclear magnetic resonance (ssNMR) spectroscopy and molecular dynamics (MD) simulations. We conducted ssNMR measurements on ATSP-7041M, a prototypical stapled peptide, to understand its interaction with lipid membranes, leading to an MD-informed model for peptide membrane permeation. Our findings reveal that ATSP-7041M adopts a stable α-helical structure upon membrane binding, facilitated by a cation-π interaction between its phenylalanine side chain and the lipid headgroup. This interaction makes the membrane-bound state energetically favorable, facilitating membrane affinity and insertion. The bound peptide displayed asymmetric insertion depths, with the C-terminus penetrating deeper (approximately 9 Å) than the N-terminus (approximately 4.3 Å) relative to the lipid headgroups. Contrary to expectations, peptide dynamics was not hindered by membrane binding and exhibited rapid motions similar to cell-penetrating peptides. These dynamic interactions and peptide-lipid affinity appear to be crucial for membrane permeation. MD simulations indicated a thermodynamically stable transmembrane conformation of ATSP-7041M, reducing the energy barrier for translocation. Our study offers an in silico view of ATSP-7041M's translocation from the extracellular to the intracellular region, highlighting the significance of peptide-lipid interactions and dynamics in enabling peptide transit through membranes.

Application of Generative Artificial Intelligence in Predicting Membrane Partitioning of Drugs: Combining Denoising Diffusion Probabilistic Models and MD Simulations Reduces the Computational Cost to One-Third

The optimal interaction of drugs with plasma membranes and membranes of subcellular organelles is a prerequisite for desirable pharmacology. Importantly, for drugs targeting the transmembrane lipid-facing sites of integral membrane proteins, the relative affinity of a drug to the bilayer lipids compared to the surrounding aqueous phase affects the partitioning, access, and binding of the drug to the target site. Molecular dynamics (MD) simulations, including enhanced sampling techniques such as steered MD, umbrella sampling (US), and metadynamics, offer valuable insights into the interactions of drugs with the membrane lipids and water in atomistic detail. However, these methods are computationally prohibitive for the high-throughput screening of drug candidates. This study shows that applying denoising diffusion probabilistic models (DDPMs), a generative AI method, to US simulation data reduces the computational cost significantly. Specifically, the models used only partial (one-third) data from the US simulations and reproduced the complete potential of mean force (PMF) profiles for three FDA-approved drugs (β2-adrenergic agonists) and ∼20 biologically relevant chemicals with known experimentally characterized bilayer locations. Intriguingly, the model can predict the solvation-free energies for partitioning and crossing the bilayer, preferred bilayer locations (low-energy well), and orientations of the ligands with high accuracy. The results indicate that DDPMs can be used to characterize the complete membrane partitioning profile of drug molecules using fewer umbrella sampling simulations at select positions along the bilayer normal (z-axis), irrespective of their amphiphilic-lipophilic-cephalophilic characteristics.

Improving the Accuracy of Permeability Data to Gain Predictive Power: Assessing Sources of Variability in Assays Using Cell Monolayers

The ability to predict the rate of permeation of new compounds across biological membranes is of high importance for their success as drugs, as it determines their efficacy, pharmacokinetics, and safety profile. In vitro permeability assays using Caco-2 monolayers are commonly employed to assess permeability across the intestinal epithelium, with an extensive number of apparent permeability coefficient (Papp) values available in the literature and a significant fraction collected in databases. The compilation of these Papp values for large datasets allows for the application of artificial intelligence tools for establishing quantitative structure-permeability relationships (QSPRs) to predict the permeability of new compounds from their structural properties. One of the main challenges that hinders the development of accurate predictions is the existence of multiple Papp values for the same compound, mostly caused by differences in the experimental protocols employed. This review addresses the magnitude of the variability within and between laboratories to interpret its impact on QSPR modelling, systematically and quantitatively assessing the most common sources of variability. This review emphasizes the importance of compiling consistent Papp data and suggests strategies that may be used to obtain such data, contributing to the establishment of robust QSPRs with enhanced predictive power.

Design Principles for Balancing Lipophilicity and Permeability in beyond Rule of 5 Space

An ab initio conformational analysis of oral beyond Rule of 5 (bRo5) drugs was complemented with measured permeability and logP(octanol) to derive design principles conferring oral bioavailability. 3D polar surface area (PSA) thresholds for oral bRo5 drugs coincided with those reported for Ro5 space. The majority of oral bRo5 drugs exceeded the Ro5 logP threshold of 5, reflecting a bias for permeability. Above 500 Da molecular weight (MW), oral drugs and highly permeable Novartis compounds occupy a narrow polarity range (topological or TPSA/MW) of 0.1-0.3 Å2 /Da, whose upper half coincides with the lower 90 percentiles of the Novartis logP set. This TPSA/MW range and 3D PSA below 100 Å2 define the "Rule of ~1 /₅" for balancing lipophilicity and permeability. Neutral TPSA, defined as TPSA minus 3D PSA occurs independent of conformation, intramolecular hydrogen bonds (IMHB) and MW, suggesting it is an intrinsic molecular property. Neutral TPSA increased in the lead optimization (LO) campaigns of three first in class de novo designed bRo5 drugs and may be a useful design parameter in bRo5 space.

How is Membrane Permeation of Small Ionizable Molecules Affected by Protonation Kinetics?

