Aop: 37


Each AOP should be given a descriptive title that takes the form “MIE leading to AO”. For example, “Aromatase inhibition [MIE] leading to reproductive dysfunction [AO]” or “Thyroperoxidase inhibition [MIE] leading to decreased cognitive function [AO]”. In cases where the MIE is unknown or undefined, the earliest known KE in the chain (i.e., furthest upstream) should be used in lieu of the MIE and it should be made clear that the stated event is a KE and not the MIE. More help

PPARα activation leading to hepatocellular adenomas and carcinomas in rodents

Short name
A short name should also be provided that succinctly summarises the information from the title. This name should not exceed 90 characters. More help
PPARalpha-dependent liver tumors in rodents

Graphical Representation

A graphical summary of the AOP listing all the KEs in sequence, including the MIE (if known) and AO, and the pair-wise relationships (links or KERs) between those KEs should be provided. This is easily achieved using the standard box and arrow AOP diagram (see this page for example). The graphical summary is prepared and uploaded by the user (templates are available) and is often included as part of the proposal when AOP development projects are submitted to the OECD AOP Development Workplan. The graphical representation or AOP diagram provides a useful and concise overview of the KEs that are included in the AOP, and the sequence in which they are linked together. This can aid both the process of development, as well as review and use of the AOP (for more information please see page 19 of the Users' Handbook).If you already have a graphical representation of your AOP in electronic format, simple save it in a standard image format (e.g. jpeg, png) then click ‘Choose File’ under the “Graphical Representation” heading, which is part of the Summary of the AOP section, to select the file that you have just edited. Files must be in jpeg, jpg, gif, png, or bmp format. Click ‘Upload’ to upload the file. You should see the AOP page with the image displayed under the “Graphical Representation” heading. To remove a graphical representation file, click 'Remove' and then click 'OK.'  Your graphic should no longer be displayed on the AOP page. If you do not have a graphical representation of your AOP in electronic format, a template is available to assist you.  Under “Summary of the AOP”, under the “Graphical Representation” heading click on the link “Click to download template for graphical representation.” A Powerpoint template file should download via the default download mechanism for your browser. Click to open this file; it contains a Powerpoint template for an AOP diagram and instructions for editing and saving the diagram. Be sure to save the diagram as jpeg, jpg, gif, png, or bmp format. Once the diagram is edited to its final state, upload the image file as described above. More help


List the name and affiliation information of the individual(s)/organisation(s) that created/developed the AOP. In the context of the OECD AOP Development Workplan, this would typically be the individuals and organisation that submitted an AOP development proposal to the EAGMST. Significant contributors to the AOP should also be listed. A corresponding author with contact information may be provided here. This author does not need an account on the AOP-KB and can be distinct from the point of contact below. The list of authors will be included in any snapshot made from an AOP. More help

J. Christopher Corton, Cancer AOP Workgroup. National Health and Environmental Effects Research Laboratory, Office of Research and Development, Integrated Systems Toxicology Division, US Environmental Protection Agency, Research Triangle Park, NC. Corresponding author for wiki entry (

Point of Contact

Indicate the point of contact for the AOP-KB entry itself. This person is responsible for managing the AOP entry in the AOP-KB and controls write access to the page by defining the contributors as described below. Clicking on the name will allow any wiki user to correspond with the point of contact via the email address associated with their user profile in the AOP-KB. This person can be the same as the corresponding author listed in the authors section but isn’t required to be. In cases where the individuals are different, the corresponding author would be the appropriate person to contact for scientific issues whereas the point of contact would be the appropriate person to contact about technical issues with the AOP-KB entry itself. Corresponding authors and the point of contact are encouraged to monitor comments on their AOPs and develop or coordinate responses as appropriate.  More help
Cataia Ives   (email point of contact)


List user names of all  authors contributing to or revising pages in the AOP-KB that are linked to the AOP description. This information is mainly used to control write access to the AOP page and is controlled by the Point of Contact.  More help
  • Chris Corton
  • Cataia Ives


The status section is used to provide AOP-KB users with information concerning how actively the AOP page is being developed, what type of use or input the authors feel comfortable with given the current level of development, and whether it is part of the OECD AOP Development Workplan and has been reviewed and/or endorsed. “Author Status” is an author defined field that is designated by selecting one of several options from a drop-down menu (Table 3). The “Author Status” field should be changed by the point of contact, as appropriate, as AOP development proceeds. See page 22 of the User Handbook for definitions of selection options. More help
Author status OECD status OECD project SAAOP status
Under development: Not open for comment. Do not cite Under Development 1.17 Included in OECD Work Plan
This AOP was last modified on April 05, 2021 18:16
The date the AOP was last modified is automatically tracked by the AOP-KB. The date modified field can be used to evaluate how actively the page is under development and how recently the version within the AOP-Wiki has been updated compared to any snapshots that were generated. More help

