phase i trial designs jud blatchford, phd biom 6649 – clinical trials april 9 th, 2015

Phase I Trial Designs

Jud Blatchford, PhD

BIOM 6649 – Clinical Trials

April 9th, 2015

Phase I Trial Designs 2

Table of Contents

1. Orientation2. Introduction3. Components of a Phase I Trial4. Phase I Trial Designs

A. Rule-Based DesignsB. Statistical Designs

5. References

Jud Blatchford, PhD


ORIENTATION

Jud Blatchford, PhD


Orientation

• Features of a Clinical Trial (CT)◦ Study of human beings◦ Prospective◦ Uses an intervention (i.e. changes some aspect of the

subjects)◦ Protects the safety of the subjects◦ Follows an approved protocol

Jud Blatchford, PhD


Orientation

• Phases of Clinical Trials◦ Phase I – First time an experimental drug or treatment is

tested in humans to examine how well the drug is tolerated◦ Phase II – Trials designed to examine if the drug or

treatment has a biological treatment effect◦ Phase III – Trials designed to assess the treatment effect on

a clinically meaningful endpoint◦ Phase IV – Post-marketing studies to gain additional

information regarding the safety of the drug or treatment

Jud Blatchford, PhD


Orientation

• Components of Study Design◦ Rationale – Establishing a legitimate reason for the study◦ Design – Detailed description of what treatments will be

administered, how they are administered, including a timeline of administration

◦ Subjects – Determining the group to be studied and how they will be assigned to treatment groups

◦ Data – Endpoint(s), obtaining data, and QA◦ Sample Size Justification – Ensuring the study will be able

to answer the scientific question with adequate power◦ Study Closure – Archiving study data, analysis files

Jud Blatchford, PhD


INTRODUCTION

Jud Blatchford, PhD


Introduction

• Phase I Clinical Trials◦ An experimental drug, treatment, chemotherapeutic agent,

cytotoxic agent, is studied—hereafter referred to as “drug”◦ Primary Goal: Safety

Investigate whether the new drug or combination of drugs can be administered safely to subjects

Investigate optimal dosing and administration of drug◦ Secondary Goal: Efficacy

Offer a treatment option to subjects who have failed other treatment regimens

Jud Blatchford, PhD


Introduction

• Underlying Assumptions◦ The drug kills both cancer cells and other cells◦ The effect is dose-dependent, therefore:

1. The efficacy of the drug increases with the dose2. The toxicity of the drug increases with the dose

◦ Logically, it would be optimal to give the subjects the highest dose of a drug that can be administered without unacceptable toxicity

◦ Fundamental Question: What is this dose?

Jud Blatchford, PhD


Maximum Tolerated Dose (MTD)

• Definition of MTD:◦ The highest dose without observing an unacceptable rate

of toxicity

• Aliases:◦ Recommended Phase 2 Dose (RP2D)◦ Phase 2 Recommended Dose (P2RD)

Jud Blatchford, PhD


COMPONENTS OF A PHASE I TRIAL

Jud Blatchford, PhD


Components of a Phase I Trial

• Definition of a Dose-Limiting Toxicity (DLT)◦ Clarify time-frame for experiencing a DLT

• Dose Levels◦ How many dose levels will be tested?◦ What will the smallest dose be?◦ What will the starting dose be?

• Subjects◦ How many subjects will be tested?◦ Will single subjects or cohorts be tested at each dose?

• What dose-escalation scheme will be employed?Jud Blatchford, PhD


Definition of a DLT

• DLTs are typically defined using the National Cancer Institute’s (NCI) Common Terminology Criteria for Adverse Events (CTCAE).

• DLTs are often grade ≥ 3 non-hematological and grade ≥ 4 hematological toxicities, which are definitely, probably, or possibly related to the drug.

• CTCAE Grades◦ 0 – No AE◦ 1 – Mild◦ 2 – Moderate◦ 3 – Severe◦ 4 – Life threatening◦ 5 – Death

• Degrees of Related◦ Unrelated◦ Unlikely◦ Possibly◦ Probably◦ Definitely

Jud Blatchford, PhD


Definition of a DLT

• The length of observation within which a DLT occurrence is “counted” should be explicitly stated in the protocol

• Typical lengths used are the first cycle of chemotherapy (often 3 weeks)

• Weight the trade-off between observation time for a DLT and efficiency in enrolling subjects

Jud Blatchford, PhD


Choosing the Starting Dose

• Goals:◦ Dose high enough to have chance of efficacy◦ Dose low enough to avoid a DLT

• Use data from animal pre-clinical studies• Scale dose by body surface area (mg/m2)• Studies that aren’t “first-in-human” studies may

be informed from previous studies using the same drug

Jud Blatchford, PhD


Choosing the Starting Dose

• Choices Used:◦ First find dose that is lethal in 10% of mice (LD10)

Standard starting dose was 10% of this dose (MELD10), if no grade 4+ AEs observed in other species (rats, dogs, etc.)

