The experimental design compared control and treated groups where each experimental group was comprised of six wistar rats (Rattus norvegicus) (n = 6). The treatments were performed by oral administration for four weeks and these groups were compared to the vehicle (non-treated, normal metabolism) and diabetic control groups. The treated groups received either the ethanol fruit extract of Withania coagulans or the standard diabetic drug, sitagliptin. The protocols used in the animal experiments were approved by the IAEC (Institutional Animal Ethical Committee) as per norms of the CPCSEA (Committee for the Purpose of Control and Supervision of Experiments on Animals), Government of India (Reg. No.1646/GO/a/12/CPCSEA valid up to 27.03.23).
Induction of type-2 diabetes: Type-2 diabetes was induced in test rats through the administration of a high-sucrose diet along with high-carbohydrate food for three weeks. Four intraperitoneal injections of dexamethasone (1.0 mg/kg) at alternate day intervals was also used to induce type 2-diabetes in the rats by following modified protocol (Martínez et al. 2016). The establishment of type-2 diabetes in the rats was determined by monitoring the level of glucose in their blood, HOMA (Homeostasis Model Assessment) indices (HOMA – IR(insulin resistance), HOMA-β% (β-cell function), and HOMA-S% (insulin sensitivity)(Chao et al. 2018).
Fruit extract, standard drug, and chemical reagents
The ethanol fruit extract of Withania coagulans was prepared using a standard Soxhlet protocol. The obtained extract was subsequently evaporated to dryness in a vacuum and the dried powder was used to formulate the extract(Poojary et al. 2015). The sitagliptin (Januvia® 50mg), was purchased from a local pharmacy in Jodhpur, India. The dose of extract (400mg/kg) was provided to the treated rats as per calculations of physiological dose (Gupta et al. 2018). Chemical reagents were purchased from a local supplier and were of a chemical grade equal to Loba Chemie Pvt Ltd. Biomedical diagnostic kits (Erba, Pvt Ltd) were used for the biochemical analysis of blood serum and a DPP-4 inhibition assay kit (Sigma Aldrich)was used for the DPP-4 inhibition assay.
Identification of the phytoconstituents present in the ethanol extract of W. coagulans fruit by LC-MS analysis
The phytoconstituents present in the ethanol extracts of W. coagulans fruit were identified by LC-MS(Liquid chromatography and Mass spectroscopy) analysis using standard protocols (Rijai et al. 2017). The LC-MS analysis was outsourced to CDRI (Central Drug Research Institute), Lucknow, India and performed by trained technicians on the appropriate equipment (ID: FEE-2, SAIF920). The HPLC samples were further analysed by Q-TOF mass spectrometry equipped with an ESI source. The analysis conditions were as follows: Full-scan mode from m/z 50 to 1200 and a source temperature of 140°C. The solvent was methanol with 0.3% formic acid. Solvents were subjected to a flow rate of 0.1 mL/min. The MS spectra were acquired in the positive ion mode. The mass fragmentations were identified using the spectrum database and mass hunter software.
Inhibition of DPP-4 activity and treatment of hyperglycemia
Two groups of rats were used to assess the impact of treatments on type 2- diabetic rats. The ethanol extract of W. coagulans fruit or the standard drug, sitagliptin, were the two administered treatments. Group – III the formulated fruit extract at a dose of 400 mg/kg BW (Body Weight) per day was administered to type -2 diabetic rats(Prasad et al. 2010). Group -IV sitagliptin at a dose of 50 mg/kg body weight per day, which is equivalent to a 50 mg oral clinical dose, was administered to another group of type 2 – diabetic rats. Group – I (vehicle control) and Group II (type-2 diabetic) rats were served as negative and positive controls, respectively. The extract and drug administration were performed by gastric intubation between 10 and 11 AM to avoid variable responses due to circadian rhythms.
In-vitro inhibition of DPP-4 activity
The DPP-4 assay was performed using the standard protocol of measuring chromatophore production by cleavage of Gly-Pro p-nitroanilide hydrochloride. The inhibition of DPP-4 by the fruit extract was determined by measuring the release of 4-nitroaniline from an assay mixture that included0.1 M Tris-HCl (pH 8.0) and 2 mM Gly-Pro p-nitroanilide (substrate). The reaction mixture was incubated at 37°C and moderated by the addition of sodium acetate buffer (pH 4.5). Absorbance was measured at 405 nm using a UV-VIS Spectrophotometer(Al-Masri et al. 2009; Chakrabarti et al. 2011). Percent inhibition was calculated using the following formula.
