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CML-4: Correlation of demographic, disease and therapy factors with toxicity and tolerability of the different TKIs



Chronic myeloid leukemia (CML) is characterized by the presence of BCR-ABL1 as a molecular marker. To target this, tyrosine kinase inhibitors (TKIs) were developed and successfully implemented in the treatment of CML. Despite this, reported resistance to TKIs resulted in the development of second and third generation TKIs. 

Currently, the optimal treatment for a patient with CML is chosen based on multiple factors such as disease stage, age, risk score, comorbidities, financial affordability, reimbursement and expected side effect profiles. The specific side effect profile varies across each TKI which consequently influences treatment selection. In addition to this, lack of response or intolerance to first-line TKIs may occur, leading to second-line treatment and sometimes a third-line treatment if necessary. A predictive accurate system to overcome this does not currently exist. However, in some cases mutation profiling is available to identify mutations to various TKIs – one of the mechanisms of resistance.

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This research aims to identify significant correlations between toxicity and tolerability with factors which improve therapy choice and QoL such as demographic factors (e.g., age, gender, ethnicity, etc.), genomic factors (e.g., mutations, genomic markers), concomitant disease, CML and/or therapy-related factors (e.g., drug excipients/coating, adverse events, events severity, timepoints, sequence, related dose reductions/increase or therapy interruptions), and prognostic factors in a large cohort of CML patients within the HARMONY Alliance Big Data platform. 

As a retrospective, non-interventional study, data from various academic and commercial CML clinical studies where Common Terminology Criteria for Adverse Events (CTCAE) and patient reported outcome (PRO) data is available will be analyzed using descriptive statistics. Setting endpoints on toxicity and Tolerability will allow to compare all variables among each other and define any significant correlation. This overall study proposes to increase prediction factors to improve understanding of the practical aspects of choosing the appropriate TKI, monitoring response and side-effects – key points leading to the long-term accurate and personalized treatment of CML patients.