The Application of High‐Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

Abstract

An early example of HTS in drug development came from the identification of new antibiotics using 96-well microtiter plates, whereby fermentations of an established actinomycetes library were incubated with a panel of microorganisms, and after optimization, a screening capacity of 10 000 fermentation broths per week was achieved. [1] In the following decades, the rapid development of molecular biology techniques and advances in automated equipment have changed how new drugs are identified, and a variety of HTS-identified drugs have been approved to treat different diseases. [2] For a comprehensive list of HTS-identified drugs, please refer to the excellent review by Macarron et al. [2] High-throughput applications can also be combined with other unbiased "Omics" technologies used to study genomics, epigenomics, transcriptomics, proteomics, and metabolomics. [3] Diabetes is an incurable disease characterized by the inability of the body to maintain normoglycemia due to decreased sensitivity of various tissues to insulin and insufficient amounts of circulating insulin, a hormone produced by pancreatic β-cells. [4] Diabetes is associated with chronic complications, like macrovascular diseases (e.g., cardiovascular diseases) and microvascular diseases (e.g., damages in nerve, kidney, eye, and foot). [5,6] More than 537 million adults are currently living with diabetes, and this number is expected to increase to 693 million by 2045. In 2021, 6.7 million deaths worldwide were attributed to diabetes, [7] which made it the ninth leading cause of death. [8] Globally, the prevalence of diabetes also brings a heavy burden to healthcare systems, with an estimated expenditure of over 966 billion U.S. dollars per year. [7] Diabetes is generally categorized into two groups: Type I diabetes mellitus (T1DM) and Type II diabetes mellitus (T2DM), but there are still some cases that have different etiologies, such as gestational diabetes mellitus and monogenic diabetes. [9] Since the discovery of insulin in 1921, [10] significant progress has been made in diabetes pharmacotherapy and therapeutic technology; [11][12][13] however, since a permanent cure is unavailable, the discovery and development of anti-diabetic drugs is still at the forefront of diabetes research. With the rapid advancement in high-throughput omics technologies, combined with accumulated genome-wide association study (GWAS) data, a better understanding of diabetes pathophysiology may one day lead to the discovery of novel therapeutic targets. [14,15] This review will During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in highthroughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseas...

A high-throughput screening approach to discover potential colorectal cancer chemotherapeutics: Repurposing drugs to disrupt 14-3-3 protein-BAD interactions

“…We found the median BRET reduction caused by compounds capable of reducing BRET was approximately 25%, and there were 416, 162, 31, and 16 PADs that reduced the BRET signal by 25% at concentrations of 200 µM, 20 µM, and 2 µM, respectively ( Figure 4B ). We next adapted our screen into 96-well plates to re-screen PADs that were effective at 20 µM, a concentration commonly used in HTS assays [36] . Of these, 101 PADs showed consistent results and were further assessed for their capacity to induce cell death in NIH-3T3 cells via Hoechst/propidium iodide incorporation assays ( Figure 4C ).…”

“…Another group of drugs also arose from our screens such that they were capable of reducing BRET reduction by more than 34%, but without notable efficacy in inducing cell death in NIH-3T3 cells. A possible explanation for this is that our screening is based on a cell-based assay [36] , and the complex intracellular environment makes it challenging to determine whether the dissociation of 14-3-3(:BAD resulted from a direct inhibitory action on 14-3-3 (or BAD, or an indirect effect on the upstream signaling pathways that promote 14-3-3(:BAD interactions. Therefore, other than disrupting 14-3-3:BAD interactions, these compounds may have additional effects, such as up-regulating anti-apoptotic BCL-2 proteins, which promote cell survival [11] .…”