Discussion
Dysregulation of MET signalling is observed in a variety of human cancers with correlations to poor clinical outcomes and drug resistance. Amplification of the MET gene, with consequent constitutive kinase activation has been reported in a number of human primary tumours. Mutations in the MET kinase domain in both sporadic and inherited forms of human renal papillary carcinomas have been previously documented (Olivero et al., 1999). MET exon 14 mutations (METΔ14) are a recurrent mechanism of MET activation and occurs in around 3–4% of lung adenocarcinoma (Frampton et al., 2015). Role of MET amplification in resistance to EGFR inhibitors is of significant interest in lung cancer therapy (Lecia V Sequist, 2020). Oncogenic dependency on MET in these various biological contexts and its therapeutic intervention by a plethora of MET-targeted agents as single agents or in combination with novel therapeutics is an area of intense investigation. This warrants a clear understanding of the exposure response relationship of MET-targeted agents in appropriate models. This study showed a robust and consistent relationship between savolitinib PK and pMET inhibition across the models tested.
Several doses/schedules were tested in the EBC-1 lung cancer CDX model to evaluate the extremes of feasible PK profiles across a wide exposure range. More frequent dosing resulted in a stronger response than discontinuous dosing, even when higher doses were administered for the intermittent schedule. This correlates with tumours requiring constant inhibition from savolitinib to drive net tumour shrinkage. This finding is consistent with conclusions derived from a previous study with savolitinib in the Hs746T CDX model (Gu et al., 2019), and studies on crizotinib, where duration of inhibition of pMET was considered the crucial factor for inhibition of tumour growth (Yamazaki et al., 2008). Interestingly, the EC50 for inhibition of pMET of 9.7 ng/ml derived from Gu et al. was higher than the 0.38 ng/ml derived from this analysis. Moreover, the degree of pMET inhibition calculated to be required to drive tumour stasis (approximately 10%) was lower than our analysis (approximately 80%) but the plasma concentration required to drive tumour stasis was similar (1.1 ng/ml vs. 2.3 ng/ml). The majority of the dataset used by Gu et al. was used in this analysis. We explored these differences and confirmed that in our model, the EC50 for the Hs746T model did not differ statistically from the other CDX and PDX models. Thus, the discrepancy was likely due to differences in modelling approaches applied in the analyses: our dataset was larger, which enabled a more robust assessment of variability to be accounted for in the parameter estimation. Where Gu et al. used a naïve pooling approach, we applied a population approach, which enabled the use of likelihood-based methods to incorporate PK data below the limit of quantification. Additionally, our approach enabled us to estimate the baseline (vehicle) values for pMET as part of the model fitting, instead of correcting for baselines upfront (Gu et al., 2019).
We used a direct response model relating PK to pMET, while Gu et al. used an effect compartment model to explain an observed time-delay between plasma PK and tumour PD. Our analysis did not support the need for a model incorporating any delay. The Gu et al. dataset consisted of PK and PD with the first measurement made at 15 minutes, and the delay calculated to be approximately 1–4 hours. In our datasets, 1 hour was usually the first time-point observed, and therefore less visible; additionally, our data consisted of pMET measurements made after several days’ dosing with no time-dependant, or accumulation of effect observed. Overall, we felt it was not critical to account for such a time-delay when simulating changes in pMET over time and relating this to tumour growth inhibition over a time period of several weeks. A more pronounced time delay between crizotinib PK and pMET inhibition was evident and thought to be due to rate-limiting pharmacokinetic distribution from plasma to the tumour (Yamazaki, 2013), which would be expected for compounds with a large volume of distribution, such as crizotinib. A PK/PD analysis of tepotinib, together with an analysis in humans, is also available and similarly supports the need for continuous inhibition of pMET (Xiong et al., 2015). The potency of tepotinib to inhibit pMET was 1.7-fold higher in humans than in mice, and given the precision of the assays available for assessing EC50 and the biological variability observed, this supports a hypothesis of similar translation of inhibitory effect from preclinical to clinical use of savolitinib (Xiong et al., 2015). Taking the EC90derived from this analysis and correcting it to account for an approximate two-fold difference in plasma protein binding between mouse and human (data unpublished), the estimated plasma concentration required to achieve 90% pMET inhibition in patients is 7 ng/ml. Data from a Phase I study in patients with advanced solid tumours show that doses above 200–400 mg achieve concentrations in excess of this value at 24 hours (Gan et al., 2019).
