Reconsidering the 3+3 Dose Escalation in Oncology Studies

Many product developers are propelled by the goal of delivering life-saving treatments as quickly and as safely as possible to patients who are left with no other options. With oncology development, the standard 3+3 dose escalation in study design in first-in-human (FIH) clinical trials is still the standard, however, we should consider alternative Phase 1 study designs to achieve the optimal recommended Phase 2 dose (RP2D) for patients before entering later stage studies.  

In recent years, we have seen the emergence of molecularly targeted agents (MTAs) and immunotherapies and a transition away from cytotoxic agents. As the oncology treatment landscape changes, it is becoming increasingly important for researchers to consider alternative study designs that determine the optimal biological dose rather than only the maximum tolerated dose (MTD).  

In March 2022, FDA released the final Guidance for Industry “Expansion Cohorts: Use in First-in-Human Clinical Trials to Expedite Development of Oncology Drugs and Biologics” which provides recommendations for leveraging expansion cohorts within FIH trials and emphasizes the importance of flexibility, safety monitoring, biomarker-driven strategies, and collaboration in the design and execution of expansion cohort studies. Expansion cohort studies can utilize both rule-based and model-based approaches, depending on the specific trial design and objectives. The guidance states these types of studies should be limited to patients with serious oncologic diseases for which there are no alternatives.  

Implementing alternative study designs to efficiently achieve a safe and optimal recommended Phase 2 dose is crucial for clinical research in 2024 and years to come. 

Rule-Based Designs 

Rule-based designs apply simple rules to allow for step-up or step-down dosing in the absence or presence of toxicities seen at each dose level. By far the most popular Phase 1 study design in oncology, the 3+3 dose escalation has been utilized in more than 95% of published Phase 1 trials in the past few decades.¹ The logistical simplicity of the design together with familiarity with the escalation rules by clinicians and researchers are likely precluding exploration and implementation of novel study designs.² Accelerated titration design (i.e., rapid intrapatient drug dose escalation) and the rolling six design (i.e., up to six patients concurrently enrolled onto a study) are extensions of the 3+3 design. 

However, continued reliance on the 3+3 dose escalation design in oncology should be questioned. Statistical simulations have shown the MTD is identified in as few as 30% of clinical trials that utilized a 3+3 design.² Furthermore, the 3+3 design exposes an unnecessary number of patients to subtherapeutic doses. Due to the number of escalations and the number of patients required to be treated at each dose level, a large proportion of patients are treated at low doses that are potentially subtherapeutic, while few patients receive doses at or close to the RP2D.³ While this may be a suitable approach for drugs where there is limited safety data, alternative designs, such as Bayesian adaptive designs or model-based approaches, are increasingly being explored to improve efficiency and accuracy in dose finding. 

Model-Based Designs 

Model-based designs use data from each dose level to model a dose-toxicity curve and provide a confidence interval for the RP2D, once achieved.³ While these designs require biostatistics expertise and statistical modeling software, model-based designs can achieve better estimations of the target probability of a DLT at the RP2D while minimizing suboptimal dosing.² Particularly for agents with a low expected toxicity profile, it may make sense to consider a model-based design, given that model-based designs assume a relationship between the study drug dose and the likelihood of occurrence of a DLT. Furthermore, in a model-based design, medical decisions are based on statistical inference, which reduces subjectivity in the dose escalation decision-making process.¹

Traditional model-based designs, such as the continual reassessment method (CRM) and efficacy-toxicity trade-offs, were introduced decades ago, and yet are still scarcely used in practice due to a perception of being too statistically complex.⁴

More recently, there has been increased interest in a combination design that incorporates the simplicity of a rule-based design with the better performance of a model-based design; a model is used for decision making but allows for the decision-making rules to be pre-tabulated before the trial begins.⁵

Model-Assisted Designs 

One such combination design, the modified toxicity probability interval (mTPI) design, is equally as simple, transparent, and costs less to implement as the 3+3 design.¹ The mTPI design requires a biostatistician to generate a decision table to be included in the protocol based on the number of planned dose levels in the study. In the decision table, the dose may be escalated, de-escalated, or eliminated based on the number of patients treated and the number of DLTs.  

‘Eliminate’ means that the current and higher doses will be eliminated from the trial to prevent treating any future patients at such doses because they are overly toxic. In a simulation of 2,000 trials comparing the operating characteristics of the 3+3 design and the mTPI design, it was concluded that compared with the 3+3 design, the mTPI design is safer, because it treats fewer patients at doses above the MTD and is more likely to identify the true MTD than the 3+3 design.¹ 

One of the drawbacks of the mTPI design and model-based designs, in general, is that while they can accelerate dose escalation by treating fewer patients at sub-therapeutic dose levels, the inclusion of one patient per dose level may also deprive the study team of data on interpatient pharmacokinetic (PK) variability.³ However, this limitation can easily be addressed by expanding the cohort size if additional PK data are needed. 

