Silver Nanoparticles from Saudi and Syrian Black Cumin Seed Extracts: Green Synthesis, ADME, Toxicity, Comparative Research, and Biological Appraisal

ABSTRACT

Objective:

The current study’s objective is to highlight the value of using plant resources to identify key bioactive molecules and implement green chemistry in research and development to meet market demand.

Materials and Methods:

The black cumin seeds (Saudi and Syria originated) were utilized to make silver nanoparticles (Ag-NPs), which were subsequently confirmed using a UV spectrophotometer and color analysis of reaction mixtures. The antibacterial activity of Ag-NPs was tested against E. coli, K. pneumoniae, and S. aureus, and antioxidant activity was measured using the DPPH assay. Swiss-ADME, pkCSM, and ProTox-II were also used to assess the pharmacokinetics, oral bioavailability, toxicity, and safety endpoints of molecules.

Result:

The antibacterial effect of Ag-NPs from Saudi-origin black cumin seeds was observed higher. In comparison to the standard, the Saudi and Syrian Ag-NPs combined displayed synergistic antibacterial effects and were found to be more susceptible to S. aureus. In comparison to the reference, the antioxidant activity of Ag-NPs indicated 60–85% radical scavenging. All molecules passed the Lipinski rule, the filter (Veber, Egan, and Muegge), PAINS, and the Brenk structural alert (zero violations), and the synthetic score was also found to be in the easy limit (1 to 2). The compounds were found to be non-substrate for p-glycoprotein, high GIA% (>90%), non-inhibitor for CYP3A4, CYP2C19, CYP2C9, CYP2D6 (except 5 and 10), Log Po/w (1.71 to 3.26), TPSA 150 2 and MR 155. The compounds likewise had high Caco2 values (log Papp >0.9) with the exception of 4 and 9 (log Papp 0.9), were non-inhibitors of P-gp-I and II and hERG I and II, and showed no AMES toxicity. Except for molecule 11, no organ damage (hepatotoxicity) or endpoint toxicity (mutagenicity, immunotoxicity, carcinogenicity, and cytotoxicity) was identified in ProTox-II.

Conclusion:

The current study sheds new light on the significance of bioactive molecules found in black cumin seeds, with molecules 3 and 6 identified as potential leads (highest GIA%, no AMES toxicity, oral rat acute and chronic toxicity, lack of renal OCT2 substrate, high total clearance, and lack of organ toxicity) for further research for a variety of medical applications.

INTRODUCTION

Nanoparticles can be created in a variety of ways, including solid reactions, chemical reactions, co-precipitation, the sol–gel process, and many others. Green synthesis of NPs has a variety of benefits over chemical synthesis in recent years, including simplicity and cost-effectiveness. Additionally, it is suitable for biomedical and food applications, and it does not require the use of dangerous chemicals, high temperatures, or high pressure. Silver nanoparticles (Ag-NPs), which have the widest range of applications in medicine, electronics, energy conservation, the environment, textile, cosmetics, and biomedicine, are often made from noble metals, such as silver, gold, platinum, and palladium.[1-3] The details of silver nanoparticles are available in supplementary text.

The black seed (Nigella sativa), also known as Habbat El Baraka in the Arab world, has been utilized for generations in many parts of the world, notably by the vast majority of the Arab population.[4] Silver metal nanoparticles, in particular, have gained special interest due to their high electrical conductivity, chemical stability, catalytic activity, and antibacterial activity.[5,6]

Lists the names and molecular characteristics of known bioactive compounds from Nigella Sativa seeds, including Thymoquinone, Dithymoquinone, Thymohydroquinone, Thymol, p-Cymene, 4-Terpineol, T-anethol, Carvacrol, Nigellicine, Nigellidine, Nigellimine, Longifolene, and Limonene.[7-10] In silico tools are now faster, simpler, and less expensive than traditional investigational procedures. The bioactive molecules are present in Table 1 and available in supplementary text.

Table 1.

Zone of inhibition of several bacteria by black cumin seed extract and silver nanoparticles in mm

Samples	Zone of inhibition (in mm) by samples
	Escherichia coli	Klebsiella pneumonia	Staphylococcus aureus
SA-E	3	1	4
SY-E	2	1	3
SAE-NP	8	3	9
SYE-NP	7	2	7
SA-SY-E	7	3	9
SA-SY-NP	15	7	17
STD-1	10	5	13
STD-2	14	7	15

SA-E=Saudi Arabia originate extract, SY-E=Syrian originate extract, SAE-NP=Saudi Arabia extract nanoparticle, SYE-NP=Syrian extract nanoparticle, SA-SY-E=Saudi and Syrian originate extract, SA-SY-NP=Saudi and Syrian originate extract nanoparticle, STD-1=Marketed black cumin seed oil and STD-2=Ampicillin antibiotic

Swiss-ADME webserver (www.swissadme.ch) provides a viable alternative to experimental drug design from natural products or synthetic chemicals, which can lead to innovative medication development.[11-13] The Swiss-ADME method was utilized to estimate the pharmacokinetics, bioavailability, drug-likeness, and medicinal chemistry friendliness of the bioactive compound for better improvement. The pkCSM web server (http://bleoberis.bioc.cam.ac.uk/pkcsm/) was used to determine the pharmacokinetic study, toxicity, and safety endpoint. ProTox-II (https://tox-new.charite.de/protox_II/index.php?site = compound_search_ similarity) was also utilized to assess the in silico organ toxicity of bioactive compounds discovered in Nigella sativa seeds. The description of the plant and collection of black cumin seeds are available in supplementary text.

MATERIALS AND METHODS

Aqueous seed extract preparation

The Nigella sativa seeds were obtained at a local market in Buraydah, Saudi Arabia’s Al-Qassim region. The cleaned seeds were finely milled into a powder after drying for 48 hours at room temperature. Following that, 10 grams of powder were combined with 250 mL of distilled water and left to soak for 24 hours.

Aqueous silver nitrate preparation: Silver nitrate solution (AgNO₃), 0.1 M, was prepared and stored in an amber-colored bottle.

Silver nanoparticle (Ag-NPs) synthesis

Seed extracts are added to an 80 mL solution of silver nitrate (AgNO3) during the synthesis of silver nanoparticles (Ag-NPs) and the optimization process in volumetric amounts (20 mL). The reaction mixture was incubated for 30 minutes, or until a color change from light yellow to pink to dark brown was visible.[14] The details of syntheis of silver nanoparticle are available in supplementary text.

Silver nanoparticles (Ag-NPs) detection and characterization

Visual color changes were used as the primary method of detecting the creation of silver nanoparticles, and these color changes show that Ag-NPs have formed. Using analyzing the material in the region of 200–1100 nm, a subsequent synthesis of silver nanoparticles was verified using a UV-Vis spectrophotometer.[14] Detection of Silver nanoparticle are available in supplementary text.

Biological evaluation of silver nanoparticles (Ag-NPs) and seed extract

Ag-NPs’ and seed extract antibacterial activity as measured by disc diffusion

In comparison to the standard, the antibacterial activity of an aqueous seed extract of black cumin (of Saudi and Syrian provenance) and its silver nanoparticle was assessed against Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. Dimethyl sulfoxide (DMSO, 0.1% v/v in PBS) was used to prepare the samples.[15,16] Details of antibacterial activity of seed extract and silver nanoparticle is available in supplementary text.

Ag-NPs’ and seed extract’s DPPH test for radical scavenging activity

The antioxidant activity of the aqueous extract of Black cumin seeds (of Saudi and Syrian origin) and its silver nanoparticle was evaluated using the scavenging ability of the stable 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical. Dissolving and diluting the samples in methanol yielded concentrations of 150, 120, 90, 60, or 30 g/mL. The absorbance of the resultant solution at 517 nm was determined using UV-Vis spectrophotometry.[15,16] Details of radical scavenging activity and cumin seeds’ phytochemical analysis are available in supplementary text.

Computational analysis of bioactive compounds in seeds of black cumin

Swiss-ADME for predicting bioactive compounds

Bioactive chemicals were depicted using the SMILES notation (simplified molecular input line entry system) in the chem. bio ultra sketch software. The SMILES notation for each bioactive molecule was put into the Swiss-ADME web tool to predict ADME and associated physicochemical parameters.[17-20] The details of Swiss-ADME and physicochemical characteristics are available in supplementary text.

Physicochemical characteristics for bioactive compounds are determined using Swiss bioavailability radar

The bioavailability radar provides a rapid look at target compounds’ drug similarity by taking six physicochemical characteristics into account: LIPO (Lipophilicity), SIZE, POLAR (Polarity), INSOLU (Insolubility), INSATU (In saturation), and FLEX (Flexibility).[21,22]

Determining the lipophilicity (log Pw/o) of bioactive compounds

The Swiss-ADME web service provides five free models for determining a molecule’s lipophilicity, including XLOGP3, WLOGP, MLOGP, SILICOS-IT, and iLOGP.[23]

Measurement of bioactive molecules’ water solubility (Log S Scale)

Swiss-ADME forecasts water solubility using two separate topological approaches.

Determining the cytochrome P-450 and P-gp of bioactive molecules

If the test molecule is expected to be a P-gp and CYP substrate, the resultant molecule will either return “Yes” or “No.”

Bioactive molecule drug-likeness prediction using a variety of criteria and filters

Swiss-ADME filters chemical libraries to exclude molecules with features that are incompatible with a decent pharmacokinetics profile utilizing five distinct rule-based filters from major pharmaceutical companies in order to improve the state of proprietary chemical collections.[24,25]

Prediction for bioactive compounds that are suitable for medicinal chemistry

These sections are meant to aid medicinal chemists in their continual attempts to discover novel medications. PAINS (Pan assay interference chemicals), also known as frequent hitters or promiscuous compounds, are substances that respond strongly in assays regardless of the protein targets.

