Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on more than 96,000 Compounds

Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks

Quantitative estimates of reaction barriers and solvent effects are essential for developing kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of 5,600 unique elementary radical reactions calculated using the M06-2X/def2-QZVP//B3LYP-D3(BJ)/def2-TZVP level of theory. A conformer search is done for each species using TPSS/def2-TZVP. Gibbs free energies of activation and of reaction for these radical reactions in 40 common solvents are obtained using COSMO-RS for solvation effects. These balanced reactions involve the elements H, C, N, O, and S, contain up to 19 heavy atoms, and have atom-mapped SMILES. All transition states are verified by an intrinsic reaction coordinate calculation. We next train a deep graph network to directly estimate the Gibbs free energy of activation and of reaction in both gas and solution phases using only the atom-mapped SMILES of the reactant and product and the SMILES of the solvent. This simple input representation avoids computationally expensive optimizations for the reactant, transition state, and product structures during inference, making our model well-suited for high-throughput predictive chemistry and quickly providing information for (retro-)synthesis planning tools. To properly measure model performance, we report results on both interpolative and extrapolative data splits and also compare to several baseline models. During training and testing, the data set is augmented by including the reverse direction of each reaction and variants with different resonance structures. After data augmentation, we have around 2 million entries to train the model, which achieves a testing set mean absolute error of 1.16 kcal mol-1 for the Gibbs free energy of activation in solution. We anticipate this model will accelerate predictions for high-throughput screening to quickly identify relevant reactions in solution, and our data set will serve as a benchmark for future studies.

Neutral rhodol-based dyes expressing localization in mitochondria

Neutral rhodol-based red emitters are shown to efficiently localize in mitochondria, as demonstrated by confocal microscopy and co-localization studies. A simple model is proposed to explain the localization mechanism of neutral molecules. The model takes into account the strong coupling between the molecular dipole moment and the electric field of the inner mitochondrial membrane.

Synthesis, molecular modelling, and antibacterial evaluation of new sulfonamide-dyes based pyrrole compounds

In this study, we synthesized new series of 5-oxo-2-phenyl-4-(arylsulfamoyl)sulphenyl) hydrazono)-4,5-dihydro-1H-pyrrole-3-carboxylate hybrids 4a-f with the goal of overcoming sulfonamide resistance and identifying novel therapeutic candidates by chemical changes. The chemical structures of the synthesized hybrids were established over the spectroscopic tools. The frontier molecular orbitals configuration and energetic possessions of the synthesized compounds were discovered utilizing DFT/B3LYP/6-311++ G** procedure. The 3D plots of both HOMO and LUMO showed comparable configuration of both HOMO and LUMO led to close values of their energies. Amongst the prepared analogues, the sulfonamide hybrids 4a-f, hybrid 4a presented potent inhibitory towards S. typhimurium with (IZD = 15 mm, MIC = 19.24 µg/mL) and significant inhibition with (IZD = 19 mm, MIC = 11.31 µg/mL) against E.coli in contrast to sulfonamide (Sulfamethoxazole) reference Whereas, hybrid 4d demonstrated potent inhibition with (IZD = 16 mm, MIC = 19.24 µg/mL) against S. typhimurium with enhanced inhibition against E. Coli, Additionally, the generated sulfonamide analogues'' molecular docking was estimated over (PDB: 3TZF and 6CLV) proteins. Analogue 4e had the highest documented binding score as soon as linked to the other analogues. The docking consequences were fitting and addressed with the antibacterial valuation.

Evaluation of the Lipophilicity of Angularly Condensed Diquino- and Quinonaphthothiazines as Potential Candidates for New Drugs

Lipophilicity is one of the most important properties of compounds required to estimate the absorption, distribution, and transport in biological systems, in addition to solubility, stability, and acid-base nature. It is crucial in predicting the ADME profile of bioactive compounds. The study assessed the usefulness of computational and chromatographic methods (thin-layer chromatography in a reversed-phase system, RP-TLC) for estimating the lipophilicity of 21 newly synthesized compounds belonging to diquinothiazines and quinonaphthiazines. In order to obtain reliable values of the relative lipophilicities of diquinothiazines and quinonaphthiazines, the partition coefficients obtained using different algorithms such as AlogPs, AClogP, AlogP, MLOGP, XLOGP2, XLOGP3, logP, and ClogP were compared with the chromatographic RM0 values of all the tested compounds measured by the experimental RP-TLC method (logPTLC). Additionally, logPTLC values were also correlated with other descriptors, as well as the predicted ADME and drug safety profiling parameters. The linear correlations of logPTLC values of the tested compounds with other calculated molecular descriptors such as molar refractivity, as well as ADME parameters (Caco-2 substrates, P-gp inhibitors, CYP2C19, and CYP3A4) generally show poor predictive power. Therefore, in silico ADME profiling can only be helpful at the initial step of designing these new candidates for drugs. The compliance of all discussed diquinothiazines and naphthoquinothiazines with the rules of Lipiński, Veber, and Egan suggests that the tested pentacyclic phenothiazine analogs have a chance to become therapeutic drugs, especially orally active drugs.

Synthesis and Chemoinformatic Analysis of Fluorinated Piperidines as 3D Fragments for Fragment-Based Drug Discovery

The concise synthesis of a small library of fluorinated piperidines from readily available dihydropyridinone derivatives has been described. The effect of the fluorination on different positions has then been evaluated by chemoinformatic tools. In particular, the compounds' pKa's have been calculated, revealing that the fluorine atoms notably lowered their basicity, which is correlated to the affinity for hERG channels resulting in cardiac toxicity. The "lead-likeness" and three-dimensionality have also been evaluated to assess their ability as useful fragments for drug design. A random screening on a panel of representative proteolytic enzymes was then carried out and revealed that one scaffold is recognized by the catalytic pocket of 3CLPro (main protease of SARS-CoV-2 coronavirus).

Integration of virtual screening of phytoecdysteroids as androgen receptor inhibitors by 3D-QSAR Model, CoMFA, molecular docking and ADMET analysis: An extensive and interactive machine learning

Ecdysteroids, a class of naturally isolated polyhydroxylated sterols, stands at a very good place in the pharmaceutical industry from their medicinal point of views like anti-inflammatory, neuroprotective, anti-microbial, anti-diabetic, antioxidant, and anti-tumor effects. Due to their excellent antioxidant and anti-microbial potential, ecdysteroids have extensive use in skin products, especially derma creams. To monitor the best anti-acne phytoecdysteroids, here made use of different computational approaches, by using the rapid, easy, cost-effective and high throughput method to screen and identify ecdysteroids as androgen receptor inhibitors. 3D-QSAR study was carried out on a dataset of ecdysteroids by using comparative molecular field analysis (CoMFA) to determine the factors responsible for the activity of compounds. Statistically a cross-validated (q2) 0.1457 and regression coefficient (r2) 0.9713 indicated the best model. Contour map results showed the influence of steric effect to enhance activity. A molecular docking analysis was done to further find out the binding sites and their anti-acne potential against three crystal structured macromolecules (PDB ID: 2REQ, 2BAC, 4EM0). Docking results were further evaluated by prime MM-GBSA analysis and findings confirmed the accuracy. Toxicity by ADMET assessment was carried out and M2 was found as lead druglike with best anti-acne activity against Propionium acnes GehA lipase bacteria after passing all filters. This research study is novel because it is representing first effort to explore ecdysteroids class for their high therapeutic output as androgen receptor inhibitor by using computational tools and expectedly led to novel scaffold for androgen receptor inhibitor. This is a novel and new approach to investigate the ecdysteroids for first time for their practical applications.

