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The time course of tumour size changes with gemcitabine. What can we learn about pharmacology?. Nick Holford Dept Pharmacology & Clinical Pharmacology University of Auckland. The Workers. Lai San Tham 3 Boon-Cher Goh 1 Wei Peng Yong 1 Ross A Soo 1 Ling-Zhi Wang 1 Soo-Chin Lee 1 - PowerPoint PPT Presentation
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The time course of tumour sizechanges with gemcitabine.What can we learn about
pharmacology?
Nick Holford
Dept Pharmacology & Clinical Pharmacology
University of Auckland
The Workers
Lai San Tham3
Boon-Cher Goh1
Wei Peng Yong1
Ross A Soo1
Ling-Zhi Wang1
Soo-Chin Lee1
How-Sung Lee2
1Department of Hematology-Oncology, National University Hospital, Singapore2Department of Pharmacology, National University of Singapore3Lilly-NUS Centre for Clinical Pharmacology, Singapore
Outline
• What does Pharmacology mean?
• Does Cancer need PKPD?
• Learning and Confirming– A reminder
• Tumor response study– A pharmacodynamic model for the time course of tumor
shrinkage in patients with ‘big cell’ lung cancer– What can we learn about pharmacology?– How can we use tumour size to predict survival?
What does this mean?
Pharmacokinetics Pharmacodynamics
Treatment Rx =Recipeor
Jupiter Symbol
Confirming
• Making sure• Outcome Expected• Analysis Assumptions Minimized
E.g. Randomized Treatment Assignment
• Questions for Drug Approval– E.g.
• Is the drug effective?• Can it be used safely in renal failure?
Learning• Exploration• Outcome Unexpected• Assumption rich analysis
–E.g. PKPD model
• Questions for Drug Science–E.g.
• How big an effect does the drug have?• What is the clearance in renal failure?
Confirming or Learning?
Sheiner LB. Learning versus confirming in clinical drug development. Clinical Pharmacology & Therapeutics 1997;61(3):275-91
"Why We're Losing the War on Cancer"
Accelerating Anticancer Agent Development and Validation Workshop June 20-22, 2007
Keynote Address: "Learning Too Little, Too Late: Why We Need a New Paradigm for the Cancer Clinical Trial"Clifton LeafFormer Executive EditorFortune
Resisting RECIST
Throwing away data?
Collaborative Effort
Singapore Study• Randomized, phase II trial
• Carboplatin at AUC of 5mg/mL*min given over one hour on day 1 of each cycle prior to the gemcitabine infusion
• Study arms– gemcitabine 750 mg/m2 over 75
minutes (Arm A) on days 1 and 8 every 3 weeks x 6 cycles
– gemcitabine 1000 mg/m2 over 30 minutes (Arm B) on days 1 and 8 every 3 weeks x 6 cycles
• No differences in outcome (survival or toxicity)
Soo RA, Wang LZ, Tham LS, Yong WP, Boyer M, Lim HL, et al. A multicentre randomised phase II study of carboplatin in combination with gemcitabine at standard rate or fixed dose rate infusion in patients with advanced stage non-small-cell lung cancer. Ann Oncol. 2006;17(7):1128-33.
Singapore Study of Tumour Size
• 56 treatment naïve patients with advanced ‘big cell’ lung cancer treated with carboplatin and gemcitabine
• 261 measurements of tumour size– largest dimension of the primary tumour measured
from CT images using electronic calipers
– Used only for RECIST category
• Measurements at protocol baseline, cycles 2, 4 and 6, and bimonthly– Actual mean follow up only 3.5 months
Gemcitabine Pharmacology
• Gemcitabine (dFdC)– Inactive pro-drug
• dFdCTP (gemcitabine triphosphate)– Intracellular, active, tri-phosphate metabolite
• dFdU– Major extracellular, inactive metabolite
Exposure Response
• Which Exposure Measure? – Dose
• Cannot distinguish PK from PD causes of variation
– AUC• Can be used to identify causes of between patient
variability through PK model linking Dose to AUC
– C(t)• Can be used to predict schedule dependence• But long computation times preclude practical
exploration of C(t) and response
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Tumour Size Drug
Why Dose by Dose Concentration Time Course Won’t Work
Concentration SpikesWith Each Cycle
Slow Tumour Response
How to Describe Delayed Drug Response?
