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Benchtop NMR spectroscopy in the analysis of substandard and falsified medicines as well as illegal drugs
Journal Pre-proof Benchtop NMR spectroscopy in the analysis of substandard and falsified medicines as well as illegal drugs Peter H.J. Keizers, Frank Bakker, Jose´ Ferreira, Paul F.K. Wackers, Dion van Kollenburg, Eva van der Aa, Amy van Beers
PII:
S0731-7085(19)31782-0
DOI:
https://doi.org/10.1016/j.jpba.2019.112939
Reference:
PBA 112939
To appear in:
Journal of Pharmaceutical and Biomedical Analysis
Received Date:
18 July 2019
Revised Date:
7 October 2019
Accepted Date:
17 October 2019
Please cite this article as: Keizers PHJ, Bakker F, Ferreira J, Wackers PFK, van Kollenburg D, van der Aa E, van Beers A, Benchtop NMR spectroscopy in the analysis of substandard and falsified medicines as well as illegal drugs, Journal of Pharmaceutical and Biomedical Analysis (2019), doi: https://doi.org/10.1016/j.jpba.2019.112939
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Benchtop NMR spectroscopy in the analysis of substandard and falsified medicines as well as illegal drugs
Peter H. J. Keizers*, Frank Bakker, José Ferreira, Paul F. K. Wackers, Dion van Kollenburg, Eva van der Aa, Amy van Beers
National Institute for Public Health and the Environment, P. O. Box 1, 3721 MA, Bilthoven, the
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Netherlands
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*Corresponding author. Tel.: +31 30274 2587. E-mail address: [email protected] (P. H. J. Keizers)
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Graphical Table of Contents
We have assessed an analytical method for the analysis of active ingredients in pharmaceutical products and illegal drugs, based on benchtop NMR spectroscopy. Within its resolution limits, benchtop NMR spectroscopy is useful in determining the identity and contents of the active ingredients in pharmaceutical products. Additionally, a simple chemometrics approach is shown to
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be useful to classify spectra into active substances, reducing the need for expert interpretation of the spectra acquired.
Highlights We have developed an analytical method for the analysis of active ingredients in pharmaceutical products and illegal drugs, based on benchtop NMR spectroscopy.
Within its resolution limits, benchtop NMR spectroscopy is useful in determining the identity of the active ingredients in pharmaceutical products.
The content of active ingredient in pharmaceutical products can be determined with an error of 10% using benchtop NMR spectroscopy.
A chemometrics approach is shown to be useful to classify spectra in order to identify the active substances present in the sample, reducing the need for expert interpretation of the spectra acquired.
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Abstract
Substandard and falsified medical products may cause harm to patients and fail to treat the diseases
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or conditions for which they were intended. It is therefore required to have analytical methods
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available to assess medical product quality. Benchtop NMR spectroscopy provides a generic, inherently quantitative, analytical method capable of separating specific signals from those of a
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matrix. We have developed an analytical method for the analysis of active ingredients in pharmaceutical products and illegal drugs, based on benchtop NMR spectroscopy. Within its resolution limits, benchtop NMR spectroscopy is useful in determining the identity of the active
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ingredients in products containing acetaminophen, aspirin, caffeine, diclofenac, ibuprofen, naproxen,
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sildenafil, tadalafil and sibutramine, cocaine, and gamma hydroxybutyric acid, with a limit of detection of about 1 mg/mL. Furthermore, the content of the active ingredient can be determined with an error of 10%. Additionally, a chemometrics approach is shown to be useful to classify spectra in order to identify the active substances present in the sample, reducing the need for expert interpretation of the spectra acquired.
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Keywords Benchtop NMR spectroscopy; falsified medicines; illegal drugs
1. INTRODUCTION Medical products that are falsified or in another way substandard may be of such poor quality that they may fail to treat the diseases or conditions for which they were intended [1]. They can even be harmful to the patient’s health. It is therefore required to recognize such products and take them
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from the market as quickly as possible. In order to do so, there is a need to have available analytical methods to assess medical product quality [2]. Generic methods offer value for money and should be applicable to determine identity and quantity of active substances, including those in illegal drugs
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and, possibly, medical devices. A semi-quantitative method is often sufficient, as the presence of an effective dose is more relevant to public health and for the legal status of a product than analytical
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perfection [3]. For new or exotic products, containing designer drugs or analogues of known active ingredients, reference standards are not always commercially available [4, 5]. This brings challenges
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to laboratories focussing on chromatography-based techniques for the verification of identity and to determine the quantity of the active ingredients. NMR spectroscopy offers the opportunity to fulfil
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all criteria listed above, by providing a generic, inherently quantitative, analytical method capable of separating specific signals from those of a matrix [6]. Nevertheless, NMR spectroscopy is an
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expensive technique in purchase and maintenance and requires dedicated and experienced operators. In recent years, considerable development in the field of benchtop NMR spectroscopy,
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seems to have overcome some of these concerns [7, 8]. Nowadays, low field benchtop spectrometers are used for structure elucidation of relatively complex molecules and to follow reaction kinetics in real time [9, 10]. It was shown that benchtop NMR spectroscopy can be used to determine the presence and contents of sildenafil in adulterated dietary supplements [11]. The latter study indicated that fingerprints of active substances can be recognized within a product matrix and that some signals are selective and strong enough to be used for quantification. It was also shown 3
that screening of herbal mixtures for synthetic cannabinoids can be performed using benchtop NMR spectroscopy [12]. In this challenging matrix, a full structural elucidation of the active substances is often not possible, but benchtop NMR is sensitive enough to pick up the presence of synthetic cannabinoids. It should be noted that sample preparation may be critical, especially for quantitative analysis and autosamplers are not yet available for the benchtop spectrometer format. Another study showed that benchtop NMR spectroscopy can be applied to distinguish drugs originating from illegal production sites [13]. Even the subtle difference between pseudo-ephedrine
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and ephedrine could be spectrally highlighted from the difference in J-coupling between the protons neighbouring the alcohol. Advanced signal processing may be required, as shown for the differentiation between types of ground coffees [14].
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Should the resolution of the benchtop-acquired spectra be too low to yield direct molecular
information, the acquired data may still be well-suited for a chemometrics approach to analyse
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similarity, as is done for instance with (near-)infrared spectroscopy [15]. Indeed, edible oils could be distinguished by a chemometrics analysis of benchtop NMR spectroscopic data, allowing even a
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quantitative model to be made to determine hazelnut oil content in olive oil [16]. Other examples include the fingerprinting of essential oils, [17], determination of the research octane number [18],
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distinction between beef and horse meat [19], as well as olive and hazelnut oils [20]. A chemometrics approach reduces the need for reference material. Furthermore, substances can be identified
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without expert interpretation of the spectra. So it seems that benchtop NMR spectroscopy could be a cost-effective extension to the equipment of
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quality control laboratories and laboratories involved in screening for unknown substances, such as the Official Medicine Control Laboratories [21]. What remains to be shown is that the methodology is robust and can be applied to a wider variety of products. With this in mind, in this work we investigate the application of benchtop NMR spectroscopy in the analysis of pharmaceutical products and street drugs. The identification and quantification of active substances is assessed using a set of over-the-counter pharmaceutical products as a reliable source of products. Subsequently, a 4
chemometrics analysis of the obtained spectra is presented, as well as an application of the validated method, using a 1.4 T benchtop NMR spectroscopy to popular street drugs and pharmaceutical products from the illegal market.
2. EXPERIMENTAL 2.1 Materials Acetaminophen, acetylsalicylic acid, caffeine, diclofenac sodium salt, ibuprofen, naproxen sodium
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salt, gamma-butyrolacton, acetanilide, 3,4,5-trichloropyridine, maleic acid, 1,3,5-trimethoxybenzene, methanol-d4, chloroform-d, deuterium oxide and dimethyl sulfoxide-d6 were all obtained from Sigma Aldrich (Zwijndrecht, the Netherlands). Cocaine HCl was obtained from Verenigde Pharmaceutische
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Fabrieken (Apeldoorn, Holland). Gamma hydroxybutyric acid (GHB) was prepared by alkaline
hydroxylation of gamma-butyrolacton [22]. Formic acid (p.a.), acetonitrile (p.a.) and ammonium
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hydroxide (p.a.) were obtained from Merck (Darmstadt, Germany). Water was obtained (ULC/MS) from Biosolve (Valkenswaard, NL). Leucine encephalin was obtained from Waters (Etten-leur, NL),
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sodium formate was obtained from Sigma Aldrich (Zwijndrecht, NL). The over-the-counter pharmaceuticals paracetamol (acetaminophen) 500 mg, aspirin (acetylsalicylic acid) 100 mg,
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diclofenac 12.5 mg, naproxen 220 mg, ibuprofen 200 mg, loperamide 2 mg, bisacodyl 5 mg and mebendazole 100 mg were obtained from a local drugstore. The streetdrugs containing cocaine and
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GHB were obtained from the Netherlands Forensic Institute (NFI). The erectile dysfunction and slimming products from the illegal market, as well as the sildenafil, tadalafil and sibutramine
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reference standards were obtained from the Health and Youth Care Inspectorate (IGJ).
2.2 UV-VIS spectroscopy
A Thermo Scientific Evolution 201 spectrometer was used (Thermo Fisher Scientific, Breda, the Netherlands), operated by Thermo Scientific Insight software and operating in absorbance mode, using an integration time of 1.00 sec., a bandwidth of 1 nm and 1 cm quartz cuvettes. 5
2.3 Benchtop NMR spectroscopy Tablets and capsule contents of pharmaceutical products were ground and mixed and an amount equivalent to the amount of about 10 milligrams of active substance was placed in a 2.0 mL amber vial. A calibrated microbalance with two digits was used to weigh. 1.0 mL of solvent was added using a positive displacement pipette, the sample was heated to 55⁰ C for 10 minutes and placed in an ultrasonic bath for 20 minutes. For quantitative analysis, 5 mg of internal standard was added to the
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sample. An internal standard was chosen from the tested set with minimal spectral overlap. The sample was filtered over cotton and approximately 0.5 mL of sample was transferred into a standard 5 mm NMR tube. Spectra were acquired using a Magritek Spinsolve carbon benchtop spectrometer
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(Magritek, Aachen, Germany) operating at 60.96 MHz for 1H (1.4 T). The spectrometer was operated by Spinsolve software version 1.6.3 (Magritek, Aachen, Germany) and spectra were processed using
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Mnova, version 11.0.0 (Mestrelab Research, Santiago de Compostela, Spain). Spectra were acquired with 32768 points, a bandwidth of 5, scans ranging from 4-40, repetition time ranging from 7 s to 60
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s, and pulse angle ranging from 30⁰ to 90⁰, at ambient temperature and calibrated on the solvent peak; 3.31 ppm for methanol-d4, 7.24 ppm for chloroform-d, 4.80 for deuterium oxide and 2.50 ppm
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for dimethyl sulfoxide-d6. Active substance content was calculated according to Rundlof et al. [23]. The efficiency of extraction of the active ingredients was determined by determining the dose from
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over-the-counter pharmaceutical products.
