1. Summation of Toxicity Data in Vitic Andrew Thresher Andrew.thresher@lhasalimited.org

2. Contents Introduction Our solution It’s design It’s implementation The next step

3. INTRODUCTION

4. Introduction Large quantity of data are being continuously generated, covering a variety of endpoints and assay protocols. Data may be required for differing uses, for example modelling, regulatory submissions, etc. How can we efficiently access this information in an appropriate manner?

5. Introduction The ICH M7 guideline states “Hazard assessment involves an initial analysis of actual and potential impurities by conducting database and literature searches for carcinogenicity and bacterial mutagenicity data…” ICH Harmonised Tripartite Guideline Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk 2014

6. Introduction The Vitic database (v2016.1.0) contains 134,367 bacterial mutagenicity records covering 7,992 compounds. To manually review all data for each compound is time- consuming and open to inter-individual variations in interpretation. To goal was to devise an automated process which would distil the database to allow efficient access to the data.

7. OUR SOLUTION

8. Our Solution: Step 1- Subsets First the data is divided into subsets by grouping experiments according to which variables are likely to affect the result. Bacterial strain Absence/presence of metabolic activation. How to balance sufficiently detailed subsets against maintaining robust subset size?

9. Our Solution: Step 2- Subset Calls Once the subsets have been defined the data is condensed to a single subset call according to the below rules: Results Present Subset Call PositiveEquivocalNegative X Positive X Equivocal X Negative XX Positive X X Conflicted XX XXX

10. Our Solution: Step 3- Summary Calls These subset calls are then combined to give a single summary call for each test compound. The summary call is given as the highest priority subset call present, where; Positive > Conflicted > Equivocal > Negative

11. Implementation Ames test 2980 positive, 937 conflicted, 33 equivocal and 3278 negative summary call generated. Chromosome aberration test Subsets based on metabolic activation (with/without) and duration of exposure. 1036 positive, 66 conflicted, 68 equivocal and 958 negative summary calls generated.

12. Implementation

13. Implemenation Local lymph node assay Not split into subsets. EC 3 values calculated. a Median EC 3 calculated for each substance. Potency classifications assigned. b Summary call derived using the subset call rules, removing negative results where the highest tested dose was lower than the median EC 3. a Basketter et.al. Journal of Applied Toxicology (1999) 19, 261-266 b Loveless et.al. Regulatory Toxicology and Pharmacology (2010) 56, 54-66

14. Implementation Local lymph node assay 848 positive, 72 conflicted, 4 equivocal and 730 negative summary calls generated. 698 median EC 3 values calculated.

15. Implementation

16. THE NEXT STEP

17. The Next Step Gather user feedback Conflicted calls Can we determine common traits which can be incorporated into the automated workflow? Expansion Can this method be applied to other test types?