According to the pH-partition hypothesis, the aqueous solution adjacent to a membrane is a mixture of the ionization states of the permeating molecule at fixed Henderson-Hasselbalch concentrations, such that each state passes through the membrane in parallel with its own specific permeability. An alternative view, based on the assumption that the rate of switching ionization states is instantaneous, represents the permeation of ionizable molecules via an effective Boltzmann-weighted average potential (BWAP). Such an assumption is used in constant-pH molecular dynamics simulations. The inhomogeneous solubility-diffusion framework can be used to compute the pH-dependent membrane permeability for each of these two limiting treatments. With biased WTM-eABF molecular dynamics simulations, we computed the potential of mean force and diffusivity of each ionization state of two weakly basic small molecules: nicotine, an addictive drug, and varenicline, a therapeutic for treating nicotine addiction. At pH = 7, the BWAP effective permeability is greater than that determined by pH-partitioning by a factor of 2.5 for nicotine and 5 for varenicline. To assess the importance of ionization kinetics, we present a Smoluchowski master equation that includes explicitly the protonation and deprotonation processes coupled with the diffusive motion across the membrane. At pH = 7, the increase in permeability due to the explicit ionization kinetics is negligible for both nicotine and varenicline. This finding is reaffirmed by combined Brownian dynamics and Markov state model simulations for estimating the permeability of nicotine while allowing changes in its ionization state. We conclude that for these molecules the pH-partition hypothesis correctly captures the physics of the permeation process. The small free energy barriers for the permeation of nicotine and varenicline in their deprotonated neutral forms play a crucial role in establishing the validity of the pH-partitioning mechanism. Essentially, BWAP fails because ionization kinetics are too slow on the time scale of membrane crossing to affect the permeation of small ionizable molecules such as nicotine and varenicline. For the singly protonated state of nicotine, the computational results agree well with experimental measurements (P1 = 1.29 × 10-7 cm/s), but the agreement for neutral (P0 = 6.12 cm/s) and doubly protonated nicotine (P2 = 3.70 × 10-13 cm/s) is slightly worse, likely due to factors associated with the aqueous boundary layer (neutral form) or leaks through paracellular pathways (doubly protonated form).

Designing Reaction Coordinate for Ion-Induced Pore-Assisted Mechanism of Halide Ions Permeation through Lipid Bilayer by Umbrella Sampling

Ion permeation mechanism through lipid membranes helps to understand cellular processes. We propose new reaction coordinates that allow ions to permeate according to their water affinity and interaction with the hydrophilic layer. Simulations were done for three different halides (F-, Cl-, and I-) in two different lipid bilayers, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1,2-dinervonoyl-sn-glycero-3-phosphocholine (DNPC). It is found that the involvement of the water molecules decreases the free energy barrier. The ions were found to follow different pathways for permeation. Formation of proper pores required a collaboration effort of the hydration shell water molecules and the hydrophilic lipid layer, which was favored in the case of Cl- ions. The optimum charge density and good water affinity of Cl- with respect to F- and I- ions helped to form the pore. The effect was prominently seen in the case of DNPC membrane because of its higher hydrophobic thickness. The umbrella sampling results were compared with other methods such as the Markov state model (MSM) and well-tempered metadynamics (WT-metaD).

Physical Chemistry of Chloroquine Permeation through the Cell Membrane with Atomistic Detail

We provide a molecular-level description of the thermodynamics and mechanistic aspects of drug permeation through the cell membrane. As a case study, we considered the antimalaria FDA approved drug chloroquine. Molecular dynamics simulations of the molecule (in its neutral and protonated form) were performed in the presence of different lipid bilayers, with the aim of uncovering key aspects of the permeation process, a fundamental step for the drug's action. Free energy values obtained by well-tempered metadynamics simulations suggest that the neutral form is the only permeating protomer, consistent with experimental data. H-bond interactions of the drug with water molecules and membrane headgroups play a crucial role for permeation. The presence of the transmembrane potential, investigated here for the first time in a drug permeation study, does not qualitatively affect these conclusions.

Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery

Passive permeation of cellular membranes is a key feature of many therapeutics. The relevance of passive permeability spans all biological systems as they all employ biomembranes for compartmentalization. A variety of computational techniques are currently utilized and under active development to facilitate the characterization of passive permeability. These methods include lipophilicity relations, molecular dynamics simulations, and machine learning, which vary in accuracy, complexity, and computational cost. This review briefly introduces the underlying theories, such as the prominent inhomogeneous solubility diffusion model, and covers a number of recent applications. Various machine-learning applications, which have demonstrated good potential for high-volume, data-driven permeability predictions, are also discussed. Due to the confluence of novel computational methods and next-generation exascale computers, we anticipate an exciting future for computationally driven permeability predictions.

Challenges in Permeability Assessment for Oral Drug Product Development

Drug permeation across the intestinal epithelium is a prerequisite for successful oral drug delivery. The increased interest in oral administration of peptides, as well as poorly soluble and poorly permeable compounds such as drugs for targeted protein degradation, have made permeability a key parameter in oral drug product development. This review describes the various in vitro, in silico and in vivo methodologies that are applied to determine drug permeability in the human gastrointestinal tract and identifies how they are applied in the different stages of drug development. The various methods used to predict, estimate or measure permeability values, ranging from in silico and in vitro methods all the way to studies in animals and humans, are discussed with regard to their advantages, limitations and applications. A special focus is put on novel techniques such as computational approaches, gut-on-chip models and human tissue-based models, where significant progress has been made in the last few years. In addition, the impact of permeability estimations on PK predictions in PBPK modeling, the degree to which excipients can affect drug permeability in clinical studies and the requirements for colonic drug absorption are addressed.

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

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

Predictions from First-Principles of Membrane Permeability to Small Molecules: How Useful Are They in Practice?

