Testing physical models of passive membrane permeation

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.

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods

The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.

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.

A Computational Physics-based Approach to Predict Unbound Brain-to-Plasma Partition Coefficient, Kp,uu

The blood-brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small-molecule central nervous system (CNS) drugs is characterized by a high unbound brain/plasma ratio (K_p,uu). While various medicinal chemistry strategies and in silico models have been reported to improve BBB penetration, they have limited application in predicting K_p,uu directly. We describe a physics-based computational approach, a quantum mechanics (QM)-based energy of solvation (E-sol), to predict K_p,uu. Prospective application of this method in internal CNS drug discovery programs highlights the utility and accuracy of this new method, which showed a categorical accuracy of 79% and an R² of 0.61 from a linear regression model.

Cell-permeable chameleonic peptides: Exploiting conformational dynamics in de novo cyclic peptide design

Membrane-traversing peptides offer opportunities for targeting intracellular proteins and oral delivery. Despite progress in understanding the mechanisms underlying membrane traversal in natural cell-permeable peptides, there are still several challenges to designing membrane-traversing peptides with diverse shapes and sizes. Conformational flexibility appears to be a key determinant of membrane permeability of large macrocycles. We review recent developments in the design and validation of chameleonic cyclic peptides, which can switch between alternative conformations to enable improved permeability through cell membranes, while still maintaining reasonable solubility and exposed polar functional groups for target protein binding. Finally, we discuss the principles, strategies, and practical considerations for rational design, discovery, and validation of permeable chameleonic peptides.

Commercially Available Cell-Free Permeability Tests for Industrial Drug Development: Increased Sustainability through Reduction of In Vivo Studies

Replacing in vivo with in vitro studies can increase sustainability in the development of medicines. This principle has already been applied in the biowaiver approach based on the biopharmaceutical classification system, BCS. A biowaiver is a regulatory process in which a drug is approved based on evidence of in vitro equivalence, i.e., a dissolution test, rather than on in vivo bioequivalence. Currently biowaivers can only be granted for highly water-soluble drugs, i.e., BCS class I/III drugs. When evaluating poorly soluble drugs, i.e., BCS class II/IV drugs, in vitro dissolution testing has proved to be inadequate for predicting in vivo drug performance due to the lack of permeability interpretation. The aim of this review was to provide solid proofs that at least two commercially available cell-free in vitro assays, namely, the parallel artificial membrane permeability assay, PAMPA, and the PermeaPad^® assay, PermeaPad, in different formats and set-ups, have the potential to reduce and replace in vivo testing to some extent, thus increasing sustainability in drug development. Based on the literature review presented here, we suggest that these assays should be implemented as alternatives to (1) more energy-intense in vitro methods, e.g., refining/replacing cell-based permeability assays, and (2) in vivo studies, e.g., reducing the number of pharmacokinetic studies conducted on animals and humans. For this to happen, a new and modern legislative framework for drug approval is required.

Permeability of New Antifungal Fluconazole Derivatives through a Lipophilic Membrane: Experiment and Modeling

Relationships between the structures of molecules and their properties form the basis of modern chemistry and lay the foundation for structure-based drug design. Being the main two determinants of bioavailability, solubility and permeability of drugs are widely investigated experimentally and predicted from physicochemical parameters and structural descriptors. In the present study, we measure the passive diffusion permeability of a series of new fluconazole derivatives with triazole and thiazolo-pyrimidine moieties connected by different linker bridges through the PermeaPad barrier-a relatively new biomimetic lipophilic membrane that has been increasingly used in recent years. The permeability coefficients of new derivatives are shown to be dependent both on the structure of the linker fragment and on the substituent in the phenyl ring of the thiazolo-pyrimidine moiety. The impact of the compound ionization state on the permeability is revealed. Reliable correlations of the permeability with the antifungal activity and distribution coefficient are found. In addition, the solubility-diffusion approach is shown to be able to successfully predict the permeability of the studied derivatives. The obtained results can be considered another step in the development of permeability databases and design of schemes for in vitro permeability prediction.

Identification of Polyphenol Derivatives as Novel SARS-CoV-2 and DENV Non-Nucleoside RdRp Inhibitors

The Coronavirus Disease 2019 (COVID-19) and dengue fever (DF) pandemics both remain to be significant public health concerns in the foreseeable future. Anti-SARS-CoV-2 drugs and vaccines are both indispensable to eliminate the epidemic situation. Here, two piperazine-based polyphenol derivatives DF-47 and DF-51 were identified as potential inhibitors directly blocking the active site of SARS-CoV-2 and DENV RdRp. Data through RdRp inhibition screening of an in-house library and in vitro antiviral study selected DF-47 and DF-51 as effective inhibitors of SARS-CoV-2/DENV polymerase. Moreover, in silico simulation revealed stable binding modes between the DF-47/DF-51 and SARS-CoV-2/DENV RdRp, respectively, including chelating with Mg²⁺ near polymerase active site. This work discovered the inhibitory effect of two polyphenols on distinct viral RdRp, which are expected to be developed into broad-spectrum, non-nucleoside RdRp inhibitors with new scaffold.

