Prediction of human intestinal absorption by GA feature selection and support vector machine regression

Synthesis, design, and optimization of a potent and selective series of pyridylpiperazines as promising antimalarial agents

An optimization of the pyridylpiperazine series against Plasmodium falciparum has been performed, exploring a structure-activity relationship carried out on the toluyl fragment of hit 1, a compound with low micromolar activity against Plasmodium falciparum discovered by high-throughput screening. After confirming the crucial role played by this aryl fragment in the antiplasmodial activity, the replacement of the ortho-methyl substituent of 1 by halogenated ones led to an improvement for four analogs, either in terms of potency, expected pharmacokinetics profile, or both. Further introduction of endocyclic nitrogens in this fragment identified two more optimized compounds, 20 and 23, which are expected to be much more metabolically stable than 1. Additional assessment of the cytotoxicity, Ligand Lipophilic Efficiency, potency against the chloroquine-resistant Dd2 strain and in silico ADMET predictions revealed a satisfactory profile for most compounds, ultimately identifying the four optimized compounds 7, 9, 20 and 23 as promising compounds for further lead optimization of this series against Plasmodium falciparum.

Chemical Composition Antioxidant and Anti-Inflammatory Activities of Myrtus communis L. Leaf Extract: Forecasting ADMET Profiling and Anti-Inflammatory Targets Using Molecular Docking Tools

Compounds derived from natural sources continue to serve as chemical scaffolds for designing prophylactic/therapeutic options for human healthcare. In this study, we aimed to systematically unravel the chemical profile and antioxidant and anti-inflammatory activities of myrtle methanolic extract (MMEx) using in vitro, in vivo, and in silico approaches. High levels of TPC (415.85 ± 15.52 mg GAE/g) and TFC (285.80 ± 1.64 mg QE/g) were observed. Mass spectrophotometry (GC-MS) analysis revealed the presence of 1,8-cineole (33.80%), α-pinene (10.06%), linalool (4.83%), p-dimethylaminobenzophenone (4.21%), thunbergol (4%), terpineol (3.60%), cis-geranyl acetate (3.25%), and totarol (3.30%) as major compounds. MMEx induced pronounced dose-dependent inhibition in all assays, and the best antioxidant activity was found with H2O2, with an IC50 of 17.81 ± 3.67 µg.mL-1. MMEx showed a good anti-inflammatory effect in vivo by limiting the development of carrageenan-induced paw edema. The pharmacokinetic profiles of the active molecules were determined using the SwissADME website, followed by virtual screening against anti-inflammatory targets including phospholipase A2 (PLA-2), cyclooxygenase-2 (COX-2), tumor necrosis factor alpha (TNF-α), interleukin-1β (IL-1β), and NF-κB. A pharmacokinetic study revealed that the molecules have good absorption, distribution, and metabolism profiles, with negative organ toxicity. Among the compounds identified by GC-MS analysis, pinostrobin chalcone, cinnamyl cinnamate, hedycaryol, totarol, and p-dimethylaminobenzophenone were observed to have good binding scores, thus appreciable anti-inflammatory potential. Our study reveals that MMEx from Algerian Myrtus communis L. can be considered to be a promising candidate for alleviating many health complaints associated with oxidative stress and inflammation.

Interpersonal neural synchronization underlies mnemonic similarity during collaborative remembering

Although collaborative remembering is a ubiquitous feature of human beings, its underlying neurocognitive process is not well understood. Here we hypothesized that interpersonal neural synchronization (INS) might underlie collaborative remembering, while real collaboration as opposed to other modes of offline collaboration should enhance INS and facilitate mnemonic similarity. To test these hypotheses, brain activity was measured simultaneously from two individuals who performed a group-based selective retrieval practice task either in a real collaboration or in a pseudo-collaboration, i.e., an individual performed the task together with a pre-recorded audio. The results showed that the memory of two individuals converged to a greater level than the chance level in real collaboration but not in control condition. Moreover, collaborative remembering was associated with significant INS increase in the prefrontal cortex (PFC) relative to the baseline in the real collaboration only. Additionally, INS increase was significantly greater in the real collaboration than in control condition. Finally, the PFC's INS increase was positively correlated with and could accurately predict the level of mnemonic similarity in real collaboration. These findings support the hypothesis that the enhanced INS underlies the cognitive process of collaborative remembering.

Computational Exploration of Fluorocyclopentenyl-purines and-pyrimidines Derivatives as Potential Inhibitors of Epidermal Growth Factor Receptor (EGFR) for the Treatment of Breast Cancer

The Epidermal Growth Factor Receptor (EGFR) is an important therapeutic target for the treatment of a variety of epithelial malignancies, including breast cancer, in which EGFR is aberrantly expressed.The fluorocyclopentenyl-purine-pyrimidines derivatives, which have previously been described as powerful compounds against breast cancer, were selected to investigate their potential against EGFR using computational tools in an effort to obtain potent inhibitors with fewer adverse effects. The molecule's chemical reactivity and stability were assessed by determining the HOMO-LUMO energy gap using density functional theory (DFT) calculations. Among all the selected compounds, PU4 displayed a HOMO-LUMO gap of 0.191 eV. Additionally, molecular docking analysis was performed to assess the binding affinities of PU4 within the active pocket of EGFR-TK. The compound PU4 showed potent interactions with EGFR exhibiting -32.3 kJ/mol binding energy which was found best as compared to gefitinib i. e., -27.4 kJ/mol which was further validated by molecular dynamics simulations and ADMET analysis. The results of these analyses indicate that the top hits obtained from the virtual screening possess the ability to act as effective EGFR inhibitor. Therefore, it is recommended to further investigate the inhibitory potential of these identified compounds using in vitro and in vivo approaches.

