INTRODUCTION:

The CYP450-1A1 enzyme is a member of the CYP450 enzyme family that plays an important role in the metabolic processing of various substances, including drugs, environmental toxins (such as polycyclic aromatic hydrocarbons and heterocyclic amines), hormones, and neurotransmitters. By removing potentially toxic substances and regulating hormone and neurotransmitter levels, CYP450-1A1 contributes in maintaining the body's health and the proper functioning of physiological processes^1,2.

CYP450-1A1 inhibitors are compounds that inhibit CYP450-1A1 enzyme activity. These inhibitors are used for a variety of medical purposes, such as reducing the metabolism of certain drugs to increase their effectiveness or prevent drug-drug interactions. An example of the use of CYP450 inhibitors is in the treatment of cancer. Some anticancer drugs are metabolized by CYP450, by inhibiting the activity of this enzyme can increase the levels of anticancer drugs in the body and potentially enhance their therapeutic effects. CYP450 inhibition may be beneficial for cancer chemoprevention³. In general, CYP450 inhibitors can be used to manipulate the metabolism of certain drugs to achieve desired therapeutic outcomes or to study enzyme pathways^3–5.

Several natural products have been found to have the potential to inhibit the CYP450 enzyme. Therefore, research to explore the potential of CYP inhibition by several natural molecules was carried out. For example, the flavone acacetin can inhibit CYP450 -mediated 7-ethoxy resorufin-O-dealkylation with IC₅₀ 80 nm, and the flavone apigenin can eliminate the formation of DNA adducts, which can be detected by activating CYP450 from benzo[a]pyrene. Flavonoids, including alpha flavones, are known to have a wide range of biological activities and are of interest as potential therapeutics for various diseases⁶. Therefore, synthesizing chemicals from the alpha flavone scaffold and studying their inhibitory effects on the CYP450 enzyme system can lead to the discovery of new drug candidates that can selectively inhibit certain CYP450 enzymes with minimal off-target effects. Several chemicals have been synthesized from the alpha flavone scaffold that has been found to have inhibitory effects on the CYP450 enzyme system. 6-Hydroxyflavone is a synthetic flavone that has been found to inhibit CYP450 enzymes, particularly CYP1A2 and CYP2C9. 6,7-Dihydroxyflavone is a synthetic flavone that has been found to inhibit CYP450 enzymes, particularly CYP2C9 and CYP3A4. It's important to note that the compounds mentioned are CYP450 inhibitors and more research is needed to understand the extent of these interactions and the potential risks associated with them^7–9.

Quantitative Structure-Activity Relationship (QSAR) and molecular docking are computational methods that can be used to predict the potential efficacy and safety of CYP450 inhibitors. QSAR uses statistical and mathematical models to relate the chemical structure of a compound to its biological activity. By analyzing the chemical features of a compound, QSAR can predict its potential inhibitory activity on CYP450 enzymes^10,11. Docking was used to predict the binding affinity of a compound to a specific protein, in this case, a CYP450 enzyme^12–15. By simulating the interactions between the compound and the enzyme, docking can predict the potential inhibitory activity of the compound on the enzyme. Both QSAR and docking are in silico methods, meaning that they are done using computer simulations and can provide a quick and cost-effective way to predict the potential inhibitory activity of a compound on CYP450 enzymes¹⁶. QSAR and docking methods are useful in predicting the activity of a compound on a specific CYP450 enzyme. In this paper, the ADMET-SAR activity of several compounds built from alpha-flavone was studied⁴. The IC₅₀ of the proposed compounds was calculated using linear and nonlinear regression based on RDKit Descriptors. The descriptors –IC₅₀ describe the quantitative structure-activity relationship (QSAR) between proposed compounds and known CYP450 and approved CYP450 inhibitors. Apart from insights into important molecular structure-activity properties for CYP450 inhibition, the present results may also guide the further design of CYP450 inhibitors.

