Molecular fields in quantitative structure–permeation relationships: the VolSurf approach

An exploratory in N-doped carbon dots as green fluorescence probes for Hg(II) ions detection

Carbon dots (CDs), as a fascinating carbon nanomaterial, have important applications in various fields due to their unique properties. The physical and chemical properties of CDs can be fine-tuned using heteroatom doping and surface functionalisation. Here, we synthesised N-doped carbon dots (N-CDs) by reacting Citric acid, which serve as the carbon core, with twenty amino acids under microwave irradiation. The fluorescence quenching of each amino acid doped CDs by Hg(II) ions was experimentally measured. Then the effect of the molecular features and chemical properties of amino acids on the fluorescence quenching of N-CDs by Hg(II) ions was investigated by using the quantitative structure-property relationship (QSPR) method. Applying different machine learning techniques including correlation-based and ReliefF algorithm feature selection approaches to choose relevant descriptors, multi-linear regression, and support vector machine to construct QSPR model, some reliable and predictive models were developed. Based on the variables used throughout the final QSPR models, hydrophobic interactions, in addition to hydrogen bonding interactions, can be considered a major factor governing the photoluminescence behaviour of different N-CDs quenched by Hg(II) ions. N-CDs derived from amino acids bearing larger hydrophobic surfaces show greater fluorescence quenching, indicating that a greater capacity to interact with Hg(II) metal ions resulting in further fluorescence quenching.

Prediction of the First-Pass Metabolism of a Drug After Oral Intake Based on Structural Parameters and Physicochemical Properties

BACKGROUND AND OBJECTIVE

The oral first-pass metabolism is a crucial factor that plays a key role in a drug's pharmacokinetic profile. Prediction of the oral first-pass metabolism based on chemical structural parameters can be useful in the drug-design process. Developing an orally administered drug with an acceptable pharmacokinetic profile is necessary to reduce the cost and time associated with evaluating the extent of the first-pass metabolism of a candidate compound in preclinical studies. The aim of this study is to estimate the first-pass metabolism of an orally administered drug.

METHODS

A set of compounds with reported first-pass metabolism data were collected. Moreover, human intestinal absorption percentage and oral bioavailability data were extracted from the literature to propose a classification system that split the drugs up based on their first-pass metabolism extents. Various structural parameters were calculated for each compound. The relations of the structural and physicochemical values of each compound to the class the compound belongs to were obtained using logistic regression.

RESULTS

Initial analysis showed that compounds with logD7.4 > 1 or a rugosity factor of > 1.5 are more likely to have high first-pass metabolism. Four different models that can predict the oral first-pass metabolism with acceptable error were introduced. The overall accuracies of the models were in the range of 72% (for models with simple descriptors) to 78% (for models with complex descriptors). Although the models with simple descriptors have lower accuracies compared to complex models, they are more interpretable and easier for researchers to utilize.

CONCLUSION

A novel classification of drugs based on the extent of the oral first-pass metabolism was introduced, and mechanistic models were developed to assign candidate compounds to the appropriate proposed classes.

Enzymatic Synthesis and Structural Modeling of Bio-Based Oligoesters as an Approach for the Fast Screening of Marine Biodegradation and Ecotoxicity

Given the widespread use of esters and polyesters in products like cosmetics, fishing nets, lubricants and adhesives, whose specific application(s) may cause their dispersion in open environments, there is a critical need for stringent eco-design criteria based on biodegradability and ecotoxicity evidence. Our approach integrates experimental and computational methods based on short oligomers, offering a screening tool for the rapid identification of sustainable monomers and oligomers, with a special focus on bio-based alternates. We provide insights into the relationships between the chemical structure and properties of bio-based oligomers in terms of biodegradability in marine environments and toxicity in benchmark organisms. The experimental results reveal that the considered aromatic monomers (terephthalic acid and 2,5-furandicarboxylic acid) accumulate under the tested conditions (OECD 306), although some slight biodegradation is observable when the inoculum derives from sites affected by industrial and urban pollution, which suggests that ecosystems adapt to non-natural chemical pollutants. While clean seas are more susceptible to toxic chemical buildup, biotic catalytic activities offer promise for plastic pollution mitigation. Without prejudice to the fact that biodegradability inherently signifies a desirable trait in plastic products, nor that it automatically grants them a sustainable "license", this study is intended to facilitate the rational design of new polymers and materials on the basis of specific uses and applications.

Discovery and Structure-Activity Relationships of Novel ssDAF-12 Receptor Modulators

The nuclear receptor ssDAF-12 has been recognized as the key molecular player regulating the life cycle of the nematode parasite Strongyloides stercoralis. ssDAF-12 ligands permit the receptor to function as an on/off switch modulating infection, making it vulnerable to therapeutic intervention. In this study, we report the design and synthesis of a set of novel dafachronic acid derivatives, which were used to outline the first structure-activity relationship targeting the ssDAF-12 receptor and to unveil hidden properties shared by the molecular shape of steroidal ligands that are relevant to the receptor binding and modulation. Moreover, biological results led to the discovery of sulfonamide 3 as a submicromolar ssDAF-12 agonist endowed with a high receptor selectivity, no toxicity, and improved properties, as well as to the identification of unprecedented ssDAF-12 antagonists that can be exploited in the search for novel chemical tools and alternative therapeutic approaches for treating parasitism such as Strongyloidiasis.

A further pocket or conformational plasticity by mapping COX-1 catalytic site through modified-mofezolac structure-inhibitory activity relationships and their antiplatelet behavior

Cyclooxygenase enzymes have distinct roles in cardiovascular, neurological, and neurodegenerative disease. They are differently expressed in different type of cancers. Specific and selective COXs inhibitors are needed to be used alone or in combo-therapies. Fully understand the differences at the catalytic site of the two cyclooxygenase (COX) isoforms is still opened to investigation. Thus, two series of novel compounds were designed and synthesized in fair to good yields using the highly selective COX-1 inhibitor mofezolac as the lead compound to explore a COX-1 zone formed by the polar residues Q192, S353, H90 and Y355, as well as hydrophobic amino acids I523, F518 and L352. According to the structure of the COX-1:mofezolac complex, hydrophobic amino acids appear to have free volume eventually accessible to the more sterically hindering groups than the methoxy linked to the phenyl groups of mofezolac, in particular the methoxyphenyl at C4-mofezolac isoxazole. Mofezolac bears two methoxyphenyl groups linked to C3 and C4 of the isoxazole core ring. Thus, in the novel compounds, one or both methoxy groups were replaced by the higher homologous ethoxy, normal and isopropyl, normal and tertiary butyl, and phenyl and benzyl. Furthermore, a major difference between the two sets of compounds is the presence of either a methyl or acetic moiety at the C5 of the isoxazole. Among the C5-methyl series, 12 (direct precursor of mofezolac) (COX-1 IC50 = 0.076 μM and COX-2 IC50 = 0.35 μM) and 15a (ethoxy replacing the two methoxy groups in 12; COX-1 IC50 = 0.23 μM and COX-2 IC50 > 50 μM) were still active and with a Selectivity Index (SI = COX-2 IC50/COX-1 IC50) = 5 and 217, respectively. The other symmetrically substituted alkoxyphenyl moietis were inactive at 50 μM final concentration. Among the asymmetrically substituted, only the 16a (methoxyphenyl on C3-isoxazole and ethoxyphenyl on C4-isoxazole) and 16b (methoxyphenyl on C3-isoxazole and n-propoxyphenyl on C4-isoxazole) were active with SI = 1087 and 38, respectively. Among the set of compounds with the acetic moiety, structurally more similar to mofezolac (SI = 6329), SI ranged between 1.4 and 943. It is noteworthy that 17b (n-propoxyphenyl on both C3- and C4-isoxazole) were found to be a COX-2 slightly selective inhibitor with SI = 0.072 (COX-1 IC50 > 50 μM and COX-2 IC50 = 3.6 μM). Platelet aggregation induced by arachidonic acid (AA) can be in vitro suppressed by the synthesized compounds, without affecting of the secondary hemostasia, confirming the biological effect provided by the selective inhibition of COX-1. A positive profile of hemocompatibility in relation to erythrocyte and platelet toxicity was observed. Additionally, these compounds exhibited a positive profile of hemocompatibility and reduced cytotoxicity. Quantitative structure activity relationship (QSAR) models and molecular modelling (Ligand and Structure based virtual screening procedures) provide key information on the physicochemical and pharmacokinetic properties of the COX-1 inhibitors as well as new insights into the mechanisms of inhibition that will be used to guide the development of more effective and selective compounds. X-ray analysis was used to confirm the chemical structure of 14 (MSA17).

