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Article Cite This: Anal. Chem. 2019, 91, 9001−9009

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Mass Spectrometry-Sensitive Probes Coupled with Direct Analysis in Real Time for Simultaneous Sensing of Chemical and Biological Properties of Botanical Drugs Zhenhao Li,†,‡ Yi Wang,† and Yiyu Cheng*,† †

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Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Zijingang Campus, Hangzhou 310058, China ‡ Zhejiang Engineering Research Center of Rare Medicinal Plants, Wuyi 321200, China S Supporting Information *

ABSTRACT: The development of botanical materials as therapeutic agents involves the meticulous assessment of safety, efficacy, and quality. Compared with small-molecule drugs, quality control of botanical drugs confronts with more significant challenges due to their inherent complexity. Current quality control methods for botanical drugs, either prevailing chemical tests or emerging biological assays, are not able to meet recent demands of multiplexing, sensitivity, and speed. Here, we propose an on-demand strategy based on a direct analysis in real time-mass spectrometry (DART-MS) platform, which is capable of simultaneously analyzing multiple constituents and bioactivities of botanical drugs. Notably, the bioactivities are assessed by a multiple-enzyme assay that adopts cleavable mass spectrometry probes as enzymatic substrates: these probes labeled with a piperazine tag make possible sensitive, multiplexed, and quantitative enzyme activity measurements. The concept is successfully demonstrated via a case study of Danshen (Salvia miltiorrhiza) Injection where simultaneous detection of 34 constituents and inhibitory activities on two target enzymes can be achieved in just minutes. This proof-of-concept application also gives evidence that combining MSsensitive probes with DART-MS can provide an environmentally friendly, highly sensitive analytical approach for botanical quality control.

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Adapted from the mainstream pharmaceutical industry, chemical testing is currently the primary approach for botanical quality control. Cutting-edge analytical techniques, including spectroscopic and chromatographic methods, have also been increasingly introduced into this realm.9,10 However, different from synthetic or highly purified drugs, botanical drugs contain often a myriad of diverse phytochemicals, which are difficult to fully characterize. Moreover, although many chemical tests are valuable to monitor specific constituents of interest or generate characteristic chemical profiles, they provide little, if any, information regarding bioactivities of botanical drugs. As a result, there has been increasing interest in developing biological assays that correlate with the drug’s known or intended mechanism of action to complement the chemical tests. This trend has also led to the emergence of different bioassays for the assessment of botanical drug activity using animals, microorganisms, cells, and enzymes.11−14 Of the bioassays that have been designed, optical schemes such as colorimetry and fluorescence represent the most widely used

or centuries, herbal medicines have been instrumental in developing both traditional and modern pharmacological therapies. Isolated phytochemicals from natural sources have been the source for new chemical entities and led to the creation of some of the best-known drugs.1−3 Less mature, but potentially even more valuable to the drug discovery community, is botanical drug development in the context of complex mixtures. In recent years, the rise of botanical drugs as therapeutic agents is gaining sharply in acceptance and popularity, particularly in chronic and multifactorial diseases.4 These mixtures may exert their effects through the interference of multiple constituents on various biological targets,5 thus holding significant promise for the treatment of complex disorders.6 Despite the great potential of botanical drugs, detailed knowledge of their compositions and biological activities is often lacking, as is evidence for their safety and therapeutic efficacy. In addition, as naturally occurring mixtures for which influencing factors (e.g., agricultural practice and manufacturing process) often are not well-defined and controlled,7,8 these drugs may exhibit considerable variability from raw materials to final products. Therefore, quality control is highly critical for ensuring the safety, efficacy, and consistency of botanical drugs. © 2019 American Chemical Society

Received: March 10, 2019 Accepted: June 17, 2019 Published: June 17, 2019 9001

DOI: 10.1021/acs.analchem.9b01251 Anal. Chem. 2019, 91, 9001−9009

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Figure 1. Overall workflow of multidimensional quality assessment of botanical drugs using MS-sensitive probes and DART-MS.

