An essential component of all three approaches to qAOP development is the need for extensive review and documentation of available data and literature.
2: Linkage of multiple models to create a mechanistic qAOP model for aromatase inhibition leading to reproductive dysfunction
The AOP for aromatase inhibition leading to reproductive dysfunction is diagrammed on the right of the figure with arrowhead indicating equivalent sections of the mechanistic qAOP. (A) A mechanistic hypothalamus-pituitary-gonad (HPG) model that simulates steroidogenesis (yellow) and conversion of testosterone (T) into estradiol (E2) by aromatase, a negative feedback to the hypothalamus/pituitary where decreasing levels of E2 cause increased synthesis of luteinizing hormone (LH; pink), LH binds to the LH receptor in the ovary and stimulates a cAMP cascade (green, purple) resulting in phosphorylation (light green, red) of transcription factor SF1, increased transport of cholesterol into mitochondria by steroid acute response protein and ultimately increased synthesis of E2.
Our objective in this paper is to examine how hypothesis testing using quantitative qAOPs can support hazard and risk decisions.
3.2 Semi-quantitative or quantitative weight of evidence qAOPs
Probabilistic qAOPs can be composed of predictive relationships that span a few events or an entire AOP and be combined with WOE analyses depending on the application.
While probabilistic qAOPs are clearly useful in estimating if an adverse outcome may occur given available data, they generally do not explicitly incorporate mechanisms of action and fail to account for regulatory and feedback control mechanisms that underlay compensatory responses.