Leon Barron

A year-long study of the spatial occurrence and relative distribution of pharmaceutical residues in sewage effluent, receiving marine waters and marine bivalves

Reports concerning the quantitative analysis of pharmaceuticals in marine ecosystems are somewhat limited. It is necessary to determine pharmaceutical fate and assess any potential risk of exposure to aquatic species and ultimately, seafood consumers. In the work presented herein, analytical methods were optimised and validated for the quantification of pharmaceutical residues in wastewater effluent, receiving marine waters and marine mussels (Mytilus spp.). Selected pharmaceuticals included two non-steroidal anti-inflammatory drugs (NSAIDs) (diclofenac and mefenamic acid), an antibiotic (trimethoprim), an antiepileptic (carbamazepine) and a lipid regulator (gemfibrozil). This paper also presents the results of an in situ study in which caged Mytilus spp. were deployed at three sites on the Irish coastline over a 1-year period. In water samples, pharmaceutical residues were determined using solid phase extraction (SPE) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). The extraction of pharmaceuticals from mussel tissues used an additional pressurised liquid extraction (PLE) step prior to SPE and LC-MS/MS. Limits of quantification between 15 and 225 ng·L(-1) were achieved in wastewater effluent, between 3 and 38 ng·L(-1) in marine surface water and between 4 and 29 ng·g(-1) dry weight in marine mussels. Method linearity was achieved for pharmaceuticals in each matrix with correlation coefficients of R(2)≥0.976. All five selected pharmaceuticals were quantified in wastewater effluent and marine surface waters. This work has demonstrated the susceptibility of the Mytilus spp. to pharmaceutical exposure following the detection of pharmaceutical residues in the tissues of this mussel species at measurable concentrations.

An LC–MS method for the determination of pharmaceutical compounds in wastewater treatment plant influent and effluent samples

Pharmaceuticals are continually introduced into the environment as a result of industrial and domestic use. In recent years they have emerged as environmental pollutants. An analytical method has been developed allowing for simultaneous detection and identification of 20 pharmaceutical compounds from various therapeutic classes using solid phase extraction (SPE) followed by liquid chromatography-electrospray ionisation mass spectrometry (LC-MS/MS). The limits of detection and limits of quantitation for the method were in the ng/L-microg/L range. The method was applied to influent and effluent samples from three wastewater treatment plants (WWTPs). Fifteen compounds were identified in the sample matrix with salicylic acid and ibuprofen being the most abundant at 9.17 and 3.20 microg/L respectively.

Multi-residue determination of pharmaceuticals in sludge and sludge enriched soils using pressurized liquid extraction, solid phase extraction and liquid chromatography with tandem mass spectrometry

An analytical method to determine a selection of 27 frequently prescribed and consumed pharmaceuticals in biosolid enriched soils and digested sludges is presented. Using a combination of pressurized liquid extraction, solid phase extraction and liquid chromatography with tandem mass spectrometry, it was possible to detect all analytes in each sample type at the low-sub ng g(-1) level. Solid phase extraction efficiencies were compared for 6 different sorbent types and it was found that Waters Oasis HLB cartridges offered enhanced selectivities with 20 analytes showing final method recoveries > or =60% in both soils and digested sludges. The method was validated for linearity, range, precision and limits of detection in both sample matrices. All analytes were then determined in sludge enriched soils as well as the precursor thermally dried sludge fertilizer produced from a primary wastewater treatment plant. Levels of the antibacterial agent triclosan were found to exceed 20 microg g(-1) in digested sludge and 5 microg g(-1) in thermally dried sludge cake. Significant traces of carbamazepine and warfarin were also detected in the above samples.

Predicting sorption of pharmaceuticals and personal care products onto soil and digested sludge using artificial neural networks

A comprehensive analytical investigation of the sorption behaviour of a large selection of over-the-counter, prescribed pharmaceuticals and illicit drugs to agricultural soils and freeze-dried digested sludges is presented. Batch sorption experiments were carried out to identify which compounds could potentially concentrate in soils as a result of biosolid enrichment. Analysis of aqueous samples was carried out directly using liquid chromatography-tandem mass spectrometry (LC-MS/MS). For solids analysis, combined pressurised liquid extraction and solid phase extraction methods were used prior to LC-MS/MS. Solid-water distribution coefficients (K(d)) were calculated based on slopes of sorption isotherms over a defined concentration range. Molecular descriptors such as log P, pK(a), molar refractivity, aromatic ratio, hydrophilic factor and topological surface area were collected for all solutes and, along with generated K(d) data, were incorporated as a training set within a developed artificial neural network to predict K(d) for all solutes within both sample types. Therefore, this work represents a novel approach using combined and cross-validated analytical and computational techniques to confidently study sorption modes within the environment. The logarithm plots of predicted versus experimentally determined K(d) are presented which showed excellent correlation (R(2) > 0.88), highlighting that artificial neural networks could be used as a predictive tool for this application. To evaluate the developed model, it was used to predict K(d) for meclofenamic acid, mefenamic acid, ibuprofen and furosemide and subsequently compared to experimentally determined values in soil. Ratios of experimental/predicted K(d) values were found to be 1.00, 1.00, 1.75 and 1.65, respectively.

