Insight into structural features of phenyltetrazole derivatives as ABCG2 inhibitors for the treatment of multidrug resistance in cancer

Machine learning models based on residue interaction network for ABCG2 transportable compounds recognition

As the one of the most important protein of placental transport of environmental substances, the identification of ABCG2 transport molecules is the key step for assessing the risk of placental exposure to environmental chemicals. Here, residue interaction network (RIN) was used to explore the difference of ABCG2 binding conformations between transportable and non-transportable compounds. The RIN were treated as a kind of special quantitative data of protein conformation, which not only reflected the changes of single amino acid conformation in protein, but also indicated the changes of distance and action type between amino acids. Based on the quantitative RIN, four machine learning algorithms were applied to establish the classification and recognition model for 1100 compounds with transported by ABCG2 potential. The random forest (RF) models constructed with RIN presented the best and satisfied predictive ability with an accuracy of training set of 0.97 and the test set of 0.96 respectively. In conclusion, the construction of residue interaction network provided a new perspective for the quantitative characterization of protein conformation and the establishment of prediction models for transporter molecular recognition. The ABCG2 transport molecular recognition model based on residue interaction network provides a possible way for screening environmental chemistry transported through placenta.

Exploring molecular fingerprints of different drugs having bile interaction: a stepping stone towards better drug delivery

Bile acids are amphiphilic substances produced naturally in humans. In the context of drug delivery and dosage form design, it is critical to understand whether a drug interacts with bile inside the gastrointestinal (GI) tract or not. This study focuses on the identification of structural fingerprints/features important for bile interaction. Molecular modelling methods such as Bayesian classification and recursive partitioning (RP) studies are executed to find important fingerprints/features for the bile interaction. For the Bayesian classification study, the ROC score of 0.837 and 0.950 are found for the training set and the test set compounds, respectively. The fluorine-containing aliphatic/aromatic group, the branched chain of the alkyl group containing hydroxyl moiety and the phenothiazine ring etc. are identified as good fingerprints having a positive contribution towards bile interactions, whereas, the bad fingerprints such as free carboxylate group, purine, and pyrimidine ring etc. have a negative contribution towards bile interactions. The best tree (tree ID: 1) from the RP study classifies the bile interacting or non-interacting compounds with a ROC score of 0.941 for the training and 0.875 for the test set. Additionally, SARpy and QSAR-Co analyses are also been performed to classify compounds as bile interacting/non-interacting. Moreover, forty-six recently FDA-approved drugs have been screened by the developed SARpy and QSAR-Co models to assess their bile interaction properties. Overall, this attempt may facilitate the researchers to identify bile interacting/non-interacting molecules in a faster way and help in the design of formulations and target-specific drug development.

Two Important Anticancer Mechanisms of Natural and Synthetic Chalcones

ATP-binding cassette subfamily G and tubulin pharmacological mechanisms decrease the effectiveness of anticancer drugs by modulating drug absorption and by creating tubulin assembly through polymerization. A series of natural and synthetic chalcones have been reported to have very good anticancer activity, with a half-maximal inhibitory concentration lower than 1 µM. By modulation, it is observed in case of the first mechanism that methoxy substituents on the aromatic cycle of acetophenone residue and substitution of phenyl nucleus by a heterocycle and by methoxy or hydroxyl groups have a positive impact. To inhibit tubulin, compounds bind to colchicine binding site. Presence of methoxy groups, amino groups or heterocyclic substituents increase activity.

Design and evaluation of pyrimidine derivatives as potent inhibitors of ABCG2, a breast cancer resistance protein

The protein ATP-binding cassette subfamily G member 2 (ABCG2) is one of the major factors behind multidrug resistance (MDR) in breast cancer. We performed three-dimensional quantitative structure-activity relationship (3D-QSAR) modelling, docking, and molecular dynamics (MD) simulation to design pyrimidine-based ABCG2 antagonists. The developed QSAR model (r 2 = 0.92, q 2 = 0.82, and good cross-validated r 2 = 0.73) dictate requirement of electrostatic, and hydrophobic fields for modulating bioactivity. Based on this rationale, we designed and screened 1010 new compounds, among them 2 (ND-510 and ND-500) exhibit excellent drug-like features. Comparative molecular docking, MM/GBSA and ADMET profiles were determined to understand the interactive poses, affinity, and drug-likeness of the designed compounds. Furthermore, MD simulations were performed with the ABCG2 receptor, and the results were compared with the two earlier synthesized active compounds. The outcomes of the study will help researchers to develop new antagonists for treatment of MDR breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03231-1.

