Mix-match synthesis of nanosynbiotics from probiotics and prebiotics to counter gut dysbiosis via AI integrated formulation profiling

Abstract

Antibiotics, improper food, and stress have created a dysbiotic state in the gut and almost 81% of the world’s population has been affected due to the pandemic of COVID-19 and the prevalence of dengue virus in the past few years. The main intent of this study is to synthesize nanosynbiotics as nu traceuticals by combining probiotics, and prebiotics with nanoformulation. The effectiveness of the nanosynbiotics was evaluated using a variety of Nutra-pharmacogenetic assays leading to an AI-integrated formulation profiling was assessed by using machine learning methods. Consequently, Acetobacter oryzoeni as a probiotic and inulin as a prebiotic has been chosen and iron-mediated nanoformulation of symbiotic is achieved. Nanosynbiotics possessed 89.4, 96.7, 93.57, 83.53, 88.53% potential powers of Nutra-pharmacogenetic assays. Artificial intelligent solid dispersion formulation of nanosynbiotics has high dissolution, absorption, distribution, and synergism, in addition, they are non-tox, non-allergen and have a docking score of − 10.83 kcal/mol, implying the best interaction with Pregnane X receptor involved in dysbiosis. The potential of nanosynbiotics to revolutionize treatment strategies through precise targeting and modulation of the gut microbiome for improved health outcomes and disease management is promising. Their transformational influence is projected to be powered by integration with modern technology and customized formulas. Further in-vivo studies are required for the validation of nanosynbiotics as nutraceuticals.

Introduction

Dysbiosis, or an imbalance of the gut microbiota, has now become a major health concern. Disturbing the symbiosis of various microbes in the digestive tract can lead to a wide range of health problems. Inflammatory bowel disease, obesity, diabetes, and even mental illness have all been linked to dysbiosis. Modern medicine has focused on maintaining a balanced gut microbiome spurring research into new approaches to this problem. The term "nutrients" combines the words "nutrients" and "drugs", referring to metabolites, which can be found in food or formulated as nutrients^1,2,3. Probiotics, which are healthy living bacteria, and prebiotics. which are indigestible components, have grown in popularity Helping Probiotics Prosper Probiotics, commonly referred to as "friendly bacteria" are necessary to maintain healthy microbes in the gut. Probiotics, on the other hand, need prebiotics as fuel to do their best^4,5. The application of these materials in nanotechnology opens exciting possibilities for cutting-edge dysbiosis treatment⁶.

Nanoformulations are used for the design and application of products manufactured at the nanoscale, typically 1–100 nm. Nanoformulations have advantages in terms of nutrients and probiotics. Drugs that improve the stability, bioavailability and target delivery of these cells, ensuring they reach the fetus under the right conditions and in the right amounts including Nanoformulations can protect these properties away from harsh gastrointestinal conditions and enzymatic breakdown, to greatly increase their efficacy⁷. We combine nanotechnology with nutrition to unveil the potential to create innovative ways to effectively manage dysbiosis. This study conducted a groundbreaking study to explore the development of nanosynbiotics, novel metabolites that combine dietary probiotic and prebiotic properties at the nanoscale to effectively restore microbial balance. These nanosynbiotics will be precisely designed to target various gut structures. We will also integrate artificial intelligence (AI) into the formulation process to further improve the effectiveness of these nanosynbiotics. We will be able to analyze big data, predict interactions, and improve medicine for better medical capabilities and AI. By doing this, we hope to radically change the way dysbiosis is managed by providing nutritional therapies with very high potential to improve gut health and overall well-being.

Materials and methods

Collection of Probiotic strain and extraction of prebiotics

An Acetobacter orzoenii probiotic was collected from cultures grown in MBBL laboratory and cultured in MRS broth. The commonly used prebiotic inulin was selected for this study. The roots of the chicory plant were cleaned, dried and finely powdered to obtain inulin. A 1:10 (root powder: water, w/v) mixture of powdered chicory root and 18 cc distilled water was added and the mixture was stirred continuously for one hour at 80 °C and then the resulting crude extract was taken filter paper to remove any byproducts that were not soluble⁸.

