Marie-Karelle Riviere

Bio: Marie-Karelle Riviere is an academic researcher from University of Paris. The author has contributed to research in topics: Pharmacodynamics & Bayesian hierarchical modeling. The author has an hindex of 7, co-authored 17 publications receiving 211 citations. Previous affiliations of Marie-Karelle Riviere include Paris Diderot University & French Institute of Health and Medical Research.

A Bayesian dose-finding design for drug combination clinical trials based on the logistic model.

Abstract: In early phase dose-finding cancer studies, the objective is to determine the maximum tolerated dose, defined as the highest dose with an acceptable dose-limiting toxicity rate. Finding this dose for drug-combination trials is complicated because of drug–drug interactions, and many trial designs have been proposed to address this issue. These designs rely on complicated statistical models that typically are not familiar to clinicians, and are rarely used in practice. The aim of this paper is to propose a Bayesian dose-finding design for drug combination trials based on standard logistic regression. Under the proposed design, we continuously update the posterior estimates of the model parameters to make the decisions of dose assignment and early stopping. Simulation studies show that the proposed design is competitive and outperforms some existing designs. We also extend our design to handle delayed toxicities. Copyright © 2014 John Wiley & Sons, Ltd.

Phase I/II dose-finding design for molecularly targeted agent: Plateau determination using adaptive randomization

Abstract: Conventionally, phase I dose-finding trials aim to determine the maximum tolerated dose of a new drug under the assumption that both toxicity and efficacy monotonically increase with the dose. This paradigm, however, is not suitable for some molecularly targeted agents, such as monoclonal antibodies, for which efficacy often increases initially with the dose and then plateaus. For molecularly targeted agents, the goal is to find the optimal dose, defined as the lowest safe dose that achieves the highest efficacy. We develop a Bayesian phase I/II dose-finding design to find the optimal dose. We employ a logistic model with a plateau parameter to capture the increasing-then-plateau feature of the dose–efficacy relationship. We take the weighted likelihood approach to accommodate for the case where efficacy is possibly late-onset. Based on observed data, we continuously update the posterior estimates of toxicity and efficacy probabilities and adaptively assign patients to the optimal dose. The simulation studies show that the proposed design has good operating characteristics. This method is going to be applied in more than two phase I clinical trials as no other method is available for this specific setting. We also provide an R package dfmta that can be downloaded from CRAN website.

Competing designs for drug combination in phase I dose‐finding clinical trials

Abstract: The aim of phase I combination dose-finding studies in oncology is to estimate one or several maximum tolerated doses (MTDs) from a set of available dose levels of two or more agents. Combining several agents can indeed increase the overall anti-tumor action but at the same time also increase the toxicity. It is, however, unreasonable to assume the same dose-toxicity relationship for the combination as for the simple addition of each single agent because of a potential antagonist or synergistic effect. Therefore, using single-agent dose-finding methods for combination therapies is not appropriate. In recent years, several authors have proposed novel dose-finding designs for combination studies, which use either algorithm-based or model-based methods. The aim of our work was to compare, via a simulation study, six dose-finding methods for combinations proposed in recent years. We chose eight scenarios that differ in terms of the number and location of the true MTD(s) in the combination space. We then compared the performance of each design in terms of correct combination selection, patient allocation, and mean number of observed toxicities during the trials. Our results showed that the model-based methods performed better than the algorithm-based ones. However, none of the compared model-based designs gave consistently better results than the others.

A Bayesian dose finding design for clinical trials combining a cytotoxic agent with a molecularly targeted agent

Abstract: Novel molecularly targeted agents (MTAs) have emerged as valuable alternatives or complements to traditional cytotoxic agents in the treatment of cancer. Clinicians are combining cytotoxic agents with MTAs in a single trial to achieve treatment synergism and better outcomes for patients. An important feature of such combinational trials is that, unlike the efficacy of the cytotoxic agent, that of the MTA may initially increase at low dose levels and then approximately plateau at higher dose levels as MTA saturation levels are reached. Therefore, the goal of the trial is to find the optimal dose combination that yields the highest efficacy with the lowest toxicity and meanwhile satisfies a certain safety requirement. We propose a Bayesian phase I–II design to find the optimal dose combination. We model toxicity by using a logistic regression and propose a novel proportional hazard model for efficacy, which accounts for the plateau in the MTA dose–efficacy curve. We evaluate the operating characteristics of the proposed design through simulation studies under various practical scenarios. The results show that the design proposed performs well and selects the optimal dose combination with high probability.

