Chlorotoxin fusion protein regulates miR-374a and TNFAIP8 expression and inhibits glioma cell proliferation and promotes apoptosis

Comparison of Oncogenes, Tumor Suppressors, and MicroRNAs Between Schizophrenia and Glioma: The Balance of Power

The risk of cancer in schizophrenia has been controversial. Confounders of the issue are cigarette smoking in schizophrenia, and antiproliferative effects of antipsychotic medications. The author has previously suggested comparison of a specific cancer like glioma to schizophrenia might help determine a more accurate relationship between cancer and schizophrenia. To accomplish this goal, the author performed three comparisons of data; the first a comparison of conventional tumor suppressors and oncogenes between schizophrenia and cancer including glioma. This comparison determined schizophrenia has both tumor-suppressive and tumor-promoting characteristics. A second, larger comparison between brain-expressed microRNAs in schizophrenia with their expression in glioma was then performed. This identified a core carcinogenic group of miRNAs in schizophrenia offset by a larger group of tumor-suppressive miRNAs. This proposed "balance of power" between oncogenes and tumor suppressors could cause neuroinflammation. This was assessed by a third comparison between schizophrenia, glioma and inflammation in asbestos-related lung cancer and mesothelioma (ALRCM). This revealed that schizophrenia shares more oncogenic similarity to ALRCM than glioma.

An update on dual targeting strategy for cancer treatment

The key issue in the treatment of solid tumors is the lack of efficient strategies for the targeted delivery and accumulation of therapeutic cargoes in the tumor microenvironment (TME). Targeting approaches are designed for more efficient delivery of therapeutic agents to cancer cells while minimizing drug toxicity to normal cells and off-targeting effects, while maximizing the eradication of cancer cells. The highly complicated interrelationship between the physicochemical properties of nanoparticles, and the physiological and pathological barriers that are required to cross, dictates the need for the success of targeting strategies. Dual targeting is an approach that uses both purely biological strategies and physicochemical responsive smart delivery strategies to increase the accumulation of nanoparticles within the TME and improve targeting efficiency towards cancer cells. In both approaches, either one single ligand is used for targeting a single receptor on different cells, or two different ligands for targeting two different receptors on the same or different cells. Smart delivery strategies are able to respond to triggers that are typical of specific disease sites, such as pH, certain specific enzymes, or redox conditions. These strategies are expected to lead to more precise targeting and better accumulation of nano-therapeutics. This review describes the classification and principles of dual targeting approaches and critically reviews the efficiency of dual targeting strategies, and the rationale behind the choice of ligands. We focus on new approaches for smart drug delivery in which synthetic and/or biological moieties are attached to nanoparticles by TME-specific responsive linkers and advanced camouflaged nanoparticles.

An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2

The TIPE2 (tumor necrosis factor-alpha-induced protein 8-like 2) protein is a major regulator of cancer and inflammatory diseases. The availability of its sequence and structure, as well as the critical amino acids involved in its ligand binding, provides insights into its function and helps greatly identify novel drug candidates against TIPE2 protein. With the current advances in deep learning and molecular dynamics simulation-based drug screening, large-scale exploration of inhibitory candidates for TIPE2 becomes possible. In this work, we apply deep learning-based methods to perform a preliminary screening against TIPE2 over several commercially available compound datasets. Then, we carried a fine screening by molecular dynamics simulations, followed by metadynamics simulations. Finally, four compounds were selected for experimental validation from 64 candidates obtained from the screening. With surprising accuracy, three compounds out of four can bind to TIPE2. Among them, UM-164 exhibited the strongest binding affinity of 4.97 µM and was able to interfere with the binding of TIPE2 and PIP2 according to competitive bio-layer interferometry (BLI), which indicates that UM-164 is a potential inhibitor against TIPE2 function. The work demonstrates the feasibility of incorporating deep learning and MD simulation in virtual drug screening and provides high potential inhibitors against TIPE2 for drug development.