Abstract 687: Implementation of inter-patient variability in semi-mechanistic model of melanoma treatment with vemurafenib

Abstract Introduction: The number of parameters in quantitative systems pharmacology (QSP), mechanistic and semi-mechanistic models is greater than in conventional PK/PD models. That is why it is not possible to implement inter-patient variability using only clinical data. The objective of this study was to describe inter-patient variability in the semi-mechanistic model of melanoma treatment with vemurafenib, a BRAF inhibitor, on the basis of published human in vitro, in vivo and clinical data, and to predict overall response from phase 2 clinical trial. Methods: Model described melanoma progression and its treatment with vemurafenib. It includes (i) proliferation, apoptosis, and transition between states (c-MET negative/positive and sensitive/resistant to BRAF inhibitor) of cancer cells; (ii) HGF effect on proliferation of melanoma cells; (iii) PK/PD of vemurafenib. Various types of data were used to extract inter-patient variability, for example: (a) in vitro data on effect of vemurafenib on melanoma cell lines [1]; (b) in vivo data on tumor growth in melanoma patients without treatment [2]; (c) in vivo data on HGF level in blood plasma of melanoma patients [3]. Results: Different virtual patients developed by the model reproduce 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 simulation of virtual patients population with clinical outcomes. An overall response rate was 70% in the model versus 53% (95% CI, 44 to 62) observed in phase 2 clinical trial [4]. Overestimation can be explained by intermittent administration and dose reduction in case of adverse events in clinical trials which were not taken into account in the model. Conclusions: Our work establishes the benefit of using of in vitro and in vivo data in addition to clinical data to predict the variability in treatment outcomes of cancer patients. [1] Stones et al. Front Genet. 2013 May 8;4:66 [2] Hartung et al. PLoS One. 2017 May 4;12(5):e0176080 [3] Hügel et al. Melanoma Res. 2016 Aug;26(4):354-60 [4] Sosman et al. N Engl J Med. 2012 Feb 23;366(8):707-14 Citation Format: Dmitry Shchelokov, Oleg Demin Jr, Oleg Demin. Implementation of inter-patient variability in semi-mechanistic model of melanoma treatment with vemurafenib [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 687.