ABSTRACT
Drug combination is a common clinical phenomenon. However, the scientific implementation of drug combination is li-mited by the weak rational evaluation that reflects its clinical characteristics. In order to break through the limitations of existing evaluation tools, examining drug-to-drug and drug-to-target action characteristics is proposed from the physical, chemical and biological perspectives, combining clinical multicenter case resources, domestic and international drug interaction public facilities with the aim of discovering the common rules of drug combination. Machine learning technology is employed to build a system for evaluating and predicting the rationality of clinical drug combinations based on "drug characteristics-repository information-artificial intelligence" strategy, which will be debugged and validated in multi-center clinical practice, with a view to providing new ideas and technical references for the safety and efficacy of clinical drug use.
Subject(s)
Artificial Intelligence , Drug Combinations , Machine Learning , TechnologyABSTRACT
The quality evaluation of compound Chinese medicines is an important but challenging issue in this research field, which has been paid much controversial due to the constrained association with clinical efficacy. Developing a methodology for quality evaluation of compound Chinese medicines related to clinical efficacy is an important measure in research on Chinese material medica quality to ensure clinical effectiveness and safety. Therefore, based on the research concept that "originating from clinic-testing in experiment-returning to clinic", and taking Xiaoke prescription as an example, the characteristic information of metabolome, proteome and microbiome are discussed from the clinical aspect, and the integrated markers associated with clinical efficacy constructed with artificial intelligence technology. Taking the integrated markers as the link and indication are connecting the clinical and basic, the main pharmacodynamic substances and key targets of Xiaoke prescription that are related to clinical efficacy are explained. Clinical samples are used for validation. Based on the main pharmacodynamic substances and key targets, methods and key technologies for chemical and biological evaluation of the quality of Xiaoke prescription are established, providing a methodology for quality evaluation of compound Chinese medicines, including clinical efficacy response indicators (related to clinic), main pharmacodynamic substances (chemical evaluation), and key targets (biological evaluation), to provide new ideas and methods for improving the quality evaluation of compound Chinese medicines.
ABSTRACT
Despite Salvia miltiorrhiza being one of the most important medicine plants in China, there is a limited availability of genomic resources, especially of the expressed sequence tag-based markers. In this study, we selected and characterized functional markers in S. miltiorrhiza, which consisted of 4,192 non-redundant expressed sequence tags (ESTs) from 10,288 identified S. miltiorrhiza ESTs in dbEST data bank. Among them, 159 simple sequence repeats (SSR) were detected, which amounted to 3.79% of the non-redundant starting sequence population. This incidence was equivalent to one EST-SSR in every 12.74 kb of S. miltiorrhiza ESTs. Among the different motifs ranging from 1 bp to 6 bp, di-nucleotide repeat motif was the most abundant (77, 48.43%), followed by tri-nucleotide (41, 25.79%), hexa-nucleotide (23, 14.47%), penta-nucleotide (12, 7.55%) and tetra-nucleotide (6, 3.77%). In 47 identified motif types, the detected frequency above 5% were GA/CT (16.35%), AG/TC (15.09%), TCA/AGT (10.69%), AT/TA (6.29%), GAAAAG/CAAAAC (6.29%) and TA/AT (5.03%). Based on flank sequence of detected SSR, a total of 83 EST-SSR primer pairs were designed and tested for the amplification efficiency, polymorphism and transferability in thirteen S. mihiorrhiza samples and other ten species from the genus Salvia. The results showed that 72 primer pairs were successfully amplified in S. miltiorrhiza samples to yield and 279 loci with an average of 3.88 loci per primer pair. The cross-transferability of S. miltiorrhiza EST-SSR markers to other ten Salvia plants was very high, ranging from 60% to 100% with an average of 85%. Further analysis of the genetic similarity based on the polymorphic bands showed the EST-SSR could detect the genetic diversity on different levels among the whole test samples and distinguish the S. miltiorrhiza from other Salvia plants effectively. It is expected that the potential markers described here would add to the repertoire of DNA markers needed for genetic analysis, linkage mapping and comparative genomics studies in S. miltiorrhiza and related Salvia genus plants.