A cross-industry forum on benchmarking critical quality attribute identification and linkage to process characterization studies.

Identification of Critical Quality Attributes (CQAs) and subsequent characterization in process development studies are the key elements of quality by design (QbD) for biopharmaceutical products. Since the inception of ICH Q8R2, several articles have been published on approaches to conducting CQA risk assessments as well as the application to process understanding. A survey was conducted by multiple companies participating in an International Consortium working group on the best practices for identifying CQAs with linkages to process characterization (PC) studies. The results indicate that the companies surveyed are using similar approaches/timing to identify CQAs during process development. Consensus was also observed among the companies surveyed with approaches to linkage of CQAs to process characterization studies leading to impact to control strategies and lifecycle management.

Adverse health effects of abuse-deterrent opioids: Evidence from the reformulation of OxyContin.

The United States is currently in the midst of the worst drug epidemic in its history, with nearly 64,000 overdose deaths in 2016. In response, pharmaceutical companies have begun introducing abuse-deterrent painkillers, pills with properties that make the drug more difficult to misuse. The first such painkiller, a reformulated version of OxyContin, was released in 2010. Previous research has found no net effect on opioid mortality, with users substituting from OxyContin toward heroin. This paper explores health effects of the reformulation beyond mortality. In particular, I show that heroin is substantially more likely to be injected than OxyContin, increasing exposure to blood-borne diseases. Exploiting variation across states in OxyContin misuse prior to the reformulation, I find relative increases in the spread of hepatitis B and C in states most likely to be affected by the reformulation. In aggregate, the estimates suggest that absent the reformulation, we would have observed approximately 76% fewer cases of hepatitis C and 53% fewer cases of hepatitis B from 2011 to 2015. I find some suggestive evidence that the reformulation also lead to increases in HIV and hepatitis A, although these findings are less robust. These findings have important implications for future policies addressing the opioid crisis.

Scientific, statistical, practical, and regulatory considerations in design space development.

The quality by design (QbD) paradigm guides the pharmaceutical industry towards improved understanding of products and processes, and at the same time facilitates a high degree of manufacturing and regulatory flexibility throughout the establishment of the design space. This review article presents scientific, statistical and regulatory considerations in design space development. All key development milestones, starting with planning, selection of factors, experimental execution, data analysis, model development and assessment, verification, and validation, and ending with design space submission, are presented and discussed. The focus is especially on frequently ignored topics, like management of factors and CQAs that will not be included in experimental design, evaluation of risk of failure on design space edges, or modeling scale-up strategy. Moreover, development of a design space that is independent of manufacturing scale is proposed as the preferred approach.

Biochemical comparison of four commercially available human α1-proteinase inhibitors for treatment of α1-antitrypsin deficiency.

Intravenous therapy with purified plasma-derived alpha1-proteinase inhibitor (α1-PI) concentrates is the only specific treatment for α1-PI deficiency. For the therapy to be safe and efficacious, α1-PI concentrates should be highly pure and contain high amounts of functional protein. This study compared the four plasma-derived α1-PI products commercially available in Europe (Respreeza, Prolastin, Alfalastin, Trypsone) by biochemical methods with respect to function, purity, structure, and chemical modifications. Respreeza had the highest level of functional protein (48.8 mg/mL) and the highest specific activity (0.862 mg active α1-PI per mg total protein). By size exclusion chromatography, Respreeza was 97.4% pure, followed by Alfalastin 88.1%, Prolastin 76.9%, and Trypsone 70.8%. By reversed phase chromatography, Respreeza had an α1-PI purity of 97.7%, followed by Trypsone 88.0%, Prolastin 78.0%, and Alfalastin 69.5%. The main protein band by sodium dodecyl sulphate-polyacrylamide gel electrophoresis was found for all products at approximately 50 kDa. Additional protein bands were found for Prolastin, Alfalastin, and Trypsone. The α1-PI products differed in cysteine oxidation state and C-terminal lysine status. α1-PI products tested differ in purity, concentration, and chemical variation. Respreeza has the highest level of purity. The impact of the non-therapeutic proteins identified has not been evaluated.

Analytical procedure validation and the quality by design paradigm.

Since the adoption of the ICH Q8 document concerning the development of pharmaceutical processes following a quality by design (QbD) approach, there have been many discussions on the opportunity for analytical procedure developments to follow a similar approach. While development and optimization of analytical procedure following QbD principles have been largely discussed and described, the place of analytical procedure validation in this framework has not been clarified. This article aims at showing that analytical procedure validation is fully integrated into the QbD paradigm and is an essential step in developing analytical procedures that are effectively fit for purpose. Adequate statistical methodologies have also their role to play: such as design of experiments, statistical modeling, and probabilistic statements. The outcome of analytical procedure validation is also an analytical procedure design space, and from it, control strategy can be set.

