Baseline Tumor Size as Prognostic Index in Patients With Advanced Solid Tumors Receiving Experimental Targeted Agents.

BACKGROUND: Baseline tumor size (BTS) has been associated with outcomes in patients with cancer treated with immunotherapy. However, the prognostic impact of BTS on patients receiving targeted therapies (TTs) remains undetermined. METHODS: We reviewed data of patients with advanced solid tumors consecutively treated within early-phase clinical trials at our institution from 01/2014 to 04/2021. Treatments were categorized as immunotherapy-based or TT-based (biomarker-matched or not). BTS was calculated as the sum of RECIST1.1 baseline target lesions. RESULTS: A total of 444 patients were eligible; the median BTS was 69 mm (IQR 40-100). OS was significantly longer for patients with BTS lower versus higher than the median (16.6 vs. 8.2 months, P < .001), including among those receiving immunotherapy (12 vs. 7.5 months, P = .005). Among patients receiving TT, lower BTS was associated with longer PFS (4.7 vs. 3.1 months, P = .002) and OS (20.5 vs. 9.9 months, P < .001) as compared to high BTS. However, such association was only significant among patients receiving biomarker-matched TT, with longer PFS (6.2 vs. 3.3 months, P < .001) and OS (21.2 vs. 6.7 months, P < .001) in the low-BTS subgroup, despite a similar ORR (28% vs. 22%, P = .57). BTS was not prognostic among patients receiving unmatched TT, with similar PFS (3.7 vs. 4.4 months, P = .30), OS (19.3 vs. 11.8 months, P = .20), and ORR (33% vs. 28%, P = .78) in the 2 BTS groups. Multivariate analysis confirmed that BTS was independently associated with PFS (P = .03) and OS (P < .001) but not with ORR (P = .11). CONCLUSIONS: Higher BTS is associated with worse survival outcomes among patients receiving biomarker-matched, but not biomarker-unmatched TT.

Dose Finding in Oncology Trials Guided by Ordinal Toxicity Grades Using Continuous Dose Levels.

We present a Bayesian adaptive design for dose finding in oncology trials with application to a first-in-human trial. The design is based on the escalation with overdose control principle and uses an intermediate grade 2 toxicity in addition to the traditional binary indicator of dose-limiting toxicity (DLT) to guide the dose escalation and de-escalation. We model the dose-toxicity relationship using the proportional odds model. This assumption satisfies an important ethical concern when a potentially toxic drug is first introduced in the clinic; if a patient experiences grade 2 toxicity at the most, then the amount of dose escalation is lower relative to that wherein if this patient experienced a maximum of grade 1 toxicity. This results in a more careful dose escalation. The performance of the design was assessed by deriving the operating characteristics under several scenarios for the true MTD and expected proportions of grade 2 toxicities. In general, the trial design is safe and achieves acceptable efficiency of the estimated MTD for a planned sample size of twenty patients. At the time of writing this manuscript, twelve patients have been enrolled to the trial.

Backfilling cohorts in phase I dose-escalation studies.

BACKGROUND: The use of 'backfilling', assigning additional patients to doses deemed safe, in phase I dose-escalation studies has been used in practice to collect additional information on the safety profile, pharmacokinetics and activity of a drug. These additional patients help ensure that the maximum tolerated dose is reliably estimated and give additional information to determine the recommended phase II dose. METHODS: In this article, we study the effect of employing backfilling in a phase I trial on the estimation of the maximum tolerated dose and the duration of the study. We consider the situation where only one cycle of follow-up is used for escalation as well as the case where there may be delayed onset toxicities. RESULTS: We find that, over a range of scenarios, the use of backfilling gives an increase in the percentage of correct selections by up to 9%. On average, for a treatment with a cycle length of 6 weeks, each additional backfilling patient reduces the trial duration by half a week. CONCLUSIONS: Backfilling in phase I dose-escalation studies can substantially increase the accuracy of estimation of the maximum tolerated dose, with a larger impact in the setting with a dose-limiting toxicity event assessment period of only one cycle. This increased accuracy and reduction in the trial duration are at the cost of increased sample size.

