Phytochemical Profiling and Molecular Docking Investigation of Avocado (Persea Americana Mill. Cultivar Hass) Leaves and Seeds: Implications for Antioxidant Activity and Health Benefits

doi:10.21203/rs.3.rs-3824132/v2

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Phytochemical Profiling and Molecular Docking Investigation of Avocado (Persea Americana Mill. Cultivar Hass) Leaves and Seeds: Implications for Antioxidant Activity and Health Benefits

https://doi.org/10.21203/rs.3.rs-3824132/v2

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Despite the rising interest in avocado leaves and seeds' health benefits, little or no research has been conducted on both their phytochemical profiles in conjunction with molecular docking investigations, particularly in relation to its antioxidant activity. Utilizing phytochemical screening, molecular docking, and ADMET predictions, this study, investigates the antioxidant properties of avocado leaves (AVL) and seeds (AVS). Results show that AVS has a high presence of flavonoids (+++), terpenoids (+++), but a low presence of phenols (+), while AVL has a high presence of tannins (+++) and phenols (++). Molecular docking studies validate two AVL (L01 and L02) and two AVS (S02 and S03) compounds based on binding affinity and interactions with 2rgu.pdb, 3mng.pdb, and 2vwi.pdb protein targets. ADMET studies indicate that AVL and AVS extracts have favourable bioavailability and health safety characteristics. Additionally, DPPH, ABTS and FRAP radical inhibition (%) results show that AVL (46.36%/DPPH, 6.67%/ABTS, 11.07%/FRAP) has lesser antioxidant activity than AVS (70.97%/DPPH, 47.35%/ABTS, 3.66%/FRAP).

Food Chemistry

Computational Chemistry

ADMET

Avocado

Molecular Docking

Phytochemical Screening

Avocado (Persea Americana Mill. Cultivar Hass) is a tropical fruit famous for its distinct flavor, creamy texture, and high nutritional content. Avocado trees (Fig. 1) flourish in subtropical and tropical climes, preferring moderate temperatures and well-drained soils (Duresa & Manaye, 2017). The characteristics of avocados go far beyond their delicious flavor, since their leaves and seeds are a veritable source of various medicinal compounds. Avocado have been widely disussed on their phytochemical composition and activities in a variety of health disorders, including anti-inflammatory (Alkhalaf et al., 2019; Batool et al., 2019; ORHEVBA & Jinadu, 2011), anti-hypertensive (Sutiningsih et al., 2022; Irawati, 2015), and anti-diabetic (Ojo et al., 2022) activities.

[Figure 1]

Recent studies have focused on the association between anti-inflammatory, anti-hypertensive, and anti-diabetic compounds found in avocado leaves (AVL) and seeds (AVS) (Ojo et al., 2022; Sutiningsih et al., 2022; Alkhalaf et al., 2019; Irawati, 2015). Several studies revealed that the bioactive compounds contained in AVL and AVS may have anti-inflammatory effects that might aid in the reduction of inflammation (Alkhalaf et al., 2019; Tabeshpour et al., 2017), which is a critical role in chronic illnesses such as diabetes, hypertension, and other metabolic disorders. However, further research is needed to corroborate these findings and support the potential use of AVL and AVS extracts as functional foods or nutraceuticals with health-promoting qualities.

Theoretically, Puspita et al., (2022) and Kusuma & Susilowati, (2018), had both used molecular docking modelling to explore the binding affinity and interaction of AVL and AVS molecules with various protein targets linked with anti-inflammatory, anti-hypertensive, and anti-diabetic properties. Furthermore, Kusuma & Susilowati and Bhatt et al., in avocado research, applied ADMET property prediction to evaluate the possible bioavailability, distribution, metabolism, excretion, and safety profiles of AVL and AVS extracts, giving critical information for their prospective usage as functional foods or nutraceuticals (Kusuma & Susilowati, 2018; Bhatt et al., 2017). Despite increased interest in the health benefits of avocado leaves and seeds, little study has been undertaken on their phytochemical profiles and molecular docking studies, particularly in connection to their antioxidant activity implications.

The goal of this study is to look at the phytochemical profiles of avocado leaves and seeds, and to perform molecular docking simulations to determine the likely compounds that can be responsible for their anti-inflammatory, anti-hypertensive, and anti-diabetic characteristics. Additionally, the study also intends to predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) features of avocado leaf and seed extracts. The overall objective for this study is to get insights into the antioxidant capacity and health benefits of avocado leaves and seeds by combining phytochemical profiling, molecular docking, and ADMET predictions. The findings of this study might help researchers better understand the important bioactive compounds in avocado leaves and seeds with respect to their antioxidant capacity, as well as their potential uses in functional foods and nutraceuticals.