In silico analysis of identified molecules using LC-HR/MS of Cupressus sempervirens L. ethyl acetate fraction against three monkeypox virus targets

Monkeypox virus is a viral disease transmitted to humans through contact with infected animals, such as monkeys and rodents, or through direct contact with the bodily fluids or lesions of infected humans. The aim of this study is to evaluate in silico the inhibition effect of eight Cupressus sempervirens L. ethyl acetate fraction identified molecules using LC-MS on three monkeypox targets such as the vaccinia virus thymidylate kinase (VTK), the viral profilin-like protein (VPP), and the viral RNA polymerase (VRP). The study consist of using molecular docking with AutoDock vina based on the lowest energy value in kcal/mol, pharmacokinetics prediction with pre-ADMET v2.0 server, and prediction of biological activity with the PASS server tool. The best complexes were subjected to molecular dynamics simulation (MD) study to confirm their stability using Desmond software. The used molecules were vitamin C, vanillic acid (Pol), Flav1 (Catechin), Flav2 (Epicatechin), Flav3 (Hyperoside), Flav4 (Luteolin), Flav5 (Taxifolin), and Flav6 (Quercetin). The results show that flavonoids are potent to VTK, VPP and effectively block the VRP channel with energy values ranging from -7.0 to -9.3 kcal/mol. Further, MD simulation supports Flav1 and, Flav2 for notable stability in the VTK binding pocket through hydrogen and hydrophobic interactions. PASS results predicted various biological activities with promising VTK and VRP inhibition activities. The studied molecules could constitute a safer alternative to current drugs, which often cause adverse side effects.Communicated by Ramaswamy H. Sarma.

Verbal information exchange enhances collective performance through increasing group identification

Information exchange is a key factor in the attainment of collective outcomes and the navigation of social life. In the current study, we investigated whether and how information exchange enhanced collective performance by combining behavioral and neuroimaging approaches from the perspective of multiparticipant neuroscience. To evaluate collective performance, we measured the collaborative problem-solving abilities of triads working on a murder mystery case. We first found that verbal information exchange significantly enhanced collective performance compared to nonverbal exchange. Moreover, both group sharing and group discussion positively contributed to this effect, with group discussion being more essential. Importantly, group identification mediated the positive effect of verbal information exchange on collective performance. This mediation was supported by higher interactive frequency and enhanced within-group neural synchronization (GNS) in the dorsolateral prefrontal cortex (DLPFC). Taken together, we provided a multiparticipant theoretical model to explain how verbal information exchange enhanced collective performance. Our findings deepen the insight into the workings of group decision-making.

Pharmacokinetic correlation of structurally modified chalcone derivatives as promising leads to treat tuberculosis

In this study, we evaluated the potential of curated structurally modified chalcone derivatives as anti-tuberculosis (TB) agents through computer-aided drug design. Compounds from the flavonoid family known as chalcones were identified by the chemical group 1,3-diaryl-2-propen-1-one. After a search of the literature, 14 outstanding structurally modified chalcones were selected and evaluated for inhibitory activity against Mycobacterium tuberculosis H37Rv targets. The therapeutic potential of the chalcones was directly based on the drug-likeness and pharmacokinetic properties of the synthesized compounds. Prompt drug selection and personalized therapy are required to prevent TB from progressing and spreading to others. Pharmacokinetic parameters helps in the identification of lead molecule, at the earlier stages of drug development.

Cupressus sempervirens L. flavonoids as potent inhibitors to xanthine oxidase: in vitro, molecular docking, ADMET and PASS studies

Excessive intake of purine-rich foods such as seafood and red meat leads to excess xanthine oxidase activity and provokes gout attacks. The aim of this paper is to evaluate in vitro and in silico, the inhibition effect of Cupressus sempervirens plant extracts (flavonoids (Cae) and alkaloids (CaK)) and its six derivative compounds on bovine xanthine oxidase (BXO). The in silico study consists of molecular docking with GOLD v4.0 based on the best PLPchem score (PLP) and prediction of biological activity with the PASS server tool. The inhibitors used were lignan (cp1), Amentoflavone (cp2), Cupressuflavone (cp3), Isocryptomerin (cp4), Hinokiflavone (cp5), and Neolignan (cp6). The in vitro results showed that CaK gives an IC50 of 3.52 ± 0.04 μg/ml. Similarly, Cae saved an IC50 of 8.46 ± 1.98 μg/ml compared with the control (2.82 ± 0.10 μg/ml). The in silico results show that cp1 was the best inhibitor model (PLP of 88.09) with approved pharmacokinetics. These findings suggest that cp1 and cp2 may offer good alternatives for the treatment of hyperuricemia; cp3 was moderate, while the others (cp4 to cp6) were considered weak inhibitors according to their PLP.Communicated by Ramaswamy H. Sarma.

Soft sensor based rapid detection of trace chlorine dioxide (ClO2) concentration in water

Chlorine dioxide (ClO2) is a widely used sterilizer and a disinfectant across a multitude of industries. When using ClO2, it is imperative to measure the ClO2 concentration to abide by the safety regulations. This study presents a novel, soft sensor method based on Fourier transform infrared spectroscopy (FTIR) spectroscopy for measurement of ClO2 concentration in different water samples varying from milli Q to wastewater. Six distinct artificial neural network models were constructed and evaluated based on three overarching statistical standards to select the optimal model. The OPLS-RF model outperformed all other models with R2, RMSE, and NRMSE values of 0.945, 0.24, and 0.063, respectively. The developed model demonstrated limit of detection and limit of quantification values of 0.1 and 0.25 ppm, respectively, for water. Furthermore, the model also exhibited good reproducibility and precision as measured by the BCMSEP (0.064). The soft sensor-based method presented in the study offers significant advantages in terms of simplicity and speedy detection. In summary, the study presents development of a soft sensor that is capable of predicting the trace content of chlorine dioxide ranging between 0.1 to 5 ppm in a water sample by connecting FTIR with an OPLS-RF model.

In Silico Screening and Identification of Antidiabetic Inhibitors Sourced from Phytochemicals of Philippine Plants against Four Protein Targets of Diabetes (PTP1B, DPP-4, SGLT-2, and FBPase)

Current oral medications for type 2 diabetes target a single main physiological mechanism. They either activate or inhibit receptors to enhance insulin sensitivity, increase insulin secretion, inhibit glucose absorption, or inhibit glucose production. In advanced stages, combination therapy may be required because of the limited efficacy of single-target drugs; however, medications are becoming more costly, and there is also the risk of developing the combined side effects of each drug. Thus, identifying a multi-target drug may be the best strategy to improve treatment efficacy. This study sees the potential of 2657 Filipino phytochemicals as a source of natural inhibitors against four targets of diabetes: PTP1B, DPP-4, SGLT-2, and FBPase. Different computer-aided drug discovery techniques, including ADMET profiling, DFT optimization, molecular docking, MD simulations, and MM/PBSA energy calculations, were employed to elucidate the stability and determine the binding affinity of the candidate ligands. Through in silico methods, we have identified seven potential natural inhibitors against PTP1B, DPP-4, and FBPase, and ten against SGLT-2. Eight plants containing at least one natural inhibitor of each protein target were also identified. It is recommended to further investigate the plants' potential to be transformed into a safe and scientifically validated multi-target drug for diabetes therapies.

