Phase I dose-escalation trials with more than one dosing regimen - PowerPoint PPT Presentation

Phase I dose-escalation trials with more than one dosing regimen Burak Kürşad Günhan 1 Sebastian Weber 2 Abdelkader Seroutou 2 Tim Friede 1 IBS-DR, Göttingen, 7 December, 2018 1 Department of Medical Statistics, University Medical Center Göttingen 2 Novartis Pharma, Basel

Introduction

Background • In drug development, earliest trials on humans = ⇒ phase I dose-escalation trials • Relationship between dose and toxicity • Aim: Maximum tolerable dose (MTD) • Small cohorts of patients, and treated in cycles • Observed toxicities: dose limiting toxicities (DLTs) and non-DLTs • Based on DLTs data in first cycle 1/27

Background (cont.) Cohort Dose Number Number MTD (mg) of pats of DLTs 2/27

Background (cont.) Cohort Dose Number Number MTD (mg) of pats of DLTs 1 1 3 0 No 2/27

Background (cont.) Cohort Dose Number Number MTD (mg) of pats of DLTs 1 1 3 0 No 2 2 5 0 No 2/27

Background (cont.) Cohort Dose Number Number MTD (mg) of pats of DLTs 1 1 3 0 No 2 2 5 0 No 3 4 4 0 No 2/27

Background (cont.) Cohort Dose Number Number MTD (mg) of pats of DLTs 1 1 3 0 No 2 2 5 0 No 3 4 4 0 No 4 8 5 1 No 2/27

Background (cont.) Cohort Dose Number Number MTD (mg) of pats of DLTs 1 1 3 0 No 2 2 5 0 No 3 4 4 0 No 4 8 5 1 No 5 8 6 1 Yes! 2/27

Background (cont.) Cohort Dose Number Number MTD (mg) of pats of DLTs 1 1 3 0 No 2 2 5 0 No 3 4 4 0 No 4 8 5 1 No 5 8 6 1 Yes! Standard Methods • Main approaches: algorithm-based (3+3) and model-based • Model-based approaches display better performance. 2/27

Bayesian Logistic Regression Model (BLRM) (Neuenschwander et al., 2008) • For dose d • Number of DLTs: r d ∼ Bin ( π d , n d ) • DLT probabilities: logit ( π d ) = log ( α 1 ) + α 2 log ( d / d ∗ ) where d ∗ is the reference dose. • Interpretation of α 1 is odds of a DLT probability at d ∗ . 3/27

Bayesian Logistic Regression Model (cont...) • Metric for dose recommendation = ⇒ Posterior distribution of π d is used. • Three categories for π d • π d < 0 . 16 Underdosing (UD) • 0 . 16 ≤ π d < 0 . 33 Targeted toxicity (TT) • π d ≥ 0 . 33 Overdosing (OD) 4/27

Bayesian Logistic Regression Model (cont...) • Metric for dose recommendation = ⇒ Posterior distribution of π d is used. • Three categories for π d • π d < 0 . 16 Underdosing (UD) • 0 . 16 ≤ π d < 0 . 33 Targeted toxicity (TT) • π d ≥ 0 . 33 Overdosing (OD) • Escalation with overdose control (EWOC) principle ⇒ P ( π d ≥ 0 . 33 ) should not exceed 0 . 25. = 4/27

Visualization of EWOC principle d = 8 mg Probability density 0.00 0.25 0.50 0.75 1.00 DLT probability ( π d ) 5/27

Visualization of EWOC principle d = 8 mg Probability density 0.25 0.33 0.00 0.16 0.50 0.75 1.00 DLT probability ( π d ) 6/27

Visualization of EWOC principle d = 8 mg Probability density UD TT OD 0.25 0.33 0.00 0.16 0.50 0.75 1.00 DLT probability ( π d ) 7/27

Visualization of EWOC principle d = 8 mg Probability density 0.35 UD TT OD 0.25 0.33 0.00 0.16 0.50 0.75 1.00 DLT probability ( π d ) 8/27

Visualization of EWOC principle d = 8 mg Probability density 0.35 > 0.25 UD TT OD 0.25 0.33 0.00 0.16 0.50 0.75 1.00 DLT probability ( π d ) 9/27

Visualization of EWOC principle d = 8 mg Probability density 0.35 > 0.25 Too toxic! UD TT OD 0.25 0.33 0.00 0.16 0.50 0.75 1.00 DLT probability ( π d ) 10/27

More than one dosing regimen • Weekly and daily regimens • BLRM does NOT allow! 11/27

More than one dosing regimen • Weekly and daily regimens • BLRM does NOT allow! • Ad-hoc approach: BLRM MAP • BLRM is used for the first regimen. • Meta-analytic-predictive (MAP) prior is derived from analysis based on first regimen. • Then, MAP prior is used to analyse the second regimen. 11/27

TITE-PK

Time-to-event pharmacokinetic model (TITE-PK) • Time-to-first-DLTs model using an exposure measure • Exposure measure based on drug pharmacokinetics • Use of planned dosing regimen and known PK parameters 5 mg/daily 20 mg/weekly 0.006 0.006 0.004 0.004 E(t) E(t) 0.002 0.002 0.000 0.000 0 1 2 3 4 0 1 2 3 4 time (weeks) time (weeks) 12/27