The BOILED-Egg model for GI absorption and brain penetration of bioactive substances

The BOILED-Egg model facilitates the computation of polarity and lipophilicity of molecules, since it delivers datasets with precision, speed, and intelligible graphical results. Furthermore, the red color indication denotes the non-substrate of c, which is shown as (PGP+), but the blue color indication shows that the molecule is actively effluxed by P-glycoprotein, which is shown as (PGP).[26-28]

Prediction of pkCSM for bioactive molecules’ safety, toxicity, and pharmacokinetics

To assess the safety and toxicity endpoints of bioactive compounds, the pkSCM web server tool (http://bleoberis.bioc.cam.ac.uk/pkcsm/) was employed.[29,30] The predicted toxicity via pkCSM are available in supplementary text.

ProTox-II predicts the oral toxicity of bioactive compounds in rats

The ProTox-II is a free web server that predicts a wide range of toxicological endpoints for numerous chemical compounds. It can be accessed at http://tox.charite.de/protox_II/.[31,32] The predicted toxicity of bioactive molecules via ProTox-II are available in supplementary text.

RESULTS AND DISCUSSION

Silver nanoparticles (Ag-NPs) production, detection, observation, and characterization

The key indicator of the creation of silver nanoparticles was the visible color shift (from creamy-yellow to dark brown). Ag-NPs have formed as seen by the color shift. The color shift figure is available in supplementary text.

Silver nanoparticles (Ag-NPs) and seed extract biological assessment

Evaluation of antibacterial effects of silver nanoparticles (Ag-NPs) and seed extract

In comparison to seeds from Syria, the extract and nanoparticle of black cumin seeds from Saudi Arabia showed more activity. The silver nanoparticle from black cumin seeds grown in Saudi Arabia showed more action compared to black cumin seed oil sold commercially and nearly identical activity to the antibiotic ampicillin. In comparison to the standard (ampicillin and commercial black cumin seed oil), the mixture of silver nanoparticles from Saudi and Syrian-origin black cumin seeds demonstrated better activity [Table 1]. Details of antibacterial effects of silver nanoparticles (Ag-NPs) and seed extract are available in supplementary text.

Radical scavenging activity assessment

According to the DPPH assay results, the sample displayed dose-dependent effect and moderate-to-high radical scavenging activity ranging from 60% to 85% at a concentration of 150 g/mL. Black cumin seeds from Saudi Arabia and Syria mixed with silver nanoparticles at a concentration of 150 g/mL inhibited the most (85%), followed by Saudi and Syrian cumin seed nanoparticles (79% and 76%, respectively) [Figure 1]. Details of radical scavenging activity result analysis are available in supplementary text.

Figure 1.

The samples were tested against concentrations of 150 μg/mL, 120 μg/mL, 90 μg/mL, 60 μg/mL, and 30 μg/mL for DPPH free radical scavenging% inhibition

For the prediction of the bioactive chemical, in silico study analysis

Analysis of Swiss-ADME web servers

Swiss-ADME developed the bioavailability radar to predict oral bioavailability based on physicochemical properties. Swiss-ADME and Drug-likeness prediction, lipophilicity (log Pw/o) and water solubility result analysis are available in supplementary text.

The predicted value of the pharmacokinetics, bioavailability, drug-likeness, and medicinal chemistry friendliness of bioactive molecules such as Thymoquinone, Dithymoquinone, Thymohydroquinone, Thymol, p-Cymene, 4-Terpineol, T-anethol, Carvacrol, Nigellicine, Nigellidine, Nigellimine, Longifolene, and Limonene which are found in Nigella sativa seeds is presented in Table 2. Except for molecules 5, 12, and 13, all bioactive compounds demonstrated higher gastrointestinal (GI) absorption rates. All compounds were found to be non-inhibitors for CYP3A4, non-inhibitors for CYP2C19 except molecule 12 (inhibitory activity), and non-inhibitors for CYP2C9 except molecules 12 and 13.

Table 2.

Different methods for predicting drug similarity and medicinal chemistry alerts for bioactive input are used by the Swiss-ADME

Const. Rules	Drug-likeness prediction by a type of rules					Medicinal chemistry alert
	Lipinski’s	Ghose	Veber	Egan	Muegge	PAINS	Brenk	Lead likeness	Accessibility Synthetic
1	Yes	Yes	Yes	Yes	No (1 viol.)	1 Alert	1 Alert	No (1 viol.)	2.83
2	Yes	Yes	Yes	Yes	Yes	0 Alert	0 Alert	Yes	4.85
3	Yes	Yes	Yes	Yes	Yes	0 Alert	1 Alert	No (1 viol.)	1.05
4	Yes	No (1 viol.)	Yes	Yes	No (2 viol.)	0	0	No (1 viol.)	1.00
5	Yes	No (1 viol.)	Yes	Yes	No (2 viol.)	0	0	No (1 viol.)	1.00
6	Yes	No (1 viol.)	Yes	Yes	No (2 viol.)	0	1	No (1 viol.)	3.28
7	Yes	No (1 viol.)	Yes	Yes	No (2 viol.)	0	0	No (1 viol.)	1.47
8	Yes	No (1 viol.)	Yes	Yes	No (2 viol.)	0	0	No (1 viol.)	1.00
9	Yes	Yes	Yes	Yes	Yes	0	0	No (1 viol.)	2.51
10	Yes	Yes	Yes	Yes	Yes	0	0	Yes	2.86
11	Yes	Yes	Yes	Yes	Yes	0	0	No (1 viol.)	1.51
12	Yes	Yes	Yes	Yes	Yes	0	1	No (2 viol.)	3.67
13	Yes	No (1 viol.)	Yes	Yes	No (2 viol.)	0	1	No (2 viol.)	3.46

Drug-likeness: Yes (0 violations), Lead likeness: 250< MW <350, Synthetic accessibility score; from 1(very easy) to 10 (very difficult), viol.- violation

Except for molecules 4–8 and 13, which had just one violation, all molecules were found to be appropriate according to the Ghose filter. Molecules 2, 3, 9, 10, 11, and 12 had zero violations for the Muegge filter, whereas molecule 1 had one [Table 2].

The BOILED-Egg model for GI absorption and brain barrier penetration of bioactive compounds

According to the results of the BOILED-Egg model, all molecules would appear in the yellow part of the egg, indicating BBB penetration, with the exception of molecule 12, which would appear in the white section of the egg, indicating GI absorption. BOILED-Egg model result analysis for GI absorption are available in supplementary file.

With the exception of molecules 2, 10, and 12 (substrate for CYP3A4), all bioactive compounds were revealed to be non-substrates for CYP2D6 and CYP3A4.

Displays the total hepatic and renal clearance for all drugs tested. In Table 3, it was discovered that every bioactive molecule had a satisfactory log total clearance in ml/min/kg. The result analysis of safety, toxicity, and pharmacokinetics via pkCSM are availabe in supplementary text.

Table 3.

Prediction of input bioactive constituents’ excretion and toxicity characteristics by pkCSM

Const.	Excretion		Toxicity prediction of bioactive constitute
	Tota-Clea.	Renal OCT2 Sub.	AMES toxicity	Max. Tol. D.	hERG-I inhib.	hERG-II inhib.	Acute toxicity oral rat	Chronic toxicity oral rat	Skin sensitization	Minnow toxicity
1	0.225	No	No	0.764	No	No	1.643	2.491	Yes	1.264
2	0.59	No	No	-0.398	No	No	1.582	1.718	No	0.488
3	0.292	No	No	0.786	No	No	1.874	2.504	Yes	1.201
4	0.247	No	No	0.591	No	No	1.898	2.328	Yes	0.539
5	0.239	No	No	0.858	No	No	1.616	2.342	Yes	0.654
6	1.269	No	No	0.79	No	No	1.78	2.02	Yes	1.604
7	0.294	No	No	1.089	No	No	1.756	2.177	Yes	0.962
8	0.243	No	No	0.561	No	No	1.828	2.299	Yes	0.83
9	0.546	No	No	0.408	No	No	2.359	0.928	No	1.634
10	0.521	No	No	-0.604	No	Yes	2.444	1.69	No	0.686
11	0.672	No	No	1.124	No	No	2.319	2.286	No	1.185
12	0.909	No	No	0.013	No	No	1.57	1.344	No	0.285
13	0.213	No	No	0.77	No	No	1.88	2.368	Yes	1.203

Tota-Clea.: Total clearance (log ml/min/kg), Renal OCT2 Sub.: Renal OCT2 substrate, Max. Tol. D.: Maximum tolerated dose in human (log mg/kg/day), hERG -I and II inhib.: hERG inhibitor I and II, Acute Toxicity Oral Rat (LD50, mol/kg), Chronic Toxicity Oral Rat (LOAEL, log mg/kg_bw/day)

ProTox-II web server’s prediction of rodent oral toxicity for bioactive compounds

Table 4 shows the results of oral acute toxicity as LD50 (mg/Kgbw) and the toxicity class for each discovered compound.

Table 4.