commercial inception, DART-MS has become an emerging tool in forensic,25 agricultural,26 environmental,27,28 and pharmaceutical29 applications as well as in natural product characterization30 and botanical quality control.31,32 Taking advantage of DART-MS, this new platform enables rapid and simultaneous detection of multiple constituents and bioactivities via MS fingerprinting analysis and a dual-enzyme assay, respectively (Figure 1). Notably, the dual-enzyme assay adopts two tag-labeled probes as enzymatic substrates for quantitative enzyme activity measurements. Each probe consists of a specific peptide sequence and 1-(2-pyrimidyl) piperazine (PP), a tag with high ionization efficiency,33 thus allowing multiplexed and sensitive detection of enzyme activities. To evaluate this platform for practical application we tested Danshen (Salvia miltiorrhiza) Injection (DSI), a Chinese medicine widely prescribed for cardiovascular and cerebrovascular diseases. Previous studies have demonstrated the drug exerts its therapeutic effects through inhibition of several critical enzymes, including thrombin and angiotensin converting enzyme (ACE).34−37 Therefore, the two target enzymes are selected as quality biomarkers14 to develop the dual-enzyme assay. The testing result demonstrates this platform can detect the chemical constituents and bioactivities in a sensitive, reliable, and efficient manner, which highlights its great potential in quality assessment of botanical drugs.

mechanisms. However, these optical sensing methods suffer from background interference or autofluorescence from constituents contained in botanical drugs,14,15 compromising the detection sensitivity and dynamic range. Moreover, few of the bioassays meet the assay requirements which include multiplexing, sensitivity, and speed. Detection approaches with multiplexing capabilities are highly valued for botanical drugs considering their multitarget mechanisms of action. In addition, biological assays that can be integrated with existing chemical tests are much sought after. Such integration capability can enable paralleled chemical and biological analyses to be achieved on the same analytical platform, thereby reducing instrument cost and variations between instruments. However, although increasing numbers of studies have focused upon the combination of chemical and biological tests to gain a comprehensive summary of botanical drug quality,16−18 almost all these analyses have thus far been performed on separate analytical platforms. Owing to its high selectivity, low detection limits, and multiplexing potential, mass spectrometry (MS) is increasing perceived to be an essential tool for detection of both large biomolecules and small molecules. Instead of directly detecting intact large biomolecules, strategies that enable the detection of tagged small molecules (or substrates) offer the advantages of higher sensitivity and lower requirements for the instrumentation.19−21 The tag labeling method can therefore be exploited to develop multiplexed bioassays in the context of botanical quality control. Development of such bioassays may also initiate a paradigm shift in detection mechanisms from optical scheme to mass spectrometry, in which interference from phytochemicals can be eliminated and integration with chemical tests can be achieved. Here, we have developed a direct analysis in real time-mass spectrometry (DART-MS)-based platform that employs newly designed MS probes for multidimensional quality assessment of botanical drugs. DART-MS is one of the most extensively used ambient mass spectrometry technique which allows for rapid and noncontact detection with minimal sample handling.22,23 This plasma-based method is featured with its atmospheric pressure chemical ionization (APCI)-like mechanisms and typically enables effective ionization of compounds with molecular weights lower than 1500 Da.24 Since its

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EXPERIMENTAL SECTION Materials. Captopril, glutamine, thrombin, and ACE were purchased from Sigma-Aldrich (St. Louis, MO). AEBSF-HCl was purchased from Selleckchem (Houston, TX). Transcinnamic acid was obtained from Aladdin (Shanghai, China). 1-(2-Pyrimidyl) piperazine (PP) was provided by Top-Peptide (Shanghai, China). A thrombin substrate, D-Phe-Pro-Arg-βAla-PP (FPRA-PP), and an ACE substrate, PP-Asp-Ser-AspLys-Pro (PP-DSDKP), were synthesized using solid-phase peptide synthesis. Details of the synthesis are given in the Supporting Information. Thirty batches of DSI were supplied by Chiatai Qingchunbao (Hangzhou, China), and batch information is detailed in the Supporting Information Table S1. Assay Protocols. MS Fingerprinting Analysis. A volume of 900 μL of DSI was placed in a 1.5 mL polypropylene tube, 9002

