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The Bayesian logistic regression method (BLRM) is a widely adopted and flexible design for finding the maximum tolerated dose in oncology phase I studies. However, the BLRM design has been criticized in the literature for being overly conservative due to the use of the overdose control rule. Recently, a discussion paper titled "Improving the performance of Bayesian logistic regression model with overall control in oncology dose-finding studies" in Statistics in Medicine has proposed an overall control rule to address the "excessive conservativeness" of the standard BLRM design. In this short communication, we discuss the relative conservativeness of the standard BLRM design and also suggest a dose-switching rule to further enhance its performance., (© 2024 John Wiley & Sons Ltd.)

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., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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 (C ss ) at the approved dose was compared to the in vitro cell potency (half-maximal inhibitory concentration (IC 50 )). 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 C ss was remarkably similar to the IC 50 . The median C ss /IC 50 value was 1.2, and 76% of the values were within 3-fold of unity. However, three drugs (encorafenib, erlotinib, and ribociclib) had a C ss /IC 50 value > 25. Seven other therapies targeting the same 3 kinases had much lower C ss /IC 50 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 C ss exceeds a potency threshold. This potency-guided approach is expected to maximize the therapeutic window thereby improving patient outcomes., (© 2020 Tango Therapeutics. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

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., (© 2021 by American Society of Clinical Oncology.)

Hairy cell leukemia is a rare indolent B-cell lymphoid malignancy. Durable remission can be obtained with purine analogues, but relapse is inevitable, and effective treatment options may be limited. Moxetumomab pasudotox is a recombinant CD22-targeting immunotoxin that has recently been approved by the United States Food and Drug Administration for the treatment of relapsed or refractory hairy cell leukemia. Approval was based on a pivotal phase III study in this unique patient population. Rationale for use, clinical trial data, and current treatment recommendations are detailed. Common adverse effects are reviewed, and management strategies for select adverse effects are suggested. Implications for contemporary practitioners are also provided, as use of this novel agent is likely to increase as follow-up studies are reported.

Phase I designs traditionally use the dose-limiting toxicity (DLT), a binary endpoint from the first treatment cycle, to identify the maximum-tolerated dose (MTD) assuming a monotonically increasing relationship between dose and efficacy. In this article, we establish a general framework for a multi-stage adaptive design where we jointly model a continuous efficacy outcome and continuous/quasi-continuous toxicity endpoints from multiple treatment cycles. The normalized Total Toxicity Profile (nTTP) is used as an illustration for quasi-continuous toxicity endpoints, and we replace DLT with nTTP to take into account multiple grades and types of toxicities. In addition, the proposed design accommodates non-monotone dose-efficacy relationships, and longitudinal toxicity data in effort to capture the adverse events from multiple cycles. Stage 1 of our design uses toxicity data to perform dose-escalation and identify a set of initially allowable (safe) doses; stage 2 of our design incorporates an efficacy outcome to update the set of allowable doses for each new cohort and randomizes the new cohort of patients to the allowable doses with emphasis towards those with higher predicted efficacy. Stage 3 uses all data from all treated patients at the end of the trial to make final recommendations. Simulations showed that the design had a high probability of making the correct dose selection and good overdose control across various dose-efficacy and dose-toxicity scenarios. In addition, the proposed design allows for early termination when all doses are too toxic. To our best knowledge, the proposed dual-endpoint dose-finding design is the first such study to incorporate multiple cycles of toxicities and a continuous efficacy outcome.

In this article, we propose and evaluate three alternative randomization strategies to the adaptive randomization (AR) stage used in a seamless Phase I/II dose-finding design. The original design was proposed by Wages and Tait in 2015 for trials of molecularly targeted agents in cancer treatments, where dose-efficacy assumptions are not always monotonically increasing. Our goal is to improve the design's overall performance regarding the estimation of optimal dose as well as patient allocation to effective treatments. The proposed methods calculate randomization probabilities based on the likelihood of every candidate model as opposed to the original design which selects the best model and then randomizes doses based on estimations from the selected model. Unlike the original method, our proposed adaption does not require an arbitrarily specified sample size for the adaptive randomization stage. Simulations are used to compare the proposed strategies and a final strategy is recommended. Under most scenarios, our recommended method allocates more patients to the optimal dose while improving accuracy in selecting the final optimal dose without increasing the overall risk of toxicity.

To shorten trial duration and improve safety of Phase I trials, we propose R-TPI, a rolling enrollment design that combines the features in model-based designs such as mTPI-2 and rule-based designs such as rolling six. R-TPI employs a novel rolling enrollment scheme, which allows concurrent patient enrollment that is faster than cohort-based enrollment. Bench-marking against rolling six, we find that the R-TPI design is as fast in completing clinical trials but with fewer toxicity events and higher chance of finding the maximum tolerated dose (MTD) in the single scenario laid out in the 2008 rolling six publication. We also find that in a broad setting involving multiple scenarios, R-TPI is generally faster, safer, and more reliable than standard designs. R-TPI is a general design that can be applied to adult and pediatric Phase I trials. It reduces the length of trial duration, leads to safer trials with fewer toxicity events, and maintains relatively a high chance of identifying the MTD.

