Search Results (13)

Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction. Full article

Cardiovascular disease has become a common ailment that endangers human health, having garnered widespread attention due to its high prevalence, recurrence rate, and sudden death risk. Ginseng possesses functions such as invigorating vital energy, enhancing vein recovery, promoting body fluid and blood nourishment, calming the nerves, and improving cognitive function. It is widely utilized in the treatment of various heart conditions, including palpitations, chest pain, heart failure, and other ailments. Although numerous research reports have investigated the cardiovascular activity of single ginsenoside, there remains a lack of systematic research on the specific components group that predominantly contribute to cardiovascular efficacy in ginseng medicinal materials. In this research, the spectrum–effect relationship, target cell extraction, and BP neural network classification were used to establish a rapid screening system for potential active substances. The results show that red ginseng extract (RGE) can improve the decrease in cell viability and ATP content and inhibit the increase in ROS production and LDH release in OGD-induced H9c2 cells. A total of 70 ginsenosides were identified in RGE using HPLC-Q-TOF-MS/MS analysis. Chromatographic fingerprints were established for 12 batches of RGE by high-performance liquid chromatography (HPLC). A total of 36 common ingredients were found in 12 batches of RGE. The cell viability, ATP, ROS, and LDH of 12 batches RGE were tested to establish gray relationship analysis (GRA) and partial least squares discrimination analysis (PLS-DA). BP neural network classification and target cell extraction were used to narrow down the scope of Spectral efficiency analysis and screen the potential active components. According to the cell experiments, RGE can improve the cell viability and ATP content and reduce the oxidative damage. Then, seven active ingredients, namely, Ginsenoside Rg1, Rg2, Rg3, Rb1, Rd, Re, and Ro, were screened out, and their cardiovascular activity was confirmed in the OGD model. The seven ginsenosides were the main active substances of red ginseng in treating myocardial injury. This study offers a reference for quality control in red ginseng and preparations containing red ginseng for the management of cardiovascular diseases. It also provides ideas for screening active ingredients of the same type of multi-pharmacologically active traditional Chinese medicines. Full article

Hawthorn is a type of natural food with significant medicinal and nutritional properties; it has been listed in the “Both Food and Drug” list by the Chinese Ministry of Health Item List since 1997. However, hawthorn varieties have complex origins, and there are significant differences in the content, type, and medicinal efficacy of the chemically active ingredients in different varieties of hawthorn. This leads to the phenomenon of mixed varieties and substandard products being passed off as high-quality. In this work, by using headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), we identified and analyzed volatile organic compounds (VOCs) in four varieties of hawthorn, establishing their characteristic fingerprints. As a result, a total of 153 peaks were detected, and 139 VOCs were also identified. As shown by the fingerprint profiles, the different hawthorn samples contained different VOCs. Meanwhile, by using principal component analysis (PCA), Euclidean distance, and partial least-squares discriminant analysis (PLS-DA), the relationship between the VOCs found in the different varieties of hawthorn was revealed. This study developed a simple, fast, accurate, and sensitive method for identifying, tracking, and evaluating hawthorn varieties. Full article

As natural products with biological activity, the quality of traditional Chinese medicines (TCM) is the key to their clinical application. Fingerprints based on the types and contents of chemical components in TCM are an internationally recognized quality evaluation method but ignore the correlation between chemical components and efficacy. Through chemometric methods, the fingerprints represented by the chemical components of TCM were correlated with its pharmacodynamic activity results to obtain the spectrum–effect relationships of TCM, which can reveal the pharmacodynamic components information related to the pharmacodynamic activity and solve the limitations of segmentation of chemical components and pharmacodynamic research in TCM. In the 20th anniversary of the proposed spectrum–effect relationships, this paper reviews its research progress in the field of TCM, including the establishment of fingerprints, pharmacodynamic evaluation methods, chemometric methods and their practical applications in the field of TCM. Furthermore, the new strategy of spectrum–effect relationships research in recent years was also discussed, and the application prospects of this technology were discussed. Full article

