Big Question to Developing Solutions: A Decade of Progress in the Development of Aquatic New Approach Methodologies from 2012 to 2022.

In 2012, 20 key questions related to hazard and exposure assessment and environmental and health risks of pharmaceuticals and personal care products in the natural environment were identified. A decade later, this article examines the current level of knowledge around one of the lowest-ranking questions at that time, number 19: "Can nonanimal testing methods be developed that will provide equivalent or better hazard data compared with current in vivo methods?" The inclusion of alternative methods that replace, reduce, or refine animal testing within the regulatory context of risk and hazard assessment of chemicals generally faces many hurdles, although this varies both by organism (human-centric vs. other), sector, and geographical region or country. Focusing on the past 10 years, only works that might reasonably be considered to contribute to advancements in the field of aquatic environmental risk assessment are highlighted. Particular attention is paid to methods of contemporary interest and importance, representing progress in (1) the development of methods which provide equivalent or better data compared with current in vivo methods such as bioaccumulation, (2) weight of evidence, or (3) -omic-based applications. Evolution and convergence of these risk assessment areas offer the basis for fundamental frameshifts in how data are collated and used for the protection of taxa across the breadth of the aquatic environment. Looking to the future, we are at a tipping point, with a need for a global and inclusive approach to establish consensus. Bringing together these methods (both new and old) for regulatory assessment and decision-making will require a concerted effort and orchestration. Environ Toxicol Chem 2024;43:559-574. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Socially Mediated Shift in Neural Circuits Activation Regulated by Synergistic Neuromodulatory Signaling.

Animals exhibit context-dependent behavioral decisions that are mediated by specific motor circuits. In social species these decisions are often influenced by social status. Although social status-dependent neural plasticity of motor circuits has been investigated in vertebrates, little is known of how cellular plasticity translates into differences in motor activity. Here, we used zebrafish (Danio rerio) as a model organism to examine how social dominance influences the activation of swimming and the Mauthner-mediated startle escape behaviors. We show that the status-dependent shift in behavior patterns whereby dominants increase swimming and reduce sensitivity of startle escape while subordinates reduce their swimming and increase startle sensitivity is regulated by the synergistic interactions of dopaminergic, glycinergic, and GABAergic inputs to shift the balance of activation of the underlying motor circuits. This shift is driven by socially induced differences in expression of dopaminergic receptor type 1b (Drd1b) on glycinergic neurons and dopamine (DA) reuptake transporter (DAT). Second, we show that GABAergic input onto glycinergic neurons is strengthened in subordinates compared with dominants. Complementary neurocomputational modeling of the empirical results show that drd1b functions as molecular regulator to facilitate the shift between excitatory and inhibitory pathways. The results illustrate how reconfiguration in network dynamics serves as an adaptive strategy to cope with changes in social environment and are likely conserved and applicable to other social species.

Social Experience Regulates Endocannabinoids Modulation of Zebrafish Motor Behaviors.

Social status-dependent modulation of neural circuits has been investigated extensively in vertebrate and invertebrate systems. However, the effects of social status on neuromodulatory systems that drive motor activity are poorly understood. Zebrafish form a stable social relationship that consists of socially dominant and subordinate animals. The locomotor behavior patterns differ according to their social ranks. The sensitivity of the Mauthner startle escape response in subordinates increases compared to dominants while dominants increase their swimming frequency compared to subordinates. Here, we investigated the role of the endocannabinoid system (ECS) in mediating these differences in motor activities. We show that brain gene expression of key ECS protein pathways are socially regulated. Diacylglycerol lipase (DAGL) expression significantly increased in dominants and significantly decreased in subordinates relative to controls. Moreover, brain gene expression of the cannabinoid 1 receptor (CB1R) was significantly increased in subordinates relative to controls. Secondly, increasing ECS activity with JZL184 reversed swimming activity patterns in dominant and subordinate animals. JZL184 did not affect the sensitivity of the startle escape response in dominants while it was significantly reduced in subordinates. Thirdly, blockage of CB1R function with AM-251 had no effect on dominants startle escape response sensitivity, but startle sensitivity was significantly reduced in subordinates. Additionally, AM-251 did not affect swimming activities in either social phenotypes. Fourthly, we demonstrate that the effects of ECS modulation of the startle escape circuit is mediated via the dopaminergic system specifically via the dopamine D1 receptor. Finally, our empirical results complemented with neurocomputational modeling suggest that social status influences the ECS to regulate the balance in synaptic strength between excitatory and inhibitory inputs to control the excitability of motor behaviors. Collectively, this study provides new insights of how social factors impact nervous system function to reconfigure the synergistic interactions of neuromodulatory pathways to optimize motor output.

Multicompartment and cross-species monitoring of contaminants of emerging concern in an estuarine habitat.

