ABSTRACT
Aim: Breast cancer has been a leading cause of mortality among women worldwide in recent years. Targeting the lysophosphatidic acid (LPA)-LPA1 pathway using small molecules could improve breast cancer therapy. Materials & methods: Thiazolidin-4-ones were developed and tested on MCF-7 cancer cells, and active compounds were analyzed for their effects on apoptosis, migration angiogenesis and LPA1 protein and gene expression. Results & conclusion: Compounds TZ-4 and TZ-6 effectively reduced the migration of MCF-7 cells, and induced apoptosis. TZ-4, TZ-6, TZ-8 and TZ-14 significantly reduced the LPA1 protein, LPA1 and angiogenesis gene expression in treated MCF-7 cells. Molecular docking and molecular dynamic simulation studies reveal the ligand interactions and stability of the LPA1-ligand complex. Developed thiazolidin-4-ones showed great potential as an LPA1-targeted approach to combating breast cancer.
Breast cancer is a major cause of death for women worldwide. Using small molecules to target the lysophosphatidic acid (LPA)LPA1 pathway could improve breast cancer treatment. We tested a type of molecule called thiazolidin-4-ones on breast cancer cells in the lab. We looked at how these molecules affected cell death, movement, blood vessel growth and the activity of the LPA1 gene and protein. Some of these molecules, such as TZ-4 and TZ-6, reduced the movement of cancer cells and caused them to die. They also decreased the levels of LPA1 protein and gene activity in the cells. We used computer simulations to see how these molecules interacted with the LPA1 protein. Our findings suggest that thiazolidin-4-ones could be a promising treatment for breast cancer by targeting LPA1.
Subject(s)
Antineoplastic Agents , Breast Neoplasms , Drug Design , Receptors, Lysophosphatidic Acid , Thiazolidines , Humans , Receptors, Lysophosphatidic Acid/antagonists & inhibitors , Receptors, Lysophosphatidic Acid/metabolism , Breast Neoplasms/drug therapy , Breast Neoplasms/pathology , Breast Neoplasms/metabolism , Female , Antineoplastic Agents/pharmacology , Antineoplastic Agents/chemistry , Antineoplastic Agents/chemical synthesis , Thiazolidines/pharmacology , Thiazolidines/chemistry , Thiazolidines/chemical synthesis , Apoptosis/drug effects , Molecular Docking Simulation , MCF-7 Cells , Molecular Structure , Structure-Activity Relationship , Cell Proliferation/drug effects , Drug Screening Assays, Antitumor , Cell Movement/drug effectsABSTRACT
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
