Integrative systems toxicology to predict human biological systems affected by exposure to environmental chemicals

In silico prediction of drug-induced nephrotoxicity: current progress and pitfalls

INTRODUCTION

Due to its role in absorption and metabolism, the kidney is an important target for drug toxicity. Drug-induced nephrotoxicity (DIN) presents a significant challenge in clinical practice and drug development. Conventional methods for assessing nephrotoxicity have limitations, highlighting the need for innovative approaches. In recent years, in silico methods have emerged as promising tools for predicting DIN.

AREAS COVERED

A literature search was performed using PubMed and Web of Science, from 2013 to February 2023 for this review. This review provides an overview of the current progress and pitfalls in the in silico prediction of DIN, which discusses the principles and methodologies of computational models.

EXPERT OPINION

Despite significant advancements, this review identified issues accentuates the pivotal imperatives of data fidelity, model optimization, interdisciplinary collaboration, and mechanistic comprehension in sculpting the vista of DIN prediction. Integration of multiple data sources and collaboration between disciplines are essential for improving predictive models. Ultimately, a holistic approach combining computational, experimental, and clinical methods will enhance our understanding and management of DIN.

Mass spectrometry in sports drug testing-Analytical approaches and the athletes' exposome

Test methods in anti-doping, most of which rely on the most modern mass spectrometric instrumentation, undergo continuous optimization in order to accommodate growing demands as to comprehensiveness, sensitivity, retrospectivity, cost-effectiveness, turnaround times, etc. While developing and improving analytical approaches is vital for appropriate sports drug testing programs, the combination of today's excellent analytical potential and the inevitable exposure of humans to complex environmental factors, specifically chemicals and drugs at the lowest levels, has necessitated dedicated research, particularly into the elite athlete's exposome. Being subjected to routine doping controls, athletes frequently undergo blood and/or urine tests for a plethora of drugs, chemicals, corresponding metabolic products, and various biomarkers. Due to the applicable anti-doping regulations, the presence of prohibited substances in an athlete's organism can constitute an anti-doping rule violation with severe consequences for the individual's career (in contrast to the general population), and frequently the question of whether the analytical data can assist in differentiating scenarios of 'doping' from 'contamination through inadvertent exposure' is raised. Hence, investigations into the athlete's exposome and how to distinguish between deliberate drug use and potential exposure scenarios have become a central topic of anti-doping research, aiming at supporting and consolidating the balance between essential analytical performance characteristics of doping control test methods and the mandate of protecting the clean athlete by exploiting new strategies in sampling and analyzing specimens for sports drug-testing purposes.

Assessing network-based methods in the context of system toxicology

Introduction: Network-based methods are promising approaches in systems toxicology because they can be used to predict the effects of drugs and chemicals on health, to elucidate the mode of action of compounds, and to identify biomarkers of toxicity. Over the years, the network biology community has developed a wide range of methods, and users are faced with the task of choosing the most appropriate method for their own application. Furthermore, the advantages and limitations of each method are difficult to determine without a proper standard and comparative evaluation of their performance. This study aims to evaluate different network-based methods that can be used to gain biological insight into the mechanisms of drug toxicity, using valproic acid (VPA)-induced liver steatosis as a benchmark. Methods: We provide a comprehensive analysis of the results produced by each method and highlight the fact that the experimental design (how the method is applied) is relevant in addition to the method specifications. We also contribute with a systematic methodology to analyse the results of the methods individually and in a comparative manner. Results: Our results show that the evaluated tools differ in their performance against the benchmark and in their ability to provide novel insights into the mechanism of adverse effects of the drug. We also suggest that aggregation of the results provided by different methods provides a more confident set of candidate genes and processes to further the knowledge of the drug's mechanism of action. Discussion: By providing a detailed and systematic analysis of the results of different network-based tools, we aim to assist users in making informed decisions about the most appropriate method for systems toxicology applications.

Linking chemicals, genes and morphological perturbations to diseases

The progress in image-based high-content screening technology has facilitated high-throughput phenotypic profiling notably the quantification of cell morphology perturbation by chemicals. However, understanding the mechanism of action of a chemical and linking it to cell morphology and phenotypes remains a challenge in drug discovery. In this study, we intended to integrate molecules that induced transcriptomic perturbations and cellular morphological changes into a biological network in order to assess chemical-phenotypic relationships in humans. Such a network was enriched with existing disease information to suggest molecular and cellular profiles leading to phenotypes. Two datasets were used for this study. Firstly, we used the "Cell Painting morphological profiling assay" dataset, composed of 30,000 compounds tested on human osteosarcoma cells (named U2OS). Secondly, we used the "L1000 mRNA profiling assay" dataset, a collection of transcriptional expression data from cultured human cells treated with approximately 20,000 bioactive small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). Furthermore, pathways, gene ontology terms and disease enrichments were performed on the transcriptomics data. Overall, our study makes it possible to develop a biological network combining chemical-gene-pathway-morphological perturbation and disease relationships. It contains an ensemble of 9989 chemicals, 732 significant morphological features and 12,328 genes. Through diverse examples, we demonstrated that some drugs shared similar genes, pathways and morphological profiles that, taken together, could help in deciphering chemical-phenotype observations.