Predicting from first-principles the rate of passive permeation of small molecules across the biological membrane represents a promising strategy for screening lead compounds upstream in the drug-discovery and development pipeline. One popular avenue for the estimation of permeation rates rests on computer simulations in conjunction with the inhomogeneous solubility-diffusion model, which requires the determination of the free-energy change and position-dependent diffusivity of the substrate along the translocation pathway through the lipid bilayer. In this Perspective, we will clarify the physical meaning of the membrane permeability inferred from such computer simulations, and how theoretical predictions actually relate to what is commonly measured experimentally. We will also examine why these calculations remain both technically challenging and overly computationally expensive, which has hitherto precluded their routine use in nonacademic settings. We finally synopsize possible research directions to meet these challenges, increase the predictive power of physics-based rates of passive permeation, and, by ricochet, improve their practical usefulness.

Permeation of a Homologous Series of NBD-Labeled Fatty Amines through Lipid Bilayers: A Molecular Dynamics Study

Permeation through biomembranes is ubiquitous for drugs to reach their active sites. Asymmetry of the cell plasma membrane (PM) has been described as having an important role in this process. Here we describe the interaction of a homologous series of 7-nitrobenz-2-oxa-1,3-diazol-4-yl (NBD)-labeled amphiphiles (NBD-Cn, n = 4 to 16) with lipid bilayers of different compositions (1-palmitoyl, 2-oleoyl-sn-glycero-3-phosphocholine (POPC):cholesterol (1:1) and palmitoylated sphingomyelin (SpM):cholesterol (6:4)), including an asymmetric bilayer. Both unrestrained and umbrella sampling (US) simulations (at varying distances to the bilayer center) were carried out. The free energy profile of NBD-Cn at different depths in the membrane was obtained from the US simulations. The behavior of the amphiphiles during the permeation process was described regarding their orientation, chain elongation, and H-bonding to lipid and water molecules. Permeability coefficients were also calculated for the different amphiphiles of the series, using the inhomogeneous solubility-diffusion model (ISDM). Quantitative agreement with values obtained from kinetic modeling of the permeation process could not be obtained. However, for the longer, and more hydrophobic amphiphiles, the variation trend along the homologous series was qualitatively better matched by the ISDM when the equilibrium location of each amphiphile was taken as reference (ΔG = 0), compared to the usual choice of bulk water.

Understanding Passive Membrane Permeation of Peptides: Physical Models and Sampling Methods Compared

The early characterization of drug membrane permeability is an important step in pharmaceutical developments to limit possible late failures in preclinical studies. This is particularly crucial for therapeutic peptides whose size generally prevents them from passively entering cells. However, a sequence-structure-dynamics-permeability relationship for peptides still needs further insight to help efficient therapeutic peptide design. In this perspective, we conducted here a computational study for estimating the permeability coefficient of a benchmark peptide by considering and comparing two different physical models: on the one hand, the inhomogeneous solubility-diffusion model, which requires umbrella-sampling simulations, and on the other hand, a chemical kinetics model which necessitates multiple unconstrained simulations. Notably, we assessed the accuracy of the two approaches in relation to their computational cost.

Free-Energy Profiles for Membrane Permeation of Compounds Calculated Using Rare-Event Sampling Methods

The free-energy profile of a compound is an essential measurement in evaluating the membrane permeation process by means of theoretical methods. Computationally, molecular dynamics (MD) simulation allows the free-energy profile calculation. However, MD simulations frequently fail to sample membrane permeation because they are rare events induced in longer timescales than the accessible timescale of MD, leading to an insufficient conformational search to calculate an incorrect free-energy profile. To achieve a sufficient conformational search, several enhanced sampling methods have been developed and elucidated the membrane permeation process. In addition to these enhanced sampling methods, we proposed a simple yet powerful free-energy calculation of a compound for the membrane permeation process based on originally rare-event sampling methods developed by us. Our methods have a weak dependency on external biases and their optimizations to promote the membrane permeation process. Based on distributed computing, our methods only require the selection of initial structures and their conformational resampling, whereas the enhanced sampling methods may be required to adjust external biases. Furthermore, our methods efficiently search membrane permeation processes with simple scripts without modifying any MD program. As demonstrations, we calculated the free-energy profiles of seven linear compounds for their membrane permeation based on a hybrid conformational search using two rare-event sampling methods, that is, (1) parallel cascade selection MD (PaCS-MD) and (2) outlier flooding method (OFLOOD), combined with a Markov state model (MSM) construction. In the first step, PaCS-MD generated initial membrane permeation paths of a compound. In the second step, OFLOOD expanded the unsearched conformational area around the initial paths, allowing for a broad conformational search. Finally, the trajectories were employed to construct reliable MSMs, enabling correct free-energy profile calculations. Furthermore, we estimated the membrane permeability coefficients of all compounds by constructing the reliable MSMs for their membrane permeation. In conclusion, the calculated coefficients were qualitatively correlated with the experimental measurements (correlation coefficient (R²) = 0.8689), indicating that the hybrid conformational search successfully calculated the free-energy profiles and membrane permeability coefficients of the seven compounds.

A study on attempt for determination of permeation kinetics of coumarin at lipid bilayer of liposomes by using capillary electrophoresis with moment analysis theory

