1. Phase I Trial Designs Jud Blatchford, PhD Colorado School of Public Health January 30 th, 2013

2. Table of Contents 1.Orientation 2.Introduction 3.Components of a Phase I Trial 4.Phase I Trial Designs A.Rule-Based Designs B.Statistical Designs 5.References Jud Blatchford, PhDPhase I Trial Designs2

3. ORIENTATION Jud Blatchford, PhDPhase I Trial Designs3

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, PhDPhase I Trial Designs4

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, PhDPhase I Trial Designs5

6. Orientation Components of Study Design ◦Rationale – Establishing a legitimate reason for the study ◦Design – Detailed description of what treatments will be administered, including a timeline of administration ◦Subjects – Determining the group to be studied and how they will be assigned to treatment groups ◦Data – Outcome measure(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, PhDPhase I Trial Designs6

7. INTRODUCTION Jud Blatchford, PhDPhase I Trial Designs7

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, PhDPhase I Trial Designs8

9. 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 dose 2.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, PhDPhase I Trial Designs9

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, PhDPhase I Trial Designs10

11. COMPONENTS OF A PHASE I TRIAL Jud Blatchford, PhDPhase I Trial Designs11

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, PhDPhase I Trial Designs12

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, PhDPhase I Trial Designs13

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, PhDPhase I Trial Designs14

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/m 2 ) Studies that aren’t “first-in-human” studies may be informed from previous studies using the same drug Jud Blatchford, PhDPhase I Trial Designs15

16. Choosing the Starting Dose Choices Used: ◦First find dose that is lethal in 10% of mice (LD 10 )  Standard starting dose was 10% of this dose (MELD 10 ), 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, PhDPhase I Trial Designs16

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, PhDPhase I Trial Designs17

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, PhDPhase I Trial Designs18

19. The Fibonacci Sequence Term (n)Value (f n )Ratio (f n / f n-1 ) 11- 211.000 322.000 431.500 551.667 681.600 7131.625 8211.615 9341.619 10551.618 11891.618 Jud Blatchford, PhDPhase I Trial Designs19

20. The Golden Ratio Jud Blatchford, PhDPhase I Trial Designs20

21. The Golden Ratio Jud Blatchford, PhDPhase I Trial Designs21

22. Modified Fibonacci Dose Escalation (MFDE) Term (n)Ratio (f n / f n-1 )Ratio (f n / f 1 )Fib. Seq.Comparison 1-1.001 Similar 22.00 1 31.673.332 41.505.003 51.407.005 61.339.338 71.3312.4413 81.3316.5921 Conservative 91.3322.1234 101.3329.5055 111.3339.3389 Jud Blatchford, PhDPhase I Trial Designs22

23. 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, PhDPhase I Trial Designs23

24. PHASE I TRIAL DESIGNS Jud Blatchford, PhDPhase I Trial Designs24

25. Rule-Based Designs 1.Traditional Escalation Rule 2.Variations of the Traditional Escalation Rule 3.Best of 5 Rule 4.Up-and-Down Designs 5.2-Stage Designs Jud Blatchford, PhDPhase I Trial Designs25

26. Traditional Escalation Rule (TER) 3 subjects receive dose d i Jud Blatchford, PhDPhase I Trial Designs26 2 or 3 DLTs 1 DLT 0 DLTs 3 more subjects at dose d i 0 DLTs (1/6 with DLT) 1—3 DLTs ( ≥ 2/6 with DLT) Stop escalation De-escalate to d i-1 Escalate - 3 subjects receive dose d i+1 Stop escalation De-escalate to d i-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.

27. 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, PhDPhase I Trial Designs27

28. 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, PhDPhase I Trial Designs28

29. Best of 5 Rule Jud Blatchford, PhDPhase I Trial Designs29 3 subjects receive dose d i 1 or 2 DLTs 0 DLTs 3 DLTs Stop escalation1 more at dose d i Escalate to dose d i+1 1/4 with DLT 3/4 with DLTs 2/4 with DLTs Escalate to dose d i+1 Stop escalation1 more at dose d i 3/5 with DLTs2/5 with DLTs Escalate to dose d i+1 Stop escalation MTD is the dose prior to the dose on which escalation stopped.

30. Up-and-Down Design (UaD) 1 subject receives dose d i Jud Blatchford, PhDPhase I Trial Designs30 0 DLT 1 DLT Escalate to dose d i+1 Perform UaD for a pre-specified number of subjects (j). MTD is the dose that would be assigned to the j+1 st subject. De-escalate to d i-1

31. Storer’s C Design (UaD-C) 1 subject receives dose d i Jud Blatchford, PhDPhase I Trial Designs31 0 DLT 1 DLT If 2 consecutive subjects with 0 DLT, escalate to dose d i+1 ; else give dose d i Perform UaD for a pre-specified number of subjects (j). MTD is the dose that would be assigned to the j+1 st subject. De-escalate to d i-1

32. Storer’s Two-Stage BC Design (UaD-BC) 1 subject receives dose d i Jud Blatchford, PhDPhase I Trial Designs32 0 DLT 1 DLT Escalate to dose d i+1 Perform UaD for a pre-specified number of subjects (j). MTD is the dose that would be assigned to the j+1 st subject. De-escalate to d i-1 1 subject receives dose d i-1 0 DLT 1 DLT If 2 consecutive subjects with 0 DLT, escalate to dose d i ; else give dose d i-1 De-escalate to d i-2 Stage 1 Stage 2

33. 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, PhDPhase I Trial Designs33

34. Statistical Designs Dose escalation guided by a statistical model of the relationship between dose and toxic response 1.Continual Reassessment Method 2.Modifications to the CRM 3.2-Stage CRM Designs 4.TITE-CRM Jud Blatchford, PhDPhase I Trial Designs34

35. 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, PhDPhase I Trial Designs35

36. 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, PhDPhase I Trial Designs36

37. 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, PhDPhase I Trial Designs37

38. Choosing a Dose-Response Function Logistic FunctionLogistic Regression Jud Blatchford, PhDPhase I Trial Designs38

39. Choosing a Dose-Response Function Hyperbolic Tangent FunctionScaled Tanh Function Jud Blatchford, PhDPhase I Trial Designs39

40. Choosing a Dose-Response Function CDF of Normal Distribution Jud Blatchford, PhDPhase I Trial Designs40

41. 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, PhDPhase I Trial Designs41

42. Distributions of DLT Occurrence By Dose Priors for Subject 1Priors for Subject 26 Separation between dose levels becoming clearer Jud Blatchford, PhDPhase I Trial Designs42 High degree of overlap of probabilities between doses

43. 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, PhDPhase I Trial Designs43

44. 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, PhDPhase I Trial Designs44

45. “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, PhDPhase I Trial Designs45

46. 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, PhDPhase I Trial Designs46

47. 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, PhDPhase I Trial Designs47

48. Example of a TITE-CRM Trial Subject accrual is instantaneous The majority of doses administered are near MTD Jud Blatchford, PhDPhase I Trial Designs48

49. 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, PhDPhase I Trial Designs49

50. 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, PhDPhase I Trial Designs50

51. 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, PhDPhase I Trial Designs51

52. REFERENCES Jud Blatchford, PhDPhase I Trial Designs52

53. 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, PhDPhase I Trial Designs53

54. 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, PhDPhase I Trial Designs54

55. 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—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, PhDPhase I Trial Designs55

56. References (Continued) 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, PhDPhase I Trial Designs56