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2.4 LC-MS
Two half-dose units of the pharmaceutical products were homogenized in 10.0 mL of methanol. These product solutions were diluted 100-fold (naproxen, acetaminophen, ibuprofen, diclofenac) or 50-fold (aspirin), in t0-eluent (containing 87% component A; 5 mM ammonium formate, adjusted to pH 3.0 using formic acid, and 13% component B; 0.1% formic acid in acetonitrile). These were 0.2 µm filtered before injection in the LC-MS. The chromatographic separation was performed using a 6
Waters AcquityTM ultra-performance liquid chromatography (UPLC) system fitted with a HSS C18 column (150 mm x 2.1 mm, 1.8 µm; Waters Chromatography B.V., Etten-Leur, NL). Detection of the analytes was carried out using a Waters SynaptTM G2 quadrupole time of flight (QTOF) mass spectrometer (Waters Chromatography B.V., Etten-Leur, NL) with a Z-spray electrospray ionization (ESI) source operating in the positive ion mode. MSE data (0.1 s MS1 at energy 10 eV, followed by 0.1 s MS2 with an energy ramp op 15-50 eV) were acquired in the resolution mode (≥ 20.000 FWHM). Chromatographic and mass data were acquired and analysed using Waters MassLynx v4.1 software.
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The presence of an analyte was confirmed by retention time, MS and MS/MS in comparison to the reference standard. The analytes were quantified from peak areas interpolated on the reference
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standard calibration curve. The method was applied before to quantify active ingredients [24].
2.5 Chemometrics classification method
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A method was developed and applied to identify products based on NMR spectra acquired on samples of these products using the software package R [25]. Three replicate spectra of three tablet
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extracts were used per product. To avoid the effects of the spectral contributions of protons exchanged in methanol-d4 and water, the spectral ranges of 3.2-3.4 and 4.6-5.0 were excluded, after
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calibrating spectra on solvent peaks. The distance between two spectra 𝑆1 and 𝑆2 is calculated as ∫|𝑆1 (𝑓) − 𝑆2 (𝑓)|d𝑓; or more precisely, since the spectra are observed only over a grid of
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frequencies {𝑓1 , 𝑓2 , … , 𝑓𝐾 }, the distance between 𝑆1 and 𝑆2 is obtained by using the trapezoidal rule to approximate the integral:
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𝐷(𝑆1 , 𝑆2 ) = ∑𝐾 𝑖=2
(|𝑆1 (𝑓𝑖−1 )−𝑆2 (𝑓𝑖−1 )|+|𝑆1 (𝑓𝑖 )−𝑆2 (𝑓𝑖 )|) 2
(𝑓𝑖 − 𝑓𝑖−1 ).
The replicate spectra 𝑆1′ , 𝑆2′ , 𝑆3′ , obtained on the same dose unit of the same substance are used with the purpose of determining whether the substance corresponds to any of the substances represented in the training data set. The set contains replicate triples (m = 1,2,3) of spectra from 𝑛 substances, say (𝑆𝑠,𝑢,1 , 𝑆𝑠,𝑢,2 , 𝑆𝑠,𝑢,3 ), 𝑠 = 1,2, … , 𝑛, 𝑢 = 1,2, … , 𝑛𝑠 , with 𝑢 standing for a unit and 𝑛𝑠
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′ for the number of units of substance 𝑠. The distances 𝐷(𝑆𝑚 , 𝑆𝑠,𝑢,𝑚′ ) are calculated for all 𝑚, 𝑚′ , 𝑠, 𝑢
and ranked increasingly to obtain: ′ ′ ′ 𝐷(𝑆𝑚 , 𝑆𝑠1 ,𝑢1 ,𝑚1′ ) ≤ 𝐷(𝑆𝑚 , 𝑆𝑠2 ,𝑢2 ,𝑚2′ ) ≤ 𝐷(𝑆𝑚 , 𝑆𝑠3 ,𝑢3 ,𝑚3′ ) ⋯ 1 2 3
where possibly 𝑚1 = 𝑚2 or even 𝑚1 = 𝑚2 = 𝑚3 , and similarly for 𝑠1 , 𝑠2 , 𝑠3 . Intuitively, the substances indicated in this list by 𝑠1 , 𝑠2 , 𝑠3 , which as suggested may very well be one the same, should be the most likely candidates for the substance in the product. If indeed 𝑠1 = 𝑠2 = 𝑠3 , as
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expected if the substance was already included in the training data set and there were at least three replicate measurements on it, the consensus is considered to be of level 1, and there is only one
substance among the three top candidates. If 𝑠1 = 𝑠2 ≠ 𝑠3 , the consensus is of level 2, there being two substances among the top three candidates. Finally, if 𝑠1 , 𝑠2 , 𝑠3 are all distinct, the consensus is
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of level 3. However, even if the consensus is of level 1, it does not follow that the distances of the new spectra to the spectra of the top candidates are small. Even if the substance is absent in the
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dataset, there will always be top candidates. Thus, in order to judge the plausibility of 𝑠1 as a
′ ,𝑆 #{ ((𝑢,𝑚),(𝑢′ ,𝑚′ )): 𝑢≠𝑢′ ∧ 𝐷(𝑆𝑠1 ,𝑢,𝑚 ,𝑆𝑠 ,𝑢′ ,𝑚′ )≥𝐷(𝑆𝑚 1 𝑠1 ,𝑢1 ,𝑚′1 )} 1
#{ ((𝑢,𝑚),(𝑢′ ,𝑚′ )): 𝑢≠𝑢′ }
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𝑞𝑠1 ,𝑚1 ≔
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candidate for a substance, the following empirical tail probability is used:
Where 𝐴 is a set and #𝐴 is used to denote the number of elements in that set. If q is big enough, it
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suggests that the distance of measurement 𝑚1 of the given substance to measurement 𝑚1′ from unit 𝑢1 of the candidate substance 𝑠1 is within the margins of the distances between the measurements
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in our database made on different units of 𝑠1 . Similarly, we consider tail probabilities 𝑞𝑠2 ,𝑚2 , 𝑞𝑠3 ,𝑚3 , etc., in order to judge the plausibility of 𝑠2 , 𝑠3 , etc., as candidates for the given substance, in conjunction with the ranking.
3. RESULTS To be able to make use of a benchtop NMR spectrometer in determining the presence and quantity of active substances in substandard and falsified pharmaceutical products and street drugs, a method 8
was developed and validated using reference active substances and commercially available over-thecounter pharmaceutical products. Validation comprised of analysing identity, linearity, sensitivity, reproducibility and recovery. Subsequently, a series of spectra obtained with the validated method were used to develop a model to identify substances present in over-the-counter pharmaceutical products. Finally, the validated method was applied to samples of illegal drugs and medicines from the illegal market.
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3.1 Validation 3.1.1 Identity
The presence of acetaminophen, aspirin, ibuprofen, diclofenac and naproxen in over-the-counter
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pharmaceutical products could be determined by comparing 1H-spectra of methanol-d4 extracts with those of reference standards. All proton signals were retrieved at the corresponding chemical shift
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within a 0.01 ppm range, in the correct peak area ratio, see Figure 1 and Table 1 and supplementary material Figures S1-S5. The low resolution NMR spectra are specific enough to distinguish the active
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substances. Peak area ratios were within 5% for the dominant signals. Hardly any other signals besides those of the active substance were observed in the extract spectra. An additional small signal
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was observed at 1.3 ppm in the pharmaceutical products, indicating a component likely originating from a common excipient. Spectra of caffeine were determined in D2O, as caffeine was used to
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determine the sensitivity and linearity (Table 1).
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3.1.2 Linearity and sensitivity
The spectra of caffeine, acetaminophen, aspirin, ibuprofen, diclofenac and naproxen show a linear relationship when plotting peak area versus the concentration of the compound. Using at least seven concentrations ranging from 1.0 to 22 mg/mL, an R2 of at least 0.99 was obtained for all compounds, see supplementary material Figure S6. The linear relationship between concentration and detector response is a requirement to quantify by making use of a single solution with an internal standard. 9
The proton signal at 7.5 ppm of 1 mg caffeine in 1 mL of chloroform-d was found with a signal-tonoise ratio of 7.2 when using a 4 scans, a 90⁰ pulse angle and 15 s repetition time 1H-experiment. Without optimizing the experiment, it should be possible to detect the presence of a pharmacologically active component at a concentration of about 1 mg/mL. The limit of detection can obviously be reduced by increasing the number of scans and optimizing the pulse length and repetition time. Nevertheless, for determining the presence of active substances in substandard and falsified pharmaceuticals and street drugs, where these are often present in milligrams per dose unit,
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the sensitivity of the benchtop NMR spectrometer should not be an issue.
3.1.3 Internal standards
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In order to use the benchtop NMR spectrometer to quantify a variety of active substances, a series of internal standards is required, in such a way that an internal standard can be chosen that provides a
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signal not overlapping with those of the substance of interest. Rundlof et al. have elegantly described such a series [23, 26], and we have adapted it in this study. We have verified the primary standard,
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acetanilide by UV-VIS spectroscopy and used this subsequently to determine the purity of the secondary standards, 3,4,5-trichloropyridine, maleic acid, 1,3,5-trimethoxybenzene, by benchtop
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NMR spectroscopy (Table 2). Absorption spectroscopy was performed according to the European Pharmacopeia [27] and was chosen as it is widely available and often used in quality control of
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reference material. Alternatively, commercially available certified reference material could be used. Additionally, this shows that benchtop NMR spectroscopy can be used to verify the quality of a
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reference standard.
3.1.4 Recovery and reproducibility
The tested pharmaceutical active ingredients acetaminophen, aspirin, ibuprofen, diclofenac and naproxen as well as the internal standards acetanilide, 3,4,5-trichloropyridine, maleic acid, 1,3,5trimethoxybenzene were well soluble in in methanol-d4. In chloroform-d, the substances acetanilide, 10
trichloropyridine, naproxen and diclofenac did not completely dissolve. In DMSO-d6, naproxen and ibuprofen did not completely dissolve either. Therefore, methanol-d4 was chosen as the first solvent of choice for the extraction of the active ingredients from the over-the-counter pharmaceutical products. Subsequently, the recoveries were determined for the active substances, see Table 3. All recoveries were well above 90%. The measured 1H-spectra show a high degree of similarity, as shown in Figure 1. However, when using the Powerscan method, with a 90⁰ pulse and a 15 s repetition time, of the Spinsolve software
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to acquire 1H-spectra of the pharmaceutical product extracts, considerable variation in peak areas was observed (Table 3). Apparently, the slow relaxation of the protons played a role in the cause of the variation as decreasing the pulse angle and increasing the repetition time had a positive effect on
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and to less than or equal to 5% in general, see Table 3.
