Addressing evidence needs in chemicals policy and regulation.
Date: 2022-02-11
Institutions: Joint Research Centre
Subjects: animal experimentation, chemical industry, chemical product, chemicals legislation, decision-making, research method, research report, risk management, toxicology

Addressing evidence 
needs in chemicals policy 
and regulation

Joint
Research
Centre

EUR 30941 EN

This  publication  is  a  Science  for  Policy  report  by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science  and 

knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific 

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JRC126724

EUR 30941 EN

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ISBN 978-92-76-45889-0

ISSN 1831-9424

doi:10.2760/9130

Luxembourg: Publications Office of the European Union, 2022 

© European Union, 2022

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The authors would like to thank Juerg Baggenstoss (Stonehaven Consulting) for his advice and contributions to this report.

How  to  cite  this  report:  Carusi,  A.,  Wittwehr,  C.,  and  Whelan,  M.,  Addressing  evidence  needs  in  chemicals  policy  and 

regulation,  EUR  30941  EN,  Publications  Office  of  the  European  Union,  Luxembourg,  2022,  ISBN  978-92-79-45889-0, 

doi:10.2760/9130, JRC126724.

Addressing evidence 
needs in chemicals policy 
and regulation

Table of
Contents

Executive summary 

1. Introduction

2. A study of stakeholder perceptions

3. Nurturing a healthy trust culture in chemicals regulation 
3.1  Trust based on a shared understanding: Transparency Plus 

3.2  Bridging and comparison

3.2.1  Methods and mechanisms

3.2.2  Science and policy: from data to evidence

3.2.3  Stakeholders and the public

4. A knowledge management framework for

transparency plus

4

8

11

15

16

16

17

19

20

22

4.1  User-centric design 

4.2  Transparency 

4.3  Validation from the outset  

4.4  Inclusion of methods and data relevant to regulation 

4.5  Clarity in levels of knowledge 

4.6  Intuitive visualisation  

4.7  Facilitating contextualisation through comparisons and 

equivalences 

4.8  Gaps should not be absences 

5.  Training, collaboration, and engagement 

References 

24

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Executive 
summary

achieve  this  is  not  straightforward.  The 
Chemicals Strategy for Sustainability (CSS) 
calls for the creation of a comprehensive 
knowledge base on chemicals, with the aim 
of  improving  the  availability  of  chemical 
data and creating a more effective science-
policy  interface.  This  comes  at  a  time 
when  the  field  of  regulatory  toxicology 
is  undergoing  profound  changes  with 
respect  to  methods  and  approaches  for 
generating evidence.

insights 

This report is the culmination of the AOP 
(Adverse  Outcome  Pathway)  Framework 
Study  (Carusi  et  al,  2021),  which  aimed 
to  gain 
into  stakeholders’ 
perceptions  of  i)  the  main  challenges 
facing chemicals regulation, ii) alternative 
approaches  to  conducting  toxicological 
studies, and iii) the role and added-value 
of the AOP Framework. The focus was on 
key stakeholders who are directly involved 
regulatory  or 
in  decision-making 

in 

4

regulation  of  chemicals 

is 
Effective 
crucial  for  health,  the  environment  and 
commerce.  Leading  global  economies 
strive  to  balance  the  benefits  of  using 
chemicals  in  practically  every  industrial, 
agricultural and consumer product, against 
the  potential  risks  they  pose  to  people 
and  our  ecosystem.  Striking  this  balance 
is  an  overarching  goal  in  the  European 
Commission’s  Chemicals  Strategy  for 
Sustainability 
(European  Commission, 
2020) - a central element of the European 
Green Deal (European Commission, 2019) 
and its Zero Pollution ambition.

At  the  same  time,  in  the  public  arena, 
chemicals-related policies and regulations 
are coming under greater public scrutiny, 
and  the  pandemic  has  focused  attention 
on how evidence is used for policy-making.

While there is agreement on the need for 
effective  evidence-based  policy,  how  to 

5

industry contexts: regulatory toxicologists, 
risk  assessors  and  risk  managers.  The 
study  revealed  that  these  stakeholders 
are  deeply  divided  on  the  best  methods 
for  obtaining  data  and  evidence  fit  for 
the  purpose  of  informing  decisions  on 
particular  substances,  and  on  policy  in 

general.  This  lack  of  consensus  leads  to 
fundamental  disagreements 
regarding 
what counts as good evidence for decision 
making in the chemicals domain. Added to 
this, there is significant mistrust between 
sectors and stakeholders, which affects the 
way that data and studies are considered.

AOP  Framework  Study  (Carusi  et  al,  2021)  found  that  the  main  challenges  of 
current chemicals regulation are:

 ▶ the science directly informing policy and regulatory decision-making often lags behind 

current science;

 ▶ there  is  a  lack  of  consensus  on  different  methods  and  approaches  in  toxicological 
sciences, exacerbated by difficulty of access to large quantities of dispersed and non-
standard data;

 ▶ there is mistrust among stakeholders in different sectors;
 ▶ there is not a shared understanding of how data is constituted as evidence for regulatory 

decisions, or for current and future policy regarding chemicals;

 ▶ in view of the likely increasingly contentious nature of chemicals and other potential 
stressors, transparency of the decision-making process in regulation and policy, for all 
stakeholders, becomes an ever greater challenge.

MAIN

   RECOMMENDATIONS:

TRUST

ENABLING ROBUST TRUST PRACTICES

Chemicals  regulation  needs  robust  trust  practices,  that  allow  for  critical 
interrogation and shared understandings of data, evidence and methods. This 
in  turn  leads  to  productive  discussions,  where  the  parties  are  not  forced  to 
agree or come to a consensus, but where their different perspectives are heard 
and understood.

TRANSPARENCY
PLUS

SHARING DATA AND UNDERSTANDING

Robust trust practices in turn depend on two related forms of transparency: 

Transparency  1:  ensures  that  all  relevant  information  is  available  to 

stakeholders;

Transparency 2:  ensures  that  all 

information 

is  understandable  to 

stakeholders coming from different perspectives. 

6

BRIDGES

BRIDGING BETWEEN PERSPECTIVES 

Understandability  from  different  perspectives  needs  bridging  between 
perspectives: that is, tools that make it easy to compare different approaches, 
to see the similarities and differences between them, and to contextualise them 
in the broader background. 

Methods
and approaches

Data and evidence,
science and policy

Stakeholders

KNOWLEDGE
INFRASTRUCTURE

MANAGING KNOWLEDGE SYSTEMS 

Knowledge management has a mediating role between the multiple perspectives 
of  different  methods  and  approaches,  and  provides  a  common  framing, 
overcoming the splintering into narrow specialisms that often characterises this 
domain. 

 
KNOWLEDGE INFRASTRUCTURE FOR TRUST: REQUIREMENTS

TRUST

SHARED UNDERSTANDING OF 
METHODS

Validation

User-centric 
design

Clear levels 
of knowledge

Inclusivity of data and methods relevant to 
regulation

Intuitive visualisations

Contextualisation, comparisons 
and equivalences

1 Deploying a user-centric design approach for the knowledge management system 

will  ensure  that  the  needs  and  purposes  of  all  users  and  potential  users  in  the 

stakeholder groups, are taken into consideration.

2 To  ensure  that  there  is  not  a  validation  deficit  as  novel  scientific  methods  and 

approaches are developed, the knowledge management framework can highlight 

validation issues, both challenges and how to address them.

7

the principles of its organisation and arrangement.

3 It should be clear at all times what types of knowledge are accessed, and what are 
4 Visualisations should be clear with respect to which form and level of knowledge 
5 The knowledge infrastructure should be as inclusive as possible regarding data and 

methods relevant to regulation, and offer tools for contextualising, comparing, and 

they are illustrating.

drawing new connections between methods and approaches.

THIS KNOWLEDGE MANAGEMENT FRAMEWORK SHOULD ALSO ACT AS A POINT OF 
CONTINUITY FOR THE FURTHER ACTIONS IN EDUCATION, COLLABORATION AND 
ENGAGEMENT

1 Education and training that aims at breadth of knowledge and appreciation of how 

multiple methods fit together rather than narrow specialism, and that includes an 

understanding of chemicals regulation as a science and policy area.

knowledge.

2 Collaboration across disciplines and sectors builds trust as well as new and useable 
3 A proactive approach to engagement of stakeholders with the knowledge produced 

and mobilised in chemicals regulation is essential.

