Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of “more is better” is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I–II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I–II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I–II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose–outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I–II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.

With the development of targeted therapy, immunotherapy, and antibody‐drug conjugates (ADCs), there is growing concern over the “more is better” paradigm developed decades ago for chemotherapy, prompting the US Food and Drug Administration (FDA) to initiate Project Optimus to reform dose optimization and selection in oncology drug development. For early‐phase oncology trials, given the high variability from sparse data and the rigidity of parametric model specifications, we use Bayesian dynamic models to borrow information across doses with only vague order constraints. Our proposed adaptive design simultaneously incorporates toxicity and efficacy outcomes to select the optimal dose (OD) in Phase I/II clinical trials, utilizing Bayesian model averaging to address the uncertainty of dose–response relationships and enhance the robustness of the design. Additionally, we extend the proposed design to handle delayed toxicity and efficacy outcomes. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed method under various practical scenarios. The results demonstrate that the proposed designs have desirable operating characteristics. A trial example is presented to demonstrate the practical implementation of the proposed designs.

A primary objective of oncology dose‐finding trials for novel therapies is to determine an optimal biological dose (OBD) that is both tolerable and therapeutically beneficial for patients in subsequent clinical trials. These new therapeutic agents are more likely to induce multiple low‐ or moderate‐grade toxicities rather than dose‐limiting toxicities. Additionally, efficacy is evaluated comprehensively, differentiating between complete remission and partial remission, as well as incorporating continuous efficacy endpoints. This important issue was highlighted in the American Statistical Association (ASA) Biopharmaceutical (BIOP) Section open forums and was a significant consideration of the FDA's “Project Optimus.” We proposed the UNITED design, a unified, transparent, and efficient Phase I/II trial design to incorporate toxicity and efficacy grades and types, as well as continuous efficacy responses, into dose‐finding and optimization. The UNITED design can handle binary, quasi‐binary, continuous, and mixed toxicity and efficacy endpoints. We further extended the UNITED design, referred to as TITE‐UNITED, to accommodate delayed toxicity and efficacy outcomes. Simulation studies showed that the UNITED and TITE‐UNITED designs have desirable operating characteristics, performing comparably to or better than existing designs. A user‐friendly software is available for practical implementation.

The integration of backfill cohorts into Phase I clinical trials has garnered increasing interest within the clinical community, particularly following the “Project Optimus” initiative by the U.S. Food and Drug Administration, as detailed in their final guidance of August 2024. This approach allows for the collection of additional clinical data to assess safety and activity before initiating trials that compare multiple dosages. For novel cancer treatments such as targeted therapies, immunotherapies, antibody-drug conjugates, and chimeric antigen receptor T-cell therapies, the efficacy of a drug may not necessarily increase with dose levels. Backfill strategies are especially beneficial as they enable the continuation of patient enrollment at lower doses while higher doses are being explored. We propose a robust Bayesian design framework that borrows information across dose levels without imposing stringent parametric assumptions on dose–response curves. This framework minimizes the risk of administering subtherapeutic doses by jointly evaluating toxicity and efficacy, and by effectively addressing the challenge of delayed outcomes. Simulation studies demonstrate that our design not only generates additional data for late stage studies but also enhances the accuracy of optimal dose selection, improves patient safety, reduces the number of patients receiving subtherapeutic doses, and shortens trial duration across various realistic trial settings.

Combination therapies involving novel agents, such as immunotherapies and targeted therapies, offer significant antitumor benefits by increasing dose intensity, targeting multiple pathways, and benefiting a broader patient population. To further explore these advantages, the National Cancer Institute (NCI) has initiated Combination Therapy Platform Trial with Molecular Analysis for Therapy Choice (ComboMATCH) to evaluate the effectiveness of new drug combinations in treating both adults and children. However, designing dose optimization trials for these combination therapies presents substantial challenges due to the complex interactions and unique mechanisms of action. To address these challenges, we propose COMPACT, a Bayesian phase I-II randomized design for combination cancer therapies that uses progression-free survival (PFS) as the primary efficacy endpoint to identify the optimal dose combination (ODC) based on restricted mean survival time (RMST). The COMPACT design jointly evaluates both toxicity and PFS, with continuous toxicity monitoring throughout the trial. Toxicity probabilities are modeled using a partial ordering assumption without relying on complex parametric models, while PFS is modeled through a Bayesian Pareto proportional hazards model with gamma-shared frailty. The trial consists of two seamlessly connected stages. In the first stage, the dose space is explored primarily based on toxicity, while PFS data are concurrently collected. In the second stage, patients are adaptively randomized to safe and potentially promising dose combinations based on PFS, and the dose combination with the highest RMST among those deemed safe is selected as the ODC. Simulation studies demonstrate that COMPACT has desirable operating characteristics and outperforms conventional designs in identifying the ODC, allocating more patients to ODC, while maintaining patient safety. Sensitivity analysis is performed to examine the robustness of the proposed design. A trial example is provided to facilitate the practical implementation of the proposed COMPACT design. The proposed COMPACT design offers a novel and robust framework for combination cancer therapies with progression-free survival end point.

In immunotherapy, both the dose and the schedule of drug administration can significantly influence therapeutic effects by modulating immune system activation. Incorporating immune response measures into clinical trial designs offers an opportunity to enhance decision‐making by leveraging their close association with therapeutic efficacy and toxicity. Motivated by settings where biomarker data indicate improved efficacy in biomarker‐positive patients, we propose a dose–schedule optimization strategy tailored to each biomarker‐defined subgroup, based on elicited utility functions that capture risk‐benefit tradeoffs. We introduce a joint modeling framework that simultaneously evaluates immune response, toxicity, and efficacy, enabling information sharing across outcome types and patient subgroups. Our approach utilizes parsimonious yet flexible models designed specifically to address challenges due to small sample sizes commonly encountered in early‐phase trials. Simulation studies demonstrate that the proposed design achieves desirable operating characteristics and effectively informs dose–schedule optimization.