Machine-Learning- and Structure-Based Virtual Screening for Selecting Cinnamic Acid Derivatives as Leishmania major DHFR-TS Inhibitors

The critical enzyme dihydrofolate reductase-thymidylate synthase in Leishmania major (LmDHFR-TS) serves a dual-purpose role and is essential for DNA synthesis, a cornerstone of the parasite's reproductive processes. Consequently, the development of inhibitors against LmDHFR-TS is crucial for the creation of novel anti-Leishmania chemotherapies. In this study, we employed an in-house database containing 314 secondary metabolites derived from cinnamic acid that occurred in the Asteraceae family. We conducted a combined ligand/structure-based virtual screening to identify potential inhibitors against LmDHFR-TS. Through consensus analysis of both approaches, we identified three compounds, i.e., lithospermic acid (237), diarctigenin (306), and isolappaol A (308), that exhibited a high probability of being inhibitors according to both approaches and were consequently classified as promising hits. Subsequently, we expanded the binding mode examination of these compounds within the active site of the test enzyme through molecular dynamics simulations, revealing a high degree of structural stability and minimal fluctuations in its tertiary structure. The in silico predictions were then validated through in vitro assays to examine the inhibitory capacity of the top-ranked naturally occurring compounds against LmDHFR-TS recombinant protein. The test compounds effectively inhibited the enzyme with IC50 values ranging from 6.1 to 10.1 μM. In contrast, other common cinnamic acid derivatives (i.e., flavonoid glycosides) from the Asteraceae family, such as hesperidin, isovitexin 4'-O-glucoside, and rutin, exhibited low activity against this target. The selective index (SI) for all tested compounds was determined using HsDHFR with moderate inhibitory effect. Among these hits, lignans 306 and 308 demonstrated the highest selectivity, displaying superior SI values compared to methotrexate, the reference inhibitor of DHFR-TS. Therefore, continued research into the anti-leishmanial potential of these C6C3-hybrid butyrolactone lignans may offer a brighter outlook for combating this neglected tropical disease.

Discovery of Guanfacine as a Novel TAAR1 Agonist: A Combination Strategy through Molecular Modeling Studies and Biological Assays

Trace amine-associated receptor 1 (TAAR1) is an attractive target for the design of innovative drugs to be applied in diverse pharmacological settings. Due to a non-negligible structural similarity with endogenous ligands, most of the agonists developed so far resulted in being affected by a low selectivity for TAAR1 with respect to other monoaminergic G protein-coupled receptors, like the adrenoreceptors. This study utilized comparative molecular docking studies and quantitative-structure activity relationship (QSAR) analyses to unveil key structural differences between TAAR1 and alpha2-adrenoreceptor (α2-ADR), with the aim to design novel TAAR1 agonists characterized by a higher selectivity profile and reduced off-target effects. While the presence of hydrophobic motives is encouraged towards both the two receptors, the introduction of polar/positively charged groups and the ligand conformation deeply affect the TAAR1 or α2-ADR putative selectivity. These computational methods allowed the identification of the α2A-ADR agonist guanfacine as an attractive TAAR1-targeting lead compound, demonstrating nanomolar activity in vitro. In vivo exploration of the efficacy of guanfacine showed that it is able to decrease the locomotor activity of dopamine transporter knockout (DAT-KO) rats. Therefore, guanfacine can be considered as an interesting template molecule worthy of structural optimization. The dual activity of guanfacine on both α2-ADR and TAAR1 signaling and the related crosstalk between the two pathways will deserve more in-depth investigation.

Impact of structural similarity on the accuracy of retention time prediction

Quantitative Structure-Retention Relationships offer a valuable tool for de-risking chromatographic methods in relation to newly formed or hypothetical compounds, arising from synthetic processes or formulation activities. They can also be used to identify optimal separation conditions, or in support of structural elucidation. In this contribution, we provide a systematic study of the relationship between the accuracy of the retention model, the size of the training set and its structural similarity to the predicted compound. We compare structural similarity expressed either on a fingerprint basis (e.g., Tanimoto index), or by Euclidean distance calculated from of subset of molecular descriptors. The results presented indicate that accurate and predictive models can be built from a small dataset containing as few as 25 compounds, provided that the training set is structurally similar to the test compound. When the training set contains compounds selected by minimizing the Euclidean distance calculated from 3 descriptors most correlated with the retention time, root mean square error of 0.48 min and correlation coefficient of 0.9464 were observed for the test sets of 104 compounds. Moreover, these models meet the Tropsha predictivity criteria. These findings potentially bring the prediction of retention times within the practical reach of pharmaceutical analysts involved in chromatographic method development. We also present an optimisation approach to select algorithm settings in order to minimize the prediction error and ensure model predictivity.

DeepGRID: Deep Learning Using GRID Descriptors for BBB Prediction

Deep Learning approaches are able to automatically extract relevant features from the input data and capture nonlinear relationships between the input and output. In this work, we present the GRID-derived AI (GrAId) descriptors, a simple modification to GRID MIFs that facilitate their use in combination with Convolutional Neural Networks (CNNs) to build Deep Learning models in a rotationally, conformationally, and alignment-independent approach we are calling DeepGRID. To our knowledge, this is the first time that GRID MIFs have been combined with CNNs in a Deep Learning approach. We applied the approach to build regression and classification models for blood-brain barrier permeation, an important factor when designing CNS drugs and conversely when designing to avoid off-target effects for CNS-inactive drugs. The VolSurf approach was one of the first to successfully model this property from three-dimensional structures, using descriptors derived from their GRID Molecular Interaction Fields (MIFs) in combination with PLS. We compared the DeepGRID models with others built using the hand-crafted VolSurf descriptors in combination with both PLS and Random Forest (RF). Both the DeepGRID and RF regression models performed best according to the % of compounds with a Geometric Mean Fold Error (GMFE) within 2-fold of the experimental data. Applying these regression models as classifiers, for the smaller 332 and 416 compound data sets all models performed well with ROC AUC values of ∼0.9 on the external test set. For the larger 2105 compound data set, the DeepGRID classifier performed the best with an AUC of 0.87 on the external test set with the RF model having an AUC of 0.84 and the original VolSurf lgBB model having an AUC of 0.83.

