Savolitinib exposure-pMET model in mouse
Across the CDX and PDX models investigated, after a single dose of
savolitinib, pMET inhibition responded to savolitinib plasma
concentrations with rapid onset and no apparent delay, as evidenced by
lack of a hysteresis loop when plotting savolitinib exposure against
inhibition of pMET (Figure 1). The observed maximum inhibition of pMET
coincided with savolitinib tmax of 1–2 h; recovery of
pMET to baseline continued to track with the PK profile. We concluded
that no delay between plasma PK and pMET inhibition was evident, and
consideration for an indirect-response model would not be required. A
sigmoidal Emax model provided adequate description of
the concentration-pMET relationship. The western blots for pMET and
total MET protein measured in the EBC-1 and MKN-45 studies presented
here are shown in Supplementary Figure S4. Within the dataset used in
this exercise, the PD response following multiple days of dosing was
investigated across several studies and shown to mirror that seen
following a single dose. Therefore, it was assumed that no change in
inhibition of pMET over time is observed and all data (single dose and
repeat dose) are plotted in Figure 1. There was no
statistically-significant difference between cell lines in
EC50 or Emax and single estimates of
0.38 ng/mL (95% CI 0.25–0.5) and 97% (95% CI 94–97) adequately
described exposure-pMET relationship across all cell lines. For some
previously published studies used in this analysis, changes in pMET were
measured using Meso Scale Discovery® (MSD). A
comparison was made between datasets generated by MSD and WB and
differences in the exposure-response parameters between the methods were
not statistically significantly different. For both methods,
EC50 estimates were within approximately 1.5-fold (ELISA
0.38 ng/ml; WB 0.25 ng/ml) with overlapping confidence intervals (ELISA
95% CI 0.27–0.54; WB 95% CI 0.15–0.42), and a resultant
insignificant drop of 6 in OFV. Figure 1 shows the final model overlaid
on observed concentration-pMET data from across all datasets used here.
Figure 2 shows a comparison between the observed time-course of pMET
inhibition measured in the EBC-1 and MKN-45 studies presented here and
model simulations. Final model parameters are shown in Supplementary
Tables S3 and S4. Goodness of fit plots are shown in Figure S5. The
model was used to derive the concentration required for near complete
inhibition (90%) of pMET (EC90 3.4 ng/mL).
Savolitinib showed dose- and schedule-dependent
anti-tumour activity in the EBC-1 NSCLC and MKN-45 gastric cancer
xenograft
models
In the EBC-1 model, there was a dose-dependent increase in the level of
anti-tumour activity with once-daily doses of 2.5 mg/kg (77% TGI), 5
mg/kg (79% TGI), 10 mg/kg (87% TGI) 30 mg/kg (76 to 94% TGI) and 100
mg/kg (58% regression) (Supplementary Table S2; Figure 3A). We found
that although 30 mg/kg BID and 100 mg/kg 2/5 both deliver approximately
the same AUC over a weekly cycle, the continuous BID dose resulted in
greater anti-tumour effects (Supplementary Table S2). Of all schedules
tested, the discontinuous schedules showed the least anti-tumour
response, with the 100 mg/kg Q2D or 4/3 resulting in TGIs similar to
mice receiving 30 mg/kg QD or 15 mg/kg BID, despite receiving twice as
much compound on a weekly basis (Supplementary Table S2; Figure 3A). The
addition of ABT increased the half-life of savolitinib in the mouse,
which increased the duration of pMET inhibition at a given dose level,
resulting in improved efficacy compared to the non ABT groups. For
example, 2.5 mg/kg QD delivers 77% TGI while 2.5 mg/kg + ABT delivers
19% regression; 30 mg/kg delivers 76 to 94% TGI whilst 30 mg/kg + ABT
delivers 75% regression. (Supplementary Table S2; Figure 3B). Similar
results of dose- and schedule-dependent anti-tumour activity were
observed in the MKN-45 gastric cancer xenograft model (Figure 3C,
Supplementary Table S5). Body weight time-course data are shown for both
the EBC-1 and MKN-45 studies in Supplementary Figure S6. In the MKN-45
study, some animals were removed mid-study due to tumour ulcerations,
but this had no statistical effect on the calculation of tumour growth
inhibition (derived from the geometric mean tumour volume). For the
PK/PD modelling, each animal was modelled individually and therefore the
data were used as is.
