Predições ADMET e por bioquímica quântica de bioativos com potencial antimicrobiano ou neuroativo

Abstract

The growing demand for new drugs with neuroactive effects as well as for antimicrobial drugs motivated this study to predict ADMET (absorption, distribution, metabolism, excretion and toxicity) profiles of bioactive compounds with eugeroic (modafinil), noradrenergic (atomoxetine), antiviral (tecovirimat) and antichagasic effects and to analyze them using quantum biochemical techniques. With the help of advanced computational tools such as ADMETlab 2.0, admetSAR, FAF-Drugs4, PreADMET, pkCSM and PRED-HERG were used to evaluate the pharmacokinetic and toxicological profiles of modafinil, highlighting its hepatic and mutagenic toxicity; atomoxetine, highlighting its hepatic, cardiovascular and mutagenic toxicity; and tecovirimat, highlighting its hepatic, respiratory and mutagenic toxicity. Subsequently, the focus was directed towards Chagas disease and was selected six compounds with antichagasic potential, among which the compound BZTS stood out for meeting all the rules of medicinal chemistry and had a favorable ADMET profile with a lower toxicity risk. Molecular modeling studies and density functional theory (DFT)-based calculations revealed robust and specific interactions between BZTS and the cruzipain enzyme, the therapeutic target of Trypanosoma cruzi, highlighting its stability and affinity in the ligand-receptor complex resulting from interactions with the amino acids GLU208 (-10.2 kcal/mol), MET68 (-4.75 kcal/mol), LEU67 (-4.08 kcal/mol), ASN69 (-3.68 kcal/mol), LEU160 (-3.38 kcal/mol), ALA138 (-1.98 kcal/mol), GLU117 (-1.97 kcal/mol) and ASP161 (-1.68 kcal/mol). In addition, the limiting orbitals HOMO (-5.85 eV) and LUMO (-3.37 eV) as well as the chemical-quantum descriptors - ionization potential (5.85 eV), electronic affinity (3.37 eV), chemical hardness (1.24 eV), softness (0.81 eV), chemical potential (4.61 eV), electronegativity (-4.61 eV) and electrophilicity (13.19 eV) - confirmed the ability of BZTS to interact effectively with biological targets, highlighting its potential as a therapeutic agent. This study highlights the effectiveness of in silico methods in the identification and characterization of promising bioactive compounds, with BZTS emerging as a promising candidate for the treatment of Chagas disease. The integration of computational prediction into drug development is critical to accelerate the discovery and optimization of safer and more effective drugs, making an important contribution to future advances in pharmacology and public health.