ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

1,3-Naphthoxazine derivatives: Synthesis, in silico pharmacokinetic studies, antioxidant and photoprotective properties

Investigation into the interactions between photoprotective agents and skin can offer a precise understanding of their biological behaviors in vitro and in vivo, providing crucial insights for generating new substances. For this purpose, we designed and synthesized a series of naphthoxazine derivatives and examined their photoprotective properties. 1,3-naphthoxazine derivatives were synthesized through the multi-component reaction of 2-naphthol, arylamines and aromatic aldehydes in the presence of copper(II) trifluoromethanesulfonate (Cu(OTf)2) and (±)-Camphor-10-sulfonic acid ((±)-CSA) catalyst system under sonication. The potential of these synthesized 1,3-naphthoxazine derivatives as antioxidants and viable organic structural-based sunscreen ingredients has been investigated. Sun protection factor (SPF) assay results showed that especially compounds 4i, 4c, 4k, 4d, 4r, and 4h had remarkably high activity (23.65, 23.57, 23.04, 21.94, 20.80, and 20.26, respectively at 900 µg/mL concentration). Additionally, antioxidant activity of the synthesized compounds was evaluated and compounds 4h, 4e, 4b, and 4j exhibited the highest activities in DPPH scavenging activity assay (86.46 %, 82.83 %, 80.78 %, and 80.65 % respectively at 400 µg/mL concentration). The synthesized compounds exhibit promising characteristics for effective UV radiation absorption, suggesting their suitability for inclusion in sunscreen formulations. Cytotoxic activity of compound 4k against normal human fibroblast cell line (MRC-5) was determined by CVDK-8 method. The results revealed that the compound provided remarkable viability (87.55 %) of MRC-5 cells at concentration of 100 µM. The study explores their efficacy in providing broad-spectrum protection against UVA and UVB rays, degradation and photostability, ADMET profile, and other pharmacokinetic properties.

Mechanistic insights into antidiabetic potential of Ficus viren against multi organ specific diabetic targets: molecular docking, MDS, MM-GBSA analysis

Ficus viren has been traditionally used to treat diabetes, and its extract inhibits carbohydrate/lipid metabolism and possesses anti-hyperglycemic potential. However, there is conflicting investigation related to F. viren extract effect on carbohydrate metabolism. Thus, bioactive and mechanism behind its antidiabetic potential is still scanty. This study explored F. viren's anti-diabetic property by identifying potential phytoconstituents and mechanism. A sequential in-silico approach was used i.e., druglikeness, molecular docking, post-docking MM-GBSA, ADMET studies, molecular dynamic simulation (MDS), and post-MDS MM-GBSA. We screened ∼32 phytoconstituents and twelve potential organ-specific diabetic targets (O.S.D.Ts i.e., IR, DPP-4, ppar-γ, ppar-α, ppar-δ, GLP-1R, SIRT-1, AMPK, GSK-3β, RAGE, and AR). Drug likeness study identified 18 druggable candidates among 32 phytoconstituents. K3A, quercetin, scutellarein, sorbifolin, and vogeline J identified as potential ligands from druggable ligands, using IR as the standard target. Subsequently, potential ligands docked with remaining O.S.D.Ts. and data showed that K3A binds strongly with AMPK, ppar-δ, DPP-4, and GSK-3β, while scutellarein binds with AR and ppar-α. Sorbifolin, quercetin, and vogeline J binds with ppar-α, ppar-γ, and RAGE, respectively. Post-docking MM-GBSA data (∆GBind) also depicted potential ligand's strong binding affinities with their corresponding targets. Thereafter, simulation data revealed that only scutellarein and sorbifolin showed dynamic stability with their respective targets, i.e., AR/ppar-α and ppar-α, respectively. Interestingly, post-MDS MM-GBSA revealed that only scutellarein exhibited strong ∆GBind of -55.08 kcal/mol and -75.48 kcal/mol with AR and ppar-α, respectively. Though, collective computational analysis supports antidiabetic potential of F. viren through AR and ppar-α modulation by scutellarein.

3D-QSAR, ADMET, and molecular docking studies of aztreonam analogs as E. colis inhibitors

Background

The development of multidrug resistant strains of extended-spectrum β-lactamase-producing Escherichia coli has become a global problem; therefore, the discovery of new antibacterial agents is the only available solution.

Objective

To improve and propose new compounds with antibacterial activity, the three-dimensional quantitative structure-activity relationship and molecular docking studies were carried out on Aztreonam analogs as E. coli inhibitors in DNA gyrase B.

Method

This study's 3D-Quantitative structure-activity relationship model was created using on the Comparative Molecular Field Analysis and the Comparative Molecular Similarity Indices Analysis. Using the Comparative Molecular Field Analysis (Q 2 = 0.73; R 2 = 0.82), excellent predictability was achieved, and the best Comparative Molecular Similarity Indices Analysis model (Q 2 = 0.88; R 2 = 0.9). The generated model's ability to predict outcomes was assessed through external validation using a test set compound and an applicability domain technique. In this study, the steric, electrostatic, and hydrogen bond acceptor fields played a key role in antibacterial activity.

Results

The results of the molecular docking revealed that the newly generated compound A6 has the highest binding affinity with DNA gyrase B. It forms 10 hydrogen bonds with amino acid residues of Asn104, Asn274, Asn132, Ser70, Ser237, Thr105, Glu273, and 2 salt bridges with amino acid residues of Ser70 and Glu273 and one pi-pi interacting with Gys271 amino acid residue in the binding site of 5G1, and this result was validated by a new assessment method. We created some novel, highly effective DNA gyrase B inhibitors based on the earlier findings, and the most accurate model predicted their inhibitory actions. The ADMET characteristics and pharmacological similarity of these novel inhibitors were also examined.

Conclusion

These findings would be very beneficial in guiding the optimization process for the identification of novel drugs that can address the issue of multiple drug resistance.

Elaboration of chitosan nanoparticles loaded with star anise extract as a therapeutic system for lung cancer: Physicochemical and biological evaluation

The research study aimed to maximize the important medical role of star anise extract (SAE) through its loading on a widely available natural polymer (chitosan, Cs). Thus, SAE loaded chitosan nanoparticles (CsNPs) was prepared. The finding illustrated the formation of spherical particles of SAE loaded CsNPs as proved by transmission electron microscope (TEM). In addition, the average particle size of CsNPs and SAE loaded CsNPs are 131.8 ± 24.63 and 318.5 ± 73.94 nm, respectively. Scanning electron microscope (SEM) showed the presence of many spherical particles deposited on the surface of CsNPs owing to the deposition of SAE on the surface and encapsulated into pores of CsNPs. It also showed the presence of elements such as sodium, potassium, copper, magnesium, zinc, calcium, and iron, as well as the elements that accompanied with CsNPs: carbon, oxygen, nitrogen, and phosphorus. The extract was rich in bioactive components, such as anethole, shikimic acid, and different flavonoids, contributing to its medicinal qualities. The bioactive molecules in SAE were assessed by chromatographic analysis. Using the agar well diffusion test, the antibacterial qualities of CsNPs and SAE loaded CsNPs were evaluated against pathogenic bacteria linked to lung illnesses. The most significant inhibition zones showed that the SAE loaded CsNPs had the most antibacterial activity. The anticancer activity using MTT assay was used in the biological assessments to determine the cytotoxicity against the NCl-H460 lung cancer cell line. The results showed that CsNPs loaded with SAE considerably decreased cell viability in a dose-dependent manner, with the most significant anticancer impact by SAE loaded CsNPs. Furthermore, in vivo tests on lung cancer therapy revealed that when compared to other treatment groups, the SAE loaded CsNPs group showed the greatest reduction in tumor biomarkers and inflammation, as seen by decreased levels of Plasma malondialdehyde (MDA), tumor protein 53 (p53), Tumor necrosis factor-alpha (TNF- alpha), and fibronectin. Results concluded that these thorough characterizations, biological assessments, and antibacterial tests have confirmed the effective integration of SAE into CsNPs. Further, SAE loaded CsNPs could be a suitable option for various biomedical applications in tackling lung cancer and the inactivation of bacterial infection.