Distinct inter-brain synchronization patterns underlying group decision-making under uncertainty with partners in different interpersonal relationships

Humans may behave in different manners when making decisions with friends and strangers. Whether the interpersonal relationship and the characteristics of the individuals in the group affected the group decision-making under uncertainty in the real-time interaction remains unknown. Using the turn-based Balloon Analogue Risk Task (BART), the present study examined the group decision-making propensity under uncertainty with partners in different interpersonal relationships and interpersonal orientations. Corresponding inter-brain synchronization (IBS) patterns at the prefrontal cortex (PFC) were also uncovered with the fNIRS-based hyperscanning approach. Behavioral results identified that dyads in the friend group exhibited the uncertainty-averse propensity when comparing with the stranger group. The fNIRS results reported that feedback-related IBS at the left inferior frontal gyrus (l-IFG) and medial frontopolar cortex (mFPC) during different feedbacks was modulated by interpersonal relationships. The IBS at all channels in the PFC during the positive and negative feedbacks, respectively, predicted the decision-making propensity under uncertainty in the stranger and friend groups based on the support vector machine (SVM) algorithm. The moderating role of the social value orientation (SVO) was also verified in the mediation effect of the dyad closeness on the decision-making propensity under uncertainty via the IBS at the right lateral frontopolar cortex (r-FPC). These findings demonstrated disparate behavioral responses and inter-brain synchronization patterns underlying group decision-making under uncertainty with partners in different interpersonal relationships.

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling

Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.

Novel 7-Chloro-4-aminoquinoline-benzimidazole Hybrids as Inhibitors of Cancer Cells Growth: Synthesis, Antiproliferative Activity, in Silico ADME Predictions, and Docking

In this study, new 7-chloro-4-aminoquinoline-benzimidazole compounds were synthesized and characterized by NMR, MS, and elemental analysis. These novel hybrids differ in the type of linker and in the substituent on the benzimidazole moiety. Their antiproliferative activities were evaluated on one non-tumor (MDCK1) and seven selected tumor (CaCo-2, MCF-7, CCRF-CEM, Hut78, THP-1, and Raji) cell lines by MTT test and flow cytometry analysis. The compounds with different types of linkers and an unsubstituted benzimidazole ring, 5d, 8d, and 12d, showed strong cytotoxic activity (the GI₅₀ ranged from 0.4 to 8 µM) and effectively suppressed the cell cycle progression in the leukemia and lymphoma cells. After 24 h of treatment, compounds 5d and 12d induced the disruption of the mitochondrial membrane potential as well as apoptosis in HuT78 cells. The drug-like properties and bioavailability of the compounds were calculated using the Swiss ADME web tool, and a molecular docking study was performed on tyrosine-protein kinase c-Src (PDB: 3G6H). Compound 12d showed good solubility and permeability and bound to c-Src with an energy of -119.99 kcal/mol, forming hydrogen bonds with Glu310 and Asp404 in the active site and other residues with van der Waals interactions. The results suggest that compound 12d could be a leading compound in the further design of effective antitumor drugs.

Design of novel quinoline derivatives as antibreast cancer using 3D-QSAR, molecular docking and pharmacokinetic investigation

Breast cancer has been one of the most challenging women's cancers and leading cause of mortality for decades. There are several studies being conducted all the time to find a cure for breast cancer. Quinoline derivatives have shown their potential as antitumor agents in breast cancer therapy. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking with aromatase enzyme (Protein Data Bank: 3S7S) studies were performed to suggest the current scenario of quinoline derivatives as antitumor agents and to refine the path of these derivatives to discover and develop new drugs against breast cancer. For developing the 3D-QSAR model, comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were included. To attain the high level of predictability, the best CoMSIA model was applied. External validation utilizing a test set has been used in order to validate the predictive capabilities of the built model. According to the findings, electrostatic, hydrophobic and hydrogen bond donor, and acceptor fields had a significant impact on antibreast cancer activity. Thus, we generated a variety of novel effective aromatase inhibitors based on prior findings and we predicted their inhibitory activity using the built model. In addition, absorption, distribution, metabolism, elimination and toxicity properties were employed to explore the effectiveness of new drug candidates.

1,2,3-Triazole-Benzofused Molecular Conjugates as Potential Antiviral Agents against SARS-CoV-2 Virus Variants

SARS-CoV-2 and its variants, especially the Omicron variant, remain a great threat to human health. The need to discover potent compounds that may control the SARS-CoV-2 virus pandemic and the emerged mutants is rising. A set of 1,2,3-triazole and/or 1,2,4-triazole was synthesized either from benzimidazole or isatin precursors. Molecular docking studies and in vitro enzyme activity revealed that most of the investigated compounds demonstrated promising binding scores against the SARS-CoV-2 and Omicron spike proteins, in comparison to the reference drugs. In particular, compound 9 has the highest scoring affinity against the SARS-CoV-2 and Omicron spike proteins in vitro with its IC50 reaching 75.98 nM against the Omicron spike protein and 74.51 nM against the SARS-CoV-2 spike protein. The possible interaction between the synthesized triazoles and the viral spike proteins was by the prevention of the viral entry into the host cells, which led to a reduction in viral reproduction and infection. A cytopathic inhibition assay in the human airway epithelial cell line (Vero E6) infected with SARS-CoV-2 revealed the effectiveness and safety of the synthesized compound (compound 9) (EC50 and CC50 reached 80.4 and 1028.28 µg/mL, respectively, with a selectivity index of 12.78). Moreover, the antiinflammatory effect of the tested compound may pave the way to reduce the reported SARS-CoV-2-induced hyperinflammation.

