Identification of cellular targets involved in cardiac failure caused by PKI in oncology: an approach combining pharmacovigilance and pharmacodynamics

• Published in: British journal of clinical pharmacology Vol. 83; no. 7; pp. 1544 - 1555
• Main Authors: Patras de Campaigno, Emilie, Bondon‐Guitton, Emmanuelle, Laurent, Guy, Montastruc, Francois, Montastruc, Jean‐Louis, Lapeyre‐Mestre, Maryse, Despas, Fabien
• Format: Journal Article
• Language: English
• Published: England John Wiley and Sons Inc 01.07.2017
• Subjects: Abl; Adverse Drug Reaction Reporting Systems - statistics & numerical data; Antineoplastic Agents - adverse effects; Antineoplastic Agents - pharmacology; cardiac failure; Databases, Factual - statistics & numerical data; Drug Interactions; Drug Safety; Heart Failure - chemically induced; Heart Failure - pathology; Humans; Molecular Targeted Therapy - adverse effects; Molecular Targeted Therapy - methods; Neoplasms - drug therapy; oncology; pharmacodynamics; Pharmacovigilance; protein kinase inhibitor; Protein Kinase Inhibitors - adverse effects; Protein Kinase Inhibitors - pharmacology; Protein-Tyrosine Kinases - antagonists & inhibitors; Protein-Tyrosine Kinases - metabolism; Proto-Oncogene Proteins c-abl - antagonists & inhibitors; Proto-Oncogene Proteins c-abl - metabolism; Signal Transduction - drug effects; World Health Organization; protein kinase inhibitor; Abl; pharmacovigilance; pharmacodynamics; oncology; cardiac failure
• ISSN: 0306-5251; 1365-2125; 1365-2125
• DOI: 10.1111/bcp.13238

Aims The aims of the present study were to evaluate the risk of cardiac failure (CF) associated with 15 anticancer protein kinase inhibitors (PKIs) through a case/noncase analysis and to identify which PK(s) and pathways are involved in PKI‐induced CF. Methods In order to evaluate the risk of CF, adjusted reporting odds ratios (aRORs) were calculated for the 15 anticancer PKIs in the World Health Organization safety report database (VigiBase®). We realised a literature review to identify 21 protein kinases (PKs) that were possibly involved in CF caused by PKIs. Pearson correlation coefficients (r) between aRORs and affinity data of the 15 PKIs for the 21 PKs were calculated to identify the cellular target most likely to be involved in PKI‐induced CF. Results A total of 141 601 individual case safety reports (ICSRs) were extracted from VigiBase® for the following PKIs: afatinib, axitinib, bosutinib, crizotinib, dasatinib, erlotinib, gefitinib, imatinib, lapatinib, nilotinib, pazopanib, ruxolitinib, sorafenib, sunitinib and vandetanib. Among them, 2594 ICSRs concerned CF. The disproportionality analysis revealed that, for dasatinib, imatinib, bosutinib, sunitinib and nilotinib, disproportionality for CF was significantly higher than for other PKIs, with aRORs of 2.52 [95% CI 2.26, 2.82], 1.79 (95% CI 1.57, 2.03), 1.73 (95% CI 1.18, 2.54), 1.67 (95% CI 1.51, 1.84) and 1.38 (95% CI 1.18, 1.61), respectively. Significant values for correlation coefficients between the product of dissociation constant (pKd) and aROR were observed for two non‐receptor protein kinases: ABL1 (non‐phosphorylated and phosphorylated forms) and ABL2 protein kinases, with values of r = 0.83 (P = 0.0001), r = 0.75 (P = 0.0014) and r = 0.78 (P = 0.0006), respectively. Conclusion We observed a higher disproportionality for CF with dasatinib, imatinib, bosutinib, sunitinib and nilotinib than with other PKIs. In addition, the study highlighted the role of ABL tyrosine kinases in CF caused by anticancer PKIs.