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Figure 2. Fingerprinting analysis of Danshen Injection by DART-MS. (A) A representative mass spectrum of Danshen Injection obtained by DART-MS in negative ionization mode. (B) Assessment of chemical consistency of 30 batches of Danshen Injection by the scores plot of principal component analysis. Chemical identification of the marked ions are detailed in the Supporting Information, Table S2. Principal component analysis was performed using the normalized peak areas of 34 detected ions in the DART-MS fingerprinting analysis. The ellipse represented the 95% confidence interval. The result indicated that the chemical profile of sample 26 was obviously different from those of the others.

and 100 μL of methanol containing 1.35 mM trans-cinnamic acid was added as an internal standard (IS1). The solution was vortexed and centrifuged briefly and then introduced by a 12 DIP-it sampler (IonSense, Saugus, MA). The DIP-it tip (a glass rod) was immersed into the tube for 2 s and transferred to a holder positioned between the MS inlet and the ion source. Thrombin and ACE Assay. Tris-HCl (10 mM, pH 8.3) was prepared as the assay buffer for the dual-enzyme assay. A volume of 20 μL each of thrombin (2 U/mL), ACE (0.6 U/ mL), FPRA-PP (0.2 mM), and PP-DSDKP (1.33 mM), 50 μL of the inhibitor solution (DSI or positive drugs), and 70 μL of assay buffer were sequentially added to a 1.5 mL polypropylene tube. The mixture was incubated for 2 h at 37 °C in a thermostated shaker at 500 rpm. After the incubation, the reaction was terminated by adding 400 μL of methanol containing 24.4 μM glutamine as an internal standard (IS2). The solution was vortexed and centrifuged briefly and then introduced by the DIP-it sampler. AEBSF-HCl and captopril were used as positive controls for thrombin and ACE, respectively. Blank samples allowing the calculation of 100% activity were prepared by replacing the inhibitor solution with assay buffer. Enzyme inhibition (in percentages) was employed as an indicator of drug activity and calculated according to the following equations:

ACE inhibition (%)(%) = [1 − (Pinhibitor /Pblank)] × 100 (2)

where ΔPinhibitor and ΔPblank were the decreases in relative peak area of FPRA-PP (normalized by IS2) for the inhibitor sample and the blank sample, respectively, and Pinhibitor and Pblank were the relative peak areas (normalized by IS2) corresponding to PP-DSD (the enzyme product of PP-DSDKP) of the inhibitor sample and the blank sample, respectively. For an easier calculation, the relative peak area of FPRA-PP in the blank sample before the enzymatic reaction was used for all samples to calculate ΔP. In addition, to investigate whether the enzymatic activity would be affected by the presence of other enzymes and substrates, single-enzyme assays were carried out to measure the enzymatic activity separately. DART-MS Setup. A DART-standardized voltage and pressure (DART-SVP) model ion source (IonSense, Saugus, MA) was coupled to an API 4000 triple quadrupole (QqQ)MS (AB SCIEX, Framingham, MA). The DART-SVP was fitted with a motorized linear rail to automatically position the DIP-it tips to the ionization region of the DART source. The rail moving speed was set at 0.2 mm/s, and the distance between the ion source orifice and the MS inlet was 10 mm. Helium gas (99.999%) was served as the carrier gas, whereas nitrogen gas (99.999%) was used as the standby gas. DART ion source was operated using the following conditions: negative (DART−, for the fingerprinting analysis) or positive (DART+, for the enzymatic assay) ionization mode; gas heater temperature, 400 °C; grid voltage, −150 V (DART−) and