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the reproducibility and accuracy. The optimized settings lowered the RSD to less than 10% in all cases
3.2 Quantitative analysis over-the-counter pharmaceutical products
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Eventually, the benchtop NMR spectrometer will be applied to quantify falsified and substandard medicines. These products may include exotic active substances for which reference standards are
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not easily available and therefore internal standards are used. To validate this approach, the content of the active substances in the over-the-counter pharmaceutical products acetaminophen, aspirin,
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ibuprofen, diclofenac and naproxen, was determined. The determined values were compared to those determined with LC-QTOF-MS, see Table 4. A QTOF-MS detector is not known for the stability
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of its response, but is used as a working horse for determining the identity of active substances in falsified and substandard products [3, 24]. It does allow for a semi-quantitative estimation of the contents to be given, which is often good enough to determine whether the active substance is present in sufficient amounts for a physiological effect and to determine the status of a product. The benchtop NMR spectrometer is foreseen to be used in addition to LC-MS so its performance should at least be equal. Both by NMR spectroscopy as LC-QTOF-MS, the declared contents of the 11
pharmaceutical products were verified. In general, the error in the measurements is much smaller in the NMR spectroscopic measurements compared to those using LC-QTOF-MS.
3.3 Classification of 1H spectra In order to identify products based on NMR spectra acquired on samples of them, without spectral interpretation, a classification procedure was evaluated. Besides the spectra of the products mentioned above and their corresponding reference standards, spectra of loperamide 2 mg,
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bisacodyl 5 mg and mebendazole 100 mg were added to increase the dataset. First, a definite classification rule was adopted. Spectra were ranked on similarity and having determined the top
three candidates, their distance, consensus, and tail probability were determined. If the consensus is
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of level 1 and the tail probability is≥ 0.75, then we propose the top candidate as the substance;
otherwise, we refrain from proposing any substance, an event described as a referral. The level of
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0.75 is chosen arbitrarily and can be fine-tuned once more substances are added to the database. Two exercises of leave-one-out cross-validation were carried out. First, the active substance of a dose
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unit was predicted based on the training data set obtained, by removing the data from that dose unit from the data set. In each case, the training data set contains data on other dose units of the
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substance being investigated, so it is expected that the method is able to identify the correct substance among the substances represented in the training data. A correct identification yields a
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consensus of level 1 and a high tail probability. Secondly, the active substance of a dose unit was predicted based on the training data set obtained by removing all the data on that substance from
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the data set. Then, the substance being investigated is not represented in the data set and one would expect the consensus to be poor and the tail probabilities to be small. In the first exercise the consensus is of level 1 in all cases and the tail probabilities are generally high (above 0.75) except in four cases; see Table 5 and Figure 2. The minimum distances are generally low (below 0.2). So if we referred all the dubious cases we might have to count with about 16% referrals, but the proportion of incorrect classifications might be close to 0. In the second exercise the 12
consensus is mostly of level 1, but the probabilities are practically all ≤ 0.5 (data not shown); if we were to follow the procedure strictly, not a single case would have been positively classified and they would have all been referred to an expert. When combining the two exercises, 20 true positives, 4 false negatives, 24 true negatives and 0 false negatives are obtained, yielding a Matthews correlation coefficient of 0.84. These results indicate that in the absence of expert interpretation, the NMR spectra obtained with the benchtop spectrometer can be used to verify the presence of an active
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substance in a pharmaceutical product using a chemometrics classification method.
3.4 Application to street drugs
Similar to the pharmaceutical products, 1H-spectra were obtained from popular street drugs such as
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cocaine and 4-hydroxybutyric acid (GHB) (Table 1 and Figure 3). As the substance GHB is a simple
molecule and the product is mostly a watery GHB containing solution, a simple and clean spectrum is
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obtained of GHB street drugs. The 1H-spectrum of street drug cocaine is more complex but matched the spectrum of reference cocaine HCl. For prosecution purposes, under Dutch law, it is sufficient to
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not investigated here.
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verify the presence of a listed narcotic in a street drug. Quantitation is not required and therefore
3.5 Application to pharmaceutical products from the illegal market
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Some erectile dysfunction products and a slimming product from the illegal market, sampled by the Health and Youth Care Inspectorate for market control reasons were analysed using the optimized
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and validated method described above. 1H-NMR spectra were acquired in DMSO-d6 (tadalafil) and methanol-d4 (sildenafil and sibutramine) extracts and compared to those of reference standards. All proton signals were retrieved at the corresponding chemical shift, in the correct ratio, verifying the identity of the active substances present (Figure 4). The doses of the active substances were determined with benchtop NMR spectroscopy using the same method as for the over-the-counter
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products, see Table 6. As a control experiment, the doses of tadalafil and sildenafil in respectively Cialis and Viagra were determined in products obtained from a pharmacy. The measured quantity of tadalafil in Cialis corresponds to the declared dose of the product. The determined amount sildenafil in Viagra is somewhat small, which may be explained by the quality of the spectrum. As sildenafil is a rather large and complex molecule, at least compared to the over-thecounter substances, a crowded spectrum is obtained. Not many isolated peaks are available that can be used to correlate to the signal of the internal standard. We chose to use the aromatic signals from
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7.0 to 8.7 ppm, which are the combined signals of three different protons and therefore the signalto-noise ratio is rather low. Perhaps sildenafil is at the border of the substances that can be quantified using our method.
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The dose in products from the illegal market do not necessarily match their declaration, a
phenomenon observed earlier with such products [3]. This unreliable dosing also explains the larger
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errors in the values obtained for the samples from the illegal market because the dose also varies between tablets. Thirdly, this becomes also apparent from the results obtained with the LC-MS.
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Where the match between NMR-based data and the LC-MS based data was good with the over-thecounter products and the erectile dysfunction products obtained at the pharmacy , now there is a
falsified products.
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4. DISCUSSION
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large discrepancy. We believe this is caused by the large inter-tablet differences in dosage in the
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A benchtop NMR spectrometer does not offer all possibilities of a superconducting magnet containing spectrometer. Breast implant impurities [28], and dermal filler crosslinking [29], are typical examples of samples that are outside of analytical reach at 60 MHz NMR spectroscopy (data not shown): a higher resolution is required to obtain the sensitivity to observe contributions of minor components. Nevertheless, fingerprint spectra can be obtained with benchtop resolution, allowing to determine the presence of components in a challenging matrix [12], or the comparison of samples using a chemometrics approach, in a way similar to other medium resolution spectroscopic 14
techniques. We have validated an analytical method based on benchtop NMR spectroscopy and applied it to illegal drugs and pharmaceutical products from the illegal market. We have shown the possibility of a chemometrics approach to determine presence of a substance without expert spectra interpretation. It should be pointed out that one cannot expect an automatic, data-driven method of identification based on NMR spectroscopy to identify substances not yet represented in the data used to build it, in particular mixtures of substances already represented. One of the purposes of the method proposed here is to refer substances not yet included in the data set to an expert, avoiding
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undue referral as much as possible. Small differences in signal to noise appeared not to be an issue in the current method, maybe because similar amounts of active substances, corresponding to 10 mg, were extracted.
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In the field of substandard and falsified medicines, we have shown that benchtop NMR spectroscopy offers an additional chemical identification technique besides the more commonly used LC-MS and
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vibration spectroscopy. The absence of a superconducting magnet brings NMR spectroscopy within financial and logistical reach. Furthermore, it offers the opportunity to quantify active substances in
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absence of a reference standard, required for LC-MS and vibration spectroscopy. This works rather well for the applications shown here, as long as significant isolated signals of both the component of
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interest and an internal standard are available. The limits of the analysis of pharmaceutical products are reached when products have more complex spectra, be it from multi-proton active substances or
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matrix components and lack of dispersion impedes proper peak assignment and area analysis. At that point the chemometrics model might be helpful, provided the corresponding database contains
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many substances in combination with enough excipients. Still, the presence of a large amount of water may result in a solvent peak interfering with peaks of interest or a reduction in the sensitivity and quality of the spectrum. The results presented here are in line with the recent work by Assemat et al. [30]. In that work, the potential of low field NMR spectroscopy in the analysis of falsified medicines was elegantly shown in
15
comparison with high field obtained data. In the current work we have tried to relate more to chromatography based methods which is more commonly applied by medicine control laboratories. In a similar way to the pharmaceutical products, the quality of reference standards can be verified. NMR spectroscopy offers the advantage over UV-VIS spectroscopy in standard verification in that the presence of water is directly observed in the spectrum, relative to the spectrum of the internal standard alone. The attraction of water is one of the primary causes for a decrease in purity. The analysis of falsified and substandard medicines is not easy as the composition of such products is
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never entirely known. Additionally, various aspects may be of interest; the presence and identity of active substances, impurities and excipients and their amounts relative to the levels of toxic concern. Hence, orthogonal techniques should be applied and results combined to be able to estimate the risk
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for the patients. Benchtop NMR spectroscopy provides an affordable additional technique based on an independent physical principle and is therefore truly orthogonal to vibration spectroscopy and
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chromatography.
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5. CONCLUSION
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Within its resolution and sensitivity limits, benchtop NMR spectroscopy is useful in determining the identity of active ingredients in pharmaceutical products, street drugs and other products from the
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illegal market. Here, we developed a chemometrics method to identify the substances in the pharmaceutical products, reducing the need for expert interpretation of the acquired NMR spectra.
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Furthermore, using an optimized and validated method, the contents of the active ingredient can be determined with an uncertainty of 10%.
Conflicts of interest The authors declare no conflicts of interest.
ACKNOWLEDGEMENTS 16
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We thank NFI and IGJ for providing samples.
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Figure 1: Overlay of 1H-NMR spectra of methanol-d4 extracts of over the counter pharmaceutical products obtained at 60 MHz, using the Powerscan settings. Spectra 1-9 in red are from aspirin, spectra 10-18 in olive are from diclofenac, spectra 19-27 in green are from ibuprofen, spectra 28-36 in blue are from naproxen, and spectra 37-45 in purple are from acetaminophen. Methanol solvent
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protons are visible at 3.31 and water protons at 4.78 ppm.
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Figure 2: Details from the chemometric analysis of three spectra from the over-the-counter pharmaceutical products. For diclofenac, naproxen and paracetamol a spectrum of a reference standard is included. The substance average tail probabilities are shown (A) and the substance average minimum distances (B) with their standard deviation.
