Bayesian Optimal Interval (BOIN) Design in Phase 1 Oncology Dose-Finding Trials: An Industry Experience Wijith Munasinghe Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development ASA Joint Statistical Meetings, July 27 – August 01, 2019

Disclosure This presentation was sponsored by AbbVie. AbbVie contributed to the design, research, and interpretation of data, writing, reviewing, and approving the publication. Wijith Munasinghe is an employee of AbbVie Inc. Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

Background The primary objective of early phase oncology trials is generally to determine a maximum tolerated dose (MTD) and/or recommended Phase 2 dose (RP2D) for subsequent clinical development. The MTD is typically defined as the highest dose with a dose-limiting toxicity (DLT) rate at or near a pre-specified target toxicity rate. Early phase oncology trials generally employ a dose escalation strategy whereby initial subjects are administered low doses of an investigational agent and subsequent subjects are administered progressively higher doses until an MTD is determined. Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

Trial Design Considerations Accuracy: have a high probability of correctly identifying the true MTD Safety: minimize the number of patients allocated to doses which exceed the MTD and control the probability of incorrectly selecting an overly toxic dose as the MTD Efficiency: allocate more patients to doses at or near the true MTD, and be able to correctly identify the true MTD with relatively modest sample size Practicality: flexibility to accommodate the specific needs and objectives of the trial, and should not impose undue complexity to the conduct of the trial Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

Motivation/Statistical Considerations Bayesian optimal interval (BOIN) designs motivated by practical consideration of effectively treating patients and minimizing the chance of exposing them to sub-therapeutic and overly toxic doses. Interval designs define dose transitions by the relative location of the observed toxicity rate at the current dose with respect to a prespecified toxicity tolerance interval. BOIN designs are intended to minimize dose assignment decision errors. Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

Details of the Method – Local BOIN Design Let pj denote the true toxicity rate for dose level j and define three point hypotheses: H0j: pj = where  is the target toxicity rate H1j : pj = 1 where 1 is the highest toxicity rate deemed sub-therapeutic H2j : pj =2 where 2 is the lowest toxicity rate deemed overly toxic H0 indicates current dose is MTD and should be retained H1 indicates current dose is sub-therapeutic and should be escalated H2 indicates current dose is overly toxic and should be de-escalated Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

Details of the Method – Local BOIN Design Under Bayesian paradigm, assign each of the three hypotheses a prior probability of being true: 0j, 1j, 2j Then can derive the interval (1j, 2j) which minimizes the decision error rate: Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

Details of the Method – Local BOIN Design The values 1j and 2j are the boundaries at which the posterior probabilities of H1 and H2, respectively, become more likely than that of H0 When 0j=1j=2j, then 1j and 2j are invariant to both the dose level j and the accumulated sample size nj When 0j=1j=2j, probability of dose escalation (or de-escalation) is zero when the observed toxicity rate at the current dose is higher (or lower) than the target toxicity rate (i.e. long-term memory coherent) Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

Dose Escalation Process Specify design parameters , 1, 2, 0j, 1j, 2j and determine resulting optimal interval (1j, 2j) Dose escalation proceeds as follows: Patients in first cohort are treated at lowest dose level At current dose level j, assume cumulative number of patients is nj with observed toxicity rate 𝑝 𝑗 . To assign a dose to the next cohort: If 𝑝 𝑗 ≤ 1j, escalate to dose level j+1 If 𝑝 𝑗 ≥ 2j, de-escalate to dose level j-1 Otherwise remain at dose level j Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

Dose Escalation Process Process continues until maximum sample size is reached or trial is terminated due to excessive toxicity or maximum cohort size reached BOIN design may also impose a dose elimination rule based on the posterior probability that the true toxicity rate at dose level j exceeds the target toxicity rate At end of the trial, select a dose level as the MTD or RP2D Authors suggest using isotonically transformed values of the observed toxicity rates to determine MTD or RP2D but any reasonable method could be used Global BOIN design is based on composite hypothesis and can be developed similar to local BOIN Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

BOIN Pros and Cons Pros Simpler interpretation and implementation: optimal interval has a simple closed-form solution and dose escalation is based on comparison of the observed DLT rate to the optimal interval Explicitly defined target toxicity rate: requires specification of a target toxicity rate, which can be tailored as appropriate for the investigational agent and/or patient population to be studied Flexible cohort size: provides an escalation decision rule for any cohort size, which allows for accommodation of over- or under-enrollment and/or patients who become unevaluable for DLT assessment Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

