Computational binding study with α7 nicotinic acetylcholine receptor of Anvylic-3288: an allosteric modulator

Lycopene potentiates wound healing in streptozotocin-induced diabetic rats

Background

Diabetes is a growing metabolic disease that is characterized by high blood sugar levels with life-threatening results. Diabetic wounds are a major problem because they do not resolve in few days. Major problems affecting wound healing are infection, age, stress, etc. at the wound site, and other associated disease conditions. Lycopene is a red pigment obtained from various fruits such as tomatoes, watermelon, and guava. It is a powerful antioxidant that scavenges reactive oxygen species and potential as nutraceuticals. It has reported antidiabetic, antioxidant, anti-obesity, anti-inflammatory, antihyperglycemic, and antiaging activities based on the literature.

Objective

The objective of the current study is to find the wound-healing potential of lycopene emulgel (LE) and report the properties of the compound.

Methods

Wound healing activity was assessed in Streptozotocin induced diabetic rats and control rats. Streptozotocin injection (55 mg/kg) was used to induce marked hyperglycaemia, compared with controls. The formulation was applied topically and was evaluated for efficacy.

Results

Treatment of rats with lycopene emulgel (LE) topical application exhibited a significant reduction of wound closure of 95.3 and 88.9% and epithelisation within 21 days.

Conclusion

The formulation was found to be novel, safe, and effective in the functional recovery of wounds.

Design, synthesis, molecular docking and pharmacological evaluation of some thiadiazole based nipecotic acid derivatives as a potential anticonvulsant and antidepressant agents

In our continuous effort to develop novel antiepileptic drug, a new series of nipecotic acid derivatives having1,3,4-thiadiazole nucleus were designed and synthesized. This study aims to improve the lipophilicity of nipecotic acid by attaching some lipophilic anchors like thiadiazole and substituted aryl acid derivatives. In our previous study, we noticed that the N-substituted oxadiazole derivative of nipecotic acid exhibited significant antiepileptic activity in the rodent model. The synthesized compounds were characterized by FT-IR, 1H-NMR, 13C-NMR, Mass, and elemental analysis. The anticonvulsant activity was evaluated by using the maximal electroshock-induced seizure model in rats (MES) and the subcutaneous pentylenetetrazol (scPTZ) test in mice. None of the compounds were found to be active in the MES model whereas compounds (TN2, TN9, TN12, TN13, and TN15) produced significant protection against the scPTZ-induced seizures model. The compounds showing antiepileptic activity were additionally evaluated for antidepressant activity by using the forced swim test, 5-hydroxytryptophan (5-HTP)-induced head twitch test, and learned helplessness test. All the molecules that showed anticonvulsant activity (TN2, TN9, TN12, TN13, and TN15), also exerted significant antidepressant effects in the animal models. The selected compounds were subjected to different toxicity studies. Compounds were found to have no neurotoxicity in the rota-rod test and devoid of hepatic and renal toxicity in 30 days repeated oral toxicity test. Further, a homology model was developed to perform the in-silico molecular docking and dynamics studies which revealed the similar binding of compound TN9 within the active binding pocket and were found to be the most potent anti-epileptic agent. The market expectation for newly developed antiepileptic thiadiazole-based nipecotic acid derivatives is significant, driven by their potential to offer improved therapeutic outcomes and reduced side effects, addressing a critical need in epilepsy treatment. These innovative compounds hold promise for meeting the demand for more effective and safer antiepileptic medications.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03897-1.

Novel Gastroprotective and Thermostable Cocrystal of Dimethyl Fumarate: Its Preparation, Characterization, and In Vitro and In Vivo Evaluation