2.1 Phytochemical screening

2.1.1 Extraction of avocado leaves and seed

The Hass avocado specie, Persea Americana Mill. Cultivar Hass was the specie of avocado selected for this study. Persea americana Mill. Cultivar Hass is one of the most widely grown and consumed avocado varieties globally, known for its creamy texture, rich flavor, and dark purple to black pebbly skin when ripe.

The leaves and ripening avocados (Persea Americana Mill. Cultivar Hass), harvested in the dry season (March – April) were purchased from the NIHORT farm in Ibadan, Nigeria. The avocado fruit was peeled, and the soft fruit's seeds were removed. Following thorough washing, the seeds were broken, the leaves were washed in clean water, and both were dried in a food dehydrator (Excalibur, model-4926T, USA) for 48 hours at 50°C. The dried seeds and leaves were then pulverized with a grinder (Rico MG "1701") and sieved to make a fine powder. The extraction methodology was tested using a modified method reported by Chaalal et al., (2019). 2 grams (g) of avocado seed and leaf powder were combined with 40 millilitres (mL) of 80% methanol solvent. Following 3 minutes (mins) of stirring the mixture was incubated for 30 minutes in a water bath at 50°C. Before filtering, the extracts were centrifuged at 4000 rpm for 15 minutes.

A 2D-structures and the nomenclature of extracted compounds are provided in Supplementary Data 6.

2.1.2 Qualitative analysis

The active phytochemicals tannins, saponins, flavonoids, terpenoids, phenols, and sterols were identified by qualitative analysis of avocado seed and leaf methanol extract.

Assessment of tannins:

Ferric chloride test- To 0.5 g of each sample, 10 ml of 5% ferric chloride was added. The presence of tannins was indicated by the appearance of greenish black or dark blue color (Batool et al., 2019).

Assessment of saponins:

Each sample (5 mg) was mixed with 5 ml of distilled water in the volumetric flask. After this accumulation, the sample was mixed vigorously for almost 15 min. The presence of saponins in the test sample was revealed by the creation of a soapy layer (Batool et al., 2019).

Assessment of flavonoids:

Each sample (2 mg) was added in 2 ml of 2 N NaOH. The presence of yellow color was the confirmation sign for flavonoids (Batool et al., 2019).

Assessment of terpenoids:

Each sample (1 mg) was taken in the test tube and 5 ml of each CHCl₃ and concentrated H₂SO₄ was added to the samples. Formation of brown layer in the midst of other two layers indicated the presence of terpenoids (Batool et al., 2019).

Assessment of phenols:

Each sample (0.5 mg) was taken in test tube, 1 ml of distilled water and 5% FeCl₃ was added in it. The formation of blue color confirms the presence of phenols (Batool et al., 2019).

Assessment of sterols:

Each sample extracts (1 ml) was taken in test tube, 3 ml of CHCl₃ was added and then 0.5 ml concentrated sulphuric acid was carefully dispensed along the walls of the test tube (Salkowski test). The presence of sterols was revealed by the emergence of reddish color in the lower layer (Batool et al., 2019).

2.1.3 Quantitative phytochemical analysis

Total Phenolic and Total Flavonoid contents:

Total phenolic was assessed and determined by a method described by Chandra et al. (2014). The test sample (0.5 ml) was mixed with 1 ml of distilled water and 2.5 ml of Folin-Ciocalteu’s reagent (5:1) in a volumetric flask. After 5 min, 2 ml of saturated sodium carbonate solution (8% w/v) in water was added to the mixture and the volume was made up to 10 ml with distilled water. The reaction was held in the dark for 30 minutes before centrifuging and the absorbance of the blue color was measured from separate samples at 765 nm (Chandra et al., 2014). The phenolic content was measured in milligrams of Gallic Acid Equivalent per gram (mg GAE/g) of extracts. Total Flavonoid was performed by a method described by Batool et al. (2019). The reaction mixture was prepared in a test tube by adding 0.5 ml sample, 0.15 ml NaNO₂ (0.5 mol/l), 0.3 M AlCl₃.6H₂O, and 5 ml methanol (30%). After 5 minutes, 2 ml of 1 M NaOH was added to the mixture, which was then read at 506 nm wavelength. Whereas, the flavonoid content was measured in milligrams of Catechin Equivalent per gram (mg CE/g) of extracts.

