2022-08-03
EffPha utilized FDA’s designated Fit-For-Purpose tool, BOIN study design, for several Phase I clinical trials
Per Section 3011 of the 21st Century Cures Act (Cures Act)1, FDA has created the Drug Development Tool (DDT) program to facilitate drug development utilizing approved methods, measures or materials, such as a biomarkers, clinical outcome assessments (COAs), and animal models for different disease areas. The qualified DDT can be used in drug development program of appropriate disease area without FDA re-assessing its suitability. If not, other non-qualified program used in during sponsor’s drug development program will need to be evaluated by relevant FDA centers. Due to the evolving nature and the lack of formal qualification for DDT, the “Fit-For-Purpose” designation was established. Currently there are three tools with FFP designation (Table 1), including Placebo/Disease Progression disease model in Alzheimer’s disease, MCP-Mod statistical model and the Bayesian Optimal Interval (BOIN) design for multiple disease areas2.
Table 1
Adapted from https://www.fda.gov/drugs/development-approval-process-drugs/drug-development-tools-fit-purpose-initiative?utm_medium=email&utm_source=govdelivery
Traditional Phase 1 Study Design for Oncology Drugs
When it comes to the Phase I clinical trial design for safety exploration of oncology drugs, the main objective is to identify the recommended Phase II dose (RP2D), which is typically the maximum tolerated dose (MTD). The ease of use and good performance are two key considerations for the selection of statistical methodology in Phase I clinical trials. The traditional 3+3 study design is commonly used by pharmaceuticals or biotech companies to explore MTD before moving to dose-finding. The popularity of 3+3 study design is based on its ease of implementation for the study nurses and clinicians, even for the medical writer to prepare the study protocol, without the need of statistician’s expertise. However, it also carries the drawback of poor targeting for MTD and often exposes more than required subjects to subtherapeutic doses. Although there are other model-based methodologies with better performance (e.g., continual reassessment method (CRM)), they require expertise/specialized software in implementation. The BOIN study design, developed by the Professor Ying Yuan of University of Texas MD Anderson Cancer Center, not only overcomes this hurdle but also provides good performance with flexibility.
BOIN Study Design
The central concept of BOIN study design is to compare the observed dose limiting toxicity (DLT) rate at the current dose with pre-specified boundaries (𝜆e: escalation boundary; 𝜆d: de-escalation boundary; between 0 – 1) for dose escalation and de-escalation. In BOIN study design (Figure 1), the subjects sequentially enrolled into the study are assigned to the designated dose and monitored if the observed toxicity rate (number of patients who experienced toxicity divided by the number of patients treated at the current dose) lands within or out of the interval set by pre-defined boundaries. If the observed toxicity rate is below the pre-defined boundaries, escalation to the next dose level proceeds. If the observed toxicity rate goes beyond pre-defined boundaries, then de-escalation to the previous dose level will take place for the next enrolled subject. A decision table for dose escalation/de-escalation can be easily generated for study site personnel to follow for the subject sequentially enrolled into the study. BOIN design allows targeting any prespecified DLT rate, with the ease of implementation over the model-based design and pre-specification of the sample size. The maximum sample size (cohort size × number of cohorts) is set independently of the number of patients treated at each dose and the study dose. The study will stop when any of the following conditions is met:
Figure 1
Source: Clin Cancer Res. 2016 Sep 1;22(17):4291-301.
Conclusion
EffPha has devoted its effort to helping clients save resources and speeding up their product development using innovative and reliable approaches. The use of BOIN design in Phase 1 oncology studies was demonstrated as efficient and was also recognized by FDA as a useful tool for drug development. EffPha’s team of scientific and statistical experts can help advance your clinical trials and provide a customized strategy for your clinical development programs. Contact us to learn how EffPha can help support your product development.
Table 1
| Tool | Disease Area | Trial Component | Issuance Date |
| Placebo/Disease Progression (CAMD) (Disease Model) | Alzheimer’s disease | Demographics, Drop-out | Jun 12, 2013 |
| MCP-Mod (Statistical Method) | Multiple | Dose-Finding | May 26, 2016 |
| Bayesian Optimal Interval (BOIN) design (Statistical Method) | Multiple | Dose-Finding | Dec 10, 2021 |
Traditional Phase 1 Study Design for Oncology Drugs
BOIN Study Design
The central concept of BOIN study design is to compare the observed dose limiting toxicity (DLT) rate at the current dose with pre-specified boundaries (𝜆e: escalation boundary; 𝜆d: de-escalation boundary; between 0 – 1) for dose escalation and de-escalation. In BOIN study design (Figure 1), the subjects sequentially enrolled into the study are assigned to the designated dose and monitored if the observed toxicity rate (number of patients who experienced toxicity divided by the number of patients treated at the current dose) lands within or out of the interval set by pre-defined boundaries. If the observed toxicity rate is below the pre-defined boundaries, escalation to the next dose level proceeds. If the observed toxicity rate goes beyond pre-defined boundaries, then de-escalation to the previous dose level will take place for the next enrolled subject. A decision table for dose escalation/de-escalation can be easily generated for study site personnel to follow for the subject sequentially enrolled into the study. BOIN design allows targeting any prespecified DLT rate, with the ease of implementation over the model-based design and pre-specification of the sample size. The maximum sample size (cohort size × number of cohorts) is set independently of the number of patients treated at each dose and the study dose. The study will stop when any of the following conditions is met:
- Pre-specified maximum sample size is reached.
- Risk of being above MTD is > 95%.
- A pre-specified number of patients have been treated at the dose predicted to be the MTD.
Figure 1
Source: Clin Cancer Res. 2016 Sep 1;22(17):4291-301.
Conclusion
EffPha has devoted its effort to helping clients save resources and speeding up their product development using innovative and reliable approaches. The use of BOIN design in Phase 1 oncology studies was demonstrated as efficient and was also recognized by FDA as a useful tool for drug development. EffPha’s team of scientific and statistical experts can help advance your clinical trials and provide a customized strategy for your clinical development programs. Contact us to learn how EffPha can help support your product development.
By Wing Chuang, Ph.D.
1 https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf
2 https://www.fda.gov/drugs/development-approval-process-drugs/drug-development-tools-fit-purpose-initiative?utm_medium=email&utm_source=govdelivery