BMS345541 is predicted as a repurposed drug for the treatment of TMZ-resistant Glioblastoma using target gene expression and virtual drug screening

Glioblastoma (GBM) is one of the most aggressive and fatal cancers, for which Temozolomide (TMZ) chemo drug is commonly used for its treatment. However, patients gradually develop resistance to this drug, leading to tumor relapse. In our previous study, we have identified lncRNAs that regulate chemoresistance through the competing endogenous RNA (ceRNA) mechanism. In this study, we tried to find FDA-approved drugs against the target proteins of these ceRNA networks through drug repurposing using differential gene expression profiles, which could be used to nullify the effect of lncRNAs and promote the sensitivity of TMZ in GBM. We performed molecular docking and simulation studies of predicted repurposed drugs and their targets. Among the predicted repurposed drugs, we found BMS345541 has a higher binding affinity towards its target protein - FOXG1, making it a more stable complex with FOXG1-DNA. The ADMET analysis of this drug BMS345541 shows a higher half-life and lower cytotoxicity level than other predicted repurposed drugs. Hence, we conjecture that this could be a better drug for increasing the sensitivity of TMZ for treating GBM patients.

In vitro and in silico studies of alpha glucosidase inhibition and antifungal activity of coffea canephora husk

The coffea canephora husk, a protected agricultural crop, is abundant in Vietnam. Examining the effects of C. canephora husk compounds on α-glucosidase and antifungal drug activity was the primary objective of this research. A cholestane-type steroid, coffeacanol A (1), was extracted from the ethyl acetate extract. Three cholestane-type derivatives (2-4) and three additional known compounds (5-7) were separated, and we used a variety of chromatographic techniques to identify a total of six substances. We used NMR to determine the chemical structures of these substances. Extensive HR-MS-ESI analysis and NMR experimental data were used to confirm the structure of the novel metabolite (1). The cholestane-type steroid was initially discovered in the Coffea canephora husk, marking the first instance in the coffee plant family to reveal chemical structures (1-7). The inhibition of α-glucosidase was found to be significantly higher in all compounds tested, with the exception of compounds (2) and (5). In vitro, the positive control showed the lowest inhibition, and the range of IC50 values was calculated to be 27.4 to 96.5 μM, which is lower than the IC50 value of 214.50 μM for the acarbose control. With an IC50 value of 27.4 μM, compound (7) showed the greatest capacity to inhibit α-glucosidase among the test compounds. The 3TOP and 2VF5 enzyme crystal structures were used for in silico docking investigations and validations of compounds (1-7). In silico calculations to explain how compound (7) shows high activity in vitro via the enzyme inhibition mechanism by residual amino acids, like Gly 1102 (B chain) and Glu 1095 (B chain), and their relative interaction with compounds (7) and acarbose. Compound (7) exhibited the best antifungal activity against Candida albicans fungus among three fungi, namely Candida albicans, Trichophyton mentagrophytes, and Trichophyton rubrum, with a MIC value of 25 μM. Compound (7) and fluconazole combined to form similar interactions in the contact ligand model, including the functional group, capping group, and linker part, which interacted fully with the 2VF5 enzyme, leading to effective in vitro inhibition.

Screening and toxicity evaluation of natural compounds as adenosine 2a and 2b receptor ligands: insights from molecular docking, dynamics, and ADMET analysis

Recent studies suggest that immunological and inflammatory responses in cardiovascular disorders, such as hypertension, myocardial infarction, ischemia injury, heart failure, arrhythmias, and atherosclerosis, may be affected by changes in the adenosine system. Pharmacological modulation of adenosine occurs through its receptor subtypes. In numerous preclinical studies, the activation of adenosine receptor 2A (A2AR) or the blockade of adenosine receptor 2B (A2BR) has shown promising results against cardiovascular diseases. This in silico study aimed to identify potential natural compounds that can activate A2AR or block A2BR without causing toxicity. Natural compounds were screened using COlleCtion of Open Natural ProdUcTs (COCONUT) or Natural Product Activity and Species Source Database (NPASS) databases to find agonists for A2AR or an antagonists/inverse agonists for A2BR. These compounds were then pre-filtered based on their toxicity profiles. The remaining compounds were subjected to molecular docking against A2AR and A2BR followed by molecular dynamics simulations were conducted. Finally, selected compounds' ADMET properties were determined using ADMETlab 2.0 web tool. Ultimately, one novel natural compound with potential agonistic activity (COCONUT IDs: CNP0450901) for A2AR and one antagonist/inverse agonist (rauwolscine) for A2BR were identified.

Antihypertensive and Antidiabetic Drug Candidates from Milkfish (Chanos chanos)-Identification and Characterization through an Integrated Bioinformatic Approach

Integrated bioinformatics tools have created more efficient and robust methods to overcome in vitro challenges and have been widely utilized for the investigation of food proteins and the generation of peptide sequences. This study aimed to analyze the physicochemical properties and bioactivities of novel peptides derived from hydrolyzed milkfish (Chanos chanos) protein sequences and to discover their potential angiotensin-converting enzyme (ACE)- and dipeptidyl peptidase-4 (DPPIV)-inhibitory activities using machine learning-based tools, including BIOPEP-UWM, PeptideRanker, and the molecular docking software HADDOCK 2.4. Nine and three peptides were predicted to have ACE- and DPPIV-inhibitory activities, respectively. The DPPIV-inhibitory peptides were predicted to inhibit the compound with no known specific mode. Meanwhile, two tetrapeptides (MVWH and PPPS) were predicted to possess a competitive mode of ACE inhibition by directly binding to the tetra-coordinated Zn ion. Among all nine discovered ACE-inhibitory peptides, only the PPPS peptide satisfied the drug-likeness analysis requirements with no violations of the Lipinski rule of five and should be further investigated in vitro.

Integrating network toxicology and molecular docking to explore the toxicity of the environmental pollutant butyl hydroxyanisole: An example of induction of chronic urticaria

The study aimed to comprehensively investigate environmental pollutants' potential toxicity and underlying molecular mechanisms, focusing on chronic urticaria (CU) induced by butylated hydroxyanisole (BHA) exposure, further drawing public awareness regarding the potential risks of environmental pollutants, applying ChEMBL, STITCH, and SwissTargetPrediction databases to predict the targets of BHA, CTD, GeneCards, and OMIM databases to collect the relevant targets of CU. Ultimately, we identified 81 potential targets of BHA-induced CU and extracted 31 core targets, including TNF, SRC, CASP3, BCL2, IL2, and MMP9. GO and KEGG enrichment analyses revealed that these core targets were predominantly involved in cancer signaling, estrogen and endocrine resistance pathways. Furthermore, molecular docking confirmed the ability of BHA to bind with core targets. The onset and development of CU may result from BHA by affecting multiple immune signaling pathways. Our study elucidated the molecular mechanisms of BHA toxicity and its role in CU induction, providing the basis for preventing and treating chronic urticaria associated with environmental BHA exposure.

Identification of potential dipeptidyl peptidase IV inhibitors from the ConMedNP library by virtual screening, and molecular dynamics methods

In this study, we screened novel dipeptidyl peptidase IV (DPP4) inhibitors from the ConMedNP library consisting of 3507 molecules. Interestingly, molecular docking, ADMET, and the anti-diabetic activity predictions suggest that three molecules, namely OTH_UD_XX06_1, GB19, and BMC_000104, have a high binding affinity toward DPP4. The molecular dynamics (MD) simulation results suggest that these hit molecules have a stable binding pose and occupy the binding pockets throughout the 200 ns simulation. The presence of intermolecular H-bonding between the ligands and DPP4 was observed throughout the simulation period. Thus, docking and MD results, predicted that the three compounds were the most potent DPP4 inhibitors that could putatively bind to the DPP4 active site via both conventional H-bonding and hydrophobic interactions. These results could aid the discovery of new drugs to treat type 2 diabetes.

Repurposing approved protein kinase inhibitors as potent anti-leishmanials targeting Leishmania MAP kinases

AIMS

Leishmaniasis, caused by the protozoan parasite poses a significant health burden globally. With a very few specific drugs, increased drug resistance it is important to look for drug repurposing along with the identification of pre-clinical candidates against visceral leishmaniasis. This study aims to identify potential drug candidates against visceral leishmaniasis by targeting leishmanial MAP kinases and screening FDA approved protein kinase inhibitors.

MATERIALS AND METHODS

MAP kinases were identified from the Leishmania genome. 12 FDA approved protein kinase inhibitors were screened against Leishmania MAP kinases. Binding affinity, ADME and toxicity of identified drug candidates were profiled. The anti-proliferative effects and mechanism of action were assessed in Leishmania, including changes in cell morphology, flagellar length, cell cycle progression, reactive oxygen species (ROS) generation, and intra-macrophage parasitic burden.

KEY FINDINGS

23 MAP kinases were identified from the Leishmania genome. Sorafenib and imatinib emerged as repurposable drug candidates and demonstrated excellent anti-proliferative effects in Leishmania. Treatment with these inhibitors resulted in significant changes in cell morphology, flagellar length, and cell cycle arrest. Furthermore, sorafenib and imatinib promoted ROS generation and reduced intra-macrophage parasitic burden, and elicited anti-leishmanial activity in in vivo experimental VL models.

