This study investigates the application of a deep learning (DL) model, specifically a message-passing neural network (MPNN) implemented through Chemprop, to predict the persistence, bioaccumulation, and toxicity (PBT) characteristics of compounds, with a focus on pharmaceuticals. We employed a clustering strategy to provide a fair assessment of the model performances. By applying the generated model to a set of pharmaceutically relevant molecules, we aim to highlight potential PBT chemicals and extract PBT-relevant substructures. These substructures can serve as structural flags, alerting drug designers to potential environmental issues from the earliest stages of the drug discovery process. Incorporating these findings into pharmaceutical development workflows is expected to drive significant advancements in creating more environmentally friendly drug candidates while preserving their therapeutic efficacy.

Human-use medicines are introduced into surface water throughout their entire life cycle, from manufacturing and consumption to improper disposal, resulting in negative effects on aquatic flora and fauna. Sustainability approaches have addressed this issue, proposing frameworks like the One Health approach. A revised definition of the rational use of medicines has also been proposed, taking into account their environmental sustainability. Building on this updated definition, this paper presents a decision-making flowchart to integrate the assessment of the impact of human medicines on surface water, outlining existing actions and proposing new ones. These actions are categorized into those with primary clinical benefits and those focused solely on environmental concerns. They include both upstream and downstream measures, such as implementing regulations similar to those in the EU and the USA, educating future healthcare practitioners, promoting green pharmacy innovations, and establishing proper disposal practices.

The presence of human pharmaceuticals in the aquatic environment is recognised internationally as an important public health and environmental issue. In Scotland, healthcare sustainability targets call for improvements to medicine prescribing and use to reduce healthcare's impact on the environment. This proof-of-concept study aimed to develop a framework on the environmental impact of pharmaceuticals to use as a knowledge support tool for healthcare professionals, focussing on pharmaceutical pollution. Nominal Group Technique was applied to achieve consensus on pharmaceuticals and modelling factors for the framework, working with a panel of cross-sector stakeholders. Bayesian Belief Network modelling was applied to predict the environmental impact (calculated from hazard and exposure factors) of selected pharmaceuticals, with Scotland-wide mapping for visualisation in freshwater catchments. The model calculated the pollution risk score of the individual pharmaceuticals, using the ratio of prescribed mass vs. mass that would not exceed the predicted no-effect concentration in the freshwater environment. The pharmaceuticals exhibited different risk patterns, and spatial variation of risk was evident (generally related to population density), with the most catchments predicted to exceed the pollution risk score for clarithromycin (probability >80 % in 35 of 40 modelled catchments). Simulated risk scores were compared against observed risk calculated as the ratio of measured environmental concentrations from national regulatory and research monitoring and predicted no-effect concentrations. The model generally overpredicted risk, likely due to missing factors (e.g. solid-phase sorption, temporal variation), low spatial resolution, and low temporal resolution of the monitoring data. This work demonstrates a novel, trans-disciplinary approach to develop tools aiding collation and integration of environmental information into healthcare decision-making, through application of public health, environmental science, and health services research methods. Future work will refine the framework with additional clinical and environmental factors to improve model performance, and develop electronic interfaces to communicate environmental information to healthcare professionals.

The One Health concept recognises the interconnectedness of humans, plants, animals and the environment. Recent research strongly supports the idea that the environment serves as a significant reservoir for antimicrobial resistance (AMR). However, the complexity of natural environments makes efforts at AMR public health risk assessment difficult. We lack sufficient data on key ecological parameters that influence AMR, as well as the primary proxies necessary for evaluating risks to human health. Developing environmental AMR 'early warning systems' requires models with well-defined parameters. This is necessary to support the implementation of clear and targeted interventions. In this review, we provide a comprehensive overview of the current tools used globally for environmental AMR human health risk assessment and the underlying knowledge gaps. We highlight the urgent need for standardised, cost-effective risk assessment frameworks that are adaptable across different environments and regions to enhance comparability and reliability. These frameworks must also account for previously understudied AMR sources, such as horticulture, and emerging threats like climate change. In addition, integrating traditional ecotoxicology with modern 'omics' approaches will be essential for developing more comprehensive risk models and informing targeted AMR mitigation strategies.

This comprehensive scientific review evaluates the effectiveness of nature-based solutions (NBS) in reducing antibiotics (ABs), combating antimicrobial resistance (AMR), and controlling pathogens in various aquatic environments at different river catchment levels. It covers conventional and innovative treatment wetland configurations for wastewater treatment to reduce pollutant discharge into the aquatic ecosystems as well as exploring how river restoration and saltmarshes can enhance pollutant removal. Through the analysis of experimental studies and case examples, the review shows NBS's potential for providing sustainable and cost-effective solutions to improve the health of aquatic ecosystems. It also evaluates the use of diagnostic indicators to predict NBS effectiveness in removing specific pollutants such as ABs and AMR. The review concludes that NBS are feasible for addressing the new challenges stemming from human activities such as the presence of ABs, AMR and pathogens, contributing to a better understanding of NBS, highlighting success stories, addressing knowledge gaps, and providing recommendations for future research and implementation.

