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We assessed the impact of the coronavirus disease 2019 (COVID-19) pandemic on the statistical analysis of time-to-event outcomes in late-phase oncology trials. Using a simulated case study that mimics a Phase III ongoing trial during the pandemic, we evaluated the impact of COVID-19-related deaths, time off-treatment and missed clinical visits due to the pandemic, on overall survival and/or progression-free survival in terms of test size (also referred to as Type 1 error rate or alpha level), power, and hazard ratio (HR) estimates. We found that COVID-19-related deaths would impact both size and power, and lead to biased HR estimates; the impact would be more severe if there was an imbalance in COVID-19-related deaths between the study arms. Approaches censoring COVID-19-related deaths may mitigate the impact on power and HR estimation, especially if study data cut-off was extended to recover censoring-related event loss. The impact of COVID-19-related time off-treatment would be modest for power, and moderate for size and HR estimation. Different rules of censoring cancer progression times result in a slight difference in the power for the analysis of progression-free survival. The simulations provided valuable information for determining whether clinical-trial modifications should be required for ongoing trials during the COVID-19 pandemic.

Potency bioassays are used to measure biological activity. Consequently, potency is considered a critical quality attribute in manufacturing. Relative potency is measured by comparing the concentration-response curves of a manufactured test batch with that of a reference standard. If the curve shapes are deemed similar, the test batch is said to exhibit constant relative potency with the reference standard, a critical requirement for calibrating the potency of the final drug product. Outliers in bioassay potency data may result in the false acceptance/rejection of a bad/good sample and, if accepted, may yield a biased relative potency estimate. To avoid these issues, the USP1032recommends the screening of bioassay data for outliers prior to performing a relative potency analysis. In a recently published work, the effects of one or more outliers, outlier size, and outlier type on similarity testing and estimation of relative potency were thoroughly examined, confirming the USP1032outlier guidance. As a follow-up, several outlier detection methods, including those proposed by the USP1010, are evaluated and compared in this work through computer simulation. Two novel outlier detection methods are also proposed. The effects of outlier removal on similarity testing and estimation of relative potency were evaluated, resulting in recommendations for best practice.

Simon's design has been widely used in oncology to conduct single arm phase II trials and to make Go/No-Go development decision. Other authors have proposed designs with decision-making frameworks that include a third, "Consider" outcome. For results in the Consider zone, a final Go/No-Go development decision must still be made; however it is typically a subjective decision based on the totality of data and the development landscape. Under this framework, the probability of continuing development when the candidate therapy is truly ineffective or the probability of stopping development when the candidate therapy is truly effective is undefined.We use a motivating example to compare end of trial decision-making between Simon's two-stage approach and a Multilevel outcome approach. We present the minimum and maximum development decision error probabilities by varying whether candidates that end in the Consider zone would ultimately continue with development or not.The Multilevel approach typically requires fewer patients, but the risk of making an incorrect drug development decision is inflated above the statistically defined Type I and Type II error rates. Compared to a Type I error rate of 20%, the Multilevel trial's maximum probability of moving forward with an ineffective therapy is 22%, 27%, and 36% for Consider zone sizes of 10%, 20%, and 30%, respectively.The Multilevel approach provides flexibility in interpreting moderate efficacy results. However, the flexibility is accomplished with a lower sample size and corresponding uncertainty in the trial outcome that increases the risk of incorrect drug development decisions.

Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug-drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies.

Stability study is a critical component for the submission and market authorization of a new drug or biological product. Long-term stability studies are required to establish the stability ...

We evaluated the relationship between benralizumab (30 mg every 4 and 8 weeks (Q4W, Q8W)) pharmacokinetic (PK) exposure and end points of asthma exacerbation rates (AERs) and change from baseline in prebronchodilator forced expiratory volume in 1 second (FEV 1) for patients with severe, uncontrolled eosinophilic asthma in the SIROCCO/CALIMA phase III trials. In empirical assessment, AER ratios in SIROCCO were similar across PK quartiles. However, the lowest PK quartile in CALIMA had reduced efficacy; low CALIMA placebo AER possibly confounded this result. In population modeling, estimated benralizumab 90% effective concentration for AER reduction was 927 ng/mL, below the Q8W dosage steady‐state average PK concentration (1,066 ng/mL). Benralizumab treatment resulted in more rapid FEV 1 improvement vs. placebo (estimated half‐maximum time: 7.6 vs. 18 days); this response was greater for patients with greater baseline eosinophil counts. These results confirmed 30 mg Q8W is the optimal benralizumab dosage for patients with severe eosinophilic asthma.

