Metabolites Identification and Mechanism Prediction of Neobavaisoflavone In Vitro and In Vivo of Rats through UHPLC-Q-Exactive Plus Orbitrap MS Integrated Network Pharmacology

Neobavaisoflavone is an important isoflavone component isolated from Psoraleae Fructus. It is used extensively worldwide because of its antibacterial, antioxidant, anti-inflammatory, anticancer, and anti-osteoporotic activities. However, there is no systematic and comprehensive research on the metabolism of neobavaisoflavone in vivo and in vitro. The study aimed to analyze the metabolic characteristics and mechanism of neobavaisoflavone for the first time. Firstly, biological samples were pretreated by the solid-phase extraction (SPE) method, methanol precipitation, and acetonitrile precipitation. Secondly, the samples were analyzed on UHPLC-Q-Exactive Plus Orbitrap MS. Thirdly, metabolites were tentatively identified based on retention time, parallel reaction monitoring strategy, diagnostic product ions, and neutral loss fragments. A total of 72 metabolites of neobavaisoflavone were tentatively identified, including 28 in plasma, 43 in urine, 18 in feces, six in the liver, and four in the liver microsome. The results suggested that neobavaisoflavone mainly underwent glucuronidation, sulfation, hydroxylation, methylation, cyclization, hydration, and their composite reactions in vivo and in vitro. In addition, nine active components with high bioavailability and 191 corresponding targets were predicted by the Swiss Drug Design database. The 1806 items of GO and 183 KEGG signaling pathways were enriched. These results showed that metabolites expanded the potential effects of neobavaisoflavone. The present study would provide the scientific basis for the further exploitation and application of neobavaisoflavone.


Introduction
Psoraleae Fructus is widely distributed in warm and sunny areas such as Sichuan, Anhui, Yunnan, Guizhou, and Guangxi in China [1], as well as India, Myanmar, and Sri Lanka [2]. Apart from various pharmacological effects, Psoraleae Fructus is often used for medicinal diets with meat, walnut kernels, wine, or other foods. It is not only delicious but also a warm tonic that can improve immunity [3,4]. Scholars have extracted a variety of chemical components from Psoraleae Fructus, such as coumarins, flavonoids, and monoterpene phenols [5]. The study of these complex components would provide help for further elucidation of clinical pharmacological effects and applications of Psoraleae Fructus [6]. Neobavaisoflavone is isoflavone extracted from Psoraleae Fructus with anti-inflammatory [7], anticancer [8], and antioxidant effects [9,10]. The researchers found that neobavaisoflavone could potently inhibit osteoclastogenesis and osteoclast functions in vivo and in vitro [11]. Some researchers also found that neobavaisoflavone was a potential whitening agent [12], in addition to the traditional pharmacological effects. Neobavaisoflavone might be widely used in whitening cosmetics in the future. Recent studies have shown that mice develop severe cholestatic liver damage after taking a certain dose of neobavaisoflavone [13,14]. Hence, the toxicity of neobavaisoflavone may manifest as liver damage. However, most of the previous studies focused on a single biological activity or pharmacological effect, and few studies systematically investigated the metabolites and mechanisms of neobavaisoflavone. Therefore, it is important to carry out a comprehensive metabolism study of neobavaisoflavone.
Metabolism refers to the biotransformation of a drug in the body. Any form of transformation and existence of drugs may be an important part of its efficacy. Therefore, metabolite identification is an indispensable step in the drug development process [15,16]. In addition, the research on the metabolism of natural products could help us to explain its pharmacological effects and predict its mechanism better [17]. At present, the combination of ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) has been widely used for the in-depth analysis of drug metabolites [18]. UHPLC-HRMS shows its superiority of high sensitivity, high selectivity, and high mass accuracy in structural identification [19,20]. Recently, UHPLC-Q-Exactive Plus Orbitrap MS has been used for improving the efficiency of metabolite identification. This technology would be a powerful tool for the characterization and identification of metabolites even at low concentrations in vivo and in vitro [21]. In this study, UHPLC-Q-Exactive Plus Orbitrap MS combined with network pharmacology was used to systematically study the metabolic characteristics of neobavaisoflavone in vivo and in vitro for the first time.
In the present study, the SPE method, methanol precipitation, and acetonitrile precipitation were used to prepare biological samples. The samples were analyzed on UHPLC-Q-Exactive Plus Orbitrap MS. The spectra were analyzed by Thermo Xcalibur 3.0 workstation. Based on parallel reaction monitoring (PRM), diagnostic product ions (DPIs), and neutral loss fragments (NLFs), neobavaisoflavone and its metabolites were preliminarily identified in plasma, urine, feces, the liver, and the liver microsome of rats. The possible metabolic pathways of neobavaisoflavone were mapped according to metabolic reactions and metabolites. The Swiss ADME and the Swiss Target Prediction databases were used to predict active metabolites and targets, and Cytoscape version 3.8.2 was used to draw an ingredienttarget network. The GO and KEGG analysis of neobavaisoflavone and its metabolites were performed on the Metascape database. Our findings elucidated the metabolic properties of neobavaisoflavone in vivo and in vitro for the first time, which might also help to explore potential active metabolites and mechanisms. It is expected to be a useful resource for drug development using natural products.  Table 1 and

