Journal of Forensic Science and Medicine

: 2020  |  Volume : 6  |  Issue : 4  |  Page : 117--125

A preliminary gas chromatography-mass spectrometry-based metabolomics study of rats ingested diazepam or clonazepam

Shiyong Fang, Jianxia Chen, Xinhua Dai, Yuzi Zheng, Hao Wu, Yingqiang Fu, Jian Li, Yi Ye, Linchuan Liao 
 Department of Forensic Analytical Toxicology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China

Correspondence Address:
Linchuan Liao
8th Floor, Fa Yi Building, No. 16, Section 3, Renmin Nan Road, Chengdu


Drug-facilitated sexual assault (DFSA) is a sexual act in which the victim is unable to give or rescind consent due to alcohol or drug intoxication, which involved the abuse of benzodiazepines around the world. Conventional techniques used for the analysis of benzodiazepines have the limitation of short detection time window due to the rapid metabolism of these drugs in body. This study aimed to investigate the characteristic changes of metabolites in the blood of rats after ingesting diazepam/clonazepam through a gas chromatography-mass spectrometry-based metabolomics method, allowing the indirect reveal of the rats ingested diazepam/clonazepam. First, we found that diazepam and clonazepam in the blood of rats could not be detected by liquid chromatography-tandem mass spectrometry after 48 h of ingestion. Then, orthogonal partial least squares discrimination analysis regression models were, respectively, constructed to determine whether the rats ingested diazepam/clonazepam after 48 h. The results showed that 5 metabolites were found to be associated with diazepam exposure, and 7 metabolites were found to be associated with clonazepam exposure, which may be characterization for the evaluation of digestion of diazepam and clonazepam in rat.

How to cite this article:
Fang S, Chen J, Dai X, Zheng Y, Wu H, Fu Y, Li J, Ye Y, Liao L. A preliminary gas chromatography-mass spectrometry-based metabolomics study of rats ingested diazepam or clonazepam.J Forensic Sci Med 2020;6:117-125

How to cite this URL:
Fang S, Chen J, Dai X, Zheng Y, Wu H, Fu Y, Li J, Ye Y, Liao L. A preliminary gas chromatography-mass spectrometry-based metabolomics study of rats ingested diazepam or clonazepam. J Forensic Sci Med [serial online] 2020 [cited 2021 Jan 18 ];6:117-125
Available from:

Full Text


Drug-facilitated sexual assault (DFSA)[1],[2] is a sexual act in which the victim is unable to give or rescind consent due to intoxication with alcohol and/or drugs that have been self-administered (opportunistic DFSA) or covertly administered by the perpetrator (predatory DFSA). In these cases, symptoms reported by victims include confusion, dizziness, drowsiness, impaired judgment, impaired memory, lack of muscle control, loss of consciousness, nausea, and reduced inhibition.[3] Benzodiazepines, especially diazepam and clonazepam are commonly involved in DFSA cases in China and other countries.[2],[4],[5] Traditional methods for identifying the presence of these drugs in biological samples include gas chromatography (GC) with a nitrogen-phosphorus detector,[6] GC-mass spectrometry (GC-MS),[7] high-performance liquid chromatography (HPLC),[8] liquid chromatography-tandem mass spectrometry (LC-MS/MS),[9],[10] and immunoassay.[11] However, these benzodiazepines will be cleared from the body over time.[12] Furthermore, it has been[13],[14] demonstrated that some female victims tend to struggle to tell anyone about the sexual assault they experienced, and more than half of them were inclined to tell someone but not the police, while only a small portion of them reported to the police about the assault.[13],[14] In practice, the dilemmas in the analysis of diazepam and clonazepam are their low concentrations in body fluids, their rapid metabolism in vivo, and even the long-time delay without instant detection, allowing the qualitative or quantitative analysis of these drugs in the biological materials collected from crime victims become difficult.[15]

