STTR Phase I: Biophotonic plant foliage optical inspection system for improved indoor molecular farming of plant-based medicines
STTR Phase I: Biophotonic plant foliage optical inspection system for improved indoor molecular farming of plant-based medicines
批准号:
2111730
负责人:
Anthony Ragone
金额:
$25.55万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-12-01 至 2022-12-31
中文摘要
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英文摘要
The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to provide safe, reliable, sustainable medicines with molecular medicine farming. Indoor farms are insulated from the impacts of weather and climate and are usually located near population centers, reducing transportation costs and fossil fuel use. The economic advantages include low-cost medicines using plants with minimal pesticides and herbicides, and with 90% lower water consumption. Plant-based pharmaceuticals markets are expected to grow yearly more than 20%. Producing medicines in plants will accelerate pharmaceutical development, due to the speed and consistency of processing plants in well-regulated controlled environments. This project will develop and demonstrate a real-time plant foliage optical inspection system that will predict pharmaceutical production in crops and signal plant stress, enabling the grower to remediate defects and optimize production. These advantages of molecular farming can be used more generally for crop foods. The proposed project advances an in-process precise chemical imager for two-dimensional understanding of where, how and at what rate protein and stressor generation occurs in the leaves of Nicotiana benthamiana plants. The imager method analyzes medical protein production and food crop growth. The test plan will modify production variables, including specialized bacteria introduced into the plants to stimulate them to generate the proteins, to enable fingerprinting the generated plant stresses and protein productivity responses. Trial cycles of two weeks will permit rapid development of algorithms to model production rates and optimization for commercial use.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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