DPIs Construction and Fragmentation Pathways of Neobavaisoflavone
The fragment ion at m/z 137.02 was formed by RDA fission. The DPIs and NLFs of neobavaisoflavone provided guidance for the subsequent identification of metabolites. The MS (a), and MS 2 (b) spectra and proposed fragmentation pathways of neobavaisoflavone were summarized, as shown in Table 1 and Figures 1-4.

Fragmentation Pattern Analysis and DPIs Determination of Neobavaisoflavone Metabolites
UHPLC-Q-Exactive Plus Orbitrap MS was used to analyze the biological samples of urine, plasma and feces, and liver and liver microsome. The spectra were analyzed by Thermo Xcalibur 3.0 workstation. Based on PRM, DPIs, and NLFs, a total of 72 metabolites were preliminarily identified. The chromatographic and mass spectrometric information from all the metabolites was summarized in Table 2.

Fragmentation Pattern Analysis and DPIs Determination of Neobavaisoflavone Metabolites
UHPLC-Q-Exactive Plus Orbitrap MS was used to analyze the biological samples of urine, plasma and feces, and liver and liver microsome. The spectra were analyzed by Thermo Xcalibur 3.0 workstation. Based on PRM, DPIs, and NLFs, a total of 72 metabolites were preliminarily identified. The chromatographic and mass spectrometric information from all the metabolites was summarized in Table 2     ponents were products of sulfation, hydroxylation (including isomers), demethylation, hydration, epoxidation, decarbonylation reduction, and methylation. The bioavailability of metabolites produced by phase II metabolism was reduced. Hence, it was speculated that the components with drug-likeness might be neobavaisoflavone, sulfated metabolites, and other metabolites formed by phase I metabolism. Finally, nine components were used for further research in network pharmacology via the Metascape database [22] (https://metascape.org/, accessed on 26 February 2022). hydration, epoxidation, decarbonylation reduction, and methylation. The bioavailability of metabolites produced by phase II metabolism was reduced. Hence, it was speculated that the components with drug-likeness might be neobavaisoflavone, sulfated metabolites, and other metabolites formed by phase I metabolism. Finally, nine components were used for further research in network pharmacology via the Metascape database [22] (https://metascape.org/, accessed on 26 February 2022). A total of 405 targets were obtained by Swiss Target Prediction, and 191 gene targets remained after removing deduplicates. On the basis of these data, an ingredient-target network was constructed through Cytoscape version 3.8.2. As shown in Figure 7, the network consisted of 200 nodes and 405 edges. There were 33 targets of neobavaisoflavone, while the metabolite (N8) had 99 targets, and the metabolite (N7) had 66 targets. It was indicated that potential pharmacological effect changed, and metabolites of neobavaisoflavone might play a larger role. A Venn diagram was drawn to distinguish neobavaisoflavone from its metabolites, as shown in Figure 8. The targets of neobavaisoflavone were all included in the targets of the metabolites, which further verified that the A total of 405 targets were obtained by Swiss Target Prediction, and 191 gene targets remained after removing deduplicates. On the basis of these data, an ingredient-target network was constructed through Cytoscape version 3.8.2. As shown in Figure 7, the network consisted of 200 nodes and 405 edges. There were 33 targets of neobavaisoflavone, while the metabolite (N8) had 99 targets, and the metabolite (N7) had 66 targets. It was indicated that potential pharmacological effect changed, and metabolites of neobavaisoflavone might play a larger role. A Venn diagram was drawn to distinguish neobavaisoflavone from its metabolites, as shown in Figure 8. The targets of neobavaisoflavone were all included in the targets of the metabolites, which further verified that the A total of 405 targets were obtained by Swiss Target Prediction, and 191 gene targets remained after removing deduplicates. On the basis of these data, an ingredient-target network was constructed through Cytoscape version 3.8.2. As shown in Figure 7, the network consisted of 200 nodes and 405 edges. There were 33 targets of neobavaisoflavone, while the metabolite (N8) had 99 targets, and the metabolite (N7) had 66 targets. It was indicated that potential pharmacological effect changed, and metabolites of neobavaisoflavone might play a larger role. A Venn diagram was drawn to distinguish neobavaisoflavone from its metabolites, as shown in Figure 8. The targets of neobavaisoflavone were all included in the targets of the metabolites, which further verified that the A total of 405 targets were obtained by Swiss Target