Discovery of a Novel Orally Active, Selective LPA Receptor Type 1 Antagonist, 4-(4-(2-Isopropylphenyl)-4-((2-methoxy-4-methylphenyl)carbamoyl)piperidin-1-yl)-4-oxobutanoic Acid, with a Distinct Molecular Scaffold

Lysophosphatidic acid receptor 1 (LPAR1) antagonists show promise as potentially novel antifibrotic treatments. In a human LPAR1 β-arrestin recruitment-based high-throughput screening campaign, we identified urea 19 as a hit with a LPAR1 IC50 value of 5.0 μM. Hit-to-lead activities revealed that one of the urea nitrogen atoms can be replaced by carbon and establish the corresponding phenylacetic amide as a lead structure for further optimization. Medicinal chemistry efforts led to the discovery of piperidine 18 as a potent and selective LPAR1 antagonist with oral activity in a mouse model of LPA-induced skin vascular leakage. The molecular scaffold of 18 shares no obvious structural similarity with any other LPAR1 antagonist disclosed so far.

Pyrimidine Schiff Bases: Synthesis, Structural Characterization and Recent Studies on Biological Activities

Recently, 5-[(4-ethoxyphenyl)imino]methyl-N-(4-fluorophenyl)-6-methyl-2-phenylpyrimidin-4-amine has been synthesized, characterized, and evaluated for its antibacterial activity against Enterococcus faecalis in combination with antineoplastic activity against gastric adenocarcinoma. In this study, new 5-iminomethylpyrimidine compounds were synthesized which differ in the substituent(s) of the aromatic ring attached to the imine group. The structures of newly obtained pyrimidine Schiff bases were established by spectroscopy techniques (ESI-MS, FTIR and 1H NMR). To extend the current knowledge about the features responsible for the biological activity of the new 5-iminomethylpyrimidine derivatives, low-temperature single-crystal X-ray analyses were carried out. For all studied crystals, intramolecular N-H∙∙∙N hydrogen bonds and intermolecular C-H∙∙∙F interactions were observed and seemed to play an essential role in the formation of the structures. Simultaneously, their biological properties based on their cytotoxic features were compared with the activities of the Schiff base (III) published previously. Moreover, computational investigations, such as ADME prediction analysis and molecular docking, were also performed on the most active new Schiff base (compound 4b). These results were compared with the highest active compound III.

Energetics of β-lactoglobulin-flavor compounds interactions

Interaction of bovine β-lactoglobulin (BLG) with several flavor compounds (FC) (2-methylpyrazine, vanillin, 2-acetylpyridine, 2- and 3-acetylthiophene, methyl isoamyl ketone, heptanone, octanone, and nonanone) was studied by high-sensitivity differential scanning calorimetry. The denaturation temperature, enthalpy, and heat capacity increment were determined at different FC concentrations. It was found that the denaturation temperature and heat capacity increment do not depend on the FC concentration, while the denaturation enthalpy decreases linearly with the FC concentration. These thermodynamic effects disclose the preferential FC binding to the unfolded form of BLG. By the obtained calorimetric data, the free energies of FC binding vs. the FC concentrations were calculated. These dependences were shown to be linear. Their slope relates closely to the overall FC affinity for the unfolded BLG in terms of the Langmuir binding model. The overall BLG affinity for FC varies from 20 M-1 (2-methylpyrazine) up to 360 M-1(nonanone). The maximal stoichiometry of the BLG-FC complexes was roughly estimated as a ratio of the length of the unfolded BLG to the molecular length of FC. Using these estimates, the apparent BLG-FC binding constants were determined. They are in the range of 0.3-8.0 M-1 and correlated strictly with the FC lipophilicity descriptor (logP).

In silico study of antibacterial tyrosyl-tRNA synthetase and toxicity of main phytoconstituents from three active essential oils

The misuse and overuse of antibiotics have resulted in antibiotic resistance. However, there are alternative approaches that could either substitute antibiotics or enhance their effectiveness without harmful side effects. One such approach is the use of terpene-rich essential oils. In this study, we aimed to demonstrate the antibacterial activity of the main components of three plant essential oils, namely Anthemis punctata, Anthemis pedunculata and Daucus crinitus. Specifically, we targeted bacterial tyrosyl-tRNA synthetase, an enzyme that plays a critical role in bacterial protein synthesis. To investigate how the phytocompounds interact with the enzyme's active sites, we employed a molecular docking study using Autodock Software Tools 1.5.7. Our findings revealed that all 28 phytocompounds bound to the enzyme's active sites with binding energies ranging from -6.96 to -4.03 kcal/mol. These results suggest that terpene-rich essential oils could be a potential source of novel antimicrobial agents.Communicated by Ramaswamy H. Sarma.

Antidepressants enter cells, organelles, and membranes

We begin by summarizing several examples of antidepressants whose therapeutic actions begin when they encounter their targets in the cytoplasm or in the lumen of an organelle. These actions contrast with the prevailing view that most neuropharmacological actions begin when drugs engage their therapeutic targets at extracellular binding sites of plasma membrane targets-ion channels, receptors, and transporters. We review the chemical, pharmacokinetic, and pharmacodynamic principles underlying the movements of drugs into subcellular compartments. We note the relationship between protonation-deprotonation events and membrane permeation of antidepressant drugs. The key properties relate to charge and hydrophobicity/lipid solubility, summarized by the parameters LogP, pKa, and LogDpH7.4. The classical metric, volume of distribution (Vd), is unusually large for some antidepressants and has both supracellular and subcellular components. A table gathers structures, LogP, PKa, LogDpH7.4, and Vd data and/or calculations for most antidepressants and antidepressant candidates. The subcellular components, which can now be measured in some cases, are dominated by membrane binding and by trapping in the lumen of acidic organelles. For common antidepressants, such as selective serotonin reuptake inhibitors (SSRIs) and serotonin/norepinephrine reuptake inhibitors (SNRIs), the target is assumed to be the eponymous reuptake transporter(s), although in fact the compartment of target engagement is unknown. We review special aspects of the pharmacokinetics of ketamine, ketamine metabolites, and other rapidly acting antidepressants (RAADs) including methoxetamine and scopolamine, psychedelics, and neurosteroids. Therefore, the reader can assess properties that markedly affect a drug's ability to enter or cross membranes-and therefore, to interact with target sites that face the cytoplasm, the lumen of organelles, or a membrane. In the current literature, mechanisms involving intracellular targets are termed "location-biased actions" or "inside-out pharmacology". Hopefully, these general terms will eventually acquire additional mechanistic details.

Deep learning algorithms applied to computational chemistry

Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems.

Investigating the bispecific lead compounds against methicillin-resistant Staphylococcus aureus SarA and CrtM using machine learning and molecular dynamics approach

Methicillin-resistant Staphylococcus aureus (MRSA) is a notorious pathogen that has emerged as a serious global health concern over the past few decades. Staphylococcal accessory regulator A (SarA) and 4,4'-diapophytoene synthase (CrtM) play a crucial role in biofilm formation and staphyloxanthin biosynthesis. Thus, the present study used a machine learning-based QSAR model to screen 1261 plant-derived natural organic compounds in order to identify a medication candidate with both biofilm and virulence inhibitory potential. Additionally, the in-silico molecular docking analysis has demonstrated significant binding efficacy of the identified hit compound, that is 85137543, with SarA and CrtM when compared to the control compound, hesperidin. Post-MD simulation analysis of the complexes depicted strong binding of 85137543 to both SarA and CrtM. Moreover, 85137543 showed hydrogen bonding with the key residues of both proteins during docking (ALA138 of SarA and ALA134 of CrtM) and post-MD simulation (LYS273 of CrtM and ASN212 of SarA). The RMSD of 85137543 was stable and consistent when bound to both CrtM and SarA with RMSDs of 1.3 and 1 nm, respectively. In addition, principal component analysis and the free energy landscape showed stable complex formation with both proteins. Low binding free energy (ΔGTotal) was observed by 85137543 for SarA (-47.92 kcal/mol) and CrtM (-36.43 kcal/mol), which showed strong binding. Overall, this study identified 85137543 as a potential inhibitor of both SarA and CrtM in MRSA.Communicated by Ramaswamy H. Sarma.

