Comparative review of novel model-assisted designs for phase I/II clinical trials.

In recent years, both model-based and model-assisted designs have emerged to efficiently determine the optimal biological dose (OBD) in phase I/II trials for immunotherapy and targeted cellular agents. Model-based designs necessitate repeated model fitting and computationally intensive posterior sampling for each dose-assignment decision, limiting their practical application in real trials. On the other hand, model-assisted designs employ simple statistical models and facilitate the precalculation of a decision table for use throughout the trial, eliminating the need for repeated model fitting. Due to their simplicity and transparency, model-assisted designs are often preferred in phase I/II trials. In this paper, we systematically evaluate and compare the operating characteristics of several recent model-assisted phase I/II designs, including TEPI, PRINTE, Joint i3+3, BOIN-ET, STEIN, uTPI, and BOIN12, in addition to the well-known model-based EffTox design, using comprehensive numerical simulations. To ensure an unbiased comparison, we generated 10,000 dosing scenarios using a random scenario generation algorithm for each predetermined OBD location. We thoroughly assess various performance metrics, such as the selection percentages, average patient allocation to OBD, and overdose percentages across the eight designs. Based on these assessments, we offer design recommendations tailored to different objectives, sample sizes, and starting dose locations.

The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies.

In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials.

Current issues in dose-finding designs: A response to the US Food and Drug Adminstration's Oncology Center of Excellence Project Optimus.

With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration's Oncology Center of Excellence initiated Project Optimus, with the goal "to reform the dose optimization and dose selection paradigm in oncology drug development." As a response to Project Optimus, the articles in this special issue of Clinical Trials review recent advances in methods for choosing the dose or schedule of a new agent with an overall objective of informing clinical trialists of these innovative designs. This introductory article briefly reviews problems with conventional methods, the regulatory changes that encourage better dose optimization designs, and provides brief summaries of the articles that follow in this special issue.

Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies.

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.

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.

Risk-benefit trade-offs and precision utilities in phase I-II clinical trials.

BACKGROUND: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life. PURPOSE: While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups. METHODS AND CONCLUSIONS: Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.

Rapid cGMP manufacturing of COVID-19 monoclonal antibody using stable CHO cell pools.

Therapeutic proteins, including monoclonal antibodies, are typically manufactured using clonally derived, stable host cell lines, since consistent and predictable cell culture performance is highly desirable. However, selecting and preparing banks of stable clones takes considerable time, which inevitably extends overall development timelines for new therapeutics by delaying the start of subsequent activities, such as the scale-up of manufacturing processes. In the context of the coronavirus disease 2019 (COVID-19) pandemic, with its intense pressure for accelerated development strategies, we used a novel transposon-based Leap-In Transposase® system to rapidly generate high-titer stable pools and then used them directly for large scale-manufacturing of an anti-severe acute respiratory syndrome coronavirus 2 monoclonal antibody under cGMP. We performed the safety testing of our non-clonal cell bank, then used it to produce material at a 200L-scale for preclinical safety studies and formulation development work, and thereafter at 2000L scale for supply of material for a Phase 1 clinical trial. Testing demonstrated the comparability of critical product qualities between the two scales and, more importantly, that our final clinical trial product met all pre-set product quality specifications. The above expediated approach provided clinical trial material within 4.5 months, in comparison to 12-14 months for production of clinical trial material via the conventional approach.

Meta-analysis of Pharmacokinetic/Pharmacodynamic Results of 3 Phase 1 Studies with Biosimilar Pegfilgrastim.

A meta-analysis using data from 3 phase 1 studies evaluated the pharmacokinetics (PK) and pharmacodynamics (PD) of Sandoz biosimilar versus US- and EU-reference pegfilgrastim. The studies included a single-dose, double-blind, 3-arm, parallel-group study (study 1); a single-dose, double-blind, 2-way crossover study (study 2); and a single-dose, double-blind, 3-way, 6-sequence crossover study (study 3). Healthy male and female subjects were randomized to receive the proposed biosimilar (all studies), US-reference biologic (studies 1 and 3), or EU-reference biologic (studies 1, 2, and 3). For PK parameters (area under the serum concentration-time curve from time of dosing and extrapolated to infinity, area under the serum concentration-time curve from the time of dosing to the last measurable concentration, and maximum observed serum concentration) and PD parameters (absolute neutrophil count area under the effect curve from the time of dosing to the last measurable concentration and maximum measured absolute neutrophil count) geometric mean ratios and 90% confidence intervals (CIs) for treatment comparisons were calculated using the meta-analysis approach with a fixed-effects model. PK/PD biosimilarity was concluded if the 90%CIs were within the equivalence margins of 0.80 to 1.25. The 90%CIs for the geometric mean ratios for the PK/PD parameters were all within the equivalence margins. Safety and tolerability were similar between the proposed biosimilar and the US- and EU-reference pegfilgrastim in healthy subjects. This meta-analysis of 3 phase 1 studies supports PK/PD similarity of Sandoz biosimilar pegfilgrastim to US- and EU-reference pegfilgrastim. No clinically meaningful differences in safety or tolerability were observed.

