AbstractBackgroundRepurposing of tyrosine kinase inhibitors (TKI), such as nilotinib, imatinib etc. for the treatment of neurodegenerative disease is considered actively. Some tyrosine kinases which are targeted by these drugs (c‐Abl, DDR) are upregulated in neurodegenerative diseases (AD and PD). Thus, discoidin domain receptors (DDRs) are important regulators of microglial state, while c‐Abl‐kinase is a key regulator of cell stress response. TKI have been shown to boost autophagy through the interaction with these targets and to allow for the clearance of toxic tau and amyloid protein aggregates. The aim of this work was to apply the quantitative systems pharmacology (QSP) model of AD amyloid and tau pathology to translate the preclinical results for prediction of potential efficacy in humans.MethodInteraction of cell metabolism with amyloid ant tau accumulation was described earlier in QSP translational model [1]. It described dynamics of multiple biomarkers in brain, CSF and plasma. Amyloid and tau exert toxic influence on protein degradation by autophagy and proteasome and transport. In this work, c‐Abl was added as one of the drivers of AD pathology through p53 regulation, tau phosphorylation and autophagy inhibition. Data for nilotinib [2] and other TKI were used for model validation. PK data for nilotinib and other TKI were used to estimate brain concentration of the drug.ResultModel describes boosting of autophagy by about 40% [3] after c‐Abl inhibition. This leads to the reduction of amyloid and tau pathology, in correspondence to the data observed in vitro. However, for amyloid and tau transgenic mice, efficacy of c‐Abl inhibition was significantly lower assuming brain concentrations of about 1 nM, and contribution of microglial processes due to DDR1 inhibition was added. Human data on SUVR and CSF p‐tau reduction after 1 year of nilotinib treatment were described satisfactorily.ConclusionThe model can be applied to simulations of the efficacy of tyrosine kinase inhibitors in neurodegeneration diseases.[1] Karelina, T. et al, (2021). CPT: PSP, 10, 545.[2] Fowler, et al, (2019). Drugs in R and D, 19(2), 149.[3] Lonskaya, I. et al, (2013). PLoS ONE, 8(12), e83914.