The Bayesian Early-Phase Seamless Transformation Platform

We offer BEST consulting service.  Contact us to inquire about it

The Bayesian early-phase seamless transformation (BEST) platform provides a fast, efficient, and powerful solution for early-phase drug development. The BEST platform allows for 1) seamless transition from dose finding (Phase 1a) to cohort expansion (Phase 1b), and to a proof-of-concept (POC) stage (Phase 2a) if needed, 2) simultaneous expansion of multiple doses in multiple indications, 3) interim decision making to graduate or terminate a dose-indication arm adaptively, 4) powerful data analysis for RP2D selection, and 5) flexible selection of different modules tailored for customized design.

The statistical innovation of BEST centers at a proprietary Bayesian hierarchical model (BHM). With this model, BEST is able to empower a master protocol for early drug development that incorporates phase 1a and phase 1b into a single trial allowing for highly efficient exploration of efficacious and safe doses in multiple indications. Due to the novel BHM, the BEST platform can improve the overall study power in selecting the promising doses and indications for late-stage drug development and eliminate toxic or inefficacious doses quickly without wasting resources. This leads to increased probability of success for the entire drug development, speeds up the process, and reduces the cost for sponsors.

Features

  • Real-time monitoring of toxicity events
  • Real-time monitoring of probability of success
  • Acceleration in enrollment of highly-efficacious doses
  • Early exclusion of the inefficacious or overly-toxic doses

Benefits

  • Smaller sample size Compared to the conventional approach (independent frequentist test for each dose-indication arm), the BEST platform is able to save about 20%-30% sample size for the trial without sacrificing the chance of finding the efficacious doses and promising indications.
  • Higher power Compared to the conventional approach (independent frequentist test for each dose-indication arm), the BEST platform has a larger (up to twice many) chance to find the efficacious dose and promising indications.
  • Flexibility The BEST platform is highly flexible, allowing investigators to plan appropriate timing for recommending any promising dose-indication arm as RP2D during the cohort expansion, and to early stop.
  • Time-saving BEST accelerates drug development due to the seamless strategy and sample size reduction.

BEST PRODUCTS

The BEST platform incorporates a suite of innovative, efficient designs and utilities for different objectives.

Solution Suites

Designs Under BEST

  • The mTPI-2 design (modified Toxicity Probability Interval Design version 2, Guo et al., 2017a): a simple, safe and efficient phase 1 dose finding design, in which all decisions can be transparently tabulated for examination before trial begins.
  • R-TPI (Rolling Toxicity Probability Interval Design, Guo et al., 2018): a fast and efficient phase 1 dose finding trial, that incorporates a rolling enrollment scheme, aiming to accelerate the trial conduct without sacrificing safety and desirability.
  • PITE (Probability Interval Design based on both Toxicity and Efficacy): a transparent, efficient and powerful solution for immuno-oncology (IO) phase 1 dose finding trials, which incorporates efficacy outcomes together with toxicity outcomes to inform dosing decisions to optimize efficacy and safety simultaneously.
  • Dual-agent Drug Combination Dose Finding designs: the state-of-art designs such as the AAA design (Lyu et al., 2018) in which multiple adaptive scheme (e.g. adaptive dose insertion and parallel patient enrollment at multiple doses) can be incorporated.
  • MUCE (MUltiple Cohort Expansion): the key feature of BEST platform that allows for expansion of multiple doses in multiple indications. With the innovative BHM, MUCE is able to save the patient resources without sacrificing the chance of finding the efficacious doses and indications.
  • SCUBA (Subgroup ClUster Based Bayesian Adaptive Design, Guo et al., 2017b): a powerful precision-medicine solution for phase 2 subgroup enrichment trials aiming to find optimal subgroups that benefit from precise treatment and increase the probability of success for the whole trial.

Other Utilities Under BEST

  • Sequential or seamless transition from dose finding to cohort expansion.
  • Sequential or seamless transition from cohort expansion to POC.
  • Interim analysis that allows early stopping for futility, superiority, or toxicity.
  • Comparison with different reference rates for different indications.
  • Flexible sample size calculation for the entire master protocol or for each component such as dose finding only, or cohort expansion only.
  • Adaptive or equal randomization across different doses during cohort expansion.

Learn More

We offer BEST consulting service.  Contact us to inquire about it

REFERENCES

  1. Guo, W., Wang, S. J., Yang, S., Lynn, H., & Ji, Y. (2017a). A Bayesian interval dose-finding design addressing Ockham's razor: mTPI-2. Contemporary clinical trials, 58, 23-33.
  2. Guo, W., Ji, Y., & Li, D. (2018). R-TPI: Rolling Toxicity Probability Interval Design to Shorten the Duration and Maintain Safety of Phase I Trials. Journal of Biopharmaceutical Statistics.
  3. Lyu, J., Ji, Y., Zhao, N., & Catenacci, D. V. (2018). AAA: triple adaptive Bayesian designs for the identification of optimal dose combinations in dual‐agent dose finding trials. Journal of the Royal Statistical Society: Series C (Applied Statistics).
  4. Guo, W., Ji, Y., & Catenacci, D. V. (2017b). A subgroup cluster‐based Bayesian adaptive design for precision medicine. Biometrics, 73(2), 367-377.