Revision dates for related pages

Page Revision Date/Time
Activation, PPARα December 28, 2020 12:48
Increase, Phenotypic enzyme activity December 28, 2020 12:32
Increase, cell proliferation (hepatocytes) January 06, 2021 16:21
Increase, Clonal Expansion of Altered Hepatic Foci January 06, 2021 16:15
Increase, hepatocellular adenomas and carcinomas December 26, 2020 10:09
Activation, PPARα leads to Increase, Phenotypic enzyme activity December 31, 2020 11:42
Increase, Phenotypic enzyme activity leads to Increase, cell proliferation (hepatocytes) December 31, 2020 13:00
Increase, cell proliferation (hepatocytes) leads to Increase, Clonal Expansion of Altered Hepatic Foci December 03, 2016 16:38
Increase, Clonal Expansion of Altered Hepatic Foci leads to Increase, hepatocellular adenomas and carcinomas December 03, 2016 16:38
Activation, PPARα leads to Increase, cell proliferation (hepatocytes) December 31, 2020 09:43
Activation, PPARα leads to Increase, hepatocellular adenomas and carcinomas December 31, 2020 09:46
Increase, cell proliferation (hepatocytes) leads to Increase, hepatocellular adenomas and carcinomas December 31, 2020 09:46
pirinixic acid November 29, 2016 18:42
Clofibrate November 29, 2016 18:42
Bis(2-ethylhexyl) phthalate November 29, 2016 18:42
Nafenopin November 29, 2016 18:42
ciprofibrate November 29, 2016 18:42
Gemfibrozil March 31, 2020 10:24
PERFLUOROOCTANOIC ACID November 29, 2016 18:42
Bezafibrate November 29, 2016 18:42
Fenofibrate November 29, 2016 18:42


In the abstract section, authors should provide a concise and informative summation of the AOP under development that can stand-alone from the AOP page. Abstracts should typically be 200-400 words in length (similar to an abstract for a journal article). Suggested content for the abstract includes the following: The background/purpose for initiation of the AOP’s development (if there was a specific intent) A brief description of the MIE, AO, and/or major KEs that define the pathway A short summation of the overall WoE supporting the AOP and identification of major knowledge gaps (if any) If a brief statement about how the AOP may be applied (optional). The aim is to capture the highlights of the AOP and its potential scientific and regulatory relevance More help

Several therapeutic agents and industrial chemicals induce liver tumors in rats and mice through the activation of the peroxisome proliferator-activated receptor alpha (PPARα). The molecular and cellular events by which PPARα activators induce rodent hepatocarcinogenesis have been extensively studied and elucidated. The weight of evidence relevant to the hypothesized AOP for PPARα activator-induced rodent hepatocarcinogenesis is summarized here. Chemical-specific and mechanistic data support concordance of temporal and dose–response relationships for the key events associated with many PPARα activators including a phthalate ester plasticizer di(2-ethylhexyl)phthalate (DEHP) and the drug gemfibrozil. The key events (KE) identified include the MIE – PPARα activation measured as a characteristic change in gene expression,  KE2 – increased enzyme activation, characteristically those involved in lipid metabolism and cell cycle control, KE3 – increased cell proliferation, KE4 – selective clonal expansion of preneoplastic foci, and the AO –  – increases in hepatocellular adenomas and carcinomas.  Other biological factors modulate the effects of PPARα activators.These modulating events include increases in oxidative stress, activation of NF-kB, and inhibition of gap junction intercellular communication. The occurrence of hepatocellular adenomas and carcinomas is specific to mice and rats. The occurrence of the various KEs in hamsters, guinea pigs, cynomolgous monkeys are generally absent.

Background (optional)

This optional subsection should be used to provide background information for AOP reviewers and users that is considered helpful in understanding the biology underlying the AOP and the motivation for its development. The background should NOT provide an overview of the AOP, its KEs or KERs, which are captured in more detail below. Examples of potential uses of the optional background section are listed on pages 24-25 of the User Handbook. More help

During the 1970s, an increased incidence of hepatocellular adenomas and carcinomas was observed in rodents treated with a variety of seemingly disparate chemicals. The common effect was an increase in the number and size of peroxisomes and these substances were labeled ‘‘peroxisome proliferators’’ (Rao & Reddy, 1996). Peroxisomes are subcellular organelles involved in long chain fatty acid catabolism through the β-oxidation cycle (de Duve, 1996). Peroxisomes increase in number and/or size following exposure to substances that perturb fatty acid homeostasis. These substances include marketed pharmaceutical agents and drug candidates, phthalate ester plasticizers or their metabolites, herbicides , solvents  and perfluorinated chemicals (Klaunig et al., 2003). Hepatocellular hypertrophy and hyperplasia, changes in apoptosis rates, Kupffer cell activation, and oxidative stress were also observed following chronic exposure of rats and mice to peroxisome proliferators (Corton, 2010).

The peroxisome proliferator-activated receptor α (PPARα) was identified after cloning from mouse DNA (Issemann & Green, 1990). PPARα along with the PPARb/d and PPARg subtypes are ligand-activated transcription factors with both DNA-binding and ligand-binding domain with variation in tissue distribution, expression during development, ligand specificity, and biological function. PPARα is expressed in metabolically active tissues, including the liver, kidney, brown fat and heart, which exhibit pleiotropic responses to peroxisome proliferators. The biological functions and role in chemical effects of PPARα has been facilitated by the PPARα-null mouse  the experimental use of which revealed that a functional PPARα was required for the obseved phenotypic effects and led to the identification of the genes involved in lipid catabolism, lipid transport, peroxisome proliferation and hepatocellular adenomas and carcinomas (Corton, 2010; Lee et al., 1995).  

Like other nuclear receptors, PPARα forms a heterodimer before translocating to the nucleus.  with another nuclear receptor family member, retinoid X receptor (RXR), the receptor for 9-cis-retinoic acid. The PPARα-RXR heterodimer binds to peroxisome proliferator response elements (PPREs) usually found in the promoter or enhancer regions. The PPRE consensus sequence consists of the sequence 5’-AACT AGGTCA A AGGTCA-3’ with PPARα occupying the 5’ position. After agonist binding to PPARα, co-repressors dissociate from the complex leading to de-acetylation, chromatin remodeling  to enable transcription (Escher & Wahli, 2000; Göttlicher et al., 1992; Moreno et al. 2010). 

