Bayesian dose regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics.

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose of a drug under a specific schedule. Evaluating drug schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) to estimate the maximum tolerated dose regimen (MTD-regimen) at the end of the dose-escalation stage of a trial. We modeled the binary toxicity via a PD endpoint and estimated the dose regimen toxicity relationship through the integration of a dose regimen PD model and a PD toxicity model. For the first model, we considered nonlinear mixed-effects models, and for the second one, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. In an extensive simulation study, the DRtox outperformed traditional designs in terms of proportion of correctly selecting the MTD-regimen. Moreover, the inclusion of PK/PD information helped provide more precise estimates for the entire dose regimen toxicity curve; therefore the DRtox may recommend alternative untested regimens for expansion cohorts. The DRtox was developed to be applied at the end of the dose-escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/PD data are collected.

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens.

Most phase I trials in oncology aim to find the maximum tolerated dose (MTD) based on the occurrence of dose limiting toxicities (DLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance. Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose-finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of DLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/PD), whereas the other DLT (DLTo ) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of DLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose-allocation stage of the trial, once all data, including PK/PD measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTD-regimen compared to the recommendation of the dose-allocation method implemented. Our joint approaches can also predict the DLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.

Plastic surgery in bariatric patients: a nationwide study of 17,000 patients on the national administrative database.

BACKGROUND: Bariatric patients are often candidates for plastic surgery. However, the rate of postbariatric procedures is not known. OBJECTIVES: The aim of this study was to analyze the rate of plastic surgery, and factors related to surgery, in bariatric patients. SETTING: University hospital, France. METHODS: This was a cohort study based on administrative data. All adult patients who received bariatric surgery in France between 2007 and 2013 were included to estimate the rate of plastic surgery and related predictive factors. Data are reported according to the reporting of studies conducted using observational routinely collected data guidelines for observational studies on administrative data. RESULTS: Among the 183,514 patients who underwent bariatric surgery in the study period, 23,120 plastic surgeries were performed on 17,695 patients, including abdominoplasty (62%), dermolipectomy of the upper or lower limbs (25%), and reconstruction of the breast (14%). The rates of plastic surgery were 13%, 18%, and 21% at 3, 5, and 7 years post-bariatric surgery, respectively. Multivariate analysis revealed that patients who had a biliopancreatic diversion or a gastric bypass had a hazard ratio of 2.67 and 2.67 for subsequent plastic surgery, respectively, compared with patients who had adjustable gastric banding. Women had a 2-fold probability of surgery compared with men (hazard ratio 2.02). Important variability in the rate of surgery was found among different hospitals; rates ranged from 6.1% to 41.3% at 5 years. CONCLUSIONS: This study showed that 21% of bariatric patients undergo plastic surgery. Large variability exists among hospitals, suggesting that several unmeasured factors may limit access to contouring surgery.