Scannotation: A Suspect Screening Tool for the Rapid Pre-Annotation of the Human LC-HRMS-Based Chemical Exposome

In an increasingly chemically polluted environment, rapidly characterizing the human chemical exposome (i.e., chemical mixtures accumulating in humans) at the population scale is critical to understand its impact on health. High-resolution mass spectrometry (HRMS) profiling of complex biological matrices can theoretically provide a comprehensive picture of chemical exposures. However, annotating the detected chemical features, particularly low-abundant ones, remains a significant obstacle to implementing such approaches at a large scale. We present Scannotation (https://github.com/scannotation/Scannotation_software), an automated and user-friendly suspect screening tool for the rapid pre-annotation of HRMS preprocessed data sets. This software tool combines several MS1 chemical predictors, i.e., m/z, experimental and predicted retention times, isotopic patterns, and neutral loss patterns, to score the proximity between features and suspects, thus efficiently prioritizing tentative annotations to verify. Scannotation and MS-DIAL4 were used to annotate blood serum samples of 75 Breton adolescents. Scannotation's combination of MS1-based chemical predictors allowed us to annotate 89 chemically diverse environmental compounds with high confidence (confirmed by MS2 when available). These compounds included 62% of emerging molecules, for which no toxicological or human biomonitoring data are reported in the literature. The complementarity observed with MS-DIAL4 results demonstrates the relevance of Scannotation for the efficient pre-annotation of large-scale exposomics data sets.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Occurrence, fate, and potential risk of pharmaceutical pollutants in agriculture: Challenges and environmentally friendly solutions

In recent years, pharmaceutical active compounds (PhACs) have attained global prevalence. The behavior of PhACs in agricultural soils is complex and depends on several factors, such as the nature of the compounds and their physicochemical characteristics, which affect their fate and potential threats to human health, ecosystems, and the environment. The detection of residual pharmaceutical content is possible in both agricultural soils and environmental matrices. PhACs are commonly found in agricultural soil, with concentrations varying significantly, ranging from as low as 0.048 ng g-1 to as high as 1420.76 mg kg-1. The distribution and persistence of PhACs in agriculture can lead to the leaching of these toxic pollutants into surface water, groundwater, and vegetables/plants, resulting in human health risks and environmental pollution. Biological degradation or bioremediation plays a critical role in environmental protection and efficiently eliminates contamination by hydrolytic and/or photochemical reactions. Membrane bioreactors (MBRs) have been investigated as the most recent approach for the treatment of emerging persistent micropollutants, including PhACs, from wastewater sources. MBR- based technologies have proven to be effective in eliminating pharmaceutical compounds, achieving removal rates of up to 100%. This remarkable outcome is primarily facilitated by the processes of biodegradation and metabolization. In addition, phytoremediation (i.e., constructed wetlands), microalgae-based technologies, and composting can be highly efficient in remediating PhACs in the environment. The exploration of key mechanisms involved in pharmaceutical degradation has revealed a range of approaches, such as phytoextraction, phytostabilization, phytoaccumulation, enhanced rhizosphere biodegradation, and phytovolatilization. The well-known advanced/tertiary removal of sustainable sorption by biochar, activated carbon, chitosan, etc. has high potential and yields excellent quality effluents. Adsorbents developed from agricultural by-products have been recognized to eliminate pharmaceutical compounds and are cost-effective and eco-friendly. However, to reduce the potentially harmful impacts of PhACs, it is necessary to focus on advanced technologies combined with tertiary processes that have low cost, high efficiency, and are energy-saving to remove these emerging pollutants for sustainable development.

Novel NIR-II fluorescent probes for biliary atresia imaging

Biliary atresia is a rare infant disease that predisposes patients to liver transplantation and death if not treated in time. However, early diagnosis is challenging because the clinical manifestations and laboratory tests of biliary atresia overlap with other cholestatic diseases. Therefore, it is very important to develop a simple, safe and reliable method for the early diagnosis of biliary atresia. Herein, a novel NIR-II fluorescence probe, HZL2, with high quantum yield, excellent biocompatibility, low cytotoxicity and rapid excretion through the liver and gallbladder was developed based on the oil/water partition coefficient and permeability. A simple fecal sample after injection of HZL2 can be used to efficiently identify the success of the mouse model of biliary atresia for the first time, allowing for an early diagnosis of the disease. This study not only developed a simple and safe method for the early diagnosis of biliary atresia with great potential in clinical translation but also provides a research tool for the development of pathogenesis and therapeutic medicines for biliary atresia.

Overview of Determination of Biopharmaceutical Properties for Development Candidate Selection

The physicochemical and biopharmaceutical properties of putative drug molecules impact their performance in both in vitro and in vivo studies. The design and selection of molecules with drug-like properties assist in the selection of drug candidates with a higher probability of success in the development process. Described in this overview are commonly used approaches for measuring compound solubility, permeability, and partitioning in drug discovery and development. The utility of these methods in the drug discovery process and product development is discussed. The evaluation of crystallinity and physicochemical stability in relation to biopharmaceutical properties and in assessing the potential for successful development is also discussed. © 2023 Wiley Periodicals LLC.

A benchmark dataset for machine learning in ecotoxicology

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, which we consider a barrier of entry for this kind of research. Additionally, model performances can only be compared across studies when the same dataset, cleaning, and splittings were used. Therefore, we provide ADORE, an extensive and well-described dataset on acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species as well as chemical properties and molecular representations. Apart from challenging other researchers to try and achieve the best model performances across the whole dataset, we propose specific relevant challenges on subsets of the data and include datasets and splittings corresponding to each of these challenge as well as in-depth characterization and discussion of train-test splitting approaches.

Hierarchical Virtual Screening of Potential New Antibiotics from Polyoxygenated Dibenzofurans against Staphylococcus aureus Strains

Staphylococcus aureus is a microorganism with high morbidity and mortality due to antibiotic-resistant strains, making the search for new therapeutic options urgent. In this context, computational drug design can facilitate the drug discovery process, optimizing time and resources. In this work, computational methods involving ligand- and structure-based virtual screening were employed to identify potential antibacterial agents against the S. aureus MRSA and VRSA strains. To achieve this goal, tetrahydroxybenzofuran, a promising antibacterial agent according to in vitro tests described in the literature, was adopted as the pivotal molecule and derivative molecules were considered to generate a pharmacophore model, which was used to perform virtual screening on the Pharmit platform. Through this result, twenty-four molecules were selected from the MolPort® database. Using the Tanimoto Index on the BindingDB web server, it was possible to select eighteen molecules with greater structural similarity in relation to commercial antibiotics (methicillin and oxacillin). Predictions of toxicological and pharmacokinetic properties (ADME/Tox) using the eighteen most similar molecules, showed that only three exhibited desired properties (LB255, LB320 and LB415). In the molecular docking study, the promising molecules LB255, LB320 and LB415 showed significant values in both molecular targets. LB320 presented better binding affinity to MRSA (-8.18 kcal/mol) and VRSA (-8.01 kcal/mol) targets. Through PASS web server, the three molecules, specially LB320, showed potential for antibacterial activity. Synthetic accessibility (SA) analysis performed on AMBIT and SwissADME web servers showed that LB255 and LB415 can be considered difficult to synthesize and LB320 is considered easy. In conclusion, the results suggest that these ligands, particularly LB320, may bind strongly to the studied targets and may have appropriate ADME/Tox properties in experimental studies.