Summary of the AOP

This section is for information that describes the overall AOP. The information described in section 1 is entered on the upper portion of an AOP page within the AOP-Wiki. This is where some background information may be provided, the structure of the AOP is described, and the KEs and KERs are listed. More help


Molecular Initiating Events (MIE)
An MIE is a specialised KE that represents the beginning (point of interaction between a stressor and the biological system) of an AOP. More help
Key Events (KE)
This table summarises all of the KEs of the AOP. This table is populated in the AOP-Wiki as KEs are added to the AOP. Each table entry acts as a link to the individual KE description page.  More help
Adverse Outcomes (AO)
An AO is a specialised KE that represents the end (an adverse outcome of regulatory significance) of an AOP.  More help
Sequence Type Event ID Title Short name
1 MIE 227 Activation, PPARα Activation, PPARα
2 KE 1170 Increase, Phenotypic enzyme activity Increase, Phenotypic enzyme activity
3 KE 716 Increase, cell proliferation (hepatocytes) Increase, cell proliferation (hepatocytes)
4 KE 1171 Increase, Clonal Expansion of Altered Hepatic Foci Increase, Clonal Expansion of Altered Hepatic Foci
5 AO 719 Increase, hepatocellular adenomas and carcinomas Increase, hepatocellular adenomas and carcinomas

Relationships Between Two Key Events (Including MIEs and AOs)

TESTINGThis table summarises all of the KERs of the AOP and is populated in the AOP-Wiki as KERs are added to the AOP. Each table entry acts as a link to the individual KER description page.To add a key event relationship click on either Add relationship: events adjacent in sequence or Add relationship: events non-adjacent in sequence.For example, if the intended sequence of KEs for the AOP is [KE1 > KE2 > KE3 > KE4]; relationships between KE1 and KE2; KE2 and KE3; and KE3 and KE4 would be defined using the add relationship: events adjacent in sequence button.  Relationships between KE1 and KE3; KE2 and KE4; or KE1 and KE4, for example, should be created using the add relationship: events non-adjacent button. This helps to both organize the table with regard to which KERs define the main sequence of KEs and those that provide additional supporting evidence and aids computational analysis of AOP networks, where non-adjacent KERs can result in artifacts (see Villeneuve et al. 2018; DOI: 10.1002/etc.4124).After clicking either option, the user will be brought to a new page entitled ‘Add Relationship to AOP.’ To create a new relationship, select an upstream event and a downstream event from the drop down menus. The KER will automatically be designated as either adjacent or non-adjacent depending on the button selected. The fields “Evidence” and “Quantitative understanding” can be selected from the drop-down options at the time of creation of the relationship, or can be added later. See the Users Handbook, page 52 (Assess Evidence Supporting All KERs for guiding questions, etc.).  Click ‘Create [adjacent/non-adjacent] relationship.’  The new relationship should be listed on the AOP page under the heading “Relationships Between Two Key Events (Including MIEs and AOs)”. To edit a key event relationship, click ‘Edit’ next to the name of the relationship you wish to edit. The user will be directed to an Editing Relationship page where they can edit the Evidence, and Quantitative Understanding fields using the drop down menus. Once finished editing, click ‘Update [adjacent/non-adjacent] relationship’ to update these fields and return to the AOP page.To remove a key event relationship to an AOP page, under Summary of the AOP, next to “Relationships Between Two Key Events (Including MIEs and AOs)” click ‘Remove’ The relationship should no longer be listed on the AOP page under the heading “Relationships Between Two Key Events (Including MIEs and AOs)”. More help

Network View

The AOP-Wiki automatically generates a network view of the AOP. This network graphic is based on the information provided in the MIE, KEs, AO, KERs and WoE summary tables. The width of the edges representing the KERs is determined by its WoE confidence level, with thicker lines representing higher degrees of confidence. This network view also shows which KEs are shared with other AOPs. More help


The stressor field is a structured data field that can be used to annotate an AOP with standardised terms identifying stressors known to trigger the MIE/AOP. Most often these are chemical names selected from established chemical ontologies. However, depending on the information available, this could also refer to chemical categories (i.e., groups of chemicals with defined structural features known to trigger the MIE). It can also include non-chemical stressors such as genetic or environmental factors. Although AOPs themselves are not chemical or stressor-specific, linking to stressor terms known to be relevant to different AOPs can aid users in searching for AOPs that may be relevant to a given stressor. More help

Life Stage Applicability

Identify the life stage for which the KE is known to be applicable. More help
Life stage Evidence
Adult High

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected. In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available in relation to this KE. More help
Term Scientific Term Evidence Link
mouse Mus musculus High NCBI
rat Rattus norvegicus High NCBI

Sex Applicability

The authors must select from one of the following: Male, female, mixed, asexual, third gender, hermaphrodite, or unspecific. More help
Sex Evidence
Male High
Female High

Overall Assessment of the AOP

This section addresses the relevant biological domain of applicability (i.e., in terms of taxa, sex, life stage, etc.) and WoE for the overall AOP as a basis to consider appropriate regulatory application (e.g., priority setting, testing strategies or risk assessment). The goal of the overall assessment is to provide a high level synthesis and overview of the relative confidence in the AOP and where the significant gaps or weaknesses are (if they exist). Users or readers can drill down into the finer details captured in the KE and KER descriptions, and/or associated summary tables, as appropriate to their needs.Assessment of the AOP is organised into a number of steps. Guidance on pages 59-62 of the User Handbook is available to facilitate assignment of categories of high, moderate, or low confidence for each consideration. While it is not necessary to repeat lengthy text that appears elsewhere in the AOP description (or related KE and KER descriptions), a brief explanation or rationale for the selection of high, moderate, or low confidence should be made. More help

