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.

Residue-Specific Impact of EDTA and Methionine on Protein Oxidation in Biotherapeutics Formulations Using an Integrated Biotherapeutics Drug Product Development Workflow.

The rational design and selection of formulation composition to meet molecule-specific and product-specific needs are critical for biotherapeutics development to ensure physical and chemical stability. This work, based on three antibody-based (mAb) proteins (mAbA, mAbB, and mAbC), evaluates residue-specific impact of EDTA and methionine on protein oxidation, using an integrated biotherapeutics drug product development workflow. This workflow includes statistical experimental design, high-throughput experimental automation and execution, structure-based in silico modeling, inferential statistical analysis, and enhanced interactive data visualization of large datasets. This oxidation study evaluates the impact of formulation parameters including pH, protein concentration, and the presence of polysorbate 80 on the oxidation of specific conserved and variable residues of mAbs A, B, and C in the presence of stressors (iron, peroxide) and/or protectants (EDTA, L-methionine). Residue-specific analysis by automated high-throughput peptide mapping demonstrates differential residue-specific effects of EDTA and methionine in protecting against oxidation, highlighting the need for molecule-specific and product-specific selection of these excipients during formulation development. Computational modeling based on a homology model and the two-shell water coordination method (WCN) was employed to gain mechanistic understanding of residue-specific oxidation susceptibility of methionine residues. The computational determinants of local solvent exposure of methionine residues showed good correlation of WCN with experimentally determined oxidation for corresponding residues. The rapid generation of high-resolution data, statistical data analysis and interactive visualization of the high-throughput residue-level data containing ∼200 unique formulations facilitate residue-specific, molecule-specific and product-specific oxidation (global and local) assessment for oxidation protectants during early development for mAbs and related mAb-based modalities.

The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA-BIOP Nonclinical Bayesian Working Group.

The use of Bayesian methods to support pharmaceutical product development has grown in recent years. In clinical statistics, the drive to provide faster access for patients to medical treatments has led to a heightened focus by industry and regulatory authorities on innovative clinical trial designs, including those that apply Bayesian methods. In nonclinical statistics, Bayesian applications have also made advances. However, they have been embraced far more slowly in the nonclinical area than in the clinical counterpart. In this article, we explore some of the reasons for this slower rate of adoption. We also present the results of a survey conducted for the purpose of understanding the current state of Bayesian application in nonclinical areas and for identifying areas of priority for the DIA/ASA-BIOP Nonclinical Bayesian Working Group. The survey explored current usage, hurdles, perceptions, and training needs for Bayesian methods among nonclinical statisticians. Based on the survey results, a set of recommendations is provided to help guide the future advancement of Bayesian applications in nonclinical pharmaceutical statistics.

Reduced scale model qualification of 5-L and 250-ml bioreactors using multivariant visualization and Bayesian inferential methods.

A novel method for the qualification of reduced scale models (RSMs) was illustrated using data from both a 250-ml advanced microscale bioreactor (ambr) and a 5-L bioreactor RSM for a 2,000-L manufacturing scale process using a CHO cell line to produce a recombinant monoclonal antibody. The example study showed how the method was used to identify process performance attributes and product quality attributes that capture important aspects of the RSM qualification process. The method uses two novel statistical approaches: multivariate dimension reduction and data visualization techniques, via partial least squares discriminant analysis (PLS-DA), and Bayesian multivariate linear modeling for inferential analysis. Bayesian multivariate linear modeling allows for individual probability distributions of the differences of the mean of each attribute for each scale, as well as joint probability statements on the differences of the means for multiple attributes. Depending on the results of this inferential procedure, PLS-DA is used to identify the process performance outputs at the different scales which have the greatest negative impact on the multivariate Bayesian joint probabilities. Experience with that particular process can then be leveraged to adjust operating conditions to minimize these differences, and then equivalence can be reassessed using the multivariate linear model.

The Use of Bayesian Hierarchical Logistic Regression in the Development of a Modular Viral Inactivation Claim.

Low pH inactivation of enveloped viruses has historically been shown to be an effective viral inactivation step in biopharmaceutical manufacturing. To date, most statistical analyses supporting modular low pH viral inactivation claims have used descriptive statistical analyses, which in many cases do not allow for probabilistic characterization of future experimental log10 reduction values (LRVs). Using Bayesian hierarchical logistic regression modeling, probability statements regarding the likelihood of successful low pH viral inactivation based on only certain process parameter settings can be derived. This type of analysis also permits statistical modeling in the presence of historical data from different experiments and right-censored data, two issues that have not as yet been satisfactorily dealt with in the literature. The characterization of the probability of successful inactivation allows creation of a modular claim stating future LRVs will be greater than or equal to some critical value, based on only certain process parameter settings of the viral inactivation unit operation. This risk-based approach, when used in conjunction with traditional descriptive statistics, facilitates coherent and cogent decision-making about modular viral clearance LRV claims.LAY ABSTRACT: Viral contamination of biologically derived drug products is a safety concern for both regulatory agencies and drug manufacturers. Validation of the removal and inactivation of model viruses is required to ensure the safety of patients receiving these drugs, and dedicated steps, including viral filtration and chemical inactivation, are often added to manufacturing processes to provide additional clearance and inactivation capabilities. One of these steps, low pH inactivation, exposes enveloped viruses to a low pH environment to reduce the potential of the virus to infect host cells. Because the viral inactivation capability of this well-understood unit operation has been demonstrated for years across many different biological drugs, many companies have begun investigating the use of the modular viral clearance claim for the low pH inactivation step. Modular claims ensure, without experimentation, that a certain level of reduction of virus will occur if specific parameters are used in the manufacturing process, allowing manufacturers to save both time and resources in the early developmental phases of biologically derived drugs. A novel type of statistical analysis is outlined in this article that when used in addition to previously used analyses allows drug manufacturers to estimate a more valid level of virus reduction in modular viral clearance claims.

Connecting Our Gut Feeling and How Our Gut Feels: The Role of Well-being Attributes in Irritable Bowel Syndrome.

BACKGROUND/AIMS: There is a close relationship between the mind and gut in the pathogenesis of functional bowel disorders. Common psychological disturbances such as depression and anxiety are not uncommon in those with irritable bowel syndrome (IBS). There is little research investigating the role of positive psychology and gastrointestinal (GI) conditions. In this pilot study we investigated the well-being attributes in those with and without IBS. METHODS: We used an anonymous online survey and recruited 416 study subjects using social media as the main method of recruitment. We gathered demographic information, GI symptoms, history of mental health issues such as anxiety and depression, assessed several well-being attributes, and finally assessed subjective well-being. We hypothesized that those with GI symptoms and IBS have lower scores in their well-being attributes compared to healthy controls. RESULTS: We observed that a history of anxiety and depression is significantly associated with GI symptoms and IBS. In addition, sense of subjective well-being and several well-being attributes are negatively associated with GI symptoms and/or IBS. Of interest, the household income showed a negative correlation with the prevalence of GI symptoms and IBS. CONCLUSIONS: Subjective well-being, and several well-being attributes that contribute to the sense of overall contentment, are negatively associated with GI symptoms and IBS. The link between subjective well-being, and GI symptoms and IBS are independent of anxiety and depression. Well-being attributes and sense of subjective well-being may be a contributory factor in clinical expression of GI symptoms or IBS consistent with the biopsychosocial model of the disease.