S04-04 In Silico Decision Support Systems Enabling Next Generation Risk Assessments for Carcinogenicity

Abstract

The ICH S1B(R1) addendum represents a significant milestone in improving human-relevant carcinogenicity testing. Utilizing relevant data and knowledge in a weight-of-evidence (WoE) assessment can now be used to determine if a two-year rat study would add value to a pharmaceutical safety package. Recent publications describing experiences in the practical implementation of the amended guidance have highlighted both opportunities and challenges from the initial application of this approach such as data gaps, documentation and interpretation. These reflections highlight the importance of developing best practice and having frameworks in place to ensure that the new regulation can be applied consistently and transparently with scientific rigour to support chemical safety decisions.

In silico decision support systems provide a means of achieving this, through organising evidence, encoding guidance, and enabling best practices. A case study was performed using such a decision support system to enable an ICH S1B(R1) addendum decision. The system was able to capture the requirements from the ICH guidance and emerging best practice. In addition, relevant data and knowledge was organised around an AOP network, thus contextualising the evidence and bringing transparency and consistency. With this in place, guided expert review can then focus a user on where additional information or review is needed to reduce uncertainty. Overall, the decision support system enables systematic, transparent and robust outcomes for complex WoE assessments and thus supports greater consensus between organisations in the decisions reached.