Current Drug Discovery Technologies - Volume 20, Issue 6, 2023

Introduction: Due to the high prevalence of Tourette's disorder among children and adolescents and its negative consequences, an appropriate and effective medical treatment with minimal complications is necessary. Therefore, this study was conducted to compare the effects of Aripiprazole and Risperidone on Tourette's disorders in children and adolescents. Methods: The statistical population of this semi-experimental study was children and adolescents aged seven to eighteen years old. They were diagnosed with Tourette's disorder based on the DSM-V criteria by the clinical interview of a child and adolescent psychiatrist in the child Psychiatry clinic of Ibne- Sina's Psychiatric Hospital (Mashhad-Iran) in 2018. A total of forty participants were selected by the convenience sampling method, and they were randomly divided into two groups treated with medicines, Risperidone or Aripiprazole, for two months. Then, the demographic information questionnaire was completed. The Y-GTSS Scale was completed. The clinical Effect Rating Scale (CGI-Tics Scale) was completed. Calculation of body mass index and medical side effects complications were completed. The evaluation was carried out at the beginning and on the second, fourth, and eighth weeks, and the results were compared. The data were analyzed using SPSS software. 14, descriptive statistics, Chi-square, and variance analysis. Results: The two groups were homogeneous in terms of demographic variables and body mass index. Despite the positive effect of both medicines, no significant difference was observed among the general scores of such disorders, the overall score of severity, Tourette's recovery, and BMI of these two groups at the intervals and the end of treatments. (p <0.05). Due to the low number of complications reported, statistical comparisons of the medical side effects were not made. Conclusion: According to the results, the two medicines, Aripiprazole and Risperidone, effectively improved the symptoms of Tourette's disorder and its overall severity. However, there were no significant statistical differences between them. Furthermore, in terms of the medical side effects, the statistical comparison between the two medicines was impossible due to the small number of complications.

Indole is known as a versatile heterocyclic building block for its multiple pharmacological activities and has a high probability of success in the race for drug candidates. Many natural products, alkaloids, and bioactive heterocycles contain indole as the active principle pharmacophore. These encourage the researchers to explore it as a lead in the drug development process. The current manuscript will serve as a torchbearer for understanding the structurally diverse class of indole derivatives with extensive pharmacological activity. The current manuscript describes the intermediates and their functional groups responsible for superior biological activity compared to the standard. The review is written to help researchers to choose leads against their target but also to provide crucial insight into the design of a hybrid pharmacophore-based approach in drug design with enhanced potential. The present reviews on the indole derivatives correlate the structures with biological activities as well as essential pharmacophores, which were highlighted. The discussion was explored under challenging targets like dengue, chikungunya (anti-viral), antihypertensive, diuretic, immunomodulator, CNS stimulant, antihyperlipidemic, antiarrhythmic, anti-Alzheimer’s, and neuroprotective, along with anticancer, antitubercular, antimicrobial, anti-HIV, antimalarial, anti-inflammatory, antileishmanial, antianthelmintic, and enzyme inhibitors. So, this review includes a discussion of 19 different pharmacological targets for indole derivatives that could be utilized to derive extensive information needed for ligand-based drug design. The article will guide the researchers in the selection, design of lead and pharmacophore, and ligand-based drug design using indole moiety.