It was tried to develop a moment analysis method for the determination of lipid membrane permeability. The first absolute and second central moments of elution peaks measured by liposome electrokinetic chromatography (LEKC) are analyzed by using moment equations. As a concrete example, elution peak profiles of coumarin in a LEKC system, in which liposomes consisting of 1-palmitoyl-2-oleoyl-sn‑glycero-3-phosphocholine (POPC) and phosphatidylserine (PS) are used as a pseudo-stationary phase, were analyzed. It seems that lipid membrane permeability of coumarin across the lipid bilayer of POPC/PS liposomes was measured by the moment analysis method because previous permeability measurements using parallel artificial membrane permeability assay (PAMPA) and Caco-2 cells indicated that coumarin is permeable across lipid bilayer. However, it was also pointed out that the moment analysis method with LEKC is not effective for the determination of lipid membrane permeability and that it provides information about adsorption/desorption kinetics at lipid bilayer of liposomes. Therefore, different moment equations were also developed for the determination of adsorption/desorption rate constants of coumarin from the LEKC data. It was demonstrated that permeation rate constants at lipid bilayer or adsorption/desorption rate constants can be determined from the LEKC data on the basis of moment analysis theory for the mass transfer phenomena of coumarin at the lipid bilayer of POPC/PS liposomes. Mass transfer kinetics of solutes at lipid bilayer should be determined under the conditions that liposomes originally be because they are self-assembling and dynamic systems formed through weak interactions between phospholipid monomers. The moment analysis method using LEKC is effective for the experimental determination of the mass transfer rate constants at the lipid bilayer of liposomes because neither immobilization nor chemical modification of liposomes is necessary when LEKC data are measured. It is expected that the results of this study contribute to the dissemination of an opportunity for the determination of permeation rate constants or adsorption/desorption rate constants at the lipid bilayer of liposomes to many researchers because capillary electrophoresis is widespread.

Predictive Molecular Models for Charged Materials Systems: From Energy Materials to Biomacromolecules

Electrostatic interactions play a dominant role in charged materials systems. Understanding the complex correlation between macroscopic properties with microscopic structures is of critical importance to develop rational design strategies for advanced materials. But the complexity of this challenging task is augmented by interfaces present in the charged materials systems, such as electrode-electrolyte interfaces or biological membranes. Over the last decades, predictive molecular simulations that are founded in fundamental physics and optimized for charged interfacial systems have proven their value in providing molecular understanding of physicochemical properties and functional mechanisms for diverse materials. Novel design strategies utilizing predictive models have been suggested as promising route for the rational design of materials with tailored properties. Here, an overview of recent advances in the understanding of charged interfacial systems aided by predictive molecular simulations is presented. Focusing on three types of charged interfaces found in energy materials and biomacromolecules, how the molecular models characterize ion structure, charge transport, morphology relation to the environment, and the thermodynamics/kinetics of molecular binding at the interfaces is discussed. The critical analysis brings two prominent field of energy materials and biological science under common perspective, to stimulate crossover in both research field that have been largely separated.

Calculation of the permeability coefficients of small molecules through lipid bilayers by free-energy reaction network analysis following the explicit treatment of the internal conformation of the solute

Biomembrane permeation represents a major barrier to pharmacokinetics. During preclinical drug discovery, the coefficients of the permeation of molecules through lipid bilayers account for a valuable property of such molecules. Therefore, the control of the permeation of molecules through lipid bilayers is an essential factor in drug design, and the estimation of the permeation phenomena is a crucial study in pharmacy. Thus, there are many published studies on the theoretical estimations of permeation coefficients. Here, we propose a molecular dynamics (MD) simulation method for estimating the permeation of small molecules through lipid bilayers based on the free-energy reaction network (FERN) analysis. In this method, the collective variables (CVs) of the free energy calculations explicitly include the conformational changes in the rotational bonds of the solute molecules. The advantages of the present method over the other method are that it is possible to estimate reaction pathways and their reaction rates, i.e., permeation coefficients or passage times, in multidimensional space spanned by CVs though conventional methods such as the umbrella sampling method and target MDs often dealt with a few degrees of freedom. To demonstrate the efficacy of our method, we calculate the coefficients of the permeation of three small aromatic peptides, namely N-acetylphenylalanineamide (Ac-Phe-NH2 or NAFA), N-acetyltyrosineamide (Ac-Tyr-NH2 or NAYA), and N-acetyltryptophanamide (Ac-Trp-NH2 or NATA), through a 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) lipid bilayer. In these cases we adopted one CV for the permeation direction and four CVs for the internal rotational coordinates. The results reveal that the permeation coefficients of NAFA, NAYA, and NATA are 1.7 × 10^-2, 0.51 × 10^-4, and 5.7 × 10^-4 cm s^-1, respectively. Compared with the experimental data, our simulation results followed the same trend, i.e., NAFA > NATA > NAYA. By analyzing the structures of metastable points of the solute molecules, our simulation result reveals that the aforementioned trend is caused by the differences in stability among their rotamers. Furthermore, we evaluate the statistical fluctuation of the rotamers, and the time scale of flipping the side chain reveals that the structures rigidify as the ligand moves deeper into the membrane.

Paradoxical Increase of Permeability and Lipophilicity with the Increasing Topological Polar Surface Area within a Series of PRMT5 Inhibitors

An imidazolone → triazolone replacement addressed the limited passive permeability of a series of protein arginine methyl transferase 5 (PRMT5) inhibitors. This increase in passive permeability was unexpected given the increase in the hydrogen bond acceptor (HBA) count and topological polar surface area (TPSA), two descriptors that are typically inversely correlated with permeability. Quantum mechanics (QM) calculations revealed that this unusual effect was due to an electronically driven disconnect between TPSA and 3D-PSA, which manifests in a reduction in overall HBA strength as indicated by the HBA moment descriptor from COSMO-RS (conductor-like screening model for real solvation). HBA moment was subsequently deployed as a design parameter leading to the discovery of inhibitors with not only improved passive permeability but also reduced P-glycoprotein (P-gp) transport. Our case study suggests that hidden polarity as quantified by TPSA-3DPSA can be rationally designed through QM calculations.