ProTox-II predicts bioactive chemical oral organ and endpoint toxicity

Const.	LD50 dose (mg/kg) prediction	Prediction toxicity class	The toxicity prediction criteria and % probability value of bioactive molecules					Average similarity %	Prediction Accuracy %
			Hepato	Carcino	Immuno.	Mutag.	Cyto.
1	2400	5	-tive (0.63)	-tive (0.63)	-tive (0.97)	-tive (0.91)	-tive (0.78)	100	100
2	2300	5	-tive (0.64)	-tive (0.57)	-tive (0.54)	-tive (0.65)	-tive (0.77)	84.76	70.97
3	1000	4	-tive (0.77)	-tive (0.73)	-tive (0.89)	-tive (0.99)	-tive (0.90)	100	100
4	640	4	-tive (0.75)	-tive (0.60)	-tive (0.93)	-tive (0.99)	-tive (0.89)	100	100
5	3	1	-tive (0.87)	Active (0.67)	-tive (0.99)	-tive (0.98)	-tive (0.89)	100	100
6	1016	4	-tive (0.80)	-tive (0.72)	-tive (0.99)	-tive (0.83)	-tive (0.88)	100	100
7	150	3	-tive (0.74)	+tive (0.57)	-tive (0.71)	-tive (0.95)	-tive (0.90)	100	100
8	810	4	-tive (0.75)	-tive (0.60)	-tive (0.96)	-tive (0.99)	-tive (0.89)	100	100
9	1300	4	-tive (0.60)	+tive (0.50)	-tive (0.99)	-tive (0.59)	-tive (0.65)	45.89	54.26
10	1000	4	-tive (0.64)	+tive (0.52)	-tive (0.95)	-tive (0.58)	-tive (0.59)	50.33	67.38
11	1300	3	+tive (0.54)	-tive (0.53)	+tive (0.54)	+tive (0.78)	-tive (0.86)	69.14	68.07
12	5000	5	-tive (0.84)	-tive (0.68)	-tive (0.95)	-tive (0.88)	-tive (0.75)	100	100
13	4400	5	-tive (0.76)	-tive (0.65)	-tive (0.95)	-tive (0.97)	-tive (0.82)	100	100

Fatal if swallowed (LD50 ≤5 mg/Kgbw)-Class I; Fatal if swallowed (5 mg/Kgbw< LD50 ≤50 mg/Kgbw)-Class II; toxic if swallowed (50 mg/Kgbw < LD50 ≤300 mg/Kgbw)-Class III; harmful if swallowed (300 mg/Kgbw< LD50 ≤2000 mg/Kgbw)-Class IV; may be harmful if swallowed (2000 mg/Kgbw<LD50 ≤5000 mg/Kgbw)-Class V; Hepato. (Hepatotoxicity), Carcino. (Carcinogenicity), Immuno. (Immunotoxicity), Mutag. (Mutagenicity), Cyto. (Cytotoxicity), -tive: Inactive, +tive: Active

Table 4 highlights the data on organ toxicity as well as the estimated projections for many toxicological endpoints generated by the ProTox-II web server. The result analysis of rodent oral toxicity via ProTox-II are available in supplementary text.

CONCLUSION

Silver nanoparticles (Ag-NPs) were examined for their antibacterial effectiveness against E. coli, K. pneumoniae, and S. aureus. The combination of silver nanoparticles (Saudi and Syrian-derived black cumin seeds) showed better effectiveness against S. aureus than the norm. A mixture of silver nanoparticles of black cumin seeds at 150 g/mL produced the highest percentage of inhibition (85%), followed by Saudi (79%), and Syrian cumin seed nanoparticles (76%). Except for molecule 12 (p-gp substrate), the percentage of GIA of compounds was found to be higher (over 90%) and they displayed positive permeability across the BBB. Except for 3, 10, and 12, all compounds were shown to be non-inhibitors for CYP3A4, CYP2C19, CYP2C9, and CYP2D6. Except for molecule 4, all of the compounds had a high Caco2 permeability (log Papp >0.9). Moreover, with the exception of molecule 2 (a P-gp-I inhibitor), all molecules are non-inhibitors of P-gp-I and II. Except for molecules 5, 10, and 12, all displayed VDss within the permissible range. Furthermore, with the exception of compound 10, none of the molecules exhibited AMES toxicity or renal OCT2 substrate, and they were non-inhibitors of hERG I and II. Except for molecule 11 (which demonstrated hepatotoxicity, mutagenicity, and immunotoxicity), all compounds were found to be organ-free. All molecules followed the Lipinski rule, Veber, Egan, and Muegge filters, and there were no violations reported. Except for molecules 1, 12, and 13 (1 violation only), the PAINS and Brenk structural alarms had zero (0) violations. Molecules 3 and 6 were identified as prospective lead candidates for future medical study because of their high GIA, absence of AMES toxicity, oral rat acute and chronic toxicity, lack of renal OCT2 substrate, high total clearance, and lack of organ toxicity. They also have a good grade for simple synthetic accessibility (1.05).

Human and animal rights

No animals/humans were used for studies that are the basis of this research.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Supplementary Text

INTRODUCTION

Due to their small size and high surface-to-volume ratio, nanoparticles (NPs) exhibit a variety of unique features compared to bulk materials, including electrical, magnetic, and optical capabilities. The features of the materials alter as the particle size gets closer to the nanoscale, and the proportion of atoms on the surface becomes important. Numerous materials are manufactured at the nanoscale for a variety of uses, including mechanical, biological, and electrical. Metal nanoparticles have a variety of previously unanticipated advantages, according to researchers in the field of nanotechnology.[1,3]

Silver nanoparticles are synthesized utilizing green processes that use less chemicals, are non-toxic, affordable, and environmentally benign. Among the various nanoparticles (Ag, Au, Fe, Pd, ZnO/Au and ZnO/Ag and quantum dots CdS), silver nanoparticles play a significant role due to their optical, electronic, chemical, photochemical, electrochemical, catalytic, magnetic, antibacterial, antiviral, antifungal, anti-inflammatory, biological labeling, and catalytic properties.[4,5] Silver nanoparticles act as an antibacterial agent, which has medical applications such as Ag-NP coated blood collection tubes, coated capsules, patches, and so on. Animal cells are unaffected by silver, however bacteria and other microbes (E. coli, Pseudomonas aeruginosa, and Staphylococcus aureus) are. Because of these characteristics, it is regarded as a bactericidal metal that is both safe and effective. Although biological approaches are frequently utilized for the manufacture of silver nanoparticles, the usage of plant extracts is being widely researched due to their advantages over others.[6,7] A number of studies have emphasized the antibacterial action of plant-synthesized nanoparticles, which have been proven to be useful in treating urinary tract infection-causing bacteria, typically Escherichia coli and Staphylococcus aureus, which demonstrate a zone of inhibition. Because of their interesting, motivating, beautiful, and astonishing qualities with a variety of uses over its parent material, many researchers have embraced eco-friendly synthesis methods for a variety of metal nanoparticles.[8,9]

Previous research has indicated that silver nanoparticles have the potential to be utilized in various antimicrobial products. The antibacterial properties of silver nanoparticles make them suitable for extending the shelf life of fruits, as well as for use in dental materials, cosmetics, water treatment, and the coating of medical devices made of stainless steel. The presence of phytochemicals in plants contributes to their ability to reduce metal compounds through their antioxidant and reducing properties. Nanoparticles exhibit efficacy against a broad spectrum of both Gram-positive and Gram-negative bacteria, as well as antifungal and antiviral properties. The methods employed for the green synthesis of metal nanoparticles are environmentally friendly, biocompatible, non-toxic, and hygienic. Flora and its components hold immense significance and play a major role in the global economy. They are utilized in the treatment of several illnesses, including infectious diseases. Overuse of antibacterial drugs has led to multiple drug resistances, resulting in inadequate disease control. Simultaneously, patients experience adverse effects associated with antibiotics. This has led to the emergence of alternative plant medicines to prevent and treat microbial infections. Phytoconstituents possess immense therapeutic potential and minimal side effects as compared to synthetic drugs.

Alkaloids, tannins, polyphenols, quinines, flavonoids, coumarins, terpenoids, lectins, and polypeptides are phytoconstituents with medicinal properties.[10,11] Nigella sativa seeds, among other plant seeds, have exhibited antibacterial, antifungal, antidiabetic, anti-allergic, anti-inflammatory, antioxidant, and anticancer effects, as well as chemical therapies. Its seeds help the body recuperate from weariness and are also used to treat respiratory and gynecological problems. Black cumin oil is extracted from Nigella seeds and used topically to treat eczema, inflammation, rashes, abscesses, boils, and other skin problems. Because of its strong antioxidant levels, black cumin seeds have been used for a variety of medical purposes for decades. Green silver nanoparticle production can enable large-scale synthesis and encourage research and development in this field. Silver metal nanoparticles, in particular, have gained special interest due to their high electrical conductivity, chemical stability, catalytic activity, and antibacterial activity. Silver nanoparticles and carrier biofilms have been used in a variety of applications due to their high antibacterial activity, including food, medicine, textiles, sunscreens, and cosmetics.[7,10]

Numerous qualities of black cumin seed oil have been documented to fight against various pathogens such as E. coli, Salmonella, Shigella, and Vibrios. Antimicrobial resistance, which is already wreaking havoc on global public health, has the potential to outnumber numerous communicable and noncommunicable diseases in terms of deaths. One of the primary causes of antimicrobial resistance is the inappropriate and unneeded use of antibiotics in people. Antibiotic use varies according to population. Antibiotic use in the outpatient sector is ten times higher in European countries than in the hospital sector. The outpatient sector in Germany contributes significantly to antibiotic resistance, accounting for 85 percent of all antibiotic prescriptions for people. However, drug and human effort waste is unavoidable during the clearance of compounds in clinical studies. In this regard, computational algorithms have been developed to predict drug pharmacokinetic characteristics, toxicity, and safety. In-silico tools are now faster, simpler, and less expensive than traditional investigational procedures. Large attrition rates of preclinical and clinical candidates have been recorded in the pharmaceutical business due to toxicity or latency of ideal pharmacokinetic features. Through structural alterations, medicinal chemists can influence a molecule’s pharmacokinetics and toxicity.

Swiss ADME webserver (www.swissadme.ch) provides a viable alternative to experimental drug design from natural products or synthetic chemicals, which can lead to innovative medication development.[11-13] The Swiss ADME method was utilized to estimate the pharmacokinetics, bioavailability, drug-likeness, and medicinal chemistry friendliness of the bioactive compound for better improvement. The pkCSM web server (http://bleoberis.bioc.cam. ac.uk/pkcsm/) was used to determine the pharmacokinetic study, toxicity, and safety end point. ProTox-II (https://tox-new.charite.de/protox_II/index.php?site=compound_search_ similarity) was also utilized to assess the in-silico organ toxicity of bioactive compounds discovered in Nigella sativa seeds. The current study involves the environmentally friendly synthesis of silver nanoparticles from Nigella Sativa seeds, as well as the evaluation of their antibacterial efficacy against Escherichia coli, Staphylococcus aureus, and Klebsiella pneumonia, as well as their antioxidant activity as measured by DPPH% in comparison to the standard. This study also used Swiss ADME, pkCSM, and ProTox-II to assess the pharmacokinetics, oral bioavailability potential, toxicity, and safety end points of the identified bioactive compounds in Nigella Sativa seeds.

