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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