PDF(Pubmed)
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.
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.
摘要：
背景：药物不良反应（ADR），这是人类临床药物毒性的表型表现，是精准临床医学的主要关注点。对药品不良反应的综合评价有助于对上市药品进行无偏监管，有助于发现成功率高的新药。
目标：在目前的实践中，药物安全性评价往往过于简化为ADR的发生或不发生。鉴于目前定性方法的局限性，迫切需要一种定量评价模型来提高药物警戒性和药物安全性的准确评估。
方法：在本研究中，我们建立了一个数学模型,即药品不良反应分类系统(ADReCS)严重程度分级模型，对于ADR严重程度的定量表征，评估ADR对人类健康影响的关键特征。该模型是通过挖掘数百万个真实世界的历史药物不良事件报告来构建的。引入了一个名为Severity_score的新参数来衡量ADR的严重程度，并确定了5个严重程度等级的评分上限和下限.
结果：ADReCS严重性分级模型与专家分级系统表现出优异的一致性（99.22％），不良事件的通用术语标准。因此，我们对129,407个药物-ADR对的6277个标准ADR的严重程度进行了分级.此外，我们通过挖掘真实世界的药物处方,计算了大量患者人群中127,763对药物-ADR对的6272种不同ADR的发生率.有了定量特征，我们展示了在系统阐明ADR机制方面的应用实例,从而发现了一系列剂量不当的药物.
结论：总之，本研究首次全面确定了ADR严重程度和ADR频率.这项努力为未来人工智能在发现高效低毒新药方面的应用奠定了坚实的基础。这也预示着临床毒性研究的范式转变，从定性描述转向定量评价。
目标：在目前的实践中，药物安全性评价往往过于简化为ADR的发生或不发生。鉴于目前定性方法的局限性，迫切需要一种定量评价模型来提高药物警戒性和药物安全性的准确评估。
方法：在本研究中，我们建立了一个数学模型,即药品不良反应分类系统(ADReCS)严重程度分级模型，对于ADR严重程度的定量表征，评估ADR对人类健康影响的关键特征。该模型是通过挖掘数百万个真实世界的历史药物不良事件报告来构建的。引入了一个名为Severity_score的新参数来衡量ADR的严重程度，并确定了5个严重程度等级的评分上限和下限.
结果：ADReCS严重性分级模型与专家分级系统表现出优异的一致性（99.22％），不良事件的通用术语标准。因此，我们对129,407个药物-ADR对的6277个标准ADR的严重程度进行了分级.此外，我们通过挖掘真实世界的药物处方,计算了大量患者人群中127,763对药物-ADR对的6272种不同ADR的发生率.有了定量特征，我们展示了在系统阐明ADR机制方面的应用实例,从而发现了一系列剂量不当的药物.
结论：总之，本研究首次全面确定了ADR严重程度和ADR频率.这项努力为未来人工智能在发现高效低毒新药方面的应用奠定了坚实的基础。这也预示着临床毒性研究的范式转变，从定性描述转向定量评价。