Biochemical analysis of blood serum
- Basic parameters: The serum parameters measured using standard methods included glucose (Ambade et al. 2017), total protein(Lowry et al. 1951), insulin(Yalow and Berson 1959), total cholesterol (Allain et al. 1974), HDL-cholesterol (Moshides 1987), triglyceride(Gottfried and Rosenberg 1973), SGOT(Reitman and Frankel 1957), SGPT(Reitman and Frankel 1957), urea (Wybenga et al. 1971), uric acid (Steele and Mansdorfer 1969), and creatinine (Mitchell 1973). The lipid profile (total cholesterol, HDL-cholesterol, LDL-cholesterol, Triglyceride and VLDL – cholesterol) was assessed following Friedewald’s formula (Jatwa et al. 2007; Parmar et al. 2014; Ram et al. 2014).
LDL-C (mg/dL) = TC (mg/dL) − HDL-C (mg/dL) − TG (mg/dL)/5.
- Total antioxidant capacity (FRAP)( Benzie and Strain 1996), catalase , SOD , GSH , and LPO activity(Buege and Aust, 1978) were also assessed using standard methods.
HOMA (Homeostatic model assessment) analysis
(HOMA-IR and HOMA-β) scores and insulin sensitivity were determined using fasting serum insulin and glucose concentrations measured at the end of the experiment. Calculations were based on the formula reported by Matthew et al. (1985) and Parekh et al. (2005) as follows (Matthews et al. 1985; Parekh et al. 2005).
Pancreatic tissues were obtained from autopsied animals after the completion of the experiments and processed for histological examination using standard methods (Ram et al. 2019). Briefly, tissues were fixed in 10% formalin, gradually dehydrated in an ethanol series, and embedded in paraffin wax. The embedded tissues were sectioned at a 5-μm thickness, stained with hematoxylin and eosin, and were then subsequently observed with a clinical microscope and photomicrographs were taken with an attached camera.
Molecular Docking analysis
The phytoconstituents identified by LC-MS analysis and the protein ligand molecular docking with the DPP-4 protein was assessed (Kaur et al. 2018; Sneha and Doss 2016). Molecular interactions of the identified compounds with DPP-4 were investigated using PyMol and Autodock 4.2. The catalytic triad of DPP-4 comprises Glu205, Glu206, and Tyr226 as the main residues and a hydrophobic core is composed of ten residues (Tyr547, Tyr667, Asn710, Val711, His740, Ser630, Ser209, Arg358, Phe357, and Val207). A high-resolution crystallographic structure of DPP-4 receptor protein (PDB ID 5y7k) was downloaded from a public protein database and processed using PyMol to extract the co-crystallised ligand inhibitor, remove water molecules, and correct the chain integration. Three-dimensional structures of the identified compounds sitagliptin, and vildagliptin (two standard drugs with DPP-4 inhibitory activity) were downloaded from the Pubchem Database. Ligands were processed using PyMol and hydrogen was added to the structures. The developed docking protocol was validated by performing re-docking with prepared co-crystalized ligand and receptor protein and maps were generated. Post-validation was conducted of the docking protocol of the individual identified compounds with DPP-4 protein. Molecular interactions, ligand conformations, and binding energies for each of the phytoconstituents and the standard drugs were obtained.
ADME/T (Absorption, distribution, Metabolism, Excretion, and Toxicity) analysis was performed using Drulito software (www.niper.gov.in/pi_dev_tools/DruLiToWeb /DruLiTo_index.html) to study the pharmacokinetics profile of the identified compounds for potential drug development(Reza and Fallahi 2020; Ubani et al. 2020). The compounds were ranked based on two filters: the Lipinski rule and the ability to pass through the blood brain barrier (BBB). The Lipinski rule states that an ideal drug molecule should weigh below 500g/mol, hydrogen bond donors should be ≤ 5, and the number of hydrogen bond acceptors should be ≤10 and have a partition coefficient ≤5. A compound with these properties would pass the BBB if the number of hydrogen bonds present is between 8-10 and no acidic groups are present in the molecule. TPSA (total polar surface area) indicates the bioavailability of the drug molecule as per Veber’s rule. ATPSA ≤140Å indicates good oral bioavailability.
Values obtained for the biochemical assessments and other data were expressed as a mean ± the standard error of mean (SEM) and the effect of treatment was analyzed by a one-way ANOVA with a post hoc Dunnett’s 𝑡-test using SPSS 22 trial version for windows(Assaad et al. 2014). The probability of significant differences between treatment means was set at p ≤ 0.05.