thrombin inhibition (%) = [1 − (ΔPinhibitor /ΔPblank )] × 100

(1) 9003

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Analytical Chemistry +100 V (DART+); pressure of helium, 35 psi (DART−) and 25 psi (DART+). A total of 34 characteristic ions in DSI and IS1 were monitored under selected ion monitoring (SIM) mode for fingerprinting analysis (Supporting Information, Table S2). The multiple reaction monitoring (MRM) transitions for the enzymatic assay were monitored at m/z 392.2 → m/z 157.4, m/z 482.5 → m/z 165.5, and m/z 147.4 → m/z 84.2 for FPRA-PP, PP-DSD (enzyme product of PP-DSDKP) and IS2 respectively. MS parameters for the selected ions and transitions, such as declustering potential (DP) and collision energy (CE), were stepwise optimized and listed in the Supporting Information, Table S3. A minimum of three replicates of each sample were analyzed and averaged together. Internal standards were used during the whole analytical procedure to correct for sampling and instrument variability, and the ratio of the analyte signal to the internal standard signal (IS1 for fingerprinting analysis and IS2 for the enzymatic assay) was used. The sole exception was that in DART-MS optimization procedure, analyte signals were not calibrated by IS because responses of IS varied under different settings. Method Validation. To ensure the proposed methodology is reliable and reproducible for the intended use, both the MS fingerprinting test and the enzymatic assay were systematically validated in accordance with guidelines of the U.S. Food and Drug Administration (FDA).38−40 Both the methods were validated in terms of intra- and interday precision, repeatability and stability, while additional validation including selectivity and matrix effect were performed for the bioassay. Sample 1 was used to prepare the quality control (QC) samples for method validation. Intraday precision was estimated by analyzing one QC sample six times within 1 day, while interday precision was examined in duplicate per day over three consecutive days. Six replicates of the QC samples were prepared under the same conditions and analyzed to measure repeatability. For stability evaluation, the QC samples were stored at 4 °C and then analyzed at 0, 2, 4, 8, 12, and 24 h, respectively. Selectivity for PP-DSD and FPRA-PP was validated by testing potential interference from the matrix, DSI, and encountered analytes. Matrix effect was determined by comparing the responses of PP-DSD and FPRA-PP in matrix and in deionized water at low, medium, and high concentrations (0.7, 7.2, and 72.3 μM for PP-DSD and 0.4, 4.2, and 41.7 μM for FPRA-PP).

ides, and adduct ions formed by these precursors (Supporting Information, Table S2). Adduct ions were also chosen for fingerprinting analysis as they correlated with the contents of corresponding constituents. All these characteristic ions were selected for fingerprinting analysis. Prior to fingerprinting analysis of DSI samples, key DARTMS settings were investigated, including gas heater temperature, source-to-MS distance, rail moving speed, grid voltage, pressure of helium, and DP. The total peak area of all ions was employed as an indicator for optimization. Since the intensities of the ions varied considerably, the min-max normalization was used to normalize the peak areas of individual ions between 0 and 1, so that each ion contributed approximately proportionately to the total peak area. The optimal settings were 400 °C, 10 mm, 0.2 mm/s, −150 V, 35 psi, and −100 V for gas heater temperature, source-to-MS distance, rail moving speed, grid voltage, pressure of helium, and DP, respectively (Supporting Information, Figure S2). The optimized DART-MS method was subsequently employed to obtained the MS fingerprints for 30 batches of DSI. In order to evaluate the batch-to-batch consistency, congruence coefficient41 and principal component analysis (PCA) were used to quantitatively characterize differences and similarities of the fingerprints. A reference fingerprint was generated by averaging all created fingerprints and employed to calculate the congruence coefficient using the angle cosine measure.41 As depicted in the Supporting Information, Figure S3, the similarities between the samples and the reference are all above 0.99, except sample 26 (with similarity of 0.96), indicating good consistency among the batches. Similarly, the PCA scores plot shows that the samples are clustered in one group with the sole exception of sample 26, which actually lies outside the 95% confidence interval (Figure 2B). More detailed investigation revealed that the intensities of many saccharide-related ions (e.g., ions 6, 7, 18, 19, 22, 23 and 31) in sample 26 were much higher than the average level, while those of phenolic acid-related ions (e.g., ions 11, 24, 27, and 34) were relatively lower. Generally, saccharides need to be removed by ethanol precipitation during the manufacture of DSI to limit the production of 5-hydroxymethylfurfural42 (a dehydration product of saccharides with potential toxicity43), whereas phenolic acids need to be concentrated in this process. The abnormal levels of these constituents in sample 26 should be a cause for concern considering their relation to the safety and effectiveness. Overall, most of the test samples showed good batch-to-batch consistency. Design and Synthesis of Cleavable MS Probes as Enzymatic Substrates. The MS probes were designed with two features in mind. One was to produce peptides that could be readily synthesized and modified. The other feature was to enhance MS detectability thus allowing highly sensitive and specific DART-MS analysis. The ACE substrate PP-Asp-SerAsp-Lys-Pro (PP-DSDKP) was a conjugate containing the SerAsp-Lys-Pro (SDKP) tetrapeptide, which was preferentially recognized and cleaved by ACE,44,45 that was linked by an Asp residue to a 1-(2-pyrimidyl) piperazine moiety. The piperazine tag improved the ionization efficiency of the substrate as the pyrimidyl group could greatly enhance the hydrophobicities, pI values, and gas-phase basicities.33 The Asp was selected as the linker because it provided two carboxylic groups for PP and Ser to react with, respectively. By using solid phase peptide synthesis, the amino acids were sequentially coupled to the Cterminal of prolinyl group and finally reacted with PP to obtain

