Collection of Probiotic strain and extraction of prebioticsAn 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-formulationProbiotic 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 nanosynbioticsUV–Vis spectrometryThe 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 assaysGastric juice resistance assayIn 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 °C12,13.$${\text{Resistance to gastric juice}}\left( \% \right) \, = \, \{ {1}00 – {\text{Absorbance after}}\,{\text{2h}}\,\,{\text{of}}\,\,{\text{incubation}}\} \times {1}00$$Resistance to Hydrogen PeroxideA 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 assayPolyoxyethanyl-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 assayThe 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 assayThe 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 analysesStructure prediction of nanosynbioticsTo 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 nanosynbioticsSolid dispersion formulation design of nanosynbioticsApproach 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 predictionDissolution 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 predictionPrediction 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 nanosynbioticsParameterize 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 nanosynbioticsPathways 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 bioavailabilitySwissADME 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 predictionToxicity 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 predictionCHAIPred 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 receptorPDB (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 receptorFor 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 receptorPrediction 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 analysisfastDRH 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 spacingDifferent 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 simulationsIn 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 receptor25,30.