8

1. Introduction

Effective  regulation  of  chemicals  is  crucial  for  protecting  human  health 
and  the  environment  while  underpinning  proper  functioning  of  the  EU 
single market and industrial competiveness and innovation. Leading global 
economies strive to balance the benefits of using chemicals in practically 
every  industrial,  agricultural  and  consumer  product,  against  the  potential 
risks  they  pose  to  people  and  our  ecosystem.  Striking  this  balance  is  an 
overarching  goal  in  the  European  Commission’s  Chemicals  Strategy  for 
Sustainability  (European  Commission,  2020),  a  central  element  of  the 
European Green Deal (European Commission, 2019) and its Zero Pollution 
ambition.

Global production of chemicals is expected to double by 2030, reflecting the 
rapidly increasing use of chemicals in almost every sector. In addition, concerns 
surrounding chemicals are becoming more prominent in public discourse. A 
recent survey of European attitudes to the environment shows that a large 
majority are worried about the impact of chemicals on the environment and 
on their health (90% and 84% respectively, Eurobarometer 2020). It’s not 
surprising therefore that policies and regulations related to chemicals are 
coming under greater public scrutiny, amplified by the COVID-19 emergency 
and concerns about possible links between vulnerability to the disease and 
environment related health factors.

With a view to ensuring that policy making and regulatory intervention in the 
EU are evidence based, the Chemicals Strategy for Sustainability (CSS) calls 
for the creation of a comprehensive knowledge base on chemicals, noting 
that this should both improve the availability of chemical data and serve the 
science-policy interface. Linked closely with this are the objectives of ‘one 
substance  one  assessment’  and  exploiting  ‘academic’  data  to  ensure  the 
best use and re-use of relevant data in different regulatory decision-making 
contexts and processes.

While there is clear agreement that policy and regulation should be based 
on evidence, there is often a lack of consensus as to what actually counts as 
evidence for specific purposes, such as forming policies to address particular 
concerns or to make a decision on how to regulate a hazardous substance. 
This lack of consensus is not surprising in a way, since regulatory toxicology 
is  going  through  profound  change  with  a  disruptive  shift  towards  New 
Approach Methodologies (NAMs) and the challenge of using new forms of 
evidence derived from them (see Box 1). Moreover, the expected levels of 
scientific uncertainty that come with embracing new approaches are being 

1. Introduction

compounded with a prevalent lack of trust between stakeholders. This poses 
a  serious  challenge  to  the  acceptance  and  uptake  of  NAMs  by  regulators 
and policy makers and risks leaving evidence lagging behind what modern 
science  can  offer.  Thus  the  evidence  base  for  regulatory  decision-making 
and policy formation is in danger of becoming eroded.

The CSS clearly identifies transparency as an important overarching factor in 
effective long term chemicals regulation. This is strongly mirrored in food law 
with the recent implementation of the new regulation on the transparency 
and sustainability of the EU risk assessment in the food chain. Transparency 
makes data and information more available and accessible in the sense that 
they are easily findable, and not hidden for instance behind institutional and 
financial barriers. In addition however, striving for transparency should also 
include making data and information more accessible in the sense of being 
understandable to different audiences. This includes being presented in a way 
that shared understanding between different audiences is encouraged and 
facilitated. For example, scientists, regulators, the public, and policy-makers 
are different audiences that should be able to access data and information 
about chemicals, and should be able to have productive discussions about 
them, that build consensus. This higher level of transparency is essential for 
developing robust evidence-based policy and trusted chemicals regulation.

This  report  addresses  these  important  aspects  of  chemicals  regulation 
for  current  and  future  needs.  Findings  and  recommendations  are  based 
on  empirical  research  facilitated  through  extensive  engagement  with  key 
stakeholder  groups  in  chemicals  regulation,  in  industry  and  governance, 
analysing their perceptions of the main challenges of chemicals regulation 
and the main obstacles to the acceptance of NAMs. A focus of this work has 
been on optimal approaches to knowledge management to reach sufficient 
shared understanding for consensus about core issues, which includes the 
aim of bridging between different scientific methods, approaches and forms 
of data and evidence.

Box 1.  The need for change in toxicity testing (Carusi et al, 2021)

The  seminal  report  ‘Toxicity  Testing  in  the  21st  century’  (Krewski  et 
al, 2010) set out a bold vision for a paradigm change in  the science 
informing policy making in order to meet the challenges of the huge 
quantities and different types of industrial chemicals, and of mixtures 
and combinations. The paradigm change proposed is in tune with the 
biomolecular and mechanistic approaches that have gained prominence 
since the technologies for conducting data intensive and quantitative 
studies have gained a maturity that sees them standardly used in many 
fields of the biosciences. The authors of the report set out how these 
new scientific methods and technologies and approaches could be best 
harnessed to address challenges in chemicals regulation, arguing that 
these challenges have outstripped the reliance on animal testing that has 
characterised science for the regulation of chemicals since its inception 
as  a  field  in  the  mid-20th  century.  However,  our  study  into  attitudes 

9

towards these new methods and technologies among those in decision-
making  roles,  or  close  to  these  roles,  shows  that  they  are  far  from 
universally accepted. Observational animal studies are still entrenched 
in regulatory toxicology, especially in the nitty-gritty of daily regulatory 
decisions. While clearly different agencies are working to devise ways 
of  bringing  New  Approach  Methodologies  (NAMs)*  into  play  (ECHA 
2016; US EPA 2020), there is still an obvious lack of a common framing 
that allows meaningful comparisons of traditional and NAM data with a 
view to more effective integration of data types and ultimately the use 
of NAMs to satisfy regulatory information requirements.

*As  proposed  in  2016  at  an  ECHA  workshop,  “NAMs  were  taken  in  a  broad 
context  to  include  in  silico  approaches,  in  chemico  and  in  vitro  assays,  as 
well  as  the  inclusion  of  information  from  the  exposure  of  chemicals  in  the 
context  of  hazard  assessment.  They  also  include  a  variety  of  new  testing 
tools,  such  as  “high-throughput  screening”  and  “high-content  methods”  e.g. 
genomics, proteomics, metabolomics; as well as some “conventional” methods 
that aim to improve understanding of toxic effects, either through improving 
toxicokinetic or toxicodynamic knowledge for substances.”

10

Addressing evidence needs in chemicals policy and regulation2. A study of stakeholder 
perceptions

The  study  undertaken  (Carusi  et  al,  2021)  aimed  to  gain  insights  into 
stakeholder perceptions of the main challenges facing chemicals regulation, 
and  the  use  of  NAMs  to  generate  toxicological  data  as  an  alternative  to 
conventional  animal  tests.  In  addition,  the  study  explored  the  level  of 
awareness  of  the  Adverse  Outcome  Pathway  (AOP)  framework  (Ankley 
et.  al.,  2010)  and  the  associated  AOP  Development  Programme  of  the 
Organisation for Economic Cooperation and Development (OECD). The AOP 
programme was established in 2012 as a knowledge management approach 
for regulatory toxicology and risk assessment and designed specifically to 
serve chemicals policy and regulation in OECD member countries.

Three main stakeholder groups involved in decision-making in regulatory or 
industry contexts were targeted: regulatory toxicologists, risk assessors and 
risk managers (Figure 1). They were engaged through a survey, interviews 
and focus groups.

11

Figure 1:  Targeted groups of the stakeholder study.

Findings of the study (Box 2) shed light on the challenges, strategies and 
practices  spoken  about  by  participants  in  interviews  and  focus  groups, 
and  written  about  by  survey  respondents  in  open  comments.  Participants 
described  issues  relating  to  the  large  number  of  chemicals  without 
sufficiently accessible and findable data, or without data of adequate quality; 
a regulatory system that struggles to keep up with new scientific advances; 
and a regulatory world that is pervaded by competing interests. These issues 
become even more prominent in discussions about non-animal methods in 
toxicology where both NAMs and the ‘gold standard’ of animal testing elicit 
a great deal of scepticism and doubt within different circles. This is clearly 
reflected in the demand for validation, and how pressure is being exerted 
from both sides of the science-policy divide.

12

There  is  compelling  evidence  that  stakeholders  are  deeply  divided  on  the 
best methods for obtaining data and evidence fit for the purpose of informing 
decisions  on  particular  substances,  and  on  policy  in  general.  This  lack  of 
consensus leads to fundamental disagreements regarding what counts as 
good evidence for decision-making in the chemicals domain. Added to this, 
there is significant mistrust between sectors and stakeholders, which affects 
the way that data and studies are considered.

Although the level of awareness of the AOP framework was modest, those 
that  were  somewhat  informed  recognised  the  knowledge  management 
aspect of the framework as a powerful tool for addressing many of the data 
challenges  of  chemicals  regulation.  There  was  recognition  that  AOPs  can 
integrate and reconcile different kinds of data from for example, animal, in 
vitro, computational, clinical, or epidemiological studies. In general though, 
the  AOP  framework  was  perceived  as  being  more  relevant  for  NAMs  and 
non-animal  approaches,  and  thereby  inheriting  the  associated  challenges, 
limitations and potential.

