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Bart Michiels

Publications and source records attributed to Bart Michiels.

4 recordsLinked to original sources

Phase II study of the efficacy and tolerability of two dosing regimens of the farnesyl transferase inhibitor, R115777, in advanced breast cancer.

PURPOSE: R115777 is an orally active farnesyl transferase inhibitor that specifically blocks farnesylation of proteins involved in growth-factor-dependent cell-signal-transduction pathways. We conducted a phase II study in 76 patients with advanced breast cancer. PATIENTS AND METHODS: Two cohorts of patients were recruited sequentially. The first cohort (n = 41) received a continuous dosing [CD] regimen of R115777 400 or 300 mg bid. The second cohort (n = 35) received 300 mg bid in a cyclical regimen of 21 days of treatment followed by 7 days of rest (intermittent dosing [ID]). RESULTS: In the CD cohort, four patients (10%) had a partial response (PR) and six patients (15%) had stable disease at > or = 24 weeks (SD). In the ID cohort, five patients (14%) had a PR and three patients (9%) had prolonged SD. The first six patients in the CD cohort treated at 400 mg bid all developed grade 3 to 4 neutropenia, so the subsequent 35 patients were treated at 300 mg bid. The incidence of hematologic toxicity was significantly lower in the ID than in the CD (300-mg bid) cohort: grade 3 to 4 neutropenia (14% v 43%; P =.016) and grade 3 to 4 thrombocytopenia (3% v 26%; P =.013). One patient in the ID cohort developed grade 2 to 3 neurotoxicity compared with 15 patients in the CD cohort (3% v 37%; P =.0004). CONCLUSION: The farnesyl transferase inhibitor R115777 has demonstrated clinical activity in patients with metastatic breast cancer, and the ID regimen has a significantly improved therapeutic index compared with the CD regimen.

Administration, Oral↗

Controlling type I error rate for fast track drug development programmes.

The U.S. Food and Drug Administration (FDA) Modernization Act of 1997 has a Section (No. 112) entitled 'Expediting Study and Approval of Fast Track Drugs' (the Act). In 1998, the FDA issued a 'Guidance for Industry: the Fast Track Drug Development Programs' (the FTDD programmes) to meet the requirement of the Act. The purpose of FTDD programmes is to 'facilitate the development and expedite the review of new drugs that are intended to treat serious or life-threatening conditions and that demonstrate the potential to address unmet medical needs'. Since then many health products have reached patients who suffered from AIDS, cancer, osteoporosis, and many other diseases, sooner by utilizing the Fast Track Act and the FTDD programmes. In the meantime several scientific issues have also surfaced when following the FTDD programmes. In this paper we will discuss the concept of two kinds of type I errors, namely, the 'conditional approval' and the 'final approval' type I errors, and propose statistical methods for controlling them in a new drug submission process.

Clinical Trials, Phase III as Topic↗

Selection models and pattern-mixture models to analyse longitudinal quality of life data subject to drop-out.

Longitudinally observed quality of life data with large amounts of drop-out are analysed. First we used the selection modelling framework, frequently used with incomplete studies. An alternative method consists of using pattern-mixture models. These are also straightforward to implement, but result in a different set of parameters for the measurement and drop-out mechanisms. Since selection models and pattern-mixture models are based upon different factorizations of the joint distribution of measurement and drop-out mechanisms, comparing both models concerning, for example, treatment effect, is a useful form of a sensitivity analysis.

Aged↗

Strategies to fit pattern-mixture models.

Whereas most models for incomplete longitudinal data are formulated within the selection model framework, pattern-mixture models have gained considerable interest in recent years (Little, 1993, 1994). In this paper, we outline several strategies to fit pattern-mixture models, including the so-called identifying restrictions strategy. Multiple imputation is used to apply this strategy to realistic settings, such as quality-of-life data from a longitudinal study on metastatic breast cancer patients.

Journal Article↗