But a potentially superior model-assisted design is the Bayesian Optimal Interval (BOIN) method because it outperforms the mTPI with higher accuracy identifying the MTD and a lower risk of overdosing patients. 

Looking Beyond the 3+3 

The limitations of the 3+3 dose escalation in oncology study design and potential for alternative designs have been discussed for decades, with little to no increase in the number of Phase 1 studies utilizing alternate designs. In 1997, a simulation comparing the 3+3 design with three accelerated titration designs was conducted. The results showed the alternate designs were favorable for several reasons:⁶  

• Reduced the duration of trials 
• Reduced the number of patients exposed to subtherapeutic doses 
• Provided an estimate of the population distribution of the MTD where the 3+3 design did not 

27 years later, these results have had seemingly little to no impact on clinical trial designs as the 3+3 design continues to be commonly utilized. 

Further substantiating the 1997 simulation, a recent comparison of 172 rule-based versus model-based oncology trials were conducted. The results showed rule-based designs took a median of 10 months longer than model-based designs to complete, fewer patients were treated at sub-optimal dose levels in model-based versus rule-based studies, and that despite the savings in time and minimization of suboptimal treatment, safety was preserved in the model-based design.⁷ 

While alternative designs have remained more the exception than the rule, FDA has begun encouraging more innovative and adaptive designs in early phase studies; FDA’s recent guidance calls out a need to consider adaptive trial designs in exploratory and dose-finding studies as a way to ensure optimal dose selection while affording the opportunity to learn more about exposure, pharmacodynamics, and variability in patient response.⁸

In addition, FDA launched Project Optimus in 2022 – an initiative from the Oncology Center of Excellence (OCE), to reform the dose optimization and dose selection paradigm in oncology drug development. The project’s mission is to educate, innovate, and collaborate with companies, academia, professional societies, regulatory authorities, and patients to establish a dosage that maximizes not only efficacy, but also safety and tolerability. 

As new drugs are brought to the clinic, it is important to understand there is no one best escalation scheme that can be applied across all scenarios. While the 3+3 dose escalation in oncology study design still may be appropriate in some situations, clinical researchers should take into consideration the mechanism of action of their drug as well as the expected toxicity profile when considering study design and resist opting for the 3+3 design in every instance for its simplicity. 

To begin the conversation around your clinical development strategy for your oncology product, contact Halloran. We’re ready when you are.

References 

1. Ji, Y., & Wang, S. J. (2013). Modified toxicity probability interval design: a safer and more reliable method than the 3+ 3 design for practical phase I trials. Journal of Clinical Oncology, 31(14), 1785.
2. Hansen, A. R., Graham, D. M., Pond, G. R., & Siu, L. L. (2014). Phase 1 trial design: is 3+ 3 the best?. Cancer Control, 21(3), 200-208. 
3. Le Tourneau, C., Lee, J. J., & Siu, L. L. (2009). Dose escalation methods in phase I cancer clinical trials. JNCI: Journal of the National Cancer Institute, 101(10), 708-720. 
4. Wheeler, G. M., Mander, A. P., Bedding, A., Brock, K., Cornelius, V., Grieve, A. P., … & Bond, S. J. (2019). How to design a dose-finding study using the continual reassessment method. BMC medical research methodology, 19(1), 1-15. 
5. Yan, F., Mandrekar, S. J., & Yuan, Y. (2017). Keyboard: a novel Bayesian toxicity probability interval design for phase I clinical trials. Clinical Cancer Research, 23(15), 3994-4003. 
6. Simon, R., Rubinstein, L., Arbuck, S. G., Christian, M. C., Freidlin, B., & Collins, J. (1997). Accelerated titration designs for phase I clinical trials in oncology. Journal of the National Cancer Institute, 89(15), 1138-1147. 
7. Brummelen, E. M. J. V., Huitema, A. D. R., Werkhoven, E. V., Beijnen, J. H., & Schellens, J. H. M. (2016). The performance of model-based versus rule-based phase I clinical trials in oncology. Journal of Pharmacokinetics and Pharmacodynamics, 43(3), 235–242. doi: 10.1007/s10928-016-9466-0. 
8. FDA. Guidance for Industry: Optimizing the Dosage of Human Prescription Drugs and Biological Products for the Treatment of Oncologic Diseases. 2023. https://www.fda.gov/media/164555/download. Accessed 13 February 2024.