A pilot randomized study of a telephone-based cognitive-behavioral stress-management intervention to reduce distress in phase 1 oncology trial caregivers.

OBJECTIVES: Caregivers of adult phase 1 oncology trial patients experience high levels of distress and face barriers to in-person supportive care. The Phase 1 Caregiver LifeLine (P1CaLL) pilot study assessed the feasibility, acceptability, and general impact of an individual telephone-based cognitive behavioral stress-management (CBSM) intervention for caregivers of phase I oncology trial patients. METHODS: The pilot study involved 4 weekly adapted CBSM sessions followed by participant randomization to 4 weekly cognitive behavioral therapy sessions or metta-meditation sessions. A mixed-methods design used quantitative data from 23 caregivers and qualitative data from 5 caregivers to examine the feasibility and acceptability outcomes. Feasibility was determined using recruitment, retention, and assessment completion rates. Acceptability was assessed with self-reported satisfaction with program content and participation barriers. Baseline to post-intervention changes in caregiver distress and other psychosocial outcomes were assessed for the 8-session intervention. RESULTS: The enrollment rate was 45.3%, which demonstrated limited feasibility based on an a priori criterion enrollment rate of 50%. Participants completed an average of 4.9 sessions, with 9/25 (36%) completing all sessions and an 84% assessment completion rate. Intervention acceptability was high, and participants found the sessions helpful in managing stress related to the phase 1 oncology trial patient experience. Participants showed reductions in worry and isolation and stress. SIGNIFICANCE OF RESULTS: The P1CaLL study demonstrated adequate acceptability and limited feasibility and provided data on the general impact of the intervention on caregiver distress and other psychosocial outcomes. Caregivers of phase 1 oncology trial patients would benefit from supportive care services; a telephone-based intervention may have more utilization and thus make a larger impact.

Dose finding studies for therapies with late-onset toxicities: A comparison study of designs.

An objective of phase I dose-finding trials is to find the maximum tolerated dose; the dose with a particular risk of toxicity. Frequently, this risk is assessed across the first cycle of therapy. However, in oncology, a course of treatment frequently consists of multiple cycles of therapy. In many cases, the overall risk of toxicity for a given treatment is not fully encapsulated by observations from the first cycle, and hence it is advantageous to include toxicity outcomes from later cycles in phase I trials. Extending the follow up period in a trial naturally extends the total length of the trial which is undesirable. We present a comparison of eight methods that incorporate late onset toxicities while not extensively extending the trial length. We conduct simulation studies over a number of scenarios and in two settings; the first setting with minimal stopping rules and the second setting with a full set of standard stopping rules expected in such a dose finding study. We find that the model-based approaches in general outperform the model-assisted approaches, with an interval censored approach and a modified version of the time-to-event continual reassessment method giving the most promising overall performance in terms of correct selections and trial length. Further recommendations are made for the implementation of such methods.

Phase I trial compensation: How much do healthy volunteers actually earn from clinical trial enrollment?

BACKGROUND/AIMS: Financial compensation for research participation is a major focus of ethical concern regarding human subject recruitment. Phase I trials are sometimes considered to be a lucrative source of income for healthy volunteers, encouraging some people to become "professional guinea pigs." Yet, little is known about how much these clinical trials actually pay and how much healthy volunteers earn from them. METHODS: As part of a mixed-methods, longitudinal study of healthy volunteers, we required participants to complete clinical trial diaries, or surveys that captured detailed information about screening and enrollment in Phase I trials. Over a 3-year period, participants provided information online or via telephone about each clinical trial for which they screened (e.g. the clinic name, the study's therapeutic area, the length of the trial, the number of nights spent in the clinic, and the study compensation), and whether they qualified for trial inclusion. Clinical trial diaries generated data about whether participants continued to screen for and enroll in clinical trials and how much money they earned from their participation. RESULTS: 131 participants routinely completed clinical trial diaries or confirmed that they had not screened for any new clinical trials. Together, these participants screened for 1001 clinical trials at 73 research facilities during a 3-year period. Overall, the median clinical trial compensation was US$3070 (range = US$150-US$13,000). Participants seeking new healthy volunteer trials tended to screen for three studies per year, participate in one or two studies, and earn roughly US$4000 annually. Participants who were unemployed earned the most income from clinical trials compared to those with full-time or part-time jobs, and those individuals whom we label "occupational" participants because of their persistent pursuit of clinical trials earned more than people who screened occasionally. Notably, the median annual trial compensation was well below US$10,000 for all employment groups, and most occupational healthy volunteers also earned less than US$10,000 each year. The 10% of participants who earned the most had a median annual income of US$18,885 from clinical trials, and there was significant volatility in these individuals' earnings from year to year. CONCLUSION: Despite the perception that Phase I enrollment can generate significant earnings, it was exceedingly rare for anyone in this study to make more than US$20,000 in a single year, and unusual to earn even between US$10,000 and US$20,000. From an ethics perspective, individual trials might appear to unduly induce enrollment by offering significant sums of money, but given our findings, the larger problem for low-income participants may be the unrealistic perception that clinical trials alone could be a way of earning a living.

