Acceptable Intake (AI) and

Permitted Daily Exposure (PDE)

Data Sharing Project for

Pharmaceutical Impurities

David Wilkinson - Senior Scientist

Dr. William Drewe - Principal Global Alliance Manager

Lhasa Limited

Established in 1983

Not-for-profit organisation & educational charity

To promote the development and use of computer-aided reasoning and information systems for the

advancement of chemistry

Creators of knowledge base, statistical and database systems

Controlled by our members (400+)

Work with members to understand and meet their needs

Pioneered collaborative data sharing projects in the chemistry-related industries

Data and Knowledge Sharing

• Began with Derek development

• Members shared feedback → knowledge → proprietary mutagenicity data → generic structure-activity relationships as toxicity alerts in Derek Nexus

• Improve predictive performance of Derek for mutagenicity prediction (before ICH M7)

• Mutual benefit: improvements to the knowledge; understand knowledge gaps; derive knowledge that would be unfeasible by one organisation alone

• These collaborations encouraged others to come forward…..which is how the Data Sharing Projects began

• Clearly defined benefit / value proposition

• Pre-competitive: the collaboration must benefit the science and have mutual benefit

• Lhasa act as honest broker: a trusted neutral partner hosting the shared data and controlling data access in accordance with the wishes of all the data donors

Data and Knowledge Sharing Projects2006

Production Intermediates data

sharing initiative

2008

Excipients data sharing initiative

2010

Aromatic Amines data sharing initiative

2010

IMI eTOX Project

2012

IMI MIP-DILI Project

2015

IMI iPiE Project

2016

Elemental Impurities data sharing initiative

2017

IMI eTRANSAFE Project

2017

AI/PDE data sharing initiative

2020

Nitrites in excipients data sharing initiative

Why share

AI/PDE

data?

Who is

involved?

How does the

project work?

How is the

project going?

Are regulators

aware of this

project?

Can I join the

project consortium?

?

What is an

AI or PDE?

Why share

AI/PDE

data?

Who is

involved?

How does the

project work?

How is the

project going?

Are regulators

aware of this

project?

Can I join the

project consortium?

?

What is an

AI or PDE?

What is an AI or PDE?

• Drug synthesis requires solvents, reagents and synthetic intermediates

• These can carry through into the drug substance as low-level impurities

• When these cannot be controlled to the required regulatory limit (ICH Q3A/B/C/D/E, M7,

TTC etc.) → Compound-specific exposure limit

• The dose of chemical that would present negligible risk to patients assuming lifetime

daily exposure

What is an AI or PDE?

AI – Acceptable Intake

• ICH M7: Mutagenic carcinogens with no evidence for a threshold mechanism

• Calculated from carcinogenic potency (TD50) and linear extrapolation to a theoretical excess cancer risk of less than 1 in 100,000 for a 50 kg individual

PDE – Permissible or Permitted Daily Exposure

• ICH M7: Mutagenic compounds with evidence for a non-linear dose-response (i.e. a threshold mechanism)

• Assumes that exposure below the threshold dose does not pose a cancer risk

• e.g. Compounds which interact with DNA but must overwhelm DNA-repair mechanisms

• Often calculated based on data from other “severe toxicity” endpoints (AF4) e.g. non-genotoxic carcinogenicity, neurotoxicity, DART etc.

Why share

AI/PDE

data?

Who is

involved?

How does the

project work?

How is the

project going?

Are regulators

aware of this

project?

Can I join the

project consortium?

?

What is an

AI or PDE?

Why Share AI/PDE Data?

• Currently sponsors develop AI/PDE limits for chemicals independently:

• Different conclusions based on the data and methodology used

• Inconsistent AI/PDE values are applied across industry or submitted to regulators

• Very time consuming

• Duplication of effort across industry

• Regulators:

• Receive different AI/PDE limits for the same chemical

• Reach different conclusions due to alternative interpretation of the toxicology data

What are the Benefits of Sharing AI/PDE Data?

• Reduce duplication of effort across industry

• Time, effort, resource and cost saving

• Cross-industry harmonisation of AI/PDE limits and methodology

• Increased confidence and consistency

• Access to a larger pool of non-public harmonised AI/PDEs

• Data used in the calculation of an AI/PDE is often from public sources

• Sharing effort rather than proprietary data

• Pre-competitive collaboration

Goals of the Lhasa Limited AI/PDE Data Sharing Project

1. Share AI/PDE data to generate a large non-public series of exposure limits for common impurities

2. Harmonise the shared AI/PDE limits and the approach taken to conduct safety assessments

3. Make the shared and harmonised AI/PDE limits available to project members through a Vitic database

4. Project members aim to use the project limits and monographs in a regulatory context

5. Access to this resource will save consortium members a significant amount of time, effort, resource and cost

Why share

AI/PDE

data?

Who is

involved?

How does the

project work?

How is the

project going?

Are regulators

aware of this

project?

Can I join the

project consortium?

?

What is an

AI or PDE?

Who is Currently Involved?

Why share

AI/PDE

data?

Who is

involved?

How does the

project work?

How is the

project going?

Are regulators

aware of this

project?

Can I join the

project consortium?

?

What is an

AI or PDE?

