Development, Evaluation and In Silico Study of QR-Enabled CogniGut Chocolate for Gut-Brain Axis Support and Digestive Wellness

Table -1

3. Plant Profile of Ingredients

A. Ajwain (Trachyspermum ammi)

• Family: Apiaceae
• Active Constituents: Thymol, γ‑terpinene, p‑cymene
• Medicinal Uses: Carminative, antimicrobial, digestive stimulant.
• Pharmacological Actions: Relaxes intestinal smooth muscle, expels gas, reduces bloating.
• Description: Small aromatic seeds used traditionally for indigestion and colic.

B. Mint (Mentha arvensis)

• Family: Lamiaceae
• Active Constituents: Menthol, menthone, limonene
• Medicinal Uses: Cooling, antispasmodic, carminative.
• Pharmacological Actions: Menthol activates TRPM8 receptors, producing cooling sensation and relieving spasms.
• Description: Aromatic herb with serrated leaves and purple flowers.

C. Brahmi (Bacopa monnieri)

• Family: Scrophulariaceae
• Active Constituents: Bacosides A & B, alkaloids, saponins
• Medicinal Uses: Adaptogen, memory enhancer, anti‑stress agent.
• Pharmacological Actions: Modulates neurotransmitters, reduces oxidative stress, strengthens gut‑brain axis.
• Description: Creeping herb with succulent leaves and small white flowers.

D. Tulsi (Ocimum sanctum)

• Family: Lamiaceae
• Active Constituents: Eugenol, ursolic acid, rosmarinic acid
• Medicinal Uses: Immunomodulatory, adaptogenic, antioxidant.
• Pharmacological Actions: Reduces stress, enhances immunity, exhibits anti‑inflammatory effects.
• Description: Aromatic shrub with green/purple leaves and purple inflorescence.

E. Ginger (Zingiber officinale)

• Family: Zingiberaceae
• Active Constituents: Gingerols, shogaols, zingerone
• Medicinal Uses: Digestive stimulant, anti‑nausea, anti‑inflammatory.
• Pharmacological Actions: Stimulates gastric motility, reduces nausea and bloating.
• Description: Underground rhizome with pungent aroma.

F. Cumin (Cuminum cyminum)

• Family: Apiaceae
• Active Constituents: Cuminaldehyde, terpenes
• Medicinal Uses: Carminative, digestive, antioxidant.
• Pharmacological Actions: Enhances pancreatic enzyme secretion and bile flow.
• Description: Small brown seeds with distinctive aroma.

G. Cardamom (Elettaria cardamomum)

• Family: Zingiberaceae
• Active Constituents: Cineole, limonene, terpinyl acetate
• Medicinal Uses: Flavor enhancer, carminative, breath freshener.
• Pharmacological Actions: Stimulates salivary secretion, improves aroma and taste.
• Description: Green pods containing aromatic seeds.

H. Cocoa (Theobroma cacao)

• Family: Malvaceae
• Active Constituents: Flavanols, theobromine, caffeine
• Medicinal Uses: Antioxidant, mood enhancer, cardiovascular support.
• Pharmacological Actions: Improves endothelial function, enhances mood, provides antioxidant protection.
• Description: Tropical tree producing pods with cocoa beans.

• Description: Viscous golden liquid produced from nectar.

I. Gum Acacia (Acacia senegal)

• Family: Fabaceae
• Active Constituents: Polysaccharides (arabic acid, glucuronic acid)
• Medicinal Uses: Emulsifier, stabilizer, prebiotic.
• Pharmacological Actions: Ensures uniform dispersion of ingredients, stabilizes chocolate texture.
• Description: Natural gum exudate from Acacia trees.

J. Cocoa Butter (Theobroma cacao)

• Family: Malvaceae
• Active Constituents: Stearic acid, palmitic acid, oleic acid
• Medicinal Uses: Base material, emollient, stabilizer.
• Pharmacological Actions: Provides smooth texture, gradual release of actives, enhances mouthfeel.
• Description: Pale yellow fat extracted from cocoa beans.

4. Formulation Table

Table -2

4. Method of Preparation

The final “optimized” formulation that came out of this study was called CogniGut Chocolate and, basically, it was picked for more evaluation studies later on . 

This chocolate batch was made through a few sequential steps like this :

1. Melting 

First, cocoa mass and cocoa butter were melted around 45–50 °C using a water bath, until a fairly smooth molten base formed.

