Skip to main content
NCBI home page
Journal of Pharmaceutical Analysis logoLink to Journal of Pharmaceutical Analysis
. 2026 May 21;16(9):101665. doi: 10.1016/j.jpha.2026.101665

A quantitative screening strategy based on activity-content ratio for prioritizing bioactive compounds in medicinal plants

Lin Chen 1, Xinxin Chen 1, Shengnan Pi 1, Ditong Huang 1, Manhuayun Zhai 1, Jincai Wang 1,⁎⁎⁎, Zhengjin Jiang 1,⁎⁎, Tingting Zhang 1,⁎
PMCID: PMC13627906  PMID: 42824724

Abstract

Accurately evaluating the intrinsic bioactivity of individual constituents, independent of their abundance, remains a major challenge in natural product screening. To address this, we proposed the activity-content ratio (ACR) as a novel index for direct intrinsic inhibitory potency characterization of constituents. Utilizing an at-line nanofractionation platform, an ACR-based screening method for acetylcholinesterase inhibitors was established. The method was validated using both mixed standards and a complex extract of Corydalis rhizoma alkaloids. Results demonstrated a strong linear correlation (R2 > 0.93) between ACR% and IC50 for 16 active constituents. High-ACR% compounds, such as berberine, palmatine, and dehydrocorydalin, exhibited significant inhibitory activities at nanomolar concentrations. The model's broad applicability was further confirmed by screening Citri reticulatae pericarpium flavonoids for α-glucosidase inhibitors, showing excellent linearity (R2 = 0.96) for the five identified active constituents. Notably, rutin displayed the highest ACR% and an IC50 of 15.21 ± 1.23 μM. The consistent performance of the ACR model across two distinct enzymatic targets and chemical families (alkaloids and flavonoids) demonstrates its reliability and broad applicability. This study provides a reliable and efficient quantitative tool for bioactivity evaluation, which is expected to greatly accelerate the discovery and quality control of bioactive medicinal plants.

Keywords: Activity-content ratio, Bioactivity evaluation, Natural products, At-line nanofractionation, High-throughput screening

Graphical abstract

graphic file with name ga1.webp

Open in a new tab

Highlights

  • •

    A novel ACR strategy for quantitative bioactivity screening was established.

  • •

    ACR% correlates with IC50, allowing direct bioactivity ranking.

  • •

    ACR model was validated with various targets and chemical families.

  • •

    19 AChE inhibitors and 19 AG inhibitors were successfully identified.

1. Introduction

Medicinal plants are a significant source for new drug leads [[1], [2], [3]]. However, a major challenge in early-stage screening lies in accurately evaluating the intrinsic bioactivity of individual constituents within complex mixtures and their contribution to overall efficacy. Conventional bioactivity-guided isolation typically employs peak-based fractionation, which is labor-intensive and often fails to adequately integrate compound abundance with efficacy, resulting in the omission of low-abundance active constituents and biased evaluation of a constituent's contribution to overall activity [4]. Although recent strategies, such as establishing fraction-level content-activity correspondence through fractionation and independent detection [5], or utilizing statistical modeling to infer content-activity relationships for individual constituent [6], have sought to address this gap, they still failed to comprehensively and intuitively depict the bioactivity profile of medicinal plants as well as establish a precise, quantitative "content-activity" relationship at the single-compound level. This limitation fundamentally constrains screening efficiency and limits the precise identification of highly potent constituents and bioactive markers.

To address these challenges, an at-line nanofractionation platform was previously established by integrating parallel chromatographic separation, mass spectrometry, and post-column bioassays, which has been successfully applied to the rapid screening of bioactive constituents in various medicinal plants [[7], [8], [9], [10], [11], [12], [13], [14]]. However, the diode array detector (DAD) conventionally employed in this platform is a non-universal detector with limited response to compounds lacking chromophores, making it difficult to directly and accurately quantify the concentration of diverse constituents in complex systems. Consequently, we incorporated a charged aerosol detector (CAD), a mass-sensitive universal detector, enabling unbiased quantification of complex constituents [15,16]. Leveraging the platform's capability for acquisition of bioactivity profiling, we further proposed and defined a novel metric: the activity-content ratio (ACR). The ACR quantifies the "potency-concentration" relationship of individual constituents in complex matrices, allowing ranking of inhibitory efficiency directly from crude extracts without requiring tedious purification steps. This approach significantly improves both the efficiency and reliability of screening high-activity constituents, and provides a more comprehensive assessment of the overall medicinal efficacy of medicinal plants.

To systematically validate the reliability and generality of the proposed ACR model, a stepwise strategy was performed. Initially, acetylcholinesterase (AChE) was selected as a model target to construct and optimize the inhibitor screening platform. The platform was then applied to screen for AChE inhibitors (AChEIs) from the alkaloids in Corydalis rhizoma. An ACR evaluation model was established using a mixture of reference standards with varying potencies and was further validated with the potential AChEIs identified from Corydalis rhizoma. Finally, to demonstrate its broad applicability, the model was extended to a different target and chemical system by screening for α-glucosidase inhibitors (AGIs) from the flavonoids of Citri reticulatae pericarpium.