Combined ligand-based and structure-based design of PDE 9A inhibitors against Alzheimer's disease

PDE9 enzyme hydrolyzes cGMP, which is involved in the regulation of synaptic plasticity through the NMDA pathway (a well-known excitotoxic target for AD) via activation of calcium/calmodulin-dependent neuronal NO synthases in the postsynaptic neurons. The inhibition of PDE9 leads to elevated cGMP levels, causing enhanced NMDA signaling and thus contributing to an increase in synaptic plasticity and stabilization. Therefore, it could be considered a pertinent target for AD drug discovery. PF-04447943 and BI-409306 targeting PDE9 are undergoing clinical trials (Phase II). The present study encompasses a pharmacophoric approach to identify potent PDE9 inhibitors using various computational methods. Pharmacophores generated from the PDB 6A3N yielded 37,554 virtual hits, which underwent drug likeliness and PAINS filtering to arrive at a few virtual leads. The leads were further subjected to extra precision docking, ADMET predictions, and molecular dynamics. The final hits, ZINC000001305675 and ZINC000000377099, showed superior docking scores of - 10.90 and - 10.30 kcal/mol and satisfactory predicted ADMET scores. The hits were subjected to molecular dynamics (MD) studies, wherein they formed stable complexes with PDE9 protein and had ligand RMSDs within acceptable limits. The processes involved in the combined ligand and structure-based strategies.

Neural mechanisms of the mood effects on third‐party responses to injustice after unfair experiences

Behavioral decision theory argues that humans can adjust their third‐party responses (e.g., punishment and compensation) to injustice by integrating unfair experiences. Typically, the mood plays an important role in such a decision‐making process. However, the underlying neurocognitive bases remain largely unclear. We first employ a modified third‐party justice game in which an allocator split an amount of money between oneself and a receiver. The participants can reapportion the money as observers by choosing from the following three costly options: compensate the receiver, accept the current allocation, or punish the allocator. Then, a second‐party pseudo interaction is conducted where participants receive more (i.e., advantageous unfair experience) or less (i.e., disadvantageous unfair experience) than others. Finally, participants perform the third‐party justice game again after unfair experiences. Here, we use functional near‐infrared spectroscopy (fNIRS) to measure participants' brain activities during third‐party responses to injustice. We find participants compensate more to the receiver after advantageous unfair experience, which involved enhanced positive emotion, weakened sense of unfairness, and is linked with increased activity in the right dorsolateral prefrontal cortex (rDLPFC). In contrast, participants punish more on the allocator after disadvantageous unfair experience, which might primarily stem from their negative emotional responses, strong sense of unfairness, and is associated with significantly decreased activity in the rDLPFC. Our results suggest that third‐party compensation and punishment involved differential psychological and neural bases. Our findings highlight the crucial roles of second‐party unfair experiences and the corresponding mood responses in third‐party responses to unfairness, and unravel the intermediate neural architecture.

Identifying potential natural inhibitors of Brucella melitensis Methionyl-tRNA synthetase through an in-silico approach

Background

Brucellosis is an infectious disease caused by bacteria of the genus Brucella. Although it is the most common zoonosis worldwide, there are increasing reports of drug resistance and cases of relapse after long term treatment with the existing drugs of choice. This study therefore aims at identifying possible natural inhibitors of Brucella melitensis Methionyl-tRNA synthetase through an in-silico approach.

Methods

Using PyRx 0.8 virtual screening software, the target was docked against a library of natural compounds obtained from edible African plants. The compound, 2-({3-[(3,5-dichlorobenzyl) amino] propyl} amino) quinolin-4(1H)-one (OOU) which is a co-crystallized ligand with the target was used as the reference compound. Screening of the molecular descriptors of the compounds for bioavailability, pharmacokinetic properties, and bioactivity was performed using the SWISSADME, pkCSM, and Molinspiration web servers respectively. The Fpocket and PLIP webservers were used to perform the analyses of the binding pockets and the protein ligand interactions. Analysis of the time-resolved trajectories of the Apo and Holo forms of the target was performed using the Galaxy and MDWeb servers.

Results

The lead compounds, Strophanthidin and Isopteropodin are present in Corchorus olitorius and Uncaria tomentosa (Cat’s-claw) plants respectively. Isopteropodin had a binding affinity score of -8.9 kcal / ml with the target and had 17 anti-correlating residues in Pocket 1 after molecular dynamics simulation. The complex formed by Isopteropodin and the target had a total RMSD of 4.408 and a total RMSF of 9.8067. However, Strophanthidin formed 3 hydrogen bonds with the target at ILE21, GLY262 and LEU294, and induced a total RMSF of 5.4541 at Pocket 1.

Conclusion

Overall, Isopteropodin and Strophanthidin were found to be better drug candidates than OOU and they showed potentials to inhibit the Brucella melitensis Methionyl-tRNA synthetase at Pocket 1, hence abilities to treat brucellosis. In-vivo and in-vitro investigations are needed to further evaluate the efficacy and toxicity of the lead compounds.

The cure for brucellosis involves a long course of treatment with a combination of antibiotics. However, some of the drugs are not recommended for very young children and pregnant women. Moreover, cases of relapse and resistance to these drugs are reported. With the Brucella Methionyl-tRNA synthetase as a target, molecular docking and virtual screening was used to identify possible drug candidates from a library of 1524 compounds obtained from edible African plants. Two lead compounds, Strophanthidin and Isopteropodin usually present in Corchorus olitorius and Uncaria tomentosa (Cat’s claw) plants showed potentials to inhibit the Brucella melitensis Methionyl-tRNA synthetase. Their bioactivities were also confirmed in their molecular dynamic simulation with the target protein. Consequently, both compounds have potentials for safety and efficacy in the treatment of brucellosis.

New styrylquinoxaline: synthesis, structural, biological evaluation, ADMET prediction and molecular docking investigations

The organic compound (E)-3-(4-methylstyryl)quinoxalin-2(1H)-one (SQO) with molecular formula C₁₇H₁₄N₂O was synthesized and analyzed using single crystal X-ray diffraction, ¹H, ¹³C NMR and FTIR spectroscopic techniques. The geometric parameters of the molecule was optimized by density-functional theory (DFT) choosing B3LYP with 6-31++G(d,p) basis set. For compatibility, the theoretical structure and experimental structure were overlapped with each other. Frontier molecular orbitals of the title compound were made, and energy gap between HOMO and LUMO was calculated. Molecular electrostatic potential map was generated finding electrophilic and nucleophilic attack centers using DFT method. Hirshfeld surface analysis (HSA) confirms active regions at the circumference of N1 atoms and O1 atoms that form intermolecular N1-H1···O1 hydrogen bond. The acute oral toxicity study was carried out according to OECD guideline, which approve that the compound SQO was non-toxic. In addition, this quinoxaline derivative was evaluated for its in vitro antidiabetic activity against α-glucosidase and α-amylase enzymes and for antioxidant activity by utilizing several tests as 1,1-diphenyl-2-picryl hydrazyl, (2,2'-azino-bis(3-ethyl benzthiazoline-6-sulfonicacid), reducing power test (FRAP) and hydrogen peroxide activity H₂O₂. The molecular docking studies were performed to investigate the antidiabetic activity of SQO and compared with the experimental results. SQO is a potent antidiabetic from both the experimental and molecular docking results. Finally, the physicochemical, pharmacokinetic and toxicological properties of SQO have been evaluated by using in silico absorption, distribution, metabolism, excretion and toxicity analysis prediction.