A
1.2
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1.0 0.8 0.6 0.4 0.2
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average tail probablity
1.4
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0.0
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B
0.3 0.2
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0.4
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average minimum distance
0.5
0.1 0.0
-0.1
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Figure 3: 1H-NMR spectra of a DMSO-d6 extract of a street sample (in red) and reference (in blue) of cocaine (A) and a D2O diluted sample of a street sample (in red) and reference (in blue) of GHB (B).
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Spectra were acquired at 60 MHz.
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Figure 4: 1H-NMR spectra of a DMSO-d6 extract of Tadaga 40 mg, above the spectrum of a tadalafil reference standard (A), a methanol-d4 extract of Aurogra-100, above the spectrum of a sildenafil reference standard (B), and a methanol-d4 extract of Slimex 15 mg above the spectrum of a sibutramine reference standard (C). Spectra were acquired at 60 MHz in presence of 3,4,5-
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trichloropyridine as internal standard.
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Tables and Figures
Table 1: The used active ingredients and their 1H-NMR data as determined at 60 MHz. s = singlet, d = doublet, dd = double doublet, q = quartet, m = multiplet. Compound
Structure
Chemical shifts (splitting,
Solvent
number of 1H) Acetaminophen
2.08 ppm (s, 3H)
methanol-d4
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6.72 ppm (d, 2H, J = 9 Hz) 7.30 ppm (d, 2H, J = 9 Hz) Aspirin
2.28 ppm (s, 3H)
methanol- d4
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7.13 (d, 1H, J = 8 Hz) 7.31 (t, 1H)
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7.62 (t, 1H)
8.02 ppm (d, 1H, J = 8 Hz) 3.32 ppm (s, 3H)
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Caffeine
D2O
3.50 ppm (s, 3H)
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3.95 ppm (s, 3H)
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Cocaine
7.91 ppm (s, 1H) 2.29 ppm (m, 6H) 2.86 ppm (s, 3H) 3.64 (s, 3H) 4.09 ppm (bd, 2H) 5.57 ppm (q, 1H, J = 8 Hz) 7.3-8.0 ppm (m, 5H)
24
methanol- d4
Diclofenac
3.63 ppm (s, 2H)
methanol- d4
6.3-7.5 ppm (m, 7H)
GHB
1.51-1.99 ppm (m, 2H)
D2O
2.11-2.37 ppm (m, 2H) 3.59 ppm (t, 2H, J = 7 Hz) 0.89 ppm (d, 6H, J = 6 Hz)
methanol- d4
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Ibuprofen
1.43 ppm (d, 2H, J = 7 Hz) 1.62-2.06 (m, 1H)
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2.45 ppm (d, 2H, J = 7 Hz) 3.67 ppm (q, 1H, J = 7 Hz)
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7.15 ppm (m, 4H) Naproxen
1.49 ppm (d, 3H, J = 7 Hz)
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3.65 ppm (m, 1H)
6.9-7.8 ppm (m, 6H)
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3.88 ppm (s, 3H)
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methanol- d4
Table 2: The internal standards used, their 1H-NMR data as determined at 60 MHz and their purity as determined by UV-VIS in methanol (acetanilide) and 1H-NMR spectroscopy in DMSO-d6 relative to acetanilide (all others). s = singlet, m = multiplet. Compound
Structure
Chemical shifts
Purity (%)
(splitting, number of 1
H)
acetanilide
2.12 ppm (s, 3H)
98 5
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7.32 ppm (m, 5H) 6.31 ppm (s, 2H)
101 2
3,4,5-trichloropyridine
8.76 ppm (s, 2H)
87 3
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maleic acid
6.09 ppm (s, 3H)
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1,3,5-
3.77 ppm (s, 9H)
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trimethoxybenzene
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101 1
Table 3: Recoveries of 10 mg of reference standards, determined over at least four samples, and the reproducibility of dose determinations determined over 15 spectra (5 dose units measured in triplicate) of pharmaceutical products, using 3,4,5-trichloropyridine as internal standard. Powerscan: 90⁰ pulse angle, 15 s repetition time and 40 scans, optimized settings: 60⁰ pulse angle, 30 s repetition time and 32 scans. Mean RSD Powerscan
Mean RSD optimized
(%)
settings (%) 4.8
104 3
12.1
Aspirin
103 2
12.8
Diclofenac
97 4
7.6
Ibuprofen
106 1
18.5
Naproxen
96 2
5.3
9.2 5.3 3.6
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Acetaminophen
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Recovery (%)
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Substance
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5.1
Table 4: Quantitation of the over-the-counter pharmaceutical products, using benchtop 1H-NMR spectroscopy, using 3,4,5-trichloropyridine as internal standard and LC-QTOF-MS. The NMR analyses were performed with five independent measurements, the LC-MS analyses were performed with two dose units measured in duplicate. Pharmaceutical
Declared dose (mg per
Dose determined by
Dose determined by
product
dose unit)
benchtop NMR
LC-QTOF-MS (mg per
spectroscopy (mg per
dose unit)
Acetaminophen
500
523 7
100
107 3
Lot: 3043510
Aspirin
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Exp. 08-2022
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Charge: BTAJRAO Exp. 01-2021
108 30
11 2
(free base)
(free base)
211 26
204 13
201
197 6
240 37
(220 Na-salt)
(free base)
(free base)
Lot: 3337244
(12 .5 K-salt)
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Exp. 09-2020
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11
200
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Lot: 3230694
533 32
11 0.2
Diclofenac
Ibuprofen
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dose unit)
Exp. 09-2022
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Naproxen
Lot: 3008971 Exp. 01-2022
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Table 5: Results of active substance prediction using the chemometrics model. The model was made from spectra acquired from extracts of pharmaceutical products, containing various active substances obtained in triplicate. Tail probability
Minimum distance
0.99
0.01
Acetaminophen
1
0.99
0.01
Acetaminophen
1
0.99
0.01
Acetaminophen
1
0.37
0.02
Aspirin
1
1.00
Aspirin
1
0.95
Aspirin
1
0.83
Bisacodyl
1
Bisacodyl
1
Bisacodyl
1
Diclofenac
1
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0.01 0.01 0.02 0.03
0.92
0.03
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1.00
0.67
0.25
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Level of consensus 1
0.95
0.14
1
1.00
0.13
1
0.99
0.13
Diclofenac
1
0.82
0.17
Loperamide
1
0.72
0.16
Loperamide
1
1.00
0.08
Loperamide
1
0.99
0.08
Mebendazole
1
0.95
0.10
Diclofenac
Diclofenac
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Acetaminophen containing analgesic Acetaminophen containing analgesic Acetaminophen containing analgesic Acetaminophen reference standard Aspirin containing analgesic Aspirin containing analgesic Aspirin containing analgesic Bisacodyl containing laxative Bisacodyl containing laxative Bisacodyl containing laxative Diclofenac containing pain reliever Diclofenac containing pain reliever Diclofenac containing pain reliever Diclofenac reference standard Loperamide containing antidiarrhoeal drug Loperamide containing antidiarrhoeal drug Loperamide containing antidiarrhoeal drug Mebendazole containing anthelminitic
Predicted substance Acetaminophen
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Spectrum
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1
0.94
0.10
Mebendazole
1
0.95
0.10
Naproxen
1
0.95
0.03
Naproxen
1
0.99
0.03
Naproxen
1
0.97
0.03
Naproxen
1
0.21
0.09
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Mebendazole
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Mebendazole containing anthelminitic Mebendazole containing anthelminitic Naproxen containing pain reliever Naproxen containing pain reliever Naproxen containing pain reliever Naproxen reference standard
30
Table 6: Benchtop NMR analysis of pharmaceutical products from the illegal market. Three dose units were analysed independently, using 3,4,5-trichloropyridine as internal standard. The average dose and its standard deviation are reported. For comparison, the LC-MS based analysis is added. Two separate dose units were analysed. N. d.: not determined. Product name
Declared active substance
Active substance and dose
Active substance and
and dose (mg per dose
by benchtop NMR
dose by LC-QTOF-MS
unit)
spectroscopy
(mg per dose unit)
Cialis 20 mg
Tadalafil 20 mg
Tadalafil 19 3
Sildenafil 50 mg
Sildenafil 40 5
Charge:
-p
A098388 Exp. 11-2006
N. d.
N. d.
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Viagra 50 mg
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(mg per dose unit)
Charge:
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7078708N Exp. 09-2012
Sildenafil 100 mg
Sildenafil 98 22
Sildenafil 87 17
Sildenafil 20 3
Sildenafil 99 20
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Aurogra-100 Lot:
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S0214M112/9e Exp. 11-2012
Sildenafil 100 mg
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Lovegra 100 mg
Lot: MF010/09 Exp. 09-2012
31
Sildenafil 100 mg
Sildenafil 50 5
Sildenafil 106 21
Slimex 15 mg
Chromium picolinate,
Sibutramine 12 1
Sibutramine 17 3
Lot: 56-20
Tea yellow alkali, alcar, L-
Exp. 05-2013
carnithine
Tadaga 40 mg
Tadalafil 40 mg
Tadalafil 72 7
Tadalafil 31 6
Malegra-100 Lot: E-134
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Exp. 05-2011
Lot: E-139
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Exp. 05-2013
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PII:
S0731-7085(19)31782-0
DOI:
https://doi.org/10.1016/j.jpba.2019.112939
Reference:
PBA 112939
To appear in:
Journal of Pharmaceutical and Biomedical Analysis
Received Date:
18 July 2019
Revised Date:
7 October 2019
Accepted Date:
17 October 2019
Please cite this article as: Keizers PHJ, Bakker F, Ferreira J, Wackers PFK, van Kollenburg D, van der Aa E, van Beers A, Benchtop NMR spectroscopy in the analysis of substandard and falsified medicines as well as illegal drugs, Journal of Pharmaceutical and Biomedical Analysis (2019), doi: https://doi.org/10.1016/j.jpba.2019.112939
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Benchtop NMR spectroscopy in the analysis of substandard and falsified medicines as well as illegal drugs
Peter H. J. Keizers*, Frank Bakker, José Ferreira, Paul F. K. Wackers, Dion van Kollenburg, Eva van der Aa, Amy van Beers
National Institute for Public Health and the Environment, P. O. Box 1, 3721 MA, Bilthoven, the
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Netherlands
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*Corresponding author. Tel.: +31 30274 2587. E-mail address: [email protected] (P. H. J. Keizers)
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Graphical Table of Contents
We have assessed an analytical method for the analysis of active ingredients in pharmaceutical products and illegal drugs, based on benchtop NMR spectroscopy. Within its resolution limits, benchtop NMR spectroscopy is useful in determining the identity and contents of the active ingredients in pharmaceutical products. Additionally, a simple chemometrics approach is shown to
1
be useful to classify spectra into active substances, reducing the need for expert interpretation of the spectra acquired.
