An introduction to Phase I dual-agent dose escalation trials

An introduction to Phase I dual-agent dose escalation trials Michael Sweeting MRC Biostatistics Unit, Cambridge 13th March 2013

Outline Introduction to phase I trials Aims, objectives and key elements. The CRM method. Dual-agent phase I trials Escalation strategies. Patient vs. Population gain. Toxicity vs. Efficacy trade-off.

Introduction to Phase I trials Aims Phase I studies involve the first experimentation of a new drug / clinical procedure in human subjects. To seek the highest possible dose subject to toxicity constraints:- This is known as the maximum tolerated dose (MTD). Based on a monotonicity assumption that the benefit (efficacy) of treatment increases with dose. Sample size Typically small: 20-50 patients. Due to ethical considerations patients are added sequentially after side-effects from previous patients have been assessed. Subjects Healthy volunteers for relatively non-toxic agents (e.g. molecular entities for CNS disorders). Patients when drugs are highly toxic (e.g. in oncology).

Key elements 1. A starting dose that will be given to the first patient:- In psychiatric trials: no observable effect level (NOEL) in animals. In oncology trials: 1 10 LD 10 in mice (one tenth of the lethal dose in 10% of mice). 2. Potential doses for experimentation. 3. Dose regime (single dose or multiple doses per individual). 4. Outcomes:- Safety (Toxicity) outcomes: often binary (e.g. occurrence of a dose-limiting toxicity (DLT) is used in cancer trials). PD measures of efficacy: often biomarker based. 5. A target level (TL) or therapeutic index:- The desired target level that corresponds to the MTD (e.g. cancer trials often propose 30% prevalence of DLT at the MTD). 6. A dose-escalation design:- Rule or model based. Cohort size:- Number individuals treated at each dose level. Sample size / stopping rules.

A dose-response curve Response θ 1 2 3MTD 4 Dose level 5 6 θ = Target level

The Continual Reassessment Method (CRM) The CRM is an adaptive design that adjusts the dose for the next cohort based on all information accrued to date. The aim is to choose the dose that is closest to the target level. d(i): dose administered to patient i. π(d;α): probability of a DLT at dose d with parameter α. f (α): prior distribution for α. Posterior distribution for α: f (α y n ) = f (α)l(α;y n ) 0 f (u)l(u;y n ) du. The dose whose posterior probability of DLT closest to the TTL, θ, is chosen, that is:- d(n + 1) = arg min E [π(ξ ;α)] θ ξ {1,...,k}

Example escalation scheme 1.00 Cohort 0 0.75 Probability of DLT 0.50 5 0 1 2 3 4 5 Dose level Previous slide Next slide

Extending to dual-agent phase I trials Drug B Synergy Antagonism Probability DLT 0.1 0.3 0.5 0.7 Drug A

Comparison of single and dual-agent phase I trials Aim Single-agent To identify a single maximum tolerated dose (MTD). Dual-agent To identify numerous MTD combinations. Escalation Single-agent Quick and safe escalation to the target level (TL). Dual-agent Quick and safe escalation to the TL, with varied experimentation of doses at the TL. Prior information Single-agent Pre-clinical, Learning from similar compounds. Dual-agent Pre-clinical, Learning from similar compounds and combinations, Results from single-agent Phase I-III studies.

Series of 1-agent CRMs Fix one drug at a single dose, escalate the other, in a series of sub-trials. Identified MTD from one sub-trial can be used to limit dose range for next sub-trial. 1 6 5 Drug B 4 3 2 1 1 2 3 4 5 6 Drug A 1 Yuan, Y. and Yin, G. Statistics in Medicine 2008; 27: 5664-78.

Using 1-dimensional CRM with partial ordering If a prior ordering of the dose combinations can be assumed then a 1-dimensional CRM can be applied. 2 Alternatively, the likelihood of each possible ordering can be assessed after each patient is recruited. 3 Can become problematic if many dose levels are used (many possible orderings). Drug B 2 1 1 2 3 Drug A 2 Kramar, A. et al. Statistics in Medicine 1999; 18: 1849-64. 3 Wages, N. A. et al. Clinical Trials 2011; 8: 380-89.