SIGNIFICANCE

Collectively, these results imply involvement of MAP kinases in infectivity and survival of the parasite and can pave the avenue for repurposing sorafenib and imatinib as anti-leishmanial agents. These findings contribute to the exploration of new treatment options for visceral leishmaniasis, particularly in the context of emerging drug resistance.

Synthesis of 2-aminothiazole sulfonamides as potent biological agents: Synthesis, structural investigations and docking studies

A simplified synthetic approach involving sulfonylation followed by amino group alkylation produced new 2-aminothiazole derivatives. UV/Vis, infrared, and NMR spectroscopies confirmed their structures. Compounds 36, 22, 34, and 35 showed strong inhibition against Jack bean and Bacillus Pasteurii urease, with IC50 values from 14.06 to 20.21 μM/mL. Compounds 20, 26, 21, 29, 30, 31, and 32 exhibited potent inhibitory effects against α-glucosidase and α-amylase, with IC50 values between 20.34 and 37.20 μM/mL. Compounds 33, 26, and 27 demonstrated potent DPPH scavenging, with IC50 values around 34.4-39.2 μM/mL. FMO analysis showed compounds 21, 22, 24, and 25 having parallel aromatic ring systems due to π cloud interactions, while compounds 32 and 38 had distinct electronic density distributions. Compound 22 had HOMO and LUMO energy gaps of 5.805 eV, with bromo and fluoro substitutions in compounds 21 and 24 slightly increasing the gaps to 6.089 eV and 6.078 eV, respectively. Nitro groups in compounds 25 and 32 reduced the gaps to 0.384 eV and 1.187 eV. All compounds demonstrated high gastrointestinal absorption, non-permeability to the blood-brain barrier, and optimal skin permeation (Log Kp between -5.83 and -6.54 cm/s). Compounds 22, 24, and 38 had promising QED scores of 0.719, 0.707, and 0.860, respectively, with synthetic accessibility scores from 2.057 to 2.517. ADMET predictions indicated minimal toxicity, cardiovascular safety, and significant inhibitory potential for CYP enzymes. Strong in silico binding affinities (binding energies -5.75 to -7.63 kcal/mol) and metabolic stability suggest these derivatives are promising candidates for further drug development.

Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation

Aim: Build a virtual screening model for ULK1 inhibitors based on artificial intelligence. Materials & methods: Build machine learning and deep learning classification models and combine molecular docking and biological evaluation to screen ULK1 inhibitors from 13 million compounds. And molecular dynamics was used to explore the binding mechanism of active compounds. Results & conclusion: Possibly due to less available training data, machine learning models significantly outperform deep learning models. Among them, the Naive Bayes model has the best performance. Through virtual screening, we obtained three inhibitors with IC50 of μM level and they all bind well to ULK1. This study provides an efficient virtual screening model and three promising compounds for the study of ULK1 inhibitors.

Computational insights into rational design and virtual screening of pyrazolopyrimidine derivatives targeting Janus kinase 3 (JAK3)

The Janus kinase 3 (JAK3) family, particularly JAK3, is pivotal in initiating autoimmune diseases such as rheumatoid arthritis. Recent advancements have focused on developing antirheumatic drugs targeting JAK3, leading to the discovery of novel pyrazolopyrimidine-based compounds as potential inhibitors. This research employed covalent docking, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) analysis, molecular dynamics modeling, and MM/GBSA (Molecular Mechanics Generalized Born Surface Area) binding free energy techniques to screen 41 in silico-designed pyrazolopyrimidine derivatives. Initially, 3D structures of the JAK3 enzyme were generated using SWISS-MODEL, followed by virtual screening and covalent docking via AutoDock4 (AD4). The selection process involved the AMES test, binding affinity assessment, and ADMET analysis, narrowing down the candidates to 27 compounds that passed the toxicity test. Further covalent docking identified compounds 21 and 41 as the most promising due to their high affinity and favourable ADMET profiles. Subsequent development led to the creation of nine potent molecules, with derivatives 43 and 46 showing exceptional affinity upon evaluation through molecular dynamics simulation and MM/GBSA calculations over 300 nanoseconds, comparable to tofacitinib, an approved RA drug. However, compounds L21 and L46 demonstrated stable performance, suggesting their effectiveness in treating rheumatoid arthritis and other autoimmune conditions associated with JAK3 inhibition.

In silico studies of benzothiazole derivatives as potential inhibitors of Anopheles funestus and Anopheles gambiae trehalase

Introduction

In malaria management, insecticides play a crucial role in targeting disease vectors. Benzothiazole derivatives have also been reported to possess insecticidal properties, among several other properties they exhibit. The female Anopheles mosquito is responsible for transmitting the malaria parasite when infected. Anopheles gambiae (Ag) and Anopheles funestus (Af) are two of the most notable Anopheles species known to spread malaria in Nigeria. Trehalase is an enzyme that breaks down trehalose. Recent research has proposed it as a viable target for inhibition since it aids in flight and stress adaptation.

Methods

This study aimed to investigate benzothiazole derivatives as potential inhibitors of trehalase of Anopheles funestus (AfTre) and Anopheles gambiae (AgTre) using toxicity profiling, molecular docking, and dynamic simulation for future insecticidal intervention. A total of 4,214 benzothiazole-based compounds were obtained from the PubChem database and subjected to screening against the 3D modelled structure of AfTre and AgTre. Compounds with some toxicity levels were optimised, and the obtained lead compounds were further investigated through molecular docking studies. Furthermore, the best hit was subjected to parameters such as RMSD, RMSF, SASA, Rg, and hydrogen bond to confirm its stability when in a complex with AfTre, and these parameters were compared to that of validamycin A (control ligand).

Results and discussion

The post-screening analysis showed binding affinities of -8.7 and -8.2 kcal/mol (compound 1), -8.2 and -7.4 kcal/mol (compound 2), compared to -6.3 and -5.1 kcal/mol (Validamycin A, a known inhibitor) against AfTre and AgTre, respectively. The molecular dynamics simulation showed that compound 1 (the best hit) had good stability when in complex with AfTre. These findings suggest that these best hits can serve as potential inhibitors for the development of novel insecticides in the control of malaria vectors.

Toxicity of Bromo-DragonFLY as a New Psychoactive Substance: Application of In Silico Methods for the Prediction of Key Toxicological Parameters Important to Clinical and Forensic Toxicology

Bromo-DragonFLY is a synthetic new psychoactive substance (NPS) that has gained attention due to its powerful and long-lasting hallucinogenic effects, legal status, and widespread availability. This study aimed to use various in silico toxicology methods to predict key toxicological parameters for Bromo-DragonFLY, including acute toxicity (LD50), genotoxicity, cardiotoxicity, health effects, and the potential for endocrine disruption. The results indicate significant acute toxicity with noticeable variations across different species, a low likelihood of genotoxic potential suggesting potential DNA damage, and a notable risk of cardiotoxicity associated with inhibition of the hERG channel. Evaluation of endocrine disruption suggests a low probability of Bromo-DragonFLY interacting with the estrogen receptor α (ER-α), indicating minimal estrogenic activity. These insights from in silico investigations are important for advancing our understanding of this NPS in forensic and clinical toxicology. These initial toxicological examinations establish a foundation for future research efforts and contribute to developing risk assessment and management strategies for using and misusing NPS.

Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study

Optimization of the ADME properties and pharmacokinetic (PK) profile of compounds is one of the critical activities in any medicinal chemistry campaign to discover a future clinical candidate. Finding ways to expedite the process to address ADME/PK shortcomings and reduce the number of compounds to synthesize is highly valuable. This article provides practical guidelines and a case study on the use of ML ADME models to guide compound design in small molecule lead optimization. These guidelines highlight that ML models cannot have an impact in a vacuum: they help advance a program when they have the trust of users, are tuned to the needs of the program, and are integrated into decision-making processes in a way that complements and augments the expertise of chemists.