Designing Dose-Finding Phase I Clinical Trials: Top 10 Questions That Should Be Discussed With Your Statistician.

In recent years, the landscape in clinical trial development has changed to involve many molecularly targeted agents, immunotherapies, or radiotherapy, as a single agent or in combination. Given their different mechanisms of action and lengths of administration, these agents have different toxicity profiles, which has resulted in numerous challenges when applying traditional designs such as the 3 + 3 design in dose-finding clinical trials. Novel methods have been proposed to address these design challenges such as combinations of therapies or late-onset toxicities. However, their design and implementation require close collaboration between clinicians and statisticians to ensure that the appropriate design is selected to address the aims of the study and that the design assumptions are pertinent to the study drug. The goal of this paper is to provide guidelines for appropriate questions that should be considered early in the design stage to facilitate the interactions between clinical and statistical teams and to improve the design of dose-finding clinical trials for novel anticancer agents.

Randomised Phase 1 clinical trials in oncology.

The aims of Phase 1 trials in oncology have broadened considerably from simply demonstrating that the agent/regimen of interest is well tolerated in a relatively heterogeneous patient population to addressing multiple objectives under the heading of early-phase trials and, if possible, obtaining reliable evidence regarding clinical activity to lead to drug approvals via the Accelerated Approval approach or Breakthrough Therapy designation in cases where the tumours are rare, prognosis is poor or where there might be an unmet therapeutic need. Constructing a Phase 1 design that can address multiple objectives within the context of a single trial is not simple. Randomisation can play an important role, but carrying out such randomisation according to the principles of equipoise is a significant challenge in the Phase 1 setting. If the emerging data are not sufficient to definitively address the aims early on, then a proper design can reduce biases, enhance interpretability, and maximise information so that the Phase 1 data can be more compelling. This article outlines objectives and design considerations that need to be adhered to in order to respect ethical and scientific principles required for research in human subjects in early phase clinical trials.

Rolling continual reassessment method with overdose control: An efficient and safe dose escalation design.

In phase 1 dose escalation studies, dose limiting toxicities (DLTs) are defined as adverse events of concern occurring during a predefined time window after first dosing of patients. Standard dose escalation designs, such as the continual reassessment method (CRM), only utilize this binary DLT information. Thus, late-onset DLTs are usually not accounted for when CRM guiding the dose escalation and finally defining the maximum tolerated dose (MTD) of the drug, which brings safety concerns for patients. Previously, several extensions of CRMs, such as the time-to-event CRM (TITE-CRM), fractional CRM (fCRM) and the data augmented CRM (DA-CRM), have been proposed to handle this issue without prolonging trial duration. However, among the model-based designs, none of the designs have explicitly controlled the risk of overdosing as in the escalation with overdose control (EWOC) design. Here we propose a novel dose escalation with overdose control design using a two-parameter logistic regression model for the probability of DLT depending on the dose and a piecewise exponential model for the time to DLT distribution, which we call rolling-CRM design. A comprehensive simulation study has been conducted to compare the performance of the rolling-CRM design with other dose escalation designs. Of note, the trial duration is significantly shorter compared to traditional CRM designs. The proposed design also retains overdose control characteristics, but might require a larger sample size compared to traditional CRM designs.

Safety and tolerability of fixed-dose combinations of ibuprofen and acetaminophen: pooled analysis of phase 1-3 clinical trials.