Developing multitarget coumarin based anti-breast cancer agents: synthesis and molecular modeling study

A new series of 7-substituted coumarin scaffolds containing a methyl ester moiety at the C4-position were synthesized and tested for their in vitro anti-proliferative activity against MCF-7 and MDA-MB-231 breast cancer cell lines using Doxorubicin (DOX) as reference. Compounds 2 and 8 showed noticeable selectivity against MCF-7 with IC50 = 6.0 and 5.8 µM, respectively compared to DOX with IC50 = 5.6 µM. Compounds 10, 12, and 14 exhibited considerable selectivity against Estrogen Negative cells with IC50 = 2.3, 3.5, and 1.9 µM, respectively) compared to DOX with (IC50 = 7.3 µM). The most promising compounds were tested as epidermal growth factor receptor and aromatase (ARO) enzymes inhibitors using erlotinib and exemestane (EXM) as standards, respectively. Results proved that compound 8 elicited the highest inhibitory activity (94.73% of the potency of EXM), while compounds 10 and 12 displayed 97.67% and 81.92% of the potency of Erlotinib, respectively. Further investigation showed that the promising candidates 8, 10, and 12 caused cell cycle arrest at G0-G1 and S phases and induced apoptosis. The mechanistic pathway was confirmed by elevating caspases-9 and Bax/Bcl-2 ratio. A set of in silico methods was also performed including docking, bioavailability ADMET screening and QSAR study.

Evaluation of the Effects of Heteroaryl Ethylene Molecules in Combination with Antibiotics: A Preliminary Study on Control Strains

The discovery of compounds with antibacterial activity is crucial in the ongoing battle against antibiotic resistance. We developed two QSAR models to design six novel heteroaryl drug candidates and assessed their antibacterial properties against nine ATCC strains, including Enterococcus faecalis, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and also Salmonella enterica and Escherichia coli, many of which belong to the ESKAPE group. We combined PB4, a previously tested compound from published studies, with GC-VI-70, a newly discovered compound, with the best cytotoxicity/MIC profile. By testing sub-MIC concentrations of PB4 with five antibiotics (linezolid, gentamycin, ampicillin, erythromycin, rifampin, and imipenem), we evaluated the combination's efficacy against the ATCC strains. To assess the compounds' cytotoxicity, we conducted a 24 h and 48 h 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on colorectal adenocarcinoma (CaCo-2) cells. We tested the antibiotics alone and in combination with PB4. Encouragingly, PB4 reduced the MIC values for GC-VI-70 and for the various clinically used antibiotics. However, it is essential to note that all the compounds studied in this research exhibited cytotoxic activity against cells. These findings highlight the potential of using these compounds in combination with antibiotics to enhance their effectiveness at lower concentrations while minimizing cytotoxic effects.

Anticancer Activity of Chalcones and Its Derivatives: Review and In Silico Studies

Chalcones are direct precursors in the biosynthesis of flavonoids. They have an α,β-unsaturated carbonyl system which gives them broad biological properties. Among the biological properties exerted by chalcones, their ability to suppress tumors stands out, in addition to their low toxicity. In this perspective, the present work explores the role of natural and synthetic chalcones and their anticancer activity in vitro reported in the last four years from 2019 to 2023. Moreover, we carried out a partial least square (PLS) analysis of the biologic data reported for colon adenocarcinoma lineage HCT-116. Information was obtained from the Web of Science database. Our in silico analysis identified that the presence of polar radicals such as hydroxyl and methoxyl contributed to the anticancer activity of chalcones derivatives. We hope that the data presented in this work will help researchers to develop effective drugs to inhibit colon adenocarcinoma in future works.

Functionalized sulfonyl anthranilic acid derivatives inhibit replication of all the four dengue serotypes

Dengue virus (DENV), a mosquito-borne flavivirus, continues to be a major public health threat in many countries and no approved antiviral therapeutics are available yet. In this work, we designed and synthesized a series of sulfonyl anthranilic acid (SAA) derivatives using a ligand-based scaffold morphing approach of the 2,1-benzothiazine 2,2-dioxide core, previously used by us to develop DENV polymerase inhibitors resulting devoid of any cell-based antiviral activity. Several derivatives based on the new SAA chemotype exhibited potent inhibition against DENV infection in the cell-based assay but did not inhibit DENV NS5 polymerase activity in the in vitro de novo initiation and elongation assays. Notably, best compounds 26 and 39 showed EC50 values in the range of 0.54-1.36 μM against cells infected with the four dengue serotypes (DENV-1-4). Time-of-drug-addition assay revealed that analogue 26 is a post-entry replication inhibitor that appears to be specific for cells of primate origin, implicating a host target with a high barrier to resistance. In conclusion, SAA derivatives offer a valuable starting point for developing effective Dengue antiviral therapeutics.

Studies of Cytotoxicity Effects, SARS-CoV-2 Main Protease Inhibition, and in Silico Interactions of Synthetic Chalcones

SARS-CoV-2 main protease (M^pro ) plays an essential role in proteolysis cleavage that promotes coronavirus replication. Thus, attenuating the activity of this enzyme represents a strategy to develop antiviral agents. We report inhibitory effects against M^pro of 40 synthetic chalcones, and cytotoxicity activities, hemolysis, and in silico interactions of active compounds. Seven of them bearing a (E)-3-(furan-2-yl)-1-arylprop-2-en-1-one skeleton (10, 28, and 35-39) showed enzyme inhibition with IC₅₀ ranging from 13.76 and 36.13 μM. Except for 35 and 36, other active compounds were not cytotoxic up to 150 μM against THP-1 and Vero cell lines. Compounds 10, and 35-39 showed no hemolysis while 28 was weakly hemotoxic at 150 μM. Moreover, molecular docking showed interactions between compound 10 and M^pro (PDBID 5RG2 and 5RG3) with proximity to cys145 and His41, suggesting a covalent binding. Products of the reaction between chalcones and cyclohexanethiol indicated that this binding could be a Michael addition type.

Inhibitors of ABCG2-mediated multidrug resistance: Lead generation through computer-aided drug design

Human breast cancer resistance protein (BCRP), known also as ABCG2, plays a major role in multiple drug resistance (MDR) in tumor cells. Through this ABC transporter, cancer cells acquire the ability of resistance to structurally and functionally unrelated anticancer drugs. Nowadays, the design of ABCG2 inhibitors as potential agents to enhance the chemotherapy efficacy is an interesting strategy. In this context, we have used computer-aided drug design (CADD) based on available data of a large series of potent inhibitors from our groups as an approach in guiding the design of effective ABCG2 inhibitors. We report therein the results on the use of the FLAPpharm method to elucidate the pharmacophoric features of one of the ABCG2 binding sites involved in the regulation of the basal ATPase activity of the transporter. The predictivity of the model was evaluated by testing three predicted compounds which were found to induce high inhibitory activity of BCRP, in the nanomolar range for the best of them.

