Adverse Outcome
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2021 ◽  
Janani Ravichandran ◽  
Bagavathy Shanmugam Karthikeyan ◽  
Areejit Samal

An adverse outcome pathway (AOP) is a compact representation of the available mechanistic information on observed adverse effects upon environmental exposure. Sharing of events across individual AOPs has led to the emergence of AOP networks. Since AOP networks are expected to be functional units of toxicity prediction, there is current interest in their development tailored to specific research question or regulatory problem. To this end, we have developed a detailed workflow to construct a comprehensive endocrine-specific AOP (ED-AOP) network. Connectivity analysis of the ED-AOP network comprising 48 AOPs reveals 7 connected components and 12 isolated AOPs. Subsequently, we apply standard network measures to perform an in-depth analysis of the two largest connected components of the ED-AOP network. Notably, the graph-theoretic analyses led to the identification of important events including points of convergence or divergence in the ED-AOP network. Detailed analysis of the largest component in the ED-AOP network gives insights on the systems-level perturbations caused by endocrine disruption, emergent paths, and stressor-event associations. In sum, the derived ED-AOP network can be used to address the current knowledge gaps in the existing regulatory framework and aid in better risk assessment of environmental chemicals.

2021 ◽  
Vol 11 (1) ◽  
Juan Lu ◽  
Ling Wang ◽  
Mohammed Bennamoun ◽  
Isaac Ward ◽  
Senjian An ◽  

AbstractOur aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.

2021 ◽  
Vol 12 ◽  
Mounika Gayathri Tirumala ◽  
Pratibha Anchi ◽  
Susmitha Raja ◽  
Mahesh Rachamalla ◽  
Chandraiah Godugu

Nanotoxicology is an emerging field employed in the assessment of unintentional hazardous effects produced by nanoparticles (NPs) impacting human health and the environment. The nanotoxicity affects the range between induction of cellular stress and cytotoxicity. The reasons so far reported for these toxicological effects are due to their variable sizes with high surface areas, shape, charge, and physicochemical properties, which upon interaction with the biological components may influence their functioning and result in adverse outcomes (AO). Thus, understanding the risk produced by these materials now is an important safety concern for the development of nanotechnology and nanomedicine. Since the time nanotoxicology has evolved, the methods employed have been majorly relied on in vitro cell-based evaluations, while these simple methods may not predict the complexity involved in preclinical and clinical conditions concerning pharmacokinetics, organ toxicity, and toxicities evidenced through multiple cellular levels. The safety profiles of nanoscale nanomaterials and nanoformulations in the delivery of drugs and therapeutic applications are of considerable concern. In addition, the safety assessment for new nanomedicine formulas lacks regulatory standards. Though the in vivo studies are greatly needed, the end parameters used for risk assessment are not predicting the possible toxic effects produced by various nanoformulations. On the other side, due to increased restrictions on animal usage and demand for the need for high-throughput assays, there is a need for developing and exploring novel methods to evaluate NPs safety concerns. The progress made in molecular biology and the availability of several modern techniques may offer novel and innovative methods to evaluate the toxicological behavior of different NPs by using single cells, cell population, and whole organisms. This review highlights the recent novel methods developed for the evaluation of the safety impacts of NPs and attempts to solve the problems that come with risk assessment. The relevance of investigating adverse outcome pathways (AOPs) in nanotoxicology has been stressed in particular.

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0255890
Emmanuelle Lesieur ◽  
Mathilde Barrois ◽  
Mathilde Bourdon ◽  
Julie Blanc ◽  
Laurence Loeuillet ◽  

