The process of drug discovery is a long back episode appearing from the
ancient times. The therapeutic applications of plants, mineral were recorded in ancient
civilizations like Chinese, Hindus, and European. The development of the new drug
from lead/hit molecules is a very expensive event considering money, manpower as
well as time. The traditional approach includes the synthesis of compounds in
laboratory which is a time-consuming process and their testing in in-vivo biological
assays. The in silico approaches are often known as high throughput screening methods
(HTS)/ virtual screening mainly applied in the early phases of drug discovery.It helps
the researchers to go deep into in silico simulation prior to wet laboratory experiments.
The statistics highlighted that there is a shift from 10% to 20% by the pharmaceutical
industries in pharmaceutical R&D on computer modeling and simulations. In late
1990s FDA identified that poor pharmacokinetic parameters (ADME/Tox) were one of
the major cause of late stage failure of drug candidates for clinical trials. In the past
decade, there was a remarkable growth of computational approach in bridging the
chemical and biological process in drug discovery pipeline.The advent of the arena of
in silico approaches was made possible by the advancement of software and hardware
computational ability and afurther increase in precision and accuracy. The incidents of
allergic diseases like bronchial asthma, allergic rhinoconjunctivitis and atopic
dermatitis are increasing. Drugs in this categories act through different targets like
inhibitors of histaminic receptors, leukotrienes,thromboxinase-A2 inhibitors, mast cell
stabilizer. In this context, the chapter tries to give emphasis on different recent targets
for antiallergic agents and abrief overview on in silico methods as well as
computational studies carried out for the targets in accelerating the drug discovery for
the antiallergic agents for controlling the above-mentioned disease.
Keywords: ADME/T, Antiallergic Agents, HTS, in Silico.