Metabolomics has been an effective method to study the changes of metabolites after the ingestion of drugs or poisons, such as diazepam,[16] dichlorvos (DDVP),[17] and paraquat.[18] For example,[19] GC-MS-based metabolomics have been developed to investigate the changes of metabolites in the blood of DDVP poisoned rats.[20] Coupled with GC-MS and UPLC-MS/MS, the metabolic profile of brodifacoum-induced toxicity can be characterized, which would result in the discovery potential biomarkers in rat plasma. Therefore, it can be known that the normal homeostasis environment of the body may be disturbed by exogenous poisons or drugs, leading to some characteristic changes in metabolites in the body. GC-MS also has the great potential in other forensic-related studies, including the detection of the metabolome of suffocated and dichlorvos poisoned rats for postmortem interval estimation, which has become an efficient method for the analysis of metabolomics.[19],[21] Furthermore, we also have explored the potential generative mechanism of ketamine-induced cystitis by urinary metabolomics in rats using GC-MS spectroscopy.[22] Thus, the GC-MS has been an excellent technique to achieve the analysis of metabolomics, which also possesses an accessible standard database and can be applied for simultaneous qualitative and quantitative analysis of multiple compounds.[23] Taking advantages of the high sensitivity, high separation efficiency, durability, low cost, and the ability to detect trace metabolites and isomers using GC-MS, we herein employed GC-MS to investigate the metabolomics after the rats ingested diazepam and clonazepam and established preliminary orthogonal partial least squares (OPLS) models determining whether the rats ingested diazepam/clonazepam using GC-MS-based metabolomics when we could not detect diazepam/clonazepam by LC-MS/MS.

 Materials and Methods

Chemicals and reagents

Diazepam (2.5 mg/tablet) and clonazepam (2.0 mg/tablet) tablets were prescribed by psychiatrists from the Mental Health Centre, West China Hospital of Sichuan University.

Diazepam and clonazepam standard solutions (purity ≥99%) were purchased from the Academy of Forensic Sciences, Shanghai; the internal standard (IS) of diazepam-d5 (100 μg/mL in methanol, 1 mL, HPLC grade) was purchased from J and K Scientific, Beijing. All standard working solutions were diluted to 100, 10, and 1 μg/mL. Ammonium acetate (purity ≥99%, HPLC grade) were obtained from CNW (Germany); methanol and acetonitrile were purchased from Thermo Fisher Scientific (China) Co., Ltd; ethyl acetate was purchased from Kelong Chemical Company (Chengdu, China); formic acid (HPLC grade) was obtained from Thermo Fisher Scientific (China) Co., Ltd; analytical-grade pyridine and heptane were purchased from Fuyu Chemical Reagent Co., Ltd., (Tianjin, China); methoxyamine hydrochloride, N, O-bis-(trimethylsilyl)-trifluoroacetamide (BSTFA) with 1% (v/v) trimethylchlorosilane (TMCS) and methyl stearate were purchased from Sigma-Aldrich (St. Louis, USA). All solutions were stored at 4°C for later analysis.

Instrument conditions

High-performance liquid chromatography-tandem mass spectrometry conditions

An LC-MS/MS8030 (Shimadzu, Japan) chromatography fitted with an auto-injection system was applied to detect the diazepam and clonazepam in blood. Chromatographic separation was achieved on a Shim-pack XR-ODSIII C18 column (2.0 mm × 75 mm, 1.6 μm, Shimadzu, Japan). The binary mobile phase consisted of 0.1% formic acid and 10 mM of ammonium acetate in deionized water (phase A) and 0.1% formic acid in acetonitrile (phase B). Elution was performed in the gradient mode as follows: From 0 to 4 min, a linear gradient from 90% to 60% B; from 5 to 4.5 min, 60% B; from 4.5 to 9 min, a linear gradient from 60% to 90% B. The temperature of the column was 40°C during the experiments.