2. Experimental

2.1. Chemical and reagents

AChE from fly heads was purchased from Shanghai Yuan-Ye Biotechnology Co., Ltd. (Shanghai, China). α-Glucosidase (AG), iodothioacetylcholine (ATCI), and Triton X-100 were obtained from Sigma-Aldrich (Steinheim, Germany). 5,5’-Dithiobis-(2-nitrobenzoic acid) (DTNB), 4-nitrophenyl α-D-glucoside (pNPG), Na2HPO4 (99%), dimethyl sulfoxide (DMSO), bovine serum albumin (BSA), galanthamine hydrobromide (98%), tetrahydropalmatine (98%), formic acid (FA, 98%), acetic acid (≥99.5%) and ethyl acetate (≥99%) were all bought from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). NaH2PO4 (99%) and protopine (98%) were purchased from Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China). Catechin (98%), berberine (98%), berberine hydrochloride (98%), coptisine (98%), jatrorrhizine (98%), lonicerin (>98%), rutin (>98%), and tangeretin (>98%) were acquired from Chengdu HerbSubstance Co., Ltd. (Chengdu, China). Tetrahydrocoptisine (98%), tetrahydroberberine (98%), dehydrocorydalin (98%), palmatine (98%), 13-methylberberine (98%), hesperidin (>98%) and nobiletin (98%) were procured from Sichuan Weikeqi Biological Technology Co., Ltd. (Chengdu, China). Liquid chromatography-mass spectrometry (LC-MS) grade acetonitrile (ACN), methanol (MeOH), and ammonia solution (25%) were supplied by Merck (Darmstadt, Germany). 384-Well plates were purchased from Corning Inc. (Corning, NY, USA). Dried slices of Corydalis rhizoma and Citri reticulatae pericarpium were obtained from Guangzhou Renweitian Trading Company (Guangzhou, China) and Guangzhou Caizhilin Pharmaceutical Company (Guangzhou, China), respectively, and authenticated by Associate Professor Tingting Zhang (Institute of Pharmaceutical Analysis, College of Pharmacy, Jinan University). Deionized water used in the experiments was purified using a Spring-R10 water system from RSJ Scientific Instruments Co., Ltd. (Xiamen, China).

2.2. Preparation of plant extracts

To obtain the extracts of the two plant extracts, standardized procedures were employed. For Corydalis rhizoma, dried slices (50 g) were ground using a multifunctional grinder (550 W, mesh size >50). The powder was subjected to ultrasonic extraction with 70% ethanol (500 mL) at 30 °C (40 kHz, 250 W) for three cycles of 30 min each. The combined extracts were filtered, concentrated using a rotary evaporator (EYELA, Tokyo, Japan) at 35 °C and 60 r/min, and freeze-dried to obtain the crude extract. This crude extract was further processed by ethyl acetate extraction, followed by concentration and vacuum drying, to yield the ethyl acetate fraction used for AChE inhibitory activity screening.

For Citri reticulatae pericarpium, the crude material was dried at 35 °C for 24 h, ground, and sieved. The powder (12 g) was mixed with 300 mL of 70% ethanol, soaked overnight, and then subjected to ultrasonic extraction (360 W) at 30 °C for three cycles of 60 min each. The combined extracts were filtered, concentrated using a rotary evaporator at 35 °C and 60 r/min, and freeze-dried to obtain the extract, which was directly used for AG inhibitory activity screening.

2.3. Enzyme activity assay

AChE activity was determined using a modified Ellman spectrophotometric method [17]. Solutions of enzyme AChE (0.5 mg/mL), substrate ATCI (0.5 mM), and colorimetric reagent DTNB (0.1 mM) were prepared by dissolving desired amount of reagents in phosphate buffer (PB; 100 mM, containing 0.02% Triton X-100, pH 7.4), respectively. For inhibition assays, 2.5 μL of test compounds or extracts (with DMSO content below 0.6%) at gradient concentrations were precisely dispensed into 384-well plates using a FinnpipetteTM Novus electronic multichannel pipette (Thermo Fisher Scientific Co., Ltd., Vantaa, Finland). 5% DMSO was used as a blank control. Subsequently, 10 μL of the AChE solution was added, followed by centrifugation at 2000 rpm for 2 min using a SC-3610 low-speed centrifuge (Anhui Zhongke Zhongjia Scientific Instrument Co., Ltd., Hefei, China). After incubation at room temperature for 10 min, 5 μL of the DTNB solution and 5 μL of the ATCI solution were successively added in dark, and then centrifuged at 2000 rpm for 1 min. Enzymatic reaction kinetics were monitored dynamically at 405 nm for 30 min under 35 °C using a PerkinElmer EnVision 2105 multimode plate reader (PerkinElmer Inc., Waltham, MA, USA). The inhibition rate was calculated according to the following equation:

Inhibition (%) = (1−S1/S0) × 100%

where, S1 and S0 represent the fitted slopes of the kinetic curve in the presence and absence of the test compounds or extracts, respectively. Data were shown as a mean ± standard deviation (SD) of three independent experiments and the eventual half-maximal inhibitory concentration (IC50) values were calculated using OriginPro 2021 (Origin Lab, Northampton, MA, USA).

The activity of AG was tested following a previously reported method [7], with minor adaptations. Detailed procedures were provided in Supplementary data.