The effect of descriptor database selection on the physicochemical characterization and prediction of water-air, octanol-air and octanol-water partition constants using the solvation parameter model

The distribution of neutral compounds in biphasic separation systems can be described by the solvation parameter model using six solute properties, or descriptors. These descriptors (McGowan's characteristic volume, excess molar refraction, dipolarity/polarizability, hydrogen-bond acidity and basicity, and the gas-liquid partition constant on n-hexadecane at 298.15 K) are curated in two publicly accessible databases for hundreds (WSU compound descriptor database) or thousands (Abraham compound descriptor database). These databases were developed independently using different approaches resulting in descriptor values that vary for many compounds. Previously, it was shown that the two descriptor databases are not interchangeable, and the WSU descriptor database consistently demonstrated improved model performance for chromatographic systems where the uncertainty in the dependent variable was minimized by suitable quality control and calibration procedures. In this report we wish to evaluate whether the same conclusions are true for models with a dependent variable containing significant measurement uncertainty. To evaluate this hypothesis, we assembled databases for water-air, octanol-air, and octanol-water partition constants reported by multiple laboratories using various measurement methods. It was found that database selection has little effect on model quality or model predictive capability but significantly affects the assignment of the contribution of individual intermolecular interactions to the dependent variable. The latter information is database specific, and a quantitative comparison of system constants should be restricted to models using the same compound descriptor database.

Leaf metabolic traits reveal hidden dimensions of plant form and function

The metabolome is the biochemical basis of plant form and function, but we know little about its macroecological variation across the plant kingdom. Here, we used the plant functional trait concept to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five metabolic functional traits reveals that plants vary on two major axes of leaf metabolic specialization-a leaf chemical defense spectrum and an expression of leaf longevity. Axes are similar for tropical and temperate species, with many trait combinations being viable. However, metabolic traits vary orthogonally to life-history strategies described by widely used functional traits. The metabolome thus expands the functional trait concept by providing additional axes of metabolic specialization for examining plant form and function.

Lipophilicity of fentalogs: Comparison of experimental and computationally derived data

Although fentanyl and a small number of derivatives used for medical or veterinary procedures are well characterized, physiochemical properties have not been determined for many of the newer fentanyl analogs. Partition coefficients (Log P) were determined for 19 fentalogs using the shake-flask method and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Experimentally determined partition coefficients were compared with computationally derived data using six independent software sources (ACD/LogP, LogKOWWIN v 1.69, miLogP 2.2, OsirisP, XLOGP 3.0, ALogPS 2.1). Fentalogs with a wide variety of structural modifications were intentionally selected, yielding Log P values ranging from 1.21 to 4.90. Comparison of experimental and computationally derived Log P values were highly correlated (R2 0.854-0.967). Overall, substructure-based modeling using fragmental methods or property-based topological approaches aligned more closely with experimentally determined Log P values. LC-MS/MS was also used to estimate pKa values for fentalogs with no previously reported data. Lipophilicity and pKa are important considerations for analytical detection and toxicological interpretation. In silico methods allow the determination of physicochemical information prior to certified reference materials being readily available for in vitro or in vivo studies. Computationally derived data can provide insight regarding physiochemical characteristics of future fentalogs and other classes of synthetic analogs that have yet to emerge.

Interpretable Attribution Assignment for Octanol-Water Partition Coefficient

With the increasing development of machine learning models, their credibility has become an important issue. In chemistry, attribution assignment is gaining relevance when it comes to designing molecules and debugging models. However, attention has only been paid to which atoms are important in the prediction and not to whether the attribution is reasonable. In this study, we developed a graph neural network model, a highly interpretable attribution model in chemistry, and modified the integrated gradients method. The credibility of our approach was confirmed by predicting the octanol-water partition coefficient (logP) and evaluating the three metrics (accuracy, consistency, and stability) in the attribution assignment.

Ru(II)-p-Cymene Complexes of Furoylthiourea Ligands for Anticancer Applications against Breast Cancer Cells

Half-sandwich Ru(II) complexes containing nitro-substituted furoylthiourea ligands, bearing the general formula [(η⁶-p-cymene)RuCl₂(L)] (1-6) and [(η⁶-p-cymene)RuCl(L)(PPh₃)]⁺ (7--12), have been synthesized and characterized. In contrast to the spectroscopic data which revealed monodentate coordination of the ligands to the Ru(II) ion via a "S" atom, single crystal X-ray structures revealed an unusual bidentate N, S coordination with the metal center forming a four-membered ring. Interaction studies by absorption, emission, and viscosity measurements revealed intercalation of the Ru(II) complexes with calf thymus (CT) DNA. The complexes showed good interactions with bovine serum albumin (BSA) as well. Further, their cytotoxicity was explored exclusively against breast cancer cells, namely, MCF-7, T47-D, and MDA-MB-231, wherein all of the complexes were found to display more pronounced activity than their ligand counterparts. Complexes 7-12 bearing triphenylphosphine displayed significant cytotoxicity, among which complex 12 showed IC₅₀ values of 0.6 ± 0.9, 0.1 ± 0.8, and 0.1 ± 0.2 μM against MCF-7, T47-D, and MDA-MB-231 cell lines, respectively. The most active complexes were tested for their mode of cell death through staining assays, which confirmed apoptosis. The upregulation of apoptotic inducing and downregulation of apoptotic suppressing proteins as inferred from the western blot analysis also corroborated the apoptotic mode of cell death. The active complexes effectively generated reactive oxygen species (ROS) in MDA-MB-231 cells as analyzed from the 2',7'-dichlorofluorescein diacetate (DCFH-DA) staining. Finally, in vivo studies of the highly active complexes (6 and 12) were performed on the mice model. Histological analyses revealed that treatment with these complexes at high doses of up to 8 mg/kg did not induce any visible damage to the tested organs.

Bridging the Gap between Differential Mobility, Log S, and Log P Using Machine Learning and SHAP Analysis

Aqueous solubility, log S, and the water-octanol partition coefficient, log P, are physicochemical properties that are used to screen the viability of drug candidates and to estimate mass transport in the environment. In this work, differential mobility spectrometry (DMS) experiments performed in microsolvating environments are used to train machine learning (ML) frameworks that predict the log S and log P of various molecule classes. In lieu of a consistent source of experimentally measured log S and log P values, the OPERA package was used to evaluate the aqueous solubility and hydrophobicity of 333 analytes. With ion mobility/DMS data (e.g., CCS, dispersion curves) as input, we used ML regressors and ensemble stacking to derive relationships with a high degree of explainability, as assessed via SHapley Additive exPlanations (SHAP) analysis. The DMS-based regression models returned scores of R² = 0.67 and RMSE = 1.03 ± 0.10 for log S predictions and R² = 0.67 and RMSE = 1.20 ± 0.10 for log P after 5-fold random cross-validation. SHAP analysis reveals that the regressors strongly weighted gas-phase clustering in log P correlations. The addition of structural descriptors (e.g., # of aromatic carbons) improved log S predictions to yield RMSE = 0.84 ± 0.07 and R² = 0.78. Similarly, log P predictions using the same data resulted in an RMSE of 0.83 ± 0.04 and R² = 0.84. The SHAP analysis of log P models highlights the need for additional experimental parameters describing hydrophobic interactions. These results were achieved with a smaller dataset (333 instances) and minimal structural correlation compared to purely structure-based models, underscoring the value of employing DMS data in predictive models.