The fate of many chemicals in the environment, particularly contaminants of emerging concern (CEC), have been characterised to a limited extent with a major focus on occurrence in water. This study presents the characterisation, distribution and fate of multiple chemicals including pharmaceuticals, recreational drugs and pesticides in surface water, sediment and fauna representing different food web endpoints in a typical UK estuary (River Colne, Essex, UK). A comparison of contaminant occurrence across different benthic macroinvertebrates was made at three sites and included two amphipods (Gammarus pulex &Crangon crangon), a polychaete worm (Hediste diversicolor) and a gastropod (Peringia ulvae). Overall, multiple contaminants were determined in all compartments and ranged from;

Biomonitoring of pesticides, pharmaceuticals and illicit drugs in a freshwater invertebrate to estimate toxic or effect pressure.

Multiple classes of environmental contaminants have been found in aquatic environments, globally. Understanding internalised concentrations in the organism could further improve the risk assessment process. The present study is concerned with the determination of several contaminant classes (107 compounds) in Gammarus pulex collected from 15 sites covering 5 river catchments across Suffolk, UK. Quantitative method performance was acceptable for 67 compounds including pharmaceuticals, pesticides, illicit drugs and drugs of abuse. A total of 56 compounds were detectable and ranged from

The Use of Molecular Descriptors To Model Pharmaceutical Uptake by a Fish Primary Gill Cell Culture Epithelium.

Modeling approaches such as quantitative structure-activity relationships (QSARs) use molecular descriptors to predict the bioavailable properties of a compound in biota. However, these models have mainly been derived based on empirical data for lipophilic neutral compounds and may not predict the uptake of ionizable compounds. The majority of pharmaceuticals are ionizable, and freshwaters can have a range of pH values that affect speciation. In this study, we assessed the uptake of 10 pharmaceuticals (acetazolamide, beclomethasone, carbamazepine, diclofenac, gemfibrozil, ibuprofen, ketoprofen, norethindrone, propranolol, and warfarin) with differing modes of action and physicochemical properties (p Ka, log S, log D, log Kow, molecular weight (MW), and polar surface area (PSA)) by an in vitro primary fish gill cell culture system (FIGCS) for 24 h in artificial freshwater. Principal component analysis (PCA) and partial least-squares (PLS) regression was used to determine the molecular descriptors that influence the uptake rates. Ionizable drugs were taken up by FIGCS; a strong positive correlation was observed between log S and the uptake rate, and a negative correlation was observed between p Ka, log D, and MW and the uptake rate. This approach shows that models can be derived on the basis of the physicochemical properties of pharmaceuticals and the use of an in vitro gill system to predict the uptake of other compounds. There is a need for a robust and validated model for gill uptake that could be used in a tiered risk assessment to prioritize compounds for experimental testing.

Prediction of bioconcentration factors in fish and invertebrates using machine learning.

The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different models and the prediction of bioconcentration in invertebrates has not been previously evaluated. A comparison of 24 linear and machine learning models is presented herein for the prediction of bioconcentration in fish and important factors that influenced accumulation identified. R2 and root mean square error (RMSE) for the test data (n = 110 cases) ranged from 0.23-0.73 and 0.34-1.20, respectively. Model performance was critically assessed with neural networks and tree-based learners showing the best performance. An optimised 4-layer multi-layer perceptron (14 descriptors) was selected for further testing. The model was applied for cross-species prediction of bioconcentration in a freshwater invertebrate, Gammarus pulex. The model for G. pulex showed good performance with R2 of 0.99 and 0.93 for the verification and test data, respectively. Important molecular descriptors determined to influence bioconcentration were molecular mass (MW), octanol-water distribution coefficient (logD), topological polar surface area (TPSA) and number of nitrogen atoms (nN) among others. Modelling of hazard criteria such as PBT, showed potential to replace the need for animal testing. However, the use of machine learning models in the regulatory context has been minimal to date and is critically discussed herein. The movement away from experimental estimations of accumulation to in silico modelling would enable rapid prioritisation of contaminants that may pose a risk to environmental health and the food chain.

Social Status-Related Differences in Motor Activity Between Wild-Type and Mutant Zebrafish.