Synthesis of synbiotics and transformation to Nano-formulation

Probiotic strains were cultured in 100 mL of MRS broth supplemented with fresh crude inulin from chicory root to produce synbiotics. Digested synbiotics were prepared after incubation for 24 to 48 h at 37 °C. The combined synbiotics were then incubated for another three days with 0.1 ml 1 M FeSO4 at 30 °C for nanoformulation. For further characterization and experimentation, the resulting nanosynbiotics were centrifuged, collected after color change, purified, and dried at 75 °C⁹.

Characterization of nanosynbiotics

UV–Vis spectrometry

The first characterization of nanosynbiotics was done by UV–Visible spectroscopy. Data analyzed in the previous literature reported that the bands of surface plasmon resonance should be in between 200 and 700 nm and the ideal range would be on the 310 nm that symbolizes the synthesis of synbiotics.

SEM (scanning electron microscopy)

Nanosynbiotics were evaluated on the basis of their structural features by using SEM (Scanning electron microscopy). Samples preparation were done on conductive tape coating with platinum-gold in order to enhance the conductivity, then tested at the current range of 80–100 kV. The diameter of nanosynbiotics was calculated by the ImageJ software followed by the data plotting in OriginPro for having ranges of size¹⁰.

EDX (energy dispersive X-ray spectroscopy)

In conjunction with SEM (scanning electron microscopy), EDX (energy dispersive X-ray spectroscopy) was used to determine the elemental composition of nanosynbiotics using the method of X-rays. The sample was prepared by coating with metal having strong conduction. Beam of X-rays was emitted by the atoms of the sample when they interacted with the beam of electrons. Each x-ray was examined by exploring the speaks produced in EDX spectra. Each peaks were characterized by comparing them with the peaks in reference with other samples¹¹.

FTIR (Fourier-transform infrared spectroscopy)

FTIR Fourier-transform infrared spectroscopy was used to identify the functional group nanosynbiotics. This technique examines the infrared spectrum of a sample in accordance with its absorption and emission features. The identification of chemical relationships in the nanosynbiotics was done by centrifuging the solution of nanosynbiotics them at 10,000 rpm for 30 min followed by analyzing them under the FTIR spectra between 4000 and 400 cm⁻¹.

Nutra-pharmacogenetic potential assays

Gastric juice resistance assay

In gastric juice assay, 1 M of HCl was added to saline (0.85%, w/v NaCl) to adjust the pH of simulated gastric juice to 2–3. 9 ml of simulated gastric juice was added to 1 ml of control (Culturelle), synbiotics and nanosynbiotics solution respectively. This mixture was incubated for 2 h at 37 °C^12,13.

$${\text{Resistance to gastric juice}}\left( \% \right) \, = \, \{ {1}00 - {\text{Absorbance after}}\,{\text{2h}}\,\,{\text{of}}\,\,{\text{incubation}}\} \times {1}00$$

Resistance to Hydrogen Peroxide

A modified approach proposed by Oberg, et al. was used to assess the resistance of nanosynbiotics to hydrogen peroxide. 10 mL of each synbiotics and nanosynbiotics concentration were combined with 10 mL of 0.85% (w/v) NaCl in separate falcon tubes with 1.5 mM H2O2, and the mixtures were then incubated for an hour.

Resistance to hydrogen peroxide (%) = {100 − Absorbance after 1 h of incubation} × 100.

Cholesterol reduction assay

Polyoxyethanyl-cholesterol sebacate, which is a water-soluble form of cholesterol, was dissolved in 10 mL of distilled water to create a cholesterol stock solution. We combined 2 mL of each synbiotics and nanosynbiotics concentration with 2 mL (33% w/v) KOH and 2 mL (96% ethanol). This mixture underwent a 2-min vortex, a 30-min incubation at 37 °C, and a 2-min cooling process. After cooling, 2 mL of injection water and 3 mL of hexane were added, and the mixture was vortexed for 2 min. The mixture was separated into two phases, and the upper layer of hexane was removed and evaporated. 50 mg of OPA were dissolved in 100 mL of glacial acetic acid, then 2 mL of the o-phthalaldehyde reagent was added and vortexed for 2 min¹⁴. At 550 nm, absorbance was measured following a 10-min rest period at room temperature. The formula was used to compute the percentage of cholesterol removed.