Background: Reliable evaluation of treatment benefit in early phase clinical trials is necessary. The time to progression ratio (TTPr), which compares successive TTP in a single patient, is a powerful criteria for determining targeted or immune therapies efficacy., Methods: We evaluated 205 TTPr in a large cohort of 177 advanced cancer patients enrolled in at least two Phase 1/1b trials (out of 2827 phase 1/1b-treated patients) at Gustave Roussy., Results: This first wide description of TTPr showed that, under the hypothesis of overall absence of treatment line effect, the median TTPr was 0.7 and that 25% of patients presented a TTPr above the conventional efficacy threshold of 1.3., Conclusions: A higher median TTPr and a larger proportion of patients above the 1.3 threshold should therefore be achieved to conclude to drug efficacy. New guidelines for TTPr interpretation and calibration are proposed, which warrant independent prospective validation.

Background: Greater understanding of the molecular classification of breast cancer has permitted the development of rational drug design strategies. In a phase I clinical trial setting, molecular profiling with next-generation sequencing of individual tumour samples has been employed to guide treatment., Methods: We conducted a retrospective evaluation of clinical outcomes of patients with metastatic breast cancer (MBC) treated in phase I clinical trials at our institution to assess the benefit of molecularly matched compared to non-matched treatments., Results: A total of 97 consecutive patients with MBC were enrolled onto ≥1 trial between 2009 and 2015. Fourteen patients participated in multiple trials, and a total of 113 trial encounters were reviewed in this retrospective study. Eighty-three percent of patients with molecular data available were able to participate in trials matched to molecular aberrations. Patients who were treated on matched studies had improved clinical benefit (RR: 1.80, p = 0.005), progression-free (HR: 0.52, p = 0.003) and overall survival (HR: 0.54, p < 0.001). Treatment was well tolerated with low rates of treatment discontinuation for toxicity (8% overall) that did not differ between groups. No toxicity-related deaths were observed., Conclusions: Molecular profiling for MBC patients in a phase I setting is feasible and aids therapeutic decisions with improved patient outcomes.

Traditionally, Phase I oncology trials evaluate the safety profile of a novel agent and identify a maximum tolerable dose based on toxicity alone. With the development of biologically targeted agents, investigators believe the efficacy of a novel agent may plateau or diminish before reaching the maximum tolerable dose while toxicity continues to increase. This motivates dose-finding based on the simultaneous evaluation of toxicity and efficacy. Previously, we investigated hierarchical modeling in the context of Phase I dose-escalation studies for multiple populations and found borrowing strength across populations improved operating characteristics. In this article, we discuss three hierarchical extensions to commonly used probability models for efficacy and toxicity in Phase I-II trials and adapt our previously proposed dose-finding algorithm for multiple populations to this setting. First, we consider both parametric and non-parametric bivariate models for binary outcomes and, in addition, we consider an under-parameterized model that combines toxicity and efficacy into a single trinary outcome. Our simulation results indicate hierarchical modeling increases the probability of correctly identifying the optimal dose and increases the average number of patients treated at the optimal dose, with the under-parameterized hierarchical model displaying desirable and robust operating characteristics., (Copyright © 2018 Elsevier Inc. All rights reserved.)

We consider the problem of incorporating historical data from a preceding trial to design and conduct a subsequent dose-finding trial in a possibly different population of patients. In oncology, for example, after a phase I dose-finding trial is completed in Caucasian patients, investigators often conduct a further phase I trial to determine the maximum tolerated dose in Asian patients. This may be due to concerns about possible differences in treatment tolerability between populations. In this study, we propose to adaptively incorporate historical data into prior distributions assumed in a new dose-finding trial. Our proposed approach aims to appropriately borrow strength from a previous trial to improve the maximum tolerated dose determination in another patient population. We define a "historical-to-current (H-C)" parameter representing the degree of borrowing based on a retrospective analysis of previous trial data. In simulation studies, we examine the operating characteristics of the proposed method in comparison with 3 alternative approaches and assess how the H-C parameter functions across a variety of realistic settings., (Copyright © 2018 John Wiley & Sons, Ltd.)

Toxicity studies in juvenile animals (JAS) are sometimes performed to support clinical trials in pediatric oncology patients, and there are differing conclusions on the value of JAS for pediatric drug development. This manuscript provides a review of the pediatric clinical data for 25 molecularly-targeted and 4 biologic anticancer therapeutics. Other publications that evaluated the value of JAS in pediatric drug development focus on differences in toxicity between juvenile animals and adult animals. The present paper examines pediatric-specific clinical findings to focus on dose setting in pediatric oncology patients and safety monitoring in terms of the potential value of JAS. Our assessment demonstrates that pediatric starting doses were safe for all 29 therapeutics examined in that no life-threatening toxicities occurred in the first cohort, and overall the ratio of the pediatric maximum tolerated dose (MTD) to the recommended adult dose was close to 1. In addition, the 4 serious adverse events (SAEs) that weren't detectable with standard monitoring plans for pediatric oncology trials would not have been detectable in a standard JAS. This review demonstrates that safe starting doses in pediatric oncology patients for these therapeutics could have been solely based on adult doses without any knowledge of findings in JAS., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Conventional phase I algorithms for finding a phase-2 recommended dose (P2RD) based on toxicity alone is problematic because the maximum tolerated dose (MTD) is not necessarily the optimal dose with the most desirable risk-benefit trade-off. Moreover, the increasingly common practice of treating an expansion cohort at a chosen MTD has undesirable consequences that may not be obvious., Patients and Methods: We review the phase I-II paradigm and the EffTox design, which utilizes both efficacy and toxicity to choose optimal doses for successive patient cohorts and find the optimal P2RD. We conduct a computer simulation study to compare the performance of the EffTox design with the traditional 3 + 3 design and the continuous reassessment method., Results: By accounting for the risk-benefit trade-off, the EffTox phase I-II design overcomes the limitations of conventional toxicity-based phase I designs. Numerical simulations show that the EffTox design has higher probabilities of identifying the optimal dose and treats more patients at the optimal dose., Conclusions: Phase I-II designs, such as the EffTox design, provide a coherent and efficient approach to finding the optimal P2RD by explicitly accounting for risk-benefit trade-offs underlying medical decisions.

