Introduction
MET (mesenchymal epithelial transition factor) is a receptor tyrosine
kinase (RTK) associated with the regulation of tissue repair,
proliferation, and survival in normal physiology (Matsumoto & Nakamura,
1996). Dysregulation of MET signalling mediates mitogenesis,
angiogenesis and invasion and has been implicated in a number of
malignancies including papillary renal cell carcinoma (PRCC), gastric
and lung cancers (Albiges et al., 2014; Awad et al., 2016; Soman,
Correa, Ruiz, & Wogan, 1991), Aberrant MET signalling occurs through a
number of mechanisms including overexpression of MET or its ligand HGF
(hepatocyte growth factor), MET gene amplification, MET gene mutation/
rearrangement and changes in ligand-induced autocrine or paracrine
signalling (Raghav et al., 2018). Furthermore, MET gene amplification
could be detected in about 5-22% of lung adenocarcinomas patients
developing acquired resistance to first generation epidermal growth
factor receptor tyrosine kinase inhibitor (EGFR TKI) (Arcila et al.,
2011; Bean et al., 2007; Engelman et al., 2007; Sequist et al., 2011).
Therefore MET has been a target of significant clinical interest and
drug discovery efforts and several strategies are being developed to
therapeutically target MET, including small molecule kinase inhibitors.
Savolitinib (AZD6094, HMPL-504, volitinib) is an oral, potent, and
highly selective MET TKI (Gavine et al., 2015; Jia et al., 2014).
A pharmacokinetic-pharmacodynamic (PK/PD) model relating savolitinib
exposure to inhibition of MET phosphorylation (pMET) and anti-tumour
activity in the Hs746T gastric model was recently published (Gu et al.,
2019). This model estimated that as little as 10% continuous inhibition
of pMET is required to prevent the tumour growing (stasis), which
equates to plasma concentration of 1.1 ng/ml. Work describing a PK/PD
model for crizotinib, an ALK/ROS1 inhibitor with MET-inhibitory
activity, has also been previously published, linking drug exposure to
inhibition of pMET (Yamazaki et al., 2008). The model was built on data
generated in athymic mice implanted with GTL16 gastric carcinoma cells
and U87MG glioblastoma cells; it indicated that >90%
inhibition of pMET is required to drive significant anti-tumour
activity, with durable inhibition desirable for optimal anti-tumour
activity. Another group investigated two MET inhibitors in pre-clinical
models, assessing PK, pMET inhibition in tumour samples, and anti-tumour
activity (Bladt et al., 2013). The two compounds were primarily
distinguished by differences in their plasma half-life, with more
durable inhibition of pMET observed for the compound with a longer
half-life, and consequently greater anti-tumour activity. The compound
with the longer half-life was taken into the clinic as MSC2156119J
(tepotinib, EMD-1214063), and tested in a Phase I trial to assess PK,
inhibition of pMET, and anti-tumour activity (Falchook et al., 2014).
Subsequently, the recommended Phase II dose was set based on achieving a
plasma exposure at steady state that delivers >90%
inhibition of pMET in the tumour over the dosing interval (Falchook et
al., 2014).
Thus, it seems that potent (>90%) and durable inhibition
of pMET is desirable to maximise anti-tumour activity. However, the data
from Gu et al., (2019) for savolitinib suggests a lower threshold, but
that analysis focussed on a single in vivo model. It is important
to confirm the requirements for savolitinib, and, since savolitinib is
being investigated for the treatment of multiple tumour types in the
clinic, an important question to address is whether the same
relationships are apparent across tumour models. Mathematical modelling
is a useful tool to integrate a number of datasets and quantitatively
address this question; such a model can then be used to predict the
likely time-course of pMET inhibition for a given dose and schedule
being tested in the clinic.