2. Herbal part getting in 

Then the pre weighed herbal powders (Ginger, Ajwain, Cumin, Mint, Tulsi, Brahmi , Cardamom ) were added little by little. Throughout this stage the temperature was kept below 50 °C, so the actives don’t get degraded.

3. Making it more stable 

Gum acacia was incorporated as an emulsifier in order to stabilize the mixture and help achieve a consistent, even dispersion of the herbal components.

4. Moulding and cooling 

Finally, the mixture was transferred into moulds and cooled at 10–15 °C for about 30 minutes, until it solidified. Afterward the chocolates were demoulded and stored in aluminium foil at room temperature.

1. 5. Nutritional Composition

Table-3

The estimated nutritional composition of CogniGut Chocolate was worked out to figure, in a rough way, the likely amount of the big nutrients in a single 10 g serving. This was done by using the amounts of each ingredient that went into the final mix, and then matching those amounts with their known nutritional values. Each ingredient’s share was considered, then the numbers were tuned proportionally, based on the final chocolate total weight.

For a 10 g serving, the formulation was estimated to supply about 58–62 kcal, along with 3.8–4.5 g carbohydrates, 0.8–1.1 g proteins, 4.5–5.2 g fats, 1.2–1.6 g dietary fibre and 0.8–1.2 g sugar. The fat amount seems relatively higher mainly because the cocoa based chocolate structure holds more lipids, while the carbohydrates come from the chocolate base plus the herbal additions. As for the dietary fibre, that part is largely linked to the plant derived herbal powders that were blended in.

Overall these figures should be seen as estimated nutritional values that came from the formulation plan and ingredient composition, not from laboratory proximate testing. So they’re meant to give a nearby nutritional snapshot for one 10 g serving of CogniGut Chocolate, not an exact laboratory result.

6. Evaluation Parameters 

A. Sensory Evaluation 

1. Appearance (Surface, Shape, Gloss) 

• Why important: First determinant of consumer acceptance and perceived quality, kind of the “look” factor you know. 
• What indicates good quality: Smooth surface, uniform geometry, glossy finish (proper tempering ,no fat bloom). 
• What it reflects: Correct crystallization of cocoa butter, proper mixing, and absence of phase separation 

2. Colour 

• Why important: Visual cue for product identity and quality, people judge it fast. 

• Expected: Uniform rich brown typical of chocolate 

• Deviations indicate: Overheating (darkening), fat bloom (whitish film), or ingredient dispersion issues, like uneven blending 

3. Odour/Aroma 

• Why important: Strongly influences perceived taste and acceptance. 

• Expected: Cocoa-forward aroma with mild herbal notes (balanced, not pungent). 

• Reflects: Stability of volatile compounds and absence of rancidity or off-odours 

4. Taste (Sweetness & Aftertaste) 

• Why important: Main driver for compliance, it’s the part that matters most. 

• Expected: Pleasant sweetness with controlled herbal aftertaste (effectively masked by cocoa + honey/stevia). 

• Reflects: Proper ratio of sweeteners and herbs; absence of bitterness or harshness 

5. Texture (Mouthfeel) 

• Why important: Key to the premium chocolate experience. 

• Expected: Smooth, creamy, non-gritty, melts cleanly without waxiness. 

• Reflects: Particle size distribution, emulsification, and fat phase behavior

B. Physicochemical Evaluation

1. Weight Variation 

• Why important: makes sure the dose is even, like per unit when someone actually uses it. 

• Method: analytical balance, with quick repeats now and then . 

• Acceptance: a tight band (for example 10 ±0.5 g per piece set) 

• Implication: if this passes, it usually means mixing was decent, moulding was precise, and the whole workflow stays repeatable. 

2. Melting Point (°C) 

• Why important: it basically controls both how it keeps over storage and how it melts in the mouth. 

• Method: capillary method. 

• Expected: around 33–35°C, roughly near body heat. 

• Implication: suggests cocoa butter crystallization is doing the right thing, and it can melt orally without getting deformed just sitting at room temperature. 

3. pH (1% Solution/Dispersion) 

• Why important: shows chemical steadiness and also how compatible it is with the oral environment. 

• Method: pH paper. 

• Expected: close to neutral, say ~6–7. 

• Implication: helps reduce irritation risk, and supports stability of any actives, even if the formulation is a bit sensitive. 