Highlights We have developed an analytical method for the analysis of active ingredients in pharmaceutical products and illegal drugs, based on benchtop NMR spectroscopy.
Within its resolution limits, benchtop NMR spectroscopy is useful in determining the identity of the active ingredients in pharmaceutical products.
The content of active ingredient in pharmaceutical products can be determined with an error of 10% using benchtop NMR spectroscopy.
A chemometrics approach is shown to be useful to classify spectra in order to identify the active substances present in the sample, reducing the need for expert interpretation of the spectra acquired.
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Abstract
Substandard and falsified medical products may cause harm to patients and fail to treat the diseases
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or conditions for which they were intended. It is therefore required to have analytical methods
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available to assess medical product quality. Benchtop NMR spectroscopy provides a generic, inherently quantitative, analytical method capable of separating specific signals from those of a
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matrix. We have developed an analytical method for the analysis of active ingredients in pharmaceutical products and illegal drugs, based on benchtop NMR spectroscopy. Within its resolution limits, benchtop NMR spectroscopy is useful in determining the identity of the active
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ingredients in products containing acetaminophen, aspirin, caffeine, diclofenac, ibuprofen, naproxen,
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sildenafil, tadalafil and sibutramine, cocaine, and gamma hydroxybutyric acid, with a limit of detection of about 1 mg/mL. Furthermore, the content of the active ingredient can be determined with an error of 10%. Additionally, a chemometrics approach is shown to be useful to classify spectra in order to identify the active substances present in the sample, reducing the need for expert interpretation of the spectra acquired.
2
Keywords Benchtop NMR spectroscopy; falsified medicines; illegal drugs
1. INTRODUCTION Medical products that are falsified or in another way substandard may be of such poor quality that they may fail to treat the diseases or conditions for which they were intended [1]. They can even be harmful to the patient’s health. It is therefore required to recognize such products and take them
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from the market as quickly as possible. In order to do so, there is a need to have available analytical methods to assess medical product quality [2]. Generic methods offer value for money and should be applicable to determine identity and quantity of active substances, including those in illegal drugs
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and, possibly, medical devices. A semi-quantitative method is often sufficient, as the presence of an effective dose is more relevant to public health and for the legal status of a product than analytical
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perfection [3]. For new or exotic products, containing designer drugs or analogues of known active ingredients, reference standards are not always commercially available [4, 5]. This brings challenges
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to laboratories focussing on chromatography-based techniques for the verification of identity and to determine the quantity of the active ingredients. NMR spectroscopy offers the opportunity to fulfil
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all criteria listed above, by providing a generic, inherently quantitative, analytical method capable of separating specific signals from those of a matrix [6]. Nevertheless, NMR spectroscopy is an
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expensive technique in purchase and maintenance and requires dedicated and experienced operators. In recent years, considerable development in the field of benchtop NMR spectroscopy,
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seems to have overcome some of these concerns [7, 8]. Nowadays, low field benchtop spectrometers are used for structure elucidation of relatively complex molecules and to follow reaction kinetics in real time [9, 10]. It was shown that benchtop NMR spectroscopy can be used to determine the presence and contents of sildenafil in adulterated dietary supplements [11]. The latter study indicated that fingerprints of active substances can be recognized within a product matrix and that some signals are selective and strong enough to be used for quantification. It was also shown 3
that screening of herbal mixtures for synthetic cannabinoids can be performed using benchtop NMR spectroscopy [12]. In this challenging matrix, a full structural elucidation of the active substances is often not possible, but benchtop NMR is sensitive enough to pick up the presence of synthetic cannabinoids. It should be noted that sample preparation may be critical, especially for quantitative analysis and autosamplers are not yet available for the benchtop spectrometer format. Another study showed that benchtop NMR spectroscopy can be applied to distinguish drugs originating from illegal production sites [13]. Even the subtle difference between pseudo-ephedrine
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and ephedrine could be spectrally highlighted from the difference in J-coupling between the protons neighbouring the alcohol. Advanced signal processing may be required, as shown for the differentiation between types of ground coffees [14].
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Should the resolution of the benchtop-acquired spectra be too low to yield direct molecular
information, the acquired data may still be well-suited for a chemometrics approach to analyse
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similarity, as is done for instance with (near-)infrared spectroscopy [15]. Indeed, edible oils could be distinguished by a chemometrics analysis of benchtop NMR spectroscopic data, allowing even a
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quantitative model to be made to determine hazelnut oil content in olive oil [16]. Other examples include the fingerprinting of essential oils, [17], determination of the research octane number [18],
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distinction between beef and horse meat [19], as well as olive and hazelnut oils [20]. A chemometrics approach reduces the need for reference material. Furthermore, substances can be identified
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without expert interpretation of the spectra. So it seems that benchtop NMR spectroscopy could be a cost-effective extension to the equipment of
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quality control laboratories and laboratories involved in screening for unknown substances, such as the Official Medicine Control Laboratories [21]. What remains to be shown is that the methodology is robust and can be applied to a wider variety of products. With this in mind, in this work we investigate the application of benchtop NMR spectroscopy in the analysis of pharmaceutical products and street drugs. The identification and quantification of active substances is assessed using a set of over-the-counter pharmaceutical products as a reliable source of products. Subsequently, a 4
chemometrics analysis of the obtained spectra is presented, as well as an application of the validated method, using a 1.4 T benchtop NMR spectroscopy to popular street drugs and pharmaceutical products from the illegal market.
2. EXPERIMENTAL 2.1 Materials Acetaminophen, acetylsalicylic acid, caffeine, diclofenac sodium salt, ibuprofen, naproxen sodium
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salt, gamma-butyrolacton, acetanilide, 3,4,5-trichloropyridine, maleic acid, 1,3,5-trimethoxybenzene, methanol-d4, chloroform-d, deuterium oxide and dimethyl sulfoxide-d6 were all obtained from Sigma Aldrich (Zwijndrecht, the Netherlands). Cocaine HCl was obtained from Verenigde Pharmaceutische
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Fabrieken (Apeldoorn, Holland). Gamma hydroxybutyric acid (GHB) was prepared by alkaline
hydroxylation of gamma-butyrolacton [22]. Formic acid (p.a.), acetonitrile (p.a.) and ammonium
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hydroxide (p.a.) were obtained from Merck (Darmstadt, Germany). Water was obtained (ULC/MS) from Biosolve (Valkenswaard, NL). Leucine encephalin was obtained from Waters (Etten-leur, NL),
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sodium formate was obtained from Sigma Aldrich (Zwijndrecht, NL). The over-the-counter pharmaceuticals paracetamol (acetaminophen) 500 mg, aspirin (acetylsalicylic acid) 100 mg,
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diclofenac 12.5 mg, naproxen 220 mg, ibuprofen 200 mg, loperamide 2 mg, bisacodyl 5 mg and mebendazole 100 mg were obtained from a local drugstore. The streetdrugs containing cocaine and
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GHB were obtained from the Netherlands Forensic Institute (NFI). The erectile dysfunction and slimming products from the illegal market, as well as the sildenafil, tadalafil and sibutramine
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reference standards were obtained from the Health and Youth Care Inspectorate (IGJ).
2.2 UV-VIS spectroscopy
A Thermo Scientific Evolution 201 spectrometer was used (Thermo Fisher Scientific, Breda, the Netherlands), operated by Thermo Scientific Insight software and operating in absorbance mode, using an integration time of 1.00 sec., a bandwidth of 1 nm and 1 cm quartz cuvettes. 5
2.3 Benchtop NMR spectroscopy Tablets and capsule contents of pharmaceutical products were ground and mixed and an amount equivalent to the amount of about 10 milligrams of active substance was placed in a 2.0 mL amber vial. A calibrated microbalance with two digits was used to weigh. 1.0 mL of solvent was added using a positive displacement pipette, the sample was heated to 55⁰ C for 10 minutes and placed in an ultrasonic bath for 20 minutes. For quantitative analysis, 5 mg of internal standard was added to the
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sample. An internal standard was chosen from the tested set with minimal spectral overlap. The sample was filtered over cotton and approximately 0.5 mL of sample was transferred into a standard 5 mm NMR tube. Spectra were acquired using a Magritek Spinsolve carbon benchtop spectrometer
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(Magritek, Aachen, Germany) operating at 60.96 MHz for 1H (1.4 T). The spectrometer was operated by Spinsolve software version 1.6.3 (Magritek, Aachen, Germany) and spectra were processed using
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Mnova, version 11.0.0 (Mestrelab Research, Santiago de Compostela, Spain). Spectra were acquired with 32768 points, a bandwidth of 5, scans ranging from 4-40, repetition time ranging from 7 s to 60
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s, and pulse angle ranging from 30⁰ to 90⁰, at ambient temperature and calibrated on the solvent peak; 3.31 ppm for methanol-d4, 7.24 ppm for chloroform-d, 4.80 for deuterium oxide and 2.50 ppm
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for dimethyl sulfoxide-d6. Active substance content was calculated according to Rundlof et al. [23]. The efficiency of extraction of the active ingredients was determined by determining the dose from
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over-the-counter pharmaceutical products.
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2.4 LC-MS
Two half-dose units of the pharmaceutical products were homogenized in 10.0 mL of methanol. These product solutions were diluted 100-fold (naproxen, acetaminophen, ibuprofen, diclofenac) or 50-fold (aspirin), in t0-eluent (containing 87% component A; 5 mM ammonium formate, adjusted to pH 3.0 using formic acid, and 13% component B; 0.1% formic acid in acetonitrile). These were 0.2 µm filtered before injection in the LC-MS. The chromatographic separation was performed using a 6
Waters AcquityTM ultra-performance liquid chromatography (UPLC) system fitted with a HSS C18 column (150 mm x 2.1 mm, 1.8 µm; Waters Chromatography B.V., Etten-Leur, NL). Detection of the analytes was carried out using a Waters SynaptTM G2 quadrupole time of flight (QTOF) mass spectrometer (Waters Chromatography B.V., Etten-Leur, NL) with a Z-spray electrospray ionization (ESI) source operating in the positive ion mode. MSE data (0.1 s MS1 at energy 10 eV, followed by 0.1 s MS2 with an energy ramp op 15-50 eV) were acquired in the resolution mode (≥ 20.000 FWHM). Chromatographic and mass data were acquired and analysed using Waters MassLynx v4.1 software.
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The presence of an analyte was confirmed by retention time, MS and MS/MS in comparison to the reference standard. The analytes were quantified from peak areas interpolated on the reference
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standard calibration curve. The method was applied before to quantify active ingredients [24].