Discovery and Crystallographic Studies of Trisubstituted Piperazine Derivatives as Non-Covalent SARS-CoV-2 Main Protease Inhibitors with High Target Specificity and Low Toxicity

The continuous spread of SARS-CoV-2 calls for more direct-acting antiviral agents to combat the highly infectious variants. The main protease (Mpro) is an promising target for anti-SARS-CoV-2 drug design. Here, we report the discovery of potent non-covalent non-peptide Mpro inhibitors featuring a 1,2,4-trisubstituted piperazine scaffold. We systematically modified the non-covalent hit MCULE-5948770040 by structure-based rational design combined with multi-site binding and privileged structure assembly strategies. The optimized compound GC-14 inhibits Mpro with high potency (IC50 = 0.40 μM) and displays excellent antiviral activity (EC50 = 1.1 μM), being more potent than Remdesivir. Notably, GC-14 exhibits low cytotoxicity (CC50 > 100 μM) and excellent target selectivity for SARS-CoV-2 Mpro (IC50 > 50 μM for cathepsins B, F, K, L, and caspase 3). X-ray co-crystal structures prove that the inhibitors occupy multiple subpockets by critical non-covalent interactions. These studies may provide a basis for developing a more efficient and safer therapy for COVID-19.

Structure-based discovery of nonopioid analgesics acting through the α2A-adrenergic receptor

Because nonopioid analgesics are much sought after, we computationally docked more than 301 million virtual molecules against a validated pain target, the α2A-adrenergic receptor (α2AAR), seeking new α2AAR agonists chemotypes that lack the sedation conferred by known α2AAR drugs, such as dexmedetomidine. We identified 17 ligands with potencies as low as 12 nanomolar, many with partial agonism and preferential Gi and Go signaling. Experimental structures of α2AAR complexed with two of these agonists confirmed the docking predictions and templated further optimization. Several compounds, including the initial docking hit '9087 [mean effective concentration (EC50) of 52 nanomolar] and two analogs, '7075 and PS75 (EC50 4.1 and 4.8 nanomolar), exerted on-target analgesic activity in multiple in vivo pain models without sedation. These newly discovered agonists are interesting as therapeutic leads that lack the liabilities of opioids and the sedation of dexmedetomidine.

Membrane Permeating Macrocycles: Design Guidelines from Machine Learning

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular "chameleonicity." Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications.

Conformational Effects on the Passive Membrane Permeability of Synthetic Macrocycles

Macrocyclic compounds (MCs) can have complex conformational properties that affect pharmacologically important behaviors such as membrane permeability. We measured the passive permeability of 3600 diverse nonpeptidic MCs and used machine learning to analyze the results. Incorporating selected properties based on the three-dimensional (3D) conformation gave models that predicted permeability with Q² = 0.81. A biased spatial distribution of polar versus nonpolar regions was particularly important for good permeability, consistent with a mechanism in which the initial insertion of nonpolar portions of a MC helps facilitate the subsequent membrane entry of more polar parts. We also examined effects on permeability of 800 substructural elements by comparing matched molecular pairs. Some substitutions were invariably beneficial or invariably deleterious to permeability, while the influence of others was highly contextual. Overall, the work provides insights into how the permeability of MCs is influenced by their 3D conformational properties and suggests design hypotheses for achieving macrocycles with high membrane permeability.

Palladium(II) Complexes of Substituted Salicylaldehydes: Synthesis, Characterization and Investigation of Their Biological Profile

