The time course of tumour size changes with gemcitabine. What can we learn about pharmacology?

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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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

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turnover

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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

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Variability inGemcitabine Dose-Response

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Why Stop Treatment?

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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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Tumour Size Gemcitabine Dose

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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