scholarly journals dfpk: An R-package for Bayesian dose-finding designs using pharmacokinetics (PK) for phase I clinical trials

2018 ◽  
Vol 157 ◽  
pp. 163-177 ◽  
Author(s):  
A. Toumazi ◽  
E. Comets ◽  
C. Alberti ◽  
T. Friede ◽  
F. Lentz ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
R Package ◽  
Dose Finding ◽  
Phase I Clinical Trials

Informed consent in phase I clinical trials: Implications for trends in design.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14077-e14077
Author(s):  
Paul Henry Frankel ◽  
Susan G. Groshen
Keyword(s):  
Clinical Trials ◽  
Informed Consent ◽  
Phase I ◽  
Dose Finding ◽  
Institutional Review Boards ◽  
Severe Toxicity ◽  
Phase I Clinical Trials ◽  
Traditional Use ◽  
Phase I Studies ◽  
Definition Of

e14077 Background: Informed Consent (IC) is a critical aspect of human subjects protection. Institutional Review Boards are tasked with insuring proper IC as one aspect of protecting participants in clinical trials. Phase I trials in oncology present special issues with IC, as often neither the risks nor the benefits are well-known. This has resulted in carefully worded IC templates for Phase I studies based on the traditional use of dose-finding designs that are geared towards finding the “Maximum Tolerated Dose (MTD)”. As the definition of this term varies by study, the implication for patient risk and informed consent are rarely discussed. Methods: We reviewed Phase I designs to present options for improving the informed consent process for Phase I oncology trials. Results: Phase I studies have seen an increase in designs based on work from the early 1990s seeking a dose that results in a targeted percent of patients experiencing a “Dose Limiting Toxicity (DLT)” to define the MTD. The most common definition of a DLT is a treatment-related toxicity that results in a particularly concerning severe toxicity (grade 3 or higher) in the first cycle of therapy and the most common rate targeted (in designs that define toxicity as a goal) is 25%. In that setting, while lower doses may have a lower likelihood of DLT, higher doses or the expansion cohort are likely to have a 25% chance of DLT if the target is pursued. This information is rarely quantitatively communicated in the informed consent. Conclusions: IRBs and investigators should consider communicating through informed consent the quantitative summary of goals of the study and related risk. For example, transparency suggests conveying when the goal (target) of the study is to find the dose where there is a one in four chance of experiencing a severe adverse event in the first cycle.

A Bayesian dose-finding procedure for phase I clinical trials based only on the assumption of monotonicity

2010 ◽  
Vol 29 (17) ◽  
pp. 1808-1824 ◽  
Author(s):  
John Whitehead ◽  
Helene Thygesen ◽  
Anne Whitehead
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Finding ◽  
Phase I Clinical Trials

scholarly journals Adaptive Dose-Finding Studies: A Review of Model-Guided Phase I Clinical Trials

2014 ◽  
Vol 32 (23) ◽  
pp. 2505-2511 ◽  
Author(s):  
Alexia Iasonos ◽  
John O'Quigley
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Escalation ◽  
Late Onset ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Design Parameters ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Trial Duration

Purpose We provide a comprehensive review of adaptive phase I clinical trials in oncology that used a statistical model to guide dose escalation to identify the maximum-tolerated dose (MTD). We describe the clinical setting, practical implications, and safety of such applications, with the aim of understanding how these designs work in practice. Methods We identified 53 phase I trials published between January 2003 and September 2013 that used the continual reassessment method (CRM), CRM using escalation with overdose control, or time-to-event CRM for late-onset toxicities. Study characteristics, design parameters, dose-limiting toxicity (DLT) definition, DLT rate, patient-dose allocation, overdose, underdose, sample size, and trial duration were abstracted from each study. In addition, we examined all studies in terms of safety, and we outlined the reasons why escalations occur and under what circumstances. Results On average, trials accrued 25 to 35 patients over a 2-year period and tested five dose levels. The average DLT rate was 18%, which is lower than in previous reports, whereas all levels above the MTD had an average DLT rate of 36%. On average, 39% of patients were treated at the MTD, and 74% were treated at either the MTD or an adjacent level (one level above or below). Conclusion This review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.