Prediction, and 191 gene targets remained after removing deduplicates. On the basis of these data, an ingredient-target network was constructed through Cytoscape version 3.8.2. As shown in Figure 7, the network consisted of 200 nodes and 405 edges. There were 33 targets of neobavaisoflavone, while the metabolite (N8) had 99 targets, and the metabolite (N7) had 66 targets. It was indicated that potential pharmacological effect changed, and metabolites of neobavaisoflavone might play a larger role. A Venn diagram was drawn to distinguish neobavaisoflavone from its metabolites, as shown in Figure 8. The targets of neobavaisoflavone were all included in the targets of the metabolites, which further verified that the metabolites had a more potent possibility. It was speculated that N7 and N8 were the main A total of 405 targets were obtained by Swiss Target Prediction, and 191 gene targets remained after removing deduplicates. On the basis of these data, an ingredient-target network was constructed through Cytoscape version 3.8.2. As shown in Figure 7, the network consisted of 200 nodes and 405 edges. There were 33 targets of neobavaisoflavone, while the metabolite (N8) had 99 targets, and the metabolite (N7) had 66 targets. It was indicated that potential pharmacological effect changed, and metabolites of neobavaisoflavone might play a larger role. A Venn diagram was drawn to distinguish neobavaisoflavone from its metabolites, as shown in Figure 8. The targets of neobavaisoflavone were all included in the targets of the metabolites, which further verified that the metabolites had a more potent possibility. It was speculated that N7 and N8 were the main A total of 405 targets were obtained by Swiss Target Prediction, and 191 gene targets remained after removing deduplicates. On the basis of these data, an ingredient-target network was constructed through Cytoscape version 3.8.2. As shown in Figure 7, the network consisted of 200 nodes and 405 edges. There were 33 targets of neobavaisoflavone, while the metabolite (N8) had 99 targets, and the metabolite (N7) had 66 targets. It was indicated that potential pharmacological effect changed, and metabolites of neobavaisoflavone might play a larger role. A Venn diagram was drawn to distinguish neobavaisoflavone from its metabolites, as shown in Figure 8. The targets of neobavaisoflavone were all included in the targets of the metabolites, which further verified that the metabolites had a more potent possibility. It was speculated that N7 and N8 were the main

. Construction and Analysis of Ingredient-Target Network
A total of 405 targets were obtained by Swiss Target Prediction, and 191 gene targets remained after removing deduplicates. On the basis of these data, an ingredient-target network was constructed through Cytoscape version 3.8.2. As shown in Figure 7, the network consisted of 200 nodes and 405 edges. There were 33 targets of neobavaisoflavone, while the metabolite (N8) had 99 targets, and the metabolite (N7) had 66 targets. It was indicated that potential pharmacological effect changed, and metabolites of neobavaisoflavone might play a larger role. A Venn diagram was drawn to distinguish neobavaisoflavone from its metabolites, as shown in Figure 8. The targets of neobavaisoflavone were all included in the targets of the metabolites, which further verified that the metabolites had a more potent possibility. It was speculated that N7 and N8 were the main components that exerted pharmacological effects among the above 9 components. Therefore, it was speculated that the metabolites M53 and M65 might be produced by the hydroxylation and sulfation of M56.

Metabolic Pathway Mechanism Analysis of Neobavaisoflavone
After the neobavaisoflavone was absorbed, 28 metabolites were detected in plasma samples, 43 in urine samples, 18 in feces samples, and six in the liver samples. In addition, four metabolites were detected in liver microsome samples. Neobavaisoflavone was detected in all samples except for liver samples. These data showed that it would be metabolized and excreted in urine and feces partially after neobavaisoflavone was absorbed into the blood through the gastrointestinal tract. The products of glucuronidation, sulfation, oxidation and cyclization were identified in plasma samples. In the urine samples, metabolites were formed by phase I metabolism, such as hydration, dehydration, oxidation, and reduction reactions. Hydroxylated products were found primarily in feces samples. There were few metabolites in liver microsome, and they were only sulfated products and methylated products in vitro. The metabolic features of neobavaisoflavone in rats are shown in Figure 5. The metabolic pathway of neobavaisoflavone is shown in Figure 6.

