Journal of biopharmaceutical statistics
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Substantial heterogeneity in treatment effects across subgroups can cause significant findings in the overall population to be driven predominantly by those of a certain subgroup, thus raising concern on whether the treatment should be prescribed for the least benefitted subgroup. Because of its low power, a nonsignificant interaction test can lead to incorrectly prescribing treatment for the overall population. This article investigates the power of the interaction test and its implications. Also, it investigates the probability of prescribing the treatment to a nonbenefitted subgroup on the basis of a nonsignificant interaction test and other recently proposed criteria.
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One of the most challenging aspects of the pharmaceutical development is the demonstration and estimation of chemical stability. It is imperative that pharmaceutical products be stable for two or more years. Long-term stability studies are required to support such shelf life claim at registration. ⋯ In this paper, we introduce two nonparametric bootstrap procedures for shelf life estimation based on accelerated stability testing, and compare them with a one-stage nonlinear Arrhenius prediction model. Our simulation results demonstrate that one-stage nonlinear Arrhenius method has significant lower coverage than nominal levels. Our bootstrap method gave better coverage and led to a shelf life prediction closer to that based on long-term stability data.
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Applications of personalized medicine are becoming increasingly prominent. A well-characterized market-ready companion diagnostic assay (CDx) is often desired for patient enrollment in device-drug pivotal clinical trial(s) so that Food and Drug Administration can ensure that appropriate clinical and analytical validation studies are planned and carried out for CDx. However, such a requirement may be difficult or impractical to accomplish. ⋯ A concordance study (or bridging study) will be required to assess the agreement between CDx and CTA in order to bridge the clinical data (e.g. overall survival) from CTA to CDx and to evaluate the drug efficacy in CDx intended use population. In this article, we will discuss statistical challenges in study design and data analysis for bridging study. Particularly, we aimed to provide statistical methods on how to estimate the drug efficacy in CDx intended use population using results from bridging study and CTA-drug pivotal clinical trial.
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We propose a method for determining the criticality of residual host cell DNA, which is characterized through two attributes, namely the size and amount of residual DNA in biopharmaceutical product. By applying a mechanistic modeling approach to the problem, we establish the linkage between residual DNA and product safety measured in terms of immunogenicity, oncogenicity, and infectivity. Such a link makes it possible to establish acceptable ranges of residual DNA size and amount. Application of the method is illustrated through two real-life examples related to a vaccine manufactured in Madin Darby Canine Kidney cell line and a monoclonal antibody using Chinese hamster ovary (CHO) cell line as host cells.