$${\text{Cholesterolremoved}}\left( \% \right) \, = \, \{ {1}00{-}{\text{residualcholesterolafter24hofincubation}}\} \, \times {1}00$$

ABTS scavenging assay

The method used to assess the radical scavenging ability of ABTS was previously published by Ilyasov et al.¹⁵ with modifications. As a stock solution, 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (2,2′-ABTS) was dissolved in a 2.45 mM potassium persulfate aqueous solution. After the components were dissolved, the mixture was allowed to sit for 24 h at room temperature in the dark before being diluted with 50% (v/v) methanol to create a working solution. A spectrophotometric cuvette was filled with 2.5 mL of working ABTS solution, 0.25 mL of each concentration of synbiotics and nanosynbiotics, and a control at concentrations between 200 and 1000 g/ml. The following equation was used to determine the ABTS scavenging¹⁶.

$${\text{ABTS scavenging}} = {1} - {\text{As}}/{\text{Ac}} \times {1}00$$

The absorbance of the test sample, As, is measured by UV–Visible Spectrophotometry at 630 nm, while the absorbance of the control sample is Ac.

Inhibition of protein denaturation assay

The anti-inflammatory efficacy of nanosynbiotics was investigated using the protein denaturation method, according to Eze et al.¹⁷. However, for the activity investigations, 2 ml of each concentration of synbiotics and nanosynbiotics were ingested in different concentrations ranging from 200, 400, 600, 800, and 1000 g/ml. 2.8 ml of phosphate-buffered saline solution (pH 6.4) and 0.2 ml of fresh hen's egg white were combined with the synthetic and nano-synthetic compounds. The reaction mixture was preheated to 70 °C for 5 min after 20 min at 37 °C of incubation. A UV–Visible spectrophotometer was used to detect the turbidity at 660 nm after the solution combination had been cooled to room temperature. Aspirin (acetylsalicylic acid), which was employed as a control, was diluted to identical amounts of 100, 200, 300, 400, and 500 g/m¹⁸. The analysis of anti-inflammatory action was carried out in triplicate, and the formula provided was used to calculate the percentage of protein inhibition.

$$\% {\text{ Inhibition of protein denaturation}} = {1}{-}{\text{At}}/{\text{Ac}} \times {1}00$$

In the formula above, At stands for the absorbance of the test sample and Ac for the absorbance of the control, both measured by UV–visible spectrophotometry at 630 nm.

AI-integrated analyses

Structure prediction of nanosynbiotics

To design the structure of nanosynbiotics, ChemDraw is a potent software programme that was frequently used in chemistry and nanotechnology. 3D structure of nanosynbiotics was designed by the help of functional groups detected in the FTIR analysis by utilizing various tool bars in ChemDraw¹⁶.

Integration of Artificial Intelligence with nanosynbiotics

Solid dispersion formulation design of nanosynbiotics

Approach to design solid dispersion formulation is considered as the most successful technique to enhance the solubility and oral bioavailability of nutraceuticals and pharmaceutical products. PharmSD is an AI or machine learning commanded platform to predict the solid dispersion formulations that allow an evaluation of different important parameters of solid dispersion designs with just a one click¹⁹.

Dissolution rate prediction

Dissolution rate prediction of nanosynbiotics was predicted with the help of tool named dissolution rate prediction in PharmSD toolkit by selecting the hot melt extrusion method²⁰.

SD (solid dispersion) stability prediction

Prediction of SD stability of nanosynbiotics was accomplished by the SD stability tool integrated with PharmSD.

Particular temperature of 25 °C ranged from 20 to 275 °C was selected for the pretreatment of nanosynbiotics²¹.

Parameterization of nanosynbiotics

Parameterize tool uses a force field based neural network potentials (NNPs) utilizing quantum level machine learning method to predict all the parameterizable bonds and angles responsible for interaction with receptors or any macromolecules. By using GAFF2 and AMI-BCC atomic charges basic force field was build²².