Crystallization has revolutionized the field of solid-state formulations by modulating the physiochemical and release profile of active pharmaceutical ingredients (APIs). Dimethyl fumarate (DF), an FDA-approved first-line drug for relapsing-remitting multiple sclerosis, has a sublimation problem, leading to loss of the drug during its processing. To tackle this problem, DF cocrystal has been prepared by using solvent evaporation technique using nicotinamide as a coformer, which has been chosen based on in silico predictions and their ability to participate in hydrogen bonding. Fourier transform infrared (FT-IR), powder X-ray diffraction (PXRD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and sublimation analysis have characterized the cocrystal and its thermostability. Comparative analysis of the release profile has been done by the dissolution and pharmacokinetic study of DF and its cocrystal. Formulated cocrystal is noncytotoxic, antioxidant and inhibits interleukin-6 and tissue necrosis factor-α in peripheral blood mononuclear cells induced by lipopolysaccharide. We have obtained a thermostable cocrystal of DF with a similar physicochemical and release profile to that of DF. The formulated cocrystal also provides a gastroprotective effect which helps counterbalance the adverse effects of DF by reducing lipid peroxidation and total nitrite levels.

Effect of sulfonamide derivatives of phenylglycine on scopolamine-induced amnesia in rats

Alzheimer's disease is a neurodegenerative disease responsible for dementia and other neuropsychiatric symptoms. In the present study, compounds 30 and 33, developed earlier in our laboratory as selective butyrylcholinesterase inhibitors, were tested against scopolamine-induced amnesia to evaluate their pharmacodynamic effect. The efficacy of the compounds was determined by behavioral experiments using the Y-maze and the Barnes maze and neurochemical testing. Both compounds reduced the effect of scopolamine treatment in the behavioral tasks at a dose of 20 mg/kg. The results of the neurochemical experiment indicated a reduction in cholinesterase activity in the prefrontal cortex and the hippocampus. The levels of antioxidant enzymes superoxide dismutase and catalase were restored compared to the scopolamine-treated groups. The docking study on rat butyrylcholinesterase (BChE) indicated tight binding, with free energies of -9.66 and -10.23 kcal/mol for compounds 30 and 33, respectively. The two aromatic amide derivatives of 2-phenyl-2-(phenylsulfonamido) acetic acid produced stable complexes with rat BChE in the molecular dynamics investigation.

Development of homology model, docking protocol and Machine-Learning based scoring functions for identification of Equus caballus's butyrylcholinesterase inhibitors

Machine learning (ML), an emerging field in drug design, has the potential to predict in silico toxicity, shape-based analysis of inhibitors, scoring function (SF) etc. In the present study, a homology model, docking protocol, and a dedicated SF have been developed to identify the inhibitors of horse butyrylcholinesterase (BChE) enzyme. Horse BChE enzyme has homology with human BChE and is a substitute for the screening of in vitro inhibitors. The developed homology model was validated and the active site residues were identified from Cavityplus to generate grid box for docking. The validation of docking involved comparison of interactions of ligands co-crystallised with human BChE and the docked poses of the corresponding ligands with horse BChE. A high degree of similarity in the interaction profiles of generated poses validated the docking protocol. Scoring of ligands was further validated by docking with known BChE inhibitors. The binding energies obtained from SF was correlated with IC₅₀ values of inhibitors through classification and regression-based methods, which indicated poor predictivity of native SF. Therefore, protein-ligand binding energy, interaction profile, and ligand descriptors were used to develop and validate the classification and regression-based models. The validated extra tree binary classifier, random forest and extra tree regression-based models were compiled as a protein-ligand SF and were made available to the users through web application and python library. ML models exhibited improved area under the curve for ROC and good correlation between the predicted and observed IC₅₀ values, than the Autodock SF. Communicated by Ramaswamy H. Sarma.

Identification of sulfonamide-based butyrylcholinesterase inhibitors using machine learning

Aim: This study reports the designing of BChE inhibitors through machine learning (ML), followed by in silico and in vitro evaluations. Methodology: ML technique was used to predict the virtual hit, and its derivatives were synthesized and characterized. The compounds were evaluated by using various in vitro tests and in silico methods. Results: The gradient boosting classifier predicted N-phenyl-4-(phenylsulfonamido) benzamide as an active BChE inhibitor. The derivatives of the inhibitor, i.e., compounds 34, 37 and 54 were potent BChE inhibitors and displayed blood-brain barrier permeability with no significant AChE inhibition. Conclusion: The ML prediction was effective, and the synthesized compounds showed the BChE inhibitory activity, which was also supported by the in silico studies.

