Single and Double Agent Oncology Dose-Escalation Designs in East 6.4: A communication tool. Shaping the Future of Drug Development

Transcription

1 Shaping the Future of Drug Development Single and Double Agent Oncology Dose-Escalation Designs in East 6.4: A communication tool Pantelis Vlachos, Ph.D. Cytel Inc. Geneva

2 Agenda What s new? Single- Agent dose escaladon Demo/Workshop CombinaDon agent dose escaladon Review of methods Demo/Workshop Q&A Webinar - - May 4,

3 Phase I Dose- Escala2on Trials Assess dose- toxicity reladonship First- in- human (FIH) studies single agent Determine maximum tolerated dose (MTD) or recommended phase II dose (RP2D) Observe Dose limidng toxicides (DLTs) CombinaDon dose finding studies (Phase Ib) Same primary objecdve as FIH studies CombinaDon of two (or more) drugs AddiDon of a new drug to a registered treatment to increase efficacy Webinar - - May 4,

4 What s new? mtpi: User specified decision table More flexible stopping rules Accelerated DtraDon CombinaDon designs And much more! Webinar - - May 4,

5 mtpi: User specified decision table Webinar - - May 4,

6 More flexible stopping rules EscalaDons condnue undl declaradon of the MTD. This dose level must meet the following condidons: 1. At least 6 padents treated at this dose level 2. and a) The probability of targeted toxicity at this dose level exceed 50% or b) A minimum of 15 padents should have already been treated in the trial Webinar - - May 4,

7 Accelerated 2tra2on Webinar - - May 4,

8 Combina2on designs Increased popularity! Webinar - - May 4,

9 Single- Agent Dose Escala2on Webinar - - May 4,

10 Phase I dose- escala2on (general frame) Only consider trials with fixed doses. A sequence of K doses, d 1,d 2,,d K, as candidates. Dose i has a toxicity probability of p i (unknown). Monotonicity : p i < p i+1 Goal: to find the MTD, defined as the highest dose with toxicity rate lower (or close to) a fixed rate, p T, e.g., p T = Webinar - - May 4,

11 Rule- based vs Model- based Single Agent Designs Bayesian? Model dose- toxicity? (number of parameters) Probability Intervals? 3+3 No No No CRM Yes Yes (1) No BLRM Yes Yes (2) Yes mtpi Yes No Yes Double Agent Designs comb2blrm Yes Yes (5) Yes PIPE Yes No No Webinar - - May 4,

12 Regulatory Guidelines FDA Guidance (Clinical ConsideraDons for TherapeuDc Cancer Vaccines) EMEA / CHMP AlternaDve Guideline on approach Clinical Trials needed in Small to meet PopulaDons design requirements Webinar - - May 4,

13 Bayesian Framework (Based on Matano. Bayesian AdapDve Designs for Oncology Phase 1 Trials, 2013) Webinar - - May 4,

14 Bayesian Logis2c Regression Model (BLRM) Webinar - - May 4,

15 Bayesian Logis2c Regression Model (BLRM) Two parameter logisdc: is the reference dose (arbitrary scaling dose) α>0 is the odds of DLT at β>0 is the increase in log- odds of DLT for unit increase in log- dose Webinar - - May 4,

16 Escala2on with Overdose Control (EWOC) Choose dose that maximizes targeted toxicity probability, given not overdosing. Webinar - - May 4,

17 Prior Specifica2on (direct vs indirect) Enter directly bivariate normal for log(α) and log(β): Indirectly: Webinar - - May 4,

18 BLRM Stopping Rules Webinar - - May 4,

19 BLRM Interim Monitoring Webinar - - May 4,

20 Predic2ve Distribu2on of DLTs Webinar - - May 4,

21 Interim Monitoring Webinar - - May 4,

22 Combina2on- Agent Dose Escala2on Webinar - - May 4,

23 Rule- based vs Model- based Single Agent Designs Bayesian? Model dose- toxicity? (number of parameters) Probability Intervals? 3+3 No No No CRM Yes Yes (1) No BLRM Yes Yes (2) Yes mtpi Yes No Yes Double Agent Designs comb2blrm Yes Yes (5) Yes PIPE Yes No No Webinar - - May 4,

24 About combina2on therapies Becoming increasingly common in the treatment of many diseases (e.g. cancer, HIV) Many designs are sdll quite naïve e.g. fix dose of one agent, and dose- escalate the other (using single- agent designs) Require simultaneous dose- escaladon Aims and objecdves must differ from single- agent trials MulDple MTDs may exist More prior informadon (from single- agent trials) InteracDon Webinar - - May 4,

25 Ra2onale Recent FDA drav guidance on Co- development of two or more unmarketed invesdgadonal drugs for use in combinadon Combina;on therapy is an important treatment modality in many disease sebngs, including cancer, cardio- vascular disease, and infec;ous diseases. Recent scien;fic advances have increased our understanding of the pathophysiological processes that underlie these and other complex diseases. This increased understanding has provided further impetus for new therapeu;c approaches using combina;ons of drugs directed at mul;ple therapeu;c targets to improve treatment response or minimize development of resistance. Webinar - - May 4,

26 Methods in East comb2blrm PIPE Webinar - - May 4,

27 The general approach Specify an inidal dose- combinadon for first cohort, x = (x A,x B ) Record the observed number of toxicides Given a parametric dose- toxicity model, π(x, θ), with priors on the parameter vector θ Update inferences to obtain new posterior distribudon Choose next dose combinadon based on A set of admissible dose combinadons A decision rule to choose between admissible doses, using the posterior distribudon ConDnue recruidng padents undl either a fixed sample size is obtained A stopping rule is sadsfied Webinar - - May 4,

