Stability analysis using mixed models: A critique of tolerance interval methods and a probabilistic solution.

Recently, tolerance interval approaches to the calculation of a shelf life of a drug product have been proposed in the literature. These address the belief that shelf life should be related to control of a certain proportion of batches being out of specification. We question the appropriateness of the tolerance interval approach. Our concerns relate to the computational challenges and practical interpretations of the method. We provide an alternative Bayesian approach, which directly controls the desired proportion of batches falling out of specification assuming a controlled manufacturing process. The approach has an intuitive interpretation and posterior distributions are straightforward to compute. If prior information on the fixed and random parameters is available, a Bayesian approach can provide additional benefits both to the company and the consumer. It also avoids many of the computational challenges with the tolerance interval methodology.

Quantitation of soluble aggregates by sedimentation velocity analytical ultracentrifugation using an optical alignment system - Aspects of method validation.

Sedimentation velocity analytical ultracentrifugation (SV-AUC) is routinely used for quantitation of soluble aggregates as an orthogonal technique to size-exclusion chromatography (SEC). SV-AUC presents many advantages over the SEC, yet lower precision of aggregate quantitation by SV-AUC often complicates comparison between aggregate values generated by these techniques and subsequent decision making. In an earlier report, we described the development of an optical alignment (OA) system and evaluated the intermediate precision of aggregate quantitation offered by the OA. Here, we determine the limit of detection (LOD) and limit of quantitation (LOQ) which can be achieved with the OA. For a common setup using three cells, the improvement lent by the OA system is almost 2.5-fold compared to the earlier reported limits. In addition, we estimate the contribution of the fitting variability and compare options to further increase the precision of aggregate quantitation by SV-AUC.

A curve similarity approach to parallelism testing in bioassay.

Potency determination via bioassay is a relative measure that requires an evaluation of parallelism between the dose-response relationships of a reference standard and a sample material. Typical approaches for assessing parallelism include difference ([Formula: see text]-value) and equivalence tests. These traditional methods rely on a statistical assessment of model parameters as opposed to direct evaluation of the similarity of the dose-response curves. We propose a simple curve similarity approach that tests the hypothesis that the sample material is a dilution or concentration of the reference standard. The test is achieved by quantifying and normalizing the total area between the two curves and provides a single composite measure of parallelism. Both a frequentist and a Bayesian approach to the test are provided. We show through a simulation study that the curve similarity approach overcomes the drawbacks of the traditional methods and is effective at detecting parallelism and non-parallelism for bioassays.

An optical alignment system improves precision of soluble aggregate quantitation by sedimentation velocity analytical ultracentrifugation.

Appropriate characterization of soluble aggregates is an important aspect of biologics development and manufacturing, and sedimentation velocity analytical ultracentrifugation (SV-AUC) is often used an orthogonal technique to size-exclusion chromatography (SEC) for this purpose. Precise quantification of low levels of soluble aggregates by SV-AUC can be adversely impacted by improper cell alignment. This report describes the development of an optical system capable of quantifying cell alignment that affords a substantial improvement compared to historical approaches.

Use of Bayesian Methods to Analyze and Visualize Content Uniformity Capability Versus United States Pharmacopeia and ASTM Standards.

The purpose of this work was to develop a straightforward and robust approach to analyze and summarize the ability of content uniformity data to meet different criteria. A robust Bayesian statistical analysis methodology is presented which provides a concise and easily interpretable visual summary of the content uniformity analysis results. The visualization displays individual batch analysis results and shows whether there is high confidence that different content uniformity criteria could be met a high percentage of the time in the future. The 3 tests assessed are as follows: (a) United States Pharmacopeia Uniformity of Dosage Units <905>, (b) a specific ASTM E2810 Sampling Plan 1 criterion to potentially be used for routine release testing, and (c) another specific ASTM E2810 Sampling Plan 2 criterion to potentially be used for process validation. The approach shown here could readily be used to create similar result summaries for other potential criteria.

Variability in Flow-Imaging Microscopy Measurements and Considerations for Biopharmaceutical Development.

Flow-imaging microscopy is widely used in the biopharmaceutical industry to characterize populations of subvisible (1-100 µm) particles due to high sensitivity and the ability to discriminate different particle morphologies. The present work provides a comprehensive assessment of the capabilities of flow-imaging microscopy by exploring the impacts of a variety of factors on the observed variability of these measurements. A novel graphical presentation is proposed to facilitate both determination of expected levels and detection of potential atypical results. Data collected across different products and container-closure systems illustrate that a substantial amount of historical experience is typically required to adequately define the expected levels of subvisible particles for any specific system. It is also shown, however, that an appropriate level of control can be demonstrated without the need to pool large numbers of containers or perform replicate measurements.

Arrhenius time-scaled least squares: a simple, robust approach to accelerated stability data analysis for bioproducts.

Defining a suitable product presentation with an acceptable stability profile over its intended shelf-life is one of the principal challenges in bioproduct development. Accelerated stability studies are routinely used as a tool to better understand long-term stability. Data analysis often employs an overall mass action kinetics description for the degradation and the Arrhenius relationship to capture the temperature dependence of the observed rate constant. To improve predictive accuracy and precision, the current work proposes a least-squares estimation approach with a single nonlinear covariate and uses a polynomial to describe the change in a product attribute with respect to time. The approach, which will be referred to as Arrhenius time-scaled (ATS) least squares, enables accurate, precise predictions to be achieved for degradation profiles commonly encountered during bioproduct development. A Monte Carlo study is conducted to compare the proposed approach with the common method of least-squares estimation on the logarithmic form of the Arrhenius equation and nonlinear estimation of a first-order model. The ATS least squares method accommodates a range of degradation profiles, provides a simple and intuitive approach for data presentation, and can be implemented with ease.