Selene-Ethylenelacticamides and N-Aryl-Propanamides as Broad-Spectrum Leishmanicidal Agents

The World Health Organization classifies Leishmania as one of the 17 “neglected diseases” that burden tropical and sub-tropical climate regions with over half a million diagnosed cases each year. Despite this, currently available anti-leishmania drugs have high toxicity and the potential to be made obsolete by parasite drug resistance. We chose to analyze organoselenides for leishmanicidal potential given the reduced toxicity inherent to selenium and the displayed biological activity of organoselenides against Leishmania. Thus, the biological activities of 77 selenoesters and their N-aryl-propanamide derivatives were predicted using robust in silico models of Leishmania infantum, Leishmania amazonensis, Leishmania major, and Leishmania (Viannia) braziliensis. The models identified 28 compounds with >60% probability of demonstrating leishmanicidal activity against L. infantum, and likewise, 26 for L. amazonesis, 25 for L. braziliensis, and 23 for L. major. The in silico prediction of ADMET properties suggests high rates of oral absorption and good bioavailability for these compounds. In the in silico toxicity evaluation, only seven compounds showed signs of toxicity in up to one or two parameters. The methodology was corroborated with the ensuing experimental validation, which evaluated the inhibition of the Promastigote form of the Leishmania species under study. The activity of the molecules was determined by the IC50 value (µM); IC50 values < 20 µM indicated better inhibition profiles. Sixteen compounds were synthesized and tested for their activity. Eight molecules presented IC50 values < 20 µM for at least one of the Leishmania species under study, with compound NC34 presenting the strongest parasite inhibition profile. Furthermore, the methodology used was effective, as many of the compounds with the highest probability of activity were confirmed by the in vitro tests performed.

Regression analysis for predicting the elasticity of liquid crystal elastomers

It is highly desirable but difficult to understand how microscopic molecular details influence the macroscopic material properties, especially for soft materials with complex molecular architectures. In this study we focus on liquid crystal elastomers (LCEs) and aim at identifying the design variables of their molecular architectures that govern their macroscopic deformations. We apply the regression analysis using machine learning (ML) to a database containing the results of coarse grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of a surrogate model generated by the regression analysis is also tested. The database contains design variables for LCE molecular architectures, system and simulation conditions, and stress-strain curves for each LCE molecular system. Regression analysis is applied using the stress-strain curves as objective variables and the other factors as explanatory variables. The results reveal several descriptors governing the stress-strain curves. To test the predictive performance of the surrogate model, stress-strain curves are predicted for LCE molecular architectures that were not used in the ML scheme. The predicted curves capture the characteristics of the results obtained from molecular dynamics simulations. Therefore, the ML scheme has great potential to accelerate LCE material exploration by detecting the key design variables in the molecular architecture and predicting the LCE deformations.

Multiassay Profiling of a Focused Small Molecule Library Reveals Predictive Bidirectional Modulation of the lncRNA MALAT1 Triplex Stability In Vitro

The rapidly accelerating characterization of RNA tertiary structures has revealed their pervasiveness and active roles in human diseases. Small molecule-mediated modulation of RNA tertiary structures constitutes an attractive avenue for the development of tools for therapeutically targeting and/or uncovering the pathways associated with these RNA motifs. This potential has been highlighted by targeting of the triple helix present at the 3'-end of the noncoding RNA MALAT1, a transcript implicated in several human diseases. This triplex has been reported to decrease the susceptibility of the transcript to degradation and promote its cellular accumulation. While small molecules have been shown to bind to and impact the stability of the MALAT1 triple helix, the small molecule properties that lead to these structural modulations are not well understood. We designed a library utilizing the diminazene scaffold, which is underexplored but precedented for nucleic acid binding, to target the MALAT1 triple helix. We employed multiple assays to holistically assess what parameters, if any, could predict the small molecule affinity and effect on triplex stability. We designed and/or optimized competition, calorimetry, and thermal shift assays as well as an enzymatic degradation assay, the latter of which led to the discovery of bidirectional modulators of triple helix stability within the scaffold-centric library. Determination of quantitative structure-activity relationships afforded predictive models for both affinity- and stability-based assays. This work establishes a suite of powerful orthogonal biophysical tools for the evaluation of small molecule:RNA triplex interactions that generate predictive models and will allow small molecule interrogation of the growing body of disease-associated RNA triple helices.

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.

Classification models and SAR analysis on thromboxane A2 synthase inhibitors by machine learning methods

Thromboxane A2 synthase (TXS) is a promising drug target for cardiovascular diseases and cancer. In this work, we conducted a structure-activity relationship (SAR) study on 526 TXS inhibitors for bioactivity prediction. Three types of descriptors (MACCS fingerprints, ECFP4 fingerprints, and MOE descriptors) were utilized to characterize inhibitors, 24 classification models were developed by support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN). Then we reduced the number of fingerprints according to the contribution of descriptors to the models, and constructed 16 extra models on simplified fingerprints. In general, Model_4D built by DNN algorithm and 67 bits MACCS fingerprints performs best. The prediction accuracy of the model on the test set is 0.969, and Matthews correlation coefficient (MCC) is 0.936. The distance between compound and model (dSTD-PRO) was used to characterize the application domain of the model. In the test set of Model_4D, dSTD-PRO of 91.5% compounds is lower than the corresponding training set threshold (threshold0.90 = 0.1055), and the accuracy of these compounds is 0.983. In addition, the important descriptors were summarized and further analyzed. It showed that aromatic nitrogenous heterocyclic groups were beneficial to improve the bioactivity of TXS inhibitors.

Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.

Design, synthesis, biological evaluation and QSAR analysis of novel N-substituted benzimidazole derived carboxamides

As a result of our previous research focussed on benzimidazoles, herein we present design, synthesis, QSAR analysis and biological activity of novel N-substituted benzimidazole derived carboxamides. Carboxamides were designed to study the influence of the number of methoxy groups, the type of the substituent placed at the benzimidazole core on biological activity. Pronounced antioxidative activity displayed unsubstituted 28 (IC₅₀ ≈ 3.78 mM, 538.81 mmolFe²⁺/mmolC) and dimethoxy substituted derivative 34 (IC₅₀ ≈ 5.68 mM, 618.10 mmolFe²⁺/mmolC). Trimethoxy substituted 43 and unsubstituted compound 40 with isobutyl side chain at N atom showed strong activity against HCT116 (IC₅₀ ≈ 0.6 µM, both) and H 460 cells (IC₅₀ ≈ 2.5 µM; 0.4 µM), being less cytotoxic towards non-tumour cell. Antioxidative activity in cell generally confirmed relatively modest antioxidant capacity obtained in DPPH/FRAP assays of derivatives 34 and 40. The 3D-QSAR models were generated to explore molecular properties that have the highest influence on antioxidative activity.

