Studies on logP, retention time and QSAR of 2-substituted phenylnitronyl nitroxides as free radical scavengers

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.

MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS

To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS²). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC₅₀) values from the CompTox database and validated for 219 chemicals and 420 MS² spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC₅₀ values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC₅₀ values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log K_OW.

α-naphthoflavone-derived cytochrome P450 (CYP)1B1 degraders specific for sensitizing CYP1B1-mediated drug resistance to prostate cancer DU145: Structure activity relationship

We previously discovered extrahepatic cytochrome P450 1B1 (CYP1B1) degraders able to overcome drug resistance toward docetaxel using a PROTACs technology, however, the underexplored structure activity relationships and poor water solubility posed a major hurdle in the development of CYP1B1 degraders. Herein, continuous efforts are made to develop more promising α-naphthoflavone (ANF)-derived chimeras for degrading CYP1B1. Guided by the strongest ANF-derived CYP1B1 degrader 3a we ever reported, 17 ANF analogues are designed and synthesized to evaluate the CYP1B1 degradation and resultant resistance reversal. In degrading CYP1B1 and sensitizing drug resistance, 4d with a 1, 5-cis triazole coupling mode at (C3') of B ring of ANF exhibited the similar potency as 3a carrying a 1, 4-trans triazole fragment at (C4') of B ring, but more obvious selectivity of 4d toward CYP1B1 over CYP1A2 is observed. When an oxygen was inserted into the linker of 4d, 4f demonstrated better water solubility, a more potent ability in degrading CYP1B1 and reversing drug resistance, and a promising selectivity. Collectively, a substitution position, an alkyne-azide cyclization and a liker type significantly affect the ability of ANF-thalidomide conjugates in eliminating drug resistance of CYP1B1-expressing DU145 (DU145/CY) cells to docetaxel via targeted CYP1B1 degradation.

Discovery of novel 2H-chromene-3-carbonyl derivatives as selective estrogen receptor degraders (SERDs): Design, synthesis and biological evaluation

Selective estrogen receptor degraders (SERDs) not only block ERα activity but degrade this receptor at the same time and are effective in relapsed ERα positive breast cancer patients who have accepted other endocrine therapies. Herein, through scaffold hopping of coumarin skeleton, a series of 2H-chromene-3-carbonyl-based SERDs with phenyl acrylic acid group as the side chain were designed and synthesized. Compound XH04 containing 7-hydroxy-2H-chromene-3-carbonyl skeleton exhibited the most potent activities in 2D (IC₅₀ = 0.8 μM) and 3D cells culture models (MCF-7) and had the best ERα binding affinity as well. Furthermore, the significant antiestrogen property of compound XH04 was confirmed by inhibiting the expression of progesterone receptor (PgR) mRNA in MCF-7 cells. On the other hand, the outgoing ERα degradation property of compound XH04 was qualitatively and quantificationally verified by immunofluorescence analysis and Western blot assay in MCF-7 cells. Besides, compound XH04 repressed the expression level of Ki67 in MCF-7 cells and induced the apoptosis increase of this tumor cells in a dose-dependent manner like approved-SERD fulvestrant (2), while compound XH04 exhibited better preliminary pharmacokinetics in human and rat liver microsomes in vitro and a lower LogD_7.4 value than fulvestrant. And further molecular docking study revealed that compound XH04 possessed a proverbial and typical binding model with ERα like other reported SERD. All these results confirmed that 7-hydroxy-2H-chromene-3-carbonyl structure could be a feasible skeleton for design of ERα antagonists including SERDs and compound XH04 is a promising candidate for further development of ERα + breast cancer therapy agents.

Protective effect of nitronyl nitroxide against hypoxia-induced damage in PC12 cells

Hypoxia induces cellular oxidative stress that is associated with neurodegenerative diseases. HPN (4'-hydroxyl-2-substituted phenyl nitronyl nitroxide), a stable nitronyl nitroxide, has excellent free radical scavenging properties. The purpose of this study was to investigate the protective effects of HPN on hypoxia-induced damage in PC12 cells. It was shown that HPN significantly attenuated hypoxia-induced loss of cell viability, release of lactate dehydrogenase (LDH), and morphological changes in PC12 cells. Moreover, hypoxic PC12 cells had increased levels of reactive oxygen species (ROS), malondialdehyde (MDA), and expression of HIF-1α and VEGF, but had reduced levels of superoxide dismutase (SOD) and catalase (CAT), and HPN reversed these changes. HPN also inhibited hypoxia-induced cell apoptosis via suppressing the expression of Bax, cytochrome c, and caspase-3, and inducing the expression of Bcl-2. These results indicate that the protective effects of HPN on hypoxia-induced damage in PC12 cells is associated with the suppression of hypoxia-induced oxidative stress and cell apoptosis. HPN could be a promising candidate for the development of a novel neuroprotective agent.