The current investigation analysed pMET inhibition and anti-tumour
activity in gastric (MKN-45) and non-small cell lung cancer (NSCLC;
EBC-1) cell line xenograft models using several doses and schedules of
savolitinib. Savolitinib has also been tested in a number of additional
cell line-derived xenograft (CDX) and patient-derived xenograft (PDX)
models carrying MET copy number gain or amplification (Schuller et al.,
2015) (Gavine et al., 2015) (Henry et al., 2016). These additional
datasets along with the EBC-1 and MKN-45 models presented here were used
to develop a mathematical model relating savolitinib exposure and pMET
inhibition to anti-tumour activity. The mathematical model was used to
determine the extent and duration of pMET inhibition needed for optimal
efficacy and to address the question of whether these requirements
differ across the CDX and PDX models tested.
Methods
Animals
Female athymic nude mice were purchased from Charles River Laboratories
(Wilmington, MA). Mice were housed under pathogen-free conditions in
individual ventilated cages at our Association for the Assessment and
Accreditation of Laboratory Animal Care accredited facility in Waltham,
MA. All animal manipulations were conducted in a biosafety cabinet
maintained under positive pressure. Mice were 5–6 weeks old at the time
of tumour implantation. All animal studies were conducted in accordance
with the guidelines established by the internal Institutional Animal
Care and Use Committee and reported following the Animal Research:
Reporting In Vivo Experiments guidelines (Kilkenny, Browne, Cuthill,
Emerson, & Altman, 2010).
Xenograft anti-tumour activity studies
Five million EBC-1 human lung cancer or ten million MKN-45 human gastric
cancer cells were injected subcutaneously in the right flank of athymic
nude mice in a volume of 0.1 mL containing 50% matrigel. Mice were
randomised based on tumour volumes using stratified sampling, and
enrolled into control and treatment groups. Oral dosing began when mean
tumour size reached ~175 mm3 for EBC-1
and ~250 mm3 for MKN-45. In order to
determine the anti-tumour activity of savolitinib in theMET -amplified gastric cancer xenograft model (MKN-45), we treated
mice daily (QD), with dose levels ranging from 2.5–25 mg/kg, and twice
a day (BID; second dose given 8 hours after the first), with dose levels
in the range 1.25–12.5 mg/kg (Supplementary Table S1). Doses and
schedules were selected to deliver a broad dose range overall and
encompass clinical exposures observed, and the BID groups had a matching
QD group receiving the same daily total dose. In the EBC-1 model, a more
extensive range of doses and dose schedules were tested including QD,
BID and intermittent schedules. The discontinuous schedules explored
included 2 days on 5 days off (2/5), 4 days on 3 days off (4/3), or
every other day (Q2D). Savolitinib was dosed alone or in combination
with a pan-CYP inhibitor (100 mg/kg intraperitoneal, 1 hour prior to
savolitinib dosing), which prolonged savolitinib PK half-life by
reducing the elimination rate so that the plasma concentration-time
profile better matches that seen in the clinic (1-aminobenzotriazole;
ABT Sigma catalog number: A3940-250mg, Lot number: 087M4078V).
Tumour volumes, body weight, and
tumour condition were recorded at least twice weekly for the duration of
the study. The tumour volume was calculated (taking length to be the
longest diameter across the tumour and width to be the corresponding
perpendicular diameter) using the formula (equation 1):
[1] \(TV=length\times\text{width}^{2}\times 0.52\)
Where TV is tumour volume, length in mm, width in mm.
Growth inhibition from the start of treatment was assessed by comparison
of the differences in tumour volume between control and treated groups
according to the following equations for percentage inhibition, and
percentage regression (equation 2 and 3).
[2]%\(\ inh=100\times\left(\frac{\text{TV}_{\text{cont}}-\text{TV}_{\text{treat}}}{\text{TV}_{\text{cont}}-1}\right)\)
[3]%\(\ reg=100\times\left(1-\text{TV}_{\text{treat}}\right)\)
Where% inh is the calculated percentage of tumour growth
inhibition,% reg is the calculated percentage of tumour growth
shrinkage, TVcont is the geometric mean tumour
volume of the control group, TVtreat is the
geometric mean tumour volume of the treated group
Savolitinib has been investigated in a number of additional CDX and PDX
models. These have been combined with MKN-45 and EBC-1 datasets,
presented here as part of the analysis (Supplementary Table S2).
Mouse pharmacokinetics and pharmacodynamics
assessments
Whole blood was collected under xylazine-ketamine anaesthesia by cardiac
puncture and plasma frozen at -80°C until analysis. PK samples were
collected at multiple time points both after single (first) dose
administration and at the end of study (repeat dose administration).
