A Comprehensive Review on the Screening Models for the Pharmacological Assessment of Antiulcer Drugs

Abstract

Background: Due to inappropriate diet, smoking, alcohol consumption, regular use of drugs like NSAIDs and sedentary lifestyle, one may feel upper abdominal pain which may be the predictor of the gastrointestinal disorder called Peptic Ulcer. When an imbalance occurs between the defensive factor and aggressive factor of the stomach, ulcer formation in the esophageal lining, stomach, or duodenum takes place. This leads to the formation of small sores that cause pain. Another condition that synergizes the abdominal pain is vomiting materials which look like coffee grounds, blood in the stool, black or tarry stools. This pain may increase after lunch or dinner. This problem persists, that often leads to the gastroenterologist's consultation. Objective: There are many antiulcer screening models present for the determination of antiulcer activity of the drug molecule. The main objective of this study is to find which model is best for the determination of antiulcer activity. Methods: A literature search was conducted on the databases namely Science direct and PubMed with the help of different keywords such as "Anti-ulcer", "In-vitro models" and "In-vivo models". The search was customized by applying the appropriate filters so as to get the most relevant articles to meet the objective of this review article. Result: There are different research and review papers based on the antiulcer screening models for the determination of antiulcer activity of new drug molecules. Conclusion: On the basis of our study, we found some useful models for the antiulcer activity of drugs and suggested that, if we use in-vitro and in-vivo methods together, then we may obtain the most relevant result in our research area.

Gastroprotective Effects of the Aqueous Extract from Taraxacum officinale in Rats Using Ultrasound, Histology, and Biochemical Analysis

“…Prostaglandin also maintains mucosal blood flow and repairs and regulates mucosal cells. NSAIDs also increase mucosal hydrogen peroxide and hydroxyl ion concentrations by inhibiting gastric peroxide, which is responsible for oxidative mucosal damage [ 17 ].…”

“…In pharmacological screening, most experiments are carried out in rodents, and the measurement of gastric lesions is performed after dissecting the stomach. Ulcer lesions can be examined macroscopically and microscopically [ 17 ]. However, these techniques used for preclinical evaluation of natural products, for example, can be improved to more accurately assess the inflammatory process at the gastric wall.…”

Effect of a Cytoprotective Dose of Dehydroleucodine, Xanthatin, and 3-Benzyloxymethyl-5H-furan-2-one on Gastric Mucosal Lesions Induced by Mast Cell Activation

“…Compound 48/80 is known to cause degranulation of connective tissue mast cells, such as peritoneal mast cells, with the release of serotonin and histamine from the cells [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. Ohta et al have shown that a single compound 48/80 treatment induces the release of endogenous biogenic amines through degranulation of connective tissue mast cells and the subsequent development of gastric mucosal lesions in male Wistar rats [ 39 ].…”

Relationship between Hormonal Modulation and Gastroprotective Activity of Malvidin and Cyanidin Chloride: In Vivo and In Silico Approach

“…Since it has not been reported in the literature which regions (binding sites) and which amino acid residues would be important to establish molecular interactions in the coupling of these anthocyanidins to these proteins, there was a need to perform the blind docking technique to first track which regions would be the most favorable for binding (according to the criterion of binding energy values), employing the method called Vina_vision, which uses a pose clustering algorithm based on the traditional AutoDock Vina software [ 32 , 33 ] implemented in Achilles Blind Docking Server [ 34 ] (available online: last accessed on 2 November 2021), which first calculates the fit of the molecules across the protein surface to find the points with the best binding affinities, then proceeds with the clustering of the poses, where the best affinity is considered as the representation of the cluster. The crystallographic structures used in this study were: SOD with PDB code 1N0J [ 35 ]; CAT with PDB code 1F4J [ 36 ], GSH with PDB code 3SQP [ 37 ] and MPO with PDB code 7LAE [ 38 ], all made available in the Protein Database PDB [ 39 , 40 ] (available online: last accessed on 2 November 2021). The protein and ligand input files were generated in pdbqt format, with AutoDock Vina parameterization.…”