Mass spectrometry analysis was carried out using a Shimadzu LC-MS-8030 triple-quadrupole mass spectrometer (Shimadzu, Japan) equipped with an electrospray ionization source. The determination was conducted in positive monitoring mode and multiple reactions monitoring (MRM) acquisition mode. The MS conditions were as follows: capillary voltage of 4.5 kV; nebulizing gas flow rate of 3 L/min; drying gas flow rate of 15 L/min, heat block temperature of 400°C and oven temperature of 40°C. Compound-dependent parameters are listed in [Table 1].{Table 1}

Gas chromatography-mass spectrometry conditions

The 7890A-5975C GC-MS (Agilent, CA, USA) was used for the analysis of metabolomics from diazepam and clonazepam. The specific parameters were as follows: Column-DB-5 MS, 0.25 mm × 30 m × 0.25 μm; sample volume: 1 μL, Carrier gas: helium; flow rate of 1.5 mL/min; inlet temperature of 260°C; split ratio of 5:1; temperature program: the initial temperature was 55°C, held for 1 min, then increased by 15°C/min to 325°C, held for final 10 min; a mass spectral analysis was performed in the full scan mode by using the mass range of 50–600 amu at a rate of 2 spectra/s. The electron impact ionization mode was set using the energy of 70 eV. The ion-source and quadrupole temperatures were maintained at 230 and 150°C, respectively.

Development and validation of a liquid chromatography-tandem mass spectrometry detection method for diazepam and clonazepam

Volumes of 100 μL of blood samples were transferred to a 2 mL Eppendorf tube and spiked with 10 μL of a 1 μg/mL IS solution, which were extracted twice with 1 mL of ethyl acetate along with vortex mixing for 5 min and centrifugation for 5 min at 12000 rpm for each time. The obtained supernatant was further transferred to 10 mL glass vial and evaporated to dryness under N2 stream at 40°C. The residue was then reconstituted with 100 μL of mobile phase (A/B, 10:90 (v/v)) and vortex mixed, 10 μL of aliquots were injected into the LC-MS/MS system for analysis.

This LC-MS/MS method has been validated according to the US Food and Drug Administration guidelines for bioanalytical method validation and related documents.[24] To investigate the specificity of LC-MS/MS detection method for the analysis of diazepam and clonazepam, six different blank blood samples were conducted to determine the presence of possible interfering chromatographic peaks. A 100 μL aliquot of blood were spiked with the diazepam and clonazepam working solutions, respectively, to obtain concentrations of 0.4, 4, 10, 40, 100, 400, and 1000 ng/mL. All blood samples were spiked with diazepam-d5 to obtain an IS (d5) concentration of 100 ng/mL. The calibration curves were measured using the ratio of the peak areas of the target ion chosen for diazepam, clonazepam and the IS (diazepam-d5). The relative standard deviation (RSD) was determined by analyzing samples spiked with diazepam and clonazepam to low, medium and high concentrations of 50, 150, and 500 ng/mL. Apparent recovery (Rapp, %) is defined as the ratio between the spiked blood samples and the standard samples. Intra-day precision (repeatability) and inter-day precision (intermediate precision) were determined by calculating the RSD (%), RSDr, and RSDR, respectively, using one-way ANOVA.

Animal experiments

Thirty-five eight-week adult Sprague Dawley rats weighted 500 ± 8 g were purchased from the Experimental Animal Centre of Sichuan University. The animals were kept in a facility with controlled conditions (temperature of 20°C ± 3°C, relative humidity of 55 ± 5%, 12/12 h light/dark cycle, water and food provided ad libitum). All the rats were adaptively fed for one week. Animal care and the experimental procedures were conducted according to the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

Blood concentration of diazepam and clonazepam by liquid chromatography-tandem mass spectrometry analysis

