Preclinical predictive markers of post-approval drug safety

批准后药物安全性的临床前预测标志物

• 批准号: 7913002
• 负责人: Ben Y Reis
• 金额: $ 31.49万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-26 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7913002
• 关键词: Adverse effects; Adverse event; Affect; Area; Biological Neural Networks; Boxing; Categories; Cause of Death; Clinical; Computer software; Data; Data Set; Databases; Dose; Drug Approval; Drug Industry; Drug toxicity; Ensure; Exposure to; Goals; Life; Linear Regressions; Logistic Regressions; Machine Learning; Marketing; Methodology; Methods; Metric; Modeling; Morbidity - disease rate; Nature; New Drug Approvals; Patients; Performance; Pharmaceutical Preparations; Phase; Predictive Value; Procedures; Public Domains; Publishing; Reaction; Receiver Operator Characteristics; Regression Analysis; Reporting; Rofecoxib; Safety; Sensitivity and Specificity; Sentinel; Societies; System; Testing; Time; Toxic effect; Training; United States Food and Drug Administration; Withdrawal; base; cerivastatin; computer based statistical methods; cost; drug development; drug discovery; innovation; mathematical algorithm; mortality; network models; novel; open source; post-market; pre-clinical; predictive modeling; public health relevance; repository; research study; troglitazone

DESCRIPTION (provided by applicant): The approval and subsequent withdrawal of widely prescribed unsafe drugs affects millions of lives annually. We have recently reported that a Bayesian network model can utilize preclinical, phase I and phase II data to predict phase III safety and efficacy with 78% accuracy. Our approach exceeds pharmaceutical industry performance. Our novel preliminary data demonstrate that identifying post-marketing safety issues independent of drug class is feasible using preclinical data only. Every drug has a unique set of preclinical dose versus primary effect curves and dose versus side effect curves. Our preliminary studies show that quantifiable features of preclinical dose versus primary effect curves predict post-approval safety withdrawal with impressive accuracy. Our objective is to build and distribute preclinical pharmacologic predictive models of post-approval clinical safety. Our specific aims are (1) to identify preclinical dose-effect indicators of post- approval drug safety and (2) to build and distribute preclinical indicator machine-learning models that predict post-approval drug safety. This proposal will deliver an open source drug safety sentinel that is based solely on preclinical data. The potential benefits to society include reduced exposure to unsafe drugs, an indicator for potentially suppressed safety data, and reduced burden on the FDA Adverse Event Reporting System and other phase IV surveillance systems. PUBLIC HEALTH RELEVANCE: The approval and subsequent withdrawal of widely prescribed unsafe drugs affects millions of lives annually. Patients affected by toxicity suffer from drug-induced morbidity and mortality, while those patients who benefited from the drug without toxicity can no longer receive it. We have recently reported that predictive models can predict efficacy and safety was accuracy. Our novel preliminary data demonstrate that predicting post-marketing safety issues independent of drug class is feasible using preclinical data only. Our objective is to build and distribute preclinical pharmacologic predictive models of post-approval clinical safety. Our goal is to deliver an open source drug safety sentinel that is based solely on preclinical data in order to reduce exposure to unsafe drugs.

描述（由申请人提供）：批准和随后撤回广泛规定的不安全药物每年都会影响数百万的生命。我们最近报告说，贝叶斯网络模型可以利用临床前，I期和II期数据来预测III期的安全性和功效，其精度为78％。我们的方法超过了制药行业的绩效。我们的新型初步数据表明，仅使用临床前数据识别独立于药物类别的市场后安全问题是可行的。每种药物都有一组独特的临床前剂量曲线，剂量与副作用曲线。我们的初步研究表明，临床前剂量与原发性效应曲线的可量化特征可预测批准后安全性的精度。我们的目标是建立和分发批准后临床安全性的临床前药物预测模型。我们的具体目的是（1）确定临床前剂量效应指标 - 批准药物安全性和（2）构建和分发临床前指标机器学习模型，以预测批准后药物安全。该提案将提供仅基于临床前数据的开源药物安全哨兵。对社会的潜在好处包括减少对不安全药物的接触，潜在抑制安全数据的指标以及减轻FDA不良事件报告系统和其他IV期监视系统的负担。 公共卫生相关性：批准和随后撤回广泛规定的不安全药物每年会影响数百万的生命。受毒性影响的患者患有药物引起的发病率和死亡率，而从没有毒性的药物中受益的患者则无法再接受。我们最近报告说，预测模型可以预测功效，安全性是准确性。我们的新型初步数据表明，仅使用临床前数据预测独立于药物类别的市场后安全问题是可行的。我们的目标是建立和分发批准后临床安全性的临床前药物预测模型。我们的目标是提供仅基于临床前数据的开源药物安全哨兵，以减少对不安全药物的接触。