Anticancer–drug-induced interstitial lung disease (DIILD): With great power comes great responsibility.

Abstract

e18699 Background: DIILD accounts for 3-5% of all ILD cases, with Anticancer agents accounting for 23-51% of all cases, and Case-Fatality Rates of 51.3%. Guidelines for anticancer therapy-related DIILD are lacking. DIILD was not often identified as an adverse event until late in drug development or after launch. Methods: Publications till Feb 2023 were reviewed for incidence, time to onset, prevention and early identification of anticancer DIILD. Results: Highest reportedincidence of DIILD was associated with targeted therapies [mTOR Inhibitors (9.5- 58%), Anti-EGFR (0.9 - 5.9%), MEK inhibitors (2.4%), CDK 4/6 inhibitors (1.6%), PARP inhibitors (0.79%)]; Chemotherapy [Bleomycin (6.8-21%), Gemcitabine(1.1-3.9%), Irinotecan (0.74%)]; ADCs (11.4%); CTLA4 inhibitors (5.44%); and Check-point inhibitors [(CPIs: 1.1-3.6%), PD-1 inhibitors (3.6%), PD-L1 inhibitors (1.3%)]. The rates of DIILD were higher in combinations compared to single-agents. Higher Mortality rates were associated with Anti-EGFR (18-51.3%), Irinotecan (24%), Bleomycin (8.1-23%), Gemcitabine (0-22%), mTOR Inhibitors (0-20%), Check-point inhibitors (all CPIs: 8-9.4%). The time of onset was as early as a week for CPIs (42% occurred within first two months), 28 days for EGFR:TKIs, 60-172 days for MEK Inhibitors, and 14-638 days for ADCs. Risk-mitigation strategies for DIILD: Identification of high-risk patients – (multiple prior lines of therapy, combination therapy, lung comorbidities, poor respiratory reserve, smokers). Early radiological diagnosis accompanied by Monitoringfor early signs of ILD with wearable technology. Conclusions: Research in Genomics and biomarkers to identify patients at risk, development of Artificial Intelligence (AI) algorithms for PFT interpretation, early radiological detection and audiometry analysis are ongoing and urgently needed.