To confirm the threshold value of the detection of diazepam and clonazepam in the blood of rats by LC-MS/MS, 10 rats were randomly divided into two groups (n = 5 per group) for the analysis of diazepam and clonazepam, respectively. For the diazepam group, five test rats were weighed and orally administered 10 mg/kg of diazepam or 2.5 mg/kg of clonazepam.[25],[26] The diazepam and clonazepam tablets were ground and dissolved in carboxymethylcellulose sodium solution before ingestion. Then, 200 μL of orbital venous blood were collected at each of the following time points: 0 h, 0.25 h, 0.5 h, 1 h, 1.5 h, 2 h, 3 h, 4 h, 8 h, 12 h, 24 h, and 36 h. The blood samples were stored at − 20°C for LC-MS/MS analysis. All pretreatment samples for the LC-MS/MS detection were measured continuously in one day.

Experiment sets for gas chromatography-mass spectrometry-based metabolomics

Fifteen rats were randomly divided into three groups (n = 5 per group) containing blank group (Group B), diazepam 48 h group (Group D), and clonazepam 48 h group (Group C). For Group D and Group C, the five rats were orally administered 10 mg/kg of diazepam and 2.5 mg/kg of clonazepam, respectively.[25],[26] For Group B, the drug solutions were replaced by ultrapure water; all feeding conditions were the same as those for Group D and Group C. After oral administration for 48 h, approximately 100 μL of orbital venous blood was collected from each animal of Group B, D, and C. The blood samples were stored at − 80°C before pretreatment and GC-MS analysis.

Forecasting sets (double-blind) for gas chromatography-mass spectrometry-based metabolomics

Another 10 rats (n = 5 per group) were randomly assigned to two groups (forecasting set of diazepam and clonazepam, respectively). Under the same conditions as experiment sets, the rats of forecasting sets were given diazepam/clonazepam or not given. The orbital venous blood samples were collected at 48 h after the oral administration. Samples were designated UD1–UD5 (to validate the diazepam model) and UC1–UC5 (to validate the clonazepam model). All these operations were completed by another experimenter.

Sample pretreatment for gas chromatography-mass spectrometry-based metabolomics

Based on the previous report,[27] the collected blood samples were treated for the GC-MS-based metabolomics analysis as following: Briefly, 50 μL blood samples were put into an Eppendorf tube, and 150 μL of acetonitrile was added to precipitate protein. The mixture was vortexed for 2 min and centrifuged at 12000 ×g for 10 min at 4°C. Then, 100 μL of the supernatant was transferred to a 300 μL GC vial, concentrated and lyophilized for 12 h. Subsequently, 30 μL of methoxyamine hydrochloride in pyridine solution was added to the vial, mixed for 2 min and finally placed in the dark for 16 h of oximation at 16°C, after which was the addition of 30 μL of BSTFA and 1% TMCS along with vortexed for 5 min and incubated at 70°C for silanization for 1 h. Then, 100 μL of the methyl stearate solution (10 μg/mL) was added, and the mixture was vortexed for 2 min and centrifuged for 15 min to collect approximately 100 μL of the supernatant to perform analysis of GC-MS. a 100 μL of ice-cold acetonitrile was put into a GC vial as a blank sample. The other steps were the same as those of the blood pretreatment. Two sets of quality control (QC) samples were obtained by collecting 50 μL blood samples from all rats. QC samples were the mixture of all blood samples in the same group, which were used to investigate the stability of the instrument during the injection process.

Considering the sequence of metabolomics analysis, two blank solutions were injected at the beginning and the end of the measurement, respectively. Then, four QCs were detected before the injection of samples, and other QCs were analyzed among every five samples to verify the stability of the method.

Data analysis

The GC-MS spectrum data were confirmed by automated mass spectral deconvolution and identification system (AMDIS) software (NIST, CA, USA) and by the National Institute of Standards and Technology (NIST) mass spectral library. Peak areas of metabolite were normalized (the ratio of individual peak area to total peak area). Subsequently, the processed data were imported into Simca-P software 14.1 (Umetrics, Umea, Sweden), and multivariate statistical analysis was performed, including principal component analysis (PCA) and OPLS-discrimination analysis (OPLS-DA).