As per regulatory guidance, it is mandatory to demonstrate comparability before and after a change is made to an analytical method for commercial lot release or manufacturing process in the area of Chemistry, Manufacturing, and Controls. The change may include transfer assay or manufacturing process to a different location. Use of statistical methods to assess comparability before and after the change across the range of interest is a regulatory requirement. Regardless of the types of the changes, comparability is often demonstrated using analytical data collected pre- and post-change. For instance, assay transfer requires the demonstration of comparable assay performance over a range of expected responses. Although there are various methods used in practice or proposed in the published literature, there is no consensus on the best practice. For measurements that exhibit linearity in the outcome variable, a general statistical framework of equivalence testing is proposed. The equivalence test can ...

A basket trial is a trial that investigates a single experimental therapy simultaneously across multiple different patient population groups. When groups are heterogeneous, the optimal analysis is independence but when groups are homogeneous the optimal analysis strategy is pooling. A Bayesian Hierarchical Model (BHM) analysis strategy offers a compromise between independence and pooling. There are multiple different parameterizations of BHMs in the literature offering different ways to account for possible lack of exchangeability and heterogeneity across the groups in the basket. We selected 4 different BHMs that fit under the same general structure but have increasing levels of complexity to accommodate group heterogeneity for comparison. Because the choice of prior for a BHM affects the behavior of the model, we first optimize each model’s prior selection in terms of maximizing the expected number of correct decisions across six efficacy scenarios. After optimization, differences between the performance of the models were small. In the context we evaluated, a simpler model may provide similar operating characteristics to a more complex model even when groups are truly heterogeneous.

OBJECTIVE Neutrophil dysregulation and the type I interferon (IFN) axis have been proposed to contribute to premature cardiovascular disease, a leading cause of mortality in patients with systemic lupus erythematosus (SLE). In the present study, we evaluated the ability of anifrolumab, a type I IFN receptor-blocking antibody, to reduce neutrophil extracellular trap (NET) formation and modulate cardiometabolic disease markers in comparison to placebo. METHODS Study subjects comprised patients with moderate-to-severe SLE who were enrolled in phase IIb of the MUSE trial (A Phase II, Randomized Study to Evaluate the Efficacy and Safety of MEDI-546 in Subjects with Systemic Lupus Erythematosus), with healthy individuals as controls. Blood samples were collected from SLE patients (n = 305) and healthy controls (n = 10-20) before the initiation of treatment (baseline) and from SLE patients after they had been treated with 300 mg of anifrolumab (n = 99) or placebo (n = 102). Baseline IFN gene signature test status was determined, and the IFN gene signature (21-gene panel) was monitored over time. Serum proteins were measured by multiplex immunoassay or ultrasensitive Simoa assay. NET complexes, cholesterol efflux capacity (CEC), and glycoprotein acetylation (GlycA) and other lipid parameters were assessed in plasma. RESULTS Formation of NET complexes and levels of tumor necrosis factor (TNF) and interleukin-10 (IL-10) were correlated with extent of type I IFN pathway activity. NET complexes and IL-10 levels were up-regulated in SLE patients compared to healthy controls (P < 0.008). The cardiometabolic disease markers CEC and GlycA were also found to be dysregulated in patients with SLE (P < 0.001 versus healthy controls). Type I IFN receptor inhibition with anifrolumab significantly reduced NET complexes and GlycA and improved CEC compared to baseline (P < 0.05) whereas no improvements were seen with placebo. Levels of TNF and IL-10 were reduced with anifrolumab compared to placebo (P < 0.05). CONCLUSION These data support a key role for type I IFNs in modulating factors contributing to SLE vasculopathy and suggest that inhibition of this pathway could decrease cardiovascular risk in individuals with SLE.