A quality by design approach for longitudinal quality attributes.

The concept of quality by design (QbD) as published in ICH-Q8 is currently one of the most recurrent topics in the pharmaceutical literature. This guideline recommends the use of information and prior knowledge gathered during pharmaceutical development studies to provide a scientific rationale for the manufacturing process of a product and provide guarantee of future quality. This poses several challenges from a statistical standpoint and requires a shift in paradigm from traditional statistical practices. First, to provide "assurance of quality" of future lots implies the need to make predictions regarding the quality given past evidence and data. Second, the quality attributes described in the Q8 guidelines are not always a set of unique, independent measurements. In many cases, these criteria are complicated longitudinal data with successive acceptance criteria over a defined period of time. A common example is a dissolution profile for a modified or extended-release solid dosage form that must fall within acceptance limits at several time points. A Bayesian approach for longitudinal data obtained in various conditions of a design of experiment is provided to elegantly address the ICH-Q8 recommendation to provide assurance of quality and derive a scientifically sound design space.

Using tolerance intervals for assessment of pharmaceutical quality.

In quality control of drug products, tolerance intervals are commonly used methods to assure a certain proportion of the products covered within a pre-specified acceptance interval. Depending on the nature of the quality attributes, the corresponding acceptance interval could be one-sided or two-sided. Thus, the tolerance intervals can also be one-sided or two-sided. To better utilize tolerance intervals for quality assurance, we reviewed the computation method and studied their statistical properties in terms of batch acceptance probability in this article. We also illustrate the application of one-sided and two-sided tolerance, as well as two one-sided tests through the examples of dose content uniformity test, delivered dose uniformity test, and dissolution test.

Testing assay linearity over a pre-specified range.

Validation of linearity is a regulatory requirement. Although many methods are proposed, they suffer from several deficiencies including difficulties of setting fit-for-purpose acceptable limits, dependency on concentration levels used in linearity experiment, and challenges in implementation for statistically lay users. In this article, a statistical procedure for testing linearity is proposed. The method uses a two one-sided test (TOST) of equivalence to evaluate the bias that can result from approximating a higher-order polynomial response with a linear function. By using orthogonal polynomials and generalized pivotal quantity analysis, the method provides a closed-form solution, thus making linearity testing easy to implement.

Dissolution curve comparisons through the F(2) parameter, a Bayesian extension of the f(2) statistic.

Dissolution (or in vitro release) studies constitute an important aspect of pharmaceutical drug development. One important use of such studies is for justifying a biowaiver for post-approval changes which requires establishing equivalence between the new and old product. We propose a statistically rigorous modeling approach for this purpose based on the estimation of what we refer to as the F2 parameter, an extension of the commonly used f2 statistic. A Bayesian test procedure is proposed in relation to a set of composite hypotheses that capture the similarity requirement on the absolute mean differences between test and reference dissolution profiles. Several examples are provided to illustrate the application. Results of our simulation study comparing the performance of f2 and the proposed method show that our Bayesian approach is comparable to or in many cases superior to the f2 statistic as a decision rule. Further useful extensions of the method, such as the use of continuous-time dissolution modeling, are considered.

Non-normal random effects models for immunogenicity assay cut point determination.

Administration of biological therapeutics can generate undesirable immune responses that may induce anti-drug antibodies (ADAs). Immunogenicity can negatively affect patients, ranging from mild reactive effect to hypersensitivity reactions or even serious autoimmune diseases. Assessment of immunogenicity is critical as the ADAs can adversely impact the efficacy and safety of the drug products. Well-developed and validated immunogenicity assays are required by the regulatory agencies as tools for immunogenicity assessment. Key to the development and validation of an immunogenicity assay is the determination of a cut point, which serves as the threshold for classifying patients as ADA positive(reactive) or negative. In practice, the cut point is determined as either the quantile of a parametric or nonparametric empirical distribution. The parametric method, which is often based on a normality assumption, may lead to biased cut point estimates when the normality assumption is violated. The non-parametric method, which yields unbiased estimates of the cut point, may have low efficiency when the sample size is small. As the distribution of immune responses are often skewed and sometimes heavy-tailed, we propose two non-normal random effects models for cut point determination. The random effects, following a skew-t or log-gamma distribution, can incorporate the skewed and heavy-tailed responses and the correlation among repeated measurements. Simulation study is conducted to compare the proposed method with the current normal and nonparametric alternatives. The proposed models are also applied to a real dataset generated from assay validation studies.