Feasibility of the liquid chromatography-tandem mass spectrometry method

The MRM chromatogram of the blank blood control, standard control, and blood spiked samples are shown in [Figure 1]. The retention times of diazepam and clonazepam were 4.835 min and 4.075 min, respectively. The obtained chromatogram showed that there was no background interference to the analysis of diazepam and clonazepam under the LC-MS/MS conditions. The analytical performance of this proposed method was shown in [Table 2], limit of detection (LOD) was determined by the analytes of the chromatographic extracts of blood samples spiked with decreasing concentration of the analytes until a signal-to-noise (S/N) ratio of 3:1 was reached. The limit of quantitation (LOQ) was estimated from the analysis as the concentration of an analyte with an S/N ratio of 10:1. The standard deviations of the intra-day and inter-day precisions of diazepam and clonazepam were >5% but <10%.The recoveries for diazepam and clonazepam were 74.3%–103.1%. The above data show that the LC-MS/MS method is stable.{Figure 1}{Table 2}

Blood concentration of diazepam and clonazepam by liquid chromatography-tandem mass spectrometry analysis

The qualitative and quantitative analysis of diazepam and clonazepam in the blood samples of rats were carried out using the calibration curves of diazepam and clonazepam in the blood after pretreatment. Then, blood concentration-time profiles were obtained [Figure 2]. At 24 h, the concentration of diazepam in the blood samples was below the LOD of this method (S/N < 3:1). For clonazepam, the time point was 36 h. Finally, the time point of 48 h after oral administration was selected for blood collection in our GC-MS-based metabolomics study to ensure that diazepam and clonazepam could not be detected in the blood by the LC-MS/MS method we developed and validated.{Figure 2}

Gas chromatography-mass spectrometry-based metabolomics

Metabolite identification

As shown in [Table 3], after derivatization and GC-MS analysis, 65 metabolites and 69 metabolites were detected in the blood of Group D and Group C, respectively, which mainly included organic acids, amino acids, carbohydrates, and lipids.{Table 3}

Multivariate statistical analysis for metabolomics

The QCs and experimental data from Group D and Group C were imported into SIMCA-P 14.1 for PCA. As shown in [Figure 3], the QCs clustered together, which means that this GC-MS method is relatively stable.{Figure 3}

The OPLS-DA analysis was performed to achieve the fundamental separation between Group B and the experimental groups were achieved, as shown in [Figure 4]. Cross-validation was used to confirm the reliability of the regression model. In the OPLS-DA model of diazepam, R2 = 0.99702 and Q2 = 0.7854. In the OPLS-DA model of clonazepam, R2 = 0.99628 and Q2 = 0.80899. R2 represents the goodness of fit of this model, and Q2 represents the model's predictive ability. R2 = 1 indicates a perfect fit of the data by the model. Q2 = 1 indicates perfect predictability. A well-fitted model is usually indicated by R2 and Q2 values >0.5, with a difference between them of no more than 0.3.[28]{Figure 4}

Identification of differential metabolites responsible for the discrimination between groups

After the OPLS-DA between Group B and the Group D/Group C, several differential metabolites were identified [Table 4]. Metabolites with VIP >1 in the OPLS-DA models and P < 0.05 in the independent samples t-test were considered to be characteristic metabolites contributing to the separation of the study groups.[29] The changes of alanine and uracil in orbital venous blood identified among experimental sets were shown as [Figure 5].{Table 4}{Figure 5}

Practical application of the orthogonal partial least squares-discrimination analysis model (forecasting sets)

The results of the predictive datasets are given in [Table 5]. The predicted probability values of UD and UC represent the probability that these rats were assigned to Group D or Group C, respectively. The results of the predictive dataset [Table 5] suggested that these two models have probable predictive ability.{Table 5}