Prediction of toluene/water partition coefficients in the SAMPL9 blind challenge: assessment of machine learning and IEF-PCM/MST continuum solvation models

In recent years the use of partition systems other than the widely used biphasic n-octanol/water has received increased attention to gain insight into the molecular features that dictate the lipophilicity of compounds. Thus, the difference between n-octanol/water and toluene/water partition coefficients has proven to be a valuable descriptor to study the propensity of molecules to form intramolecular hydrogen bonds and exhibit chameleon-like properties that modulate solubility and permeability. In this context, this study reports the experimental toluene/water partition coefficients (log P_tol/w) for a series of 16 drugs that were selected as an external test set in the framework of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) blind challenge. This external set has been used by the computational community to calibrate their methods in the current edition (SAMPL9) of this contest. Furthermore, the study also investigates the performance of two computational strategies for the prediction of log P_tol/w. The first relies on the development of two machine learning (ML) models, which are built up by combining the selection of 11 molecular descriptors in conjunction with either the multiple linear regression (MLR) or the random forest regression (RFR) model to target a dataset of 252 experimental log P_tol/w values. The second consists of the parametrization of the IEF-PCM/MST continuum solvation model from B3LYP/6-31G(d) calculations to predict the solvation free energies of 163 compounds in toluene and benzene. The performance of the ML and IEF-PCM/MST models has been calibrated against external test sets, including the compounds that define the SAMPL9 log P_tol/w challenge. The results are used to discuss the merits and weaknesses of the two computational approaches.

Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods

Glutamic acid is a non-essential amino acid involved in multiple metabolic pathways. Of high importance is its relationship with glutamine, an essential fuel for cancer cell development. Compounds that can modify glutamine or glutamic acid behaviour in cancer cells have resulted in attractive anticancer therapeutic alternatives. Based on this idea, we theoretically formulated 123 glutamic acid derivatives using Biovia Draw. Suitable candidates for our research were selected among them. For this, online platforms and programs were used to describe specific properties and their behaviour in the human organism. Nine compounds proved to have suitable or easy to optimise properties. The selected compounds showed cytotoxicity against breast adenocarcinoma, lung cancer cell lines, colon carcinoma, and T cells from acute leukaemia. Compound 2Ba5 exhibited the lowest toxicity, and derivative 4Db6 exhibited the most intense bioactivity. Molecular docking studies were also performed. The binding site of the 4Db6 compound in the glutamine synthetase structure was determined, with the D subunit and cluster 1 being the most promising. In conclusion, glutamic acid is an amino acid that can be manipulated very easily. Therefore, molecules derived from its structure have great potential to become innovative drugs, and further research on these will be conducted.

Appraisal of Pancreatic Lipase Inhibitory Potential of Ziziphus oenoplia (L.)Mill. Leaves by In Vitro and In Silico Approaches

Pancreatic lipase is one of the crucial lipolytic enzymes of the gut that actively facilitates the digestion and absorption of the dietary triglycerides and cholesteryl esters. Although it has been deemed as one of the most reliable targets for the treatment of obesity and/or dyslipidemia, to date, orlistat is the only known FDA-approved, effective, oral pancreatic lipase inhibitor available for clinical use apart from the centrally acting antiobesity agents. However, it is known to be associated with adverse gastrointestinal and renal complications. In this study, we attempted to assess the antioxidant and porcine pancreatic lipase inhibitory potentials of Ziziphus oenoplia (L.)Mill. leaves through a systematic combination of in vitro and in silico approaches. Among the four different extracts including petroleum ether extract, ethyl acetate extract, ethanolic extract, and aqueous extract obtained through successive solvent extraction, the ethyl acetate extract has outperformed the other extracts and orderly displayed competent peroxide scavenging (IC₅₀ value: 267.30 μg/mL) and porcine pancreatic lipase inhibitory (IC₅₀ value: 444.44 μg/mL) potentials compared to the selected reference compounds: ascorbic acid (IC₅₀ value: 251.50 μg/mL) and orlistat (IC₅₀ value: 502.51 μg/mL) in the selected in vitro assay models. In addition, based on the molecular docking simulations of the six essential phytoconstituents of the leaves of Ziziphus oenoplia (L.)Mill. and their respective chemical analogues against the crystal structure of pancreatic lipase-colipase complex (PDB ID: 1LPB), four best-ranked molecules (PubChem CIDs: 15515703, 132582306, 11260294, and 44440845) have been proposed. Further, among these, the interaction potentials of the two top-ranked molecules (PubChem CIDs: 132582306 and 15515703) were analyzed through molecular dynamics (MD) simulations at a trajectory of 100 ns. Finally, absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters were theoretically predicted for all of the molecules using Swiss ADME and ADMET lab2.0. In conclusion, Ziziphus oenoplia (L.)Mill. leaves could become a prominent source for various potent bioactive compounds that may serve as prospective leads for the development of clinically cognizable pancreatic lipase inhibitors, provided their pharmacokinetic and in particular toxicity properties are thoroughly optimized.

Preliminary study to identify CXCR4 inhibitors as potential therapeutic agents for Alzheimer's and Parkinson's diseases

Neurodegenerative disorders (NDDs) are known to exhibit genetic overlap and shared pathophysiology. This study aims to find the shared genetic architecture of Alzheimer's disease (AD) and Parkinson's disease (PD), two major age-related progressive neurodegenerative disorders. The gene expression profiles of GSE67333 (containing samples from AD patients) and GSE114517 (containing samples from PD patients) were retrieved from the Gene Expression Omnibus (GEO) functional genomics database managed by the National Center for Biotechnology Information. The web application GREIN (GEO RNA-seq Experiments Interactive Navigator) was used to identify differentially expressed genes (DEGs). A total of 617 DEGs (239 upregulated and 379 downregulated) were identified from the GSE67333 dataset. Likewise, 723 DEGs (378 upregulated and 344 downregulated) were identified from the GSE114517 dataset. The protein-protein interaction networks of the DEGs were constructed, and the top 50 hub genes were identified from the network of the respective dataset. Of the four common hub genes between two datasets, C-X-C chemokine receptor type 4 (CXCR4) was selected due to its gene expression signature profile and the same direction of differential expression between the two datasets. Mavorixafor was chosen as the reference drug due to its known inhibitory activity against CXCR4 and its ability to cross the blood-brain barrier. Molecular docking and molecular dynamics simulation of 51 molecules having structural similarity with Mavorixafor was performed to find two novel molecules, ZINC49067615 and ZINC103242147. This preliminary study might help predict molecular targets and diagnostic markers for treating Alzheimer's and Parkinson's diseases. Insight Box Our research substantiates the therapeutic relevance of CXCR4 inhibitors for the treatment of Alzheimer's and Parkinson's diseases. We would like to disclose the following insights about this study. We found common signatures between Alzheimer's and Parkinson's diseases at transcriptional levels by analyzing mRNA sequencing data. These signatures were used to identify putative therapeutic agents for these diseases through computational analysis. Thus, we proposed two novel compounds, ZINC49067615 and ZINC103242147, that were stable, showed a strong affinity with CXCR4, and exhibited good pharmacokinetic properties. The interaction of these compounds with major residues of CXCR4 has also been described.

A Small Molecule That In Vitro Neutralizes Infection of SARS-CoV-2 and Its Most Infectious Variants, Delta, and Omicron

The COVID-19 pandemic has underscored the urgent need to develop highly potent and safe medications that are complementary to the role of vaccines. Specifically, it has exhibited the need for orally bioavailable broad-spectrum antivirals that are able to be quickly deployed against newly emerging viral pathogens. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) and its variants Delta and Omicron are still a major threat to patients of all ages. In this brief report, we describe that the small molecule CD04872SC was able to neutralize SARS-CoV2 infection with a half-maximal effective concentration (EC50) = 248 μM. Serendipitously, we also were able to observe that CD04872SC inhibited the infection of the SARS-CoV-2 variants; Delta (EC50 = 152 μM) and Omicron (EC50 = 308 μM). These properties may define CD04872SC as a potential broad-spectrum candidate lead for the development of treatments for COVID-19.

