Abstract
Effectiveness of chemotherapy treatment for brain-related disorders is primarily limited by the difficulty these drugs have, in crossing the Blood-Brain Barrier (BBB). With the help of artificial intelligence, it is possible to predict the ability of a molecule or drug to cross the BBB, in silico. By initially predicting the values of 17 physicochemical and topological structural descriptors of 136 drugs approved by the Food and Drug Administration (FDA) or European Medicines Agency (EMA) recognized for their recreational or therapeutic effects on the central nervous system, it was possible to establish the structural profile of any drug or molecule that penetrates the BBB. This structural profile was settled using algorithms developed by the company Chemaxon and published in the Drug Data Bank. In a second step, the structural profile of 60 approved antitumor drugs, regardless of the type of cancer they target, was set up using their physicochemical and topological descriptors. Comparing the structural profile of these antitumor drugs with those of drugs known to act on the Central Nervous System (CNS) enabled us to select, those anticancer drugs, that cross the blood-brain barrier and could be suitable for treating brain tumors, like gliomas, and glioblastomas. To verify our approach, we established and compared the structural profile of the five main drugs clinically used and recommended for the treatment of glioblastoma to the reference structural profile of drugs that penetrate the blood-brain barrier. The results confirmed clinical observations, that only few anticancer drugs are effective in treating glioblastoma due to the limited or absent penetration of the BBB. Furthermore, developing the physicochemical and structural topological profile of molecules that penetrate the BBB can help to reorient drugs for a new therapeutic application related to the CNS and assist the medicinal chemist in the design of molecules targeting brain pathologies.
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