Abstract: The European BorderSens project leverages voltammetric sensors, developed with end-users' input, to rapidly and accurately detect illicit drugs. By embracing practicalities and validation, this technology has the potential to combat the illicit drug problem.

Abstract: More and more countries and organisations emphasise the value of harm reduction measures in the context of illicit drug use and abuse. One of these measures is drug checking, a preventive action that can represent a quick win by tailored consultation on the risks of substance use upon analytical screening of a submitted sample. Unlike drop-in centres that operate within a fixed setting, enabling drug checking in a harm reduction context at events requires portable, easy to use analytical approaches, operated by personnel with limited knowledge of analytical chemistry. In this case study, four different approaches were compared for the characterisation of 3,4-methylenedioxymethamphetamine samples and this in the way the approaches would be applied today in an event context. The four approaches are mid-infrared (MIR), near-infrared, and Raman spectroscopy, which are today used in drug checking context in Belgium, as well as an electrochemical sensor approach initially developed in the context of law enforcement at ports. The MIR and the electrochemical approach came out best, with the latter allowing for a direct straightforward analysis of the percentage 3,4-methylenedioxymethamphetamine (as base equivalent) in the samples. However, MIR has the advantage that, in a broader drug checking context, it allows to screen for several molecules and so is able to identify unexpected active components or at least the group to which such components belong. The latter is also an important advantage in the context of the growing emergence of new psychotropic substances. MIR, NIR, Raman spectroscopy, and an electrochemical sensor (Narcoreader (R)) for MDMA analysis were compared in a realistic harm reduction context. NIR and Raman failed in simple library approaches. MIR and Narcoreader (R) were preferred. MIR came out as first choice. MIR and Narcoreader (R) have complementary (dis)advantages and could be used in a two-step approach: MIR for screening and Narcoreader (R) for dosage/risk evaluation of MDMA samples.image

Abstract: Illicit drugs are harmful substances, posing a threat to the health and safety of society. Policies, such as supply reduction and harm reduction, are in place to combat the illicit drug problem. Science can play a substantial role in this fight, by providing tools that enable these policies to be successfully enforced. One example are on-site detection tools, i.e. sensors that allow the on-site identification of an illicit drug in a sample of interest. Several technologies, such as color tests and portable spectroscopic techniques, are currently employed for this goal. Although these are valuable techniques, there is an opportunity for voltammetry, an electrochemical technique, to make an impactful addition to this repertoire of on-site detection tools. Despite its attractive features (low-cost, portable, short analysis time, indifference to color,...), voltammetric illicit drug sensor have failed to make an impact in real scenarios. The work outlined in this PhD thesis aims to change this by bringing the technology from the lab to the field. Strategic choices, fueled by feedback from end-users, were made to further develop those specific aspects of the technology that previously haltered the technology to fulfill its potential. A detection algorithm was introduced that converts the voltammetric output into a clear-cut interpretation thereof, opening up the technology to end-users without prior knowledge of the technology. A sensor that allows qualitative and quantitative detection of the psychoactive drug MDMA was introduced, and importantly, validated on a large set of 212 confiscated samples. A state-of-the art mobile application and adequate sampling methodology were developed, alongside other, often more practical studies and product developments, to evolve the technology into a product that truly creates value for end-users. Important steps towards multidrug detection were made with a festival sensor and a flowchart based on visual appearance that ties together a variety of voltammetric single sensors into a single multidrug sensing approach. Last but not least, multiple valorization aspects were researched, including a market study and an analysis to determine the optimal commercialization strategy. Overall, this PhD thesis has facilitated the transition of the voltammetric illicit drug sensing technology from lab to on-site application. The final application creates value for end-users, and is ready to make an impact in real on-site scenarios.