Stakeholders believe that the knowledge management capacity of the AOP 
framework could be extended and tailored to meet many aims of the CSS, 
acting as a mediating tool for enhancing trust and transparency in chemicals 
regulation. It was appreciated that not only does the framework give open 
access to integrated, synthesised and curated knowledge, it also acts as a 
common framing that makes knowledge understandable across the barriers 
of discipline and sector. As such, it forms a basis for building trust through 
transparency in both important senses: access  and understandability, and 
can  act  as  a  scaffold  for  collaboration  and  dialogue  across  actors  and 
regulated sectors.

Box 2.  Findings of the stakeholder study (Carusi et al, 2021) 

Main challenges to chemicals regulation

 ▶ The quantity and quality of data. Data gaps, the need for prioritisation, 
the lack of standardisation were all identified as major challenges. 
There are specific types of data that are lacking – most prominently 
exposure  data  –  and  there  are  areas  that  are  particularly  lacking 
in  data  –  mentioned  were  neurotoxicity,  endocrine  disruption  and 
combined toxicity or mixtures, and new chemicals generally.

 ▶ There is widespread disagreement and a lack of consensus regarding 
which  methods  or  types  of  studies  are  likely  to  produce  reliable 
results.

 ▶ Regulatory guidelines are out of date, unclear or lacking.

 ▶ The variability of data interpretation can mean, for example, a single 
submission  can  be  interpreted  differently  by  different  regulatory 
agencies. This leads to the sector being perceived as unpredictable, 

Addressing evidence needs in chemicals policy and regulation2. A study of stakeholder perceptions

and the process of going through regulation as lacking in transparency 
and governed by chance.

 ▶ Chemicals  regulation  lags  behind  current  scientific  advances, 
especially regarding biomolecular and mechanistic approaches. This 
is due to a variety of reasons including workloads, training, actual 
or perceived innate conservativeness, age demographics, as well as 
socio-economic factors.

 ▶ Chemicals regulation is insufficiently science driven. The claim that 
the regulation of chemicals is insufficiently science driven, and overly 
driven by other interests, such as political or economic interests, was 
frequently made.

 ▶ There is a high degree of mistrust among different sectors involved in 
chemicals regulation. While this seemed most marked in relationships 
between industry and regulators, it was also present in relationships 
between other stakeholders.

 ▶ Mistrust  and  lack  of  consensus  about  reliability  of  methods  and 

approaches are closely inter-related.

Main challenges for NAMs

 ▶ There is an even greater lack of consensus regarding the standing of 

non-animal methods and their use for chemicals regulation.

13

inherent  to  the  regulatory  context 

 ▶ There are two main sources of challenges to non-animal methods: 
inherent 
factors 
conservativeness,  lack  of  training  in  new  methods,  and  pressures 
from  policy  makers.  Disagreement  over  the  methods  and  their 
usefulness  and  usability  in  regulatory  science  are  manifested  in 
many ways, described in the points below.

include 

 ▶ The  demand  for  non-animal  studies  to  be  proven  reliable  is  often 
made in terms of a demand for validation or for validated studies. 
The validation process is time consuming and costly. However, there 
is  not  universal  agreement  on  what  the  criteria  for  validation  are, 
or even, it seems, of how to conduct the debate about what those 
criteria ought to be. Validation in this context is not a strictly scientific 
process, but depends to a large extent on the regulatory purposes of 
the studies.

 ▶ The regulatory paradigm is overwhelmingly geared towards animal 
studies, and so as long as the benchmarks and problem formulation 
for non-animal studies are set by animal studies, it is unlikely that 
they will make much headway. Seeing these two forms of studies 
in a potential ‘replacement’ relationship (where non-animal studies 
are deemed acceptable only when they are able to meet standards 
set by animal studies) is a major obstacle. Integration rather than 

replacement appeared as a compromise position that would enable 
non-animal studies to be developed so that they could become more 
viable.

 ▶ While non-animal studies are often seen as too uncertain to be used 
in regulatory decision-making, there is a pressing question of how 
certain animal studies in fact are, and whether they should continue 
to  be  considered  as  a  ‘gold  standard’;  this  is  closely  associated 
with the question of how uncertainty is tolerated and managed in 
different sectors.

14

Addressing evidence needs in chemicals policy and regulation3. Nurturing a healthy trust 
culture in chemicals regulation

Trust is the cornerstone of good governance in any domain. Trust, however, 
comes  in  many  forms,  and  some  forms  are  better  at  sustaining  robust 
knowledge practices, especially in domains like chemical regulation, which 
are  often  controversial.  Some  forms  of  trust  lead  to  ‘groupthink’  and 
closed  knowledge  practices,  and  an  overly  trusting  relationship  between 
stakeholders can lead to a lack of interrogation and oversight. Some degree 
of scepticism can be healthy. A healthy trust culture for governance – that 
is, where there are likely to be competing interests and perspectives – is one 
that allows for an interrogation of evidence based on transparency and a 
shared understandings among different stakeholder groups.

Understanding  who  the  key  audiences  are,  the  stakeholders  in  chemicals 
regulation,  is  the  all-encompassing  priority  of  any  trust-building  system. 
Identifying  the  broad  categories  of  stakeholders  is  fairly  straightforward: 
the main direct stakeholder groups in the chemicals domain are the public 
(often  represented  by  Non-Governmental  Organisations),  regulators,  and 
industry, sub-divided into different organisations, institutions, agencies and 
groups.  Relations  between  these  main  stakeholder  groups  often  revolve 
around  knowledge:  access  to  it;  ways  of  managing,  organising,  validating 
and prioritising it; and ways of mobilising it, for example, for regulation or 
for the production and consumption of goods. This brings in another major 
stakeholder:  scientists,  who  could  fall  into  any,  or  none,  of  these  main 
stakeholder  groups  (individually  or  institutionally).  The  mistrust  between 
stakeholder groups needs to be addressed head-on, by fostering engagement 
and dialogue between stakeholder groups.

We know that transparency is essential for trust. What we need to work further 
on is how to ensure transparency in practice, what should be transparent, and 
most  importantly,  for  whom.  Transparency  requires  access  to  information 
and  an  insight  into  the  provenance  of  information  (what  is  the  source  of 
the  information,  and  what  interests  is  it  associated  with).  Further  to  this, 
transparency  requires  cognitive  access  too,  that  is,  the  understandability 
of  information  for  different  audiences.  Knowledge  management  is  the 
indispensable support for creating transparent and shared understandings 
of the reasoning behind often competing claims made by different parties in 
chemicals regulation.

15

16

In  this  section,  we  describe  the  main  aspects  of  a  healthy  trust  culture 
based on shared understanding. In Section 4, we outline the features of a 
knowledge management framework to support this.

3.1 Trust  based  on  a  shared  understanding: 
Transparency Plus

In highly contested domains where there are frequently controversies and 
disagreements, consensus on conclusions and decisions based on evidence 
is difficult to achieve. A cornerstone of transparency in the chemicals domain 
is the access to all relevant information by all parties, and the declaration 
of  potential  conflicts  of  interest.  Beyond  the  interests  that  are  potentially 
at  play,  and  even  assuming  full  openness  and  access,  there  will  still  be 
multiple perspectives on what constitutes data and evidence. A great deal of 
disagreement is generated by the splintering of these multiple perspectives, 
and  the  absence  of  a  common  framework  for  thinking  about  different 
forms  of  data  and  evidence,  and  the  variety  of  methods  and  approaches 
that  generate  them.  Trust  and  consensus  in  chemicals  regulation  could 
be  improved  by  working  to  provide  a  common  framework  for  multiple 
perspectives on an issue. In this way, the focus is shifted from the decisions 
themselves towards the ways in which decisions are arrived at. Stakeholders 
can  share  in  the  understanding  of  the  basis  of  decisions,  without  having 
helped to make the decision. This transparency plus concept goes beyond 
the  typical  understanding  of  transparency:  it  not  only  makes  available 
the data on which decisions are made, but also tries to make explicit the 
reasoning  behind  it,  for  audiences  different  than  those  who  produced  the 
data.  For  example,  it  makes  explicit  why  certain  methods  were  used  to 
obtain the data, how those methods may be contextualised and compared 
to other methods, and why they are considered reliable and adequate. The 
reasoning showing how data supports specific decisions is also made clear. 
A common framing makes it possible for stakeholders to make comparisons 
between different forms of data and evidence and thus the right knowledge 
management tool will go a long way to supporting shared understandings 
among multiple perspectives.