Molecular Initiating Event - PPARα activation

The MIE was characterized using two types of measurements: the earliest is the genomic changes representing the activation of PPARα; (Rooney et al., 2018; Corton et al., 2020; Hill et al., 2020; Lewis et al., 2020) Previously, increases in enzyme activity were used as a measure of PPARα activation . (Liu et al., 1996; David et al., 1999; Isenberg et al., 2000; Klaunig et al., 2003; Kondo et al., 2019)

Microarray profiling was used to show that alteration of gene expression following PPARα activation was almost completely abolished in PPARα-null mice s (Anderson et al., 2001, 2004a,b; Corton et al., 2004; Rosen et al.,2008a,b; Woods et al., 2007c; Ren et al., 2009, 2010; Rosen et al., 2008, 2010; Rosen et al., 2017). The genes that were dependent on PPARα were those involved in lipid homeostasis and the cell cycle.

The development of large gene expression databases TG-GATEs and DrugMatrix enabled that statistical analysis of whole genome expression profiles that form the basis of the genomic MIEs (Svoboda et al., 2019; Igarashi et al., 2015) From these data, A gene expression signature of 131 PPARα-dependent genes was built using microarray profiles from the livers of wild-type and PPARα-null mice. A quantitative measure of this expression signature is a measure of similarity/correlation between the PPARα signature and positive and negative test sets is provided by the Running Fisher test (Kupershmidt et al., 2010; Rooney et al., 2018; Corton et al., 2020).

Key Event #2 - Increased Phenotypic Enzyme Activity 

Increased activity in acyl coenzyme A oxidase, peroxisomal beta-oxidase, carnitine acetyl transferase, catalase and others have been observed uniformly in rats and mice following PPARα activation (Corton et al. 2014; Cunningham et al. 2010; Klaunig et al. 2003; NTP, 2007). 

Key Event #3 - Increased Cell Proliferation

Increased cell proliferation is observed following PPARα and is measured by increases in BrdU labeling index or liver weight.  great deal of work has been conducted to identify the mechanistic events that lead to alterations in cell growth by PPARα activators. Early studies focused mainly on growth factors secreted from Kupffer cells are activated by PPARα activation and may secrete tumor necrosis factor α (TNFα), interleukin-1α (IL-1α), interleukin-1β (IL-1β) and likely other cytokines (Bojes et al., 1997; Rolfe et al.,1997 Holden et al., 2000). Cell proliferation following PPARα did not occur in vivo with pretreatment with antibodies to either TNFα or TNFα receptor 1 (TNFR1) nor in PPARα null mice (West et al., 1999; Bojes et al., 1997; Rolfe et al.,1997 Anderson et al., 2001; Lawrence et al., 2001b).

PPARα reduces expression of the miRNA let-7c in the liver that, in turn, reduces the expression of the c-Myc gene. The resulting increased c-Myc expression is likely a causal factor in increased cell proliferation following PPARα activation (Shah et al., 2007; Qu et al., 2014)

Key Event #4 - Clonal Expansion of Altered Hepatic Foci

ATPase-negative foci and basophilic foci were observed following occurrence of the MIE in rats (Marsman & Popp, 1994).

PPARα activators promote the growth of chemically- and spontaneously-induced lesions through enhanced cell replication (Cattley & Popp, 1989; Cattley et al., 1991; Isenberg et al., 1997; Marsman et al., 1988). ATPase-negative foci and GGT- weakly basophilic foci were observed following occurrence of the MIE in rats but GGT+ and GST-P+ foci were not observed (Grasl-Kraupp et al. 1993c; Marsman & Popp, 1994).

PPARα activators increase cell proliferation in AHF to about half-again that of normal cells measured by labeling index (Grasl-Kraupp et al. 1993b). Continued exposure to PPARα activators causes selective increases in DNA replication within these liver foci in comparison to normal hepatocytes (Isenberg et al., 1997).

Although, apoptosis was found to be increased in both normal cells and AHF by PPARα activation, the increase in foci cell proliferation was sufficient to outweigh the increase in focal apoptosis (Isenberg et al., 1997).

Adverse Outcome - Hepatocellular tumors in mice and rats

Increases in hepatocellular adenomas and carcinomas has been observed in both rats and mice in a dose-dependent fashion after treatment with PPARα activators. Corton et al. (2014) provide a summary of the dose-response data for di-2-ethylhexyl phthalate (DEHP). Similar increases have been observed after treatement with other PPARα activators. The section on Quantitative Understanding below presents some of these data. 

