Next Article in Journal
Chemical Analyses and Therapeutic Properties of Plant Extracts
Previous Article in Journal
Triazole-Estradiol Analogs Induce Apoptosis and Inhibit EGFR and Its Downstream Pathways in Triple Negative Breast Cancer
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Affinity Ultrafiltration Mass Spectrometry for Screening Active Ingredients in Traditional Chinese Medicine: A Review of the Past Decade (2014–2024)

1
State Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
2
School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
*
Authors to whom correspondence should be addressed.
Molecules 2025, 30(3), 608; https://doi.org/10.3390/molecules30030608
Submission received: 25 December 2024 / Revised: 17 January 2025 / Accepted: 27 January 2025 / Published: 30 January 2025
(This article belongs to the Section Analytical Chemistry)

Abstract

Discovering targets in natural products is a critical and challenging task in new drug development. Rapid and efficient screening of active ingredients from complex systems like traditional Chinese medicine (TCM) is now crucial in drug research. Affinity ultrafiltration (AUF) technology is widely used to screen active ingredients in natural medicines. AUF-liquid chromatography–mass spectrometry (AUF-LC-MS) leverages the affinity between natural medicine extracts and targets to isolate active ingredients from complex matrices, employing LC-MS for detection and activity assessment. This review discusses the developments in employing AUF-LC-MS to analyze TCM and TCM compound preparations over the last decade. This review succinctly presents the advantages and limitations of AUF-LC-MS, illustrating its benefits through the example of screening for active ingredients in natural pharmaceuticals.

Graphical Abstract

1. Introduction

Traditional Chinese medicine (TCM), a remarkable medical resource with a long history, has unique advantages in preventing and treating various diseases, particularly in the control of major epidemics and clinical treatment. Approximately 35% of the global pharmaceutical market annually derives directly or indirectly from natural products, predominantly from plant sources (25%), with microbial sources (13%) and animal sources (3%) following [1]. The active ingredients of TCM form the material basis for its therapeutic effects and serve as an important source of biologically active compounds. However, TCM and its formulations often contain numerous chemical components, and the complexity of these mixtures makes the evaluation and identification of active ingredients highly challenging. Thus, identifying the active ingredients in TCM is a critical scientific challenge in its modernization and a significant bottleneck in its global development.
The traditional strategy for researching active ingredients in TCM involves “chemical extraction and separation, molecular structure identification, and pharmacological activity evaluation” [2]. Although effective, this strategy is cumbersome and time-consuming, making it challenging to efficiently screen active structures. Modern pharmacological research indicates that a drug’s affinity for biological macromolecules is the first step in its mechanism of action, and the drug target is the critical starting point for its therapeutic effects in vivo [3]. Small molecules in TCM regulate biological processes and exert medicinal effects by interacting with target proteins in organisms. Consequently, molecular targeting methods for drug screening, based on disease-related biomacromolecules, have emerged.
Affinity ultrafiltration (AUF)–liquid chromatography (LC)–mass spectrometry (MS) is a solution affinity selection platform that separates target–ligand complexes in solution via ultrafiltration. It serves as a powerful tool for identifying active molecules within complex natural products. Compared with traditional methods, AUF is simple to operate, and it significantly reduces screening time and lowers the consumption of samples and reagents. The technology enables the online integration of various detection instruments, allowing for an accurate reflection of the interaction between the natural conformation of active substances and receptors. Due to its high sensitivity and strong selectivity, AUF-LC-MS holds unique value in small-molecule drug discovery and has garnered widespread attention from the pharmaceutical community. Before that, Chen et al. [4] also provided an overview, summary, and outlook on AUF-LC-MS technology. On this basis, this review provides a more comprehensive review of the basic principles, characteristics, and influencing factors of AUF-LC-MS technology, and summarizes its application in the screening of bioactive components of medicinal plants in the past ten years. For example, Panax ginseng has many functions such as enhancing immunity, anti-fatigue, and antioxidants. Panax ginseng is rich in saponins, which have a wide range of benefits for the human body. Modern pharmacological research shows that the most important ones are ginsenosides Rg1, Re, and Rb1 [5,6,7]. In recent years, researchers have used α-glucosidase, acetylcholinesterase, Monoamine oxidase type-B, and N-methyl-D-aspartic acid as targets, and adopted AUF-LC-MS technology to screen out 24, 16, 7, and 3 active ingredients, respectively [8,9]. In addition, 5, 12, and 32 active ingredients were also screened from Coptis chinensis Franch, Salvia miltiorrhiza Bge., Curcuma longa, etc. [10,11]. Please refer to Figure 1 for specific contents, which provide a certain scientific basis for rapid targeted screening of active ingredients in medicinal plants. This review also discusses the adaptability of this technology to a wider range of natural products and its combination with other analytical techniques, and prospects for its development, so that AUF technology can be widely used internationally.

2. AUF-MS: An Overview

AUF combines affinity capture with ultrafiltration, facilitating high-throughput compound screening [12]. Developed in 1981, this technique initially evaluated ultrafiltration’s theoretical and experimental applications in clinical serum binding assays [13]. Discovering drug target proteins is crucial for drug research [14]. In the late 1990s, AUF became widely used in targeted drug discovery and an indispensable tool for many pharmaceutical companies. Over the past decade, significant advancements have been made in AUF in terms of membrane materials, separation properties, and system optimization. Many new affinity membrane materials have been developed recently to enhance the selectivity and performance of the membranes. The separation capabilities of these membranes are enhanced by introducing various affinity ligands or through surface modifications. For instance, the use of hydrophilic polymers, nanomaterials, or composite materials enhances the affinity and anti-fouling properties of these membranes [15,16,17]. To enhance their separation performance, researchers have improved the separation abilities of AUF membranes by combining various affinity ligands, such as different antibodies, proteins, or small molecules. Particularly in complex biological systems, this multi-level separation significantly enhances the purity and efficiency of target molecule separation [18]. Additionally, AUF technology has increasingly adopted automation and intelligent control systems to enhance operational efficiency. For example, the use of real-time sensors to monitor membrane status, in combination with machine learning algorithms for automatic adjustments, enhances both the performance and the operational ease of the membrane system [19]. The ultrafiltration screening method is based on ligand–receptor-specific binding, with screening potential active ligands binding to the target protein by disease-specific characteristics [20]. First, the ligand mixture is combined with the receptor. After ultrafiltration, the ligand dissociates from the receptor, or the binding part is directly observed. Finally, the potential active ingredients are analyzed by LC-MS. AUF-MS is mainly divided into centrifugal ultrafiltration-MS (CU-MS) and pulsed ultrafiltration-MS (PU-MS). In both methods, the basic principle of small-molecule screening is the same: ligand enrichment is achieved through the selectivity of a semi-permeable membrane.
The CU-MS by ultrafiltration chamber and LC-MS platform operate independently, necessitating manual injection of ultrafiltration samples into the LC-MS system, hence the term “off-line ultrafiltration”. CU-MS employs commercial ultrafiltration centrifuge tubes to screen compounds, offering straightforward procedures and good reproducibility. Chen et al. [21] developed an off-line ultrafiltration-LC-MS platform to screen for inhibitors of α-glucosidase and pancreatic lipase. Fifteen potential ligands, including glucomoringin, 3-caffeoylquinic acid, and quinic acid, were quickly screened and identified from Moringa oleifera leaf extracts. The study identified 14 potential α-glucosidase ligands and 10 potential pancreatic lipase ligands. Feng et al. [22] captured 12 phytochemicals with varying affinities for topoisomerase I, topoisomerase II, COX-2, and ACE2 from Dysosma versipellis root and stem extracts by using an off-line ultrafiltration-LC-electrospray ionization (ESI)-MS/MS model. In vitro antiproliferation tests demonstrated that podophyllotoxin and quercetin had the strongest inhibition rates on A549 and HT-29 cells, whereas kaempferol exhibited a significant dose-dependent effect on COX-2. Additionally, quercetin exhibited a strong inhibitory effect on ACE2 (Figure 2).
PU-MS consists of a flow chamber, a magnetic stirrer, and an ultrafiltration membrane [23]. It is an online combination of PU and electrospray MS. After the test sample and target protein are added to the flow chamber, the ligand–receptor complex and inactive components can be separated by applying pressure. Unlike CU-MS, this technology is an online affinity MS screening method. Hence, it is also referred to as online ultrafiltration. PU-MS was first proposed by van Breemen et al. [24] to screen potential compounds binding to target receptors from complex systems. Adenosine deaminase inhibitors were successfully identified from a combinatory chemical library of 20 adenosine analogs by using this method. Beverly et al. [25] utilized PU-MS to evaluate a 35 μL binding chamber’s ability to screen ligands forming noncovalent complexes with protein targets. They found that the platform quickly screened and enriched the carbonic anhydrase inhibitor acetazolamide from bacterial fermentation broth extracts, completing the process in 5 min only.
Compared with PU, CU cannot be integrated with MS online. Additionally, the concentration polarization during centrifugal ultrafiltration can reduce the filtration speed, and in severe cases, cause protein adsorption and deposition on the membrane surface, affecting free drug transport. Thus, CU is primarily used for screening small-molecule active compounds within a limited range. By contrast, PU, which easily integrates with LC-MS to form an automated, high-throughput system, is more effective for describing receptor–ligand binding characteristics, drug metabolism, and product identification. In conclusion, the advantage of ultrafiltration-based methods lies in their ability to rapidly provide binding information between drug targets and compounds. These methods can be used to study the synergistic or antagonistic effects of multiple compounds.
The history of MS traces back to the early 20th century with the invention of the parabolic mass spectrometer by J.J. Thomson. In 1919, Aston developed the first velocity-focusing MS, marking a significant milestone in the field. Initially, MS was primarily used to determine the atomic weight of elements and isotopes. With advancements in ion optics theory, the technology continually improved, and by the late 1950s, it was widely applied in the analysis of inorganic and organic compounds. Owing to its high sensitivity, accuracy, and resolution, MS has become one of the most crucial analytical techniques in life sciences, medicine, and chemistry [26]. The advent of MS technology, particularly soft ionization methods like ESI and matrix-assisted laser desorption/ionization (MALDI), has extended the application of MS to the early stages of drug discovery, specifically in the identification of lead compounds [27]. Compared with earlier detection methods, MS does not require derivatization or isotope labeling, thereby expanding the range of applicable compounds, accelerating detection, and enhancing sensitivity and specificity. Thus, integrating MS with target affinity techniques—referred to as target molecule affinity-MS—has made drug screening more efficient and effective. In recent years, numerous MS techniques have been developed to address the increasing demand for analyzing and identifying specific components within complex substrates from multiple perspectives. These include techniques such as AUF-LC-MS, ESI-Q-TOF-MS, ultrahigh-performance LC (UPLC)–Orbitrap–(time-of-flight) TOF-MS, MALDI-TOF-MS, LC-MS, GC-MS, FT-ICR-MS, and DART-MS.
Based on the above explanation, the ultrafiltration method effectively enriches and separates ligands that bind to target proteins while being easy to operate and cost-effective. AUF can screen ligand–protein complexes from unbound substances, and when combined with LC and MS, it enables rapid separation and identification of potential active ingredients. It can identify target substances at various concentrations, and it is suitable for analyzing small quantities of complex mixtures such as combinatorial compound libraries and extracts or fractions of medicinal plants. When AUF is combined with LC-MSn, the high sensitivity of MS compensates for the limitations of LC in detecting minute components with low sample content [28]. As a high-throughput method, AUF-LC-MS performs well in screening active substances without stringent sample size requirements and offers additional advantages such as simplicity of operation and strong specificity. However, this method has certain limitations: false positives resulting from nonspecific adsorption in the ultrafiltration process typically need to be addressed through parallel control experiments using an inactivated target protein group or a serum protein replacement group. Additionally, ultrafiltration screening is primarily based on the affinity between the target protein and the ligand. As a result, while it evaluates the ligand’s affinity for the target protein, it does not directly reflect the ligand’s biological activity [29].