Monitoring the quality consistency of traditional Chinese medicines, or herbal medicines (HMs), is the basis of assuring the efficacy and safety of HMs during clinical applications. The purpose of this work was to characterize the difference in hydrophilic antioxidants and related bioactivities between Flos Chrysanthemum (JH) and its wild relatives (Chrysanthemum indicum L.; YJH) based on the establishment of fingerprint–efficacy relationship modeling. The concentrations of the total phenolics and flavonoids of JH samples were shown to be generally higher than those of YJH, but the concentration distribution ranges of YJH were significantly greater compared to JH samples, possibly related to environmental stress factors leading to the concentration fluctuations of phytochemicals during the growth and flowering of Chrysanthemum cultivars. Correspondingly, the total antioxidant capabilities of JH were greatly higher than those of YJH samples, as revealed by chemical assays, including DPPH and ABTS radical scavenging activities and FRAP assays. In addition, cellular-based antioxidant activities confirmed the results of chemical assays, suggesting that the differences in antioxidant activities among the different types of Chrysanthemums were obvious. The extracts from YJH and JH samples showed significant α-glucosidase inhibitory activity and lipase-inhibitory activity, implying the modulatory effects on lipid and glucose metabolisms, which were also confirmed by an untargeted cell-based metabolomics approach. The selected common peaks by similarity analysis contributed to the discrimination of YJH and JH samples, and the modeling of the fingerprint–bioactivity relationship identified neochlorogenic acid, isochlorogenic acid A, and linarin as efficacy-associated chemical markers. These results have demonstrated that integrating HPLC fingerprints and the analysis of similarity indexes coupled with antioxidant activities and enzyme-inhibitory activities provides a rapid and effective approach to monitoring the quality consistency of YJH/JH samples. Full article

The Danshen-Honghua (DH) herbal pair exhibits a synergistic effect in protecting the cerebrovascular system from ischemia/reperfusion injury, but the therapeutic effect on vascular dementia (VaD) has not been clarified, and the main active ingredient group has not been clarified. In this work, the chemical constituents in DH herbal pair extract were characterized by UHPLC-QTOF MS, and a total of 72 compounds were identified. Moreover, the DH herbal pair alleviated phenylhydrazine (PHZ)-induced thrombosis and improved bisphenol F (BPF)- and ponatinib-induced brain injury in zebrafish. Furthermore, the spectrum-effect relationship between the fingerprint of the DH herbal pair and the antithrombotic and neuroprotective efficacy was analyzed, and 11 chemical components were screened out as the multi-component combination (MCC) against VaD. Among them, the two compounds with the highest content were salvianolic acid B (17.31 ± 0.20 mg/g) and hydroxysafflor yellow A (15.85 ± 0.19 mg/g). Finally, we combined these 11 candidate compounds as the MCC and found that it could improve thrombosis and neuronal injury in three zebrafish models and rat bilateral common carotid artery occlusion (BCCAO) model, which had similar efficacy compared to the DH herbal pair. This study provides research ideas for the treatment of VaD and the clinical application of the DH herbal pair. Full article

PMP-HPLC, FT-IR, and HPSEC fingerprints of 10 batches of polysaccharides from Saposhnikoviae Radix with different production areas and harvest times have been prepared, and the chemometrics analysis was performed. The anti-allergic activity of 10 batches of Saposhnikoviae Radix polysaccharide (SP) was evaluated, and the spectrum-effect relationship of the 10 batches of SP was analyzed by gray correlation degree with the chromatographic fingerprint as the independent variable. The results showed that the PMP-HPLC, HPSEC, and FT-IR fingerprints of 10 batches of SP had a high similarity. Two monosaccharides (rhamnose and galactose), the polysaccharide fragment Mn = 8.67 × 10⁶~9.56 × 10⁶ Da, and the FT-IR absorption peak of 892 cm⁻¹ can be used as the quality control markers of SPs. All 10 batches of SP could significantly inhibit the release of β-HEX in RBL-231 cells, and the polysaccharides harvested from Inner Mongolia in the winter had the best anti-allergic activity. The spectrum-effect relationship model showed that the monosaccharide composition and molecular weight were related to the anti-allergic activity of the SPs. Multiple fingerprints combined with spectrum-effect relationship analysis can evaluate and control the quality of SPs from the aspects of overall quality and efficacy, which has more application value. Full article

