Develop and Commercialize the Bayesian Dose-Response Modeling System and Services
开发贝叶斯剂量反应建模系统和服务并将其商业化
基本信息
- 批准号:10222676
- 负责人:
- 金额:$ 107.41万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-08-15 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAdoptionAdverse effectsAdvocateBenchmarkingBiological ModelsBusinessesChemical ExposureChemicalsCommunitiesComputer softwareConfidence IntervalsDataData ReportingDependenceDevelopmentDoseDreamsEducational workshopEmploymentEpidemiologyEuropeanEvaluationFood SafetyFoundationsGenomicsGoalsGovernmentGovernment AgenciesHealthIndianaIndustryKnowledgeLibrariesLiteratureMethodologyMethodsModelingNo-Observed-Adverse-Effect LevelOnline SystemsPhaseProcessPublic HealthResearchRisk AssessmentSafetyServicesSmall Business Technology Transfer ResearchSolidStandardizationStatutes and LawsStreamSystemTestingToxic effectToxicologyTrainingUncertaintyUnited States Environmental Protection AgencyUniversitiesauthoritybasecommercializationdesignexperimental studyimprovedprogramsprototyperesearch and developmentresponseskillssuccesstool
项目摘要
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.
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。
项目成果
期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Using Prior Toxicological Data to Support Dose-Response Assessment─Identifying Plausible Prior Distributions for Dichotomous Dose-Response Models.
- DOI:10.1021/acs.est.2c05872
- 发表时间:2022-11-15
- 期刊:
- 影响因子:11.4
- 作者:Shao, Kan;Ji, Chao;Chiu, Weihsueh
- 通讯作者:Chiu, Weihsueh
A computational system for Bayesian benchmark dose estimation of genomic data in BBMD.
- DOI:10.1016/j.envint.2022.107135
- 发表时间:2022-03
- 期刊:
- 影响因子:11.8
- 作者:Ji C;Weissmann A;Shao K
- 通讯作者:Shao K
Benchmark dose modeling for epidemiological dose-response assessment using prospective cohort studies.
使用前瞻性队列研究进行流行病学剂量反应评估的基准剂量模型。
- DOI:10.1111/risa.14196
- 发表时间:2024
- 期刊:
- 影响因子:0
- 作者:DePretis,Francesco;Zhou,Yun;Xun,Pengcheng;Shao,Kan
- 通讯作者:Shao,Kan
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Kan Shao其他文献
Kan Shao的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Kan Shao', 18)}}的其他基金
Quantitative dose-response characterization for liver carcinogenicity with non-mutagenic modes of action
非诱变作用模式下肝脏致癌性的定量剂量反应表征
- 批准号:
10542807 - 财政年份:2020
- 资助金额:
$ 107.41万 - 项目类别:
Quantitative dose-response characterization for liver carcinogenicity with non-mutagenic modes of action
非诱变作用模式下肝脏致癌性的定量剂量反应表征
- 批准号:
10318949 - 财政年份:2020
- 资助金额:
$ 107.41万 - 项目类别:
Quantitative dose-response characterization for liver carcinogenicity with non-mutagenic modes of action
非诱变作用模式下肝脏致癌性的定量剂量反应表征
- 批准号:
9892742 - 财政年份:2020
- 资助金额:
$ 107.41万 - 项目类别:
Develop and Commercialize the Bayesian Dose-Response Modeling System and Services
开发贝叶斯剂量反应建模系统和服务并将其商业化
- 批准号:
10081313 - 财政年份:2018
- 资助金额:
$ 107.41万 - 项目类别:
相似海外基金
Investigating the Adoption, Actual Usage, and Outcomes of Enterprise Collaboration Systems in Remote Work Settings.
调查远程工作环境中企业协作系统的采用、实际使用和结果。
- 批准号:
24K16436 - 财政年份:2024
- 资助金额:
$ 107.41万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
WELL-CALF: optimising accuracy for commercial adoption
WELL-CALF:优化商业采用的准确性
- 批准号:
10093543 - 财政年份:2024
- 资助金额:
$ 107.41万 - 项目类别:
Collaborative R&D
Unraveling the Dynamics of International Accounting: Exploring the Impact of IFRS Adoption on Firms' Financial Reporting and Business Strategies
揭示国际会计的动态:探索采用 IFRS 对公司财务报告和业务战略的影响
- 批准号:
24K16488 - 财政年份:2024
- 资助金额:
$ 107.41万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10107647 - 财政年份:2024
- 资助金额:
$ 107.41万 - 项目类别:
EU-Funded
Assessing the Coordination of Electric Vehicle Adoption on Urban Energy Transition: A Geospatial Machine Learning Framework
评估电动汽车采用对城市能源转型的协调:地理空间机器学习框架
- 批准号:
24K20973 - 财政年份:2024
- 资助金额:
$ 107.41万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10106221 - 财政年份:2024
- 资助金额:
$ 107.41万 - 项目类别:
EU-Funded
De-Adoption Beta-Blockers in patients with stable ischemic heart disease without REduced LV ejection fraction, ongoing Ischemia, or Arrhythmias: a randomized Trial with blinded Endpoints (ABbreviate)
在没有左心室射血分数降低、持续性缺血或心律失常的稳定型缺血性心脏病患者中停用β受体阻滞剂:一项盲法终点随机试验(ABbreviate)
- 批准号:
481560 - 财政年份:2023
- 资助金额:
$ 107.41万 - 项目类别:
Operating Grants
Our focus for this project is accelerating the development and adoption of resource efficient solutions like fashion rental through technological advancement, addressing longer in use and reuse
我们该项目的重点是通过技术进步加快时装租赁等资源高效解决方案的开发和采用,解决更长的使用和重复使用问题
- 批准号:
10075502 - 财政年份:2023
- 资助金额:
$ 107.41万 - 项目类别:
Grant for R&D
Engage2innovate – Enhancing security solution design, adoption and impact through effective engagement and social innovation (E2i)
Engage2innovate — 通过有效参与和社会创新增强安全解决方案的设计、采用和影响 (E2i)
- 批准号:
10089082 - 财政年份:2023
- 资助金额:
$ 107.41万 - 项目类别:
EU-Funded
Collaborative Research: SCIPE: CyberInfrastructure Professionals InnoVating and brOadening the adoption of advanced Technologies (CI PIVOT)
合作研究:SCIPE:网络基础设施专业人员创新和扩大先进技术的采用 (CI PIVOT)
- 批准号:
2321091 - 财政年份:2023
- 资助金额:
$ 107.41万 - 项目类别:
Standard Grant