Lipid Composition Is Critical for Accurate Membrane Permeability Prediction of Cyclic Peptides by Molecular Dynamics Simulations

Cyclic peptides have attracted attention as a promising pharmaceutical modality due to their potential to selectively inhibit previously undruggable targets, such as intracellular protein-protein interactions. Poor membrane permeability is the biggest bottleneck hindering successful drug discovery based on cyclic peptides. Therefore, the development of computational methods that can predict membrane permeability and support elucidation of the membrane permeation mechanism of drug candidate peptides is much sought after. In this study, we developed a protocol to simulate the behavior in membrane permeation steps and estimate the membrane permeability of large cyclic peptides with more than or equal to 10 residues. This protocol requires the use of a more realistic membrane model than a single-lipid phospholipid bilayer. To select a membrane model, we first analyzed the effect of cholesterol concentration in the model membrane on the potential of mean force and hydrogen bonding networks along the direction perpendicular to the membrane surface as predicted by molecular dynamics simulations using cyclosporine A. These results suggest that a membrane model with 40 or 50 mol % cholesterol was suitable for predicting the permeation process. Subsequently, two types of membrane models containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and 40 and 50 mol % cholesterol were used. To validate the efficiency of our protocol, the membrane permeability of 18 ten-residue peptides was predicted. Correlation coefficients of R > 0.8 between the experimental and calculated permeability values were obtained with both model membranes. The results of this study demonstrate that the lipid membrane is not just a medium but also among the main factors determining the membrane permeability of molecules. The computational protocol proposed in this study and the findings obtained on the effect of membrane model composition will contribute to building a schematic view of the membrane permeation process. Furthermore, the results of this study will eventually aid the elucidation of design rules for peptide drugs with high membrane permeability.

Antibiotic Pyrrolomycin as an Efficient Mitochondrial Uncoupler

Pyrrolomycins C (Pyr_C) and D (Pyr_D) are antibiotics produced by Actinosporangium and Streptomyces. The mechanism of their antimicrobial activity consists in depolarization of bacterial membrane, leading to the suppression of bacterial bioenergetics through the uncoupling of oxidative phosphorylation, which is based on the protonophore action of these antibiotics [Valderrama et al., Antimicrob. Agents Chemother. (2019) 63, e01450]. Here, we studied the effect of pyrrolomycins on the isolated rat liver mitochondria. Pyr_C was found to be more active than Pyr_D and uncoupled mitochondria in the submicromolar concentration range, which was observed as the mitochondrial membrane depolarization and stimulation of mitochondrial respiration. In the case of mitoplasts (isolated mitochondria with impaired outer membrane integrity), the difference in the action of Pyr_C and Pyr_D was significantly less pronounced. By contrast, in inverted submitochondrial particles (SMPs), Pyr_D was more active as an uncoupler, which caused collapse of the membrane potential even at the nanomolar concentrations. The same ratio of the protonophoric activity of Pyr_D and Pyr_C was obtained by us on liposomes loaded with the pH indicator pyranine. The protonophore activity of Pyr_D in the planar bilayer lipid membranes (BLMs) was maximal at ~pH 9, i.e., at pH values close to pK_a of this compound. Pyr_D functions as a typical anionic protonophore; its activity in the BLM could be reduced by the addition of the dipole modifier phloretin. The difference between the protonophore activity of Pyr_C and Pyr_D in the mitochondria and BLMs can be attributed to a higher ability of Pyr_C to penetrate the outer mitochondrial membrane.

Optimization of an in Silico Protocol Using Probe Permeabilities to Identify Membrane Pan-Assay Interference Compounds

Membrane pan-assay interference compounds (PAINS) are a class of molecules that interact nonspecifically with lipid bilayers and alter their physicochemical properties. An early identification of these compounds avoids chasing false leads and the needless waste of time and resources in drug discovery campaigns. In this work, we optimized an in silico protocol on the basis of umbrella sampling (US)/molecular dynamics (MD) simulations to discriminate between compounds with different membrane PAINS behavior. We showed that the method is quite sensitive to membrane thickness fluctuations, which was mitigated by changing the US reference position to the phosphate atoms of the closest interacting monolayer. The computational efficiency was improved further by decreasing the number of umbrellas and adjusting their strength and position in our US scheme. The inhomogeneous solubility-diffusion model (ISDM) used to calculate the membrane permeability coefficients confirmed that resveratrol and curcumin have distinct membrane PAINS characteristics and indicated a misclassification of nothofagin in a previous work. Overall, we have presented here a promising in silico protocol that can be adopted as a future reference method to identify membrane PAINS.

Phospholipid-porphyrin conjugates: deciphering the driving forces behind their supramolecular assemblies

Phospholipid-porphyrin conjugates (PL-Por) are nowadays considered as a unique class of building blocks that can self-assemble into supramolecular structures that possess multifunctional properties and enhanced optoelectronics characteristics compared to their disassembled counterparts. However, despite their versatile properties, little is known about the impact of the packing parameter of PL-Por conjugates on their assembling mechanism and their molecular organization inside these assemblies. To gain a better understanding on their assembling properties, we synthesized two new series of PL-Por conjugates with different alkyl sn2-chain lengths linked via an amide bond to either pheophorbide-a (Ph_xLPC) or pyropheophorbide-a (Pyr_xLPC). By combining a variety of experimental techniques with molecular dynamics (MD) simulations, we investigated both the assembling and optical properties of the PL-Por either self-assembled or when incorporated into lipid bilayers. We demonstrated that independently of the linker length, Ph_xLPC assembled into closed ovoid structures, whereas Pyr_xLPC formed rigid open sheets. Interestingly, Pyr_xLPC assemblies displayed a significant red shift and narrowing of the Q-band indicating the formation of ordered J-aggregates. The MD simulations highlighted the central role of the interaction between porphyrin cores rather than the length difference between the two phospholipid chains in controlling the structure of the lipid bilayer membranes and thus their optical properties. Indeed, while Ph_xLPC have the tendency to form inter-leaflet π-stacked dimers, Pyr_xLPC conjugates formed dimers within the same leaflet. Altogether, this work could be used as guidelines for the design of new PL-Por conjugates that self-assemble into bilayer-like supramolecular structures with tunable morphology and optical properties.