3.2 Bridging and comparison 

Making  comparisons  is  a  key  aspect  of  scientific  reasoning:  for  example, 
assessing the reproducibility of a study is essentially an exercise of making 
comparisons  and  is  a  major  factor  in  building  credibility  and  confidence. 
Comparability  is  facilitated  by  different  means:  through  common  data 
and  reporting  standards,  terminology  and  vocabulary,  classification  and 
measurement systems, diagrams and other visualisations. These are all ways 
of supporting, or grounding comparability. Our study shows that for many 
stakeholders  in  decision-making  roles,  there  is  a  failure  of  comparability 
between established methods and NAMs for toxicity testing. This does not 
mean that there may not in principle be some form of comparability possible, 
only that it is either not available, it is contentious or not universally accepted.

Addressing evidence needs in chemicals policy and regulation3. Nurturing a healthy trust culture in chemicals regulation

This is where bridging between different approaches and methods becomes 
extremely  important.  Bridging  consists  in  making  connections  between 
different approaches or methods either by highlighting connections that are 
already in place or by forging new ones. Bridging can also involve identifying 
and articulating a current lack of connection and what might constitute a 
potential  connection.  In  this  way,  the  absence  is  not  simply  a  gap,  but  is 
highlighted  as  a  space  where  questions  can  be  raised,  and  where  further 
action is needed. Absences like this will also be one aspect of the knowledge 
pool  relating  to  a  particular  substance,  adverse  effect  or  other  point  of 
interest, and it will be possible to see not only where information is missing, 
but where it is present and in which forms.

There are two main forms of bridging that need to occur in order to achieve 
better science-informed chemicals regulation (Figure 2): the first is bridging 
between different methods and approaches to probe a question of mutual 
scientific  interest;  the  second  is  bridging  between  science  and  policy  or 
regulatory decision-making.

Established 
toxicity test 
methods

New methods 
and 
mechanisms

Bridging
across scientiﬁc methods and approaches

17

Data

Evidence

Bridging
across science and policy

Figure 2:  Methods and mechanisms; data and evidence.

3.2.1  Methods and mechanisms

Our  study  shows  that  there  is  a  worrying  lack  of  consensus  regarding 
the  methods  and  approaches  for  obtaining  data.  This  spills  over  into 
disagreements  about  the  trustworthiness  of  the  data,  and  whether  the 
data can or should be used as evidence to inform decision-making. In the 
chemicals domain, shared understanding of reasons is shared understanding 
of methods for producing data and evidence.

The last decades have seen a seismic shift of the biosciences towards cellular 
and  biomolecular  approaches,  with  considerable  emphasis  on  biological 
mechanisms. This shift has gained momentum through the development of 
technologies for in vitro testing, and for data intensive and computational 
studies.  It  has  affected  all  aspects  of  the  scientific  process,  from  the 

formation of research questions to identifying the focus of studies. The rise 
of mechanistic information in toxicology occurs against this background.

By the nature of the case, there are not one-to-one comparisons between 
these mechanistic approaches and the whole organism based approaches 
on which established toxicology is based. However, this is not new in toxicity 
testing (Figure 3), as there are not one-to-one comparisons between animal 
studies  and  the  human  populations  for  which  they  are  models.  These 
comparisons take a lot of time to become established, and identifying the 
appropriate comparisons emerges alongside the refinement of the approach 
and emerging knowledge in the respective domain. While there is a wealth 
of  experience  in  making  comparisons  in  the  scientific  community,  not  all 
terms of comparison are predictable in advance, and some will emerge as 
the science does.

18

a)

b)

Addressing evidence needs in chemicals policy and regulation3. Nurturing a healthy trust culture in chemicals regulation

formation of research questions to identifying the focus of studies. The rise 

of mechanistic information in toxicology occurs against this background.

c)

By the nature of the case, there are not one-to-one comparisons between 

these mechanistic approaches and the whole organism based approaches 

on which established toxicology is based. However, this is not new in toxicity 

testing (Figure 3), as there are not one-to-one comparisons between animal 

studies  and  the  human  populations  for  which  they  are  models.  These 

comparisons take a lot of time to become established, and identifying the 

appropriate comparisons emerges alongside the refinement of the approach 

and emerging knowledge in the respective domain. While there is a wealth 

of  experience  in  making  comparisons  in  the  scientific  community,  not  all 

terms of comparison are predictable in advance, and some will emerge as 

the science does.

19

Figure 3:  Examples of common ways of drawing comparisons in toxicology. Dose 
response curves are now a standard and familiar way of making comparisons in 
regulatory toxicology, for example, between a) substances or b) model organisms, 
c) they are supported by tables that make the calculations explicit. Similar means 
of making comparisons for new methods and across methods will emerge. a) © 
Liu et al, 2017, doi:10.3892/mmr.2017.6552, CC BY-NC-ND 4.0; b) © Cortajarena 
et al, 2003, doi:10.1074/jbc.M208552200, CC BY 4.0; c) © Nair and Jacob 2016, 
doi:10.4103/0976-0105.177703, CC BY-NC-SA 3.0.

Making  the  role  of  methods  more  explicit,  and  bridging  across  methods 
will also indirectly support the ability to compare methods, using familiar 
terms of comparison, or showing up where new terms of comparison may 
be forged. This in turn supports further discussions of the extent to which 
methods may stand in for each other, or may complement each other and 
potentially be integrated.

Knowledge  management  can  provide  a  powerful  means  for  supporting 
comparisons,  through  explicitly  linking  together  familiar  terms  or  through 
forging new ones. This is further discussed in Section 4.3.

3.2.2  Science and policy: from data to evidence

The independence of science, and its ability to inform decisions and regulation 
effectively are not necessarily in tension. Scientific data can and should be 
gathered  independently  of  any  specific  policy  decisions;  however  it  needs 
to be gathered in a form that fits the needs of policy, especially in an area 
like chemicals regulation that is constantly at the science-policy interface 
(Dilling et al, 2105; Lemos et al, 2012; Mach et al, 2020; Millstone, 2009). 
There  is,  and  has  to  be,  a  dialogue  between  science  and  policy  regarding 
what those forms of evidence are, as these cannot be imposed unilaterally 
by policy.

The key point is that not all data are evidence for decision-making or for 
policy  formation.  An  early  historical  example  are  the  Guidelines  from  the 

20

US  Environmental  Protection  Agency  (EPA)  published  between  1983  and 
1986, which act as a bridge between science and policy, establishing which 
forms  of  data  would  be  relevant  to  decision-making,  and  could  become 
evidence  for  them  (Demortain,  2020).  A  more  current  example  are  the 
EFSA  Guidelines  for  dose-response  assessment  (EFSA  2016).  Knowledge 
management  needs  to  support  the  transformation  of  data  into  evidence: 
that  is,  how  science  can  produce  data  that  can  be  taken  up  as  evidence. 
Optimally, there is a two-way relationship between science on one hand, and 
regulatory decisions and policy on the other, with scientists providing data 
that can be used as evidence to support decisions and policy; and regulators 
and  policy-makers  adapting  the  forms  of  evidence  they  can  use  to  make 
the  best  use  of  relevant,  new  and  emerging  forms  of  data.  Importantly, 
there  needs  to  be  a  convergence  on  the  key  questions  and  problems  for 
data and evidence provided by science to have a means of hooking up with 
policy. These dialogues between scientists, regulators and policymakers can 
be supported through a common framework for formulating questions and 
presenting evidence, information and data.

3.2.3  Stakeholders and the public

Chemicals regulation is a nexus of social, economic, political and scientific 
concerns and interests. As such, it is not surprising that it attracts controversies 
and disputes; equally the need for it to adhere to the strictest requirements 
of transparency in decision-making is well recognised. Perhaps less talked 
about have been two other closely interconnected aspects: the first is the 
link between transparency and accessibility; and the second is the link to the 
stakeholders for whom information should be transparent (and accessible).

Transparency  means  that  the  bases  for  making  decisions  should  be 
open  and  not  hidden;  however,  in  the  fullest  sense,  it  also  means  that 
the  information  and  evidence  on  which  decisions  are  based  need  to  be 
accessible; that is, not only open, if someone takes the trouble to find them, 
but easily accessible too, not requiring extra burden in terms of time, effort 
or knowhow to understand. There are many different stakeholder groups in 
chemicals regulation with different types and levels of specialist knowledge. 
This  study  highlighted  how  important  it  is  for  people  in  decision-making 
roles who are not directly involved in scientific research, and who may not 
be ‘scientific insiders’, to have the type of access to knowledge that comes 
with enhanced transparency, or transparency plus. The expertise, experience 
and  expectations  of  regulators  in  everyday  decision-making,  as  well  as 
policymakers in policy formation, play a crucial role in determining whether 
data  from  a  wide  variety  of  methods  and  approaches  is  accepted,  and 
therefore actually admitted as evidence in, for example, a dossier submitted 
to a regulatory agency.