Pathway analysis of nanosynbiotics

Pathways analysis of nanosynbiotics was accessed by PathwayMap web server. It uses ECFP4 fingerprints, self-normalizing neural networks and data from various reactomes through which it can forecast in which in how many pathways nanosynbiotics are involved after their absorption in the body²³.

Physiochemical properties and oral bioavailability

SwissADME is an online server though which all the physiochemical properties, boiled-egg analysis and oral bioavailability of nanosynbiotics were examined. By giving the 3D structure of nanosynbiotics as input all the parameters were explored²⁴.

Toxicity prediction

Toxicity of nanosynbiotics were accompanied by using Protox-II tool which is an online server that offers various type of toxicity or negative potential predictions at individual organ levels. It utilizes predictive models based on different structural signals and chemical properties²⁵.

Allergenicity prediction

CHAIPred tool predicted the allergenicity of nanosynbiotics. It gathers all the historical data of allergic compounds as reference and compares it with given compound as input²⁶.

Structure retrieval of PXR receptor

PDB (Protein Data Bank) is a wide platform or the retrieval of protein 3D structure. PXR (Pregnane X receptor) was selected as the targeted receptor for nanosynbiotics and its structure was retrieved from PDB.

Titration and protonation of PXR receptor

For the interaction and molecular dynamic simulations, preparation of Pregnane X receptor (PXR) protein that was retrieved from PDB (Protein Data Bank) was achieved through Protein Prepare tool. PROPKA 3.1 added the missing atoms to titrate the protonation states and the optimization of H-networks was done by PDB2PQR 2.1²⁷.

Residue communities of PXR receptor

Prediction of residual communities’ prediction of PXR receptor was accomplished by the Leri tool. This is significant for the targeted interaction with the nanosynbiotics for proper involvement in biological processes.

Molecular docking and MM/PB(GB)SA analysis

fastDRH is an open access web service that was utilized for the molecular interaction of PXR receptor with nanosynbiotics followed by MM/PB (GB)SA analysis for the calculation of binding free energies. Different engines were incorporated into this we server such as AutoDock Vina and AutoDock-GPU docking scores²⁸.

Calculation of interatomic spacing

Different interatomic spacings present between PXR receptor and nanosynbiotics were explored using PLIP (Protein–Ligand Interaction Profiler). It examines various hydrogen bonds, ionoc bonding, covalent and no-covalent interactions between protein and ligand²⁹.

Molecular dynamic simulations

In order to predict the dynamic behavior of PXR protein, molecular dynamic simulations were done by using SiBIOLEAD tool, which is a potent calculating platform well-known for its accuracy in simulating biomolecular systems. It uses the force field (FF) system to forecast that how the protein is acting when nanosynbiotics interact with PXR receptor^25,30.

Results

Collection of Probiotic strain and extraction of prebiotics

In MRS media, Acetobacter oryzoeni was cultured and extraction of inulin was done by the roots of chicory plants which is brown in color as depicted in Fig. 1.

Figure 1

Crude extract of inulin from chicory plant’s root as prebiotic.

Synthesis of synbiotics and transformation to Nano-formulation

Synbiotics were synthesized by the combination of equal amount of Acetobacter oryzoeni cultured in MRS medium and the inulin. After fermentation as mentioned in the material and methods the light brown color was converted into cloudy whitish color, approving the synthesis of synbiotics as shown in Fig. 2A,B. Transformation to nano-formulation was confirmed due to the color change from cloudy whitish to dark brown color after addition and incubation of 1 M of FeSO4 solution in the solution of synbiotics as depicted in Fig. 2C.

Figure 2

(A–C) Synthesized synbiotics in falcon tubes (light brown to cloudy whitish color) from Acetobacter oryzoeni probiotic in combination with inulin as prebiotic; change of color from yellow whitish to dark brown by the reaction of FeSO4 solution with synbiotics.

Characterization of nanosynbiotics

UV–Vis spectrometry

After the hourly intervals, the UV–Vis Spectrophotometer was used to conduct the wavelength scan. The best wavelength range for iron-mediated nanosynbiotics is 250–350 nm, with 310 nm being the ideal wavelength as portrayed in the Fig. 3A.

