Abstract:
In classical approach of drug discovery involving medicinal plants, large number
of extracts are prepared by using different parts of plants and solvents. The biological activity of these extracts are assessed using some standard test system.
Extracts which are found active are fractionated to get different fractions. These
fractions are assessed using the suitable test system. Fractions which are found
active are processed further to get pure compounds, which are again tested for
bioactivity. At every stage (extracts, fractions and pure compounds), lot of test
data is generated. Statisticians play an Important role in evaluating such 4ata to
get promising compounds. If the activity of any compound is over- estimated,
then chances of it getting dropped at preclinical or clinical level will be higher,
and if under-estimated and not pursued further, we may loose a good drug.
Depending upon the disease and observations of response, a suitable method is
applied to ascertain the significantly active compounds. For example, to get a
lead against diabetes one has to select a suitable animal model such as rat, mice,
etc. N number of animals are considered in two groups. Both group animals are
loaded with glucose. One randomly taken group is administered the compound
under test, while the other one remains untreated and acts as control. The serum
glucose level of the animals is measured at different predecided time points.