The general public increasingly demand to know more about chemicals in 
products  and  present  in  the  environment  and  civil  groups  frequently  take 
action of their own to lobby governments. For example, this has been very 
evident in the case of glyphosate, and also neonicotinoids in pesticides, both 

Addressing evidence needs in chemicals policy and regulation3. Nurturing a healthy trust culture in chemicals regulation

of which were the subject of petitions that garnered millions of signatures1. 
With greater awareness of the issues relating to climate and the environment, 
and currently living through a global pandemic and experiencing for ourselves 
the many complexities in the relationship between science and policy, the 
general  public  has  a  greater  appetite  for  understanding  more  about  how 
the decisions that affect their lives are made. Studies of public reactions to 
science-policy relationships are no doubt already underway; however, even 
a  cursory  look  at  current  news  items  and  social  media  show  the  intense 
interest  in  this  relationship  and  participation  in  discussions  by  the  public, 
elicited by topics such as the continued use of animal testing for cosmetics 
reported in The Guardian2 (Gilbert and Grover, 2021) or the science/policy 
relationship  during  the  Covid19  pandemic,  as  reported  in  The  Scientist3 
(Kwon, 2020).

Acceptance of different methods for basing decisions about chemicals will 
ultimately  lie  with  the  broad  public,  and  it  should  be  possible  for  people 
to  participate  in  the  discussions  in  this  domain.  Indeed,  they  will  engage 
in  those  discussions  anyway,  the  difference  is  how  and  with  what  levels 
of  knowledge  and  understanding.  The  issue  of  public  engagement  goes 
well  beyond  the  remit  of  this  report.  However,  the  recommendations 
made here calling for shared understandings of multiple perspectives are 
in  accordance  with  a  deliberative  approach  to  public  engagement,  which 
empowers members of the public to participate actively in discussions (for 
example, National Consumer Council 2008). Knowledge management that 
organises  knowledge  so  as  to  make  it  clearer  how  different  approaches 
relate to each other supports these more deliberative ways of engaging the 
public in matters that impact on all of us directly. We need further research 
to understand how to contextualise knowledge management for chemicals 
regulation in the broader health, environment, social and economic issues 
that directly relate to public concerns.

21

1  The European Citizens’ Initiative, ‘Ban Glyphosate and protect people and the environment from 
toxic  pesticides’  was  submitted  in  October  2017,  with  over  1  million  supporters  (https://europa.
eu/citizens-initiative/ban-glyphosate-and-protect-people-and-environment-toxic-pesticides_en), 
while  the  ‘Save  Bees  and  Farmers’  initiative  has  garnered  over  600  000  signatures  (https://www.
savebeesandfarmers.eu/eng).
2  https://www.theguardian.com/environment/2021/aug/19/hundreds-of-uk-and-eu-cosmetics-
products-contain-ingredients-tested-on-animals
3  https://www.the-scientist.com/careers/science-and-policy-collide-during-the-pandemic-67882

22

4. A  knowledge  management 
framework for transparency plus

More  efficient  knowledge  management  is  frequently  called  for  to  address 
the  challenges  of  very  high  quantities  of  data  and  information  dispersed 
across  different  sources  and  in  different  formats  and  standards.  Efficient 
knowledge management is targeted for particular purposes, since there is 
not a form of universal knowledge management that will suit every need 
and use. In chemicals regulation, the aim of knowledge management is to 
ensure that scientific knowledge about hazard and risk becomes useable as 
scientific evidence for regulatory decisions and policy.

This  has  always  been  the  aspiration  of  the  AOP  framework.  The  AOP 
framework  is  well  represented  in  the  literature  on  regulatory  toxicology 
(Carusi  et  al,  2021),  and  is  in  a  good  position  to  become  an  important 
resource for chemicals regulation in two main respects:

•  bridging  between  mechanistic  knowledge  on  toxicological  hazard 
and the forms of evidence that feed into decision-making and policy 
formation;
supporting shared understanding of evidence.

• 

In  this  section,  we  list  the  requirements  for  adapting  and  extending  the 
AOP framework that will enable it to better fulfil these roles in chemicals 
regulation. This designed evolution will see the framework bridging across 
established and emerging methods in toxicology, enabling data produced for 
example by NAMs and human biomonitoring to become useable as evidence 
for regulatory decisions and policy.

Box 3.  Main findings related to the AOP framework (Carusi et al, 

2021)

Challenges

 ▶ Even though the survey showed high levels of awareness of the AOP 
framework  and  AOP  Knowledge  Base,  there  were  lower  levels  of 
awareness at focus groups and during fieldwork observations.

 ▶ The AOP framework is faced with many of the same challenges as 

non-animal methods.

4. A knowledge management framework for transparency plus

 ▶ The lack of exposure data in AOPs is the most important ‘missing 
data’  diminishing  the  usefulness  of  the  AOP  framework  for  many 
research  participants.  This  may  be  because  many  participants  are 
more motivated to do risk assessment than hazard identification and 
assessment.

 ▶ Lack  of  interest  from  regulators  in  mechanistic  processes  was 
mentioned  by  many  research  participants.  Mechanisms  are 
‘blackboxed’,  and  there  are  no  incentives,  and  many  disincentives, 
for opening up the black box.

 ▶ Further important aspects perceived as making the framework and 
approach less useful are the absence of toxicokinetics, and with this 
the absence of quantification, and reliance on observational studies. 
In  addition,  chemical  agnosticism  was  perceived  as  a  significant 
drawback.

 ▶ A  further  issue  is  that  of  the  balance  between  simplicity  and 
complexity. For many, the AOP framework is seen as overly simple, 
even simplistic, due to what is perceived as its linearity. Introducing 
more  of  the  feedback  loops  and  inter-relationships  that  would 
make  AOPs  more  biologically  ‘realistic’  threatens  to  make  AOPs 
unmanageably complex. It seems that the linear visual representation 
of the AOP leads to many misunderstandings regarding how linear 
AOPs actually are.

 ▶ There are still too few completed AOPs, and they are extremely time 
consuming to develop. More completed AOPs are needed in order to 
show the utility of the approach. In the same vein, more participation 
by the regulatory community and more collaboration is needed.

 ▶ The AOP Knowledge Base still has several usability issues, for example 
relating to the navigation and searches; but most importantly, it lacks 
some key information.

Benefits

 ▶ The  AOP  framework  is  recognised  as  bringing  many  benefits  with 
respect  to  knowledge  management,  the  identification  of  data 
gaps,  the  integration  of  data,  and  as  an  aid  to  study  design  and 
development of new test strategies.

 ▶ The  framework  is  seen  as  contributing  to  more  integrated  and 
holistic approaches, and an important part of Integrated Approaches 
to  Testing  and  Assessment  (IATA).  It  could  also  play  a  role  in  the 
ability  to  potentially  move  towards  testing  very  large  numbers  of 
chemicals rather than relying on discrete observational studies. This 
data intensive approach would be mechanistic in nature, and may 
facilitate  fundamental  changes  in  regulatory  toxicology,  such  as  a 

23

24

shift towards a different understanding of validation that takes more 
account of biological plausibility, and a shift towards intermediate-
effect related risk factors instead of conventional apical endpoints.

 ▶ An  interesting  possibility  for  the  AOP  framework  is  in  framing 
arguments  around  chemicals  and  the  different  studies  one  might 
conduct, and acting as a mediator between different perspectives.

4.1 User-centric design

An  enterprise  to  develop  a  knowledge  management  framework  that  is 
actually used by stakeholders has to start out by thinking about who these 
stakeholders  are,  and  how  best  to  engage  them  in  the  development  and 
application of the framework. This engagement needs to be thought about 
from the outset and users need to be included in the design process at every 
step.  An  analysis  of  stakeholders  is  discussed  in  Carusi  et  al  (2018);  this 
needs further fine-tuning and extension. The user group is the beginning of a 
wider network or community of practice that will ensure ongoing interaction 
between  different  stakeholders.  It  is  important  to  understand  how  to 
engage with these different stakeholders through the way that knowledge 
is made transparent and accessible, and how to contextualise it for better 
understanding.