Table 1.

Major bioactive molecules found in black cumin seeds (Nigella sativa).

Sr. No.	Bioactive constitute	2D Molecular structure & Smile notation	Pub chem CID	Retro synthesis %	The % of contents	Molecular formula
1	Thymoquinone	CC(C)C(C(C=C1C)=O)=CC1=O	10281	100	30-48%	C10H12O2
2	Dithymoquinone	O=C(C(C(C)C)=C3)C(C(C12)(C)C(CC(C(C)C)C1=O)=O)C2(C)C3=O	398941	0.00	7-15%	C20H24O4
3	Thymohydroqui none	CC(C)C1=C(O[H])C=C(C)C(O[H])=C1	95779	100	7-15%	C10H14O2
4	Thymol	CC1=CC(O)=C(C(C)C)C=C1	6989	100	18.4%- 24%	C10H14O
5	p-Cymene	CC1=CC=C(C(C)C)C=C1	7463	90	7-15%	C10H14
6	4-Terpineol	CC1=CCC(C(C)C)(O)CC1	5325830	>60%	2-7%	C10H18O
7	T-anethol	COC1=CC=C(/C([H])=C([H])/C)C=C1	637563	-	1-4%	C10H12O
8	Carvacrol	CC1=C(O)C=C(C(C)C)C=C1	10364	100	6-12%	C10H14O
9	Nigellicine	OC(C3=C1C(N2CCCCN23)=CC(C)=CC1=O)=O	11402337	33.33	<1%	C13H14N2O3
10	Nigellidine	O=C3C=C(C)C=C2N1CCCCN1C(C4=CC=C(O)C=C4)=C23	136828302	100	<1%	C18H18N2O2
11	Nigellimine	CC1=NC=CC2=C1C=C(OC)C(OC)=C2	20725	100	<1%	C12H13NO2
12	Longifolene	CC1(C)[C@H]2C(CC[C@H]2C3=C)[C@]3(C)CCCC1	289151	00	1-8%	C15H24
13	Limonene	C=C(C)C1CC=C(C)CC1	22311	39	<1%	C10H16

MATERIALS AND METHODS

A concise description of the plant species

Nigella sativa L. is a herbaceous plant in the Ranunculaceae family. It is also known as black cumin and is native to the Mediterranean region. It is also grown in the Arab desert, Northern Africa, and parts of Asia. Black cumin seeds are well-known for their capacity to treat a wide range of conditions, including infectious disorders. The percentage of bioactive compounds in seeds changes according to geographical and agricultural conditions, which influences their biological activity. These seeds have a strong taste and a nutty scent and are spherical, black, and about 1mm in diameter. They are used as a spice as well as a flavoring agent.[11-13]

Collection of black cumin seeds

It is straightforward to obtain black cumin seed at the local market in Buraydah, Al-Qassim, Kingdom of Saudi Arabia. Among the many therapeutic herbs, black cumin is gaining reputation as a miraculous herb with a deep historical and religious history due to its broad spectrum of pharmacological potential, as discovered by various research. Nigella Sativa’s black seeds or black cumin have great value as traditional medicine in several countries and are highly regarded as a herbal or chemical cure for the treatment of a variety of ailments. Extensive research on the seeds has revealed their biological activities and therapeutic potential, including diuretic, antihypertensive, antidiabetic, anticancer, skin disorders, immunomodulatory, analgesic, antimicrobial, anthelmintics, anti-inflammatory, rheumatism, spasmolytic, bronchodilator, gastroprotective, hepatoprotective, renal protective, and antioxidant properties. The seeds are also used as a liver tonic, digestive aid, anti-diarrheal, appetite stimulant, emmenagogue, to boost milk production in nursing mothers, to fight parasitic infections, and to boost the immune system.[4-7]

Aqueous seed extract preparation

The Nigella sativa seeds were obtained at a local market in Buraydah, Saudi Arabia’s Al-Qassim region. The seeds were rinsed many times with double distilled water to remove any surface contaminants. The cleaned seeds were finely milled into a powder after drying for 48 hours at room temperature. Following that, 10 grams of powder were combined with 250 mL of distilled water and left to soak for 24 hours. After that, the soaking mixture was filtered using Whatman No. 1 filter paper. The extracted solution was used as a stock solution and kept at 4°C until the testing step.[14]

Aqueous silver nitrate preparation: Silver nitrate solution (AgNO3), 0.1 M, was prepared and store in an amber-coloured bottle.

Silver nanoparticle (Ag-NPs) synthesis

Seed extracts are added to an 80 mL solution of silver nitrate (AgNO3) during the synthesis of silver nanoparticle (Ag-NPs) and optimization process in volumetric amounts (20 mL). The mixtures were kept in a variety of settings, including exposure to heat and sunlight. The reaction mixture was incubated for 30 minutes, or until a color change from light yellow to pink to dark brown was visible. After the reaction mixture had been left standing for 24 hours, a color shift in the form of silver nanoparticles was noticed. Centrifugation was used to remove extra silver ions from the particles while operating at 10,000 rpm for 15 minutes. Three cycles of centrifugation using double-distilled water are required to completely extract all silver colloids. The sample was kept in screw-capped vials in an aseptic environment so that it could be further characterized and used.[14]

Silver nanoparticles (Ag-NPs) detection and characterisation

Visual color changes were used as the primary method of detecting the creation of silver nanoparticles, and these color changes show that Ag-NPs have formed. It took around 96 hours for the colorless solution to turn brown after the reaction mixture was left undisturbed. The entire procedure was done in a warm environment. using analysing the material in the region of 200-1100 nm, a subsequent synthesis of silver nanoparticles was verified using a UV-Vis spectrophotometer.[14]

Biological Evaluation of Silver Nanoparticles (Ag-NPs) and Seed Extract

Ag-NPs’ and seed extract antibacterial activity as measured by disc diffusion

In comparison to standard, the antibacterial activity of an aqueous seed extract of black cumin (of Saudi and Syrian provenance) and its silver nanoparticle was assessed against Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. Dimethyl sulfoxide (DMSO, 0.1% v/v in PBS) was used to prepare the samples. On sterile petri plates, the prepared nutritional agar was poured, and 17-hour-growing cultures of E. coli, K. pneumoniae, and S. aureus were swabbed on to the agar plates. The sterile discs were placed inverted on the swabbed plate while being impregnated with a solution of silver nanoparticles and a positive control medication. As a negative control, an empty sterile disc was preserved. The zones of inhibition on the plates were determined after an overnight incubation at room temperature.[14,15]

The agar diffusion method was developed to be a viable alternative to the agar and broth tube dilution techniques. In the agar diffusion approach, plant extracts from Saudi and Syrian Black origin seed extract and a popular antibiotic, Ampicillin, are transmitted through a hole in the agar plate. During the incubation period, the tested material diffuses into the agar media that has been sowed with the test microorganism. The antibacterial action of extracts and silver nanoparticles was observed in inhibitory zones surrounding the hole. The size of the filter paper disk or hole, the amount of compound supplied to the disk or hole, the kind and concentration of the agar, the thickness and pH of the medium, the tested microbial strain, and the incubation temperature are all factors that influence the size of the inhibitory zones

Ag-NPs’ and seed extract’s DPPH test for radical scavenging activity

The antioxidant activity of the aqueous extract of Black cumin seeds (of Saudi and Syrian origin) and its silver nanoparticle was evaluated using the scavenging ability of the stable 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical. Dissolving and diluting the samples in methanol yielded concentrations of 150, 120, 90, 60, or 30 g/mL. A freshly prepared 0.04% DPPH solution in methanol was added to each concentration sample. The mixture was shaken and incubated for 30 minutes in a 37°C oven. The absorbance of the resultant solution at 517 nm was determined using UV-Vis spectrophotometry. Ascorbic acid was utilized as a positive control at a concentration comparable to the test sample. Utilizing the provided formula, the percentage of inhibition used to report the tested sample’s ability to scavenge DPPH radicals. % DPPH Inhibition = [(Acontrol –Asample)/Acontrol] × 100. where Acontrol denotes the absorbance of the DPPH solution and Asample denotes the absorbance of the test sample (DPPH solution plus chemical).[15]

Black cumin seeds’ phytochemical analysis

The process of extracting, identifying, and screening medicinally active compounds from plant seeds is known as phytochemical screening. Phytochemicals are the chemical substances that plants make in their seeds to help them resist diseases from fungi, bacteria, and plant viruses as well as to be consumed by insects and other animals. Phytochemical screening is used to confirm the presence of different phytochemicals. Major classes of phytoconstituents as steroids, saponins, terpenoids, flavonoids, and alkaloids are analysed for presence in the black cumin seeds oil (Table 2).[16]

Table 2.

The phytochemical analysis of black cumin seeds.

Sr. No.	Chemical class	Sample S	Sample Y	M1	M2
1.	Steroids	+tive	+tive	+tive	+tive
2.	Terpenoids	- tive	- tive	- tive	- tive
3.	Flavanoids	- tive	- tive	- tive	- tive
4.	Saponons	- tive	- tive	- tive	- tive
5.	Alkaloids	+ tive	+ tive	- tive	- tive

Sample S: Saudi origin seeds, Sample Y: Syrian origin seeds, M1: One brand marked black cumin seeds oil and M2: Second brand marked black cumin seeds oil.