3.2.3.2 Determination of Antioxidant activity

Ferric Reducing Antioxidant Power (FRAP) Test

A 2.5ml of 0.2M Phosphate buffer and 2.5ml of 1% potassium ferricyanide was added to 1ml of extract in a test tube. Reaction mixture was incubated at 50^oC for 20 minutes, 2.5ml of 10% of trichloroacetic acid was added before centrifuging at 2000rpm for 10 minutes. 2.5ml of distilled water was added to the supernatant. After adding 0.5ml of 0.1% Fecl₃, reaction mixture was incubated in dark at room temperature for 10 minutes before reading absorbance at 700nm.

$$\text{%}Scavenging=\frac{\left(\left|control\right|-\left|sample\right|\right)}{\left|control\right|}x100$$

2,2-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) Test

ABTS (2,2-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid)) radical scavenging activity was conducted using method as adopted by (Dudonné et al., 2009) with slight modification. ABTS radical cation was produced by reacting 7mM ABTS with 2.45mM potassium persulphate then stored in dark for 12-16hrs at room temperature. To 0.1ml of extract, 3ml of ABTS radical added. Absorbance was read at 734nm using UV-VIS spectrophotometer (model T80) after incubating in dark for 30 minutes.

$$\text{%}Scavenging=\frac{\left({A}_{o}-{A}_{s}\right)}{{A}_{o}}X100$$

Where A_o = Absorbance of control and A_s = Absorbance of sample

2,2-diphenyl-1-picryhydrazyl (DPPH) Test

DPPH free radical scavenging was done according to method by Kumar et al., (2013) 0.1mM DPPH was prepared in ethanol, 3ml of 2,2-diphenyl-1-picryhydrazyl was added to 1ml of sample extract then stored in dark for 30 minutes before reading absorbance was read using UV-VIS spectrophotometer (model T80) at 517nm.

$$\text{%}\in hibition=\frac{\left({A}_{c}-{A}_{s}\right)}{{A}_{c}}X100$$

Where A_c = Absorbance of control and A_s = Absorbance of sample

2.2 Gas Chromatography-Mass Spectrometry (GC-MS) analysis

The Agilent 6890N gas chromatography, which has an autosampler and is coupled to a mass spectrometer, was used for this study. A one microliter of the sample was injected in the pulsed spitless mode on a fused silica column with a film thickness of 0.15 micrometres and a 30m x 0.25mm ID. The column head pressure was kept at 20 psi while helium gas was employed as the carrier gas, resulting in a constant flow rate of 1 ml/min. The column temperature was first maintained at 55°C for 0.4 minutes, then increased to 200°C at a rate of 25°C per minute, then to 280°C at a rate of 8°C per minute, and finally to 300°C at a rate of 25°C per minute, kept for 2 minutes. The list of Avocado Seed and Leave compounds identified by the GC-MS analysis are shown in Supplementary Data 1–4, respectively.

3.1 Ligand preparation

In this study, ligands were created by modelling their chemical structures in ChemDraw^® Ultra version 12.0.2.1076 software (Mills, 2006). The Molecular Mechanics Force Field (MM-FF 99) (O’Boyle et al., 2011) technique, which was developed using the Avogadro version 1.2.0 software program (Allinger et al., 1989), was used to optimize the ligands. We were able to compute the energy of the ligands and adjust their structures to increase their stability and interactions with target proteins using this technique. The optimized ligands were stored as .pdb files for further analysis and usage in molecular docking simulations.

3.2 Receptor preparation

In this inquiry, receptors were retrieved and downloaded from the rcsb.org portal (Rose et al., 2016) for further processing. The receptors were cleaned with the BIOVIA^® DS version 2021 software program (BIOVIA, 2021), which removed crystallized water and the heteroatoms associated with it, resulting in a clean receptor structure. After that, the cleaned receptor structure was stored in .pdb format. The receptor structure was then optimized, polar hydrogen added, and Geister charge assigned using the Chimera^® version 35.15.1443 software program (Goddard et al., 2005). The improved receptor structure was then stored in the autodock’.pdbqt format, where it was ready for docking. This process resulted in a clean, optimized receptor structure that was acceptable for molecular docking simulations.

