Halogenated naphthochalcones and structurally related naphthopyrazolines with antitumor activity

Chalcone: A Promising Bioactive Scaffold in Medicinal Chemistry

Chalcones are a class of privileged scaffolds with high medicinal significance due to the presence of an α,β-unsaturated ketone functionality. Numerous functional modifications of chalcones have been reported, along with their pharmacological behavior. The present review aims to summarize the structures from natural sources, synthesis methods, biological characteristics against infectious and non-infectious diseases, and uses of chalcones over the past decade, and their structure–activity relationship studies are detailed in depth. This critical review provides guidelines for the future design and synthesis of various chalcones. In addition, this could be highly supportive for medicinal chemists to develop more promising candidates for various infectious and non-infectious diseases.

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.

Development of Simple and Accurate in Silico Ligand-Based Models for Predicting ABCG2 Inhibition

The ATP binding cassette transporter ABCG2 is a physiologically important drug transporter that has a central role in determining the ADMET (absorption, distribution, metabolism, elimination, and toxicity) profile of therapeutics, and contributes to multidrug resistance. Thus, development of predictive in silico models for the identification of ABCG2 inhibitors is of great interest in the early stage of drug discovery. In this work, by exploiting a large public dataset, a number of ligand-based classification models were developed using partial least squares-discriminant analysis (PLS-DA) with molecular interaction field- and fingerprint-based structural description methods, regarding physicochemical and fragmental properties related to ABCG2 inhibition. An in-house dataset compiled from recently experimental studies was used to rigorously validated the model performance. The key molecular properties and fragments favored to inhibitor binding were discussed in detail, which was further explored by docking simulations. A highly informative chemical property was identified as the principal determinant of ABCG2 inhibition, which was utilized to derive a simple rule that had a strong capability for differentiating inhibitors from non-inhibitors. Furthermore, the incorporation of the rule into the best PLS-DA model significantly improved the classification performance, particularly achieving a high prediction accuracy on the independent in-house set. The integrative model is simple and accurate, which could be applied to the evaluation of drug-transporter interactions in drug development. Also, the dominant molecular features derived from the models may help medicinal chemists in the molecular design of novel inhibitors to circumvent ABCG2-mediated drug resistance.

Synthesis and biological evaluation of halogenated phenoxychalcones and their corresponding pyrazolines as cytotoxic agents in human breast cancer

Novel halogenated phenoxychalcones 2a–f and their corresponding N-acetylpyrazolines 3a–f were synthesised and evaluated for their anticancer activities against breast cancer cell line (MCF-7) and normal breast cell line (MCF-10a), compared with staurosporine. All compounds showed moderate to good cytotoxic activity when compared to control. Compound 2c was the most active, with IC₅₀ = 1.52 µM and selectivity index = 15.24. Also, chalcone 2f showed significant cytotoxic activity with IC₅₀ = 1.87 µM and selectivity index = 11.03. Compound 2c decreased both total mitogen activated protein kinase (p38α MAPK) and phosphorylated enzyme in MCF-7 cells, suggesting its ability to decrease cell proliferation and survival. It also showed the ability to induce ROS in MCF-7 treated cells. Compound 2c exhibited apoptotic behaviour in MCF-7 cells due to cell accumulation in G2/M phase and elevation in late apoptosis 57.78-fold more than control. Docking studies showed that compounds 2c and 2f interact with p38alpha MAPK active sites.

Discovery of Anticancer Agents from 2-Pyrazoline-Based Compounds

As nitrogen-containing five-membered heterocyclic structural units, the substituted pyrazole derivatives have a broad spectrum of pharmacological activities, especially 4,5-dihydro-1H-pyrazoles that also commonly known as 2-pyrazolines. Since 2010, considerable studies have been found that the 2-pyrazoline derivatives possess potent anticancer activities. In the present review, it covers the pyrazoline derivatives reported by literature from 2010 till date (2010-2019). This review aims to establish the relationship between the anticancer activities variation and different substituents introduced into a 2-pyrazoline core, which could provide important pharmacophore clues for the discovery of new anticancer agents containing 2-pyrazoline scaffold.

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning

Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug–drug interactions. The prediction of BCRP inhibition can facilitate evaluating potential drug resistance and drug–drug interactions in early stage of drug discovery. Here we reported a structurally diverse dataset consisting of 1098 BCRP inhibitors and 1701 non-inhibitors. Analysis of various physicochemical properties illustrates that BCRP inhibitors are more hydrophobic and aromatic than non-inhibitors. We then developed a series of quantitative structure–activity relationship (QSAR) models to discriminate between BCRP inhibitors and non-inhibitors. The optimal feature subset was determined by a wrapper feature selection method named rfSA (simulated annealing algorithm coupled with random forest), and the classification models were established by using seven machine learning approaches based on the optimal feature subset, including a deep learning method, two ensemble learning methods, and four classical machine learning methods. The statistical results demonstrated that three methods, including support vector machine (SVM), deep neural networks (DNN) and extreme gradient boosting (XGBoost), outperformed the others, and the SVM classifier yielded the best predictions (MCC = 0.812 and AUC = 0.958 for the test set). Then, a perturbation-based model-agnostic method was used to interpret our models and analyze the representative features for different models. The application domain analysis demonstrated the prediction reliability of our models. Moreover, the important structural fragments related to BCRP inhibition were identified by the information gain (IG) method along with the frequency analysis. In conclusion, we believe that the classification models developed in this study can be regarded as simple and accurate tools to distinguish BCRP inhibitors from non-inhibitors in drug design and discovery pipelines.