Safe and Effective Kinase Inhibitors for the Treatment of Gynecological Cancers: In Silico Approach
Aims: This study aimed at studying various types of gynecological cancers and the available therapeutics to investigate safe and effective drugs.
Background: Cancer is the most common cause of mortality throughout the world. When the statistics are considered for gynecological cancers, ovarian, cervical, and uterine cancers are among the most prevalent types. They have the worst prognosis and the highest mortality rate, and by the year 2040 significant increase in mortality rate is predicted.
Objective: The major limitation with the development of anti-cancer therapeutics for gynecological cancers is the safety of the therapeutics for the fetus as well as the mother. Various medicinal classes of natural to synthetic therapeutics have been reported, including kinase inhibitors as the most promising category of anti-cancer drugs.
Method: A dataset of kinase inhibitory clinically approved anticancer agents was derived through literature review. A QSAR based approach, i.e., VEGAQSAR, has been applied to evaluate the reproductive and developmental toxicity of the selected class of kinase inhibitors.
Result: In the present work, the promising category of anticancer kinase inhibitors has been investigated for its toxicity potential with the help of in silico approach. The anti-cancer kinase inhibitors were categorized based on non-toxic or toxic properties towards reproductive and developmental toxicity.
Conclusion:Early prediction of the available or proposed anti-cancer therapeutics for their contribution towards developmental and reproductive toxicity is an important criterion for their use in pregnancy-associated cancers. The investigation of the toxicity profile of available anti-cancer kinase therapeutics will be helpful to design and develop novel and safe anti-cancer drugs in the near future.
Other: The study outcomes will benefit the current anticancer drug development efforts.Journal Title:
Current Drug Metabolism