Mechanistic Insights into Passive Membrane Permeability of Drug-like Molecules from a Weighted Ensemble of Trajectories

Passive permeability of a drug-like molecule is a critical property assayed early in a drug discovery campaign that informs a medicinal chemist how well a compound can traverse biological membranes, such as gastrointestinal epithelial or restrictive organ barriers, so it can perform a specific therapeutic function. However, the challenge that remains is the development of a method, experimental or computational, which can both determine the permeation rate and provide mechanistic insights into the transport process to help with the rational design of any given molecule. Typically, one of the following three methods are used to measure the membrane permeability: (1) experimental permeation assays acting on either artificial or natural membranes; (2) quantitative structure–permeability relationship models that rely on experimental values of permeability or related pharmacokinetic properties of a range of molecules to infer those for new molecules; and (3) estimation of permeability from the Smoluchowski equation, where free energy and diffusion profiles along the membrane normal are taken as input from large-scale molecular dynamics simulations. While all these methods provide estimates of permeation coefficients, they provide very little information for guiding rational drug design. In this study, we employ a highly parallelizable weighted ensemble (WE) path sampling strategy, empowered by cloud computing techniques, to generate unbiased permeation pathways and permeability coefficients for a set of drug-like molecules across a neat 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine membrane bilayer. Our WE method predicts permeability coefficients that compare well to experimental values from an MDCK-LE cell line and PAMPA assays for a set of drug-like amines of varying size, shape, and flexibility. Our method also yields a series of continuous permeation pathways weighted and ranked by their associated probabilities. Taken together, the ensemble of reactive permeation pathways, along with the estimate of the permeability coefficient, provides a clearer picture of the microscopic underpinnings of small-molecule membrane permeation.

Optimizing peptide inhibitors of SARS-Cov-2 nsp10/nsp16 methyltransferase predicted through molecular simulation and machine learning

Coronaviruses, including the recent pandemic strain SARS-Cov-2, use a multifunctional 2'-O-methyltransferase (2'-O-MTase) to restrict the host defense mechanism and to methylate RNA. The nonstructural protein 16 2'-O-MTase (nsp16) becomes active when nonstructural protein 10 (nsp10) and nsp16 interact. Novel peptide drugs have shown promise in the treatment of numerous diseases and new research has established that nsp10 derived peptides can disrupt viral methyltransferase activity via interaction of nsp16. This study had the goal of optimizing new analogous nsp10 peptides that have the ability to bind nsp16 with equal to or higher affinity than those naturally occurring. The following research demonstrates that in silico molecular simulations can shed light on peptide structures and predict the potential of new peptides to interrupt methyltransferase activity via the nsp10/nsp16 interface. The simulations suggest that misalignments at residues F68, H80, I81, D94, and Y96 or rotation at H80 abrogate MTase function. We develop a new set of peptides based on conserved regions of the nsp10 protein in the Coronaviridae species and test these to known MTase variant values. This results in the prediction that the H80R variant is a solid new candidate for potential new testing. We envision that this new lead is the beginning of a reputable foundation of a new computational method that combats coronaviruses and that is beneficial for new peptide drug development.

Calculation of Permeability Coefficients from Solute Equilibration Dynamics: An Assessment of Various Methods

Predicting the rate at which substances permeate membrane barriers in vivo is crucial for drug development. Permeability coefficients obtained from in vitro studies are valuable for this goal. These are normally determined by following the dynamics of solute equilibration between two membrane-separated compartments. However, the correct calculation of permeability coefficients from such data is not always straightforward. To address these problems, here we develop a kinetic model for solute permeation through lipid membrane barriers that includes the two membrane leaflets as compartments in a four-compartment model. Accounting for solute association with the membrane allows assessing various methods in a wide variety of conditions. The results showed that the often-used expression Papp= β × r/3 is inapplicable to very large or very small vesicles, to moderately or highly lipophilic solutes, or when the development of a significant pH gradient opposes the solute’s flux. We establish useful relationships that overcome these limitations and allow predicting permeability in compartmentalised in vitro or in vivo systems with specific properties. Finally, from the parameters for the interaction of the solute with the membrane barrier, we defined an intrinsic permeability coefficient that facilitates quantitative comparisons between solutes.

Lipid21: Complex Lipid Membrane Simulations with AMBER

We extend the modular AMBER lipid force field to include anionic lipids, polyunsaturated fatty acid (PUFA) lipids, and sphingomyelin, allowing the simulation of realistic cell membrane lipid compositions, including raft-like domains. Head group torsion parameters are revised, resulting in improved agreement with NMR order parameters, and hydrocarbon chain parameters are updated, providing a better match with phase transition temperature. Extensive validation runs (0.9 μs per lipid type) show good agreement with experimental measurements. Furthermore, the simulation of raft-like bilayers demonstrates the perturbing effect of increasing PUFA concentrations on cholesterol molecules. The force field derivation is consistent with the AMBER philosophy, meaning it can be easily mixed with protein, small molecule, nucleic acid, and carbohydrate force fields.

