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.