Thomas H. Miller

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis.

The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concentration level rely on analytical reference standards for chromatographic retention time (tR) and mass spectral comparisons. Chromatographic tR prediction can play a role in increasing confidence in suspect screening efforts for new compounds in the environment, especially when standards are not available, but reliable methods are lacking. The current work focuses on the development of artificial neural networks (ANNs) for tR prediction in gradient reversed-phase liquid chromatography and applied along with HRMS data to suspect screening of wastewater and environmental surface water samples. Based on a compound tR dataset of >500 compounds, an optimized 4-layer back-propagation multi-layer perceptron model enabled predictions for 85% of all compounds to within 2min of their measured tR for training (n=344) and verification (n=100) datasets. To evaluate the ANN ability for generalization to new data, the model was further tested using 100 randomly selected compounds and revealed 95% prediction accuracy within the 2-minute elution interval. Given the increasing concern on the presence of drug metabolites and other transformation products (TPs) in the aquatic environment, the model was applied along with HRMS data for preliminary identification of pharmaceutically-related compounds in real samples. Examples of compounds where reference standards were subsequently acquired and later confirmed are also presented. To our knowledge, this work presents for the first time, the successful application of an accurate retention time predictor and HRMS data-mining using the largest number of compounds to preliminarily identify new or emerging contaminants in wastewater and surface waters.

Prediction of chromatographic retention time in high-resolution anti-doping screening data using artificial neural networks

The computational generation of gradient retention time data for retrospective detection of suspected sports doping species in postanalysis human urine sample data is presented herein. Retention data for a selection of 86 compounds included in the London 2012 Olympic and Paralympic Games drug testing schedule were used to train, verify, and test a range of computational models for this purpose. Spiked urine samples were analyzed using solid phase extraction followed by ultrahigh-pressure gradient liquid chromatography coupled to electrospray ionization high-resolution mass spectrometry. Most analyte retention times varied ≤0.2 min over the relatively short runtime of 10 min. Predicted retention times were within 0.5 min of experimental values for 12 out of 15 blind test compounds (largest error: 0.97 min). Minimizing the variance in predictive ability across replicate networks of identical architecture is presented for the first time along with a quantitative discussion of the contribution of each selected molecular descriptor toward the overall predicted value. The performance of neural computing predictions for isobaric compound retention time is also discussed. This work presents the application of neural networks to the prediction of gradient retention time in archived high-resolution urine analysis sample data for the first time in the field of anti-doping.

DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing

Highlights • Blood DNA methylation profiles of 1156 individuals were assessed for age correlation.• Stepwise regression identified 23 age-associated CpG sites in DNA from blood.• A machine learning model based on 16 markers predicted age with a mean error of 3.8 years.• The model predicted age successfully for twins and ‘diseased’ individuals.• A new NGS-based method was combined with machine learning for age prediction.

Pharmaceuticals in the freshwater invertebrate, Gammarus pulex, determined using pulverised liquid extraction, solid phase extraction and liquid chromatography-tandem mass spectrometry

The development, characterisation and application of a new analytical method for multi-residue PPCP determination in the freshwater amphipod, Gammarus pulex are presented. Analysis was performed using pulverised liquid extraction (PuLE), solid phase extraction (SPE) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Qualitative method performance offered excellent limits of detection at <20 ng g(-1) for 18 out of 29 compounds. For quantitative application, linearity and precision were considered acceptable for 10 compounds across the ng-μg g(-1) range (R2≥0.99; ≤20% relative standard deviation respectively). The method was applied to the analysis of G. pulex and river water sourced from six tributaries of the River Thames. Carbamazepine, diazepam, nimesulide, trimethoprim and warfarin were determined in G. pulex samples at low ng g(-1) (dry weight) concentrations across these sites. Temazepam and diclofenac were also detected, but were not quantifiable. Six pharmaceuticals were quantified in surface waters across the eight sites at concentrations ranging from 3 to 344 ng L(-1). The possibility for confirmatory detection and subsequent quantification of pharmaceutical residues in benthic organisms such as G. pulex will enable further understanding on the susceptibility and ecological effects of PPCPs in the aquatic environment.

Artificial neural network modelling of pharmaceutical residue retention times in wastewater extracts using gradient liquid chromatography-high resolution mass spectrometry data

The modelling and prediction of reversed-phase chromatographic retention time (tR) under gradient elution conditions for 166 pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on tR measurements is presented. Measured tR for some compounds in influent wastewater varied by >1min in comparison to tR in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of <1.3min at the 75th percentile of all measured tR data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 blind test compounds in wastewater matrices lay at or above R(2)=0.92. Finally, the model was evaluated for application to the semi-targeted identification of pharmaceutical residues during a weeklong wastewater sampling campaign. The model successfully identified native compounds at a rate of 83±4% and 73±5% in influent and effluent extracts, respectively. The use of an HRMS database and the optimised ANN model was also applied to shortlisting of 37 additional compounds in wastewater. Ultimately, this research will potentially enable faster identification of emerging contaminants in the environment through more efficient post-acquisition data mining.

The first attempt at non-linear in silico prediction of sampling rates for Polar Organic Chemical Integrative Samplers (POCIS)

Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d(-1) (RTD-model) and 0.03 ± 0.03 L d(-1) (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d(-1) and 0.0437 ± 0.02 L d(-1), respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations.