FragExplorer: GRID-Based Fragment Growing and Replacement

Understanding which chemical modifications can be made to known ligands is a key aspect of structure-based drug design and one that was pioneered by the software GRID. We developed FragExplorer with the explicit aim of showing GRID users which fragments would best match the GRID molecular interaction fields in a protein binding site, given a bound ligand as a starting point. Users can grow ligands or replace existing moieties; the R-Group Exploration mode identifies all potential R-Groups and searches for replacements automatically; the Scaffold Exploration mode does the same for all potential scaffolds. For a ligand with three points of variation, R-Group Exploration will typically explore a chemical space of 10¹⁶ potential molecules; including Scaffold Exploration increases this to 10²². FragExplorer was designed to be integrated within an interactive 3D Editor/Designer; therefore, the speed of computation was an important consideration; a typical fragment search takes 20 seconds. In a fragment reprediction test, FragExplorer demonstrates an overall fragment retrieval rate of 55%, increasing to 69% for smaller fragments. At a 90% substructural match, the retrieval rate increases to ∼80%. We also show how the approach could have been used to hop from olmesartan to azilsartan or to optimize a p38 MAP kinase lead to a compound that bears similarity to a known nanomolar inhibitor.

Pharmacophore-Based Discovery of Substrates of a Novel Drug/Proton-Antiporter in the Human Brain Endothelial hCMEC/D3 Cell Line

A drug/proton-antiporter, whose the molecular structure is still unknown, was previously evidenced at the blood-brain barrier (BBB) by functional experiments. The computational method could help in the identification of substrates of this solute carrier (SLC) transporter. Two pharmacophore models for substrates of this transporter using the FLAPpharm approach were developed. The trans-stimulation potency of 40 selected compounds for already known specific substrates ([3H]-clonidine) were determined and compared in the human brain endothelial cell line hCMEC/D3. Results. The two pharmacophore models obtained were used as templates to screen xenobiotic and endogenous compounds from four databases (e.g., Specs), and 45 hypothetical new candidates were tested to determine their substrate capacity. Psychoactive drugs such as antidepressants (e.g., imipramine, desipramine), antipsychotics/neuroleptics such as phenothiazine derivatives (chlorpromazine), sedatives anti-histamine-H1 drugs (promazine, promethazine, triprolidine, pheniramine), opiates/opioids (e.g., hydrocodone), trihexyphenidyl and sibutramine were correctly predicted as proton-antiporter substrates. The best performing pharmacophore model for the proton-antiporter substrates appeared as a good predictor of known substrates and allowed the identification of new substrate compounds. This model marks a new step in the characterization of this drug/proton-antiporter and will be of great use in uncovering its substrates and designing chemical entities with an improved influx capability to cross the BBB.

Destabilizers of the thymidylate synthase homodimer accelerate its proteasomal degradation and inhibit cancer growth

Drugs that target human thymidylate synthase (hTS), a dimeric enzyme, are widely used in anticancer therapy. However, treatment with classical substrate-site-directed TS inhibitors induces over-expression of this protein and development of drug resistance. We thus pursued an alternative strategy that led us to the discovery of TS-dimer destabilizers. These compounds bind at the monomer-monomer interface and shift the dimerization equilibrium of both the recombinant and the intracellular protein toward the inactive monomers. A structural, spectroscopic, and kinetic investigation has provided evidence and quantitative information on the effects of the interaction of these small molecules with hTS. Focusing on the best among them, E7, we have shown that it inhibits hTS in cancer cells and accelerates its proteasomal degradation, thus causing a decrease in the enzyme intracellular level. E7 also showed a superior anticancer profile to fluorouracil in a mouse model of human pancreatic and ovarian cancer. Thus, over sixty years after the discovery of the first TS prodrug inhibitor, fluorouracil, E7 breaks the link between TS inhibition and enhanced expression in response, providing a strategy to fight drug-resistant cancers.

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications

Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly desirable for in silico drug discovery and medicinal chemistry applications. In this work, we revised a well‐established molecular mechanics' force field and applied a hybrid quantum mechanics and machine learning approach to parametrize the hydrogen‐bonding (HB) potentials of small molecules, improving this aspect of the molecular description. Approximately 66,000 molecules were chosen from available drug databases and subjected to density functional theory calculations (DFT). For each atom, the molecular electrostatic potential (EP) was extracted and used to derive new HB energy contributions; this was subsequently combined with a fingerprint‐based description of the structural environment via partial least squares modeling, enabling the new potentials to be used for molecules outside of the training set. We demonstrate that parameter prediction for molecules outside of the training set correlates with their DFT‐derived EP, and that there is correlation of the new potentials with hydrogen‐bond acidity and basicity scales. We show the newly derived MIFs vary in strength for various ring substitution in accordance with chemical intuition. Finally, we report that this derived parameter, when extended to non‐HB atoms, can also be used to estimate sites of reaction.

Selection of antileishmanial sesquiterpene lactones from SistematX database using a combined ligand-/structure-based virtual screening approach

Leishmaniasis refers to a complex of diseases, caused by the intracellular parasitic protozoans belonging to the genus Leishmania. Among the three types of disease manifestations, the most severe type is visceral leishmaniasis, which is caused by Leishmania donovani, and is diagnosed in more than 20,000 cases annually, worldwide. Because the current therapeutic options for disease treatment are associated with several limitations, the identification of new potential leads/drugs remains necessary. In this study, a combined approach was used, based on two different virtual screening (VS) methods, which were designed to select promising antileishmanial agents from among the entire sesquiterpene lactone (SL) dataset registered in SistematX, a web interface for managing a secondary metabolite database that is accessible by multiple platforms on the Internet. Thus, a ChEMBL dataset, including 3159 and 1569 structures that were previously tested against L. donovani amastigotes and promastigotes in vitro, respectively, was used to develop two random forest models, which performed with greater than 74% accuracy in both the cross-validation and test sets. Subsequently, a ligand-based VS assay was performed against the 1306 SistematX-registered SLs. In parallel, the crystal structures of three L. donovani target proteins, N-myristoyltransferase, ornithine decarboxylase, and mitogen-activated protein kinase 3, and a homology model of pteridine reductase 1 were used to perform a structure-based VS, using molecular docking, of the entire SistematX SL dataset. The consensus analysis of these two VS approaches resulted in the normalization of probability scores and identified 13 promising, enzyme-targeting, antileishmanial SLs from SistematX that may act against L. donovani. A combined approach based on two different virtual screening methods (structure-based and ligand-based) was performed using an in-house dataset composed of 1306 sesquiterpene lactones to identify potential antileishmanial (Leishmania donovani) structures.

Neuroprotective Effect of 1,4-Naphthoquinones in an In Vitro Model of Paraquat and 6-OHDA-Induced Neurotoxicity

Targeted screening using the MTT cell viability test with a mini-library of natural and synthetic 1,4-naphthoquinones and their derivatives was performed in order to increase the survival of Neuro-2a neuroblastoma cells in in vitro paraquat and 6-hydroxydopamine models of Parkinson’s disease. As a result, 10 compounds were selected that could protect neuronal cells from the cytotoxic effects of both paraquat and 6-hydroxydopamine. The five most active compounds at low concentrations were found to significantly protect the activity of nonspecific esterase from the inhibitory effects of neurotoxins, defend cell biomembranes from lytic destruction in the presence of paraquat and 6-hydroxydopamine, and normalize the cell cycle. The protective effects of these compounds are associated with the suppression of oxidative stress, decreased expression of reactive oxygen species and nitric oxide formation in cells and normalization of mitochondrial function, and restoration of the mitochondrial membrane potential altered by neurotoxins. It was suggested that the neuroprotective activity of the studied 1,4-NQs is attributable to their pronounced antioxidant and free radical scavenging activity and their ability to reduce the amount of reactive oxygen species formed by paraquat and 6-hydroxydopamine action on neuronal cells. The significant correlation between the neuroprotective properties of 1,4-naphthoquinones and Quantitative Structure–Activity Relationship descriptors describing the physicochemical properties of these compounds means that the hydrophobicity, polarity, charge, and shape of the molecules can be of decisive importance in determining the biological activity of studied substances.