Statistical evaluation of several methods for cut-point determination of immunogenicity screening assay.

The cut point of the immunogenicity screening assay is the level of response of the immunogenicity screening assay at or above which a sample is defined to be positive and below which it is defined to be negative. The Food and Drug Administration Guidance for Industry on Assay Development for Immunogenicity Testing of Therapeutic recommends the cut point to be an upper 95 percentile of the negative control patients. In this article, we assume that the assay data are a random sample from a normal distribution. The sample normal percentile is a point estimate with a variability that decreases with the increase of sample size. Therefore, the sample percentile does not assure at least 5% false-positive rate (FPR) with a high confidence level (e.g., 90%) when the sample size is not sufficiently enough. With this concern, we propose to use a lower confidence limit for a percentile as the cut point instead. We have conducted an extensive literature review on the estimation of the statistical cut point and compare several selected methods for the immunogenicity screening assay cut-point determination in terms of bias, the coverage probability, and FPR. The selected methods evaluated for the immunogenicity screening assay cut-point determination are sample normal percentile, the exact lower confidence limit of a normal percentile (Chakraborti and Li, 2007) and the approximate lower confidence limit of a normal percentile. It is shown that the actual coverage probability for the lower confidence limit of a normal percentile using approximate normal method is much larger than the required confidence level with a small number of assays conducted in practice. We recommend using the exact lower confidence limit of a normal percentile for cut-point determination.

Quality assurance test of delivered dose uniformity of multiple-dose inhaler and dry powder inhaler drug products.

The delivered dose uniformity is one of the most critical requirements for dry powder inhaler (DPI) and metered dose inhaler products. In 1999, the Food and Drug Administration (FDA) issued a Draft Guidance entitled Nasal Spray and Inhalation Solution, Suspension, and Spray Drug Products-Chemistry, Manufacturing and Controls Documentation and recommended a two-tier acceptance sampling plan that is a modification of the United States Pharmacopeia (USP) sampling plan of dose content uniformity (USP34<601>). This sampling acceptance plan is also applied to metered dose inhaler (MDI) and DPI drug products in general. The FDA Draft Guidance method is shown to have a near-zero probability of acceptance at the second tier. In 2000, under the request of The International Pharmaceutical Aerosol Consortium, the FDA developed a two-tier sampling acceptance plan based on two one-sided tolerance intervals (TOSTIs) for a small sample. The procedure was presented in the 2005 Advisory Committee Meeting of Pharmaceutical Science and later published in the Journal of Biopharmaceutical Statistics (Tsong et al., 2008). This proposed procedure controls the probability of the product delivering below a pre-specified effective dose and the probability of the product delivering over a pre-specified safety dose. In this article, we further propose an extension of the TOSTI procedure to single-tier procedure with any number of canisters.

One- and two-stage Arrhenius models for pharmaceutical shelf life prediction.

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. However, during drug development to facilitate formulation and dosage form selection, an accelerated stability study with stressed storage condition is preferred to quickly obtain a good prediction of shelf life under ambient storage conditions. Such a prediction typically uses Arrhenius equation that describes relationship between degradation rate and temperature (and humidity). Existing methods usually rely on the assumption of normality of the errors. In addition, shelf life projection is usually based on confidence band of a regression line. However, the coverage probability of a method is often overlooked or under-reported. 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.

Statistical considerations in setting product specifications.

According to ICH Q6A (1999), a specification is defined as a list of tests, references to analytical procedures, and appropriate acceptance criteria, which are numerical limits, ranges, or other criteria for the tests described. For drug products, specifications usually consist of test methods and acceptance criteria for assay, impurities, pH, dissolution, moisture, and microbial limits, depending on the dosage forms. They are usually proposed by the manufacturers and subject to the regulatory approval for use. When the acceptance criteria in product specifications cannot be pre-defined based on prior knowledge, the conventional approach is to use data from a limited number of clinical batches during the clinical development phases. Often in time, such acceptance criterion is set as an interval bounded by the sample mean plus and minus two to four standard deviations. This interval may be revised with the accumulated data collected from released batches after drug approval. In this article, we describe and discuss the statistical issues of commonly used approaches in setting or revising specifications (usually tighten the limits), including reference interval, (Min, Max) method, tolerance interval, and confidence limit of percentiles. We also compare their performance in terms of the interval width and the intended coverage. Based on our study results and review experiences, we make some recommendations on how to select the appropriate statistical methods in setting product specifications to better ensure the product quality.