3. Advantages and Characteristics of AUF-LC-MS

Currently, various methods exist for screening active ingredients, such as cell membrane chromatography, magnetic bead screening, UV-visible spectroscopy, nuclear magnetic resonance (NMR), fluorescence, and electrochemical methods (Table 1) [30,31,32,33,34,35]. Compared with these methods, the combination of AUF and MS for screening small-molecule active substances in TCM offers several advantages, including ease of operation, high sensitivity, and specific results. Traditional chromatographic methods based on optical or radioactive substances often encounter matrix interference [36]. This interference complicates the identification and analysis of complex components in natural products. For instance, UV-visible spectroscopy measures α-glucosidase activity by hydrolyzing p-nitrophenyl-α-D-glucopyranoside, producing p-nitrophenol, detectable at 400 nm [37]. However, NMR is time-consuming and not well suited for rapid inhibitor screening. Additionally, fluorescence and electrochemical methods suffer from significant interference issues [38,39]. Consequently, a rapid and accurate method to screen active compounds with inhibitory effects is urgently needed. The ligand matrix does not affect the screening process of affinity MS. Thus, this unique advantage renders it particularly suitable for screening active ingredients in complex systems, especially traditional medicinal plants.
Owing to its high sensitivity and selectivity, AUF-LC-MS has been effectively utilized to isolate and identify target substances from complex samples, playing a pivotal role in extracting active molecules from natural products. In AUF, researchers study the interactions between small drug molecules and biological targets in solution. Binding between AUF receptors and ligands occurs in solution, which avoids alterations in their properties from labeling or chemical coupling to solid supports, thereby preserving their natural conformation and interactions. Ultrafiltration requires only small quantities of the target, and some protein targets can be reused, making it a viable option when targets are costly, scarce, or available in limited quantities [44]. Alternatively, the retention capability of the ultrafiltration membrane allows for the direct selection of active components that bind to target substances without the need for pretreatment, such as in immobilized enzyme online MS and cell membrane chromatography-MS [45,46]. AUF-MS enables rapid determination of binding constants between biological targets and small drug molecules while concurrently providing activity data for these molecules. In the combined AUF-MS approach, AUF exhibits robust specificity and screening capabilities for small ligands in complex mixtures. Meanwhile, LC-MS offers potent functionality for efficient separation and structural identification, effectively minimizing matrix interference.

4. Factors Affecting AUF Screening

AUF-LC-MS is extensively utilized for screening active ingredients from complex substrates because of its high-throughput capabilities. However, this technique has limitations, including the possibility that some identified candidates may not exhibit the expected activity or may show elevated activity, leading to potential false positives [47]. Various factors must be considered during the experimental process, including the concentration of the target and screening substances, the material of the ultrafiltration membrane, the selection of the dissociation solvent, the interception volume, the co-incubation time, the centrifugal speed, and the solution pH, to mitigate false-positive or false-negative results. The screening conditions must be optimized to ensure the high efficiency and specificity of the screening results, and operations should be rationally designed and standardized. Additionally, the design of negative control experiments is crucial for reducing false positives and improving the accuracy of the results (Figure 3).

4.1. Concentration of the Target and the Screened Substances

The concentrations of targets and screening substances are critical factors influencing the affinity filtration process. If the ligand concentration is significantly higher than that of the target protein, it may prevent some active ingredients from binding to the target proteins because ligand binding to target proteins is inherently competitive, leading to false negative results. Conversely, if the ligand concentration is too low, it may enhance nonspecific adsorption, thus increasing the likelihood of false positives. These false positives are often due to the nonspecific binding of the compound to the target protein. Yang et al. [48] were the first to verify AUF-LC screening results to eliminate false positives by using competitive binding experiments. In fact, competitive binding experiments not only eliminate false positives but also exclude ligands that bind to different sites than those of competitively binding compounds. Wang et al. [47] evaluated the feasibility of using competitive binding experiments combined with AUF-LC to identify xanthine oxidase (XOD) inhibitors in Perilla frutescens (L.) Britt., aiming to reduce false positives. In the experiment, P. frutescens extracts were incubated with XOD-free, XOD-present, or XOD-blocked active sites before ultrafiltration, and the total binding degree and specific binding degree of each compound were calculated on the basis of peak area. The results indicated that AUF-LC significantly reduced the number of false positives identified. However, this method cannot eliminate all false positives and may exclude some effective inhibitors.
Therefore, a thorough methodological review is essential to obtain reliable binding results. The equilibrium dissociation constant (KD) is a critical metric for evaluating the interaction between a ligand and its target protein, with each component having its own distinct KD value. The KD values of the receptor and target ligand should be closely matched; otherwise, significant discrepancies may result in false positives or false negatives. In general, the receptor concentration should be close to the KD value of the weakest ligand. If the ligand concentration is too high, only ligands with strong binding affinity could bind to the target protein at competitive binding sites. Therefore, in actual experiments, the ligand concentration should be equal to or less than that of the receptor. Wang et al. [49] developed an AUF-UPLC method to directly determine the KD of compounds in P. frutescens extracts and their target proteins, including the KD determination for α-glucosidase ligands in the ethyl acetate fraction of P. frutescens. The recovery rate, binding degree, and signal-to-noise ratio of α-glucosidase ligands in PFEA were determined using AUF-LC, followed by KD calculation using the proposed equilibrium. Oleanolic acid and apigenin were identified as high-affinity ligands of α-glucosidase, with KDs of 44.9 and 88.5 μM, respectively. These values were consistent with the results from isothermal titration calorimetry, kinetic analysis, and molecular docking simulations. The results demonstrate that this method is simple and easy to implement, allowing direct determination of KD values for compounds in natural product extracts without the need for internal standards or calibration agents. Optimizing these methods can enhance the screening accuracy and reliability of AUF-LC-MS, providing a robust foundation for the identification of active ingredients in complex substrates.

4.2. Ultrafiltration Membrane Material

In AUF-LC-MS, ultrafiltration membranes separate ligand–receptor complexes from unbound components. The selection of ultrafiltration membranes primarily involves two factors: pore size and material [40]. An ideal ultrafiltration membrane should effectively retain the target biological macromolecules while preventing leakage or clogging. The pore size should be less than one-third of the biomacromolecule’s size to ensure effective retention [12]. Selecting the appropriate pore size improves separation efficiency and prevents leakage of unbound components. An ideal ultrafiltration membrane material should minimize specific adsorption with potential ligands and receptors. Common ultrafiltration membrane materials include polyvinyl fluoride, polysulfone, polyether ketone, and methylcellulose [50]. These materials exhibit low nonspecific binding and are therefore widely used in ultrafiltration membrane production. Selecting the appropriate pore sizes and materials optimizes the separation efficiency of AUF-LC-MS and enhances the accuracy and reliability of the experiment, ensuring the authenticity of ligand–receptor interactions and reducing false-positive results.

4.3. Choice of Dissociation Solvent

The complex components, diverse structures, and varying polarities of TCM extracts make it challenging to successfully dissociate ligands from the affinity target while minimizing nonspecific adsorption, a key factor affecting screening results. Two main methods are currently used to denature enzymes: adding acid to the dissociation solvent to inactivate the enzyme in a low pH environment or using organic solvents for enzyme denaturation.
However, using organic solvent-based dissociation solutions only can sometimes increase nonspecific adsorption. Some related studies have demonstrated that acid-containing organic solvents, as opposed to those with organic solvents only, can effectively reduce nonspecific adsorption of non-affinity interacting substances. For example, Xie et al. [51] used a methanol–water (90:10) mixture to screen potential TCM components targeting 5-lipoxygenase and cyclooxygenase-2. Comparison of the ultrafiltrate chromatograms between the experimental and control groups revealed significant differences in the peak areas of active ingredients, with lower signals for nonspecifically adsorbed substances. Conversely, some researchers have successfully screened small-molecule inhibitors of cyclooxygenase and glutathione reductase from TCM by using dissociation solutions containing organic solvents only [52,53]. The findings indicate that different dissociation solutions yield varying effects, necessitating multiple experimental attempts to optimize dissociation conditions.
In summary, selecting an appropriate dissociation solvent is essential for reducing nonspecific adsorption and enhancing the accuracy of screening results. Multiple experimental attempts are recommended to identify the optimal dissociation conditions by comparing results, thereby effectively screening the active ingredients in TCM extracts.

