Original end-to-end smart diagnosis framework of systematic critical quality attributes meets FDA standards of phytomedicine by biosensor and multi-information fusion coupled with AI algorithm†
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Abstract
Great progress has been made in phytomedicine manufacturing based on the development of green chemistry. However, identifying critical variation sources and exploring interactive coupling mechanisms to ultimately feedback to manufacturing remain rigorous challenges. Thus, an end-to-end smart diagnosis framework (ETE-SDF) of systematic critical quality attributes (CQAs) was proposed and applied to the phytomedicine product Xiaoer Xiaoji Zhike oral liquid. Firstly, a strategy integrating high electron mobility transistor biosensors and ultra-high performance liquid chromatography-mass spectroscopy was developed. A total of 92 chemical constituents were discovered, and then 10 biological CQAs were successfully identified. Secondly, this framework implemented an overall variation source data basis containing 18 000 biological data points, 318 400 chemical data points, and 900 physical data points. Subsequently, a voting decision-fusion strategy was developed and verified so that the aforementioned big data could be mapped to six artificial intelligence diagnosis strategies. Then, they formed systematic CQAs. The diagnostic accuracy of abnormal samples increased from 61% to 93.3%. Finally, a novel end-to-end mass transfer traceability pathway was developed. It demonstrated that the quality standard grade of Xiaoer Xiaoji Zhike oral liquid was grade III and at the level of 2–3 Sigma. Remarkably, inspissation was diagnosed as the low smart process that called for the most improvement. The temperature of inspissation was the first parameter that needed to be optimized to improve product quality. These results formed a higher reliability pharmaceutical management paradigm that had a positive impact on cost, safety, and sustainability as required by FDA standards.
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