Purpose: The pharmacology, pharmacokinetics, clinical efficacy and safety, cost, and place in therapy of elotuzumab for treatment of relapsed or refractory multiple myeloma (MM) are reviewed., Summary: Elotuzumab is a humanized monoclonal antibody that targets the signaling lymphocytic activation molecule (SLAM) protein SLAMF7 and facilitates an antibody-dependent cellular cytotoxicity interaction between myeloma cells and natural killer cells. Elotuzumab has U.S. marketing approval for use in combination with lenalidomide and dexamethasone in patients with relapsed or refractory MM who have received 1-3 previous therapies; this regimen is among the preferred regimens for relapsed or refractory MM recommended by the National Comprehensive Cancer Network (NCCN). A Phase III trial involving 321 patients demonstrated a median progression-free survival duration of 19.4 months with elotuzumab plus lenalidomide and dexamethasone versus 14.9 months with lenalidomide and dexamethasone alone (hazard ratio for progression or death, 0.70; 95% confidence interval, 0.57-0.85; p < 0.001). Common adverse effects of elotuzumab-lenalidomide-dexamethasone therapy include hematologic toxicities, fatigue, pyrexia, diarrhea, constipation, muscle spasms, and cough. Elotuzumab plus bortezomib and dexamethasone is an NCCN-recommended alternative option for relapsed or refractory MM., Conclusion: While elotuzumab plus lenalidomide and dexamethasone is a promising regimen for patients with MM, it is only one of several regimens recommended by NCCN for relapsed or refractory MM. Key factors in patient selection for elotuzumab therapy include adverse effects, prior treatments received, and cost considerations., (Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.)

We present a Bayesian adaptive design for dose finding in cancer phase I clinical trials. The goal is to estimate the maximum tolerated dose (MTD) after possible modification of the dose range during the trial. Parametric models are used to describe the relationship between the dose and the probability of dose-limiting toxicity (DLT). We investigate model reparameterization in terms of the probabilities of DLT at the minimum and maximum available doses at the start of the trial. Trial design proceeds using escalation with overdose control (EWOC), where at each stage of the trial we seek the dose of the agent such that the posterior probability of exceeding the MTD of this agent is bounded by a feasibility bound. At any time during the trial, we test whether the MTD is below or above the minimum and maximum doses, respectively. If during the trial there is evidence that the MTD is outside the range of doses, we extend the range of doses and complete the trial with the planned sample size. At the end of the trial, a Bayes estimate of the MTD is proposed. We evaluate design operating characteristics in terms of safety of the trial design and efficiency of the MTD estimate under various scenarios and model misspecification. The methodology is further compared to the original EWOC design. We showed by comprehensive simulation studies that the proposed method is safe and can estimate the MTD more efficiently than the original EWOC design.

Background: Phase I trials are designed to determine the safety, tolerability, and recommended phase 2 dose of therapeutic agents for subsequent testing. The dose-finding paradigm has thus traditionally focused on identifying the maximum tolerable dose of an agent or combination therapy under the assumption that there is a non-decreasing relationship between dose-toxicity and dose-efficacy. The dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle. A novel endpoint, the total toxicity profile, was previously developed to account for the multiple toxicity types and grades experienced in the first cycle. More recently, this was extended to a repeated measures design based on the total toxicity profile to account for longitudinal toxicities over multiple treatment cycles in the absence of within-patient correlation., Methods: In this work, we propose to extend the design in the presence of within-patient correlation. Furthermore, we provide a framework to detect a toxicity time trend (toxicity increasing, decreasing, or stable) over multiple treatment cycles. We utilize a linear mixed model in the Bayesian framework, with the addition of Bayesian risk functions for decision-making in dose assignment., Results: The performance of this design was evaluated using simulation studies and real data from a phase I trial. We demonstrated that using available toxicity data from all cycles of treatment improves the accuracy of maximum tolerated dose identification and allows for the detection of a time trend. The performance is consistent regardless of the strength of the within-patient correlation. In addition, the use of a quasi-continuous total toxicity profile score significantly increased the power to detect time trends compared to when binary data only were used., Conclusion: The increased interest in molecularly targeted agents and immunotherapies in oncology necessitates innovative phase I study designs. Our proposed framework provides a tool to tackle some of the challenges presented by these novel agents, specifically through the ability to understand patterns of toxicity over time, which is important in the cases of cumulative or late toxicities.

