Artificial Intelligence and Advanced Melanoma: Treatment Management Implications.

Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by "intelligent" machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.

Quantum Chemical and Molecular Dynamics Studies of MUC1 Calix[4,8]arene Scaffold Based Anticancer Vaccine Candidates.

Functional antitumor vaccine constructs are the basis for active tumor immunotherapy, which is useful in the treatment of many types of cancers. MUC1 is one key glycoprotein for targeting and designing new strategies for multicomponent vaccines. Two self-adjuvant tetravalent vaccine candidates were prepared by clustering four or eight PDTRP MUC1 core epitope sequences on calixarene scaffolds. In this work, the different activities of two molecules with calix[4]arene and calix[8]arene skeleton are rationalized. Quantum mechanics, docking, and molecular dynamics structural optimization were first carried out followed by metadynamics to calculate the energy profiles. Further insights were obtained by complementarity studies of molecular fields. The molecular modeling results are in strong agreement with the experimental in vivo immunogenicity data. In conclusion, the overall data shows that, in the designing of anticancer vaccines, scaffold flexibility has a pivotal role in obtaining a suitable electrostatic, hydrophobic, and steric complementarity with the biological target.

New Brilliant Blue G Derivative as Pharmacological Tool in Retinal Surgery.

Our study was aimed at assessing the retinal binding of a new synthetic Brilliant Blue G (BBG) derivative (pure benzyl-Brilliant Blue G; PBB) ophthalmic formulation, to improve vitreoretinal surgery procedure. Protein affinity of the new molecule was evaluated in vitro (cell-free assay) and in silico. Furthermore, an ex vivo model of vitreoretinal surgery was developed by using porcine eyes to assess the pharmacological profile of PBB, compared to commercial formulations based on BBG and methyl-BBG (Me-BBG). PBB showed a higher affinity for proteins (p < 0.05), compared to BBG and Me-BBG. In vitro and in silico studies demonstrated that the high selectivity of PBB could be related to high lipophilicity and binding affinity to fibronectin, the main component of the retinal internal limiting membrane (ILM). The PBB staining capabilities were evaluated in porcine eyes in comparison with BBG and Me-BBG. Forty microliters of each formulation were slowly placed over the retinal surface and removed after 30 s. After that, ILM peeling was carried out, and the retina collected. BBG, Me-BBG, and PBB quantification in ILM and retina tissues was carried out by HPLC analysis. PBB levels in the ILM were significantly (p < 0.05) higher compared to BBG and Me-BBG formulations. On the contrary, PBB showed a much lower (p < 0.05) distribution in retina (52 ng/mg tissue) compared to BBG and Me-BBG, in particular PBB levels were significantly (p < 0.05) lower. Therefore, the new synthetic Brilliant Blue derivative (PBB) showed a great ILM selectivity in comparison to underneath retinal layers. In conclusion, these findings had high translational impact with a tangible improving in ex vivo model of retinal surgery, suggesting a future use during surgical practice.