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

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.