Desenvolvimento de modelos de QSAR para identificação de substratos e inibidores de CYP3A4 - Silva, Flávia Cristina da (2015)
The discovery and development of drugs consist of a complex process, requiring the integration of various strategic areas such as knowledge, innovation, technology, management and high investments in Research, Development and Innovation (RD&I). No drug can be approved for use in humans without first go through extensive studies aimed at ensuring its effectiveness and safety. On the other hand, a drug that inhibits the activity of a metabolic enzyme cytochrome P450 family (CYP450) can affect the pharmacokinetics of other drugs, resulting in drug-drug interactions (DDIs), which potentially lead to side effects and toxic effects. The main oxidative enzymes responsible for drug metabolism have as main representatives CYP450 superfamily, wherein the CYP3A4 isoform is the most important because it is responsible for metabolizing approximately 50% of the drugs on the market. Several computational methods have been developed as a strategy to predict human metabolism in the early stages of research and development of drugs. In silico models of metabolism have advantages such as faster, lower cost and ease of operation when compared to traditional models in vitro and in vivo. The work aimed mainly at the development of Quantitative Relations between models chemical structure and activity / property (QSAR / QSPR) robust and predictive, to identify CYP3A4 substrates and inhibitors. To this were collected, integrated and prepared larger data sets available in the literature substrates and inhibitors of CYP3A4. Several QSAR models were generated and validated for both properties using a workflow that contemplated carefully the recommendations of the Organization for Economic Co-operation Development (OECD). The combination of different descriptors and machine learning methods have led to obtain robust and predictive QSAR models, with correct classification rate (CCR) ranging from 0.65 to 0.83 and 0.69 to 0.89 of coverage, showing a statistically significant values for classification of compounds with high accuracy whether or not substrates of CYP3A4 substrates. The binary Morgan RFgenerated model to classify compounds inhibitors and non-inhibitors also proved highly robust and predictive with sensitivity values of 0.77 and accuracy of 0.76, and the Morgan-RF model multiclass obtained values of 0.68 sensitivity and 0.69 for accuracy. The map of predicted probability proved useful as it could encode major structural fragments to classify compounds inhibitors or not CYP3A4 inhibitors. In conclusion, have been developed and validated many QSAR to predict the interaction with the CYP450 enzyme that may be useful in the early stages of the development of new drugs. The next step is the online availability of the models obtained in LabMol server (http://labmol.farmacia.ufg.br).