2.4. Establishment and operation of the at-line nanofractionation platform

The at-line nanofractionation platform was constructed based on a Prominence LC-20AT Modular high-performance liquid chromatography (HPLC) System equipped with a DAD (Shimadzu, Kyoto, Japan). The separations of mixed standard-1 (MS1, composed of galantamine hydrobromide, berberine hydrochloride, and catechin), mixed standard-2 (MS2, composed of catechin, protopine, berberine, and tetrahydroberberine), and the ethyl acetate extract of Corydalis rhizoma were conducted on an RD-C18 column (4.6 mm i. d. × 250 mm, 5 μm) produced by Hangzhou Zhongpu Technology Co., Ltd. (Hangzhou, China). The separation of the ethanol extract of Citri reticulatae pericarpium was performed on an RD-C18 column (4.6 mm i. d. × 250 mm, 3 μm) produced by Hangzhou Zhongpu Technology Co., Ltd. (Hangzhou, China). All separations were conducted at a flow rate of 0.5 mL/min. The detailed LC conditions were provided in the corresponding figure captions. Following LC separation, the eluate from the DAD was split in a 1:2 ratio (v/v), with one-third directed to a mass spectrometer for structural characterization. The remaining two-thirds were collected into 384-well plates using a CTC PAL nanofraction collector (CTC Analytics AG, Zwingen, Switzerland) operated by in-house written control code. All samples were filtered through a 0.22 μm organic membrane filter prior to injection.

Quadrupole time-of-flight tandem mass spectrometry (Q-TOF-MS/MS) analysis was performed on an AB SCIEX ZenoTOF 7600 system (AB SCIEX, Framingham, MA, USA). The electrospray ionization (ESI) parameters were set as follows: ion source gas pressure: 55 psi; curtain gas pressure: 35 psi; ion source temperature: 500 °C. In positive mode, capillary voltage: 5500 V; TOF-MS scan range: 50 to 1500 Da; ion accumulation time: 0.25 s; declustering potential: 80 V; collision energy: 10 V; TOF-MS/MS scan range: 50 to 1000 Da; ion accumulation time: 0.1 s; declustering potential: 80 V; collision energy: 45 V (±20 V). In negative mode, capillary voltage: −4500 V; TOF-MS scan range: 50 to 1500 Da; ion accumulation time: 0.25 s; declustering potential: −80 V; collision energy: −10 V; TOF-MS/MS scan range: 50 to 1000 Da; ion accumulation time: 0.1 s; declustering potential: −80 V; collision energy: −45 V (±20 V). MS/MS data were processed using SCIEX OS software ver. 1.5.

The plates containing the collected fractions were subsequently vacuum-dried at 35 °C for 48 h under reduced pressure (−0.1 MPa). The dried fractions were then subjected to the enzyme bioassay according to the described procedure in Section 2.3. Enzymatic activity in each well was determined by calculating the initial slope of the reaction curve using Microsoft Excel. The collection time for each fraction was extracted from the event logs of the CTC PAL autoinjector. A bioactive inhibition profile was generated by plotting the calculated "1−Inhibition (%)" or "slope" (Y-axis) against the corresponding collection time of each well (X-axis). Signal-to-noise (S/N) ratios of active peaks were calculated based on the bioactivity profile, defined as the ratio of peak height to baseline noise. An S/N > 3 was adopted as the threshold for identifying active peaks; peaks falling below this criterion were not considered active. To identify the bioactive constituents in a mixture, the bioactivity profile was aligned with the parallel HPLC chromatogram. All experiments were performed in triplicate.

2.5. Quantitative analysis by HPLC-DAD-CAD and calculation of ACR

The MS2s and plant extracts were analyzed using a Vanquish HPLC system (Thermo Fisher Scientific, Germering, Germany) equipped with a DAD and a CAD. The CAD parameters were configured as follows: data collection rate: 10 Hz; filter setting: 1.0; evaporation temperature: 35 °C. The concentration gradients of individual constituents in the MS2s are listed in Table S1. Chromatographic data for bioactivity obtained from Section 2.4 were imported into OriginPro 9.0 software, where peak integration functionality was applied to calculate active peak areas (AA). Simultaneously, the peak areas (AC) determined by CAD were manually integrated using Chromeleon 7.3.1 software. Distinct integration strategies were employed for chromatographic peaks with different characteristics to ensure the accuracy and consistency of quantitative analysis. For well-separated and symmetrical peaks, the full-peak integration method was applied. For tailing peaks, half-peak integration was performed based on the normally distributed portion of the leading edge. For incompletely separated co-eluting peaks, they were treated as a single combined peak and integrated as a whole. In the ACR analysis, the combined peak of co-eluting peaks was not included in the linear correlation analysis between ACR% and IC50, but was only used as a reference for overall activity contribution.

For each active constituent, the ratio of its AA to AC was calculated and defined as the ACR. Subsequently, the ACR values of all active constituents in a plant extract were summed to obtain the total ACR. The ACR of each active peak was then divided by this total value to yield the percentage contribution of that peak's ACR (referred to as ACR%).