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RESULTS AND DISCUSSION DART-MS Fingerprinting Analysis for Chemical Consistency Assessment. In order to establish a reliable DART-MS fingerprint for quality assessment, a three-step procedure is proposed as follows: (i) a full scan is performed for DSI, and characteristic ions are selected and identified; (ii) operating parameters of both DART-SVP and QqQ-MS are optimized to improve detection sensitivity; (iii) the ions are monitored by SIM to generate the fingerprint for each batch, and similarity analysis is applied for consistency assessment. Figure 2A shows a full-scan mass spectrum of DSI obtained by DART-MS in negative ionization mode. Putative identification of the ions was assigned based on molecular weight, literature and library matching, and further confirmed via MS/MS fragmentation. Moreover, an additional liquid chromatography−high resolution-mass spectrometry analysis was conducted to corroborate the identification result (Supporting Information, Table S4 and Figure S1). A total of 34 ions were identified, including phenolic acids, mono- and oligosacchar9004

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Figure 3. Typical DART-MS spectra of enzymatic substrates and their enzyme products in positive ionization mode. (A) PP-DSDKP (the substrate of ACE). (B) PP-DSD (the enzyme product of PP-DSDKP). (C) Limits of quantification of PP-DSD and SDKP (unlabeled substrate) obtained in water. (D) FPRA-PP (the substrate of thrombin). (E) A-PP (the enzyme product of FPRA-PP). (F) Limits of quantification of FPRA-PP and FPR (unlabeled substrate) obtained in water. Ions marked with * are background ions.

PP-DSDKP. The substrate was examined for purity by HPLC and further characterized by HPLC−MS (Supporting Information, Figure S4A,B). The enzymatic cleavage occurred at the Asp-Lys bond, forming two products: PP-DSD and KP (Supporting Information, Figure S4C). As shown in Figure 3A,B, PP-DSDKP and PP-DSD shared many common fragment ions in DART-MS due to similarity in structure, while the quasi-molecular ion [M + H]+ of PP-DSD at m/z 482.5 was specific. The MRM transition was therefore developed as m/z 482.5 → m/z 165.5 for PP-DSD, which overlapped with none of the other chemicals present in the assay environment. The sensitivity that PP labeling provided to peptide detection was evaluated by comparing limits of quantification (LOQs) of PP-DSD and SDKP (MRM transition, m/z 226.5 → m/z 125.2), which were determined at a signal-to-noise ratio (S/N) of around 10. Figure 3C shows that PP-DSD produced nearly 125 times higher MS signal than SDKP, whose LOQs were 49 ng/mL and 6050 ng/mL, respectively.

The thrombin substrate D-Phe-Pro-Arg-β-Ala-PP (FPRAPP) consisted of a D-Phe-Pro-Arg (FPR) tripeptide, a β-Ala residue, and a PP moiety. D-Phe-Pro-Arg was a hydrophilic peptide sequence specific to thrombin.46 The β-Ala was selected as a linker because both FPRD-PP and FPR-PP showed much lower reaction efficiency with thrombin compared to FPRA-PP (Supporting Information, Figure S5), possibly due to steric effects. The peptide FPRA was first prepared by solid phase synthesis and then reacted with PP in the liquid phase to form FPRA-PP, followed by an HPLC purification step. The enzyme products of the substrate were FPR and β-Ala-PP (Supporting Information, Figure S6). By comparing the full-scan DART-MS spectra of the substrate and the products, a dominant y2 ion at m/z 392.2 was observed for FPRA-PP, which showed good specificity and repeatability (Figure 3D,E). Therefore, this ion was selected as the precursor ion for transition design, and the optimal MRM transition for FPRA-PP was m/z 392.2 → m/z 157.4. LOQs of FPRA-PP and FPR were also determined (Figure 3F), and FPRA-PP yielded approximately 7 times greater MS signal 9005