5. Screening Technology and Application

5.1. High-Throughput Screening (HTS) of Active Ingredients of TCM

Efficient and rapid screening of active ingredients from complex systems, such as TCM, remains a key challenge in modern pharmaceutical research. Traditional methods of chemical separation, structural identification, and activity screening face the following several issues: unclear objectives, cumbersome procedures, high workload, lengthy processes, and potential loss of active ingredients. Recent pharmacological research has demonstrated that the affinity between drugs and biological macromolecules—such as enzymes, receptors, DNA, and RNA—is crucial for drug action. Molecular targeting strategies for drug screening have emerged, focusing on disease-related biological macromolecules as targets. Ultrafiltration offers excellent separation and minimizes matrix interference, whereas LC-MS provides powerful analytical capabilities for the rapid identification of multiple components. Combining these technologies to discover small-molecule active ingredients in TCM holds significant potential. Recently, this combined approach has been successfully applied to the screening of lead compounds, compound libraries, and active ingredients from natural products.
Numerous studies have confirmed that this method rapidly screens and identifies complex ligands in natural products (Figure 4). In recent years, scientists have frequently combined AUF with MS detection to screen active ingredients in combinatory chemical libraries, identifying novel inhibitors of key targets like α-glucosidase. α-Glucosidase is a key enzyme in carbohydrate hydrolysis, cleaving the α-1,4-glucoside bond at the non-reducing end of oligosaccharides, thereby releasing glucose and raising blood sugar levels. α-Glucosidase inhibitors reduce glucose production by inhibiting this enzyme’s activity, and they are widely used in the treatment of type 2 diabetes mellitus (T2DM) [54]. Although some α-glucosidase inhibitors derived from microorganisms, such as acarbose and voglibose, are used clinically, they can cause severe gastrointestinal side effects [55,56]. Natural α-glucosidase inhibitors from medicinal plants offer potential as alternative treatments for T2DM due to their low toxicity. Consequently, researchers have recently screened potential α-glucosidase inhibitors from various natural plants, including Cichorium glandulosum Boiss. et Huet, a chicory species in the Asteraceae family and a traditional Uighur medicinal plant. C. glandulosum is listed as a “medicinal food homology” item in the 2015 Catalogue of Homologous Medicine and Food by the National Health and Family Planning Commission of China. Studies have shown that chicory exhibits significant hypoglycemic activity and inhibits α-glucosidase [57,58]. Chen et al. [59] used AUF-LC-MS to screen and identify four potential α-glucosidase inhibitors from C. glandulosum seed extract to further investigate its hypoglycemic components. The preliminary identification included esculetin, chlorogenic acid, isochlorogenic acid B, and osochlorogenic acid A. Subsequently, Abudurexiti A et al. [60] used AUF to screen C. glandulosum extracts, identifying the following six potential α-glucosidase inhibitors: quercetin, lactucin, 3-O-methylquercetin, hyperoside, lactucopicrin, and isochlorogenic acid B. Potential α-glucosidase inhibitors have been screened from various natural plants, including the leaves of Rubus suavissimus and Inonotus obliquus and the roots of Siraitia grosvenorii [61,62,63]. The screening results of α-glucosidase-targeted active ingredients are detailed in Table 2.
Medicinal plants have been widely used to treat various diseases for thousands of years owing to their value as natural resources. Extracting biologically active compounds from medicinal plants has become a major focus of research worldwide. Chemical components in medicinal plants often have low abundance, complex structures, and multiple biological targets. The active ingredients and mechanisms of action are often challenging to define precisely. AUF-LC-MS is well suited for screening active ingredients in complex natural products. This technology combines the separation and analytical strengths of AUF and LC-MS, facilitating HTS and rapid identification of bioactive components in complex natural products. Andrographis paniculata (Burm. f.) Wall. ex Nees is derived from the dried aboveground parts of the plant. It exhibits a broad range of pharmacological activities in vivo and in vitro studies, with anti-inflammatory effects being the most prominent. Cyclooxygenase-2 (COX-2) is a key enzyme in prostaglandin (PG) synthesis, and its inhibitors are effective anti-inflammatory agents. Jiao [64] developed an AUF-based analytical method combined with UPLC and quadrupole TOF-MS (BAUF-UPLC-Q-TOF-MS) for rapid screening and identification of COX-2 ligands. Five COX-2 inhibitors were identified from A. paniculata extracts. Apart from its anti-inflammatory properties, A. paniculata exhibits immunomodulatory and antiviral effects. Feng [65] screened 11 potential ligands from A. paniculata targeting COX-2, IL-6, and ACE2.
In addition to the previously mentioned disease-related targets, AUF-MS can be used to screen 24 target active ingredients, including lipase, thrombin, and tyrosinase (TYR). Lipase catalyzes the hydrolysis of fats (lipids). Lipase inhibitors regulate lipids by inhibiting the catalytic activity of human pancreatic lipase, a key enzyme in triacylglycerol hydrolysis, aiding in the control or treatment of obesity-related conditions. TYR is a rate-limiting enzyme in melanin production. Albinism is a genetic disorder caused by mutations in the TYR gene, leading to impaired TYR production. Thrombin (FIIα) is a key enzyme in thrombosis and a downstream component of the coagulation pathway. It converts fibrinogen into fibrin and coagulation factor XIII into factor XIIIα. This process combines with calcium ions to form the fibrin network, a critical step in thrombosis. Consequently, FIIα has gained widespread attention as a target for antithrombotic therapies. Table 2 summarizes the applications of AUF-MS in screening natural product extracts from January 2014 to May 2024.
Table 2. List of active ingredients of natural products screened by AUF technology.
Table 2. List of active ingredients of natural products screened by AUF technology.
Target ProteinNatural ProductsActive IngredientsRef.
α-GlucosidasePanax GinsengTwenty-four compounds[8]
Rhizoma CoptidisJatrorrhizine, epiberberine, coptisine, palmatine, berberine[10]
Moringa oleifera leavesFourteen compounds[21]
Perilla frutescensNine compounds[49]
Cichorium glandulosumEsculetin, chlorogenic acid, isochlorogenic acid B, isochlorogenic acid A[59]
Cichorium glandulosumQuercetin, lactucin, 3-O-methylquercetin, hyperoside, lactucopicrin, isochlorogenic acid B[60]
Rubus suavissimus leavesTwenty-six compounds[61]
Inonotus obliquus(E)-4-(3,4-dihydroxyphenyl) but-3-en-2-one[62]
Siraitia grosvenorii RootsSeventeen compounds[63]
Cichorium glundulosum rootBaicalin, lactupicrin[66]
Trifolium pratenseDaidzin, ononin, daidzein, genistein, fomononetin, biochanin A[67]
Cyclocarya paliurus leavesMainly damarane-type triterpenoid saponins[68]
Radix AstragaliThirteen prototype isoflavonoids and one monohydroxylated metabolic isoflavonoid[69]
Scutellaria baicalensis GeorgiBaicalin, wogonoside, 5,7,3,2′,6′-pentahydroxy flflavanone, chrysin-6-C-arabinosyl-8-C-glucoside, chrysin-6-C-glucosyl-8-C-arabinoside, wogonin[70]
Polygonatum odoratumFive phenethyl cinnamides and four homoisoflavanones[71]
Scutellaria baicalensis GeorgiBaicalein, baicalein, wogonin, chrysin, oroxylin A[72]
Ginkgo bilobaEleven compounds[73]
Buddleja FlosThirteen phenylethanoid glycosides and twenty flavonoids[74]
Cercis chinensisTwelve compounds[75]
Cyclooxygenase 2Dysosma versipellisNine compounds [22]
Anemarrhenae rhizomaTimosaponin A-II, timosaponin A-III, timosaponin B-II, timosaponin B-III, anemarrhenasaponin I[51]
Andrographis paniculataAndrographolide, 14-deoxy-11,12-didehydroandrographiside, andrographidine E, andrographidine D, deoxyandrographolide[64]
Andrographis paniculataEleven compounds [65]
Kadsura coccineaTwenty-one compounds [76]
Rhamnus davuricaVitexin, Taxifolin, Aromadendrin, Kaempferol 7-O-glucoside, berberine III, apigenin, kaempferol, rhamnocitrin, sakuranetin, questin, physcion[77]
Trifolium pratense L.Rothindin, ononin, daidzein, trifoside, pseudobaptigenin, formononetin, biochanin A[78]
Paris polyphyllaPolyphyllin I, II, VI, VII[79]
Curcuma longaThirteen compounds [80]
Sinopodophyllum hexandrumRutin, quercetin 3-O-glucoside, kaempferol 3-O-glucoside, β-Apopicropodophyllin, quercetin, isorhamnetin, kaempferol, podophyllotoxin[81]
Saussurea obvallataConiferin, syringin, roseoside, grasshopper ketone[81]
Moutan cortexGallic acid, methyl gallate, galloylpaeoniflflorin, 1,2,3,6-Tetra-O-galloyl-β-D-glucose, 1,2,3,4,6-Penta-O-galloyl-β-D-glucopyranose [82]
Xanthine oxidasePerilla frutescensKaempferol-3-O-rutinoside, rosmarinic acid, methyl-rosmarinic acid, apigenin, 4′,5,7-trimethoxyflflavone were identifified, from total eleven compounds [47]
Trifolium pratenseDaidzin, ononin, daidzein, genistein, fomononetin, biochanin A[67]
Panax japlcus var.24(R)-majoroside R1, chikusetsusaponin IVa, oleanolic acid-28-O-β-D-glucopyranoside, notoginsenoside Fe, ginsenoside Rb2, ginsenoside Rd[83]
Flos ChrysanthemumLuteolin-7-O-glucoside, apigenin-7-O-glucoside, luteolin, apigenin[84]
Selaginella tamariscinaAmentoflavone, robustaflavone[85]
Celery seeds (Apium graveolens L.)Luteolin-7-O-apinosyl glucoside, luteolin-7-O-glucoside, luteolin-7-O-malonyl apinoside, luteolin-7-O-6′-malonyl glucoside, luteolin, apigenin, chrysoeriol[86]
the roots of Lindera reflexa HemslPinosylvin, pinocembrin, methoxy-5-hydroxy-trans-stilbene[87]
Azadirachta indicaCarnosic acid[88]
Ligusticum chuanxiongIsochlorogenic acid C, senkyunolide I[89]
Curcumae RhizomaFifteen compounds[89]
Curcuma phaeocaulis ValetonFifteen compounds[90]
Polygonum AmplexicauleGallic acid, procyanidin B2-3″O-gallate, 11-O-galloylbergenin, (−)-epicatechin gallate, di-galloyl-O-bergenin[91]
Salvia miltiorrhiza Bge.Seventeen compounds[92]
AcetylcholinesterasePanax ginsengGinsenoside Ro, Rb2, Rg1, Re, Rf, Rb1, Rc, Rb3, Rd, Rs1, Ra6, chikusetsusaponin IVa, gypenoside XVl, compound O, pseudoginseoside Rc1, zingibroside R1[9]
Terminalia chebula fruits Mainly gallotannins and ellagitannins[68]
Fibraurea recisa Pierre.Twelve compounds [93]
Coptis chinensis FranchColumbamine, jatrorrhizine, coptisine, palmatine, berberine[94]
Zanthoxylum nitidumJatrorrhizine, columbamine, skimmianine, palmatine, epiberberine [94]
Azadirachta indicaD-(+)-catechin, (−)-epicatechin, carnosol[95]
Hedyotis diffusaQuercetin-3-O-sophoroside, quercetin-3-O-[2-O-(6-O-E-sinapoyl)-β-D-glucopyranosyl]-β-D-glucopyanoside, quercetin-3-O-[2-O-(6-O-E-feruloyl)-β-D-glucopy-ranosyl]-β-D-glucopyranoside, (E)-6-O-p-coumaroyl scandoside methyl ester[96]
Topoisomerase I Dysosma versipellisTwelve compounds [22]
Lycoris radiateHippeastrine, camptothecin[44]
Rhamnus davurica Pall.Eleven compounds[77]
Paris polyphyllaPolyphyllin I, II, VI, VII[79]
Sinopodophyllum hexandrumIsocorydine, rutin, quercetin 3-O-glucoside, kaempferol 3-O-glucoside, β-apopicropodophyllin, quercetin, isorhamnetin, kaempferol, podophyllotoxin [80]
Rhamnus davuricaAromadendrin, naringeninb, apigenin, quercetina, rhamnocitrinb, sakuranetin, questinb, physcionb[97]
Arachidonate 5-lipoxygenaseSaposhnikovia divaricata (Turcz.) SchischkPrim-O-glucosylcimifugin, 4′-O-β-D-glucosyl-5-O-methylvisamminol, cimifugin, sec-O-glucosylhamaudol[98]
Smilax glabra Roxb.Astilbin, isoastilbin, engelitin, isoengelitin, resveratrol[98]
Pueraria lobatapuerarin, daidzin, 3′-methoxy-puerarin, 3′-hydroxy-puerarin, daidzein[98]
Carthamus tinctoriusHydroxyl safflower yellow A, anhydrosafflor yellow B[98]
Radix Saposhnikoviae viaPrim-O-glucosylcimifugin, cimifugin, 5-O-methylvisamminol, sec-O-glucosylhamaudol, hamaudol[99]
Topoisomerase IIDysosma versipellisTwelve compounds [22]
Paris polyphyllaPolyphyllin I, II, VI, VII[79]
Sinopodophyllum hexandrumIsocorydine, rutin, quercetin 3-O-glucoside, quercetin, isorhamnetin, kaempferol[80]
Augmented realityLysimachia christinae1,5-di-hydroxy-1,5-di-[(E)-3-(4-hydroxyphenyl)-2-propenoic]-3-pentanonyl[100]
Peruvian tea plant infusionsChlorogenic acid, 3,5-di-O-caffeoylquinic acid, 1,3,5-tri-O-caffeoylquinic acid[101]
Hypericum laricifolium Juss.Protocatechuic acid, chlorogenic acid, caffeic acid, kaempferol 3-O-glucuronide, quercetin, kaempferol[102]
NeuraminidasePolygonum cuspidatumTrans-polydatin, cis-polydatin, emodin-1-O-β-D-glucoside, emodin-8-O-β-D-glucoside, emodin[103]
Baphicacanthus cusia2,4(1H,3H)-quinazolinedione, 4(3H)-quinazolinone, 2(3H)-benzoxazolone, tryptanthrin, indirubin[104]
Angelica pubescensThirteen compounds [105]
Angiotensin-converting enzyme 2Dysosma versipellisTwelve compounds [22]
Andrographis paniculataEleven compounds [65]
Sinopodophyllum hexandrumIsocorydine, rutin, quercetin 3-O-glucoside, kaempferol 3-O-glucoside, β-apopicropodophyllin, isorhamnetin, kaempferol, podophyllotoxin[83]
Pancreatic lipaseMoringa oleifera leavesEleven compounds[21]
Dendrobium officinaleVicenin II, isoschaftoside, schaftoside, vitexin 2″-O-glucoside, vitexin 2″-O-rhamnoside, rutin, isoquercetrin, kaempferol 3-O-β-D-glucopyranoside, naringenine, linolenic acid, palmitic acid[106]
Protein Tyrosine Phosphatase-1BPuerariae Lobatae RadixDaidzin, Puerarin[107]
Black tea(−)-epicatechin-3-O-gallate, epigallocatechin gallate, positive control[107]
Estrogen receptor Arnebia euchromaTwenty-one compounds [108]
G-quadruplex DNAMacleaya cordataProtopine, allocryptopine, sanguinarne, chelerythrine[109]
Superoxide dismutase Azadirachta indicaGallic acid, protocatechuic acid, (−)-epicatechin[88]
Lactate dehydrogenaseTrifolium pratenseBiochanin A, genistein, fomononetin, ononin[67]
Monoamine oxidase type-BPanax ginsengGinsenoside Rg1, Re, Rb1, Rc, Ro, Rb2, Rd[9]
N-Methyl-D-aspartic acidPanax ginsengGinsenoside Rg1, Re, Rf[9]
Matrix Metallopeptidase 2Smilax glabra Roxb., Smilax china L., Saposhnikovia divaricate (Turcz.) SchischkResveratrol, engelitin, asibinn, 4′-O-β-D-glucosyl-5-O-methylvisamminol, cimifugin, prim-O-glucosylcimifugin, sec-O-glucosylhamaudol[110]
UDP-glucuronosyltransferase 1A1Polygonum multiflorum rootCis-2,3,5,4′-tetrahydroxystilbene-2-O-β-glucoside, trans-2,3,5,4′-tetrahydroxystilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucoside, emodin[111]
Interleukin-6Andrographis paniculataEleven compounds [65]
TyrosinaseDryopteris crassirhizoma rhizomeTwenty-two compounds[112]
Lactate dehydrogenasesAzadirachta IndicaCarnosol [95]
Epidermal growth factor receptor erbB1Psoralea FructusPsorachalcone A, psoralen, bakuchalcone [113]

