Dose optimization during drug development: whether and when to optimize

Dose selection of novel anticancer drugs: exposing the gap between selected and required doses

Historically, dose selection of anticancer drugs has mainly been based on establishing the maximum tolerated dose in phase 1 clinical trials with a traditional 3 plus 3 design. In the era of targeted therapies and immune-modulating agents, this approach does not necessarily lead to selection of the most favourable dose. This strategy can introduce potentially avoidable toxicity or inconvenience for patients. Multiple changes in drug development could lead to more rational dose selection, such as use of better predictive preclinical models, adaptive and randomised trial design, evaluation of multiple dose levels in late-phase development, assessment of target activity and saturation, and early biomarker use for efficacy and safety evaluation. In this Review, we evaluate the rationale and validation of dose selection in each phase of drug development for anticancer drugs approved by the European Medicines Agency and US Food and Drug Administration from Jan 1, 2020, to June 30, 2023, and give recommendations for dose optimisation to improve safety and patient convenience. In our evaluation, we classified 20 (65%) of the 31 recently registered anticancer agents as potential candidates for dose optimisation, which could be achieved either by reducing the dose (n=10 [32%]) or adjusting the dosage regimen (n=10 [32%]). Dose selection seemed to be adequately justified for nine (29%) of the drugs, whereas the reviewed data were inconclusive for formulating a recommendation on dose optimisation for two (6%) of the drugs.

Optimus-Era Dose Finding for Rare Cancers

SUMMARY

Advances in cancer biology and diagnostics have led to the recognition of a multitude of rare cancer subtypes, emphasizing the pressing need for strategies to accelerate drug development for patients with these cancers. This paper addresses the unique challenges of dose finding in trials that accrue small numbers of patients with rare cancers; strategies for dose optimization are proposed, in line with evolving approaches to dose determination in the age of the US Food and Drug Administration's Project Optimus.

Rethinking dose optimization in drug development: A goal of Project Optimus is to educate and collaborate with drug developers to select doses of drugs under investigation with a focus on maximizing efficacy, safety, and tolerability

This news section offers Cancer readers timely information on events, public policy analysis, topical issues, and personalities. In this issue, the launch of Project Optimus in 2022 by the US Food and Drug Administration (FDA) represents a much‐needed shift in the way that drug trials in cancer are conducted. In addition, the FDA approves the first immunotherapy drug for nasopharyngeal carcinoma, and Cancer Statistics 2024 reports on cancer facts and trends by the American Cancer Society.

Statistical and practical considerations in planning and conduct of dose-optimization trials

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.

Backfilling Patients in Phase I Dose-Escalation Trials Using Bayesian Optimal Interval Design (BOIN)

In recent years, there has been increased interest in incorporation of backfilling into dose-escalation clinical trials, which involves concurrently assigning patients to doses that have been previously cleared for safety by the dose-escalation design. Backfilling generates additional information on safety, tolerability, and preliminary activity on a range of doses below the maximum tolerated dose (MTD), which is relevant for selection of the recommended phase II dose and dose optimization. However, in practice, backfilling may not be rigorously defined in trial protocols and implemented consistently. Furthermore, backfilling designs require careful planning to minimize the probability of treating additional patients with potentially inactive agents (and/or subtherapeutic doses). In this paper, we propose a simple and principled approach to incorporate backfilling into the Bayesian optimal interval design (BOIN). The design integrates data from the dose-escalation and backfilling components of the design and ensures that the additional patients are treated at doses where some activity has been seen. Simulation studies demonstrated that the proposed backfilling BOIN design (BF-BOIN) generates additional data for future dose optimization, maintains the accuracy of the MTD identification, and improves patient safety without prolonging the trial duration.

Sotorasib plus Panitumumab in Refractory Colorectal Cancer with Mutated KRAS G12C

BACKGROUND

KRAS G12C is a mutation that occurs in approximately 3 to 4% of patients with metastatic colorectal cancer. Monotherapy with KRAS G12C inhibitors has yielded only modest efficacy. Combining the KRAS G12C inhibitor sotorasib with panitumumab, an epidermal growth factor receptor (EGFR) inhibitor, may be an effective strategy.

METHODS

In this phase 3, multicenter, open-label, randomized trial, we assigned patients with chemorefractory metastatic colorectal cancer with mutated KRAS G12C who had not received previous treatment with a KRAS G12C inhibitor to receive sotorasib at a dose of 960 mg once daily plus panitumumab (53 patients), sotorasib at a dose of 240 mg once daily plus panitumumab (53 patients), or the investigator's choice of trifluridine-tipiracil or regorafenib (standard care; 54 patients). The primary end point was progression-free survival as assessed by blinded independent central review according to the Response Evaluation Criteria in Solid Tumors, version 1.1. Key secondary end points were overall survival and objective response.