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Analytical Chemistry versus FPR (MRM transition, m/z 262.5 → m/z 120.2). It is worth noting that due to the absence of chromatographic separation in DART-MS-based analysis, selection of proper ions or ion transitions is critical for avoiding false (negative or positive) findings. Dual-Enzyme Assay Optimization. Both ACE and thrombin assays were evaluated and optimized regarding the DART-MS settings affecting MS response, incubation conditions affecting the enzyme activity, as well as workup procedures affecting the matrix effect. Results of the relevant measurements are summarized in the Supporting Information, Figures S7−S12. Key DART-MS settings for PP-DSD and FPRA-PP were investigated, including gas heater temperature, source-to-MS distance, grid voltage, and pressure of helium. In this assay PPDSD and FPRA-PP needed to be measured simultaneously for multiplexed detection purpose, while the optimal settings for the two analytes might differ, thus parameter compatibility should be taken into consideration. After optimization (Supporting Information, Figures S7 and S8), the settings were determined at 400 °C, 10 mm, 100 V, and 25 psi for the gas heater temperature, source-to-MS distance, grid voltage, and helium pressure, respectively, which provided favorable responses for both analytes, and were also compatible with the settings of MS fingerprinting analysis. The incubation conditions were optimized in terms of enzyme and substrate concentrations and incubation time. The activity of ACE was determined by measuring the production of PP-DSD. As depicted in the Supporting Information, Figure S9A, the peak area of PP-DSD increased gradually along with the elevated concentrations of ACE, while it began to level off at 0.1 U/mL. A similar trend was observed for the substrate concentration, where a plateau started from 0.17 mM (Supporting Information, Figure S9B). Different incubation time was also compared to provide sufficient time for the enzyme−substrate reaction, and 2 h was found necessary to complete the reaction under the tested conditions (Supporting Information, Figure S9C). On the basis of these measurements, the assay conditions for ACE were set at 0.06 U/mL, 0.13 mM, and 2 h for the final concentrations of ACE and PPDSDKP and the incubation time, respectively. Conditions for thrombin were optimized in a similar fashion whereas the peak area decrease (ΔP) of FPRA-PP was employed to indicate the enzyme activity. The final concentrations of thrombin and FPRA-PP were determined at 0.2 U/mL and 0.02 mM, respectively (Supporting Information, Figure S9D,E). The reaction velocity of FPRA-PP to thrombin was relatively fast, and 1 h was sufficient to achieve a full reaction (Supporting Information, Figure S9F). Since thrombin and ACE were coincubated in this multiplexed assay, the incubation time was set at 2 h to offer sufficient reaction time for both enzymes. The lack of a separation step makes the DART-MS approach vulnerable to matrix effects, often resulting in pronounced signal suppression and low detection sensitivity. In this work, matrix effects mainly arose from the buffer and the stop solution. Therefore, the two factors were investigated by comparing LOQs of PP-DSD and FPRA-PP in different reagent systems. As shown in the Supporting Information, Figures S10 and S11, higher buffer concentrations and the presence of NaCl helped to keep the enzyme activities while leading to severe signal suppression. These results indicate the trade-off between matrix effects and enzyme activity needs to be carefully considered. Finally, Tris-HCl at 10 mM devoid of

