1. Jud Blatchford, PhD BIOM 6649 – Clinical Trials April 9th, 2015
Phase I Trial Designs
Jud Blatchford, PhD
BIOM 6649 – Clinical Trials
April 9th, 2015

2. Table of Contents Orientation Introduction
Components of a Phase I Trial
Phase I Trial Designs
Rule-Based Designs
Statistical Designs
References
Jud Blatchford, PhD
Phase I Trial Designs

3. Orientation
Jud Blatchford, PhD
Phase I Trial Designs

4. 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
Phase I Trial Designs

5. 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
Phase I Trial Designs

6. 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
Phase I Trial Designs

7. Introduction
Jud Blatchford, PhD
Phase I Trial Designs

8. 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
Phase I Trial Designs

9. Introduction Underlying Assumptions
The drug kills both cancer cells and other cells
The effect is dose-dependent, therefore:
The efficacy of the drug increases with the dose
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
Phase I Trial Designs

10. 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
Phase I Trial Designs

11. Components of a Phase I Trial
Jud Blatchford, PhD
Phase I Trial Designs

12. 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
Phase I Trial Designs

13. 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
Phase I Trial Designs

14. 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
Phase I Trial Designs

15. 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
Phase I Trial Designs

16. 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
Phase I Trial Designs

17. 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
Phase I Trial Designs

18. 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
Phase I Trial Designs

19. The Fibonacci Sequence
Term (n)
Value (fn)
Ratio (fn / fn-1) 1 - 2
1.000 3
2.000 4
1.500 5
1.667 6 8
1.600 7 13
1.625 21
1.615 9 34
1.619 10 55
1.618 11 89
Jud Blatchford, PhD
Phase I Trial Designs

20. The Golden Ratio
Jud Blatchford, PhD
Phase I Trial Designs

21. The Golden Ratio
Jud Blatchford, PhD
Phase I Trial Designs

22. Fibonacci Sequence in Nature
Jud Blatchford, PhD
Phase I Trial Designs

23. Spirals in a Pine Cone Clockwise from Center
Counter-clockwise from Center
Jud Blatchford, PhD
Phase I Trial Designs

24. Modified Fibonacci Dose Escalation (MFDE)
Term (n)
Ratio (fn / fn-1)
Ratio (fn / f1)
Fib. Seq.
Comparison 1 -
1.00
Similar 2
2.00 3
1.67
3.33 4
1.50
5.00 5
1.40
7.00 6
1.33
9.33 8 7
12.44 13
16.59 21
Conservative 9
22.12 34 10
29.50 55 11
39.33 89
Jud Blatchford, PhD
Phase I Trial Designs

25. 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

26. Phase I Trial Designs
Jud Blatchford, PhD
Phase I Trial Designs

27. Rule-Based Designs Traditional Escalation Rule
Variations of the Traditional Escalation Rule
Best of 5 Rule
Up-and-Down Designs
2-Stage Designs
Jud Blatchford, PhD
Phase I Trial Designs

28. Traditional Escalation Rule (TER)
3 subjects receive dose di
0 DLTs
1 DLT
2 or 3 DLTs
Escalate - 3 subjects receive dose di+1
3 more subjects at dose di
Stop escalation
De-escalate to di-1
0 DLTs
(1/6 with DLT)
1—3 DLTs
(≥ 2/6 with DLT)
Escalate - 3 subjects receive dose di+1
Stop escalation
De-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.
Jud Blatchford, PhD
Phase I Trial Designs

29. Evaluating the TER Benefits Criticisms 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
Many subjects treated at low, ineffective doses
At least 2 subjects treated at level above MTD
The true MTD is underestimated
Jud Blatchford, PhD
Phase I Trial Designs

30. 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
Phase I Trial Designs

31. Best of 5 Rule 3 subjects receive dose di 0 DLTs 1 or 2 DLTs 3 DLTs
Escalate to dose di+1
1 more at dose di
Stop escalation
1/4 with DLT
2/4 with DLTs
3/4 with DLTs
Escalate to dose di+1
1 more at dose di
Stop escalation
2/5 with DLTs
3/5 with DLTs
Escalate to dose di+1
Stop escalation
MTD is the dose prior to the dose on which escalation stopped.
Jud Blatchford, PhD
Phase I Trial Designs

32. Up-and-Down Design (UaD)
1 subject receives dose di
0 DLT
1 DLT
Escalate to dose di+1
De-escalate to 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.
Jud Blatchford, PhD
Phase I Trial Designs

33. Storer’s C Design (UaD-C)
1 subject receives dose di
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
Perform UaD for a pre-specified number of subjects (j).
MTD is the dose that would be assigned to the j+1st subject.
Jud Blatchford, PhD
Phase I Trial Designs

34. Storer’s Two-Stage BC Design (UaD-BC)
1 subject receives dose di
0 DLT
1 DLT
Escalate to dose di+1
De-escalate to di-1
Stage 2
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
Perform UaD for a pre-specified number of subjects (j).
MTD is the dose that would be assigned to the j+1st subject.
Jud Blatchford, PhD
Phase I Trial Designs

35. Accelerated Titration Designs
Extension by Simon of Storer’s work
Design 1: TER
Designs 2—4
Stage 1: Single subjects until first DLT or second grade 2 AE
Stage 2: TER
Design 2: Toxicities observed in first cycle only
Design 3: Toxicities may be observed in any cycle
Design 4: Same as 3 except escalation factor is 2.0
Jud Blatchford, PhD
Phase I Trial Designs

36. Statistical Designs
Dose escalation guided by a statistical model of the relationship between dose and toxic response
Continual Reassessment Method
Modifications to the CRM
2-Stage CRM Designs
TITE-CRM
Jud Blatchford, PhD
Phase I Trial Designs