5.2. Screening of Active Ingredients in TCM Compound Preparations

TCM compound preparations are formulated on the basis of TCM theory. Their chemical components are highly complex, making it challenging to rapidly screen and identify active ingredients using conventional analytical methods. Historically, clarifying the bioactive components and mechanisms of action in single medicinal plants has been difficult, let alone in natural drug formulas, due to their low content, complex chemical structures, and multicomponent, multitarget effects. AUF-LC-MS remains one of the most powerful tools for screening active compounds from complex natural products (Table 3).
In recent years, Ronghua Dai’s research group [114] has employed AUF-LC-MS to study the interactions between extracts of Zishen Pills, a TCM compound preparation, and biological target proteins. COX-2 is a key enzyme that catalyzes the conversion of arachidonic acid (AA) into PGs. It is specifically induced during inflammation, degeneration, and tumorigenesis. The research group employed AUF-LC-MS to investigate the interaction between Zishen Pill extract and COX-2, selecting celecoxib and glipizide as positive and negative controls, respectively. The study identified 20 compounds that specifically bind to COX-2, 8 of which are potential COX-2 inhibitors. Their structures were elucidated using Fourier transform ion cyclotron resonance MS. Further validation was conducted using in vitro COX-2 inhibition assays and molecular docking studies.
Additionally, the research group further investigated the interaction between Zishen Pills and 5-lipoxygenase (5-LOX) inhibitors [115]. It was found that 5-LOX plays a crucial role in inflammatory processes, and it is a key enzyme in the metabolism of AA to leukotriene A4 (LTA4). The research team optimized the concentration of 5-LOX enzyme, incubation conditions (temperature and time), pH, and ionic strength based on prior experiments to achieve more accurate screening results. The screening results indicated that six compounds may possess potential 5-LOX inhibitory activity, with anemarrhenasaponin I, timosaponin AI, nyasol, and demethyleneberberine demonstrating significant enzyme inhibition. Further, structure–activity relationship studies revealed that the hydroxyl group is essential for ligand binding to the 5-LOX protein, followed by the aromatic ring, which engages in π–π interactions with amino acid residues in the 5-LOX protein. This study provides a scientific foundation for the development of 5-LOX inhibitors.

6. Fingerprint Analysis of the Active Components of TCM

The fingerprint analysis of active ingredients in TCM is crucial for quality control and evaluation. Although traditional chemical fingerprints can reflect the overall characteristics of TCM, they are limited because the selected chemical components may not correspond directly to those that produce clinical effects. Therefore, integrating high-throughput screening technologies, such as AUF-LC-MS, to identify active ingredients in TCM and further obtain their biological fingerprints can address the limitations of chemical fingerprints and offer a novel approach for evaluating the efficacy of TCM.
Recently, Mingquan Guo’s research group [121] has made significant progress in studying Rhamnus davurica Pall. by using AUF-LC-MS. They established an AUF-LC-MS-based method to successfully screen and identify ligands in R. davurica that are potentially active against therapeutic targets like top I and COX-2 [77]. The study identified 12 potential top I ligands and 11 potential COX-2 ligands, further demonstrating that these components exhibit anti-inflammatory and anti-proliferative activities in vitro. This study not only proposes a novel method to reveal the diverse active ingredients of TCM and their potential targets but also underscores the importance of biological fingerprint analysis in TCM research.
By integrating bioaffinity technology with MS, the characteristics of active ingredients in TCM can be understood more comprehensively, providing a more scientific basis for its quality control. This approach not only addresses the limitations of traditional chemical fingerprinting but also enhances the accuracy of TCM efficacy evaluation. Future research should focus on exploring the biological fingerprints of various TCMs to advance the quality standardization and modernization of TCM, ultimately supporting its broader application in clinical practice.

7. Analysis of Metabolites of Small-Molecule Drugs

In the analysis of small-molecule drug metabolites, modern analytical methods are diverse and highly efficient, with LC-MS being particularly prominent. This technology not only efficiently separates and detects drugs and their metabolites but also provides detailed structural information and supports metabolic pathway research, significantly advancing the fields of pharmacokinetics and pharmacodynamics. AUF-LC-MS, a pretreatment technique, has been widely applied in drug metabolism research. This method combines ultrafiltration technology with online LC-MS analysis to rapidly and efficiently assess the metabolic rate and extent of drugs at affinity targets like liver microsomes.
Van Breemen and colleagues [122] successfully used AUF-LC-MS to evaluate the metabolic characteristics of tricyclic psychotropic drugs like promethazine and to reveal the structural features of their main metabolites. Huang et al. [99] demonstrated the potential of AUF-LC-MS in studying the pharmacological activity of natural products. They employed this technique to screen for potential lipoxygenase inhibitors in Saposhnikovia divaricata (Trucz.) Schischk. They also identified multiple metabolic pathways by using semi-preparative HPLC separation and in vitro cytochrome P450 metabolism studies, offering new approaches for evaluating the medicinal value of natural products. Methodologically, the advantage of AUF-LC-MS lies in its simplicity and high-throughput capabilities, making it particularly suitable for metabolite analysis and structural identification of complex samples. This technology not only allows researchers to quickly obtain pharmacokinetic data but also provides valuable structure–activity relationship information during drug design and optimization.

8. Summary and Outlook

In recent years, TCM has demonstrated unique advantages in treating complex diseases owing to its multicomponent and multitarget characteristics. Traditional methods often struggle to analyze the chemical components and pharmacological mechanisms of TCM. Bioaffinity MS offers a novel approach to address this issue. Notably, AUF-LC-MS has been widely applied in screening active ingredients in TCM owing to its high efficiency and simplicity. AUF-LC-MS shows significant potential in TCM research. This approach involves combining medicinal plant extracts with specific protein targets, using ultrafiltration to separate the conjugates, and then identifying the bound active ingredients through LC-MS. Recent studies have shown that the AUF-LC-MS yielded remarkable results in screening targets such as α-glucosidase, cyclooxygenase-2, and thrombin. This study found that compounds isolated from traditional Chinese medicine by using this method exhibited excellent enzyme inhibitory activity, with high selectivity and specificity.
Although the AUF-LC-MS method holds promising prospects for screening active ingredients in TCM, it still possesses limitations and faces various challenges. First, given the complex nature of TCM compounds, molecular interactions may compromise analytical accuracy. Future efforts might incorporate computational biology techniques to predict and confirm inter-component interactions, thereby enhancing the accuracy of screening and analysis for potentially active components. Secondly, employing multi-stage ultrafiltration membranes or a series of ultrafiltration tubes could facilitate the development of multichannel or high-throughput AUF systems, significantly enhancing the efficiency and precision of multi-target screening. Additionally, despite the therapeutic benefits of TCM volatiles like monoterpenes and sesquiterpenes, their volatile and low-density nature leads to immobilization and trapping challenges during the AUF process. Improvements might be achieved by employing tightly sealed reaction vessels, developing specialized ultrafiltration membranes, or operating in low-temperature conditions to minimize the loss of volatile components. Given the unique characteristics of volatile components, integrating auxiliary technologies such as gas chromatography for pretreatment or post-treatment might improve screening accuracy and efficiency. Moreover, false positives and nonspecific binding restrict the wider application of this technique. Utilizing enzyme denaturation controls, together with enzyme activity assays and molecular docking, can significantly mitigate nonspecific binding and boost screening accuracy.
Considering that MS and LC analysis tools have become more miniaturized and automated, the application of AUF-LC-MS is expected to become more widespread and in-depth. In the future, the use of this technology in screening TCM active ingredients should extend beyond common targets to include more significant protein targets. This approach could facilitate the discovery of new drugs and enhance the understanding of the pathogenesis of complex diseases. Therefore, AUF-MS is a powerful tool for identifying and studying the mechanisms of active ingredients in TCM. With ongoing innovations and improvements, this method is likely to play a more significant role in natural product research and new drug development. Future research should focus on overcoming current technical bottlenecks and identifying more disease-related protein targets, which would advance modern research on TCM.

Author Contributions

Y.H.: Literature Review, Writing—Original Draft Preparation, and Conceptualization; X.Z.: Literature Review and Table Arrangement; M.Y.: Table Arrangement and Drawing Preparation; D.Y.: Drawing Preparation; L.C.: Literature Review; C.T.: Conceptualization, Writing—Review and Editing, and Supervision; Y.Z.: Funding Acquisition, Conceptualization, and Supervision. All authors have read and agreed to the published version of the manuscript.

Funding

The authors gratefully acknowledge the financial support from the National Key Research and Development Program of China (No. 2023YFC3504400).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare that they have no competing financial interests or personal relationships that may have influenced the work reported in this study.