This paper addresses dose finding in clinical trials where individuals exhibit biologic characteristics that alter the toxicity risks of the individuals. In these situations, instead of determining a dose that works for every patient, the trial aims to identify a dosing algorithm that prescribes dose according to the patient's biomarker or pharmacokinetic expression. Specifically, we aim to dose patients around a pre-specified level of area under the curve of irinotecan concentration using the patients' baseline phenotypes that predict drug clearance. We propose least squares recursion procedures to estimate the dosing algorithm sequentially with an aim to treat patients in the trial around the true unknown dosing algorithm, and introduce a novel application of an eigenvalue theory that guarantees convergence to the true dosing algorithms. Our simulation study demonstrates that using an eigenvalue constraint improves the efficiency of the recursion by as large as 40%, while concentrating in-trial patient allocation around the true dosing algorithm. We also provide practical guidance on design calibration, and design future irinotecan studies based on data from our first trial., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Seamless phase I/II dose-finding trials are attracting increasing attention nowadays in early-phase drug development for oncology. Most existing phase I/II dose-finding methods use sophisticated yet untestable models to quantify dose-toxicity and dose-efficacy relationships, which always renders them difficult to implement in practice. To simplify the practical implementation, we extend the Bayesian optimal interval design from maximum tolerated dose finding to optimal biological dose finding in phase I/II trials. In particular, optimized intervals for toxicity and efficacy are respectively derived by minimizing probabilities of incorrect classifications. If the pair of observed toxicity and efficacy probabilities at the current dose is located inside the promising region, we retain the current dose; if the observed probabilities are outside of the promising region, we propose an allocation rule by maximizing the posterior probability that the response rate of the next dose falls inside a prespecified efficacy probability interval while still controlling the level of toxicity. The proposed interval design is model-free, thus is suitable for various dose-response relationships. We conduct extensive simulation studies to demonstrate the small- and large-sample performance of the proposed method under various scenarios. Compared to existing phase I/II dose-finding designs, not only is our interval design easy to implement in practice, but it also possesses desirable and robust operating characteristics., (Copyright © 2017 John Wiley & Sons, Ltd.)

Background: Dose-escalation trials aim to identify the maximum tolerated dose and, importantly, the recommended phase II dose (RP2D) and rely on the occurrence of dose-limiting toxicities (DLTs) during the first treatment cycle. Molecularly targeted agents (MTAs) often follow continuous and prolonged administrations, displaying a distinct toxicity profile compared to conventional chemotherapeutics, and classical DLT criteria might not be appropriate to evaluate MTAs' toxicity. We investigated this issue in children., Methods: The Innovative Therapies for Children with Cancer Consortium (ITCC) phase I trials of novel anticancer agents between 2004 and 2015 were analysed. Data from investigational product, trial design, items defining DLT/RP2D were extracted. A survey on dose-escalation process, DLTs and RP2D definition was conducted among the ITCC clinical trials committee members., Results: Thirteen phase I trials with 15 dose-escalation cohorts were analysed. They explored 11 MTAs and 2 novel cytotoxics; 12 evaluated DLT during cycle 1. Definition of DLT was heterogeneous: Grade III-IV haematologic toxicities that were transient or asymptomatic and grade III-IV non-haematological toxicities manageable with adequate supportive care were often excluded, whereas some included dose intensity or grade II toxicities into DLT. None of the studies considered delayed toxicity into the RP2D definition., Conclusion: DLTs should be homogeneously defined across trials, limiting the number of exceptions due to specific toxicities. Dose escalation should still be based on safety data from cycle 1, but delayed and overall toxicities, pharmacokinetic parameters and pharmacodynamic data should be considered to refine the final RP2D. The evaluation of long-term toxicity in the developing child cannot be adequately addressed in early trials., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Introduction: Paediatric dose-finding studies are challenging to perform due to ethical reasons, the limited number of available patients and restricted number of blood samples. In certain cases, the adult pharmacokinetic (PK) exposure can be used as target for dose finding in paediatrics. The aim of this study was to investigate the performance of a paediatric phase I dose-finding clinical trial in silico., Methods: Using an adult pharmacokinetic model, clinical trial simulations were performed to determine the power of a proposed clinical trial design. Power was defined as the fraction of 1000 trials with an area under the plasma concentration-time curve at steady-state (AUC 0-24,SS ) within ±20% of the adult geometric mean AUC 0-24,SS . Different scenarios were compared to optimise the design of the trial. To show the potential of this framework for similar compounds, the current simulation method was also evaluated with adult and paediatric data from literature on sunitinib., Results: At the starting dose of 300 mg/m 2 , the power of the trial design was 66.9%. Power did not improve by dose escalation to 350 mg/m 2 (65.3%). Power increased to 78.9% with inclusion of 10 patients per trial. Paediatric sunitinib PK data were adequately predicted from adult data with a mean prediction error of 1.80%., Conclusion: The performance of PK-based clinical trials in paediatrics can be predicted and optimised through PK modelling and simulation. Application of this approach enables clinical trials in paediatrics to be performed as efficiently as possible while protecting the child from unnecessary harm., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Introduction: A number of phase I immunotherapy trials for cancer patients incorporate the absolute lymphocyte count (ALC) as an inclusion criteria. This study aims to assess whether ALC is associated with a lack of response to anti-PD-1/PD-L1 in early clinical trials., Methods: All consecutive patients treated with anti-PD-1/PD-L1 monotherapy in phase I trials in our institution between December 2011 and January 2014 were reviewed. Baseline ALC, neutrophil-to-lymphocyte ratio (NLR), Royal-Marsden Hospital (RMH) prognostic score, objective response rate (ORR) and disease control rate (DCR = SD + PR + CR, stable disease (SD), partial response (PR), complete response (CR)) defined by Response Evaluation Criteria In Solid Tumours (RECIST) version 1.1 were retrieved., Results: Out of a total of 167 patients, 48 (28.7%) and 8 patients (4.8%) had baseline ALCs of <1 G/l and <0.5 G/l, respectively. The RECIST change (%) was not correlated with ALC (G/l) (Spearman's rho = -0.06, P = 0.43). We did not observe any difference in terms of ORR (8.3% versus 15.1%, P = 0.32) or of DCR (58.3% versus 61.3%, P = 0.73) between patients with ALC <1 G/l versus >1 G/l. When using 0.5 G/l as ALC threshold, we did not find any difference either in ORR or in DCR. In a multivariate Cox regression analysis, baseline ALC was not associated with overall survival, whereas the RMH and the number of previous lines of treatment remained independent prognostic factors., Conclusions: Baseline ALC was not associated with response to anti-PD-1/PD-L1 in cancer patients enrolled in phase I trials. Patients should not be excluded from early phase clinical trials testing immune checkpoints blockers because of ALC., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Introduction: Most trials investigating new drugs around the world, including phase I trials, are conducted in outpatient clinics. However, in Japan, regulatory authority requirements and traditional domestic guidelines often require hospitalization of phase I study participants., Patients and Methods: Patients participating in single-agent phase I clinical trials at National Cancer Center Hospital between December 1996 and August 2014 were monitored. Toxicity requiring hospitalization is defined as toxicity that needs intensive treatment. Study designs were classified into three types: first-in-human (FIH) study, dose-escalation study (conventional dose-escalation study to determine maximum tolerated dose (MTD) in Japanese patients), and dose-finding study (to assess safety and pharmacokinetic profiles up to the MTD previously determined in the West)., Results: A total of 945 patients who participated in a variety of single-agent phase I clinical trials between December 1996 and August 2014 were included in this study. Patients participated in one of three study types: dose-escalation (n = 582, 62%), first-in-human (n = 129, 14%), or dose-finding (n = 234, 25%). A total of 76 study drugs were evaluated as part of this pool of phase I studies. Subdivided by mechanism of action, 20 (26%) were cytotoxic, 50 (66%) were molecularly targeted, and 6 (8%) were immune checkpoint inhibitor. Thirty-six patients (3.8%) had severe toxicities requiring hospitalization during the first cycle. The overall number of toxicities requiring hospitalization and/or grade 4 toxicities during any cycle was 5.0%., Conclusions: The frequency of severe toxicity that needs to be hospitalized was unexpectedly low. The data did not demonstrate the need for hospitalization in the phase I trials, suggesting that phase I trials in Japan could be conducted in outpatient settings.