Computational analysis of bioactive compounds in seeds of black cumin

Swiss website server for computer-based research

A Swiss-web server was used to forecast the pharmacokinetics (ADME), bioavailability, drug similarity, and medicinal chemistry friendliness of chemical compounds. The web tool predicts bioavailability radar using six physicochemical criteria, including lipophilicity, size, polarity, solubility, flexibility, and saturation, in order to find chemical compounds that are similar to medications. The tool includes ADME properties such as blood-brain barrier (BBB) permeability, passive human gastrointestinal absorption (HIA), and substrate or non-substrate for glycoprotein (P-gp) as assessed positively or negatively in the BOILED-Egg model. Daina et al. developed the generalized-born and solvent accessible surface area (GB/SA) model, which was used to calculate lipophilicity (Log p/w) features such as iLOGP for n-octanol and water.[17] Cheng et al. developed the XLOGP3 atomistic technique, which incorporates corrective elements and a knowledge-based library. WLOGP was designed for an entirely atomistic technique based on the Wildman and Crippen fragment system.[18] M-LOGP is an example of a topological technique that relies on a linear connection and 13 molecular descriptors, according to Moriguchi et al.’s research.[19] and Daina et al.’s hybrid SILICOS-IT approach is based on 27 pieces and 7 topological descriptors.[17]

Swiss-ADME for predicting bioactive compounds

A web server hosting the Swiss Institute of Bioinformatics’ Swiss-ADME submission page (www.swissadme.ch) was used to access Swiss-ADME and study the ADME behavior of bioactive chemicals discovered in black cumin seeds. Bioactive chemicals were depicted using the SMILES notation (simplified molecular input line entry system) in the chem. bio ultra sketch software. The SMILES notation for each bioactive molecule was put into the Swiss-ADME web tool to predict ADME and associated physicochemical parameters. The list is meant to contain one input molecule per line with numerous inputs described using SMILES, and the results are displayed in tables, graphs, and an excel spreadsheet for each molecule.[20,21]

Physicochemical characteristics for bioactive compounds are determined using Swiss bioavailability radar

A number of characteristics influence molecule bioavailability, including solubility, stability due to stomach and colonic pH, metabolism by gut bacteria, absorption across the intestinal wall, active efflux mechanism, and first-pass metabolic impacts. However, assessing a compound’s bioavailability solely on its physicochemical properties might be difficult. The term “bioavailability” refers to the amount and rate at which a substance’s active component (medication or metabolite) enters systemic circulation and finally reaches the site of action. Lipinski’s rule of five states that a molecule will be more bioavailable if it contains five hydrogen bond donors, ten hydrogen bond acceptors, 500 daltons of molecular mass, five partition co-efficients with log P-values, and ten rotatable bonds [20] The two-dimensional chemical structure utilizing SMILES was displayed in the first portion of the Swiss web tool. The bioavailability radar provides a rapid look at target compounds’ drug similarity by taking six physicochemical characteristics into account: LIPO (Lipophilicity), SIZE, POLAR (Polarity), INSOLU (Insolubility), INSATU (In saturation), and FLEX (Flexibility). Lipophilicity: XLOGP-3 (-0.7 to + 5.0), Size: MW (150 to 500 g/mol), TPSA (20 to 1300A2) Polarity Solubility: log S should not be greater than 6. Flexibility: no more than 9 rotatable bonds; saturation: no less than 0.25 carbon fraction in the sp3 hybridization. Other physiochemical properties include the number of H-bond acceptors or donors, the number of heavy atoms, and molar refractivity.[21,22]

Determining the lipophilicity (log Pw/o) of bioactive compounds

Lipophilicity is an important element in drug discovery and design since it complements the most informative and successful physicochemical property in medicinal chemistry.[23] The Swiss-ADME web service provides five free models for determining a molecule’s lipophilicity, including XLOGP3, WLOGP, MLOGP, SILICOS-IT, and iLOGP. XLOGP3 is an atomistic technique that includes correction factors and a library of knowledge. WLOGP is a purely atomistic technique based on a fragmental basis. MLOGP is a topological approach that is based on a linear connection and uses 13 molecular descriptors.[18-19] SILICOS-IT is a hybrid approach that employs 27 pieces as well as 7 topological descriptors. iLOGP is a physics-based approach that uses the generalized-born and solvent accessible surface area (GB/SA) model to determine the free energies of solvation in n-octanol and water. The arithmetic mean of the values anticipated by the five recommended approaches is the consensus log P o/w.[12,13] Lipophilicity can be measured empirically as partition coefficients (log P) or distribution coefficients (log D). The partition equilibrium of a unionized solute in water and an immiscible organic solvent is represented by log P. Lipophilicity increases as log P values increase.[23]

Measurement of Bioactive Molecules’ Water Solubility (Log S Scale)

Swiss-ADME forecasts water solubility using two separate topological approaches. The first is based on the ESOL model, which categorizes solubility as insoluble (10), weakly soluble (6), moderately soluble (4), soluble -2, and extremely highly soluble (numbers range from 0). The second technique, developed from Ali et al, 2012, categorizes solubility as insoluble (10), weakly soluble (6), moderately soluble (4), soluble -2, and extremely highly soluble (numbers range from 0). Both techniques differ from the usual solubility equation in that they do not take the melting point into account. The correlation between anticipated and experimental values, on the other hand, was strong, with R2 values of 0.69 and 0.81, respectively. SILICOS-IT developed the third predictor in Swiss-ADME, which categorizes solubility as insoluble-10, weakly soluble-6, moderately soluble-2, and extremely highly soluble with values ranging from 0, and corrects the linear coefficient by molecular weight, with an R2 value of 0.75. Log S is the decimal logarithm of molar solubility in water (log S). Swiss-ADME additionally offers qualitative solubility classes in addition to mol/l and mg/ml solubility. A molecule’s solubility is greatly influenced by the solvent employed, as well as the ambient temperature and pressure. The saturation concentration is the point at which adding more solute does not increase its concentration in solution. A drug is deemed highly soluble if the highest dose strength dissolves in 250 mL or less of aqueous media over the pH range of 1 to 7.5.[23,24]

Determining the cytochrome P-450 and P-gp of bioactive molecules

Pharmacokinetics is a critical component in the creation of novel medications since it describes how a substance behaves in an organism when administered therapeutically. According to Hay et al., a rapid assessment of ADME during the discovery phase considerably reduced the percentage of pharmacokinetics-related failure during the clinical phases. Swiss-ADME employs the support vector machine (SVM) algorithm for binary classification of datasets containing known substrates/non-substrates or inhibitors/non-inhibitors. If the test molecule is expected to be a P-gp and CYP substrate, the resultant molecule will either return “Yes” or “No.” The SVM model for the P-gp substrate had a training set of 1033 molecules and a test set of 415 molecules. The SVM model for the Cytochrome P-450 1A2 inhibitor molecule was developed using 9145 molecules as the training set and 3000 molecules as the test set. The SVM model for the Cytochrome P-450 2C19 inhibitor was trained on 9272 molecules before being evaluated on 3000 molecules. The SVM model for the Cytochrome P-450 2C9 inhibitor compound has 5940 molecules in the training set and 2075 molecules in the test set. The SVM model for the Cytochrome P-450 2D6 inhibitor molecule had a training set of 3664 molecules and a test set of 1068 molecules. The SVM model for the Cytochrome P-450 3A4 inhibitor compound had a training set of 7518 molecules and a test set of 2579 molecules.[24,25]

Bioactive molecule drug likeness prediction using a variety of criteria and filters

Swiss-ADME filters chemical libraries to exclude molecules with features that are incompatible with a decent pharmacokinetics profile utilizing five distinct rule-based filters from major pharmaceutical companies in order to improve the state of proprietary chemical collections. The Lipinski filter (Pfizer) is the original rule of five that categorizes tiny molecules based on physicochemical property profiles and includes MW less than 500, MLOGP 4.15, N or O 10, NH or OH 5, and NH or OH 5. Lipinski believes that all nitrogen and oxygen are H-bond acceptors, while all nitrogen and oxygen containing at least one hydrogen are H-bond donors. Furthermore, aliphatic fluorines are neither donors nor acceptors of aniline nitrogen.[26] The Ghose filter (Amgen) describes small molecules based on their physical-chemical properties, the presence of functional groups, and their substructures. The qualifying range for tiny molecules is between 20 and 70 atoms, with molecular weights ranging from 160 to 480 Da, WlogP values ranging from -0.4 to 5.6, and molar refractivity (MR) values ranging from 40 to 130.[24-26]

The Veber filter (GSK filter) model considers a molecule to be drug-like if it has fewer than 10 rotatable bonds, a TPSA of 140 or less, and 12 or fewer H-bond donors and acceptors. Reduced TPSA correlates with higher penetration rate, whereas more rotatable bond numbers reduce permeation rate.[26,27] Compounds having these characteristics have a high oral bioavailability. The Egan filter (Pharmacia filter) hypothesizes that mechanisms involved in medication absorption alter the membrane permeability of a small molecule. This model portrays a molecule as a medicine with a WLOGP of 5.88 and a TPSA of 131.6. The Egan computer model for human passive intestinal absorption (HIA) of small compounds provides for active transport and efflux routes in order to accurately predict medication absorption.[20] The Muegge filter (also known as the Bayer filter), a self-sufficient pharmacophore point filter, separates drug-like and non-drug-like substances. If a molecule meets the following criteria, it is represented as a medicine using this model: TPSA 150; number of rings; number of carbon atoms; number of heteroatoms; number of rotatable bonds; number of carbon atoms more than one; number of H-bond acceptors; and number of H-bond donors, respectively. The Abbott bioavailability score seeks to predict if a chemical will have at least 10% oral bioavailability in rats or measurable Caco-2 permeability, which predicts whether a substance will have F>10% in a rat model based on the predominant charge at physiologic pH. It focuses on quickly screening chemical libraries to find the optimum compounds for synthesis.[27]