Exploring EZH2-Proteasome Dual-Targeting Drug Discovery through a Computational Strategy to Fight Multiple Myeloma

Multiple myeloma is an incurable plasma cell neoplastic disease representing about 10-15% of all haematological malignancies diagnosed in developed countries. Proteasome is a key player in multiple myeloma and proteasome inhibitors are the current first-line of treatment. However, these are associated with limited clinical efficacy due to acquired resistance. One of the solutions to overcome this problem is a polypharmacology approach, namely combination therapy and multitargeting drugs. Several polypharmacology avenues are currently being explored. The simultaneous inhibition of EZH2 and Proteasome 20S remains to be investigated, despite the encouraging evidence of therapeutic synergy between the two. Therefore, we sought to bridge this gap by proposing a holistic in silico strategy to find new dual-target inhibitors. First, we assessed the characteristics of both pockets and compared the chemical space of EZH2 and Proteasome 20S inhibitors, to establish the feasibility of dual targeting. This was followed by molecular docking calculations performed on EZH2 and Proteasome 20S inhibitors from ChEMBL 25, from which we derived a predictive model to propose new EZH2 inhibitors among Proteasome 20S compounds, and vice versa, which yielded two dual-inhibitor hits. Complementarily, we built a machine learning QSAR model for each target but realised their application to our data is very limited as each dataset occupies a different region of chemical space. We finally proceeded with molecular dynamics simulations of the two docking hits against the two targets. Overall, we concluded that one of the hit compounds is particularly promising as a dual-inhibitor candidate exhibiting extensive hydrogen bonding with both targets. Furthermore, this work serves as a framework for how to rationally approach a dual-targeting drug discovery project, from the selection of the targets to the prediction of new hit compounds.

Development of 2D, 3D-QSAR and Pharmacophore Modeling of Chalcones for the Inhibition of Monoamine Oxidase B

BACKGROUND

Selective and reversible types of MAO-B inhibitors have emerged as promising candidates for the management of neurodegenerative diseases. Several functionalized chalcone derivatives were shown to have potential reversible MAO-B inhibitory activity, which have recently been reported from our laboratory.

METHODS

With the experimental results of about 70 chalcone derivatives, we further developed a pharmacophore modelling, and 2D and 3D- QSAR analyses of these reported chalcones for MAO-B inhibition.

RESULTS

The 2D-QSAR model presented four variables (MATS7v, GATS 1i and 3i, and C-006) from 143 Dragon 7 molecular descriptors, with a r2 value of 0.76 and a Q2cv for cross-validation equal to 0.72. An external validation also was performed using 11 chalcones, obtaining a Q2ext value of 0.74. The second 3D-QSAR model using MLR (multiple linear regression) was built starting from 128 Volsurf+ molecular descriptors, being identified as 4 variables (Molecular descriptors): D3, CW1 and LgS11, and L2LGS. Adetermination coefficient (r2) value of 0.76 and a Q2cv for cross-validation equal to 0.72 were obtained for this model. An external validation also was performed using 11 chalcones and a Q2ext value of 0.74 was found.

CONCLUSION

This report exhibited a good correlation and satisfactory agreement between experiment and theory.

Structural bioinformatics used to predict the protein targets of remdesivir and flavones in SARS-CoV-2 infection

BACKGROUND

During the current SARS-CoV-2 pandemic, the identification of effective antiviral drugs is crucial. Unfortunately, no specific treatment or vaccine is available to date.

OBJECTIVE

Here, we aimed to predict the interactions between SARS-CoV-2 proteins and protein targets from the human body for some flavone molecules (kaempferol, morin, pectolinarin, myricitrin, and herbacetin) in comparison to synthetic compounds (hydroxychloroquine, remdesivir, ribavirin, ritonavir, AMD-070, favipiravir).

METHODS

Using MOE software and advanced bioinformatics and cheminformatics portals, we conducted an extensive analysis based on various structural and functional features of compounds, such as their amphiphilic field, flexibility, and steric features. The structural similarity analysis of natural and synthetic compounds was performed using Tanimoto coefficients. The interactions of some compounds with SARS-CoV-2 3CLprotease or RNA-dependent RNA polymerase were described using 2D protein-ligand interaction diagrams based on known crystal structures. The potential targets of considered compounds were identified using the SwissTargetPrediction web tool.

RESULTS

Our results showed that remdesivir, pectolinarin, and ritonavir present a strong structural similarity which may be correlated to their similar biological activity. As common molecular targets of compounds in the human body, ritonavir, kaempferol, morin, and herbacetin can activate multidrug resistance-associated proteins, while remdesivir, ribavirin, and pectolinarin appear as ligands for adenosine receptors.

CONCLUSION

Our evaluation recommends remdesivir, pectolinarin, and ritonavir as promising anti-SARS-CoV-2 agents.

Insights into the molecular properties underlying antibacterial activity of prenylated (iso)flavonoids against MRSA

High resistance towards traditional antibiotics has urged the development of new, natural therapeutics against methicillin-resistant Staphylococcus aureus (MRSA). Prenylated (iso)flavonoids, present mainly in the Fabaceae, can serve as promising candidates. Herein, the anti-MRSA properties of 23 prenylated (iso)flavonoids were assessed in-vitro. The di-prenylated (iso)flavonoids, glabrol (flavanone) and 6,8-diprenyl genistein (isoflavone), together with the mono-prenylated, 4'-O-methyl glabridin (isoflavan), were the most active anti-MRSA compounds (Minimum Inhibitory Concentrations (MIC) ≤ 10 µg/mL, 30 µM). The in-house activity data was complemented with literature data to yield an extended, curated dataset of 67 molecules for the development of robust in-silico prediction models. A QSAR model having a good fit (R²_adj 0.61), low average prediction errors and a good predictive power (Q²) for the training (4% and Q²_LOO 0.57, respectively) and the test set (5% and Q²_test 0.75, respectively) was obtained. Furthermore, the model predicted well the activity of an external validation set (on average 5% prediction errors), as well as the level of activity (low, moderate, high) of prenylated (iso)flavonoids against other Gram-positive bacteria. For the first time, the importance of formal charge, besides hydrophobic volume and hydrogen-bonding, in the anti-MRSA activity was highlighted, thereby suggesting potentially different modes of action of the different prenylated (iso)flavonoids.

Examination of Urinary Excretion of Unchanged Drug in Humans and Preclinical Animal Models: Increasing the Predictability of Poor Metabolism in Humans

PURPOSE

A dataset of fraction excreted unchanged in the urine (fe) values was developed and used to evaluate the ability of preclinical animal species to predict high urinary excretion, and corresponding poor metabolism, in humans.