◦ Find the highest dose for which the most sensitive animals investigated had no AEs

Starting dose is 1/3 of this level (scaled)◦ Find the minimal dose for which any toxicity is seen (TDL)

Starting dose is 1/3 of the TDL

Jud Blatchford, PhD


Choosing the Number of Dose Levels

• Testing more dose levels to accurately estimate the MTD creates a more cumbersome trial, and may require more subjects

• Common number of levels is 4 to 7• Observed number has ranged from 3 to 14

Jud Blatchford, PhD


Choosing the Dose Levels

• Desire to progress through possible doses in a quick (e.g. exponential) manner

• Ethical considerations should guide the dose escalation scheme used

• Linear sequence of numbers may be inefficient◦ 20, 40, 60, 80, 100, 120, 140, …

• Famous sequence of increasing numbers:◦ 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, …

Jud Blatchford, PhD


The Fibonacci Sequence

Term (n) Value (fn) Ratio (fn / fn-1)

1 1 -

2 1 1.000

3 2 2.000

4 3 1.500

5 5 1.667

6 8 1.600

7 13 1.625

8 21 1.615

9 34 1.619

10 55 1.618

11 89 1.618

Jud Blatchford, PhD


The Golden Ratio

Jud Blatchford, PhD


Fibonacci Sequence in Nature

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Spirals in a Pine Cone

Clockwise from Center Counter-clockwise from Center

Jud Blatchford, PhD


Modified Fibonacci Dose Escalation (MFDE)

Term (n) Ratio (fn / fn-1) Ratio (fn / f1) Fib. Seq. Comparison

1 - 1.00 1

Similar

2 2.00 2.00 1

3 1.67 3.33 2

4 1.50 5.00 3

5 1.40 7.00 5

6 1.33 9.33 8

7 1.33 12.44 13

8 1.33 16.59 21

Conservative9 1.33 22.12 34

10 1.33 29.50 55

11 1.33 39.33 89

Jud Blatchford, PhD


Ethical Considerations

• Approach the MTD from below (under-estimates MTD)

◦ Bracketing the MTD is unbiased and more efficient

• Expected efficacy is minimal◦ Historical response rate is 11%; temp. stable rate is 34%◦ 40% expect a cure

• Subjects suffer significant toxicity◦ Rate of grade 4 toxicity is 14%; death rate is 0.5%

• What subjects are told is very important

Jud Blatchford, PhD


PHASE I TRIAL DESIGNS

Jud Blatchford, PhD


Rule-Based Designs

1. Traditional Escalation Rule2. Variations of the Traditional Escalation Rule3. Best of 5 Rule4. Up-and-Down Designs5. 2-Stage Designs

Jud Blatchford, PhD


Traditional Escalation Rule (TER)

3 subjects receive dose di

Jud Blatchford, PhD

2 or 3 DLTs1 DLT0 DLTs

3 more subjects at dose di

0 DLTs(1/6 with DLT)

1—3 DLTs(≥ 2/6 with DLT)

Stop escalationDe-escalate to di-1

Escalate - 3 subjects receive dose di+1

Escalate - 3 subjects receive dose di+1

Stop escalationDe-escalate to di-1

De-escalate until a level is reached where at least 6 subjects are treated and at most 1 DLT occurs.MTD is the highest dose where at least 6 subjects were treated with at most 1 DLT.


Evaluating the TER

Benefits

• Conservative escalation• Ease of implementation

◦ Rules regarding dose assignment are clear

◦ Statistical models not fit after each subject

• Design is robust• Will arrive at reasonable

estimate of MTD

Criticisms

• Many subjects treated at low, ineffective doses

• At least 2 subjects treated at level above MTD

• The true MTD is underestimated

Jud Blatchford, PhD


Variations to TER

• After escalation stops, fill out all lower levels until at least 6 subjects are treated at each level

• Treat subjects at a dose level between the level where escalation stopped and the next lower level

Jud Blatchford, PhD


Best of 5 Rule

Jud Blatchford, PhD

3 subjects receive dose di

1 or 2 DLTs0 DLTs 3 DLTs

Stop escalation1 more at dose diEscalate to dose di+1

1/4 with DLT 3/4 with DLTs2/4 with DLTs

Escalate to dose di+1 Stop escalation1 more at dose di

3/5 with DLTs2/5 with DLTs

Escalate to dose di+1 Stop escalation

MTD is the dose prior to the dose on which escalation stopped.