Domain of Applicability

The relevant biological domain(s) of applicability in terms of sex, life-stage, taxa, and other aspects of biological context are defined in this section. Biological domain of applicability is informed by the “Description” and “Biological Domain of Applicability” sections of each KE and KER description (see sections 2G and 3E for details). In essence the taxa/life-stage/sex applicability is defined based on the groups of organisms for which the measurements represented by the KEs can feasibly be measured and the functional and regulatory relationships represented by the KERs are operative.The relevant biological domain of applicability of the AOP as a whole will nearly always be defined based on the most narrowly restricted of its KEs and KERs. For example, if most of the KEs apply to either sex, but one is relevant to females only, the biological domain of applicability of the AOP as a whole would be limited to females. While much of the detail defining the domain of applicability may be found in the individual KE and KER descriptions, the rationale for defining the relevant biological domain of applicability of the overall AOP should be briefly summarised on the AOP page. More help

Sex Differences in PPARα activators

For some PPARα activators, differences in the carcinogenic effects can be observed in both rats and mice (Astill et al., 1996; Butala et al., 1997; Kluwe et al., 1985; Gold and Zeiger, 1997; Hollander and Wiegand, 1978; Malley et al., 1995; Shirasu 1987a, 1987b; U.S. EPA, 2000a, 2000b) These differences may reflect variation in the formation of a metabolite that activates PPARα.

PPARα Activation in the Fetus, Neonate and Adult

PPARα mRNA and protein has been detected in the fetuses of both rats and mice prior to birth (Balasubramaniyan et al. 2005; Beck et al. 1992). Assembly of peroxisomal proteins into peroxisomes and peroxisomal enzyme activity are detectable in late gestation Stefanini et al. 1989; Wilson et al., 1991). Catalase and palmitoyl CoA oxidase activities were first detected in the GD15 Wistar rat fetus (Cibelli et al., 1988).

Stefanini et al. (1995) found no statistically significant differences in numerical density or volume density of peroxisomes in livers of 14-, 21-, or 35-day F344 rat neonates and no differences between neonate groups and adult females. No differences were found between 14- or 21-day neonates or between these groups and adult F344 rats in peroxisomal β-oxidation; the numerical density and volume density of liver peroxisomes were also comparable among groups (Stefanini et al., 1999). No differences were reported for the specific volume density among 7-, 8.5-, 10-, 13-, or 17-day Wistar-derived rat neonates or compared with adults (Wiebel et al., 1969; Staubli et al., 1977).

Direct exposure of the neonate to PPARα agonists results in an increase in peroxisomal enzyme activities and an increase in the numerical density or volume of peroxisomes; the increases in these parameters are comparable to those observed in young adult or adult rats (Staubli et al., 1977; Dostal et al., 1987; Yamoto et al., 1996; Yu et al., 2001).

Species Differences in PPARα Activation

Whilst mice and rats are responsive to PPARα activator-induced liver cancer and associated responses, hamsters, guinea pigs, and primates, including humans, are less sensitive (Ashby et al., 1994; Bentley et al., 1993; Cattley et al., 1998; Doull et al., 1999).

In side-by-side assays human PPARα were less sensitive than rodent PPARα to chemical activation. Hypolipidemic agents and environmental chemicals activated rat or mouse PPARα with greather potency and greater efficacy than human PPARα (Takacs & Abbott, 2007; Maloney & Waxman, 1999; Lapinskas et al., 2005)

Essentiality of the Key Events

An important aspect of assessing an AOP is evaluating the essentiality of its KEs. The essentiality of KEs can only be assessed relative to the impact of manipulation of a given KE (e.g., experimentally blocking or exacerbating the event) on the downstream sequence of KEs defined for the AOP. Consequently evidence supporting essentiality is assembled on the AOP page, rather than on the independent KE pages that are meant to stand-alone as modular units without reference to other KEs in the sequence.The nature of experimental evidence that is relevant to assessing essentiality relates to the impact on downstream KEs and the AO if upstream KEs are prevented or modified. This includes: Direct evidence: directly measured experimental support that blocking or preventing a KE prevents or impacts downstream KEs in the pathway in the expected fashion. Indirect evidence: evidence that modulation or attenuation in the magnitude of impact on a specific KE (increased effect or decreased effect) is associated with corresponding changes (increases or decreases) in the magnitude or frequency of one or more downstream KEs.When assembling the support for essentiality of the KEs, authors should organise relevant data in a tabular format. The objective is to summarise briefly the nature and numbers of investigations in which the essentiality of KEs has been experimentally explored either directly or indirectly. See pages 50-51 in the User Handbook for further definitions and clarifications.  More help

Transgenic or knockout animals can often provide a powerful counterfactual demonstration supporting the identification of a KE (Phillips and Goodman 2006; Simon et al. 2014) Such work demonstrates the necessity of a given KE studies show that preventing a given KE also prevents the occurrence of downstream KEs. Hence, the PPARα-null mouse has provided critical evidence identifying the MIE as well as subsequent downstream KEs.

Global gene expression profiling revealed that alteration of gene expression following PPARα-activation was almost completely abolished in PPARα-null mice at multiple time points (Anderson et al., 2004a,b; Corton et al., 2004; Li et al. 2018; Woods et al., 2007; Rosen et al., 2008. 2010, 2017; Sanderson et al. 2008; Ren et al., 2010; Wang et al. 2020). Following PPARα activation, wild-type mice exhibited increased hepatocyte proliferation compared to untreated controls and no increase was observed in PPARα-null mice (Peters et al., 1997, 1998; Valles et al., 2003; Laughter et al., 2004; Wolf et al., 2008). Apoptosis was suppressed in hepatocytes from wild-type mice but not in those from the knockouts (Hasmall et al., 2000). Finally, chronic treatment with PPARα activators resulted in 100% incidence of hepatocellular tumor in wild-type mice but the knockouts remained unaffected (Peters et al., 1997; Hays et al., 2005).

KE#2, increased enzyme activity, KE#3, increased cell proliferation and KE#4, clonal expansion of preneoplastic lesions have been recognized as KEs in many carcinogenic modes of action and are not specific to PPARα activation.

