3 rd ICPAD Workshop Amsterdam November 8 th & 9 th

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1 A generalisable pharmacokinetic-pharmacodynamic (PKPD) model of savolitinib, a novel MET tyrosine kinase inhibitor, to explore extent and duration of target inhibition required for optimal efficacy across a range of tumour xenograft models Rhys DO Jones 1, Mike Grondine 2, Alexandra Borodovsky 2, Maryann San Martin 2, Michelle DuPont 2, Celina D Cruz 2, Alwin Schuller 2, Ryan Henry 2, Klas Petersson 3, Tarjinder Sahota 4, Ghada F. Ahmed 4 1 Oncology IMED Biotech Unit, AstraZeneca, Cambridge, UK; 2 Oncology IMED Biotech Unit, AstraZeneca, Gatehouse Park, Waltham, US; 3 qpharmetra LLC, Karolinska Institutet Science Park, Hälsovägen 7, Huddinge; 4 Quantitative Clinical Pharmacology, Early Clinical Development IMED Biotech Unit, AstraZeneca, Cambridge, UK 3 rd ICPAD Workshop Amsterdam November 8 th & 9 th

2 Introduction Pharmacokinetics Pharmacodynamics TGI Savolitinib Savolitinib (AZD6094, HMPL-504, volitinib) is an orally bioavailable, potent and selective small-molecule MET kinase inhibitor 1 3 Currently in clinical development as monotherapy for PRCC 4 and in combination with osimertinib for NSCLC 5 Demonstrates anti-tumour activity across a spectrum of MET amplified CDX/PDX models including EBC-1, H441, H820, H1993 (lung); RCC-43b, RCC-47 (PRCC); MKN-45, SNU-5, LN-36, Hs746T (gastric) CDX, cell-line xenograft; NSCLC, non small cell lung cancer; PDX, primary explant xenograft; PRCC, papillary renal cell carcinoma; TGI, tumour growth inhibition Objectives of current study Establish a model linking (1) drug exposure to pmet suppression and (2) pmet suppression to inhibition of tumour growth Utilise the model to derive the level and duration of pmet suppression needed for optimal efficacy Can a model (partially) explain the heterogeneity in anti-tumour activity observed across xenograft models tested? 1. Hua et al. AACR 106th Annual Meeting. Abstract CT ; 2. Jia et al. J Med Chem. 2014;57(18): ; 3. Gavine et al. Mol Oncol 2015;9:323 33; 4. NCT Accessed 24 October 2018; 5. NCT Accessed 24 October 2018

3 CDX and PDX xenograft models explored for PD and anti-tumour activity Tumour MET status Indication Other known mutations Approximate dose that delivers tumour stasis on continuous daily dose (mg/kg) RCC43b Amp(8), ratio 3.2 PRCC BRCA1 m 10 RCC47 Amp(8), ratio 1.5 PRCC 5 SNU-5 Hs746T Amp(21), ratio 3.4Chr7 ploidy (>3 copies) Amp(13), ratio 2.7, exon 14 Gastric P53, IDH2, CDH1 8 Gastric P53, BRCA, APC 5 MKN-45 Amp(12), ratio 2.6 Gastric p53 20 EBC-1 Amp(20), ratio 3.5 Sq NSCLC P53, ATM 40 NCI-1993 Amp(11.3), ratio 2.5 NSCLC P53, STK11 40 NCI-H441 CN gain 3, ratio.72 NSCLC P53, KRAS 100 HLXF-036LN Chr 7 trisomy, 3 copies NSCLC p53 CDX, cell-line xenograft; PD, pharmacodynamic; PDX, primary explant xenograft

4 Savolitinib drives rapid and extensive suppression of pmet in an exposure-dependent manner Consistent relationship across xenografts investigated EC 50 = 0.35 ng/ml EC 90 = 3 ng/ml pmet = Baseline 1 E max. C p C p + EC 50 In vivo pmet inhibition explored across 10 CDX/PDX models E max function with a single EC 50 adequately describes data across all 10 models MET status therefore not a covariate for pmet inhibition CDX, cell-line xenograft; EC 50, 50% of maximum effect; E max, maximum effect; PDX, primary explant xenograft; pmet, phosphorylated MET

