Objectives: Next generation anaplastic lymphoma kinase inhibitors (ALKi) have shown tremendous promise in circumventing on-target resistance mutations in ALK+ non-small cell lung cancer (NSCLC). However, additional on-target mutations and bypass signaling can allow for resistance to ALKi therapies to emerge (Yoda 2018, Schneider 2023). In this setting, inhibition of signaling nodes that act at the junction of multiple bypass mechanisms, such as those achieved by PF-07284892 (ARRY-558), an allosteric SHP2 inhibitor (SHP2i), offers a promising combination strategy in conjunction with ALKi (Drilon et al. 2023). In this work, we describe the development of a quantitative systems pharmacology (QSP) model to support and enhance mechanistic interpretation of clinical response to combination strategies between small molecule therapies for ALK+ NSCLC.
Methods: This work makes use of several in vitro and in vivo datasets, including a set of resistant patient-derived cell lines, to calibrate a QSP signaling and tumor growth model and capture the range of resistance mechanisms and efficacy variability in response to combination ALKi & SHP2i treatments. The model incorporates pharmacokinetic simulations to drive a signaling pathway model including both drug effects and potential resistance mechanisms. The signaling pathway in turn is used to drive a simulation of tumor growth and to predict growth inhibition under treatment.
Results: We use the model to contextualize mechanistic understanding from preclinical experiments and to better understand potential clinical resistance mechanisms and combination efficacy in translation to the clinic. The model is shown to capture the combination drug effect across varying levels of resistance. Simulations for ALK+ NSCLC supported the hypothesis that the SHP2i can re-sensitize resistant patients to ALKi.
Citations: Drilon A, et al. Cancer Discovery (2023): 1789-1801. Schneider, J. et al. Nature Cancer 4.3 (2023): 330-343. Yoda, S, et al. Cancer Discovery 8.6 (2018): 714-729.