3. Results and discussion

3.1. Establishment and validation of the AChEIs screening platform

AChE activity was determined based on the slope of the linear region (5–15 min, indicated by the orange shaded area in Figs. 1A and B) of the kinetic curves. Optimal enzyme and substrate concentrations were identified via single-factor experiments, yielding suitable slopes at 0.5 mg/mL of AChE and 0.5 mM of ATCI (Figs. 1A and B). The IC50 values of the reference inhibitors, berberine hydrochloride (0.25 ± 0.04 μM) and galantamine hydrobromide (0.16 ± 0.01 μM), were consistent with previously literature reported data [18,19], and their dose-response curves are shown in Figs. 1C and D, respectively. The assay yielded a Z' factor of 0.775 (Fig. S1), which exceeds the acceptable threshold (Z' ≥ 0.5), thereby confirming its suitability for high-throughput screening of AChEIs in complex matrices [20].

Fig. 1.

Fig. 1

Open in a new tab

Enzyme assay system optimization and dose-response curves of positive inhibitors on acetylcholinesterase (AChE) activity. (A) Enzymatic reaction curves at different concentrations of AChE; (B) at different concentrations of iodothioacetylcholine (ATCI); (C) berberine hydrochloride; (D) galantamine hydrobromide. IC50: half-maximal inhibitory concentration; A405: absorbance at 405 nm.

To further validate the performance of the proposed platform, the MS1 model was established using galantamine hydrobromide and berberine hydrochloride as the positive controls and catechin as the negative control. As shown in Fig. 2A, the fractionation frequency was optimized to 9 s/well. Highly consistent activity profiles across repeated experiments confirmed the stability and reliability of the platform (Fig. 2B). In summary, this proposed platform is capable of effectively identifying AChE-inhibiting constituents from mixtures.

Fig. 2.

Fig. 2

Open in a new tab

Feasibility verification of the at-line nanofractionation platform using the mixed standard-1 (MS1) model. (A) Optimization of the collection time. (B) Reproducibility of the platform. Mobile phase A: water containing 0.1% formic acid; mobile phase B: acetonitrile containing 0.1% formic acid. Isocratic elution with 40% B. AChE: acetylcholinesterase.

3.2. Screening of AChEIs in the ethyl acetate extract of Corydalis rhizoma

The ethyl acetate extract of Corydalis rhizoma demonstrated significant inhibitory activity against AChE (IC50 = 0.90 ± 0.05 μg/mL, Fig. S2), and was therefore selected for further investigation. An extended gradient program (110 min) was employed to achieve optimal separation of structurally similar constituents. As shown in Fig. 3, 19 distinct inhibitory peaks were observed, suggesting the presence of multiple potentially bioactive constituents. S/N analysis confirmed that all 19 active peaks exhibited S/N > 3 (Table S2), indicating reliable signal responses. Based on Q-TOF-MS/MS data analysis and comparison with relevant literature [[21], [22], [23]], the identification results are presented in Table S3 and the corresponding MS/MS spectra are provided in Figs. S3 and S4. All 19 compounds were identified as isoquinoline alkaloids, including seven protoberberine-type alkaloids, six tetrahydroprotoberberine-type alkaloids, and the remaining classified as aporphine-type and protopine-type alkaloids. Notably, 13-methylberberine is reported herein for the first time to possess AChE-inhibiting properties. Modern pharmacological studies have shown that isoquinoline alkaloids possess broad pharmacological effects and significant bioactivity, particularly in mitigating neuronal damage associated with neurodegenerative diseases [24]. Their active constituents exert neuroprotective effects through multiple mechanisms: suppressing neuroinflammatory responses, counteracting oxidative stress, modulating autophagy, inhibiting intracellular calcium overload, improving mitochondrial dysfunction, promoting angiogenesis, and enhancing neuronal repair and regeneration [[25], [26], [27]].

Fig. 3.

Fig. 3

Open in a new tab

Screening results of acetylcholinesterase inhibitors of 1 mg/mL ethyl acetate extract in Corydalis rhizoma. The mobile phase consisted of (A) water containing 0.2% acetic acid (adjusted to pH 5.0 with ammonium hydroxide) and (B) acetonitrile containing 0.2% acetic acid. The gradient program was: 0 min, 20% B, 35 min, 23% B, 65 min, 30% B, 105 min, 100% B, 110 min, 100% B. TIC: total ion chromatogram; UV: ultraviolet.

3.3. Establishment of an ACR-based model for evaluating the efficiency of active constituents

To quantitatively assess the inhibitory potency of individual constituents, an ACR model was developed. The MS2 model consisted of positive controls with varying inhibitory potencies, including protopine (IC50 = 57.7 μM, moderate-low inhibition), tetrahydroberberine (IC50 = 15.3 μM, moderate inhibition), and berberine (IC50 = 0.14 μM, high inhibition), along with catechin as a negative control. In this model, the key metric ACR calculated by the ratio of active area to content area (AA/AC) was used to reflect the intrinsic inhibitory efficiency per unit concentration of a constituent. Validation results demonstrated that when half-peak integration was applied to the berberine peaks, both the AA and AC exhibited a good linear relationship with concentration across different concentration levels (Fig. S5). This confirmed the reliability of this method for integrating tailing peaks.

