EXPLORATION OF PLANT BIOACTIVE FROM CASSIA FISTULA LEAVES FOR THE TREATMENT OF OVARIAN CANCER: AN INTEGRATIVE APPROACH

Ramanathan K, Shanthi V, Kanika Verma

Abstract


ABSTRACT
Objective: Paclitaxel is one of the most effective anticancer agents. It is used as a chemotherapy agent for a spectrum of cancer types. However,
paclitaxel resistance is one of the foremost problems for chemotherapy. Most importantly, an emergence of paclitaxel resistance due to mutation
(F270V) in β-tubulin has been extremely deliberated in recent years. With the rise of paclitaxel-resistant mutation in β-tubulin, there is a need to add
a novel inhibitor from natural source, as they have less chance of getting resistance additionally less side effects. Keeping this in mind, we have utilized
experimental and in silico approaches to isolate the potent inhibitor for β-tubulin target protein.
Methods: We have extracted phytocompounds from Cassia fistula plant, and the structures were recognized with the help of gas chromatographymass
spectrometry
technique.
Subsequently,
oral
bioavailability
and
toxicity
analysis
were
executed
for
the
extracted
compounds
by
employing

MOLINSPIRATION
and OSIRIS program,
respectively.
Furthermore,
docking analysis
was
performed
using
YASARA
algorithm.
In
addition,
bioactivity

analysis
for
the screened
compounds was
performed
using prediction
of activity
spectra
for
substances program.
Results: The results from our analysis clearly depict that HOP-22(29)-EN-3.BETA.-OL could be a promising inhibitor for the treatment of cancer and
provide direction for future research. Further in vitro and in vivo exploration is also required to identify whether HOP-22(29)-EN-3.BETA.-OL have
anticancer effect or not.
Conclusion: The combination of computational approach and experimental analysis provides an easy approach to identify novel candidate for the
target protein β-tubulin.
Keywords: Phytochemicals, Gas chromatography mass spectrometry, Bioavailability, Molecular docking, Prediction of activity spectra for substances
prediction.


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DOI: http://dx.doi.org/10.22159/DOI:%2010.22159/ajpcr.2016.v9i5.13187

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