Improving Toxicity

Predictions using Data

and Knowledge Sharing

liz.covey-crump@lhasalimited.org

Dr Liz Covey-Crump

In Silico Prediction of Toxicity

• Toxicity Risk assessment of chemicals is required in

several areas:-

• Deliberate exposure – e.g. Drugs and Personal Care

• ‘Accidental’ human exposure – agrochemical,

manufacturing, packaging etc

• In Silico predictions can be used to:-

• Predict, support and explain experimental results

• In some cases in lieu of testing (e.g. ICH M7 – mutagenic

impurity assessment)

In Silico Prediction of Toxicity

• Advantages of an In Silico prediction?

• Cost/Time effective

• No need to synthesise compound

• Reproducible

• Reduces animal testing

• Can provide mechanistic information

• Can be used to help inform next steps as part of intelligent

testing strategy and/or defined approach

• Different In Silico methodologies

• Statistical

• Knowledge Base Focus for this presentation

Knowledge Based Expert Systems

Qualitative

Predictions

Transparent(What underlying data

has been used to make

the prediction)

Structure Activity Relationship (SAR)

methodology

Broad Range of endpoints

Example – Using a

knowledgebase system

Enter Structure

• Multiple structure entry methods

Processing

Prediction – Substructure

Prediction – Comments & Supporting Information

Alert 884: alpha-Dihalo or trihalo ketone or aldehyde

This alert describes the mutagenicity of alpha-dihalo or alpha-trihalo ketones and aldehydes.

Alpha-dihalo or alpha-trihalo ketones and aldehydes globally exhibit mutagenicity…….both in the preseence

and in the absence of metabolic activation……. A Lhasa Limited member donated alpha-dihalo ketone shows

positive Ames results in strains …………………..Alpha-dihalo or alpha-trihalo ketones and aldehydes are

electrophilic species that are capable of directly alkylating DNA. The electrophilicity of the carbon atom alpha

to the carbonyl is enhanced by both the carbonyl group and the halogen atoms.

The scope of this alert has been defined by the available Ames test data for alpha-dihalo or alpha-trihalo

ketones and aldehydes, including also a compound donated by a Lhasa Limited member…………………..

Skin Sensitisation Prediction

The Importance of Knowledge

and Data Sharing

Why Share Data?

• Cover gaps in chemical space within in silico models• e.g. 25% of Derek Nexus alerts for mutagenicity have been

built using proprietary data

• Donation of proprietary data can fill the gaps to allow:-• Modelling of chemical space unique for an organisation

• Improve predictivity for chemical space of highest relevance

• Generalise models for mutual benefit

• Encouraging collaboration which benefits the scientific

community• Standardisation of methodologies

• Standardisation of approach and workflows

Data Sharing Coverage

Highly relevant proprietary data

A representation of chemical coverage in the eTOX database when

compared to marketed drugs

Visualisation produced in StarDrop (Optibrium Ltd, Cambridge, UK)

eTOX 25/10/16 DrugBank Approved v5.0.3

Data/Knowledge Sharing Case

Studies

Using proprietary data to

generate new/modify

mutagenicity alerts

Mutagenicity in Derek Nexus

Metrics (%) Results

Data

setSe Sp PP NP Acc TP FP TN FN Total

Public 83 75 79 79 79 2908 762 2247 595 6512

• 140 mutagenicity alerts

• 25% of alerts contain proprietary data

• Comprehensive coverage of endpoint• Aromatic amines and boronic acids are still of significant interest

and require refinement

• Derek Nexus performance against public data is very good

How do we analyse member data?

Data Analysis

Member data set - Performance

• 1261 compounds

• Mainly negative results• Bias = 77% negative

• 114 FP

• 117 FN

Mutagenicity

Metrics (%) Results

Data

setSe Sp PP NP Acc TP FP TN FN Total

Public 83 75 79 79 79 2908 762 2247 595 6512

Member 59 88 60 88 82 168 114 862 117 1261

Member data set – New/Modified alert summary

• 5 new alerts

• Amine (x4)

