In-process microbial testing: statistical properties of a rapid alternative to compendial enumeration methods.

UNLABELLED: In-process tests are used between manufacturing steps to avoid the cost of further processing material that is apt to fail its final tests. Rapid microbiological methods that return simple negative or positive results are attractive in this context because they are faster than the compendial methods used at product release. However, using a single such test will not reliably detect barely unacceptable material (sensitivity) without generating an undesirable number of false rejections (poor specificity). We quantify how to achieve a balance between the risks of false acceptance and false rejection by performing multiple rapid microbiological methods and applying an acceptance rule. We show how the end user can use a simple (and novel) graph to choose a sample size, the number of samples, and an acceptance rule that yield a good balance between the two risks while taking cost (number of tests) into account. LAY ABSTRACT: In-process tests are used between manufacturing steps to avoid the cost of further processing material that is apt to fail its final tests. Rapid microbiological methods that return simple negative or positive results are attractive in this context because they are faster than the compendial methods used at product release. However, using a single such test will not reliably detect barely unacceptable material (sensitivity) without generating an undesirable number of false rejections (poor specificity). We quantify how to achieve a balance between the risks of false acceptance and false rejection by performing multiple rapid microbiological methods and applying an acceptance rule. We show how the end user can use a simple (and novel) graph to choose a sample size, the number of samples, and an acceptance rule that yield a good balance between the two risks while taking cost (number of tests) into account.

Challenges and opportunities in implementing the FDA default parametric tolerance interval two one-sided test for delivered dose uniformity of orally inhaled products.

The goal of this article is to discuss considerations regarding implementation of the parametric tolerance interval two one-sided test (PTI-TOST) for delivered dose uniformity (DDU) of orally inhaled products (OIPs). That test was proposed by FDA in 2005 as an alternative to the counting test described in the 1998 draft FDA guidance for metered dose inhalers and dry powder inhalers. The 2005 PTI-TOST, however, still has not found much use in practice despite the general desirability of parametric approaches in modern pharmaceutical quality control. A key reason for its slow uptake is that it rejects, with high probability, batches whose quality is considered acceptable by all other published regulatory and pharmacopeial standards as well as by the DDU specifications for many approved OIPs. Manufacturers therefore continue using nonparametric counting tests for control of DDU. A simulated case study presented here compares the consequences of the PTI-TOST compared to the counting test. The article discusses three possibilities that would help increase the uptake of the PTI-TOST approach, namely: product-specific quality standards, a different default standard suitable for the majority of OIPs, and integration of the PTI-TOST with a continuous verification control strategy rather than using it as an isolated-batch (transactional) end-product testing. In any of these efforts, if a parametric test is used, it is critical not to set the target quality close to, or at the boundary of the process/product capabilities, because PTI tests are designed to reject with high probability the identified target quality.

Stability models for sequential storage.

Some drugs are intended for sequential storage under two different storage conditions. If the data for each condition are analyzed separately, predicting assay and other responses after T1 months at one condition followed by T2 months at the other condition is non-trivial for several reasons. First, the two analyses will give different intercept terms. What should one do about that? Second, how would one calculate the confidence limits for combined storage? Third, what if prior storage at one condition affects the slope at the other condition? This paper proposes a simple ANCOVA model containing two slope terms, one for each storage condition. When multiple batches and/or packages are involved, it is easily generalized to two sets of slope terms. Confidence limits are straightforward and can be calculated using existing commercial software. With properly designed data, one can test whether prior storage at one condition affects the slope at the other condition. If no such effect is significant, very useful extrapolations can be made. Temperature excursions, model reduction and curvilinear dependencies are discussed.

Inspiratory efforts achieved in use of the Technosphere insulin inhalation system.

OBJECTIVE: The Technosphere Insulin (TI) inhalation system comprises TI powder premetered into unit dose cartridges and the patient-friendly, reusable, breath-powered MedTone inhaler. This high-resistance system uses a patient's inspiratory effort to effect TI powder de-agglomeration and promote subsequent deep-lung delivery. This study reports on flow and pressure data achieved by patients with diabetes using the MedTone system. METHOD: MedTone inhalers containing empty cartridges were adapted with pneumotach measuring devices to capture inhalation profiles. The measuring apparatuses had negligible impact on the nominal MedTone system resistance level of 0.117 kPa(0.5)/liters/min. Each of 56 subjects inhaled twice to mimic TI clinical study dosing instructions. Achieved inhalation profiles were characterized by peak inspiratory flow (PIF), peak inspiratory pressure (PIP), and average pressure drop from the time of PIP to 4 s (P(avg)). RESULTS: The achieved mean PIF (+/- standard deviation [SD]) in all subjects was 26.74 (+/-6.06) liters/min after the first inhalation and was similar to the mean PIF of 26.25 (+/-6.23) liters/min achieved after the second inhalation. Mean PIP (+/-SD) achieved by subjects was 8.49 (+/-2.86) and 8.1 (+/-2.99) kPa, and mean P(avg) drop (+/-SD) in all subjects was 6.53 (+/-2.24) and 6.09 (+/-2.08) kPa after the respective inhalations. CONCLUSION: Patients with diabetes demonstrated consistent inhalation efforts over two inhalations using the MedTone system. The achieved PIFs and PIPs demonstrate the capacity of this population to obtain sufficient inspiratory effort necessary for delivery of TI using the MedTone inhaler. Adequate postpeak pressures were also revealed, further supporting reliable and sustained inhalation efforts.

NIR chemical imaging to guide/support BMS-561389 tablet formulation development.

The objective of this study was to determine whether the particle size of extra-granular tartaric acid affects the uniformity of its distribution within BMS-561389 tablets. A near-infrared imaging technique was used to assess the distribution of tartaric acid near the surface of tablet tops and bottoms. Three batches of BMS-561389 tablets were manufactured using three lots of granular tartaric acid having different particle size distributions. Near-Infrared chemical images were acquired on the tops and bottoms of 15 tablets from each lot. Spectra were collected from 1350 to 1600 nm in 10 nm increments and 16 co-added scans at each wavelength. Data were analyzed using ISys 3.1 (Spectral Dimensions, Inc.) Chemical Imaging Software. Data analysis consisted of preprocessing, principal component analysis, and image analysis of the principal component scores image. It was feasible to map tartaric acid particles near the surface of BMS-561389 tablets using near infrared chemical imaging. The tartaric acid particle size statistics based on image analysis results correlated well with pre-compaction measurements using a laser-light scattering method. The image analysis results indicate that segregation of tartaric acid between tablet tops and bottoms was apparent in tablets lots containing both the largest and intermediate-size tartaric acid particles. For tablets made with the smallest tartaric acid particles, differences between tablet tops and bottoms in either the number of tartaric acid particles or the percent tablet surface area covered by tartaric acid were not statistically different at the 95% confidence level.