MATERIALS AND METHODS:

QSAR Study

The IC₅₀ value of a substance is a measure of its potency as an inhibitor of a specific enzymatic activity. IC₅₀ stands for "inhibitory concentration at 50%", which means the concentration of a substance that is required to inhibit 50% of the enzymatic activity. The IC₅₀ value is a relative measure and can change depending on the source of enzyme, substrate, and conditions of the assay, so it's important to compare IC₅₀ values within the same experimental conditions. Table 1 consists of IC₅₀of some compounds that was used as a predictor for alpha-flavone analogs in this study. The datasets were obtained from Gilson et al.³, and datasets with missing or infinite IC₅₀ were removed. The processed datasets contain 402 ligands. The SMILES duplication check found 131 ligands have similar canonical SMILES. The removal of canonical SMILES duplication left 323 datasets to be further studied. The pIC₅₀ mean value of 323 datasets was 11.42 with a standard deviation of 1.21, a minimum value of 9.00, a median of 11.18, and a maximum value of 15.04. The 20 structures of the compounds obtained by random sampling from processed datasets are shown in Figure 1.

Table 1. Training datasets obtained from RCSB PDB

Ligand SMILES	Monomer-ID	Target Name	Target Source	IC50 (nM)
COc1ccc2c3oc(cc(=O)c3cc(OC)c2c1OC)-c1ccc(F)cc1	50507600	Cytochrome P450 1A	Homo sapiens	1.00
COc1ccc(OC)c2c1c(OC)cc1c2oc(cc1=O)-c1ccc2[nH]ncc2c1	50562217	Cytochrome P450 1A	Homo sapiens	1.60
COc1ccc(OC)c2c1c(OC)cc1c2oc(cc1=O)-c1ccc(Cl)cn1	50562252	Cytochrome P450 1A	Homo sapiens	1.60
COc1ccc(OC)c2c3oc(cc(=O)c3cc(OC)c12)-c1ccc(Cl)cc1	50081717	Cytochrome P450 1A1	Homo sapiens	1.80
COc1ccc(OC)c2c1c(OC)cc1c2oc(cc1=O)-c1cccc(F)n1	50562250	Cytochrome P450 1A	Homo sapiens	2.30
COc1cc(cc(OC)c1OC)-c1cc(=O)c2ccc3ccccc3c2o1	50519905	Cytochrome P450 1A	Homo sapiens	3.00
COc1ccc(OC)c2c3oc(cc(=O)c3cc(OC)c12)-c1cscn1	50562225	Cytochrome P450 1A	Homo sapiens	3.10
Clc1ccc(cc1)-c1cc(=O)c2ccc3ccccc3c2o1	50159640	Cytochrome P450 1A	Homo sapiens	4.30
COc1ccc(OC)c2c3oc(cc(=O)c3cc(OC)c12)-c1cccs1	50562224	Cytochrome P450 1A	Homo sapiens	4.40
COc1ccc(OC)c2c1c(OC)cc1c2oc(cc1=O)-c1ccccc1	50081709	Cytochrome P450 1A1	Homo sapiens	4.80
COc1ccc(OC)c2c1c(OC)cc1c2oc(cc1=O)-c1ccccc1	50081709	Cytochrome P450 1A	Homo sapiens	4.80
Clc1cccc(c1)-c1cc(=O)c2ccc3ccccc3c2o1	50159655	Cytochrome P450 1A	Homo sapiens	5.20
COc1ccc(OC)c2c3oc(cc(=O)c3cc(OC)c12)-c1ccccn1	50562229	Cytochrome P450 1A	Homo sapiens	5.40
Nn1c(NN=C2C(=O)Nc3ccccc23)nnc(Cc2ccccc2)c1=O	50380109	Cytochrome P450 1A1	Rattus norvegicus	5.42
COc1ccc(Cc2nnc(NN=C3C(=O)Nc4ccccc34)n(N)c2=O)cc1	50380110	Cytochrome P450 1A1	Rattus norvegicus	5.53
CC(=O)NN1C2=NN(C(C)=O)C(C)(N2N=C(Cc2ccccc2)C1=O)c1ccc(Cl)cc1	50380107	Cytochrome P450 1A1	Rattus norvegicus	5.69
COc1ccc(OC)c2c1c(OC)cc1c2oc(cc1=O)-c1cccc(Cl)n1	50562251	Cytochrome P450 1A	Homo sapiens	5.70
COc1ccc(OC)c2c3oc(cc(=O)c3cc(OC)c12)-c1ccccc1F	50081710	Cytochrome P450 1A1	Homo sapiens	5.90
COc1ccc(OC)c2c3oc(cc(=O)c3cc(OC)c12)-c1cccc(Cl)c1	50081716	Cytochrome P450 1A1	Homo sapiens	6.40