Importance Use of expansion cohorts (EC) in phase I trials is increasing. However, the utility of phase I EC in aiding drug development is unclear. We sought to determine factors associated with the inclusion of EC in phase I studies and the impact of EC on subsequent clinical development. Methods We performed a systematic review of all phase I trials published in the Journal of Clinical Oncology between June 2004 and May 2014. Presence of an EC, number of participants, funding source, class of agent, tumor type, and maximum tolerated dose (MTD)/recommended phase 2 dose (RP2D) were identified. Subsequent conduct of phase II studies and FDA approval of the study agent was also assessed. Results We identified 252 phase I studies. An EC was included in 105 studies. Average accrual on EC studies was 47 compared to 31 in studies without EC (p < 0.0001). There was no impact of time on the inclusion of EC. Only 4 % of phase I studies with an EC provided sample size justification. Source of funding had the only significant association with inclusion of EC. Addition of a phase I EC did not impact the phase I MTD/RP2D, subsequent phase II trial, or FDA approval. Conclusion The importance of including an EC in phase I trials is subject to ongoing debate. Our results demonstrated little benefit to including EC in phase I studies. These findings support that innovative design strategies are needed to optimize the utility of EC in phase I studies.

We present a cancer phase I clinical trial design of a combination of two drugs with the goal of estimating the maximum tolerated dose curve in the two-dimensional Cartesian plane. A parametric model is used to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity. The model is re-parameterized in terms of the probabilities of toxicities at dose combinations corresponding to the minimum and maximum doses available in the trial and the interaction parameter. Trial design proceeds using cohorts of two patients receiving doses according to univariate escalation with overdose control (EWOC), where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current dose of the other agent. The maximum tolerated dose curve is estimated as a function of Bayes estimates of the model parameters. Performance of the trial is studied by evaluating its design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD curve and under model misspecifications for the true dose-toxicity relationship. The method is further extended to accommodate discrete dose combinations and compared with previous approaches under several scenarios. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

The majority of phase I methods for multi-agent trials have focused on identifying a single maximum tolerated dose combination (MTDC) among those being investigated. Some published methods in the area have been based on the notion that there is no unique MTDC and that the set of dose combinations with acceptable toxicity forms an equivalence contour in two dimensions. Therefore, it may be of interest to find multiple MTDCs for further testing for efficacy in a phase II setting. In this paper, we present a new dose-finding method that extends the continual reassessment method to account for the location of multiple MTDCs. Operating characteristics are demonstrated through simulation studies and are compared with existing methodology. Some brief discussion of implementation and available software is also provided. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

