Antiproliferative, proapoptotic, and tumor‐suppressing effects of the novel anticancer agent alsevirone in prostate cancer cells and xenografts

Khan, Irina I.; Karshieva, Saida Sh; Sokolova, Darina V.; Спирина, Т. С.; Zolottsev, Vladimir A.; Latysheva, Alexandra S.; Anisimova, Natalia; Комарова, М В; Yakunina, Marina N; Nitetskaya, Tatiana A; Misharin, A. Yu.; Pokrovsky, Vadim S.

doi:10.1002/ardp.202100316

Cited by 1 publication

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References 33 publications

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“…Since the activities distribution for both databases is highly concentrated around the average, the 20% of intermediate compounds presents a narrow window of activities between 6 and 16 µM. Noteworthy, several studies report anti-cancer compounds with activity against PC-3 and DU-145 in the range of 1-20 µM [37][38][39], in agreement with the ranges identified for the classification of active and inactive compounds. Of note, the dataset of compounds showed an overall low degree of similarity, despite most of the activity data were distributed in a restricted range of values.…”

Section: Activity Profilingsupporting

confidence: 69%

Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

2022

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Prostate cancer is the most common type of cancer in men. The disease presents good survival rates if treated at the early stages. However, the evolution of the disease in its most aggressive variant remains without effective therapeutic answers. Therefore, the identification of novel effective therapeutics is urgently needed. On these premises, we developed a series of machine learning models, based on compounds with reported highly homogeneous cell-based antiproliferative assay data, able to predict the activity of ligands towards the PC-3 and DU-145 prostate cancer cell lines. The data employed in the development of the computational models was finely-tuned according to a series of thresholds for the classification of active/inactive compounds, to the number of features to be implemented, and by using 10 different machine learning algorithms. Models’ evaluation allowed us to identify the best combination of activity thresholds and ML algorithms for the classification of active compounds, achieving prediction performances with MCC values above 0.60 for PC-3 and DU-145 cells. Moreover, in silico models based on the combination of PC-3 and DU-145 data were also developed, demonstrating excellent precision performances. Finally, an analysis of the activity annotations reported for the ligands in the curated datasets were conducted, suggesting associations between cellular activity and biological targets that might be explored in the future for the design of more effective prostate cancer antiproliferative agents.

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Section: Activity Profilingsupporting

confidence: 69%