Receptor used for this study are:

Human Dipeptydylpeptase (IV) (2rgu.pdb) (Lai et al., 2014)

Human peroxiredoxin (V) (3mng.pdb) (Saftić Martinović et al., 2023)

Human serine/threonine kinase (2vwi.pdb) (Vidyanagar et al., 2018)

3.3 Molecular Docking studies

The Autodock Vina tool, version 1.2.0 (Trott & Olson, 2010) was used via Pyrx software (Dallakyan & Olson, 2015) to perform molecular docking simulations in this investigation. The docking methodology consisted of verifying the protein's active site by docking known ligands retrieved from the RCSB portal. This guaranteed that the selected compounds were assessed at the receptor's active site. Based on the validation results, a box size was established for each receptor and documented in Table 1. The screening procedure was then initiated, with the determined box size for each receptor. For each simulation cycle, the exhaustiveness or run time was set to 10. The docked score was recorded and compared to the standard drug after each simulation round. This technique enabled an accurate assessment of the compounds' binding affinity as well as the identification of prospective lead compounds for further development.

Table 1

Box-Dimension used for the molecular docking simulation.
Receptor	Box size Dimension
2RGU	Center: 43.7 × 54.4 × 34.64 Dimension: 39.8 × 32.46 × 29.45
3MNG	Center: 9.093 × 43.73 × 19.68 Dimension: 37.34 × 32.46 × 36.83
2VWI	Center: 93.87 × 157.39 × -21.04 Dimension: 41.25 × 40.79 × 42.76

[Table 1]

3.4 Visualization and plotting tools.

All docking visualization were done on the BIOVIA^® DS version 2021 (BIOVIA, 2021).

Graphical plots were done using two Python libraries called Pandas (McKinney, 2011), Matplotlib (Barrett et al., 2005) and Scipy on LibreOffice version 7.5 (Virtanen et al., 2020).

3.5 ADMET property studies

ADMETlab version 2.0 website at https://admetmesh.scbdd.com/ (Xiong et al., 2021) was used to conduct pharmacokinetics, toxicity, and drug-likeness studies to predict the ADMET properties of compounds obtained from both leave and seed compounds. The server calculated Ames’ oral toxicity, water solubility, brain-blood barrier crossing, cytochrome' inhibition, the Lipinski rule of five (Lipinski, 2002), and other properties. The five conditions for Lipinski violation includes: Molecular weight ≤ 500, mlogP ≤ 4.15, number of N and O ≤ 10, NH ≤ 5 and OH ≤ 5 (Lipinski, 2002).

4.1 GC-MS analysis

Supplementary Data, 3–4 and 1–2 show the gas chromatogram of AVL and AVS compounds extracts, respectively. Results show how AVL (Supplementary Data 7A) detected more abundant compounds than AVS (Supplementary Data 7B). It’s observed that from retention time 4mins – 14mins and 20mins – 21mins, AVL showed greater abondance of similar compounds to AVS. The detected AVS and AVL compounds are shown in Supplementary Data 1–2 and 3–4, respectively.

4.2 Phytochemical screening

4.2.1 Qualitative phytochemical screening analysis

The results of qualitative phytochemical screening for AVL and AVS revealed distinct phytochemical profiles. AVS was found to be high in flavonoids (+++), terpenoids (+++), and low in phenols (+), whereas AVL was high in tannins (+++) and low in phenols (+). Saponins were absent in AVL (+) but present in minor quantities in AVS. Steroids were not found in either the AVL or the AVS.

Table 2

Qualitative analytical result for AVL and AVS compounds.
Phytochemicals	Avocado Leaves (AVL)	Avocado Seeds (AVS)
Tannin	+++	+
Saponin	-	+
Flavonoid	+	+++
Terpenoid	+	+++
Steroid	-	-
Phenol	++	+
Note: (+++) = strongly present; (++) = mildly present; (+) = slightly present; (-) = absent (Tadhani & Subhash, 2006)

4.2.2 Quantitative phytochemical screening

This study conducted a quantitative phytochemical screening of avocado seeds to determine variations in the phenolic and flavonoid contents of AVL and AVS. The phenolic and flavonoid content were measured in milligrams of Gallic Acid Equivalent per gram (mg GAE/g) and milligrams of Catechin Equivalent per gram (mg CE/g), respectively, and the results are shown in Table 3.

As shown in Table 3, AVL has a significantly higher phenolic content (48.083 ± 1.176 mg GAE/g) than AVS (16.11 ± 0.71 mg GAE/g), while AVS has a significantly higher flavonoid content (3.58 ± 0.38 mg CE/g) than AVL (0.71 ± 0.78 mg CE/g).