Figure 3

(A) UV visible spectrum of synthesized iron mediated nanosynbiotics; (B) Microscopic image of nanosynbiotics by Scanning electron microscope having the average size of 40nm; (C) EDX spectrum of nanosynbiotics having different peaks of various elements especially the peak of Fe lies on 0.25 keV with concentration of 6–7 number of counts; Energy (KeV) on y-axis and number of counts or concentration of elements on x-axis; (D) FTIR spectrum of nanosynbiotics; length of peaks at x-axis and transmittance at y-axis.

Scanning electron microscopy (SEM)

A typical scanning electron micrograph (bead-like shape) revealed that the nanosynbiotics were evenly spherical and present in clusters. Additionally, Image J software revealed that the nanosynbiotics' typical crystalline size ranged between 35 and 45 nm in both size and diameter. The synthesized nanosynbiotics' average diameter was discovered to be 40 nm. The pictures created by SEM and shown by ImageJ software are presented in the Fig. 3B below.

Energy-dispersive X-ray spectroscopy

To determine the elemental composition, the measured diffracted rays are measured. The results are presented on the screen, and an image graph is provided to help comprehend the findings. Figure 3 represents the spectrum that has energy in (KeV) on y-axis and number of counts on x-axis showing different peaks of various elemental components such as C, O, P, S and Fe. The total weight and atomic percentages for each element present in nanosynbiotics are displayed in the Table 1. SEM image of elemental composition of synthesized nanosynbiotics is represented in Fig. 3C.

Table 1 Elemental composition of nanosynbiotics as predicted by EDX analysis.

Fourier-transform infrared spectroscopy

The identification of secondary metabolites in nanosynbiotics was carried out using Fourier Transform Infrared (FTIR) spectroscopy. As seen in Fig. 3D, the FTIR spectra from the nanosynbiotics produced using Acetobacter oryzoeni as probiotics, inulin as a prebiotic, and FeSO4 solution to transform into nano- formulation showed significant absorption peaks at 1000, 1600, 2200, and 3250 cm⁻¹ which sowed that there is presence of significant C–O (Carbohydrate group), C=O (Carbonyl group), C=C (Alkene group) and NH₂ (Aminoacidic group) respectively.

Nutra-pharmacogenetic potential assays

Gastric juice resistance assay

This assay was to determine that the synthesized nanosynbiotics can resist the gastric juice of stomach or gastrointestinal tract or not, as supplements has the ability to resist it. At 1000 µg/ml concentration, nanosynbiotics showed the highest gastric juice resistance with mean of 89.4 ± 0.56. While as compared to the control, on every concentration nanosynbiotics showed more efficient values than control and simple synbiotics used. The trend of resistance to gastric juice resistance assay of control, synbiotics and nanosynbiotics is depicted in the Fig. 4A.

Figure 4

(A) Graph showing the trend of resistance to gastric juice; (B) graph showing the trend of reduction in cholesterol level; (C) graph showing the trend of resistance to hydrogen peroxide; (D) ABTS scavenging trend; (E) trends of inhibition of protein denaturation by nanosynbiotics, synbiotics respectively.

Resistance to hydrogen peroxide assay

This assay was used to check the resistance property of nanosynbiotics against hydrogen peroxide as compared to synbiotics and the control (Culturelle). In the obtained results, 1000 µg/ml concertation of nanosynbiotics has the highest resistance power with the mean value of 96.7 against hydrogen peroxide in comparison to control and synbiotics. While control, synbiotics, and nanosynbiotics has a trend from low to high concentration directly proportional to efficiency as shown in the Fig. 4B.

Cholesterol reduction assay

The cholesterol reduction assay aimed to evaluate the ability of nanosynbiotics to lower cholesterol levels in the body. Atorvastatin served as the control, a commonly used drug for cholesterol management. Nanosynbiotics, particularly at a concentration of 1000 µg/ml, demonstrated the highest cholesterol reduction efficacy with a mean value of 93.57 ± 0.49, surpassing synbiotics (82.5%) and atorvastatin (78.2%). The results are represented in Fig. 4C that highlights the superior cholesterol-reducing potential of nanosynbiotics.

