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Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.
Yue, Qi-Xuan; Ding, Ruo-Fan; Chen, Wei-Hao; Wu, Lv-Ying; Liu, Ke; Ji, Zhi-Liang.
Afiliação
  • Yue QX; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
  • Ding RF; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
  • Chen WH; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
  • Wu LY; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
  • Liu K; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
  • Ji ZL; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
J Med Internet Res ; 26: e48572, 2024 May 03.
Article em En | MEDLINE | ID: mdl-38700923
ABSTRACT

BACKGROUND:

Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates.

OBJECTIVE:

In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety.

METHODS:

In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades.

RESULTS:

The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 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 mechanisms and thereby discovered a list of drugs with improper dosages.

CONCLUSIONS:

In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos / Mineração de Dados / Big Data Limite: Humans Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Canadá

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos / Mineração de Dados / Big Data Limite: Humans Idioma: En Revista: J Med Internet Res Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Canadá