Host P2X7R-p38MAPK axis mediated intra-macrophage leishmanicidal activity of Spergulin-A

Current drugs are inefficient for the treatment of visceral leishmaniasis an immunosuppressive ailment caused by Leishmania donovani. Regrettably, there is no plant-origin antileishmanial drug present. P2X₇R is constitutively present on macrophage surfaces and can be a putative therapeutic target in intra-macrophage pathogens with function attributes towards inflammation, host cell apoptosis, altered redox, and phagolysosomal maturation by activating p₃₈MAPK. Here we demonstrated that the initial interaction of Spergulin-A (Sp A), a triterpenoid saponin with RAW 264.7 macrophages was mediated through P2X₇R involving the signaling cascade intermediates Ca⁺⁺, p₃₈MAPK, and NF-κβ. Phospho (P)-p38MAPK involvement is shown to have specific and firm importance in leishmanial killing with increased NF-κβp65. Phago-lysosomal maturation by Sp A also campaigns for another contribution of P2X₇R. In vivo evaluation of the anti-leishmanial activity of Sp A was monitored through expression analyses of P2X₇R, P-p38MAPK, and NF-κβp65 in murine spleen and bone-marrow macrophages and supported Sp A being a natural compound of leishmanicidal functions which acted through the P2X₇R-p38MAPK axis.

Quantum parameter analysis of the adsorption mechanism by freshly formed ferric hydroxide for synthetic dye and antibiotic wastewaters

In this study, the adsorption effect by freshly formed ferric hydroxide (FFFH) for the removal of 47 synthetic dye and antibiotic wastewaters under different pH conditions (i.e., pH = 4, 7, and 10) was investigated. The average total organic carbon (TOC) removal rates (R_exp) of pollutants under acidic, neutral, and alkaline conditions were 27.10 ± 3.21%, 15.16 ± 2.48%, and 9.72 ± 2.81%, respectively. By analyzing the characteristics of FFFH measured by SEM, XRD, FT-IR, TGA and BET, the properties of pollutants, and the values of R_exp, one can conclude that the large specific surface area and rich hydroxyl groups on the surface of FFFH were the reasons for its adsorption capacity for organic pollutants, and the electrostatic adsorption was the main reason for higher removal rate in acidic condition. Subsequently, to better elucidate the intrinsic factors influencing the removal rates at the molecular structure level, three optimal quantitative structure-activity relationship (QSAR) models were established by using multiple linear regression (MLR) analysis. Results of model validations (e.g., regression coefficient, internal and external verifications, and Y-randomization) showed that the established models exhibited excellent stability, reliability, and robustness with the values of R² = 0.7544, 0.7764, 0.7528, Q²_INT = 0.6451, 0.6836, 0.6228, and Q²_EXT = 0.5890, 0.6029, 0.7298 under acidic, neutral, and alkaline conditions, respectively. The results of quantum parameter analysis revealed that the adsorption mechanism of FFFH for dyes and antibiotics mainly includes the activity of adsorption site, the behavior of electron transfer and the strength of molecular polarity.

VKNG-1 Antagonizes ABCG2-Mediated Multidrug Resistance via p-AKT and Bcl-2 Pathway in Colon Cancer: In Vitro and In Vivo Study

The emergence of multidrug resistance (MDR) to chemotherapeutic drugs is a major problem in the therapy of cancer. Knowledge of the mechanisms of drug resistance in cancer is necessary for developing efficacious therapies. ATP-binding cassette (ABC) transporters are transmembrane proteins that efflux chemotherapeutic drugs from cancer cells, thereby producing MDR. Our research efforts have led to the discovery of VKNG-1, a compound that selectively inhibits the ABCG2 transporter and reverses resistanctabe to standard anticancer drugs both in vitro and in vivo. VKNG-1, at 6 µM, selectively inhibited ABCG2 transporter and sensitized ABCG2-overexpressing drug-resistant cancer cells to the ABCG2 substrate anticancer drugs mitoxantrone, SN-38, and doxorubicin in ABCG2-overexpressing colon cancers. VKNG- 1 reverses ABCG2-mediated MDR by blocking ABCG2 efflux activity and downregulating ABCG2 expression at the mRNA and protein levels. Moreover, VKNG-1 inhibits the level of phosphorylated protein kinase B (PKB/p-AKT), and B-cell lymphoma-2 (Bcl-2) protein which may overcome resistance to anticancer drugs. However, the in vitro translocation of ABCG2 protein did not occur in the presence of 6 µM of VKNG-1. In addition, VKNG-1 enhanced the anticancer efficacy of irinotecan in ABCG2- overexpressing mouse tumor xenografts. Overall, our results suggest that VKNG-1 may, in combination with certain anticancer drugs, represent a treatment to overcome ABCG2-mediated MDR colon cancers.

