Discovery of Novel Small Molecule Dual Inhibitor Targeting Toll-Like Receptors 7 and 9.

The aberrant secretion of proinflammatory cytokines by immune cells is the principal cause of inflammatory diseases, such as systemic lupus erythematosus and rheumatoid arthritis. Toll-like receptor 7 (TLR7) and TLR9, sequestered to the endosomal compartment of dendritic cells and macrophages, are closely associated with the initiation and progression of these diseases. Therefore, the development of drugs targeting dysregulated endosomal TLRs is imperative to mitigate systemic inflammation. Here, we applied the principles of computer-aided drug discovery to identify a novel low-molecular-weight compound, TLR inhibitory compound 10 (TIC10), and its potent derivative (TIC10g), which demonstrated dual inhibition of TLR7 and TLR9 signaling pathways. Compared to TIC10, TIC10g exhibited a more pronounced inhibition of the TLR7- and TLR9-mediated secretion of the proinflammatory cytokine tumor necrosis factor-α in a mouse macrophage cell line and mouse bone marrow dendritic cells in a concentration-dependent manner. While TIC10g slightly prevented TLR3 and TLR8 activation, it had no impact on cell surface TLRs (TLR1/2, TLR2/6, TLR4, or TLR5), indicating its selectivity for TLR7 and TLR9. Additionally, mechanistic studies suggested that TIC10g interfered with TLR9 activation by CpG DNA and suppressed downstream pathways by directly binding to TLR9. Western blot analysis revealed that TIC10g downregulated the phosphorylation of the p65 subunit of nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) and mitogen-activated protein kinases (MAPKs), including extracellular-signal-regulated kinase, p38-MAPK, and c-Jun N-terminal kinase. These findings indicate that the novel ligand, TIC10g, is a specific dual inhibitor of endosomal TLRs (TLR7 and TLR9), disrupting MAPK- and NF-κB-mediated proinflammatory gene expression.

Role of Thioredoxin-Interacting Protein in Diseases and Its Therapeutic Outlook.

Thioredoxin-interacting protein (TXNIP), widely known as thioredoxin-binding protein 2 (TBP2), is a major binding mediator in the thioredoxin (TXN) antioxidant system, which involves a reduction-oxidation (redox) signaling complex and is pivotal for the pathophysiology of some diseases. TXNIP increases reactive oxygen species production and oxidative stress and thereby contributes to apoptosis. Recent studies indicate an evolving role of TXNIP in the pathogenesis of complex diseases such as metabolic disorders, neurological disorders, and inflammatory illnesses. In addition, TXNIP has gained significant attention due to its wide range of functions in energy metabolism, insulin sensitivity, improved insulin secretion, and also in the regulation of glucose and tumor suppressor activities in various cancers. This review aims to highlight the roles of TXNIP in the field of diabetology, neurodegenerative diseases, and inflammation. TXNIP is found to be a promising novel therapeutic target in the current review, not only in the aforementioned diseases but also in prolonged microvascular and macrovascular diseases. Therefore, TXNIP inhibitors hold promise for preventing the growing incidence of complications in relevant diseases.

Modeling structure-activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics.

Coronaviruses induce severe upper respiratory tract infections, which can spread to the lungs. The nucleocapsid protein (N protein) plays an important role in genome replication, transcription, and virion assembly in SARS-CoV-2, the virus causing COVID-19, and in other coronaviruses. Glycogen synthase kinase 3 (GSK3) activation phosphorylates the viral N protein. To combat COVID-19 and future coronavirus outbreaks, interference with the dependence of N protein on GSK3 may be a viable strategy. Toward this end, this study aimed to construct robust machine learning models to identify GSK3 inhibitors from Food and Drug Administration-approved and investigational drug libraries using the quantitative structure-activity relationship approach. A non-redundant dataset consisting of 495 and 3070 compounds for GSK3α and GSK3ß, respectively, was acquired from the ChEMBL database. Twelve sets of molecular descriptors were used to define these inhibitors, and machine learning algorithms were selected using the LazyPredict package. Histogram-based gradient boosting and light gradient boosting machine algorithms were used to develop predictive models that were evaluated based on the root mean square error and R-squared value. Finally, the top two drugs (selinexor and ruboxistaurin) were selected for molecular dynamics simulation based on the highest predicted activity (negative log of the half-maximal inhibitory concentration, pIC50 value) to further investigate the structural stability of the protein-ligand complexes. This artificial intelligence-based virtual high-throughput screening approach is an effective strategy for accelerating drug discovery and finding novel pharmacological targets while reducing the cost and time.

Sequence variants in three genes underlying leukodystrophy in Pakistani families.

Leukodystrophies (LDs) are a heterogeneous group of rare and progressive genetic diseases that affect brain, spinal cord, and often the peripheral nerves. They are characterized by abnormal development or destruction of the myelin sheath of the brain. This study was aimed to search for the causative variants in three unrelated consanguineous families presented with LD. Detailed clinical investigations were carried out on probands in three unrelated consanguineous families of Pakistani origin. Targeted gene sequencing and Whole Exome Sequencing (WES) were performed for variant identification. Candidate variants were checked for co-segregation with the phenotype using Sanger sequencing. Public databases including ExAC, gnomAD, dbSNP, and the 1,000 Genome Project were searched to determine frequencies of the alleles. Conservation of the missense variants was ensured by aligning orthologous protein sequences from diverse vertebrate species. Targeted gene sequencing identified a novel homozygous missense mutation [c.2135G > A, p.(Arg712His) in the ATP Binding Cassette Subfamily D Member 1 (ABCD1; OMIM# 300371) in three affected siblings in family A.WES followed by validation by Sanger sequencing revealed previously reported homozygous missense variants [c.162C > A; p.(Asn54Lys)] in ASPA (OMIM# 608034) in family B and [c.361G > C,p.(Gly121Arg)] in ARSA (OMIM# 607574) in family C. Investigation of three families underlies importance of WES as an amazing diagnostic tool for conclusive determination of a specific type of LD. Further, the study would assist in carrier testing and prenatal diagnosis of the affected families. In addition, searching for common variants in the genes causing LD would help in designing low-cost targeted variation testing in patients.