• Boronic acid

• 4 modifications to existing alerts

• Azide, hydrazoic acid or azide salt

• Alkyl aldehyde

• Arylhydrazine

• Arylboronic acid or derivative

• 4 potential new alerts/alert modifications

• require more data/mechanistic support

Results – Member data - Mutagenicity

Results – Public data - Mutagenicity

Alert modification case study

Alkylhydrazines are tricky to predict…

• Alkylhydrazines tend to be at best weakly positive in the

Ames test

• Conflicting evidence as to strain activity for compounds

of this class

• Inconsistencies in published Ames test results…• weak activity

• high toxicity

• facile oxidation

• The mechanism of mutagenic activity has yet to be fully

elucidated• Different mechanistic pathways may furthermore contribute

to the differences in observed strain specificity between

alkylhydrazines

The problem with alert 28 (alkyl hydrazine)

• A pharmaceutical company found that alert 28 was over-

firing for their proprietary data.

• The member provided Lhasa with the relevant structures.

?

‘Alert 28 – alkyl hydrazine’ modification

Anonymised

structure

Ames positive

compounds

Ames negative

compounds

Mechanistic

rationalOld alert New alert

3 4 Hydrolysable Positive Positive

0 3

Sufficient

hydrolysis not

expected

Positive Negative

Improving the prediction of

skin sensitisation

Skin Sensitisation Data Sharing

7 new alerts

5 modifications to

existing alerts

Public data (n = 2611) BMS (n = 467)

Data/Knowledge Overview

History of Toxicity Data/Knowledge Sharing at Lhasa

Time

2018

Knowledge Shared

Structures + Toxicity Data

Preclinical

ClinicalDegree of Sharing/Value of Data

Structures +Ames Data

Ongoing Initiatives

• 12 Japanese Pharmaceutical companies have shared

mutagenicity data – analysis is underway

• Japanese NIHS (Regulatory) Collaboration continues

(14th year)

• Several other data sharing initiatives ongoing at Lhasa

Data and Knowledge Sharing

• Data sharing groups benefits:-

• Data can be used to prevent repeat testing

• Data can be used to make decisions/help prioritise

• Verification of the results of a model

• Improvement of models

• Set common quality standards

• Standardisation of workflows etc

Acknowledgements

• Dr Donna MacMillan

• Dr Rachael Tennant

• Changing regulatory guidance has led to the need to build

knowledge of EI levels in excipients

http://www.ich.org/products/guidelines/quality/article/quality-

guidelines.html

Elemental Impurities Data Sharing Initiative (EI)

Elemental Impurities Data Sharing Initiative (EI)

• Q3D :-

• Section 5 - Information for this risk assessment includes but is

not limited to: data generated by the applicant, information

supplied by drug substance and/or excipient manufacturers

and/or data available in published literature.

• Section 5.5 - The data that support this risk assessment can

come from a number of sources that include, but are not limited

to:

• Prior knowledge;

• Published literature;

• Data generated from similar processes;

• Supplier information or data;

• Testing of the components of the drug product;

• Testing of the drug product.

A proactive action from the pharma industry regarding the

compliance with the regulatory guidelines ICH Q3D for elemental

impurities.

Elemental Impurities Data Sharing Initiative

Facilitate more scientifically driven elemental impurities risk

assessments under ICH Q3D, and reduce unnecessary

testing as part of the elemental impurities risk assessment

efforts.

Elemental Impurities Data Sharing Initiative

• The data to be shared is the analytical data generated to

establish the levels of trace metals within batches of excipients

used in the manufacture of pharmaceuticals

• Aims to save time and provide further evidence/robustness

when partners are setting the limits for the elemental impurities

in the API

• Lhasa acts as the ‘honest broker’ and hosts the data within a

custom version of Vitic and facilitates the data sharing group

How will the EI database be used?

• The database will provide a better assessment of which

materials represent a more significant risk than others

• Indicate where the risk is real and where it is negligible

• Reduce the amount of testing that is needed

• The EI database can be used as key supportive information in

conjunction with some product specific test data for a risk

assessment

• Various case studies have been presented in a Lhasa vICGMhttps://www.lhasalimited.org/publications/?custom_in_RelatedEvent=

4032&orderby=Name

Publication on the database pending

Elemental Impurities Data Sharing Consortium

• What the Consortium do?

Discussing and agreeing upon the scientific direction of the

project

Contributing expertise and knowledge

Monitoring the data provided by the member organisations

and ensuring it meets predefined quality standards

Recommending priorities for work on the project

Elemental Impurities Data Sharing Consortium