Detection of morphine and data processing using surface plasmon resonance imaging sensor

Jianuo Sun; Haokun Ke; Jinghan Wang; Xianchao Du; Hongxia Hao; Hong Zhou

doi:10.1515/gps-2024-0067

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Detection of morphine and data processing using surface plasmon resonance imaging sensor

Jianuo Sun , Haokun Ke , Jinghan Wang , Xianchao Du , Hongxia Hao und Hong Zhou

Veröffentlicht/Copyright: 19. Oktober 2024

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Abstract

Based on the surface plasmon resonance imaging (SPRi) instrument, we established a new method of analyzing morphine in urine by processing a calibration curve. According to an inhibition immunoassay, gradient concentration of morphine and morphine-BSA fixed on the chip competitively combine with morphine antibody on the chip. Given the three mathematical models, the data of SPRi signals generated from SPRi with morphine was processed to obtain the calibration curve. Ultrafiltration was used to pretreat blank urine samples with adding morphine, and then investigated the advantages and disadvantages of each model. With a limit detection of 6.57 ng·mL⁻¹, the method and mathematical models can provide robust support for SPRi sensors used in further environmental detection, such as the epidemiological study of sewage.

Keywords: SPRi; inhibition immunoassay; morphine; calibration curve

1 Introduction

At present, the abuse of opioids, especially the cases of death caused by taking or injecting opioids, is increasing. The primary metabolite of opioids in the human body is morphine. Therefore, the qualitative and quantitative detection of morphine in the body fluids of these people is the direct basis for judging whether they take opium drugs [1]. The typical drug detection methods include coloration assay [2], immunoassay [3], and chromatography assay [4], among which the coloration assay is simple and rapid. However, detecting traces or drugs of similar chemical structure is challenging, and impurities would significantly interfere with the results. Chromatographic assay, like gas chromatography [5] or liquid chromatography [6], which can accurately analyze the target drugs qualitatively and quantitatively under low detection limits, has become the laboratory’s most widely used analytical method. However, the complex pretreatment of samples and large-scale instruments limit its use in on-the-spot rapid detection. The basic principle of immunoassay is the specificity and reversibility of the binding reaction of antigen and antibody. Enzyme-linked immunosorbent assay (ELISA), also called enzyme immunoassay, is a standard immunoassay for drug analysis [7], which has a relatively low accuracy rate and specificity, resulting in false positive results. Nieddu et al. [8] showed that even if the concentration of methamphetamine in the urine is as high as 10,000 ng·mL⁻¹, it cannot be detected by the ELISA kits, indicating a false negative. In addition, morphine is metabolized quickly in the body, and the morphine concentration in the urine of opioid abusers will soon drop to below 300 ng·mL⁻¹ within 1–1.5 days [9]. The researchers analyzed effluent samples collected from different locations in New York City using by ultra-high performance liquid chromatography-tandem mass spectrometry, and the results showed that levels in raw influent wastewater samples (133.0–258.3 ng·L⁻¹) and water from the sewage overflow area were positive (10.7 ng·L⁻¹) [10].

Surface plasmon resonance (SPR) is an optical instrument used to characterize the change of surface refractive index and show the properties of materials attached to the metal surface in the SPR instrument [11]. SPR technology was first used by Liedberg [12] in the 1980s to detect the interaction between IgG antibody and its antigen. Then, this technology was applied in the field of biosensors and quickly penetrated it. In the 1990s, many mature commercial SPR instruments appeared [13], pushing SPR sensor research to a rapid development stage [14–16]. Compared with the traditional detection methods, SPR sensors show better rapidity and sensitivity. In addition, it can obtain the concentration, affinity, kinetic constant, and specificity of analytes, which has shown great potential in drug detection.

