Digital identification of Aucklandiae radix, Vladimiriae radix, and Inulae radix based on multivariate algorithms and UHPLC-QTOF-MS analysis.

INTRODUCTION: The identification of Aucklandiae Radix (AR), Vladimiriae Radix (VR), and Inulae Radix (IR) based on traits and microscopic features is susceptible to the state of samples and the subjective awareness of personnel, and the identification based on a few or single chemical compositions is a cumbersome and time-consuming procedure and fails to rationally and effectively utilize the information of unknown components and is not specificity enough. OBJECTIVES: This study aimed to improve the identification efficiency, strengthen supervision, and realize digital identification of three Chinese medicines. Ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) combined with multivariate algorithms was used to explore the digital identification of AR, VR, and IR. MATERIALS AND METHODS: UHPLC-QTOF-MS was used to analyze AR, VR, and IR. The MS data combined with multivariate algorithms such as partial least squares discrimination analysis (PLS-DA) and artificial neural networks (ANNs) was used to filter important variables and data modeling. Finally, the optimal model was selected for the digital identification of three herbs. RESULTS: The results showed that three herbs can be distinguished on the whole level, and through feature screening, 591 characteristic variables combined with multivariate algorithms to construct data models. The ANN model was the best with accuracy = 0.983, precision = 0.984, and external verification showed the reliability and practicability of ANN model. CONCLUSION: ANN model combined with MS data is of great significance for tdigital identification of AR, VR, and IR. It is an important reference for developing the digital identification of traditional Chinese medicines at the individual level based on UHPLC-QTOF-MS and multivariate algorithms.

Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor.

BACKGROUND: The study of drug-target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure-activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs. RESULTS: After comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R2) are all greater than 0.94. Compared to the quantitative models reported in literatures, the RF models developed in this paper have higher accuracy and wide applicability. In addition, E-state molecular descriptors associated with molecular vibrations and normalized Moreau-Broto autocorrelation (G3), Moran autocorrelation (G4), transition-distribution (G7) protein descriptors are of higher importance in the quantification of DTIs. CONCLUSION: Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.

Identification Analysis of Angelicae sinensis radix and Angelicae pubescentis radix Based on Quantized "Digital Identity" and UHPLC-QTOF-MSE Analysis.

Angelicae sinensis radix (ASR) and Angelicae pubescentis radix (APR), as traditional herbal medicines, are often confused and doped in the material market. However, the traditional identification method is to characterize the whole herb with a single or a few components, which do not have representation and cannot realize the effective utilization of unknown components. Consequently, the result is not convincing. In addition, the whole process is time-consuming and labor-intensive. To avoid the confusion and adulteration of ASR and APR as well as to strengthen quality control and improve identification efficiency, in this study, a UHPLC-QTOF-MSE method was used to analyze ASR and APR. Based on digital representation, the shared data with high ionic strength were extracted from different batches of the same herbal medicine as their "digital identity". Further, the above "digital identity" was used as the benchmark for matching and identifying unknown samples to feedback on matching credibility (MC). The results showed that based on the "digital identities" of ASR and APR, the digital identification of two herbal samples can be realized efficiently and accurately at the individual level. And the matching credibility (MC) was higher than 94.00%, even if only 1% of APR or ASR in the mixed samples can still be identified efficiently and accurately. The study is of great practical significance for improving the efficiency of the identification of ASR and APR, cracking down on adulterated and counterfeit drugs, and strengthening the quality control of ASR and APR. In addition, it has important reference significance for developing nontargeted digital identification of herbal medicines at the individual level based on UHPLC-QTOF-MSE and "digital identity", which is beneficial to the construction of digital Chinese medicine and digital quality control.

Comprehensive Analysis of Bile Medicines Based on UHPLC-QTOF-MSE and Machine Learning.