5 Data from a wide range of doses and schedules informed the relationship between pmet and anti-tumour effect (e.g. EBC-1 model) Group Dose level (mg/kg) # Schedule Average weekly dose (mg) Average weekly AUC (μg.h/ml) TGI Rationale for dose A 2.5 QD % Low dose B ABT BID % reg Match C C 5 QD % Match B D 15 + ABT Q2D % Match J E 10 QD % Match F, H F 30 Q2D % Match E, G, H G 15 + ABT QD % reg, 70% reg Match O, F H 30 QD 4/ % Match E, F I 100 QD 2/ % Match J, K, L J 15 BID % Match I, K, L Doses & schedules selected to deliver (1) broad dose range overall; (2) match clinical exposures; (3) match total weekly exposure following different schedules 1-Aminobenzotriazole (ABT) (CYP450 inh) added to some dose groups to match human exposure profile (T 1/2 ) *Model-based simulations performed to explore the level & duration of pmet inhibition required for optimal efficacy K 30 QD %, 76%, 89% Match I, J, L L 30 + ABT QD % reg Match I, J, K M 100 Q2D %, 70% Match N, O N 100 QD 4/ % Match M, O O 30 BID % Match G, M, N P 100 QD % reg High dose Q Veh R5 Veh + ABT 9% pmet, phosphorylated MET; TGI, tumour growth inhibition *Mouse Pop-PK/PD model established using end of study samples, and from separate single dose groups # Key to schedules: QD once daily; BID twice daily; Q2D once every other day; QD 4/3 once daily dosing 4 days on/3 days off; QD 2/5 once daily dosing 2 days on/5 days off

6 An integrated PopPK-pMET-TGI model adequately describes average and individual anti-tumour activity across doses and schedules tested in EBC-1 Population/individual predictions compared against observed tumour growth data dtv dt = TV k growth. 1 pmet sup 1 pmet sup slope γ Last administered dose Observations Tumour growth model (TGI) represented by turnover compartment with pmet driving tumour shrinkage and set to be proportional to intrinsic growth (more on this later) Individual Predictions 6 Population Predictions pmet, phosphorylated MET; TGI, tumour growth inhibition

7 Effect Near-max suppression of pmet throughout the dosing interval is needed to achieve optimal tumour shrinkage Regressions Stasis >90% pmet suppression for >80% of time required to drive net tumour shrinkage Maximum tumour shrinkage seen when achieving >90% pmet inhibition continuously High-dose intermittent schedules (eg 100 mg/kg 2/5) resulted in inferior tumour shrinkage compared to lower-dose continuous schedules eg (30 mg/kg BID) 7 pmet, phosphorylated MET % of time above IC90

8 Range in sensitivity to TGI across CDX / PDX models observed following dosing with savolitinib Some CDX / PDX models are more sensitive than others e.g. Hs746T (orange) requires less sustained pmet inhibition than H441 (Red) model Overall, optimal efficacy observed across models when driving durable inhibition of pmet >90% CDX, cell-line xenograft; PDX, primary explant xenograft; pmet, phosphorylated MET; TGI, tumour growth inhibition

9 Rate of growth inhibition drops significantly when pmet suppression < 80 to 90 % Simulating the relationship between pmet suppression and momentaneous growth rate k growth Slope describes steepness of slope γ describes how sharp the intersection is to a more exponential effect Zero on y-axis is tumour stasis -ve values on y-axis is tumour shrinkage Each tumour model has a different baseline growth rate k tot = k growth 1 pmet sup 1 pmet sup slope pmet suppression (fitted on a transformed scale) drives tumour growth inhibition Effect of pmet suppression increases disproportionately at > 80 to 90 % suppression Require high pmet inhibition for durable effect Do tumour dynamics play a role in the sensitivity observed? γ 9

10 There is evidence that doubling time of PDXs is an important predictor of drug sensitivity Internal analysis of Novartis data (Gao et al., 2015 use of PDXs for patient selection) Control group doubling (DT) plotted against treated DT / control DT shows trend for 3 compounds Suggested that slower growing tumours are less sensitive to drug effects than rapidly growing tumours DT, doubling time; PDX, primary explant xenograft

11 E Driving tumour shrinkage as a function of pmet inhibition and intrinsic growth rate of the tumour Incorporating tumour dynamics partly explains differences in sensitivity across CDX / PDX models EBC1, LN36, H820 H441 Hs746t MKN45, H1993 RCC47 SNU5, RCC43b E = pmet sup 1 pmet sup slope γ k growth Broadly, two groups of sensitivity observed across tumour models - a minimum of ~80 % and >90 % pmet suppressions. Accounting for tumour growth rate (doubling time) in model offers opportunity to scale from mouse to human 11

12 Summary Savolitinib was tested in a range of MET amplified CDX/PDX models Savolitinib modulates pmet in vivo in an exposure dependant manner with a consistent EC 50 across CDX / PDX models tested Optimal efficacy driven by high and durable suppression of pmet Different levels of sensitivity observed in anti-tumour activity across CDX / PDX models Testing in multiple models offers an assessment of variability in response that is not available when relying on a single model The range of observed sensitivies to target suppression across CDX / PDX models was partly explained by modelling intrinsic tumour growth rate as a function of pmet suppression Opportunity to translate anti-tumour activity from mouse to human by adjusting the growth inhibition parameter relative to the intrinsic growth (doubling time) of clinical tumours CDX, cell-line xenograft; pmet, phosphorylated MET; PDX, primary explant xenograft