Antiviral activity of amide-appended α-hydroxytropolones against herpes simplex virus-1 and -2

α-Hydroxytropolones (αHTs) have potent antiviral activity against herpes simplex virus-1 and -2 (HSV-1 and HSV-2) in cell culture, including against acyclovir-resistant mutants, and as a result have the potential to be developed as antiviral drugs targeting these viruses. We recently described a convenient final-step amidation strategy to their synthesis, and this was used to generate 57 amide-substituted αHTs that were tested against hepatitis B virus. The following manuscript describes the evaluation of this library against HSV-1, as well as a subset against HSV-2. The structure-function analysis obtained from these studies demonstrates the importance of lipophilicity and rigidity to αHT-based anti-HSV potency, consistent with our prior work on smaller libraries. We used this information to synthesize and test a targeted library of 4 additional amide-appended αHTs. The most potent of this new series had a 50% effective concentration (EC₅₀) for viral inhibition of 72 nM, on par with the most potent αHT antivirals we have found to date. Given the ease of synthesis of amide-appended αHTs, this new class of antiviral compounds and the chemistry to make them should be highly valuable in future anti-HSV drug development.

Traditional herbal compounds as candidates to inhibit the SARS-CoV-2 main protease: an in silico study

COVID-19, a disease caused by the SARS-CoV-2 virus, is responsible for a pandemic since March 2020 and it has no cure. Therefore, herein, different theoretical methods were used to obtain potential candidates from herbal compounds to inhibit the SARS-CoV-2 main protease (M^pro). Initially, the 16 best-scored compounds were selected from a library containing 4066 ligands using virtual screening by molecular docking. Among them, six molecules (physalin B 5,6-epoxide (PHY), methyl amentoflavone (MAM), withaphysalin C (WPC), daphnoline or trilobamine (TRI), cepharanoline (CEP) and tetrandrine (TET)) were selected based on Lipinski's rule and ADMET analysis as criteria. These compounds complexed with the M^pro were submitted to triplicate 100 ns molecular dynamics simulations. RMSD, RMSF, and radius of gyration results show that the overall protein structure is preserved along the simulation time. The average ΔG_binding values, calculated by the MM/PBSA method, were -41.7, -55.8, -45.2, -38.7, -49.3, and -57.9 kcal/mol for the PHY-M^pro, MAM-M^pro, WPC-M^pro, CEP-M^pro, TRI-M^pro, and TET-M^pro complexes, respectively. Pairwise decomposition analyses revealed that the binding pocket is formed by His41-Val42, Met165-Glu166-Leu167, Asp187, and Gln189. The PLS regression model generated by QSPR analysis indicated that non-polar and polar groups with the presence of hydrogen bond acceptors play an important role in the herbal compounds-M^pro interactions. Overall, we found six potential candidates to inhibit the SARS-CoV-2 M^pro and highlighted key residues from the binding pocket that can be used for future drug design. Communicated by Ramaswamy H. Sarma.

APPLICATION OF MICELLAR LIQUID CHROMATOGRAPHY TO MODEL ECOTOXICITY OF PESTICIDES. COMPARISON WITH IMMOBILIZED ARTIFICIAL MEMBRANE CHROMATOGRAPHY AND N-OCTANOL-WATER PARTITIONING

The potential of Micellar Liquid Chromatography (MLC) to model ecotoxicological endpoints for a series of pesticides was investigated. To exploit the flexibility in MLC conditions, different surfactants were employed and retention mechanism was tracked and compared to Immobilized Artificial Membrane (IAM) chromatographic retention and n-octanol- water partitioning, logP. Neutral polyoxyethylene (23) lauryl ether (Brij-35), anionic sodium dodecyl sulfate (SDS) and cationic cetyltrimethylammonium bromide (CTAB) were used in presence of PBS at pH=7.40 and acetonitrile as organic modifier when necessary. Similarities/ dissimilarities between MLC retention and IAM or logP were investigated by Principal Component Analysis (PCA) and Liner Solvation Energy Relationships (LSER). LSER revealed that hydrogen bonding acidity is the most important factor for differentiation between MLC and IAM or logP. The impact of hydrogen bonding is exemplified in the relationships of MLC retention factors with IAM or logP, which necessitate the inclusion of a relevant descriptor. PCA further revealed that MLC retention factors are clustered together with IAM indices and logP within a broader ellipse formed by ecotoxicological endpoints, involving LC₅₀/ EC₅₀ values of six aquatic organisms namely Rainbow Trout, Fathead Minnow, Bluegill Sunfish, Sheepshead Minnow, Eastern Oyster and Water Flea as well as LD₅₀ values of Honey Bee, thus justifying their use to construct relevant models. Satisfactory specific models for individual organisms, as well as general fish models, were obtained, in most cases, upon combination of MLC retention factors with Molecular Weight (MW) or/ and hydrogen bond parameters. All models were evaluated and compared to previously reported IAM and logP based models using an external validation data set. Predictions with Brij-35 and SDS based models were comparable, although slightly inferior than those obtained with IAM, while they were in all cases better than those obtained with logP. CTAB led to a satisfactory prediction model for Honey Bee, but it was found less suitable for aquatic organisms.

Dimensionally reduced machine learning model for predicting single component octanol-water partition coefficients

MF-LOGP, a new method for determining a single component octanol-water partition coefficients ([Formula: see text]) is presented which uses molecular formula as the only input. Octanol-water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make [Formula: see text] predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average [Formula: see text] = 0.77 ± 0.007, [Formula: see text] = 0.52 ± 0.003, and [Formula: see text] = 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ([Formula: see text] = 0.42-1.54, [Formula: see text] = 0.09-1.07, and [Formula: see text] = 0.32-0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures.

Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity

Lipophilicity, as measured by the partition coefficient between octanol and water (log P), is a key parameter in early drug discovery research. However, measuring log P experimentally is difficult for specific compounds and log P ranges. The resulting lack of reliable experimental data impedes development of accurate in silico models for such compounds. In certain discovery projects at Novartis focused on such compounds, a quantum mechanics (QM)-based tool for log P estimation has emerged as a valuable supplement to experimental measurements and as a preferred alternative to existing empirical models. However, this QM-based approach incurs a substantial computational cost, limiting its applicability to small series and prohibiting quick, interactive ideation. This work explores a set of machine learning models (Random Forest, Lasso, XGBoost, Chemprop, and Chemprop3D) to learn calculated log P values on both a public data set and an in-house data set to obtain a computationally affordable, QM-based estimation of drug lipophilicity. The message-passing neural network model Chemprop emerged as the best performing model with mean absolute errors of 0.44 and 0.34 log units for scaffold split test sets of the public and in-house data sets, respectively. Analysis of learning curves suggests that a further decrease in the test set error can be achieved by increasing the training set size. While models directly trained on experimental data perform better at approximating experimentally determined log P values than models trained on calculated values, we discuss the potential advantages of using calculated log P values going beyond the limits of experimental quantitation. We analyze the impact of the data set splitting strategy and gain insights into model failure modes. Potential use cases for the presented models include pre-screening of large compound collections and prioritization of compounds for full QM calculations.

Revisiting the Use of Quantum Chemical Calculations in LogPoctanol-water Prediction

The partition coefficients of drug and drug-like molecules between an aqueous and organic phase are an important property for developing new therapeutics. The predictive power of computational methods is used extensively to predict partition coefficients of molecules. The application of quantum chemical calculations is used to develop methods to develop structure-activity relationship models for such prediction, either based on molecular fragment methods, or via direct calculation of solvation free energy in solvent continuum. The applicability, merits, and shortcomings of these developments are revisited here.