Discovery of an 8-oxoguanine regulator PCBP1 inhibitor by virtual screening and its synergistic effects with ROS-modulating agents in pancreatic cancer

Introduction: Drugs that target reactive oxygen species (ROS) metabolism have progressed the treatment of pancreatic cancer treatment, yet their efficacy remains poor because of the adaptation of cancer cells to high concentration of ROS. Cells cope with ROS by recognizing 8-oxoguanine residues and processing severely oxidized RNA, which make it feasible to improve the efficacy of ROS-modulating drugs in pancreatic cancer by targeting 8-oxoguanine regulators. Methods: Poly(rC)-binding protein 1 (PCBP1) was identified as a potential oncogene in pancreatic cancer through datasets of The Cancer Genome Atlas (TCGA) project and Gene Expression Omnibus (GEO). High-throughput virtual screening was used to screen out potential inhibitors for PCBP1. Computational molecular dynamics simulations was used to verify the stable interaction between the two compounds and PCBP1 and their structure-activity relationships. In vitro experiments were performed for functional validation of silychristin. Results: In this study, we identified PCBP1 as a potential oncogene in pancreatic cancer. By applying high-throughput virtual screening, we identified Compound 102 and Compound 934 (silychristin) as potential PCBP1 inhibitors. Computational molecular dynamics simulations and virtual alanine mutagenesis verified the structure-activity correlation between PCBP1 and the two identified compounds. These two compounds interfere with the PCBP1-RNA interaction and impair the ability of PCBP1 to process RNA, leading to intracellular R loop accumulation. Compound 934 synergized with ROS agent hydrogen peroxide to strongly improve induced cell death in pancreatic cancer cells. Discussion: Our results provide valuable insights into the development of drugs that target PCBP1 and identified promising synergistic agents for ROS-modulating drugs in pancreatic cancer.

Exploring novel Apalutamide analogues as potential therapeutics for prostate cancer: design, molecular docking investigations and molecular dynamics simulation

Introduction: Prostate cancer (PC) ranks as the second most frequent type of cancer in men and is the fourth largest cause of mortality worldwide. Androgenic hormones such as testosterone and dihydrotestosterone are crucial for the development and progression of the prostate gland. Androgenic hormones bind to androgen receptors (AR) and trigger the synthesis of many genes that stimulate the growth of prostate cells, initiating PC growth. Apalutamide (APL) is a non-steroidal antiandrogen drug used to treat PC; however, it also causes a variety of toxicities and resistance during the treatment. Methods: The purpose of this study was to computationally identify new and safer analogues of APL, focusing on improved pharmacokinetic properties and reduced toxicity. Drug likeness (DL) and drug score (DS) were also calculated. Docking studies on the designed analogues were conducted to predict their binding affinities and compare their orientations with the ligands in the original crystal structure. Molecular dynamics (MD) simulation of docked ligands was done using Schrödinger suite. Results: We generated a total of 1,415 analogues for different groups of APL using the bioisosteric approach. We selected 80 bioisosteres based on pharmacokinetic profiles, DL and DS score predictions, and found that the designed APL bioisosteres were optimal to good compared to APL. Analogues APL19, APL35, APL43, APL76, and APL80, formed hydrogen bonds with protein (PDB ID: 5T8E) which is similar hydrogen bonding to the standard (APL). The MD simulation result confirmed that APL43 and APL80 complexes were stable during the 100 nS run. Discussion: The results suggest that the APL analogues, particularly APL43 and APL80, are predicted to be potential antiandrogen drugs for the treatment of prostate cancer.

A new framework for novel analogues of pazopanib as potent and selective human carbonic anhydrase inhibitors: Design, repurposing rational, synthesis, crystallographic, in vivo and in vitro biological assessments

Herein, we describe the design and synthesis of novel aryl pyrimidine benzenesulfonamides APBSs 5a-n, 6a-c, 7a-b, and 8 as pazopanib analogues to explore new potent and selective inhibitors for the CA IX. All APBSs were examined in vitro for their promising inhibition activity against a small panel of hCAs (isoforms I, II, IX, and XII). The X-ray crystal structure of CA I in adduct with a representative APBS analogue was solved. APBS-5m, endowed with the best hCA IX inhibitory efficacy and selectivity, was evaluated for antiproliferative activity against a small panel of different cancer cell lines, SK-MEL-173, MDA-MB-231, A549, HCT-116, and HeLa, and it demonstrated one-digit IC50 values range from 2.93 μM (MDA-MB-231) to 5.86 μM (A549). Furthermore, compound APBS-5m was evaluated for its influence on hypoxia-inducible factor (HIF-1α) production, apoptosis induction, and colony formation in MDA-MB-231 cancer cells. The in vivo efficacy of APBS-5m as an antitumor agent was additionally investigated in an animal model of Solid Ehrlich Carcinoma (SEC). In order to offer perceptions into the conveyed hCA IX inhibitory efficacy and selectivity in silico, a molecular docking investigation was also carried out.

Toxicity of New Psychoactive Substance (NPS): Threo-4-methylmethylphenidate (4-Mmph) - Prediction of toxicity using in silico methods

This study represents the first application of in silico methods to evaluate the toxicity of 4-methylphenidate (4-Mmph), a new psychoactive substance (NPS). Using advanced in silico toxicology tools, it was feasible to anticipate key aspects of 4-Mmph's toxicological profile, including acute toxicity (LD50), genotoxicity, cardiotoxicity, and possible endocrine disruption. The findings indicate significant acute toxicity with variability among species, a high potential for adverse effects in the gastrointestinal system and lungs, a low genotoxic potential, a significant likelihood of skin irritation, and a notable cardiotoxicity risk associated with hERG channel inhibition. Evaluation of endocrine disruption revealed a low likelihood that 4-Mmph interacts with the estrogen receptor alpha (ER-α), indicating minimal estrogenic activity. These insights, derived from in silico studies, play a crucial role in improving the comprehension of 4-Mmph in forensic and clinical toxicology. These initial toxicological inquiries establish the foundation for future investigations and help formulate risk assessment and management strategies regarding the use and abuse of NPS. This article is part of a larger project funded by the Polish Ministry of Education and Science, titled "Toxicovigilance, Poisoning Prevention, and First Aid in Poisoning with Xenobiotics of Current Clinical Importance in Poland" (Grant Number SKN/SP/570184/2023).

Structure-based design, synthesis, and biological characterization of indolylarylsulfone derivatives as novel human immunodeficiency virus type 1 inhibitors with potent antiviral activities and favorable drug-like profiles

In the current antiretroviral landscape, continuous efforts are still needed to search for novel chemotypes of human immunodeficiency virus type 1 (HIV-1) inhibitors with improved drug resistance profiles and favorable drug-like properties. Herein, we report the design, synthesis, biological characterization, and druggability evaluation of a class of non-nucleoside reverse transcriptase inhibitors. Guided by the available crystallographic information, a series of novel indolylarylsulfone derivatives were rationally discovered via the substituent decorating strategy to fully explore the chemical space of the entrance channel. Among them, compound 11h bearing the cyano-substituted benzyl moiety proved to be the most effective inhibitor against HIV-1 wild-type and mutant strains (EC50 = 0.0039-0.338 μM), being far more potent than or comparable to etravirine and doravirine. Besides, 11h did not exhibit cytotoxicity at the maximum test concentration. Meanwhile, the binding target of 11h was further confirmed to be reverse transcriptase (IC50 = 0.055 μM). Preliminary structure-activity relationship were discussed to guide further optimization work. Molecular docking and dynamics simulation studies were investigated in detail to rationalize the biological evaluation results. Further drug-likeness assessment indicated that 11h possessed excellent physicochemical properties. Moreover, no apparent hERG blockade liability and cytochrome P450 inhibition were observed for 11h. Notably, 11h was characterized by favorable in vitro metabolic stability with moderate clearance rates and long half-lives in human plasma and liver microsomes. Overall, 11h holds great promise as an ideal Anti-HIV-1 lead compound due to its potent antiviral efficacy, low toxicity, and favorable drug-like profiles.

Investigating mechanistic insights of curcumin in blocking the Interleukin-8 signaling pathway associated with Breast Cancer: An in-silico approach

Interleukin-8 (IL-8) is a chemokine, a type of signaling molecule that has a role in immunological responses and inflammation. In recent years, IL-8 is additionally related to cancer growth and recurrence. Breast cancer growth, progression, and metastatic development are all linked to IL-8. Breast cancer cells are known to develop faster when IL-8 stimulates their proliferation and survival. It can also cause angiogenesis, or the creation of new blood vessels, which is necessary for tumor nutrition and growth. IL-8 and curcumin have been subjects of interest in drug design, particularly in the context of inflammation-related disorders and cancer. This study aims to give an overview of the role of IL-8. Inhibitor-based treatment approaches were being used to target IL-8 with curcumin. Molecular docking method was employed to find a potential interaction to supress competitive inhibition of IL-8 with curcumin. PASS analysis and ADMET characteristics were also being carried out. In the end, IL-8 complexed with curcumin is chosen for MD simulations. Overall, our results showed that during the simulation, the complex stayed comparatively stable. It is also possible to investigate curcumin further as a possible treatment option. The combined results imply that IL-8 and their genetic alterations can be studied in precision cancer therapeutic treatments, utilizing target-driven therapy and early diagnosis.

