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

ABSTRACT: 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.

SUBMITTER: Wu Q

PROVIDER: S-EPMC8718398 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:This study was undertaken to systematically assess the utilities and performance of ontology-based semantic analysis in adverse outcome pathway (AOP) research. With an increasing number of AOPs developed by scientific domain experts to organize toxicity information and facilitate chemical risk assessment, there is a pressing need for objective approaches to evaluate the biological coherence and quality of these AOPs. Powered by ontologies covering a wide range of biological domains, abundant phenotypic data annotated ontologically, and some sophisticated knowledge computing tools, semantic analysis has great potential in this area of application. With the events in the AOP-Wiki first annotated into logical definitions and then grouped into phenotypic profiles by individual AOPs, the coherence and quality of AOPs were assessed at several levels: paired key event relationships (KER), all possible event pair combinations within AOPs, and the phenotypic profiles of AOPs, genes, biological pathways, human diseases, and selected chemicals. The semantic similarities were assessed at all these levels based on a unified cross-species vertebrate phenotype ontology encompassing the logical definitions of AOP events as well as many other domain ontologies. A substantial number of KERs and AOPs in the AOP-Wiki were found to be semantically coherent. These same coherent AOPs also mapped to many more genes, pathways, and diseases biologically aligned with the intended chain of events therein leading to their respective adverse outcomes. Significantly, these findings imply that semantic analysis should also have utilities in developing future AOPs by selecting candidate events from either the existing AOP-Wiki events or a broader collection of ontology terms semantically similar to the molecular initiating events or adverse outcomes of interest. In addition, semantic analysis enabled AOP networks to be constructed at the level of phenotypic profiles based on similarities, complementing those based on event sharing by bringing genes, pathways, diseases, and chemicals into the networks too-thus greatly expanding the biological scope and our understanding of AOPs.

Project description:BACKGROUND:Older patients are at high risk for experiencing Adverse Drug Events (ADEs) during hospitalization. To be able to reduce ADEs in these vulnerable patients, hospitals first need to measure the occurrence of ADEs, especially those that are preventable. However, data on preventable ADEs (pADEs) occurring during hospitalization in older patients are scarce, and no 'gold standard' for the identification of ADEs exists. METHODOLOGY:The study was conducted in three hospitals in the Netherlands in 2007. ADEs were retrospectively identified by a team of experts using a comprehensive and structured patient chart review (PCR) combined with a trigger-tool as an aid. This ADE identification strategy was applied to a cohort of 250 older hospitalized patients. To estimate the intra- and inter-rater reliabilities, Cohen's kappa values were calculated. PRINCIPAL FINDINGS:In total, 118 ADEs were detected which occurred in 62 patients. This ADE yield was 1.1 to 2.7 times higher in comparison to other ADE studies in older hospitalized patients. Of the 118 ADEs, 83 (70.3%) were pADEs; 51 pADEs (43.2% of all ADEs identified) caused serious patient harm. Patient harm caused by ADEs resulted in various events. The overall intra-rater agreement of the developed strategy was substantial (? = 0.74); the overall inter-rater agreement was only fair (? = 0.24). CONCLUSIONS/SIGNIFICANCE:The ADE identification strategy provided a detailed insight into the scope of ADEs occurring in older hospitalized patients, and showed that the majority of (serious) ADEs can be prevented. Several strategy related aspects, as well as setting/study specific aspects, may have contributed to the results gained. These aspects should be considered whenever ADE measurements need to be conducted. The results regarding pADEs can be used to design tailored interventions to effectively reduce harm caused by medication errors. Improvement of the inter-rater reliability of a PCR remains challenging.

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

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