2.5 Chemometrics classification method
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A method was developed and applied to identify products based on NMR spectra acquired on samples of these products using the software package R [25]. Three replicate spectra of three tablet
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extracts were used per product. To avoid the effects of the spectral contributions of protons exchanged in methanol-d4 and water, the spectral ranges of 3.2-3.4 and 4.6-5.0 were excluded, after
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calibrating spectra on solvent peaks. The distance between two spectra 𝑆1 and 𝑆2 is calculated as ∫|𝑆1 (𝑓) − 𝑆2 (𝑓)|d𝑓; or more precisely, since the spectra are observed only over a grid of
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frequencies {𝑓1 , 𝑓2 , … , 𝑓𝐾 }, the distance between 𝑆1 and 𝑆2 is obtained by using the trapezoidal rule to approximate the integral:
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𝐷(𝑆1 , 𝑆2 ) = ∑𝐾 𝑖=2
(|𝑆1 (𝑓𝑖−1 )−𝑆2 (𝑓𝑖−1 )|+|𝑆1 (𝑓𝑖 )−𝑆2 (𝑓𝑖 )|) 2
(𝑓𝑖 − 𝑓𝑖−1 ).
The replicate spectra 𝑆1′ , 𝑆2′ , 𝑆3′ , obtained on the same dose unit of the same substance are used with the purpose of determining whether the substance corresponds to any of the substances represented in the training data set. The set contains replicate triples (m = 1,2,3) of spectra from 𝑛 substances, say (𝑆𝑠,𝑢,1 , 𝑆𝑠,𝑢,2 , 𝑆𝑠,𝑢,3 ), 𝑠 = 1,2, … , 𝑛, 𝑢 = 1,2, … , 𝑛𝑠 , with 𝑢 standing for a unit and 𝑛𝑠
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′ for the number of units of substance 𝑠. The distances 𝐷(𝑆𝑚 , 𝑆𝑠,𝑢,𝑚′ ) are calculated for all 𝑚, 𝑚′ , 𝑠, 𝑢
and ranked increasingly to obtain: ′ ′ ′ 𝐷(𝑆𝑚 , 𝑆𝑠1 ,𝑢1 ,𝑚1′ ) ≤ 𝐷(𝑆𝑚 , 𝑆𝑠2 ,𝑢2 ,𝑚2′ ) ≤ 𝐷(𝑆𝑚 , 𝑆𝑠3 ,𝑢3 ,𝑚3′ ) ⋯ 1 2 3
where possibly 𝑚1 = 𝑚2 or even 𝑚1 = 𝑚2 = 𝑚3 , and similarly for 𝑠1 , 𝑠2 , 𝑠3 . Intuitively, the substances indicated in this list by 𝑠1 , 𝑠2 , 𝑠3 , which as suggested may very well be one the same, should be the most likely candidates for the substance in the product. If indeed 𝑠1 = 𝑠2 = 𝑠3 , as
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expected if the substance was already included in the training data set and there were at least three replicate measurements on it, the consensus is considered to be of level 1, and there is only one
substance among the three top candidates. If 𝑠1 = 𝑠2 ≠ 𝑠3 , the consensus is of level 2, there being two substances among the top three candidates. Finally, if 𝑠1 , 𝑠2 , 𝑠3 are all distinct, the consensus is
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of level 3. However, even if the consensus is of level 1, it does not follow that the distances of the new spectra to the spectra of the top candidates are small. Even if the substance is absent in the
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dataset, there will always be top candidates. Thus, in order to judge the plausibility of 𝑠1 as a
′ ,𝑆 #{ ((𝑢,𝑚),(𝑢′ ,𝑚′ )): 𝑢≠𝑢′ ∧ 𝐷(𝑆𝑠1 ,𝑢,𝑚 ,𝑆𝑠 ,𝑢′ ,𝑚′ )≥𝐷(𝑆𝑚 1 𝑠1 ,𝑢1 ,𝑚′1 )} 1
#{ ((𝑢,𝑚),(𝑢′ ,𝑚′ )): 𝑢≠𝑢′ }
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𝑞𝑠1 ,𝑚1 ≔
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candidate for a substance, the following empirical tail probability is used:
Where 𝐴 is a set and #𝐴 is used to denote the number of elements in that set. If q is big enough, it
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suggests that the distance of measurement 𝑚1 of the given substance to measurement 𝑚1′ from unit 𝑢1 of the candidate substance 𝑠1 is within the margins of the distances between the measurements
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in our database made on different units of 𝑠1 . Similarly, we consider tail probabilities 𝑞𝑠2 ,𝑚2 , 𝑞𝑠3 ,𝑚3 , etc., in order to judge the plausibility of 𝑠2 , 𝑠3 , etc., as candidates for the given substance, in conjunction with the ranking.
3. RESULTS To be able to make use of a benchtop NMR spectrometer in determining the presence and quantity of active substances in substandard and falsified pharmaceutical products and street drugs, a method 8
was developed and validated using reference active substances and commercially available over-thecounter pharmaceutical products. Validation comprised of analysing identity, linearity, sensitivity, reproducibility and recovery. Subsequently, a series of spectra obtained with the validated method were used to develop a model to identify substances present in over-the-counter pharmaceutical products. Finally, the validated method was applied to samples of illegal drugs and medicines from the illegal market.
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3.1 Validation 3.1.1 Identity
The presence of acetaminophen, aspirin, ibuprofen, diclofenac and naproxen in over-the-counter
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pharmaceutical products could be determined by comparing 1H-spectra of methanol-d4 extracts with those of reference standards. All proton signals were retrieved at the corresponding chemical shift
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within a 0.01 ppm range, in the correct peak area ratio, see Figure 1 and Table 1 and supplementary material Figures S1-S5. The low resolution NMR spectra are specific enough to distinguish the active
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substances. Peak area ratios were within 5% for the dominant signals. Hardly any other signals besides those of the active substance were observed in the extract spectra. An additional small signal
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was observed at 1.3 ppm in the pharmaceutical products, indicating a component likely originating from a common excipient. Spectra of caffeine were determined in D2O, as caffeine was used to
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determine the sensitivity and linearity (Table 1).
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3.1.2 Linearity and sensitivity
The spectra of caffeine, acetaminophen, aspirin, ibuprofen, diclofenac and naproxen show a linear relationship when plotting peak area versus the concentration of the compound. Using at least seven concentrations ranging from 1.0 to 22 mg/mL, an R2 of at least 0.99 was obtained for all compounds, see supplementary material Figure S6. The linear relationship between concentration and detector response is a requirement to quantify by making use of a single solution with an internal standard. 9
The proton signal at 7.5 ppm of 1 mg caffeine in 1 mL of chloroform-d was found with a signal-tonoise ratio of 7.2 when using a 4 scans, a 90⁰ pulse angle and 15 s repetition time 1H-experiment. Without optimizing the experiment, it should be possible to detect the presence of a pharmacologically active component at a concentration of about 1 mg/mL. The limit of detection can obviously be reduced by increasing the number of scans and optimizing the pulse length and repetition time. Nevertheless, for determining the presence of active substances in substandard and falsified pharmaceuticals and street drugs, where these are often present in milligrams per dose unit,
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the sensitivity of the benchtop NMR spectrometer should not be an issue.
3.1.3 Internal standards
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In order to use the benchtop NMR spectrometer to quantify a variety of active substances, a series of internal standards is required, in such a way that an internal standard can be chosen that provides a
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signal not overlapping with those of the substance of interest. Rundlof et al. have elegantly described such a series [23, 26], and we have adapted it in this study. We have verified the primary standard,
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acetanilide by UV-VIS spectroscopy and used this subsequently to determine the purity of the secondary standards, 3,4,5-trichloropyridine, maleic acid, 1,3,5-trimethoxybenzene, by benchtop
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NMR spectroscopy (Table 2). Absorption spectroscopy was performed according to the European Pharmacopeia [27] and was chosen as it is widely available and often used in quality control of
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reference material. Alternatively, commercially available certified reference material could be used. Additionally, this shows that benchtop NMR spectroscopy can be used to verify the quality of a
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reference standard.
3.1.4 Recovery and reproducibility
The tested pharmaceutical active ingredients acetaminophen, aspirin, ibuprofen, diclofenac and naproxen as well as the internal standards acetanilide, 3,4,5-trichloropyridine, maleic acid, 1,3,5trimethoxybenzene were well soluble in in methanol-d4. In chloroform-d, the substances acetanilide, 10
trichloropyridine, naproxen and diclofenac did not completely dissolve. In DMSO-d6, naproxen and ibuprofen did not completely dissolve either. Therefore, methanol-d4 was chosen as the first solvent of choice for the extraction of the active ingredients from the over-the-counter pharmaceutical products. Subsequently, the recoveries were determined for the active substances, see Table 3. All recoveries were well above 90%. The measured 1H-spectra show a high degree of similarity, as shown in Figure 1. However, when using the Powerscan method, with a 90⁰ pulse and a 15 s repetition time, of the Spinsolve software
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to acquire 1H-spectra of the pharmaceutical product extracts, considerable variation in peak areas was observed (Table 3). Apparently, the slow relaxation of the protons played a role in the cause of the variation as decreasing the pulse angle and increasing the repetition time had a positive effect on
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and to less than or equal to 5% in general, see Table 3.
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the reproducibility and accuracy. The optimized settings lowered the RSD to less than 10% in all cases
3.2 Quantitative analysis over-the-counter pharmaceutical products
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Eventually, the benchtop NMR spectrometer will be applied to quantify falsified and substandard medicines. These products may include exotic active substances for which reference standards are
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not easily available and therefore internal standards are used. To validate this approach, the content of the active substances in the over-the-counter pharmaceutical products acetaminophen, aspirin,
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ibuprofen, diclofenac and naproxen, was determined. The determined values were compared to those determined with LC-QTOF-MS, see Table 4. A QTOF-MS detector is not known for the stability
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of its response, but is used as a working horse for determining the identity of active substances in falsified and substandard products [3, 24]. It does allow for a semi-quantitative estimation of the contents to be given, which is often good enough to determine whether the active substance is present in sufficient amounts for a physiological effect and to determine the status of a product. The benchtop NMR spectrometer is foreseen to be used in addition to LC-MS so its performance should at least be equal. Both by NMR spectroscopy as LC-QTOF-MS, the declared contents of the 11
pharmaceutical products were verified. In general, the error in the measurements is much smaller in the NMR spectroscopic measurements compared to those using LC-QTOF-MS.