• Exposure has delayed effect– Time course of drug concentration is complete within a few hours
– Binding of drug to DNA probably rapid
– Effect of DNA damage on cell proliferation probably slow
– Time course of tumour response takes weeks
ExposureTdt
dExposureeffect ,2/1/)2ln(
• KPD Effect Compartment Model for Exposure– What “apparent half-life” of drug would explain the effect time
course?– Can be based on Dose or AUC– C(t) not required
Tumour Size Model
Semi-mechanisticNatural history of tumour growth has rapid growth with asymptote
Feedback inhibits growth
Drug effect expresses pathophysiological mechanism
SizeSizeT
EffectRateIndt
dSize
turnover
1
Drug Effect on TumourFormation Rate
Tumour Turnover Kinetics Simple Feedback
“KPD” Drug Effect and Tumour Turnover
• Effect assumed to slow rate of proliferation of new tumour cells
KPD Model Tumour Model
Drug Effect Model
T1/2,effect describes delay in drug effect Tturnover describes delay in tumour esponse
EffectCompartment
Tumour TurnoverCompartment
CeDose
CeEEffect
50
max1Dose
effectTk
,2/11
)2ln(
SizeSizekEffectRateIndt
dSize )( 2
turnoverTk
12
)(1 CeDosekdt
dCe
“KPD” plus Turnover
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Variability inGemcitabine Dose-Response
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2525 2626 2727 2828
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ID
Why Stop Treatment?
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ObsIndiv PredPop PredAe g
Which Exposure Metric?
• No better fit with dFdCTP (or dfdU) compared to gemcitibine AUC
• No better fit with individual AUC compared with dose of gemcitabine
• Dose is the simplest exposure metric
Tumour Size Turnover and PD Parameters
ParameterFinal Estimate
BSV% 95% CI
Size0, tumor size at baseline (cm) 6.7 54.6 (5.7, 7.8)
Tturnover, Tumor turnover (cm x week) 10.7 24.7 (2.0, 17.7)
Dose50, Gemcitabine dose at 50% tumor
size shrinkage (mg) 3156 136 (536, 16417)
T1/2,effect, KPD Effect half-life (week) 2.5 29 (0.61, 12.0)
Residual Error
Proportional error 12% - (9, 16)
BSV=Between Subject Variability (apparent coefficient of variation)95%CI=Empirical confidence interval from 1000 bootstraps
What Did We Learn About Pharmacology?
• No evidence that differences in exposure time course [C(t)] can influence tumour response
• No evidence that intracellular metabolite is better then dose as a predictor of tumour response– Anti-tumour mechanism may be different from haematological
toxicities
• Unable to learn about influence of dose and duration of infusion– Uninformative design
Didn’t learn very much!
What Could We Learn About Pharmacology?
• Can quantitate individual sensitivity (ED50) and time course (effect and tumour ‘half-lives’)
• Complements toxicity based models e.g. Friberg myelosuppression model (optimal dosing?)
• Link to survival probability (Claret et al 2009, Wang et al 2009)
FDA Model Linking Tumour Size with Survival
Wang Y, Sung C, Dartois C, Ramchandani R, Booth BP, Rock E, Gobburu J. Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development. Clin Pharmacol Ther. 2009;86(2):167-74.
Wang (FDA) Tumour Size Model
Empirical modelNo dose or exposure information used
“A model with mixed exponential-decay (shrinkage) and linear-growth (progression) components described the time course of tumor change
where TSi(t) is the tumor size at time t for the ith individual, BASEi isthe baseline tumor size, SRi is the exponential tumor shrinkage rateconstant, and PRi is the linear tumor progression rate.”
