Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands.

Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logßML) and potentiometric sensitivity (PSML) of 200 ligands in complexes with the heavy metal ions Cu2+, Cd2+, and Pb2+. In result, the logßML models developed for four ions showed good performance with square correlation coefficients (R2) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R2 of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease.

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and ß-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

Design, Synthesis and Evaluation of Novel (E)-N'-((1-(4-chlorobenzyl)-1H-indol-3-yl)methylene)-2-(4-oxoquinazolin-3(4H)-yl)acetohydrazides as Antitumor Agents.

BACKGROUND: Herein, we have designed and synthesized a series of the novel (E)-N'-((1-(4-chlorobenzyl)- 1H-indol-3-yl)methylene)-2-(4-oxoquinazolin-3(4H)-yl)acetohydrazides (5) as potent small molecules activating procaspase- 3. The compounds were designed by the amalgamation of structural features of PAC-1 (the first procaspase-3 activator) and oncrasin-1, one potential anticancer agent. METHODS: The target acetohydrazides (5a-m) were prepared via the Niementowski condensation of anthranilic acid (1a) or 5-substituted-2-aminobenzoic acid (1b-m) and formamide. The compound libraries were evaluated for their cytotoxicity, caspase-3 activation, cell cycle analysis, and apoptosis. In addition, computational chemistry is also performed. RESULTS: A biological evaluation revealed that all thirteen compounds designed and synthesized showed strong cytotoxicity against three human cancer cell lines (SW620, colon cancer; PC-3, prostate cancer; NCI-H23, lung cancer) with eight compounds (5a, 5c-i, 5k), which were clearly more potent than both PAC-1 and oncrasin-1. In this series, four compounds, including 5c, 5e, 5f, and 5h, were the most potent members with approximately 4- to 5-fold stronger than the reference compounds PAC-1 and oncrasin-1 in terms of IC50. In comparison to 5-FU, these compounds were even 18- to 29-fold more potent in terms of cytotoxicity in three human cell lines tested. In the caspase activation assay, the caspase activity was activated to 285% by compound 5e compared to PAC-1, the first procaspase activating compound, which was used as a control. Our docking simulation revealed that compound 5e was a potent allosteric inhibitor of procaspase-3 through chelation of inhibitory zinc ion. Physicochemical and ADMET calculations for 5e provided useful information of its suitable absorption profile and some toxicological effects that need further optimization to be developed as a promising anticancer agent. CONCLUSION: Compound 5e has emerged as a potential hit for further design and development of caspases activators and anticancer agents.