Table 3.

Characterisation of ten mechanistic qAOPs

Model purpose	Adverse outcome	Mechanistic knowledge and associated data								Quantitative approach	Regulatory applicability	References
		OECD AOP-Wikia	Type of AOPb	Type of chemical model applied to	Data type	Adjacent KERs	Biological level(s)	D/C–Rc	T–Rd
Association of MIE to AO at higher level of biological organisations	Increased frequency of spontaneous tail contractions	No	LAOP	Single chemical	In vivo experimental data	√	Molecular, tissue, organ	√	√	Statistical analysis	Ecological risk assessment	Yozzo et al. (2013)
Mechanism of CuO engineered nanoparticles toxicity	Mortality	No	LAOP	Nanoparticles	In vitro experimental data	√	Molecular, cellular, organ, organism	√	√	Linear regression, one-compartment toxicokinetic model	Ecological risk assessment	Muller et al. (2015)
Development of a qAOP network	Egg production	No	AOPN	Single chemical	In vitro and in vivo experimental data	√	Molecular, cellular, tissue, organ, individual	√	√	Statistical analysis	Ecological risk assessment	Margiotta-Casaluci et al. (2016)
Development of a qAOP and potential applications	Population declining trajectory (reproductive dysfunction)	AOP ID 25	LAOP	Single chemical	Empirical data	√	Molecular, cellular, tissue, organ, individual, population	√	√	A mechanistic model, a compartment model, a statistical model, a density-dependent population matrix model	Ecological risk assessment	Conolly et al. (2017)
Development of a qAOP on developmental neurotoxicity	Brain malformation	AOP ID 42	LAOP	Single chemical	In vivo experimental data	√f	Molecular, cellular, tissue, organ	√	√	Mathematical equations (exponential regression)	Human risk assessment	Hassan et al. (2017)
Development of a cross-species qAOP	Mortality increase, population declining trajectory	AOP ID 150	LAOP	Mixtures	In vitro experimental data on COS-7 cells	√f	Molecular, organism, population	√	–	Linear regression, statistical analysis	Ecological risk assessment	Doering et al. (2018)
Simulation of the mechanism of toxicity	Abnormalities at facial primordia branchial arches	No	LAOP	Single chemicals	In vitro experimental data, in vivo and in silico data	√	Molecular, cellular, tissue, organ	√	√	Multistage dose–response model, Bayesian analysis	Ecological risk assessment	Battistoni et al. (2019)
Define the taxonomic domain of applicability of an existing qAOP	Decreased fecundity	AOP ID 25	LAOP	Single chemical	In vivo experimental data	√	Cellular, tissue, organ, individual	√	√	Regression, statistical analysis	Ecological risk assessment	Doering et al. (2019)
Quantification of qKERs with available data in a modular manner	Decrease in population; Impairment of memory and learning	AOPs IDs 25 and 48	LAOP	Single chemicals	Empirical data	√f		√	–	Linear regression (response-response function)	Screening or prioritisation	Foran et al. (2019)
Comparison between probabilistic and mechanistic approaches	Nephron attrition leading to chronic kidney disease	AOP ID 284	LAOP	Single chemicals	In vitro experimental data on human RPTEC/TERT1 cells, AOP construction	√	Molecular, cellular, tissue, organ	√	√	Empirical dose–response model, systems biology model	Human health risk assessment	Zgheib et al. (2019)e

^aNumbers represent the indices (XXX) of the AOP in the AOP-Wiki available at https://aopwiki.org/aops/XXX

^bLinear AOP (LAOP), AOP Network (AOPN)

^cDose/Concentration–Response (D/C–R)

^dTime–Response (T–R) describing the time-course behaviour

^eModel represents a combination of both probabilistic and mechanistic approaches

^fNon-adjacent KERs were modelled as well