This study aimed to investigate the characteristic changes of metabolites in the blood of rats after ingesting diazepam/clonazepam, and establish preliminary OPLS models for determining whether the rats ingested diazepam/clonazepam using GC-MS-based metabolomics, when diazepam and clonazepam could not be detected in the blood by LC-MS/MS. Then, the predictive datasets for the OPLS-DA models of diazepam/clonazepam were respectively used to validate this metabolomics method. Previous studies of DFSA cases usually emphasize the selection of biological samples, specimen extraction, different types of detection technologies, or improvement of detection methods. Villain et al.[30] used hair samples to detect zopiclone in DFSA cases. Negrusz et al.[3] described the application of various biological materials in DFSA cases. In contrast, our study pays more attention to the characteristic changes in the metabolites of the body caused by diazepam or clonazepam exposure.

In terms of metabolomics data analysis, high-dimensional data generated by chromatography and mass spectrometry can be analyzed by partial least squares discrimination analysis (PLS-DA), which may solve the problems of dimensionality reduction, classification visualization, and feature selection.[31] However, many metabolites in metabolomics studies are highly correlated and have no relationship with classification. It is impossible to concentrate the classification information on the first two or three principal components using the PLS-DA method, resulting in poor classification and visualization. In this study, to eliminate as many irrelevant interferences as possible, we selected the OPLS-DA (orthogonal PLS-DA) model to obtain more specific differential metabolites among the experimental groups. OPLS-DA is a combination of orthogonal signal correction (OSC) and partial least squares (PLS). The most important feature of this model is that it can remove variation from a data matrix X that is orthogonal to the response matrix Y.[32],[33] Therefore, the classification information is mainly concentrated in a principal component, which makes the model simple and easy to explain. The discriminant effect and visualization effect of the score maps are visible. Therefore, the OPLS-DA classification model can be more easily applied to our study.

The differential metabolites in these two models were similar, which may be explained by the fact that diazepam and clonazepam are both benzodiazepines. Therefore, the changes of metabolites in the rat body are similar after taking these drugs. In this metabolomics study, two characteristic metabolites were uracil and alanine specific for diazepam and clonazepam [Figure 5]. Uracil is a pyrimidine nucleotide in the body, which metabolite is alanine, a neurotransmitter existed in the brain. It has been demonstrated by Anastasia et al.[34] that alanine can sensitize and regulate chloride channels. Besides, Wu et al.[35] also reported that the alanine could activate GABA receptors as a neurotransmitter through in vitro experiments. Consistent with these references, the metabolism mechanisms of benzodiazepines are similar: benzodiazepines act on a complex of GABA receptors and chloride channels. By enhancing the activity of GABA and further opening chloride channels, chloride ions can enter the cells in large quantities, causing hyperpolarization of nerve cells, which ultimately plays a central inhibitory role in the brain.[36] In this study, we found that the concentration of uracil was decreased while that of alanine increased, which might lead to higher levels of alanine in the blood. Alanine may then enter the brain through the blood–brain barrier. It is worth pointing out that uracil and alanine may be the characteristic differential metabolites in the blood after the rats ingested diazepam or clonazepam. Nevertheless, there are some limitations to this study. Drugs other than diazepam and clonazepam are involved in DFSA cases. More drugs related to DFSA cases may need to be studied. This study is only an animal experiment, and there may be metabolic differences between humans and animals. However, as a preliminary study, these results can be used to further expand the detection methods in DFSA cases, and they may even provide ideas for forensic identification in other cases.


It has been demonstrated that uracil and alanine may as the characteristic metabolites in blood after the rats ingested diazepam or clonazepam, which transformation may provide a basis for the evaluation whether the body has ingested these drugs.

Financial support and sponsorship

The study was financially supported by the Project of the National Natural Sciences Foundation of China (81373239) and The Innovation and Business Starting-oriented training program of College Students in Sichuan Province (C2020113713).

Conflicts of interest

There are no conflicts of interest.