NaCl was selected as the assay buffer, as high detection sensitivity in low-molarity buffer significantly outweighed the loss in enzyme activity. Additionally, two stop solutions, i.e., concentrated HCl (1 M) and methanol, were also compared. Although both solutions compromised the detection sensitivity, methanol had a minor influence on LOQs compared with HCl (Supporting Information, Figure S12). Moreover, 400 μL of methanol was found to be sufficient to completely terminate the enzymatic reaction. Thus, methanol was chosen as the stop solution. Dual-Enzyme Assay for Biological Activities Assessment. A total of 30 batches of DSI were subject to the dualenzyme analysis, and their inhibitions on ACE and thrombin were simultaneously measured to indicate drug activity. As illustrated in Figure 4A, the samples showed a range of ACE

Figure 4. Assessment of biological activity of Danshen Injection by a dual-enzyme assay: (A) graphical representation of ACE and thrombin inhibitions of 30 batches of Danshen Injection and (B) linear correlation obtained between ACE and thrombin inhibitions.

inhibitions from 28% to 41% with a mean at 33% and a range of thrombin inhibitions from 20% to 30% with a mean at 24%. This indicated that DSI exerted a more potent inhibitory activity on ACE than on thrombin. The interbatch coefficients of variation (CVs) of ACE and thrombin inhibitions were 9.5% and 9.2%, respectively, indicating the good biological consistency of the samples. Interestingly, a weak correlation is observed (r = 0.39, p < 0.05) when plotting thrombin inhibition against ACE inhibition (Figure 4B). It could be attributed to the fact that some active constituents in DSI, such as danshensu (ion 5), rosmarinic acid (ion 20), and salvianolic acid B (ion 33) exhibit inhibitory activities against both the enzymes.14,34,47−49 Therefore, variations in the contents of these constituents may result in similar changes in ACE and thrombin inhibitions. Another interesting finding was that 9006

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assay conditions. The results are also in accordance with some previous studies,52,53 which demonstrated that enzymatic assays with different enzymes and substrates could be performed in parallel, whereas coincubations of enzymes belonging to the same class might impaired the enzymatic activity. Method Validation. For both the fingerprinting analysis and the enzymatic assay, CVs of precision (intra- and interday), repeatability, and stability were within 18%, indicating that the established method was precise, repeatable, and stable during the analytical procedure (Supporting Information, Table S5). The selectivity analysis (Supporting Information, Figure S16) showed that no interferences were detected from the matrix, DSI, and other components in the bioassay cassette. The average matrix effects at the three concentrations were 31%, 34%, and 33% for PP-DSD and 27%, 29%, and 21% for FPRA-PP, respectively (Supporting Information, Table S6). Although matrix effect was severe due to the influence of the buffer and the stop solution, its extent was relatively consistent. Also, the high MS response of the substrates enabled sensitive detection even under such conditions. The positive controls (i.e., captopril and AEBSF-HCl) were employed to assess test validity. Dose−response relationships of captopril and AEBSF-HCl against ACE and thrombin were determined, respectively. The IC50 of captopril was in the nanomolar range (4.3 nM), while AEBSF-HCl exhibited IC50 of micromolar range (8.4 μM), which were comparable to literature data.14,54 These results supported that the current method was of reasonable reliability and applicable to quality assessment of botanical drugs.

