PENGEMBANGAN MODEL HUBUNGAN KUANTITATIF STRUKTUR-AKTIVITAS UNTUK INHIBITOR ANGIOTENSIN CONVERTING ENZYME
Hypertension or high blood pressure is one of the most common cardiovascular diseases. One of the therapies for hypertension is using Angiotensin Converting Enzyme (ACE) inhibitors. ACE inhibitor drugs currently available, still have several problems, such as symptom complex and serious side effe...
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| Main Author: | |
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/69302 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Hypertension or high blood pressure is one of the most common cardiovascular diseases. One of
the therapies for hypertension is using Angiotensin Converting Enzyme (ACE) inhibitors. ACE
inhibitor drugs currently available, still have several problems, such as symptom complex and
serious side effect. Therefore, it is necessary to develop better ACE inhibitors. In the development
of ACE inhibitor compounds, it is needed to select the best candidate for lead compounds to
minimize development failures. One method that can be done is using a computational method
based on the quantitative structure-activity relationship (QSAR). This study aimed to develop a
QSAR model that can be used as a guide in designing new ACE inhibitors and to predict the potential
activity of a compound as an ACE inhibitor. This research was conducted using the in-silico method,
which utilizes computer simulations and databases. Data were collected from databases obtained
from the results of previous studies, which were accessed online on the internet. The best HKSA
model is Log(1/IC50) = ?35.134 + 1.606(Log P) + 0.265(Log P
2
) ? 0.159(MR) + 0.001(MR2
) + 0.066(PSA)
? 0.0002(PSA2
) + 0.646(DM) ? 0.078(DM2
) – 180.449(HOMO) – 255.887(HOMO2
) – 47.508(LUMO)
+ 224.355(LUMO2
). The results showed that the model obtained by the nonlinear regression
method with six molecular descriptors, which gives predictions close to the experimental data, has
good statistical parameters and a random residual plot is the better model.
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