We have previously shown that N-1-sulfonylpyrimidine derivatives have strong antiproliferative activity on human tumor cell lines, whereby 1-(p-toluenesulfonyl)cytosine showed good selectivity with regard to normal cells and was easily synthesized on a large scale. In the present work we have used an interdisciplinary approach to elucidate the compounds’ mechanistic class. An augmented number of cell lines (11) has allowed a computational search for compounds with similar activity profiles and/or mechanistic class by integrating our data with the comprehensive DTP–NCI database. We applied supervised machine learning methodology (Random Forest classifier), which offers information complementary to unsupervised algorithms commonly used for analysis of cytostatic activity profiles, such as self-organizing maps. The computational results taken together with cell cycle perturbation and apoptosis analysis of the cell lines point to an unusual mechanism of cytostatic action, possibly a combination of nucleic acid antimetabolite activity and a novel molecular mechanism.
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This work was supported by the Rudjer Boskovic Institute’s spin-off company BioZyne d.o.o., and the Ministry of Science, Education and Sport of Croatia. We are very grateful to Dr. David Covell of the National Cancer Institute’s Developmental Therapeutics Program for supplying the data set with mechanistic classes of compounds.
Fran Supek and Marijeta Kralj contributed equally to this work.
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Supek, F., Kralj, M., Marjanović, M. et al. Atypical cytostatic mechanism of N-1-sulfonylcytosine derivatives determined by in vitro screening and computational analysis. Invest New Drugs 26, 97–110 (2008). https://doi.org/10.1007/s10637-007-9084-1
- Antitumor agents
- In vitro screening
- Random Forest