Abstract
Recent decades have shown a good progress in all the fields of the advanced diagnostic capabilities and personalized medicine. The smart contrast agents (SCAs) that may appear the important element of the proper diagnosis specification should become the standard in a normal clinical practice. Most widely used contrast agents are the kinetically inert and thermodynamically stable complexes of organic ligands with Gd(III) metal ion. However, known safety concerns associated with high toxicity and severe side effects due to demetallization reactions leading to the formation of free Gd(III) ion necessitate the demand in the contrast agents of new generation. It is expected that one of the alternatives will be ML complexes with essential paramagnetic metals such as the Mn(II), Mn(III) and Fe(III). In this study, the experimental data on Mn(II)-based contrast agents (CAs) including the key functional characteristics such as the relaxation enhancement characteristics, kinetic inertness and thermodynamic stability were analyzed. The machine learning methods were used in a combination with the physicochemical and substructure(topology)-based features to estimate the missed experimental data concerned the thermodynamic stability and relaxivity characteristics evaluated at certain conditions. The analysis of the experimental data has shown that the bispidine scaffold can be considered as the reference point for the ligand search as providing with a sufficient compromise for all the investigated functional characteristics of CAs. The analysis of the literature suggests using iodine and fluorine substituents for the electronic and steric hindrance thus preventing the cleavage of the bonds while the histidine residue can be recommended for the pH sensitivity of MRI CAs while targeting the OATPs (organic anion transporting polypeptides) to achieve the hepatobiliary clearance as well as for the enhanced contrast for hepatobiliary system thus personalizing the MRI investigation.
