Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia

Simple Summary Tyrosine kinase inhibitor (TKI) therapy is often taken indefinitely by chronic myeloid leukemia (CML) patients. However, little is known about the long-term or under-reported TKI side effects due to limited longitudinal clinical data on these relatively new drugs. We utilize novel cross-domain text mining and machine learning to comprehensively assess relationships between TKIs and possible adverse events (AEs) across 30+ million PubMed publications. The top 10% of predicted AEs were mapped to ten physiological function-based foci: hematology, glucose, iron, cardiovascular, thyroid, inflammation, kidney, gastrointestinal, neuromuscular, and others. Study results provided new insight into the breadth of adverse events with TKI usage. Results-based recommendations were devised for proactive patient monitoring protocols as a function of perceived AE risk. Additionally, AEs were mapped to specific TKI types to assist in personalized TKI selection. Tyrosine kinase inhibitors (TKIs) are prescribed for chronic myeloid leukemia (CML) and some other cancers. The objective was to predict and rank TKI-related adverse events (AEs), including under-reported or preclinical AEs, using novel text mining. First, k-means clustering of 2575 clinical CML TKI abstracts separated TKIs by significant (p < 0.05) AE type: gastrointestinal (bosutinib); edema (imatinib); pulmonary (dasatinib); diabetes (nilotinib); cardiovascular (ponatinib). Next, we propose a novel cross-domain text mining method utilizing a knowledge graph, link prediction, and hub node network analysis to predict new relationships. Cross-domain text mining of 30+ million articles via SemNet predicted and ranked known and novel TKI AEs. Three physiology-based tiers were formed using unsupervised rank aggregation feature importance. Tier 1 ranked in the top 1%: hematology (anemia, neutropenia, thrombocytopenia, hypocellular marrow); glucose (diabetes, insulin resistance, metabolic syndrome); iron (deficiency, overload, metabolism), cardiovascular (hypertension, heart failure, vascular dilation); thyroid (hypothyroidism, hyperthyroidism, parathyroid). Tier 2 ranked in the top 5%: inflammation (chronic inflammatory disorder, autoimmune, periodontitis); kidney (glomerulonephritis, glomerulopathy, toxic nephropathy). Tier 3 ranked in the top 10%: gastrointestinal (bowel regulation, hepatitis, pancreatitis); neuromuscular (autonomia, neuropathy, muscle pain); others (secondary cancers, vitamin deficiency, edema). Results suggest proactive TKI patient AE surveillance levels: regular surveillance for tier 1, infrequent surveillance for tier 2, and symptom-based surveillance for tier 3.

Automated summary

This study comprehensively assessed the relationship between TKIs and adverse events using text mining and machine learning methods. It provided new insights into the breadth of adverse events caused by TKI usage and made recommendations for proactive monitoring and personalized drug selection.