A semi-mechanistic model of targeted therapy for melanoma
Dmitrii Shchelokov (1), Oleg Demin Jr (2)
(1) Lomonosov Moscow State University, Faculty of Biology, Moscow, Russia (2) InSysBio, Moscow, Russia
Introduction: Targeted therapy with BRAF inhibitors has resulted in significant progress in the treatment of metastatic melanoma bearing BRAF V600E mutation. Since 2011 FDA had approved a series of targeted inhibitors of MAPK pathway, the first of them was BRAF inhibitor vemurafenib . Despite the initial success of clinical outcomes, most patients ultimately develop drug resistance and relapse. The recent studies evidence that HGF/c-MET pathway plays a crucial role in the development of drug resistance mediated by tumor microenvironment . Implementation of these experimental findings could help to develop a more realistic model of targeted therapy for melanoma and to evaluate the potential role of c-MET inhibitors in melanoma treatment.
• To develop a semi-mechanistic model of targeted therapy for melanoma (using vemurafenib as an example) which is able to describe the emergence of drug resistance during therapy
• To describe inter-patient variability in response to vemurafenib treatment
• To explore the effect of c-MET inhibitors in combination with BRAF inhibitor vemurafenib for different types of virtual patients
Methods: The model comprises of 6 ordinary differential equations (ODEs): 4 of them describe cellular dynamics and other 2 describe pharmacokinetics (PK) of the drug. PK model and parameters for vemurafenib were taken from FDA clinical pharmacology and biopharmaceutics review . The cellular block of the model includes 4 various cell states (c-MET negative/positive and sensitive/resistant to BRAF inhibitor melanoma cells, respectively) and describes proliferation, apoptosis, and transition between cell states, as well as effects of vemurafenib and HGF on the rate of proliferation. An effect of c-MET inhibitors was simulated by decreasing parameter Emax of HGF stimulatory effect on proliferation of c-MET positive cells. Tumor volume was defined as an explicit function of a total number of melanoma cells and used to measure change from baseline during the therapy.
To introduce an inter-patient variability in the model for further multiple simulations we compiled available published data and tried to estimate variability for several numbers of parameters. Also, we tried to estimate the amount of pre-existing drug-resistant cells in the tumor on the basis of clinical data on time to progression. It is important to note due to the lack of data we are able only to assume the probability distribution of each parameter and approximately to estimate mean and variance. The R package ‘stats’ were used for random generation of parameters sets according to their function of distribution (R v3.2.1) .
Results: Developed model qualitatively reproduces all types of tumor response to vemurafenib monotherapy according to RECIST criteria: complete response, partial response, stable disease and progressive disease. To evaluate the predictive ability of the model in a quantitative manner we compared results of multiple simulations with clinical outcomes. An overall response rate was 70% versus 53% (95% CI, 44 to 62) observed in phase 2 clinical trial . The model tends to overestimate the treatment effect possibly due to we did not take into account an intermittent administration and dose reduction in case of adverse events. The subsequent analysis of the multiple simulations revealed a correlation between maximal response and parameter Emax of HGF effect which depends on c-MET expression level. This observation is supported by experimental data which shows that HGF rescue strongly correlates with c-MET expression by melanoma cells . The model predicts that usage of c-MET inhibitors in combination with BRAF inhibitors could improve on response and overall response rate, as well as delay or avoid BRAF-inhibitors resistance development and relapse.
Conclusion: The present work focused on the development of a semi-mechanistic model of targeted therapy for melanoma which is able to adequately describe clinical data and drug resistance development. Obtained results are consistent with recent experimental observations and confirm the hypothesis about the role of HGF/c-MET pathway in the development of resistance to BRAF-inhibitors. To conclude our results show a potential perspective of BRAF/c-MET inhibitors combination therapy for melanoma to improve overall response rate and overcome drug resistance.
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