References

  1. Calixto, J. The role of natural products in modern drug discovery. An. Acad. Bras. Cienc. 2019, 91, e20190105. [Google Scholar] [CrossRef]
  2. Song, H.; Yang, H.; Gao, W.; Chen, J.; Li, P. A progress on the key technologies for discovery of bioactive compounds from traditional Chinese medicines. World Sci. Technol. Mod. Tradit. Chin. Med. 2016, 18, 1093–1098. [Google Scholar]
  3. Li, S.P.; Zhao, J.; Yang, B. Strategies for quality control of Chinese medicines. J. Pharm. Biomed. Anal. 2011, 55, 802–809. [Google Scholar] [CrossRef] [PubMed]
  4. Chen, G.; Huang, B.; Guo, M. Current advances in screening for bioactive components from medicinal plants by affinity ultrafiltration mass spectrometry. Phytochem. Anal. 2018, 29, 375–386. [Google Scholar] [CrossRef] [PubMed]
  5. Dong, W.; Zhao, J.; Zhong, F.; Zhu, W.; Jiang, J.; Wu, S.; Yang, D.; Li, D.; Quan, L. Biotransformation of Panax ginseng extract by rat intestinal microflora: Identification and quantification of metabolites using liquid chromatography-tandem mass spectrometry. J. Ginseng Res. 2017, 41, 540–547. [Google Scholar] [CrossRef] [PubMed]
  6. Jeong, D.; Irfan, M.; Kim, S.; Kim, S.; Oh, J.; Park, C.; Kim, H.; Rhee, M. Ginsenoside Rg3-enriched red ginseng extract inhibits platelet activation and in vivo thrombus formation. J. Ginseng Res. 2017, 41, 548–555. [Google Scholar] [CrossRef] [PubMed]
  7. Zhou, Q.; Jiang, L.; Xu, C.; Luo, D.; Zeng, C.; Liu, P.; Yue, M.; Liu, Y.; Hu, X.; Hu, H. Ginsenoside Rg1 inhibits platelet activation and arterial thrombosis. Thromb. Res. 2014, 133, 57–65. [Google Scholar] [CrossRef]
  8. Wang, H.; Fan, C.; Lin, Z.; Yin, Q.; Zhao, C.; Peng, P.; Zhang, R.; Wang, Z.; Du, J.; Wang, Z. Screening of potential α-Glucosidase inhibitors from the roots and rhizomes of Panax Ginseng by affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS method. Molecules 2023, 28, 2069. [Google Scholar] [CrossRef]
  9. Yu, L.; Wei, F.; Liang, J.; Ren, G.; Liu, X.; Wang, C.; Yuan, J.; Zeng, J.; Luo, Y.; Bi, Y.; et al. Target molecular-based neuroactivity screening and analysis of Panax ginseng by affinity ultrafiltration, UPLC-QTOF-MS and molecular docking. Am. J. Chin. Med. 2019, 47, 1345–1363. [Google Scholar] [CrossRef]
  10. Tang, Y.; Li, S.; Li, S.; Yang, X.; Qin, Y.; Liu, C.; Zhang, Y. Screening and isolating potential α-glucosidase inhibitors from Rhizoma Coptidis by ultrafiltration LC-PDA-ESI/MS combined with high-speed countercurrent chromatography and reverse-phase medium-pressure liquid chromatography. Med. Chem. Res. 2017, 26, 3384–3394. [Google Scholar] [CrossRef]
  11. Lan, Z.; Yang, R.; Wang, H.; Xue, X.; Sun, Y.; Wang, S.; Zhang, Y.; Meng, J. Rapid identifying of COX-2 inhibitors from turmeric (Curcuma longa) by bioaffinity ultrafiltration coupled with UPLC-Q Exactive-Orbitrap-MS and zebrafish-based in vivo validation. Bioorg. Chem. 2024, 147, 107357. [Google Scholar] [CrossRef] [PubMed]
  12. Wu, S.; Yang, H.; Li, P. Application of the affinity ultrafiltration coupled with LC-MS technology in screening active components of traditional Chinese medicines. Acta Pharm. Sin. 2016, 51, 1060–1067. [Google Scholar]
  13. Whitlam, J.B.; Brown, K.F. Ultrafiltration in serum protein binding determinations. J. Pharm. Sci. 1981, 70, 146–150. [Google Scholar] [CrossRef]
  14. Babine, R.E.; Bender, S.L. Molecular recognition of protein-ligand complexes: Applications to drug design. Chem. Rev. 1997, 97, 1359–1472. [Google Scholar] [CrossRef] [PubMed]
  15. Zimmermann, R.; Freudenberg, U.; Schweiss, R.; Küttner, D.; Werner, C. Hydroxide and hydronium ion adsorption—A survey. Curr. Opin. Colloid Interface Sci. 2010, 15, 196–202. [Google Scholar] [CrossRef]
  16. Yang, X.; Hsia, T.; Merenda, A.; AL-Attabi, R.; Dumee, L.; Thang, S.; Kong, L. Constructing novel nanofibrous polyacrylonitrile (PAN)-based anion exchange membrane adsorber for protein separation. Sep. Purif. Technol. 2022, 285, 120364. [Google Scholar] [CrossRef]
  17. Ye, J.; Wang, X.; Chu, J.; Yao, D.; Zhang, Y.; Meng, J. Electrospun poly(styrene-co-maleic anhydride) nanofibrous membrane: A versatile platform for mixed mode membrane adsorbers. Appl. Surf. Sci. 2019, 484, 62–71. [Google Scholar] [CrossRef]
  18. Li, J.; Ge, J.; Yin, Y.; Zhong, W. Multiplexed affinity-based protein complex purification. Anal. Chem. 2008, 80, 7068–7074. [Google Scholar] [CrossRef]
  19. Tran, T.; Gustavsson, R.; Martinsson, E.; Bergqvist, F.; Axen, A.; Lundström, I.; Mandenius, C.; Aili, D. In-line fiber optical sensor for detection of IgG aggregates in affinity chromatography. J. Chromatogr. A 2024, 1730, 465129. [Google Scholar] [CrossRef]
  20. Cloutier, T.E.; Comess, K.M. Library Screening Using Ultrafiltration and Mass Spectrometry. In Mass Spectrometry in Medicinal Chemistry: Applications in Drug Discovery, Methods and Principles in Medicinal Chemistry; Wanner, K.T., Höfner, G., Eds.; Wiley-VCH: Weinheim/Berlin, Germany, 2007; pp. 157–183. [Google Scholar] [CrossRef]
  21. Chen, G.; Xu, Y.; Wu, J.; Li, N.; Guo, M. Hypoglycemic and hypolipidemic effects of Moringa oleifera leaves and their functional chemical constituents. Food Chem. 2020, 333, 127478. [Google Scholar] [CrossRef]
  22. Feng, H.; Chen, G.; Zhang, Y.; Guo, M. Potential multifunctional bioactive compounds from Dysosma versipellis explored by bioaffinity ultrafiltration-HPLC/MS with Topo I, Topo II, COX-2 and ACE2. J. Inflamm. Res. 2022, 15, 4677–4692. [Google Scholar] [CrossRef] [PubMed]
  23. Chen, C.-J.; Chen, S.; Woodbury, C.P.; Venton, D.L. Pulsed ultrafiltration characterization of binding. Anal. Biochem. 1998, 261, 164–182. [Google Scholar] [CrossRef]
  24. Van Breemen, R.B.; Huang, C.R.; Nikolic, D.; Woodbury, C.P.; Zhao, Y.; Venton, D.L. Pulsed ultrafiltration mass spectrometry: A new method for screening combinatorial libraries. Anal. Chem. 1997, 69, 2159–2164. [Google Scholar] [CrossRef] [PubMed]
  25. Beverly, M.B.; West, P.; Julian, R.K. Evaluation of a micro volume pulsed ultrafiltration cell for screening ligands in non-covalent complexes. Comb. Chem. High Throughput Screen. 2002, 5, 65–73. [Google Scholar] [CrossRef]
  26. Yates, J.R., III. A century of mass spectrometry: From atoms to proteomes. Nat. Methods 2011, 8, 633–637. [Google Scholar] [CrossRef]
  27. Bennett, J.L.; Nguyen, G.T.H.; Donald, W.A. Protein-Small Molecule Interactions in Native Mass Spectrometry. Chem. Rev. 2022, 122, 7327–7385. [Google Scholar] [CrossRef]
  28. Wieboldt, R.; Zweigenbaum, J.; Henion, J. Immunoaffinity ultrafiltration with ion spray HPLC/MS for screening small-molecule libraries. Anal. Chem. 1997, 690, 1683–1691. [Google Scholar] [CrossRef]
  29. Jian, J.; Chen, H.; Hong, Q.; Wang, L.; Zhao, Y.; Li, L.; Zhang, T.; Zhou, H.; Jiang, Z. Research progress in screening technology for active pharmaceutical ingredients of natural products based on liquid chromatography separation. Acta Pharm. Sin. 2020, 55, 1504–1510. [Google Scholar]
  30. Ma, W.; Wang, C.; Liu, R.; Wang, N.; Lv, Y.; Dai, B.; He, L. Advances in cell membrane chromatography. J. Chromatogr. A 2021, 1639, 461916. [Google Scholar] [CrossRef] [PubMed]
  31. Zhang, Y.; Shi, S.; Chen, X.; Peng, M. Functionalized magnetic nanoparticles coupled with mass spectrometry for screening and identification of cyclooxygenase-1 inhibitors from natural products. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2014, 960, 126–132. [Google Scholar] [CrossRef]
  32. Jin, P.; Chen, L.; Zhong, J.; Yuan, T.; Gan, L.; Huang, J.; Wang, L.; Fan, H.; Lin, C. Screening and identification of lipase inhibitors extracted from Dioscorea nipponica Makino by UV-vis and HPLC coupled to UPLC-Q-TOF-MS/MS. Int. J. Biol. Macromol. 2023, 230, 123427. [Google Scholar] [CrossRef] [PubMed]
  33. Shishodia, S.; Nuñez, R.; Strohmier, B.P.; Bursch, K.L.; Goetz, C.J.; Olp, M.D.; Jensen, D.R.; Fenske, T.G.; Ordonez-Rubiano, S.C.; Blau, M.E.; et al. Selective and cell-active PBRM1 bromodomain inhibitors discovered through NMR fragment screening. J. Med. Chem. 2022, 65, 13714–13735. [Google Scholar] [CrossRef]
  34. Yan, F.; He, S.; Han, X.; Wang, J.; Tian, X.; Wang, C.; James, T.D.; Cui, J.; Ma, X.; Feng, L. High-throughput fluorescent screening of β-lactamase inhibitors to improve antibiotic treatment strategies for tuberculosis. Biosens. Bioelectron. 2022, 216, 114606. [Google Scholar] [CrossRef]
  35. Liang, Q.; Huang, Y.; Wang, M.; Kuang, D.; Yang, J.; Yi, Y.; Shi, H.; Li, J.; Yang, J.; Li, G. An electrochemical biosensor for SARS-CoV-2 detection via its papain-like cysteine protease and the protease inhibitor screening. Chem. Eng. J. 2023, 452, 139646. [Google Scholar] [CrossRef] [PubMed]
  36. Mulabaqal, V.; Calderón, A.I. Development of an ultrafiltration-liquid chromatography/mass spectrometry (UF-LC/MS) based ligand-binding assay and an LC/MS based functional assay for Mycobacterium tuberculosis Shikimate Kinase. Anal. Chem. 2010, 82, 3616–3621. [Google Scholar] [CrossRef]
  37. Zhang, S.; Wang, X.; Wang, X.; Fan, X.; Liu, K.; Sa, Y.; Wilson, G.; Ma, X.; Chen, G. Establishment and application of a screening method for α-glucosidase inhibitors based on dual sensing and affinity chromatography. J. Chromatogr. A 2024, 1720, 464822. [Google Scholar] [CrossRef]
  38. Liu, Y.; Hu, J.; Lin, L.; Yang, B.; Huang, M.; Chang, M.; Huang, X.; Dai, Z.; Sun, S.; Ren, L.; et al. Overcoming the fluorescent interference during Raman spectroscopy detection of microplastics. Sci. Total Environ. 2023, 897, 165333. [Google Scholar] [CrossRef]
  39. Leong, I.; Kishimoto, S.; Tsutsui, M.; Taniguchi, M. Interference of electrochemical ion diffusion in nanopore sensing. iScience 2022, 25, 105073. [Google Scholar] [CrossRef]
  40. Johnson, B.; Nikolic, D.; van Breemen, R. Applications of pulsed ultrafiltration-mass spectrometry. Mass Spectrom. Rev. 2002, 21, 76–86. [Google Scholar] [CrossRef] [PubMed]
  41. He, L.; Wang, S.; Geng, X. Coating and fusing cell membranes onto a silica surface and their chromatographic characteristics. Chromatographia 2001, 54, 71–76. [Google Scholar] [CrossRef]
  42. Yasuda, M.; Wilson, D.; Fugmann, S.; Moaddel, R. Synthesis and characterization of SIRT6 protein coated magnetic beads: Identification of a novel inhibitor of SIRT6 deacetylase from medicinal plant extracts. Anal. Chem. 2011, 83, 7400–7407. [Google Scholar] [CrossRef]
  43. Zhang, H.; Yao, J.; Xiao, G.; Xie, J.; Mao, S.; Sun, C.; Yao, J.; Yan, J.; Tu, P. Discovery of drug targets based on traditional Chinese medicine microspheres (TCM-MPs) fishing strategy combined with bio-layer interferometry (BLI) technology. Anal. Chim. Acta 2024, 1305, 342542. [Google Scholar] [CrossRef]
  44. Zhao, H.; Zhang, Y.; Xiao, C. Advances in the study of affinity selection-ultrafiltration/HPLC-MS. Acta Pharm. Sin. 2009, 44, 1084–1088. [Google Scholar]
  45. Lohmann, W.; Karst, U. Generation and identification of reactive metabolites by electrochemistry and immobilized enzymes coupled on-line to liquid chromatography/mass spectrometry. Anal. Chem. 2007, 79, 6831–6839. [Google Scholar] [CrossRef]
  46. Wang, S.; Sun, M.; Zhang, Y.; Du, H.; He, L. A new A431/cell membrane chromatography and online high performance liquid chromatography/mass spectrometry method for screening epidermal growth factor receptor antagonists from Radix sophorae flavescentis. J. Chromatogr. A 2010, 1217, 5246–5252. [Google Scholar] [CrossRef] [PubMed]
  47. Wang, Z.; Kwon, S.H.; Hwang, S.H.; Kang, Y.-H.; Lee, J.-Y.; Lim, S.S. Competitive binding experiments can reduce the false positive results of affinity-based ultrafiltration-HPLC: A case study for identification of potent xanthine oxidase inhibitors from Perilla frutescens extract. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2017, 1048, 30–37. [Google Scholar] [CrossRef]
  48. Yang, Z.; Zhang, Y.; Sun, L.; Wang, Y.; Gao, X.; Cheng, Y. An ultrafiltration high-performance liquid chromatography coupled with diode array detector and mass spectrometry approach for screening and characterizing tyrosinase inhibitors from mulberry leaves. Anal. Chim. Acta 2012, 719, 87–95. [Google Scholar] [CrossRef]
  49. Wang, Z.; Zuo, G.; Hwang, S.H.; Kwon, S.H.; Kang, Y.-H.; Lee, J.-Y.; Lim, S.S. Affinity measurement of ligands in Perilla frutescens extract towards α-glucosidase using affinity-based ultrafiltration-high-performance liquid chromatography. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2019, 1125, 121725. [Google Scholar] [CrossRef] [PubMed]