PA-CRM: A continuous reassessment method for pediatric phase I oncology trials with concurrent adult trials.

Pediatric phase I trials are usually carried out after the adult trial testing the same agent has started, but not completed yet. As the pediatric trial progresses, in light of the accrued interim data from the concurrent adult trial, the pediatric protocol often is amended to modify the original pediatric dose escalation design. In practice, this is done frequently in an ad hoc way, interrupting patient accrual and slowing down the trial. We developed a pediatric-continuous reassessment method (PA-CRM) to streamline this process, providing a more efficient and rigorous method to find the maximum tolerated dose for pediatric phase I oncology trials. We use a discounted joint likelihood of the adult and pediatric data, with a discount parameter controlling information borrowing between pediatric and adult trials. According to the interim adult and pediatric data, the discount parameter is adaptively updated using the Bayesian model averaging method. Numerical study shows that the PA-CRM improves the efficiency and accuracy of the pediatric trial and is robust to various model assumptions.

Survival outcome and prognostic model of patients with colorectal cancer on phase 1 trials.

Background Patients with metastatic colorectal cancer (mCRC) who progress on standard therapies may be eligible for phase I trials. To better delineate the risk-benefit ratio, we assessed toxicities, clinical outcomes and prognostic factors. Methods Records of mCRC patients on phase I trials at our institution over 18 years were reviewed. Univariable (UVA) and multivariable analyses (MVA) were undertaken and a prognostic model developed. Results There were 187 enrollments on 37 phase I trials. Median age was: 59 (29-83) years and number of prior therapies: 3 (0-8). The clinical benefit rate (CBR): response (5.6%) + stable disease, was 43.1%. Median progression free survival (PFS) and overall survival (OS) was 7.7 weeks and 43.7 weeks, respectively. The MVA identified age > 60 years (HR 1.63, p < 0.004), albumin<3.5 g/dL (HR 3.69, p < 0.001), direct bilirubin>ULN (HR1.69, p < 0.01), and WBC ≥ 5.2 k/uL (HR 1.97, p < 0.001) as negative prognostic factors. A risk score based on the MVA revealed that patients with a score of 0-1 had an improved OS (58.7 weeks) compared to a score of 2 (49.9 weeks, p < 0.01) and 3 (14.1 weeks, p < 0.001). Conclusions Phase 1 trials may offer similar or better clinical outcome for mCRC patients than standard third line therapies; the prognostic model could assist in selecting appropriate patients.

How to design a dose-finding study using the continual reassessment method.

