Lhasa Limited Collaborative Data &

Knowledge Sharing Projects

Dr. Liz Covey-Crump

Liz.covey-crump@lhasalimited.org

Acknowledgements: Dr Crina Heghes, Dr Will Drewe

Presentation Outline

• History of Data Sharing

• Knowledge Sharing

• Lhasa Limited Member projects

• Case Study - Elemental Impurities Data Sharing

• EU Funded Projects

• Regulatory Collaborations

• New Project Proposals

• PDE/AI

2

LHASA User Group is established in the

UK and Lhasa UK Limited is created as a

not-for-profit and charitable company to

coordinate the development of the LHASA knowledge base

Lhasa UK Limited adopts the DEREK system

(from Schering Agrochemicals) and

coordinates the development of its structure-toxicity knowledge base.

1983 201620001989 2003 2006

Data is shared by members in order to

improve Derek (and other knowledgebase systems).

The Vitic toxicity database system is launched

The first data from the

Intermediates data sharing

initiative is released to the group

Various other data sharing

initiatives have arisen from this

starting point

History of Lhasa Data Sharing

Time1983 2016 Future

Degree of Sharing/Value of Data

Knowledge Shared

Structures +Ames Data

Structures + Analytical Data

Structures +Toxicity Data

Future, preclinical + clinical

LHASA

Using member data for alert development

25% of Derek Mutagenicity alerts are based on proprietary data

Member data set – New/modified alerts from a

recent member project

• 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

Knowledge Sharing Benefits

• Successful data sharing projects have led to

improvements in mutagenicity chemical space coverage

• Predictivity of (large) public data sets improved by a few

percentage points

• Major improvements in predictivity of proprietary data

• e.g. for 2 recent member projects 17% and 18% increase in

Sensitivity and 4% and 9% increase in Positive Predictivity

for members 1 and 2, respectively.

• Benefits both Lhasa and all members

• e.g. from 2 recent member projects, 18 new alerts/alert

modifications are being implemented into Derek Nexus

Data Sharing Projects

• Aromatic Amines – genotoxicity data sharing and research.

• Reduction in testing, improved models, highlighting

discrepancies

• Excipients - excipient vehicle toxicity data from single and repeat

dose studies.

• Reduction in testing and useful for planning experiments

• Production Intermediates – mutagenicity and clastogenicity

data for production intermediates.

• Reduction in testing & improved models

• Elemental Impurities - analytical data for trace metals within

batches of excipients with relevance for ICH Q3D.

• New guidelines relating to elemental impurities from the

International Conference on Harmonization (ICH), Q3D

Guideline for Elemental Impurities (2014) have presented the

pharmaceutical industry with new challenges.

Elemental Impurities - Background

• A proactive action from the pharma industry regarding the

compliance with the regulatory guidelines ICH Q3D for

elemental impurities.

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

guidelines.html

• Project useful for pharmaceutical companies wishing to assess

the potential elemental impurities in their final drug products,

but will also be relevant for suppliers of excipients.

Background

• Facilitate more scientifically driven elemental impurities risk

assessments under ICH Q3D, and reduce unnecessary testing

as part of the elemental impurities risk assessment efforts.

• The data being shared is the analytical data generated to

establish the levels of trace metals within batches of excipients

used in the manufacture of pharmaceuticals.

Objective

Elemental Impurities Data Sharing Consortium

• The Consortium comprises of representatives chosen by the

organisations contributing to the Vitic Elemental Impurities

database. This group is led by an elected chairperson and has

various responsibilities, including:

• 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

• Vitic Nexus has been selected as the database to be used for the

storage of these data.

• A database schema and data entry guidelines were drawn up.

Contributed data conforming to these guidelines were uploaded into

the database and shared between consortium members.

Database Overview

Database Overview

• The Elementals database v2016.1.0 contains the following

number of records:

52 records in the Excipient table.

123 records in the Elementals table.

• Next released planned for November 2016 will contain about:

~450 records in the Excipient table.

~1350 records in the Elementals table.

Database Overview

Number of studies reporting measurements for each element within

the Vitic Elemental Impurities Database.

Database Overview

Class 1 Compounds Class 2a Compounds

LOD = Limit of Detection, LOQ = Limit of Quantification

Database overview

Database Overview

• There is good alignment across companies in the consortium

on the intended use of the database.

• Data that supports a risk assessment, when coupled with

other product specific information

• Unlikely in the short term (2016/17) to be used as a sole

source of data that justifies no long term product testing is

required

• As the dataset becomes more extensive (2017/18) then an

entirely paper based risk assessment is the goal

• Can be used to highlight excipients (either individually or on

a class basis) that might be a higher risk of;

• exceeding PDEs

• being more prone to variability in EI levels

How do we envisage the database being used?

• Strong desire to publish the data when a critical mass has

been reached

• Industry consortium differentiate between the real and

negligible risks.

• Gaining of regulatory support & “endorsement” for the use of

a database in risk assessments

2017 timeframe

• Good alignment on methodology & validation requirements

How do we envisage the database being used?