Finally, in an initiation-promotion study, 25% of wild-type mice receiving only diethylnitrosamine (DEN) developed tumors (25%) whereas 63% of wild-type mice receiving both DEN and a PPARα activator developed tumors. (Glauert et al., 2006).

Evidence Assessment

The biological plausibility, empirical support, and quantitative understanding from each KER in an AOP are assessed together.  Biological plausibility of each of the KERs in the AOP is the most influential consideration in assessing WoE or degree of confidence in an overall hypothesised AOP for potential regulatory application (Meek et al., 2014; 2014a). Empirical support entails consideration of experimental data in terms of the associations between KEs – namely dose-response concordance and temporal relationships between and across multiple KEs. It is examined most often in studies of dose-response/incidence and temporal relationships for stressors that impact the pathway. While less influential than biological plausibility of the KERs and essentiality of the KEs, empirical support can increase confidence in the relationships included in an AOP. For clarification on how to rate the given empirical support for a KER, as well as examples, see pages 53- 55 of the User Handbook.  More help

Evidence supporting PPARα activation as the MIE

  • All PPARα activators exhibit a characteristic genomic signature that was used to characterize the MIE (Corton et al. 2020; Hill et al. 2020; Rooney et al. (2018).
  • The potency of PPARα activation is roughly proportional to the potency of the chemical as an inducer of the liver tumor response (Klaunig et al., 2003).
  • Characteristic gene expression changes do not occur in PPARα-null mice (Anderson et al., 2004a,b; Corton et al., 2004; Rosen et al.,2008, 2017; Ren et al. 2009, 2010;  Woods et al., 2007c).

Evidence supporing KE2 - Increased enzyme activity

  • Increased activity of lipid metabolizing enzymes following PPARα has been repeated demonstrated (Amacher et al 1997; Klaunig et al. 2003; Belury et al. 1998; Corton et al. 2000).

Weaknesses in the evidence

  • Hepatocytes from PPARα-null mice can respond PPARα activators if adjacent to wild-type hepatocytes (Weglarz & Sandgren, 2004).

Evidence supporting KE3 - Increased Cell Proliferation

  • PPARα activators increase cell proliferation.
  • Increases in cell proliferation after exposure to a number of PPARα activators were abolished in PPARα-null mice.

Evidence supporting KE4 - Clonal Expansion of altered foci

  • Clonal expansion is consistently induced in mice and rats by diverse PPARα activators (Marsman & Popp 1994).
  • Clonal expansion and the appearance of foci and tumors by PPARα activators were not observed in PPARα-null mice after exposure to PPARα activators (Hays et al. 2005; Peters et al. 1997).

Evidence supporting the AO - Hepatocellular Adenomas and Carcinomas

  • Chronic treatment with PPARα activators produced hepatocellular neoplasia in 100% of wild-type mice and not in PPARα-null mice (Hays et al., 2005; Peters et al., 1997).

Strength and Specificity of the Evidence

The activation of PPARα is specific this AOP, (AOP 37) whereas the downstream key events are common to the neoplastic process in the rodent liver, e.g AOP 107 and AOP 41. Data supporting each key event were determined to be strong in that several studies support that key event as part of the AOP using multiple PPARα activators from multiple laboratories with no or limited evidence of contradiction. 

Quantitative Understanding

Some proof of concept examples to address the WoE considerations for AOPs quantitatively have recently been developed, based on the rank ordering of the relevant Bradford Hill considerations (i.e., biological plausibility, essentiality and empirical support) (Becker et al., 2017; Becker et al, 2015; Collier et al., 2016). Suggested quantitation of the various elements is expert derived, without collective consideration currently of appropriate reporting templates or formal expert engagement. Though not essential, developers may wish to assign comparative quantitative values to the extent of the supporting data based on the three critical Bradford Hill considerations for AOPs, as a basis to contribute to collective experience.Specific attention is also given to how precisely and accurately one can potentially predict an impact on KEdownstream based on some measurement of KEupstream. This is captured in the form of quantitative understanding calls for each KER. See pages 55-56 of the User Handbook for a review of quantitative understanding for KER's. More help

To understand the dose-response of the PPARα genomic MIE over time, the level of MIE activation of seven most active substances from the TG-GATEs database were fit to a gain-loss model used a dose range normalized over the range of zero to one at each of the time-points, i.e., 3, 6 and 9 hours and 1, 3, 7, 14 and 28 days. The gain-loss model is shown below as Eq. 1 (Watt and Judson, 2018). 

Gain-loss function used to model the Genomic MIE

Gain-loss function. mu[i] = response at the ith dose; x[i] = the ith dose; tp = maximal response as -log(p-value); gw = Hill parameter for gain; ga = dose at the half-maximal gain; lw = Hill parameter for loss; la = dose at the half maximal loss. The model was applied without constraints.

The gain-loss model was chosen because the response variable -log(p-value) from the Running Fisher test did not increase monotonically with dose. This model provided a flexible means of fitting all dose responses for the PPARα agonists in the TG-GATES database at all times. The fits were compared to the data and both visual comparisons and calculated AICs revealed very good fits. The fitted curves were plotted against both dose and time in both 2D and 3D surface plots.

2D and 3D surface plots of the Genomic MIE for 3 PPARα activators

As noted, for all substances, MIE activation does not rise monotonically over dose or time. These fluctuations are likely due to variations in cofactor availability or access to the site of transcription (Gaillard et al., 2006; Koppen et al., 2009; Kupershmidt et al., 2010; Ong et al., 2010; Chow et al., 2011; De Vos et al., 2011; Simon et al., 2015).