Objective To determine whether bladder size is associated with an unfavorable neonatal outcome, in the case of first-trimester megacystis. Materials and methods This was a retrospective observational study between 2009 and 2019 in two prenatal diagnosis centers. The inclusion criterion was an enlarged bladder (> 7 mm) diagnosed at the first ultrasound exam between 11 and 13+6 weeks of gestation. The main study endpoint was neonatal outcome based on bladder size. An adverse outcome was defined by the completion of a medical termination of pregnancy, the occurrence of in utero fetal death, or a neonatal death. Neonatal survival was considered as a favorable outcome and was defined by a live birth, with or without normal renal function, and with a normal karyotype. Results Among 75 cases of first-trimester megacystis referred to prenatal diagnosis centers and included, there were 63 (84%) adverse outcomes and 12 (16%) live births. Fetuses with a bladder diameter of less than 12.5 mm may have a favorable outcome, with or without urological problems, with a high sensitivity (83.3%) and specificity (87.3%), area under the ROC curve = 0.93, 95% CI (0.86–0.99), p< 0.001. Fetal autopsy was performed in 52 (82.5%) cases of adverse outcome. In the 12 cases of favorable outcome, pediatric follow-up was normal and non-pathological in 8 (66.7%). Conclusion Bladder diameter appears to be a predictive marker for neonatal outcome. Fetuses with smaller megacystis (7–10 mm) have a significantly higher chance of progressing to a favorable outcome. Urethral stenosis and atresia are the main diagnoses made when first-trimester megacystis is observed. Karyotyping is important regardless of bladder diameter.

Anthony R Hart ◽  
Chakra Vasudevan ◽  
Paul D Griffiths ◽  
Nicola Foulds ◽  
Hilary Piercy ◽  

2021 ◽  
Vol 50 (Supplement_1) ◽  
Stephanie Choi ◽  
Adrienne Gordon ◽  
Lisa Hilder ◽  
Amanda Henry ◽  
Jon A. Hyett ◽  

Abstract Background Abnormal fetal growth is a risk factor for perinatal mortality and morbidity. There is considerable debate about the choice and performance of growth charts to classify newborns as small or large for gestational age (SGA and LGA) as a proxy for the at-risk infants. Several international charts have been proposed to be adopted worldwide. We aim to evaluate the performance of commonly-used growth charts (including international INTERGROWTH-21st-standards) for predicting adverse outcomes among SGA and LGA babies. Methods A population cohort of 2.4 million singleton births (24+0–40+6 weeks) delivered in Australia, 2004–2013. Performance was evaluated by prevalence, relative risk and diagnostic accuracy for adverse outcome based on AUC. Results There was wide variation in SGA and LGA classification across charts. For example, compared to other charts, the INTERGROWTH-21st-standards classified half of the number of term-SGA babies (prevalence: 3-4% vs. 7-10%) (&lt;10th-centile) and double the number of LGA babies (prevalence: 24-25% vs. 8-18%) (&gt;90th-centile), resulting in a smaller cohort of term-SGA at higher-risk of adverse outcome, and a larger LGA cohort with lower-risk of adverse outcome. All charts performed poorly for detecting adverse outcomes (AUC range for a composite outcome: 0.49-0.68) and across birthweight centiles. Conclusions Significant differences in the classification of newborns and the chart performance raises concerns about whether the INTERGROWTH-21st-standards are applicable to a multi-ethnic population such as Australia. Key messages Significant differences in the classification of newborns and the relatively poor predictive ability of growth charts means that over reliance on infant size alone may misclassify, and thus miss at-risk infants.

2021 ◽  
Vol 9 (3) ◽  
pp. 2-13
Thania Rios Rossi Lima ◽  
Nathália Pereira de Souza ◽  
Lílian Cristina Pereira ◽  
João Lauro Viana de Camargo ◽  

Introduction: Over the last two decades, chemical safety assessment and regulatory toxicology have progressed from empirical science based on direct observation of apical adverse outcomes in whole organisms to a predictive practice that infers outcomes and risks on the basis of accumulated understanding of toxicological mechanisms and modes of action. Objective: To provide general concepts on how Adverse Outcome Pathways (AOPs) are developed and examples related to skin sensitization, endocrine, disruption, and mitochondrial dysfunction. Method: Narrative review based on data of the scientific literature relevant to the theme addressed and on the experience of the authors. Results: An AOP framework provides a systematic approach to organize knowledge about mechanisms of toxicity that may inform analytical domains in regulatory decision-making. AOPs are open structures that may indicate not only data gaps in the understanding of a toxicity process, but also testing procedures that will generate the necessary knowledge to fill those gaps. Every AOP should be continuously refined through the collaborative efforts of the scientific community. Depending on the amount and detail of information that is successively inserted, AOP may progress from the stage of a putative AOP to the stages of qualitative and quantitative AOPs, which are more fit-for-purpose to support regulatory decision-making. Conclusions: Continuous collaboration between AOP developers within the scientific community and the regulatory corps toward the development of this mechanistic structure will support the advancement of toxicological sciences, regardless of its immediate application for regulatory purposes.

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