Synthesis and evaluation of dihydrofuro[2,3-b]pyridine derivatives as potent IRAK4 inhibitors

Construction of IRAK4 inhibitor activity prediction model based on machine learning

Interleukin-1 receptor-associated kinase 4 (IRAK4) is a crucial serine/threonine protein kinase that belongs to the IRAK family and plays a pivotal role in Toll-like receptor (TLR) and Interleukin-1 receptor (IL-1R) signaling pathways. Due to IRAK4's significant role in immunity, inflammation, and malignancies, it has become an intriguing target for discovering and developing potent small-molecule inhibitors. Consequently, there is a pressing need for rapid and accurate prediction of IRAK4 inhibitor activity. Leveraging a comprehensive dataset encompassing activity data for 1628 IRAK4 inhibitors, we constructed a prediction model using the LightGBM algorithm and molecular fingerprints. This model achieved an R2 of 0.829, an MAE of 0.317, and an RMSE of 0.460 in independent testing. To further validate the model's generalization ability, we tested it on 90 IRAK4 inhibitors collected in 2023. Subsequently, we applied the model to predict the activity of 13,268 compounds with docking scores less than - 9.503 kcal/mol. These compounds were initially screened from a pool of 1.6 million molecules in the chemdiv database through high-throughput molecular docking. Among these, 259 compounds with predicted pIC50 values greater than or equal to 8.00 were identified. We then performed ADMET predictions on these selected compounds. Finally, through a rigorous screening process, we identified 34 compounds that adhere to the four complementary drug-likeness rules, making them promising candidates for further investigation. Additionally, molecular dynamics simulations confirmed the stable binding of the screened compounds to the IRAK4 protein. Overall, this work presents a machine learning model for accurate prediction of IRAK4 inhibitor activity and offers new insights for subsequent structure-guided design of novel IRAK4 inhibitors.

Quercetin inhibits NF-kB and JAK/STAT signaling via modulating TLR in thymocytes and splenocytes during MSG-induced immunotoxicity: an in vitro approach

BACKGROUND

The most widely used food additive monosodium glutamate (MSG) has been linked to immunopathology. Conversely, quercetin (Q), a naturally occurring flavonoid has been demonstrated to have immunomodulatory functions. Therefore, the purpose of the study is to determine if quercetin can mitigate the deleterious effects of MSG on immune cells, and the possible involvement of TLR, if any.  METHODS AND RESULTS: This study was conducted on Q, to determine how it affects the inflammatory response triggered by MSG in primary cultured thymocytes and splenocytes from rats (n = 5). Q shielded cells by augmenting cell survival and decreasing lactate dehydrogenase leakage during MSG treatment. It decreased IL-1β, IL-6, IL-8, and TNF-α expression and release by hindering NF-kB activation and by inhibiting the JAK/STAT pathway. Moreover, Q prevented NLRP3 activation, lowered IL-1β production, and promoted an anti-inflammatory response by increasing IL-10 production. Q reduced MSG-induced cellular stress and inflammation by acting as an agonist for PPAR-γ and LXRα, preventing NF-kB activation, and lowering MMP-9 production via increasing TIMP-1. Additionally, Q neutralized free radicals, elevated intracellular antioxidants, and impeded RIPK3, which is involved in inflammation induced by oxidative stress, TNF-α, and TLR agonists in MSG-treated cells. Furthermore, it also modulated TYK2 and the JAK/STAT pathway, which exhibited an anti-inflammatory effect.

CONCLUSIONS

MSG exposure is associated with immune cell dysfunction, inflammation, and oxidative stress, and Q modulates TLR to inhibit NF-kB and JAK/STAT pathways, providing therapeutic potential. Further research is warranted to understand Q's downstream effects and explore its potential clinical applications in inflammation.