4. Texture Uniformity 

• Why important : gives consistency from one unit to the next, so the mouthfeel and release are more predictable. 

• Method: visual inspection / manual check. 

• Implication: points toward effective mixing, and good dispersion of powders or extracts, not clumping and not “patchy” spots. 

5. Surface Stability (Bloom/Cracking) 

• Why important : tells you how physically stable it is during storage, not only chemically. 

• Method: just visual observation across time, again and again. 

• Expected: no fat bloom, no sugar bloom , and no cracking. 

• Implication: indicates tempering was correct, formulation stays steady, and packaging is doing its part.

7. Results of Evaluation

Sensory Results

Table -4

• Physicochemical Results

Table - 5

The formulation, demonstrated acceptable up to excellent sensory properties. The smooth glossy look kinda confirms that tempering was on point and that the fat crystals stayed stable , like a nice formation. A uniform colour also points to an even dispersion of cocoa solids along with the herbal components, nothing weird or patchy. The pleasant cocoa aroma, with mild herbal hints, suggests that volatile herbal odours were masked properly . The taste—sweet with a mild herbal aftertaste—seems to show good balancing between sweeteners (stevia + honey) and the botanicals. Texture was described as smooth and creamy, which supports adequate particle size reduction and proper emulsification , overall.

Physiochemically, the weight variation (10 ± 0.5 g) indicates consistent dosing and decent process control. The melting point (33–35°C) is well suited for an oral melt, stable at room temperature yet it melts close to body temperature for fast release. The pH (6 ± 0.1) sits in a near neutral range, so it looks compatible with oral conditions and it also helps ingredient stability. Moisture content (1.5 ± 0.2%) is low, which supports a longer shelf life, while also limiting microbial growth and reducing bloom formation. No surface cracking or bloom showed up, so the physical stability seems good and storage behaviour was appropriate.

All things considered, the evaluation results kind of validate that the formulation is organoleptically acceptable and physiochemically stable, suitable as a chocolate-based delivery system.

Figure 1: Evaluation tests

8. In Silico Study

Table -6

Docking Results

ADME Interpretation

• Thymol, Menthol, Eugenol → High GI absorption
• Bacoside → Low absorption
• BBB permeation → Yes (except bacoside)
• Bioavailability → Moderate

Toxicity Prediction

• No AMES toxicity
• No major cardiac risk
• Mild hepatotoxicity (thymol)
• Skin sensitization possible

1. Molecular Docking

Molecular docking was used to look at the possible interactions among four chosen phytoconstituents from CogniGut Chocolate, like thymol, menthol, bacoside and eugenol, with molecular targets that relate to digestive activity and the gut–brain axis kind of mechanisms. The ligand–target pairs we picked were thymol–α-amylase, menthol–pancreatic lipase, bacoside–acetylcholinesterase (AChE), and eugenol–GABA receptor, A 

Docking itself was carried out with AutoDock Vina v1.2.7, using its scoring function plus a randomised, stochastic search strategy.

Protein Preparation

The target protein structures were fetched from the Protein Data Bank (PDB) and then prepped a bit prior to docking. Basically , non essential crystallographic waters and other unnecessary hetero bits were taken out, but cofactors plus key structural pieces were kept, when it actually made sense. After that, polar hydrogens were added, and the receptors got transformed into the specific docking format that was required. The targets chosen were porcine pancreatic α-amylase (PDB: 1PPI; 2.20 Å), the pancreatic lipase–colipase complex (PDB: 1LPB; 2.46 Å), acetylcholinesterase from Tetronarce californica (PDB: 1EVE; 2.50 Å), and a human α1β2γ2 GABA receptor (PDB: 6D6U; 3.92 Å).

Ligand Preparation

The three dimesional structures of thymol, menthol, bacoside, and eugenol were prepared by generating some molecular geometries , then adding hydrogen atoms, and finally assigning the right protonation states, you know. The ligands were converted into the docking-ready format, keeping rotatable bonds so there is this conformational freedom during the docking process, even if it may not look like much at first.

Docking Procedure

The prepared receptors were considered like rigid structures, while the ligand flexibility stayed there. Grid boxes were placed around the selected predicted binding regions of each target , and then the docking poses were ranked via the AutoDock Vina scoring function. After that, the top ranked poses were picked for further analysis , basically.

For the grid-center coordinates grid dimensions, exhaustiveness, the number of generated poses and the number of docking replicates , those were set using whatever options were already in the original AutoDock Vina configuration files. So these values should be reported exactly as they appear , from the saved docking configuration /output .