Xenograft tumour growth model and savolitinib
effect
Across the datasets presented here (EBC-1 and MKN-45) and those that
were previously published and used in this analysis, no delay between
start of savolitinib treatment and onset of effect was observed, thus
the Simeoni model (equation 5) could be reduced to a single compartment
of growing tumour cells (T) (equation 8), growing with rate constant
(λ0). Both exponential and linear growth rates
(λ1, linear and λ0, exp) were tested for
each cell line and informed by data from control (vehicle-treated)
cohorts (equations 9 and 10, ϕ fixed to 20). λQ is the
ratio of the growth rates, used in the change point calculation. The TGI
data showed that there was a negative correlation between growth and
inhibitory effect (i.e. faster growing cells had a higher inhibition
rate and vice versa). Therefore, the effect (E) of pMET suppression on
tumour inhibition rate was modelled as proportional to the growth rate
of the cell line (equation 11). E thus becomes a factor of the intrinsic
growth rates of the tumour model rather than an inhibitory rate
constant. When E=1, the inhibition equals the growth, and tumour stasis
is reached. During the periods where E>1, inhibition is
greater than growth and the tumour is shrinking. Incorporating a
growth-dependent drug effect into the model has led to a better model
fit and significant drop in OFV (ΔOFV = 461.8, Χ2p<0.0001).
[8] \(\ \ \frac{\text{dT}}{\text{dt}}=G-G\bullet E\)
[9] \(\ \text{\ λ}_{Q}=\frac{\lambda_{0i}\bullet T}{\lambda_{1}}\)
[10] \(\ \ G=T\bullet\lambda_{0i}\bullet\frac{1}{{(1+\lambda_{Q}^{\phi})}^{\frac{1}{\phi}}}\)
[11] \(E={{(pMET}_{T}\bullet slope)}^{\gamma}\)
[12]\(\ \lambda_{0i}=\lambda_{0}\bullet\frac{T^{5}}{\left(T^{5}+T_{\text{frac}}^{5}\right)}+\lambda_{0}\bullet\lambda_{\text{frac}}\bullet\left(1-\frac{T^{5}}{\left(T^{5}+T_{\text{frac}}^{5}\right)}\right)\)
The data did not support estimation of an Emaxrelationship for pMET effect (described in equation 11 above). A
power-linear function was not worse in model fit than the
Emax model according to Aikaiki information criteria
(ΔOFV = -0.25, k=1) (equation 13).
[13]\(Effect=\ {(slope\ \bullet\ \text{pMET}_{T})}^{\gamma}\)
For the studies where post-treatment regrowth data were available, a
slight delay in regrowth could be observed for the groups where
significant tumour size reduction had occurred. Small tumours, below a
threshold value (Tfrac, as a proportion of the baseline
size) had a reduced growth rate (λfrac), and a
size-dependent effect on growth was found to statistically improve the
model fit (ΔOFV = 348.4, Χ2 p<0.0001). The
reduced growth rate λ0i is related to the nominal growth
rate for that cell line through equation 12. All anti-tumour activity
model parameter estimates are shown in Supplementary Table S4.
Overall, the model presented here adequately describes the population
average and individual tumour growth curves with representative
comparisons between simulated time-course and observed data, shown in
Supplementary Figure S7, and goodness of fit plots are shown in
Supplementary Figure S5.
Calculating the level and duration of pMET inhibition
required for tumour
regression
Using the model above, the degree and duration of pMET inhibition
required to drive anti-tumour activity were determined across the CDX
and PDX models investigated. The EBC-1 model had the most extensive
dataset available and offered a good way to demonstrate the relationship
between inhibition of pMET and anti-tumour activity across the doses and
schedules tested. When the time above 90% pMET inhibition for a weekly
cycle is plotted against anti-tumour activity (calculated as the change
in growth rate compared to vehicle), maximum anti-tumour activity is
observed when pMET inhibition of >90% is continuously
achieved (Figure 4A). Across the ten CDX and PDX models analysed, all
models see >90% inhibition which drives anti-tumour
activity; however, variable sensitivity was observed in terms of the
duration above 90% required to drive tumour regressions. This can be
shown by calculating the time above 90% inhibition of pMET at a tested
dose that delivers tumour stasis (stasis offers a useful endpoint to
make a direct comparison) for each CDX and PDX model (Table 2); the most
sensitive model (Hs746T) required only approximately 6 h, whilst the
least sensitive model (NCI-H441) required continuous coverage. Setting a
threshold value, in this case 90% inhibition, is arbitrary but useful
to easily make comparisons across datasets and to enable a baseline
understanding of the target modulation requirements without applying
more in-depth modelling. However, this can be improved upon using the
mathematical model presented here with a structure that describes the
anti-tumour activity as being dependent on the baseline growth rate of
the tumour, and this provides a partial explanation for the level of
variation seen across these models. This mathematical relationship can
be used to plot the relationship between percentage of pMET inhibition
and the ratio of inhibition rate of growth due to savolitinib, over the
baseline growth rate of the tumour (Figure 4B). When this ratio is 1,
the rate of inhibition is equal to the tumour growth rate and the tumour
is not growing (stasis), whilst values below 1 indicate the tumour is
seeing net growth. Values above 1 indicate net tumour shrinkage. This
relationship demonstrates that the rate of inhibition of tumour growth
initially increases proportional to pMET suppression, but then the
inhibitory rate on tumour growth increases disproportionately above 80%
to 90% pMET suppression. This illustrates the benefit of achieving
durable near-maximal inhibition of pMET in driving tumour shrinkage to
maximise the tumour inhibitory rate.