As illustrated in Fig. 4 and Table 1, AC and AA decreased correspondingly with decreasing concentrations of positive compounds in the MS2 solutions. Protopine demonstrated stable ACR values (3.4–3.6) across tested concentrations, indicating robust concentration-independent behavior. Berberine exhibited stable ACR values within the range of 160–170 at lower concentrations, with a substantial decrease observed at the highest test concentration due to saturation of the activity response. Tetrahydroberberine exhibited ACR fluctuations between 9.2 and 9.9, aligning with its intermediate IC50 value. These results confirmed a clear negative correlation between ACR and IC50 (berberine > tetrahydroberberine > protopine), validating the ACR model as a rational and effective tool for ranking the inhibitory efficiency of bioactive compounds without the need for prior purification.

Fig. 4.

Fig. 4

Open in a new tab

Feasibility verification of the activity-content ratio (ACR) to evaluate the acetylcholinesterase (AChE) inhibition of constituents. The mobile phase consisted of (A) water containing 0.2% acetic acid (adjusted to pH 5.0 with ammonium hydroxide) and (B) acetonitrile containing 0.2% acetic acid. The gradient program was: 0 min, 45% B, 10 min, 75% B, 10.01 min, 100% B, 18 min, 100% B. CAD: charged aerosol detector; UV: ultraviolet; MS2: mixed standard-2.

Table 1.

The activity-content ratio (ACR) of each positive compound in mixed standards-2 (MS2s) at different concentrations.

ACR MS2-1 MS2-2 MS2-3 MS2-4
Protopine 3.55 3.51 3.50 3.49
Berberine 58.7 167 164 162
Tetrahydroberberine 9.23 9.38 9.61 9.86
Open in a new tab

ACR: activity-content ratio; MS2: mixed standard-2.

3.4. Validation of the ACR model using the ethyl acetate extract of Corydalis rhizoma

To further evaluate the practical applicability of the ACR model, it was applied to analyze the AChE inhibitory efficacy of alkaloid constituents in the ethyl acetate extract of Corydalis rhizoma. Among the 19 compounds screened in Section 3.3, IC50 values were experimentally determined for nine compounds (dose-response curves shown in Fig. S6), while IC50 values for the remaining seven compounds were obtained from the literature [[28], [29], [30], [31], [32], [33]]. These 16 compounds were included in the correlation analysis, and as shown in Fig. 5, Fig. 6, a strong negative linear correlation was observed between ACR% and the corresponding IC50 values. To further validate the predictive stability of ACR across different activity ranges, the compounds were divided into two groups for analysis. The seven compounds with higher ACR% exhibited a strong linear correlation with their IC50 values (Fig. 6A, R2 = 0.9387, P = 0.0003), while the nine compounds with lower ACR% also showed a strong linear correlation (Fig. 6B, R2 = 0.9785, P < 0.0001). This indicates that ACR maintains predictive performance not only for highly potent compounds but also for those with moderate to low activity. These data are presented as a double-bar chart in Fig. S7. Notably, constituents with high ACR% such as berberine, palmatine, and dehydrocorydalin showed particularly high potency (IC50 < 1 μM).

Fig. 5.

Fig. 5

Open in a new tab

Schematic diagram of bioactive peaks and charged aerosol detector (CAD) peaks integration for the ethyl acetate extract of Corydalis rhizoma. AChE: acetylcholinesterase.

Fig. 6.

Fig. 6

Open in a new tab

Linear correlation between activity-content ratio percent (ACR%) and half-maximal inhibitory concentration (IC50) values. (A) Compounds with higher ACR%; (B) compounds with lower ACR%.

3.5. Extension and validation of the ACR model for AGIs screening

To further validate the applicability of the ACR activity evaluation model across different targets and classes of medicinal plants, this study employed a previously established at-line nanofractionation-based platform for screening AGIs. The AG inhibitory profile of the Citri reticulatae pericarpium extract (Fig. 7) revealed 18 distinct active peaks and their S/N ratios are summarized in Table S4. After comparison with the literature, 15 active constituents were successfully identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) as ten flavonoids and five other compounds [34,35] (detailed structural information in Table S5 and Figs. S8 and S9).

Fig. 7.

Fig. 7

Open in a new tab

High-throughput screening result of the potential α-glucosidase inhibitors in Citri reticulatae pericarpium. The mobile phase consisted of (A) water containing 0.1% formic acid and (B) methanol containing 0.1% formic acid. The gradient program was: 0 min, 10% B, 15 min, 30% B, 45 min, 50% B, 55 min, 60% B, 65 min, 65% B, 75 min, 65% B, 80 min, 70% B, 85 min, 85% B, 100 min, 100% B, 110 min, 100% B. TIC: total ion chromatogram; ESI: electrospray ionization; UV: ultraviolet; CAD: charged aerosol detector.

Among these, peaks 1–5 represented co-eluting components that could not be fully resolved. Since the ACR model requires single-compound resolution for reliable activity-content assignment, this combined peak region was excluded from the ACR calculation. The ACR and ACR% results for the remaining constituents are presented in Table 2. Data in Table 2 confirmed that rutin was the most prominent compound in the ACR evaluation, exhibiting the highest ACR value and a potent IC50 of 15.21 ± 1.23 μM. A negative correlation between IC50 and ACR% was also observed for several other compounds (Fig. 8), with specific numerical data provided in Table S6. This trend aligns with the consistent relationship between ACR% and IC50 established in Section 3.4, collectively confirming the effectiveness of the ACR model in guiding the identification of high-potency active constituents across structurally diverse compounds.