Figure 1. The structure of 20 compounds was used to predict IC₅₀ of alpha-naphthoflavone analogs.

Figure 3. The scaffold of alpha-naphthoflavone⁵

Docking studies

Chemical structures were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and formatted with USCF Chimera. The three-dimensional structure for human CYP450 (PDB: 4I8V)²⁰ was downloaded from the Protein Data Bank ^21,22, and the removal of counter-ions, crystallographic waters, and other ligands was done using USCF Chimera.

Atomic charges and solvation parameters were added using USCF Chimera. Docking simulations centered on the catalytic site of human CYP450 were carried out using AutoDock Vina in PyRx ^23–25. The top-ranked binding modes and protein-ligand interactions were visualized with Biovia Studio Visualizer. The grid box for the docking study was obtained from the position of PDB: 4I8V native ligand and tabulated in Table 2.

Table 2. Grid box parameters for docking analysis

Chain	Centers			Size
A	-15	36	-29	15	15	15
B	-30	84	-3	15	15	15
C	-50	125	11	15	15	15
D	-15	87	-61	15	15	15

RESULT:

Quantitative structure-activity relationships (QSARs)

QSAR (Quantitative Structure-Activity Relationship) is a computational method that is used to predict the biological activity of a compound based on its chemical structure. QSAR models are often used to identify compounds that are likely to be active against a specific target, such as a specific enzyme like CYP450. RDKit is an open-source software toolkit for cheminformatics and machine learning, it can be used to build QSAR models for different chemical activities. The process of building a QSAR model typically involves collecting a set of compounds with known activity against the target enzyme. Molecular descriptors represent the compounds' numerical values that describe the chemical structure of the compounds. Machine learning algorithms were applied to develop a predictive model that correlates the molecular descriptors to the activity of the compounds. The models were validated using a set of compounds with a known activity that was not used to train the model. The trained model was used to predict the activity of new compounds, which can then be experimentally tested to confirm the predictions. The boxplot and histogram of the pIC₅₀ of obtaining datasets used in this study are shown in Figure 4. The mean, median, minimum, and maximum values of the IC₅₀ were 11.37, 11.53, 9.00, and 15.04 nm, respectively. The IC₅₀ standard deviation was 1.25. The fingerprint similarity between training dataset compounds compared to alpha-naphthoflavone is shown in Figure 5. The average Tanimoto similarity of the compounds was about 21.21%.

(a) (b)

Figure 4. The boxplot (a) and histogram (b) of pIC₅₀ values of CYP450 ligand.

Figure 5. The Tanimoto similarity between alpha-naphthoflavone and the test compounds

RDKit provides a wide range of molecular descriptor calculation methods, molecular fingerprints, and machine learning algorithms, which can be used to build QSAR models for different enzymatic activities of CYP450²⁶. The QSAR study was carried out by predicting the IC₅₀ of research data conducted by Santes-Palacios et al. 2020, Chaudhary and Willett 2006^4,5. Some IC₅₀ from one research were significantly different from the others. Molecular weight descriptors have values higher than the others. The descriptor's feature data distribution is shown in Figure 6.

