Collecting unit operation performance data on manufacturing processes has many benefits. Besides assessing the health of assets, the data can also be analyzed to drive process improvement activities, to assist in establishing practical operating ranges for new products, and can be used to facilitate process development activities in the laboratory ultimately leading to more robust tech transfers of processes to the manufacturing facility. In this paper, we will describe how to obtain operational variability on the basis of performance data extracted from current and/or historical manufacturing unit operations and its practical uses. Although, the concept was developed initially as a small molecule process design tool, its flexibility is underscored by including an example of how a parenteral manufacturing site used it to identify and correct an issue before it translated into unplanned downtime or product loss.
SUMMARY Randomized controlled trials (RCTs) are the gold standard for evaluation of the efficacy and safety of investigational interventions. If every patient in an RCT were to adhere to the randomized treatment, one could simply analyze the complete data to infer the treatment effect. However, intercurrent events (ICEs) including the use of concomitant medication for unsatisfactory efficacy, treatment discontinuation due to adverse events, or lack of efficacy may lead to interventions that deviate from the original treatment assignment. Therefore, defining the appropriate estimand (the appropriate parameter to be estimated) based on the primary objective of the study is critical prior to determining the statistical analysis method and analyzing the data. The International Council for Harmonisation (ICH) E9 (R1), adopted on November 20, 2019, provided five strategies to define the estimand: treatment policy, hypothetical, composite variable, while on treatment, and principal stratum. In this article, we propose an estimand using a mix of strategies in handling ICEs. This estimand is an average of the “null” treatment difference for those with ICEs potentially related to safety and the treatment difference for the other patients if they would complete the assigned treatments. Two examples from clinical trials evaluating antidiabetes treatments are provided to illustrate the estimation of this proposed estimand and to compare it with the estimates for estimands using hypothetical and treatment policy strategies in handling ICEs.
During the development of a pharmaceutical chemical process, it is vital to establish a control strategy that will ensure the process performance and fitness for use of the active pharmaceutical ingredient (API), which in turn is essential to the drug product performance and its fitness for use. As part of the control strategy, it is very important to understand and establish critical elements of the process, one of which is the establishment of the critical process parameters that impact the critical quality attributes (CQAs) of the API. In this paper, we are proposing a method for determining the criticality of a process parameter and whether it should be listed in the common technical document as critical. By using routine process control capability across a variety of operating conditions and equipment configurations, a risk-based approach is used to identify parameters that could have a potential of impacting the CQAs of the API. Beyond establishing criticality, and understanding the operational variability, the knowledge gathered from these approaches can also be used to facilitate the efficient mapping of a multivariate design space.
Cardiovascular (CV) outcome trials (CVOTs) of type 2 diabetes mellitus (T2DM) therapies have mostly used randomized comparison with placebo to demonstrate non-inferiority to establish that the investigational drug does not increase CV risk. Recently, several glucagon-like peptide 1 receptor agonists (GLP-1 RA) and sodium glucose cotransporter 2 inhibitors (SGLT-2i) demonstrated reduced CV risk. Consequently, future T2DM therapy trials could face new ethical and clinical challenges if CVOTs continue with the traditional, placebo-controlled design. To address this challenge, here we review the methodologic considerations in transitioning to active-controlled CVOTs and describe the statistical design of a CVOT to assess non-inferiority versus an active comparator and if non-inferiority is proven, using novel methods to assess for superiority versus an imputed placebo. Specifically, as an example of such methodology, we introduce the statistical considerations used for the design of the “Effect of Tirzepatide versus Dulaglutide on Major Adverse Cardiovascular Events (MACE) in Patients with Type 2 Diabetes” trial (SURPASS CVOT). It is the first active-controlled CVOT assessing antihyperglycemic therapy in patients with T2DM designed to demonstrate CV efficacy of the investigational drug, tirzepatide, a dual glucose-dependent insulinotropic polypeptide and GLP-1 RA, by establishing non-inferiority to an active comparator with proven CV efficacy, dulaglutide. To determine the efficacy margin for the hazard ratio, tirzepatide versus dulaglutide, for the composite CV outcome of death, myocardial infarction, or stroke (MACE-3), which is required to claim superiority versus an imputed placebo, the lower bound of efficacy of dulaglutide compared with placebo was estimated using a hierarchical Bayesian meta-analysis of placebo-controlled CVOTs of GLP-1 RAs. SURPASS CVOT was designed so that when the observed upper bound of the 95% confidence interval of the hazard ratio is less than the lower bound of efficacy of dulaglutide, it demonstrates non-inferiority to dulaglutide by preserving at least 50% of the CV benefit of dulaglutide as well as statistical superiority of tirzepatide to a theoretical placebo (imputed placebo analysis). The presented methods adding imputed placebo comparison for efficacy assessment may serve as a model for the statistical design of future active-controlled CVOTs.
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