A more extensive analysis was conducted for the three PPARalpha agonists shown above to understand the KERs between the genomic MIE and downstream KEs. For all these analyses, the MIE response at 7 days was chosen as the most representative due to the generally steeper response. The 7 day response was also steepest for the PPARalpha activators fenofibrate, benzbromarone, benzodiarone and simvastatin (not shown).

Relationship between the Genomic MIE and Tumor Frequency

To assess this relationship, the dose-response of all three PPARalpha activation at all times measured in the TG-GATES database were plotted along with the corresponding tumor frequency.

Figure. Comparison of the Genomic MIE at all time points from TG-GATES compared to tumor frequency. The 7-day Genomic MIE used in the analyses below is shown with a thick violet line. The tumor frequencies are shown with dashed lines. For clofibrate, the tumor plots show hepatocellular tumors in males and female. For gemfibrozil, the tumor plot shows hepatocellular carcinomas. For WY-14,643, the tumor plot shows the tumor frequency from combined studies in the carcdb at

 The apparent potency of Gemfibrozil (middle plot) increased with time. This trend was weaker for Clofibrate. For gemfibrozil, the potency was higher than the other two PPARalpha activators at all time points. For all chemicals, the  efficacy of the MIE response at the 7-day time point is generally proportional to the tumor frequency at the highest dose and is apparent in the table below. The value for efficacy was tp in the gain-loss equation

Comparison of Efficacy Values for three PPARalpha activators at a range of time points
Time point Clofibrate Gemfibrozil WY-14,643
3h 6.36 3.73 37.7
6h 11.4 4.49 42.8
9h 18.9 24.6 48.6
24h 20.8 29.7 51.4
3d 20.7 19.4 50.2
7d 23.6 20.3 62.0
14d 23.2 35.0 54.7
28d 40.4 18.4 53.5
Max. Tumor Freq. 0.26 0.36 1.0

KERs for PPARalpha Activation by Clofibrate

The figure below displays the analysis.

KER Figure for Clofibrate. A. Detailed surface plot of the genomic MIE in dose and time. B. Dose-response plots of the genomic MIE (solid line and open circles) and the tumor response in males and female rats from Hartig et al. (1982) (dotted line and gender symbols). C. Equation and parameters for the genomic MIE response at 7 d. D. KERs between the MIE and measures of subsequent KEs. Top: KE#1: Peroxisome volume % of the liver vs. MIE; Upper and lower middle: KE#2: liver weight and labeling index v. MIE. Both are measures of cell proliferation. Bottom: tumor respose in males and female rats v. the MIE response. The dotted lines in this plot and in B are the second order multistage model fits to the tumor response data.

The KER between the MIE and the AO is revealed by plots B and the bottom panel in D: tumors don't occur until the MIE is sustained at an MIE level of between 10 and 15. Fatty acid CoA oxidase is a lipid metabolizing enzyme and thus a measure of KE#1 and rises more steeply in female rats. The two responses measuring KE#2 also show male-female differences. Labeling index is a measure of cell proliferation and rises more steeply than in females with increasing MIE levels. Liver weight, another measure of KE#2 changes little in females and more in males, consistent with labeling index, which is a more direct measure of cell proliferation. The tumor response in both males and females trends upward somewhere between and MIE level of 10 to 15. Specifying a response level related to tumors would require a bioassay with more doses.

KERs for PPARalpha Activation by Gemfibrozil

The figure below displays the analysis.


KER Figure for Gemfibrozil. A. Detailed surface plot of the genomic MIE in dose and time. B. Dose-response plots of the genomic MIE (solid line and open circles) and the tumor response in males and female rats from pooled data  (dotted line and gender symbols). C. Equation and parameters for the genomic MIE response at 7 d. D. KERs between the MIE and measures of subsequent KEs. Top: KE#1: Peroxisome volume % of the liver vs. MIE; Upper and lower middle: KE#2: liver weight and labeling index v. MIE. Both are measures of cell proliferation. Bottom: tumor respose in males and female rats v. the MIE response. The dotted lines in this plot and in B are the second order multistage model fits to the tumor response data.

The KER between the MIE and the AO is revealed by plots B and the bottom panel in D: the MIE reaches a plateau at an MIE level of 20 corresponding to a dose of 30 mg/kg/d; an increase in the frequency of neoplastic nodules to 4% occurs at this dose and MIE level; an increase in the frequency of carcinomas to 8% also occurs at this dose level. At a dose of 100 mg/kg/d corresponding to the plateau value of the MIE at 20, the frequency of neoplastic nodules rises to 36% and for carcinomas to 10%.

The steepness in the rise of the MIE between doses of 0 and 30 mg/kg/d occurs along with any level of increase in downstream KEs. In the four graphs in D, Acyl CoA oxidase is a lipid metabolizing enzyme and thus a measure of KE#1 and continues to rise at the plateau level of the MIE. Labeling index as a measure of KE#2 continued to rise from 4% in controls to 26% at a dose of 510 mg/kg/d. At the highest dose of 1300 mg/kg/d, the MIE was at the plateau level of 20 and Labeling Index was 7%. Relative Liver weight, a less direct measure of KE#2, rose to between 55 and 60 mg liver / g BW and remained at the level at the three highest doses with the same MIE level of 20 (Cunningham et al. 2010)

The tumor response was evident only in male rats. (Fitzgerald et al. 1981). Specifying a range of the MIE corresponding to an increase in tumors because of the apparently steep dose-response curve of the MIE [B], and the lack of any doses between 0 and 30 in either the TG-GATES database or the bioassay (Corton et al. 2020; Fitzgerald et al. 1981; Hill et al. 2020; Rooney et al. 2018).

