Abstract

Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. The effectivenes of cancer vaccines in inducing CD8+Tcell responses remains a challenge, resulting in a need for testing more potent adjuvants. In previous clinical trials it has been determined the safetyand immunogenicity of vaccination against melanoma-related antigens employing MART-1,gp100, and tysosinase paptides combined with the TLR-9 agonist PF-3512676 and local GM-CSFin-oil emulsion.Using continuous monitoring of safety and a two-stage design for immunological efficacy, More than 20 immune-response evaluable patients were targetted. Vaccinations were given subcutaneously ondays 1 and 15 per cycle (1 cycle=28 days) for up to 13 cycles. Structure-based virtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. That said, virtual screening is heavily dependent on detailed understanding of the tertiary or quaternary structure of the protein target of interest, including knowledge of the relevant binding pocket. Here, in Biogenea we have for the first time discovered a Computer-aided new cluster Simulation of algorithms and a Ligand-Based Virtual Screening approach through a Support Vector and Information Fusion Bayesian Machine on Glioma Growth Morphology generation of a MART-1 (26-35,27L), gp100 (209-217, 210M), and tyrosinase (368-376, 370D) mimicking activator with a promising PF-3512676 and GM-CSF clinical outcome in metastatic melanoma.

Keywords

Computer Simulation, Glioma Growth, Morphology Computer designed, Safe, immunogenic, pharmacophoric activator, mimicking physicochemical properties, MART-1 (26-35,27L), gp100 (209-217, 210M), tyrosinase (368-376, 370D) inadjuvant, PF-3512676 and GM-CSF, clinical outcome, metastatic melanoma, new cluster, algorithms, Ligand-Based Virtual Screening approach, Support Vector, Information Fusion Bayesian Machine, Computer Simulation of Glioma Growth and Morphology; glioma, glioblastoma, computer simulation, 3-D, tumor growth, tumor morphology, mathematical model, cancer model.