1Xiang P, Shen M, Drummer OH. Review: Drug concentrations in hair and their relevance in drug facilitated crimes. J Forensic Leg Med 2015;36:126-35.
2LeBeau M, Andollo W, Hearn WL, Baselt R, Cone E, Finkle B, et al. Recommendations for toxicological investigations of drug-facilitated sexual assaults. J Forensic Sci 1999;44:227-30.
3Negrusz A, Gaensslen RE. Analytical developments in toxicological investigation of drug-facilitated sexual assault. Analytical Bioanalytical Chem 2003;376:1192-7.
4Birkler RI, Telving R, Ingemann-Hansen O, Charles AV, Johannsen M, Andreasen MF. Screening analysis for medicinal drugs and drugs of abuse in whole blood using ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC-TOF-MS)-toxicological findings in cases of alleged sexual assault. Forensic Sci Int 2012;222:154-61.
5Chen H, Xiang P, Shen M. The role of segmental analysis of clonazepam in hair in drug facilitated cases. Fa Yi Xue Za Zhi 2017;33:252-7.
6Toth M, Bereczki A, Drabant S, Nemes KB, Varga B, Grezal G, et al. Gas chromatography nitrogen phosphorous detection (GC-NPD) assay of tofisopam in human plasma for pharmacokinetic evaluation. J Pharm Biomed Analysis 2006;41:1354-9.
7Alvarez-Freire I, Brunetti P, Cabarcos-Fernandez P, Fernandez-Liste A, Tabernero-Duque MJ, Bermejo-Barrera AM. Determination of benzodiazepines in pericardial fluid by gas chromatography-mass spectrometry. J Pharm Biomed Analysis 2018;159:45-52.
8Behnoush B, Sheikhazadi A, Bazmi E, Fattahi A, Sheikhazadi E, Saberi Anary SH. Comparison of UHPLC and HPLC in benzodiazepines analysis of postmortem samples: A case-control study. Medicine (Baltimore) 2015;94:e640.
9De Boeck M, Missotten S, Dehaen W, Tytgat J, Cuypers E. Development and validation of a fast ionic liquid-based dispersive liquid-liquid microextraction procedure combined with LC-MS/MS analysis for the quantification of benzodiazepines and benzodiazepine-like hypnotics in whole blood. Forensic Sci Int 2017;274:44-54.
10Cheze M, Villain M, Pepin G. Determination of bromazepam, clonazepam and metabolites after a single intake in urine and hair by LC-MS/MS. Application to forensic cases of drug facilitated crimes. Forensic Sci Int 2004;145:123-30.
11Huang W, Moody DE. Immunoassay detection of benzodiazepines and benzodiazepine metabolites in blood. J Analytical Toxicol 1995;19:333-42.
12Breimer DD, Jochemsen R. Clinical pharmacokinetics of hypnotic benzodiazepines: A summary. Br J Clin Pharmacol 2012;16:277S-8S.
13Ministry of Justice, Home Office, Office for National Statistics, an Overview of Sexual Offending in England and Wales. Available from:[Last accessed 2018 Nov 02].
14Johnson H. Why doesn't she just report it? Apprehensions and contradictions for women who report sexual violence to the police. Canadian J Women Law 2017;29:36-59.
15Adamowicz P, Kala M. Simultaneous screening for and determination of 128 date-rape drugs in urine by gas chromatography-electron ionization-mass spectrometry. Forensic Sci Int 2010;198:39-45.
16Li P, Wei DD, Wang JS, Yang MH, Kong LY. H NMR metabolomics to study the effects of diazepam on Anisatin induced convulsive seizures. J Pharm Biomed Analysis 2016;117:184-94.