(-)-Meptazinol-melatonin hybrids as novel dual inhibitors of cholinesterases and amyloid-β aggregation with high antioxidant potency for Alzheimer's therapy

The multifactorial pathogenesis of Alzheimer's disease (AD) implicates that multi-target-directed ligands (MTDLs) intervention may represent a promising therapy for AD. Amyloid-β (Aβ) aggregation and oxidative stress, two prominent neuropathological hallmarks in patients, play crucial roles in the neurotoxic cascade of this disease. In the present study, a series of novel (-)-meptazinol-melatonin hybrids were designed, synthesized and biologically characterized as potential MTDLs against AD. Among them, hybrids 7-7c displayed higher dual inhibitory potency toward cholinesterases (ChEs) and better oxygen radical absorbance capacity (ORAC) than the parental drugs. Furthermore, compound 7c could effectively inhibit Aβ self-aggregation, showed favorable safety and the blood-brain barrier (BBB) permeability. Therefore, 7c may serve as a valuable candidate that is worthy of further investigations in the treatment of AD.

Scaffold and cell line based approaches for QSAR studies on anticancer agents

Importance of 2D QSAR in drug discovery, lower number of descriptors containing models shows best statistical parameters, number of involved scaffolds in models affects the statistical values.

A quantitative structure- property relationship of gas chromatographic/mass spectrometric retention data of 85 volatile organic compounds as air pollutant materials by multivariate methods

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of retention times of volatile organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structure of compounds. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and artificial neural network (ANN). The stepwise regression was used for the selection of the variables which gives the best-fitted models. After variable selection ANN, MLR methods were used with leave-one-out cross validation for building the regression models. The prediction results are in very good agreement with the experimental values. MLR as the linear regression method shows good ability in the prediction of the retention times of the prediction set. This provided a new and effective method for predicting the chromatography retention index for the volatile organic compounds.

Chemical and biological evaluation of some new antipyrine derivatives with particular properties

Starting from 4-amino-antipyrine, six new compounds were synthesized and characterized. The new compounds contain moieties with particular properties, such are ionophore (benzo-15-crown-5), fluorescent (nitrobenzofurazan), stable free radical (nitroxide), or other types of biological active residues, like nitroderivatives, antipyrine or isoniazid residues. They were fully characterized by appropriate means ((1)H and (13)C NMR, IR, UV-Vis, fluorescence, EPR, elemental analysis) and some of their biological properties were evaluated. Hydrophobicity (R(M0), log P), total antioxidant capacity (TAC), and antimicrobial properties are also presented and discussed.

Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models

BACKGROUND

QSAR is among the most extensively used computational methodology for analogue-based design. The application of various descriptor classes like quantum chemical, molecular mechanics, conceptual density functional theory (DFT)- and docking-based descriptors for predicting anti-cancer activity is well known. Although in vitro assay for anti-cancer activity is available against many different cell lines, most of the computational studies are carried out targeting insufficient number of cell lines. Hence, statistically robust and extensive QSAR studies against 29 different cancer cell lines and its comparative account, has been carried out.

RESULTS

The predictive models were built for 266 compounds with experimental data against 29 different cancer cell lines, employing independent and least number of descriptors. Robust statistical analysis shows a high correlation, cross-validation coefficient values, and provides a range of QSAR equations. Comparative performance of each class of descriptors was carried out and the effect of number of descriptors (1-10) on statistical parameters was tested. Charge-based descriptors were found in 20 out of 39 models (approx. 50%), valency-based descriptor in 14 (approx. 36%) and bond order-based descriptor in 11 (approx. 28%) in comparison to other descriptors. The use of conceptual DFT descriptors does not improve the statistical quality of the models in most cases.

CONCLUSION

Analysis is done with various models where the number of descriptors is increased from 1 to 10; it is interesting to note that in most cases 3 descriptor-based models are adequate. The study reveals that quantum chemical descriptors are the most important class of descriptors in modelling these series of compounds followed by electrostatic, constitutional, geometrical, topological and conceptual DFT descriptors. Cell lines in nasopharyngeal (2) cancer average R2 = 0.90 followed by cell lines in melanoma cancer (4) with average R2 = 0.81 gave the best statistical values.

Benzyl 1,2,3,5,11,11a-hexahydro-3,3-dimethyl-1-oxo-6H-imidazo[3',4':1,2]pyridin[3,4-b]indole-2-substituted acetates: One-pot-preparation, anti-tumor activity, docking toward DNA and 3D QSAR analysis

To discover the anti-tumoral indoles a series of benzyl 1,2,3,5,11,11a-hexahydro-3,3-dimethyl-1-oxo-6H-imidazo[3',4':1,2]pyridin[3,4-b]indole-2-substituted acetates (2a-n) are prepared via one-pot-preparation. The IC(50) values of 2a-n in vitro against human lung carcinoma, prostate cancer, nasopharyngeal carcinoma, vincristine-resistant KB subline and human breast carcinoma cells range from 40 nM to 60 microM. On Sarcoma 180 (S180) tumor-bearing mouse model four of them (2e,g,h,i) significantly inhibited the tumor growth. At the dose of 0.1mg/kg the efficacy of the most potent 2h was equal to that of 1.0mg/kg of doxorubicin. In contrast to doxorubicin, at 1.0mg/kg of dose 2e,g,h,i did not induce the treated S180 mice to have organ atrophy and body emaciation. The healthy mice receiving 10, 100 and 500 mg/kg of 2e,g,h,i gave no any neurotoxic response. Even up to the dose of 500 mg/kg the healthy mice occurred no death. The interaction of 2a-n with DNA was confirmed by the fluorescence quenching experiments and automated flexible ligand docking. By 3D QSAR analysis the IC(50) values of 2a-n against prostate cancer cells were correlated with the structures and conformations of their side chains. To increase the data related to their physical-chemical properties the experimental LogP values were also provided.