Up-and-Down Design (UaD)

1 subject receives dose di

Jud Blatchford, PhD

0 DLT 1 DLT

Escalate to dose di+1

Perform UaD for a pre-specified number of subjects (j).MTD is the dose that would be assigned to the j+1st subject.

De-escalate to di-1


Storer’s C Design (UaD-C)


Jud Blatchford, PhD

0 DLT 1 DLT

If 2 consecutive subjects with 0 DLT, escalate to dose di+1;

else give dose di


De-escalate to di-1


Storer’s Two-Stage BC Design (UaD-BC)


Jud Blatchford, PhD

0 DLT 1 DLT

Escalate to dose di+1


De-escalate to di-1

1 subject receives dose di-1

0 DLT 1 DLT

If 2 consecutive subjects with 0 DLT, escalate to dose di; else give dose di-1

De-escalate to di-2

Stag

e 1

Stag

e 2


Accelerated Titration Designs

Extension by Simon of Storer’s workDesign 1: TERDesigns 2—4

Stage 1: Single subjects until first DLT or second grade 2 AEStage 2: TER

Design 2: Toxicities observed in first cycle onlyDesign 3: Toxicities may be observed in any cycleDesign 4: Same as 3 except escalation factor is 2.0

Jud Blatchford, PhD


Statistical Designs

Dose escalation guided by a statistical model of the relationship between dose and toxic response1. Continual Reassessment Method2. Modifications to the CRM3. 2-Stage CRM Designs4. TITE-CRM

Jud Blatchford, PhD


Continual Reassessment Method (CRM)

• First proposed by O’Quigley in 1990• Subjects are enrolled individually• A dose-toxicity function is assumed

◦ f(d | α) = Pr{DLT | α}

• After each patient completes observation, the estimate of α is updated

• Strategy is to assign the dose closest to the estimated MTD to each subject

Jud Blatchford, PhD


Considerations for the CRM

• Number of dose levels• Initial estimates of toxicity rates at each dose level• Target rate of DLT (θ)• Dose-toxicity function• Escalation restrictions• Number of subjects to be treated

Jud Blatchford, PhD


Considerations

Number of Dose Levels

• Typically between 3 and 8• In general, as the number of

dose levels in the trial increases, the number of subjects needed to accurately estimate the MTD will increase

Initial Estimates of Toxicity

• The estimates should bound the target rate (θ)

◦ The CRM is not robust when doses tested do not induce toxicity

Jud Blatchford, PhD


Choosing a Dose-Response Function

Logistic Function Logistic Regression

Jud Blatchford, PhD

Let p = Pr{DLT}

Solving for p we have:

One-parameter model:



Hyperbolic Tangent Function

=

Scaled Tanh Function

Pr{DLT}

Jud Blatchford, PhD



CDF of Normal Distribution

P{DLT}

Jud Blatchford, PhD


The Method of CRM

• Dose-toxicity function and θ are chosen a-priori• Function is re-fit (i.e. new estimate of α is

obtained) after each subject’s observed toxicity◦ New function is determined from the a-priori function and

the vector of observed toxicities◦ Curve shifts to the right without toxicity; left with toxicity

• Next subject is treated at the dose level whose Pr{DLT} is closest to θ

Jud Blatchford, PhD


Distributions of DLT Occurrence By Dose

Priors for Subject 1 Priors for Subject 26

• Separation between dose levels becoming clearer

Jud Blatchford, PhD

• High degree of overlap of probabilities between doses


Evaluating the CRM

Benefits

• Few subjects are treated at low, ineffective doses

• Subjects are treated at doses believed at the time to be the most efficacious, yet safe

Criticisms

• Starting dose is too high• Dose escalation is too

aggressive• Trial length is too long

Jud Blatchford, PhD


Modified CRM

• Start at the lowest dose level under consideration• Enroll two or three subjects at each cohort• Constrain dose escalation to increase by at most

one dose level

Jud Blatchford, PhD


“Practical” CRM

• Proposed by Piantadosi• Based on pre-clinical toxicity data:

◦ Choose dose that would produce low (10%) rate of DLT◦ Choose dose that would produce high (90%) rate of DLT◦ Estimate dose/toxicity curve that fits these 2 points

