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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

基本信息

批准号：
7692175
负责人：
ASHER D SCHACHTER
金额：
$ 31.6万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
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/7692175
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

项目摘要

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）：广泛处方的不安全药物的批准和随后的撤销每年影响数百万人的生命。我们最近报道，贝叶斯网络模型可以利用临床前、I期和II期数据预测III期安全性和有效性，准确率为78%。我们的方法超越了制药行业的表现。我们新颖的初步数据表明，仅使用临床前数据识别独立于药物类别的上市后安全性问题是可行的。每一种药物都有一套独特的临床前剂量与初始效应曲线以及剂量与副作用曲线。我们的初步研究表明，临床前剂量与主要效应曲线的可量化特征预测批准后的安全停药具有令人印象深刻的准确性。我们的目标是建立和分发临床前药理学预测模型，以预测批准后的临床安全性。我们的具体目标是(1)确定批准后药物安全性的临床前剂量效应指标，(2)建立和分发预测批准后药物安全性的临床前指标机器学习模型。该提案将提供一个完全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，这是潜在被抑制的安全性数据的一个指标，并减轻了FDA不良事件报告系统和其他IV期监测系统的负担。