Five palladium(II) complexes of substituted salicylaldehydes (X-saloH, X = 4-Et2N (for 1), 3,5-diBr (for 2), 3,5-diCl (for 3), 5-F (for 4) or 4-OMe (for 5)) bearing the general formula [Pd(X-salo)2] were synthesized and structurally characterized. The crystal structure of complex [Pd(4-Et2N-salo)2] was determined by single-crystal X-ray crystallography. The complexes can scavenge 1,1-diphenyl-picrylhydrazyl and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radicals and reduce H2O2. They are active against two Gram-positive (Staphylococcus aureus and Bacillus subtilis) and two Gram-negative (Escherichia coli and Xanthomonas campestris) bacterial strains. The complexes interact strongly with calf-thymus DNA via intercalation, as deduced by diverse techniques and via the determination of their binding constants. Complexes interact reversibly with bovine and human serum albumin. Complementary insights into their possible mechanisms of bioactivity at the molecular level were provided by molecular docking calculations, exploring in silico their ability to bind to calf-thymus DNA, Escherichia coli and Staphylococcus aureus DNA-gyrase, 5-lipoxygenase, and membrane transport lipid protein 5-lipoxygenase-activating protein, contributing to the understanding of the role complexes 1–5 can play both as antioxidant and antibacterial agents. Furthermore, in silico predictive tools have been employed to study the chemical reactivity, molecular properties and drug-likeness of the complexes, and also the drug-induced changes of gene expression profile (as protein- and mRNA-based prediction results), the sites of metabolism, the substrate/metabolite specificity, the cytotoxicity for cancer and non-cancer cell lines, the acute rat toxicity, the rodent organ-specific carcinogenicity, the anti-target interaction profiles, the environmental ecotoxicity, and finally the activity spectra profile of the compounds.

Exploring the potential neurotoxicity of vaping vitamin E or vitamin E acetate

Serious adverse health effects have been reported with the use of vaping products, including neurologic disorders and e-cigarette or vaping product use-associated lung injury (EVALI). Vitamin E acetate, likely added as a diluent to cannabis-containing products, was linked to EVALI. Literature searches were performed on vitamin E and vitamin E acetate-associated neurotoxicity. Blood brain barrier (BBB) penetration potential of vitamin E and vitamin E acetate were evaluated using cheminformatic techniques. Review of the literature showed that the neurotoxic potential of inhalation exposures to these compounds in humans is unknown. Physico-chemical properties demonstrate these compounds are lipophilic, and molecular weights indicate vitamin E and vitamin E acetate have the potential for BBB permeability. Computational models also predict both compounds may cross the BBB via passive diffusion. Based on literature search, no experimental nonclinical studies and clinical information on the neurotoxic potential of vitamin E via inhalation. Neurotoxic effects from pyrolysis by-product, phenyl acetate, structurally analogous to vitamin E acetate, suggests vitamin E acetate has potential for central nervous system (CNS) impairment. Cheminformatic model predictions provide a theoretical basis for potential CNS permeability of these inhaled dietary ingredients suggesting prioritization to evaluate for potential hazard to the CNS.

From computer-aided drug discovery to computer-driven drug discovery

Computational chemistry and structure-based design have traditionally been viewed as a subset of tools that could aid acceleration of the drug discovery process, but were not commonly regarded as a driving force in small molecule drug discovery. In the last decade however, there have been dramatic advances in the field, including (1) development of physics-based computational approaches to accurately predict a broad variety of endpoints from potency to solubility, (2) improvements in artificial intelligence and deep learning methods and (3) dramatic increases in computational power with the advent of GPUs and cloud computing, resulting in the ability to explore and accurately profile vast amounts of drug-like chemical space in silico. There have also been simultaneous advancements in structural biology such as cryogenic electron microscopy (cryo-EM) and computational protein-structure prediction, allowing for access to many more high-resolution 3D structures of novel drug-receptor complexes. The convergence of these breakthroughs has positioned structurally-enabled computational methods to be a driving force behind the discovery of novel small molecule therapeutics. This review will give a broad overview of the synergies in recent advances in the fields of computational chemistry, machine learning and structural biology, in particular in the areas of hit identification, hit-to-lead, and lead optimization.

Eyes on Lipinski's Rule of Five: A New "Rule of Thumb" for Physicochemical Design Space of Ophthalmic Drugs

The study objective was to investigate molecular thermodynamic properties of approved ophthalmic drugs and derive a framework outlining physicochemical design space for product development. Unlike the methodology used to obtain molecular descriptors for assessment of drug-like properties by Lipinski's Rule of 5 (Ro5), this work presents a retrospective approach based on in silico analysis of molecular thermodynamic properties beyond Ro5 parameters (ie, free energy of distribution/partitioning in octanol/water, dynamic polar surface area, distribution coefficient, and solubility at physiological pH) by using 145 marketed ophthalmic drugs. The study's focus was to delineate inherent molecular parameters explicitly important for ocular permeability and absorption from topical eye drops. A comprehensive parameter distribution analysis on ophthalmic drugs’ molecular properties was performed. Frequencies in distribution analyses provided groundwork for physicochemical parameter limits of molecular thermodynamic properties having impact on corneal permeability and topical ophthalmic drug delivery. These parameters included free energy of partitioning (ΔG_o/w) calculated based on thermodynamic free energy equation, distribution coefficient at physiological pH (clog D_pH7.4), topological polar surface area (TPSA), and aqueous solubility (S_int, S_pH7.4) with boundaries of clog D_pH7.4 ≤4.0, TPSA ≤250 Ų, ΔG_o/w ≤20 kJ/mol (4.8 kcal/mol), and solubility (S_int and S_pH7.4) ≥1 μM, respectively. The theoretical free energy of partitioning model streamlined calculation of changes in the free energy of partitioning, Δ(ΔG_o/w), as a measure of incremental improvements in corneal permeability for congeneric series. The above parameter limits are proposed as “rules of thumb” for topical ophthalmic drugs to assess risks in developability.

