Impact of Clinical Parameters in the Intrahost Evolution of HIV-1 Subtype B in Pediatric Patients: A Machine Learning Approach

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Effect of HIV/HCV Co-Infection on the Protease Evolution of HIV-1B: A Pilot Study in a Pediatric Population

This pilot study evaluates in pediatric patients the impact of HIV/HCV coinfection in the molecular evolution of the HIV-1 subtype B protease (HIV-1BPR). For this study, HIV-1B/HCV coinfected (15) and HIV-1B monoinfected (56) patients with available HIV-1B pol sequences were enrolled. Both groups of patients had comparable gender frequencies and average age, time of infection, antiretroviral treatment (ART) exposure and time under ART. Prevalence of drug resistance mutations (DRM), genetic diversity, number of synonymous (d_S) and non-synonymous (d_N) mutations per site and selection pressures (d_N - d_S) in the HIV-1BPR were estimated and compared between mono- and coinfected patients. Both HIV-1B populations presented similar genetic diversity (0.050 ± 0.02 vs. 0.045 ± 0.01) and d_S (0.074 ± 0.03 vs. 0.078 ± 0.04). In turn, in coinfected patients the HIV-1BPR had higher d_N (0.045 ± 0.01 vs. 0.024 ± 0.01) and d_N-d_S (-0.026 ± 0.02 vs. -0.048 ± 0.04) values, and less amino acid sites under purifying selection (4.2% vs. 42.1%) than in monoinfected patients. Accordingly, in co-infection with HCV, the HIV-1BPR sites 50, 53, 82, 84 and 88 - associated with resistance to PIs - were under neutral evolution, whereas these sites were under purifying selection in monoinfected patients. This pilot study suggests that HIV-1B may evolve differently in the presence than in the absence of HCV.