In oncology, combinations of drugs are often used to improve treatment efficacy and/or reduce harmful side effects. Dual-agent phase I clinical trials assess drug safety and aim to discover a maximum tolerated dose combination via dose-escalation; cohorts of patients are given set doses of both drugs and monitored to see if toxic reactions occur. Dose-escalation decisions for subsequent cohorts are based on the number and severity of observed toxic reactions, and an escalation rule. In a combination trial, drugs may be administered concurrently or non-concurrently over a treatment cycle. For two drugs given non-concurrently with overlapping toxicities, toxicities occurring after administration of the first drug yet before administration of the second may be attributed directly to the first drug, whereas toxicities occurring after both drugs have been given some present ambiguity; toxicities may be attributable to the first drug only, the second drug only or the synergistic combination of both. We call this mixture of attributable and non-attributable toxicity semi-attributable toxicity. Most published methods assume drugs are given concurrently, which may not be reflective of trials with non-concurrent drug administration. We incorporate semi-attributable toxicity into Bayesian modelling for dual-agent phase I trials with non-concurrent drug administration and compare the operating characteristics to an approach where this detail is not considered. Simulations based on a trial for non-concurrent administration of intravesical Cabazitaxel and Cisplatin in early-stage bladder cancer patients are presented for several scenarios and show that including semi-attributable toxicity data reduces the number of patients given overly toxic combinations. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd., (© 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

Phase I studies with anticancer drugs are used to evaluate safety and tolerability and to choose a recommended phase II dose (RP2D). Traditionally, phase I trial designs are rule-based, but for several years there is a trend towards model-based designs. Simulations have shown that model-based designs perform better, faster and are safer to establish the RP2D than rule-based designs. However, the superiority of model-based designs has never been confirmed based on true trial performance in practice. To aid evidence-based decisions for designing phase I trials, we compared publications of model-based and rule-based phase I trials in oncology. We reviewed 172 trials that have been published in the last 2 years and assessed the following operating characteristics: efficiency (trial duration, population size, dose-levels), patient safety (dose-limiting toxicities (DLTs)) and treatment optimality (percentage of patients treated below and at or above the recommended phase 2 dose). Our results showed a non-significant but clinically relevant difference in trial duration. Model-based trials needed 10 months less than rule-based trials (26 versus 36 months; p = 0.25). Additionally, fewer patients were treated at dose-levels below the RP2D (31 % versus 40 %; p = 0.73) while safety was preserved (13 % DLTs versus 14 % DLTs). In this review, we provide evidence to encourage the use of model-based designs for future phase I studies, based on a median of 10 months of time gain, acceptable toxicity rates and minimization of suboptimal treatment.

Phase I oncology clinical trials are designed to identify the optimal dose that will be recommended for phase II trials. In pediatric oncology, the conduct of those trials raises specific challenges, as the disease is rare with limited therapeutic options. In addition, the tolerance profile is known from adult trials. This paper provides a review of the major recent developments in the design of these trials, inspired by the need to cope with the specific challenges of dose finding in cancer pediatric oncology. We reviewed simulation studies comparing designs dedicated to address these challenges. We also reviewed the design used in published dose-finding trials in pediatric oncology over the period 2009-2014. Three main fields of innovation were identified. First, designs that were developed in order to relax the rules for more flexible inclusions. Second, methods to incorporate data emerging from adult studies. Third, designs accounting for toxicity evaluation at repeated cycles in pediatric oncology. In addition to this overview, we propose some further directions for designing pediatric dose-finding trials., (Published by Elsevier Inc.)

Background: Immunostimulatory monoclonal antibodies (imAbs) targeting immune checkpoint molecules are revolutionizing oncology not only regarding cancer therapeutics and clinical care, but also from a drug development point of view. A handful of first-generation molecules have been approved so far based on their tremendous efficacy, after an expedited development phase that has challenged most paradigms established in the era of conventional cytotoxic therapy and to some extent molecularly targeted agents. A huge wave of second-generation imAbs is just entering into phase 1 trials now, in monotherapy or in combination. In order to maximize their chances of success in early phase trials, and eventually for patients' benefit, their clinical development has to benefit from lessons learnt from previous imAbs phase 1 trials., Materials and Methods: We reviewed the early clinical development of anti-cytotoxic T-lymphocyte antigen 4 and anti-programmed death-1 receptor/ligand. Particularities of each agent, including safety, dose--toxicity and dose--efficacy relationships, scheduling, pharmacokinetics (PK), pharmacodynamics (PD), trial design, biomarkers, response assessment and overall drug development strategies, are described and challenged., Results: As opposed to conventional cytotoxic agents, dose of imAbs is not linearly associated with efficacy and toxicity. Therefore, the definition of a minimal immunologically active dose could be proposed. Traditional patient eligibility criteria might also be revisited as the toxicity profile and mechanism of toxicity--immune-related adverse events--are mostly known and their physiopathology somehow less unexpected than with molecularly targeted small molecules. Most challenging are the comprehensive investigation of complex PK and PD characteristics as well as the definition of patient selection biomarkers. Finally, the early focus on efficacy (and not only dose confirmation) in expansion cohorts challenges the traditional phase 1/2/3 drug development process., Conclusion: Several drug development paradigms have been challenged by imAbs. Here, we discuss novel approaches for an efficient and successful drug development of these agents., (© The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Advances in our knowledge of the molecular pathogenesis of cancer have led to increased interest in molecularly targeted agents (MTAs), which target specific oncogenic drivers and are now a major focus of cancer drug development. MTAs differ from traditional cytotoxic agents in various aspects, including their toxicity profiles and the potential availability of predictive biomarkers of response. The landscape of phase I oncology trials is evolving to adapt to these novel therapies and to improve the efficiency of drug development. In this Review, we discuss new strategies used in phase I trial design, such as novel dose-escalation schemes to circumvent limitations of the classic 3 + 3 design and enable faster dose escalation and/or more-precise dose determinations using statistical modelling; improved selection of patients based on genetic or molecular biomarkers; pharmacokinetic and pharmacodynamic analyses; and the early evaluation of efficacy - in addition to safety. Indeed, new expedited approval pathways that can accelerate drug development require demonstration of efficacy in early phase trials. The application of molecular tumour profiling for matched therapy and the testing of drug combinations based on a strong biological rationale are also increasingly seen in phase I studies. Finally, the shift towards multi-institutional trials and centralized study management results in consequent implications for institutions and investigators. These issues are also highlighted herein.