• Use the dose/toxicity curve to find dose for θ• Treat three subjects at this level, then re-estimate

the dose-toxicity curve, dose for θ, and tx 3 more• Repeat until target dose changes by < 10%

Jud Blatchford, PhD


2-Stage CRM Designs

• Stage 1: TER◦ “2 + 2” is a more common first stage than “3 + 3”◦ Continue until first toxicity is observed

• Stage 2: CRM◦ After first toxicity, fit the dose-response curve using the

toxicity data accrued thus far◦ Choose dose for next cohort of 2 as dose with estimated

rate of DLT closest to θ

Jud Blatchford, PhD


Time-to-Event CRM (TITE-CRM)

• Builds on the CRM described thus far• Uses information from subjects accrued, even if

they haven’t finished observation period◦ Subjects with DLT are given full weight◦ Subjects without DLT are given weight t/T.

• Allows subjects to be enrolled without waiting for prior cohorts to finish

◦ Benefits studies with delayed toxicity (e.g. radiation studies)

Jud Blatchford, PhD


Example of a TITE-CRM Trial

• Subject accrual is instantaneous• The majority of doses administered are near MTD

Jud Blatchford, PhD


Additional TITE-CRM Considerations

• Choice of weight function◦ Uniform toxicities may use a linear function◦ Expecting late toxicities may use a convex function◦ Expecting early toxicities may use a concave function

• Setting a Margin (i.e. upper limit) on toxicity◦ If θ = 0.20 and Margin = 0.05, dose for next subject will be

dose closest to 0.20 and not greater than 0.25

• Determine cumulative time exposure (B) before allowing escalation (e.g. B = 2)

Jud Blatchford, PhD


Design Comparisons

• Fitting a model to the data will improve the accuracy of the MTD found by rule-based designs

• Model-guided designs only perform well if assumptions are met (θ in range of doses tested)

• Conflicting results when designs compared• Few comparisons made on “level playing field”• Both rule-based and model-guided designs are in

common use, for good reason

Jud Blatchford, PhD


Important Future Work

• “Individualized” Designs◦ Development of designs allowing for within-subject dose

escalation◦ Development of designs for targeted agents

• Designs for trials expecting minimal toxicity

Jud Blatchford, PhD


Pharmacokinetic Profiles

• Often of interest in a phase 1 study• Studies how the body processes the drug• Parameters of interest are typically:

◦ Cmax – Maximum concentration of drug◦ Tmax – Time until the maximum concentration of drug◦ λ – Elimination constant – describes rate of loss from body◦ T1/2 – Half-life of the drug◦ AUC – Area under the concentration curve

Jud Blatchford, PhD


Calculation of PK Parameters

• Cmax & Tmax – Directly from table of results• λ = Taken from regression of ln(C) on time• T1/2

◦ Create exponential decay equation from above regression◦ Solve for T when the concentration is half of reference amt

• AUC = AUC0-t + AUCt-∞

◦ AUC0-t – Use trapezoidal rule◦ AUCt-∞ = Ct / λ

Jud Blatchford, PhD


Example Calculations

• From regression of ln(C) on time we get:◦ ln (C) = 2.25 – 0.58 T, so λ = 0.58 (not -0.58)◦ C = exp(2.25) * exp(-0.58)T

◦ C = 9.5(-.56)T

◦ 4.75 = 9.5(-0.56)T to solve for T1/2

◦ ½ = -0.56T

◦ ln(1/2) = T*ln(-0.56)◦ T = 1.19 so T1/2 = 1.19 hours

• AUC = 12.03 + 0.06/0.58 = 12.18

Jud Blatchford, PhD


PK Design Considerations

• Sampling Times◦ Important to have accurate estimates of Cmax

◦ Cluster several times around expected Tmax

• Sampling Period◦ Important to have accurate estimate of λ◦ US FDA requires times to capture at least 3 half-lives after

Tmax

• Bioequivalence Trials◦ Create 90% CIs for ln[µ(A)] - ln[µ(B)], µ = PK parameter◦ Check if all CIs are within 0.80 to 1.25 range

Jud Blatchford, PhD


REFERENCES

Jud Blatchford, PhD


References

1989. Storer BE. Design and Analysis of Phase I Clinical Trials. Biometrics, 45, 925—937.1990. O’Quigley J, Pepe M, and Fisher L. Continual Reassessment Method: A Practical Design for

Phase I Clinical Trials in Cancer. Biometrics, 46, 33—48.1993. Korn EL, and Simon R. Using the Tolerable-Dose Diagram in the Design of Phase I

Combination Chemotherapy Trials. Journal of Clinical Oncology, 11 (4), 794—801.