Illicit and pharmaceutical drug consumption estimated via wastewater analysis. Part A:chemical analysis and drug use estimates

This paper presents, for the first time, community-wide estimation of drug and pharmaceuticals consumption in England using wastewater analysis and a large number of compounds. Among groups of compounds studied were: stimulants, hallucinogens and their metabolites, opioids, morphine derivatives, benzodiazepines, antidepressants and others. Obtained results showed the usefulness of wastewater analysis in order to provide estimates of local community drug consumption. It is noticeable that where target compounds could be compared to NHS prescription statistics, good comparisons were apparent between the two sets of data. These compounds include oxycodone, dihydrocodeine, methadone, tramadol, temazepam and diazepam. Whereas, discrepancies were observed for propoxyphene, codeine, dosulepin and venlafaxine (over-estimations in each case except codeine). Potential reasons for discrepancies include: sales of drugs sold without prescription and not included within NHS data, abuse of a drug with the compound trafficked through illegal sources, different consumption patterns in different areas, direct disposal leading to over estimations when using parent compound as the drug target residue and excretion factors not being representative of the local community. It is noticeable that using a metabolite (and not a parent drug) as a biomarker leads to higher certainty of obtained estimates. With regard to illicit drugs, consistent and logical results were reported. Monitoring of these compounds over a one week period highlighted the expected recreational use of many of these drugs (e.g. cocaine and MDMA) and the more consistent use of others (e.g. methadone).

Ion chromatography-mass spectrometry: a review of recent technologies and applications in forensic and environmental explosives analysis.

The development and application of ion chromatography (IC) coupled to mass spectrometry (MS) is discussed herein for the quantitative determination of low-order explosives-related ionic species in environmental and forensic sample types. Issues relating to environmental explosives contamination and the need for more confirmatory IC-MS based applications in forensic science are examined. In particular, the compatibility of a range of IC separation modes with MS detection is summarised along with the analytical challenges that have been overcome to facilitate determinations at the ng-μg L(-1) level. Observed trends in coupling IC to inductively coupled plasma and electrospray ionisation mass spectrometry form a particular focus. This review also includes a discussion of the relative performance of reported IC-MS methods in comparison to orthogonal ion separation-based, spectrometric and spectroscopic approaches to confirmatory detection of low-order explosives. Finally, some promising areas for future research are highlighted and discussed with respect to potential IC-MS applications.

Simultaneous determination of trace oxyhalides and haloacetic acids using suppressed ion chromatography-electrospray mass spectrometry

A new analytical procedure for the simultaneous determination of trace oxyhalides and haloacetic acids (HAs) in drinking water and aqueous soil extracts is described. The method uses micro-bore ion chromatography (IC) coupled with suppressed conductivity (SC) and electrospray ionization mass spectrometric detection (ESI-MS). The IC-SC-ESI-MS system included a secondary flow of 100% MeOH, which was added to the column eluate (post-suppressor) and resulted in a significant increase in sensitivity for all analytes. All ESI-MS parameters were optimized for HA analysis and sensitivity quantitatively compared to suppressed conductivity. Full analytical performance characteristics for the developed method are presented for monochloro-, monobromo-, dichloro-, dibromo-, trichloro-, bromochloro, chlorodifluoro-, trifluoro-, dichlorobromo- and dibromochloroacetic acid, as well as the oxyhalides iodate, bromate, chlorate and perchlorate. In the case of the HAs, an optimised 25-fold SPE preconcentration method meant all analytes could be readily detected well below the USEPA 60mug/L regulatory limit using conductivity and/or ESI-MS. The IC-ESI-MS method was applied to the determination of oxyhalides and HAs in both soil extracts and drinking water samples. Soil samples were extracted using ultra pure water with subsequent determination of perchlorate at 1.68mug/g of soil. A drinking water sample containing HAs was preconcentrated using LiChrolut EN solid phase extraction cartridges with subsequent sulphate and chloride removal. Total HAs were determined at 13mug/L.

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.