Kinase inhibitors (KIs) represent a clinically important class of anticancer agents, as KIs have shown the potential for curative and long-term remission. Currently, there are 28 KIs on the market, and more than 150 KIs in clinical development. However, a major side effect of many KIs is cardiotoxicity (CT) manifesting as loss of contractile function, which can lead to heart failure. Unlike many other transient drug-induced toxicities, KI-induced CT has long term implications on the quality-of-life and mortality of patients. To illustrate, the leading long-term cause of death of breast cancer patients is not cancer, but cardiovascular complications. Furthermore, unexpected CT of newly developed KIs can result in late-stage failure in clinical drug development, preventing otherwise potentially highly effective KIs to reach patients. The enormous costs of ~€1,500M for successfully developing a new drug is largely caused by such late stage failures due to unexpected toxicity including CT. Ultimately, these costs are paid for by society. Thus, an urgent need exists for approaches for CT risk minimization of newly developed KIs.
The mechanisms of KI-induced CT are still poorly understood. Previous research has focussed on specific KIs and identified some important molecular components associated with CT. However, comprehensive approaches to obtain insight in, and predict CT are still lacking. In contrast, for other forms of CT such as QT-prolongation, the limited number of mechanisms is relatively well understood (e.g. hERG channel blocking). This insight has been successfully used in drug development to predict QT-prolongation by a combination of experimental models and mathematical modelling. For KI-induced toxicity, however, it is unlikely that there exists a single, or limited number of mechanisms underlying KI-induced toxicity. The human ‘kinome’ consists of more than 500 kinases. Many if not most kinase inhibitors have been shown to inhibit a multitude of 'off-target' kinases, i.e. interacting with kinases other than the intended therapeutic target. Such ‘off-target’ interactions can result in toxicities including CT. The CT-inducing KI sunitinib is one typical example of this concept. Sunitinib not only binds to its therapeutic target the VEGF receptor, it also inhibits over 50 other kinases at therapeutic concentrations, which appear to contribute to its CT profile. The crucial role of kinases in CT has also been confirmed by others. Given the currently poorly understood and the multitude of mechanisms for KI-induced CT, a multi-disciplinary systems pharmacology approach to identify predictive signatures for CT is proposed in this project. Such model-based signatures can be related to chemical structure properties of existing KIs, to optimize chemical structures of newly developed KIs for CT risk. In case of KI-induced CT, the power of such structural modification approaches was demonstrated for the CT-inducing KI imatinib, which was structurally re-engineered to lower the risk of CT, and was confirmed in vivo.
The research objective of this project is to develop systems pharmacology models for KI-induced CT, to identify predictive network-based dynamically weighted signatures for KI-induced CT. These signatures can form the basis for designing new KIs with minimized CT risk. A multi-disciplinary approach combining state-of-the-art computational modelling and experimental data generation will be used. This work is done by: 1) Selection of KIs and CT-modifying drugs from clinical adverse event databases. 2) Experimental characterization and network modelling of CT-associated biological networks in cardiomyocytes. 3) Dynamical modelling of CT networks and identification of predictive signatures.
