Rui (Sammi) Tang Biostatistics Associate Director, Vertex Bayesian Model Screening Platform Design for Randomized Phase II Combo Therapy Oncology Trials Rui (Sammi) Tang Biostatistics Associate Director, Vertex Collaborators: Jing Shen, Ying Yuan

Disclaimer The presentation reflects presenter’s own views and do not represent the view of Vertex; The presenter is not speaking on behalf of Vertex.

Overview Motivation Challenges and Proposed Method Simulation Results Summary Conclusion and Discussion

An Era of Immunotherapies in Oncology Source: Morrissey, K., Yuraszeck, T., Li, C.-C., Zhang, Y. and Kasichayanula, S. (2016), Immunotherapy and Novel Combinations in Oncology: Current Landscape, Challenges, and Opportunities. Clinical And Translational Science, 9: 89–104. doi:10.1111/cts.12391

Selected List of Combination Immunotherapies in Clinical Development Source: Morrissey, K., Yuraszeck, T., Li, C.-C., Zhang, Y. and Kasichayanula, S. (2016), Immunotherapy and Novel Combinations in Oncology: Current Landscape, Challenges, and Opportunities. Clinical And Translational Science, 9: 89–104. doi:10.1111/cts.12391

Challenges Combination therapies may dramatically improve the outcome for cancer patients Combination is a future direction of cancer therapy development Discovery of effective combinations is a challenging endeavor Nearly 200 molecules approved by the FDA by 2016 Over 15 immunotherapy agents Unfeasible: experimentally testing every possible combination Urgently NEED: Innovative study design to efficiently identify effective combination therapies

Motivatonal Trial Setting and Challenges Primary investigation compound 1 Multiple Back-up compounds (Similar MOA) Multiple immunotherapies as backbone choices The same disease indication How to select the most efficient combination therapy(ies) quickly?

Screen Selection Design (SSD) Recruit N patients Randomized 1:1:1:…:1 (x arms) Simon two stage approach applied to each arm H0: P0 <=20% H1: P1>40% Arm1 Arm2 … Arm x Bayesian stopping rule could also apply (BSD) No arms meet the criteria to proceed to next stage Treatments with positive results are recommended Apply selection strategy with arm comparison to pick the winner Can not take the advantage of combination therapy; Ignored the potential relationship between backbone or compound arms;

Proposed Design and Method Bayesian Model Screening Platform Design (BMSPD) For simplicity and without loss of generality Assume efficacy endpoint is an event of response following a Bernoulli distribution Other efficacy endpoint could apply to the same concept of design The Bayesian hierarchical logistic model is used to model the rate of response Let i and j denote the compound and backbone k indicate the index of patients assign to the compound i and backbone j.

Proposed Design and Method Cont. The hierarchical model setting allows borrowing information across different backbone groups (j) within a compound (i) Instead of setting default vague prior to 𝜎 2 , we allow choosing 𝜎 2 from 106, 104,10, 1, 0.1 to construct 5 model candidates representing weak to strong borrowing strength models Using DIC based model selection criterion to select the best fitted model among the candidates after the first stage Can add and early drop arm(s) We propose to have n interim look during the trial and the model prior are determined after the sth interim (typically s=1) At the end of the trial, the non stopping arm(s) who has the largest winning probability Pr(p_ij = max(p_ij) |data) will be selected for future development. Other threshold selection rule can also apply The specifications of s will be determined by the study team according to practical reasons An extreme case would be evaluating data after each subject's outcome like the Zhou et al. (2008) BATTLE trial

BMSPD Design Process

Simulation Set up Simulation runs =1000 Assume a set-up of a 3 (compound) by 3 (backbone) arms with pij corresponding to the response rate of compound i and backbone j combination. Response rate Backbone 1 Backbone 2 Backbone 3 Compound 1 P11 P12 P13 Compound 2 P22 P23 Compound 3 P31 P32 P33

Simulation Results for trivial cases All arms are equally non-efficacious(bad) or with moderate efficacy   Case Scenario SSD BMSPD BSD Sample size Percentage of no arm selected 1. All bad arms 81 1 63 2. All moderate arms 100 0.71 103 0.86 104 0.88 SSD: Screen Selection Design BMSPD: Bayesian Model Screening Platform Design( proposed method) BSD: Bayesian Screening Selection (Basic Case of Bayesian framework)

Simulation Results for Cases at Least 1 Superior Arm Exists SSD BMSPD BSD Sample size Selection Percentage 3. Compound effect 137 0.78 144 0.88 120 0.62 4. Backbone effect 140 0.73 152 0.79 105 0.49 5. Total random 163 0.81 157 0.91 153 0.87 6. 121 0.71 117 0.80 112 0.74 7. Additive effect 167 169 171 8. Synergy effect 161 172 0.89 164 0.83 9. Drug-drug interaction 141 0.85 143 0.90 142 Compound effect case: the low selection rate of BSD where the priors of variance depends on hyper prior of IG mistakenly borrowed too much information cross the backbones and neutralized the compound effect. Similarly, in backbone effect case the BSD mistakenly borrowed too much information across compound and neutralized the backbone effect. Selection rate= Percentage of selecting superior arm(s)

A R shiny tool is being develop for the method It is a very interactive tool for trial fitting and simulation Layout Outline flowchart Uploading files of trial set up Trial summary Trial fitting Simulated trial

BMSPD – Uploading files

BMSPD – Fitting outputs

BMSPD – Trial simulation

Conclusion and Discussion Our approach is flexible and can stop arm early if unsatisfied response rate observed. In cases where no arm meets an acceptance rate, our method is comparable with BSM claiming no arm is selected, both are better than SSD. In cases where superior arm(s) existed, our method can select the superior arm(s) with highest probability comparing to that of SSD and BSD. The model tends to select the prior with high borrowing strength when response rates are homogeneous across backbone and/or compounds; and select the prior with moderate/mild borrowing strength when the less homogeneity assumed across arms The number of subjects enrolled were compatible across all three methods Finally, a web based application is being developed and available to users soon.

Thank you

Acknowledgment Research Collaborators: Jing Shen (Gilead) ; Ying Yuan (MD Anderson) Thank my management Bo Yang (Vertex) and Yang Song (Vertex) for the review comments

Back up

Simulation set up detials The sample size of SSD is determined by Simon's two stage design, ph2simon(p0=0.5, pa= 0.75, ep1 = 0.1, ep2=0.2): r1/n1 = 5/9, r/n =13/22, corresponding to 75% early stopping given p0 is correct after the first stage [Yap, Pettitt and Billingham (2013)]. The sample size of the two Bayesian approach BSD and BMSPD is defined as (7, 7, 8) for a 3-stage design. The early stopping rule for BMSPD and BSD are set as 𝑃𝑟 𝑝 𝑖𝑗 > 𝜃 1 𝑑𝑎𝑡𝑎)≤ 𝛿 𝑙 where 𝜃 1 = 0.7 and 𝛿 𝑙 = 0.1, which corresponds to 70% early stopping given p= 0.5 and prior is M0 at the end of the first stage.