The c-Abl inhibitor, radotinib induces apoptosis in multiple myeloma cells via mitochondrial-dependent pathway

In-silico study for the identification of potential destabilizers between the spike protein of SARS-CoV-2 and human ACE-2

The emergence of the new SARS-CoV-2 virus, which causes the disease known as COVID-19, has generated a pandemic that has plunged the world into a health crisis. The infection process is triggered by the direct binding of the receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2 to the angiotensin-converting enzyme 2 (ACE2) of the host cell. In the present study, virtual screening techniques such as molecular docking, molecular dynamics, calculation of free energy using the GBSA method, prediction of drug similarity, pharmacokinetic, and toxicological properties of various ligands interacting with the RBD-ACE2 complex were applied. The ligands radotinib, hinokiflavone, and ginkgetin were identified as potential destabilizers of the RBD-ACE2 interaction, which could produce their pharmacological effect by interacting at an allosteric site of ACE2, with affinity energy values of -10.2 ± 0.1, -9.8 ± 0.0, and -9.4 ± 0.0 kcal/mol, indicating strong receptor affinity. The complex with hinokiflavone showed the highest conformational stability and rigidity of the dynamic simulation and also obtained the best binding free energy of the three molecules, with an energy of -215.86 kcal/mol.

Radotinib attenuates TGFβ -mediated pulmonary fibrosis in vitro and in vivo: exploring the potential of drug repurposing

BACKGROUND

Tyrosine kinase (TK) plays a crucial role in the pathogenesis of idiopathic pulmonary fibrosis. Here, we aimed to investigate whether radotinib (Rb) could inhibit pulmonary fibrosis by inhibiting TK in vitro and in vivo.

METHODS

The antifibrotic effects of Rb in transforming growth factor-β (TGF-β)1-stimulated A549 cells were determined using real-time polymerase chain reaction, western blotting, and immunocytochemistry assays. Rb inhibition of bleomycin-induced lung fibrosis in Sprague Dawley (SD) rats was determined by histopathological and​ immunohistochemical analyses. Rb-interfering metabolites were analyzed using LC-MS/MS.

RESULTS

Rb concentrations of up to 1000 nM did not affect the viability of A549 cells, but Rb (30 nM) significantly reduced expression of TGF-β1 (10 ng/mL)-induced ECM factors, such as Snail, Twist, and F-actin. Rb also regulated TGF-β1-overexpressed signal cascades, such as fibronectin and α-smooth muscle actin. Furthermore, Rb attenuated the phosphorylation of Smad2 and phosphorylation of kinases, such as, extracellular signal-regulated kinase, and protein kinase B. In the inhibitory test against bleomycin (5 mg/kg)-induced lung fibrosis, the Rb (30 mg/kg/daily)-treated group showed a half-pulmonary fibrosis region compared to the positive control group. In addition, Rb significantly reduced collagen type I and fibronectin expression in the bleomycin-induced fibrotic region of SD rats. Further, the identified metabolite pantothenic acid was not altered by Rb.

CONCLUSION

Taken together, these results indicate that Rb inhibits TGF-β1-induced pulmonary fibrosis both in vitro and in vivo. These findings suggest that Rb may be an effective treatment for pulmonary fibrosis-related disorders and idiopathic pulmonary fibrosis.

Radotinib inhibits multiple myeloma cell proliferation via suppression of STAT3 signaling

Multiple myeloma (MM) is a hematological cancer causing from accumulated abnormal plasma cells. STAT3 overexpression in MM appears to be mediated by a variety of factors, and it may be associated with an adverse prognosis and play a role in microenvironment-dependent treatment resistance. Unfortunately, MM remains an incurable disease, as relapse is very common. Therefore, there is urgent need to develop new treatment options for MM. Radotinib is a novel anti-cancer drug, currently approved in South Korea for the treatment of chronic myeloid leukemia patients. It is an oral, multitargeted inhibitor of receptor tyrosine kinases, including BCR-ABL, c-KIT, PDGFR, and Src family kinases. However, little is known about the effects of radotinib on multiple myeloma cells. However, little is known about the effects of radotinib on multiple myeloma cells. But even tinip almost not known about the impact of multiple myeloma cells. Moreover, nothing is known about how it affects STAT3 and JAK2. In this study, we analyzed the effect of radotinib on multiple myeloma cells. Herein, Moreover, nothing is known about how it. Moreover, not all is known about how the affects STAT3 and JAK2. We investigated the effect of radotinib on the STAT3 signaling pathway in MM cells, including several MM cell lines and mouse models. So we investigated the effect of radotinib on MM cells, including several MM cell lines and mouse models. Interestingly, radotinib induced apoptosis, and inhibited cell proliferation in MM cells including RPMI-8226, MM.1S, U266B1, and IM-9 cells. Moreover, radotinib treatment significantly increased the number Annexin V-positive cells and G0/G1-phase cells. In addition, radotinib treatment in various MM cells strongly suppressed the activity and expression of STAT3 and JAK2 proteins. We also observed that diverse proteins related to the STAT3 signaling pathway, including c-Myc, Bcl-xL, Mcl-1, cyclin D1 and cyclin D3, were powerfully inhibited by radotinib treatment in MM cells. Furthermore, radotinib significantly suppressed MM cell growth in a xenograft animal model using IM-9 cells. In conclusion, radotinib may play an important role as a candidate agent for MM treatment.

Network controllability solutions for computational drug repurposing using genetic algorithms

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdős-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.