Helen Strickland

Considerations for the Development and Practice of Cascade Impaction Testing, Including a Mass Balance Failure Investigation Tree

APSD MEASUREMENTS OF OINDP are performed in order to characterize the size distribution of particles emitted from the OINDP device. APSD measurements are performed during drug product development for characterization studies, clinical release, and stability studies. In addition, some type of APSD measurement is usually required for release of the final commercial product as part of a comprehensive program to ensure quality of marketed batches. These measurements are made using a CI/MSLI that fractionates the incoming aerosol into several classes with well-defined limits in terms of aerodynamic particle size. It is normal to collect data from a CI/MSLI measurement initially as mass of API collected on each of the components of the apparatus (e.g., induction port, pre-separator (if used), stages of the CI/MSLI, and back-up filter). After determining the mass of API on each component of the apparatus (normally via HPLC with spectrophotometric detection or via direct spectrophotometric analysis), the arithmetic sum of the obtained individual values is calculated, expressed as % of target delivery per actuation, and is referred to as the mass balance (MB). MB is useful in determining whether an expected mass of drug has been captured by the impactor to provide a reliable measurement of the APSD, but by itself does not ensure that the APSD results are valid. MB should therefore not be used alone as a system suitability test when assessing APSD. The PQRI Particle Size Distribution Mass Balance Working Group was formed in late 2001 to examine several issues concerning the MB specification recommendations in the following FDA Guidances for Industry:

Relative Precision of Inhaler Aerodynamic Particle Size Distribution (APSD) Metrics by Full Resolution and Abbreviated Andersen Cascade Impactors (ACIs): Part 1

The purpose of this study was to compare relative precision of two different abbreviated impactor measurement (AIM) systems and a traditional multi-stage cascade impactor (CI). The experimental design was chosen to provide separate estimates of variability for each impactor type. Full-resolution CIs are useful for characterizing the aerosol aerodynamic particle size distribution of orally inhaled products during development but are too cumbersome, time-consuming, and resource-intensive for other applications, such as routine quality control (QC). This article presents a proof-of-concept experiment, where two AIM systems configured to provide metrics pertinent to QC (QC-system) and human respiratory tract (HRT-system) were evaluated using a hydrofluoroalkane-albuterol pressurized metered dose inhaler. The Andersen eight-stage CI (ACI) served as the benchmark apparatus. The statistical design allowed estimation of precision with each CI configuration. Apart from one source of systematic error affecting extra-fine particle fraction from the HRT-system, no other bias was detected with either abbreviated system. The observed bias was shown to be caused by particle bounce following the displacement of surfactant by the shear force of the airflow diverging above the collection plate of the second impaction stage. A procedure was subsequently developed that eliminated this source of error, as described in the second article of this series (submitted to AAPS PharmSciTech). Measurements obtained with both abbreviated impactors were very similar in precision to the ACI for all measures of in vitro performance evaluated. Such abbreviated impactors can therefore be substituted for the ACI in certain situations, such as inhaler QC or add-on device testing.

Improved Quality Control Metrics for Cascade Impaction Measurements of Orally Inhaled Drug Products (OIPs)

This study of aerodynamic mass-weighted particle size distribution (APSD) data from orally inhaled products (OIPs) investigated whether a set of simpler (than currently used) metrics may be adequate to detect changes in APSD for quality control (QC) purposes. A range of OIPs was examined, and correlations between mass median aerodynamic diameter and the ratio of large particle mass (LPM) to small particle mass (SPM) were calculated. For an Andersen cascade impactor, the LPM combines the mass associated with particle sizes from impactor stage 1 to a product-specific boundary size; SPM combines the mass of particles from that boundary through to terminal filter. The LPM–SPM boundary should be chosen during development based on the full-resolution impactor results so as to maximize the sensitivity of the LPM/SPM ratio to meaningful changes in quality. The LPM/SPM ratio along with the impactor-sized mass (ISM) are by themselves sufficient to detect changes in central tendency and area under the APSD curve, which are key in vitro quality attributes for OIPs. Compared to stage groupings, this two-metric approach provides better intrinsic precision, in part due to having adequate mass and consequently better ability to detect changes in APSD and ISM, suggesting that this approach should be a preferred QC tool. Another advantage is the possibility to obtain these metrics from the abbreviated impactor measurements (AIM) rather than from full-resolution multistage impactors. Although the boundary is product specific, the testing could be accomplished with a basic AIM system which can meet the needs of most or all OIPs.

Relative Precision of Inhaler Aerodynamic Particle Size Distribution (APSD) Metrics by Full Resolution and Abbreviated Andersen Cascade Impactors (ACIs): Part 2—Investigation of Bias in Extra-Fine Mass Fraction with AIM-HRT Impactor

The purpose of this study was to resolve an anomalously high measure of extra-fine particle fraction (EPF) determined by the abbreviated cascade impactor possibly relevant for human respiratory tract (AIM-HRT) in the experiment described in Part 1 of this two-part series, in which the relative precision of abbreviated impactors was evaluated in comparison with a full resolution Andersen eight-stage cascade impactor (ACI). Evidence that the surface coating used to mitigate particle bounce was laterally displaced by the flow emerging from the jets of the lower stage was apparent upon microscopic examination of the associated collection plate of the AIM-HRT impactor whose cut point size defines EPF. A filter soaked in surfactant was floated on top of this collection plate, and further measurements were made using the same pressurized metered-dose inhaler-based formulation and following the same procedure as in Part 1. Measures of EPF, fine particle, and coarse particle fractions were comparable with those obtained with the ACI, indicating that the cause of the bias had been identified and removed. When working with abbreviated impactors, this precaution is advised whenever there is evidence that surface coating displacement has occurred, a task that can be readily accomplished by microscopic inspection of all collection plates after allowing the impactor to sample ambient air for a few minutes.