Escalation strategies for 2-dimensional models: Admissible next doses 4 Diagonal (or Fast ) escalation. Non-diagonal (or Slow ) escalation. 6 6 5 5 Drug B 4 3 Drug B 4 3 2 2 1 1 1 2 3 4 5 6 Drug A 1 2 3 4 5 6 Drug A A third strategy: Allow any previously experimented dose combination to be admissible. 4 Sweeting, MJ. and Mander, AP. Pharm. Stats. 2012;11(3):258-266.

Escalation strategies: Decision rules Patient gain The next cohort is treated at our best estimate of the MTD. Patient gain used in most CRM methodology. Variance (population) gain The next cohort is treated at the dose that will provide the most information regarding the dose-toxicity surface. This dose is the one that will provide most benefit to the population (by giving more precise estimates at the end of the trial). Hybrid patient/variance gain In dual-agent trials, we want doses close to the TL, but also allow experimentation across the dose-toxicity surface. Hence this requires a trade-off between Patient and Population Gain.

An example trial in practice: using non-diagonal escalation n = 0 1.0 Drug B Probability DLT 0.1 0.3 0.5 0.7 1.0 Drug A Previous slide Next slide

An example trial in practice: diagonal escalation with population gain function n = 30 1.0 Drug B Probability DLT 0.1 0.3 0.5 0.7 1.0 Drug A

Incorporating measures of efficacy There is increasing interest in developing dose finding methods that incorporate both toxicity and efficacy endpoints. Considered as a phase I-II trial. A trade-off must be made between efficacy and toxicity. Response Toxicity Efficacy MED MTD Dose

EffTox contours of desirability 5 Dose-Finding Based on Efficacy Toxicity Trade-Offs Prob(TOXICITY) 1.0 π * 3 q π * 1 1.0 p Prob(EFFICACY) Figure 1. Efficacy toxicity trade-off contours for the 5 Pentostatin trial. The target contour C is given by the solid Thall, PF. and Cook, line, and JD. the Biometrics three elicited2004; target points 60: 684-693. that determine C are C L(q) π * 2 This may be used to construct a that partition Π, with the points desirable. Given any p Cand z> hz (p) =q if q L(p) and ρ(p)/ρ hz (p) =(1 (1 pe )/z, pt /z). W not be a subset of Π for some z, sin contours inside Π we define Cz = { contour in Π obtained by shifting e point q in Π such that ρ(p)/ρ(q) = ordered by their desirabilities and contours is a partition of Π, the co on Π. We require f(πe) to be strictly given (πe,πt ) C and ɛ>0, prov Π, it must be the case that (πe + ɛ C and hence is more desirable than (πe, πt + ɛ) Π must be on a con desirable than (πe, πt ). In partic binary outcome case, the rectang line segments from (π E, πt )to(π (1, πt ) does not satisfy this admis the convenient form πt = f(πe) the applications described here, fit pairs subject to the constraint th πe such that {πe,f(πe)} C. O elliptical contour, should work as w 4.2 The Trade-Off-Based Algorithm Initially, the physician must provid dose for the first cohort, N, c, a used in the acceptability criteria

Summary Escalation strategies more complex in two-agent trials. Generally require twice as many patients as single-agent trials. May wish for varied experimentation around MTD contour:- Allows more drug combinations to be recommended for phase II experimentation. Such an objective requires sequential learning about MTD contour to choose next optimal dose. Non-diagonal escalation rarely behave in a step-like manner, and may get stuck in regions where one drug is given at a low dose. Need to consider carefully trade-off between:- Patient and Population gain. Toxicity and Efficacy. In summary, more consideration needs to be given to these design aspects in order to gain optimal information from the available patients. Most of these designs are disappointingly under used in practice.