Structural modification of 2-phenylquinoline-4-carboxylic acid containing SIRT3 inhibitors for the cancer differentiation therapy

Inhibition of SIRT3 exhibited potency in triggering leukemic cell differentiation. In discovery of potent SIRT3 inhibitors for cancer differentiation therapy, structural modification was performed on the previously developed lead compound P6. A total of 33 compounds were designed and synthesized. In the enzyme inhibitory assay, several molecules S18, S26, S27 and T5 showed potent SIRT3 inhibitory activity with IC50 value of 0.53, 1.86, 5.06, and 2.88 μM, respectively. Moreover, the tested compounds exhibited SIRT3 inhibitory selectivity over SIRT1 and SIRT2. Compounds S27 and T5 were potent in inhibition the growth of MM1.S and RPMI-8226 cells in the in vitro antiproliferative test. Significantly, representative compounds, especially S27 and T5, promoted differentiation of tested MM cells in the cellular morphological evaluation, accompanied by increasing the expression of differentiation antigen CD49e and human immunoglobulin light chain lambda and kappa. Additionally, molecule S18 without antiproliferative potency itself, showed significant inhibitory activity against growth factor IL-6 induced RPMI-8226 cell proliferation. Collectively, potent SIRT3 selective inhibitors with MM cell differentiation potency were developed for further discovery of anticancer drugs.

Hybrid fragment-SMILES tokenization for ADMET prediction in drug discovery

BACKGROUND

Drug discovery and development is the extremely costly and time-consuming process of identifying new molecules that can interact with a biomarker target to interrupt the disease pathway of interest. In addition to binding the target, a drug candidate needs to satisfy multiple properties affecting absorption, distribution, metabolism, excretion, and toxicity (ADMET). Artificial intelligence approaches provide an opportunity to improve each step of the drug discovery and development process, in which the first question faced by us is how a molecule can be informatively represented such that the in-silico solutions are optimized.

RESULTS

This study introduces a novel hybrid SMILES-fragment tokenization method, coupled with two pre-training strategies, utilizing a Transformer-based model. We investigate the efficacy of hybrid tokenization in improving the performance of ADMET prediction tasks. Our approach leverages MTL-BERT, an encoder-only Transformer model that achieves state-of-the-art ADMET predictions, and contrasts the standard SMILES tokenization with our hybrid method across a spectrum of fragment library cutoffs.

CONCLUSION

The findings reveal that while an excess of fragments can impede performance, using hybrid tokenization with high frequency fragments enhances results beyond the base SMILES tokenization. This advancement underscores the potential of integrating fragment- and character-level molecular features within the training of Transformer models for ADMET property prediction.

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Application of natural-products repurposing strategy to discover novel FtsZ inhibitors: Bactericidal evaluation and the structure-activity relationship of sanguinarine and its analogs

The novel bactericidal target-filamentous temperature-sensitive protein Z (FtsZ)-has drawn the attention of pharmacologists to address the emerging issues with drug/pesticide resistance caused by pathogenic bacteria. To enrich the structural diversity of FtsZ inhibitors, the antibacterial activity and structure-activity relationship (SAR) of natural sanguinarine and its analogs were investigated by using natural-products repurposing strategy. Notably, sanguinarine and chelerythrine exerted potent anti-Xanthomonas oryzae pv. oryzae (Xoo) activity, with EC50 values of 0.96 and 0.93 mg L-1, respectively, among these molecules. Furthermore, these two compounds could inhibit the GTPase activity of XooFtsZ, with IC50 values of 241.49 μM and 283.14 μM, respectively. An array of bioassays including transmission electron microscopy (TEM), fluorescence titration, and Fourier transform infrared spectroscopy (FT-IR) co-verified that sanguinarine and chelerythrine were potential XooFtsZ inhibitors that could interfere with the assembly of FtsZ filaments by inhibiting the GTPase hydrolytic ability of XooFtsZ protein. Additionally, the pot experiment suggested that chelerythrine and sanguinarine demonstrated excellent curative activity with values of 59.52% and 54.76%, respectively. Excitedly, these two natural compounds also showed outstanding druggability, validated by acceptable drug-like properties and low toxicity on rice. Overall, the results suggested that chelerythrine was a new and potential XooFtsZ inhibitor to develop new bactericide and provided important guiding values for rational drug design of FtsZ inhibitors. Notably, our findings provide a novel strategy to discover novel, promising and green bacterial compounds for the management of plant bacterial diseases.

Flavonoids as potential KRAS inhibitors: DFT, molecular docking, molecular dynamics simulation and ADMET analyses

KRAS mutations linked with cancer. Flavonoids were docked against KRAS G12C and G12D receptors. Abyssinone III, alpha naphthoflavone, beta naphthoflavone, abyssinone I, abyssinone II and beta naphthoflavone, genistin, daidzin showed good docking scores against KRAS G12C and G12D receptors, respectively. The MD simulation data revealed that Rg, RMSD, RMSF, and SASA values were within acceptable limits. Alpha and beta naphthoflavone showed good binding energies with KRAS G12C and G12D receptors. DFT and MEP analysis highlighted the nucleophilic and electrophilic zones of best-docked flavonoids. A novel avenue for the control of KRAS G12C and G12D mutations is made possible by flavonoids.

Screening for compounds with bioaccumulation potential in breast milk using their retention behavior in two-dimensional gas chromatography

Discovery of emerging pollutants in breast milk will be helpful for understanding the hazards to human health. However, it is difficult to identify key compounds among thousands present in complex samples. In this study, a method for screening compounds with bioaccumulation potential was developed. The method can decrease the number of compounds needing structural identification because the partitioning properties of bioaccumulative compounds can be mapped onto GC×GC chromatograms through their retention behaviors. Twenty pooled samples from seven provinces in China were analyzed. 1,286 compounds with bioaccumulation potential were selected from over 3,000 compounds. Sixty-two compounds, including aromatic compounds, phthalates, and phenolics etc., were identified with a high level of confidence and then quantified. Among them, twenty-seven compounds were found for the first time in breast milk. Three phthalate plasticizers and two phenolic antioxidants were found in significantly higher concentrations than other compounds. A toxicological priority index approach was applied to prioritize the compounds considering their concentrations, detection frequencies and eight toxic effects. The prioritization indicated that 13 compounds, including bis(2-ethylhexyl) phthalate, dibutyl phthalate, 1,3-di-tert-butylbenzene, phenanthrene, 2,6-di-tert-butyl-1,4-benzoquinone, 2,4-di-tert-butylphenol, and others, showed higher health risks. Meanwhile, some compounds with high risk for a particular toxic effect, such as benzothiazole and geranylacetone, were still noteworthy. This study is important for assessing the risks of human exposure to organic compounds.

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

Integrating network pharmacology and computational biology to propose Yiqi Sanjie formula's mechanisms in treating NSCLC: molecular docking, ADMET, and molecular dynamics simulation

Background

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related deaths globally. Current treatments often do not fully meet efficacy and quality of life expectations. Traditional Chinese medicine (TCM), particularly the Yiqi Sanjie formula, shows promise but lacks clear mechanistic understanding. This study addresses this gap by investigating the therapeutic effects and underlying mechanisms of Yiqi Sanjie formula in NSCLC.

Methods

We utilized network pharmacology to identify potential NSCLC drug targets of the Yiqi Sanjie formula via the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Compounds with favorable oral bioavailability and drug-likeness scores were selected. Molecular docking was conducted using AutoDock Vina with structural data from the Protein Data Bank and PubChem. Molecular dynamics (MD) simulations were performed with Desmond Molecular Dynamics System, analyzing interactions up to 500 nanoseconds using the OPLS4 force field. ADMET predictions were executed using SwissADME and ADMETlab 2.0, assessing pharmacokinetic properties.

Results

Using network pharmacology tools, we performed Search Tool for the Retrieval of Interaction Genes/Proteins (STRING) analysis for protein-protein interaction, Kyoto Encyclopedia of Genes and Genomes (KEGG) for pathway enrichment, and gene ontology (GO) for functional enrichment, identifying crucial signaling pathways and biological processes influenced by the hit compounds bifendate, xambioona, and hederagenin. STRING analysis indicated substantial connectivity among the targets, suggesting significant interactions within the cell cycle regulation and growth factor signaling pathways as outlined in our KEGG results. The GO analysis highlighted their involvement in critical biological processes such as cell cycle control, apoptosis, and drug response. Molecular docking simulations quantified the binding efficiencies of the identified compounds with their targets-CCND1, CDK4, and EGFR-selected based on high docking scores that suggest strong potential interactions crucial for NSCLC inhibition. Subsequent MD simulations validated the stability of these complexes, supporting their potential as therapeutic interventions. Additionally, the novel identification of ADH1B as a target underscores its prospective significance in NSCLC therapy, further expanded by our comprehensive bioinformatics approach.

