Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 2, 2023
Date Accepted: Mar 18, 2024
The urge for paradigm-shift: mining real-world big data to characterize adverse drug reaction quantitatively
ABSTRACT
Background:
Clinical drug toxicity is a major concern in precision clinical medicine. A comprehensive evaluation of clinical drug toxicity is helpful for unbiased supervision of marketed drugs and the discovery of new drugs with high success rates.
Objective:
Nowadays, evaluation of clinical drug toxicity is often oversimplified to occurrence or non-occurrence of adverse drug reactions (ADRs). Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and accurate assessment of clinical drug toxicity.
Methods:
In this study, we developed an ADReCS severity-grading model for quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The mathematical model was constructed by mining millions of real-world historical adverse drug event (ADE) reports. A new parameter called Severity_score was introduced to measure the ADR severity, and upper and lower score boundaries were determined for five severity grades.
Results:
The ADReCS severity-grading model showed excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events (CTCAE). Hence, we graded the severity of 6,277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6,272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanism and thereby discovered a list of improper dosage drugs.
Conclusions:
In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. The endeavor establishes a strong foundation for future artificial intelligence applications in the discovery of new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research from qualitative description to quantitative evaluation.
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