Traditionally, phase II cancer trials test a binary endpoint formed from a dichotomisation of the continuous change in tumour size. Directly testing the continuous endpoint provides considerable gains in power, although also results in several statistical issues. One such issue is when complete responses, i.e. complete tumour removal, are observed in multiple patients; this is a problem when normality is assumed. Using simulated data and a recently published phase II trial, we investigate how the choice of test affects the operating characteristics of the trial. We propose using parametric tests based on the censored normal distribution, comparing them to the t-test and Wilcoxon non-parametric test. The censored normal distribution fits the real dataset well, but simulations indicate its type-I error rate is inflated, and its power is only slightly higher than the t-test. The Wilcoxon test has deflated type I error. For two-arm designs, the differences are much smaller. We conclude that the t-test is suitable for use when complete responses are present, although positively skewed data can result in the non-parametric test having higher power., (© The Author(s) 2011.)

Exciting recent advancements in deep-sequencing technology have enabled a rapid and cost-effective molecular characterization of patient-derived tumor samples. Incorporating these innovative diagnostic technologies into early clinical trials could significantly propel implementation of precision medicine by identifying genetic markers predictive of sensitivity to agents. It may also markedly accelerate drug development and subsequent regulatory approval of novel agents. Particularly noteworthy, a high-response rate in a Phase II trial involving a biomarker-enriched patient cohort could result in a regulatory treatment approval in rare histologies, which otherwise would not be a candidate for a large randomized clinical trial. Furthermore, even if a trial does not meet its statistical endpoint, tumors from a few responders should be molecularly characterized as part of the new biomarker-mining processes. In order to accommodate patient screening and accelerate the accrual process, institutions conducting early clinical trials need to be a part of a multi-institution clinical trials network. Future clinical trial design will incorporate new biomarkers discovered by a 'phenotype-to-genotype' effort with an appropriate statistical design. To help advance such changes, the National Cancer Institute has recently reformed the existing early phase clinical trials network. A new clinical trial network, the Experimental Therapeutics Clinical Trials Network (ET-CTN), was begun and, in addition to its pre-existing infrastructure, an up-to-date clinical trial registration system, clinical trial monitoring system including electronic database and a central Institutional Review Board were formed. Ultimately, these reforms support identifying the most appropriate therapy for each tumor type by incorporating state-of-the-art molecular diagnostic tools into early clinical trials., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Purpose: Patients with high-grade gliomas (HGG) are frequently excluded from first-in-human solid tumor trials because of perceived poor prognosis, excessive toxicities, concomitant drug interactions, and poor efficacy. We conducted an analysis of outcomes from select, single-agent phase I studies in patients with HGG. We compared outcomes to pooled analysis of published studies in solid tumors with various molecular and cytotoxic drugs evaluated as single agents or as combinations., Patient and Methods: Individual records of patients with recurrent HGG enrolled onto Adult Brain Tumor Consortium trials of single-agent, cytotoxic or molecular agents from 2000 to 2008 were analyzed for baseline characteristics, toxicities, responses, and survival., Results: Our analysis included 327 patients with advanced, refractory HGG who were enrolled onto eight trials involving targeted molecular (n=5) and cytotoxic (n=3) therapies. At enrollment, patients had a median Karnofsky performance score of 90 and median age of 52 years; 62% were men, 63% had glioblastoma, and the median number of prior systemic chemotherapies was one. Baseline laboratory values were in an acceptable range to meet eligibility criteria. Patients were on the study for a median of two cycles (range,

Phase I oncology clinical trials are designed to identify the optimal dose that will be recommended for phase II trials. This dose is typically defined as the dose associated with a certain probability of severe toxicity at cycle 1, although toxicity is repeatedly measured over cycles on an ordinal scale. Recently, a proportional odds mixed-effect model for ordinal outcomes has been proposed to (i) identify the optimal dose accounting for repeated events and (ii) to provide some framework to explore time trend. We compare this approach to a method based on repeated binary variables and to a method based on an under-parameterized model of the dose-time toxicity relationship. We show that repeated binary and ordinal outcomes both improve the accuracy of dose-finding trials in the same proportion; ordinal outcomes are, however, superior to detect time trend even in the presence of nonproportional odds models. Moreover, less parameterized models led to the best operating characteristics. These approaches are illustrated on two dose-finding phase I trials. Integration of repeated measurements is appealing in phase I dose-finding trials., (Copyright © 2015 John Wiley & Sons, Ltd.)

The main role of early phase clinical trials in cancer is to determine the dose to take forward for future clinical study. However, study design is changing in order to account for the change in focus of drug development toward molecularly targeted agents.