Network Pharmacology of Neobavaisoflavone and Its Main Metabolites
From the above metabolic pathways, it is speculated that neobavaisoflavone might be one of the main components that exerted its efficacy. The Swiss ADME database (http://www.swissadme.ch/, accessed on 26 February 2022) and the Swiss Target Prediction database (http://www.swisstargetprediction.ch/, accessed on 26 February 2022) were used to screen the active metabolites and their targets. Neobavaisoflavone and its eight metabolites had high bioavailability scores and drug-likeness (Table 3). These eight components were products of sulfation, hydroxylation (including isomers), demethylation, hydration, epoxidation, decarbonylation reduction, and methylation. The bioavailability of metabolites produced by phase II metabolism was reduced. Hence, it was speculated that the components with drug-likeness might be neobavaisoflavone, sulfated metabolites, and other metabolites formed by phase I metabolism. Finally, nine components were used for further research in network pharmacology via the Metascape database [22] (https://metascape.org/, accessed on 26 February 2022).
After the neobavaisoflavone was absorbed, 28 metabolites were detected in plasma samples, 43 in urine samples, 18 in feces samples, and six in the liver samples. In addition, four metabolites were detected in liver microsome samples. Neobavaisoflavone was detected in all samples except for liver samples. These data showed that it would be metabolized and excreted in urine and feces partially after neobavaisoflavone was absorbed into the blood through the gastrointestinal tract. The products of glucuronidation, sulfation, oxidation and cyclization were identified in plasma samples. In the urine samples, metabolites were formed by phase I metabolism, such as hydration, dehydration, oxidation, and reduction reactions. Hydroxylated products were found primarily in feces samples. There were few metabolites in liver microsome, and they were only sulfated products and methylated products in vitro. The metabolic features of neobavaisoflavone in rats are shown in Figure 5. The metabolic pathway of neobavaisoflavone is shown in Figure 6.

Network Pharmacology of Neobavaisoflavone and Its Main Metabolites
From the above metabolic pathways, it is speculated that neobavaisoflavone mig be one of the main components that exerted its efficacy. The Swiss ADME databa (http://www.swissadme.ch/, accessed on 26 February 2022) and the Swiss Target Pred tion database (http://www.swisstargetprediction.ch/, accessed on 26 February 2022) we used to screen the active metabolites and their targets. Neobavaisoflavone and its eig metabolites had high bioavailability scores and drug-likeness (Table 3). These eight co ponents were products of sulfation, hydroxylation (including isomers), demethylatio hydration, epoxidation, decarbonylation reduction, and methylation. The bioavailabil of metabolites produced by phase II metabolism was reduced. Hence, it was speculat

Construction and Analysis of Ingredient-Target Network
A total of 405 targets were obtained by Swiss Target Prediction, and 191 gene targets remained after removing deduplicates. On the basis of these data, an ingredient-target network was constructed through Cytoscape version 3.8.2. As shown in Figure 7, the network consisted of 200 nodes and 405 edges. There were 33 targets of neobavaisoflavone, while the metabolite (N8) had 99 targets, and the metabolite (N7) had 66 targets. It was indicated that potential pharmacological effect changed, and metabolites of neobavaisoflavone might play a larger role. A Venn diagram was drawn to distinguish neobavaisoflavone from its metabolites, as shown in Figure 8. The targets of neobavaisoflavone were all included in the targets of the metabolites, which further verified that the metabolites had a more potent possibility. It was speculated that N7 and N8 were the main components that exerted pharmacological effects among the above 9 components.