  50. Yang, L.; Zhao, B.; Li, C.; Wang, K.; Zhang, T.; Wang, S.; Liu, R.; He, J. Research progress on screening active components from medicinal plants based on affinity ultrafiltration coupled with LC-MS technology. Chin. J. Exp. Tradit. Med. Formulae 2021, 27, 196–208. [Google Scholar]
  51. Xie, L.; Lee, D.Y.-W.; Shang, Y.; Cao, X.; Wang, S.; Liao, J.; Zhang, T.; Dai, R. Characterization of spirostanol glycosides and furostanol glycosides from anemarrhenae rhizoma as dual targeted inhibitors of 5-lipoxygenase and Cyclooxygenase-2 by employing a combination of affinity ultrafiltration and HPLC/MS. Phytomedicine 2020, 77, 153284. [Google Scholar] [CrossRef] [PubMed]
  52. Zhu, H.; Liu, S.; Li, X.; Song, F.; Liu, Z.; Liu, S. Bioactivity fingerprint analysis of cyclooxygenase-2 ligands from radix Aconiti by ultrafiltration-UPLC-MSn. Anal. Bioanal. Chem. 2013, 405, 7437–7445. [Google Scholar] [CrossRef] [PubMed]
  53. Mulabagal, V.; Calderón, A.I. Development of binding assays to screen ligands for Plasmodium falciparum thioredoxin and glutathione reductases by ultrafiltration and liquid chromatography/mass spectrometry. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2010, 878, 987–993. [Google Scholar] [CrossRef]
  54. McDougall, G.J.; Stewart, D. The inhibitory effects of berry polyphenols on digestive enzymes. BioFactors 2005, 23, 189–195. [Google Scholar] [CrossRef]
  55. Mohan, S.; Eskandari, R.; Pinto, B.M. Naturally occurring sulfonium-ion glucosidase inhibitors and their derivatives: A promising class of potential antidiabetic agents. Acc. Chem. Res. 2014, 47, 211–225. [Google Scholar] [CrossRef]
  56. Kihara, Y.; Ogami, Y.; Tabaru, A.; Unoki, H.; Otsuki, M. Safe and effective treatment of diabetes mellitus associated with chronic liver diseases with an alphaglucosidase inhibitor, acarbose. J. Gastroenterol. 1997, 32, 777–782. [Google Scholar] [CrossRef] [PubMed]
  57. Xin, X.; Wu, H.; Lv, Q.; Aisa, H.A. Hypoglycemic Effect of Extracts from Cichorium glandulosum. Nat. Prod. Res. Dev. 2012, 24, 234–238. [Google Scholar]
  58. Dalar, A.; Konczak, I. Cichorium intybus from Eastern Anatolia: Phenolic composition, antioxidant and enzyme inhibitory activities. Ind. Crops Prod. 2014, 60, 79–85. [Google Scholar] [CrossRef]
  59. Chen, H.; Qin, H.; Long, F.; Yu, W.; Wang, Y.; Chen, L.; Li, Q.; Chen, W.; Qin, D.; Han, B. Screening of high-affinity α-glucosidase inhibitors from cichorium glandulosum boiss. et Hout seed based on ultrafiltration liquid chromatography-mass spectrometry and molecular docking. Anal. Chem. 2017, 45, 889–897. [Google Scholar]
  60. Abudurexiti, A.; Abdurahman, A.; Zhang, R.; Zhong, Y.; Lei, Y.; Qi, S.; Hou, W.; Ma, X. Screening of α-Glucosidase inhibitors in Cichorium glandulosum Boiss. et Huet extracts and study of interaction mechanisms. ACS Omega 2024, 9, 19401–19417. [Google Scholar] [CrossRef]
  61. Liu, M.; Li, X.; Liu, Q.; Xie, S.; Chen, M.; Wang, L.; Feng, Y.; Chen, X. Comprehensive profiling of α-glucosidase inhibitors from the leaves of Rubus suavissimus using an off-line hyphenation of HSCCC, ultrafiltration HPLC-UV-MS and prep-HPLC. J. Food Compos. Anal. 2020, 85, 103336. [Google Scholar] [CrossRef]
  62. Wang, Y.; Guo, L.; Liu, C.; Zhang, Y.; Li, S. Isolation of potential α-glucosidase inhibitor from Inonotus obliquus by combining ultrafiltration-liquid chromatography and consecutive high-speed countercurrent chromatography. Anal. Methods 2021, 13, 918–924. [Google Scholar] [CrossRef]
  63. Lu, F.; Sun, J.; Jiang, X.; Song, J.; Yan, X.; Teng, Q.; Li, D. Identification and isolation of α-Glucosidase inhibitors from Siraitia grosvenorii roots using bio-affinity ultrafiltration and comprehensive chromatography. Int. J. Mol. Sci. 2023, 24, 10178. [Google Scholar] [CrossRef]
  64. Jiao, J.; Yang, Y.; Wu, Z.; Li, B.; Zheng, Q.; Wei, S.; Wang, Y.; Yang, M. Screening cyclooxygenase-2 inhibitors from Andrographis paniculata to treat inflammation based on bio-affinity ultrafiltration coupled with UPLC-Q-TOF-MS. Fitoterapia 2019, 137, 104259. [Google Scholar] [CrossRef] [PubMed]
  65. Feng, H.; Chen, G.; Guo, M. Exploring multifunctional components from Andrographis paniculata by affinity ultrafiltration with three molecular targets. Food Chem. 2023, 404, 134515. [Google Scholar] [CrossRef] [PubMed]
  66. Chen, H.; Ma, S.; Jiang, M.; Wang, Q.; Zang, J.; Qin, H.; Chen, W.; Han, B. Screening of α-glucosidase inhibitors from roots of Cichorium glundulosum by UF-LC-MS and molecular docking. Chin. Tradit. Herb. Drugs 2019, 50, 344–351. [Google Scholar]
  67. Hao, Y.; Liu, C.; Li, S.; Wang, Y.; Hou, W.; Wu, T. Screening of bioactive ligands in Trifolium pratense byaffinity ultrafiltration mass spectrometry. North Hortic. 2019, 17, 102–107. [Google Scholar]
  68. Li, Y. Studies on affinity screening acetylcholinesterase and α-glucosidase inhibitors from traditional Chinese medicines and interactions with enzymes. PhD Thesis, Lanzhou University, Lanzhou, Gansu, China, 2023. [Google Scholar]
  69. Zhao, H.; Zhang, Y.; Guo, Y.; Shi, S. Identification of major α-glucosidase inhibitors in Radix Astragali and its human microsomal metabolites using ultrafiltration HPLC-DAD-MSn. J. Pharm. Biomed. Anal. 2015, 104, 31–37. [Google Scholar] [CrossRef]
  70. Wang, J.; Liu, S.; Li, S.; Song, F.; Zhang, Y.; Liu, Z.; Liu, C. Ultrafiltration LC-PDA-ESI/MS combined with reverse phase-medium pressure liquid chromatography for screening and isolation potential α-glucosidase inhibitors from Scutellaria baicalensis Georgi. Anal. Methods 2014, 6, 5918–5924. [Google Scholar] [CrossRef]
  71. Zhou, X.; Liang, J.; Zhang, Y.; Zhao, H.; Guo, Y.; Shi, S. Separation and purification of α-glucosidase inhibitors from Polygonatum odoratum by stepwise high-speed counter-current chromatography combined with Sephadex LH-20 chromatography target-guided by ultrafiltration-HPLC screening. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2015, 985, 149–154. [Google Scholar] [CrossRef] [PubMed]
  72. Yang, J.; Luo, J.; Kong, L. Determination of α-glucosidase inhibitors from Scutellaria baicalensis using liquid chromatography with quadrupole time of flight tandem mass spectrometry coupled with centrifugal ultrafiltration. Chin. J. Nat. Med. 2015, 13, 208–214. [Google Scholar] [CrossRef]
  73. Wu, B.; Song, H.; Zhou, X.; Liu, X.; Gao, W.; Dong, X.; Li, H.; Li, P.; Yang, H. Screening of minor bioactive compounds from herbal medicines by in silico docking and the trace peak exposure methods. J. Chromatogr. A 2016, 1436, 91–99. [Google Scholar] [CrossRef] [PubMed]
  74. Xie, L.; Fu, Q.; Shi, S.; Li, J.; Zhou, X. Rapid and comprehensive profiling of α-glucosidase inhibitors in Buddleja flos by ultrafiltration HPLC-QTOF-MS/MS with diagnostic ions filtering strategy. Food Chem. 2021, 344, 128651. [Google Scholar] [CrossRef] [PubMed]
  75. Hong, Y.; Liao, X.; Chen, Z. Screening and characterization of potential α-glucosidase inhibitors from Cercis chinensis Bunge fruits using ultrafiltration coupled with HPLC-ESI-MS/MS. Food Chem. 2022, 372, 131316. [Google Scholar] [CrossRef]
  76. Sun, X. Study on cyclooxygenase-2 inhibitors in Kadsura coccinea based on affinity ultrafiltration coupled with high-performance liquid chromatography and quadruple time-of-flight mass spectrometry. PhD Thesis, Zhengzhou University, Zhengzhou, Henan, China, 2020. [Google Scholar]
  77. Chen, G.; Wu, J.; Li, N.; Guo, M. Screening for anti-proliferative and anti-inflammatory components from Rhamnus davurica Pall. using bio-affinity ultrafiltration with multiple drug targets. Anal. Bioanal. Chem. 2018, 410, 3587–3595. [Google Scholar] [CrossRef]
  78. Hou, W.; Li, S.; Li, S.; Shi, D.; Liu, C. Screening and isolation of cyclooxygenase-2 inhibitors from Trifolium pratense L. via ultrafiltration, enzyme-immobilized magnetic beads, semi-preparative high-performance liquid chromatography and high-speed counter-current chromatography. J. Sep. Sci. 2019, 42, 1133–1143. [Google Scholar] [CrossRef]
  79. Chen, G.; Guo, M. Rapid re-evaluation of bioactive saponins from Paris polyphylla using affinity ultrafiltration-LC/MS with multiple drug targets. Int. J. Mass Spectrom. 2018, 434, 87–92. [Google Scholar] [CrossRef]
  80. Feng, H.; Chen, G.; Zhang, Y.; Guo, M. Potential multiple bioactive components from Sinopodophyllum hexandrum explored by affinity ultrafiltration with four drug targets. Phytomed. Plus 2022, 2, 100219. [Google Scholar] [CrossRef]
  81. Wang, W.; Mei, L.; Yue, H.; Tao, Y.; Liu, Z. Targeted isolation of cyclooxygenase-2 inhibitors from Saussurea obvallata using affinity ultrafiltration combined with preparative liquid chromatography. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2023, 1217, 123620. [Google Scholar] [CrossRef]
  82. Zou, C.; Chen, Q.; Li, J.; Lin, X.; Xue, X.; Cai, X.; Chen, Y.; Sun, Y.; Wang, S.; Zhang, Y.; et al. Identification of potential anti-inflammatory components in Moutan Cortex by bio-affinity ultrafiltration coupled with ultra-performance liquid chromatography mass spectrometry. Front. Pharmacol. 2024, 15, 1358640. [Google Scholar] [CrossRef] [PubMed]
  83. Li, S.; Tang, Y.; Liu, C.; Li, J.; Guo, L.; Zhang, Y. Development of a method to screen and isolate potential xanthine oxidase inhibitors from Panax japlcus var via ultrafiltration liquid chromatography combined with counter-current chromatography. Talanta 2015, 134, 665–673. [Google Scholar] [CrossRef]
  84. Song, H.; Zhang, H.; Fu, Y.; Mo, H.; Zhang, M.; Chen, J.; Li, P. Screening for selective inhibitors of xanthine oxidase from Flos Chrysanthemum using ultrafiltration LC-MS combined with enzyme channel blocking. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2014, 961, 56–61. [Google Scholar] [CrossRef] [PubMed]
  85. Wang, J.; Liu, S.; Ma, B.; Chen, L.; Song, F.; Liu, Z.; Liu, C. Rapid screening and detection of XOD inhibitors from S. tamariscina by ultrafiltration LC-PDA-ESI-MS combined with HPCCC. Anal. Bioanal. Chem. 2014, 406, 7379–7387. [Google Scholar] [CrossRef] [PubMed]
  86. Gan, X.; Peng, B.; Chen, L.; Jiang, Y.; Li, T.; Li, B.; Liu, X. Identification of xanthine oxidase inhibitors from celery seeds using affinity ultrafiltration-liquid chromatography-mass spectrometry. Molecules 2023, 28, 6048. [Google Scholar] [CrossRef] [PubMed]
  87. Fu, Y.; Yang, J.; Chen, S.; Sun, X.; Zhao, P.; Xie, Z. Screening, and identification of the binding position, of xanthine oxidase inhibitors in the roots of Lindera reflexa Hemsl using ultrafiltration LC-MS combined with enzyme blocking. Biomed. Chromatogr. 2019, 33, e4577. [Google Scholar] [CrossRef]
  88. Fan, M.; Chen, G.; Guo, M. Potential antioxidative components in Azadirachta indica revealed by bio-affinity ultrafiltration with SOD and XOD. Antioxidants 2022, 11, 658. [Google Scholar] [CrossRef] [PubMed]
  89. Zhang, Q.; Yang, Y.; Li, S.; Wang, Y.; Yang, F.; Chen, H.; Xia, Z. An ultrafiltration and high performance liquid chromatography coupled with diode array detector and mass spectrometry approach for screening and characterizing thrombin inhibitors from Rhizoma Chuanxiong. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2017, 1061–1062, 421–429. [Google Scholar] [CrossRef] [PubMed]
  90. Lan, Z.; Zhang, Y.; Sun, Y.; Wang, L.; Huang, Y.; Cao, H.; Wang, S.; Meng, J. Identifying of anti-thrombin active components from Curcumae rhizoma by affinity-ultrafiltration coupled with UPLC-Q-Exactive Orbitrap/MS. Front. Pharmacol. 2021, 10, 769021. [Google Scholar] [CrossRef]
  91. Huang, C.; Yan, S.; Zhao, Y.; He, X.; Huang, S.; Zhao, H.; Yuan, Z.; Xu, G. Determination of anti-thrombin active components in Polygonum amplexicaule radix by affinity-ultrafiltration (AUF) and high-performance-tandem mass spectrometry (HPLC-MS/MS). Anal. Lett. 2024, 57, 1632–1645. [Google Scholar] [CrossRef]
  92. Hu, H.; Lai, L.; Wang, S.; Xie, Y. Ultra-filtration affinity mass spectrometry coupled with molecular docking to explore effective form of blood-activating and stasis-resolving active ingredient group in Salvia miltiorrhiza. Chin. Tradit. Herb. Drugs 2024, 55, 7217–7229. [Google Scholar]
  93. He, Z.; Lyu, N.; Nan, M.; Zhao, Y.; He, Y.; Meng, L.; Sun, J.; Zhang, L. Sereening and structure characterization of acetylcholinesterase inhibitorsfrom total alkaloids of Fibraurea recisa Pierre. by target molecule affinity-liquid chromatography-tandem mass spectrometry. Chin. J. Anal. Chem. 2017, 45, 211–216. [Google Scholar]