28 comb2blrm: Model components Model has three components which stand for 1. Single- agent 1 toxicity, represented by parameters α 1, β 1 2. Single- agent 2 toxicity, represented by parameters α 2, β 2 3. InteracDon, represented by parameter η. π 12 is the probability of a DLT for dose combinadon (d 1, d 2 ), odds 12 = π 12 /(1 - π 12 ) = α 1 d 1 β1 + α 2 d 2 β2 + α 3 (d 1 β1 d 2 β2 ) β3 To ensure interpretadon of the parameters, we simplify the model as odds 12 = odds 12 0 x exp(η d 1 d 2 ) Webinar - - May 4,

29 Prior distribu2on For the single- agent parameters proceeds as in the univariate BLRM For the interacdon log- odds muldplier η we use η ~ N(m η, s 2 η ) which allows for synergisdc and antagonisdc interacdon m η, s 2 η determined from two prior quandles of η, for example the median (set to 0 for the case of no a priori evidence for interacdon) and 97.5% quandle. If it is known that only synergisdc interacdon is possible, a prior for η confined to posidve values could be used (e.g., log- normal). Webinar - - May 4,

30 Escala2on with Overdose Control (EWOC) Choose dose combinadon that maximizes targeted toxicity probability, given not overdosing (set of admissible doses). Webinar - - May 4,

31 comb2blrm priors Webinar - - May 4,

32 Mul2ple true MTDs Webinar - - May 4,

33 Mul2ple true MTDs Webinar - - May 4,

34 Interval Probabili2es by Dose Webinar - - May 4,

35 PIPE: Design components Target a MTD contour (MTC) A- priori and a- posteriori the probability of toxicity (π 12 ) at a specific dose combinadon is a Beta random variable Posterior probability of toxicity at each dose level easily calculated MTC needs to sadsfy monotonicity assumpdon to drive dose escaladon Next dose combinadon is chosen from a set of admissible doses that are close to the MTC Webinar - - May 4,

36 Discrete Dose Combina2on Space π ij represent unknown true probabilides of toxicity at each dose combinadon Assume strict monotonicity Webinar - - May 4,

37 MTC monotonicity For an I x J matrix, there exist ( (I+J)@J ) monotonic contours Webinar - - May 4,

38 Priors in PIPE: Uniform Webinar - - May 4,

39 Priors in PIPE: Uniform UnrealisDc prior DLTs? Too strong prior sample sizes? Webinar - - May 4,

40 Priors in PIPE: Weak Prior DLTs assume all combinadons are 'safe (if p T = 0.33) But prior belief is weak Webinar - - May 4,

41 Process Dose a new cohort of padents on best combinadon Record the number of DLTs For each dose combinadon calculate the posterior DLT probability Calculate the probability of being above the TTL (averaged over the contour distribudon) for Safety Use the most likely contour for Decision making * Mander (2015) Finding the right dose in response adapdve trials Webinar - - May 4,

42 Product of Beta Tail Probabili2es Bayesian update: ( π ij Y, α ij, β ij ) ~Beta( α ij + r ij, β ij + n ij r ij ) for dose combinadon d ij Define tail probability p ij p T Y =F( p T ; r ij, n ij, α ij, β ij ) where F() is the cdf of Beta. Product of tail probabilides P MTC= C s Y = i,j (1 p ij C s [i,j] ) p ij 1 C s [i,j] * Mander (2015) Finding the right dose in response adapdve trials Webinar - - May 4,

43 MTC monotonicity Use most likely contour for Decision Making: define admissible doses Average over all contours for Safety Constraint: expected probability of exceeding p T Webinar - - May 4,

44 Define closest doses to MTC Select one of "closest" doses Avoid dose skipping If there are muldple closest doses to the MTC then the doses are chosen with smallest sample size (random if Des) Webinar - - May 4,

45 Dose Skipping: Neighborhood Cannot select doses outside dashed line (* = highest combinadon) Neighborhood constraint: Not more than one dose level higher than current dose combinadon. Webinar - - May 4,

46 Dose Skipping: Non- Neighborhood Cannot select doses outside dashed line (* = highest combinadons) Non- Neighborhood constraint: Not more than one dose level higher than any previously visited combinadon.. Webinar - - May 4,

47 References 3+3 B.E. Storer. Design and analysis of phase I clinical trials. Biometrics, 45: , mtpi Y. Ji, P. Liu, Y. Li, and N. Bekele. A modified toxicity probability interval method for dose finding trials. Clinical trials, 7: , CRM J. O Quigley, M. Pepe, and L. Fisher. ConDnual reassessment method: A pracdcal design for phase I clinical trials in cancer. Biometrics, 46:33-48, S.N. Goodman, M.L. Zahurak, and S Piantadosi. Some pracdcal improvements in the condnual reassessment method for phase I studies. StaDsDcs in Medicine, 14: , 1995 BLRM B. Neuenschwander, M. Branson, and T. Gsponer. Clinical aspects of the Bayesian approach to phase I cancer trials. StaDsDcs in Medicine, 27: , L. W. Huson and N. Kinnersley. Bayesian fi ng of a logisdc dose response curve with numerically derived priors. PharmaceuDcal StaDsDcs, 8: , 2009 Combination B. Neuenschwander, et al. A Bayesian Industry Approach to Phase I CombinaDon Trials in Oncology. StaDsDcal Methods in Drug CombinaDon Studies, , 2015 A.P. Mander and M.J. SweeDng. A product of independent beta probabilides dose escaladon design for dual- agent phase I trials. StaDsDcs in Medicine, 34: , 2015 Webinar - - May 4,

48 Demo Time Webinar - - May 4,

49 Pantelis Vlachos, Ph.D. Cytel Inc. Geneva Connect with Pantelis on LinkedIn Webinar - - May 4,