Targeted metabolomics of Gammarus pulex following controlled exposures to selected pharmaceuticals in water

The effects of pharmaceuticals and personal care products (PPCPs) on aquatic organisms represent a significant current concern. Herein, a targeted metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS) is presented to characterise concentration changes in 29 selected metabolites following exposures of aquatic invertebrates, Gammarus pulex, to pharmaceuticals. Method performance revealed excellent linearity (R2 > 0.99), precision (0.1–19%) and lower instrumental limits of detection (0.002–0.20 ng) for all metabolites studied. Three pharmaceuticals were selected representing the low, middle and high range of measured acute measured toxicities (of a total of 26 compounds). Gammarids were exposed to both the no-observed-adverse-effect-level (NOAEL) and the lowest-observed-adverse-effect-level (LOAEL) of triclosan (0.1 and 0.3 mg L− 1), nimesulide (0.5 and 1.4 mg L− 1) and propranolol (100 and 153 mg L− 1) over 24 h. Quantitative metabolite profiling was then performed. Significant changes in metabolite concentrations relative to controls are presented and display distinct clustered trends for each pharmaceutical. Approximately 37% (triclosan), 33% (nimesulide) and 46% (propranolol) of metabolites showed statistically significant time-related effects. Observed changes are also discussed with respect to internal concentrations of the three pharmaceuticals measured using a method based on pulverised liquid extraction, solid phase extraction and LC-MS/MS. Potential metabolic pathways that may be affected by such exposures are also discussed. This represents the first study focussing on quantitative, targeted metabolomics of this lower trophic level benthic invertebrate that may elucidate biomarkers for future risk assessment.

Assessing the reliability of uptake and elimination kinetics modelling approaches for estimating bioconcentration factors in the freshwater invertebrate, Gammarus pulex

This study considers whether the current standard toxicokinetic methods are an accurate and applicable assessment of xenobiotic exposure in an aquatic freshwater invertebrate. An in vivo exposure examined the uptake and elimination kinetics for eight pharmaceutical compounds in the amphipod crustacean, Gammarus pulex by measuring their concentrations in both biological material and in the exposure medium over a 96 h period. Selected pharmaceuticals included two anti-inflammatories (diclofenac and ibuprofen), two beta-blockers (propranolol and metoprolol), an anti-depressant (imipramine), an anti-histamine (ranitidine) and two beta-agonists (formoterol and terbutaline). Kinetic bioconcentration factors (BCFs) for the selected pharmaceuticals were derived from a first-order one-compartment model using either the simultaneous or sequential modelling methods. Using the simultaneous method for parameter estimation, BCF values ranged from 12 to 212. In contrast, the sequential method for parameter estimation resulted in bioconcentration factors ranging from 19 to 4533. Observed toxicokinetic plots showed statistically significant lack-of-fits and further interrogation of the models revealed a decreasing trend in the uptake rate constant over time for rantidine, diclofenac, imipramine, metoprolol, formoterol and terbutaline. Previous published toxicokinetic data for 14 organic micro-pollutants were also assessed and similar trends were identified to those observed in this study. The decreasing trend of the uptake rate constant over time highlights the need to interpret modelled data more comprehensively to ensure uncertainties associated with uptake and elimination parameters for determining bioconcentration factors are minimised.

Uptake, biotransformation and elimination of selected pharmaceuticals in a freshwater invertebrate measured using liquid chromatography tandem mass spectrometry

Methods were developed to assess uptake and elimination kinetics in Gammarus pulex of nine pharmaceuticals (sulfamethazine, carbamazepine, diazepam, temazepam, trimethoprim, warfarin, metoprolol, nifedipine and propranolol) using targeted LC-MS/MS to determine bioconcentration factors (BCFs) using a 96 h toxicokinetic exposure and depuration period. The derived BCFs for these pharmaceuticals did not trigger any regulatory thresholds and ranged from 0 to 73 L kg−1 (sulfamethazine showed no bioconcentration). Metabolism of chemicals can affect accurate BCF determination through parameterisation of the kinetic models. The added selectivity of LC-MS/MS allowed us to develop confirmatory methods to monitor the biotransformation of propranolol, carbamazepine and diazepam in G. pulex. Varying concentrations of the biotransformed products; 4-hydroxypropranolol sulphate, carbamazepine-10,11-epoxide, nordiazepam, oxazepam and temazepam were measured following exposure of the precursor compounds. For diazepam, the biotransformation product nordiazepam was present at higher concentrations than the parent compound at 94 ng g−1 dw. Overall, the results indicate that pharmaceutical accumulation is low in these freshwater amphipods, which can potentially be explained by the rapid biotransformation and excretion.

Environmental monitoring of urban streams using a primary fish gill cell culture system (FIGCS)

The primary fish gill cell culture system (FIGCS) is an in vitro technique which has the potential to replace animals in whole effluent toxicity tests. In the current study FIGCS were transported into the field and exposed to filtered (0.2μm) river water for 24h from 4 sites, on 2 different sampling dates. Sites 1 and 2 are situated in an urban catchment (River Wandle, London, UK) with site 1 downstream of a sewage treatment work; site 3 is located in a suburban park (River Cray, Kent, UK), and site 4 is more rural (River Darent, Kent, UK). The change in transepithelial electrical resistance (TER), the expression of the metal responsive genes metallothionein A (mta) and B (mtb), cytochrome P450 1A1 (cyp1a1) and 3A27 (cyp3a27), involved in phase 1 metabolism, were assessed following exposure to sample water for 24h. TER was comparable between FIGCS exposed to 0.2μm filtered river water and those exposed to synthetic moderately soft water for 24h. During the first sampling time, there was an increase in mta, cyp1a1 and cyp3a27 gene expression in epithelium exposed to water from sites 1 and 2, and during the second sampling period an increase in cyp3a27 gene expression at sites 1 and 4. Urban river water is a complex mixture of contaminants (e.g., metals, pesticides, pharmaceuticals and polyaromatic hydrocarbons) and the increase in the expression of genes encoding mta, cyp1a1 and cyp3a27 in FIGCS is indicative of the presence of biologically active pollutants.