Figure 6. Descriptors data distribution

The regression models of pIC₅₀ as a function of chemical descriptors give an r² score, adjusted r² score, and RMSE as shown in Figure 7. The regression models shown were only the regression that have an r² score greater than 0.7. Random Forest regression produces the lowest RMSE while Neighbors regression produces the highest RMSE.

Figure 7. Plots of r² score adjusted r² score, and RMSE of chemical descriptors as independent variables and pIC₅₀ as dependent variables.

The predicted IC₅₀ based on the models developed produces the IC₅₀ that was described in Figure 8. Some models give higher or lower than others, the predicted IC₅₀ distribution of each compound was presented in a boxplot as shown in Figure 9.

Table 3. The average IC₅₀ of 30 compounds studied

No	Compounds	Mean pIC₅₀	IC₅₀	STD
0	C₂₀H₁₃Br	3.6087	36.9173	0.3249
1	C₂₀H₁₃Cl	4.3157	74.8638	0.4273
2	C₁₉H₁₂O₂	4.1989	66.6155	0.7376
3	C₁₉H₁₂O₂	4.4239	83.4202	0.7467
4	C₁₉H₁₂ClN	4.0630	58.1490	0.4734
5	C₁₉H₁₂IN	4.8779	131.3493	0.8905
6	C₁₉H₁₂OS	3.8042	44.8891	0.5752
7	C₂₀H₁₃Br	4.1407	62.8463	0.4594
8	C₁₉H₁₃F	7.4582	1733.9819	1.1814
9	C₁₉H₁₄N⁺	5.1416	170.9972	1.1099
10	C₁₉H₁₄N⁺	4.8022	121.7767	0.8485
11	C₂₀H₁₃Cl	4.2067	67.1328	0.4455
12	C₁₉H₁₄N⁺	4.6307	102.5838	1.0812
13	C₂₀H₁₃Cl	4.2306	68.7576	0.4134
14	C₂₀H₁₃Br	3.9684	52.8990	0.3773
15	C₂₀H₁₃Cl	4.2199	68.0283	0.4016
16	C₁₉H₁₂O	7.5077	1821.9980	0.9097
17	C₁₉H₁₃Br	5.9253	374.3905	1.2838
18	C₁₉H₁₂O₂	4.4838	88.5717	0.6580
19	C₁₉H₁₂ClN	4.1047	60.6275	0.4477
20	C₁₉H₁₁IO₂	4.2072	67.1655	0.7116
21	C₁₉H₁₂O₂	4.3852	80.2536	0.7584
22	C₁₉H₁₂IN	4.7673	117.5993	0.8149
23	C₁₉H₁₆ClN	6.4959	662.3976	0.4710
24	C₁₉H₁₂O₂	4.3845	80.1946	0.7662
25	C₁₉H₁₅N	4.6889	108.7342	0.8108
26	C₁₉H₁₄ClN	4.0898	59.7290	0.5520
27	C₁₉H₁₄N₂	5.8609	351.0530	0.6284
28	C₂₀H₁₃Cl	4.2594	70.7661	0.4357
29	C₂₀H₁₃Br	3.6087	36.9173	0.3250

The Tanimoto similarity based on Morgan fingerprints of the C₂₀H₁₃Br, C₂₀H₁₃Br, and C₁₉H₁₂OS compared to alpha-naphthoflavone were shown in Figure 10. The green color indicated a similar sub-structure, while the pink color indicated the differences.

Figure 10. The similarity maps of the C₂₀H₁₃Br, C₂₀H₁₃Br, and C₁₉H₁₂OS compared to alpha-naphthoflavone.

The IC₅₀ obtained was classified based on their values. The compounds with IC₅₀ below 100 nM were classified as ‘Active’, while compounds with IC₅₀ above 100 nm were classified as ‘Inactive’. The number of active compounds from the 30 compounds studied was 19. For comparison, the 30 compounds were also tested using SwissADME. The activity of the compounds compared to those based on SwissADME calculation was shown in Figure 11. The active inhibitors were described in ‘Yellow’ color. The similarity of SwissADME calculation compared to machine learning was 63.33%.