Thymol–α-Amylase:

Thymol turned out to have a predicted docking score of −5.9 kcal/mol, so that looks like a favorable kind of interaction with the chosen α-amylase binding place.

Figure 2: Thymol Docking Result

Menthol–Pancreatic Lipase:

Menthol had this predicted docking score of −6.7 kcal/mol, so it looked like there was a favourable kind of interaction around the pancreatic lipase binding area , in general.

Figure 3: Menthol Docking Result

Bacoside–AChE:

Bacoside shows the  most favorable predicted docking score across the evaluated combinations, it landed at −9.1 kcal/mol against AChE. For the dataset we have right now, that is the best computational score, but it still   does not, on its own, really confirm experimentally strong binding.

Figure 4: Bacoside Docking Result

Eugenol–GABA receptor:

Eugenol showed a predicted docking score of −6.7 kcal/mol, against the human α1β2γ2 GABA receptor. The +7.6 kcal/mol value that appeared in the docking output corresponded to the reference/template entry, so it was not the eugenol score. Because of that, −6.7 kcal/mol was kept for interpretation.

Figure 5: Eugenol Docking Result

Docking Score Interpretation:

The docking scores were treated mostly like relative, computational estimates using the same kind of docking protocol. General labels like “weak”, “moderate”, “strong” or “very strong” were not given, because AutoDock Vina scores depend on the scoring model and the search settings, so it’s kind of indirect. In that context bacoside showed the best predicted docking score, menthol and eugenol basically looked similar in their predicted scores , and thymol ended up with the less favorable score for the set that was tested. Also these numbers shouldn’t be viewed as experimentally determined binding affinities or anything like that.

Docking Protocol Validation

The docking protocol was kind of evaluated, using the crystallographic benzamidine- containing complex PDB ID 4COF as reference system. Redocking of the crystallographic ligand and the comparison of experimental vs predicted poses ,were done by looking at RMSD. In general, an RMSD around ≤2.0 Å is usually treated as acceptable for reproducing a crystallographic binding pose, that’s the idea. Still, the real RMSD you got from the redocking experiment needs to be reported, not just a guess. And you should only claim successful validation if the redocking and the RMSD calculation were genuinely carried out.

Overall Docking Finding

Overall, the docking study kind of gave computational backup for the proposed multi-target way of action of CogniGut Chocolate. Thymol looked like it could interact with α-amylase (−5.9 kcal/mol), menthol with pancreatic lipase (−6.7 kcal/mol), bacoside with AChE (−9.1 kcal/mol) and eugenol with the GABA A receptor (−6.7 kcal/mol). Among the combinations that were tested, bacoside turned out to have the most favorable predicted docking score. These results are computational and more like idea-generating steps , so they still need experimental validation, before any firm conclusions about pharmacological effects can really be stated.

2. SwissADME Analysis

The physicochemical as well as pharmacokinetic properties of thymol menthol bacoside, and eugenol were estimated with the SwissADME web server which was made by the Swiss Institute of Bioinformatics. This platform gives computational forecasts for physicochemical traits, stomach and intestinal uptake, blood brain barrier (BBB) permeability, overall drug-likeness, and medicinal chemistry parameters. The delivered outputs are basically in silico predictions and should be seen as early signs, not as things confirmed in actual bench experiments

Comparative SwissADME profile of selected compounds

Table 7

The SwissADME prediction suggested that thymol, menthol and eugenol had high predicted gastrointestinal absorption, while bacoside looked like low predicted GI absorption. It also looked like BBB permeability was supported for thymol, menthol and eugenol , so in the computational model there’s a possibility of central nervous system exposure. On the other hand bacoside showed several drug likeness rule violations, because it has a bigger molecular size and higher polarity. So, overall it seems the predicted pharmacokinetic profile might be rather different. Of course, these are in-silico observations, not experimentally validated pharmacokinetic outcomes, and they should not be treated as confirmed results.

3. Toxicity Prediction

We predicted the toxicity profile of the chosen bioactive compounds by using the pkCSM platform, it uses graph based machine learning models to approximate different toxicity related endpoints. The numbers you see here should be taken as computational toxicity predictions, not as experimentally confirmed toxicity outcomes, so keep in mind they are only an estimate, not real bench results.

Comparative pkCSM toxicity prediction

 Tabl

Table -8