A frequency response analysis (FRA) is a well-known technique for evaluating the mechanical stability of a power transformer’s active part components. FRA’s measuring practices have been industrialised and are codified in IEEE and IEC standards. However, because there is no valid coding in the standard, the interpretation of FRA data is still far from being a widely acknowledged and authoritative approach. This study proposes an innovative fault segmentation and localisation technique based on FRA data. The algorithm is based on regression analysis to estimate the repeatability and relationship between the FRA fingerprint and the latest measured data. Initially, the measuring frequency is discretised into three regions to narrow the location of the fault; the regression model of the fingerprint and current FRA data are then evaluated. As a benchmark, two statistical indicators are the employed benchmark against the proposed method. Finally, the proposed scheme identifies and characterises various transformer conditions, such as healthy windings, axial and radial winding deformations, core deformation and electrical faults. The database used in this study consists of FRA measurements from 70 mineral-oil-immersed power transformers of different designs, ratings and manufacturers that were physically inspected for various faults and comparable frequency regions. The results achieved corroborate the efficacy of the proposed regression analysis fault recognition algorithm (RAFRA) model for transformer fault diagnosis using FRA. Further recommendations are made to address the reproducibility concerns induced by multiple FRA testing conditions. Full article

Building the capacity of efficiently determining the provenance of food products represents a crucial step towards the sustainability of the global food system. Despite species specific empirical examples of multi-tracer approaches to provenance, the precise benefit and efficacy of multi-tracers remains poorly understood. Here we show why, and when, data fusion of bio-tracers is an extremely powerful technique for geographical provenance discrimination. Specifically, we show using extensive simulations how, and under what conditions, geographical relationships between bio-tracers (e.g., spatial covariance) can act like a spatial fingerprint, in many naturally occurring applications likely allowing rapid identification with limited data. To highlight the theory, we outline several statistic methodologies, including artificial intelligence, and apply these methodologies as a proof of concept to a limited data set of 90 individuals of highly mobile Sockeye salmon that originate from 3 different areas. Using 17 measured bio-tracers, we demonstrate that increasing combined bio-tracers results in stronger discriminatory power. We argue such applications likely even work for such highly mobile and critical fisheries as tuna. Full article

O⁶-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O⁶-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED₅₀ values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q²_Loo = 0.83, R² = 0.87, Q²_ext = 0.67, and R²_ext = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors. Full article

Zhi zhu xiang (ZZX for short) is the root and rhizome of Valeriana jatamansi Jones, which is a Traditional Chinese Medicine (TCM) used to treat various mood disorders for more than 2000 years, especially anxiety. The aim of the present work was to identify the bioactive chemical markers in Zhi zhu xiang improving anxiety in rats by a fingerprint-efficacy study. More specifically, the chemical fingerprint of ZZX samples collected from 10 different regions was determined by High Performance Liquid Chromatography (HPLC) and the similarity analyses were calculated based on 10 common characteristic peaks. The anti-anxiety effect of ZZX on empty bottle stimulated rats was examined through the Open Field Test (OFT) and the Elevated Plus Maze Test (EPM). Then we measured the concentration of CRF, ACTH, and CORT in rat’s plasma by the enzyme-linked immune sorbent assay (ELISA) kit, while the concentration of monoamine and metabolites (NE, DA, DOPAC, HVA, 5-HT, 5-HIAA) in the rat’s cerebral cortex and hippocampus was analysed by HPLC coupled with an Electrochemical Detector. At last, the fingerprint-efficacy study between chemical fingerprint and anti-anxiety effect of ZZX was accomplished by partial least squares regression (PLSR). As a result, we screened out four compounds (hesperidin, isochlorogenic acid A, isochlorogenic acid B and isochlorogenic acid C) as the bioactive chemical markers for the anti-anxiety effect of ZZX. The fingerprint-efficacy study we established might provide a feasible way and some elicitation for the identification of the bioactive chemical markers for TCM. Full article

