Based on Nucleotides Analysis of Tumor Cell Lines to Construct and Validate a Prediction Model of Mechanisms of Chemotherapeutics

Title:Based on Nucleotides Analysis of Tumor Cell Lines to Construct and Validate a Prediction Model of Mechanisms of Chemotherapeutics

Volume: 16 Issue: 5

Author(s): Cuichai Liu, Fang Wang, Xi Liu, Min Liu, Zheng Liu and Lixin Sun

Affiliation:

Keywords: HPLC, mechanisms, model validation, nucleotides, prediction model, tumor cell lines.

Abstract: Cancer is one of the diseases that seriously threaten to human life worldwide. Up to now, chemotherapy remains to be a critical means of cancer treatment, thus the development of chemotherapeutical drugs has become a top priority. An ion pair high performance liquid chromatography (ion pair RP-HPLC) was established for analyzing intracellular nucleotides of tumor cell lines. In this article, a partial least-squares discriminant analysis (PLS-DA) prediction model of mechanisms of chemotherapeutics was established based on four types of drugs with different mechanisms, including antimetabolic agents, antineoplastic agents that affect protein synthesis, agents directly acting on DNA, and RNA interference agents. Then four anti-tumor agents commonly used in clinical were used to validate the availability of the prediction model. Three natural compounds, including 16- dehydropregnenolone (16-DHP), apigenin (API) and diosgenin (DIO), were reported to display anti-tumor effect with unclear mechanisms. The three components were applied to this prediction model firstly. In conclusion, the recognition model was proved to be accurate and feasible to some degree and might become a promising auxiliary method in the process of chemotherapeutic drugs development.

Cite this article as:

Liu Cuichai, Wang Fang, Liu Xi, Liu Min, Liu Zheng and Sun Lixin, Based on Nucleotides Analysis of Tumor Cell Lines to Construct and Validate a Prediction Model of Mechanisms of Chemotherapeutics , Anti-Cancer Agents in Medicinal Chemistry 2016; 16 (5) . https://dx.doi.org/10.2174/1871520615666150803124353