Crossing of the Cystic Barriers of Toxoplasma gondii by the Fluorescent Coumarin Tetra-Cyclopeptide

FR235222 is a natural tetra-cyclopeptide with a strong inhibition effect on histone deacetylases, effective on mammalian cells as well as on intracellular apicomplexan parasites, such as Toxoplasma gondii, in the tachyzoite and bradyzoite stages. This molecule is characterized by two parts: the zinc-binding group, responsible for the binding to the histone deacetylase, and the cyclic tetrapeptide moiety, which plays a crucial role in cell permeability. Recently, we have shown that the cyclic tetrapeptide coupled with a fluorescent diethyl-amino-coumarin was able to maintain properties of cellular penetration on human cells. Here, we show that this property can be extended to the crossing of the Toxoplasma gondii cystic cell wall and the cell membrane of the parasite in its bradyzoite form, while maintaining a high efficacy as a histone deacetylase inhibitor. The investigation by molecular modeling allows a better understanding of the penetration mechanism.

A comprehensive study on the effect of backbone stereochemistry of a cyclic hexapeptide on membrane permeability and microsomal stability

Backbone stereochemistry of cyclic peptides has been reported to have a great influence on microsomal stability and membrane permeability, two important factors that determine oral bioavailability. Here, we comprehensively investigated the correlation between the backbone stereochemistry of cyclic hexapeptide stereoisomers and their stability in liver microsomes, as well as passive membrane permeability.

Stress impact of liposomes loaded with ciprofloxacin on the expression level of MepA and NorB efflux pumps of methicillin-resistant Staphylococcus aureus

One mechanism of ciprofloxacin resistance is attributed to chromosomal DNA-encoded efflux pumps such as the MepA and NorB proteins. The goal of this research is to find a way to bypass Staphylococcus aureus' efflux pumps. Because of its high membrane permeability and low association with NorB and MepA efflux proteins, a liposome-encapsulating antibiotic is one of the promising, cost-effective drug carriers and coating mechanisms for overcoming active transport of methicillin-resistant S. aureus (MRSA) multidrug-resistant efflux protein . The calculated "Log Perm RRCK" membrane permeability values of 1,2-distearoyl-sn-glycerol-3-phosphocholine (DSPC) ciprofloxacin liposome-encapsulated (CFL) showed a lower negative value of - 4,652 cm/s and greater membrane permeability than ciprofloxacin free (CPF). The results of RT-qPCR showed that cationic liposomes containing ciprofloxacin in liposome-encapsulated form (CFL) improved CPF antibacterial activity and affinity for negatively charged bacterial cell surface membrane in comparison to free drug and liposome, as it overcame several resistance mechanisms and reduced the expression of efflux pumps. Ciprofloxacin liposome-encapsulated (CFL) is therefore more effective than ciprofloxacin alone. Liposomes can be combined with a variety of drugs that interact with bacterial cell efflux pumps to maintain high sustained levels of antibiotics in bacterial cells.

Molecular Simulation Studies on the Interactions of Bilirubin at Different States with a Lipid Bilayer

The unconjugated bilirubin (BR) may penetrate through the cell membrane and cause a severe cytotoxicity. However, the molecular mechanism underlying the penetration of BR into the cell membrane is still largely unknown. In this work, we systematically investigate the interaction of BR and a lipid bilayer under different conditions by using all-atom molecular dynamics simulations. It is found that BR at the Z,Z conformation can easily enter into the interior of the lipid bilayer due to its hydrophobicity. However, when BR transforms from the Z,Z conformation to the E,E conformation (after the blue-light emission), its penetration ability is greatly reduced (especially at its ionized state). This study may offer useful physical insights into the effect of phototherapy on the penetration behavior and the cytotoxicity of the unconjugated BR.

Effect of Flexibility, Lipophilicity, and the Location of Polar Residues on the Passive Membrane Permeability of a Series of Cyclic Decapeptides

Cyclic peptides have received increasing attention over the recent years as potential therapeutics for "undruggable" targets. One major obstacle is, however, their often relatively poor bioavailability. Here, we investigate the structure-permeability relationship of 24 cyclic decapeptides that share the same backbone N-methylation pattern but differ in their side chains. The peptides cover a large range of values for passive membrane permeability as well as lipophilicity and solubility. To rationalize the observed differences in permeability, we extracted for each peptide the population of the membrane-permeable conformation in water from extensive explicit-solvent molecular dynamics simulations and used this as a metric for conformational rigidity or "prefolding." The insights from the simulations together with lipophilicity measurements highlight the intricate interplay between polarity/lipophilicity and flexibility/rigidity and the possible compensating effects on permeability. The findings allow us to better understand the structure-permeability relationship of cyclic peptides and extract general guiding principles.

Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods

The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sensitivity (SEN). To improve performance, ensemble models were built to predict the BBB permeability of compounds. In this study, in silico ensemble-learning models were developed using 3 machine-learning algorithms and 9 molecular fingerprints from 1757 chemicals (integrated from 2 published data sets) to predict BBB permeability. The best prediction performance of the base classifier models was achieved by a prediction model based on an random forest (RF) and a MACCS molecular fingerprint with an ACC of 0.910, an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.957, a SEN of 0.927, and a specificity of 0.867 in 5-fold cross-validation. The prediction performance of the ensemble models is better than that of most of the base classifiers. The final ensemble model has also demonstrated good accuracy for an external validation and can be used for the early screening of CNS drugs.

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.

Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.

Structure, solubility, and permeability relationships in a diverse middle molecule library

To develop methodology to predict the potential druggability of middle molecules, we examined the structure, solubility, and permeability relationships of a diverse library (HKDL ver.1) consisting of 510 molecules (359 natural product derivatives, 76 non-natural products, 46 natural products, and 29 non-natural product derivatives). The library included peptides, depsipeptides, macrolides, and lignans, and 476 of the 510 compounds had a molecular weight in the range of 500-2000 Da. The solubility and passive diffusion velocity of the middle molecules were assessed using the parallel artificial membrane permeability assay (PAMPA). Quantitative values of solubility of 471 molecules and passive diffusion velocity of 287 molecules were obtained, and their correlations with the structural features of the molecules were examined. Based on the results, we propose a method to predict the passive diffusion characteristics of middle molecules from their three-dimensional structural features.

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.

Benchmark of Generic Shapes for Macrocycles

Macrocycles target proteins that are otherwise considered undruggable because of a lack of hydrophobic cavities and the presence of extended featureless surfaces. Increasing efforts by computational chemists have developed effective software to overcome the restrictions of torsional and conformational freedom that arise as a consequence of macrocyclization. Moloc is an efficient algorithm, with an emphasis on high interactivity, and has been constantly updated since 1986 by drug designers and crystallographers of the Roche biostructural community. In this work, we have benchmarked the shape-guided algorithm using a dataset of 208 macrocycles, carefully selected on the basis of structural complexity. We have quantified the accuracy, diversity, speed, exhaustiveness, and sampling efficiency in an automated fashion and we compared them with four commercial (Prime, MacroModel, molecular operating environment, and molecular dynamics) and four open-access (experimental-torsion distance geometry with additional “basic knowledge” alone and with Merck molecular force field minimization or universal force field minimization, Cambridge Crystallographic Data Centre conformer generator, and conformator) packages. With three-quarters of the database processed below the threshold of high ring accuracy, Moloc was identified as having the highest sampling efficiency and exhaustiveness without producing thousands of conformations, random ring splitting into two half-loops, and possibility to interactively produce globular or flat conformations with diversity similar to Prime, MacroModel, and molecular dynamics. The algorithm and the Python scripts for full automatization of these parameters are freely available for academic use.

Structure-Permeability Relationship of Semipeptidic Macrocycles-Understanding and Optimizing Passive Permeability and Efflux Ratio

We herein report the first thorough analysis of the structure-permeability relationship of semipeptidic macrocycles. In total, 47 macrocycles were synthesized using a hybrid solid-phase/solution strategy, and then their passive and cellular permeability was assessed using the parallel artificial membrane permeability assay (PAMPA) and Caco-2 assay, respectively. The results indicate that semipeptidic macrocycles generally possess high passive permeability based on the PAMPA, yet their cellular permeability is governed by efflux, as reported in the Caco-2 assay. Structural variations led to tractable structure-permeability and structure-efflux relationships, wherein the linker length, stereoinversion, N-methylation, and peptoids site-specifically impact the permeability and efflux. Extensive nuclear magnetic resonance, molecular dynamics, and ensemble-based three-dimensional polar surface area (3D-PSA) studies showed that ensemble-based 3D-PSA is a good predictor of passive permeability.

