Background: Kinase inhibition is an increasingly popular strategy for pharmacotherapy of human diseases. Although many of these agents have been described as "targeted therapy", they will typically inhibit multiple kinases with varying potency. Pre-clinical model testing has not predicted the numerous significant toxicities identified during clinical development. The purpose of this study was to develop a bioinformatics-based method to predict specific adverse events (AEs) in humans associated with the inhibition of particular kinase targets (KTs).
Methods: The AE frequencies of protein kinase inhibitors (PKIs) were curated from three sources (PubMed, Thompson Physician Desk Reference and PharmGKB), and affinities of 38 PKIs for 317 kinases, representing >50% of the predicted human kinome, were collected from published in vitro assay results. A novel quantitative computational method was developed to predict associations between KTs and AEs that included a whole panel of 71 AEs and 20 PKIs targeting 266 distinct kinases with K(d)<10microM. The method calculated an unbiased, kinome-wide association score via linear algebra on (i) the normalized frequencies of AEs associated with 20 PKIs and (ii) the negative log-transformed dissociation constant of kinases targeted by these PKIs. Finally, a reference standard was calculated by applying Fisher's exact test to the co-occurrence of indexed Pubmed terms (p0.05, and manually verified) for AE and associated kinase targets (AE-KT) pairs from standard literature search techniques. We also evaluated the enrichment of predictions between the quantitative method and the literature search by Fisher's exact testing.
Results: We identified significant associations among already empirically well established pairs of AEs (e.g. diarrhea and rash) and KTs (e.g. EGFR). The following less well recognized AE-KT pairs had similar association scores: diarrhea-(DDR1;ERBB4), rash-ERBB4, and fatigue-(CSF1R;KIT). With no filtering, the association score identified 41 prioritized associations involving 7 AEs and 19 KTs. Among them, eight associations were reported in the literature review. There were only 78 out of a total of 4522 AE-KT pairs meeting the evaluation threshold, indicating a strong association between the predicted and the text mined AE-KT pairs (p=3x10(-7)). As many of these drugs remain in development, a larger volume of more detailed data on AE-PKI associations is accessible only through non-public databases. These prediction models will be refined with these data and validated through dedicated prospective human studies. CONCLUSION AND FUTURE DIRECTIONS: Our in silico method can predict associations between kinase targets and AE frequencies in human patients. Refining this method should lead to improved clinical development of protein kinase inhibitors, a large new class of therapeutics. http://www.lussierlab.org/publication/PAS/.