Voxtalisib Hydrochloride

Although hypertrophic scarring affects ∼91% of burn patients annually, there is no drug to prevent this common complication. Hypertrophic scars are a result of dysregulated wound healing, characterised by persistent myofibroblast transformation and the excessive accumulation of extracellular matrix (ECM). Due to the multi-mechanistic nature of the scarring process, target-based approaches for identifying novel drugs have failed. Primary human dermal fibroblasts, derived from burn scar tissue, were exposed to transforming growth factor-beta 1 (TGF-β1) to induce myofibroblast transformation. A phenotypic screening assay, measuring alpha-smooth muscle actin (α-SMA) expression, was developed to screen 1,954 approved drugs. Drugs that elicited >80% inhibition of α-SMA expression, and >80% cell viability were progressed as candidate drugs. Anti-myofibroblast activity of the candidates was confirmed before investigating their effects on extracellular matrix (ECM) production and keratinocyte epithelial-mesenchymal transition (EMT). TGF-β1 induced myofibroblast transformation in primary human dermal fibroblasts (E_max = >3 ng/mL). The assay was optimised and validated (Z' = 0.59), before screening 1,954 approved drugs. 90 drugs were identified as hits and hydroxypyridone anti-fungals selected for further testing. Concentration-response curves for these drugs confirmed their concentration-dependent anti-myofibroblast activity (IC₅₀ = 1.4 - 16.7 μM). Hydroxypyridone anti-fungals were also found to successfully reduce ECM production and keratinocyte EMT. This is the first study to screen approved drugs in primary human dermal fibroblasts. Hydroxypyridone anti-fungals were found to prevent myofibroblast transformation, ECM production and keratinocyte EMT suggesting they could be repurposed to prevent hypertrophic scarring.

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP-BEZ235, GDC-0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.