INTRODUCTION: The continual reassessment method (CRM) is a model-based design for phase I trials, which aims to find the maximum tolerated dose (MTD) of a new therapy. The CRM has been shown to be more accurate in targeting the MTD than traditional rule-based approaches such as the 3 + 3 design, which is used in most phase I trials. Furthermore, the CRM has been shown to assign more trial participants at or close to the MTD than the 3 + 3 design. However, the CRM's uptake in clinical research has been incredibly slow, putting trial participants, drug development and patients at risk. Barriers to increasing the use of the CRM have been identified, most notably a lack of knowledge amongst clinicians and statisticians on how to apply new designs in practice. No recent tutorial, guidelines, or recommendations for clinicians on conducting dose-finding studies using the CRM are available. Furthermore, practical resources to support clinicians considering the CRM for their trials are scarce. METHODS: To help overcome these barriers, we present a structured framework for designing a dose-finding study using the CRM. We give recommendations for key design parameters and advise on conducting pre-trial simulation work to tailor the design to a specific trial. We provide practical tools to support clinicians and statisticians, including software recommendations, and template text and tables that can be edited and inserted into a trial protocol. We also give guidance on how to conduct and report dose-finding studies using the CRM. RESULTS: An initial set of design recommendations are provided to kick-start the design process. To complement these and the additional resources, we describe two published dose-finding trials that used the CRM. We discuss their designs, how they were conducted and analysed, and compare them to what would have happened under a 3 + 3 design. CONCLUSIONS: The framework and resources we provide are aimed at clinicians and statisticians new to the CRM design. Provision of key resources in this contemporary guidance paper will hopefully improve the uptake of the CRM in phase I dose-finding trials.

Cancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents.

Drug combination trials are increasingly common nowadays in clinical research. However, very few methods have been developed to consider toxicity attributions in the dose escalation process. We are motivated by a trial in which the clinician is able to identify certain toxicities that can be attributed to one of the agents. We present a Bayesian adaptive design in which toxicity attributions are modeled via copula regression and the maximum tolerated dose (MTD) curve is estimated as a function of model parameters. The dose escalation algorithm uses cohorts of two patients, following the continual reassessment method (CRM) scheme, where at each stage of the trial, we search for the dose of one agent given the current dose of the other agent. The performance of the design is studied by evaluating its operating characteristics when the underlying model is either correctly specified or misspecified. We show that this method can be extended to accommodate discrete dose combinations.

Understanding how coping strategies and quality of life maintain hope in patients deliberating phase I trial participation.

OBJECTIVE: This study aimed to understand how hope and motivation of patients considering phase I trial participation are affected by psychological factors such as coping strategies and locus of control (LoC) and general well-being as measured by the quality of life (QoL). METHODS: An exploratory cross-sectional study was performed in patients with incurable cancer (N = 135) referred to our phase I unit for the first time. Patients were potentially eligible for phase I trial participation and participated in our study while deliberating phase I trial participation. We used questionnaires on hope, motivation to participate, coping, LoC, and QoL. To investigate the nature and magnitude of the relationships between the scales, a structural equation modeling (SEM) was fitted to the data. RESULTS: Hope significantly predicted the motivation to participate in phase I trials. Predictors of hope were a combination of flexible and tenacious goal pursuit (both P < .01), internal LoC (P < .01), and QoL (P < .01). The SEM showed an exact fit to the data, using a null hypothesis significance test: chi-square (8) = 9.30, P = .32. CONCLUSIONS: Patients considering phase I trial participation seem to use a pact of tenacious and flexible coping and control to stay hopeful. Furthermore, hope and QoL positively affected each other. The psychological pact may promote an adaptation enabling them to adjust to difficult circumstances by unconsciously ignoring information, called dissonance reduction. This mechanism may impair their ability to provide a valid informed consent. We suggest including a systematic exploration of patients' social context and values before proposing a phase I trial.

A Bayesian adaptive design for cancer phase I trials using a flexible range of doses.

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.

Incoherent dose-escalation in phase I trials using the escalation with overdose control approach.

A desirable property of any dose-escalation strategy for phase I oncology trials is coherence: if the previous patient experienced a toxicity, a higher dose is not recommended for the next patient; similarly, if the previous patient did not experience a toxicity, a lower dose is not recommended for the next patient. The escalation with overdose control (EWOC) approach is a model-based design that has been applied in practice, under which the dose assigned to the next patient is the one that, given all available data, has a posterior probability of exceeding the maximum tolerated dose equal to a pre-specified value known as the feasibility bound. Several methodological and applied publications have considered the EWOC approach with both feasibility bounds fixed and increasing throughout the trial. Whilst the EWOC approach with fixed feasibility bound has been proven to be coherent, some proposed methods of increasing the feasibility bound regardless of toxicity outcomes of patients can lead to incoherent dose-escalation. This paper formalises a proof that incoherent dose-escalation can occur if the feasibility bound is increased without consideration of preceding toxicity outcomes, and shows via simulation studies that only small increases in the feasibility bound are required for incoherent dose-escalations to occur.