The applications of SPR in the detection of morphine have been reported. Sakai et al. [17] developed an SPR sensor based on the immune reaction between morphine-bovine serum albumin (MOP-BSA) and antibody and verified the feasibility of morphine detection. However, the SPR method is based on the combination of antigen and antibody, and the ratio of antigen and antibody depends on the proportion of antigen and antibody in the reaction system. Therefore, the design or selecting the appropriate mathematical model and then establishing the calibration curve is the basis of accurate quantitative analysis of morphine with SPR sensor.

In this study, a surface plasmon resonance imaging (SPRi) instrument was applied to detect morphine on gold surface sensor chips, combining with charge coupled device (CCD) spectroscopy to realize a multipoint monitor [18]. Initially, morphine antigen was immobilized on an SPRi chip. A blend of morphine antibody and morphine was then introduced to flow across the chip surface. The detection principle of the SPR sensor involves changes in the refractive index on the sensor chip’s surface, which generate signal variations. As illustrated in Figure 1, the chip surface is coated with a gold film to support surface plasmon waves. Specific probe molecules (e.g., antigen) are fixed on the sensor chip. Polarized light from the light source is coupled to the gold film through a prism. At a specific angle, photons couple with free electrons on the metal surface to form surface plasmon waves. The flow cell above the sensor chip has inlets and outlets for analyte solutions. The analyte (e.g., morphine and its antibody) binds to the probe molecules on the chip surface, changing the local refractive index and altering the SPR conditions (e.g., reflected light intensity or angle). These changes are recorded by the CCD detector, producing SPR images. By varying the morphine concentration, a calibration curve was generated based on the response values. SPRi technology allows array-chip, real-time, label-free detection of molecular interactions, providing high sensitivity, and data credibility.

Figure 1

SPRI detection schematic: the chip is mounted to the bottom of a triangular prism, the detection cell directs the solution flow through the chip, and the detector collects and converts the signal. Enlargement shown: (A) carboxy-base surface of chip, (B) immobilization of the MOP-BSA, and (C) the competitive binding of MOP-BSA and morphine to morphine antibody on the surface of the chip.

For the molecular weight of morphine, which is less than 10 kDa, we use inhibition immunoassay to perform a qualitative and quantitative analysis of morphine (Figure 1). With the mixture solution of morphine mAb and morphine flowing through the chip surface, the response SPRi signals were obtained. The concentration of morphine was taken as the independent variable and the received response signal value as the dependent variable. The standard curve was established by polynomial, Log-logit, and four-parameter logistical mathematical models and the recovery experiment evaluated these three kinds of standard curves.

2 Experimental

2.1 Chemicals and reagents

MOP-BSA (10 mg·mL⁻¹) and anti-morphine monoclonal antibody (1 mg·mL⁻¹) were purchased from EastCoast Bio. Co., Ltd. Standard samples (10 mmol·mL⁻¹) of morphine, cocaine, methamphetamine, 3,4-methylenedioxymethamphetamine, ketamine, caffeine, and codeine were provided by the Institute of Forensic Science, Ministry of Public Security. N-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC; 5 g, 98%) was purchased from Beijing Innochem Technology Co., Ltd. N-hydroxysuccinimide (NHS; 98%) was purchased from Sigma-Aldrich LLC. Ethanolamine (AR, 99%, 500 mL) was purchased from Shanghai Macklin Biochemical Co., Ltd. Phosphate buffer (PBS; 500 mL, pH = 7.4) was purchased from Sinopharm Chemical Reagent Co., Ltd. Glycine (≥99%, 100 g) was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. 11-Mercaptoundecanoic acid (98%) was purchased from Sun Chemical Technology (Shanghai) Co., Ltd.

Ethanolamine diluent (pH = 8.5, 1 mol·L⁻¹) was prepared for the blocking solution. Mixture solution of hydrochloric acid and glycine (pH = 2) was prepared for the regeneration solution. Piranha solution was prepared with a volume ratio of sulfuric acid to water of 7:3.