Table 2.

The activity-content ratio (ACR) information of active constituents determined through α-glucosidase inhibitors system.

No. Compound ACR ACR%
1−5 / / /
6 3'-Methoxypuerarin 225.04 0.85
7 Dihydroxypentmethoxyflavone 99.91 0.38
8 2-O-caffeoylglucaric acid 201.38 0.76
9 5-(β-D-glucopyranosyloxy)-2-hydroxybenzoic acid 2387.78 9.00
10 Lonicerin 3161.77 11.92
11 Rutin 9387.84 35.38
12 Pteryxin 1134.42 4.28
13 Narcissoside 300.66 1.13
14 Narirutin 296.37 1.12
15 Homoplantaginin 295.65 1.11
16 Hesperidin 5267.80 19.85
17 Nobiletin 1545.19 5.82
18 Tangeretin 2228.99 8.40
Open in a new tab

Fig. 8.

Fig. 8

Open in a new tab

Linear relationship between half-maximal inhibitory concentration (IC50) values and corresponding activity-content ratio percent (ACR%) of active compounds in the ethanol extract of Citri reticulatae pericarpium.

4. Conclusions

In this study, we successfully developed and validated a novel quantitative screening strategy based on the ACR model for the efficient prioritization of bioactive compounds in medicinal plants. The model demonstrated high reliability and broad applicability across distinct enzymatic targets (AChE and AG) and diverse chemical families such as alkaloids derived from Corydalis rhizoma and flavonoids obtained from Citri reticulatae pericarpium. A robust negative correlation between ACR% and IC50 values was established, confirming the ACR's capability to serve as a predictive indicator for intrinsic inhibitory potency. Compared with traditional bioactivity-guided isolation and statistical modeling methods, the ACR method offers distinct advantages: it integrates separation, quantification, and activity detection into a single analytical run, enabling the simultaneous acquisition of activity-content relationships for all active constituents with only one injection. This eliminates the laborious process of stepwise isolation, purification, and IC50 determination required by conventional approaches. Furthermore, its time-based fractionation strategy effectively addresses the limitation of traditional methods, which often miss low-abundance active constituents due to dependence on real-time signal thresholds. In parallel, the integrated data analysis allows the direct establishment of activity-content relationships at the single-compound level, thus avoiding the cumulative errors associated with multi-step separation and measurement. The practical application of this strategy was further demonstrated by the rapid identification of 34 enzyme inhibitors, including high-potency AChEIs such as berberine and palmatine, as well as AGIs like rutin and hesperidin.

Beyond accelerating the drug discovery pipeline, the ACR model establishes a critical link between chemical composition and pharmacological effect, providing a scientific basis for activity-based quality control. By enabling activity contribution ranking across different enzymatic targets, the model offers a rational approach for selecting appropriate bioactive markers tailored to specific therapeutic indications or roles in herbal formulations. These highly active constituents can serve as reliable bioactivity markers to predict the overall efficacy of medicinal plants across different batches or origins. Notably, the ACR model is well-suited for complex samples containing non-volatile or semi-volatile components such as flavonoids and alkaloids, for which CAD exhibits consistent responses within structurally similar classes. For samples containing volatile constituents or those requiring cross-class quantitative comparison, the applicability can be extended through alternative detection strategies or sample pretreatment approaches. Future work will focus on extending the ACR model to a broader range of biological targets and more complex sample systems, as well as exploring its integration with artificial intelligence to establish a higher-throughput, intelligent platform for the discovery of bioactive natural product constituents.

CRediT authorship contribution statement

Lin Chen: Writing – original draft, Methodology, Data curation, Conceptualization. Xinxin Chen: Validation, Software, Methodology. Shengnan Pi: Methodology, Investigation, Data curation. Ditong Huang: Investigation, Data curation. Manhuayun Zhai: Writing – review & editing, Supervision. Jincai Wang: Resources, Methodology, Funding acquisition. Zhengjin Jiang: Writing – review & editing, Supervision, Methodology, Funding acquisition. Tingting Zhang: Writing – review & editing, Supervision, Funding acquisition, Conceptualization.

Declaration of competing interest

The author, Zhengjin Jiang, is an Editorial Board Member for this journal and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no known conflict of interest or personal relationships that could have influenced the work reported here.

Acknowledgements

We gratefully appreciate the financial support from the National Natural Science Foundation of China (82473879, 82304437 and 82204342) and the Guangzhou Science and Technology Program-Key Research and development Program (2025B03J0084).

Footnotes

Peer review under responsibility of Xi'an Jiaotong University.

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jpha.2026.101665.

Contributor Information

Jincai Wang, Email: jcwang@jnu.edu.cn.

Zhengjin Jiang, Email: jzjjackson@hotmail.com.

