Biomedical Engineering Reference
In-Depth Information
and selective NCC blockers [
52
]. This group also performed SAR study on 2-, 5-,
and 6-position of 1,4-dihydropyridine derivatives to identify selective and an
injectable NCC blockers with high aqueous solubility [
53
]. Structure-activity
study of
L
-cysteine-based NCC blockers was carried out by Seko et al. and selec-
tivity toward this channel over LCC was established [
54
].
Lars et al. also reported recently, synthesis and SAR of novel 2-arylthiazo-
lidinones as selective NCC blockers [
55
]. Selective NCC blockers belonging to
4-amino-piperidine derivatives were analyzed by Zhang et al. for potent analgesic
activity in animal models [
56
]. Tendori et al. have performed design, synthesis, and
preliminary pharmacological evaluation of 4-aminopiperidine derivatives
containing the structural motifs of verapamil and flunarizine as NCC blockers for
the treatment of pain [
57
]. Gerald et al. performed flunarizine and lomerizine
scaffold-based design and synthesis of potent NCC blockers, which showed good
efficacy toward central nervous system in rat seizure model [
58
]. Structure-activity
relationship study of diphenylpiperazine NCC inhibitors exhibiting both
antiallodynic and antihyperalgesic activity was carried out by Hassan et al. for the
treatment of neuropathic pain in the spinal nerve ligation model of rat [
59
] (Table
2
).
5 Chemoinformatics Study on NCC Blockers
In silico techniques such as QSAR modeling, molecular docking, pharmacophore
mapping, and virtual screening have proven their usefulness in pharmaceutical
research for the selection/identification and/or design/optimization of new chemical
entities. QSAR is one of the most important areas in chemoinformatics and its
advances have widened the scope of rational drug design and the search for the
mechanism of drug action. It is a well-established fact that the chemical and
pharmacological effects of a compound are closely related to its physicochemical
properties, which can be calculated by various methods from the molecular struc-
ture. Once a reliable QSAR model is created, it is possible to predict the activity of
new molecules, and to learn, which structural factors play an important role in the
modeled biological response. A reliable QSAR model can also identify and
describe important structural features of the molecules that are relevant to
variations in molecular activities. These models are useful because they rationalize
a large number of experimental observations and allow for saving both time and
money in the drug discovery process. In addition, in silico methods can expand
screenings to molecules that do not exist physically in the chemical collections,
therefore compensating for some of the most important limitations of the high-
throughput methods. Several excellent case studies and review articles were
published in this field of research [
60
-
68
]. Gupta et al. developed 2D-QSAR
models for diverse classes of CCBs. These models have the ability to identify and
describe important physicochemical features of the molecules that underpin
variations in its molecular activity [
69
,
70
].