Caulophyllum robustum Maxim (CRM) is a Chinese folk medicine with significant effect on treatment of rheumatoid arthritis (RA). This study was designed to explore the spectrum-effect relationships between high-performance liquid chromatography (HPLC) fingerprints and the anti-inflammatory effects of CRM. Seventeen common peaks were detected by fingerprint similarity evaluation software. Among them, 15 peaks were identified by Liquid Chromatography-Mass Spectrometry (LC-MS). Pharmacodynamics experiments were conducted in collagen-induced arthritis (CIA) mice to obtain the anti-inflammatory effects of different batches of CRM with four pro-inflammation cytokines (TNF-α, IL-β, IL-6, and IL-17) as indicators. These cytokines were suppressed at different levels according to the different batches of CRM treatment. The spectrum-effect relationships between chemical fingerprints and the pro-inflammation effects of CRM were established by multiple linear regression (MLR) and gray relational analysis (GRA). The spectrum-effect relationships revealed that the alkaloids (N-methylcytisine, magnoflorine), saponins (leiyemudanoside C, leiyemudanoside D, leiyemudanoside G, leiyemudanoside B, cauloside H, leonticin D, cauloside G, cauloside D, cauloside B, cauloside C, and cauloside A), sapogenins (oleanolic acid), β-sitosterols, and unknown compounds (X3, X17) together showed anti-inflammatory efficacy. The results also showed that the correlation between saponins and inflammatory factors was significantly closer than that of alkaloids, and saponins linked with less sugar may have higher inhibition effect on pro-inflammatory cytokines in CIA mice. This work provided a general model of the combination of HPLC and anti-inflammatory effects to study the spectrum-effect relationships of CRM, which can be used to discover the active substance and to control the quality of this treatment. Full article

This work investigated the spectrum-effect relationships between high performance liquid chromatography (HPLC) fingerprints and the anti-benign prostatic hyperplasia activities of aqueous extracts from Saxifraga stolonifera. The fingerprints of S. stolonifera from various sources were established by HPLC and evaluated by similarity analysis (SA), hierarchical clustering analysis (HCA) and principal component analysis (PCA). Nine samples were obtained from these 24 batches of different origins, according to the results of SA, HCA and the common chromatographic peaks area. A testosterone-induced mouse model of benign prostatic hyperplasia (BPH) was used to establish the anti-benign prostatic hyperplasia activities of these nine S. stolonifera samples. The model was evaluated by analyzing prostatic index (PI), serum acid phosphatase (ACP) activity, concentrations of serum dihydrotestosterone (DHT), prostatic acid phosphatase (PACP) and type II 5α-reductase (SRD5A2). The spectrum-effect relationships between HPLC fingerprints and anti-benign prostatic hyperplasia activities were investigated using Grey Correlation Analysis (GRA) and partial least squares regression (PLSR). The results showed that a close correlation existed between the fingerprints and anti-benign prostatic hyperplasia activities, and peak 14 (chlorogenic acid), peak 17 (quercetin 5-O-β-d-glucopyranoside) and peak 18 (quercetin 3-O-β-l-rhamno-pyranoside) in the HPLC fingerprints might be the main active components against anti-benign prostatic hyperplasia. This work provides a general model for the study of spectrum-effect relationships of S. stolonifera by combing HPLC fingerprints with a testosterone-induced mouse model of BPH, which can be employed to discover the principle components of anti-benign prostatic hyperplasia bioactivity. Full article