A statistical approach to determining criticality of residual host cell DNA.

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.

Colon specific CODES based Piroxicam tablet for colon targeting: statistical optimization, in vivo roentgenography and stability assessment.

This study was aimed to statistically optimize CODES™ based Piroxicam (PXM) tablet for colon targeting. A 3(2) full factorial design was used for preparation of core tablet that was subsequently coated to get CODES™ based tablet. The experimental design of core tablets comprised of two independent variables: amount of lactulose and PEG 6000, each at three different levels and the dependent variable was %CDR at 12 h. The core tablets were evaluated for pharmacopoeial and non-pharmacopoeial test and coated with optimized levels of Eudragit E100 followed by HPMC K15 and finally with Eudragit S100. The in vitro drug release study of F1-F9 was carried out by change over media method (0.1 N HCl buffer, pH 1.2, phosphate buffer, pH 7.4 and phosphate buffer, pH 6.8 with enzyme ß-galactosidase 120 IU) to select optimized formulation F9 that was subjected to in vivo roentgenography. Roentgenography study corroborated the in vitro performance, thus providing the proof of concept. The experimental design was validated by extra check point formulation and Diffuse Reflectance Spectroscopy revealed absence of any interaction between drug and formulation excipients. The shelf life of F9 was deduced as 12 months. Conclusively, colon targeted CODES™ technology based PXM tablets were successfully optimized and its potential of colon targeting was validated by roentgenography.

A Bayesian design space for analytical methods based on multivariate models and predictions.

The International Conference for Harmonization (ICH) has released regulatory guidelines for pharmaceutical development. In the document ICH Q8, the design space of a process is presented as the set of factor settings providing satisfactory results. However, ICH Q8 does not propose any practical methodology to define, derive, and compute design space. In parallel, in the last decades, it has been observed that the diversity and the quality of analytical methods have evolved exponentially, allowing substantial gains in selectivity and sensitivity. However, there is still a lack of a rationale toward the development of robust separation methods in a systematic way. Applying ICH Q8 to analytical methods provides a methodology for predicting a region of the space of factors in which results will be reliable. Combining design of experiments and Bayesian standard multivariate regression, an identified form of the predictive distribution of a new response vector has been identified and used, under noninformative as well as informative prior distributions of the parameters. From the responses and their predictive distribution, various critical quality attributes can be easily derived. This Bayesian framework was then extended to the multicriteria setting to estimate the predictive probability that several critical quality attributes will be jointly achieved in the future use of an analytical method. An example based on a high-performance liquid chromatography (HPLC) method is given. For this example, a constrained sampling scheme was applied to ensure the modeled responses have desirable properties.

Ambiguous assignment of release data in stability studies.

Release data are often used improperly in stability studies by reproducing the same data and assigning it to each subset of a lot. The proper use of release data in stability studies is explored in this paper. The maximum likelihood solution is determined and compared with multiple imputation and one ad hoc procedure. Maximum likelihood is seen not to lead to a practical solution. One such procedure is found to compare favorably with multiple imputation.

Introduction of a theoretical splashing degree to assess the performance of low-viscosity oils in filling of capsules.

These days an alternative to soft capsules is liquid-filled hard capsules. Their filling technology was investigated earlier with highly viscous formulations, while hardly any academic research focused on low-viscosity systems. Accordingly, this work addressed the filling of such oils that are splashing during the dosing process. It was aimed to first study capsule filling, using middle-chain triglycerides as reference oil, in order to then evaluate the concept of a new theoretical splashing degree for different oils. A laboratory-scale filling machine was used that included capsule sealing. Thus, the liquid encapsulation by microspray technology was employed to seal the dosage form. As a result of the study with reference oil, the filling volume and the temperature were found to be significant for the rate of leaking capsules. The filling volume was also important for weight variability of the capsules. However, most critical for this variability was the diameter of the filling nozzle. We proposed a power law for the coefficient of weight variability as a function of the nozzle diameter and the obtained exponent agreed with the proposed theory. Subsequently, a comparison of different oils revealed that the relative splashing degree shared a correlation with the coefficient of the capsule weight variability (Pearson product moment correlation of r=0.990). The novel theoretical concept was therefore found to be predictive for weight variability of the filled capsules. Finally, guidance was provided for the process development of liquid-filled capsules using low-viscosity oils.