Table 3

Quantitative mean and standard deviation data of AVL and AVS phenolic, flavonoid, DPPH and EC₅₀ activity.
	Phenolic content (mg/ GAEg)	Flavonoid content (mg/ CE g)
AVL	48.083 ± 1.176 ^a,c	0.71 ± 0.07
AVS	16.11 ± 0.78 ^c	3.58 ± 0.38
Means in the same row with different superscripts are significantly (p < 0.05) different. Data are mean ± SD at n = 3.

Table 4, however, shows the antioxidant assays (DPPH, ABTS, and FRAP). Results show that DPPH radical inhibition increases in the order of AVL (46.36%) to AVS (70.97%), which is comparable to that of ascorbic acid at 93.28%. The EC50 for the DPPH antioxidant activity also reflects a higher antioxidant activity for AVS (0.35) than AVL (0.54). Furthermore, AVS inhibited ABTS radicals by 47.35%, better than 6.67% and comparable to Vitamin C’ (57.13%). Likewise, the EC₅₀ activity shows higher activity in AVS (0.14) than in AVL (0.96). On the other hand, AVL inhibited FRAP radicals by 11.07% more than AVS (3.66%), while Vitamin C inhibited FRAP radicals by 81.55%. As expected, the EC₅₀ activity shows a similar trend as the FRAP inhibition pattern of AVL, AVS, and Vitamin C.

Table 4

Antioxidant (DPPH, ABTS and FRAP) activity data of AVL and AVS.
	DPPH (% inhibition)	EC₅₀ (DPPH) (mg/mL)	ABTS (% inhibition)	EC₅₀ (ABTS) (mg/mL)	FRAP (% inhibition)	EC₅₀ (FRAP) (mg/mL)
Avocado leaves AVL	46.36 ^a,b	0.54	6.67 ^a,b	0.96 ^a,b	11.07 ^a,b	3.07 ^c
Avocado seeds AVS	70.97 ^a,b,c	0.35	47.35 ^a,b	0.14	3.66 ^a,c	9.25 ^a,b,c
Vitamin C	93.28 ^a,b	0.27	57.13 ^a,c	0.11	81.55 ^b,c	0.42
Means in the same row with different superscripts are significantly (p < 0.05) different. Data are mean ± SD at n = 3.

4.3 Molecular Docking studies

A computational approach known as molecular docking predicts the binding orientation and affinity of a small molecule ligand to a target protein. The purpose of this study was to gain insight into the interaction between avocado leaf and seed components and a therapeutic target protein, as well as to forecast the potential effectiveness and specificity of avocado leaf and seed as a drug candidate (Ou-Yang et al., 2012; Caporuscio & Tafi, 2011). A validation visualization was performed prior to docking to confirm the active sites of each receptor. The validation compounds were not docked of this study but were only used to visualize the docked complex downloaded from the RCSB web portal (Rose et al., 2016). The validation compounds were discovered at the protein receptor's active site, while their binding sites are circled in red (Fig. 2). The dock-score for the validation compounds ranges from − 9.2 (kcal/mol) to -8.2 (kcal/mol).

[Figure 2]

The compound-residue interaction for the validation compound in Fig. 2 helps to understand that all other docking score calculations for all receptor-AVL and receptor-AVS compounds were performed precisely at the active site range. The docked score for the compounds from the avocado leaf’s ranges from − 8.5 to -4.3 kcal/mol for 2RGU receptor, -7.7 to -3.7 kcal/mol for 2VWI receptor, and − 6.5 to -3.7 kcal/mol for 3MNG. The docked score for compounds obtained from the seed ranges from − 8.1 to -5.1 kcal/mol for 2RGU, -7.9 to -4.8 kcal/mol for 2VWI, and − 4.1 to -6.8 kcal/mol for 3MNG receptor, respectively.

[Figure 3]

For the anti-diabetes studies, the docking scores for Leave compounds L01, L02, L03, L04, and Seed compounds S01, S02, S03, and S04 all outperformed metformin (-4.9 kcal/mol) (2RGU) (Fig. 3). Using the Human serine/threonine kinase (2VWI) receptor, L01, L02 and seed compounds, S02 and S03 are avocado compounds that outperformed amlodipine's binding score (-6.6 kcal/mol) (Fig. 3). The avocado compounds that outperformed diclofenac's binding score (-5.5 kcal/mol) in anti-inflammatory studies using the human PrxV (3MNG) receptor were leave compounds L01 and L02 and seed compounds S02 and S03 (Fig. 3 and Table 5). The docking score results show that avocado leaf compounds L01 and L02, along with avocado seed compounds S02 and S03, are the validated compounds by the docking process.