3.3 Classification of 1H spectra In order to identify products based on NMR spectra acquired on samples of them, without spectral interpretation, a classification procedure was evaluated. Besides the spectra of the products mentioned above and their corresponding reference standards, spectra of loperamide 2 mg,
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bisacodyl 5 mg and mebendazole 100 mg were added to increase the dataset. First, a definite classification rule was adopted. Spectra were ranked on similarity and having determined the top
three candidates, their distance, consensus, and tail probability were determined. If the consensus is
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of level 1 and the tail probability is≥ 0.75, then we propose the top candidate as the substance;
otherwise, we refrain from proposing any substance, an event described as a referral. The level of
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0.75 is chosen arbitrarily and can be fine-tuned once more substances are added to the database. Two exercises of leave-one-out cross-validation were carried out. First, the active substance of a dose
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unit was predicted based on the training data set obtained, by removing the data from that dose unit from the data set. In each case, the training data set contains data on other dose units of the
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substance being investigated, so it is expected that the method is able to identify the correct substance among the substances represented in the training data. A correct identification yields a
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consensus of level 1 and a high tail probability. Secondly, the active substance of a dose unit was predicted based on the training data set obtained by removing all the data on that substance from
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the data set. Then, the substance being investigated is not represented in the data set and one would expect the consensus to be poor and the tail probabilities to be small. In the first exercise the consensus is of level 1 in all cases and the tail probabilities are generally high (above 0.75) except in four cases; see Table 5 and Figure 2. The minimum distances are generally low (below 0.2). So if we referred all the dubious cases we might have to count with about 16% referrals, but the proportion of incorrect classifications might be close to 0. In the second exercise the 12
consensus is mostly of level 1, but the probabilities are practically all ≤ 0.5 (data not shown); if we were to follow the procedure strictly, not a single case would have been positively classified and they would have all been referred to an expert. When combining the two exercises, 20 true positives, 4 false negatives, 24 true negatives and 0 false negatives are obtained, yielding a Matthews correlation coefficient of 0.84. These results indicate that in the absence of expert interpretation, the NMR spectra obtained with the benchtop spectrometer can be used to verify the presence of an active
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substance in a pharmaceutical product using a chemometrics classification method.
3.4 Application to street drugs
Similar to the pharmaceutical products, 1H-spectra were obtained from popular street drugs such as
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cocaine and 4-hydroxybutyric acid (GHB) (Table 1 and Figure 3). As the substance GHB is a simple
molecule and the product is mostly a watery GHB containing solution, a simple and clean spectrum is
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obtained of GHB street drugs. The 1H-spectrum of street drug cocaine is more complex but matched the spectrum of reference cocaine HCl. For prosecution purposes, under Dutch law, it is sufficient to
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not investigated here.
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verify the presence of a listed narcotic in a street drug. Quantitation is not required and therefore
3.5 Application to pharmaceutical products from the illegal market
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Some erectile dysfunction products and a slimming product from the illegal market, sampled by the Health and Youth Care Inspectorate for market control reasons were analysed using the optimized
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and validated method described above. 1H-NMR spectra were acquired in DMSO-d6 (tadalafil) and methanol-d4 (sildenafil and sibutramine) extracts and compared to those of reference standards. All proton signals were retrieved at the corresponding chemical shift, in the correct ratio, verifying the identity of the active substances present (Figure 4). The doses of the active substances were determined with benchtop NMR spectroscopy using the same method as for the over-the-counter
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products, see Table 6. As a control experiment, the doses of tadalafil and sildenafil in respectively Cialis and Viagra were determined in products obtained from a pharmacy. The measured quantity of tadalafil in Cialis corresponds to the declared dose of the product. The determined amount sildenafil in Viagra is somewhat small, which may be explained by the quality of the spectrum. As sildenafil is a rather large and complex molecule, at least compared to the over-thecounter substances, a crowded spectrum is obtained. Not many isolated peaks are available that can be used to correlate to the signal of the internal standard. We chose to use the aromatic signals from
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7.0 to 8.7 ppm, which are the combined signals of three different protons and therefore the signalto-noise ratio is rather low. Perhaps sildenafil is at the border of the substances that can be quantified using our method.
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The dose in products from the illegal market do not necessarily match their declaration, a
phenomenon observed earlier with such products [3]. This unreliable dosing also explains the larger
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errors in the values obtained for the samples from the illegal market because the dose also varies between tablets. Thirdly, this becomes also apparent from the results obtained with the LC-MS.
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Where the match between NMR-based data and the LC-MS based data was good with the over-thecounter products and the erectile dysfunction products obtained at the pharmacy , now there is a
falsified products.
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4. DISCUSSION
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large discrepancy. We believe this is caused by the large inter-tablet differences in dosage in the
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A benchtop NMR spectrometer does not offer all possibilities of a superconducting magnet containing spectrometer. Breast implant impurities [28], and dermal filler crosslinking [29], are typical examples of samples that are outside of analytical reach at 60 MHz NMR spectroscopy (data not shown): a higher resolution is required to obtain the sensitivity to observe contributions of minor components. Nevertheless, fingerprint spectra can be obtained with benchtop resolution, allowing to determine the presence of components in a challenging matrix [12], or the comparison of samples using a chemometrics approach, in a way similar to other medium resolution spectroscopic 14
techniques. We have validated an analytical method based on benchtop NMR spectroscopy and applied it to illegal drugs and pharmaceutical products from the illegal market. We have shown the possibility of a chemometrics approach to determine presence of a substance without expert spectra interpretation. It should be pointed out that one cannot expect an automatic, data-driven method of identification based on NMR spectroscopy to identify substances not yet represented in the data used to build it, in particular mixtures of substances already represented. One of the purposes of the method proposed here is to refer substances not yet included in the data set to an expert, avoiding
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undue referral as much as possible. Small differences in signal to noise appeared not to be an issue in the current method, maybe because similar amounts of active substances, corresponding to 10 mg, were extracted.
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In the field of substandard and falsified medicines, we have shown that benchtop NMR spectroscopy offers an additional chemical identification technique besides the more commonly used LC-MS and
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vibration spectroscopy. The absence of a superconducting magnet brings NMR spectroscopy within financial and logistical reach. Furthermore, it offers the opportunity to quantify active substances in
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absence of a reference standard, required for LC-MS and vibration spectroscopy. This works rather well for the applications shown here, as long as significant isolated signals of both the component of
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interest and an internal standard are available. The limits of the analysis of pharmaceutical products are reached when products have more complex spectra, be it from multi-proton active substances or
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matrix components and lack of dispersion impedes proper peak assignment and area analysis. At that point the chemometrics model might be helpful, provided the corresponding database contains
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many substances in combination with enough excipients. Still, the presence of a large amount of water may result in a solvent peak interfering with peaks of interest or a reduction in the sensitivity and quality of the spectrum. The results presented here are in line with the recent work by Assemat et al. [30]. In that work, the potential of low field NMR spectroscopy in the analysis of falsified medicines was elegantly shown in
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comparison with high field obtained data. In the current work we have tried to relate more to chromatography based methods which is more commonly applied by medicine control laboratories. In a similar way to the pharmaceutical products, the quality of reference standards can be verified. NMR spectroscopy offers the advantage over UV-VIS spectroscopy in standard verification in that the presence of water is directly observed in the spectrum, relative to the spectrum of the internal standard alone. The attraction of water is one of the primary causes for a decrease in purity. The analysis of falsified and substandard medicines is not easy as the composition of such products is
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never entirely known. Additionally, various aspects may be of interest; the presence and identity of active substances, impurities and excipients and their amounts relative to the levels of toxic concern. Hence, orthogonal techniques should be applied and results combined to be able to estimate the risk
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for the patients. Benchtop NMR spectroscopy provides an affordable additional technique based on an independent physical principle and is therefore truly orthogonal to vibration spectroscopy and
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chromatography.
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5. CONCLUSION
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Within its resolution and sensitivity limits, benchtop NMR spectroscopy is useful in determining the identity of active ingredients in pharmaceutical products, street drugs and other products from the
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illegal market. Here, we developed a chemometrics method to identify the substances in the pharmaceutical products, reducing the need for expert interpretation of the acquired NMR spectra.
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Furthermore, using an optimized and validated method, the contents of the active ingredient can be determined with an uncertainty of 10%.
Conflicts of interest The authors declare no conflicts of interest.
ACKNOWLEDGEMENTS 16
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We thank NFI and IGJ for providing samples.
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19.
M. Voigt, R. Legner, S. Haefner, A. Friesen, A. Wirtz, and M. Jaeger, Using fieldable spectrometers and chemometric methods to determine RON of gasoline from petrol stations: A comparison of low-field 1H NMR@ 80 MHz, handheld RAMAN and benchtop NIR. Fuel, 2019. 236: p. 829-835. W. Jakes, A. Gerdova, M. Defernez, A. D. Watson, C. McCallum, E. Limer, I. J. Colquhoun, D. C. Williamson, and E. K. Kemsley, Authentication of beef versus horse meat using 60 MHz 1H NMR spectroscopy. Food Chem, 2015. 175: p. 1-9. T. Parker, E. Limer, A. D. Watson, M. Defernez, D. Williamson, and E. K. Kemsley, 60 MHz (1)H NMR spectroscopy for the analysis of edible oils. Trends Analyt Chem, 2014. 57(100): p. 147158. Council of Europe. General European OMCL Network (GEON). 2018 9 August 2018]; Available via: https://www.edqm.eu/en/general-european-omcl-network-geon. L. A. Ciolino, M. Z. Mesmer, R. D. Satzger, A. C. Machal, H. A. McCauley, and A. S. Mohrhaus, The chemical interconversion of GHB and GBL: forensic issues and implications. J Forensic Sci, 2001. 46(6): p. 1315-23. T. Rundlof, I. McEwen, M. Johansson, and T. Arvidsson, Use and qualification of primary and secondary standards employed in quantitative (1)H NMR spectroscopy of pharmaceuticals. J Pharm Biomed Anal, 2014. 93: p. 111-7. B. J. Venhuis, P. H. J. Keizers, A. van Riel, and D. de Kaste, A cocktail of synthetic stimulants found in a dietary supplement associated with serious adverse events. Drug Test. Anal., 2014. 6(6): p. 578-81. R Development Core Team. R: A language and environment for statistical computing. 2008, R Foundation for Statistical Computing: Vienna. T. Rundlof, M. Mathiasson, S. Bekiroglu, B. Hakkarainen, T. Bowden, and T. Arvidsson, Survey and qualification of internal standards for quantification by 1H NMR spectroscopy. J Pharm Biomed Anal, 2010. 52(5): p. 645-51. European Pharmacopoeia Commission. European Pharmacopoeia, in Absorption spectrophotometry, ultraviolet and visible (2.2.25.). 2017, EDQM: Strasbourg. p. 3552. P. H. J. Keizers, M. J. Vredenbregt, F. Bakker, D. de Kaste, and B. J. Venhuis, Chemical fingerprinting of silicone-based breast implants. J Pharm Biomed Anal, 2015. 102: p. 340-5. P. H. J. Keizers, C. Vanhee, E. M. W. van den Elzen, W. H. de Jong, B. J. Venhuis, H. M. Hodemaekers, P. Schwillens, and D. G. W. Lensen, A high crosslinking grade of hyaluronic acid found in a dermal filler causing adverse effects. J Pharm Biomed Anal, 2018. 159: p. 173178. G. Assemat, S. Balayssac, A. Gerdova, V. Gilard, C. Caillet, D. Williamson, and M. MaletMartino, Benchtop low-field (1)H Nuclear Magnetic Resonance for detecting falsified medicines. Talanta, 2019. 196: p. 163-173.
na
18.