Computer-aided identification of potential inhibitors against Necator americanus glutathione S-transferase 3

Hookworm infection is caused by the blood-feeding hookworm gastrointestinal nematodes. Its harmful effects include anemia and retarded growth and are common in the tropics. A current control method involves the mass drug administration of synthetic drugs, mainly albendazole and mebendazole. There are however concerns of low efficacy and drug resistance due to their repeated and excessive use. Although, Necator americanus glutathione S-transferase 3 (Na-GST-3) is a notable target, using natural product libraries for computational elucidation of promising leads is underexploited. This study sought to use pharmacoinformatics techniques to identify compounds of natural origins with the potential to be further optimized as promising inhibitors. A compendium of 3182 African natural products together with five known helminth GST inhibitors including Cibacron blue was screened against the active sites of the Na-GST-3 structure (PDB ID: 3W8S). The hit compounds were profiled to ascertain the mechanisms of binding, anthelmintic bioactivity, physicochemical and pharmacokinetic properties. The AutoDock Vina docking protocol was validated by obtaining 0.731 as the area under the curve calculated via the receiver operating characteristics curve. Four compounds comprising ZINC85999636, ZINC35418176, ZINC14825190, and Dammarane Triterpene13 were identified as potential lead compounds with binding energies less than -9.0 kcal/mol. Furthermore, the selected compounds formed key intermolecular interactions with critical residues Tyr95, Gly13 and Ala14. Notably, ZINC85999636, ZINC14825190, and dammarane triterpene13 were predicted as anthelmintics, whilst all the four molecules shared structural similarities with known inhibitors. Molecular modelling showed that the compounds had reasonably good binding free energies. More so, they had high binding affinities when screened against other variants of the Na-GST, namely Na-GST-1 and Na-GST-2. Ligand quality assessment using ligand efficiency dependent lipophilicity, ligand efficiency, ligand efficiency scale and fit quality scale showed the molecules are worthy candidates for further optimization. The inhibitory potentials of the molecules warrant in vitro studies to evaluate their effect on the heme regulation mechanisms.

Sharing Happy Stories Increases Interpersonal Closeness: Interpersonal Brain Synchronization as a Neural Indicator

Our lives revolve around sharing emotional stories (i.e., happy and sad stories) with other people. Such emotional communication enhances the similarity of story comprehension and neural across speaker-listener pairs. The theory of Emotions as Social Information Model (EASI) suggests that such emotional communication may influence interpersonal closeness. However, few studies have examined speaker-listener interpersonal brain synchronization (IBS) during emotional communication and whether it is associated with meaningful aspects of the speaker-listener interpersonal relationship. Here, one speaker watched emotional videos and communicated the content of the videos to 32 people as listeners (happy/sad/neutral group). Both speaker and listeners’ neural activities were recorded using EEG. After listening, we assessed the interpersonal closeness between the speaker and listeners. Compared with the sad group, sharing happy stories showed a better recall quality and a higher rating of interpersonal closeness. The happy group showed higher IBS in the frontal cortex and left temporoparietal cortex than the sad group. The relationship between frontal IBS and interpersonal closeness was moderated by sharing happy/sad stories. Exploratory analysis using support vector regression (SVR) showed that the IBS could also predict the ratings of interpersonal closeness. These results suggest that frontal IBS could serve as an indicator of whether sharing emotional stories facilitate interpersonal closeness. These findings improve our understanding of emotional communication among individuals that guides behaviors during interpersonal interactions.

Identification of Terpenoids From Abrus precatorius Against Parkinson's Disease Proteins Using In Silico Approach

Parkinson’s disease (PD) is the second major neuro-degenrative disorder that causes morbidity and mortality among older populations. Terpenoids were reported as potential neuro-protective agents. Therefore, this study seeks to unlock the inhibitory potential of terpenoids from Abrus precatorius seeds against proteins involve in PD pathogenesis. In this study, in silico molecular docking of 5 terpenoids derived from high-performance liquid chromatography (HPLC) analysis of A. precatorius seeds against α-synuclein, catechol-o-methyltransferase, and monoamine oxidase B which are markers of PD was performed using Autodock vina. The absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) of the hits were done using Swiss ADME predictor and molecular dynamic (MD) simulation of the hit-protein complex was performed using Desmond Schrodinger software. Five out of 6 compounds satisfied the ADME/Tox parameters and showed varying degrees of binding affinities with selected proteins. Drimenin-α-synuclein complex showed the lowest binding energy of −9.1 kcal/mol followed by interaction with key amino acid residues necessary for α-synuclein inhibition. The selection of this complex was justified by its stability in MD simulation conducted for 10 ns and exhibited stable interaction in terms of root mean square deviation (RMSD) and root mean square deviation error fluctuation (RMSF) values.

Urea-thiazole/benzothiazole hybrids with a triazole linker: synthesis, antimicrobial potential, pharmacokinetic profile and in silico mechanistic studies

Some urea-thiazole/benzothiazole hybrids with a triazole linker were synthesized via Cu(I)-catalysed click reaction. After successfully analysed by various spectral techniques including FTIR, NMR and HRMS, antimicrobial screening of the synthesized hybrids along with their precursors was carried out against two Gram (+) bacteria (Staphylococcus aureus and Bacillus endophyticus), two Gram (-) bacteria (Escherichia coli and Pseudomonas fluorescens) and two fungi (Candida albicans and Rhizopus oryzae). All the synthesized compounds (4a-4l) displayed better biological response than the standard fluconazole against both of the tested fungi. Compounds 4h and 4j were found to be the most active compounds against R. oryzae and C. albicans, respectively. Molecular docking of hybrid 4j and its alkyne precursor 1b in the active site of C. albicans target sterol 14-α demethylase was also performed and was also supported by molecular dynamics studies. In silico ADME prediction of synthesized urea-thiazole/benzothiazole hybrids with a triazole linker and their alkyne precursors was also predicted.