  94. Liu, M.; Liu, Q.; Chen, M.; Huang, X.; Chen, X. Large-scale separation of acetylcholinesterase inhibitors from Zanthoxylum nitidum by pH-zone-refining counter-current chromatography target-guided by ultrafiltration high-performance liquid chromatography with ultraviolet and mass spectrometry screening. J. Sep. Sci. 2019, 42, 1194–1201. [Google Scholar] [CrossRef] [PubMed]
  95. Fan, M.; Rakotondrabe, T.F.; Chen, G.; Guo, M. Antiparasiticactivity and potential active compounds from Azadirachta indica revealed by affinity ultrafiltration chromatography-mass spectrometry with acetylcholinesterase and lactate dehydrogenases. J. Anal. Test. 2024, 8, 403–414. [Google Scholar] [CrossRef]
  96. Li, J.; Yang, G.; Shi, W.; Fang, X.; Han, L.; Cao, Y. Anti-Alzheimer’s disease active components screened out and identified from Hedyotis diffusa combining bioaffinity ultrafiltration LC-MS with acetylcholinesterase. J. Ethnopharmacol. 2022, 296, 115460. [Google Scholar] [CrossRef]
  97. Chen, G.; Guo, M. Screening for natural inhibitors of Topoisomerases I from Rhamnus davurica by affinity ultrafiltration and high-performance liquid chromatography-mass spectrometry. Front. Plant Sci. 2017, 8, 1521. [Google Scholar] [CrossRef]
  98. Zhao, A.; Li, L.; Li, B.; Zheng, M.; Tsao, R. Ultrafiltration LC-ESI-MSn screening of 5-lipoxygenase inhibitors from selected Chinese medicinal herbs Saposhnikovia divaricata, Smilax glabra, Pueraria lobata and Carthamus tinctorius. J. Funct. Foods 2016, 24, 244–253. [Google Scholar] [CrossRef]
  99. Huang, Y.; Yu, M.; Wu, T.; Hou, W.; Liu, C.; Li, S. Development of a method to screen and isolate lipoxidase inhibitors from Radix saposhnikoviae via ultrafiltration liquid chromatography combined with metablism in vivo. Phytochem. Anal. 2020, 31, 937–947. [Google Scholar] [CrossRef]
  100. Wang, Z.; Hwang, S.H.; Lim, S.S. Characterization of DHDP, a novel aldose reductase inhibitor isolated from Lysimachia christinae. J. Funct. Foods 2017, 37, 241–248. [Google Scholar] [CrossRef]
  101. Wang, Z.; Hwang, S.H.; Quispe, Y.N.G.; Arce, P.H.G.; Lim, S.S. Investigation of the antioxidant and aldose reductase inhibitory activities of extracts from Peruvian tea plant infusions. Food Chem. 2017, 231, 222–230. [Google Scholar] [CrossRef]
  102. Quispe, Y.N.G.; Hwang, S.H.; Wang, Z.; Zuo, G.; Lim, S.S. Screening in vitro targets related to diabetes in herbal extracts from Peru: Identification of active compounds in Hypericum laricifolium Juss. by offline high-performance liquid chromatography. Int. J. Mol. Sci. 2017, 18, 2512. [Google Scholar] [CrossRef]
  103. Wang, L.; Chen, M.; Sun, Q.; Yang, Y.; Rong, R. Discovery of the potential neuraminidase inhibitors from Polygonum cuspidatum by ultrafiltration combined with mass spectrometry guided by molecular docking. J. Sep. Sci. 2023, 46, 2200937. [Google Scholar] [CrossRef]
  104. Fan, X.; Li, Y.; Wu, T.; Cheng, Z. Screening and identification of neuraminidase inhibitors from Baphicacanthus cusia by a combination of affinity ultrafiltration, HPLC-MS/MS, molecular docking, and fluorescent techniques. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2023, 1231, 123924. [Google Scholar] [CrossRef]
  105. Tian, Z.; Sun, L.; Chi, B.; Du, Z.; Zhang, X.; Liu, Y. Affinity ultrafiltration and UPLC-HR-Orbitrap-MS based screening of neuraminidase inhibitors from Angelica pubescens. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2022, 1208, 123398. [Google Scholar] [CrossRef]
  106. Tao, Y.; Cai, H.; Li, W.; Cai, B. Ultrafiltration coupled with high-performance liquid chromatography and quadrupole-time-of-flight mass spectrometry for screening lipase binders from different extracts of Dendrobium officinale. Anal. Bioanal. Chem. 2015, 407, 6081–6093. [Google Scholar] [CrossRef] [PubMed]
  107. Sun, R. Research of active components in natural products based on ultrafiltration-affnity mass spectrometry sereening and the hypoglyeemic effect study in vitro and in vivo. PhD Thesis, Shanghai Jiao Tong University, Shanghai, China, 2018. [Google Scholar]
  108. Zhu, L.; Ma, S.; Liu, M.; Li, K.; Shuai, E.; Wang, Z.; Li, S.; Zhang, S.; Cai, W. Screening and characterization estrogen receptor ligands from Arnebia euchroma (Royle) Johnst. via affinity ultrafiltration LC-MS and molecular docking. Front. Plant Sci. 2022, 13, 1012553. [Google Scholar] [CrossRef] [PubMed]
  109. Gao, Y.; Qin, W.; Ge, Y.; Sun, Y.; Yan, Y.; Zeng, Y.; Wang, F. Sereening of G-quadruplex ligands from Macleaya cordata extract bycontrast ultrafiltration with liquid chromatography-mass spectrometryand molecular docking. Chin. J. Chin. Mater. Med. 2020, 45, 3908–3914. [Google Scholar]
  110. Li, L.; Li, B.; Zhang, H.; Zhao, A.; Han, B.; Liu, C.; Tsao, R. Ultrafiltration LC-ESI-MSn screening of MMP-2 inhibitors from selected Chinese medicinal herbs Smilax glabra Roxb., Smilax china L. and Saposhnikovia divaricata (Turcz.) Schischk as potential functional food ingredients. J. Funct. Foods 2015, 15, 389–395. [Google Scholar] [CrossRef]
  111. Jiang, Y.; Zhang, C.; Zheng, X.; Zhao, Z.; Li, H. Simultaneously screening multiple UGT1A1 inhibitors from Polygonum multiflorum root using ultrafiltration LC-MS. Biomed. Chromatogr. 2022, 36, e5300. [Google Scholar] [CrossRef]
  112. Wang, Z.; Wang, N.; Han, D.; Yan, H. Characterization of tyrosinase inhibitors in Dryopteris crassirhizoma rhizome using a combination of high-speed counter-current chromatography, cffinity-based ultrafiltration, and liquid chromatography-tandem mass spectrometry. Front. Nutr. 2022, 9, 862773. [Google Scholar] [CrossRef]
  113. Jiang, B.; Chen, S.; Qian, W.; Yan, X.; Li, Y. Screening of effective components of Psoralea Fructus for endometriacancer by affinity ultrafiltration chromatography-mass spectrometry. Chin. Tradit. Herb. Drugs 2024, 55, 4663–4669. [Google Scholar]
  114. Huai, J.; Zhao, X.; Wang, S.; Xie, L.; Li, Y.; Zhang, T.; Cheng, C.; Dai, R. Characterization and screening of cyclooxygenase-2 inhibitors from Zi-shen pill by affinity ultrafiltration-ultra performance liquid chromatography mass spectrometry. J. Ethnopharmacol. 2019, 241, 111900. [Google Scholar] [CrossRef]
  115. Wang, S.; Huai, J.; Shang, Y.; Xie, L.; Cao, X.; Liao, J.; Zhang, T.; Dai, R. Screening for natural inhibitors of 5-lipoxygenase from Zi-shen pill extract by affinity ultrafiltration coupled with ultra performance liquid chromatography-mass spectrometry. J. Ethnopharmacol. 2020, 254, 112733. [Google Scholar] [CrossRef] [PubMed]
  116. Zhang, H.; Zhang, X.; Jiang, H.; Xu, C.; Tong, S.; Yan, J. Screening and identification of α-glucosidase inhibitors from Shenqi Jiangtang Granule by ultrafiltration liquid chromatography and mass spectrometry. J. Sep. Sci. 2018, 41, 797–805. [Google Scholar] [CrossRef]
  117. Song, H.; Chen, J.; Hong, J.; Hao, H.; Qi, L.; Lu, J.; Fu, Y.; Wu, B.; Yang, H.; Li, P. A strategy for screening of high-quality enzyme inhibitors from herbal medicines based on ultrafiltration LC-MS and in silico molecular docking. Chem. Commun. 2015, 51, 1494–1497. [Google Scholar] [CrossRef]
  118. Xiao, S.; Yu, R.; Ai, N.; Fan, X. Rapid screening natural-origin lipase inhibitors from hypolipidemic decoctions by ultrafiltration combined with liquid chromatography-mass spectrometry. J. Pharm. Biomed. Anal. 2015, 104, 67–74. [Google Scholar] [CrossRef] [PubMed]
  119. Zhang, H.; Xu, C.; Tian, Q.; Zhang, Y.; Zhang, G.; Guan, Y.; Tong, S.; Yan, J. Screening and characterization of aldose reductase inhibitors from Traditional Chinese medicine based on ultrafiltration-liquid chromatography mass spectrometry and in silico molecular docking. J. Ethnopharmacol. 2021, 264, 113282. [Google Scholar] [CrossRef]
  120. Fu, W.; Shentu, C.; Chen, D.; Qiu, J.; Zong, C.; Yu, H.; Zhang, Y.; Chen, Y.; Liu, X.; Xu, T. Network pharmacology combined with affinity ultrafiltration to elucidate the potential compounds of Shaoyao Gancao Fuzi Decoction for the treatment of rheumatoid arthritis. J. Ethnopharmacol. 2024, 330, 118268. [Google Scholar] [CrossRef]
  121. Chen, G.; Li, X.; Saleri, F.; Guo, M. Analysis of flavonoids in Rhamnus davurica and its antiproliferative activities. Molecules 2016, 21, 1275. [Google Scholar] [CrossRef]
  122. Van Breemen, R.B.; Nikolic, D.; Bolton, J.L. Metabolic screening using on-line ultrafiltration mass spectrometry. Drug Metab. Dispos. 1998, 26, 85–90. [Google Scholar] [PubMed]
Figure 1. AUF operation flow chart.
Figure 1. AUF operation flow chart.
Molecules 30 00608 g001
Figure 2. Schematic drawing of AUF-LC-MS screening of potential active ingredients.
Figure 2. Schematic drawing of AUF-LC-MS screening of potential active ingredients.
Molecules 30 00608 g002
Figure 3. Ultrafiltration membrane filtration principle diagram.
Figure 3. Ultrafiltration membrane filtration principle diagram.
Molecules 30 00608 g003
Figure 4. AUF application example diagram.
Figure 4. AUF application example diagram.
Molecules 30 00608 g004
Table 1. Comparison between common screening methods and AUF.
Table 1. Comparison between common screening methods and AUF.
Screening TechnologyFeaturesCompare with AUFRef.
Traditional chemical separation methodsThe traditional strategy for researching active ingredients in TCM involves “chemical extraction and separation, molecular structure identification, and pharmacological activity evaluation”.This cumbersome operation and long cycle reduce the efficiency of active ingredient discovery.[2]
Cell chromatographyAble to separate target molecules through specific interactions with stationary phases (e.g., resin, silica gel, etc.), suitable for applications requiring high-purity separations.Its operation is complex and expensive, and it is not suitable for rapid separation of large-volume samples.[40,41]
Magnetic bead adsorption screeningDifferent affinity ligands can be modified on the surface to improve selectivity for specific targets; separation using a magnetic field is easy to operate and does not require complex equipment.This operation is complex and costly and is not suitable for large-scale and high-throughput processing.[42,43]
UV-visible spectroscopySuitable for fast, non-destructive quantitative analysis, especially for solution samples with absorbing properties.Its resolution and sensitivity have certain limitations, and it has certain requirements for sample concentration.[32]
Nuclear magnetic resonance (NMR) technologyIt can provide detailed information on the molecular structure and reveal the chemical environment, three-dimensional structure, dynamic behavior of molecules, etc. It can also perform quantitative analysis.The sample concentration is required to be higher, and the instrument cost and operation difficulty are greater.[33]
Fluorescence technology screeningSuitable for occasions requiring high sensitivity, rapid screening, and dynamic monitoring.The instrument costs more and requires fluorescent labeling. Improper labeling may affect the results.[34]
Electrochemical methodIt is very effective for applications requiring high sensitivity, fast response, and real-time monitoring, especially for the detection of low-concentration substances.Suitable for small-molecule analysis in liquid samples, but interference issues in complex substrates may affect data accuracy.[35]
Table 3. List of active ingredients in compound preparations screened by AUF technology.
Table 3. List of active ingredients in compound preparations screened by AUF technology.
TargetCompound PreparationActive IngredientsRef.
Cyclooxygenase-2Zi-shen PillTwenty compounds[114]
5-lipoxygenaseZi-shen PillSix compounds[115]
α-GlucosidaseShenqi Jiangtang granuleGinsenoside Rc, ginsenoside Rh1, notoginsenoside Fe, quinquenoside L10, schisandrin, isoschisandrin, gomisin D, gomisin J, pregomisin, schisantherin D[116]
Xanthine oxidaseMai-Luo-Ning injection3,4-dicaffeoylquinic acid, 3,5-dicaffeoylquinic acid[117]
LipaseWu-Ling-San, Ze-Xie decoction, Xiao-Xian-Xiong decoction, Xiao Chai-HudecoctionSixteen compounds[118]
Augmented realityShenqi Jiangtang granuleGinsenoside Rg1, Rf, Rb1, Rh1, Rd, Rg6, Rg3, Rh7, Rh2, Calycosin, Astragaloside A, Notoginsenoside Ft1, Tigloylgomisin H, Gomisin J, K3, E, Schisandrin A[119]
Tumor necrosis factor-αShaoyao Gancao Fuzi DecoctionGlycyrrhizic acid, paeoniflorin, formononetin, isoliquiritigenin, benzoyl mesaconitine, glycyrrhetinic acid[120]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

