应用多标准决策分析技术和决策支持框架,以告知植物选择剂的指定和决策
Segaran P Pillai1,2, Julia Fruetel3, Todd West4
1Office of the Commissioner, Food and Drug Administration, Silver Spring, MD, United States.
Frontiers in bioengineering and biotechnology
|September 28, 2023
概括
美国农业部是美国农业部.
科学领域:
- 农业科学 农业科学
- 生物安全是生物安全.
- 风险评估 风险评估
背景情况:
- 美国农业部 (USDA) 农业选择剂和毒素部门 (DASAT) 保持了威胁美国作物的生物制剂清单.
- 该清单由主题专家 (中小企业) 每两年审查一次,以根据风险对代理商进行排名.
- 当前的审查过程可能会从强化分析方法中受益,以确保可靠的决策.
研究的目的:
- 评估多标准决策分析 (MCDA) 和决策支持框架 (DSF) 的适用性,用于USDA DASAT每两年对选定代理商的审查.
- 评估当前的选择代理商和非选择代理商,以进行全面的风险评估.
- 确定这些分析方法是否可以提高药剂指定的准确性和一致性.
主要方法:
- 对16种病原体进行了文献审查,对植物健康和生物恐怖主义风险的9个标准进行了对照.
- 利用主题专家 (SME) 的评估和公布的数据进行评分.
- 应用MCDA技术根据农业后果和生物恐怖主义风险对代理人进行排名.
- 采用DSF逻辑树与特异性病原体数据来识别低关注的病原体.
主要成果:
- MCDA通常与现有名称保持一致,适当地对高风险药物进行排名.
- 在MCDA中的分析值确定了当前精选代理商指定的潜在例外.
- DSF有效地确定了可能被排除在进一步考虑之外的病原体.
- 无论是MCDA还是DSF方法都得出了一致的结论,表明了它们的分析价值.
结论:
- MCDA和DSF提供了强大的分析方法,以支持USDA DASAT在选择代理商审查的决策过程.
- 这些方法可以提高对农业生物制剂风险评估的客观性和一致性.
- 这些发现表明,这些定量决策支持工具在生物安全和农业保护方面发挥了宝贵的作用.
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