Abstract:
In the current work, interaction studies of five Non-Steroidal Anti-Inflammatory Drugs
(NSAIDs) and four Angiotensin Converting enzyme (ACE) enzyme amino acids with the
antihypertensive drug, Lisinopril have been carried out on the molecular level using Quantum
Mechanical Molecular Orbital Calculations with Density Functional Theory (DFT) and
Hartree-Fock (HF) method using 6-31G basis set. Investigation of interactions was conducted
with the aim of observing the comparative pharmacodynamics interaction studies of NSAIDs
and amino acids with Lisinopril by scrutinizing the changes in geometric and electronic
parameters of NSAIDs and amino acids of before and after complex formation with
Lisinopril. Geometric parameter revealed increased electronic charge distribution on NSAIDs
as compared to amino acids when interacted with lisinopril. Electronic parameters gave a
measure of electron donating and electron accepting character of Lisinopril. Both geometric
and electronic parameter revealed that electrostatic attraction and hence complex formation of
NSAIDs with lisinopril is stronger as compared to that with amino acids. So in a competitive
reaction NSAIDs complex formation is more favorable. According to the band gap value of
ƐHOMO (lisinopril) - ƐLUMO (NSAIDs) lowest value of band gap is for aspirin which showed
highest probability of electron transfer from lisinopril to aspirin as compared to other
NSAIDs. This confirmed the formation of most strong charge transfer complex formation of
aspirin-lisinopril and distortion of lisinopril leading to decrease in its antihypertensive effect.
It was also observed that ƐLUMO of aspirin-lisinopril complex becomes more negative thus
showing increased stability of aspirin-lisinopril complex. This investigation ultimately leads
to reveal the drug interactions caused by NSAIDs in hypertension patients and suggests that
aspirin should not be prescribed with Lisinopril. Decrease in antihypertensive effect of
antihypertensive drugs by NSAIDs was also supported by discrete modeling, an approach of
system biology.