Claret Tumour Size Model
Log growth for tumor (no asymptote unlike Gompertz)D(t)*exp(-λ*t) is identical to the effect compartment modelR(t) ‘resistance’ function cannot be distinguished from drug elimination
Tumour Size and SurvivalWang/Claret
FDA Advisory Committee for Pharmaceutical Sciences and Clinical Pharmacology Meeting, March 18–19, 2008. http://www.fda.gov/ohrms/dockets/ac/08/briefing/2008-4351b1-01-FDA.pdf
Tumour SizeAt 8 weeks
Tumour Size Predictions
FDA Advisory Committee for Pharmaceutical Sciences and Clinical Pharmacology Meeting, March 18–19, 2008. http://www.fda.gov/ohrms/dockets/ac/08/briefing/2008-4351b1-01-FDA.pdf
Tumour Size ImprovesPrediction of Survival
FDA Advisory Committee for Pharmaceutical Sciences and Clinical Pharmacology Meeting, March 18–19, 2008. http://www.fda.gov/ohrms/dockets/ac/08/briefing/2008-4351b1-01-FDA.pdf
Which Patient Will Survive Longer?
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Wang et al. and Claret et al. use tumour size at a fixed time to predict survival.These patients have the same 8 week tumour size but different response time course
Survival models should include time course of tumour size!
Way to go!
Backup
Commentary Clinical Pharmacology and Therapeutics
Bruno R, Claret L. On the use of change in tumor size to predict survival in clinical oncology studies: toward a new paradigm to design and evaluate phase II studies. Clin Pharmacol Ther. 2009;86(2):136-8.
1. “Drug-independent models that link biomarker response to clinical end points are critical to support early (end of phase II) clinical decisions.
2. In oncology, change in tumor size (a biomarker of drug effect evaluated in phase II) is linked to survival (a phase II end point) in some solid tumors.
3. Change in tumor size can be used as a primary end point in the design and evaluation of phase II studies and in supporting go/no-go decisions and phase II study design.”
FDA Conclusions
FDA Advisory Committee for Pharmaceutical Sciences and Clinical Pharmacology Meeting, March 18–19, 2008. http://www.fda.gov/ohrms/dockets/ac/08/briefing/2008-4351b1-01-FDA.pdf
Clinical Response
ResponseArm A, 750mg/m2 over
75mins (n= 38)Arm B, 1000mg/m2
over 30mins (n=38)
Partial Response Rate (%) 34 42
Median Survival (days) 212 287
1-year Survival Rate (%) 31.6 35.6
Table 2. Summary of clinical response
Conclusion – no significant difference between treatments
Toxicity
Table 3. Hematologic toxicities expressed as percentage of patients
Arm A, 750mg/m2 over 75min
Arm B, 1000mg/m2 over 30min
p valuesToxicity Grade 3-4 Grade 3-4
Anemia 31% 33% 1.0
Neutropenia 68% 75% 0.61
Thrombocytopenia 69% 52% 0.10
Conclusion – no significant difference between treatments
Which Exposure Metric?
Structural Model Description Objective Function Values
2 Compartments - KPD, Turnover
Emax
AUCgemcitabine 521.2
AUCdFdU 524.0
AUCdFdCTP 522.0
Dosegemcitabine 520.8Sigmoid Emax
AUCgemcitabine 522.0
AUCdFdU 523.4
AUCdFdCTP 522.9
Dosegemcitabine 532.2AUCgemcitabine gemcitabine AUC-driven model
AUCdFdU dFdU AUC-driven model AUCdFdCTP dFdCTP AUC-driven model
Dosegemcitabine gemcitabine Dose-driven model
Tumour Size – Effect of GemcitabineVisual Predictive Check
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Scatterplot Comparison Median and 95% Interval Comparison
Tumour Size Gemcitabine Dose
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Individual Predicted Size
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Visual Predictive Check
FDA Advisory Committee for Pharmaceutical Sciences and Clinical Pharmacology Meeting, March 18–19, 2008. http://www.fda.gov/ohrms/dockets/ac/08/briefing/2008-4351b1-01-FDA.pdf