New approach for local delivery of rapamycin by bioadhesive PLGA-carbopol nanoparticles

Local delivery of antiproliferative drugs encapsulated in biodegradable nanoparticles has shown promise as an experimental strategy for preventing vascular restenosis development. The general aim of this work was to develop polymeric nanoparticle carriers with bioadhesive properties, and to evaluate its adjuvant potential for local, intramural delivery of rapamycin for inhibition of restenosis. The bioadhesive rapamycin-loaded PLGA nanoparticles were obtained by applying carbopol 940 of different concentrations as stabilizer and bioadhesive agent. The resultant nanoparticles were characterized concerning physicochemical properties such as morphology, particle size, zeta potential, entrapment efficiency, drug loading, drug release in vitro, stability in vitro as well as the arterial uptake and retention ability in an ex-vivo model. The results revealed that carbopol could serve as a better stabilizer in the preparation of rapamycin-loaded PLGA nanoparticles compared with PVA, and the physicochemical characteristics of the obtained PLGA nanoparticles were affected by the concentration of carbopol. Furthermore, it was found that carbopol could impart the nanoparticles with bioadhesive properties, improving the rentention and uptake of nanoparticles in the arterial wall, benefiting the nanoparticles for efficient localization of therapeutic agents in restenosis site. Cell viability assay results showed that blank PLGA-carbopol nanoparticles exhibited low toxicity and excellent biocompatibility and rapamycin-loaded nanoparticles with a smaller particle size (< 200 nm) had an increased antiproliferative effect on cells in comparison to free drug. These results indicated that this research might provide a potential experimental basis for the further study of carbopol stabilized bioadhesive nanoparticles against restenosis in vivo.

Differential neuropsychopharmacological influences of naturally occurring tropane alkaloids anisodamine versus scopolamine

Two naturally occurring tropane alkaloids, anisodamine and scopolamine, structurally dissimilar in one OH group, are well established as muscarinic acetylcholine receptor (mAChR) antagonists in clinic and basic research. However, experimental evidence for central effects of anisodamine is limited and conflicting compared with that of scopolamine. In the present study, Morris water maze test, long-term potentiation (LTP) recording and receptor radioligand binding assays were used to explore the disparity in neuropsychopharmacological influences of anisodamine versus scopolamine and possible mechanisms. Anisodamine, at 10-40-fold higher doses than those of scopolamine, did not produce any spatial cognitive deficits as scopolamine, but tended to improve cognition at the repeated high doses. LTP in vivo was then adopted to predict BBB permeability of the muscarinic antagonists following systemic drug administration. Contrary to scopolamine, anisodamine did not influence the formation of LTP in the CA(1) region of rat hippocampus at 40-fold higher dose than that of scopolamine. Additionally, receptor radioligand binding assays (RRLBA) revealed that the binding affinity of anisodamine to mice brain mAChR was much lower than that of scopolamine. The findings suggested that anisodamine did not impair cognition nor depress LTP primarily due to its poor BBB permeability. This work enlarged knowledge of structure-activity relationship among tropane alkaloids, meanwhile providing evidence for more reasonable drug prescription in clinic.

Computer-aided prediction for medicinal chemistry via the Internet

Some computational tools for medicinal chemistry freely available on the Internet were compared to examine whether the results of prediction obtained with different methods coincided or not. It was shown that the correlation coefficients varied from 0.65 to 0.90 for log P (seven methods), from 0.01 to 0.73 for aqueous solubility (four methods), and from 0.19 to 0.73 for drug-likeness (three methods). While for log P estimates, reasonable average pairwise correlation was found, for aqueous solubility and drug-likeness it was rather poor. Therefore, using computational tools freely available via the Internet, medicinal chemists should evaluate their accuracy versus experimental data for particular series of compounds. In contrast to prediction of above mentioned properties, which can be done with several Internet tools, wide profiling of biological activity can be obtained only with PASS Inet (http://www.ibmc.msk.ru/PASS). PASS Inet was tested by a dozen medicinal chemists for compounds from different chemical series with various kinds of biological activity, and in the majority of cases the results of prediction coincided with the experiments. New anxiolytics, antiarrhythmics, antileishmanials, and other biologically active agents have been identified on this basis. The advantages and limitations of computer-aided predictions for medicinal chemistry via the Internet are discussed.