Discovery of BVDU as a promising Drug for autoimmune diseases Therapy by Dendritic-cell-based functional screening

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

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput 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 diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

Neuroprotective Potential of Dendritic Cells and Sirtuins in Multiple Sclerosis

Myeloid cells, including parenchymal microglia, perivascular and meningeal macrophages, and dendritic cells (DCs), are present in the central nervous system (CNS) and establish an intricate relationship with other cells, playing a crucial role both in health and in neurological diseases. In this context, DCs are critical to orchestrating the immune response linking the innate and adaptive immune systems. Under steady-state conditions, DCs patrol the CNS, sampling their local environment and acting as sentinels. During neuroinflammation, the resulting activation of DCs is a critical step that drives the inflammatory response or the resolution of inflammation with the participation of different cell types of the immune system (macrophages, mast cells, T and B lymphocytes), resident cells of the CNS and soluble factors. Although the importance of DCs is clearly recognized, their exact function in CNS disease is still debated. In this review, we will discuss modern concepts of DC biology in steady-state and during autoimmune neuroinflammation. Here, we will also address some key aspects involving DCs in CNS patrolling, highlighting the neuroprotective nature of DCs and emphasizing their therapeutic potential for the treatment of neurological conditions. Recently, inhibition of the NAD⁺-dependent deac(et)ylase sirtuin 6 was demonstrated to delay the onset of experimental autoimmune encephalomyelitis, by dampening DC trafficking towards inflamed LNs. Thus, a special focus will be dedicated to sirtuins’ role in DCs functions.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Personalized immunoglobulin aptamers for detection of multiple myeloma minimal residual disease in serum

Multiple myeloma (MM) is a neoplasm of plasma cells that secrete patient specific monoclonal immunoglobulins. A recognized problem in MM treatment is the early recognition of minimal residual disease (MRD), the major cause of relapse. Current MRD detection methods (multiparameter flow cytometry and next generation sequencing) are based on the analysis of bone marrow plasma cells. Both methods cannot detect extramedullary disease and are unsuitable for serial measurements. We describe the methodology to generate high affinity DNA aptamers that are specific to a patient’s monoclonal Fab region. Such aptamers are 2000-fold more sensitive than immunofixation electrophoresis and enabled detection and quantification of MRD in serum when conventional MRD methods assessed complete remission. The aptamer isolation process that requires small volumes of serum is automatable, and Fab specific aptamers are adaptable to multiple diagnostic formats including point-of-care devices.

Tapia-Alveal, Olsen and Worgall develop a novel strategy for patient-specific multiple myeloma diagnostics platform using DNA aptamers. The high affinity DNA aptamers enabled detection of minimal residual disease (MRD) when conventional MRD methods assessed complete remission and are adaptable to multiple diagnostic formats including point-of-care devices.

Scale-Up of a Heck Alkenylation Reaction: Application to the Synthesis of an Amino-Modifier Nucleoside ‘Ruth Linker’

Ruth linker is a C5 pyrimidine modified nucleoside analogue widely utilized for the incorporation of a primary amine in a synthetic oligonucleotide. The increasing demand for non-radioactive labeling, detection of biomolecules, and assembly of COVID-19 test kits has triggered a need for scale-up of Ruth linker. Herein, an efficient protocol involving a palladium-catalyzed Heck alkenylation is described. The synthesis has been optimized with a goal of low catalyst concentration, column-free isolation, high product purity, reproducibility, and shorter reaction time. The scalability and utility of the process have been demonstrated successfully on a 100 g scale (starting material). Additionally, for scale-up of the Heck alkenylation protocol, 7-phospha-1,3,5-triaza-adamantanebutane sulfonate (PTABS) as the coordinating caged phosphine ligand was also synthesized on a multigram scale after careful optimization of the conditions.

Novel therapeutic drug identification and gene correlation for fatty liver disease using high-content screening: Proof of concept

Non-alcoholic fatty liver disease (NAFLD) is a problem in obese people caused by increasing intake of high-calorie food such as fructose implicated in the elevated prevalence. It is necessary to identify novel drugs to develop effective therapies. In this study, we combined LOPAC® (The Library of Pharmacologically Active Compounds) and High-Content screening to identify compounds that significantly reduced intracellular lipid droplets (LD) after high fat medium (HFM) treatment. Among 1280 compounds, we identified 239 compounds that reduced LD by >50%. Of these, 17 maintained cell viability. Nine of them were selected for validation using normal primary hepatocytes, of which five compounds showed dose-dependent efficacy. Whole genome transcriptomic network analysis was performed to construct the underlying regulatory network. There were 831 (711 up-regulated and 120 down-regulated genes) and 3480 (2009 up-regulated and 1471 down-regulated genes) genes that showed a significant change (>2-fold; p < 0.05) after 12 and 24 h HFM treatment, respectively. Gene enrichment and pathway analysis showed several immune responses mediated by MIF, IL-17, TLR, and IL-6. These compounds modulate lipogenesis via GSK3β and CREB1, which is followed by an alteration in the expression of several downstream genes related to hepatocellular carcinoma and hepatitis. CREB1 is a core transcription factor and may be a potential therapeutic target for liver disease. In conclusion, this proof of concept provides a strategy for identifying novel drugs for treatment of fatty liver disease as well as elucidates their underlying mechanisms. This research provides opportunity for developing future pharmaceutical therapeutics.