Toxicity-dependent feasibility bounds for the escalation with overdose control approach in phase I cancer trials.

Phase I trials of anti-cancer therapies aim to identify a maximum tolerated dose (MTD), defined as the dose that causes unacceptable toxicity in a target proportion of patients. Both rule-based and model-based methods have been proposed for MTD recommendation. The escalation with overdose control (EWOC) approach is a model-based design where the dose assigned to the next patient is one that, given all available data, has a posterior probability of exceeding the MTD equal to a pre-specified value known as the feasibility bound. The aim is to conservatively dose-escalate and approach the MTD, avoiding severe overdosing early on in a trial. The EWOC approach has been applied in practice with the feasibility bound either fixed or varying throughout a trial, yet some of the methods may recommend incoherent dose-escalation, that is, an increase in dose after observing severe toxicity at the current dose. We present examples where varying feasibility bounds have been used in practice, and propose a toxicity-dependent feasibility bound approach that guarantees coherent dose-escalation and incorporates the desirable features of other EWOC approaches. We show via detailed simulation studies that the toxicity-dependent feasibility bound approach provides improved MTD recommendation properties to the original EWOC approach for both discrete and continuous doses across most dose-toxicity scenarios, with comparable performance to other approaches without recommending incoherent dose escalation. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Modelling semi-attributable toxicity in dual-agent phase I trials with non-concurrent drug administration.

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.

Sequential monitoring of Phase I dose expansion cohorts.

A relatively recent development in the design of Phase I dose-finding studies is the inclusion of expansion cohort(s), that is, the inclusion of several more patients at a level considered to be the maximum tolerated dose established at the conclusion of the 'pure' Phase I part. Little attention has been given to the additional statistical analysis, including design considerations, that we might wish to consider for this more involved design. For instance, how can we best make use of new information that may confirm or may tend to contradict the estimate of the maximum tolerated dose based on the dose escalation phase. Those patients included during the dose expansion phase may possess different eligibility criteria. During the expansion phase, we will also wish to have an eye on any evidence of efficacy, an aspect that clearly distinguishes such studies from the classical Phase I study. Here, we present a methodology that enables us to continue the monitoring of safety in the dose expansion cohort while simultaneously trying to assess efficacy and, in particular, which disease types may be the most promising to take forward for further study. The most elementary problem is where we only wish to take account of further toxicity information obtained during the dose expansion cohort, and where the initial design was model based or the standard 3+3. More complex set-ups also involve efficacy and the presence of subgroups. Copyright © 2016 John Wiley & Sons, Ltd.

A Bayesian adaptive design for estimating the maximum tolerated dose curve using drug combinations in cancer phase I clinical trials.

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.

Hyperglycemia and Phosphatidylinositol 3-Kinase/Protein Kinase B/Mammalian Target of Rapamycin (PI3K/AKT/mTOR) Inhibitors in Phase I Trials: Incidence, Predictive Factors, and Management.