Prediction for bioactive compounds that is suitable for medicinal chemistry

These sections are meant to aid medicinal chemists in their continual attempts to discover novel medications. Such compounds, in particular, have been shown to be active in a range of assays, which can be viewed as prospective beginning points for further investigation. PAINS (Pan assay interference chemicals), also known as frequent hitters or promiscuous compounds, are substances that respond strongly in assays regardless of the protein targets. If such moieties are detected in the molecule under consideration, Swiss-ADME issues warnings. In an alternative approach, Brenk considers molecules that are smaller and less hydrophobic than those covered by “Lipinski’s rule of 5” in order to improve the possibilities for lead optimization. Substances with potentially mutagenic, reactive, or unfavorable groups, such as nitro groups, sulfates, phosphates, 2-halopyridines, and thiols, were eliminated first. The ClogP/ClogD ratio must be between 0 and 4, the number of hydrogen-bond donors and acceptors must be between 4 and 7, and the number of heavy atoms must be between 10 and 27 according to the Brenk model. Furthermore, only chemicals with restricted complexity are accepted as therapeutic drugs, defined as having no more than 5 rings, no more than 2 fused rings, and no more than 8 rotatable links. Lead similarity is a concept used in high throughput screening (HTS) to generate high affinity leads that require the utilization of extra interactions during the lead optimization phase. Leads are exposed to chemical modifications that will most likely result in size reduction and an increase in lipophilicity, making them less hydrophobic than drug-like molecules. The rule-based strategy for lead optimization employs molecules with molecular weights ranging from 100 to 350 Da and ClogP values ranging from 1 to 3.0, which are usually recognized as preferable to drug-like compounds and, hence, lead-like compounds.[27,28]

The BOILED-Egg model for GI absorption and brain penetration of bioactive substances

The molecules’ BOILED-Egg model predicted that the ability of GI absorption and the permeability of the blood-brain barrier would be demonstrated. GI absorption and blood-brain barrier (BBB) penetration are critical in the pharmaceutical development process and can be improved by screening chemical libraries. The BOILED-Egg model facilitates in the computation of polarity and lipophilicity of molecules since it delivers datasets with precision, speed, and intelligible graphical results. The BOILED-Egg model creates a rapid, impulsive, accurately imitating, yet noisy technique to forecast passive GI absorption for drug discovery and study. The white zone (GI tract) in the BOILED-Egg model reflects a high chance of passive absorption, whereas the yellow section (BBB) represents a high risk of BBB penetration. Furthermore, the red color indication denotes the non-substrate of Pgp, which is shown as (PGP+), but the blue color indication shows that the molecule is actively effluxed by P-glycoprotein, which is shown as (PGP).[27,28]

Prediction of pkCSM for Bioactive Molecules’ safety, toxicity and pharmacokinetics

To assess the safety and toxicity end points of bioactive compounds, the pkSCM web server tool (http://bleoberis.bioc.cam.ac.uk/pkcsm/) was employed. A variety of safety criteria were also tested and identified, including hERG liability (inhibition of dofetilide binding), LD50 (lethal rat acute toxicity), hepatotoxicity, cutaneous sensitization, AMES toxicity, and cellular toxicity. The key human cytochrome P450 (CYP) isoforms involved in drug metabolism were also discovered, including CYP2C9, CYP2D6, and CYP3A4. Total clearance and renal OCT2 substrate were both expected to quantitatively influence the mechanisms via which chemicals are expelled. The percentage of molecules absorbed by the human digestive tract (%) was calculated using the pkCSM web server. Almost all drugs, with varying affinities, are in an equilibrium between the bound and unbound states with serum proteins in plasma. Only an unbound drug can interact with the predicted molecular targets. The capacity of a drug to attach to complete blood proteins so influences its effectiveness. The steady-state volume of distribution (VDss) and fraction unbound (Fu) of molecules in plasma might also be calculated using pkCSM. VDss, an important factor, contributes in the recommendation of a drug’s total dose.[29,30]

ProTox-II predicts the oral toxicity of bioactive compounds in rats.

The ProTox-II is a free web server that predicts a wide range of toxicological endpoints for numerous chemical compounds. It can be accessed at http://tox.charite.de/protox_II/. This tool uses molecular similarity, pharmacophores, fragment propensities, and machine-learning models to predict some toxicity endpoints, such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21), and toxicity targets. The ProTox-II platform is divided into five distinct classification steps generated by various computational models: (1) acute toxicity (oral toxicity model with five different toxicity classes); (2) organ toxicity model; (3) toxicological and genotoxicological endpoints, primarily immunotoxicity, cytotoxicity, mutagenicity, and carcinogenicity; (4) toxicological pathways; and (5) toxicity targets. Toxic dosages for oral acute poisoning are given as LD50 values in mgKgbw. In terms of toxicity endpoint and organ toxicity prediction, the predictive models are based on data from both in vitro (such as Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, immunotoxicity assays, and others) and in vivo (such as carcinogenicity, hepatotoxicity) experiments. This computational prediction method can be used to identify whether specific chemical compounds have the potential to interact with biological functions and create negative health effects in a timely and effective manner.[31,32]

ProTox, a web server for forecasting rodent oral toxicity, was published in 2014. There are several advantages to using the ProTox-II website instead of current computational models. The ProTox-II web server (https://tox-new.charite.de/protox_II/index.php?site=compound_search_ similarity) contains information on both chemical and molecular target knowledge. The classification of the prediction scheme into various levels of toxicity, such as oral toxicity, organ toxicity (hepatotoxicity), toxicological endpoints (such as mutagenicity, carcinogenicity, cytotoxicity, and immunotoxicity), toxicological pathways (AOPs), and toxicity targets, is a unique feature of the ProTox-II webserver, and it provides insights into the potential molecular mechanisms underlying such toxic responses. The new ProTox-II version incorporates chemical similarity, pharmacophore-based, fragment propensities, most common features, and machine learning models to predict numerous toxicity endpoints.[31,32]

ProTox-II, which consists of 33 models and is a freely accessible computational toxicity prediction website, has predicted the greatest number of toxicological endpoints. Toxicology endpoints such as mutagenicity, carcinogenicity, and many more can be used to assess a substance’s toxicity. It can also be assessed qualitatively, for example, binary (active or inert) for certain cell types and assays, or indication areas such as cytotoxicity, immunotoxicity, and hepatotoxicity. It can also be quantified mathematically, such as LD50 (lethal dosage) values. This is one of the most significant in silico models with a lot of informational data. Data from DSSTox (Distributed structure searchable toxicity), and other sources CEBS: a comprehensive annotated database of toxicological data, Liver Tox: a database of clinical and research information on DILI (Drug-induced Liver injury), and Tox21 datasets have largely supported the interpretation of data from large-scale high-throughput assays with hundreds to thousands of biological endpoints, such as toxicity pathway identification.

RESULTS AND DISCUSSION

Silver nanoparticles (Ag-NPs) production, detection, observation, and characterization

In comparison to other techniques, sunlight irradiation was shown to be quite successful, and homogenized seed extract demonstrated increased nanoparticle formation. So, for the creation of nanoparticles, a homogenized seed extract and sunlight exposure method was selected. The homogenized seeds extract was placed in a bottle with 100 mL of 1 mM silver nitrate and exposed to sunlight for the purpose of producing silver nanoparticles in large quantities. The key indicator of the creation of silver nanoparticles was the visible color shift (from creamy-yellow to dark brown). Ag-NPs have formed as seen by the color shift (Fig. 1). By analyzing a sample with a wavelength between 200 and 1100 nm, a UV-Vis spectrophotometer was able to validate the silver nanoparticles’ synthesis.

Figure 1.

The colour observation for silver nanoparticle synthesis; A: When exposed to sunshine, a creamy-yellow substance turns dark brown [B]; C: When exposed to sunlight, a light-yellow substance turns dark brown [D]; and E: When exposed to sunlight, a creamy substance turns brown [F].

Silver nanoparticles (Ag-NPs) and seed extract biological assessment

Evaluation of antibacterial effects of silver nanoparticles (Ag-NPs) and seed extract

E. coli, K. pneumoniae, and S. aureus were used as test organisms to determine the antibacterial activity of black cumin seeds of Saudi and Syrian origin extract and its silver nanoparticles (Ag-NPs) samples. All samples are shown to be less susceptible to K. pneumoniae than to E. coli and more sensitive to S. aureus. All black cumin seed extract samples and silver nanoparticles (Ag-NPs) samples were examined for zone of inhibition and contrasted with commercial black cumin oil and ampicillin antibiotic. In comparison to seeds from Syria, the extract and nanoparticle of black cumin seeds from Saudi Arabia shown more activity. The silver nanoparticle from black cumin seeds grown in Saudi Arabia shown more action compared to black cumin seed oil sold commercially and nearly identical activity to the antibiotic ampicillin. In comparison to the standard (Ampicillin and commercial black cumin seed oil), the mixture of silver nanoparticles from Saudi and Syrian-origin black cumin seeds demonstrated better activity (Table 3 & Fig. 2).

Originate Extract, SA-SY-NP: Saudi and Syrian Originate Extract Nanoparticle, STD-1: Marketed black cumin seed oil and STD-2: Ampicillin antibiotic.

Figure 2.

Zones of inhibition (measured in mm) of several samples against E. coli, K. pneumoniae and S. aureus.

Table 2 shows the results of a phytochemical analysis of all samples of black cumin oil, showing that the oil from seeds grown in Saudi Arabia and Syria included alkaloids whereas steroid was present in all samples. Additionally, black seed oil is said to contain 82.9% unsaturated fats and 17% saturated fats. As a result, the presence of alkaloids, steroids, and fatty acids in black cumin oil samples may be responsible for its antibacterial properties. Different antibacterial activity may occur from a variety of circumstances, such as extraction technique, drying, storage, location, agricultural practices, and harvesting conditions, among others, which cause chemical variance.