ABTS scavenging assay

The ABTS test assessed nanosynbiotics antioxidant capacity in scavenging ABTS in aqueous solutions. Nanosynbiotics demonstrated enhanced antioxidant efficacy at a concentration of 1000 g/ml, with a mean percentage of 83.53 0.49. Synbiotics and the control, ascorbic acid (Vitamin C), both had decreased antioxidant capacities. Figure 4D depicts the growing trend in antioxidant capacities of control, synbiotics, and nanosynbiotics.

Inhibition of protein denaturation assay

With 1000 µg /ml of nanosynbiotics and 84.5% of synbiotics, the maximum mean value of inhibition of protein denaturation for egg albumin protein was reported to be 88.53 0.49. The data unmistakably suggest that nanosynbiotics are more effective than synbiotics and the experiment's "control" drug, aspirin, at inhibiting the egg albumin protein. Figure 4E, below shows trends in the anti-inflammatory effects of Aspirin, nanosynbiotics, and synbiotics.

AI-based analyses

Structure prediction of nanosynbiotics

The structure of nanosynbiotics was predicted by ChemDraw with the help of functional groups C–O (Carbohydrate group), C=O (Carbonyl group), C=C (Alkene group) and NH2 (Aminoacidic group), as observed in FTIR analysis. The core structure of iron based nanosynbiotics has been drawn by the functional groups as reported in the literature as well. The 3D structure of nanosynbiotics is shown in the Fig. 5.

Figure 5

3D structure of nanosynbiotics with highlighted functional group.

Integration of artificial intelligence with nanosynbiotics

Solid dispersion formulation of nanosynbiotics

One effective method for increasing the oral bioavailability and solubility of nutraceuticals is solid dispersion. PharmSD is a tool for predicting solid dispersion in formulations. Modern AI or machine learning techniques were utilized by the platform, allowing speedy examination of several important solid dispersion parameters with just a mouse click.

Dissolution rate prediction

The dissolution rate of solid dispersion formulation of nanosynbiotics was observed by PharmSD. This dissolution rate is in the increasing manner as the maximum rate was at 62% at 60 min. The trend of increasing dissolution rate (y-axis) and time (x-axis) is shown in the Fig. S1.

SD stability prediction

The SD stability of nanosynbiotics was explored through PharmSD, as the has the probability of 0.74 for 3 months while for 6 months it has the probability of 0.58, meaning that nanosynbiotics has a good stability for 3 or 6 months as shown in the Table 2.

Table 2 SD stability of nanosynbiotics with probability for 3 and 6 months.

Parameterization of nanosynbiotics

The parameterization of nanosynbiotics were done by Parameterize tool. Neural network potentials based on quantum level machine learning approach predicted the energies of each residue of SD formulation of nanosynbiotics as shown in Fig. S2 and diagnostic energies are shown in Table 3 below. All the diagnostic energies including bond, angle axis etc. in which range it will interact in the GI tract.

Table 3 Diagnostic energies of Solid dispersed formulation of nanosynbiotics.

Pathway analysis of nanosynbiotics

The pathways in which nanosynbiotics can be involved are energy, nucleotide, amino acids, vitamins metabolism, biogenesis of other secondary metabolites, digestive system and environmental adaptation. The pathways in which they can treat and minimize the effects of Neurogenerative disorders, specific cancer types, cardiovascular diseases, various infectious bacterial diseases, and parasitic diseases as shown in the Fig. S3 presenting in light and dark blue lines.

Physiochemical properties and oral bioavailability

Nanosynbiotics' physiochemical characteristics, oral bioavailability radar, and boiled egg analysis have all been studied using SwissADME. Table 4 displays the physiochemical properties of nanosynbiotics. Bioavailability radar shows the red line lies in pink region depicting that nanosynbiotics have ideal range of lipophilicity, size, polarity, and insolubility as represented in Fig. 6A and gastrointestinal absorption of nanosynbiotics predicted that they are able to absorb in GI tract (white region) at maximum level as shown in the boiled-egg analysis in Fig. 6B.

Table 4 Docking score and calculation of free binding energies with 10 different poses.

Figure 6

(A) Bioavailability radar of nanosynbiotics; ideal region (pink), non-ideal region (white). (B) Boiled-egg analysis of nanosynbiotics; nanosynbiotics (red dot), gastrointestinal tract (white region), blood brain barrier (yellow region).