Conclusions

Our research demonstrates the potential of integrating network pharmacology and computational biology to elucidate the mechanisms of the Yiqi Sanjie formula in NSCLC treatment. The identified compounds could lead to novel targeted therapies, especially for patients with overexpressed targets. The discovery of ADH1B as a therapeutic target adds a new dimension to NSCLC treatment strategies. Further studies, both in vitro and in vivo, are needed to confirm these computational findings and advance these compounds towards clinical trials.

Marine fungal diversity unlocks potent antivirals against monkeypox through methyltransferase inhibition revealed by molecular dynamics and free energy landscape

The escalating threat posed by the Monkeypox virus (MPXV) to global health necessitates the urgent discovery of effective antiviral agents, as there are currently no specific drugs available for its treatment, and existing inhibitors are hindered by toxicity and poor pharmacokinetic profiles. This study aimed to identify potent MPXV inhibitors by screening a diverse library of small molecule compounds derived from marine fungi, focusing on the viral protein VP39, a key methyltransferase involved in viral replication. An extensive virtual screening process identified four promising compounds-CMNPD15724, CMNPD28811, CMNPD30883, and CMNPD18569-alongside a control molecule. Rigorous evaluations, including re-docking, molecular dynamics (MD) simulations, and hydrogen bond analysis, were conducted to assess their inhibitory potential against MPXV VP39. CMNPD15724 and CMNPD30883, in particular, demonstrated a superior binding affinity and stable interactions within the target protein's active site throughout the MD simulations, suggesting a capacity to overcome the limitations associated with sinefungin. The stability of these VP39-compound complexes, corroborated by MD simulations, provided crucial insights into the dynamic behavior of these interactions. Furthermore, Principal Component Analysis (PCA) based free energy landscape assessments offered a detailed understanding of the dynamic conformational changes and energetic profiles underlying these compounds' functional disruption of VP39. These findings establish CMNPD15724, CMNPD28811, CMNPD30883, and CMNPD18569 as promising MPXV inhibitors and highlight marine fungi as a valuable source of novel antiviral agents. These compounds represent potential candidates for further experimental validation, advancing the development of safer and more effective therapeutic options to combat this emerging viral infection.

Potential mechanism of Luoshi Neiyi prescription in endometriosis based on serum pharmacochemistry and network pharmacology

Introduction

Endometriosis (EMs) is characterized by ectopic growth of active endometrial tissue outside the uterus. The Luoshi Neiyi prescription (LSNYP) has been extensively used for treating EMs in China. However, data on the active chemical components of LSNYP are insufficient, and its pharmacological mechanism in EMs treatment remains unclear. This study aimed to explore the potential mechanism of LSNYP for EMs through network pharmacology based on the components absorbed into the blood.

Methods

Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry was used to analyze blood components, and a series of network pharmacology strategies were utilized to predict targets of these components and EMs. Protein-protein interaction (PPI) network analysis, component-target-disease network construction, gene ontology (GO) functional enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. Additionally, molecular docking, molecular dynamics simulations, and in vitro and in vivo experiments were conducted to validate the HIF1A/EZH2/ANTXR2 pathway associated with hypoxic pathology in EMs.

Results

Thirty-four absorbed components suitable for network pharmacology analysis were identified, and core targets, such as interleukin 6, EGFR, HIF1A, and EZH2, were founded. Enrichment results indicated that treatment of EMs with LSNYP may involve the regulation of hypoxia and inflammatory-related signaling pathways and response to oxidative stress and transcription factor activity. Experimental results demonstrated that LSNYP could decrease the expression of HIF1A, ANTXR2, YAP1, CD44, and β-catenin, and increased EZH2 expression in ectopic endometrial stromal cells and endometriotic tissues. Molecular docking and molecular dynamics simulations manifested that there was stable combinatorial activity between core components and key targets of the HIF1A/EZH2/ANTXR2 pathway.

Conclusion

LSNYP may exert pharmacological effects on EMs via the HIF1A/EZH2/ANTXR2 pathway; hence, it is a natural herb-related therapy for EMs.

In silico evaluation of potential breast cancer receptor antagonists from GC-MS and HPLC identified compounds in Pleurotus ostreatus extracts

Introduction: Pharmacotherapeutic targets for breast cancer include the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (EGFR). Inhibitors of these receptors could be interesting therapeutic candidates for the treatment and management of breast cancer (BC). Aim: This study used GC-MS and HPLC to identify bioactive compounds in Pleurotus ostreatus (P. ostreatus) extracts and applied in silico methods to identify potent EGFR, ER, and PR inhibitors from the compounds as potential drug candidates. Method: GC-MS and HPLC were used to identify bioactive chemicals in P. ostreatus extracts of aqueous (PO-A), methanol (PO-M), ethanol (PO-E), chloroform (PO-C), and n-hexane (PO-H). The ER, PR, and EGFR model optimization and molecular docking of compounds/control inhibitors in the binding pocket were simulated using AutoDock Vina in PyRx. The drug-likeness, pharmacokinetic, and pharmacodynamic features of prospective docking leads were all anticipated. Result: The results indicated the existence of 29 compounds in PO-A, 36 compounds in PO-M and PO-E, 42 compounds in PO-C, and 22 compounds in PO-H extracts. With ER, only o-tolylamino-acetic acid (4-nitro-benzylidene)-hydrazide (-7.5 kcal mol-1) from the ethanolic extract could bind to the receptor. PR and EGFR, on the other hand, identified several compounds with higher binding affinities than the control. Ergotaman-3',6',18-trione (-8.1 kcal mol-1), 5,10-diethoxy-2,3,7,8-tetrahydro-1H,6H-dipyrrolo[1,2-a:1',2'-d]pyrazine (-7.8 kcal mol-1) from the aqueous extract; o-tolylamino-acetic acid (4-nitro-benzylidene)-hydrazide (-8.4 kcal mol-1) from the ethanolic extract had better binding affinity compared to progesterone (-7.7 kcal mol-1). Likewise, ergotaman-3',6',18-trione (-9.7 kcal mol-1) from the aqueous extract and phenol, 2,4-bis(1,1-dimethyl ethyl) (-8.2 kcal mol-1) from the chloroform extract had better binding affinities compared to the control, gefitinib (-7.9 kcal mol-1) with regards to EGFR. None of the PO-H or PO-M extracts outperformed the control for any of the proteins. Phenols and flavonoids such as quercetin, luteolin, rutin, chrysin, apigenin, ellagic acid, and naringenin had better binding affinity to PR and EGFR compared to their control. Conclusion: The identified compounds in the class of phenols and flavonoids were better lead molecules due to their ability to strongly bind to the proteins' receptors. These compounds showed promising drug-like properties; they could be safe and new leads for creating anticancer medicines.

Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides

Using amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model based on graph attention mechanisms for peptide generation. By harnessing the advantages of graph attention mechanisms, this model effectively captured the connectivity structures between amino acid residues in peptides. Simultaneously, leveraging reinforcement learning's strength in guiding optimal sequence searches provided a novel approach to peptide design and optimization. This model introduces an actor-critic framework with real-time feedback loops to achieve dynamic balance between attributes, which can customize the generation of multiple peptides for specific targets and enhance the affinity between peptides and targets. Experimental results demonstrate that the generated drug-like peptides meet specified absorption, distribution, metabolism, excretion, and toxicity properties and bioactivity with a success rate of over 90$\%$, thereby significantly accelerating the process of drug-like peptide generation.

Exploring the Potential of Cyclic Peptidyl Antitumor Agents Derived from Natural Macrocyclic Peptide Phakellistatin

The exploration of novel anticancer compounds based on natural cyclopeptides has emerged as a pivotal paradigm in the contemporary advancement of macrocyclic pharmaceuticals. Phakellistatin 13 is a cycloheptapeptide derived from the brown snubby sponge and exhibits remarkable antitumor activity. In this study, we have designed and synthesized a series of chiral cyclopeptides incorporating the rigid isoindolinone moiety at various sites within the natural cycloheptapeptide Phakellistatin 13, with the aim of investigating conformationally constrained cyclopeptides as potential antitumor agents. Cyclopeptide 3, comprising alternating l-/d-amino acid residues, exhibited promising antihepatocellular carcinoma effects. Detailed biological experiments have revealed that Phakellistatin 13 analogs effectively inhibit the proliferation of tumor cells and induce apoptosis and autophagy, while also causing cell cycle arrest through the modulation of the p53 and mitogen-activated protein kinase (MAPK) signaling pathway. This study not only provides valuable insights into chemical structural modifications but also contributes to a deeper understanding of the biological mechanisms underlying the development of natural cyclopeptide-based drugs.