2.2 Instruments

Experimental data were generated with PlexArray™ HT SPRi sensors equipped with Nanocapture™ SPR chips, which were purchased from Plexera Biotechnology Co., Ltd. YC-F20 shaker was purchased from Suzhou Jiangdong Precision Instrument Co., Ltd. TG-15M centrifuge was purchased from Hunan Pingfan Science and Technology Co., Ltd. Ultrafiltration centrifuge tubes (5 mL, molecular weight cut-off value: 3 kDa) were purchased from Sigma-Aldrich LLC.

2.3 Immobilization of MOP-BSA

The SPRi chip (coated with gold) was soaked in the Piranha solution for 1 h to remove all organic substances and to hydroxylate the chip. After washing the chips with deionized water and drying it by nitrogen gas, the SPRi chip was then soaked in 1 mmol·L⁻¹ 11-mercaptoundecanoic acid solution for 24 h, which make the surface of chip self-assembled with carboxyl groups.

EDC solution of 0.4 mol·L⁻¹ and 0.1 mol·L⁻¹ NHS solution were mixed in equal volume as activators for the carboxyl groups. The SPRi chip was soaked in an activation solution and vibrated on a shaker for half an hour. Then, the chip was taken out, rinsed, and blow-dried dried.

The SPRi chip was placed on the prepared mold board. 10, 50, 100, 250, 500, and 1,000 µg·mL⁻¹ concentrations of MOP-BSA conjugates were successively spotted in the marked chip regions and then laid flat until the solution was dry. The unconjugated sites on the chip surface were blocked with 1 mol·L⁻¹ ethanolamine solution immersed for 1 h. The polydimethylsiloxane microflow cell was fixed on the chip by pressing through the workbench.

2.4 SPRi analysis

PlexArray HT is a high-throughput SPRi platform, which mainly includes an optical sensing detection pool and a CCD detector, which produces grayscale images with different brightnesses. PlexArray HT is a high-throughput SPRi platform that mainly includes an optical sensing detection pool and a CCD detector, which produce grayscale images with different brightnesses. Load the SPRi chip into the instrument. PBS buffers as a blank control were flowed as the mobile phase first at a flow rate of 2 µL·s⁻¹ for 5 min and the stability of the baseline was observed. After the baseline was stable, the sample was injected. The detection of each sample needs three steps: combination, dissociation, and regeneration. In the combination step, the mobile phase of the sample to be tested, flowing for 5 min at a flow rate of 2 µL·s⁻¹. The sample to be tested will combine with the chip surface, increasing the SPRi signal value. In the dissociation step, PBS buffer was used as the mobile phase at a flow rate of 2 µL·s⁻¹ for 5 min. During this time, SPRi signal value tended to be stable. The chip was regenerated with glycine hydrochloric acid solution to remove the binding antibody. The time of regeneration was 3 min at a flow rate of 5 µL·s⁻¹.

2.5 Optimum of morphine antibody concentration

Different concentrations of morphine mAb 1, 5, 15, 25, 50, and 100 µg·mL⁻¹ were successively flowed over the surface of the SPRi chip to obtain the SPRi signal value and investigated the optimal mass concentration of the antibody.

2.6 Inhibition immunoassay

Morphine antibody was mixed with a gradient concentration of morphine and diluted with PBS buffer. The mixture solutions of optimal concentration of morphine antibody and 0, 1, 5, 12.5, 16, 20, 25, 30, 50, 125, and 250 ng·mL⁻¹ morphine were finally prepared. Each concentration of antibody was repeated three times, and the corresponding SPRi signal value was obtained.

