Predictions, Data and ICH M7

Sales Manager

Krishan Patel

Krishan.Patel@lhasalimited.org

In silico workflow under M7

Expert Review

2 in silico predictionsexpert + statistical

Databases, in-house, literature..

Known mutagen

Both predict positive

Both predict negative

Ames testLimit according to TTC or present purge argument for loss

Treat as non-mutagenic

Knownnon-mutagen

Disagree / fail to predict

Evaluate drug substance, impurities, degradants, intermediates…

Agenda

• Derek Nexus and Sarah Nexus• Introduction

• Demonstration

• Differences between Derek Nexus and Sarah Nexus

• Lhasa’s ICH M7 Tool• Demonstration

• Questions

What is Derek?

• Knowledge based expert system

• Structure Activity Relationship (SAR)

• Rules written by scientists from detailed expert analysis

• Two step process:

✓ Identifies toxicophores

✓ Makes qualitative predictions

• Transparent – provides supporting references &

examples

Alerts

• An Alert is a set of structural features in a molecule,

that make a user suspect that the substance may show

a particular effect

• Currently, the Certified Derek Knowledge Base has 890

alerts and 74 endpoints with 9 parent endpoints

• 134 of these alerts are for mutagenicity

Knowledge base search

Skin permeability

Presence oftoxicophore

Molecular weight

Species

Metabolicactivation

Demonstration

What is Sarah?

Statistical approach for prediction of mutagenicity

Positive and negative predictions

Defined Applicability Domain

Transparent – supported with example structures

Methodology

Fragment

Dictionary

Fragmentation

Decision tree

leading to

Hypotheses

Hypothesis

Mining

Self

OrganisationHierarchical Network

SOHN

Ames Experimental

Data (curated)

Demonstration

Differences Between Derek and Sarah

Data

• Uses all Lhasa data

• Includes consortia &

donated confidential data +

data mined on-site

• Only uses non-confidential

data

Methodology

• Expert system

• Human-written rules based

upon data & knowledge

• Statistical model

• Machine-learning model using

a hierarchical network

Scope of alert • Hand-written Markush • Fragments learnt by model

Interpretability

• References

• Expert commentary

• Mechanistic explanation

• Some supporting examples

• Transparent methodology

• Learning summarised by

hypothesis

• Direct access to training set

• Confidence in prediction

M7 Functionality

• With a Derek and Sarah licence you can run an ‘ICH M7

prediction’

• Allows you to quickly see if the two systems agree or

disagree

• One Click Reporting

Demonstration

Any questions?