Modelling tumour inhibitory effects as a function of tumour growth is a novel approach with the potential to improve translation of tumour growth inhibition from pre-clinical to clinical. We showed that tumour growth inhibition was dependent on the intrinsic growth rate of the tumour, with slower growing tumours being less sensitive to the suppression of pMET signalling. The CDX and PDX models showed that a constant concentration of savolitinib that delivered 80% pMET suppression could prevent net tumour growth in more sensitive models, whilst the least sensitive models required >95% pMET suppression. In practice, savolitinib concentrations in mouse fluctuate by several orders of magnitude over the dosing interval, and at a dose that brings about concentrations that result in continuous cover above EC90, there will be periods when pMET suppression far exceeds EC90. This is desirable to achieve tumour shrinkage and maximise anti-tumour activity. This model behaviour was consistent with results observed in vivo, where we saw strong tumour shrinkage for the most sensitive models (e.g. Hs746T), whilst tumour stasis was only achievable in the least sensitive (e.g. NCI-H441). Moreover, the inhibitory effect on tumour growth increased exponentially above these threshold values, illustrating the benefit of strongly inhibiting MET continuously for maximum efficacious effect. This supports setting the PK/PD requirement for dosing to achieve >90% suppression continuously. Differences in tumour growth rate do not completely explain the heterogeneity in response, observed across the different models, and this is not unexpected as other factors may drive differences in sensitivity to MET inhibition such as additional mutations other than MET .
Our approach to modelling growth inhibition rate as a function of growth rate of the tumour was consistent with the concepts described previously (Norton, 1977). The modification to the Simeoni equation used in this analysis, with linear growth phase and the decreased growth rate for smaller tumours, yields a similar behaviour as the Norton-Simon model for growth over the span of tumour sizes observed.
Due to tumour heterogeneity, anti-cancer therapies show variability in efficacy across a patient population and across disease indications, creating a challenge when trying to model this pre-clinically. Modelling data using multiple mouse models is important to avoid relying on a single, and usually sensitive, model to assess pre-clinically the range in exposure requirements to translate to the clinic. Having PD and efficacy data available from as many models as presented, builds on the work of Gu et al., where the PD and efficacy effects were explored in one model (the most sensitive in this dataset), and provides further insights on the level of drug exposure required to cover different sensitivities to pathway modulation (Gu et al., 2019). Furthermore, the model presented offers the potential to incorporate tumour dynamics into the translation of anti-tumour effects from a mouse to the patient, by accounting for the differences in the doubling time between a fast-growing mouse tumour and slower-growing human tumours. This could improve the effectiveness of the translation of mouse datasets of tumour efficacy to the patient, and it will be interesting to compare simulations run using this model against patient tumour growth curves in future analyses.
Building a mathematical PK/PD modelling framework is a valuable means to integrate extensive datasets of exposure, pharmacodynamic response and tumour growth to define the target inhibition requirements (and potential variability) for optimal efficacy. Savolitinib is being investigated in combination with osimertinib in NSCLC (SAVANNAH: NCT03778229, TATTON: NCT02143466 and ORCHARD: NCT 03944772), and the modelling approach presented here provides a relevant starting point for the combination setting (Ahn et al., 2019; Choueiri et al., 2017; Lecia V Sequist, 2020; Oxnard et al., 2019; Yu et al., 2019). A PK/PD model relating osimertinib exposure to effects on EGF receptor phosphorylation and tumour growth inhibition has been previously published (Yates et al., 2016). Therefore, along with additional data exploring the combination effect in pre-clinical models, this model could be combined with the osimertinib PK/PD model to explore the combination effects of the two agents on PD response and efficacy.
Overall, these results suggest that high and durable levels of MET inhibition are needed for optimal monotherapy in preclinical models. This modelling framework has the potential to translate tumour growth inhibition from mice to humans by adjusting the growth inhibition parameter relative to the intrinsic growth rate of clinical tumours.

Acknowledgments

The authors acknowledge Lars Lindbom of qPharmetra LLC (Stockholm, Sweden), for assistance with the early modelling work.
This manuscript was funded by AstraZeneca, Cambridge, UK, the manufacturer of savolitinib.
The authors would like to acknowledge Bernadette Tynan, MSc, of Ashfield Healthcare Communications, Macclesfield, UK, part of UDG Healthcare plc, for medical writing support that was funded by AstraZeneca in accordance with Good Publications Practice (GPP3) guidelines (http://www.ismpp.org/gpp3).

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