Purpose: Potential cytochrome P-450 (CYP) drug-drug interactions in adults with metastatic solid tumors and their effect on eligibility for Phase I clinical trials were characterized., Methods: This study included adult patients with metastatic solid tumors seen by a medical oncologist from January 2008 through July 2011. The medications used by these patients were identified. Each medication's potential for interacting with CYP isozymes was also characterized. Medication changes required to meet Phase I trial eligibility criteria were also reviewed., Results: Data from 1773 patients were analyzed: 1489 were not enrolled in a Phase I trial and 284 were enrolled in a Phase I trial. Polypharmacy was significantly more prevalent in the group enrolled in a Phase I trial compared with those not enrolled (95% versus 80%, p < 0.001). The majority of patients not enrolled in a Phase I trial were taking at least one CYP isozyme inhibitor (87%) and at least one CYP isozyme inducer (45%). In a separate analysis, four Phase I trials were evaluated. Of 295 screened patients, 3.2% could not enroll due to concurrent medications. Charts from 74 enrolled patients revealed 655 concurrent medications—93 medications required further review for eligibility involving 51 (69%) of patients. Of the 93 medications, 38 (41%) were stopped and 41 (44%) were changed for the study., Conclusion: Polypharmacy and the use of medications that interact with CYP isoyzmes were common in adult patients with metastatic solid tumors. Patients enrolling in Phase I studies often require medication changes to meet eligibility requirements., (Copyright © 2015 by the American Society of Health-System Pharmacists, Inc. All rights reserved.)

Background: Most persons with cancer living in the United States are older than 65 years of age; however, in general, elderly persons are under-represented in clinical trials and outcomes data are lacking., Methods: Outcomes data were analyzed of elderly participants (≥65 years of age) enrolled in phase 1 clinical trials and the results compared with those of younger patients. All consecutive, single-center, phase 1 oncology trials initiated and completed at the H. Lee Moffitt Cancer Center & Research Institute between 1997 and 2007 were included. Patient data (including survival, response, and toxicity rates) were extracted from a cancer registry database and electronic medical records at Moffitt Cancer Center., Results: After excluding multi-institution trials, we analyzed 39 trials for a total of 1,162 enrolled study participants, 32.7% of whom were elderly. Among patients who underwent transplantation, median survival rates were worse in those who were elderly compared with those who were younger (44.9 vs 32.9 months; P = .0037). However, in the no-transplantation setting, participants who were elderly had a median survival rate of 10.9 months (95% confidence interval [CI]: 8.9-13.1) compared with 8.8 months (95% CI: 7.9-10.3) in those who were younger (P = .15). Both groups had similar overall response rates (15.2% vs 13.1%) and similar treatment-related mortality rates (1% vs 0.9%, respectively). Adverse events occurring among the elderly and younger participants were not statistically significant., Conclusions: Survival, response, toxicity, and treatment-related mortality rates were not significantly different between the elderly and younger phase 1 trial participants in the no-transplantation setting. Regardless of the complex pharmacological profiles and logistical issues involved in treating the elderly population, our data imply that elderly study participants do at least as well as their younger counterparts, contributing to the justification of increasing the phase 1 trial enrollment of elderly patients.

The goal of a phase I clinical trial in oncology is to find a dose with acceptable dose-limiting toxicity rate. Often, when a cytostatic drug is investigated or when the maximum tolerated dose is defined using a toxicity score, the main endpoint in a phase I trial is continuous. We propose a new method to use in a dose-finding trial with continuous endpoints. The new method selects the right dose on par with other methods and provides more flexibility in assigning patients to doses in the course of the trial when the rate of accrual is fast relative to the follow-up time., (Copyright © 2014 John Wiley & Sons, Ltd.)

Background: Phase I trials often include a dose expansion cohort (DEC), in which additional patients are treated at the estimated maximum tolerated dose (MTD) after dose escalation, with the goal of ensuring that data are available from more than six patients at a single dose level. However, protocols do not always detail how, or even if, the additional toxicity data will be used to reanalyze the MTD or whether observed toxicity in the DEC will warrant changing the assigned dose. A DEC strategy has not been statistically justified., Methods: We conducted a simulation study of two phase I designs: the "3+3" and the Continual Reassessment Method (CRM). We quantified how many patients are assigned the true MTD using a 10 to 20 patient DEC and how a sensible reanalysis using the DEC changes the probability of selecting the true MTD. We compared these results with those from an equivalently sized larger CRM that does not include a DEC., Results: With either the 3+3 or CRM, reanalysis with the DEC increased the probability of identifying the true MTD. However, a large CRM without a DEC was more likely to identify the true MTD while still treating 10 or 15 patients at this dose level., Conclusions: Where feasible, a CRM design with no explicit DEC is preferred to designs that fix a dose for all patients in a DEC., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

The aim of phase I combination dose-finding studies in oncology is to estimate one or several maximum tolerated doses (MTDs) from a set of available dose levels of two or more agents. Combining several agents can indeed increase the overall anti-tumor action but at the same time also increase the toxicity. It is, however, unreasonable to assume the same dose-toxicity relationship for the combination as for the simple addition of each single agent because of a potential antagonist or synergistic effect. Therefore, using single-agent dose-finding methods for combination therapies is not appropriate. In recent years, several authors have proposed novel dose-finding designs for combination studies, which use either algorithm-based or model-based methods. The aim of our work was to compare, via a simulation study, six dose-finding methods for combinations proposed in recent years. We chose eight scenarios that differ in terms of the number and location of the true MTD(s) in the combination space. We then compared the performance of each design in terms of correct combination selection, patient allocation, and mean number of observed toxicities during the trials. Our results showed that the model-based methods performed better than the algorithm-based ones. However, none of the compared model-based designs gave consistently better results than the others., (Copyright © 2014 John Wiley & Sons, Ltd.)