37. 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
Phase I Trial Designs

38. 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
Phase I Trial Designs

39. Considerations Number of Dose Levels Initial Estimates of Toxicity
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
The estimates should bound the target rate (θ)
The CRM is not robust when doses tested do not induce toxicity
Jud Blatchford, PhD
Phase I Trial Designs

40. Choosing a Dose-Response Function
Logistic Function
Logistic Regression
Let p = Pr{DLT}
ln 𝑝 1−𝑝 = 𝛽0+𝛽1(𝐷𝑜𝑠𝑒)
Solving for p we have:
𝑝= 𝑒 𝛽0+𝛽1(𝐷𝑜𝑠𝑒) 1+ 𝑒 𝛽0+𝛽1(𝐷𝑜𝑠𝑒)
One-parameter model:
𝑝= 𝑒 3+α(𝐷𝑜𝑠𝑒) 1+ 𝑒 3+α(𝐷𝑜𝑠𝑒)
Jud Blatchford, PhD
Phase I Trial Designs

41. Choosing a Dose-Response Function
Hyperbolic Tangent Function
Scaled Tanh Function
𝑇𝑎𝑛ℎ= 𝑒 𝑥 − 𝑒 −𝑥 𝑒 𝑥 + 𝑒 −𝑥 = 𝑒 2𝑥 −1 𝑒 2𝑥 +1
Pr{DLT}= 𝑒 2(𝐷𝑜𝑠𝑒) −1 2𝑒 2(𝐷𝑜𝑠𝑒) −α
Jud Blatchford, PhD
Phase I Trial Designs

42. Choosing a Dose-Response Function
CDF of Normal Distribution
P{DLT}= 1 2𝜋 𝜎 −∞ 𝐷𝑜𝑠𝑒 𝑒 − (𝑥−𝜇) 2 2𝜎 2 𝑑𝑥
Jud Blatchford, PhD
Phase I Trial Designs

43. 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
Phase I Trial Designs

44. Distributions of DLT Occurrence By Dose
Priors for Subject 1
Priors for Subject 26
High degree of overlap of probabilities between doses
Separation between dose levels becoming clearer
Jud Blatchford, PhD
Phase I Trial Designs

45. Evaluating the CRM Benefits Criticisms
Few subjects are treated at low, ineffective doses
Subjects are treated at doses believed at the time to be the most efficacious, yet safe
Starting dose is too high
Dose escalation is too aggressive
Trial length is too long
Jud Blatchford, PhD
Phase I Trial Designs

46. 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
Phase I Trial Designs

47. “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
Phase I Trial Designs

48. 2-Stage CRM Designs Stage 1: TER Stage 2: CRM
“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
Phase I Trial Designs

49. 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
Phase I Trial Designs

50. Example of a TITE-CRM Trial
Subject accrual is instantaneous
The majority of doses administered are near MTD
Jud Blatchford, PhD
Phase I Trial Designs

51. 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
Phase I Trial Designs

52. 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
Phase I Trial Designs

53. 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
Phase I Trial Designs

54. 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
Phase I Trial Designs

55. 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
Phase I Trial Designs

56. 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 = /0.58 = 12.18
Jud Blatchford, PhD
Phase I Trial Designs

57. 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
Phase I Trial Designs

58. References
Jud Blatchford, PhD
Phase I Trial Designs

59. References
1989. Storer BE. Design and Analysis of Phase I Clinical Trials. Biometrics, 45, 925— O’Quigley J, Pepe M, and Fisher L. Continual Reassessment Method: A Practical Design for Phase I Clinical Trials in Cancer. Biometrics, 46, 33— 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— 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— Faries D. Practical Modifications of the Continual Reassessment Method for Phase I Cancer Clinical Trials. Journal of Biopharmaceutical Statistics, 4 (2), 147— Piantadosi S, and Liu G. Improved Designs for Dose Escalation Studies Using Pharmacokinetic Measurements. Statistics in Medicine, 15, 1605— 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— Durham SD, Flournoy N, and Rosenberger WF. A Random Walk Rule for Phase I Clinical Trials. Biometrics, 53, 745—760.
Jud Blatchford, PhD
Phase I Trial Designs

60. 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— Friedman LM, Furberg CD, and DeMets DL. Fundamentals of Clinical Trials. Springer Whitehead J, and Williamson D. Bayesian Decision Procedures Based on Logistic Regression Models for Dose-Finding Studies. Journal of Biopharmaceutical Statistics, 8 (3), 445— 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— Cheung YK, and Chappell R. Sequential Designs for Phase I Clinical Trials with Late-Onset Toxicities. Biometrics, 56, 1177— 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— 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— 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
Phase I Trial Designs

61. References (Continued)
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— Potter PM. Adaptive Dose Finding for Phase I Clinical Trials of Drugs Used for Chemotherapy of Cancer. Statistics in Medicine, 21, 1805— 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— Ivanova A, Montazer-Haghighi A, Mohanty SG, and Durham SD. Improved Up-and-Down Designs for Phase I Trials. Statistics in Medicine, 22, 69— Stylianou M, and Follmann DA. The Accelerated Biased Coin Up-and-Down Design in Phase I Trials. Journal of Biopharmaceutical Statistics, 14 (1), 249— 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 New England Journal of Medicine, 352, 895— Crowley J, and Ankerst DP. Handbook of Statistics in Clinical Oncology. Chapman and Hall/CRC.
Jud Blatchford, PhD
Phase I Trial Designs

62. References (Continued)
Potter DM. Phase I Studies of Chemotherapeutic Agents in Cancer Patients: A Review of the Designs. Journal of Biopharmaceutical Statistics, 16, 579—604. DOI: /
Jud Blatchford, PhD
Phase I Trial Designs