Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer.

AIMS: The aims of this study were (i) to develop a modelling framework linking change in tumour size during treatment to survival probability in metastatic ovarian cancer; and (ii) to model the appearance of new lesions and investigate their relationship with survival and disease characteristics. METHODS: Data from a randomized Phase III clinical trial comparing carboplatin monotherapy to gemcitabine plus carboplatin combotherapy in 336 patients with metastatic ovarian cancer were used. A population model describing change in tumour size based on drug treatment information was established and its relationship with time to appearance of new lesions and survival were investigated with time to event models. RESULTS: The tumour size profiles were well characterized as evaluated by visual predictive checks. Metastasis in the liver at enrolment and change in tumour size up to week 12 were predictors of time to appearance of new lesions. Survival was predicted based on the patient tumour size and ECOG performance status at enrolment and on appearance of new lesions during treatment and change in tumour size up to week 12. Tumour size and survival data from a separate study were adequately predicted. CONCLUSIONS: The proposed models simulate tumour dynamics following treatment and provide a link to the probability of developing new lesions as well as to survival. The models have potential to be used for optimizing the design of late phase clinical trials in metastatic ovarian cancer based on early phase clinical study results and simulation.

Phase 1/2 study of ocaratuzumab, an Fc-engineered humanized anti-CD20 monoclonal antibody, in low-affinity FcγRIIIa patients with previously treated follicular lymphoma.

This phase 2 study assessed the safety and efficacy of ocaratuzumab, a humanized anti-CD20 monoclonal antibody. Fifty patients with previously treated follicular lymphoma (FL) and a low-affinity genotype of FcγRIIIa received ocaratuzumab 375 mg/m(2) weekly for 4 weeks. Grade 3/4/5 adverse events (AEs) were reported in 11/1/1 patients, respectively. Serious AEs were reported by 11/50 patients, and three discontinued due to AEs. One patient died from aspiration pneumonia due to possibly drug-related nausea and vomiting. Investigator-assessed response rate was 30% (15/50), including four complete responses (CR), three CR unconfirmed (CRu) and eight partial responses (PR). Investigator-assessed median Progression-free survivial (PFS) was 38.3 weeks. Ocaratuzumab's pharmacokinetic profile was similar to that reported for rituximab. Lymphocyte subset analysis showed significant, selective reduction of B-cells during and after ocaratuzumab treatment. Ocaratuzumab at this dose and schedule is active and well tolerated in patients with previously treated FL with low affinity FcγRIIIa genotypes. ClinTrials registry number: NCT00354926.

Randomization by minimization for unbalanced treatment allocation.

Minimization is a dynamic randomization technique that has been widely used in clinical trials for achieving a balance of prognostic factors across treatment groups, but most often it has been used in the setting of equal treatment allocations. Although unequal treatment allocation is frequently encountered in clinical trials, an appropriate minimization procedure for such trials has not been published. The purpose of this paper is to present novel strategies for applying minimization methodology to such clinical trials. Two minimization techniques are proposed and compared by probability calculation and simulation studies. In the first method, called naïve minimization, probability assignment is based on a simple modification of the original minimization algorithm, which does not account for unequal allocation ratios. In the second method, called biased-coin minimization (BCM), probability assignment is based on allocation ratios and optimized to achieve an 'unbiased' target allocation ratio. The performance of the two methods is investigated in various trial settings including different number of treatments, prognostic factors and sample sizes. The relative merits of the different distance metrics are also explored. On the basis of the results, we conclude that BCM is the preferable method for randomization in clinical trials involving unequal treatment allocations. The choice of different distance metrics slightly affects the performance of the minimization and may be optimized according to the specific feature of trials.