2.7 Ultrafiltration displacement of the urine sample

Based on our previous research, ultrafiltration displacement is an effective pretreatment method to remove 99% of the proteins and salts from the urine, significantly reducing the potential interference [19]. The addition of morphine and morphine antibody to the blank urine samples were prepared, making the concentration of morphine antibody at the optimal mass concentration and the morphine concentration at 18, 20, 22, 25, and 30 ng·mL⁻¹. The morphine-added urine samples were then added into the ultrafiltration tube and centrifuged for 15 min at a speed of 10,000 rpm. After centrifuging, repeatedly wash the membrane of the inner tube of the ultrafiltration tube with PBS buffer to reduce the adhesion of antibody to the membrane. Add PBS buffer into the inner tube, centrifuge again and wash the membrane repeatedly. The solution in the inner tube was taken out and detected by the SPRi instrument.

2.8 Selectivity experiment

A mixture containing antibodies against six drugs (cocaine, methamphetamine, 3,4-methylenedioxymethamphetamine, ketamine, caffeine, and morphine) was prepared with each drug at a concentration of 30 ng·mL⁻¹. Additionally, another mixture combining morphine antibody with codeine was prepared at the same concentration. The specificity of this method for detecting morphine was evaluated according to the previously described procedures.

Furthermore, antibodies against the six drugs (cocaine, methamphetamine, 3,4-methylenedioxymethamphetamine, ketamine, caffeine, and morphine) were combined into a single mixture. This mixture of antibodies was then mixed with a solution containing morphine adjusted to a concentration of 100 ng·mL⁻¹. A preliminary screening test for morphine was conducted, and the SPRi signal values of these two groups were compared.

3 Results and analysis

3.1 Optimization of MOP-BSA and antibody concentrations

Gradient concentrations of antibody solutions were flown over the multipoints of the surface of SPRi chips. As Figure 2 shows, the SPRi signals generated from 10, 50, 100, 250, 500, and 1,000 µg·mL⁻¹ concentrations of the MOP-BSA fixed on the chip were recorded. As is shown in Table 1, the fitting result of the Langmuir equation (Y = (R _max × k × x ^1−c)/(1 + R _max × k × x ^1−c)) from the data calculated saturated binding amounts (R _max) and absorption constants (k). The R _max values rose with the increase of concentration of MOP-BSA. When the concentration of BSA changed from 500 to 1,000 µg·mL⁻¹, the increase in SPRi signal value was not obvious, indicating that the chip surface tends to be saturated. Thus, 1,000 µg·mL⁻¹ concentration of the MOP-BSA is optimal.

Figure 2

The variation curve of SPRi signal value against different morphine antibody concentrations obtained from six concentrations of MOP-BSA on the chip. Each diagram represents a detection point of different MOP-BSA concentrations (unit µg·mL⁻¹).

Table 1

Fitting result of the Langmuir equation (Y = (R _max × k × x ^1−c)/(1 + R _max × k × x ^1−c)), Y represents SPR signal, x represents the concentration of MOP-BSA, k represents absorption constants, and R _max represents the maximum binding amounts

x (µg·mL⁻¹)	k	c	R _max	r ²
10	1.14 ± 0.08	0.43 ± 0.07	1.68 ± 0.05	0.9927
50	0.72 ± 0.07	0.20 ± 0.11	3.97 ± 0.14	0.9849
100	0.42 ± 0.07	−0.14 ± 0.15	7.98 ± 0.23	0.9839
250	0.50 ± 0.07	−0.13 ± 0.16	9.14 ± 0.26	0.9829
500	0.01 ± 0.01	−1.43 ± 0.30	15.84 ± 0.44	0.9934
1,000	0.02 ± 0.01	−0.88 ± 0.22	18.33 ± 0.53	0.9933

The principle of antibody dosage in indirect competitive reaction: to make morphine in the sample and MOP-BSA fixed on the chip surface compete effectively for morphine antibody, the concentration of antibody prepared should be close to saturation specifically binding to the antigen of the chip surface. The relationship between different antibody mass concentrations and the corresponding SPRi signal is shown in Figure 2. Morphine antibody’s mass concentration of 15 µg·mL⁻¹ can generate enough SPRi signal value, and the curve enters the plateau period. The signal value generated from morphine mAb concentrations higher than 25 µg·mL⁻¹ would be slightly higher but make the cost rise. In addition, the activity of antibodies is also an important factor. Different batches, storage environments, and even transportation environments would differentiate the antibody activity. For the reliability of the experimental results, the data should be obtained from the same tube of antibodies. Therefore, the intensity of the SPRi signal and the dosage of antibodies should be balanced. In conclusion, we elected 15 µg·mL⁻¹ concentration of morphine mAb as the optimum antibody concentration.