Radical Scavenging Activity Assessment

The DPPH test, which measures absorbance at 517 nm due to the production of stable DPPH radicals, was used to examine the radical scavenging ability of black cumin seeds extract and silver nanoparticles (Ag-NPs) samples. The purple color of the DPPH solution changed to yellow at all tested dosages of each drug, indicating the compounds’ potential as radical scavengers. According to the DPPH assay results, the sample displayed dose-dependent effect and moderate-to-high radical scavenging activity ranging from 60% to 85% at a concentration of 150 g/mL (Table 4). Black cumin seeds from Saudi Arabia and Syria mixed with silver nanoparticles at a concentration of 150 g/mL inhibited the most (85%), followed by Saudi and Syrian cumin seed nanoparticles (79% and 76%, respectively).

An aqueous extract of cumin seeds from Syria demonstrated modest radical scavenging activity (60%) when compared to other samples at a concentration of 150 g/mL. Silver nanoparticles of Saudi origin cumin seeds extract had the highest DPPH scavenging activity (51%), while Syrian origin cumin seeds extract had the lowest (33%), which is comparable to ascorbic acid (53%), at the lowest measured concentration of 30 g/mL. At a concentration of 90 g/mL, the aqueous extract of cumin seeds from Saudi Arabia and Syria displayed significant percentage scavenging action (50% and 48%, respectively) when compared to normal ascorbic acid. Cumin seeds from Saudi Arabia were shown to have exceptional scavenging activity in their nanoparticles when compared to other samples (Table 4, Fig. 3).

Table 4.

The extract and silver nanoparticles from black cumin seeds DPPH radical-scavenging % inhibition.

Sr. No.	Samples	150µg	120µg	90µg	60µg	30µg
1	SA-E	61	57	50	44	35
2	SY-E	60	55	48	42	33
3	SAE-NP	79	74	69	61	51
4	SYE-NP	76	71	65	58	49
5	SA-SY-E	63	58	52	45	39
6	SA-SY-NP	85	79	73	64	51
7	A-Acid	84	80	75	63	53

SA-E: Saudi Arabia Originate Extract, SY-E: Syrian Originate Extract, SAE-NP: Saudi Arabia Extract Nanoparticle, SYE-NP: Syrian Extract Nanoparticle, SA-SY-E: Saudi and Syrian Originate Extract, SA-SY-NP: Saudi and Syrian Originate Extract Nanoparticle, and A-Acid: Ascorbic acid (used as positive control). Concentration in μg/mL

For the prediction of the bioactive chemical, in-silico study analysis

Analysis of Swiss-ADME web servers

We used the free Swiss-ADME web program to examine the ADME properties of substances for this study. Furthermore, researchers and scientists from all around the world can use the predicted values to build potential semi- and synthetic medications for a number of uses. This tool by Lipinski et al. contains the Lipinski (Pfizer) filter, the original implementation of the rule-of-five, as well as a function for drug-likeness prediction. Swiss-ADME developed the bioavailability radar to predict oral bioavailability based on physicochemical properties. To forecast medicinal chemistry approaches, the pan assay interference compounds (PAINS) structural alert, the root of structural alert, or the Lilly Med Chem filters have been utilized. These filters are used to cleanse chemical libraries of compounds that are likely to be unstable, reactive, poisonous, or to interfere with biological experiments because they are unspecific frequent hits, dyes, or aggregators. The synthetic accessibility (SA) score is based on the assumption that the frequency of molecular fragments in’really’ achievable compounds correlates with the ease of synthesis. The molecular synthetic accessibility score, which runs from 1 to 10 (easy to very difficult to create), was used to characterize the developed and validated technique.

The predicted value of the pharmacokinetics, bioavailability, drug-likeness, and medicinal chemistry friendliness of bioactive molecules such as Thymoquinone, Dithymoquinone, Thymohydroquinone, Thymol, p-Cymene, 4-Terpineol, T-anethol, Carvacrol, Nigellicine, Nigellidine, Nigellimine, Longifolene, and Limonene which are found in Nigella sativa seeds is presented in Table 5-7. Except for molecules 5, 12, and 13, all bioactive compounds demonstrated higher gastrointestinal (GI) absorption rates. With the exception of molecule 12 (which had negative blood-brain permeability), all bioactive chemicals had blood-brain permeability measured, and the results demonstrated that this was true for all of the molecules. Molecules 2 (-6.92), 9 (-6.82), 10 (-6.02), and 11 (-5.81) showed the lowest skin permeation values (log Kp, cm/s). Molecules 12 (-3.64) and 13 (-3.89) had the lowest log Kp, cm/s, values for skin permeation (Table 5). Except for molecule 12 (which showed P-gp as a substrate), all bioactive chemicals were found to be non-substrates for p-glycoprotein (P-gp). All compounds were found to be non-inhibitors for CYP3A4, non-inhibitors for CYP2C19 except molecule 12 (inhibitory activity), and non-inhibitors for CYP2C9 except molecules 12 and 13. These findings were made in order to identify cytochrome P450 inhibitory action. With the exception of 5 and 10, all of the other compounds were discovered to be CYP2D6 uninhibitory. With the exception of molecule 9 (bioavailability scores:0.85), all bioactive compounds had the same bioavailability values (0.55) (Table 5).

Table 5.

Swiss-ADME forecasts bioactive constituents’ pharmacokinetic characteristics.

Cons.	GIA %	BBB	Log Kp (cm/s)	CYP Inhibitor Interaction and Bioavailability Score
				P-gp subs.	CYP1A2 inhibit.	CYP2C19 inhibit.	CYP2C9 inhibit.	CYP2D6 inhibit.	CYP3A4 inhibit.	Bioa. Score
1	High	Yes	-5.74	-tive	-tive	-tive	-tive	-tive	-tive	0.55
2	High	Yes	-6.92	-tive	-tive	-tive	-tive	-tive	-tive	0.55
3	High	Yes	-5.23	-tive	+tive	-tive	-tive	-tive	-tive	0.55
4	High	Yes	-4.87	-tive	+tive	-tive	-tive	-tive	-tive	0.55
5	Low	Yes	-4.21	-tive	-tive	-tive	-tive	+tive	-tive	0.55
6	High	Yes	-4.93	-tive	-tive	-tive	-tive	-tive	-tive	0.55
7	High	Yes	-4.86	-tive	+tive	-tive	-tive	-tive	-tive	0.55
8	High	Yes	-4.74	-tive	+tive	-tive	-tive	-tive	-tive	0.55
9	High	Yes	-6.82	-tive	-tive	-tive	-tive	-tive	-tive	0.85
10	High	Yes	-6.02	+tive	+tive	-tive	-tive	+tive	-tive	0.55
11	High	Yes	-5.81	-tive	+tive	-tive	-tive	-tive	-tive	0.55
12	Low	No	-3.64	-tive	-tive	+tive	+tive	-tive	-tive	0.55
13	Low	Yes	-3.89	-tive	-tive	-tive	+tive	-tive	-tive	0.55

Cons.: Bioactive constitute, Log Kp (Skin permeation) value: More negative value of log Kp (permeability coefficient), less skin permeant drug molecule, GIA: Gastrointestinal Absorption, CYP-inhibitor: Cytochrome-P inhibitor. BBB: Blood Brain Barrier, P-gp Subs.: P-Glycoprotein Substrate, Bioa. Score: Bioavailability Score, -tive: No (non-substrate and non-inhibitor), +tive: yes (inhibitor and substrate), inhibit.- inhibitor,

The cutoff values for the physicochemical characteristics were determined using the Lipinski’s rule of five (ROF), the Ghose’s, the Veber’s, and the bioavailability score. For bioactive molecules, the molecular parameters MW (molecular weight in Dalton), HBD (hydrogen bond donor), HBA (hydrogen bond acceptor), log P (lipophilicity log), log S (aqueous solubility), TPSA (topological polar surface area), nRot (number of rotatable bonds), and MR (molar refractivity) were evaluated. For the prediction of the aforementioned qualities, the Swiss vector machine method was used. For all bioactive substances, the projected value of the aqueous solubility descriptor (Log S) was found to be less than zero (-2.55 to -5.38) (Log S 0). The only moderately soluble molecule, with Log S (Ali: -5.38) and Log S (ESOL: -4.73), was molecule 12. All compounds’ Log Po/w (iLOGP) lipid solubilities fell within the range of 1.71 to 3.26, which is below the 5 (Table 6). All bioactive compounds were found to have molecular weights between 134 and 330, which is less than 600. Hydrogen-bonded acceptor (HBA) and hydrogen-bonded donor (HBD) were also discovered to have molecular weights between 4 and 1, which is less than 10, and 2 to 1, respectively, which is less than 5. All molecules’ topological polar surface areas (TPSAs) were determined to be between 68 and 9 (below 150 Å2), their number of rotatable bonds (nRots) to be between 1 and 2 (below 10) and their molar refractive (MR) values to be between 91 and 47 (below 155) (Table 6).

Table 6.

Swiss ADME predictions of bioactive compounds’ Physicochemical Properties and Drug-Likeness.