Development of a Content Uniformity Test Suitable for Sample Sizes between 30 and 100

Spectroscopic methods such as near infrared (NIR) for content uniformity (CU) allow the measurement of significantly more dosage units per batch than required by traditional pharmacopoeial testing of 10 to 30 units. Sandell et al. proposed a nonparametric counting test for sample sizes greater than 100. There are manufacturing situations such as small batch sizes or restrictions due to sampling equipment that will yield medium sample sizes between 30 and 100 units per batch. This work provides alternative CU tests for medium sample sizes.

Process Validation in the Twenty-First Century

Process validation is a continuous series of activities that are aligned to the product lifecycle; it is no longer a one-off activity. The process validation lifecycle consists of three major stages: (1) process design, (2) process qualification, and (3) continued process verification. Each stage corresponds to an increasing level of scientific understanding of the product and the manufacturing process. A systematic process validation plan integrates science, risk management, and statistics to collect and evaluate appropriate data throughout the product lifecycle. The process validation plan must fulfill the requirement to provide documented evidence of the manufacturing process’ ability to consistently produce finished drug product that meets specifications in relation to identity, strength, quality and purity. The evidence is established through statistical tools that seek to identify, detect and control sources of variation that impact on the critical quality attributes of the finished drug product. Acceptance sampling, tolerance intervals, control charting and sample size justification through standard and Bayesian approaches are fundamental elements of a comprehensive lifecycle approach to process validation. This chapter provides an overview of regulatory and statistical aspects of process validation as set forth in the 2011 FDA guidance on process validation and other guidances with emphasis on Stage 2—Process Performance Qualification and Stage 3—Continuous Process Verification.

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.

Process Capability and Statistical Process Control

Pharmaceutical drug substances and drug products are manufactured following a well-defined process in units of batches. Drug product batches consist of a large number of dosage units (tablets, capsules, patches, syringes, vials, etc.) Such processes have inherent variability in critical quality attributes (CQA) as a consequence of incipient differences in starting materials, environmental conditions, equipment control setting and other uncontrolled sources of variability at both the batch and dosage unit levels. Regulatory and business considerations intended to safeguard patient health, require careful statistical monitoring of the batch’s CQAs. Adherence to specifications of every batch of drug product is a regulatory requirement for marketing. This chapter summarizes the statistical methods used to carry out the statistical characterization of the manufacturing process’s ability to consistently produce acceptable product (process capability) and associated monitoring tools (statistical process control). For reporting purposes, a process capability index (e.g. Cpk) or process performance index (e.g. Ppk) is calculated based on two quantities: (1) the variability of the CQA (standard deviation, σ), and (2) the comparison of that variability with the specification, typically as the quotient of the specification width to 6σ. Statistical process control refers to graphical methods that permit detection of departures from target over time of manufacture by revealing process shifts greater than capability or inherent variability alone would allow. Details of these charts are described along with additional rules for increased vigilance in detecting assignable causes (beyond capability) of variation. Alternative charts for serially correlated data are also shown, and recommendations for the use of these tools in practical applications and for reporting quality indices are provided.

Physical Causes of APSD Changes in Aerosols from OIPs and Their Impact on CI Measurements

The successful implementation of AIM and/or EDA principles to the in vitro assessment of inhalable aerosols emitted from OIPs requires the user of such methods to have a basic understanding of how these particles and/or droplets interact with the human respiratory tract (HRT) upon inhalation. Such processes are inextricably governed by the underlying physical processes associated with these semi-stable systems, and all of the changes influencing particle size affect the entire APSD. This chapter looks at both aspects in some detail, in particular paying attention to how small changes in APSD might be detected by full-resolution CI systems. The information presented herein is a prelude to Chap. 9, in which case studies are presented to demonstrate the sensitivity of EDA metrics to such changes.

Performance of the Population Bioequivalence (PBE) Statistical Test Using an IPAC-RS Database of Delivered Dose from Metered Dose Inhalers

This article reports performance characteristics of the population bioequivalence (PBE) statistical test recommended by the US Food and Drug Administration (FDA) for orally inhaled products. A PBE Working Group of the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) assembled and considered a database comprising delivered dose measurements from 856 individual batches across 20 metered dose inhaler products submitted by industry. A review of the industry dataset identified variability between batches and a systematic lifestage effect that was not included in the FDA-prescribed model for PBE. A simulation study was designed to understand PBE performance when factors identified in the industry database were present. Neglecting between-batch variability in the PBE model inflated errors in the equivalence conclusion: (i) The probability of incorrectly concluding equivalence (type I error) often exceeded 15% for non-zero between-batch variability, and (ii) the probability of incorrectly rejecting equivalence (type II error) for identical products approached 20% when product and between-batch variabilities were high. Neglecting a systematic through-life increase in the PBE model did not substantially impact PBE performance for the magnitude of lifestage effect considered. Extreme values were present in 80% of the industry products considered, with low-dose extremes having a larger impact on equivalence conclusions. The dataset did not support the need for log-transformation prior to analysis, as requested by FDA. Log-transformation resulted in equivalence conclusions that depended on the direction of product mean differences. These results highlight a need for further refinement of in vitro equivalence methodology.