The compound-receptor interaction results for AVL (Fig. 4) and AVS (Fig. 5), show that the length of the hydrogen, van der Waals, and other non-conventional hydrogen bonds formed for the avocado leave compounds did not exceed 6.8 Å length, which is in agreement with suggestions from Ahmed et al., (2022) and Falade et al., (2021) while it did not exceed 7.0 Å length for the seed compounds. This could be one of the reasons for the docking score's high binding energies.

4.4 ADMET studies

Table 5 summarizes the findings of the Absorption Distribution Metabolism Excretion and Toxicity (ADMET) study. Previous studies, such as Falade et al. (Falade et al., 2021), de Souza Neto et al. (de Souza Neto et al., 2020), and van de Waterbeemd & Gifford (van de Waterbeemd & Gifford, 2003), have suggested a range of acceptable results for ADMET property studies. Using SMILE formula notations, Xiong et al. (Xiong et al., 2021) and Kiefer et al. (Kiefer et al., 2009) successfully trained supervised models to predict the ADMET properties of compounds.

Solubility concentrations for L01 (-6.88), L02 (-6.765), S02 (-7.122), and S03 (-7.346) were found to be lower than the recommended range of -3.0 to -6.0 mol/L, as suggested by (Falade et al., 2021). Further dissolution techniques, such as decoction or alcohol extraction, can be used to optimize this abnormality for both the leaf and seed compounds. The human absorption rate was found to be good and safe for L01, L02, S02 and S03, indicating their potential as drug candidates. None of these four qualified compounds were found to cross the blood-brain barrier, indicating that they would not alter neurological functions nor cause addiction in patients (Ahmed et al., 2022). According to Kiefer et al., CYP450 inhibition for all drug candidates was found to be within the acceptable range of 1 violation at maximum (Kiefer et al., 2009). All potential drug candidates' half-lives were found to be between 0.6 and 3 hours, as recommended by de Souza Neto et al. (de Souza Neto et al., 2020). All four drug candidates tested negative for hERG blockers, indicating that they do not have the potential to weaken the human heart pulse rate, progressively. The AMES toxicity prediction test was negative for all drug candidates, and the Lipinski drug-like test confirmed that all four drug candidates were "good enough as a drug candidate."

Table 6

Absorption, Distribution, Metabolism and Excretion (ADMET) property prediction for compounds identified in AVL and AVS with the current market drugs for hypertension (Amlodipine®), diabetes (Metformin®) and inflamation (Diclofenac®).
Compounds	Water Solubility (mol/L)	HIA (%)	BBB	CYP inhibition?	Half-life (Hours)	HERG blocker?	AMES Toxicity	Lipinski violations?
AVL
L01	-6.89	>= 90	Negative	44931	0.8	Inactive	Negative	0/5
L02	-6.77	>= 90	Negative	0/5	1.1	Inactive	Negative	0/5
AVS
S02	-7.12	>= 90	Negative	0/5	1.2	Inactive	Negative	0/5
S03	-7.35	>= 90	Negative	44931	1.1	Inactive	Negative	0/5
STANDARD DRUGS
Metformin	-1.05	>= 90	Negative	0/5	2.2	Inactive	Negative	0/5
Diclofenac	-4.77	>= 90	Negative	44931	2.8	Inactive	Negative	0/5
Amlodipine	-2.71	>= 90	Negative	44931	2	Inactive	Negative	0/5
HIA = Human Intestinal Absorption; BBB = Blood-Brain-Barrier; CYP inhibition = Cytochrome 450 inhibition; HERG blockers = Human Either-a-go heart blocker; AMES Toxicity = Bruce Ames Toxicity test method