The effects of molecular weight and orientation on the membrane permeation and partitioning of polycyclic aromatic hydrocarbons: a computational study

Membrane permeation and the partitioning of polycyclic aromatic hydrocarbons (PAHs) are crucial aspects affecting their carcinogenicity and mutagenicity. However, a clear understanding of these processes is still rare due to the difficulty of determining the details experimentally. Here, the interactions between PAHs and lipid bilayers were studied by molecular simulations, mainly to check the influence of molecular weight and orientation. The liposome-water partition coefficient (K_LW), transmembrane time (τ), and permeability coefficient (P) of the PAHs were calculated by integrating free energy profiles from umbrella sampling. For selected PAHs, the membrane adsorption is a spontaneous process. The preferred location is near the CC bond and the orientation is related to the molecular structure. The P values of all the PAHs are basically the same order of magnitude, which means that the molecular weight contributes little to the process. As for K_LW and τ, they show obvious increases with different molecular weights. Unconstrained simulations showed that a flat orientation on the membrane surface would prevent PAHs from being transported through the membrane. Highly hydrophobic driving forces are not always good for the absorption of PAHs, especially the formation of aggregates. In addition, the orientations and energetic barriers of PAHs near the midplane of the lipid bilayer explain the different transitions of high- and low-weight PAHs. This work provides molecular level details relating to the interactions of PAHs with lipid membranes, with significance for understanding the health effects of PAHs.

Membrane Permeability of Modified Butyltriphenylphosphonium Cations

The alkyltriphenylphosphonium (TPP) group is the most widely used vector targeted to mitochondria. Previously, the length of the alkyl linker was varied as well as structural modifications in the TPP phenyl rings to obtain the optimal therapeutic effect of a pharmacophore conjugated with a lipophilic cation. In the present work, we synthesized butyltriphenylphosphonium cations halogenated and methylated in phenyl rings (C₄TPP-X) and measured electrical current through a planar lipid bilayer in the presence of C₄TPP-X. The permeability of C₄TPP-X varied in the range of 6 orders of magnitude and correlates well with the previously measured translocation rate constant for dodecyltriphenylphosphonium analogues. The partition coefficient of the butyltriphenylphosphonium analogues obtained by calculating the difference in the free energy of cation solvation in water and octane using quantum chemical methods correlates well with the permeability values. Using an ion-selective electrode, a lower degree of accumulation of analogues with halogenated phenyl groups was found on isolated mitochondria of rat liver, which is in agreement with their permeability decrease. Our results indicate the translocation of the butyltriphenylphosphonium cations across the hydrophobic membrane core as rate-limiting stage in the permeability process rather than their binding/release to/from the membrane.

Mutant p53 reactivator SLMP53-2 hinders ultraviolet B radiation-induced skin carcinogenesis

The growing incidence of skin cancer (SC) has prompted the search for additional preventive strategies to counteract this global health concern. Mutant p53 (mutp53), particularly with ultraviolet radiation (UVR) signature, has emerged as a promising target for SC prevention based on its key role in skin carcinogenesis. Herein, the preventive activity of our previously disclosed mutp53 reactivator SLMP53-2 against UVR-induced SC was investigated. The pre-treatment of keratinocyte HaCaT cells with SLMP53-2, before UVB exposure, depleted mutp53 protein levels with restoration of wild-type-like p53 DNA-binding ability and subsequent transcriptional activity. SLMP53-2 increased cell survival by promoting G1-phase cell cycle arrest, while reducing UVB-induced apoptosis through inhibition of c-Jun N-terminal kinase (JNK) activity. SLMP53-2 also protected cells from reactive oxygen species and oxidative damage induced by UVB. Moreover, it enhanced DNA repair through upregulation of nucleotide excision repair pathway and depletion of UVB-induced DNA damage, as evidenced by a reduction of DNA in comet tails, γH2AX staining and cyclobutane pyrimidine dimers (CPD) levels. SLMP53-2 further suppressed UVB-induced inflammation by inhibiting the nuclear translocation and DNA-binding ability of NF-κB, and promoted the expression of key players involved in keratinocytes differentiation. Consistently, the topical application of SLMP53-2 in mice skin, prior to UVB irradiation, reduced cell death and DNA damage. It also decreased the expression of inflammatory-related proteins and promoted cell differentiation, in UVB-exposed mice skin. Notably, SLMP53-2 did not show signs of skin toxicity for cumulative topical use. Overall, these results support a promising protective activity of SLMP53-2 against UVB-induced SC.

Evaluating the cross-membrane dynamics of a charged molecule on lipid films with different surface curvature

Does the curvature of a phospholipid membrane influence the permeability of the lipid bilayers? This is a question of great importance yet hard to answer. In this work the permeability of a positively charged rod like probing molecule (D289 dye) on the bilayers of DOPG lipid vesicles was investigated using angle resolved second harmonic generation method. It was revealed that the permeability of D289 on the surface of small vesicles with ∼ 100 nm diameter was notably lower than that on giant vesicles with ∼ 1000 nm diameter. With the increasing of temperature or the introducing of dimethyl sulfoxide (DMSO) in the solutions, the D289 permeability of the lipid bilayers was notably enhanced as expected, on both the small and the giant vesicles. Still, the D289 permeability of the lipid film with more curvature is lower than the relatively flat film in all these cases. This work demonstrated a general protocol for the investigating of surface permeability of lipid films with various curvature.

Designing new antitubercular isoniazid derivatives with improved reactivity and membrane trafficking abilities

Isoniazid (INH) is one of the two most effective first-line antitubercular drugs and is still used at the present time as a scaffold for developing new compounds to fight TB. In a previous study, we have observed that an INH derivative, an hydrazide N'-substituted with a C₁₀acyl chain, was able to counterbalance its smaller reactivity with a higher membrane permeability. This resulted in an improved performance against the most prevalent Mycobacterium tuberculosis (Mtb) resistant strain (S315T), compared to INH. In this work, we have designed two new series of INH derivatives (alkyl hydrazides and hydrazones) with promising in silico properties, namely membrane permeabilities and spontaneous IN* radical formation. The kinetics, cytotoxicity, and biological activity evaluations confirmed the in silico predictions regarding the very high reactivity of the alkyl hydrazides. The hydrazones, on the other hand, showed very similar behavior compared to INH, particularly in biological tests that take longer to complete, indicating that these compounds are being hydrolyzed back to INH. Despite their improved membrane permeabilities, the reactivities of these two series are too high, impairing their overall performance. Nevertheless, the systematic data gathered about these compounds have showed us the need to find a balance between lipophilicity and reactivity, which is paramount to devise better INH-based derivatives aimed at circumventing Mtb resistance.