Anti-melanogenesis and anti-tyrosinase properties of aryl-substituted acetamides of phenoxy methyl triazole conjugated with thiosemicarbazide: Design, synthesis and biological evaluations

A series of aryl phenoxy methyl triazole conjugated with thiosemicarbazides were designed, synthesized, and evaluated for their tyrosinase inhibitory activities in the presence of l-dopa and l-tyrosine as substrates. All the compounds showed tyrosinase inhibition in the sub-micromolar concentration. Among the derivatives, compound 9j bearing benzyl displayed exceptionally high potency against tyrosinase with IC₅₀ value of 0.11 μM and 0.17 μM in the presence of l-tyrosine and l-dopa as substrates which is significantly lower than that of kojic acid as the positive control with an IC₅₀ value of 9.28 μM for l-tyrosine and 9.30 μM for l-dopa. According to Lineweaver-Burk plot, 9j demonstrated an uncompetitive type of inhibition in the kinetic assay. Also, in vitro antioxidant activities determined by DPPH assay recorded an IC₅₀ value of 68.43 μM for 9i. The melanin content of 9j was determined on B16F10 melanoma human cells which demonstrated a significant reduction of the melanin content. Moreover, the binding energies corresponding to the same ligand as well as computer-aided drug-likeness and pharmacokinetic studies were also carried out. Compound 9j also possessed metal chelation potential correlated to its high anti-TYR activity.

Hispidin, Harmaline, and Harmine as potent inhibitors of bovine xanthine oxidase: Gout treatment, in vitro, ADMET prediction, and SAR studies

Alkaloids and phenols are potent inhibitors family for many enzymes used in many therapies. We aim to evaluate in vitro and in silico, the inhibition effect of Hispidin, Harmaline, and Harmine as pure molecules to bovine milk xanthine oxidase (BXO), Molecular docking and SAR study with GOLD was done to explain the mechanism of action related to its inhibition, ADMET parameters were checked to confirm their pharmacokinetics (PK) using preADMET 2.0 server, we classified our inhibitors by applying five drug-likeness rules, the best-ranked inhibitors were chosen based on the approved ADMET properties, drug-likeness qualifications, and the best PLPchem score generated by GOLD. The in vitro results show important inhibition activity to BXO comparing to the control with an IC50 of 39.72 ± 3.60 µM, 51.00 ± 1.0 µM, and 48.52 ± 1.76 µM for Hispidin, Harmaline, and Harmine respectively. The in silico results show that Hispidin was the best inhibitor model with approved ADMET properties and qualification in all drug-likeness rules; Harmaline was saved second-best model to BXO with suitable ADMET properties and qualified in most drug-likeness rules. Eventually, Harmine was ranked third potent inhibitor model with acceptable ADMET properties, drug-likeness rules, and PLPchem score. The tested inhibitors could be significant in drug discovery, especially in treating gout disease; therefore, drug development, including clinical trials, should be done with promising results.

Virtual screening and molecular simulation study of natural products database for lead identification of novel coronavirus main protease inhibitors

3CL like protease (3CLpro or Mpro) is one of the main proteases of 2019-nCoV. The 3CLpro is a nonstructural protein of SARS-CoV and has an essential role in viral replication and transcription, thus, could be a potential target for anti-SARS drug development. The present study employed ligand- and structure-based approaches to identify the potent inhibitors of 2019-nCoV protease. The e-pharmacophore developed from 3CLpro-1 yielded virtual hits, that were subjected through drug likeliness and PAINS filters to remove interfering compounds. Further comprehensive docking studies, free energy calculations and ADMET studies resulted in two virtual leads- MolPort-000-410-348 and MolPort-002-530-156. The compounds MolPort-000-410-348 and MolPort-002-530-156 displayed good docking score of -12.09 and -13.38 Kcal/mol and free binding energy of -63.34 ± 2.03 and -61.52 ± 2.24 Kcal/mol, respectively. The compounds also exhibited satisfactory predicted ADMET profile and were subjected to molecular dynamic (MD) studies. The MD simulation produced stable complexes of these ligands with 3CLpro protein and ligand RMSD in acceptable limits. Communicated by Ramaswamy H. Sarma.

α-Mangostin and its derivatives against estrogen receptor alpha

Estrogen receptor alpha (ERα) acts as the transcription factor and the main therapeutic target against breast cancer. One of the compounds that has been shown to act as an ERα is α-mangostin. However, it still has weaknesses due to its low solubility and low potent activity. In this study, α-mangostin was modified by substituting -OH group at C6 using benzoyl derivatives through a step by step in silico study, namely pharmacokinetic prediction (https://preadmet.bmdrc.kr/adme/), pharmacophore modeling (LigandScout 4.1), molecular docking simulation (AutoDock 4.2), molecular dynamics simulation (AMBER 16) and a binding free energy analysis using MM-PBSA method. From the computational studies, three compounds which are derived from α-mangostin (AMB-1 (-9.84 kcal/mol), AMB-2 (-6.80 kcal/mol) and AMB-10 (-12.42 kcal/mol)) have lower binding free energy than α-mangostin (-1.77 kcal/mol), as evidenced by the binding free energy calculation using the MM-PBSA method. They can then be predicted to have potent activities as ERα antagonists.Communicated by Ramaswamy H. Sarma.