RESULTS

After a median follow-up of 7.8 months (range, 0.1 to 13.9), the median progression-free survival was 5.6 months (95% confidence interval [CI], 4.2 to 6.3) and 3.9 months (95% CI, 3.7 to 5.8) in the 960-mg sotorasib-panitumumab and 240-mg sotorasib-panitumumab groups, respectively, as compared with 2.2 months (95% CI, 1.9 to 3.9) in the standard-care group. The hazard ratio for disease progression or death in the 960-mg sotorasib-panitumumab group as compared with the standard-care group was 0.49 (95% CI, 0.30 to 0.80; P = 0.006), and the hazard ratio in the 240-mg sotorasib-panitumumab group was 0.58 (95% CI, 0.36 to 0.93; P = 0.03). Overall survival data are maturing. The objective response was 26.4% (95% CI, 15.3 to 40.3), 5.7% (95% CI, 1.2 to 15.7), and 0% (95% CI, 0.0 to 6.6) in the 960-mg sotorasib-panitumumab, 240-mg sotorasib-panitumumab, and standard-care groups, respectively. Treatment-related adverse events of grade 3 or higher occurred in 35.8%, 30.2%, and 43.1% of patients, respectively. Skin-related toxic effects and hypomagnesemia were the most common adverse events observed with sotorasib-panitumumab.

CONCLUSIONS

In this phase 3 trial of a KRAS G12C inhibitor plus an EGFR inhibitor in patients with chemorefractory metastatic colorectal cancer, both doses of sotorasib in combination with panitumumab resulted in longer progression-free survival than standard treatment. Toxic effects were as expected for either agent alone and resulted in few discontinuations of treatment. (Funded by Amgen; CodeBreaK 300 ClinicalTrials.gov number, NCT05198934.).

Novel Clinical Trial Designs with Dose Optimization to Improve Long-term Outcomes

Conventional designs for choosing a dose for a new therapy may select doses that are unsafe or ineffective and fail to optimize progression-free survival time, overall survival time, or response/remission duration. We explain and illustrate limitations of conventional dose-finding designs and make four recommendations to address these problems. When feasible, a dose-finding design should account for long-term outcomes, include screening rules that drop unsafe or ineffective doses, enroll an adequate sample size, and randomize patients among doses. As illustrations, we review three designs that include one or more of these features. The first illustration is a trial that randomized patients among two cell therapy doses and standard of care in a setting where it was assumed on biological grounds that dose toxicity and dose-response curves did not necessarily increase with cell dose. The second design generalizes phase I-II by first identifying a set of candidate doses, rather than one dose, randomizing additional patients among the candidates, and selecting an optimal dose to maximize progression-free survival over a longer follow-up period. The third design combines a phase I-II trial and a group sequential randomized phase III trial by using survival time data available after the first stage of phase III to reoptimize the dose selected in phase I-II. By incorporating one or more of the recommended features, these designs improve the likelihood that a selected dose or schedule will be optimal, and thus will benefit future patients and obtain regulatory approval.

Skipping a pillar does not make for strong foundations: Pharmacokinetic-pharmacodynamic reasoning behind the shape of dose-response relationships in oncology

Dose-response analysis is often applied to the quantification of drug-effect especially for slowly responding disease end points where a comparison is made across dose levels after a particular period of treatment. It has long been recognized that exposure - response is more appropriate than dose-response. However, trials necessarily are designed as dose-response experiments. Second, a wide range of functional forms are used to express relationships between dose and response. These considerations are also important for clinical development because pharmacokinetic (PK; and variability) plus pharmacokinetic-pharmacodynamic modeling may allow one to anticipate the shape of the dose-response curve and so the trial design. Here, we describe how the location and steepness of the dose response is determined by the PKs of the compound being tested and its exposure-response relationship in terms of potency (location), efficacy (maximum effect) and Hill coefficient (steepness). Thus, the location (50% effective dose [ED50 ]) is dependent not only on the potency (half-maximal effective concentration) but also the compound's PKs. Similarly, the steepness of the dose response is shown to be a function of the half-life of the drug. It is also shown that the shape of relationship varies dependent on the assumed time course of the disease. This is important in the context of drug-discovery where the in vivo potencies of compounds are compared as well as when considering an analysis of summary data (for example, model-based meta-analysis) for clinical decision making.