Const.	Physicochemical Properties						Water Solubility		Lipophilicity
	N- rota	N- HBA	N- HBD	TPSA (A°2)	MR	MW	Log S (ESOL)	Log S (Ali)	Log Po/w (iLOGP)	Log Po/w (Consensus)
1	1	2	0	34.14	47.52	164.20	-2.18	-2.55	1.99	1.85
2	2	4	0	68.28	91.72	330.42	-2.99	-3.02	2.45	2.63
3	1	2	2	40.46	50.03	166.22	-3.03	-3.45	2.10	2.39
4	1	1	1	20.23	48.01	150.22	-3.19	-3.40	2.32	2.80
5	1	0	0	0.00	45.99	134.22	-3.63	-3.81	2.51	3.50
6	1	1	1	20.23	48.80	154.25	-2.78	-3.36	2.51	2.60
7	2	1	0	9.23	47.83	148.20	-3.11	-3.17	2.55	2.79
8	1	1	1	20.23	48.01	150.22	-3.31	-3.60	2.24	2.82
9	1	3	1	64.23	68.15	246.26	-2.55	-2.34	1.71	1.45
10	1	2	1	47.16	88.65	294.35	-3.95	-3.58	2.57	2.82
11	2	3	0	31.35	59.69	203.24	-3.00	-2.74	2.38	2.29
12	0	0	0	0.00	71.69	218.38	-4.73	-5.38	3.26	4.81
13	1	0	0	0.00	47.12	136.23	-3.50	-4.29	2.72	3.37

Const.: Bioactive molecules, N-HBA: Number of H-bond acceptors, N-HBD: Number of H-bond donors, N-rota: Number of rotatable bonds, MW: Molecular weight in Dalton, MR: Molar Refractivity, TPSA: Topological polar surface area in A°2, iLOGP: A simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. The ideal range: Lipophilicity log or Moriguchi octane-water partition coefficient (M log P) ≤ 5, aqueous solubility descriptor (Ali log S) ≤ 0, molecular weight (MW) ≤ 600, hydrogen-bonded acceptor (HBA) ≤ 10, hydrogen-bonded donor (HBD) ≤ 5, molar refractivity (MR) ≤ 155, number of rotatable bonds (nRot) ≤ 10, and topological polar surface area (TPSA) ≤150 Å2.

The bioactive molecules like Dithymoquinone, Thymoquinone, Thymohydroquinone, Thymol, p-Cymene, 4-Terpineol, T-anethol, Carvacrol, Nigellicine, Nigellidine, Nigellimine, Longifolene, and Limonene were predicted as drug-likeness by a type of well-known rules and found suitable by Lipinski rule, Veber filter, and Egan filter, where 0 violation was observed. Except for molecules 4-8 and 13, which had just one violation, all molecules were found to be appropriate according to the Ghose filter. Molecules 2, 3, 9, 10, 11, and 12 had zero violations for the Muegge filter, whereas molecule 1 had one (Table 7). The bioactive molecules were predicted for medicinal chemistry friendliness and lead likeness and found zero (0) violations for molecule 2 and 10, except molecule 1, 3-9 and 11 (only 1 violation) and molecule 12 and 13 (2 violation). The PAINS structural alert was found 0 violation for all bioactive molecules except molecule 1 (1 violation only). The Brenk structural alert was also found 0 violation for all bioactive molecule except molecule 1, 3, 6, 12 and 13 (only 1 violation). The synthetic accessibility score for molecule 3,4,5 and 8 was found one (1), while molecule 7 (1.47), molecule 1 (2.83), molecule 2 (4.85), molecule 6 (3.28), molecule 10 (2.51), molecule 11 (2.86), molecule 12 (1.51), molecule 12 (3.67) and molecule 13 (3.46) (Table 7).

The BOILED-Egg model for GI absorption and brain barrier penetration of bioactive compounds

The BOILED-Egg model of bioactive chemicals predicted the ability of gastrointestinal (GI) absorption and the permeability of the blood-brain barrier penetration. According to the results of the BOILED-Egg model, all molecules would appear in the yellow part of the egg, indicating BBB penetration, with the exception of molecule 12, which would appear in the white section of the egg, indicating GI absorption. With the exception of molecule 10, which is shown in blue (P-gp positive) and suggests a P-gp substrate, all compounds were discovered on P-glycoprotein (PGP) to be red (P-gp negative). Figure 4 shows the BOILED-Egg plot of every bioactive chemical. All of the bioactive chemicals in black cumin seeds can potentially be used as therapeutic candidates, according to the overall prediction results, which also revealed similarities between their bioactive molecules and several ADME, bioavailability radar, and BOILED-Egg indicators.

Figure 4.

The BOILED-Egg model representation for gastrointestinal absorption (HIA) in white and brain penetration (BBB) in yellow, as well as blue and red PGP positive and negative areas for bioactive substances, that shown in the WLOGP-versus-TPSA graph.

Prediction of safety, toxicity, and pharmacokinetics via the pkCSM web server

The volume of distribution (VDss) in log L/kg in the human body (low if <−0.15 and high if >0.45) and the percentage of unbound dose in the human body (F-unb), which is plasma proteins (Fu), are two additional crucial factors that aid in determining the distribution of molecules. Molecules 5 (0.686), 10 (0.548), and 12 (0.799) displayed high volumes of distribution (VDss), whereas molecule 9 (-0.582) displayed the lowest value and the remaining molecules all displayed levels below the threshold. All bioactive chemicals were absorbed more than 90% of the way through the human digestive tract. With the exception of molecules 2, 10, and 12 (substrate for CYP3A4), all bioactive compounds were revealed to be non-substrates for CYP2D6 and CYP3A4. The results are shown in Table 8.

Following the assessment of these parameters, the drug excretion and toxicity data for hERG-I, AMES, oral rat acute and chronic toxicity, and minnow toxicity were evaluated. Table 8 displays the total hepatic and renal clearance for all drugs tested. The key finding from these data was that molecules with clog P < 3 and TPSA > 75Å were 2.5 times more harmless than those with clog P > 3 and TPSA <75Å, which are thought to be very harmful in short-term animal experiments. High likelihood of toxicity exists for lipophilicity with small polar functions. The analysis demonstrates that toxicity happens when log P > 3. In contrast, the value of TPSA has little to no effect on the drug’s toxicity. Therefore, it is evident that early log P and TPSA prediction is very beneficial for drug development in order to prevent chemical waste. The renal OCT2 substrate and AMES toxicity were not detected in all bioactive compounds. Except for compound 10 (an inhibitor of hERG II), all bioactive chemicals were found to not inhibit hERG I or II. In Table 9, it was discovered that every bioactive molecule had a satisfactory log total clearance in ml/min/kg.

Table 8.

Drug kinetics characteristics and input prediction for bioactive component by pkCSM.

Const.	Absorption						Distribution				Metabolism substrate
	W-Sol	Caco-2 Per.	IAH%	Skin- Per.	P- gp-1	P- gp-II	VDss	F-unb	BBB Per.	CNS Per.	CYP2D6 Subst.	CYP3A4 Subst.
1	-1.915	1.632	98.247	-2.468	No	No	0.034	0.526	0.393	-2.196	No	No
2	-4.284	1.03	99.474	-3.364	Yes	No	-0.01	0.123	-0.069	-2.128	No	Yes
3	-2.487	1.068	92.688	-2.826	No	No	0.189	0.366	0.245	-1.561	No	No
4	-2.315	0.862	93.239	-1.944	No	No	0.314	0.276	0.358	-1.281	No	No
5	-4.197	1.393	94.547	-1.017	No	No	0.686	0.213	0.531	-1.398	No	No
6	-2.313	1.497	93.555	-2.154	No	No	0.209	0.516	0.557	-2.463	No	No
7	-3.205	1.443	95.39	-1.585	No	No	0.387	0.264	0.576	-1.701	No	No
8	-2.415	0.957	92.422	-2.058	No	No	0.254	0.27	0.377	-1.202	No	No
9	-2.678	0.683	96.795	-2.796	No	No	-0.582	0.481	-0.153	-2.974	No	No
10	-4.685	1.294	94.705	-3.019	No	No	0.548	0.112	-0.019	-1.955	No	Yes
11	-2.813	1.328	97.133	-2.338	No	No	-0.009	0.293	0.293	-2.391	No	No
12	-5.97	1.408	95.139	-1.776	No	No	0.799	0.164	0.791	-1.995	No	Yes
13	-3.568	1.403	95.898	-1.721	No	No	0.396	0.484	0.725	-2.37	No	No

W-sol: Water solubility (log mol/L), Caco-2 Per.; A molecule’s Caco-2 cell permeability, whether low or high (if log Papp (10−6cm/s), the permeability is considered to be high if log Papp > 0.9 and the permeability considered to be low if log Papp <0.9, P-gp-I & II: P-Glycoprotein inhibitor-I & II, % IAH: The percentage of Intestinal absorption in human (% Absorbed >30), VDss (human):

Volume distribution in human body (log L/kg) (low if<−0.15 & high if >0.45), F-unb; The fraction of unbound dose in human body, Skin-Per. (log Kp): Skin permeability, BBB per.: Blood brain barrier permeability, CNS Per.: CNS permeability, CYP2D6 Subst.: CYP2D6 Substrate and CYP3A4 Subst.: CYP3A4 substrate.

ProTox-II web server’s prediction of rodent oral toxicity for bioactive compounds

The ProTox-II web server was used in this study to analyze the toxicity of bioactive chemicals using in silico prediction methodologies. Table 10 shows the results of oral acute toxicity as LD50 (mg/Kgbw) and the toxicity class for each discovered compound. With the exception of molecules 5 and 7, which showed classes 1 and 3, the toxicity profile test findings demonstrated that none of the compounds possessed acute toxicity (based on LD50 and toxicity class values). It should be highlighted that based on the predictions, molecule 5 had a predicted LD50 of 3 mg/kgbw, with 100% average similarity and prediction accuracy. As a result, I was allocated to the toxicity class. The majority of the evaluated bioactive compounds had 100% average similarity and prediction accuracy, on average.

Except for molecule 11 (mutagenicity and immunotoxicity), none of the examined bioactive compounds demonstrated mutagenicity, immunotoxicity, or cytotoxicity, according to the end point toxicity prediction data. Carcinogenicity was demonstrated by molecules 5, 7, and 9–10. The ProTox-II web server may also forecast organ toxicity, in particular hepatotoxicity (Liver damage), which has been assessed for tested bioactive compounds due to the liver’s critical role in the body as a site for molecular metabolism. The organ predicted result likewise showed that, with the exception of molecule 11, all bioactive compounds were inactive due to hepatotoxicity, however the forecast accuracy (probability score) was only 54%. Table 10 highlights the data on organ toxicity as well as the estimated projections for many toxicological endpoints generated by the ProTox-II web server.