A Narrative Literature Review of Natural Language Processing Applied to the Occupational Exposome

The evolution of the Exposome concept revolutionised the research in exposure assessment and epidemiology by introducing the need for a more holistic approach on the exploration of the relationship between the environment and disease. At the same time, further and more dramatic changes have also occurred on the working environment, adding to the already existing dynamic nature of it. Natural Language Processing (NLP) refers to a collection of methods for identifying, reading, extracting and untimely transforming large collections of language. In this work, we aim to give an overview of how NLP has successfully been applied thus far in Exposome research. Methods: We conduct a literature search on PubMed, Scopus and Web of Science for scientific articles published between 2011 and 2021. We use both quantitative and qualitative methods to screen papers and provide insights into the inclusion and exclusion criteria. We outline our approach for article selection and provide an overview of our findings. This is followed by a more detailed insight into selected articles. Results: Overall, 6420 articles were screened for the suitability of this review, where we review 37 articles in depth. Finally, we discuss future avenues of research and outline challenges in existing work. Conclusions: Our results show that (i) there has been an increase in articles published that focus on applying NLP to exposure and epidemiology research, (ii) most work uses existing NLP tools and (iii) traditional machine learning is the most popular approach.

Emerging Bioinformatics Methods and Resources in Drug Toxicology

Assessing the drug safety at an early stage of a drug discovery program is a critical issue. With the recent advances in molecular biology and genomic, massive amounts of generated and accumulated data by advanced experimental technologies such as RNA sequencing or proteomics start to be at the disposal of the scientific community. Innovative and adequate bioinformatic methods, tools, and protocols are required to analyze properly these diverse and extensive data sources with the aim to identify key features that are related to toxicity observations. Furthermore, the assessment of drug safety can be performed across multiple scales of complexity from molecular, cellular to phenotypic levels; therefore, the application of network science contributes to a better interpretation of the drug's exposure effect on human health. Here, we review databases containing toxicogenomics and chemical-phenotype information, as well as appropriated bioinformatics approaches that are currently used to analyze such data. Extension to others methods such as dose-responses, time-dependent processes, and text mining is also presented giving an overview of suitable tools available for a best practice of drug safety analysis.

Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome

Background: The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment.

Methods: To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space. Considering the drugs and their associations to biological events, such as molecular initiating event and key event, a bipartite network was developed. This bipartite network was projected into a monopartite network capturing the event–event linkages. Nevertheless, such transformation of a bipartite network to a monopartite network had a huge risk of information loss. A way to solve this problem is to quantify the network reduction. We calculated two scoring systems, one measuring the uncertainty and a second one describing the loss of coverage on the developed event–event network to better investigate events from AOPs linked to drugs.

Results: This AON analysis allowed us to identify biological events that are highly connected to drugs, such as events involving nuclear receptors (ER, AR, and PXR/SXR). Furthermore, we observed that the number of events involved in a linkage pattern with drugs is a key factor that influences information loss during monopartite network projection. Such scores have the potential to quantify the uncertainty of an event involved in an AON, and could be valuable for the weight of evidence assessment of AOPs. A case study related to infertility, more specifically to “decrease, male agenital distance” is presented.

Conclusion: This study highlights that computational approaches based on network science may help to understand the complexity of drug health effects, with the aim to support drug safety assessment.

Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach

BACKGROUND

Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19.

OBJECTIVES

To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied.

METHODS

As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis.

RESULTS

We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising.

CONCLUSIONS

Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction.

