Dose optimization during drug development: whether and when to optimize

Oncology Dose Optimization: Tailored Approaches to Different Molecular Classes

Oncology dose optimization during the era of chemotherapy focused on identifying the maximum tolerated dose (MTD) for registrational trials, often resulting in significant toxicity. The advent of molecular targeted drugs and immunotherapies offers the potential to achieve similar efficacy with lower doses and fewer side effects, as maximal efficacy is often reached at doses below the MTD. Recent FDA guidance outlines expectations for improving dose optimization in oncology drug development. This review presents a framework for tailored dose optimization by categorizing oncology molecules into four distinct classes based on their mechanisms of action and clinical activities: small molecule targeted therapies and antibody-drug conjugates (Class 1), large molecule antagonists (Class 2), cancer immunotherapy agonists (Class 3), and molecules with limited or no single-agent activity (Class 4). Unique dose optimization considerations for each class are discussed, supported by illustrative case examples. To enhance robust dose decision-making and optimize patient resource utilization, we propose using proof of activity as a gate for initiating dose expansion with one or multiple dose levels. This review emphasizes the importance of integrating all relevant preclinical data, disease knowledge, and clinical measurements and highlights the essential role of quantitative pharmacology and statistical modeling in optimizing doses.

Exposure-Response-Based Multiattribute Clinical Utility Score Framework to Facilitate Optimal Dose Selection for Oncology Drugs

PURPOSE

The advent of new therapeutic modalities highlighted deficiencies in the traditional maximum tolerated dose approach for oncology drug dose selection and prompted the Food and Drug Administration (FDA)'s Project Optimus initiative, which suggests that sponsors take a holistic approach, including efficacy, safety, and pharmacokinetic (PK) and pharmacodynamic data, in conjunction with integrated exposure-response (ER) analyses. However, this method comes with an inherent challenge of the collation of the multisource data. To address this issue, an ER-based clinical utility score (CUS) framework, combining benefit and risk into a single measurement, was developed.

METHODS

Model-predicted outcomes for each clinically relevant end point, informed by ER modeling, are converted to a CUS using a user-defined utility function. Thereafter, individual CUS is integrated into a single score with user-defined weighting for each end point. The user-defined weighting feature allows the user to incorporate expert knowledge/understanding into weighing the product's benefit versus risk profile.

RESULTS

To validate the framework, data were leveraged from over 50 oncology programs from 2019 to 2023 on the basis of FDA new drug application/biologics license application review packages and/or related literature studies. Five representative cases were selected for in-depth evaluation. Results showed that the optimal benefit-risk ratio (highest CUS) was consistently observed at PK exposures synonymous with recommended doses. A recurring theme across cases was a greater emphasis on safety over efficacy in oncology drug dose determination.

CONCLUSION

The ER-based CUS framework offers a strategic tool to navigate the complexities of dose selection in oncology programs. It serves as a pillar to the importance of integrative data analysis, aligning with the vision of Project Optimus, and demonstrates its potential in guiding dose optimization by balancing therapeutic benefits against risk.

Sex differences in the pharmacokinetics of anticancer drugs: a systematic review

BACKGROUND

In addition to the effect of body weight, a patient's sex can influence the pharmacokinetics (PK) of anticancer agents, and thereby their activity and safety. The magnitude and relevance of sex differences, however, are currently unclear.

METHODS

We carried out a systematic review of published studies (clinical, n ≥ 10) on Food and Drug Administration (FDA)-approved (on 31 January 2022) anticancer drugs (excluding hormonal agents), aiming to identify significant PK differences between male and female patients. A difference of ≥20% on PK parameters (clearance or trough concentration) was considered significant. The methodological quality was assessed using the National Institutes of Health study quality assessment tool. This systematic review was conducted according to the PRISMA2020 guidelines and a previously published protocol, which was registered in the PROSPERO database (number 291008).

RESULTS

Data on 99 anticancer agents (for a total of 1643 abstracts and European Medicines Agency/FDA documents) were screened. The final dataset included 112 articles and 8 European Medicines Agency/FDA documents. The median size of a study cohort was 445 patients (range: 12-6468 patients). Significant PK differences (>+20% in clearance or apparent clearance in women) were identified for 14 drugs, and potentially significant PK differences (due to conflicting reports) for another 8 drugs. None of the studies included sex-based summaries to assess whether the observed differences in PK may impact the efficacy or safety profile.

CONCLUSIONS

Significant sex differences in PK have been identified including commonly used drugs of different classes, such as 5-fluorouracil, doxorubicin, paclitaxel, regorafenib, atezolizumab, and temozolomide. The risk-benefit ratio for such anticancer drugs is likely to be improved by the development of sex-specific dosing strategies. Additional sex-based PK-pharmacodynamic analyses are recommended during dose optimisation and are to be conducted in line with the FDA Project Optimus guidance. They should be reported even if no association between the patients' sex and the activity and/or toxicity of an anticancer drug has been identified.

Repurposing propofol for breast cancer therapy through promoting apoptosis and arresting cell cycle

Breast cancer is the most prevalent cancer among women worldwide, characterized by a high mortality rate and propensity for metastasis. Although surgery is the standard treatment for breast cancer, there is still no effective method to inhibit tumor metastasis and improve the prognosis of patients with breast cancer after surgery. Propofol, one of the most widely used intravenous anesthetics in surgery, has exhibited a positive association with improved survival outcomes in patients with breast cancer post‑surgery. However, the underlying molecular mechanism remains to be elucidated. The present study revealed that triple negative breast cancer cells, MDA‑MB‑231 and 4T1, exposed to propofol exhibited a significant decrease in cell viability. Notably, propofol exhibited minimal cytotoxic effects on HUVECs under the same conditions. Furthermore, propofol significantly inhibited the migration and invasion ability of MDA‑MB‑231 and 4T1 cells. Propofol promoted apoptosis in 4T1 cells through upregulation of Bax and cleaved caspase 3, while downregulating B‑cell lymphoma‑extra large. Concomitantly, propofol induced cell cycle arrest of 4T1 cells by downregulating cyclin E2 and phosphorylated cell division cycle 6. Furthermore, propofol exhibited excellent anticancer efficacy in a 4T1 breast cancer allograft mouse model. The present study sheds light on the potential of propofol as an old medicine with a novel use for breast cancer treatment.

Dose Optimization in Oncology Drug Development: An International Consortium for Innovation and Quality in Pharmaceutical Development White Paper

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case-by-case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence-based approach to optimize oncology drug dosing regimens effectively.

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.