Development, testing, parameterisation, and calibration of a human PBK model for the plasticiser, di (2-ethylhexyl) adipate (DEHA) using in silico, in vitro and human biomonitoring data

Introduction: A physiologically based biokinetic model for di (2-ethylhexyl) adipate (DEHA) based on a refined model for di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the metabolism and biokinetics of DEHA following a single oral dosage of 50 mg to two male and two female volunteers. Methods: The model was parameterized using in vitro and in silico methods such as, measured intrinsic hepatic clearance scaled from in vitro to in vivo and algorithmically predicted parameters such as plasma unbound fraction and tissue:blood partition coefficients (PCs). Calibration of the DEHA model was achieved using concentrations of specific downstream metabolites of DEHA excreted in urine. The total fractions of ingested DEHA eliminated as specific metabolites were estimated and were sufficient for interpreting the human biomonitoring data. Results: The specific metabolites of DEHA, mono-2-ethyl-5-hydroxyhexyl adipate (5OH-MEHA), mono-2-ethyl-5-oxohexyl adipate (5oxo-MEHA), mono-5-carboxy-2-ethylpentyl adipate (5cx-MEPA) only accounted for ∼0.45% of the ingested DEHA. Importantly, the measurements of adipic acid, a non-specific metabolite of DEHA, proved to be important in model calibration. Discussion: The very prominent trends in the urinary excretion of the metabolites, 5cx-MEPA and 5OH-MEHA allowed the important absorption mechanisms of DEHA to be modelled. The model should be useful for the study of exposure to DEHA of the general human population.

Development, testing, parameterisation, and calibration of a human PBPK model for the plasticiser, di-(2-ethylhexyl) terephthalate (DEHTP) using in silico, in vitro and human biomonitoring data

A physiologically based pharmacokinetic model for di-(2-ethylhexyl) terephthalate (DEHTP) based on a refined model for di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the metabolism and biokinetics of DEHTP following a single oral dose of 50 mg to three male volunteers. In vitro and in silico methods were used to generate parameters for the model. For example, measured intrinsic hepatic clearance scaled from in vitro to in vivo and plasma unbound fraction and tissue:blood partition coefficients (PCs) were predicted algorithmically. Whereas the development and calibration of the DPHP model was based upon two data streams, blood concentrations of parent chemical and first metabolite and the urinary excretion of metabolites, the model for DEHTP was calibrated against a single data stream, the urinary excretion of metabolites. Despite the model form and structure being identical significant quantitative differences in lymphatic uptake between the models were observed. In contrast to DPHP the fraction of ingested DEHTP entering lymphatic circulation was much greater and of a similar magnitude to that entering the liver with evidence for the dual uptake mechanisms discernible in the urinary excretion data. Further, the absolute amounts absorbed by the study participants, were much higher for DEHTP relative to DPHP. The in silico algorithm for predicting protein binding performed poorly with an error of more than two orders of magnitude. The extent of plasma protein binding has important implications for the persistence of parent chemical in venous blood-inferences on the behaviour of this class of highly lipophilic chemicals, based on calculations of chemical properties, should be made with extreme caution. Attempting read across for this class of highly lipophilic chemicals should be undertaken with caution since basic adjustments to PCs and metabolism parameters would be insufficient, even when the structure of the model itself is appropriate. Therefore, validation of a model parameterized entirely with in vitro and in silico derived parameters would need to be calibrated against several human biomonitoring data streams to constitute a data rich source chemical to afford confidence for future evaluations of other similar chemicals using the read-across approach.

In Vitro and In Silico Evaluations of Boswellia carterii Resin Dermocosmetic Activities

Boswellia carterii is a plant species belonging to the Burseraceae family. It grows up in trees or shrubs, and it is known for producing an aromatic resin commonly named frankincense or olibanum. This resin has been used in traditional medicine to treat various conditions such as inflammations, gastrointestinal disorders and traumatic injuries. Virtual screening and molecular docking are two in silico approaches used to predict potential interactions between ligands and the active site of a protein. These approaches are mainly used in natural product chemistry and pharmacology as a screening tool to select plant extracts or fractions for in vitro testing, as well as for the prediction of mechanisms of action. The aim of this research is the in silico and in vitro evaluations of the potential collagenase and elastase inhibitory activities of Boswellia carterii resin organic extracts (viz., methanol, n-hexane and ethyl acetate). The obtained results revealed that methanol and n-hexane exhibited the best collagenase inhibitory activity with values superior to 85%, whereas the methanol and ethyl acetate showed the highest elastase inhibition activity with inhibition values ranging between 40 and 60%. The molecular docking prediction confirmed the experimental results; moreover, the visualization of the ligand–protein interactions showed that the main compounds of the organic extracts may have mechanisms of action similar to the positive controls. Those findings are very promising and open new perspectives for the exploitation of Boswellia carterii resin as active agents for the development of anti-aging cosmeceuticals.

Microwave assisted synthesis of 2-amino-4-chloro-pyrimidine derivatives: Anticancer and computational study on potential inhibitory action against COVID-19

We report microwave synthesis of seven unique pyrimidine anchored derivatives (1–7) incorporating multifunctional amino derivatives along with their in vitro anticancer activity and their activity against COVID-19 in silico. 1–7 were characterized by different analytical and spectroscopic techniques. Cytotoxic activity of 1–7 was tested against HCT116 and MCF7 cell lines, whereby 6 exhibited highest anticancer activity on HCT116 and MCF7 with EC₅₀ values of 89.24 ± 1.36 µM and 89.37 ± 1.17 µM, respectively.

Molecular docking was performed for derivatives (1–7) on main protease for SARS-CoV-2 (PDB ID: 6LU7). Results revealed that most of the derivatives had superior or equivalent affinity for the 3CLpro, as determined by docking and binding energy scores. 6 topped the rest with highest binding energy score of −8.12 kcal/mol with inhibition constant reported as 1.11 µM. ADME, drug-likeness, and pharmacokinetics properties of 1–7 were tested using Swiss ADME tool. Toxicity analysis was done with pkCSM online server.

All derivatives showed high GI absorption. Except 1 and 3, all derivatives showed blood brain barrier permeability. Most derivatives showed negative logKp values suggesting derivatives are less skin permeable and bioavailability score of all derivatives was 0.55. The toxicity analysis demonstrated that all derivatives have no skin sensitization properties. 6 and 7 showed maximum tolerated dose (Human) values of −0.03 and −0.018, respectively and absence of AMES toxicity.

QSPR study on Hydrophobicity of Pt(II) complexes with surface electrostatic potential-based descriptors

Pt(II) complexes play an important role in bioinorganic chemistry due to their antitumor activities. In the present study, we focused on building predictive models for the hydrophobicity of Pt(II) complexes. A five-parameter model, integrating frontier orbital energies (E_HOMO, E_LUMO) and descriptors derived from electrostatic potentials on molecular surface, was firstly constructed by using multiple linear regression (MLR) method. Mechanistic interpretations of the introduced descriptors were elucidated in terms of intermolecular interactions in the n-octanol/water partition system. Then, four up-to-date modeling methods, including support vector machine (SVM), least-squares support vector machine (LSSVM), random forest (RF) and Gaussian process (GP), were utilized to build the nonlinear models. Systematical validations including leave-one-out cross-validation, the validation for test set, as well as a very rigorous Monte Carlo cross-validation (MCCV) were performed to verify the reliability of the constructed models. The peak, median and integralR_ext² values of the best GP model are 0.88, 0.86 and 0.84, respectively. The root mean squared errors for the test set (RMSEP) of the MLR, SVM, LSSVM and GP models fall in the range of 0.62-0.71. Although they are not superior to prior models built with the use of a number of descriptors, the results are satisfactory. Applicability domain of the model was evaluated.