Molecular Transformers: Adaptive Multitarget Ligands for Esterase-Induced Transition from Analgesics to Anesthetics

Multitarget strategies are essential in addressing complex diseases, yet developing multitarget-directed ligands (MTDLs) is particularly challenging when aiming to engage multiple therapeutic targets across different tissues. Here, we present a molecular transformer strategy, enhancing traditional MTDLs. By utilizing esterase-driven hydrolysis, this approach mimics the adaptive nature of transformers for enabling molecules to modify their pharmacological effects in response to the biological milieu. By virtual screening and biological evaluation, we identified KGP-25, a novel compound initially targeting the voltage-gated sodium channel 1.8 (Nav1.8) in the peripheral nervous system (PNS) for analgesia, and later the γ-aminobutyric acid subtype A receptor (GABAA) in the central nervous system (CNS) for general anesthesia. Our findings confirm KGP-25's dual efficacy in cellular and animal models, effectively reducing opioid-related side effects. This study validates the molecular transformer approach in drug design and highlights its potential to overcome the limitations of conventional MTDLs, paving new avenues in innovative therapeutic strategies.

In silico and in vitro chemometrics, cell toxicity and permeability of naringenin 8-sulphonate and derivatives

Background

Sulphur containing natural compounds are among the most biologically relevant metabolites in vivo. Naringenin 8-sulphonate from Parinari excelsa Sabine was evaluated in a previous work, demonstrating ability to act as a natural anti-inflammatory. Although the interference of this molecule against different inflammatory mediators was described, there is no information regarding its potential toxicity and pharmacokinetics, which are essential for its capacity to reach its therapeutic targets. In fact, despite the existence of reports on naringenin ADMET properties, the influence of sulphation patterns on them remains unknown.

Objectives

This work aims to assess the in vitro pharmacokinetic and toxicological behavior of naringenin 8-sulphonate, as well as to understand the importance of the presence and position of the sulphur containing group for that.

Methods

Naringenin 8-sulphonate physicochemical and ADMET properties were investigated using in silico tools and cell-based in vitro models. At the same time, naringenin and naringenin 4'-O-sulphate were investigated to evaluate the impact of the sulphonate group on the results. ADMETlab 2.0 in silico tool was used to predict the compounds' physicochemical descriptors. Pharmacokinetic properties were determined experimentally in vitro. While MRC-5 lung fibroblasts and HaCaT keratinocytes were used to evaluate the cytotoxicity of samples through MTT and LDH assays, Caco-2 human intestinal epithelial cells were used for the determination of genotoxicity, through alkaline comet assay, and as a permeability model to assess the ability of compounds to cross biological barriers.

Results

Experimental determinations showed that none of the compounds was cytotoxic. In terms of genotoxicity, naringenin 8-sulphonate and naringenin caused significant DNA fragmentation, whereas naringenin 4'-O-sulphate did not. When it comes to permeability, the two sulphur-containing compounds with a sulphur containing group were clearly less capable to cross the Caco-2 cell barrier than naringenin.

Conclusion

In this study, we conclude that the sulphur containing group from naringenin 8-sulphonate is disadvantageous for the molecule in terms of ADMET properties, being particularly impactful in the permeability in intestinal barrier models. Thus, this work provides important insights regarding the role of flavonoids sulphation and sulphonation upon pharmacokinetics and toxicity.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

In-silico screening of bioactive compounds of Moringa oleifera as potential inhibitors targeting HIF-1α/VEGF/GLUT-1 pathway against Breast Cancer

OBJECTIVES

Breast cancer is among the most heterogeneous and aggressive diseases and a foremost cause of death in women globally. Hypoxic activation of HIF-1α in breast cancers triggers the transcription of a battery of genes encoding proteins that facilitate tumor growth and metastasis and is correlated with a poor prognosis. Based on the reported cytotoxic and anti-cancer properties of Moringa oleifera (Mo), this study explores the inhibitory effect of bioactive compounds from M. oleifera and breast cancer target proteins HIF-1α, VEGF, and GLUT-1 in silico.

METHODS

The X-ray crystallographic structures of HIF-1α, VEGF, and GLUT1 were sourced from the Protein Data Bank (PDB) and docked with 70 3D PubChem structures of bioactive compounds of M. oleifera using AutoDock Vina, and binding modes were analyzed using Discovery Studio. Five compounds with the highest binding energies were selected and further drug-likeness, oral bioavailability, ADME, and toxicity profiles were analyzed using SwissADME, ADMETSaR, and ADMETlab 3.0 web server.

RESULTS

Out of the screened 70 bioactive compounds, the top five compounds with the best binding energies were identified namely Apigenin, Ellagic Acid, Isorhamnetin, Luteolin, and Myricetin with each receptor. Molecular docking results indicated that the ligands interact strongly with the target HIF-1α, VEGF, and GLUT-1 receptors through hydrogen bonds and hydrophobic interactions. These compounds showed favorable drug-like and pharmacokinetic properties, possessed no substantial toxicity, and were fairly bioavailable.

CONCLUSIONS

Results suggested that the compounds possess strong potential in developing putative lead compounds targeting HIF-1α that are safe natural plant-based drugs against breast cancer.

Combining In Vitro, In Vivo, and Network Pharmacology Assays to Identify Targets and Molecular Mechanisms of Spirulina-Derived Biomolecules against Breast Cancer

The current research employed an animal model of 7,12-dimethylbenz(a)anthracene (DMBA)-induced mammary gland carcinogenesis. The estrogen receptor-positive human breast adenocarcinoma cell line (MCF-7) was used for in vitro analysis. This was combined with a network pharmacology-based approach to assess the anticancer properties of Spirulina (SP) extract and understand its molecular mechanisms. The results showed that the administration of 1 g/kg of SP increased the antioxidant activity by raising levels of catalase (CAT) and superoxide dismutase (SOD), while decreasing the levels of malonaldehyde (MDA) and protein carbonyl. A histological examination revealed reduced tumor occurrence, decreased estrogen receptor expression, suppressed cell proliferation, and promoted apoptosis in SP protected animals. In addition, SP disrupted the G2/M phase of the MCF-7 cell cycle, inducing apoptosis and reactive oxygen species (ROS) accumulation. It also enhanced intrinsic apoptosis in MCF-7 cells by upregulating cytochrome c, Bax, caspase-8, caspase-9, and caspase-7 proteins, while downregulating Bcl-2 production. The main compounds identified in the LC-MS/MS study of SP were 7-hydroxycoumarin derivatives of cinnamic acid, hinokinin, valeric acid, and α-linolenic acid. These substances specifically targeted three important proteins: ERK1/2 MAPK, PI3K-protein kinase B (AKT), and the epidermal growth factor receptor (EGFR). Network analysis and molecular docking indicated a significant binding affinity between SP and these proteins. This was verified by Western blot analysis that revealed decreased protein levels of p-EGFR, p-ERK1/2, and p-AKT following SP administration. SP was finally reported to suppress MCF-7 cell growth and induce apoptosis by modulating the PI3K/AKT/EGFR and MAPK signaling pathways suggesting EGFR as a potential target of SP in breast cancer (BC) treatment.

DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.

Exploring Bioactive Compounds of Rauvolfia tetraphylla L. (RT) for 3CLprotease of SARS-CoV2: GC-MS Analysis and In-Silico Studies

Studies on the bioactive phytochemicals found in traditional medicinal plants are growing. This study focuses on Rauvolfia tetraphylla L. and its unique bioactive chemical composition. Previous research has demonstrated the plant's antimicrobial properties due to this composition. In this study, however, we also aim to investigate the antiviral properties of the plant. Rauvolfia tetraphylla L. has long been used for medicinal purposes. It is primarily located in Mexico, Central America, the West Indies, and northern South America. Along with checking out its in-silico SARS-CoV-2 activity, current work also evaluates the leaf extracts qualitative phytochemical, antioxidant, and cytotoxicity properties. While several conventional procedures were employed in the bio active compounds and phytochemical study that identified multiple phytochemicals, compounds derived from plants will be the most effective substitution with unfavorable side effects. The focus of this work is on in silico analysis, which determines the experimental plants activity against SARS-CoV-2 using molecular docking and pharmacokinetic analysis. We identified 20 phytochemical compounds from the GC-MS data of the plant, out of these 12 compounds failed to meet ADMET properties and the remaining 8 compounds passed TOPKAT Ames Mutagenicity. These compounds were docked against one important protein 3CLpro (PDB ID: 7DPV) that is implicated in the development of SARS-CoV-2. Docking studies have demonstrated binding results with maximum score and three compounds showed promising results. The results of this study highlighted the potential efficacy of (E,E,E,E,E,E)-()-2,6,10,15,19,23-hexamethyltetracosa-1,6,10,14,18,22-hexaen-3-ol, α-Tocospiro A, and α-Tocopherol. Furthermore, a thorough examination of the in-silico data indicates that the leaf has the potential to be a powerful source of medication and an efficient therapy in the future.

