In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as in silico and in vitro information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.
Keywords: ADMET, Absorption, distribution, metabolism, excretion, and toxicity; AO, Adverse outcome; AOP, Adverse outcome pathway; Adverse Outcome Pathway; BBB, Blood-brain-barrier; BDNF, Brain-derived neurotrophic factor; Bayesian hierarchical model; CAS RN, Chemical Abstracts Service Registry Number; CI, Credible interval CKE, Common key event; CNS, Central nervous system; CRA, Chemical risk assessment; Common Key Event; DAG, Directed acyclic graph; DNT, Developmental neurotoxicity; DTXSID, The US EPA Comptox Chemical Dashboard substance identifier; Developmental Neurotoxicity; EC, Effective concentration; HDI, Highest density interval; IATA, Integrated Approaches to Testing and Assessment; KE, Key event; KER, Key event relationship; LDH, Lactate dehydrogenase; MCMC, Markov chain Monte Carlo; MIE, Molecular initiating event; NAM, New approach methodology; New Approach Methodology; OECD, Organisation for Economic Cooperation and Development; P-gp, P-glycoprotein; PBK, Physiologically-based kinetic; QSAR, Quantitative structure-activity relationship; SMILES, Simplified molecular input line entry system; qAOP, Quantitative adverse outcome pathway.
© 2021 The Author(s).