19
Figure 1: Overlay of 1H-NMR spectra of methanol-d4 extracts of over the counter pharmaceutical products obtained at 60 MHz, using the Powerscan settings. Spectra 1-9 in red are from aspirin, spectra 10-18 in olive are from diclofenac, spectra 19-27 in green are from ibuprofen, spectra 28-36 in blue are from naproxen, and spectra 37-45 in purple are from acetaminophen. Methanol solvent
Jo
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lP
re
-p
ro of
protons are visible at 3.31 and water protons at 4.78 ppm.
20
Figure 2: Details from the chemometric analysis of three spectra from the over-the-counter pharmaceutical products. For diclofenac, naproxen and paracetamol a spectrum of a reference standard is included. The substance average tail probabilities are shown (A) and the substance average minimum distances (B) with their standard deviation.
A
1.2
ro of
1.0 0.8 0.6 0.4 0.2
-p
average tail probablity
1.4
lP
re
0.0
na
B
0.3 0.2
ur
0.4
Jo
average minimum distance
0.5
0.1 0.0
-0.1
21
Figure 3: 1H-NMR spectra of a DMSO-d6 extract of a street sample (in red) and reference (in blue) of cocaine (A) and a D2O diluted sample of a street sample (in red) and reference (in blue) of GHB (B).
Jo
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na
lP
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-p
ro of
Spectra were acquired at 60 MHz.
22
Figure 4: 1H-NMR spectra of a DMSO-d6 extract of Tadaga 40 mg, above the spectrum of a tadalafil reference standard (A), a methanol-d4 extract of Aurogra-100, above the spectrum of a sildenafil reference standard (B), and a methanol-d4 extract of Slimex 15 mg above the spectrum of a sibutramine reference standard (C). Spectra were acquired at 60 MHz in presence of 3,4,5-
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-p
ro of
trichloropyridine as internal standard.
23
Tables and Figures
Table 1: The used active ingredients and their 1H-NMR data as determined at 60 MHz. s = singlet, d = doublet, dd = double doublet, q = quartet, m = multiplet. Compound
Structure
Chemical shifts (splitting,
Solvent
number of 1H) Acetaminophen
2.08 ppm (s, 3H)
methanol-d4
ro of
6.72 ppm (d, 2H, J = 9 Hz) 7.30 ppm (d, 2H, J = 9 Hz) Aspirin
2.28 ppm (s, 3H)
methanol- d4
-p
7.13 (d, 1H, J = 8 Hz) 7.31 (t, 1H)
re
7.62 (t, 1H)
8.02 ppm (d, 1H, J = 8 Hz) 3.32 ppm (s, 3H)
lP
Caffeine
D2O
3.50 ppm (s, 3H)
na
3.95 ppm (s, 3H)
Jo
ur
Cocaine
7.91 ppm (s, 1H) 2.29 ppm (m, 6H) 2.86 ppm (s, 3H) 3.64 (s, 3H) 4.09 ppm (bd, 2H) 5.57 ppm (q, 1H, J = 8 Hz) 7.3-8.0 ppm (m, 5H)
24
methanol- d4
Diclofenac
3.63 ppm (s, 2H)
methanol- d4
6.3-7.5 ppm (m, 7H)
GHB
1.51-1.99 ppm (m, 2H)
D2O
2.11-2.37 ppm (m, 2H) 3.59 ppm (t, 2H, J = 7 Hz) 0.89 ppm (d, 6H, J = 6 Hz)
methanol- d4
ro of
Ibuprofen
1.43 ppm (d, 2H, J = 7 Hz) 1.62-2.06 (m, 1H)
-p
2.45 ppm (d, 2H, J = 7 Hz) 3.67 ppm (q, 1H, J = 7 Hz)
re
7.15 ppm (m, 4H) Naproxen
1.49 ppm (d, 3H, J = 7 Hz)
lP
3.65 ppm (m, 1H)
6.9-7.8 ppm (m, 6H)
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3.88 ppm (s, 3H)
25
methanol- d4
Table 2: The internal standards used, their 1H-NMR data as determined at 60 MHz and their purity as determined by UV-VIS in methanol (acetanilide) and 1H-NMR spectroscopy in DMSO-d6 relative to acetanilide (all others). s = singlet, m = multiplet. Compound
Structure
Chemical shifts
Purity (%)
(splitting, number of 1
H)
acetanilide
2.12 ppm (s, 3H)
98 5
ro of
7.32 ppm (m, 5H) 6.31 ppm (s, 2H)
101 2
3,4,5-trichloropyridine
8.76 ppm (s, 2H)
87 3
re
-p
maleic acid
6.09 ppm (s, 3H)
lP
1,3,5-
3.77 ppm (s, 9H)
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trimethoxybenzene
26
101 1
Table 3: Recoveries of 10 mg of reference standards, determined over at least four samples, and the reproducibility of dose determinations determined over 15 spectra (5 dose units measured in triplicate) of pharmaceutical products, using 3,4,5-trichloropyridine as internal standard. Powerscan: 90⁰ pulse angle, 15 s repetition time and 40 scans, optimized settings: 60⁰ pulse angle, 30 s repetition time and 32 scans. Mean RSD Powerscan
Mean RSD optimized
(%)
settings (%) 4.8
104 3
12.1
Aspirin
103 2
12.8
Diclofenac
97 4
7.6
Ibuprofen
106 1
18.5
Naproxen
96 2
5.3
9.2 5.3 3.6
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Acetaminophen
ro of
Recovery (%)
-p
Substance
27
5.1
Table 4: Quantitation of the over-the-counter pharmaceutical products, using benchtop 1H-NMR spectroscopy, using 3,4,5-trichloropyridine as internal standard and LC-QTOF-MS. The NMR analyses were performed with five independent measurements, the LC-MS analyses were performed with two dose units measured in duplicate. Pharmaceutical
Declared dose (mg per
Dose determined by
Dose determined by
product
dose unit)
benchtop NMR
LC-QTOF-MS (mg per
spectroscopy (mg per
dose unit)
Acetaminophen
500
523 7
100
107 3
Lot: 3043510
Aspirin
-p
Exp. 08-2022
re
Charge: BTAJRAO Exp. 01-2021
108 30
11 2
(free base)
(free base)
211 26
204 13
201
197 6
240 37
(220 Na-salt)
(free base)
(free base)
Lot: 3337244
(12 .5 K-salt)
na
Exp. 09-2020
lP
11
200
ur
Lot: 3230694
533 32
11 0.2
Diclofenac
Ibuprofen
ro of
dose unit)
Exp. 09-2022
Jo
Naproxen
Lot: 3008971 Exp. 01-2022
28
Table 5: Results of active substance prediction using the chemometrics model. The model was made from spectra acquired from extracts of pharmaceutical products, containing various active substances obtained in triplicate. Tail probability
Minimum distance
0.99
0.01
Acetaminophen
1
0.99
0.01
Acetaminophen
1
0.99
0.01
Acetaminophen
1
0.37
0.02
Aspirin
1
1.00
Aspirin
1
0.95
Aspirin
1
0.83
Bisacodyl
1
Bisacodyl
1
Bisacodyl
1
Diclofenac
1
-p
0.01 0.01 0.02 0.03
0.92
0.03
re
1.00
0.67
0.25
lP
Jo
ro of
Level of consensus 1
0.95
0.14
1
1.00
0.13
1
0.99
0.13
Diclofenac
1
0.82
0.17
Loperamide
1
0.72
0.16
Loperamide
1
1.00
0.08
Loperamide
1
0.99
0.08
Mebendazole
1
0.95
0.10
Diclofenac
Diclofenac
ur
Acetaminophen containing analgesic Acetaminophen containing analgesic Acetaminophen containing analgesic Acetaminophen reference standard Aspirin containing analgesic Aspirin containing analgesic Aspirin containing analgesic Bisacodyl containing laxative Bisacodyl containing laxative Bisacodyl containing laxative Diclofenac containing pain reliever Diclofenac containing pain reliever Diclofenac containing pain reliever Diclofenac reference standard Loperamide containing antidiarrhoeal drug Loperamide containing antidiarrhoeal drug Loperamide containing antidiarrhoeal drug Mebendazole containing anthelminitic
Predicted substance Acetaminophen
na
Spectrum
29
1
0.94
0.10
Mebendazole
1
0.95
0.10
Naproxen
1
0.95
0.03
Naproxen
1
0.99
0.03
Naproxen
1
0.97
0.03
Naproxen
1
0.21
0.09
ro of
Mebendazole
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lP
re
-p
Mebendazole containing anthelminitic Mebendazole containing anthelminitic Naproxen containing pain reliever Naproxen containing pain reliever Naproxen containing pain reliever Naproxen reference standard
30
Table 6: Benchtop NMR analysis of pharmaceutical products from the illegal market. Three dose units were analysed independently, using 3,4,5-trichloropyridine as internal standard. The average dose and its standard deviation are reported. For comparison, the LC-MS based analysis is added. Two separate dose units were analysed. N. d.: not determined. Product name
Declared active substance
Active substance and dose
Active substance and
and dose (mg per dose
by benchtop NMR
dose by LC-QTOF-MS
unit)
spectroscopy
(mg per dose unit)
Cialis 20 mg
Tadalafil 20 mg
Tadalafil 19 3
Sildenafil 50 mg
Sildenafil 40 5
Charge:
-p
A098388 Exp. 11-2006
N. d.
N. d.
re
Viagra 50 mg
ro of
(mg per dose unit)
Charge:
lP
7078708N Exp. 09-2012
Sildenafil 100 mg
Sildenafil 98 22
Sildenafil 87 17
Sildenafil 20 3
Sildenafil 99 20
na
Aurogra-100 Lot:
ur
S0214M112/9e Exp. 11-2012
Sildenafil 100 mg
Jo
Lovegra 100 mg
Lot: MF010/09 Exp. 09-2012
31
Sildenafil 100 mg
Sildenafil 50 5
Sildenafil 106 21
Slimex 15 mg
Chromium picolinate,
Sibutramine 12 1
Sibutramine 17 3
Lot: 56-20
Tea yellow alkali, alcar, L-
Exp. 05-2013
carnithine
Tadaga 40 mg
Tadalafil 40 mg
Tadalafil 72 7
Tadalafil 31 6
Malegra-100 Lot: E-134
ro of
Exp. 05-2011
Lot: E-139
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lP
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-p
Exp. 05-2013
32