Plasma samples were analysed for parent concentration using a protein
precipitation extraction procedure followed by LC-MS/MS detection;
analysed and processed using MassLynx and TargetLynx, respectively
(Waters, Milford, MA, USA).
For assessment of target engagement, tumours were excised and frozen at
-80°C until the time of analysis. Analysis was conducted by Western blot
(WB).
Tumour fragments were homogenised in lysis buffer. Proteins, separated
by SDS-PAGE, were transferred to nitrocellulose membranes, and incubated
with the following antibodies (all at 1:1000 in TBST BSA 3%): pMET
(Y1234/1235) Cell Signaling Technology (CST) 3077, MET CST 8198,
followed by incubation with a secondary horseradish
peroxidase-conjugated antibody and chemiluminescence detection. The
lower limit of quantification for pMET was set to 1% of cell-specific
baseline pMET value.
Pharmacokinetic-pharmacodynamic analysis and
software
Population PK/PD analysis was carried out using nonlinear mixed effects
modelling as implemented in NONMEM 7.3 (ICON Development Solutions,
Elicott City, MD, USA) and the GNU GFortran compiler (Versions 4.6.0,
[ftp://ftp.globomaxnm.com/Public/nonmem7/compilers]). The
first-order conditional estimation with η-ε interaction (FOCE-I) was
used. Model selection was based on the change in NONMEM objective
function value (OFV; for nested models), Aikaiki information criteria
(for non-nested models), and visual improvements in the diagnostic
plots. For nested models, decreases in the OFV of at least 6.63
(p<0.01; degrees of freedom = 1) and 10.83 (x2,
p<0.001; degrees of freedom = 1) were used as cut-off values
for forward inclusion and backward elimination, respectively.
Post-processing of NONMEM analysis results was carried out in R version
3.0.2 (Comprehensive R Network, http://cran.r-project.org). The
precision of the parameter estimates was evaluated throughout the model
development by examining the asymptotic standard errors calculated using
the covariance routine in NONMEM and, for final models, using a
bootstrap analysis was carried out using Perl-speaks-NONMEM, versions
3.5.3 and 4.2.0 (Lindbom, Pihlgren, & Jonsson, 2005).
Population pharmacokinetic
model
Data from all CDX and PDX experiments were pooled together for
population PK analysis. Both one- and two-compartment PK models with
first-order absorption and elimination were initially tested to fit the
mouse plasma savolitinib PK. The exposure-dependent clearance was
modelled via Michaelis-Menten kinetics, and absorption was captured by a
dose-dependent relative bioavailability. Furthermore, the effect of ABT
was incorporated on maximum elimination (Vmax) to
account for additional reduction in elimination rate as an estimated
change in Vmax conditioned on the categorical variable
of ABT present (yes/no). Slight variations in exposure for some studies
were accounted for by estimating separate bioavailability for these
studies.
Exposure-pMET model
Exploratory analyses of savolitinib concentration versus percentage
inhibition from baseline in pMET (as a marker of target suppression)
showed a concentration-dependent response. Plotting the data in this way
also showed a lack of hysteresis and therefore no or minimal time delay
between plasma concentration and change in pMET. As a result, a direct
response model linking the time course of plasma concentration to the
time course of phosphorylated MET was developed applying plasma
concentration in the central PK compartment as driving force
(Supplementary Figure S1). The concentration-pMET relationship was
fitted to a Hill equation (equation 4):
[4]\(pMET=\text{base}_{\text{pMET}}\left(1-\frac{E_{\max}{C_{p}}^{\gamma}}{{\text{EC}_{50}}^{\gamma}+{C_{p}}^{\gamma}}\right)\)
where \(\text{base}_{\text{pMET}}\) is the baseline tumour pMET level,\(C_{p}\) is the savolitinib plasma concentration, \(E_{\max}\) is the
maximum suppression at infinite savolitinib concentration,\(\text{EC}_{50}\) is the concentration at which 50% of the maximum
suppression is achieved, and \(\gamma\) is the Hill factor. Covariate
testing included cell line type on EC50 and/or
Emax (if range of data allowed estimation of a separate
Emax) and method of detection (WB vs ELISA) as
categorical variables.