GO Analysis of Target
The difference in gene targets might result in pharmacological changes. The enriched terms in BP, MF, and CC categories were selected according to p values less than 0.01. To neobavaisoflavone (Figure 9), the target proteins were mainly involved in the positive regulation of MAP kinase activity, positive regulation of peptidyl-tyrosine phosphorylation, and regulation of MAP kinase activity in the BP category; the target proteins were classified into steroid hormone receptor activity, nuclear receptor activity and steroid binding in the MF category; the target proteins were mainly involved in integral component of postsynaptic membrane, an intrinsic component of the postsynaptic membrane and presynaptic membrane in the CC category.  To neobavaisoflavone metabolites (Figure 10), the target proteins were mainly involved in the cellular response to nitrogen compound, response to peptide, and cellular response to organonitrogen compound in the BP category; the target proteins were classified into phosphatase binding, protein serine/threonine/tyrosine kinase activity and protein kinase activity in the MF category; the target proteins were classified into an integral component of the presynaptic membrane, an intrinsic component of the presynaptic membrane and presynaptic membrane in the CC category. The GO enrichment analysis results verified that the metabolites expanded the functional areas and types compared to neobavaisoflavone.   The difference in gene targets might result in pharmacological changes. The enriched terms in BP, MF, and CC categories were selected according to p values less than 0.01. To neobavaisoflavone (Figure 9), the target proteins were mainly involved in the positive regulation of MAP kinase activity, positive regulation of peptidyl-tyrosine phosphorylation, and regulation of MAP kinase activity in the BP category; the target proteins were classified into steroid hormone receptor activity, nuclear receptor activity and steroid binding in the MF category; the target proteins were mainly involved in integral component of postsynaptic membrane, an intrinsic component of the postsynaptic membrane and presynaptic membrane in the CC category. To neobavaisoflavone metabolites (Figure 10), the target proteins were mainly involved in the cellular response to nitrogen compound, response to peptide, and cellular

KEGG Analysis of Target
KEGG pathways were enriched from the Metascape database (p < 0.01). Then the relationships between pathways and pharmacological effect mechanism were predicted from the KEGG PATHWAY database (https://www.kegg.jp/kegg/pathway.html, accessed on 28 February 2022). The pathways of neobavaisoflavone were mainly involved in Tyrosine metabolism, Estrogen signaling pathway, Ovarian steroidogenesis, Gap junction, Prostate cancer, Serotonergic synapse, and Chemical carcinogenesis-reactive oxygen species (Figure 11). The descriptions of these pathways from the KEGG PATHWAY database could be corroborated in the literature about neobavaisoflavone. For the neobavaisoflavone metabolites, the number of pathways increased significantly. The pathways mainly involved were Pathways in Cancer, PI3K-Akt signaling pathway, Chemical carcinogenesisreceptor activation, Endocrine resistance, Neuroactive ligand-receptor interaction, Lipid and atherosclerosis, cAMP signaling pathway, Progesterone-mediated oocyte maturation, Serotonergic synapse, Notch signaling pathway, and so on ( Figure 12). These confirmed that the neobavaisoflavone had anti-inflammatory and anti-cancer effects. In addition, it could be speculated that neobavaisoflavone had potential antioxidant, whitening, antiosteoporosis, and estrogen-like effects through querying the KEGG database and the literature [9,10,12]. However, the Steroid hormone biosynthesis pathway is related to lipid metabolism. This finding showed that neobavaisoflavone might interfere with lipid metabolism to produce hepatotoxicity after being absorbed into the blood. Meanwhile, the result was mutually confirmed by the published literature [14]. This discovery will provide a new idea for the study of the toxicity mechanism of neobavaisoflavone. To sum up, the KEGG analysis results further verified that the metabolites research provided a new way to study the pharmacological and toxicological mechanisms of neobavaisoflavone. Then the relationships between pathways and pharmacological effect mechanism were predicted from the KEGG PATHWAY database (https://www.kegg.jp/kegg/pathway.html, accessed on 28 February 2022). The pathways of neobavaisoflavone were mainly involved in Tyrosine metabolism, Estrogen signaling pathway, Ovarian steroidogenesis, Gap junction,

Discussion
In this study, a total of 72 metabolites of neobavaisoflavone were initially identified, including 28 in plasma samples, 43 in urine, 18 in feces, six in the liver, and four in liver microsome. Neobavaisoflavone underwent glucuronidation, sulfation, hydroxylation, methylation, cyclization, hydration, and other reactions, then these metabolites were transported to various organs to exert pharmacological effects.
The neobavaisoflavone and eight active metabolites with drug-likeness properties, high bioavailability, and possible pharmacological effects were predicted and screened to perform GO and KEGG analysis. Then their mechanisms of action were predicted by analyzing pathways. For the pathway of tyrosine metabolism, the synthesis of tyrosinase structural analogs that competed with tyrosine might effectively inhibit the production of melanin [12]. This mechanism of neobavaisoflavone might be applied to research whitening cosmetics. Tyrosine might promote the formation of melanin and relieve the symptoms of vitiligo [23], which is a traditional pharmacological effect of neobavaisoflavone. Amino acids associated with this pathway might act as nutritional supplements, then neobavaisoflavone might enhance immunity. Therefore, it was speculated that neobavaisoflavone might play the role of whitening, treating vitiligo, and improving immunity by regulating this pathway. The estrogen signaling pathway was related to many physiological processes of mammals, including reproduction, cardiovascular protection, and bone integrity [24]. The other pathways were related to anticancer and estrogen-like effects [11,25,26]. Besides the pharmacological mechanisms of anti-osteoporosis, anti-cancer, and estrogen-like effects, the pathways were also related to anti-inflammatory, antioxidant, neuroactive, and adrenergic effects [27,28]. In addition, the results of KEGG analysis also showed that neobavaisoflavone might interfere with lipid metabolism to produce toxicity through the steroid hormone biosynthesis pathway, which was consistent with the literature [14] that the neobavaisoflavone had certain hepatotoxicity. In a word, it was indicated that the metabolites provided more possibilities compared with neobavaisoflavone in pharmacological effects.