Network biology approach to human tissue-specific chemical exposome

Human exposure to environmental chemicals is a major contributor to the global disease burden. To characterize the external exposome it is important to assess its chemical components and to study their impact on human health. Biomonitoring studies measure the body burden of environmental chemicals detected in biospecimens from a wide range of the population. The detection of these chemicals in biospecimens (and, hence, human tissues) is considered an important biomarker of human exposure. However, there is no readily available resource that compiles such exposure data for human tissues from published literature, and no studies that explore the patterns in the associations between tissue-specific exposures and human diseases. We present Human Tissue-specific Exposome Atlas (TExAs), a compilation of 380 environmental chemicals detected across 27 human tissues. TExAs is accessible via a user friendly webserver: https://cb.imsc.res.in/texas. We compare the chemicals in TExAs with 55 global chemical regulations, guidelines, and inventories, which represent several categories of the external exposome of humans. Further to understand the potential implications on human health of chemicals detected across human tissues, we employ a network biology approach and explore possible chemical exposure-disease associations. Ensuing analyses reveal the possibilities of disease comorbidities and demonstrate the application of network biology in unraveling complex disease associations due to chemical exposure.

Sports drug testing and the athletes' exposome

Similar to the general population, elite athletes are exposed to a complex set of environmental factors including chemicals and radiation and also biological and physical stressors, which constitute an exposome that is, unlike for the general population, subjected to specific scrutiny for athletes due to applicable antidoping regulations and associated (frequent) routine doping controls. Hence, investigations into the athlete's exposome and how to distinguish between deliberate drug use and different contamination scenarios has become a central topic of antidoping research, as a delicate balance is to be managed between the vital and continually evolving developments of sensitive analytical techniques on the one hand, and the risk of the athletes' exposome potentially causing adverse analytical findings on the other.

Mn(II)-Mn(III)-Mn(IV) redox cycling inhibits the removal of methylparaben and acetaminophen mediated by horseradish peroxidase: New insights into the mechanism

Catalyzed oxidative coupling reactions mediated by enzyme have been proposed as an effective remediation strategy to remove micropollutants, however, little is known about how the Mn(II) redox cycling affects the horseradish peroxidase (HRP)-mediated reactions in wastewater treatment. Here, we explored the removal of two pharmaceuticals and personal care products (PPCPs), methylparaben (MeP) and acetaminophen (AAP), in HRP-mediated reaction system with dissolved Mn (II). It was found that the conversion rate of AAP was about 284 times higher than that of MeP, and Mn (II) significantly inhibited HRP-catalyzed MeP removal but had little influence on that of AAP. X-ray photoelectron spectroscopy (XPS) and theoretical calculations demonstrated that HRP converted Mn(II) into Mn(III), and then generated MnO₂ colloid, which inhibited the removal of the substrates. Moreover, the results of theoretical calculations also showed that the binding energy between HRP and Mn was 27.68 kcal/mol, which was higher than that of MeP (25.24 kcal/mol) and lower than that of AAP (30.19 kcal/mol). Therefore, when MeP and Mn (II) coexisted in the reaction system, HRP preferentially reacted with Mn(II), which explained the different impacts of Mn (II) on the removal of MeP and AAP. Additionally, Mn (II) significantly altered the product distribution by decreasing the amount of polymerization products. Overall, our work here revealed the roles of Mn (II) in the removal of MeP and AAP mediated by HRP, having strong implications for an accurate assessment of the influence of Mn(II) redox cycling on the removal of PPCPs in wastewater treatment.

CTD Anatomy: analyzing chemical-induced phenotypes and exposures from an anatomical perspective, with implications for environmental health studies

The Comparative Toxicogenomics Database (CTD) is a freely available public resource that curates and interrelates chemical, gene/protein, phenotype, disease, organism, and exposure data. CTD can be used to address toxicological mechanisms for environmental chemicals and facilitate the generation of testable hypotheses about how exposures affect human health. At CTD, manually curated interactions for chemical-induced phenotypes are enhanced with anatomy terms (tissues, fluids, and cell types) to describe the physiological system of the reported event. These same anatomy terms are used to annotate the human media (e.g., urine, hair, nail, blood, etc.) in which an environmental chemical was assayed for exposure. Currently, CTD uses more than 880 unique anatomy terms to contextualize over 255,000 chemical-phenotype interactions and 167,000 exposure statements. These annotations allow chemical-phenotype interactions and exposure data to be explored from a novel, anatomical perspective. Here, we describe CTD's anatomy curation process (including the construction of a controlled, interoperable vocabulary) and new anatomy webpages (that coalesce and organize the curated chemical-phenotype and exposure data sets). We also provide examples that demonstrate how this feature can be used to identify system- and cell-specific chemical-induced toxicities, help inform exposure data, prioritize phenotypes for environmental diseases, survey tissue and pregnancy exposomes, and facilitate data connections with external resources. Anatomy annotations advance understanding of environmental health by providing new ways to explore and survey chemical-induced events and exposure studies in the CTD framework.