The USP1032guidelines recommend the screening of bioassay data for outliers prior to performing a relative potency (RP) analysis. The guidelines, however, do not offer advice on the size or type of outlier that should be removed prior to model fitting and calculation of RP. Computer simulation was used to investigate the consequences of ignoring the USP1032guidance to remove outliers. For biotherapeutics and vaccines, outliers in potency data may result in the false acceptance/rejection of a bad/good lot of drug product. Biological activity, measured through a potency bioassay, is considered a critical quality attribute in manufacturing. If the concentration-response potency curve of a test sample is deemed to be similar in shape to that of the reference standard, the curves are said to exhibit constant RP, an essential criterion for the interpretation of a RP. One or more outliers in the concentration-response data, however, may result in a failure to declare similarity or may yield a biased RP estimate. Concentration-response curves for test and reference were computer generated with constant RP from four-parameter logistic curves. Single outlier, multiple outlier, and whole-curve outlier scenarios were explored for their effects on the similarity testing and on the RP estimation. Though the simulations point to situations for which outlier removal is unnecessary, the results generally support the USP1032recommendation and illustrate the impact on the RP calculation when application of outlier removal procedures are discounted.

To ensure the safety, efficacy, and quality, regulatory authorities require that multiple critical quality attributes of a drug must conform to certain specification limits. Manufacturers m...

Stability studies are required for the registration of all pharmaceutical products to ensure the quality, safety, and efficacy of the products throughout the shelf life. Moreover, stability data ar...

Throughout the life cycle of a biotechnological drug product, changes and improvements of manufacturing processes are common. It is required by the regulatory bodies that manufacturers establish adequate and appropriate comparability between pre-change and post-change products. The goals of comparability assessments are to demonstrate the comparability and consistency of product quality before and after change and to demonstrate that the changes do not have an adverse effect on safety and efficacy of the drug products. A well-planned and detailed comparability protocol may facilitate the approval of process changes and expedite the production and distribution of lifesaving drug products to patients. Analytical comparability is the foundation of all comparability exercise. Therefore, a sound comparability protocol should carefully select relevant critical quality attributes, prepare a flexible testing plan, and predefine appropriate acceptance criteria. Selection of analytical methods and acceptance criteria for determining comparability may be the most challenging step in the comparability study. In this article, a risk-based approach is proposed to setting the acceptance criteria for analytical comparability during the pharmaceutical process change. The proposed method is applied to a late-phase process comparability study, illustrating the value of using prior information and historical data. The Bayesian methodology allows the manufacturer to utilize accumulated scientific knowledge and manufacture experience during the life cycle of pharmaceutical development and the risk assessment enables control of the probability of out-of-specification, thus protecting the patients’ risks.

Changes of manufacturing processes are common. It is required by the regulatory agencies that manufacturers establish adequate and appropriate comparability between pre-change and post-change products. The goals of comparability assessments are to demonstrate the comparability and consistency of product quality before and after change and to demonstrate that the changes do not have an adverse effect on safety and efficacy of the drug products. Accelerated or stressed stability studies may shed light on drug quality under stressed environmental conditions and on product differences in the degradation pathways. Comparability of accelerated stability data may provide further evidence on the impact of process change. Equivalence test has been recommended to demonstrate the comparability of stability profiles for accelerated stability studies. Selection of appropriate acceptance criteria for determining comparability is one of the most challenging steps in the comparability studies. Because of the inherent heterogeneity of biologics, the stability profiles may vary considerably from batch to batch. It is more challenging to set the acceptance criteria for comparing the accelerated stability data for biologics. In this article, we present an approach for determining the acceptance criteria and necessary sample sizes for accelerated comparability studies for biologics.

