Reply: Predicting Adverse Events to Thiopurines in IBD: Are We a Step Closer?

To the Editors:

We thank Sharip et al for their insightful comments on our article published in this issue of Inflammatory Bowel Diseases.¹ Despite the ever-growing therapeutic armamentarium of biologics and newer small molecules, thiopurines continue to remain first-line treatment in inflammatory bowel disease, particularly in resource-limited healthcare systems.^2-4 There is robust evidence to support the concurrent use of thiopurines to enhance the efficacy as well as reducing immunogenicity to anti-tumor necrosis factor α agents.^5,6 As highlighted in the editorial commentary, there is clearly a need to bridge the gap in terms of understanding the genomic basis of thiopurine toxicity. We are grateful to the authors for acknowledging and highlighting our contribution to the pharmacogenomics of thiopurine-induced toxicity. In our study, we used the GenePy metric to assess the pathogenic burden across 46 candidate genes including TPMT and NUDT15 in a large cohort of patients with paediatric IBD. This computational signal compression tool that was developed in 2019 by the University of Southampton has been validated and applied across other genomic projects since.^7-9 GenePy reflects an individual’s personal burden of pathogenic variation for a given gene and can be applied across a whole gene network representing specific signaling pathways. As an example, GenePy can be applied across the NOD2 signaling pathway in inflammatory bowel disease cohorts to inform, develop, and refine predictive models of inflammatory bowel disease phenotype and disease progression.¹⁰ As described in our article, the GenePy technique can be applied to assess drug responses and toxicity to various other drugs including biologics using targeted signaling pathways. The illustrated example shows how GenePy can be applied for assessing treatment outcomes including efficacy and toxicity to anti-tumor necrosis factor α therapy using targeted signaling pathways (Figure 1). As a future project, we hope that it would allow development of a predictive model supporting clinical decision making personalized to individual patient profiles.

Figure 1.

Schematic representation of downstream tumor necrosis factor α (TNFα) signaling. Multiple downstream molecules regulate the anti-inflammatory effect of anti-TNF therapies. Biologics blocking TNFα have relatively frequent off-target adverse events including increased infection risk, paradoxical psoriasis, and increased malignancy risk. Understanding the genetic risk and protective roles of specific genes within this pathway using GenePy may enable identification of patients at risk of side effects and help to optimize treatment with anti-TNF therapy in inflammatory bowel disease.

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Funding

None.

Conflicts of Interest

No conflicts of interest in relation to the submitted work.

References

Author notes