Homeobox protein Msx2 as a molecular defence mechanism for preventing ossification in ligament fibroblasts

Abstract: ABSTRACT Ligaments and tendons are comprised of tough yet flexible connective tissue. Little is known, however, about the precise characteristics of the cells in ligaments and tendons due to the absence of specific markers and cell lines. We recently reported a periodontal ligament cell line, PDL-L2, with suppressed Runx2/Osf2 transcriptional activity and an inability to form mineralized nodules. The present study demonstrates that the homeobox protein Msx2 is a key factor in suppressing those two functions. Msx2 colocalizes with Runx2/Osf2 and suppresses its activity cooperatively, acting with another corepressor, TLE1, as a complex to recruit histone deacetylase 1 activity. Reverse transcription-PCR and in situ hybridization demonstrated that Msx2 expression is higher in periodontal ligament and tendon cells than in osteoblasts. Stable reduction of Msx2 expression in PDL-L2 cells induces osteoblastic differentiation, thereby causing matrix mineralization. Conversely, stable, forced Msx2 expression in MC3T3-E1 cells prevented osteoblast differentiation and matrix mineralization. Msx2-induced suppression of osteoblast differentiation was repressed by bone morphogenetic protein 2. In addition, Msx2 was downregulated in a symptom- and calcification-dependent manner at the affected region in patients with ossification of the posterior longitudinal ligament. Our findings indicate that Msx2 plays a central role in preventing ligaments and tendons from mineralizing.

The changing landscape of phase I trials in oncology.

Abstract: Advances in our knowledge of the molecular pathogenesis of cancer have led to increased interest in molecularly targeted agents (MTAs), which target specific oncogenic drivers and are now a major focus of cancer drug development. MTAs differ from traditional cytotoxic agents in various aspects, including their toxicity profiles and the potential availability of predictive biomarkers of response. The landscape of phase I oncology trials is evolving to adapt to these novel therapies and to improve the efficiency of drug development. In this Review, we discuss new strategies used in phase I trial design, such as novel dose-escalation schemes to circumvent limitations of the classic 3 + 3 design and enable faster dose escalation and/or more-precise dose determinations using statistical modelling; improved selection of patients based on genetic or molecular biomarkers; pharmacokinetic and pharmacodynamic analyses; and the early evaluation of efficacy - in addition to safety. Indeed, new expedited approval pathways that can accelerate drug development require demonstration of efficacy in early phase trials. The application of molecular tumour profiling for matched therapy and the testing of drug combinations based on a strong biological rationale are also increasingly seen in phase I studies. Finally, the shift towards multi-institutional trials and centralized study management results in consequent implications for institutions and investigators. These issues are also highlighted herein.

Bayesian optimal interval design for dose finding in drug-combination trials:

Abstract: Interval designs have recently attracted enormous attention due to their simplicity and desirable properties. We develop a Bayesian optimal interval design for dose finding in drug-combination trials. To determine the next dose combination based on the cumulative data, we propose an allocation rule by maximizing the posterior probability that the toxicity rate of the next dose falls inside a prespecified probability interval. The entire dose-finding procedure is nonparametric (model-free), which is thus robust and also does not require the typical "nonparametric" prephase used in model-based designs for drug-combination trials. The proposed two-dimensional interval design enjoys convergence properties for large samples. We conduct simulation studies to demonstrate the finite-sample performance of the proposed method under various scenarios and further make a modication to estimate toxicity contours by parallel dose-finding paths. Simulation results show that on average the performance of the proposed design is comparable with model-based designs, but it is much easier to implement.

A phase Ib dose-escalation study of the MEK inhibitor trametinib in combination with the PI3K/mTOR inhibitor GSK2126458 in patients with advanced solid tumors

Abstract: Introduction This Phase Ib trial investigated the safety, tolerability, and recommended phase 2 dose for the pan-PI3K/mTOR inhibitor, GSK2126458 (GSK458), and trametinib combination when administered to patients with advanced solid tumors. Patients and Methods Patients with advanced solid tumors received escalating doses of GSK458 (once or twice daily, and continuous or intermittent) and trametinib following a zone-based 3 + 3 design to determine the maximum tolerated dose (MTD). Assessments included monitoring for adverse events and response, and evaluating pharmacokinetic (PK) measures. Archival tissue and circulating free DNA samples were collected to assess biomarkers of response in the PI3K and RAS pathways. Results 57 patients were enrolled onto the continuous dosing cohort and 12 patients onto an intermittent BID dosing cohort. Two MTDs were established for the continuous daily dosing: 2 mg of GSK458 with 1.0 mg of trametinib or 1.0 mg of GSK458 with 1.5 mg of trametinib; no MTD was determined in the intermittent dosing cohort. The most frequent adverse events were rash (74 %) and diarrhea (61 %). Dose interruptions due to adverse events occurred in 42 % of patients. No significant PK interaction was observed. One patient achieved partial response and 12 patients had stable disease >16 weeks. Mutations in RAS/RAF/PI3K were detected in 70 % of patients, but no pattern emerged between response and mutational status. Conclusion GSK458 plus trametinib is poorly tolerated, due to skin and GI-related toxicities. Responses were minimal, despite enrichment for PI3K/RAS pathway driven tumors, which may be due to overlapping toxicities precluding sufficient dose exposure.