BACKGROUND: Dysregulation of the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) pathway is implicated in human cancer growth and progression. Agents targeting this pathway are associated with hyperglycemia due to interaction with the insulin-glucose regulatory axis. Identifying the predictive factors for hyperglycemia in patients treated with these agents may help direct future management. MATERIALS AND METHODS: Clinical characteristics and outcomes of patients treated consecutively with PI3K, AKT, or mTOR inhibitors in the Drug Development Unit, The Royal Marsden (RM) National Health Service (NHS) Foundation Trust, between 2007 and 2012 were recorded. Baseline variables and their association with grade 3 hyperglycemia (Common Terminology Criteria for Adverse Events, version 3.0) were analyzed by using the chi-square test and Fisher exact test for categorical variables and binary logistic regression for continuous variables. RESULTS: A total of 341 patients were treated in 12 phase I trials of PI3K/AKT/mTOR inhibitors, and 298 patients (87.4%) developed hyperglycemia. Hyperglycemia was grade 1 in 217 (72.8%) and grade 2 in 61 (20.5%) patients, respectively. Grade ≥3 hyperglycemia was seen in 6.7% of patients (n = 20). According to the chi-square test, age <65 years (p = .03), history of diabetes (p = .003), and treatment with AKT and dual PI3K/mTOR inhibitors (p < .0005) predicted the occurrence of grade 3 hyperglycemia. Of 24 patients requiring intervention, 20 received metformin, 2 dietary advice, 1 insulin, and 1 both metformin and insulin. One patient required dose reduction. There were no permanent drug discontinuations, and no hyperglycemia-related dose-limiting toxicities were observed; thus, the recommended phase II dose was not affected by the hyperglycemia observed in our cohort. CONCLUSION: Hyperglycemia is common in patients treated with PI3K/AKT/mTOR inhibitors; however, it is manageable with conventional treatment. Predictive factors of age, history of diabetes, and administration of AKT and dual PI3K/mTOR inhibitors warrant prospective validation. IMPLICATIONS FOR PRACTICE: This study reviewed the clinical data of 341 patients treated in 12 phase I trials of agents targeting phosphatidylinositol3-kinase (PI3), protein kinase B (AKT), and mammalian target of rapamycin (mTOR), as well as dual inhibitors. Hyperglycemia was evident in 87.4% of patients but was ≥grade 3 in just 6.7%. Age <65 years, history of diabetes, and treatment with AKT and dual PI3K/mTOR inhibitors were each associated with grade 3 hyperglycemia. Management of patients was uncomplicated, and no permanent drug discontinuations were necessary. Despite the small study size, these findings support continued caution about enrolling patients with a history of diabetes into such trials. However, clinicians may be reassured, pending prospective validation of these results, that significant hyperglycemia is not frequent and, when it occurs, is manageable.

Adaptive dose modification for phase I clinical trials.

Most phase I dose-finding methods in oncology aim to find the maximum-tolerated dose from a set of prespecified doses. However, in practice, because of a lack of understanding of the true dose-toxicity relationship, it is likely that none of these prespecified doses are equal or reasonably close to the true maximum-tolerated dose. To handle this issue, we propose an adaptive dose modification (ADM) method that can be coupled with any existing dose-finding method to adaptively modify the dose, when it is needed, during the course of dose finding. To reflect clinical practice, we divide the toxicity probability into three regions: underdosing, acceptable, and overdosing regions. We adaptively add a new dose whenever the observed data suggest that none of the investigational doses are likely to be located in the acceptable region. The new dose is estimated via a nonparametric dose-toxicity model based on local polynomial regression. The simulation study shows that ADM substantially outperforms the similar existing method. We applied ADM to a phase I cancer trial. Copyright © 2016 John Wiley & Sons, Ltd.

Dimension of model parameter space and operating characteristics in adaptive dose-finding studies.

Adaptive, model-based, dose-finding methods, such as the continual reassessment method, have been shown to have good operating characteristics. One school of thought argues in favor of the use of parsimonious models, not modeling all aspects of the problem, and using a strict minimum number of parameters. In particular, for the standard situation of a single homogeneous group, it is common to appeal to a one-parameter model. Other authors argue for a more classical approach that models all aspects of the problem. Here, we show that increasing the dimension of the parameter space, in the context of adaptive dose-finding studies, is usually counter productive and, rather than leading to improvements in operating characteristics, the added dimensionality is likely to result in difficulties. Among these are inconsistency of parameter estimates, lack of coherence in escalation or de-escalation, erratic behavior, getting stuck at the wrong level, and, in almost all cases, poorer performance in terms of correct identification of the targeted dose. Our conclusions are based on both theoretical results and simulations. Copyright © 2016 John Wiley & Sons, Ltd.