Develop and Commercialize the Bayesian Dose-Response Modeling System and Services

项目摘要 化学品风险评价作为一种重要的工具，广泛应用于工业和监管机构， 评价化学品毒性，以支持化学品注册、安全性评价和接触限制 发展剂量-反应评估中最显著的改进之一-所需的定量 风险评估的一个步骤-是基准剂量（BMD）方法的发展，以更好地利用 毒理学信息，以促进化学品的毒性评估。虽然BMD方法已经被 由美国环境保护署（EPA）和欧洲食品安全局（EFSA）倡导， 它的科学优势（如对实验设计的依赖性较小， 多年来，该方法在实际风险评估中的应用一直是 这主要受到一些重要限制的阻碍，其中之一是缺乏可靠的建模系统， 支持不同部门BMD建模的一致实践。因此，基于贝叶斯 在STTR项目的第一阶段成功建立了基准剂量建模系统（BBMD）原型， 第二阶段的目标是进一步开发BBMD系统，以满足剂量方面更多样化的需求， 作为一个重要组成部分，扩大系统的用户基础， 商业化其理由是，鉴于BMD建模的实际实施相对有限， 工业界和一些政府机构的剂量反应评估，证明和改善了 在现阶段，BMD方法的实用性而不是重复方法更合适 提高BMD方法的接受度，为公司创造商机。到 为了实现这一目标，我们将追求三个具体目标：（1）发展贝叶斯BMD建模方法 用软件对典型流行病学剂量-反应数据进行处理;（2）建立贝叶斯BMD建模方法 （3）将BBMD升级为数据计算， 管理系统，用于执行、存储和分发经专家小组批准的BMD分析。的 该项目的成功将填补阻碍大规模采用弹道导弹防御方法的多个空白， 工业和政府。同时，梦科技将加大BBMD系统的影响力， 通过一系列渠道扩大用户群，将剂量-反应建模平台商业化， 支持化学品风险评估的服务。

PROJECT SUMMARY Chemical risk assessment is widely applied in industries and regulatory agencies as an important tool to evaluate chemical toxicity in support of chemical registration, safety evaluation, and exposure limitation development. One of the most notable improvements in dose-response assessment - a required quantitative step in risk assessment - is the development of benchmark dose (BMD) methodology to better utilize toxicological information to facilitate toxicity evaluation of chemicals. Although the BMD method has been advocated by the US Environmental Protection Agency (EPA) and European Food Safety Authority (EFSA) for its scientific advantages (such as less dependency on the design of experiments and more plausible interpretation on uncertainty) for years, the employment of the method in practical risk assessment has been significantly hindered by a few important limitations, one of which is the lack of a reliable modeling system to support consistent practice of BMD modeling across different sectors. Therefore, based on the Bayesian benchmark dose modeling system (BBMD) prototype successfully built in Phase I of the STTR project, the objective of Phase II is to further the development of the BBMD system to meet more diverse needs in dose- response assessment and to enlarge the user base of the system as an essential component for commercialization. The rational is that, given relatively limited practical implementation of BMD modeling for dose-response assessment in industry and some government agencies, demonstrating and improving the utility of the BMD method rather than sophisticating the methodology are more appropriate at the current stage to enhance the acceptance of BMD method and then create business opportunities for the company. To accomplish this objective, three specific aims will be pursued: (1) develop a Bayesian BMD modeling approach with software for typical epidemiological dose-response data; (2) develop a Bayesian BMD modeling approach with software for high-throughput dose-response data; (3) upgrade the BBMD to a data computation and management system to perform, store, and distribute BMD analyses approved by a panel of experts. The success of the project will fill multiple gaps that hamper the large-scale adoption of BMD methodology in industry and government. Meanwhile, Dream Tech will increase the influence of the BBMD system and build up user base through an array of channels to commercialize the dose-response modeling platform and services in support of chemical risk assessment.