Following the GC-MS results from Supplementary Data 1–4 and 7, it can be inferred that AVS may contain more volatile compounds than AVL. The presence of tannins in AVL indicates its potential therapeutic effects in the treatment of diseases such as cancer, diabetes, and cardiovascular disease, because of its antioxidant and anti-inflammatory properties. Similarly, from Table 2, showing the phenols found in AVL and AVS are potent antioxidants that can protect cells from oxidative damage and reduce the risk of developing chronic diseases. AVS's high flavonoid content suggests that it can protect cells from oxidative damage, reduce the risk of chronic diseases, and provide anti-inflammatory, anticancer, and neuroprotective effects (Kafelau et al., 2022; Idris et al., 2009). Terpenoids are also found in high presence in AVS and in low concentrations in AVL, indicating that they are bioactive chemicals with anti-inflammatory, anticancer, and antioxidant properties that have been linked to potential therapeutic benefits in the treatment of a variety of disorders such as asthma, arthritis, and cancer (Chandra et al., 2014). However, the absence of saponins in AVL suggests that it may be limited in its ability to provide anti-inflammatory, antioxidant, and anticancer properties in comparison to AVS (Falade et al., 2021). It is worth noting, from Table 2 that neither AVL nor AVS contained any steroids. It can be inferred from the qualitative phytochemical results that, that AVL and AVS may have different health benefits and therapeutic implications. However, further studies are needed to confirm these potential health benefits and to determine the best therapeutic doses. The results of this study are like those obtained by previous researchers such as, (Kafelau et al., 2022; Duresa & Manaye, 2017; Idris et al., 2009), indicating that these findings are consistent.

Phenols and flavonoids are phytochemical groups known for their antioxidant capabilities, quantitatively, the higher phenolic concentration in AVL suggests (Table 4) that it may have more antioxidant activity than AVS (Duresa & Manaye, 2017). The higher phenolic content of AVL may contribute to its potential therapeutic benefits in preventing chronic diseases caused by oxidative stress (Chaalal et al., 2019; Dudonne et al., 2009). The higher flavonoid content of AVS, on the other hand, suggests that it may have better antioxidant and health-promoting properties than AVL (Alkhalaf et al., 2019). This could be attributed to flavonoids' potent antioxidant and anti-inflammatory properties, which can protect against a variety of health problems. According to higher inhibition percentages and lower EC₅₀ values in the DPPH and ABTS assays, AVS appeared to have more antioxidant activity than AVL, according to the results on Table 4. Although considerably underperformed Vitamin C in this assay, AVL showed stronger FRAP radical suppression than AVS. These disparities in antioxidant activity can be explained by understanding that different radical inhibition tests have different mechanisms of action (Chaalal et al. 2019). While the main objective of the DPPH and ABTS tests are to evaluate the capacity of antioxidants to scavenge stable radicals (DPPH and ABTS radicals), the FRAP assay evaluates the reducing capacity of antioxidants (Bahru et al., 2019; Chaalal et al. 2019).

According to the study's findings, it can be inferred that AVL and AVS have distinct phenolic and flavonoid profiles, which may have therapeutic implications. Because of its higher phenolic concentration, AVL may be more effective as an antioxidant, whereas AVS may have better antioxidant and other health-promoting qualities due to its higher flavonoid concentration. However, more research is equally needed to confirm these potential health benefits and to determine the best therapeutic doses.

Ahmed et al., (2022) and Falade et al., (2021) established that a good docking score should not be less than the dock score of the measuring standard drug. Hence, it can be inferred from results from Table 5 and Fig. 3 that leave compounds L01 (Stigmasta-5,22-dien-3-ol), L02 (campestenol), L03 (2(1H)-Naphthalenone), L04 (n-Hexadecanoic acid) and Seed compounds S01 (9,17-Octadecadienal), S02 (Ergost-5-en-3-ol), S03 (Stigmastan-3,5,22-trien) and S04 (n-Hexadecanoic acid) all has binding energies or scores that exceeds that of metformin (-4.9 kcal/mol) for the anti-diabetes studies (2RGU). For the anti-hypertension studies, against the Human serine/threonine kinase (2VWI) receptor, L01 (Stigmasta-5,22-dien-3-ol), L02 (campestenol) and seed compounds, S02 (Ergost-5-en-3-ol) and S03 (Stigmastan-3,5,22-trien) are the avocado compounds that exceeded the binding score of amlodipine’ (-6.6 kcal/mol). For the anti-inflammatory studies, against the human PrxV (3MNG) receptor, leave compounds, L01 (Stigmasta-5,22-dien-3-ol) and L02 (campestenol) and seed compounds, S02 (Ergost-5-en-3-ol) and S03 (Stigmastan-3,5,22-trien) are the avocado compounds that exceeded the binding score of diclofenac (-5.5 kcal/mol). Results from the docking score can be summarized that avocado leave compounds: L01 (Stigmasta-5,22-dien-3-ol) and L02 (campestenol) with avocado seed compounds, S02 (Ergost-5-en-3-ol) and S03 (Stigmastan-3,5,22-trien) are the validated compounds by the docking process. Ligand – Receptor interaction results from Supplementary Data 6, Figs. 4 & 5, shows that the hydrogen, van der Waals and other non-conventional hydrogen – bonds length formed for the avocado leave compounds did not exceed 6.8 Å length while that of the seed compounds did not exceed 7.0 Å of length. This can be an important reason for the high binding energies recorded in the docking score. This result is supported by Dzouemo et al., (2022) stated that the ligand – receptor interaction at the binding site of a potential drug candidate must not exceed 7.0 Å.

