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

项目总结 化学品风险评估作为一种重要工具被广泛应用于行业和监管机构 评估化学品毒性，以支持化学品注册、安全评估和暴露限制 发展。剂量反应评估中最显著的改进之一--所需的定量 风险评估的步骤-是开发基准剂量(BMD)方法以更好地利用 提供毒理学信息，以便于评估化学品的毒性。尽管BMD方法已经被 由美国环境保护局(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较少，更可信 对不确定性的解释)多年来，该方法在实际风险评估中的应用 受到一些重要限制的显著阻碍，其中之一是缺乏可靠的建模系统来 支持跨不同行业的BMD建模的一致实践。因此，基于贝叶斯的 基准剂量模拟系统(BBMD)原型在STTR项目第一阶段成功建立， 第二阶段的目标是进一步发展BBMD系统，以满足更多样化的剂量需求。 反应评估和扩大系统用户基础，作为以下工作的重要组成部分 商业化。理由是，鉴于骨密度建模的实际实施相对有限， 工业和一些政府机构的剂量反应评估，示范和改进 在现阶段，使用BMD方法比复杂的方法更合适 提高对BMD方法的接受度，从而为公司创造商机。至 为了实现这一目标，将追求三个具体目标：(1)开发一种贝叶斯骨密度建模方法 (2)开发了贝叶斯骨密度建模方法 具有高通量剂量反应数据的软件；(3)将BBMD升级为数据计算和 执行、存储和分发经专家小组批准的骨密度分析的管理系统。这个 该项目的成功将填补阻碍大规模采用BMD方法的多个空白 工业界和政府。同时，梦想科技将增加BBMD系统的影响力，并建立 通过一系列渠道将剂量-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.