Figure 11. The comparison of activity class from ADME analysis and Machine learning (ML) of the 30 compounds studied.

The BOILEDEgg analysis, Figure 12, indicated that C₂₀H₁₃Br and C₁₉H₁₂OS have low gastrointestinal absorption not penetrated to brain barrier they were P-gp substrate, a CYP1A2 inhibitor, and a CYP2C19 inhibitor^27,28. While, C₂₀H₁₃Br has high gastrointestinal absorption, capable to penetrate the brain barrier, and also acts as a P-gp substrate, CYP1A2 inhibitor, and CYP2C19 inhibitor. The other remaining compounds were distributed inside the yolk, white, and outside of the BOILEgg calculation, Figure 12 (b).

Figure 14. The BOILEDEgg calculation of 30 compounds studied.

Alpha-naphthoflavone analogs CYP450 enzyme docking

CYP450 is a member of the cytochrome P450 family of enzymes, which was a group of heme-thiolate proteins that catalyze the oxidation of a wide variety of substrates. The structure of a CYP450 enzyme (4I8V) consists of several different regions. The N-terminal domain was the region involved in the binding of the substrate to the enzyme. The heme-binding domain is the region that binds the heme group, which is a prosthetic group that is essential for the enzyme's activity. The α-helix domain (A) is a region that was involved in the stability of the enzyme and the binding of the electron transfer proteins. The β-sheet domain (B) is the region involved in the stability of the enzyme and the binding of the electron transfer proteins. The C-terminal domain is the region involved in the binding of electron transfer proteins. The enzyme's active site where the substrate binds is located at the interface of the N-terminal and the heme-binding domain. And the heme group is located at the center of the enzyme, it acts as an electron carrier, accepting and donating electrons during the enzymatic reaction²⁰. The structure of CYP450 is shown in Figure 13.

The docking of three naphthoflavone analogs (10-bromo-1-phenyl-phenanthrene (0), 1-phenylthioxanthen-9-one (6), and 2-bromo-1-phenyl-phenanthrene (29)) that have low IC₅₀ values were carried out using AutoDock Vina. Alpha-naphthoflavone native ligand and protoporphyrin IX containing Fe were present in conjunction with the CYP450 (4I8V) enzymes. The structure of protoporphyrin, compounds 0, 6, and 29 is shown in Figure 14.

Figure 13. The structure of CYP450 (4I8V.pdb) consists of four chains A (red), B (yellow), C (green), and D (blue) with alpha-naphthoflavone in black spheres.

The interaction between chain D and compound 0 (Figure 18 a) has a binding energy of -11.2 with 1 active torsion between atoms: C13_14 and C14_15. Similar docking features were obtained for compound 6 (Figure 18 b), it has -10.9 binding energy with 1 active torsion between atoms: C12_14 and C13_15. Two Pi-Sulfur interactions were observed for compound 6 interaction with receptor phenylalanine at A:222 and A:274. Compound 29 has -12.4 binding energy with active torsion between atoms: C14_15 and C15_16. One Pi-Sigma interaction was observed in compound 29 (Figure 18 c) with chain D which was not observed in other compounds or other chains.

The docking results suggest that the three compounds, namely compound 0, compound 6, and compound 29, have a significant binding affinity with the CYP450-1A1 enzyme system. The binding energy values between these compounds and the receptor were below -10 kc/mol, indicating that these compounds have strong binding interactions with the enzyme system. The active torsion observed in the docking results between atoms C13_14 and C14_15 for compound 0 and compound 29, and between atoms C12_14 and C13_15 for compound 6, indicates that these torsions contribute to the binding affinity of these compounds with the receptor. This suggests that modifications of these torsions could be made to further improve the binding affinity and potency of these compounds as CYP450-1A1 inhibitors.