Based on UHPLC-QTOF-MSE analysis and quantized processing, combined with machine learning algorithms, data modeling was carried out to realize digital identification of bear bile powder (BBP), chicken bile powder (CIBP), duck bile powder (DBP), cow bile powder (CBP), sheep bile powder (SBP), pig bile powder (PBP), snake bile powder (SNBP), rabbit bile powder (RBP), and goose bile powder (GBP). First, 173 batches of bile samples were analyzed by UHPLC-QTOF-MSE to obtain the retention time-exact mass (RTEM) data pair to identify bile acid-like chemical components. Then, the data were modeled by combining support vector machine (SVM), random forest (RF), artificial neural network (ANN), gradient boosting (GB), AdaBoost (AB), and Naive Bayes (NB), and the models were evaluated by the parameters of accuracy (Acc), precision (P), and area under the curve (AUC). Finally, the bile medicines were digitally identified based on the optimal model. The results showed that the RF model constructed based on the identified 12 bile acid-like chemical constituents and random forest algorithm is optimal with ACC, P, and AUC > 0.950. In addition, the accuracy of external identification verification of 42 batches of bile medicines detected at different times is 100.0%. So based on UHPLC-QTOF-MSE analysis and combined with the RF algorithm, it can efficiently and accurately realize the digital identification of bile medicines, which can provide reference and assistance for the quality control of bile medicines. In addition, hyodeoxycholic acid, glycohyodeoxycholic acid, and taurochenodeoxycholic acid, and so forth are the most important bile acid constituents for the identification of nine bile medicines.

Quantification of "Cold-Hot" Medicinal Properties of Chinese Medicines Based on Primary Metabolites and Fisher's Analysis.

BACKGROUND: Chinese medicinal properties (CMP) are an important part of the basic theory of traditional Chinese medicines (TCMs). Quantitative research on the properties of TCMs is of great significance to deepen the understanding and application of the theory of drug properties and promoting the modernization of TCMs. However, these studies are limited to strong subjectivity or distinguish different drug properties based on certain indicators since CMP studies are diverse. OBJECTIVE: To realize quantitative comparison of same medicinal properties of different Chinese medicines. METHOD: To solve the above problem, we proposed and explored quantification of Chinese medicinal properties (QMP) and the quantification value of medicinal properties "R". The correlation between primary metabolites and "cold-hot" medicinal properties was explored on the premise of material basis of Chinese herbal medicines and Fisher's analysis. Based on indicators related to "cold-hot" medicinal properties, we utilized quantitative values "R" to characterize the strength or weakness of "cold-hot" medicinal properties. RESULTS: According to QMP, the same medicinal properties were quantified and compared by quantification value of medicinal properties that expressed by alphabet "R". The general theoretical formula of "R" deduced is R = (‖l‖ × cos θ)/‖L‖ = ∑ i=1 n j i p i /∑ i=1 n p i 2, in which n ≥ 1. In the light of formula of "R" and indicators related to "cold-hot" medicinal properties, we got "R" value of "cold-cool" and "warm-hot" medicinal properties. "R" values of "cold-cool" medicinal properties of Phellodendri chinensis cortex, Coptidis rhizoma, and Menthae haplocalycis herba were 0.63, 1.00, and 0.49, respectively. The result showed that Coptidis rhizoma is the most "cold-cool", followed by Phellodendri chinensis cortex, with Menthae haplocalycis herba is the weakest in the three Chinese medicines, consistent with cognition of TCM theory. CONCLUSION: QMP has certain guiding significance for the quantification of "cold and hot" drug properties. "R" is feasible to realize the quantitative comparison of the same drug properties of different traditional Chinese medicine, which is helpful to promote process of modern Chinese medicine construction.

TCM-ISP: A Comprehensive Intelligent Service Platform for Industry Chain of Traditional Chinese Medicines.

In order to promote information interaction, intelligent regulation, and scale management in Chinese medicines industry, in this paper, a Chinese medicines intelligent service platform with characteristics of flexibility, versatility, and individuation was designed under the guidance of theoretical model of intelligent manufacturing of Chinese medicines (TMIM). TCM-ISP is a comprehensive intelligent service platform that can be flexibly applied to all links of Chinese medicines industry chain, which realizes data integration and real-time transmission as well as intelligent-flexible scheduling of equipment in response to different demand. The platform took logical framework of data flow as the core and adopts the modular design in which microcontroller and sensor module are independent to obtain overall design scheme of TCM-ISP that contains the diagram of overall framework, hardware structure, and software technology. Then, on the groundwork of overall design scheme and modern science technology, TCM-ISP was successfully constructed with flexible, intelligent, and networked characteristics in which TTL-USB and TTL-RS485S were utilized to build unified interface between boards with supporting hot-plugging mode. The results of platform tests show that TCM-ISP can not only successfully realize the integration, real-time transmission, and display of data information but also well accomplish remote intelligent-flexible control of equipment and allow flexible configuration and expansion of sensors and devices according to the needs of each link in TCM's industry chain. It is of great practical significance to the pursuit of intelligent manufacturing of Chinese medicines and the promotion of modernization of Chinese medicines industry.