Robust Bayesian Algorithm for Targeted Compound Screening in Forensic Toxicology

Toward the General Mechanistic Model of Liquid Chromatographic Retention

Large datasets of chromatographic retention times are relatively easy to collect. This statement is particularly true when mixtures of compounds are analyzed under a series of gradient conditions using chromatographic techniques coupled with mass spectrometry detection. Such datasets carry much information about chromatographic retention that, if extracted, can provide useful predictive information. In this work, we proposed a mechanistic model that jointly explains the relationship between pH, organic modifier type, temperature, gradient duration, and analyte retention based on liquid chromatography retention data collected for 187 small molecules. The model was built utilizing a Bayesian multilevel framework. The model assumes (i) a deterministic Neue equation that describes the relationship between retention time and analyte-specific and instrument-specific parameters, (ii) the relationship between analyte-specific descriptors (log P, pK_a, and functional groups) and analyte-specific chromatographic parameters, and (iii) stochastic components of between-analyte and residual variability. The model utilizes prior knowledge about model parameters to regularize predictions which is important as there is ample information about the retention behavior of analytes in various stationary phases in the literature. The usefulness of the proposed model in providing interpretable summaries of complex data and in decision making is discussed.

Liquid Chromatography High Resolution Mass Spectrometry in Forensic Toxicology: What Are the Specifics of Method Development, Validation and Quality Assurance for Comprehensive Screening Approaches?

The use of High Resolution Mass Spectrometry (HRMS) has increased over the past decade in clinical and forensic toxicology, especially for comprehensive screening approaches. Despite this, few guidelines of this field have specifically addressed HRMS issues concerning compound identification, validation, measurement uncertainty and quality assurance. To fully implement this technique, certainly in an era in which the quality demands for laboratories are ever increasing due to various norms (e.g. the International Organization for Standardization's ISO 17025), these specific issues need to be addressed. This manuscript reviews 26 HRMS-based methods for qualitative systematic toxicological analysis (STA) published between 2011 and 2021. Key analytical data such as samples matrices, analytical platforms, numbers of analytes and employed mass spectral reference databases/libraries as well as the studied validation parameters are summarized and discussed. The article further includes a critical review of targeted and untargeted data acquisition approaches, available HRMS reference databases and libraries as well as current guidelines for HRMS data interpretation with a particular focus on identification criteria. Moreover, it provides an overview on current recommendations for the validation and determination measurement uncertainty of qualitative methods. Finally, the article aims to put forward suggestions for method development, compound identification, validation experiments to be performed, and adequate determination of measurement uncertainty for this type of wide-range qualitative HRMS-based methods.

The Future of Analytical and Interpretative Toxicology: Where are We Going and How Do We Get There?

(Forensic) toxicology has faced many challenges, both analytically and interpretatively, especially in relation to an increase in potential drugs of interest. Analytical toxicology and its application to medicine and forensic science have progressed rapidly within the past centuries. Technological innovations have enabled detection of more substances with increasing sensitivity in a variety of matrices. Our understanding of the effects (both intended and unintended) have also increased along with determination and degree of toxicity. However, it is clear there is even more to understand and consider. The analytical focus has been on typical matrices such as blood and urine but other matrices could further increase our understanding, especially in postmortem (PM) situations. Within this context, the role of PM changes and potential redistribution of drugs requires further research and identification of markers of its occurrence and extent. Whilst instrumentation has improved, in the future, nanotechnology may play a role in selective and sensitive analysis as well as bioassays. Toxicologists often only have an advisory impact on pre-analytical and pre-interpretative considerations. The collection of appropriate samples at the right time in an appropriate way as well as obtaining sufficient circumstance background is paramount in ensuring an effective analytical strategy to provide useful results that can be interpreted within context. Nevertheless, key interpretative considerations such as pharmacogenomics and drug-drug interactions as well as determination of tolerance remain and in the future, analytical confirmation of an individual's metabolic profile may support a personalized medicine and judicial approach. This should be supported by the compilation and appropriate application of drug data pursuant to the situation. Specifically, in PM circumstances, data pertaining to where a drug was not/may have been/was contributory will be beneficial with associated pathological considerations. This article describes the challenges faced within toxicology and discusses progress to a future where they are being addressed.

Deep Q-learning for the selection of optimal isocratic scouting runs in liquid chromatography

An important challenge in chromatography is the development of adequate separation methods. Accurate retention models can significantly simplify and expedite the development of adequate separation methods for complex mixtures. The purpose of this study was to introduce reinforcement learning to chromatographic method development, by training a double deep Q-learning algorithm to select optimal isocratic scouting runs to generate accurate retention models. These scouting runs were fit to the Neue-Kuss retention model, which was then used to predict retention factors both under isocratic and gradient conditions. The quality of these predictions was compared to experimental data points, by computing a mean relative percentage error (MRPE) between the predicted and actual retention factors. By providing the reinforcement learning algorithm with a reward whenever the scouting runs led to accurate retention models and a penalty when the analysis time of a selected scouting run was too high (> 1h); it was hypothesized that the reinforcement learning algorithm should by time learn to select good scouting runs for compounds displaying a variety of characteristics. The reinforcement learning algorithm developed in this work was first trained on simulated data, and then evaluated on experimental data for 57 small molecules - each run at 10 different fractions of organic modifier (0.05 to 0.90) and four different linear gradients. The results showed that the MRPE of these retention models (3.77% for isocratic runs and 1.93% for gradient runs), mostly obtained via 3 isocratic scouting runs for each compound, were comparable in performance to retention models obtained by fitting the Neue-Kuss model to all (10) available isocratic datapoints (3.26% for isocratic runs and 4.97% for gradient runs) and retention models obtained via a "chromatographer's selection" of three scouting runs (3.86% for isocratic runs and 6.66% for gradient runs). It was therefore concluded that the reinforcement learning algorithm learned to select optimal scouting runs for retention modeling, by selecting 3 (out of 10) isocratic scouting runs per compound, that were informative enough to successfully capture the retention behavior of each compound.

An artificial neural network system to identify alleles in reference electropherograms

Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells them about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. This process of interpreting the electropherograms can be time consuming and is prone to subjective differences between analysts. Recently it was demonstrated that artificial neural networks could be used to classify information within an electropherogram as allelic (i.e. representative of a DNA fragment present in the DNA extract) or as one of several different categories of artefactual fluorescence that arise as a result of generating an electropherogram. We extend that work here to demonstrate a series of algorithms and artificial neural networks that can be used to identify peaks on an electropherogram and classify them. We demonstrate the functioning of the system on several profiles and compare the results to a leading commercial DNA profile reading system.

A New Bayesian Approach for Estimating the Presence of a Suspected Compound in Routine Screening Analysis

A novel method for compound identification in liquid chromatography-high resolution mass spectrometry (LC-HRMS) is proposed. The method, based on Bayesian statistics, accommodates all possible uncertainties involved, from instrumentation up to data analysis into a single model yielding the probability of the compound of interest being present/absent in the sample. This approach differs from the classical methods in two ways. First, it is probabilistic (instead of deterministic); hence, it computes the probability that the compound is (or is not) present in a sample. Second, it answers the hypothesis "the compound is present", opposed to answering the question "the compound feature is present". This second difference implies a shift in the way data analysis is tackled, since the probability of interfering compounds (i.e., isomers and isobaric compounds) is also taken into account.