pMET tumour growth inhibition
model
The relationship between pMET and tumour growth inhibition (TGI) was
sequentially modelled keeping concentration‑pMET relationship fixed. The
Simeoni model was used as the basis of tumour dynamic modelling (Simeoni
et al., 2004). The percent pMET suppression was first transformed from
0–1 to 0–∞ scale (equation 5) to allow for stable estimation and
assumed to be the sole driver of tumour growth inhibition:
[5] \(\text{\ \ pMET}_{T}=\left(\frac{\text{pMET}}{1-pMET}\right)\)
Linear, power-linear (equation 6) and Emax (equation 7)
pMET-tumour kill relationships were explored as following:
[6] \(\mathbf{\ }\ Effect=\ {(slope\ \bullet\ \text{pMET}_{T})}^{\gamma}\)
[7] \(\mathbf{\ }\ Effect=E_{\max}\frac{\text{pMET}_{T}}{(\text{pMET}_{T}+\text{pMET}_{50})}\)
where \(\text{pMET}_{50}\) is the transformed pMET supression required
for 50% maximal effect. Covariate testing explored the possibility of
cell line specific pMET-tumour kill parameters. Supplementary Figure S1
shows a schematic of the mathematical model used to link savolitinib
plasma concentration to MET phosphorylation that drives anti-tumour
activity.
Model diagnostics
qualification
Diagnostic plots of observed data vs population and individual
predictions were examined for overall fit and lack of bias in the
predicted values. Plots of conditional weighted residuals and
between-subject variability were inspected for evidence of systematic
lack of fit, and to confirm the absence of bias in the error
distributions. The final model was determined on the basis of maximised
likelihood (lowest OFV), physiological plausibility of parameter values,
and successful numerical convergence. Model parameter values should
preferably have a standard error of estimation no higher than 50% of
the parameter estimate, and there should not be significant bias in the
random effects estimates.
To verify that residuals were randomly distributed around zero, and that
no significant structural bias remained, individual weighted residuals
and conditional weighted residuals were plotted versus the population
predictions and versus time, stratified by dose and/or study. A normal
quantile plot and a density plot of conditional weighted residuals were
used to validate approximate normal error distribution with a mean of
approximately zero.
Results
Pharmacokinetic properties and population PK model for
savolitinib in
mice
Mouse PK data from the two main studies in the MKN-45 and EBC-1 models
alongside PK data from other studies (listed in Supplementary Table S1)
have been used to define the mouse population PK model. It has been
shown previously that savolitinib has a biphasic profile following
intravenous administration and an elimination half-life of 1.5 h (Gu et
al., 2013), and these data were used alongside the oral data (described
below) to estimate the PK model parameters. Following oral dosing,
savolitinib was rapidly absorbed, with maximal concentrations occurring
between 1-2 h. A dose-proportional increase in exposure was observed up
to 30 mg/kg and, above 30 mg/kg, there was a greater than proportional
increase in exposure with dose, and a lengthening of the elimination
half-life. This observation was consistent with saturation of a
clearance mechanism. The co-dosing of cytochrome P450 inhibitor ABT at
100 mg/kg in the EBC-1 study also prolonged the half-life of savolitinib
to 3.5 h, which was more consistent with that seen in patients (3 to 6
h); a comparison of the simulated mouse PK profile with and without ABT
is shown in Supplementary Figure S2A. The structural PK model was
two-compartment with first order absorption and Michaelis–Menten
elimination applied (ΔOFV = 15.8, x2 p<0.01 compared to
one-compartment model). Accounting for effect of ABT on
Vmax rather than on Km led to a drop of
3.6 in OFV (same number of parameters).
Final mouse PK model parameters are shown in Table 1. Comparisons of
model simulations against representative observed data from the EBC-1
and MKN-45 studies are shown in Supplementary Figure S2B–D. Overall,
the model adequately described the plasma concentration–time profiles
observed across dose levels and studies investigated; goodness of fit
plots are shown in Supplementary Figure S3.