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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.

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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.

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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.

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Exposure to persistent organic pollutants (POPs), as defined by the Stockholm Convention, may alter biological systems and cause toxic effects. Computational studies appear to be a relevant approach to increase our understanding of the molecular mechanisms triggered by POPs. We investigated the use of a systems toxicology approach to explore the effects of POPs on human health. A protein-protein association network (PPAN) was developed based on known POP-protein interactions. This model was used to predict protein complexes for several candidate POPs, including dicofol, methoxychlor, and perfluorooctanoic acid (PFOA), that are listed or proposed to be listed as POPs by the Stockholm Convention. Integration of multiple data sources (pathways, disease annotations, adverse outcome pathways) involving the identified protein complexes was performed independently in order to reveal putative risk factors for human health. This approach revealed that several systems may be disturbed by these candidate POPs, mainly the reproductive, metabolic and nervous systems. This study highlights that a computational systems toxicology approach may help to decipher putative biological mecha­nisms of poorly studied chemicals and link them to possible adverse effects with the aim to support regulatory assessment and trigger new epidemiological and experimental studies. In order to develop more accurate computational models as alternative methods to animal testing, the next challenge will be to integrate more data according to the findable, acces­sible, interoperable and reusable (FAIR) data principles.

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Despite of their therapeutic effects, drug's exposure may have negative effects on human health such as adverse drug reaction (ADR) and side effects (SE). Adverse drug events (ADEs), that correspond to an event occurring during the drug treatment (i.e. ADR and SE), is not necessarily caused by the drug itself, as this is the case with medical errors and social factors. Due to the complexity of the biological systems, not all ADEs are known for marketed drugs. Therefore, new and effective methods are needed to determine potential risks, including the development of computational strategies. We present an ADE association network based on 90,827 drug-ADE associations between 930 unique drug and 6221 unique ADE, on which we implemented a scoring system based on a pull-down approach for prediction of drug-ADE combination. Based on our network, ADEs proposed for three drugs, safinamide, sonidegib, rufinamide are further discussed. The model was able to identify, already known drug-ADE associations that are supported by the literature and FDA reports, and also to predict uncharacterized associations such as dopamine dysregulation syndrome, or nicotinic acid deficiency for the drugs safinamide and sonidegib respectively, illustrating the power of such integrative toxicological approach.

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Physiologically-based pharmacokinetic models are increasingly applied for pediatric dose selection along with traditional methods such as allometry and population pharmacokinetic models. We report a retrospective evaluation of the three methods. Pediatric population pharmacokinetic models sourced from literature for a subset of eight compounds were used to predict clearances for children < 2 years when they were within the modeled age range (interpolation, N = 11) or including those outside the modeled age range (interpolation and extrapolation, N = 18). Pediatric/adult clearance ratios were evaluated with a strict performance criterion of 0.8-1.25 and with twofold criteria. For children > 2 years, 58-75% of the clinical studies (N = 10) met the strict criteria, and > 80% of the clinical studies were predicted within twofold by all three methods. For children < 2 years, physiologically-based pharmacokinetic, allometry with age-dependent exponents, and pediatric population pharmacokinetic models predict 54%, 82%, and 64% within twofold of the observed, respectively.

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OBJECTIVE: To investigate the effects of culture supernatant of human amnion mesenchymal stem cells (hAMSCs-CS) on biological characteristics of human fibroblasts. METHODS: (1) hAMSCs were isolated from deprecated human fresh amnion tissue of placenta and then sub-cultured. The morphology of hAMSCs on culture day 3 and hAMSCs of the third passage were observed with inverted phase contrast microscope. (2) Two batches of hAMSCs of the third passage were obtained, then the expression of vimentin of cells was observed with immunofluorescence method, and the expression of cell surface marker CD90, CD73, CD105, and CD45 was detected by flow cytometer. (3) hAMSCs-CS of the third passage at culture hour 72 were collected, and the content of insulin-like growth factor Ⅰ (IGF-Ⅰ), vascular endothelial growth factor (VEGF), epidermal growth factor (EGF), and basic fibroblast growth factor (bFGF) were detected by enzyme-linked immunosorbent assay. (4) Human fibroblasts were isolated from deprecated human fresh prepuce tissue of circumcision and then sub-cultured. Human fibroblasts of the third passage were used in the following experiments. Cells were divided into blank control group and 10%, 30%, 50%, and 70% hAMSCs-CS groups according to the random number table (the same grouping method below), with 48 wells in each group. Cells in blank control group were cultured with DMEM/F12 medium containing 2% fetal bovine serum (FBS), while cells in the latter 4 groups were cultured with DMEM/F12 medium containing corresponding volume fraction of hAMSCs-CS and 2% FBS. The proliferation activity of cells was detected by cell counting kit 8 and microplate reader at culture hour 12, 24, 48, and 72, respectively, and corresponding volume fraction of hAMSCs-CS which causing the best proliferation activity of human fibroblasts was used in the following experiments. (5) Human fibroblasts were divided into blank control group and 50% hAMSCs-CS group and treated as in (4), with 4 wells in each group, at post scratch hour (PSH) 0 (immediately after scratch), 12, 24, 48, and 72, the migration distance of cells was observed and measured with inverted phase contrast microscope. (6) Human fibroblasts were grouped and treated as in (5), with 3 battles in each group, and apoptosis rate of cells was detected by flow cytometer. Data were processed with analysis of variance of factorial design, analysis of variance for repeated measurement, one-way analysis of variance, LSD test, and t test. RESULTS: (1) On culture day 3, most hAMSCs were in large form, and spindle-shaped with much prominences like fibroblasts or in flat polygonal shape. hAMSCs of the third passage were spindle-shaped. The expression of vimentin of hAMSCs of the third passage was strongly positive, and the expressions of surface markers CD90, CD73, and CD105 of the cells were positive, while the expression of CD45 of the cells was negative. (2) The content of IGF-Ⅰ, VEGF, EGF, and bFGF in hAMSCs-CS were respectively (11.7±1.0), (316±68), (6.1±0.4), and (1.49±0.05) pg/mL. (3) At culture hour 12-72, the proliferation activity of human fibroblasts in each hAMSCs-CS group was significantly higher than that in blank control group (with P values below 0.01), and the proliferation activity of human fibroblasts in 50% hAMSCs-CS group was the highest. (4) The width of scratch in two groups was nearly the same at PSH 0. The migration distance of cells in 50% hAMSCs-CS group was significantly longer than that in blank control group at PSH 12-72 (with P values below 0.01). (5) The apoptosis rate of human fibroblasts in blank control group was (16.2±2.4)%, which was significantly higher than that in 50% hAMSCs-CS group [(7.4±3.6)%, t=6.710, P<0.01]. CONCLUSIONS: hAMSCs-CS can promote proliferation and migration of human fibroblasts and inhibit the apoptosis of human fibroblasts.

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