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 Agenda What’s new? Single-‐Agent dose escalaDon • Demo/Workshop • CombinaDon agent dose escalaDon • Review of methods • Demo/Workshop • Q&A • • Webinar -‐-‐ May 4, 2016 2 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, 2016 3 What’s new? • • • • • mTPI: User specified decision table More flexible stopping rules Accelerated DtraDon CombinaDon designs And much more! Webinar -‐-‐ May 4, 2016 4 mTPI: User specified decision table Webinar -‐-‐ May 4, 2016 5 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, 2016 6 Accelerated 2tra2on Webinar -‐-‐ May 4, 2016 7 Combina2on designs Increased popularity! Webinar -‐-‐ May 4, 2016 8 Single-‐Agent Dose Escala2on Webinar -‐-‐ May 4, 2016 9 Phase I dose-‐escala2on (general frame) Only consider trials with fixed doses. • A sequence of K doses, d1,d2,…,dK, as candidates. • Dose i has a toxicity probability of pi (unknown). • Monotonicity : pi < pi+1 • Goal: to find the MTD , defined as the highest dose with toxicity rate lower (or close to) a fixed rate, pT, e.g., pT = 0.30. Webinar -‐-‐ May 4, 2016 10 Rule-‐based vs Model-‐based 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 comb2BLRM Yes Yes (5) Yes PIPE Yes No No Single Agent Designs Double Agent Designs Webinar -‐-‐ May 4, 2016 11 Regulatory Guidelines • FDA Guidance (Clinical ConsideraDons for TherapeuDc Cancer Vaccines) eeded meet • EMEA / CHMP AlternaDve Guideline oan pproach Clinical Tnrials in Sto mall PopulaDons design requirements Webinar -‐-‐ May 4, 2016 12 Bayesian Framework (Based on Matano. Bayesian AdapDve Designs for Oncology Phase 1 Trials, 2013) Webinar -‐-‐ May 4, 2016 13 Bayesian Logis2c Regression Model (BLRM) Webinar -‐-‐ May 4, 2016 14 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, 2016 15 Escala2on with Overdose Control (EWOC) • Choose dose that maximizes targeted toxicity probability, given not overdosing. Webinar -‐-‐ May 4, 2016 16 Prior Specifica2on (direct vs indirect) • Enter directly bivariate normal for log(α) and log(β): • Indirectly: Webinar -‐-‐ May 4, 2016 17 BLRM Stopping Rules Webinar -‐-‐ May 4, 2016 18 BLRM Interim Monitoring Webinar -‐-‐ May 4, 2016 19 Predic2ve Distribu2on of DLTs Webinar -‐-‐ May 4, 2016 20 Interim Monitoring Webinar -‐-‐ May 4, 2016 21 Combina2on-‐Agent Dose Escala2on Webinar -‐-‐ May 4, 2016 22 Rule-‐based vs Model-‐based 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 comb2BLRM Yes Yes (5) Yes PIPE Yes No No Single Agent Designs Double Agent Designs Webinar -‐-‐ May 4, 2016 23 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, 2016 24 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, 2016 25 Methods in East comb2BLRM PIPE Webinar -‐-‐ May 4, 2016 26 The general approach • Specify an iniDal dose-‐combinaDon for first cohort, x = (xA,xB) • 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, 2016 27 comb2BLRM: Model components • Model has three components which stand for 1. Single-‐agent 1 toxicity, represented by parameters α1, β1 2. Single-‐agent 2 toxicity, represented by parameters α2, β2 3. InteracDon, represented by parameter η. • π12 is the probability of a DLT for dose combinaDon (d1, d2), odds12 = π12 /(1 -‐ π12 ) = α1 d1 β1 + α2 d2 β2 + α3(d1 β1 d2 β2 ) β3 To ensure interpretaDon of the parameters, we simplify the model as odds12 = odds12 0 x exp(η d1 d2) Webinar -‐-‐ May 4, 2016 28 Prior distribu2on • For the single-‐agent parameters proceeds as in the univariate BLRM • For the interacDon log-‐odds mulDplier η we use η ~ N(mη, s2η) which allows for synergisDc and antagonisDc interacDon • mη, s2η 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, 2016 29 Escala2on with Overdose Control (EWOC) • Choose dose combinaDon that maximizes targeted toxicity probability, given not overdosing (set of admissible doses). Webinar -‐-‐ May 4, 2016 30 comb2BLRM priors Webinar -‐-‐ May 4, 2016 31 Mul2ple true MTDs Webinar -‐-‐ May 4, 2016 32 Mul2ple true MTDs Webinar -‐-‐ May 4, 2016 33 Interval Probabili2es by Dose Webinar -‐-‐ May 4, 2016 34 PIPE: Design components • Target a MTD contour (MTC) • A-‐priori and a-‐posteriori the probability of toxicity (π12) 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, 2016 35 Discrete Dose Combina2on Space • πij represent unknown true probabiliDes of toxicity at each dose combinaDon • Assume strict monotonicity Webinar -‐-‐ May 4, 2016 36 MTC monotonicity • For an I x J matrix, there exist (█■(𝐼+𝐽)@𝐽 ) monotonic contours Webinar -‐-‐ May 4, 2016 37 Priors in PIPE: Uniform Webinar -‐-‐ May 4, 2016 38 Priors in PIPE: Uniform • UnrealisDc prior DLTs? Too strong prior sample sizes? Webinar -‐-‐ May 4, 2016 39 Priors in PIPE: Weak • Prior DLTs assume all combinaDons are 'safe‘ (if pT = 0.33) • But prior belief is weak Webinar -‐-‐ May 4, 2016 40 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, 2016 41 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 – 𝑃𝑀𝑇𝐶=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, 2016 42 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, 2016 43 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, 2016 44 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, 2016 45 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, 2016 46 References 3+3 B.E. Storer. Design and analysis of phase I clinical trials. Biometrics, 45:925-‐937, 1989. mTPI Y. Ji, P. Liu, Y. Li, and N. Bekele. A modified toxicity probability interval method for dose finding trials. Clinical trials, 7:653-‐656, 2010. 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, 1990. S.N. Goodman, M.L. Zahurak, and S Piantadosi. Some pracDcal improvements in the conDnual reassessment method for phase I studies. StaDsDcs in Medicine, 14:1149-‐1161, 1995 BLRM B. Neuenschwander, M. Branson, and T. Gsponer. Clinical aspects of the Bayesian approach to phase I cancer trials. StaDsDcs in Medicine, 27:2420-‐2439, 2008. L. W. Huson and N. Kinnersley. Bayesian fi€ng of a logisDc dose– response curve with numerically derived priors. PharmaceuDcal StaDsDcs , 8: 279–286, 2009 Combination B. Neuenschwander, et al. A Bayesian Industry Approach to Phase I CombinaDon Trials in Oncology. StaDsDcal Methods in Drug CombinaDon Studies, 95-‐135, 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, 34:1261-‐1276, 2015 Webinar -‐-‐ May 4, 2016 47 Demo Time Webinar -‐-‐ May 4, 2016 48 Pantelis Vlachos, Ph.D. [email protected] Cytel Inc. | Geneva Connect with Pantelis on LinkedIn Webinar -‐-‐ May 4, 2016 49
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