3.2 Acquirement of the calibration curve

The SPRi signal curve (average value from three groups of experiments) generated from the mixture of the optimal antibody concentration (15 µg·mL⁻¹) and gradient concentrations of morphine is shown in Figure 3(a). We take the average value of 20 points (440–459 s) near the highest value of each curve and use this as the dependent variable. Then, draw the plot with the corresponding morphine mass concentration as the independent variable. The results are shown in Figure 3(b). The SPRi signal value decreases with the increase of morphine concentration, and the changing trend of the curve is a process from small to large and then to small.

Figure 3

(a) SPRi signal curve from different concentrations of morphine, (b) scattered data points of SPRi signal values against concentrations of morphine, (c) the signal interface of SPRi detector, and (d) the SPRi image of morphine samples at a range of concentrations on the sensor chip.

All samples were measured three times in parallel (relative standard deviation [RSD] < 3%), and using PBS buffer flow through the prepared BSA-morphine chip as a blank control, the resulting signal value was detected as noise. According to the theory of signal-to-noise ratio of 3 (S/N = 3), the limit of detection (LOD) was obtained to be 6.57 ng·mL⁻¹.

There are many immunoassays that have developed their own analysis system. Radioimmunoassay (RIA) in the medical field is a typical one [20]. Over longer than a decade, a complete quality control system, including the establishment of a calibration curve, has been established in RIA. In an RIA process, radiolabeled and unlabeled antigens (the latter is the substance to be tested) compete for antibodies with a fixed concentration. Based on the radioactivity values of the conjugates of antigen and antibody under the conditions of different concentrations of unlabeled antigen, a calibration curve is obtained. Comparing with the RIA and SPRi indirect competitive methods, we found that the MOP-BSA fixed on the surface of the chip was equal to the radiolabeled antigen, the morphine was equivalent to the unlabeled antigen, and the radioactivity of the conjugates of antigen and antibody was comparable to the SPRi signal value generated from the combination of MOP-BSA immobilized on the surface of the chip and the morphine antibody. Therefore, the typical polynomial [21], Log-logit [21], and four-parameter logistic [22] models in RIA could be applied to establish the SPRi calibration curve.

3.2.1 Polynomial model

Y = a x 3 + b x 2 + cx + d

The SPRi signal value generated from 0 ng·mL⁻¹ concentration of morphine is R ₀. The signal value corresponding to a certain concentration of morphine is R, and the inhibition rate/% = (R ₀ − R)/R ₀ × 100. Take the morphine concentration as an independent variable and the inhibition rate as the dependent variable, and then fit the standard curve with cubic function as a mathematical model in Origin. The results are shown in Figure 4. Although the function offers a good correlation (R ² = 0.9997) in the range of 2.5–30 ng·mL⁻¹ morphine concentration, it can only simulate the dose–response relationship in a particular region, which lacks universal applicability. Moreover, it is difficult to judge the existence of bad points.

Figure 4

Polynomial equation fitting diagram.

3.2.2 Log-logit model

lg Y 100 − Y = a + b lg x

Log-logit model is the most common mathematical method to linearize S-curve. Similarly, take the inhibition rate/% as y and the morphine concentration as x. y′ = lg [y/(100 − y)]; X′ = lg x. After Log-logit transferring, S-type curve is transferred into monotonic function. The fitting results from Origin software are shown in Figure 5, with the detection range of 2.5–125 ng·mL⁻¹, R ² = 0.9624.