Identification of sulfonamide based butyrylcholinesterase inhibitors through scaffold hopping approach

Butyrylcholinesterase (BChE), a hydrolytic enzyme, is responsible for the termination of the action of acetylcholine besides acetylcholinesterase (AChE) in the synaptic cleft of the brain. The alteration in the enzyme level, in patients with the progression of Alzheimer's disease, makes it a therapeutic target. In the present study, we developed BChE inhibitors through scaffold hopping by exploring two previously reported compounds, i.e., 1,4-bis((4-chlorophenyl) sulfonyl)-3,6-diphenylpiperazine-2,5-dione and N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide, to afford scaffold and pharmacophore fragments, respectively. The N,2-diphenyl-2-(phenylsulfonamido)acetamide derivatives, thus designed, were synthesised and screened for the inhibition of AChE and BChE enzymes. Compounds 30 and 33 were found to be most active against BChE among the derivatives, with IC₅₀ values of 7.331 ± 0.946 and 10.964 ± 0.936 μM, respectively. The compounds displayed a non-competitive mode of inhibition along with BBB permeability and good cell viability on SH-SY5Y cell line. The molecular docking analysis of the compounds with BChE showed interactions with Trp82, Trp231, Leu286, and His438. The molecular dynamics study revealed the stability of the protein-ligand complexes.

Improved machine learning scoring functions for identification of Electrophorus electricus's acetylcholinesterase inhibitors

Structure-based drug design (SBDD) is an important in silico technique, used for the identification of enzyme inhibitors. Acetylcholinesterase (AChE), obtained from Electrophorus electricus (ee), is widely used for the screening of AChE inhibitors. It shares structural homology with the AChE of human and other organisms. Till date, the three-dimensional crystal structure of enzyme from ee is not available that makes it challenging to use the SBDD approach for the identification of inhibitors. A homology model was developed for eeAChE in the present study, followed by its structural refinement through energy minimisation. The docking protocol was developed using a grid dimension of 84 × 66 × 72 and grid point spacing of 0.375 Å for eeAChE. The protocol was validated by redocking a set of co-crystallised inhibitors obtained from mouse AChE, and their interaction profiles were compared. The results indicated a poor performance of the Autodock scoring function. Hence, a batch of machine learning-based scoring functions were developed. The validation results displayed an accuracy of 81.68 ± 1.73% and 82.92 ± 3.05% for binary and multiclass classification scoring function, respectively. The regression-based scoring function produced [Formula: see text] and [Formula: see text] values of 0.94, 0.635 and 0.634, respectively.

Syringic acid, a novel thyroid hormone receptor-β agonist, ameliorates propylthiouracil-induced thyroid toxicity in rats

The aim of this study was to evaluate the potential of syringic acid (SA) against propylthiouracil (PTU)-induced hypothyroidism in rats. SA at a prestandardized dose, 50 mg/kg/day, was orally administered to PTU-induced hypothyroid rats for 30 days, and alterations in the levels of serum triiodothyronine (T₃ ), thyroxine (T₄ ), thyrotropin (TSH), alanine transaminase (ALT), and aspartate transaminase (AST); tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6); total cholesterol (CHOL) and triglycerides (TG); hepatic lipid peroxidation (LPO) and antioxidants (superoxide dismutase, catalase, glutathione peroxidase, and glutathione content), as well as histological changes in liver and thyroid were examined. The molecular interactions of the ligand, SA, with thyroid-related protein targets, such as human thyroid hormone receptor β (hTRβ), and thyroid peroxidase (TPO) protein, were studied using molecular docking. Whereas in hypothyroid animals, T₄ , T₃ , and antioxidants were decreased, there was an increase in TSH, TNF-α, IL-6, ALT, AST, and hepatic LPO; administration of SA in PTU-induced animals reversed all these indices to near normal levels. SA also improved the histological features of liver and thyroid gland. Our study clearly demonstrates SA as a novel thyroid agonist for augmenting the thyroid functions in rats. Molecular docking analysis reveals that SA possesses good binding affinity toward both the targets, hTRβ and TPO. Through this approach, for the first time we provide the evidence for SA as a novel thyroid agonist and suggest a receptor-mediated mechanism for its thyroid stimulatory potential.