Antiviral activity of astragaloside II, astragaloside III and astragaloside IV compounds against dengue virus: Computational docking and in vitro studies

This study was aimed to identify the phytocompounds possessing anti-dengue virus activity using in silico and in vitro approaches. A total of 7000 phytocompounds were virtually screened against protein targets (envelope, NS2b/NS3, and NS5) of dengue virus using iGEMDOCK and individually docked using Maestro 10.7 module of Schrödinger software. In vitro cytotoxicity and antiviral studies were performed using vero cell line. Finally, three phytocompounds namely astragaloside II, astragaloside III, and astragaloside IV were screened based on their highest binding energy values against protein targets. Astragaloside III exhibited the highest interaction energy value of -8.718 kcal/mol and -8.447 kcal/mol against envelope, and NS2b/NS3 targets, respectively. Astragaloside IV exhibited -7.244 kcal/mol against SAM site, and -9.179 kcal/mol against RNA cap site of NS5 targets. In silico ADMET analysis revealed that astragaloside II, III, and IV were non-mutagenic and non-carcinogenic in nature and these compounds were also non-toxic to vero cells upto 1000 μg/mL. Against dengue virus serotype 3, astragaloside II exhibited substantial antiviral activity at the concentration of 1.56 μg/mL followed by astragaloside III at 6.25 μg/mL and astragaloside IV at 12.5 μg/mL. Also, against dengue serotype 1, astragaloside II showed the maximum antiviral activity at 1.56 μg/mL followed by astragaloside III and IV at 3.125 μg/mL. This study concludes that astragaloside II, III, and IV compounds had potential in vitro anti-dengue virus activity.

Identification of Potential COX-2 Inhibitors for the Treatment of Inflammatory Diseases Using Molecular Modeling Approaches

Non-steroidal anti-inflammatory drugs are inhibitors of cyclooxygenase-2 (COX-2) that were developed in order to avoid the side effects of non-selective inhibitors of COX-1. Thus, the present study aims to identify new selective chemical entities for the COX-2 enzyme via molecular modeling approaches. The best pharmacophore model was used to identify compounds within the ZINC database. The molecular properties were determined and selected with Pearson’s correlation for the construction of quantitative structure–activity relationship (QSAR) models to predict the biological activities of the compounds obtained with virtual screening. The pharmacokinetic/toxicological profiles of the compounds were determined, as well as the binding modes through molecular docking compared to commercial compounds (rofecoxib and celecoxib). The QSAR analysis showed a fit with R = 0.9617, R² = 0.9250, standard error of estimate (SEE) = 0.2238, and F = 46.2739, with the tetra-parametric regression model. After the analysis, only three promising inhibitors were selected, Z-964, Z-627, and Z-814, with their predicted pIC₅₀ (−log IC₅₀) values, Z-814 = 7.9484, Z-627 = 9.3458, and Z-964 = 9.5272. All candidates inhibitors complied with Lipinski’s rule of five, which predicts a good oral availability and can be used in in vitro and in vivo tests in the zebrafish model in order to confirm the obtained in silico data.

Phytosterols demonstrate selective inhibition of COX-2: In-vivo and in-silico studies of Nicotiana tabacum

The inhibition of cyclooxygenase-1 (COX-1) enzyme by Nonsteroidal anti-inflammatory drugs (NSAIDs) exposes the gastrointestinal mucosa to peptic injuries. Selective inhibition of COX-2 generates surpassing anti-inflammatory drug candidates with reduced side effects over current NSAIDs. Phytosterols consumption is reported to decrease the risk of cardiovascular problems. Reports on the selective inhibition of COX-2 by phytosterols are scarce. The present study assesses the anti-inflammatory potentials of phytosterols from Nicotiana tabacum (of the family Solanaceae) through selective inhibition of COX-1 and/or COX-2. Virtual High Throughput Screening (vHTS) and Molecular Docking of phytochemicals from Nicotiana tabacum against the catalytic pockets of COX-1 and COX-2 were used to identify the lead bioactive(s) components of the plant. The hit phytosterols were isolated, histopathological examination of the stomach, in-vivo COX-1/COX-2 mRNAs expression patterns in the liver through reverse transcription-polymerase chain reactions, and enzymes activities of Nicotiana tabacum phytosterol isolates (NTPI) in HCl/ethanol-induced inflammation in Wistar rats were all investigated. Formation of hydrogen bonds favour selective inhibition of COX-2 while hydrophobic interactions favour selective inhibition of COX-1. NTPI demonstrates inhibition of COX-2 by down-regulate the expression of COX-2 mRNA and were ineffective against the expression COX-1 mRNA. NTPI demonstrates hepatoprotective abilities by improving the antioxidant defense system of the liver. Histopathological analyses show NTPI at 50 mg/kg bodyweight regenerates the parietal cells and maintain the gastrointestinal architecture. Drug likeness prediction and ADME toxicity screening show that phytosterols possess good oral bioavailability with no side effects. Phytosterols are selective inhibitors of COX-2, they are hepatoprotective, regenerate parietal cells, and non-toxic.

Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices

Magnetorheological elastomer (MRE) is a type of magnetic soft material consisting of ferromagnetic particles embedded in a polymeric matrix. MRE-based devices have characteristics of adjustable stiffness and damping properties, and highly nonlinear and hysteretic force–displacement responses that are dependent on external excitations and applied magnetic fields. To effectively implement the devices in mitigating the hazard vibrations of structures, numerically traceable and computationally efficient models should be firstly developed to accurately present the unique behaviors of MREs, including the typical Payne effect and strain stiffening of rubbers etc. In this study, the up-to-date phenomenological models for describing hysteresis response of MRE devices are experimentally investigated. A prototype of MRE isolator is dynamically tested using a shaking table in the laboratory, and the tests are conducted based on displacement control using harmonic inputs with various loading frequencies, amplitudes and applied current levels. Then, the test results are used to identify the parameters of different phenomenological models for model performance evaluation. The procedure of model identification can be considered as solving a global minimization optimization problem, in which the fitness function is the root mean square error between the experimental data and the model prediction. The genetic algorithm (GA) is employed to solve the optimization problem for optimal model parameters due to its advantages of easy coding and fast convergence. Finally, several evaluation indices are adopted to compare the performances of different models, and the result shows that the improved LuGre friction model outperforms other models and has optimal accuracy in predicting the hysteresis response of the MRE device.

Prediction of human intestinal absorption using micellar liquid chromatography with an aminopropyl stationary phase

The extent of human intestinal absorption (HIA) for a drug is considered to be an important pharmacokinetic parameter which must be determined for orally administered drugs. Traditional experimental methods relied upon animal testing and are renowned for being time consuming and expensive as well as being ethically unfavourable. As a result, the development of alternative methods to evaluate a drug's pharmacokinetics is crucial. Micellar liquid chromatography is considered to be one of these methods that can replace the use of animals in the prediction of HIA. In this study, the combination of an aminopropyl column with the biosurfactant sodium deoxycholate bile salt was used in the experimental determination of micelle-water partition coefficients (log P_mw ) for a group of compounds. Multiple linear regression was then used for the prediction of HIA using the experimentally determined log P_mw along with other molecular descriptors, leading to the construction of a model equation of R²  = 85% and a prediction power represented by R² _Pred. = 72%. The use of micellar liquid chromatography with an aminopropyl column in combination with sodium deoxycholate was found to be a good method for the prediction of human intestinal absorption, providing data for a far wider range of compounds compared with previous studies.

