To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KER:452

Relationship: 452


The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

AchE Inhibition leads to Increased Mortality

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Acetylcholinesterase inhibition leading to acute mortality non-adjacent High Moderate Cataia Ives (send email) Under Development: Contributions and Comments Welcome Under Development

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help
  • Acetylcholinesterase (AChE) inhibition leads to mortality via overstimulation of neuronal cholinergic signalling pathways that control factors essential for respiration (Costa in Casarett and Doull's).

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help
Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help
  • Acetylcholinesterase inhibition impacts numerous bodily functions through its effect on the neurotransmitter, acetylcholine. Acetylcholinesterase catalyzes acetylcholine degradation, thereby preventing sustained activation of acetylcholine receptors. Acetylcholine levels controls of respiration and heart rate, skeletal muscle contraction, vasodilation and blood pressure. Thus, biological plausibility that AChE inhibition leads to mortality due to its effect on critical bodily functions, specifically respiration, as well as cardiovascular effects in some cases.

  • The biological plausibility for this KER is backed by numerous lines of evidence. 

    • Direct evidence linking AChE inhibition to mortality also comes from controlled studies in the laboratory and field.

    • Further, organophosphate (OP) nerve agents are a class of AChE inhibitors and are amongst the most powerful poisons known to man”. OP nerve agents include soman, sarin, cyclosarin, tabun and VX. Non-experimental evidence linking OPs to mortality in humans is based on OP use in warfare as a biological weapon, during the 1995 Tokoyo terrorist attack, as an agent in suicide attempts, and in laboratory accidents (Figueiredo, 2018).

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help
  • Development of AChE Tolerance: Under certain circumstances, tolerance to AChE inhibition can develop, instead of mortality. Rats exposed to acutely toxic, near-lethal amounts of AChE inhibitor become tolerant. Adaptation to AChE inhibitor has been described in humans in association with Myasthenia graves, asthenic syndrome, and after long exposure to some insecticides. (Stavinoha, 1969)(The reference listed here references 3 papers on rats published between ‘52-’64).

  • In vivo AChE Inhibition Measurement Challenges

    • Correlating in vivo measures of AChE inhibition with mortality endpoints have not always been successful possibly due to interference from other esterases and partitioning issues across tissues (Wilson 2010).

    • A QSAR (quantitative structure activity relationship) model developed to predict the acute LC50 for rainbow trout (Oncorhynchus mykiss) using the pI50 (concentration that inhibits AChE by 50%) found a statistically relevant linear relationship, but the model only explained 59% of the variation in toxicity observed for the series of carbamates tested (Call et al., 1989). QSAR models to estimate fish toxicity (LC50) for a series of OPs based on the reaction rate constants associated with inhibition of AChE in electric eel did result in a significant model, but the model only explained 23% of the variation in toxicity (De Bruijn and Hermens 1993).

  • Challenges correlating In vivo and In vitro AChE Activity Measurements: Although relationships can be made between the in vitro AChE inhibition and in vivo toxicity values observed for direct acting OPs and carbamates, these relationships typically are not significant (Wilson 2010). Factors contributing to the failure of these correlations include the tissue analyzed, method used to assay AChE or acetylcholine, organism life stage, dose compared to body size, and metabolic differences including detoxification pathways (Wilson 2010; Ludke et al., 1975; Hamadain and Chambers, 2001).

Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help
This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help
Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help
Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help
Taxonomic Applicability
  • Russom et al performed two parallel approaches to examine AChE sensitivity across multiple taxa: insects, crustaceans, fish, amphibians, mollusks, annelids, and plants. They generated species sensitivity curves from empirical evidence pulled from systemic searches for acute lethality toxicity data for terrestrial and aquatic species in the ECOTOX database. Daphnids were consistently found to be highly sensitive to organophosphates and carbamates. Next, they used the Daphnia pulex AChE protein sequence was used as the query sequence to make cross-species susceptibility predictions. There was strong agreement between the empirical evidence and the species sensitivity predictions based on the protein sequence similarity approach. Insects and crustaceans include the species most sensitive the AChE inhibition, followed by fish and amphibians and then by mollusks and annelids (Russom, 2014; LaLone, 2013). 

  • Amongst fish, amphibians, mammals and birds, Wallace summarized comparative sensitivities from multiple studies. Across these groups, birds are highly sensitive to AChE inhibition, mammals are moderately sensitive and fish and amphibians are the least sensitive to AChE inhibition.

Life Stage Applicability
  • Studies in zebrafish have shown that mortality coincides with the onset of organogenesis for dichlorvos and diazinon and with the end of organogenesis/onset of hatching for chlorpyrifos (Watson, 2014) 

  • In Xenopus, OP-induced mortality occurs at a time well after organogenesis and before the physiological changes associated with metamorphosis. At the peak of Xenopus mortality, the larva was swimming actively, had a well-developed mouth, and was in the process of developing hind limbs (stage 49) (Watson, 2014) 


List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help
  • Carey JL, Dunn C, Gaspari RJ., Central respiratory failure during acute organophosphate poisoning. Respir Physiol Neurobiol. 2013 Nov 1;189(2):403-10.