Toxicity prediction

Protox-II was applied to predict the toxicity radar of nanosynbiotics. The blue circle in the center of radar represents the nanosynbiotics, each line shows different types of toxicities and the length of the lines shows the percentage of toxicity relevant to each parameter as shown in Fig. 7. The blue dot didn’t cross any distance meaning that it has 0 toxicity.

Figure 7

Toxicity radar of nanosynbiotics; Radar (orange), Lines (black), length (percentage of toxicity) and Nanosynbiotics (blue dot).

Allergenicity prediction

The allergenicity of nanosynbiotics was predicted using the ChAIPred tool. Nanosynbiotics were predicted to be non-allergen with the value of − 0.00004, depicting as they cannot cause any type of allergy in the human body as well in animals.

Structure retrieval of PXR receptor

3D structure of PXR (Pregnane X receptor) was retrieved from PDB. PXR receptor was selected for the interaction analysis of nanosynbiotics with the gastrointestinal tract or gut, as these receptors are the main proteins that are present in the gut microbiome. The PDB id of PXR receptor is 4XHD. The tertiary structure of PXR receptor is shown in Fig. 8.

Figure 8

3D structure of PXR (Pregnane X receptor); Chains (red, orange, yellow, blue and green).

Titration and protonation of PXR receptor

The main purpose of titrating and protonating PXR receptor was to ready the protein for molecular docking and simulations. As titration and protonation enhance the charge capability of protein to interacts with the subsequent ligand. Figure 9 shows the amino acids on y-axis and pKa on x- axis while the blue lines show the less pKa value (proton is held loosely) of each amino acid and the red lines shows the more pKa values (proton is held tightly) of titration and protonation with each value. Most of the amino acids were balanced and ready for further analysis.

Figure 9

Protonation graph of H-bond network for PXR receptor; Amino acids (y-axis) and pKa (x-axis), Less protonation (blue), More protonation (red).

Molecular docking and MM/PB(GB)SA analysis

fastDRH is an open-access web tool for protein–ligand complex analysis and molecular docking. Computation of the structure-truncated MM/PB(GB)SA free energy, energy decomposition of each residue based on different poses was accessed. The docking score, PB score and GB score with each binding pose are displayed in the Table 4. Energy boxplot of the top10 potential hotspot residues and energy heat map of top 30 residues are shown in the Fig. 10A,B respectively.

Figure 10

(A) Energy boxplot of the top10 potential hotspot residues; Residue (x-axis) and Energy (y-axis). (B) Energy heat map of top 30 residues; Residues (x-axis) and Docking poses (y-axis).

Calculation of interatomic spacing

Different interatomic bonds and spacing between PXR receptor and nanosynbiotics are displayed in the Table 5. The presentation of different bonds between nanosynbiotics and PXR receptor is shown in the Fig. 11.

Table 5 Interatomic bonds and spacing between PXR receptor and nanosynbiotics.

Figure 11

Presentation of interatomic spacing between nanosynbiotics and PXR receptor.

Molecular dynamic simulations

Promising results were obtained from the simulated analysis of the docked complex of the PXR receptor and nano synbiotics using the SiBIOLEAD server. The interaction between the PXR receptor and nanosynbiotics was projected. Figure 12 analyses shows (A) Graph of RMSD (B) Peak Graph of RMSF (C) Graph of solvent accessible surface (D) Graph of secondary structure (E) Graph of hydrogen bonds and (F) Graph of GROMACS energies These findings show that nanosynbiotics can form complexes with the PXR receptor strongly and that this complex has the potential to be used for a variety of purposes in nanomedicine and drug delivery system.

Figure 12

Molecular dynamic simulations mediated maps of PXR receptor and Nanosynbiotics; (A) graph of RMSD. (B) Peak Graph of RMSF. (C) Graph of solvent accessible surface. (D) Graph of secondary structure. (E) Graph of hydrogen bonds and (F) Graph of total energies.

