Publications by authors named "G Amendola"

Non-small cell lung cancer (NSCLC), the leading cause of cancer-related mortality worldwide, poses a formidable challenge due to its heterogeneity and the emergence of resistance to targeted therapies. While initially effective, first- and third-generation EGFR-tyrosine kinase inhibitors (TKIs) often fail to control disease progression, leaving patients with limited treatment options. To address this unmet medical need, we explored the therapeutic potential of multitargeting agents that simultaneously inhibit two key signalling pathways, the mesenchymal-epithelial transition factor (c-MET) and the G protein-coupled receptor Smoothened (SMO), frequently dysregulated in NSCLC.

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Cancer's persistent growth often relies on its ability to maintain telomere length and tolerate the accumulation of DNA damage. This study explores a computational approach to identify compounds that can simultaneously target both G-quadruplex (G4) structures and poly(ADP-ribose) polymerase (PARP)1 enzyme, offering a potential multipronged attack on cancer cells. We employed a hybrid virtual screening (VS) protocol, combining the power of machine learning with traditional structure-based methods.

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Transient abnormal myelopoiesis (TAM) is a Down syndrome-related pre-leukaemic condition characterized by somatic mutations in the haematopoietic transcription factor GATA-1 that result in exclusive production of its shorter isoform (GATA-1). Given the common hallmark of altered miRNA expression profiles in haematological malignancies and the pro-leukaemic role of GATA-1, we aimed to search for miRNAs potentially able to modulate the expression of GATA-1 isoforms. Starting from an prediction of miRNA binding sites in the GATA-1 transcript, miR-1202 came into our sight as potential regulator of GATA-1 expression.

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Angular pregnancies are rare and difficult to diagnose. Evidence suggests they are associated with a higher risk of intrauterine growth restriction and abnormal third stage of labor due to a retained placenta. The lack of standardized AP diagnostic criteria impacts on their correct identification and makes the treatment of potential complications challenging.

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Article Synopsis
  • The text introduces PyRMD2Dock, a new computational tool that merges the Ligand-Based Virtual Screening (LBVS) tool PyRMD with AutoDock-GPU, improving the efficiency of virtual screening in drug discovery.
  • It highlights that PyRMD2Dock allows researchers to quickly analyze large chemical databases to find compounds with the strongest binding affinity to target proteins.
  • The study emphasizes the effectiveness and speed of PyRMD2Dock, demonstrating its potential to expedite the identification of new drug candidates, and notes that it's an open-source tool available on GitHub.
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