Small molecule drugs for atopic dermatitis, rheumatoid arthritis, and hereditary angioedema

Exploring the mechanisms of artemisinin and its derivatives in the treatment of atopic dermatitis based on network pharmacology and molecular docking: A review

This study investigates the therapeutic mechanisms of artemisinin (ARS) and its derivatives in atopic dermatitis (AD) using network pharmacology and molecular docking. Molecules and disease targets were screened using public databases, including SwissTargetPrediction, PharmMapper, and Genecards. Core targets were identified, and a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape for topological analysis. Relevant data were obtained from the DAVID database for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Molecular docking of ARS and its derivatives with target genes was performed using AutoDock, with results visualized in Pymol. A functional PPI network was established, and molecular docking demonstrated strong binding activity between ARS derivatives and target protein. Mitogen-Activated Protein Kinase14 (MAPK14) and Mitogen-Activated Protein Kinase10 (MAPK10) was found to be a common target for their treatment of AD. ARS and its derivatives may treat AD by modulating pathways such as Prolactin signaling, cancer pathways, neuroactive ligand-receptor interaction, and IL-17 signaling. ARS and its derivatives have the potential to treat AD. Artemisinin, artesunate, dihydroartemisinin, artemether, artemisinin and artemisinone could potentially treat AD by targeting MAPK14 and MAPK10.

Rapamycin ameliorates senescence of periodontal ligament stem cells and promotes their osteogenesis via the PI3K/AKT pathway

Periodontal ligament stem cells (PDLSCs) have been regarded as ideal candidates for tissue regeneration due to their excellent self-renewal and multipotent differentiation ability. Rapamycin (RAPA) is reported to play an important role in the regulation of biological properties of stem cells and a variety of physiological processes. This study investigates whether RAPA could ameliorate the senescence and accelerate the osteogenic differentiation of PDLSCs, particularly the regenerative potential in a rat calvarial bone defect model, and the underlying mechanisms involved. β-galactosidase staining, quantitative real-time polymerase chain reaction, and western blot analysis were performed to assess the effects of RAPA on senescent PDLSCs. The osteogenic differentiation ability of PDLSCs was detected by alkaline phosphatase staining and activity, Alizarin Red S staining, and gene and protein levels of osteogenesis-related markers. The underlying signaling pathways were investigated via RNA transcriptome sequencing analysis and WB tests. Calvarial bone defects in rat were treated with PDLSCs pre-incubated with or without RAPA and/or H2O2. The results showed that RAPA could enhance the osteogenic potentials of PDLSCs via PI3K/AKT signaling pathway, and reversed H2O2-induced senescence and osteogenic differentiation inhibition of PDLSCs. Moreover, calvarial defects transplanted with RAPA-treated PDLSCs showed significantly greater new bone formation compared with other groups, and also improved the H2O2-induced impairment of bone formation, whether by micro-computed tomography examination or by histological analysis. Collectively, RAPA was capable of promoting osteogenic differentiation of PDLSCs via PI3K/AKT signaling pathway in vitro, facilitating calvarial bone regeneration and reversing H2O2-induced impairment of osteogenic differentiation and cell senescence in PDLSCs.

Once-Daily Oral Berotralstat for Long-Term Prophylaxis of Hereditary Angioedema: The Open-Label Extension of the APeX-2 Randomized Trial

BACKGROUND

Berotralstat is a first-line, once-daily oral plasma kallikrein inhibitor approved for prophylaxis of hereditary angioedema (HAE) attacks in patients 12 years or older.

OBJECTIVE

This analysis examined the safety and effectiveness of long-term prophylaxis with berotralstat.

METHODS

APeX-2 was a phase 3, parallel-group, multicenter trial in patients with HAE caused by C1-inhibitor deficiency (NCT03485911). Part 1 was a randomized, double-blind, placebo-controlled evaluation of 150 and 110 mg of berotralstat over 24 weeks. In part 2, berotralstat-treated patients continued the same treatment, and placebo-treated patients were re-randomized to 150 or 110 mg of berotralstat for 24 weeks. In part 3, all patients were treated with open-label berotralstat at 150 mg, which could be continued for up to an additional 4 years. In part 3, the primary endpoint was long-term safety and tolerability. Secondary endpoints included HAE attack rates and quality of life (QoL).

RESULTS

Eighty-one patients entered part 3. Treatment-emergent adverse events (TEAEs) occurred in 82.7% of patients, with most being mild or moderate in severity. The most common TEAEs were nasopharyngitis, urinary tract infection, abdominal pain, arthralgia, coronavirus infection, and diarrhea. Drug-related TEAEs occurred in 14.8% of patients, but none were serious. For patients who completed 96 weeks of berotralstat treatment (n = 70), the mean (standard error) change in attack rate from baseline was -2.21 (0.20) attacks/mo. Clinically meaningful improvements in QoL were also observed, with the largest improvements in the functioning domain.

CONCLUSION

Berotralstat was generally well tolerated, provided rapid and sustained reductions in HAE attacks and improved QoL over 96 weeks.

Is Atopic Dermatitis Only a Skin Disease?

Atopic dermatitis (AD) is a chronic, pruritic, inflammatory dermatosis that imposes significant patient and population burdens. In addition to the cutaneous signs and symptoms, growing evidence suggests that AD is systemic in nature. Certain diseases can possibly co-occur with AD as a result of coincidental exposure to similar environmental factors. However, it is also suspected that they are linked to the pathogenesis of AD through more complex genetic and immunological mechanisms, but these correlations remain less understood. It is of great need to seek explanations for the higher frequency of the number of cardiovascular, autoimmune, neurological, psychiatric, and metabolic disorders that have been observed in epidemiologic investigations among AD patients. Moreover, analysing the immunology of chronic inflammation and its correction, activation, or suppression may prevent the development of a variety of comorbidities. As comorbid diseases in patients diagnosed with AD may potentially go undetected, physicians should be aware of them.

Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition

The accurate prediction of potential associations between microRNAs (miRNAs) and small molecule (SM) drugs can enhance our knowledge of how SM cures endogenous miRNA-related diseases. Given that traditional methods for predicting SM-miRNA associations are time-consuming and arduous, a number of computational models have been proposed to anticipate the potential SM-miRNA associations. However, several of these strategies failed to eliminate noise from the known SM-miRNA association information or failed to prioritize the most significant known SM-miRNA associations. Therefore, we proposed a model of Graph Convolutional Network with Layer Attention mechanism for SM-MiRNA Association prediction (GCNLASMMA). Firstly, we obtained the new SM-miRNA associations by matrix decomposition. The new SM-miRNA associations, as well as the integrated SM similarity and miRNA similarity were subsequently incorporated into a heterogeneous network. Finally, a graph convolutional network with an attention mechanism was used to compute the reconstructed SM-miRNA association matrix. Furthermore, four types of cross validations and two types of case studies were performed to assess the performance of GCNLASMMA. In cross validation, global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation achieved excellent performance. Numerous hypothesized associations in case studies were confirmed by experimental literatures. All of these results confirmed that GCNLASMMA is a trustworthy association inference method.