Predictors for establishing recommended phase 2 doses: analysis of 320 dose-seeking oncology phase 1 trials

A population pharmacokinetic model of AT9283 in adults and children to predict the maximum tolerated dose in children with leukaemia

AIMS

AT9283 is used to treat patients with solid tumours and patients with leukaemia. However, the maximum tolerated dose (MTD) for children with leukaemia remains unknown due to early termination of the Phase I trial. The aim of this study was to develop a population model of AT9283 to describe the pharmacokinetics in adults and children and to estimate the MTD in children with leukaemia.

METHODS

Data from Phase I dose-escalation studies in adults and children were used to build a population pharmacokinetic model (NONMEM v7.3). Potential covariates investigated included body weight, body surface area (BSA), glomerular filtration rate (GFR), age and sex. Model-derived area under the concentration-time curve was used to investigate the relationship between dose and exposure in adults and children.

RESULTS

The plasma concentrations of AT9283 (n = 1770) from 92 patients (53 adults, 39 children) were used to build a two-compartment model with all pharmacokinetic parameters scaled using body weight. Renal function (GFR), but not BSA, was a significant covariate for the clearance of AT9283. In children with leukaemia (median weight 16 kg), a flat dose of 500 mg 72 h-1 provided similar drug exposures at the MTD as the adult population. The estimated MTD for children with leukaemia, therefore, is 30 mg kg-1  72 h-1 .

CONCLUSION

For adults, GFR was a significant predictor of clearance, whilst body-weight based dosing was more useful than BSA in determining the drug exposure in children. The MTD was estimated to be 30 mg kg-1  72 h-1 children with leukaemia.

Older breast cancer survivors: can interaction analyses identify vulnerable subgroups? A report from the American Cancer Society Studies of Cancer Survivors

PURPOSE/OBJECTIVES

To explore interactions among personal, cancer, aging, and symptom variables relative to physical function (PF) in older adult breast cancer survivors to better identify vulnerable subgroups.

DESIGN

Secondary analysis of the American Cancer Society Studies of Cancer Survivors II.

SETTING

U.S. population-based mail and telephone survey.

SAMPLE

2,885 breast cancer survivors from 14 different state cancer registries stratified by cancer type and time since diagnosis. A total of 184 female breast cancer survivors, aged 70 years or older, had complete data on variables of interest and were, therefore, included in this analysis.

METHODS

Chi-Square Automatic Interaction Detector (CHAID) analysis was used to examine variable interactions.

MAIN RESEARCH VARIABLES

PF, symptom bother, comorbidity, social support, length of survivorship, treatment, stage, body mass index, physical activity, emotional health, and personal characteristics.

FINDINGS

An interaction effect between symptom bother and comorbidity was found in 39% of older adult breast cancer survivors, and an interaction effect between symptom bother and marital status was found in 40%. The most vulnerable group (8%) had high symptom bother and more than four comorbid conditions.

CONCLUSIONS

Symptom bother, comorbidity, and marital status were found to have significant interactions such that high comorbidity and high symptom bother were significantly related to lower PF. Married participants with lower symptom bother had significantly higher PF scores. Comorbidity may be the best predictor of PF for the extreme ends of the symptom bother continuum. Advancing age alone was not a sufficient predictor of PF in this analysis.

IMPLICATIONS FOR NURSING

Specific attention to symptom reports, comorbidity, and marital status can guide identification of older adult cancer survivors in need of ongoing survivorship care. The findings support use of a comprehensive assessment and tailored approach to care based on factors other than age.

KNOWLEDGE TRANSLATION

CHAID interaction analysis may be useful in exploring complex nursing problems, such as the needs of older adult cancer survivors, and help oncology nurses develop appropriate interventions and referrals.

Dose-finding designs using a novel quasi-continuous endpoint for multiple toxicities

The aim of a phase I oncology trial is to identify a dose with an acceptable safety profile. Most phase I designs use the dose-limiting toxicity, a binary endpoint, to assess the unacceptable level of toxicity. The dose-limiting toxicity might be incomplete for investigating molecularly targeted therapies as much useful toxicity information is discarded. In this work, we propose a quasi-continuous toxicity score, the total toxicity profile (TTP), to measure quantitatively and comprehensively the overall severity of multiple toxicities. We define the TTP as the Euclidean norm of the weights of toxicities experienced by a patient, where the weights reflect the relative clinical importance of each grade and toxicity type. We propose a dose-finding design, the quasi-likelihood continual reassessment method (CRM), incorporating the TTP score into the CRM, with a logistic model for the dose-toxicity relationship in a frequentist framework. Using simulations, we compared our design with three existing designs for quasi-continuous toxicity score (the Bayesian quasi-CRM with an empiric model and two nonparametric designs), all using the TTP score, under eight different scenarios. All designs using the TTP score to identify the recommended dose had good performance characteristics for most scenarios, with good overdosing control. For a sample size of 36, the percentage of correct selection for the quasi-likelihood CRM ranged from 80% to 90%, with similar results for the quasi-CRM design. These designs with TTP score present an appealing alternative to the conventional dose-finding designs, especially in the context of molecularly targeted agents.