Time-course measurements of drugs and metabolites transferred from fingertips after drug administration: usefulness of fingerprints for drug testing

Determination of carbamazepine in fingerprints: a feasibility study to evaluate adherence in epilepsy patients

Background: Fingerprint drug concentrations can be used as a noninvasive and convenient alternative to evaluate adherence to pharmacotherapy. Methods: Fingerprints were applied over glass slides, extracted and analyzed by ultra-high performance LC-MS/MS. The assay and drug adherence questionnaires were applied to 30 epilepsy patients. Results: The assay had linearity in the range 0.05-10 ng fingerprint-1, with precision of 2.16-7.9% and accuracy of 95.0-102.8%. Carbamazepine (CBZ) levels in fingerprints were stable at 45°C for 15 days. Concentrations in patient samples were 0.06-9.28 ng fingerprint-1. A significant difference (p = 0.003) was found between CBZ concentrations in fingerprints between patient groups divided as low and medium/high adherence. Conclusion: This method can potentially be applied to the identification of epilepsy patients with low adherence to CBZ pharmacotherapy.

Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

Potential to Use Fingerprints for Monitoring Therapeutic Levels of Isoniazid and Treatment Adherence

A fingerprint offers a convenient, noninvasive sampling matrix for monitoring therapeutic drug use. However, a barrier to widespread adoption of fingerprint sampling is the fact that the sample volume is uncontrolled. Fingerprint samples (n = 140) were collected from patients receiving the antibiotic isoniazid as part of their treatment, as well as from a drug-naive control group (n = 50). The fingerprint samples were analyzed for isoniazid (INH) and acetylisoniazid (AcINH), using liquid chromatography high-resolution mass spectrometry. The data set was analyzed retrospectively for metabolites known to be present in eccrine sweat. INH or AcINH was detected in 89% of the fingerprints collected from patients and in 0% of the fingerprints collected from the control group. Metabolites lysine, ornithine, pyroglutamic acid, and taurine were concurrently detected alongside INH/AcINH and were used to determine whether the fingerprint sample was sufficient for testing. Given a sufficient sample volume, the fingerprint test for INH use has sensitivity, specificity, and accuracy of 100%. Normalization to taurine was found to reduce intradonor variability. Fingerprints are a novel and noninvasive approach to monitor INH therapy. Metabolites can be used as internal markers to demonstrate a sufficient sample volume for testing and reduce intradonor variability.

Finger sweat analysis enables short interval metabolic biomonitoring in humans

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

Toxicological hair analysis: Pre-analytical, analytical and interpretive aspects

Background and aims Hair analysis for drug detection is one of the widely accepted imperative techniques in the field of forensic toxicology. The current study was designed to investigate the efficacy of chromatography for detection of drugs of abuse in hair. Method A comprehensive review of articles from last two decades on hair analyses via PubMed and similar resources was performed. Issues concerning collection, decontamination and analytical techniques are summarised. Physiochemical nature of hair, mechanism of drug incorporation and its stability in hair are briefly discussed. Furthermore, various factors affecting results and interpretation are elucidated. Result A hair sample is chosen over traditional biological samples such blood, urine, saliva or tissues due to its inimitable ability to provide a longer time frame for drug detection. Its collection is almost non-invasive, less cumbersome and does not involve any specialised training/expertise. Recent advances in analytical technology have resulted in better sensitivity, reproducibility and accuracy, thus providing a new arena of scientific understanding and test interpretation. Conclusion Though recent studies have yielded many insights into drug binding and drug incorporation in hair, the major challenge in hair analysis lies in the interpretation of results, which may be affected by external contamination and thus lead to false-positives. Therefore, there is a need for more sensitive and selective analysis methods to be developed in order to minimise factors that induce the effect of melanin, age and so on, and this would certainly provide a new dimension to hair analysis and its applications.

Noninvasive Detection of Cocaine and Heroin Use with Single Fingerprints: Determination of an Environmental Cutoff

BACKGROUND

Recent publications have explored the possibility of using fingerprints to confirm drug use, but none has yet dealt with environmental contamination from fingertips. Here we explored the possibility of establishing an environmental cutoff for drug testing from a single fingerprint.

METHODS

Fingerprint samples (n = 100) were collected from the hands of 50 nondrug users before and after handwashing to establish separate environmental cutoff values and testing protocols for cocaine, benzoylecgonine, heroin, and 6-monoacetylmorphine. The cutoff was challenged by testing the fingerprints of drug-free volunteers after shaking hands with drug users. Fingerprints from patients who testified to taking cocaine (n = 32) and heroin (n = 24) were also collected and analyzed.

RESULTS

A different cutoff value needed to be applied, depending on whether the fingerprints were collected as presented or after handwashing. Applying these cutoffs gave a 0% false-positive rate from the drug-free volunteers. After application of the cutoff, the detection rate (compared to patient testimony) for washed hands of patients was 87.5% for cocaine use and 100% for heroin use.

CONCLUSIONS

Fingerprints show enhanced levels of cocaine, heroin, and their respective metabolites in patients who testified to taking the substances, compared with the population of naïve drug users surveyed, and a cutoff (decision level) can be established. The cutoff is robust enough to account for small increases in analyte observed after secondary transfer.

Analysis of Urine, Oral fluid and Fingerprints by Liquid Extraction Surface Analysis Coupled to High Resolution MS and MS/MS - Opportunities for Forensic and Biomedical Science

Liquid Extraction Surface Analysis (LESA) is a new, high throughput tool for ambient mass spectrometry. A solvent droplet is deposited from a pipette tip onto a surface and maintains contact with both the surface and the pipette tip for a few seconds before being re-aspirated. The technique is particularly suited to the analysis of trace materials on surfaces due to its high sensitivity and low volume of sample removal. In this work, we assess the suitability of LESA for obtaining detailed chemical profiles of fingerprints, oral fluid and urine, which may be used in future for rapid medical diagnostics or metabolomics studies. We further show how LESA can be used to detect illicit drugs and their metabolites in urine, oral fluid and fingerprints. This makes LESA a potentially useful tool in the growing field of fingerprint chemical analysis, which is relevant not only to forensics but also to medical diagnostics. Finally, we show how LESA can be used to detect the explosive material RDX in contaminated artificial fingermarks.