A Combined Quantitative Structure-Activity Relationship Research of Quinolinone Derivatives as Androgen Receptor Antagonists

Cheminformatic Analysis and Machine Learning Modeling to Investigate Androgen Receptor Antagonists to Combat Prostate Cancer

Prostate cancer (PCa) is a major leading cause of mortality of cancer among males. There have been numerous studies to develop antagonists against androgen receptor (AR), a crucial therapeutic target for PCa. This study is a systematic cheminformatic analysis and machine learning modeling to study the chemical space, scaffolds, structure-activity relationship, and landscape of human AR antagonists. There are 1678 molecules as final data sets. Chemical space visualization by physicochemical property visualization has demonstrated that molecules from the potent/active class generally have a mildly smaller molecular weight (MW), octanol-water partition coefficient (log P), number of hydrogen-bond acceptors (nHA), number of rotatable bonds (nRot), and topological polar surface area (TPSA) than molecules from intermediate/inactive class. The chemical space visualization in the principal component analysis (PCA) plot shows significant overlapping distributions between potent/active class molecules and intermediate/inactive class molecules; potent/active class molecules are intensively distributed, while intermediate/inactive class molecules are widely and sparsely distributed. Murcko scaffold analysis has shown low scaffold diversity in general, and scaffold diversity of potent/active class molecules is even lower than intermediate/inactive class molecules, indicating the necessity for developing molecules with novel scaffolds. Furthermore, scaffold visualization has identified 16 representative Murcko scaffolds. Among them, scaffolds 1, 2, 3, 4, 7, 8, 10, 11, 15, and 16 are highly favorable scaffolds due to their high scaffold enrichment factor values. Based on scaffold analysis, their local structure-activity relationships (SARs) were investigated and summarized. In addition, the global SAR landscape was explored by quantitative structure-activity relationship (QSAR) modelings and structure-activity landscape visualization. A QSAR classification model incorporating all of the 1678 molecules stands out as the best model from a total of 12 candidate models for AR antagonists (built on PubChem fingerprint, extra trees algorithm, accuracy for training set: 0.935, 10-fold cross-validation set: 0.735 and test set: 0.756). Deeper insights into the structure-activity landscape highlighted a total of seven significant activity cliff (AC) generators (ChEMBL molecule IDs: 160257, 418198, 4082265, 348918, 390728, 4080698, and 6530), which provide valuable SAR information for medicinal chemistry. The findings in this study provide new insights and guidelines for hit identification and lead optimization for the development of novel AR antagonists.

Discovery and Identification of Pyrazolopyramidine Analogs as Novel Potent Androgen Receptor Antagonists

Androgen receptor (AR), an important target in the current androgen derivation therapy, plays a critical role in the development and progress of prostate cancer (PCa). Nonsteroidal antiandrogens, such as enzalutamide and bicalutamide, are commonly used in clinic to treat PCa. Though they are very effective at the beginning, drug resistance problem appears after about 18 months. One of the reasons is that these antiandrogens share similar structure skeleton. Therefore, it is urgent to discover novel antiandrogens with different skeletons for resistance problem. Herein, we combined structure- and ligand-based methodologies for virtual screening chemical databases to identify potent AR antagonists. Then the cytotoxic activities of the screened hit samples were evaluated by using LNCaP prostate cancer cells. Virtual screening and biological evaluation assay results suggest that several chemicals with novel pyrazolopyrimidine skeleton can inhibit the proliferation of prostate cancer cells with similar, or even higher, bioactivities to bicalutamide. AR reporter gene assay experiments proved that Compound III showed potential antagonistic effects. In addition, molecular dynamics simulations results proved that Compound III can properly bind to AR and prevent helix 12 (H12) from closing to distort the formation of activation function 2 (AF2) site, resulting in the invalid transcription. Hence, pyrazolopyrimidine was discovered as a novel, potent and promising antiandrogen skeleton deserved to be further studied.

Synthesis and Acaricidal Activities of Scopoletin Phenolic Ether Derivatives: QSAR, Molecular Docking Study and in Silico ADME Predictions

Thirty phenolic ether derivatives of scopoletin modified at the 7-hydroxy position were synthesized, and their structures were confirmed by IR, ¹H-NMR, 13C-NMR, MS and elemental analysis. Preliminary acaricidal activities of these compounds against female adults of Tetranychus cinnabarinus (Boisduval) were evaluated using the slide-dip method. The results indicated that some of these compounds exhibit more pronounced acaricidal activity than scopoletin, especially compounds 32, 20, 28, 27 and 8 which exhibited about 8.41-, 7.32-, 7.23-, 6.76-, and 6.65-fold higher acaricidal potency. Compound 32 possessed the the most promising acaricidal activity and exhibited about 1.45-fold higher acaricidal potency against T. cinnabarinus than propargite. Statistically significant 2D-QSAR model supports the observed acaricidal activities and reveals that polarizability (HATS5p) was the most important parameter controlling bioactivity. 3D-QSAR (CoMFA: q² = 0.802, r² = 0.993; CoMSIA: q² = 0.735, r² = 0.965) results show that bulky substituents at R₄, R₁, R₂ and R₅ (C₆, C₃, C₄, and C₇) positions, electron positive groups at R₅ (C₇) position, hydrophobic groups at R₁ (C₃) and R₂ (C₄), H-bond donors groups at R₁ (C₃) and R₄ (C₆) will increase their acaricidal activity, which provide a good insight into the molecular features relevant to the acaricidal activity for further designing novel acaricidal agents. Molecular docking demonstrates that these selected derivatives display different bide modes with TcPMCA1 from lead compound and they interact with more key amino acid residues than scopoletin. In silico ADME properties of scopoletin and its phenolic ether derivatives were also analyzed and showed potential to develop as good acaricidal candidates.

A facile graphene oxide based sensor for electrochemical detection of prostate anti-cancer (anti-testosterone) drug flutamide in biological samples

An electrochemical sensor based on graphene oxide modified glassy carbon electrode for the determination of anti-cancer drug flutamide.

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.