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.