sample 26 showed no obvious difference in activity compared with other samples, although a large difference was observed in their MS fingerprints. A potential reason for this inconformity was that the constituents responsible for the chemical difference were biologically inert on the enzymes. It also reminds us that both chemical and biological assays own their respective pros and cons, and either assay alone may not be sufficient to ensure quality and thus therapeutic consistency. Consequently, the combination of different assays, such as chemical fingerprinting to assess batch-to-batch consistency and clinically relevant bioassays to measure drug potency and activity, is increasingly advocated by academics, the pharmaceutical industry, and regulatory agencies,50 since it provides a more comprehensive control of botanical drugs. Inspired by combinatorial chemistry, we notice that the correlation between MS fingerprinting and enzyme inhibition may be exploited to rapidly screen potential active constituents targeting ACE and thrombin. The peak areas of the ions and the enzyme inhibitions were modeled using multiple linear regression (MLR) with proper optimization. As a result, four ions, namely 20 (rosmarinic acid), 14 (protocatechualdehyde), 5 (danshensu), and 33 (salvianolic acid B), were identified as key variables positively correlated with ACE inhibition, and ion 20 (rosmarinic acid), 30 (rosmarinic acid + danshensu), and 3 (caffeic acid) were found to be highly associated with thrombin inhibition. The result corresponded well with some bioactivity studies14,47,48,51 while highlighting that protocatechualdehyde could be investigated as a potential anti-ACE agent. However, validation studies are necessary to confirm the screening result by using purified compounds. Duplex Analysis versus Simplex Analysis. In this work, the ACE and thrombin assays were combined in one single experiment with the goal of increasing sample throughput and speeding up data acquisition. However, in multiplexed setups, the enzymatic activity may be influenced by the presence of other substrates and/or enzymes. Therefore, simplex assays with single enzymes and their substrates were conducted, and the results were compared with those of the duplex assay. In the simplex format, the samples displayed ACE inhibitions from 25% to 40% with a mean at 31% and a range of thrombin inhibitions from 21% to 33% with a mean at 25% (Supporting Information, Figure S13). In the Bland-Altman test (Supporting Information, Figure S14), the mean of enzyme inhibitions obtained by the duplex and simplex assays was calculated and plotted against the difference between both measurements. The mean difference of ACE inhibition was 2.5% with the 95% limits of agreement (i.e., mean ± 1.96 × standard deviation) ranging from −0.9% to 5.9%, and the mean difference of thrombin inhibition was −1.5% with the 95% limits of agreement ranging from −4.8% to 1.8%, indicating that results from the duplex and simplex assays were in good agreement. Furthermore, the dose−response relationship between ACE and thrombin was investigated by comparing the enzyme activities in the duplex and simplex assays. As depicted in the Supporting Information, Figure S15, the thrombin activity was not significantly changed in the presence of ACE of different concentrations. Similarly, the ACE activity was not obviously influenced by thrombin ranging from 0.01 to 0.4 U/mL. However, reduction of ACE activity could be observed under higher concentrations of thrombin, and the relative activities of ACE were approximately 81% and 72% with thrombin of 0.5 and 0.6 U/mL, respectively. These results proved that the two enzymatic reactions did not interfere significantly under the

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CONCLUSION This work demonstrates for the first time the feasibility of tag labeling coupled with DART-MS for multidimensional measurement of botanical drug quality. Through proper optimization of experimental settings, paralleled fingerprinting and enzymatic analyses can be achieved on the same analytical platform within minutes. Moreover, MS probes labeled with 1(2-pyrimidyl) piperazine enable highly sensitive and multiplexed detection of enzyme activities. The proposed method was successfully employed to assess the quality of DSI in a reliable and efficient manner. The present study not only constitutes a valuable addition to the arsenal of botanical quality control but also is essential for the emerging paradigm shift in analytical instrumentation. However, it is important to highlight that the current method is not selective enough to discriminate isomers or isobars. Hence, there is a need for front-end separation technologies, such as high-resolution mass spectrometry (HRMS) or ion mobility spectrometry (IMS) that allow for a multidimensional separation without sacrificing the total analysis time. Efforts are ongoing to modify the probes to increase enzyme−substrate reaction velocity as it is now the major rate-limiting step. With the established methodology herein, future work can be extended to diverse applications, such as dual-target inhibitor screening and MSbased lead screening from natural sources.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.9b01251. 9007

DOI: 10.1021/acs.analchem.9b01251 Anal. Chem. 2019, 91, 9001−9009

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Synthesis methods of the MS-sensitive probes; HPLC and HPLC−MS methods for purity examination and structure confirmation of the probes; parameter optimization of MS fingerprinting and bioassays; comparisons of duplex and simplex biological assays; and method validation information (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +86-571-87951138. ORCID

Zhenhao Li: 0000-0001-8279-8981 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We would like to thank Dr. Xiaohui Fan and Dr. Xiaoping Zhao for helpful comments and discussions. This work is financially supported by the National Natural Science Foundation of China (Grant No. 81803714) and the Fundamental Research Funds for the Central Universities (Grant No. 2019QNA7041).

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DOI: 10.1021/acs.analchem.9b01251 Anal. Chem. 2019, 91, 9001−9009

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DOI: 10.1021/acs.analchem.9b01251 Anal. Chem. 2019, 91, 9001−9009