OBJECTIVES: An ibuprofen (IBU)/acetaminophen (APAP) fixed-dose combination (FDC) for over-the-counter (OTC) use was developed with the goal of providing the same effective analgesic activity as full doses of the individual monocomponents, while reducing individual monocomponent drug exposures. Here, the safety and tolerability of the FDC is characterized using pooled safety data from phase 1-3 clinical trials in the FDC development program. METHODS: We conducted a pooled safety analysis of data from 7 clinical trials: three phase 1 pharmacokinetic trials, a phase 2 proof-of-concept trial, and three phase 3 trials (a single- and a multiple-dose trial in a dental pain model and a single-dose trial in an induced-fever model). Safety and tolerability of the FDC were assessed by adverse events (AEs) for the total group and subgroups (age, sex, race). RESULTS: A total of 1,477 participants were enrolled in the 7 trials; 715 were treated with FDC IBU/APAP, 432 with IBU monotherapy, 330 with APAP monotherapy, and 156 with placebo. Most subjects were white (86.5%), and 44% were female. Two trials enrolling 195 adolescents accounted for 13.2% of the overall study population. All-causality treatment-emergent AEs (TEAEs) occurred in 19.7% of the 1477 participants. Nausea (13.5%), vomiting (7.4%), dizziness (4.5%), headache (1.2%), and feeling hot (1.0%) were the only TEAEs reported in ≥1% of subjects. Treatment-related AEs occurred in 1.8% of the subjects in the overall population. The incidence of AEs, including treatment-related AEs, was consistently lower in all active treatment groups than in the placebo group; this also applied to subgroups according to sex, race, and age, including adolescents aged 12-17 years. The higher rate of AEs with placebo was likely due to lack of pain/fever control. CONCLUSION: Single-dose or short-course FDC IBU/APAP OTC use was well tolerated, with an AE profile similar to its IBU and APAP monocomponents. CLINICALTRIALS.GOV REGISTRATION: NCT01559259; NCT02912650; NCT02837952; NCT02761980. The pharmacokinetic studies (n = 3) did not require registration.

Assessment of various continual reassessment method models for dose-escalation phase 1 oncology clinical trials: using real clinical data and simulation studies.

BACKGROUND: The continual reassessment method (CRM) identifies the maximum tolerated dose (MTD) more efficiently and identifies the true MTD more frequently compared to standard methods such as the 3 + 3 method. An initial estimate of the dose-toxicity relationship (prior skeleton) is required, and there is limited guidance on how to select this. Previously, we compared the CRM with six different skeletons to the 3 + 3 method by conducting post-hoc analysis on a phase 1 oncology study (AZD3514), each CRM model reduced the number of patients allocated to suboptimal and toxic doses. This manuscript extends this work by assessing the ability of the 3 + 3 method and the CRM with different skeletons in determining the true MTD of various "true" dose-toxicity relationships. METHODS: One thousand studies were simulated for each "true" dose toxicity relationship considered, four were based on clinical trial data (AZD3514, AZD1208, AZD1480, AZD4877), and four were theoretical. The 3 + 3 method and 2-stage extended CRM with six skeletons were applied to identify the MTD, where the true MTD was considered as the largest dose where the probability of experiencing a dose limiting toxicity (DLT) is ≤33%. RESULTS: For every true dose-toxicity relationship, the CRM selected the MTD that matched the true MTD in a higher proportion of studies compared to the 3 + 3 method. The CRM overestimated the MTD in a higher proportion of simulations compared to the 3 + 3 method. The proportion of studies where the correct MTD was selected varied considerably between skeletons. For some true dose-toxicity relationships, some skeletons identified the true MTD in a higher proportion of scenarios compared to the skeleton that matched the true dose-toxicity relationship. CONCLUSION: Through simulation, the CRM generally outperformed the 3 + 3 method for the clinical and theoretical true dose-toxicity relationships. It was observed that accurate estimates of the true skeleton do not always outperform a generic skeleton, therefore the application of wide confidence intervals may enable a generic skeleton to be used. Further work is needed to determine the optimum skeleton.

Optimizing the Therapeutic Window of Targeted Drugs in Oncology: Potency-Guided First-in-Human Studies.

Many targeted therapies are administered at or near the maximum tolerated dose (MTD). With the advent of precision medicine, a larger therapeutic window is expected. Therefore, dose optimization will require a new approach to early clinical trial design. We analyzed publicly available data for 21 therapies targeting six kinases, and four poly (ADP-ribose) polymerase inhibitors, focusing on potency and exposure to gain insight into dose selection. The free average steady-state concentration (Css ) at the approved dose was compared to the in vitro cell potency (half-maximal inhibitory concentration (IC50 )). Average steady-state area under the plasma concentration-time curve, the fraction unbound drug in plasma, and the cell potency were taken from the US drug labels, US and European regulatory reviews, and peer-reviewed journal articles. The Css was remarkably similar to the IC50 . The median Css /IC50 value was 1.2, and 76% of the values were within 3-fold of unity. However, three drugs (encorafenib, erlotinib, and ribociclib) had a Css /IC50 value > 25. Seven other therapies targeting the same 3 kinases had much lower Css /IC50 values ranging from 0.5 to 4. These data suggest that these kinase inhibitors have a large therapeutic window that is not fully exploited; lower doses may be similarly efficacious with improved tolerability. We propose a revised first-in-human trial design in which dose cohort expansion is initiated at doses less than the MTD when there is evidence of clinical activity and Css exceeds a potency threshold. This potency-guided approach is expected to maximize the therapeutic window thereby improving patient outcomes.