Tingting Zhang, Email: tzhtt008@jnu.edu.cn.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (14.5MB, docx)

References

  • 1.Atanasov A.G., Zotchev S.B., Dirsch V.M., et al. Natural products in drug discovery: advances and opportunities. Nat. Rev. Drug Discov. 2021;20:200–216. doi: 10.1038/s41573-020-00114-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Fu G., Pan M., Sun Q., et al. Natural product-based strategies targeting inflammation in Alzheimers disease: mechanisms, drug delivery, and clinical trials. J. Pharm. Anal. 2025 [Google Scholar]
  • 3.Li Y., Lin Z., Jin H., et al. The anti-hyperuricemia potential of bioactive natural products and extracts derived from traditional Chinese medicines: a review and perspective. J. Pharm. Anal. 2025;15 doi: 10.1016/j.jpha.2024.101183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Weller M.G. A unifying review of bioassay-guided fractionation, effect-directed analysis and related techniques. Sensors. 2012;12:9181–9209. doi: 10.3390/s120709181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Wang R., Liu Y., Zhou H., et al. Integration of micro-fractionation, high-performance liquid chromatography-ultraviolet detector-charged aerosol detector-mass spectrometry analysis and cellular dynamic mass redistribution assay to accelerate alkaloid drug discovery. J. Chromatogr. A. 2020;1616 doi: 10.1016/j.chroma.2019.460779. [DOI] [PubMed] [Google Scholar]
  • 6.Caesar L., Kellogg J., Kvalheim O., et al. Integration of biochemometrics and molecular networking to identify antimicrobials in Angelica keiskei. Planta Med. 2018;84:721–728. doi: 10.1055/a-0590-5223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Liu R., Kool J., Jian J., et al. Rapid screening α-glucosidase inhibitors from natural products by at-line nanofractionation with parallel mass spectrometry and bioactivity assessment. J. Chromatogr. A. 2021;1635 doi: 10.1016/j.chroma.2020.461740. [DOI] [PubMed] [Google Scholar]
  • 8.Chen Z., Wang J., Yuan J., et al. Rapid screening of neuraminidase inhibitors using an at-line nanofractionation platform involving parallel oseltamivir-sensitive/resistant neuraminidase bioassays. J. Chromatogr. A. 2023;1687 doi: 10.1016/j.chroma.2022.463693. [DOI] [PubMed] [Google Scholar]
  • 9.Jian J., Yuan J., Fan Y., et al. High-resolution bioassay profiling with complemented sensitivity and resolution for pancreatic lipase inhibitor screening. Molecules. 2022;27:6923. doi: 10.3390/molecules27206923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Wang J., Huang X., Mei J., et al. Screening of trypsin inhibitors in Cotinus coggygria Scop. extract using at-line nanofractionation coupled with semi-preparative reverse-phase liquid chromatography. J. Chromatogr. A. 2023;1691 doi: 10.1016/j.chroma.2023.463817. [DOI] [PubMed] [Google Scholar]
  • 11.Huang Y., Guo X., Wang Z., et al. High-resolution neuraminidase inhibition profiling of Arnebia euchroma (Royle) I.M. Johnst. based on HR-MS and target isolation: an example study of anti-infectious constituents in traditional Chinese medicine. J. Ethnopharmacol. 2024;319 doi: 10.1016/j.jep.2023.117074. [DOI] [PubMed] [Google Scholar]
  • 12.Chang Y., Tian H., Qu J., et al. Establishment of an at-line nanofractionation-based screening platform for rapid identification of influenza PAN/PAN I38T inhibitors from Artemisiae Argyi Folium. J. Pharm. Anal. 2026;16 doi: 10.1016/j.jpha.2025.101402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Fan Y., Wang J., Jian J., et al. High-throughput discovery of highly selective reversible hMAO-B inhibitors based on at-line nanofractionation. Acta Pharm. Sin. B. 2024;14:1772–1786. doi: 10.1016/j.apsb.2024.01.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Yuan J., Zheng Z., Wang Z., et al. Integrating high-resolution bioassay profiling with affinity-based ligand fishing for unveiling galloylated derivatives as novel catechol-O-methyltransferase inhibitors in Paeonia lactiflora Pall. J. Pharm. Anal. 2026;16 doi: 10.1016/j.jpha.2025.101449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Yuan J., Chang Y., Gu R., et al. Multi-detector based at-line nanofractionation strategy accelerates discovery of influenza PA endonuclease inhibitors from Terminalia chebula Retz. J. Chromatogr. A. 2025;1762 doi: 10.1016/j.chroma.2025.466398. [DOI] [PubMed] [Google Scholar]
  • 16.Wang C., Chao I., Qin Y., et al. Comparison for quantification of eight components in Alpinia officinarum Hance by using high-performance liquid chromatography coupled with diode array detector and charged aerosol detector with individual and substitute reference compound. J. Pharm. Biomed. Anal. 2022;210 doi: 10.1016/j.jpba.2021.114545. [DOI] [PubMed] [Google Scholar]
