Abstract Tuberculosis (TB) and human immunodeficiency virus (HIV) coinfection is a severe global health challenge with high morbidity and mortality rates. In this study, we employed a novel bioinformatics approach to gain a comprehensive understanding of the state of TB/HIV coinfection. Using a multi-dimensional graph-based clustering methodology coupled with systems biology, cheminformatics, and predictive modeling, we identified several key pathways associated with infectious and autoimmune diseases, immune and inflammatory responses, cardiovascular dysfunctions, and metabolic processes. We discovered therapeutic biomarkers regulated by established therapeutic chemicals and developed robust machine learning-based quantitative structure-activity relationship (ML-QSAR) models to identify effective drug candidates. Our models successfully identified S5105 proanthocyanidin as a promising modulator of key inflammatory biomarkers TNF-α, IL1B, and IFNG. Furthermore, we analyzed the influence of environmental factors, such as arsenic, air pollutants, and carbon monoxide, on the progression and occurrence of TB/HIV coinfection. Our findings revealed that these toxicants can trigger a cascade of inflammatory responses, leading to lung fibrosis and a cytokine storm that exacerbates the existing immune dysregulation in coinfected individuals. Additionally, the impact of air pollutants on cardiovascular health and neurological complications, such as AIDS-Dementia Complex, adds to the complexity of managing TB/HIV coinfection. Thus, a multidimensional methodology can significantly enhance drug discovery and environmental toxicological efforts by integrating diverse data sources and analytical techniques to uncover complex interactions, identify potential therapeutic targets, and assess the impact of environmental toxins on health with greater precision.
Shyamal et al. (Thu,) studied this question.