Background: Antipsychotics interfere with virtually all hallmarks of cancer, including angiogenesis. Vascular endothelial growth factor receptors (VEGFRs) and platelet-derived growth receptors (PDGFRs) play crucial roles in angiogenesis and represent targets of many anti-cancer agents. We assessed and compared the binding effects of antipsychotics and receptor tyrosine kinase inhibitors (RTKIs) on VEGFR2 and PDGFRα. Methods: FDA-approved antipsychotics and RTKIs were retrieved from DrugBank. VEGFR2 and PDGFRα structures were obtained from Protein Data Bank and loaded on Biovia Discovery Studio software to remove nonstandard molecules. Molecular docking was carried out using PyRx and CBDock to determine the binding affinities of protein-ligand complexes. Results: Risperidone exerted the highest binding effect on PDGFRα (-11.0 Kcal/mol) as compared to other antipsychotic drugs and RTKIs. Risperidone also demonstrated a stronger binding effect on VEGFR2 (-9.6 Kcal/mol) than the RTKIs, pazopanib (-8.7 Kcal/mol), axitinib (-9.3 Kcal/mol), vandetanib (-8.3 Kcal/mol), lenvatinib ( -7.6 Kcal/mol) and sunitinib (-8.3 Kcal/mol). Sorafenib (an RTKI), however, exhibited the highest VEGFR2 binding affinity of -11.7 Kcal/mol. Conclusion: Risperidone's superior binding affinity with PDGFRα when compared to all reference RTKIs and antipsychotic drugs, as well as its stronger binding effect on VEGFR2 over the RTKIs, sunitinib, pazopanib, axitinib, vandetanib, and lenvatinib, imply that it could be repurposed to inhibit angiogenic pathways and subjected to pre-clinical and clinical trials for cancer therapy.

Background: The tyrosine kinase epidermal growth factor receptor (TK-EGFR) has recently been identified as a useful target for anticancer treatments. The major concern for current EGFR inhibitors is resistance due to mutation, which can be overcome by combining more than one pharmacophore into a single molecule. Aim and Objective: In the present study, various hybrids of 1,3,4-oxadiazole-chalcone derivatives were gauged for their EGFR inhibitory potential. Method: The design of 1,3,4-oxadiazole-chalcone hybrid derivatives was carried out and in silico studies, viz., molecular docking, ADME, toxicity, and molecular simulation, were performed as EGFR inhibitors. Twenty-six 1,3,4-oxadiazole-chalcone hybrid derivatives were designed using the combilib tool of the V life software. AutoDock Vina software was used to perform in silico docking studies, while SwissADME and pkCSM tools were used to analyse molecules for ADME and toxicity. Desmond software was used to run the molecular simulation. Result: Around 50% of molecules have shown better binding affinity as compared to standard and cocrystallized ligands. Conclusion: Molecule 11 was found to be a lead molecule that has the highest binding affinity, good pharmacokinetics, good toxicity estimates and better protein-ligand stability.

More than two hundred years ago, taurine was first isolated from materials derived from animals. It is abundantly found in a wide range of mammalian and non-mammalian tissues and diverse environments. Taurine was discovered to be a by-product of the metabolism of sulfur only a little over a century and a half ago. Recently, there has been a renewed academic interest in researching and exploring various uses of the amino acid taurine, and recent research has indicated that it may be useful in the treatment of a variety of disorders, including seizures, high blood pressure, cardiac infarction, neurodegeneration, and diabetes. Taurine is currently authorised for the therapy of congestive heart failure in Japan, and it has shown promising results in the management of several other illnesses as well. Moreover, it was found to be effective in some clinical trials, and hence it was patented for the same. This review compiles the research data that supports the prospective usage of taurine as an antibacterial, antioxidant, anti-inflammatory, diabetic, retinal protective, and membrane stabilizing agent, amongst other applications

Drug discovery and development have been sped up because of the advances in computational science. In both industry and academics, artificial intelligence (AI) has been widely used. Machine learning (ML), an important component of AI, has been used in a variety of domains, including data production and analytics. One area that stands to gain significantly from this achievement of machine learning is drug discovery. The process of bringing a new drug to market is complicated and time-consuming. Traditional drug research takes a long time, costs a lot of money, and has a high failure rate. Scientists test millions of compounds, but only a small number make it to preclinical or clinical testing. It is crucial to embrace innovation, especially automated technologies, to lessen the complexity involved in drug research and avoid the high cost and lengthy process of bringing a medicine to the market. A rapidly developing field, a branch of artificial intelligence called machine learning (ML), is being used by numerous pharmaceutical businesses. Automating repetitive data processing and analysis processes can be achieved by incorporating ML methods into the drug development process. ML techniques can be used at numerous stages of the drug discovery process. In this study, we will discuss the steps of drug discovery and methods of machine learning that can be applied in these steps, as well as give an overview of each of the research works in this field.