1993. Mick R, and Ratain MJ. Model-Guided Determination of Maximum Tolerated Dose in Phase I Clinical Trials: Evidence for Increased Precision. Journal of the National Cancer Institute, 85 (3), 217—223.

1994. Faries D. Practical Modifications of the Continual Reassessment Method for Phase I Cancer Clinical Trials. Journal of Biopharmaceutical Statistics, 4 (2), 147—164.

1996. Piantadosi S, and Liu G. Improved Designs for Dose Escalation Studies Using Pharmacokinetic Measurements. Statistics in Medicine, 15, 1605—1618.

1996. Smith TL, Lee JJ, Kantarjian HM, Legha SS, and Raber MN. Design and Results of Phase I Cancer Clinical Trials: Three-Year Experience at M. D. Anderson Cancer Center. Journal of Clinical Oncology, 14 (1), 287—295.

1997. Durham SD, Flournoy N, and Rosenberger WF. A Random Walk Rule for Phase I Clinical Trials. Biometrics, 53, 745—760.

Jud Blatchford, PhD


References (Continued)

1997. Simon R, Freidlin B, Rubinstein L, Arbuck SG, Collins J, and Christian MC. Accelerated Titration Designs for Phase I Clinical Trials in Oncology. Journal of the National Cancer Institute, 89 (15), 1138—1147.

1998. Friedman LM, Furberg CD, and DeMets DL. Fundamentals of Clinical Trials. Springer.1998. Whitehead J, and Williamson D. Bayesian Decision Procedures Based on Logistic

Regression Models for Dose-Finding Studies. Journal of Biopharmaceutical Statistics, 8 (3), 445—467.

1999. Reiner E, Paoletti X, and O’Quigley J. Operating Characteristics of the Standard Phase I Clinical Trial Design. Computational Statistics and Data Analysis, 30, 303—315.

2000. Cheung YK, and Chappell R. Sequential Designs for Phase I Clinical Trials with Late-Onset Toxicities. Biometrics, 56, 1177—1182.

2000. Eisenhauer EA, O’Dwyer PJ, Christian M, and Humphrey JS. Phase I Clinical Trial Design in Cancer Drug Development. Journal of Clinical Oncology, 18 (3), 684—692.

2000. Wang O, and Faries DE. A Two-Stage Dose Selection Strategy in Phase I Trials with Wide Dose Ranges. Journal of Biopharmaceutical Statistics, 10 (3), 319—333.

2001. Lin Y, and Shih WJ. Statistical Properties of the Traditional Algorithm-Based Designs for Phase I Cancer Clinical Trials. Biostatistics, 2 (2), 203—215.

Jud Blatchford, PhD



2001. Ishizuka N, and Ohashi Y. The Continual Reassessment Method and Its Applications: A Bayesian Methodology for Phase I Cancer Clinical Trials. Statistics in Medicine, 20, 2661—2681.

2002. Potter PM. Adaptive Dose Finding for Phase I Clinical Trials of Drugs Used for Chemotherapy of Cancer. Statistics in Medicine, 21, 1805—1823.

2003. Agrawal M, and Emanuel EJ. Ethics of Phase I Oncology Studies: Reexamining the Arguments and Data. Journal of the American Medical Association, 290 (8), 1075—1082.

2003. Ivanova A, Montazer-Haghighi A, Mohanty SG, and Durham SD. Improved Up-and-Down Designs for Phase I Trials. Statistics in Medicine, 22, 69—82.

2004. Stylianou M, and Follmann DA. The Accelerated Biased Coin Up-and-Down Design in Phase I Trials. Journal of Biopharmaceutical Statistics, 14 (1), 249—260.

2005. Horstmann E, McCabe MS, Grochow L, Yamamoto S, Rubinstein L, Budd T, Shoemaker D, Emanuel EJ, and Grady C. Risks and Benefits of Phase I Oncology Trials, 1991 Through 1992. New England Journal of Medicine, 352, 895—904.

2006. Crowley J, and Ankerst DP. Handbook of Statistics in Clinical Oncology. Chapman and Hall/CRC.

Jud Blatchford, PhD



2006. Potter DM. Phase I Studies of Chemotherapeutic Agents in Cancer Patients: A Review of the Designs. Journal of Biopharmaceutical Statistics, 16, 579—604. DOI: 10.1080/10543400600860295.

Jud Blatchford, PhD

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