Incorporating physiologically relevant mobile phases in micellar liquid chromatography for the prediction of human intestinal absorption

Micellar liquid chromatography is a popular method used in the determination of a compound's lipophilicity. This study describes the use of the obtained micelle-water partition coefficient (log Pmw ) by such a method in the prediction of human intestinal absorption (HIA). As a result of the close resemblance of the novel composition of the micellar mobile phase to that of physiological intestinal fluid, prediction was deemed to be highly successful. The unique micellar mobile phase consisted of a mixed micellar mixture of lecithin and six bile salts, i.e. a composition matching that found in the human intestinal environment, prepared in ratios resembling those in the intestine. This is considered to be the first method to use a physiological mixture of biosurfactants in the prediction of HIA. As a result, a mathematical model with high predictive ability (R2 PRED  = 81%) was obtained using multiple linear regression. The micelle-water partition coefficient (log Pmw ) obtained from micellar liquid chromatography was found to be a successful tool for prediction where the final optimum model included log Pmw and polar surface area as key descriptors with high statistical significance for the prediction of HIA. This can be attributed to the nature of the mobile phase used in this study which contains the lecithin-bile salt complex, thus forming a bilayer system and therefore mimicking absorption across the intestinal membrane.

Formation of a Bile Salt-Drug Hydrogel to Predict Human Intestinal Absorption

The unique character of bile salts to self-assemble into hydrogels in the presence of halide salts was exploited in this work to facilitate the prediction of human intestinal absorption (%HIA) for a set of 25 compounds. This was achieved by firstly incorporating each compound separately within the process of gel formation to create a series of gel-drug membranes. Scanning electron microscopy analysis of the freeze-dried samples of the blank bile salt hydrogels and drug-loaded bile salt hydrogels indicated a unique microstructure made of a network of intertwined fibrils. Drug-loaded sodium deoxycholate hydrogels were then utilized as the donor phase to study permeability using flow-through and static diffusion cells. The resulting values of the release-permeability coefficient (K_p) were then analyzed, along with other molecular descriptors, for the %HIA using multiple linear regression. Overall, when comparing predicted values (using the systems presented in this study) with known literature values, it can be seen that both methods (i.e., using static and flow-through cells) had good predictability with R²_PRED values of 79.8% and 79.7%, respectively. This study therefore proposes a novel, accurate, and precise way to predict HIA for compounds of pharmaceutical interest using a simple in vitro permeation system. It is important to develop alternatives to the current methods used in prediction of HIA, which are expensive and time-consuming or include the use of animals. Therefore, the proposed method in this study being economic and time-saving provides superiority over these current methods and suggests the possibility of its use as an alternate to such methods for prediction of HIA.

Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues

A relatively larger dataset consisting of 970 compounds was collected. Classification RF models were established based on different training sets and different descriptors. model validation and evaluation.

Discovery and Optimization of Materials Using Evolutionary Approaches

Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries.

Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches

Human intestinal absorption (HIA) of the drugs administered through the oral route constitutes an important criterion for the candidate molecules. The computational approach for predicting the HIA of molecules may potentiate the screening of new drugs. In this study, ensemble learning (EL) based qualitative and quantitative structure-activity relationship (SAR) models (gradient boosted tree, GBT and bagged decision tree, BDT) have been established for the binary classification and HIA prediction of the chemicals, using the selected molecular descriptors. The structural diversity of the chemicals and the nonlinear structure in the considered data were tested by the similarity index and Brock-Dechert-Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in the literature. All the statistical criteria parameters derived for the performance of the constructed SAR models were above their respective thresholds suggesting for their robustness for future applications. In complete data, the qualitative SAR models rendered classification accuracy of >99%, while the quantitative SAR models yielded correlation (R(2)) of >0.91 between the measured and predicted HIA values. The performances of the EL-based SAR models were also compared with the linear models (linear discriminant analysis, LDA and multiple linear regression, MLR). The GBT and BDT SAR models performed better than the LDA and MLR methods. A comparison of our models with the previously reported QSARs for HIA prediction suggested for their better performance. The results suggest for the appropriateness of the developed SAR models to reliably predict the HIA of structurally diverse chemicals and can serve as useful tools for the initial screening of the molecules in the drug development process.

Advances in computationally modeling human oral bioavailability

Although significant progress has been made in experimental high throughput screening (HTS) of ADME (absorption, distribution, metabolism, excretion) and pharmacokinetic properties, the ADME and Toxicity (ADME-Tox) in silico modeling is still indispensable in drug discovery as it can guide us to wisely select drug candidates prior to expensive ADME screenings and clinical trials. Compared to other ADME-Tox properties, human oral bioavailability (HOBA) is particularly important but extremely difficult to predict. In this paper, the advances in human oral bioavailability modeling will be reviewed. Moreover, our deep insight on how to construct more accurate and reliable HOBA QSAR and classification models will also discussed.

How do antimalarial drugs reach their intracellular targets?

Drugs represent the primary treatment available for human malaria, as caused by Plasmodium spp. Currently approved drugs and antimalarial drug leads generally work against parasite enzymes or activities within infected erythrocytes. To reach their specific targets, these chemicals must cross at least three membranes beginning with the host cell membrane. Uptake at each membrane may involve partitioning and diffusion through the lipid bilayer or facilitated transport through channels or carriers. Here, we review the features of available antimalarials and examine whether transporters may be required for their uptake. Our computational analysis suggests that most antimalarials have high intrinsic membrane permeability, obviating the need for uptake via transporters; a subset of compounds appear to require facilitated uptake. We also review parasite and host transporters that may contribute to drug uptake. Broad permeability channels at the erythrocyte and parasitophorous vacuolar membranes of infected cells relax permeability constraints on antimalarial drug design; however, this uptake mechanism is prone to acquired resistance as the parasite may alter channel activity to reduce drug uptake. A better understanding of how antimalarial drugs reach their intracellular targets is critical to prioritizing drug leads for antimalarial development and may reveal new targets for therapeutic intervention.