Anti-Infectives Restore ORKAMBI® Rescue of F508del-CFTR Function in Human Bronchial Epithelial Cells Infected with Clinical Strains of P. aeruginosa

Chronic infection and inflammation are the primary causes of declining lung function in Cystic Fibrosis (CF) patients. ORKAMBI^® (Lumacaftor-Ivacaftor) is an approved combination therapy for Cystic Fibrosis (CF) patients bearing the most common mutation, F508del, in the cystic fibrosis conductance regulator (CFTR) protein. It has been previously shown that ORKAMBI^®-mediated rescue of CFTR is reduced by a pre-existing Pseudomonas aeruginosa infection. Here, we show that the infection of F508del-CFTR human bronchial epithelial (HBE) cells with lab strain and four different clinical strains of P. aeruginosa, isolated from the lung sputum of CF patients, decreases CFTR function in a strain-specific manner by 48 to 88%. The treatment of infected cells with antibiotic tobramycin or cationic antimicrobial peptide 6K-F17 was found to decrease clinical strain bacterial growth on HBE cells and restore ORKAMBI^®-mediated rescue of F508del-CFTR function. Further, 6K-F17 was found to downregulate the expression of pro-inflammatory cytokines, interleukin (IL)-8, IL-6, and tumor necrosis factor-α in infected HBE cells. The results provide strong evidence for a combination therapy approach involving CFTR modulators and anti-infectives (i.e., tobramycin and/or 6K-F17) to improve their overall efficacy in CF patients.

Pegylated triarylmethanes: Synthesis, antimicrobial activity, anti-proliferative behavior and in silico studies

We describe herein the synthesis, characterization and biological studies of novel PEGylated triarylmethanes. Non-symmetrical and symmetrical triarylmethanes series have been synthesized by Friedel-Crafts hydroxyalkylation or directly from bisacodyl respectively followed by a functionalization with PEG fragments in order to increase bioavailability and biological effectiveness. The antimicrobial activity was investigated against Gram-positive and Gram-negative foodborne pathogens and against Candida albicans, an opportunistic pathogenic yeast. The anti-biocidal activity was also studied using Staphylococcus aureus as a reference bacterium. Almost all PEGylated molecules displayed an antifungal activity comparable with fusidic acid with MIC values ranging from 6.25 to 50 μg/mL. Compounds also revealed a promising antibiofilm activity with biofilm eradication percentages values above 80% for the best molecules (compounds 4d and 7). Compounds 7 and 8b showed a modest antiproliferative activity against human colorectal cancer cell lines HT-29. Finally, in silico molecular docking studies revealed DHFR and DNA gyrase B as potential anti-bacterial targets and in silico predictions of ADME suggested adequate drug-likeness profiles for the synthetized triarylmethanes.

Prediction of Passive Membrane Permeability by Semi-Empirical Method Considering Viscous and Inertial Resistances and Different Rates of Conformational Change and Diffusion

Membrane permeability is an important property of drugs in adsorption. Many prediction methods work well for small molecules, but the prediction of middle-molecule permeability is still difficult. In the present study, we modified a classical permeability model based on Fick's law to study passive membrane permeability. The model consisted of the distribution of solute from water to membrane and the diffusion of solute in each solvent. The diffusion coefficient is the inverse of the resistance, and we examined the inertial resistance in addition to the viscous resistance, the latter of which has been widely used in permeability prediction. Also, we examined three models changing the balance between the diffusion of solute in membrane and the conformational change of solute. The inertial resistance improved the prediction results in addition to the viscous resistance. The models worked well not only for small molecules but also for middle molecules, whose structures have more conformational freedom.

Understanding Cell Penetration of Cyclic Peptides

Approximately 75% of all disease-relevant human proteins, including those involved in intracellular protein-protein interactions (PPIs), are undruggable with the current drug modalities (i.e., small molecules and biologics). Macrocyclic peptides provide a potential solution to these undruggable targets because their larger sizes (relative to conventional small molecules) endow them the capability of binding to flat PPI interfaces with antibody-like affinity and specificity. Powerful combinatorial library technologies have been developed to routinely identify cyclic peptides as potent, specific inhibitors against proteins including PPI targets. However, with the exception of a very small set of sequences, the vast majority of cyclic peptides are impermeable to the cell membrane, preventing their application against intracellular targets. This Review examines common structural features that render most cyclic peptides membrane impermeable, as well as the unique features that allow the minority of sequences to enter the cell interior by passive diffusion, endocytosis/endosomal escape, or other mechanisms. We also present the current state of knowledge about the molecular mechanisms of cell penetration, the various strategies for designing cell-permeable, biologically active cyclic peptides against intracellular targets, and the assay methods available to quantify their cell-permeability.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

PerMM: A Web Tool and Database for Analysis of Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules

The PerMM web server and database were developed for quantitative analysis and visualization of passive translocation of bioactive molecules across lipid membranes. The server is the first physics-based web tool that calculates membrane binding energies and permeability coefficients of diverse molecules through artificial and natural membranes (phospholipid bilayers, PAMPA-DS, blood-brain barrier, and Caco-2/MDCK cell membranes). It also visualizes the transmembrane translocation pathway as a sequence of translational and rotational positions of a permeant as it moves across the lipid bilayer, along with the corresponding changes in solvation energy. The server can be applied for prediction of permeability coefficients of compounds with diverse chemical scaffolds to facilitate selection and optimization of potential drug leads. The complementary PerMM database allows comparison of computationally and experimentally determined permeability coefficients for more than 500 compounds in different membrane systems. The website and database are freely accessible at https://permm.phar.umich.edu/ .