Oxidation Mechanism and Toxicity Evolution of Linalool, a Typical Indoor Volatile Chemical Product

Linalool, a high-reactivity volatile chemical product (VCP) commonly found in cleaning products and disinfectants, is increasingly recognized as an emerging contaminant, especially in indoor air. Understanding the gas-phase oxidation mechanism of linalool is crucial for assessing its impact on atmospheric chemistry and human health. Using quantum chemical calculations and computational toxicology simulations, we investigated the atmospheric transformation and toxicity evolution of linalool under low and high NO/HO2· levels, representing indoor and outdoor environments. Our findings reveal that linalool can undergo the novel mechanisms involving concerted peroxy (RO2·) and alkoxy radical (RO·) modulated autoxidation, particularly emphasizing the importance of cyclization reactions indoors. This expands the widely known RO2·-dominated H-shift-driven autoxidation and proposes a generalized autoxidation mechanism that leads to the formation of low-volatility secondary organic aerosol (SOA) precursors. Toxicological analysis shows that over half of transformation products (TPs) exhibited higher carcinogenicity and respiratory toxicity compared to linalool. We also propose time-dependent toxic effects of TPs to assess their long-term toxicity. Our results indicate that the strong indoor emission coupled with slow consumption rates lead to significant health risks under an indoor environment. The results highlight complex indoor air chemistry and health concerns regarding persistent toxic products during indoor cleaning, which involves the use of linalool or other VCPs.

Systematic computational strategies for identifying protein targets and lead discovery

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein-ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.

Prognostic role of TEAD4 in TNBC: in-silico inhibition of the TEAD4-YAP interaction by flufenamic acid analogs

Triple-negative breast cancer (TNBC) poses a significant global health challenge due to its highly aggressive nature and invasive characteristics. Dysregulation of the Hippo pathway, a key regulator of various biological processes, is observed in TNBC, and its inhibition holds promise for impeding cancer growth. This in-silico analysis investigates the role of Transcriptional Enhanced Associate Domain 4 (TEAD4) in TNBC and its interaction with Yes Associated Protein (YAP) in cancer progression. Our results demonstrate that TEAD4 upregulation is linked to poor prognosis in TNBC, emphasizing its critical role in the disease. Moreover, we identify CID44521006, an analog of Flufenamic acid, as a potential therapeutic compound capable of disrupting the TEAD4-YAP interaction by binding to the YAP-binding domain of TEAD4. These findings underscore the significance of TEAD4 in TNBC and propose CID44521006 as a promising candidate for therapeutic intervention. The study contributes valuable insights to advance treatment options for TNBC, offering a potential avenue for the development of targeted therapies against this aggressive form of breast cancer.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00239-8.

Impact of Some Natural and Artificial Sweeteners Consumption on Different Hormonal Levels and Inflammatory Cytokines in Male Rats: In Vivo and In Silico Studies

Substituting sugar with noncaloric sweeteners prevents overweight and diabetes development. They come in two types: artificial, like aspartame and sucralose, and natural, such as sorbitol. This research aimed to assess the effects of sucrose and these sweeteners on nutritional parameters, hematological parameters, hormones, and anti- and pro-inflammatory cytokines in male rats. Thirty rats had been separated into five groups. The results showed the highest significant increase in body weight gain, total food intake, and feed efficiency noticed in the aspartame group followed by sucralose, sucrose, and sorbitol, respectively. In contrast to RBCs and platelets, all sweeteners significantly reduced the hemoglobin level, Hct %, and WBC count. The aspartame group showed the highest decline in glycoproteins, steroids, and T3, and T4 hormones and a dramatic elevation in thyroid stimulating hormone, eicosanoid, and amine hormones compared with the control group. A vigorous elevation in anti- and proinflammatory cytokine levels was observed in the aspartame group, followed by sucralose, sucrose, and sorbitol groups. Aspartame has the highest docking scores when studying the interactions of sweeteners and a target protein associated with hormones or cytokines using in silico molecular docking, with the best absorption, distribution, metabolism, elimination, and toxicity properties compared to the remaining sweeteners.

Database-aided ultrahigh-performance liquid chromatography Q-Exactive-Orbitrap tandem mass spectrometry putatively identifies 16 unexpected compounds and three anticounterfeiting pharmacopoeia quality markers for Perillae Fructus

RATIONALE

Perillae Fructus (PF) is a common traditional Chinese medicine (TCM) for the treatment of asthma. It has not been effectively characterized by rosmarinic acid (RosA), which is currently designed as the sole quality indicator in the Chinese Pharmacopoeia.

METHODS

This study introduced a database-aided ultrahigh-performance liquid chromatography equipped with quadrupole-Exactive-Orbitrap mass spectrometry (UHPLC/Q-Exactive-Orbitrap MS/MS) technology to putatively identify the compounds in PF, followed by literature research, quantum chemical calculation, and molecular docking to screen potential quality markers (Q-markers) of PF.

RESULTS

A total of 27 compounds were putatively identified, 16 of which had not been previously found from PF. In particular, matrine, scopolamine, and RosA showed relatively high levels of content, stability, and drug-likeness. They exhibited interactions with the asthma-related target and demonstrated the TCM properties of PF.

CONCLUSIONS

The database-aided UHPLC/Q-Exactive-Orbitrap MS/MS can identify at least 27 compounds in PF. Of these, 16 compounds are unexpected, and three compounds (matrine, scopolamine, and RosA) should be considered anticounterfeiting pharmacopoeia Q-markers of PF.

Exploring the role of candidalysin in the pathogenicity of Candida albicans by gene set enrichment analysis and evolutionary dynamics

AIMS

To explore the pathogenic mechanisms of Candida albicans (C. albicans), focusing on its impact on human health, particularly through invasive infections in the gastrointestinal and respiratory tracts.

METHODS

In this study, we evaluated the demographic and clinical profiles of 7 pneumonia patients. Meanwhile, we used Gene Set Enrichment Analysis (GSEA) and Evolutionary Dynamics method to analyze the role of candidalysin in C. albicans pathogenicity.

RESULTS

By analyzing genomic data and conducting biomedical text mining, we identified novel mutation sites in the candidalysin coding gene ECE1-III, shedding light into the genetic diversity within C. albicans strains and their potential implications for antifungal resistance. Our results revealed significant associations between C. albicans and respiratory as well as gastrointestinal diseases, emphasizing the fungus's role in the pathogenesis of these diseases. Additionally, we identified a new mutation site in the C. albicans strain YF2-5, isolated from patients with pneumonia. This mutation may be associated with its heightened pathogenicity.

CONCLUSION

Our research advances the understanding of C. albicans pathogenicity and opens new avenues for developing targeted antifungal therapies. By focusing on the molecular basis of fungal virulence, we aim to contribute to the development of more effective treatment strategies, addressing the challenge of multidrug resistance in invasive fungal infections.

O-Alkyl derivatives of ferulic and syringic acid as lipophilic antioxidants: effect of the length of the alkyl chain on the improvement of the thermo-oxidative stability of sunflower oil

Lipid oxidation is the major cause of the deterioration of fat-containing foods, especially those containing polyunsaturated fatty acids (PUFAs). Antioxidant additives of synthetic origin are added to matrices rich in PUFAs, such as sunflower oil (SO). However, there is controversy regarding their safety, and their low solubility in both water and fat has led to the search for new covalent modifications through lipophilicity. This work presents the synthesis of O-alkyl acid derivatives from ferulic and syringic acids and the study of their antioxidant capacity and effect on the thermoxidative degradation of SO. Antioxidant activities were evaluated by employing ferric reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assays in a concentration range of 10-100 µg mL-1. The IC50 values for DPPH scavenging activity ranged from 15.61-90.43 µg mL-1. The results of the FRAP assay for both O-alkyl ferulic (3a-f) and syringic (5a-f) series revealed a "cut-off" effect on antioxidant activity in carbon five (C5). Thermoxidation study of additives 3b-c and 5b-c showed a decrease in the slope of extinction coefficients K 232 and K 270 in comparison with SOcontrol. Furthermore, 3c presented higher antioxidant activity than 3b and 1, with a power to decrease the thiobarbituric acid reactive species (TBARS) 6 times higher than SOcontrol at 220 °C. Additives 5b-c exerted a protective effect on the thermoxidation of SO. The results suggest that increasing lipophilic and thermal properties of antioxidants through O-alkyl acid derivatization is an effective strategy for accessing lipophilic antioxidant additives with potential use in food matrices.