This study conducted a network pharmacology-based analysis to simultaneously discern a broad spectrum of potential environmental risks and health effects of antidepressants, a common class of pharmaceutical emerging contaminants (PECs) possessing a complex pharmacological profile, and in silico predict the adverse phenotypes potentially occurring in fish associated with exposure to antidepressants and their mixtures under realistic exposure scenarios. Results showed that 24 of the included 39 antidepressants had been detected worldwide in water environment across 50 countries. Using the environmentally realistic exposure scenario for China as an example, the predicted blood concentrations of antidepressant residues that were generated based on the Fish Plasma Model ranged from 37.89 (Alprazolam) to 16,772.05 (Sertraline) ng/L in exposed fish. Hazard-based bioactivity network without regard to concentration data was composed of 148 potential targets and 701 antidepressant-target interactions. After filtering each antidepressant-target interaction node using the predicted drug concentrations in the blood of fish under realistic exposure scenarios in China, an environmental risk-based network was refined and showed that 11 targets, including muscarinic acetylcholine receptor M1, alpha-2B adrenergic receptor, serotonin 2 A receptor, etc. might be modulated by antidepressants at concentrations equal to or below the environmental exposure levels and their mixtures in fish. Environmentally relevant concentrations of antidepressants in water samples from China might perturb the behavior, stress response, phototaxis, and development in exposed fish.

Parasitic vector-borne diseases (VBDs) represent nearly 20% of the global burden of infectious diseases. Moreover, the spread of VBDs is enhanced by global travel, urbanization, and climate change. Treatment of VBDs faces challenges due to limitations of existing drugs, as the potential for side effects in nontarget species raises significant environmental concerns. Consequently, considering environmental risks early in drug development processes is critically important. Here, we examine the environmental risk assessment process for veterinary medicinal products in the European Union and identify major gaps in the ecotoxicity data of these drugs. By highlighting the scarcity of ecotoxicological data for commonly used antiparasitic drugs, we stress the urgent need for considering the One Health concept. We advocate for employing predictive tools and nonanimal methodologies such as New Approach Methodologies at early stages of antiparasitic drug research and development. Furthermore, adopting progressive approaches to mitigate ecological risks requires the integration of nonstandard tests that account for real-world complexities and use environmentally relevant exposure scenarios. Such a strategy is vital for a sustainable drug development process as it adheres to the principles of One Health, ultimately contributing to a healthier and more sustainable world.

The United Nations set "The 2030 Agenda for Sustainable Development," which includes the Sustainable Development Goals (SDGs), a collection of 17 global goals designed to be a "blueprint to achieve a better and more sustainable future for all". Although only mentioned in one of the seventeen goals (goal 3), we argue that drugs in general, and growing drug pollution in particular, affects the SDGs in deeper, not readily apparent ways. So far, the emerging problem of drug pollution has not been sufficiently addressed. Here, we outline and discuss how drug pollution can affect SDGs and even threaten their achievement.

The increasing production of modern medication emerges as a new source of environmental pollution. The scientific community is interested in developing alternative, ecological therapies in asthma. Halotherapy proved its benefits in asthma diagnosis, treatment, and prevention and may represent a reliable therapeutic addition to the allopathic treatment, due to its ecological and environment-friendly nature, in order to prevent or prolong the time to exacerbations in patients with asthma. We aimed to review up-to-date research regarding halotherapy benefits in asthma comprehensively. We searched the electronic databases of PubMed, MEDLINE, EMBASE for studies that evaluated the exposure of asthmatic patients to halotherapy. Eighteen original articles on asthma were included. Five studies in adults and five in children assessed the performance of hypertonic saline bronchial challenges to diagnose asthma or vocal cord dysfunction in asthmatic patients. Three papers evaluated the beneficial effects of halotherapy on mucociliary clearance in asthmatic adults. The therapeutic effect of halotherapy on acute or chronic asthma was appraised in three studies in adults and one in children. The preventive role was documented in one paper reporting the ability of halotherapy to hinder nocturnal asthma exacerbations. All studies seem to sustain the overall positive effects of halotherapy as adjuvant therapy on asthma patients with no reported adverse events. Halotherapy is a crucial natural ally in asthma, but further evidence-based studies on larger populations are needed.