Systematic review of gender bias in vortioxetine clinical trials.

This paper evaluates gender bias in the published clinical trials of Vortioxetine. We conducted a systematic review of controlled clinical trials of Vortioxetine for the treatment of depression. The literature search was performed using MEDLINE and following the corresponding international recommendations. We identified 42 articles, of which 23 were included. The proportion of women ranged from 47%-75% and the percentage of women included in the 10,404 total patients sample was 65%. The separate analysis of the main variable between the subpopulations of men and women was only carried out in 3/23 publications included. In contrast, 6/23 trials analyzed secondary variables separated by sex. No trials discussed the results separately by sex. The proportion of women included was slightly higher than that in clinical trials of other antidepressants. However, the analysis of the main result or secondary variables by sex, as well as discussing the results separately by sex, are scarce. This gives rise to gender bias in these works.

Early 3+3 Trial Dose-Escalation Phase I Clinical Trial Design and Suitability for Immune Checkpoint Inhibitors.

PURPOSE: Despite the expansion of immune checkpoint inhibitor (ICI) indications, the relationship between ICI dose and toxicity or response is not well established. To understand this correlation, we performed a meta-analysis of ICI trials that used dose escalation. EXPERIMENTAL DESIGN: We searched PubMed and abstracts presented at (inter)national meetings for trials using FDA-approved ICIs. The reported rates of grade 3-5 adverse events (G3-5 AE), immune-related adverse events (irAE), and response were correlated with doses within each ICI using marginal exact generalized linear models. RESULTS: A total of 74 trials (7,469 patients) published between January 2010 and January 2017 were included. For ipilimumab, the incidence of G3-5 AEs was 34% with a significant 27% reduced risk in lower doses (P = 0.002). However, no relationship was observed between dose and irAEs or response. For nivolumab, the incidence of G3-5 AEs was 20.1% which was lower in non-small cell lung cancer (NSCLC) compared with renal cell carcinoma (RCC) or melanoma (P ≤ 0.05) with no dose-toxicity relationship. In melanoma and NSCLC, a dose-response association was observed, which was not observed in RCC. For pembrolizumab, the incidence of G3-5 AEs was 13.3%, which was lower in melanoma compared with NSCLC (P = 0.03) with no dose-toxicity relationship. In melanoma, lower dose levels correlated with decreased odds of response (P = 0.01), a relationship that was not observed in NSCLC. CONCLUSIONS: Our analysis shows a lack of consistent dose-toxicity or dose-response correlation with ICIs. Therefore, dose escalation is not an appropriate design to conduct ICI studies. Here we present an innovative trial design for immune-modulating agents.

Phase 1 Trial of MLN0128 (Sapanisertib) and CB-839 HCl (Telaglenastat) in Patients With Advanced NSCLC (NCI 10327): Rationale and Study Design.

INTRODUCTION: There are currently no approved targeted therapies for lung squamous-cell carcinoma (LSCC) and KRAS-mutant lung adenocarcinoma (LUAD). About 30% of LSCC and 25% of KRAS-mutant LUAD exhibit hyperactive NRF2 pathway activation through mutations in NFE2L2 (the gene encoding NRF2) or its negative regulator, KEAP1. Preclinical data demonstrate that these tumors are uniquely sensitive to dual inhibition of glycolysis and glutaminolysis via mammalian target of rapamycin (mTOR) and glutaminase inhibitors. This phase 1 study was designed to assess safety and preliminary activity of the mTOR inhibitor MLN0128 (sapanisertib) in combination with the glutaminase inhibitor CB-839 HCl. METHODS: Phase 1 dose finding will use the queue-based variation of the 3 + 3 dose escalation scheme with the primary endpoint of identifying the recommended expansion dose. To confirm the acceptable tolerability of the recommended expansion dose, patients will subsequently enroll onto 1 of 4 expansion cohorts (n = 14 per cohort): (1) LSCC harboring NFE2L2 or (2) KEAP1 mutations, or (3) LUAD harboring KRAS/(KEAP1 or NFE2L2) coalterations, or (4) LSCC wild type for NFE2L2 and KEAP1. The primary endpoint of the dose expansion is to determine the preliminary efficacy of MLN0128/CB-839 combination therapy. CONCLUSION: This phase 1 study will determine the recommended expansion dose and preliminary efficacy of MLN0128 and CB-839 in advanced non-small-cell lung cancer with a focus on subsets of LSCC and KRAS-mutant LUAD harboring NFE2L2 or KEAP1 mutations.