Abstract: This work presents the results of a novel application for the fast on-site screening of cocaine and its main cutting agents in suspicious and confiscated samples. The methodology behind the novel application consists of portable electrochemical detection coupled with a peak-recognition algorithm for automated result output generation, validated both in laboratory and field settings. Currently used field tests, predominantly colorimetric tests, are lacking accuracy, often giving false positive or negative results. This presses the need for alternative approaches to field testing. By combining portable electrochemical approaches with peak-recognition algorithms, an accuracy of 98.4% concerning the detection of cocaine was achieved on a set of 374 powder samples. In addition, the approach was tested on multiple 'smuggled', colored cocaine powders and cocaine mixtures in solid and liquid states, typically in matrices such as charcoal, syrup and clothing. Despite these attempts to hide cocaine, our approach succeeded in detecting cocaine during on-site screening scenarios. This feature presents an advantage over colorimetric and optical detection techniques, which can fail with colored sample matrices. This enhanced accuracy on smuggled samples will lead to increased efficiency in confiscation procedures in the field, thus significantly reducing societal economic and safety concerns and highlighting the potential for electrochemical approaches in on-the-spot identification of drugs of abuse.

Abstract: Forensic drug laboratories are confronted with increasing amounts of drugs and a demand for faster results that are directly available on-site. In addition, the drug market is getting more complex with hundreds of new psychoactive substances (NPS) entering the market in recent years. Rapid and on-scene presumptive drug testing therefore faces a shift from manual colorimetric tests towards approaches that can detect a wider range of components and process results automatically. Electrochemical detection offers these desired characteristics, making it a suitable candidate for on-site drug detection. In this study, a two-step electrochemical sensor is introduced for the detection of MDMA and 2C-B. Firstly, a direct electrochemical analysis was performed to detect MDMA. Validation experiments on over 70 substances revealed that 2C-B was the only frequently encountered drug that gave a false positive result for MDMA in this first analysis. A second step using in-situ derivatization was subsequently introduced. To this end, formaldehyde was used for N-methylation of 2C-B thereby enhancing its electrochemical profile. The enriched electrochemical fingerprint in the second step allowed for clear differentiation between MDMA and 2C-B. The applicability of this approach was demonstrated with 71 ecstasy tablets seized by the Amsterdam Police. The MDMA/2C-B sensor correctly identified all 39 MDMA-containing tablets and 10 out of 11 tablets containing 2C-B. Most notably, correct results were also obtained for dark colored tablets in which both spectroscopic analysis and colorimetric tests failed due to obscured signals.

Abstract: The added value of supervised Machine Learning (ML) methods to determine the Absolute Configuration (AC) of compounds from their Vibrational Circular Dichroism (VCD) spectra was explored. Among all ML methods considered, Random Forest (RF) and Feedforward Neural Network (FNN) yield the best performance for identification of the AC. At its best, FNN allows near-perfect AC determination, with accuracy of prediction up to 0.995, while RF combines good predictive accuracy (up to 0.940) with the ability to identify the spectral areas important for the identification of the AC. No loss in performance of either model is observed as long as the spectral sampling interval used does not exceed the spectral bandwidth. Increasing the sampling interval proves to be the best method to lower the dimensionality of the input data, thereby decreasing the computational cost associated with the training of the models.

Abstract: Wearable electrochemical sensors capable of noninvasive monitoring of chemical markers represent a rapidly emerging digital-health technology. Recent advances toward wearable continuous glucose monitoring (CGM) systems have ignited tremendous interest in expanding such sensor technology to other important fields. This article reviews for the first time wearable electrochemical sensors for monitoring therapeutic drugs and drugs of abuse. This rapidly emerging class of drug-sensing wearable devices addresses the growing demand for personalized medicine, toward improved therapeutic outcomes while minimizing the side effects of drugs and the related medical expenses. Continuous, noninvasive monitoring of therapeutic drugs within bodily fluids empowers clinicians and patients to correlate the pharmacokinetic properties with optimal outcomes by realizing patient-specific dose regulation and tracking dynamic changes in pharmacokinetics behavior while assuring the medication adherence of patients. Furthermore, wearable electrochemical drug monitoring devices can also serve as powerful screening tools in the hands of law enforcement agents to combat drug trafficking and support on-site forensic investigations. The review covers various wearable form factors developed for noninvasive monitoring of therapeutic drugs in different body fluids and toward on-site screening of drugs of abuse. The future prospects of such wearable drug monitoring devices are presented with the ultimate goals of introducing accurate real-time drug monitoring protocols and autonomous closed-loop platforms toward precise dose regulation and optimal therapeutic outcomes. Finally, current unmet challenges and existing gaps are discussed for motivating future technological innovations regarding personalized therapy. The current pace of developments and the tremendous market opportunities for such wearable drug monitoring platforms are expected to drive intense future research and
commercialization efforts.