  • 17.Ellman G.L., Courtney K.D., Andres V., jr., et al. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem. Pharmacol. 1961;7:88–95. doi: 10.1016/0006-2952(61)90145-9. [DOI] [PubMed] [Google Scholar]
  • 18.Özbek H., Güvenalp Z., Yılmaz G., et al. In vitro anticholinesterase activity and molecular docking studies of coumarin derivatives isolated from roots of Heptaptera cilicica. Med. Chem. Res. 2018;27:538–545. [Google Scholar]
  • 19.Singh Y.P., Kumar H. Berberine derivatives as inhibitors of acetylcholinesterase: a systematic review. Chem. Biol. Drug Des. 2023;102:1592–1603. doi: 10.1111/cbdd.14337. [DOI] [PubMed] [Google Scholar]
  • 20.Zhang J., Chung T.D.Y., Oldenburg K.R. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. SLAS Discov. 1999;4:67–73. doi: 10.1177/108705719900400206. [DOI] [PubMed] [Google Scholar]
  • 21.Jeong E.K., Lee S.Y., Yu S., et al. Identification of structurally diverse alkaloids in Corydalis species by liquid chromatography/electrospray ionization tandem mass spectrometry. Rapid Commun. Mass Spectrom. 2012;26:1661–1674. doi: 10.1002/rcm.6272. [DOI] [PubMed] [Google Scholar]
  • 22.Cheng X.Y., Shi Y., Zhen S.L., et al. HPLC-MS analysis of ethanol extract of Corydalis yanhusuo and simultaneous determination of eight protoberberine quaternary alkaloids by HPLC-DAD. J. Chromatogr. Sci. 2010;48:441–444. doi: 10.1093/chromsci/48.6.441. [DOI] [PubMed] [Google Scholar]
  • 23.Ding B., Zhou T., Fan G., et al. Qualitative and quantitative determination of ten alkaloids in traditional Chinese medicine Corydalis yanhusuo W.T. Wang by LC-MS/MS and LC-DAD. J. Pharm. Biomed. Anal. 2007;45:219–226. doi: 10.1016/j.jpba.2007.06.009. [DOI] [PubMed] [Google Scholar]
  • 24.Plazas E., Avila M M.C., Muñoz D.R., et al. Natural isoquinoline alkaloids: pharmacological features and multi-target potential for complex diseases. Pharmacol. Res. 2022;177 doi: 10.1016/j.phrs.2022.106126. [DOI] [PubMed] [Google Scholar]
  • 25.Li J., Wu Y., Dong S., et al. Research progress on neuroprotective effects of isoquinoline alkaloids. Molecules. 2023;28:4797. doi: 10.3390/molecules28124797. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Shang X., Yang C., Morris-Natschke S.L., et al. Biologically active isoquinoline alkaloids covering 2014–2018. Med. Res. Rev. 2020;40:2212–2289. doi: 10.1002/med.21703. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Wang D., Qin L., Jing C., et al. Biologically active isoquinoline alkaloids covering 2019–2022. Bioorg. Chem. 2024;145 doi: 10.1016/j.bioorg.2024.107252. [DOI] [PubMed] [Google Scholar]
  • 28.Tao Y., Jiang Y., Li W., et al. Zeolite based solid-phase extraction coupled with UPLC-Q-TOF-MS for rapid analysis of acetylcholinesterase binders from crude extract of Corydalis yanhusuo. RSC Adv. 2016;6:98476–98486. [Google Scholar]
  • 29.Pecic S., McAnuff M.A., Harding W.W. Nantenine as an acetylcholinesterase inhibitor: SAR, enzyme kinetics and molecular modeling investigations. J. Enzym. Inhib. Med. Chem. 2011;26:46–55. doi: 10.3109/14756361003671078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Senol Deniz F.S., Ekhteiari Salmas R., Emerce E., et al. Cholinesterase inhibitory and in silico toxicity assessment of Thirty-four isoquinoline alkaloids-berberine as the lead compound, CNS Neurol. Disord. Drug Targets. 2024;23:773–783. doi: 10.2174/1871527322666230417083053. [DOI] [PubMed] [Google Scholar]
  • 31.Bozkurt B., Ulkar D., Nurlu N., et al. Variability of isoquinoline alkaloid profiles and anticholinesterase activities with binding-mode predictions of Glaucium flavum population. Chem. Biodivers. 2024;21 doi: 10.1002/cbdv.202301865. [DOI] [PubMed] [Google Scholar]
  • 32.Cao T.Q., Ngo Q.T., Seong S.H., et al. Cholinesterase inhibitory alkaloids from the rhizomes of Coptis chinensis. Bioorg. Chem. 2018;77:625–632. doi: 10.1016/j.bioorg.2018.01.038. [DOI] [PubMed] [Google Scholar]
  • 33.Galarce-Bustos O., Pavón J., Henríquez-Aedo K., et al. Detection and identification of acetylcholinesterase inhibitors in Annona cherimola Mill. by effect-directed analysis using thin-layer chromatography-bioassay-mass spectrometry. Phytochem. Anal. 2019;30:679–686. doi: 10.1002/pca.2843. [DOI] [PubMed] [Google Scholar]
  • 34.Yang W., Liu M., Chen B., et al. Comparative analysis of chemical constituents in Citri Exocarpium Rubrum, Citri Reticulatae Endocarpium Alba, and Citri Fructus Retinervus. Food Sci. Nutr. 2022;10:3009–3023. doi: 10.1002/fsn3.2897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Yang M., Jiang Z., Wen M., et al. Chemical variation of Chenpi (Citrus peels) and corresponding correlated bioactive compounds by LC-MS metabolomics and multibioassay analysis. Front. Nutr. 2022;9 doi: 10.3389/fnut.2022.825381. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.docx (14.5MB, docx)

Articles from Journal of Pharmaceutical Analysis are provided here courtesy of Xi'an Jiaotong University

RESOURCES