Figure 5

Log-logit equation fitting diagram.

3.2.3 Four-parameter logistic model

Y = a − d 1 + ( x / c ) b + d

In the four-parameter logistic equation, Y represents the inhibition rate (%), x represents the concentration of morphine, a represents the minimum inhibition rate (the inhibition rate when without morphine), d represents the maximum inhibition rate, c represents the concentration of morphine as the SPRi signal value is half of the maximum value, i.e., IC₅₀, and b is a influence factor for the slope. The fitting result was obtained from Origin software. As is shown in Figure 6, the experimental results show a good correlation with the four-parameter logistic equation (the correlation coefficient (R ²) is 0.9996), and the quantitative detection range is IC₂₀–IC₈₀, i.e., 17.70–34.66 ng·mL⁻¹.

Figure 6

Four-parameter logistic equation fitting diagram.

3.3 Evaluation of three mathematical models

Three concentrations of samples containing blank urine and standard samples (30, 25, 22, 20, 18 ng·mL⁻¹) were tested. The experimental results are shown in Table 2. The RSD (n = 5) ranged from 1.52% to 2.77%, indicating that the SPRi signal value from the ultrafiltration displacement pretreatment is stable at the same concentration, which can be used as a pretreatment method. Three sets of data are brought into three mathematical models for the measured value and recovery rate. The polynomial model shows a strong adaptability in a certain range of concentration and gets a high correlation coefficient in the fitting. The recovery rate is also good (108.44–99.80%). However, the model shows a limited general applicability, and it is difficult to judge the scope of quantitative analysis, especially when there exist bad points. Log-logit model has a wide detection range of 2.5–125 ng·mL⁻¹. However, the correlation of fitting curve is relatively poor (R ² = 0.9624), so the recovery results deviate relatively greatly (93.22–131.80%). The four-parameter logistic model fits the curve well in the range of 1–250 ng·mL⁻¹. Moreover, a high correlation coefficient (R ² = 0.9996) and a reasonable recovery rate (94.60–105.30%) are obtained. However, the quantitative detection range is only within the range of IC₂₀–IC₈₀ (17.70–34.66 ng·mL⁻¹). The function change rate higher or lower than this range is too small, and the signal is in a significant high or low value, which is not suitable for quantitative analysis. Although the polynomial model has good adaptability, it does not have universal applicability, especially in the future when using SPRi instrument for quantitative analysis of other small drug molecules, it is difficult to predict its fitting range. Although Log-logit model shows a wide detection range, the correlation of the fitting curve is poor. So, it can only be applicable for the primary evaluation of morphine content in samples. The four-parameter logistic model has the advantages of strong adaptability and good stability, but the detection range is relatively narrow. Because of the complexity of antigen–antibody reaction, the mathematical model we fit at present origins from the shape of the curve itself, but not the principal of antigen–antibody reaction.

Table 2

Recovery rate of morphine in urine samples (n = 5)

Morphine (ng·mL⁻¹)	Mathematical models	Measured value (ng·mL⁻¹)	Recovery (%)	RSD (%)
30	Polynomial	29.94	99.80	2.77
	Log-logit	39.54	131.80
	Logistic	31.61	105.37
25	Polynomial	26.05	104.20	1.52
	Log-logit	25.18	100.60
	Logistic	24.15	96.60
22	Polynomial	23.68	107.64	1.99
	Log-logit	20.65	93.86
	Logistic	21.48	97.64
20	Polynomial	21.35	106.75	1.85
	Log-logit	22.54	112.70
	Logistic	18.92	94.60
18	Polynomial	19.52	108.44	2.22
	Log-logit	16.78	93.22
	Logistic	18.61	103.39

3.4 Selectivity and specificity

The morphine antibody, at its optimal concentration, was combined with six different drugs (morphine, cocaine, methamphetamine, 3,4-methylenedioxymethamphetamine, ketamine, and caffeine) as the mobile phase flowed across the chip surface. Figure 7 displays the resultant signal values, which closely resemble those observed when the morphine antibody was mixed solely with morphine. Furthermore, when the morphine antibody was mixed with the other five drugs, minimal inhibition rates were observed (<3%). These findings indicate that, except for morphine, the remaining five drugs did not interact with the target antibodies, thereby confirming no interference with morphine detection.

