ICH M7 Knowledge and Data Sources Management

ICH M7 Knowledge and

Data Sources

Management

[email protected]

Dr Liz Covey-Crump

Scope

• Sources of Data and Knowledge for ICH M7

Assessments

• Knowledge Management for ICH M7 Assessments

Sources of Data and Knowledge – ICH M7

• Public Databases

• Literature Searches

• In Silico Tools

• In-house Databases

• Shared knowledge

• Shared Data

Shared Knowledge for ICH M7

• Lhasa Aim – Predict mutagenicity for all chemical space

• To achieve this proprietary data needs to be used for

refinement of Derek Nexus mutagenicity alerts, public

data sources are not enough.

• Over the last 20 years a lot of proprietary in-house

mutagenicity data has been shared

• Result – 33% of Derek mutagenicity alerts have been

developed and/or modified using proprietary data

• Validations can also be carried out using proprietary data

KNOWLEDGE SHARING

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

• 132 alerts for mutagenicity

• 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

Member data set - Performance

• 1,261 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

• Requires more data/mechanistic support

Results – Member data - Mutagenicity

Results – Public data - Mutagenicity

Placeholder Japanese Data Sharing

• Info to be provided Friday 3rd May

DATA SHARING for ICH M7

Data Sharing

• Synthetic Intermediates – Ames Data

• 1,587 Structures – 26,592 records

• Aromatic Amines - Ames Data

• 778 structures – 11,280 records

Use of Shared Data sets

• Data Source• Avoid repeat testing

• Use within regulatory submission

• Make decisions/help to prioritise

• Validation of in silico tools• By Lhasa, Industry and Regulators

• Improvement of in silico tools

• Shared understanding

Scope

• Sources of Data and Knowledge for ICH M7

Assessments

• Knowledge Management for ICH M7 Assessments

ICH M7 Workflow

• Is there any public data?

• Is there any in-house data or shared data?

• What do the in silico systems say?

• If there are any concerns, are they more concerning than the API itself

• What do you think as an expert? Can you add more knowledge?

• Should you test (Ames), control, limit according to TTC or make an argument for Purge?

• Where can I store my expert decision and supporting

information?• For submission

• For future reference

Setaria: Integrated ICH M7 Workflow Tool

Gathering Evidence

and Arriving at a

Conclusion

Storing

Assessments in a

Searchable Way

Gathering Evidence

ICH M7 Assessment

Experimental Data

Public Carc Study Data

Public Mut Study Data

Data Sharing Initiatives

In-House Data

in silicoPredictions Expert Rule

Based

Statistical Based

Expert Assessment

Storing Assessments

Searchable Storage of

Assessments: Crucial

Common impurities are

regularly assessed over

time

Multiple individuals completing

ICH M7 assessments

Need to assess the

performance of in silico tools

Setaria: Streamlining ICH M7 Workflow

Gathering Evidence

and Arriving at a

Conclusion

Storing

Assessments in a

Searchable Way

Searchable Storage of

Assessments: Crucial

Common impurities are

regularly assessed over

time

Multiple individuals

completing ICH M7

assessments

Need to assess the

performance of in silico tools


Previously Reviewed

Reassess and

Document

Cmpd


Pulling all ICH M7 appropriate information into a single repository means easier, less time consuming, and less

costly assessments

Exposing full assessments reduces the likelihood of duplicating effort between individuals and teams over time, and provides an insight into the impact of further

testing on other projects

Extensive and flexible searching facilitates review of conflicting in silico predictions and assay results, allowing the directed sharing of data to improve predictions for in-

house chemical space

Single Point

of Truth per

Compound

Project

Centric

Storage

Improve

Performance

of in silico

“Setaria saves significant time when searching

for genotoxicity findings and has delivered improved

visibility of our data across the business.”

Jim Harvey, Head of Computational Toxicology at GSK

Lhasa Limited

Granary Wharf House, 2 Canal Wharf

Leeds, LS11 5PS

Registered Charity (290866)

Company Registration Number 01765239

+44(0)113 394 6020

[email protected]

www.lhasalimited.org

Shared Data and Knowledge provides a very

important source of additional information for ICH

M7 Assessments

A Knowledge management system can be useful

for managing results from an ICH M7 Workflow

Conclusions

ICH M7 Knowledge and Data Sources Management

Documents

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