# Statistical and Clustering Analysis of Safety Profiles of FDA-Approved Protein Kinase Inhibitors
## 4 Discussion  
This study provides a detailed analysis of AE profiles associated with FDA-approved PKIs using a large dataset. By examining AE patterns, outliers, and clustering based on kinase target specificity, our findings align with recent studies [36; 37; 38; 39; 40; 41; 42; 43; 44] and contribute to a more nuanced understanding of PKI safety profiles. Several studies have analyzed the safety profile of drugs acting on several targets within the PKI class (EGFR-TKIs [42; 43], immune checkpoint inhibitors (ICIs) [37], JAK inhibitors [38], VEGFR-TKIs, ALK inhibitors [44], and CDK 4/6 inhibitors [39; 41]) using disproportionality analysis techniques, including Reporting Odds Ratio and Information Component, to discern significant associations between therapeutic agents and AEs. Major findings included elevated risks of thromboembolic events with ICIs, malignant skin neoplasms with JAK inhibitors, and various gastrointestinal, hepatic, and dermatologic toxicities with EGFR-TKIs. Our analysis shows that several AEs such as death, diarrhea, fatigue, nausea and vomiting are highly prevalent among PKIs. Death emerged as a frequent AE, probably due to the advanced-stage indications and severe underlying diseases for which PKIs are often prescribed. This observation aligns with recent studies that report high AE frequencies in late-stage cancer therapies, where treatment is administered to heavily pretreated and more vulnerable populations [45; 46; 47]. The lack of comprehensive causality assessment data restricts the capacity to draw a conclusion about the relationship between PKI treatment and mortality outcomes. Gastrointestinal and hematological AEs, such as diarrhea, nausea, neutropenia, and anemia, are also common, consistent with the cytotoxic and antiproliferative effects of PKIs on rapidly dividing cells in the gastrointestinal tract and bone marrow [48]. Outlier analysis highlighted specific AEs with significant variability, such as hypertension, falls, and palmar-plantar erythrocytesesthesia syndrome. These AEs likely indicate drugs with distinct safety profiles or unique AE susceptibilities, consistent with findings from other studies that identified specific PKIs with elevated risk profiles in particular AEs [36; 37; 38; 39; 40; 41; 42; 43; 44]. Notably, tucatinib had the highest number of outliers, which suggests it has an atypical AE profile and may require additional clinical monitoring. Most RTK inhibitors clustered in Clusters 1 and 2, suggesting a shared safety profile among compounds that inhibit the EGFR and VEGFR pathways, [42; 48; 49; 50]. Conversely, NRTK inhibitors showed more distinct clustering patterns, particularly for inhibitors targeting JAK, BTK, and SYK, which were predominantly in Cluster 1. This clustering may reflect immune-mediated AEs commonly associated with these targets, [51; 52]. STK inhibitors clustered primarily in Cluster 2, exhibiting AE profiles that differ significantly from those of RTK and NRTK inhibitors. Lipid kinase inhibitors, distributed across Clusters 1, 2, and 4, are associated with the PI3K/AKT pathway. PI3K/AKT pathway inhibition results in varied safety outcomes, as previously described in the literature [53], highlighting the need for close monitoring when these inhibitors are prescribed. However, this study has several limitations, including the underreporting and overreporting of the FAERS database, which also includes reports received from consumers that cannot be confirmed by a healthcare professional. Moreover, the presence of confounding factors, such as combination therapies commonly used in oncology or comorbidities, affect the AE analysis and induce bias. This initial analysis will be improved moving forward by refining the data to reduce the bias and including data registries with extended follow-up periods to capture the full spectrum of AEs associated with PKIs. Integrating genomic and target-specific data could provide deeper insights into the mechanisms driving AEs, optimize risk mitigation strategies and improve therapeutic outcomes.