Computational study to select the capable anthracycline derivatives through an overview of drug structure-specificity an
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ORIGINAL PAPER
Computational study to select the capable anthracycline derivatives through an overview of drug structure‑specificity and cancer cell line‑specificity Fereydoun Sadeghi1 · Abbas Afkhami1 · Tayyebe Madrakian1 · Raouf Ghavami2 Received: 30 March 2020 / Accepted: 13 August 2020 © Institute of Chemistry, Slovak Academy of Sciences 2020
Abstract A comprehensive study on anthracycline derivatives was done. A quantitative structure–activity relationship (QSAR) study on the half-maximal inhibitory concentration (IC50) of these analogs was developed. These antitumor compounds are used as topoisomerase II enzyme inhibitors. Genetic algorithm (GA) was applied for feature extraction. Multiple linear regression (MLR) was established based on the GA. High stability and robustness of the model were evaluated by leave-one-out 2 cross-validation (LOO-CV), Y-randomization, and external test set (R2 = 0.879, Q2LOO = 0.857, RMSE = 0.148, Rmax = 0.224, F = 45.4, PRESS = 0.541). This model was generalized to 29 analogs with the quantitative or qualitative in vitro observations for their p IC50 to be calculated. Good agreement between experimental observations and calculated pIC50, indicated that the model was reliable. This result also showed that probably the drug structural-specific is preferred to the cancer cell line-specific in such analogs. Furthermore, the developed model was generalized to 49 other analogs to select potent drug candidates. To do so, four criteria were used simultaneously including (i) effective inhibitory range, (ii) leverage value of structural similarity, (iii) GATS8v value as an important descriptor, and (iv) substituent effect. This approach resulted in the discrimination of 11 candidates. Graphic abstract
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11696-020-01321-z) contains supplementary material, which is available to authorized users. Extended author information available on the last page of the article
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Chemical Papers
Keywords Anthracycline derivatives · Drug structural-specific · QSAR, substituent effect · 3D-MoRSE descriptors · Prodrug strategy
Introduction One of the most commonly diagnosed diseases is cancer. In 2012, 1 million people died just because of breast and ovarian cancers (Alam and Khan 2017). Among the antitumor drugs, anthracyclines have an essential role as the chemotherapeutic agents. In this class of anticancer drugs, doxorubicin is the most common one which is used as topoisomerase II inhibitors (Laatsch 2008). Daunorubicin, idarubicin, nemorubicin, valrubicin, and sabarubicin are other most important analogs (Nadas and Sun 2006; Orlandi et al. 2005; Krohn 2008; Sabnis et al. 2012; Bellarosa et al. 2005). These drugs are mostly used against cancer cell lines of MCF-7 human breast, SK-OV-3 ovarian, and HL-60 leukemia. The most important mechanisms provided in the explanation of the action of these drugs are (i) inhibition of the topoisomerase II enzyme in double-strand DNA br
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