A computational approach for classification of HIV drug resistance based on the self-consistent extreme classifier

The development of viral resistance can significantly reduce the effectiveness of therapy. Human immunodeficiency virus type 1 is the cause of chronic immune dysfunction, leading to the development of co-infections and serious complications. Despite worldwide progress and consolidated efforts to overcome HIV drug resistance, the development of novel approaches for rational drug therapy of HIV infection is still needed for building models with high accuracy of prediction and that can be applied for evaluation of resistance against wide variety of inhibitors. Our study is dedicated to the development of a novel computational ML-driven approach for the ternary classification of HIV protease, reverse transcriptase, and integrase sequences. Binary classification approaches naturally are not applicable to capture clinically important intermediate resistance levels, motivating the use of a ternary classification model.

For the model development we used the Self-Consistent Extreme Classifier. One-versus-rest and one-versus-one ternary approaches were applied to sequences related resistance data from Stanford University HIV Drug Resistance Database (StDB).

For the final classifiers we selected the most appropriate models with 0.913 sensitivity, 0.894 specificity, 0.741 precision and 0.953 area under ROC, all values provided in average. We tested our approach in a clinical task and performed prospective validation for eight sequences of HIV protease and reverse transcriptase obtained from treatment-naive HIV-positive male patients. We performed a prediction and compared the results with the therapeutic outcome, in particular, with the viral load decline at 24 weeks.

The results of the prospective validation are generally consistent with the results of the therapeutic outcome and confirm the possibility of using the developed approach for the selection of the most appropriate therapeutic regimens.