Furthermore, the Pi-Sulfur and Pi-Sigma interactions observed between these compounds and the phenylalanine residues of the receptor suggest that these residues play a significant role in the binding interactions. This highlights the importance of these residues in the enzymatic activity of CYP450-1A1 and provides insight into the structural basis of the inhibitory activity of these compounds.

In conclusion, the docking results suggest that the three compounds, namely compound 0, compound 6, and compound 29, have the potential to be developed as selective CYP450-1A1 inhibitors. Further optimization of these compounds based on the active torsions and binding interactions observed in the docking results could lead to the discovery of more potent and selective CYP450-1A1 inhibitors, which could be useful in manipulating the metabolism of certain drugs to achieve desired therapeutic effects.

DISCUSSION:

The study presented the use of QSAR modeling and molecular docking techniques in predicting the biological activity of compounds against the CYP450 enzyme. The study investigated a set of compounds with known activity against the target enzyme and correlated their molecular descriptors to their activity using several regression models. The trained model was validated using a set of compounds with a known activity that was not used to train the model. The validated regression models were applied to predict the activity of new compounds based on their IC₅₀ values. In addition to predicting the biological activity of compounds, the study classified compounds based on their IC₅₀ values. Those below 100 nM were classified as 'Active,' and those above 100 nM were classified as 'Inactive.' This classification system helps in identifying compounds that could potentially inhibit the CYP450 enzyme, which is useful in drug discovery and development. The study found 19 active compounds out of the 30 studied, and these compounds were further analyzed using molecular docking.

The three most active naphthoflavone analogs (Compound C₂₀H₁₃Br, C₂₀H₁₃Br, and C₁₉H₁₂OS) were further studied. The study also carried out a molecular docking analysis of three naphthoflavone analogs classified as active compounds. The compounds' binding energies were analyzed concerning the CYP450 enzymes' chains A, B, C, and D. The analysis found that the three compounds showed similar docking features to their native ligand binding energies, indicating their potential to inhibit the CYP450 enzyme.

Overall, the study demonstrates the potential of using QSAR modeling and molecular docking techniques in predicting the biological activity of compounds against the CYP450 enzyme. The findings of the study could be used in drug discovery and development to identify potential inhibitors of the enzyme, which could lead to the development of new drugs for the treatment of various diseases. The study's results provide valuable insights into the structure-activity relationships of compounds and could aid in the design of new drugs with higher potency and fewer side effects.

CONCLUSION:

Thirty-three alpha-naphthoflavone analogs were investigated for inhibition of human recombinant CYP1A1. Most of the alpha-naphthoflavone analogs showed low activities toward the CYPs tested. Three of the compounds (Compound C₂₀H₁₃Br, C₂₀H₁₃Br, and C₁₉H₁₂OS) showed strong inhibitory activities toward CYP1A1. The QSAR analyses suggest that out of 30 alpha-naphthoflavone analogs 19 compounds have CYP450 1A1 inhibition potency. The QSAR regression models predict IC₅₀ of compounds 0, 6, and 29 were 36.9173, 44.8891, and 36.9173 nM. The binding energies between these three compounds with chains A, B, C, and D of CYP450-1A1 were below -10 kc/mol. Thus, the interactions between these three compounds with CYP450-1A1 were significant. QSAR and molecular docking results might be relevant in the optimization of alpha-naphthoflavone analog's potential as CYP450-1A1 inhibitors.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

ACKNOWLEDGMENTS:

The authors gratefully acknowledge financial support from the Institute Tektology Sepuluh Nopember for this work, under the project scheme of the Publication Writing and IPR Incentive Program PPHKI 2025.

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Received on 09.04.2023 Revised on 05.02.2024

Accepted on 13.07.2024 Published on 27.03.2025

Available online from March 27, 2025

Research J. Pharmacy and Technology. 2025;18(3):1346-1356.

DOI: 10.52711/0974-360X.2025.00195

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