A  user-centric  design  process  ensures  that  the  framework  meets  the 
requirements  of  the  people  it  is  aimed  at.  This  increases  its  chances  of 
being used; at the same time, members of the broader community are more 
likely  to  trust  and  be  willing  to  try  something  that  their  peers  have  been 
involved in developing (Kalafatis et al, 2015). Engagement can take many 
different  forms,  so  long  as  it  is  a  two-way  communication  process  that 
makes feedback very easy. For example, training events are opportunities 
for engagement if they proactively include active feedback targeted at the 
framework itself. Usability is not an add-on at the end of the design and 
implementation process, but is rather where the process begins: use-ability: 
the question ‘for which uses, of which people?’ being asked from the outset.

This  can  be  achieved  by  including  user  groups  in  design  workshops  and 
activities, getting feedback on prototypes and at each stage of development, 
in an iterative and agile cycle. It is necessary to target users carefully. There 
are  a  variety  of  strategies  for  choosing  user  groups,  one  is  to  target  on 
specific gatekeeper communities – such as those who produce guidelines for 
regulatory use; or to target more broadly - such as a cross-section of people 
working on regulatory decisions in industry and regulatory agencies.

4.2 Transparency

The basic requirements of transparency are access to all relevant information 
and openness about the sources of information and any potential conflicts of 

Addressing evidence needs in chemicals policy and regulation4. A knowledge management framework for transparency plus

interest. Which information users will want to see should be included in the 
user-centric design sessions, and incorporated into the knowledge platform.

4.3 Validation from the outset 

Validation of methods and technologies for producing data is crucial to the 
relationship  among  the  different  disciplines  that  inform  policy  (Patterson, 
Whelan  &  Worth  2021),  and  between  science  and  policy.  Scientists  often 
treat  validation  as  a  purely  technical  issue,  missing  the  critical  role  it 
plays in building trust in science-based regulation and policy. Validation is 
often  viewed  as  something  that  comes  last  in  the  ‘scientific  pipeline’.  On 
the  contrary,  an  effective  validation  process  should  start  with  the  science 
and  develop  alongside  it.  Validation  should  be  placed  front  and  centre  of 
knowledge management.

There are many different forms of validation and they cannot all be treated 
in the same way. Peer review and endorsement by reputable organisations 
or bodies are forms of validation that are already represented in the AOP 
Knowledge  Base.  Information  about  these  existing  validations  need  to 
become even clearer and easily accessible to users.

The demand for validation is often directed at the methods for producing 
outputs,  rather  than  directly  on  the  outputs.  A  validated  method  (be  it 
a  testing  protocol,  an  animal  or  computational  model,  or  the  specific 
measurements, instruments and technologies used by a method) is one that 
meets agreed standards set by an authoritative body or organization, and 
that has acceptance from those who will implement its results (for example, 
see  Corsini  et  al,  2021).  The  study  shows  that  the  main  obstacles  to  the 
adoption  of  new  and  emerging  methods  and  approaches  is  their  lack  of 
validation – or at least, the lack of agreed standards for validation. Many of 
the so-called ‘alternative’ methods in regulatory sciences are considered to 
be validated for other forms of science, in that they are used for generating 
results that are in principle acceptable in various scientific domains. What is 
deemed to be missing from many new scientific approaches is specifically 
validation for regulatory purposes. While the lack of agreed and accepted 
validation  of  alternative  methods  is  the  main  obstacle  to  their  use  for 
regulatory  purposes,  the  validation  of  established  methods  using  animal 
studies is also called into question. Far from being just one surmountable 
obstacle among others, the demand for validation of alternative methods 
would itself demand a reconstitution of the regulatory paradigm for scientific 
evidence. The issues around the validation of methods should be made more 
explicit in the knowledge framework, in the ways suggested in Sections 4.3 
– 4.7.

Validation is the gatekeeper of acceptance and the demand for validation 
stands proxy for trust. As such the validation process of any test method has 
both scientific and social aspects. Scientific research targets the criteria that 
will yield the most robust results (for example, criteria that are reproducible 

25

26

and informative); however, what is deemed to be robust, informative and, most 
importantly, relevant and useful, depends on the community of scientists, 
the  purpose  that  the  validation  criteria  have  to  fulfil,  and  the  context  in 
which  they  will  be  used.  In  regulatory  and  policy  settings,  validation  is  a 
crucial element of the transformation of data into evidence.

Validation  is  a  critical  strand  that  runs  through  the  whole  knowledge 
management system. Effective knowledge management for bringing science 
to policy will incorporate three levels of validity: the validity of the data and 
information  used  to  assemble  the  AOP  –  including  making  the  methods 
as explicit as possible; the validity of the way the information is presented 
and weighted within the AOP (e.g. in accordance with compliance checks or 
internal reviews); and the validity of the overall AOP as determined by meeting 
standards of external review and endorsement of that process by a regulatory 
body. In addition to supporting current approaches to validation, the knowledge 
management system can also facilitate new approaches to validation, through 
for example the measures discussed in points 4.3 to 4.7 below.

4.4 Inclusion of methods and data relevant to 
regulation

The  AOP  framework  has  been  designed  primarily  to  capture  mechanistic 
knowledge  relating  to  biological  and  toxicological  processes  or  pathways. 
Therefore,  currently,  the  scope  is  limited  to  a  fairly  restricted  area  of  the 
overall framework for the next generation of risk science. Claims about the 
benefits  of  having  a  common  framework  for  organising  knowledge  about 
mechanistic pathways (for example, Edwards et al, 2016) were borne out by 
the comments of many of the stakeholders who participated in the study. 
The  AOP  framework  has  generally  been  appreciated  for  the  way  it  fulfils 
– or potentially could fulfil – this role. The ultimate aim of the framework 
for  organising  mechanistic  knowledge  is  to  improve  the  availability  of 
mechanistic information to regulators, and to better serve regulatory needs 
(e.g. Villeneuve et. al. 2014, 2020). This study suggests that bridging between 
data and the forms of evidence used by regulators and policy makers would 
help to achieve this (see Section 3.2.2).

There  is  a  demand  for  quantitative  data  regarding  exposures  and  dose-
response, which is not surprising given that these are the very forms of data 
that comprise the central pillars of regulatory risk assessment that has been 
standardised  in  guidelines  since  the  1980s.  Toxicokinetic  data  (including 
metabolic biotransformation of exogenous chemicals within the body) are 
also frequently looked for, as is the identification of (chemical) stressors that 
trigger an AOP. Only by adding or linking to such chemical-specific data can 
the knowledge within the AOP framework actually be applied in a practical 
way  to  hazard  or  risk  assessment.  This  aspect  therefore  will  need  to  be 
carefully considered and discussed with potential users.

Addressing evidence needs in chemicals policy and regulation4. A knowledge management framework for transparency plus

Since the AOP framework, in its current form, is a tool that assists primarily in 
hazard identification and characterisation and not directly in risk assessment, 
the most important current absences from the framework are data about 
stressors and about dose-response. Extending the AOP framework to bridge 
across from data about pathways to these forms of data, and continuing on 
the programme to make AOPs quantitative, will facilitate the transformation 
of data about mechanisms into evidence that can be taken into account in 
hazard characterization.

Figure  4  shows  how  the  AOP  framework  might  be  extended  in  order  to 
bridge between science and policy more effectively.

a)

b)

27

Figure 4:  Broadening the AOP Framework so that it is more inclusive of data and 
methods relevant to regulation. a) is a diagram from Krewski et al, (2020) showing 
a  framework  for  the  next  generation  of  toxicity  testing,  encompassing  different 
forms of data. b) is an expansion of the middle box of a), which is data relevant to 
risk assessment. The green box is what the AOP Framework currently focuses on; 
this could be broadened to the red and thee blue boxes in order to become more 
inclusive. Reprinted by permission from Springer Nature Customer Service Centre 
GmbH: Springer Nature, Archives of Toxicology, Krewski et al, doi:10.1007/s00204-
019-02613-4, © Springer-Verlag GmbH Germany (2020).

Ultimately,  any  proposed  extensions  should  be  considered  and  discussed 
as a part of user-centric design, since even if there are no extensions, the 
reasons  for  borderlines  being  drawn  where  they  are  should  be  clear  and 
understood (see Section 4.7 below).

28

4.5 Clarity in levels of knowledge

The AOP framework is consistently presented as a conceptual framework for 
organising knowledge about biological sequences of events that go from an 
initial  perturbation  at  a  molecular  level,  to  an  adverse  human  health  or 
environmental  outcome  that  is  of  regulatory  relevance  and  concern. 
Knowledge is organised around three kinds of events: Molecular Initiating 
Events  (MIE),  Key  Events  (KE),  and  Adverse  Outcomes  (AO);  and  the 
relationship between these, the Key Event Relationship (KER). This gives rise 
to the well-known AOP diagram.