Phytochemical Variability, In Vitro and In Vivo Biological Investigations, and In Silico Antibacterial Mechanisms of Mentha piperita Essential Oils Collected from Two Different Regions in Morocco

The objective of this work is to explore the phytochemical profile of Mentha piperita essential oils (MPEO) collected from two different Moroccan regions using gas chromatography-mass spectrophotometer (GC-MS) and to investigate their antioxidant, anti-inflammatory, antidiabetic and, antimicrobial effects using in vivo and in vitro assays. The chemical constituent of MPEO from the Azrou zone is dominated by carvone (70.25%), while MPEO from the Ouazzane zone is rich in Menthol (43.32%) and Menthone (29.4%). MPEO from Ouezzane showed higher antioxidant activity than EO from Azrou. Nevertheless, EO from Ouezzane considerably inhibited 5-Lipoxygenase (IC50 = 11.64 ± 0.02 µg/mL) compared to EO from Azro (IC50 = 23.84 ± 0.03 µg/mL). Both EOs from Azrou and Ouazzane inhibited the α-amylase activity in vitro, with IC50 values of 131.62 ± 0.01 µg/mL and 91.64 ± 0.03 µg/mL, respectively. The EOs were also tested for minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). The discdiffusion test revealed that MPEOs from both regions have significant antibacterial efficacy, and MPEOs from the north region showed the highest effect. The gram-positive bacteria were the most susceptible organisms. The MIC concentrations were in the range of 0.05 to 6.25 mg/mL, and the MBC concentrations were within 0.05-25.0 mg/mL. The MBC/MIC index indicated that MPEO has strong bactericidal effects.

A Study of Absorption and Selected Molecular Physicochemical Properties of Some Antipsychotic Drugs

Antipsychotic drugs are commonly prescribed for different mental disorders and can be classified into two main groups: the first which contain originally developed antipsychotics of the first generation or typical antipsychotics and the other group with newly developed antipsychotics or atypical antipsychotics of the second generation. In this study, eleven antipsychotic drugs (chlorpromazine, flupentixol, haloperidol, zuclopenthixol, aripiprazole, clozapine, olanzapine, quetiapine, risperidone, sertindole, ziprasidone) were investigated to evaluate significance of their molecular physicochemical properties (lipophilicity, aqueous solubility, polar surface area, molecular weight, volume value and acidity) for their bioavailability. Relationships between literature available intestinal absorption data of antipsychotic drugs and their lipophilicity descriptor with one additional molecular descriptor, investigated using multiple linear regression analysis provided high correlations for molecular descriptors, Mw, Vol, pKa, as additional independent variables. Values of correlation coefficients (R2) were ranged from 0.951 (for Vol) above 0.944 (for Mw) to 0.923 (for pKa).

Fast Prediction of Lipophilicity of Organofluorine Molecules: Deep Learning-Derived Polarity Characters and Experimental Tests

Fast and accurate estimation of lipophilicity for organofluorine molecules is in great demand for accelerating drug and materials discovery. A lipophilicity data set of organofluorine molecules (OFL data set), containing 1907 samples, is constructed through density functional theory (DFT) calculations and experimental measurements. An efficient and interpretable model, called PoLogP, is developed to predict the n-octanol/water partition coefficient, log P_o/w, of organofluorine molecules on the basis of the descriptors of polarization, which is a combination of polarity descriptors, including the molecular polarity index and molecular polarizability (α), and hydrogen bond (HBs) index, consisting of the number of donors (N_HB^D) and acceptors (N_HB^A and N_HB-F^A). The present PoLogP with a combination of polarity descriptors is demonstrated to perform better than the dipole moment (μ) alone for the F-contained molecules. With the aid of a multilevel attention graph convolutional neural network model, the fast generation of polarity descriptors of organofluorine molecules could be achieved with the DFT accuracy based only on a topological molecular graph structure. The performance of PoLogP is further validated on synthesized organofluorine molecules and 2626 non-fluorinated molecules with satisfactory accuracy, highlighting the potential usage of PoLogP in high-throughput screening of the functional molecules with the desired solubility in various solvent media.

Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions

Efficient prediction of the partition coefficient (log P) between polar and non-polar phases could shorten the cycle of drug and materials design. In this work, a descriptor, named 〈q - ACSFs〉_conf, is proposed to take the explicit polarization effects in the polar phase and the conformation ensemble of energetic and entropic significance in the non-polar phase into consideration. The polarization effects are involved by embedding the partial charge directly derived from force fields or quantum chemistry calculations into the atom-centered symmetry functions (ACSFs), together with the entropy effects, which are averaged according to the Boltzmann distribution of different conformations taken from the similarity matrix. The model was trained with high-dimensional neural networks (HDNNs) on a public dataset PhysProp (with 41 039 samples). Satisfactory log P prediction performance was achieved on three other datasets, namely, Martel (707 molecules), Star & Non-Star (266) and Huuskonen (1870). The present 〈q - ACSFs〉_conf model was also applicable to n-carboxylic acids with the number of carbons ranging from 2 to 14 and 54 kinds of organic solvent. It is easy to apply the present method to arbitrary sized systems and give a transferable atom-based partition coefficient.

Identification of Potential New Aedes aegypti Juvenile Hormone Inhibitors from N-Acyl Piperidine Derivatives: A Bioinformatics Approach

Aedes aegypti mosquitoes transmit several human pathogens that cause millions of deaths worldwide, mainly in Latin America. The indiscriminate use of insecticides has resulted in the development of species resistance to some such compounds. Piperidine, a natural alkaloid isolated from Piper nigrum, has been used as a hit compound due to its larvicidal activity against Aedes aegypti. In the present study, piperidine derivatives were studied through in silico methods: pharmacophoric evaluation (PharmaGist), pharmacophoric virtual screening (Pharmit), ADME/Tox prediction (Preadmet/Derek 10.0^®), docking calculations (AutoDock 4.2) and molecular dynamics (MD) simulation on GROMACS-5.1.4. MP-416 and MP-073 molecules exhibiting ΔG binding (MMPBSA −265.95 ± 1.32 kJ/mol and −124.412 ± 1.08 kJ/mol, respectively) and comparable to holo (ΔG binding = −216.21 ± 0.97) and pyriproxyfen (a well-known larvicidal, ΔG binding= −435.95 ± 2.06 kJ/mol). Considering future in vivo assays, we elaborated the theoretical synthetic route and made predictions of the synthetic accessibility (SA) (SwissADME), lipophilicity and water solubility (SwissADME) of the promising compounds identified in the present study. Our in silico results show that MP-416 and MP-073 molecules could be potent insecticides against the Aedes aegypti mosquitoes.

Lipophilicity prediction of three photosensitizers by liquid-liquid extraction, HPLC, and DFT methods

Photodynamic therapy (PDT) is a method of treating precancerous diseases and malignant neoplasms. The efficacy of PDT depends on different parameters such as light dosimetry, oxygen availability, and photophysical and physical-chemical properties of the photosensitizer. In PDT, a photosensitizer is activated using light to promote oxygen photosensitization and cellular transport plays a key role in the reach of it to the desired tissue. In particular, to know the effectiveness of the drug delivery in PDT and its dosage forms to target damaged organs, along with such characteristics as water solubility, it is important to know the ability of a substance to penetrate through cell membrane or accumulate in it. Lipophilicity is used to quantify the earlier-described abilities. We evaluated the lipophilicity of three selected photosensitizers (PS) (protoporphyrin IX, pyropheophorbide-a and photofrin) by means of three different methods: octanol-water distribution methods (shake-flask), reversed-phase high-performance liquid chromatography (HPLC) and theoretical calculations based on density functional theory (DFT). We describe and compare the results of these various methods.

Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models

Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fu_mic) from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (r²=0.86) or mouse (r²=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes (r² of >0.89, n = 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human fu_mic using four different in silico models for a set of 53-60 compounds. The correlation (r²) and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r²=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r²<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.