scholarly journals A comparison between the continual reassessment method and D-optimum design for dose finding in phase I clinical trials

2016 ◽  
Vol 53 (2) ◽  
pp. 69-82
Author(s):  
M. Iftakhar Alam
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Optimum Design ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Parameter Estimates ◽  
Phase I Clinical Trials ◽  
Continual Reassessment Method ◽  
Continual Reassessment ◽  
Asymptotic Variability

AbstractThe continual reassessment method is a model-based procedure, described in the literature, used to determine the maximum tolerated dose in phase I clinical trials. The maximum tolerated dose can also be found under the framework of D-optimum design, where information is gathered in such a way so that asymptotic variability in the parameter estimates in minimised. This paper investigates the two methods under some realistic settings to explore any potential differences between them. Simulation studies for six plausible dose-response scenarios show that D-optimum design can work well in comparison with the continual reassessment method in many cases. The D-optimum design is also found to allocate doses from the extremes of the design region to the patients in a trial.

Investigating the disparate enrollment of older adults on phase I clinical trials: Evolving participation patterns of patients 65 years and older w advanced cancer on phase I trials.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 12044-12044
Author(s):  
Ishwaria Mohan Subbiah ◽  
Aman Buzdar ◽  
Ecaterina Elena Ileana Dumbrava ◽  
Siqing Fu ◽  
Filip Janku ◽  
...  
Keyword(s):  
Older Adults ◽  
Clinical Trials ◽  
Phase I ◽  
Oldest Old ◽  
Phase 1 ◽  
Single Agent ◽  
Primary Objective ◽  
Dose Finding ◽  
Phase I Trials ◽  
Phase I Clinical Trials

12044 Background: While safety and dose-finding remain the primary objective of Phase 1 trials, the potential for clinical benefit has taken a greater meaning in the last decade with the novel therapies. With data from phase I trials being submitted for regulatory approval, the finer details of these studies are under even more scrutiny: in particular, do the trial participants reflect the general patient population for whom the drug may be indicated? To that end, we investigated age-based enrollment on phase I clinical trials over time. Methods: We queried a prospectively maintained database at a major phase I trials center to identify eligible patients and demographic + clinical variables including phase I trial characteristics, age at date of enrollment into 3 age-based cohorts: AYA ages 15-39y, mid-age 40-64y, older adults aged 65y+. We calculated descriptive statistics, and explored correlations (Pearson/Spearman) and associations (linear regression) between age and independent variables. Results: Over a 3-year period (1/1/17 to 12/31/19), we identified 6267 pts enrolled on 338 phase I trials. Median overall age 58.4y (range 15.5-95.1y). 729 (12%, median age 34.8y) were AYA, 3652 (58%, median age 55.4y) mid-age and 1886 (30%, median 70y) older adults, of whom 870 pts were aged 70-79y and 76 pts aged 80y+ (18 being >85y). There was no association b/w senior participation and year of enrollment (2017 31%, 2018 29%, 2019 30%, b/w age and type of therapy (i.e. targeted vs immunotherapy, etc.) or b/w age and # of drugs given on trial (single agent vs combo) (all p > 0.05). Conclusions: Older adults remain underrepresented on phase I trials esp. when compared to incidence of cancer in that age group (30% enrollment vs 60% incidence), a discordance more staggering in the oldest old pts (85y+; only 18 pts enrolled over 3 yrs when compared to 140,690 pts 85y+ w a new cancer dx in just 2019). Once enrolled, older adults received similar types of phase I therapies with comparable number of drugs as compared to middle age patients, i.e. older adults were just as likely to get immunotherapy or targeted therapy as well mono- vs combo therapy as mid-age pts. [Table: see text]

Dose-finding designs in pediatric phase I clinical trials: Comparison by simulations in a realistic timeline framework

