Orally bioavailable HCV NS5A inhibitors of unsymmetrical structural class

Nakamura, Hiroshi; Fujioka, Satoko; Terui, Takashi; Okuda, Satoshi; Kondo, Kentaro; Tamatani, Yoshinori; Akagi, Yusuke; Komoda, Yoshiyuki; Kinoshita, Wataru; Ito, Shigeaki; Maeda, Kenichiro; Ukaji, Yutaka; Inaba, Takashi

doi:10.1016/j.bmcl.2020.127361

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…For this study, a dataset of 36 asymmetric inhibitors of HCV NS5A was used [14,19]. The chemical structures of these derivatives were drawn and were pre-optimized using the molecular mechanics' force field MMFF94 of the ChemDraw package.…”

Section: Data Preparationmentioning

confidence: 99%

Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus

et al. 2022

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Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.

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Section: Data Preparationmentioning

confidence: 99%