METHODS

A literature review of fe values in rats, dogs, and monkeys was conducted for all Biopharmaceutics Drug Disposition Classification System (BDDCS) Class 3 and 4 drugs (n=352) and a set of Class 1 and 2 drugs (n=80). The final dataset consisted of 202 total fe values for 135 unique drugs. Human and animal data were compared through correlations, two-fold analysis, and binary classifications of high (fe ≥30%) versus low (<30%) urinary excretion in humans. Receiver Operating Characteristic curves were plotted to optimize animal fe thresholds.

RESULTS

Significant correlations were found between fe values for each animal species and human fe (p<0.05). Sixty-five percent of all fe values were within two-fold of human fe with animals more likely to underpredict human urinary excretion as opposed to overpredict. Dogs were the most reliable predictors of human fe of the three animal species examined with 72% of fe values within two-fold of human fe and the greatest accuracy in predicting human fe ≥30%. ROC determined thresholds of ≥25% in rats, ≥19% in dogs, and ≥10% in monkeys had improved accuracies in predicting human fe of ≥30%.

CONCLUSIONS

Drugs with high urinary excretion in animals are likely to have high urinary excretion in humans. Animal models tend to underpredict the urinary excretion of unchanged drug in humans.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity at cholinergic synapses in various regions of the nervous system. The inhibition of acetylcholinesterase is frequently used to treat Alzheimer's disease. In this study, a merged BindingDB and ChEMBL dataset containing molecules with reported half-maximal inhibitory concentration (IC₅₀) values for AChE (7032 molecules) was used to build machine learning classification models for selecting potential AChE inhibitors from the SistematX dataset (8593 secondary metabolites). A total of seven fivefold models with accuracy above 80% after cross-validation were obtained using three types of molecular descriptors (VolSurf, DRAGON 5.0, and bit-based fingerprints). A total of 521 secondary metabolites (6.1%) were classified as active in this stage. Subsequently, virtual screening was performed, and 25 secondary metabolites were identified as potential inhibitors of AChE. Separately, the crystal structure of AChE in complex with (-)-galantamine was used to perform molecular docking calculations with the entire SistematX dataset. Consensus analysis of both methodologies was performed. Only eight structures achieved combined probability values above 0.5. Finally, two sesquiterpene lactones, structures 15 and 24, were predicted to be able to cross the blood-brain barrier, which was confirmed in the VolSurf+ quantitative model, revealing these two structures as the most promising secondary metabolites for AChE inhibition among the 8593 molecules tested. A consensus analysis of classification models and molecular docking calculations identified four potential inhibitors of acetylcholinesterase from the SistematX dataset (8593 structures).

Permeability prediction in the beyond-Rule-of 5 chemical space: Focus on cyclic hexapeptides

Cyclic peptides (CPs) are gaining more and more relevance in drug discovery. Since one of their main drawbacks is poor permeability, the discovery of new orally available CP drugs requires computational tools that predict CP permeability in very early drug discovery. In this study we used a literature dataset of 62 cyclic hexapeptides to evaluate the performances of a number of in silico tools based on different computational theory to model and rationalize PAMPA and Caco-2 permeability values. In particular, we submitted the dataset to a) online calculators, b) QSPR strategies, c) a physics-based tool, d) a mixed approach and e) a kinetic method. This latter is an emergent strategy in which a few relevant conformations retrieved from a set of molecular dynamics (MD) simulations by the Markov State Model (MSM) are used to establish the compounds permeability. Both free and commercial software were used. Results were compared with a model based on experimental physicochemical descriptors. All the computational approaches but online calculators performed quite well and show that lipophilicity and not polarity is the main determinant of the investigated event. A second major outcome of the study is that the impact of flexibility on the permeability of the considered dataset cannot be unambiguously assessed. Finally, our comparative analysis, which also included not common applied strategies, allowed a sound evaluation of the pros and cons of the applied computational approaches.

Identification of Kaurane-Type Diterpenes as Inhibitors of Leishmania Pteridine Reductase I

The current treatments against Leishmania parasites present high toxicity and multiple side effects, which makes the control and elimination of leishmaniasis challenging. Natural products constitute an interesting and diverse chemical space for the identification of new antileishmanial drugs. To identify new drug options, an in-house database of 360 kauranes (tetracyclic diterpenes) was generated, and a combined ligand- and structure-based virtual screening (VS) approach was performed to select potential inhibitors of Leishmania major (Lm) pteridine reductase I (PTR1). The best-ranked kauranes were employed to verify the validity of the VS approach through LmPTR1 enzyme inhibition assay. The half-maximal inhibitory concentration (IC₅₀) values of selected bioactive compounds were examined using the random forest (RF) model (i.e., 2β-hydroxy-menth-6-en-5β-yl ent-kaurenoate (135) and 3α-cinnamoyloxy-ent-kaur-16-en-19-oic acid (302)) were below 10 μM. A compound similar to 302, 3α-p-coumaroyloxy-ent-kaur-16-en-19-oic acid (302a), was also synthesized and showed the highest activity against LmPTR1. Finally, molecular docking calculations and molecular dynamics simulations were performed for the VS-selected, most-active kauranes within the active sites of PTR1 hybrid models, generated from three Leishmania species that are known to cause cutaneous leishmaniasis in the new world (i.e., L. braziliensis, L. panamensis, and L. amazonensis) to explore the targeting potential of these kauranes to other species-dependent variants of this enzyme.

Imidazothiazole-based potent inhibitors of V600E-B-RAF kinase with promising anti-melanoma activity: biological and computational studies

A series of imidazothiazole derivatives possessing potential activity against melanoma cells were investigated for molecular mechanism of action. The target compounds were tested against V600E-B-RAF and RAF1 kinases. Compound 1zb is the most potent against both kinases with IC₅₀ values 0.978 and 8.2 nM, respectively. It showed relative selectivity against V600E mutant B-RAF kinase. Compound 1zb was also tested against four melanoma cell lines and exerted superior potency (IC₅₀ 0.18-0.59 µM) compared to the reference standard drug, sorafenib (IC₅₀ 1.95-5.45 µM). Compound 1zb demonstrated also prominent selectivity towards melanoma cells than normal skin cells. It was further tested in whole-cell kinase assay and showed in-cell V600E-B-RAF kinase inhibition with IC₅₀ of 0.19 µM. Compound 1zb induces apoptosis not necrosis in the most sensitive melanoma cell line, UACC-62. Furthermore, molecular dynamic and 3D-QSAR studies were done to investigate the binding mode and understand the pharmacophoric features of this series of compounds.

Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis

Pharmaceutical drug development relies heavily on the use of Reversed-Phase Liquid Chromatography methods. These methods are used to characterize active pharmaceutical ingredients and drug products by separating the main component from related substances such as process related impurities or main component degradation products. The results presented here indicate that retention models based on Quantitative Structure Retention Relationships can be used for de-risking methods used in pharmaceutical analysis and for the identification of optimal conditions for separation of known sample constituents from postulated/hypothetical components. The prediction of retention times for hypothetical components in established methods is highly valuable as these compounds are not usually readily available for analysis. Here we discuss the development and optimization of retention models, selection of the most relevant structural molecular descriptors, regression model building and validation. We also present a practical example applied to chromatographic method development and discuss the accuracy of these models on selection of optimal separation parameters.

