Experimental and Computational Evaluation of Garlic (Allium Sativum) for its Potential Activity Against 15-Lipoxygenase–Driven Inflammatory Disorders

The perennial bulb-producing plant known as garlic (Allium sativum) is a member of the Liliaceae family's genus Allium [1]. Garlic has been cultivated extensively throughout the world since antiquity and has been utilized extensively as a growth enhancer and feed ingredient.  In addition to its therapeutic qualities in alternative medicine, it has a distinct flavour and scent [2].

This plant's use and knowledge date back thousands of years.[3] It has been shown to have potential benefits for preventing cancer [ 4]. There are several studies in the literature that show eating garlic lowers the risk of stomach cancer [5,6].

This study also looks into the best bioactive chemicals found in garlic in order to assist researchers working on drug design. As previously mentioned, garlic contains 0.1–0.36% volatile oil; these volatile chemicals are thought to be the primary cause of the majority of garlic’s pharmacological characteristics. Allin, allicin, ajoene, allyl propyl, diallyl, trisulfide, s- S-allyl cysteine, vinyl dithiines, S-allyl mercaptan cysteine, and other sulphur compounds are among the 36 sulphur compounds found in garlic [7-13]. 

In this study, we used a bioinformatics technique to examine the primary bioactive ingredients in garlic. In fact, bioinformatics has advanced to the point where it is possible to forecast medical data. The early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically engineered and environmentally friendly next-generation medications has transformed illness treatment through approaches using open-access in silico technologies [14,15].

The aromatic spice known as garlic (Allium sativum) is widely utilized as a culinary addition because of its unique flavour and possible medical uses. In various civilizations, it has long been used for both culinary and medicinal purposes [16]. With a broad range of pharmacological effects, including antiatherosclerosis, anti-inflammatory, hypolipidemic, hypo glycaemic, anticoagulation, anticancer, chemo preventive, antimicrobial, and hepatoprotective properties, it is currently recognized as one of the most effective disease-preventive dietary ingredients [17,18,19]

 It is regarded as the second most widely used Allium species, along with onions (Allium cepa L.), and is used to treat a number of common illnesses, including the common cold, flu, snake bites, and high blood pressure [20]. On the other hand, aged garlic extract (AGE), which is made from aged garlic, is a traditional herbal medicine that has been demonstrated to boost immunity and hence prevent heart disease and cancer. Numerous sulphur compounds have been found in raw garlic and its processed products, and these compounds have been used in a variety of preparations [21].

Garlic's most physiologically active sulphur-containing component, allicin [S-(2-propenyl)-2- -2-propene-1-sulfinothioate], is what gives it its flavour and aroma [22,23].

Compared to cooked garlic, raw garlic exhibited greater antioxidant activity [24].

The oxidation of free polyunsaturated fatty acids (PUFAs) to produce hydroperoxides is catalysed by oxidative enzymes such as lipoxygenases (LOXs), which are activated by elevated ROS levels. These hydroperoxides are implicated in the molecular. pathophysiology of numerous chronic inflammatory illnesses [25].

 Low-density lipoproteins' (LDLs') oxidative alteration is modulated by 15-LOX, which also produces pro-inflammatory leukotrienes that in turn cause atherosclerotic lesions to form [26].

Fig- 1 Chemical constituents in garlic

Table -1 Showing anti – inflammatory activity organosulfur compound in garlic

METHODOLOGY:

1. ADME (http://www.swissadme.ch/index.php)
2. PDB database (https://share.google/s5Us4Ugp73FFufJEY)

A. ADME:

Swiss ADME (for ligand/drug-like molecule evaluation) Swiss ADME is used to predict ADME (Absorption, Distribution, Metabolism, Excretion) and drug-likeness of small molecules.

Steps:

1. Open Swiss ADME:

Go to http://www.swissadme.ch

2. Input molecules:  

You can draw a chemical structure using the molecular editor, or Paste SMILES notation (can be obtained from PubChem, Chem Spider, etc.). Multiple molecules can be pasted (one per line).

3. Submit query:

Click on “Run” after entering the molecule(s).