Furthermore, according to the findings of this study, Table 5 shows that all four drug candidates that passed the docking screening (L01, L02, S02, and S03) have the potential to be used in the treatment of diabetes, hypertension, and inflammation in patients. The results confirms that the therapeutical efficacy of avocado seed and leaf will range from diabetes to hypertension to inflammation. Some compounds' low solubility concentrations can be addressed by using additional dissolution techniques, as suggested by Falade et al. (2021). The AMES toxicity prediction and Lipinski drug-like test results indicate that the drug candidates are safe for human consumption. None of the four qualified compounds were found to cross the blood-brain barrier, which means they are unlikely to cause neurological damage or addiction in patients. The findings of this study can be used to guide future research and development of these drug candidates for the treatment of diabetes, hypertension, and inflammation.

In conclusion, the findings from this research were able to provide more meaningful insights into the anti-inflammatory, anti-hypertensive, and anti-diabetic characteristics of avocado leaves (AVL) and seeds (AVS). The molecular docking simulation findings demonstrated that the binding energies of AVL compounds L01 (Stigmasta-5,22-dien-3-ol) and L02 (campestenol), as well as AVS compounds S02 (Ergost-5-en-3-ol) and S03 (Stigmastan-3,5,22-trien), exceeded those of conventional drugs (control drugs) used to treat diabetes, hypertension, and inflammation. This implies that these bioactive compounds might be interesting drug candidates to investigate in further drug development procedures. Furthermore, the findings suggest that the therapeutic efficacy of avocado varies, with inflammation having the most potential, followed by hypertension and diabetes. Additionally, AVL had a larger phenolic content, indicating greater antioxidant activity, but AVS had a higher flavonoid concentration, indicating greater total antioxidant and health-promoting qualities. Our findings shine light on the phytochemical profile and molecular docking studies of AVL and AVS, indicating their potential antioxidant action and health advantages. In understanding the findings of this work, it is also crucial to remember the limits of molecular docking and ADMET property prediction, and more research is needed to confirm these findings in vivo and examine their clinical implications.

Overall, this study adds to the expanding body of literature on avocado and its health advantages, while also emphasizing the need for more research in this field to fully explicate the potential therapeutic uses of avocado leaves and seeds.

Research funding

This research did not receive any specific grant from funding agencies in the public, commercial or non-profit sectors.

Conflict of Interest

The authors declare that they have no competing interest

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Table 5 is available in the Supplementary Files section.

The authors declare no competing interests.

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Phytochemical Profiling and Molecular Docking Investigation of Avocado (Persea Americana Mill. Cultivar Hass) Leaves and Seeds: Implications for Antioxidant Activity and Health Benefits

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Abstract

Figures

1.0 Introduction

2.0 Experimental methodology

2.1 Phytochemical screening

2.1.1 Extraction of avocado leaves and seed

2.1.2 Qualitative analysis

2.1.3 Quantitative phytochemical analysis

3.2.3.2 Determination of Antioxidant activity

2,2-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) Test

2,2-diphenyl-1-picryhydrazyl (DPPH) Test

2.2 Gas Chromatography-Mass Spectrometry (GC-MS) analysis

3.0 Computational methodology

3.1 Ligand preparation

3.2 Receptor preparation

3.3 Molecular Docking studies

3.4 Visualization and plotting tools.

3.5 ADMET property studies

4.0 Results

4.1 GC-MS analysis

4.2 Phytochemical screening

4.2.1 Qualitative phytochemical screening analysis

4.2.2 Quantitative phytochemical screening

4.3 Molecular Docking studies

4.4 ADMET studies

5.0 Discussion

6.0 Conclusions

Declarations

References

Table

Additional Declarations

Supplementary Files

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