Time-to-event pharmacokinetic model (TITE-PK) (cont.) • A time-varying Poisson process • Hazard is given by h ( t ) = β E ( t ) = ⇒ H ( t ) = β AUC E ( t ) . • Follow-up time until the end of cycle 1 ( t ∗ ) • Dosing regimen (amount d and frequency f ) • End-of-cycle 1 DLT probability P ( T ≤ t ∗ | d , f ) = 1 − S ( t ∗ | d , f ) S ( t ∗ | d , f ) = exp ( − H ( t ∗ | d , f )) 13/27

Time-to-event pharmacokinetic model (TITE-PK) (cont.) • E ( t ) is scaled using reference regimen ( d ∗ and f ∗ ) at t ∗ : AUC E ( t ∗ | d ∗ , f ∗ ) = 1 . • Analogous to reference dose in the BLRM • Crucial for prior specification 14/27

Time-to-event pharmacokinetic model (TITE-PK) (cont.) • E ( t ) is scaled using reference regimen ( d ∗ and f ∗ ) at t ∗ : AUC E ( t ∗ | d ∗ , f ∗ ) = 1 . • Analogous to reference dose in the BLRM • Crucial for prior specification • TITE-PK is implemented in Stan . 14/27

Simulations

Settings • Comparison of the performance: TITE-PK vs BLRM • Data generation under each model separately • Using exactly same decision criteria r d ≥ 6 where d is declared as the MTD, etc. 15/27

Settings • Comparison of the performance: TITE-PK vs BLRM • Data generation under each model separately • Using exactly same decision criteria r d ≥ 6 where d is declared as the MTD, etc. • Consider two different set of scenarios • Only daily regimen 1, 2, 4, 8, 15, 30 mg/daily • Firstly weekly regimen, then daily regimen 8, 16, 32, 64, 115, 230 mg/weekly • Choice of prior: Matching a priori DLT probabilities 15/27

Dose-toxicity scenarios Daily regimen 1.00 0.75 DLT probabilities 0.50 0.33 0.25 0.16 0.00 1 2 4 8 15 30 Dose (mg/daily) 16/27

Dose-toxicity scenarios Daily regimen Scenario 1.00 1) 75% less toxic 2) 25% less toxic 3) Prior medians 0.75 4) 25% more toxic DLT probabilities 5) 75% more toxic 6) Very toxic 0.50 0.33 0.25 0.16 0.00 1 2 4 8 15 30 Dose (mg/daily) 17/27

Performance measures I. Average proportion of patients in UD ( < 16 % ) 18/27

Performance measures I. Average proportion of patients in UD ( < 16 % ) II. Average proportion of patients in TT (16 % − 33 % ) III. Average proportion of patients in OD ( ≥ 33 % ) 18/27

Performance measures I. Average proportion of patients in UD ( < 16 % ) II. Average proportion of patients in TT (16 % − 33 % ) III. Average proportion of patients in OD ( ≥ 33 % ) IV. Proportion of trials with MTD in UD ( < 16 % ) V. Proportion of trials with MTD in TT (16 % − 33 % ) VI. Proportion of trials with MTD in OD ( ≥ 33 % ) 18/27

Performance measures I. Average proportion of patients in UD ( < 16 % ) II. Average proportion of patients in TT (16 % − 33 % ) III. Average proportion of patients in OD ( ≥ 33 % ) IV. Proportion of trials with MTD in UD ( < 16 % ) V. Proportion of trials with MTD in TT (16 % − 33 % ) VI. Proportion of trials with MTD in OD ( ≥ 33 % ) Stopped (eg. too toxic) Average N Average DLT 18/27

Results Measure III: Average proportion of patients in OD ( ≥ 33 % ) III 30 20 value 10 0 c c s c c c i i n i i i x x x x x a o o o o o i t t d t t t s s e e e y s s r r r m o o e e e V l l m m r % % o ) i % % 6 r 5 5 P 7 2 5 5 2 7 ) ) ) 3 1 2 ) ) 4 5 scenario 19/27

Results: TITE-PK vs BLRM Measure III: Average proportion of patients in OD ( ≥ 33 % ) III Method 30 BLRM TITE−PK value 20 10 0 c c s c c c i i n i i i x x x x x a o o o o o i t t d t t t s s e e e y s s r r r m o o e e e V l l m m r % % o ) i % % 6 r 5 5 P 7 2 5 5 2 7 ) ) ) 3 1 2 ) ) 4 5 scenario 20/27

I II III 80 60 30 60 40 20 40 20 10 20 0 0 0 IV V VI 15 80 60 60 10 40 40 5 20 20 0 0 0 Stopped AveDLT AveN 30 4 60 3 BLRM 20 40 2 TITE−PK 10 20 1 0 0 0 c c s c c c c c s c c c c c s c c c i i n i i i i i n i i i i i n i i i x x x x x x x x x x x x x x x o o a o o o o o a o o o o o a o o o i i i t t d t t t t t d t t t t t d t t t s s e e e y s s e e e y s s e e e y s s r r r s s r r r s s r r r m m m e e o o e e e o o e e e o o e m m V m m V m m V l l l l l l r r r % % % % % % o ) o ) o ) i % % 6 i % % 6 i % % 6 r r r 5 5 5 5 5 5 P P P 7 2 5 5 7 2 5 5 7 2 5 5 ) 2 7 ) 2 7 ) 2 7 ) ) ) ) ) ) 3 3 3 1 2 1 2 1 2 ) ) ) ) ) ) 4 5 4 5 4 5 I. Prop of patients in UD II. Prop of patients in TT III. Prop of patients in OD 21/27 IV. Trials with MTD in UD V. Trials with MTD in TT VI. Trials with MTD in OD