Animals and Drug Administration
Male SD rats weighing 200 ± 10 g were obtained from Jinan Pengyue Laboratory Animal Technology Co., Ltd. and raised at Binzhou Medical University. All the rats were living under stable controlled conditions at standard temperature (24 ± 2 • C) and humidity (50 ± 10%) and kept on a 12 h light/12 h dark regime with free access to food and water. Following 3 days of continuous acclimatization to the environment, the rats were randomly divided into two groups: the control group (n = 3) and the treatment group (n = 3) [29,30]. The treatment group was given neobavaisoflavone reference, which was suspended in normal saline by gavage at a dose of 150 mg/kg, while the control group was administered with an equal amount of normal saline. The rats were administered for 3 days consecutively and were fasted but had free access to water 12 h before the experiment. The study was conducted in accordance with the Institutional Animal Care and Use Committee in Binzhou Medical University (2021-085) recognized principles for the use and care of laboratory animals.
Probe heater temperature was 320 • C. The stepped normalized collision energy (NCE) was set at 15, 30, and 45. S-Lens RF Level was 50.00.

Data Processing
A Thermo Xcalibur 3.0 workstation was used for data acquisition and processing. In order to obtain as many ESI-MS/MS fragment ions as possible for the metabolites of neobavaisoflavone, the signal peaks in the positive ion mode with an intensity of not less than 40,000 and the negative ion mode with an intensity of 10,000 were selected for identification. Predictive settings for all parent and fragment ions were based on exact molecular mass, elemental composition, and possible reactions. The parameters were set as follows: C (5-40), H (5-60), O (2-20), S (0-2), N (0-3), and for the number of cyclic unsaturated double bonds (RDB) (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), the mass accuracy error was within 10 ppm. In addition, the metabolites were selected by Swiss ADME and the targets were predicted using Swiss Target Prediction. Then the Metascape database was used to perform GO analysis and KEGG analysis, and the Cytoscape of version 3.8.2 was used to draw an ingredient-target network diagram. The visual analysis of GO and KEGG enrichment results was performed using Bioinformatics online platform.

Conclusions
In this study, different biological samples were pretreated by the SPE method, methanol precipitation, and acetonitrile precipitation for studies in vitro and in vivo. A total of 72 metabolites of neobavaisoflavone were initially identified based on UHPLC-Q-Exactive Plus Orbitrap MS, including 28 in plasma samples, 43 in urine, 18 in feces, six in the liver, and four in liver microsome. A relatively systematic metabolism characterization of neobavaisoflavone was obtained. These results indicated that neobavaisoflavone was mainly excreted in urine after oral administration in rats. Neobavaisoflavone underwent glucuronidation, sulfation, hydroxylation, methylation, cyclization, hydration, and other reactions, then these metabolites were transported to various organs to exert pharmacological effects. The eight active metabolites with high bioavailability and possible pharmacological effects were predicted by network pharmacology. The differences in the pharmacological effects of the neobavaisoflavone and the eight metabolites were compared. These nine components with drug-likeness properties and high bioavailability were screened to perform GO and KEGG analysis. We found that the metabolites provided more possibilities compared with neobavaisoflavone in pharmacological effects. The complex compounds of medicinal plants might serve as a lead for the development of novel drugs. The study on metabolites of natural products would be a useful resource for drug development. In the present study, the metabolic characteristics of neobavaisoflavone in vivo and in vitro were systematically elucidated for the first time. Hence, this would provide the scientific basis for the exploitation of neobavaisoflavone in the food, pharmaceutical, cosmetic and other industries.