Physics-Based Method for Modeling Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules

Assessment of permeability is a critical step in the drug development process for selection of drug candidates with favorable ADME properties. We have developed a novel physics-based method for fast computational modeling of passive permeation of diverse classes of molecules across lipid membranes. The method is based on heterogeneous solubility-diffusion theory and operates with all-atom 3D structures of solutes and the anisotropic solvent model of the lipid bilayer characterized by transbilayer profiles of dielectric and hydrogen bonding capacity parameters. The optimal translocation pathway of a solute is determined by moving an ensemble of representative conformations of the molecule through the dioleoyl-phosphatidylcholine (DOPC) bilayer and optimizing their rotational orientations in every point of the transmembrane trajectory. The method calculates (1) the membrane-bound state of the solute molecule; (2) free energy profile of the solute along the permeation pathway; and (3) the permeability coefficient obtained by integration over the transbilayer energy profile and assuming a constant size-dependent diffusivity along the membrane normal. The accuracy of the predictions was evaluated against experimental permeability coefficients measured in pure lipid membranes (for 78 compounds, R² was 0.88 and rmse was 1.15 log units), PAMPA-DS (for 280 compounds, R² was 0.75 and rmse was 1.59 log units), BBB (for 182 compounds, R² was 0.69 and rmse was 0.87 log units), and Caco-2/MDCK assays (for 165 compounds, R² was 0.52 and rmse was 0.89 log units).

In vitro and in silico studies of the membrane permeability of natural flavonoids from Silybum marianum (L.) Gaertn. and their derivatives

BACKGROUND

In recent years the number of natural products used as pharmaceuticals, components of dietary supplements and cosmetics has increased tremendously requiring more extensive evaluation of their pharmacokinetic properties.

PURPOSE

This study aims at combining in vitro and in silico methods to evaluate the gastrointestinal absorption (GIA) of natural flavonolignans from milk thistle (Silybum marianum (L.) Gaertn.) and their derivatives.

METHODS

A parallel artificial membrane permeability assay (PAMPA) was used to evaluate the transcellular permeability of the plant main components. A dataset of 269 compounds with measured PAMPA values and specialized software tools for calculating molecular descriptors were utilized to develop a quantitative structure-activity relationship (QSAR) model to predict PAMPA permeability.

RESULTS

The PAMPA permeabilities of 7 compounds constituting the main components of the milk thistle were measured and their GIA was evaluated. A freely-available and easy to use QSAR model predicting PAMPA permeability from calculated physico-chemical molecular descriptors was derived and validated on an external dataset of 783 compounds with known GIA. The predicted permeability values correlated well with obtained in vitro results. The QSAR model was further applied to predict the GIA of 31 experimentally untested flavonolignans.

CONCLUSIONS

According to both in vitro and in silico results most flavonolignans are highly permeable in the gastrointestinal tract, which is a prerequisite for sufficient bioavailability and use as lead structures in drug development. The combined in vitro/in silico approach can be used for the preliminary evaluation of GIA and to guide further laboratory experiments on pharmacokinetic characterization of bioactive compounds, including natural products.

Applications of nanotechnology in drug delivery to the central nervous system

In recent years, the researchers and drug designers have given growing attention to new nanotechnology strategies to improve drug delivery to the central nervous system (CNS). Nanotechnology has a great potential to affect the treatment of neurological disorders, mainly Alzheimer's disease, Parkinson's disease, brain tumors, and stroke. With regard to neurodegeneration, several studies showed that nanomaterials have been successfully used for the treatments of CNS disorders. In this regard, nanocarriers have facilitated the targeted delivery of chemotherapeutics resulting in the efficient inhibition of disease progression in malignant brain tumors. Therefore, the most efficacious application of nanomaterials is the use of these substances in the treatment of CNS disease that enhances the overall effect of drug and highlights the importance of nano-therapeutics. This study was conducted to review the evidence on the applications of nanotechnology in designing drug delivery systems with the ability to cross through the blood-brain barrier (BBB) in order to transfer the therapeutic agents to the CNS.