In Silico Based Structural and Fingerprint Analysis of Structurally Diverse AT1 inhibitors

Background and Objective:

The development of pharmacologically active molecules for the treatment of hypertension and other cardiovascular diseases are important nowadays. In the present investigation, computational techniques have been implemented on Angiotensin II Type 1 (AT1) antagonists to develop better predictive models.

Methods:

Quantitative Structure Activity Relationship (QSAR) and structural patterns/fragments analyses were performed using physicochemical descriptors and MACCS Fingerprints calculaced from AT1 inhibitors collected from the literature.

Results:

The significant models developed have been validated by Leave One Out (LOO) and test set methods, which exhibit considerable Q2 values (>0.65 for the training set and >0.5 for the test set) and the R2pred values for the models are also >0.5. The applicability of the contributed descriptors in these models revealed that the chlorine atom, dipole moment, hydrogen bond donor atoms and electrostatic potential are negatively contributing, and the presence of bond between heavy atoms and the carbon atom connected with small side chain and topological polar vdW surface area are favorable for the AT1 antagonistic activity. The MACCS Fingerprints showed that the presence of atoms (kind of heavy atoms), such as N, O, and S, connected with other heteroatoms or carbon or any other atoms, through single or double bonds are predominantly present in highly active molecules. The presence of halogens, long chain alkanes, halogenated alkanes, and sulfur atoms attached with nitrogen through any atoms are responsible for decreased AT1 antagonistic activity.

Conclusion:

The results have provided additional information on the structural patterns of the compounds based on its MACCS Fingerprints, which may be used for further characterization and design of novel AT1 inhibitors.

New quinoxaline compounds as DPP-4 inhibitors and hypoglycemics: design, synthesis, computational and bio-distribution studies

The current work represents the design and synthetic approaches of a new set of compounds 6–10 bearing the 1,4-dimethyl-2,3-dioxo-1,2,3,4-tetrahydroquinoxaline-6-sulfonamide scaffold. The biological evaluation revealed that most of the new compounds were promising selective dipeptidyl peptidase-IV (DPP-4) inhibitors and in vivo hypoglycemic agents utilizing linagliptin as a standard drug. The acute toxicity examination confirmed the safety profile of all compounds. Molecular docking studies related the significant DPP-4 suppression activity of compounds 9a, 10a, 10f, 10g to their nice fitting in the active pocket of DPP-4. In addition, the molecular dynamic study exhibited the stability of both 10a and 10g within the active site of DPP-4. The QSAR study showed that the difference between the predicted activities is very close to the experimental suppression effect. Moreover, both compounds 10a and 10g obeyed Lipinski's rule, indicating their efficient oral bioavailability. Compound 10a was radiolabeled, forming the ¹³¹I-SQ compound 10a to study the pharmacokinetic profile of this set of compounds. The biodistribution pattern hit the target protein since the tracer accumulated mainly in the visceral organs where DPP-4 is secreted in a high-level, thus with consequent stimulation of insulin secretion, leading to the target hypoglycemic effect.

The current work represents the design and synthetic approaches of a new set of compounds 6–10 bearing the 1,4-dimethyl-2,3-dioxo-1,2,3,4-tetrahydroquinoxaline-6-sulfonamide scaffold.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Synthesis, Cytotoxic Activity Evaluation and Quantitative Structure-Activity Analysis of Substituted 5,8-Dihydroxy-1,4-Naphthoquinones and their O- and S-Glycoside Derivatives Tested Against Neuro-2a Cancer Cells

Based on 6,7-substituted 2,5,8-trihydroxy-1,4-naphtoquinones (1,4-NQs) derived from sea urchins, five new acetyl-O-glucosides of NQs were prepared. A new method of conjugation of per-O-acetylated 1-mercaptosaccharides with 2-hydroxy-1,4-NQs through a methylene spacer was developed. Methylation of 2-hydroxy group of quinone core of acetylthiomethylglycosides by diazomethane and deacetylation of sugar moiety led to 28 new thiomethylglycosidesof 2-hydroxy- and 2-methoxy-1,4-NQs. The cytotoxic activity of starting 1,4-NQs (13 compounds) and their O- and S-glycoside derivatives (37 compounds) was determined by the MTT method against Neuro-2a mouse neuroblastoma cells. Cytotoxic compounds with EC50 = 2.7-87.0 μM and nontoxic compounds with EC50 > 100 μM were found. Acetylated O- and S-glycosides 1,4-NQs were the most potent, with EC50 = 2.7-16.4 μM. Methylation of the 2-OH group innaphthoquinone core led to a sharp increase in the cytotoxic activity of acetylated thioglycosidesof NQs, which was partially retained for their deacetylated derivatives. Thiomethylglycosides of 2-hydroxy-1,4-NQs with OH and MeO groups in quinone core at positions 6 and 7, resprectively formed a nontoxic set of compounds with EC50 > 100 μM. A quantitative structure-activity relationship (QSAR) model of cytotoxic activity of 22 1,4-NQ derivatives was constructed and tested. Descriptors related to the cytotoxic activity of new 1,4-NQ derivatives were determined. The QSAR model is good at predicting the activity of 1,4-NQ derivatives which are unused for QSAR models and nontoxic derivatives.

Predicting the Permeability of Macrocycles from Conformational Sampling - Limitations of Molecular Flexibility

Macrocycles constitute superior ligands for targets that have flat binding sites but often require long synthetic routes, emphasizing the need for property prediction prior to synthesis. We have investigated the scope and limitations of machine learning classification models and of regression models for predicting the cell permeability of a set of denovo-designed, drug-like macrocycles. 2D-Based classification models, which are fast to calculate, discriminated between macrocycles that had low-medium and high permeability and may be used as virtual filters in early drug discovery projects. Importantly, stereo- and regioisomer were correctly classified. QSPR studies of two small sets of comparator drugs suggested that use of 3D descriptors, calculated from biologically relevant conformations, would allow development of more precise regression models for late phase drug projects. However, a 3D permeability model could only be developed for a rigid series of macrocycles. Comparison of NMR based conformational analysis with in silico conformational sampling indicated that this shortcoming originates from the inability of the molecular mechanics force field to identify the relevant conformations for flexible macrocycles. We speculate that a Kier flexibility index of ≤10 constitutes a current upper limit for reasonably accurate 3D prediction of macrocycle cell permeability.

Understanding the Metabolism of Proteolysis Targeting Chimeras (PROTACs): The Next Step toward Pharmaceutical Applications

Hetero-bifunctional PROteolysis TArgeting Chimeras (PROTACs) represent a new emerging class of small molecules designed to induce polyubiquitylation and proteasomal-dependent degradation of a target protein. Despite the increasing number of publications about the synthesis, biological evaluation, and mechanism of action of PROTACs, the characterization of the pharmacokinetic properties of this class of compounds is still minimal. Here, we report a study on the metabolism of a series of 40 PROTACs in cryopreserved human hepatocytes at multiple time points. Our results indicated that the metabolism of PROTACs could not be predicted from that of their constituent ligands. Their linkers' chemical nature and length resulted in playing a major role in the PROTACs' liability. A subset of compounds was also tested for metabolism by human cytochrome P450 3A4 (CYP3A4) and human aldehyde oxidase (hAOX) for more in-depth data interpretation, and both enzymes resulted in active PROTAC metabolism.