17Yang J, Wang H, Xu W, Hao D, Du L, Zhao X, et al. Metabolomic analysis of rat plasma following chronic low-dose exposure to dichlorvos. Human Experim Toxicol 2013;32:196-205.
18Wang Z, Ma J, Zhang M, Wen C, Huang X, Sun F, et al. Serum Metabolomics in Rats after Acute Paraquat Poisoning. Biol Pharm Bull 2015;38:1049-53.
19Dai X, Fan F, Ye Y, Lu X, Chen F, Wu Z, et al. An experimental study on investigating the postmortem interval in dichlorvos poisoned rats by GC/MS-based metabolomics. Legal Med (Tokyo) 2018;36:28-36.
20Yan H, Qiao Z, Shen B, Xiang P, Shen M. Plasma metabolic profiling analysis of toxicity induced by brodifacoum using metabonomics coupled with multivariate data analysis. Forensic Sci Int 2016;267:129-35.
21Wu Z, Lu X, Chen F, Dai X, Ye Y, Yan Y, et al. Estimation of early postmortem interval in rats by GC-MS-based metabolomics. Leg Med (Tokyo) 2018;31:42-8.
22Wu Z, Chen F, Wu H, Chen J, Wei Q, Fu Y, et al. Urinary metabonomics of rats withketamine-induced cystitis using GC-MS spectroscopy, Int J Clin Experim Pathol 2018;11:558-67.
23Monteiro MS, Carvalho M, Bastos ML, Pinho PG. Metabolomics analysis for biomarker discovery: Advances and challenges. Curr Med Chem 2013;20:257-71.
24US FDA. Guidance for Industry: Bioanalytical Method Validation; 2018.
25Slupski W, Trocha M, Rutkowska M. Pharmacodynamic and pharmacokinetic interactions between simvastatin and diazepam in rats. Pharmacol Rep 2017;69:943-52.
26Wang L, Edge JH, Ono J, Walson PD. Pharmacokinetics of clonazepam in developing rats. Chin Med J 1997;105:726-31.
27Carpenter RA, Hollowell RH, Hill KM. Determination of the metabolites of the herbicide dimethyl tetrachloroterephthalate in drinking water by high-performance liquid chromatography with gas chromatography/mass spectrometry confirmation. Analytical Chem 1997;69:3314-20.
28Triba MN, Le Moyec L, Amathieu R, Goossens C, Bouchemal N, Nahon P, et al. PLS/OPLS models in metabolomics: The impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Molecular BioSyst 2015;11:13-9.
29Werner E, Croixmarie V, Umbdenstock T, Ezan E, Chaminade P, Tabet JC, et al. Mass spectrometry-based metabolomics: Accelerating the characterization of discriminating signals by combining statistical correlations and ultrahigh resolution. Analytical Chem 2008;80:4918-32.
30Villain M, Cheze M, Tracqui A, Ludes B, Kintz P. Testing for zopiclone in hair application to drug-facilitated crimes. Forensic Sci Int 2004;145:117-21.
31Barker M, Rayens W. Partial least squares for discrimination. J Chem 2003;17:166-73.
32Wold S, Antti H, Lindgren F, Öhman J. Orthogonal signal correction of near-infrared spectra. Chem Intelligent Lab Syst 1998;44:175-85.
33Westerhuis JA, van Velzen EJ, Hoefsloot HC, Smilde AK. Multivariate paired data analysis: Multilevel PLSDA versus OPLSDA. Metabolomics 2010;6:119-28.
34Bakardjiev A, Bauer K. Transport of β-alanine and biosynthesis of carnosine by skeletal muscle cells in primary culture. Europ J Biochem 1994;225:617-23.
35Wu FS, Gibbs TT, Farb DH. Dual activation of GABAA and glycine receptors by β-alanine: Inverse modulation by progesterone and 5α-pregnan-3α-ol-20-one. Europ J Pharmacol 1993;246:239-46.
36Vinkers CH, Tijdink JK, Luykx JJ, Vis R. Choosing the correct benzodiazepine: Mechanism of action and pharmacokinetics. Ned Tijdschr Geneeskd 2012;155:A4900.