How Does the AI/PDE Project Work? – Data Flow

Consortium

members share

AI/PDE monographs

in agreed format

Vitic schema:

• Structure, CAS, name etc.

• AI/PDE limit data

• Peer review status “pending”

• Hyperlink

Vitic database of

shared AI/PDE data

released to consortium

members for intra-

project peer-review

How Does the AI/PDE Project Work? – Peer-Review

• Refined monographs re-

submitted to Lhasa

• Update AI/PDE data

• Update the peer review

status to a “completion date”

• Include public AI/PDE data• Lhasa assign members specific

monographs for peer-review

• Each monograph is

independently peer-reviewed by

two different members

Collaborative

refinement and

harmonisation

• Peer review comments

re-submitted to Lhasa &

sent to the sharing

members

Vitic database of

harmonised AI/PDE

data released to

consortium

members

Collaborative Refinement and Harmonisation

• Series of technical project-wide teleconferences (TCs) focused on

harmonisation

• Involve industry experts from the project consortium

• Address the peer-review comments raised for each monograph / limit

• Debate the technical detail of each shared AI/PDE limit and the methodology applied

• Highly valuable for harmonising the approach to PDE limit setting and further

enhance the quality standard and harmonisation of each monograph / limit

• Led to the development of the project monograph template → promotes

consistency, standardisation and best practice

Collaborative

refinement and

harmonisation

Monograph Template• The project uses a monograph

template, which has led to a

standardisation across the monographs,

aiding consistency and best practices.

• PDEs should be calculated for the oral route, with other routes being optional

• Agreement not to include physicochemical properties as add no

value to the derivation of the PDE

• Agreement not to reference GSH, DNELs and OELs

• Agreement to consistently extrapolate the NOAEL derived from 5d/week to 7d/week

as in ICH Q3C

Why share

AI/PDE

data?

Who is

involved?

How does the

project work?

How is the

project going?

Are regulators

aware of this

project?

Can I join the

project consortium?

?

What is an

AI or PDE?

Database

• The current release of the database is v2020.1.0

• Total number of substances = 147

• Total number of records = 225

• Of those, 51 structures have monographs / limits shared and harmonised by the project consortium

• Three annual cycles of data sharing have been completed

• The database also contains public data from several sources, including;

• ICH Guidelines e.g. M7, Q3C, Q3D

• Journal articles e.g. Bercu et al. 2018

Annual Consortium Survey

Each year Lhasa Limited run a survey of the project consortium members to gain their feedback

• In 2019, 5 of the 9 consortium members responded to the survey

Highlights include:

• Did members read monographs they had not peer reviewed before discussions?

• Should the same data sharing and peer-review process be followed in 2020?

• Did members feel that the peer-review process led to limit harmonisation across the consortium?

• Have members adopted any of the shared and harmonised limits internally?

Annual Consortium Survey

• Did members feel that shared monographs were enhanced as a result of consortium peer-review?

• Did members have greater confidence in applying the harmonised limits in their work relative to those which were developed only in-house?

• Whether any of the harmonised project limits had been used in a regulatory submission?

• Whether the project is meeting the consortium’s needs?

Why share

AI/PDE

data?

Who is

involved?

How does the

project work?

How is the

project going?

Are regulators

aware of this

project?

Can I join the

project consortium?

?

What is an

AI or PDE?

Regulatory Interaction

The project has been described to several regulatory authorities including FDA CDER, EMA SWP, MHRA,

CDE Taiwan

“The quality of AI/PDE limits/monographs submitted to regulators vary significantly - well developed monographs to a couple of paragraphs summarising the pivotal study used. Regulators were interested in the quality threshold for what is shared?”

• The data shared is developed by industry experts from the consortium

• Agreed monograph template is applied which enhances the quality and consistency of approach

• Monographs and limits are peer-reviewed independently by industry experts from the consortium → harmonised AI / PDE limit

“Regulators see different limits submitted for the same chemical from industry”

• Harmonising limits across the consortium reduces the likelihood of this from project members

“It would be helpful for regulators if submitters highlighted the pivotal study used for PDE calculation, or submitted the citation if possible”

• Updated monograph template to include “pivotal study citation”

• The rationale for selection of the pivotal study is described within each monograph

Why share

AI/PDE

data?

Who is

involved?

How does the

project work?

How is the

project going?

Are regulators

aware of this

project?

Can I join the

project consortium?

?

What is an

AI or PDE?

Can I Join the Project Consortium?

This depends on your specific organisation and your goals / needs

Annually you will be required to:

• Share two AI / PDE limits and monographs which have been documented to align to the project monograph template

• Peer-review four AI / PDE limit monographs which have been shared by others and provide comment

• Attend and contribute to project TCs – procedural, peer-review, special discussion topics, etc.

• Be a member of Lhasa and pay the project fee

Additional requirements on joining the project

The consortium is open to new members and wider participation in this project

For more information on data sharing or to join the AI/ PDE Data Sharing Project please contact

Global.Alliances@lhasalimited.org

mailto:Global.Alliances@lhasalimited.org

Lhasa Limited

Granary Wharf House, 2 Canal Wharf

Leeds, LS11 5PS

Registered Charity (290866)

Company Registration Number 01765239

+44(0)113 394 6020

info@lhasalimited.org

www.lhasalimited.org

Thank you

&

Questions