4. Results provided:

You will get various predictions like physio chemical properties: molecular weight, Log P, H-bond donors/acceptors. Lipinski’s rule of five (drug-likeness check). Pharmacokinetics: GI absorption, BBB penetration, P-g p substrate, CYP450 inhibition. Water solubility predictions. BOILED-Egg model for passive absorption and brain penetration.

5. Export results:

You can download results as a PDF or copy the data.

B. PDB database:

(Protein Data Bank – structural data for macromolecules) PDB is used to obtain 3D structures of target proteins, nucleic acids, and complexes.

Steps:

1. Open PDB: Go to https://www.rcsb.org
2. Search protein/target: Enter protein name, PDB ID, or keywords (e.g., COX-2, PDE4).
3. Select suitable structure: Check resolution (lower Å = better quality). Prefer structures with ligands or cofactors if docking studies are planned.
4. Download structure: Download in PDB format (needed for docking). Additional files like FASTA sequence, ligand information also available.
5. Visualize structure:  Use visualization tools like pie MOL, Discovery Studio, or UCSF Chimera to explore protein-ligand interactions. --- Connecting Both:

• Use PDB to get the protein structure. Use Swiss ADME to evaluate your ligand properties.
• Together, they prepare you for molecular docking and in-silico ADMET studies.

BY USING VINA DOCKING METHOD, WE HAVE CALCULATED THE AFFINITY OF 36 ALLICIN DERIVATIVES AGAINST 15- LOX ENZYME FOR ANTI- INFLAMMATORY PROPERTIES:

From above observation table we have observed that Gamma –L- glutamyl – S – allyl -L – cysteine shows higher affinity towards the receptors for anti- inflammatory activity than the other derivatives mentioned in this table.

 Gamma- L-glutamyl - S- allyl - L –cysteine

Boiled Egg:

Fig – 2 Boiled egg of Gamma – glutamyl – S- allyl - L – cysteine

This figure is called the “BOILED-Egg model” (Brain or Intestinal Estimated permeation method), a graphical     representation used in drug discovery and medicinal chemistry. It predicts whether a small molecule can likely be absorbed in the human intestine (HIA) and/or penetrate the blood–brain barrier (BBB) based only on two key physicochemical descriptors:

WLOGP (lipophilicity) → plotted on the X-axis

Topological Polar Surface Area (TPSA) → plotted on the Y-axis.

1. The “white” region (egg white)

Molecules falling in this region are predicted to have high probability of human intestinal absorption (HIA).

These compounds are orally bioavailable candidates.

2. The “yellow” region (yolk)

Molecules in this region are predicted to have high probability of blood–brain barrier penetration (BBB).

3. Outside the egg.

Molecules located outside both zones are less likely to be absorbed through the intestine or cross the BBB.

P-glycoprotein (PGP) prediction:

Blue circles (PGP+) → Molecules predicted to be substrates of P-glycoprotein efflux pump, which may reduce CNS penetration and intestinal absorption.

Red circles (PGP–) → Molecules predicted not to be effluxes by PGP, hence more likely to remain in the system.

Significance:

The BOILED-Egg gives a visual, simple, and rapid prediction tool for early drug design.

It avoids the need for computationally expensive simulations by using only two descriptors (lipophilicity & polarity).

It helps researchers decide whether a molecule is suitable for oral delivery or as a CNS drug candidate.

In this figure:

The molecule appears outside the boiled egg, meaning it is predicted to have low human intestinal absorption and no significant BBB penetration.

Its PGP marker (red circle) shows it is not a substrate of P-glycoprotein (PGP–).

TARGET PREDICTION:

Fig – 2 Target Prediction

Fig 3 Molecular modelling tool

The ligand shows stable binding within the protein active site, driven by a combination of hydrogen bonding, hydrophobic interactions, and possible aromatic stacking interactions. These interactions suggest that the ligand has a good binding affinity and could potentially act as an inhibitor or modulator of the target protein’s activity.

Table-2 The Swiss ADME web tool was employed to predict the physicochemical properties, ADME characteristics, pharmacokinetic behaviour, drug-likeness, and medicinal chemistry suitability of Gamma-L-glutamyl-S-ally -L–cysteine.