BOIN Pros and Cons (Cont.) Re-escalation permitted: allows for potential re-escalation subsequent to de-escalation, particularly when the prior de-escalation decision was based on a small cohort size Safety rule: incorporates a safety rule which can eliminate doses which have a high posterior probability of excessive toxicity Typical BOIN dose escalation rule illustrated in table below: Based on target DLT rate of 0.33 and optimal interval of (0.260, 0.395), but rule can be tailored to any target rate Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

BOIN Pros and Cons (Cont.) Comparable or better operating characteristics to 3+3, mTPI-2/Keyboard, CRM: generally yields relatively high probability of correctly identifying the true MTD and with relatively low variability; efficiently allocate more patients at or near true MTD3,8,13 Combination dosing: can be extended to accommodate dose escalation decision rules for dual-agent dose-finding including parallel escalation and pre-selection5 Incorporation of efficacy outcomes and late onset toxicities: an extension of the BOIN design to incorporate binary efficacy outcomes (BOIN-ET)6 and late onset toxicities (TITE-BOIN)14 into dose assignment decisions has recently been proposed Late onset tox: common for molecularly targeted agents and immunotherapy; longer DLT assessment period OR rapid accrual; TITE-BOIN gives real time dosing decisions similar to rolling six design table for decision using # complete DLT period, # still pending (standardized total follow-up time), # DLT; continuous accrual without scarifying patient safety; simple and good performance and better over-dose control; invariant to length of assessment window and robust; software http://www.trialdesign.org BOIN-ET: non-cytotoxic agents find optimal dose when monotonicity assumption is not required; minimize the posterior prob of inappropriate dose assignment in terms of both tox and effi; eff cut-off point and tox cut-off interval; 6 hypotheses to determine optimal values of those cut-offs with non-informative priors; ignore correlation between tox and eff Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

BOIN Pros and Cons (Cont.) Intermediate doses: require a fixed set of pre-specified dose levels and inefficient if exploration of previously unspecified and/or intermediate dose levels are warranted Aggregates data: do not aggregate data from all available subjects across all dose levels to continually refine the estimated dose-toxicity relationship Cohort backfilling: this may be considered for various purposes, including enrollment of subjects from a different geographic region to meet regional regulatory requirements or enrollment of subjects with a different tumor type to confirm no differences in tolerability profiles, at a given dose level and can potentially lead to inconsistencies between the BOIN escalation rule and the current dose cohort Intermediate doses – inefficient for BOIN: large sample, back-and forth, more DLTs overall Cohort backfilling (concurrent enrollment at higher doses while backfilling lower doses) – inconsistencies/ambiguity for BOIN: each dose level in isolation and no aggregate data, Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

BOIN Pros and Cons (Cont.) Extends to incorporate other information: inefficient or not practical (comparison to model based designs4,9) if extended to include data other than DLTs, including multiple patient populations with potentially different safety profiles, different dosing schedules or routes of administration, other adverse events of interest which do not otherwise meet DLT criteria; can not accommodate effect markers, anti-tumor activity, pharmacokinetic exposure or pharmacodynamic endpoints which are not binary outcomes, etc.12,15,16 Cytopenia not considered DLTs for liquid tumors but solid tumors does and so lower MTD. CRM: Pooled data, dose skipping, parallel escalation, separate MTD identification, intermediate doses Joint modeling provide quantitative estimates of both benefit and risk, different shape of dose-response (flat or plateau beyond a specific dose) Inefficient for BOIN: more subjects, lower MTD for solid Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

Conclusions The BOIN design is particularly well-suited for studies with fixed dose levels, and standard cohort enrollment and dose-limiting toxicity (DLT) assessments BOIN design has operational and conceptual simplicity Cytopenia not considered DLTs for liquid tumors but solid tumors does and so lower MTD. CRM: Pooled data, dose skipping, parallel escalation, separate MTD identification, intermediate doses Joint modeling provide quantitative estimates of both benefit and risk, different shape of dose-response (flat or plateau beyond a specific dose) Inefficient for BOIN: more subjects, lower MTD for solid Invited Session: Complex Innovative Designs in Practice of Early Phase Drug Development - BOIN; JSM July 2019

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