Figure 5:  The standard AOP diagram, showing the molecular initiating event (MIE), 
two key events (KE1 and KE2) and the Adverse Outcome (AO) in boxes, linked in a 
linear fashion by arrrows, which indicate the Key Event Relationships.

While the core papers setting out the framework (such as Villeneuve et. al., 
2014, Ankley et. al., 2010) describe it as a tool for organising knowledge, 
AOPs are frequently taken as representing biological pathways, and therefore, 
as having some characteristics of models or diagrams that are frequently 
used  in  biology  to  describe  a  process.  This  ‘misguided’  interpretation  of 
AOPs  is  the  real  target  of  many  of  the  criticisms  of  AOPs  such  as  being 
oversimplifications  of  complex  processes  or  depicting  processes  as  being 
linear  when  in  reality  many  feedback  loops  are  involved.  This  ambiguity 
should  be  avoided  since  the  criticisms  easily  deflect  from  the  real  value 
of  the  framework.  One  hypothesis  is  that  this  ambiguity  arises  more  for 
users who have a molecular biology background in all the different forms 
that can take, from experimental biology to bioinformatics and everything in 
between. Other users who do not have this background, find the diagram very 
useful, precisely because it provides a common basis for discussion. People 
from the medical domain actually find the depiction of AOPs very familiar 
since they match well with the conceptualisation of disease progression (e.g. 
stages of cancer development). This is something to be tested with user-
centric design processes.

It  is  very  important  to  disambiguate  the  emblematic  diagrams  of  the 
framework, and to clearly differentiate between diagrams used for knowledge 
management and diagrams used for depicting or modelling the domain. In 
addition, it is important to distinguish between knowledge regarding the data 
and outcomes of studies (the what), and knowledge regarding the methods 
and approaches that gave rise to them (the how).

4.6 Intuitive visualisation 

Previously,  knowledge  management  was  achieved  through  different  kinds 
of  documents  setting  out  guidelines,  tables,  definitions,  standards,  and 

Addressing evidence needs in chemicals policy and regulation4. A knowledge management framework for transparency plus

so  on.  These  documents  have  been  the  material  form  of  frameworks  for 
knowledge management. Nowadays, online platforms are more appropriate 
for the large amounts of information and the preference for digital access. 
In addition, there is a strong appetite for visual rendering of knowledge to 
make it more navigable and digestible. This is what is greatly appreciated by 
users of the AOP Wiki, as emerged from the focus groups and user studies 
conducted (Carusi et al, 2021). These visualisations mediate between the 
different perspectives brought by different studies, approaches and interests: 
they  are  literally  a  medium  for  bringing  them  together,  and  at  the  same 
time, they play an intermediary role between perspectives, supporting steps 
towards greater agreement and productive discussions. In this sense, they 
are bridging technologies. Because they play such a pivotal role, their design 
is  extremely  important.  In  fact,  the  design  of  the  visualisation  frequently 
goes hand-in-hand with the design of the framework, as it focuses attention 
on  the  component  parts,  relationships  between  them,  and  the  definitions 
and classifications of terms that will be used by each of these.

The previous section discussed the importance of ensuring that it is clear 
which knowledge is being managed, and the key role of diagrams in this. 
Visualisations  of  mechanisms  are  very  common  in  the  disciplines  that 
toxicology  draws  upon,  and  therefore  people  viewing  them  are  likely  to 
project upon them their own expectations of what the diagrams mean. It is 
essential that the visualisations associated with the knowledge management 
framework be tested on potential users and if possible, co-developed with 
them. As the formats of visualisations are agreed upon, so too will the forms 
and classifications of information be defined.

4.7 Facilitating 
comparisons and equivalences

contextualisation 

through 

The importance of comparisons has been already highlighted as a key aspect 
of scientific reasoning (Section 3.2). Not just any similarity or difference is 
relevant to scientific comparisons. Rather, comparisons that are relevant are 
based on common terms, or on grounds that are shared by other scientists. 
We refer to this support for building up the terms or grounds for comparison 
as systems of equivalences (Carusi, Whelan & Wittwehr, 2019). That is, they 
are usually not isolated terms, but whole sets of systematically organised 
factors of different types, running right through scientific paradigms.

The  over-arching  comparison  in  in  vivo  toxicological  studies  for  human 
safety  assessment  is  between  non-human  animals  and  humans.  However 
to support this comparison, a number of points of equivalence have to be 
established,  that  make  the  dose-response  relationships  in  non-human 
animals  equivalent  to  dose-response  relationships  in  human  animals.  An 
entire  system  of  equivalences  comprising  which  endpoints  are  relevant, 
how  to  measure  dose-response,  and  the  rules  for  extrapolation,  has  been 
established as the grounds for this comparison. The system of equivalences 
becomes refined into a more fine-grained network of inter-related ways of 

29

giving  evidence  for  how  something  observed  in  one  kind  of  organism  can 
be considered equivalent to what might be observed in another. Using other 
methods and approaches now requires their own systems of equivalences: 
with established test methods, and also within and across new methods. The 
exact form and content of these systems of equivalences cannot be imposed 
on  the  communities,  but  needs  to  emerge  from  them.  However,  they  can 
be supported through continuous engagement and facilitation and through 
making explicit the data and information that may be relevant to them.

Box 4.  Comparisons are based on equivalences: either already 

existing or to be constructed or discovered

The dose response curves in Figure 3a,b allow different substances or 
species to be compared through mapping them onto the dose response 
curves.  The  dose  response  curve  is  itself  something  they  have  in 
common, an equivalence, that allows them to be compared. The table 
in Figure  3c operates in the same way: it establishes comparison by 
bringing  the  different  studies  into  a  common  framework  where  they 
have categories in common, such as ‘reference body weight’ and can 
thus be compared. 

Where there is not a pre-established set of equivalences which allow 
different items of interest to be compared, they have to be created. This 
may occur through diagrams. 

One of the interviews carried out in this study was with a cardiologist 
working closely with AOPs. He stressed the need to contextualise AOPs 
and any new methodology in the context of what people are already 
familiar with. He had devised this way of juxtaposing the AOP diagram 
with diagrams that his audience of fellow cardiologists would be familiar 
with already. In this way they can understand how the AOP relates to 
their world, and to those items that they are already interested in and 
concerned  with.  In  the  regulatory  domain,  even  if  regulators  are  not 
actively doing science in their role, will frequently have a background in 
biological sciences.

Diagrams can provide a common frame for new methods and approaches, 
alongside those that they are already familiar with, through juxtaposing 
diagrams and mapping them onto one another (as below), or by using 
the conventions of diagrams and other illustrations that are common 
in  the  audience’s  world.  Good  communicators  are  able  to  use  these 
devices spontaneously, and the process can be assisted by participant 
design techniques with participants from different communities. 

30

Addressing evidence needs in chemicals policy and regulation4. A knowledge management framework for transparency plus

The  two  diagrams  below  show  how  comparisons  between  different 
approaches can be made, by showing what needs to be worked on in 
order to make useful comparisons.

Body in vivo

Well in vitro

▶ Blood ﬂow
▶ Diﬀerent built up Structure of 

the system, no plastic 
▶ Exposure in mg/kg/day
▶ Exposure is life time exposure
▶ Complex system

Equivalence 
needed

▶ Static
▶ Well has plastic (migration of 

chemicals to plastic)

▶ Exposure in uM
▶ Exposure is minutes, hrs, 

possible days

▶ Usually one cell line type

The concentration at target site is usually in uM
• Translation of in vitro nominal concentration to free concentration is the key
• Translation from external exposure dose to internal exposure dose is essential

In silico / computational 
modelling

In vivo
external dose

Lung

INHALATION

Internal concentration

d
o
o
l
b

s
u
o
n
e
V

GI-tract

ORAL

Liver

METABOLISM

d
o
o
l
b

l

a

i
r
e
t
r
A

Kidney

EXCRETION

Skin

DERMAL

31

In vitro
concentration

In silico / computational 
modelling

Internal (cellular) response

Equivalence:
External dose as a function 
of internal response

e
s
n
o
p
s
e
r

l

a
n
r
e
t
n
I

.

m
o
c
n
o
c
i
t
a
F
©
s
n
o
c
i

l

External dose

Other ways of making equivalences that allow for comparisons that are 
commonly used are graphs, tables, vocabularies and ontologies.