Figure 7

Specificities for morphine and other drugs.The symbols are the mean of at least three repetitive measurements and the error bars are the standard deviation.

However, the detection of the morphine and codeine mixture exhibited cross-reactivity, as illustrated in Figure 7. The structural similarity between morphine and codeine, differing by only one functional group, resulted in the antibodies being unable to distinguish between them specifically. Consequently, both morphine and codeine are bound to the morphine antibodies under these conditions.

Due to the SPRi sensor’s ability to detect multiple resonance points simultaneously, we utilized an SPRi chip immobilized with various BSA conjugates to screen unknown compounds in the samples. As shown in Figure 8, when a mixture of antibodies against six drugs flowed over the chip surface, signals were observed at all corresponding points on the chip. Upon adding 100 ng·mL⁻¹ of morphine to the antibody mixture, the signal intensity at points containing the morphine antigen decreased significantly. In contrast signals at other points showed negligible change (<3%), indicating specific detection of morphine in the mixture. This type of preliminary screening can be conducted prior to detailed analysis to determine the presence of multiple drugs in unknown samples.

Figure 8

Changes in signal values of points on the SPRi chip in the absence (left) and presence (right) of 100 ng·mL⁻¹ morphine. The symbols are the mean of at least three repetitive measurements and the error bars are the standard deviation.

4 Conclusion

In this research, relying on an SPRi instrument, we developed a detection method for morphine qualitatively and quantitatively. The method shows good sensitivity with the lower LOD to 6.57 ng·mL⁻¹. Moreover, the detection time is less than 20 min, and complex pretreatment is not required. Compared with large instruments such as liquid chromatography, SPRi detection has the advantages of rapid detection and simultaneous detection of multiple samples. Compared with rapid detection methods such as colloidal gold and immunoassay strips, SPRi detection has advantages in detection limit and detection accuracy [23–27]. This equipment can be mounted on a test vehicle or miniaturized for use in the field because it does not have the exact high environmental requirements for vibration, airborne humidity, contaminants, and dust as larger laboratory equipment. We processed the data through three mathematical models and evaluated the correlation coefficient and recovery rate of each model. Each method has its own disadvantages; however, from the current practice, it is a relatively ideal way to use the four-parameter logistic model to fit the curve. This SPRi immuno-based sensor technology and data process can be further applied to detect many other small molecular drugs or combined with smartphones for on-the-spot detection [28,29].

# Indicates co-first author: Jianuo Sun and Haokun Ke are co-first authors, and they have made equally important contributions to the research work.

Acknowledgements

This work was supported by the Academician Foundation of the Forensic Center of Ministry of Public Security in China (2011/23323131), the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China (81871523), the Opening Project of Key Laboratory of Evidence Science (China University of Political Science and Law), the Ministry of Education (2021KFKTO5, 2019KFKT01), and the Fundamental Research Funds for the Central Universities.

Author contributions: Jianuo Sun: writing, validation, data analysis, and visualization; Haokun Ke: investigation, methodology, formal analysis, and writing – original draft; Jinghan Wang: writing – review & editing; Xianchao Du: visualization, editing, and formal analysis; Hongxia Hao: supervision, methodology, experimental design, funding acquisition, and review; Hong Zhou: supervision, resources, and project administration.
Conflict of interest: Authors state no conflict of interest.
Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2024-03-27

Accepted: 2024-08-08

Published Online: 2024-10-19

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/gps-2024-0067

Schlagwörter für diesen Artikel

SPRi; inhibition immunoassay; morphine; calibration curve

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