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients

Developing models for predicting molecular properties of organic compounds is imperative for drug development and environmental safety; however, development of such models that have high predictive power and are independent of the compounds used is challenging. To overcome the challenges, we used a thermodynamics-based theoretical derivation to construct models for accurately predicting molecular properties. The free energy change that determines a property equals the sum of the free energy changes (ΔG_Fs) caused by the factors affecting the property. By developing or selecting molecular descriptors that are directly proportional to ΔG_Fs, we built a general linear free energy relationship (LFER) for predicting the property with the molecular descriptors as predictive variables. The LFER can be used to construct models for predicting various specific properties from partition coefficients. Validations show that the models constructed according to the LFER have high predictive power and their performance is independent of the compounds used, including the models for the properties having little correlation with partition coefficients. The findings in this study are highly useful for applications in drug development and environmental safety.

Prediction of Chameleonic Efficiency

Chameleonic properties, i. e., the capacity of a molecule to hide polarity in non-polar environments and expose it in water, help achieving sufficient permeability and solubility for drug molecules with high MW. We present models of experimental measures of polarity for a set of 24 FDA approved drugs (MW 405-1113) and one PROTAC (MW 1034). Conformational ensembles in aqueous and non-polar environments were generated using molecular dynamics. A linear regression model that predicts chromatographic apparent polarity (EPSA) with a mean unsigned error of 10 Ų was derived based on separate terms for donor, acceptor, and total molecular SASA. A good correlation (R² =0.92) with an experimental measure of hydrogen bond donor potential, Δlog P_oct-tol , was found for the mean hydrogen bond donor SASA of the conformational ensemble scaled with Abraham's A hydrogen bond acidity. Two quantitative measures of chameleonic behaviour, the chameleonic efficiency indices, are introduced. We envision that the methods presented herein will be useful to triage designed molecules and prioritize those with the best chance of achieving acceptable permeability and solubility.

Large-Scale Membrane Permeability Prediction of Cyclic Peptides Crossing a Lipid Bilayer Based on Enhanced Sampling Molecular Dynamics Simulations

Membrane permeability is a significant obstacle facing the development of cyclic peptide drugs. However, membrane permeation mechanisms are poorly understood. To investigate common features of permeable (and nonpermeable) designs, it is necessary to reproduce the membrane permeation process of cyclic peptides through the lipid bilayer. We simulated the membrane permeation process of 100 six-residue cyclic peptides across the lipid bilayer based on steered molecular dynamics (MD) and replica-exchange umbrella sampling simulations and predicted membrane permeability using the inhomogeneous solubility-diffusion model and a modified version of it. Furthermore, we confirmed the effectiveness of this protocol by predicting the membrane permeability of 56 eight-residue cyclic peptides with diverse chemical structures, including some confidential designs from a pharmaceutical company. As a result, a reasonable correlation between experimentally assessed and calculated membrane permeability of cyclic peptides was observed for the peptide libraries, except for strongly hydrophobic peptides. Our analysis of the MD trajectory demonstrated that most peptides were stabilized in the boundary region between bulk water and membrane and that for most peptides, the process of crossing the center of the membrane is the main obstacle to membrane permeation. The height of this barrier is well correlated with the electrostatic interaction between the peptide and the surrounding media. The structural and energetic features of the representative peptide at each vertical position within the membrane were also analyzed, revealing that peptides permeate the membrane by changing their orientation and conformation according to the surrounding environment.

Recent Experimental Developments in Studying Passive Membrane Transport of Drug Molecules

The ability to measure the passive membrane permeation of drug-like molecules is of fundamental biological and pharmaceutical importance. Of significance, passive diffusion across the cellular membranes plays an effective role in the delivery of many pharmaceutical agents to intracellular targets. Hence, approaches for quantitative measurement of membrane permeability have been the topics of research for decades, resulting in sophisticated biomimetic systems coupled with advanced techniques. In this review, recent developments in experimental approaches along with theoretical models for quantitative and real-time analysis of membrane transport of drug-like molecules through mimetic and living cell membranes are discussed. The focus is on time-resolved fluorescence-based, surface plasmon resonance, and second-harmonic light scattering approaches. The current understanding of how properties of the membrane and permeant affect the permeation process is discussed.

Design of Thioether Cyclic Peptide Scaffolds with Passive Permeability and Oral Exposure

Advances in the design of permeable peptides and in the synthesis of large arrays of macrocyclic peptides with diverse amino acids have evolved on parallel but independent tracks. Less precedent combines their respective attributes, thereby limiting the potential to identify permeable peptide ligands for key targets. Herein, we present novel 6-, 7-, and 8-mer cyclic peptides (MW 774-1076 g·mol-1) with passive permeability and oral exposure that feature the amino acids and thioether ring-closing common to large array formats, including DNA- and RNA-templated synthesis. Each oral peptide herein, selected from virtual libraries of partially N-methylated peptides using in silico methods, reflects the subset consistent with low energy conformations, low desolvation penalties, and passive permeability. We envision that, by retaining the backbone N-methylation pattern and consequent bias toward permeability, one can generate large peptide arrays with sufficient side chain diversity to identify permeability-biased ligands to a variety of protein targets.