 
 
 
 
 
Ideally,  an  effective  knowledge  management  framework  makes  existing 
equivalences explicit; and also makes explicit where there are not currently 
equivalences  –  as  is  often  the  case  for  example  between  non-animal 
methods and animal methods, and between different non-animal methods. 
At  the  least,  this  facilitates  discussions  and  invites  hypotheses  regarding 
what other equivalences might be made, and what further research needs 
to  be  conducted  in  order  to  relate  different  methods  and  approaches  to 
the  same  topic.  Spanning  the  distance  between  in  vitro  and  in  vivo  in  a 
one-for-one  way  is  rarely  achievable;  however,  it  is  possible  to  work  out 
systems that enable comparisons and equivalences between many in vitro 
studies, or between in vitro and other studies, that eventually begin together 
to form substantial evidence that on another level, they are comparable or 
equivalent to in vivo studies. Making these explicit is also necessary for more 
robust reproducibility of studies.

4.8 Gaps should not be absences

The  advantage  of  collecting  knowledge  in  one  place  about  a  question  of 
interest is that we are able to see both what we know and probably more 
importantly, what we do not know. This can relate to what is known (data 
or evidence, ‘knowing what’) and how it is known (methods and approaches, 
‘knowing how’). In terms of what is known, there can be an absence of data: 
for  example,  even  though  there  is  more  data  than  is  manageable  about 
chemical substances, there is still an absence of data for a very large number 
of chemical substances, and for combinations or mixtures of chemicals. In 
terms of the ‘how’, there is often a lack of knowledge on what aspects should 
be considered to trust different methods. This requires more of a spotlight 
on the methods themselves, making it explicit how they arrive at outputs, 
and  comparing  and  contrasting  with  other  methods.  These  gaps  become 
more evident when available data, information and knowledge are brought 
together, but still, it is helpful to foreground the gaps and not to allow them 
merely  to  be  absences.  When  they  are  foregrounded,  they  become  active 
aspects  of  ongoing  conversations  and  potentially  gain  more  attention  as 
sites where action can be or should be taken.

32

Addressing evidence needs in chemicals policy and regulation5. Training, collaboration, and 
engagement

Knowledge  management  for  shared  understandings  should  be  continuous 
with, and support, training, collaboration and engagement.

Training  for  breadth:  People  working  in  regulatory  toxicological 
research,  risk  assessment  or  risk  management  are  in  short  supply,  and 
becoming  increasingly  so  as  an  older  generation  is  nearing  retirement, 
without  an  upcoming  surge  of  new  recruits  (Carusi  et  al,  2021).  One  of 
the most frequently cited reasons for the resistance to new methods and 
approaches  by  participants  in  our  study  is  lack  of  training  on  the  part  of 
regulators,  and  others  working  in  this  domain  in  different  capacities.  The 
need for training in new methods and approaches to regulatory toxicology 
cannot be overstated, especially as younger generations are coming into the 
domain of chemical risk assessment and risk management. Indeed, training 
should  be  seen  also  as  a  means  to  make  the  domain  more  attractive  to 
them.

Training for breadth is twofold: people and skills.

People: training should encompass a broad range of people in different roles, 
from different backgrounds, and at different educational levels. Training for 
those who will potentially work in the chemical regulation domain in different 
roles is a priority; however it is also important to engage with students at 
pre-university levels, and in CPD and other lifelong learning contexts.

Skills:  training  is  often  focused  on  content.  However,  content  regularly 
becomes obsolete, or needs to be re-contextualised when new knowledge 
comes to the fore. It is more effective to train in skills for processing content: 
that is, for contextualising, evaluating and assessing content, and potentially 
acting  on  it.  Training  for  breadth  here  means  developing  the  skills  of 
considering issues from multiple perspectives, rather than from only one or 
two narrowly defined specialisms. For example, it is training that encourages 
relating  methods  and  approaches  to  each  other,  and  thinks  about  how  to 
build  a  shared  understanding  of  an  issue  potentially  with  others  who  are 
from different backgrounds.

33

Knowledge  management  itself  is  a  skill  of  processing  content,  and  the 
proposed knowledge management system has huge potential to be used as 
a continuous resource throughout these events.

Collaboration  for  trust:  Participants  in  the  study  pointed  out 
the  lack  of  collaboration  between  sectors,  agencies,  and  organisations. 
Without  collaboration,  the  conditions  for  trust  to  be  seeded  and  allowed 
to grow are simply not there. There is a circuit of trust involved in a good 
knowledge  management  system:  it  will  enable  better  collaboration,  but 
without collaboration it cannot be as good as it needs to be. These feedback 
circuits are typical of complex systems, and these are the systems we need 
to  become  better  at  managing.  In  addition,  the  co-production  of  useable 
knowledge  that  is  typical  of  strong  collaborations  across  disciplines  and 
sectors,  creates  a  more  effective  science-policy  relationship  (Lemos  et  al, 
2018).

Workshops for user-centric design are a good way of seeding collaboration; 
in  addition,  these  can  be  supplemented  by  many  other  methodologies 
for  actively  engaging  people  in  productive  discussions  on  complex  issues, 
appropriately  pitched  at  different  levels  of  expertise  and  combinations 
of  stakeholders,  targeting  different  bridging  points.  Distributed  online 
discussions for participatory deliberation are easily accommodated on the 
knowledge management system, and reinforce all of its functionalities, as 
exemplified in other community building knowledge bases (see for example 
Kinoshita & Strobel 2006).

Deliberative  public  engagement:  The  role  of  the  public  was 
mentioned  by  several  study  participants;  however  beyond  that,  recent 
and current events show how the role of the public is pivotal in regulatory 
matters. A global pandemic and ongoing climate crisis have highlighted the 
role of science in informing regulation and policy, and not always in ways 
that lead to confidence on the part of the public. It is beyond the remit of this 
report to make specific recommendations regarding public engagement in 
the chemicals domain. The suggested knowledge management framework is 
in line with a deliberative approach to public engagement which encourages 
stakeholders to consider issues from multiple points of view and helps to 
build  up  shared  understandings  (National  Consumer  Council  2008).  How 
to  relate  the  knowledge  management  system  with  health,  environmental, 
social and economic concerns is an urgent question. However it is answered, 
the  knowledge  management  system  should  maintain  an  openness  to 
deliberative participation on the part of the public, and may well be used 
as  a  framework  for  discussions  and  engagement  events.  The  question  of 
who are target users comes to the fore here, as does the need to have a 
more nuanced view of the composition of ‘the public’, and its role in shaping 
debates about regulation and policy.

34

Addressing evidence needs in chemicals policy and regulation5. Training, collaboration, and engagement

Table 1. A summary of the main recommendations in this document, who should 
carry them out, and what they should do in order to carry out the recommendation.

Recommendations Who 

What 

User-centric design

Development team 

Validation at the forefront Development team and user 

group 

Development team

Development team and user 
group

Include or link to methods 
and data relevant to 
regulation

Gaps should not be 
absences

Clarify levels of 
knowledge

Development  team  and  user 
group 

Intuitive visualisations

Facilitate contextualisation 
through comparisons and 
equivalences

Training for breadth

Training and outreach team and 
development team

invite  user  group 
Identify  and 
members; work to establish ongoing 
engagement  of  user  groups;  hold 
series  of  user  group  meetings  to 
engage  members  with  ongoing 
the  knowledge 
development  of 
management system. 

Events  or  activities  to  support 
inclusive  conversations  among  the 
most directly involved stakeholders 
as  to  which  are  the  aspects  of 
studies  that  are  most  relevant  to 
validation,  and  how  can  they  be 
made  more  explicit  in  knowledge 
management.

Clarify  existing  validation  status: 
for  example,  endorsement  by  an 
organisation  should  also  have 
information about that organisation 
that is easily accessible.

forms 

different 

Consider 
of 
extensions  of  the  AOP  Framework 
in  discussion  with  the  user  group: 
what should be present.

35

In the same discussion: what should 
not be present, and how to indicate 
that  there  is  an  exclusion  and 
explain it.

User-centered  participant  design 
sessions  to  design  visualisations 
that communicate across audiences 
and  enable  them  to  contextualise 
information, draw comparisons, and 
discern  where  new  information  is 
needed.

Continue developing the knowledge 
management 
system  alongside 
training toolkits that build up ability 
to  engage  with  different  methods 
and approaches. 

Collaboration for trust

User group 

User group meetings 

Development team

Knowledge  management  system 
should  support  online  discussions 
and 
example, 
through pro-active moderation. 

exchanges, 

for 

Deliberative public 
engagement

Public institutions

Broader user group including 
public 

Development team

Activities  to  engage  the  public 
with  chemicals  regulation  through 
deliberative participation. 

Activities  to  develop  capacity  of 
knowledge management system to 
support public engagement. 

36

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