PDF(Pubmed)
Abstract：
The dynamically evolving science of pharmacology requires AI technology to advance a new path for drug development. The author proposes generative AI for future drugs, identifying suitable drug molecules, uncharacteristically to previous generations of medicines, incorporating the wisdom, experience, and intuit of traditional materia medica and the respective traditional medicine practitioners. This paper describes the guiding principles of the new drug development, springing from the tradition and practice of Tibetan medicine, defined as the Interactive Nutrient Process (INP). The INP provides traditional knowledge and practitioner\'s experience, contextualizing and teaching the new drug therapy. An illustrative example of the outcome of the INP is a potential small molecule drug, 6-Shogaol and related shogaol derivatives, from ginger roots (Zingiber officinalis fam. Zingiberaceae) evaluated clinically for 12 months for biological markers of iron homeostasis in patients with the myelodysplastic syndromes (MDS). The study\'s preliminary results indicate that 6-Shogaol and related shogaols may improve iron homeostasis in low-risk/intermediate-1 MDS patients without objective or subjective side effects.
摘要：
药理学的动态发展需要AI技术来推进药物开发的新道路。作者提出了未来药物的生成AI，确定合适的药物分子，与前几代药物不同，融入智慧，经验,以及传统医学和各自的传统医学从业者的直觉。本文阐述了新药开发的指导原则,从藏医的传统和实践中，定义为交互式营养过程(INP)。INP提供传统知识和从业者的经验，新药物疗法的情境化和教学。INP结果的一个说明性例子是一种潜在的小分子药物，6-Shogaol和相关的Shogaol衍生物，从姜根(生姜。姜科）对骨髓增生异常综合征（MDS）患者的铁稳态生物学标志物进行了12个月的临床评估。该研究的初步结果表明，6-Shogaol和相关Shogaol可以改善低危/中度-1MDS患者的铁稳态，而没有客观或主观副作用。