If you’re interested to find out more…………..

• Webinar planned for the 7th December 2016 at 4pm GMT

https://www.lhasalimited.org/events/webinar-practical-

implementation-and-the-role-of-excipient-data-in-a-risk-

based-approach/4032

Or you can directly register here:

https://attendee.gotowebinar.com/register/46972064093

93346308

Or contact crina.heghes@lhasalimited.org

• Next database release planned for 11th November 2016

EU Projects

EU Projects – MIP-DILI

• Commenced in 2012 as a 5 year IMI funded project.

• 26 collaborating partners from EFPIA, SME and academia.

• Concerned with Mechanism Based Integration Systems for

the Prediction of Drug Induced Liver Injury (MIP-DILI).

• Lhasa’s major role is as the “honest broker”:

• Collecting, collating and securely managing project data

through Vitic Nexus.

• Scientific research – Hepatotoxicity prediction.

EU Projects - iPiE

• Commenced in 2016 as a 4 year IMI funded project.

• 25 collaborating partners from EFPIA, SME and academia.

• Aims to develop frameworks to support the environmental

testing of new pharmaceuticals and to help prioritise testing

of legacy APIs.

• Lhasa’s major role is as the “honest broker”:

• Collecting, collating and securely managing project data

through Vitic Nexus.

EU Projects – EU-ToxRisk

• Commenced in 2016 as a 6 year IMI funded project.

• 39 collaborating partners from industry, SME and academia.

• Aims to establish an animal-free, mechanism-based

approach to chemical safety testing for repeated dose

systemic toxicity (liver, kidney, lung and nervous system) and

DART.

• Lhasa’s major role is:

• The development of metabolic predictive methods in the

context of project case studies.

With Regulatory Authorities

• FDA Research Collaboration Agreement

• Aims to make use of publicly releasable FDA data to construct, improve

and validate Lhasa's software for toxicity, metabolism and chemical

degradation prediction.

• Current 5 year agreement commenced in 2011 following a previously

successful collaboration, renewal Oct 2016.

• A series of reprotoxicity alerts have been developed in Derek Nexus

within this agreement from shared in vivo DART data.

• FDA provides feedback from a regulatory perspective to develop our

science and establish best practice through joint publication.

• NIHS Project

• Ongoing for over 10 years

• Hepatotoxicity and Genotoxicity Focus

• (Q)SAR Challenge

New Project PDE/AI Project

Project Vision

“AI/PDE data sharing would enhance the safety of drug substances and your risk

assessment workflow by allowing you to rapidly access a harmonised and agreed upon

series of AIs/PDEs for commonly used reagents/solvents and use these AIs/PDEs

consistently across industry within regulatory submissions, thus saving you time, effort

and cost.”

Background and NeedShare AI/PDE Safety Assessment Data

• The synthesis of new pharmaceutical APIs involves the use of substances which may exert

toxic effects and therefore pose a risk to human health if present as an impurity.

• Management of impurities in medicinal products is regulated by a series of guidelines:

ICH Q3A(R2), 2006; ICH Q3B(R2), 2006; ICH Q3C(R5), 2011; ICH M7 (step 4), 2014

• Calculation of AIs/PDEs are required in regulatory submissions under these guidelines

• Calculating these AIs/PDEs involves a significant time and resource burden

• The data involved in generating an AI/PDE is often non-proprietary

• AI/PDE data therefore lends itself to a data sharing project

• Time/resource savings

• Avoids duplication of effort

• There may be a need to align how AIs/PDEs are calculated for a given chemical

• Different safety factors could be applied (F1-F5) regulators could therefore be presented with inconsistent

AIs/PDEs for a given chemical

Basic Concept of the ProjectShare AI/PDE Safety Assessment Data

1. Generate an agreed upon series of AIs/PDEs for common impurities in APIs

(reagents/solvents) of highest value for use in regulatory submissions

• Identification of chemical short-list of common interest (e.g. frequently used in synthesis)

2. Harmonise the approach to conduct safety assessments and derive an AI/PDE

• Industry accepted process developed and agreed upon joint publication (?)

• Agree on the calculation principles for AI/PDE data (as detailed in ICH Q3C – for example align Fn risk factors)

• Agree a standardised reporting format

Lead towards consistent application across industry and within regulatory submissions

3. The agreed upon AI/PDE data should then be made available through a database that

can be searched by structure, CAS, etc.

PDE/AI Web Conference

1st November 1500 – 1600 (GMT) – all organisations

welcome

Contact will.drewe@lhasalimited.org

Conclusions

• Data and Knowledge sharing have shown benefits for:-

• Saving time and money

• Reduction of testing

• Improving models

• Standardising methodology/approach across Industry

• Highlighting discrepancies

• There has a considerable increase in the volume of

knowledge/data shared since LL inception in 1983

Thank you

Lhasa Members for

Sharing Knowledge and

Data

Any Questions?