Structure-Based Discovery of ABCG2 Inhibitors: A Homology Protein-Based Pharmacophore Modeling and Molecular Docking Approach

ABCG2 is an ABC membrane protein reverse transport pump, which removes toxic substances such as medicines out of cells. As a result, drug bioavailability is an unexpected change and negatively influences the ADMET (absorption, distribution, metabolism, excretion, and toxicity), leading to multi-drug resistance (MDR). Currently, in spite of promising studies, screening for ABCG2 inhibitors showed modest results. The aim of this study was to search for small molecules that could inhibit the ABCG2 pump. We first used the WISS MODEL automatic server to build up ABCG2 homology protein from 655 amino acids. Pharmacophore models, which were con-structed based on strong ABCG2 inhibitors (IC₅₀ < 1 μM), consist of two hydrophobic (Hyd) groups, two hydrogen bonding acceptors (Acc2), and an aromatic or conjugated ring (Aro|PiR). Using molecular docking method, 714 substances from the DrugBank and 837 substances from the TCM with potential to inhibit the ABCG2 were obtained. These chemicals maybe favor synthesized or extracted and bioactivity testing.

Effects of ATP-binding cassette transporter G2 in extracellular vesicles on drug resistance of laryngeal cancer cells in in vivo and in vitro

Drug resistance is one of the main factors limiting the efficacy of chemotherapy in patients with laryngeal cancer; thus, it is important to investigate the drug resistance of laryngeal cancer. In the present study, the mechanism of the regulation of drug resistance in laryngeal cancer cells by ATP-binding transporter G2 (ABCG2) that is present in the extracellular vesicles (EVs) released by drug-resistant cells was studied in vivo and in vitro. A cisplatin (CDDP)-resistant cell line (AMC-HN-8/CDDP) was established from AMC-HN-8 cells by continuous exposure to increasing concentrations of CDDP. The EVs extracted from the culture medium of AMC-HN-8/CDDP and AMC-HN-8 cells were termed EVs1 and EVs2, respectively. Following 48-h treatment of AMC-HN-8 cells with EVs1 or EVs2, the cells were designated as AMC-HN-8-EVs1 or AMC-HN-8-EVs2. Nude mice bearing AMC-HN-8-EVs1 and AMC-HN-8 cell-derived xenograft tumors were established to detect the effects of EVs on drug resistance. The resistance index of AMC-HN-8/CDDP cells to CDDP was 5.60, which was determined by the MTT assay. The mRNA and protein expression levels of ABCG2 in AMC-HN-8/CDDP cells and EVs1 were significantly higher compared with those in AMC-HN-8 cells and EVs2, respectively (P<0.01). The ABCG2 mRNA and protein levels, and the proliferation index of AMC-HN-8-EVs1 cells were significantly higher compared with those of AMC-HN-8-EVs2 and AMC-HN-8 cells (P<0.01), whereas the apoptotic rate was significantly lower (P<0.01). The mean volume of subcutaneous tumor xenografts in the test group (inoculated with AMC-HN-8-EVs1 cells and intraperitoneally injected with 3 mg/kg CDDP) was significantly higher compared with that in the control group (inoculated with AMC-HN-8 cells and intraperitoneally injected with 3 mg/kg CDDP) (P<0.01), whereas the apoptotic rate of tumor cells was significantly lower (P<0.01). The ABCG2 mRNA and the protein expression levels in the tumor cells of the test group were significantly higher compared with those in the blank (inoculated with AMC-HN-8 cells and was intraperitoneally injected with normal saline) and control groups (P<0.01). The high expression levels of ABCG2 in laryngeal carcinoma cells affected the drug resistance of the cells. The EVs released by drug-resistant cells upregulated the expression of ABCG2 and induced drug resistance in laryngeal carcinoma cells, which may be dependent on the ABCG2 gene carried by the EVs.