  • Figueiredo TH, Apland JP, Braga MFM, Marini AM. Acute and long-term consequences of exposure to organophosphate nerve agents in humans. Epilepsia. 2018 Oct;59 Suppl 2:92-99. doi: 10.1111/epi.14500. Epub 2018 Aug 29.

  • Kobayashi H, Yuyama A, Kajita T, Shimura K, Ohkawa T, Satoh K. 1985. Effects of insecticidal carbamates on brain acetylcholine content, acetylcholinesterase activity and behavior in mice. Toxicol Lett 29:153–159.

  • Costa.  Toxic effects of pesticides.  In Casarett and Doull's Toxicology: The Basic Science of Poisons. 9th ed. pp 1055-1106.

  • Grue CE, Shipley BK. 1984. Sensitivity of nestling and adult starling to dicrotophos, an organophosphate pesticide. Environ Res 35:454–465.

  • Li M, Zheng C, Kawada T, Inagaki M, Uemura K, Sugimachi M. Adding the acetylcholinesterase inhibitor, donepezil, to losartan treatment markedly improves long-term survival in rats with chronic heart failure. Eur J Heart Fail. 2014 Oct;16(10):1056-65. doi: 10.1002/ejhf.164. Epub 2014 Sep 8.

  • Wadia, R. S., Sadagopan, C., Amin, R. B., and Sardesai, H.V. 1974. Neurological manifestations of organophosphorus insecticide poisoning. J Neurol Neurosurg Psychiatry. 37(7): 841–847.

  • Gaspari RJ, Paydarfar D. Pathophysiology of respiratory failure following acute dichlorvos poisoning in a rodent model. Neurotoxicology. 2007 May;28(3):664-71.

  • Yen,J., S. Donerly, E.D. Levin, and E.A. Linney","Differential Acetylcholinesterase Inhibition of Chlorpyrifos, Diazinon and Parathion in Larval Zebrafish",Neurotoxicol. Teratol.33(6): 735-741,2011,Fish; MORT/ACHE

  • Behra M, Cousin X, Bertrand C, Vonesch JL, Biellmann D, Chatonnet A, Strähle U. Acetylcholinesterase is required for neuronal and muscular development in the zebrafish embryo. Nat Neurosci. 2002 Feb;5(2):111-8.

  • Sivam, SP, Hoskins, B. Ho, IK. 1984. An assessment of comparative acute toxicity of diisopropylfluorophosphate, tabun, sarin, and soman in relation to cholinergic and GABAergic enzyme activities in rats. Toxicological Sciences, 4(4), 531-538.

  • Swiergosz-Kowalewska, R., Molenda, P., Halota, A. 2014. Effects of chemical and thermal stress on acetylcholinesterase activity in the brain of the bank vole, Myodes glareolus. Ecotoxicology and Environmental Safety, 106, 204-212.

  • Coppage, D.L. 1972. Organophosphate Pesticides: Specific Level of brain AChE inhibition related to death in sheepshead minnows. Transactions of the American Fisheries Society. 101 (3), 534-536.

  • Gungordu,A. 2013. Comparative Toxicity of Methidathion and Glyphosate on Early Life Stages of Three Amphibian Species:  Pelophylax ridibundus, Pseudepidalea viridis, and Xenopus laevis. Aquat. Toxicol.140/141, 220-228.

  • Gromysz-Kalkowska, K., Szubartowska, E. 1993. Evaluation of Fenitrothion Toxicity to Rana temporaria L. 50:116-124.

  • Lu Y, Park Y, Gao X, Zhang X, Yoo J, Pang X-P, Jiang H, Zhu KY. 2012. Cholinergic and non-cholinergic functions of two acetylcholinesterase genes revealed by gene-silencing in Tribolium castaneum. Sci Rep 2:1-7.

  • Pant, Radha, and S. K. Katiyar. 1983. “Effect of Malathion and Acetylcholine on the Developing Larvae OfPhilosamia Ricini (Lepidoptera: Saturniidae).” Journal of Biosciences 5 (1): 89–95.

  • Velki,M., and B.K. Hackenberger. 2012. Species-Specific Differences in Biomarker Responses in Two Ecologically Different Earthworms Exposed to the Insecticide Dimethoate. Comp. Biochem. Physiol. C Toxicol. Pharmacol.156(2): 104-112.

  • Calisi, A., Lionetto, M.G., Schettino, T. 2011. Biomarker response in the earthworm Lumbricus terrestris exposed to chemical pollutants. Science of the Total Environment. 409, 4456-4464.

  • Maxwell, D.M., Brecht, K.M., Koplovitz, I., Sweeney, R.E. 2006. Acetylcholinesterase inhibition: does it explain the toxicity of organophosphorus compounds? Archives of Toxicology. 80:756.