Use of zebrafish as a model organism in biomedical research has led to the generation of many genetically modified mutant lines to investigate various aspects of developmental and cellular processes. However, the broader effects of the underlying mutations on social and motor behavior remain poorly examined. Here, we compared the dynamics of social interactions in the Tüpfel long-fin nacre mutant line, which lacks skin pigmentation, to wild-type zebrafish; and we determined whether status-dependent differences in escape and swimming behavior existed within each strain. We show that despite similarities in aggressive activity, Tüpfel long-fin nacre pairs exhibit unstable social relationships characterized by frequent reversals in social dominance compared to wild-type pairs. The lack of strong dominance relationships in Tüpfel long-fin nacre pairs correlates with weak territoriality and overlapping spatial distribution of dominants and subordinates. Conversely, wild-type dominants displayed strong territoriality that severely limited the movement of subordinates. Additionally, the sensitivity of the startle escape response was significantly higher in wild-type subordinates compared to dominants. However, status-related differences in sensitivity of escape response in Tüpfel long-fin nacre pairs were absent. Finally, we present evidence suggesting that these differences could be a consequence of a disruption of proper visual social signals. We show that in wild-type pairs dominants are more conspicuous, and that in wild-type and Tüpfel long-fin nacre pairings wild-type fish are more likely to dominate Tüpfel long-fin nacres. Our results serve as a cautionary note in research design when morphologically engineered zebrafish for color differences are utilized in the study of social behavior and central nervous system function.

A review of the pharmaceutical exposome in aquatic fauna.

Pharmaceuticals have been considered 'contaminants of emerging concern' for more than 20 years. In that time, many laboratory studies have sought to identify hazard and assess risk in the aquatic environment, whilst field studies have searched for targeted candidates and occurrence trends using advanced analytical techniques. However, a lack of a systematic approach to the detection and quantification of pharmaceuticals has provided a fragmented literature of serendipitous approaches. Evaluation of the extent of the risk for the plethora of human and veterinary pharmaceuticals available requires the reliable measurement of trace levels of contaminants across different environmental compartments (water, sediment, biota - of which biota has been largely neglected). The focus on pharmaceutical concentrations in surface waters and other exposure media have therefore limited both the characterisation of the exposome in aquatic wildlife and the understanding of cause and effect relationships. Here, we compile the current analytical approaches and available occurrence and accumulation data in biota to review the current state of research in the field. Our analysis provides evidence in support of the 'Matthew Effect' and raises critical questions about the use of targeted analyte lists for biomonitoring. We provide six recommendations to stimulate and improve future research avenues.

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.

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

The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person's lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2-90 years) analysed with Illumina's genome-wide methylation platforms (27K/450K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R2=0.92, mean absolute error (MAE)=4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R2=0.96) with a MAE=3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE=7.1 years) and a cohort of 1011 disease state individuals (MAE=7.2 years). Furthermore, we highlighted the age markers' potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R2=0.96) with a MAE=3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq® platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE=7.5years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility.

Social Status-Dependent Shift in Neural Circuit Activation Affects Decision Making.

In a social group, animals make behavioral decisions that fit their social ranks. These behavioral choices are dependent on the various social cues experienced during social interactions. In vertebrates, little is known of how social status affects the underlying neural mechanisms regulating decision-making circuits that drive competing behaviors. Here, we demonstrate that social status in zebrafish (Danio rerio) influences behavioral decisions by shifting the balance in neural circuit activation between two competing networks (escape and swim). We show that socially dominant animals enhance activation of the swim circuit. Conversely, social subordinates display a decreased activation of the swim circuit, but an enhanced activation of the escape circuit. In an effort to understand how social status mediates these effects, we constructed a neurocomputational model of the escape and swim circuits. The model replicates our findings and suggests that social status-related shift in circuit dynamics could be mediated by changes in the relative excitability of the escape and swim networks. Together, our results reveal that changes in the excitabilities of the Mauthner command neuron for escape and the inhibitory interneurons that regulate swimming provide a cellular mechanism for the nervous system to adapt to changes in social conditions by permitting the animal to select a socially appropriate behavioral response.SIGNIFICANCE STATEMENT Understanding how social factors influence nervous system function is of great importance. Using zebrafish as a model system, we demonstrate how social experience affects decision making to enable animals to produce socially appropriate behavior. Based on experimental evidence and computational modeling, we show that behavioral decisions reflect the interplay between competing neural circuits whose activation thresholds shift in accordance with social status. We demonstrate this through analysis of the behavior and neural circuit responses that drive escape and swim behaviors in fish. We show that socially subordinate animals favor escape over swimming, while socially dominants favor swimming over escape. We propose that these differences are mediated by shifts in relative circuit excitability.

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.

Bipolar Disorder.

Bipolar disorder is a chronic mental health disorder that is frequently encountered in primary care. Many patients with depression may actually have bipolar disorder. The management of bipolar disorder requires proper diagnosis and awareness or referral for appropriate pharmacologic therapy. Patients with bipolar disorder require primary care management for comorbidities such as cardiovascular and metabolic disorders.

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 (R(2)>0.99), precision (0.1-19%) and lower instrumental limits of detection (0.002-0.20ng) 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 0.3mgL(-1)), nimesulide (0.5 and 1.4mgL(-1)) and propranolol (100 and 153mgL(-1)) over 24h. 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 ranitidine, 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.

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.

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.

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.