Quantitative structure activity relationship (QSAR) modelling of the degradability rate constant of volatile organic compounds (VOCs) by OH radicals in atmosphere

The reaction with hydroxyl radicals (•OH) is an important way to remove the most volatile organic compounds (VOCs) in atmospheric environment. Thus, the reaction rate constant (k_OH) is important for assessing the persistence and exposure risk of VOCs, and is of great significance in evaluating the ecological risk of volatile organic chemicals. Fukui indices and bond order have a large effect on the degradation of VOCs, but so far, quantitative structure activity relationship (QSAR) models for VOCs degradation have rarely been considered these two factors. In this study, these two momentous factors will be considered along with other relevant quantitative parameters. A total of 180 substances are divided into training set (144 substances) and test set (36 substances), which are used to build and validate quantitative structure activity relationship (QSAR) models, respectively. Internal, external verification and y-randomization tests showed that the established model had excellent stability and reliability. The energy of the highest occupied molecular orbital (E_HOMO), the possibility of being attacked by radicals (f (0)_n) and the breaking of chemical bonds (BO_x) are the main factors affecting VOCs removal. Finally, the scope of the application domain was determined and the robustness of the model was further verified.

Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis

The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Currently, it has been emerged as one of the best biological targets for the designing of small molecule drugs capable of eliminating multidrug resistance in breast cancer. In order to gain insights into the relationship between the molecular structure of compounds and the ABCG2 inhibition, a multi-QSAR approach using different methods was performed on a dataset of 294 ABCG2 inhibitors with diverse scaffolds. The best models obtained by different chemometric methods have the following statistical characteristics: Monte Carlo Optimization-based QSAR (sensitivity = 0.905, specificity = 0.6255, accuracy = 0.756, and MCC = 0.545), Bayesian classification model (sensitivity = 0.735, specificity = 0.775, and concordance = 0.757); structural and physicochemical interpretation analysis-random forest method (balance accuracy = 0.750, sensitivity = 0.810, and specificity = 0.700). Additionally, structural fingerprints modulating the ABCG2 inhibitory properties were identified from the best models of each method and also validated with each other. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints modulating ABCG2 inhibition.

Exploring the structural aspects of ureido-amino acid-based APN inhibitors: a validated comparative multi-QSAR modelling study

The zinc-dependent enzyme aminopeptidase N (APN) is a member of the M1 metalloenzyme family. The multi-functionality of APN as a peptidase, a receptor and a signalling molecule has provided it the access to influence a number of disease conditions namely viral diseases, angiogenesis, cellular metastasis and invasion including different cancer conditions. Hence, the development of potent APN inhibitors is a possible route for the treatment of diseases related to the activity of APN. In this study, different QSAR approaches have been adopted to identify the structural features of a group of hydroxamate-based ureido-amino acid derivative APN inhibitors. This study was able to identify different constitutional aspects of these APN inhibitors which are important for their inhibitory potency. Additionally, some of these observations were also aligned with the observations of previously performed QSAR studies conducted on different APN inhibitors. Therefore, the results of this study may help to design potent and effective APN inhibitors in the future.

Discriminations of active from inactive HDAC8 inhibitors Part II: Bayesian classification study to find molecular fingerprints

In continuation of our earlier work (Doi: 10.1080/07391102.2019.1661876), a statistically validated and robust Bayesian model was developed on a large diverse set of HDAC8 inhibitors. The training set comprised of 676 small molecules and 293 compounds were considered as test set molecules. The findings of this analysis will help to explore some major directions regarding the HDAC8 inhibitor designing approach. Acrylamide (G1-G3, G9), N-substituted 2-phenylimidazole (G4-G8, G9, G12-G13, G16-G19), benzimidazole (G10-G11), piperidine substituted pyrrole (G13-G14) groups, alkyl/aryl amide (G15) and aryloxy carboxamide (G20) fingerprints were found to play a crucial role in HDAC8 inhibitory activity whereas -CH-N=CH- (B1, B4-B6, B14) motif, benzamide (B2-B3, B9-B13, B16-B17) groups and heptazepine (B7-B8, B15, B18-B20) group were found to influence negatively the HDAC8 inhibitory activity. The importance of such fingerprints was further validated by the HDAC8 enzyme and related inhibitor interactions at the receptor level. These results are in close agreement with those of our previous work that validate each other. Moreover, this comparative learning may enrich future endeavours regarding the designing strategy of HDAC8 inhibitors.