Discussion

Acetobacter oryzoeni was grown to extract inulin from chicory plant roots, resulting in synbiotics that were turned into nanosynbiotics with FeSO4. UV–Vis spectroscopy revealed a wavelength range that is appropriate for nanosynbiotics. SEM imaging revealed spherical nanosynbiotics with an average diameter of 40 nm. The elemental composition was validated by energy-dispersive X-ray spectroscopy. C–O, C=O, C=C, and NH2 groups were detected using Fourier-transform infrared spectroscopy. In-vitro tests revealed resistance to gastric juice, hydrogen peroxide, cholesterol lowering, and ABTS scavenging properties. Physicochemical characteristics, oral bioavailability, and stability were predicted using in-silico studies. A pathway analysis indicated the possibility of therapeutic engagement³¹. Toxicity and allergenicity forecasts suggested that the product was safe. Strong interactions were suggested by molecular docking and dynamics with the PXR receptor. Simulations showed stable PXR-nanosynbiotics complexes, which might be useful in nanomedicine and drug delivery. We synthesized nanosynbiotics from Acetobacter oryzoeni and inulin and demonstrated their stability, spherical form, and elemental composition. In-vitro tests revealed resistance to gastric juice, hydrogen peroxide, cholesterol lowering, and antioxidant capabilities in the nanosynbiotics. In-silico studies suggested that the compound will have good physicochemical characteristics, oral bioavailability, and stability. Strong interactions of AI-based model designed on the basis of solid dispersion formulation nanosynbiotics with the PXR receptor were discovered using molecular docking and dynamics, good SD stability predictions, and more bioavailability indicating potential uses in nanomedicine and drug delivery. Overall, the thorough evaluations demonstrate the therapeutic and biological potential of nanosynbiotics.

Successful nanosynbiotics production and characterization has intriguing implications for biomedical applications. Their gastrointestinal resistance, antioxidant capabilities, and possible interactions with the PXR receptor all point to medicinal promise³². The in-silico studies provide additional evidence for their stability, bioavailability, and safety. PharmSD uses different algorithms to design AI-based models and the model designed in this study named as SD formulation design of nanosynbiotics was based on supervised and reinforcement learning methods which is for the training and validation purpose respectively. The probability values of SD stability given 0.74 and 0.58 for 3 and 6 months respectively confirmed the authenticity of the dissolution rates predictions of SD formulation of nanosynbiotics. These findings pave the way for the development of nanosynbiotics as effective candidates for targeted medication delivery, probiotic formulations, and nutraceuticals, with potential uses in treating gastrointestinal problems and inflammation. More research and clinical trials are needed to fully realize their medicinal potential²⁵. While the study gives useful information, several limitations must be acknowledged. The intricacies of in vivo circumstances may not be entirely replicated in vitro, and extrapolating findings to human reactions needs caution. The study concentrated on particular elements of nanosynbiotics, and larger systemic impacts may require more investigation¹⁷. Furthermore, the in-silico predictions are based on computational models, and real-world differences may influence the outcomes. These constraints highlight the importance of rigorous study and validation in a variety of experimental contexts before wider use or clinical translation. Future studies should focus on the long-term safety and efficacy of nanosynbiotics, considering potential cumulative effects and individual differences. Exploration of various in vivo models, including human clinical trials, will improve knowledge of the influence of nanosynbiotics on health. Further research into the particular molecular pathways behind reported effects may yield more specific insights. Exploring innovative applications, such as tailored nanosynbiotics formulations, has the potential to advance precision medicine in the realm of microbiome-based treatments.

Conclusion

In this comprehensive study about nanosynbiotics derived from Acetobacter oryzoaeni as probiotics and inulin as prebiotics were synthesized and investigated, which were nano-formulated nanosynbiotics with excellent physicochemical properties, stability, resistance to gastric juice and hydrogen peroxide. Computational simulations predicted their participation in several mechanisms of action, and highlighted their potential therapeutic applications, such as lipid reduction and utility in antioxidant Molecular docking simulations with PXR receptors revealed significant correlations, paving the way for further research in nanomedicine. Despite these encouraging findings, and acknowledging their shortcomings, future research should address long-term safety issues and conduct human clinical trials to further determine the effects of nanosynbiotics. This discovery adds to the growing knowledge of nanosynbiotics, opening the door to potential advances in microbial-targeted therapies and personalized medicine.