KERs for PPARalpha Activation by WY-14,643

The figure below displays the analysis.

KER Figure for WY-14,643. A. Detailed surface plot of the genomic MIE in dose and time. B. Dose-response plots of the genomic MIE (solid line and open circles) and the tumor response in males and female rats from pooled data  (dotted line and gender symbols). C. Equation and parameters for the genomic MIE response at 7 d. D. KERs between the MIE and measures of subsequent KEs. Top: KE#1: Peroxisome volume % of the liver vs. MIE; Upper and lower middle: KE#2: liver weight and labeling index v. MIE. Both are measures of cell proliferation. Bottom: tumor respose in males and female rats v. the MIE response. The dotted lines in this plot and in B are the second order multistage model fits to the tumor response data.

The combined tumor response was obtained from the carcdb at Because the studies used no more than three dose levels (and a single dose in some), the results from four of the studies were combined. The table below shows the dose response and primary sources.

Dose (mg/kg/d)













Hayashi et al. 1994; Lalwani et al. 1981; Marsman & Popp 1984; Reddy et al. 1979

























The MIE rises steeply and falls less steeply with increasing dose (A). The KER between the MIE and the AO is revealed by plots B and the bottom panel in D: the MIE reaches a plateau at an MIE level of over 30 corresponding to a dose of 10 mg/kg/d; an increase in the frequency of combined liver tumors to 15% occurs a dose of 14 mg/kg/d. Tumor frequency increases monotonically with dose and the MIE falls slightly to about 26 over the dose range of 10 to 100. 

The KER between the MIE and the AO is revealed by plots B and the bottom panel in D: the MIE reaches a plateau at an MIE level of over 30 corresponding to a dose of 10 mg/kg/d; an increase in the frequency of combined liver tumors to 15% occurs a dose of 14 mg/kg/d. Tumor frequency increases monotonically with dose and the MIE falls slightly to about 26 over the dose range of 10 to 100.  At a dose of 100 mg/kg/d corresponding to the plateau value of the MIE at 20, the frequency of neoplastic nodules rises to 36% and for carcinomas to 10%.

Similar to the KERs for Gemfibrozil, increases in KE#1 were not observed until the peak of the MIE response and continued as dose continued to rise (D, upper plot). 

Considerations for Potential Applications of the AOP (optional)

At their discretion, the developer may include in this section discussion of the potential applications of an AOP to support regulatory decision-making. This may include, for example, possible utility for test guideline development or refinement, development of integrated testing and assessment approaches, development of (Q)SARs / or chemical profilers to facilitate the grouping of chemicals for subsequent read-across, screening level hazard assessments or even risk assessment. While it is challenging to foresee all potential regulatory application of AOPs and any application will ultimately lie within the purview of regulatory agencies, potential applications may be apparent as the AOP is being developed, particularly if it was initiated with a particular application in mind. This optional section is intended to provide the developer with an opportunity to suggest potential regulatory applications and describe his or her rationale.To edit the “Considerations for Potential Applications of the AOP” section, on an AOP page, in the upper right hand menu, click ‘Edit.’ This brings you to a page entitled, “Editing AOP.” Scroll down to the “Considerations for Potential Applications of the AOP” section, where a text entry box allows you to submit text. In the upper right hand menu, click ‘Update AOP’ to save your changes and return to the AOP page or 'Update and continue' to continue editing AOP text sections.  The new text should appear under the “Considerations for Potential Applications of the AOP” section on the AOP page. More help

This AOP has no potential application for human risk assessment save to provide a case example of the mode of action of a type of non-genotoxic carcinogen (Wolf et al. 2019; Doe et al. 2019; Cohen et al. 2019). The application to ecological risk assessment is also unlikely. Although some PPARα activators occur in the enviroment, e.g. perfluorinated chemicals, the levels may not be sufficient to produce effects in wild rodent populations.

Several large retrospective epidemiological studies observed no elevated risk of death from liver cancer associated with chronic treatment with PPARα activatorsr (Peters et al., 2005). Over a decade of chronic use of these pharmaceuticals was not associated with liver tumors in large human cohorts  (Frick et al., 1987; Huttunen et al., 1994). 

PPARα activators do not induce cell proliferation or suppress apoptosis in human hepatocytes cultured in vitro (Goll et al., 1999; Hasmall et al., 1999, 2000b; Perrone et al., 1998; Williams & Perrone, 1995). In non-human primates, PPARα activators did not induce cell proliferation in vitro or in vivo (Doull et al., 1999).

In summary, humans are not responsive to the effects of PPARα activators as are mice and rats. 


List the bibliographic references to original papers, books or other documents used to support the AOP. More help

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Anderson, S. P., Dunn, C. S., Cattley, R. C., & Corton, J. C. (2001b). Hepatocellular proliferation in response to a peroxisome proliferator does not require TNFalpha signaling. Carcinogenesis, 22(11), 1843-1851.

Anderson, S. P., Howroyd, P., Liu, J., Qian, X., Bahnemann, R., Swanson, C., Kwak, M. K., Kensler, T. W., & Corton, J. C. (2004b). The transcriptional response to a peroxisome proliferator-activated receptor alpha agonist includes increased expression of proteome maintenance genes. J Biol Chem, 279(50), 52390-52398.

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