Biological assays (bioassays) are procedures to estimate the potency of a substance by studying its effects on living organisms, tissues, and cells. Bioassays are essential tools for gaining insight into biologic systems and processes including, for example, the development of new drugs and monitoring environmental pollutants. Two of the most important parameters of bioassay performance are relative accuracy (bias) and precision. Although general strategies and formulas are provided in USP , a comprehensive understanding of the definitions of bias and precision remain elusive. Additionally, whether there is a beneficial use of data transformation in estimating intermediate precision remains unclear. Finally, there are various statistical estimation methods available that often pose a dilemma for the analyst who must choose the most appropriate method. To address these issues, we provide both a rigorous definition of bias and precision as well as three alternative methods for calculating relative standard deviation (RSD). All methods perform similarly when the RSD ≤10%. However, the USP estimates result in larger bias and root-mean-square error (RMSE) compared to the three proposed methods when the actual variation was large. Therefore, the USP method should not be used for routine analysis. For data with moderate skewness and deviation from normality, the estimates based on the original scale perform well. The original scale method is preferred, and the method based on log-transformation may be used for noticeably skewed data.LAY ABSTRACT: Biological assays, or bioassays, are essential in the development and manufacture of biopharmaceutical products for potency testing and quality monitoring. Two important parameters of assay performance are relative accuracy (bias) and precision. The definitions of bias and precision in USP 〈1033〉 are elusive and confusing. Another complicating issue is whether log-transformation should be used for calculating the intermediate precision. It is often challenging for analysts to pick the most appropriate estimation method. To address these issues, we give a rigorous definition of bias and precision and provide three alternative methods of calculating RSD. We demonstrate that the RSD formula in USP 〈1033〉 tends to have larger bias and root-mean-square error (RMSE) than the three proposed methods.

Assessment of anti-drug antibodies (ADAs) for neutralizing activity is important for the clinical development of biopharmaceuticals. Two types of neutralizing antibody (NAb) assays (competitive ligand-binding assay [CLBA] and cell-based assay [CBA]) are commonly used to characterize neutralizing activities. To support the clinical development of benralizumab, a humanized, anti–interleukin-5 receptor α, anti-eosinophil monoclonal antibody, we developed and validated a CLBA and a CBA. The CLBA and CBA were compared for sensitivity, drug tolerance, and precision to detect NAbs in serum samples from clinical trials. The CLBA was more sensitive (27.1 and 37.5 ng/mL) than the CBA (1.02 and 1.10 μg/mL) in detecting NAbs to benralizumab for the polyclonal and monoclonal ADA controls, respectively. With the same polyclonal ADA control, the CLBA detected 250 ng/mL of ADA in the presence of 100 ng/mL of benralizumab, whereas the CBA detected 1.25 μg/mL of ADA in the presence of 780 ng/mL of benralizumab. In 195 ADA-positive samples from 5 studies, 63.59% (124/195) and 16.9% (33/195) were positive for NAb as measured by the CLBA and the CBA, respectively. ADA titers were strongly correlated (Pearson’s correlation coefficient r = 0.91; n = 195) with CLBA titers. Moreover, the CLBA titer correlated with CBA percentage inhibition in the CBA-positive samples (Spearman’s coefficient r = 0.50; n = 33). Our data demonstrated advantages of the CLBA in various aspects and supported the choice of the CLBA as a NAb assay for the phase III trials.

Introduction: Benralizumab is an anti-eosinophil monoclonal antibody. In the randomized, controlled Phase III SIROCCO ( Lancet . 2016;388:2115–27) and CALIMA ( Lancet . 2016;388:2128–41) trials, benralizumab 30 mg every 4 weeks and 30 mg every 8 weeks (Q8W) significantly increased FEV 1 for patients with severe asthma receiving high-dosage ICS/LABA with baseline blood eosinophils ≥300 cells/µl. Aims and Objectives: We aimed to characterize change of prebronchodilator FEV 1 from baseline in placebo- and benralizumab-treated patients with severe asthma receiving high-dosage ICS/LABA by using population modeling. Methods: Pooled FEV 1 data (N=2,244) were modeled sequentially to characterize effects of placebo and benralizumab. A likelihood ratio test evaluated effects of demographic and disease covariates on FEV 1 changes from baseline. Results: Height and age were identified as covariates for baseline FEV 1 and placebo effect. Benralizumab was associated with more rapid improvement of FEV 1 (estimated half-maximum time: 7.6 d) than placebo (estimated half-life: 18 d). Estimated typical placebo and benralizumab treatment effects were 184 mL and 96 mL, respectively. Benralizumab efficacy was greater in patients with greater baseline eosinophil counts, with a trend toward improved efficacy in patients with more exacerbations in past year. Benralizumab dosages, steady-state exposure, anti-drug antibody (ADA), body weight, and concomitant medications did not significantly affect efficacy. Conclusions: The efficacy plateau for prebronchodilator FEV 1 was reached with benralizumab Q8W. This regimen minimized the impact of PK variability associated with ADA or heavy body weight on efficacy.

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.