2012 ◽  
Vol 33 (4) ◽  
pp. 657-665 ◽  
Author(s):  
A. Doussau ◽  
B. Asselain ◽  
M.C. Le Deley ◽  
B. Geoerger ◽  
F. Doz ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Finding ◽  
Phase I Clinical Trials

scholarly journals Optimal biological dose: a systematic review in cancer phase I clinical trials

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
J. Fraisse ◽  
D. Dinart ◽  
D. Tosi ◽  
C. Bellera ◽  
C. Mollevi
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Phase I ◽  
Dose Escalation ◽  
Phase 1 ◽  
Dose Finding ◽  
Cancer Clinical Trials ◽  
Phase I Clinical Trials ◽  
Cancer Trial ◽  
Biological Dose

Abstract Background Classical phase 1 dose-finding designs based on a single toxicity endpoint to assess the maximum tolerated dose were initially developed in the context of cytotoxic drugs. With the emergence of molecular targeted agents and immunotherapies, the concept of optimal biological dose (OBD) was subsequently introduced to account for efficacy in addition to toxicity. The objective was therefore to provide an overview of published phase 1 cancer clinical trials relying on the concept of OBD. Methods We performed a systematic review through a computerized search of the MEDLINE database to identify early phase cancer clinical trials that relied on OBD. Relevant publications were selected based on a two-step process by two independent readers. Relevant information (phase, type of therapeutic agents, objectives, endpoints and dose-finding design) were collected. Results We retrieved 37 articles. OBD was clearly mentioned as a trial objective (primary or secondary) for 22 articles and was traditionally defined as the smallest dose maximizing an efficacy criterion such as biological target: biological response, immune cells count for immunotherapies, or biological cell count for targeted therapies. Most trials considered a binary toxicity endpoint defined in terms of the proportion of patients who experienced a dose-limiting toxicity. Only two articles relied on an adaptive dose escalation design. Conclusions In practice, OBD should be a primary objective for the assessment of the recommended phase 2 dose (RP2D) for a targeted therapy or immunotherapy phase I cancer trial. Dose escalation designs have to be adapted accordingly to account for both efficacy and toxicity.

Translation of Innovative Designs Into Phase I Trials

2007 ◽  
Vol 25 (31) ◽  
pp. 4982-4986 ◽  
Author(s):  
André Rogatko ◽  
David Schoeneck ◽  
William Jonas ◽  
Mourad Tighiouart ◽  
Fadlo R. Khuri ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Well Being ◽  
Adaptive Designs ◽  
Dose Finding ◽  
Therapeutic Window ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Statistical Studies ◽  
Statistical Designs

Purpose Phase I clinical trials of new anticancer therapies determine suitable doses for further testing. Optimization of their design is vital in that they enroll cancer patients whose well-being is distinctly at risk. This study examines the effectiveness of knowledge transfer about more effective statistical designs to clinical practice. Methods We examined abstract records of cancer phase I trials from the Science Citation Index database between 1991 and 2006 and classified them into clinical (dose-finding trials) and statistical trials (methodologic studies of dose-escalation designs). We then mapped these two sets by tracking which trials adopted new statistical designs. Results One thousand two hundred thirty-five clinical and 90 statistical studies were identified. Only 1.6% of the phase I cancer trials (20 of 1,235 trials) followed a design proposed in one of the statistical studies. These 20 clinical studies showed extensive lags between publication of the statistical paper and its translation into a clinical paper. These 20 clinical trials followed Bayesian adaptive designs. The remainder used variations of the standard up-and-down method. Conclusion A consequence of using less effective designs is that more patients are treated with doses outside the therapeutic window. Simulation studies have shown that up-and-down designs treated only 35% of patients at optimal dose levels versus 55% for Bayesian adaptive designs. This implies needless loss of treatment efficacy and, possibly, lives. We suggest that regulatory agencies (eg, US Food and Drug Administration) should proactively encourage the adoption of statistical designs that would allow more patients to be treated at near-optimal doses while controlling for excessive toxicity.

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