He, Y.; Zhao, X.; Yu, M.; Yang, D.; Chen, L.; Tang, C.; Zhang, Y. Affinity Ultrafiltration Mass Spectrometry for Screening Active Ingredients in Traditional Chinese Medicine: A Review of the Past Decade (2014–2024). Molecules 2025, 30, 608. https://doi.org/10.3390/molecules30030608

AMA Style

He Y, Zhao X, Yu M, Yang D, Chen L, Tang C, Zhang Y. Affinity Ultrafiltration Mass Spectrometry for Screening Active Ingredients in Traditional Chinese Medicine: A Review of the Past Decade (2014–2024). Molecules. 2025; 30(3):608. https://doi.org/10.3390/molecules30030608

Chicago/Turabian Style

He, Yuqi, Xinyan Zhao, Muze Yu, Di Yang, Lian Chen, Ce Tang, and Yi Zhang. 2025. "Affinity Ultrafiltration Mass Spectrometry for Screening Active Ingredients in Traditional Chinese Medicine: A Review of the Past Decade (2014–2024)" Molecules 30, no. 3: 608. https://doi.org/10.3390/molecules30030608

APA Style

He, Y., Zhao, X., Yu, M., Yang, D., Chen, L., Tang, C., & Zhang, Y. (2025). Affinity Ultrafiltration Mass Spectrometry for Screening Active Ingredients in Traditional Chinese Medicine: A Review of the Past Decade (2014–2024). Molecules, 30(3), 608. https://doi.org/10.3390/molecules30030608

Article Metrics

Back to TopTop