Design Principles for Intestinal Permeability of Cyclic Peptides

One of the most exciting facets of cyclic peptides is that they have the potential to be orally bioavailable, despite having physical properties well beyond the traditional "Rule-of-5" chemistry space (Lipinski et al., Adv Drug Deliv Rev. 23(1): 3-25, 1997). An important component of meeting this challenge is to design cyclic peptides with good intestinal permeability. Here we discuss the design principles for intestinal permeability that have been developed in recent year. These principles can be subdivided into three regimes: physical property guidelines, design strategies for the macrocyclic ring, and design strategies for side chains. The most important overall aims are to minimize solvent-exposed polarity while keeping size, flexibility, and lipophilicity within favorable ranges, thereby allowing peptide chemists to achieve intestinal permeability in addition to other important properties for their compounds, such as solubility and binding affinity. Here we describe a variety of design strategies that have been developed to help peptide chemists in this endeavor.

Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning

Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA), and effects of partial charge sets, CGenFF, AM1-BCC, and OPLS, on the performance of the predictions were discussed. (D)HDGB-based models improved the predictions over the two-state implicit membrane models, and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions, although it could not outperform the physics-based approach in terms of correlations.

Identification of peptidomimetics as novel chemical probes modulating fibroblast growth factor 14 (FGF14) and voltage-gated sodium channel 1.6 (Nav1.6) protein-protein interactions

The voltage-gated sodium (Nav) channel is the molecular determinant of action potential in neurons. Protein-protein interactions (PPI) between the intracellular Nav1.6 C-tail and its regulatory protein fibroblast growth factor 14 (FGF14) provide an ideal and largely untapped opportunity for development of neurochemical probes. Based on a previously identified peptide FLPK, mapped to the FGF14:FGF14 PPI interface, we have designed and synthesized a series of peptidomimetics with the intent of increasing clogP values and improving cell permeability relative to the parental lead peptide. In-cell screening using the split-luciferase complementation (LCA) assay identified ZL0177 (13) as the most potent inhibitor of the FGF14:Nav1.6 channel complex assembly with an apparent IC₅₀ of 11 μM. Whole-cell patch-clamp recordings demonstrated that ZL0177 significantly reduced Nav1.6-mediated transient current density and induced a depolarizing shift of the channel voltage-dependence of activation. Docking studies revealed strong interactions between ZL0177 and Nav1.6, mediated by hydrogen bonds, cation-π interactions and hydrophobic contacts. All together these results suggest that ZL0177 retains some key features of FGF14-dependent modulation of Nav1.6 currents. Overall, ZL0177 provides a chemical scaffold for developing Nav channel modulators as pharmacological probes with therapeutic potential of interest for a broad range of CNS and PNS disorders.

Reliable and Performant Identification of Low-Energy Conformers in the Gas Phase and Water

Prediction of compound properties from structure via quantitative structure-activity relationship and machine-learning approaches is an important computational chemistry task in small-molecule drug research. Though many such properties are dependent on three-dimensional structures or even conformer ensembles, the majority of models are based on descriptors derived from two-dimensional structures. Here we present results from a thorough benchmark study of force field, semiempirical, and density functional methods for the calculation of conformer energies in the gas phase and water solvation as a foundation for the correct identification of relevant low-energy conformers. We find that the tight-binding ansatz GFN-xTB shows the lowest error metrics and highest correlation to the benchmark PBE0-D3(BJ)/def2-TZVP in the gas phase for the computationally fast methods and that in solvent OPLS3 becomes comparable in performance. MMFF94, AM1, and DFTB+ perform worse, whereas the performance-optimized but far more expensive functional PBEh-3c yields energies almost perfectly correlated to the benchmark and should be used whenever affordable. On the basis of our findings, we have implemented a reliable and fast protocol for the identification of low-energy conformers of drug-like molecules in water that can be used for the quantification of strain energy and entropy contributions to target binding as well as for the derivation of conformer-ensemble-dependent molecular descriptors.

MacroEvoLution: A New Method for the Rapid Generation of Novel Scaffold-Diverse Macrocyclic Libraries

Macrocycles are a structural class bearing great promise for future challenges in medicinal chemistry. Nevertheless, there are few flexible approaches for the rapid generation of structurally diverse macrocyclic compound collections. Here, an efficient method for the generation of novel macrocyclic peptide‐based scaffolds is reported. The process, named here as “MacroEvoLution”, is based on a cyclization screening approach that gives reliable access to novel macrocyclic architectures. Classification of building blocks into specific pools ensures that scaffolds with orthogonally addressable functionalities are generated, which can easily be used for the generation of structurally diverse compound libraries. The method grants rapid access to novel scaffolds with scalable synthesis (multi gram scale) and the introduction of further diversity at a late stage. Despite being developed for peptidic systems, the approach can easily be extended for the synthesis of systems with a decreased peptidic character.