Peptide set test: a peptide-centric strategy to infer differentially expressed proteins.

MOTIVATION: The clinical translation of mass spectrometry-based proteomics has been challenging due to limited statistical power caused by large technical variability and inter-patient heterogeneity. Bottom-up proteomics provides an indirect measurement of proteins through digested peptides. This raises the question whether peptide measurements can be used directly to better distinguish differentially expressed proteins. RESULTS: We present a novel method called the peptide set test, which detects coordinated changes in the expression of peptides originating from the same protein and compares them to the rest of the peptidome. Applying our method to data from a published spike-in experiment and simulations demonstrates improved sensitivity without compromising precision, compared to aggregation-based approaches. Additionally, applying the peptide set test to compare the tumor proteomes of tamoxifen-sensitive and tamoxifen-resistant breast cancer patients reveals significant alterations in peptide levels of collagen XII, suggesting an association between collagen XII-mediated matrix reassembly and tamoxifen resistance. Our study establishes the peptide set test as a powerful peptide-centric strategy to infer differential expression in proteomics studies. AVAILABILITY AND IMPLEMENTATION: Peptide set test (PepSetTest) is publicly available at https://github.com/JmWangBio/PepSetTest.

Integrated Proteomics Characterization of NLRP3 Inflammasome Inhibitor MCC950 in Monocytic Cell Line Confirms Direct MCC950 Engagement with Endogenous NLRP3.

Inhibition of the NLRP3 inflammasome is a promising strategy for the development of new treatments for inflammatory diseases. MCC950 is a potent and selective small-molecule inhibitor of the NLRP3 pathway and has been validated in numerous species and disease models. Although the capacity of MCC950 to block NLRP3 signaling is well-established, it is still critical to identify the mechanism of action and molecular targets of MCC950 to inform and derisk drug development. Quantitative proteomics performed in disease-relevant systems provides a powerful method to study both direct and indirect pharmacological responses to small molecules to elucidate the mechanism of action and confirm target engagement. A comprehensive target deconvolution campaign requires the use of complementary chemical biology techniques. Here we applied two orthogonal chemical biology techniques: compressed Cellular Thermal Shift Assay (CETSA) and photoaffinity labeling chemoproteomics, performed under biologically relevant conditions with LPS-primed THP-1 cells, thereby deconvoluting, for the first time, the molecular targets of MCC950 using chemical biology techniques. In-cell chemoproteomics with inlysate CETSA confirmed the suspected mechanism as the disruption of inflammasome formation via NLRP3. Further cCETSA (c indicates compressed) in live cells mapped the stabilization of NLRP3 inflammasome pathway proteins, highlighting modulation of the targeted pathway. This is the first evidence of direct MCC950 engagement with endogenous NLRP3 in a human macrophage cellular system using discovery proteomics chemical biology techniques, providing critical information for inflammasome studies.

DOSE-L1000: unveiling the intricate landscape of compound-induced transcriptional changes.

MOTIVATION: The LINCS L1000 project has collected gene expression profiles for thousands of compounds across a wide array of concentrations, cell lines, and time points. However, conventional analysis methods often fall short in capturing the rich information encapsulated within the L1000 transcriptional dose-response data. RESULTS: We present DOSE-L1000, a database that unravels the potency and efficacy of compound-gene pairs and the intricate landscape of compound-induced transcriptional changes. Our study uses the fitting of over 140 million generalized additive models and robust linear models, spanning the complete spectrum of compounds and landmark genes within the LINCS L1000 database. This systematic approach provides quantitative insights into differential gene expression and the potency and efficacy of compound-gene pairs across diverse cellular contexts. Through examples, we showcase the application of DOSE-L1000 in tasks such as cell line and compound comparisons, along with clustering analyses and predictions of drug-target interactions. DOSE-L1000 fosters applications in drug discovery, accelerating the transition to omics-driven drug development. AVAILABILITY AND IMPLEMENTATION: DOSE-L1000 is publicly available at https://doi.org/10.5281/zenodo.8286375.

Whole-cage randomization for animal studies with unequal cage or group sizes.

Following good statistical practice, in vivo study investigators allocate animals into two or more treatment groups using a randomization routine to eliminate selection bias and balance known and unknown confounding factors. For some studies, however, randomization at the individual animal level cannot be implemented. For example, for studies that involve co-housed male mice, an animal-level randomization can place unfamiliar mice together in the same cage, which can trigger fighting. To meet the ethical obligations to enhance the welfare of an animal used in science, the experimental procedures are, therefore, often modified, and male mice, possibly from the same brood, may be housed together. It follows that animal allocation into groups must proceed at the whole-cage level. Given the small sample sizes in animal studies, controlling baseline variables can be quite challenging. The difficulty greatly increases with a whole-cage randomization restriction. When the number of animals per cage or the treatment group sizes are unequal, there is no algorithm in the literature to perform the task. We propose a novel, fast, and reliable algorithm to provide a whole-cage randomization that balances one or more baseline variables across groups. The algorithm was applied to a realistic example dataset.

Multiomics links global surfactant dysregulation with airflow obstruction and emphysema in COPD.

Rationale: Pulmonary surfactant is vital for lung homeostasis as it reduces surface tension to prevent alveolar collapse and provides essential immune-regulatory and antipathogenic functions. Previous studies demonstrated dysregulation of some individual surfactant components in COPD. We investigated relationships between COPD disease measures and dysregulation of surfactant components to gain new insights into potential disease mechanisms. Methods: Bronchoalveolar lavage proteome and lipidome were characterised in ex-smoking mild/moderate COPD subjects (n=26) and healthy ex-smoking (n=20) and never-smoking (n=16) controls using mass spectrometry. Serum surfactant protein analysis was performed. Results: Total phosphatidylcholine, phosphatidylglycerol, phosphatidylinositol, surfactant protein (SP)-B, SP-A and SP-D concentrations were lower in COPD versus controls (log2 fold change (log2FC) -2.0, -2.2, -1.5, -0.5, -0.7 and -0.5 (adjusted p<0.02), respectively) and correlated with lung function. Total phosphatidylcholine, phosphatidylglycerol, phosphatidylinositol, SP-A, SP-B, SP-D, napsin A and CD44 inversely correlated with computed tomography small airways disease measures (expiratory to inspiratory mean lung density) (r= -0.56, r= -0.58, r= -0.45, r= -0.36, r= -0.44, r= -0.37, r= -0.40 and r= -0.39 (adjusted p<0.05)). Total phosphatidylcholine, phosphatidylglycerol, phosphatidylinositol, SP-A, SP-B, SP-D and NAPSA inversely correlated with emphysema (% low-attenuation areas): r= -0.55, r= -0.61, r= -0.48, r= -0.51, r= -0.41, r= -0.31 and r= -0.34, respectively (adjusted p<0.05). Neutrophil elastase, known to degrade SP-A and SP-D, was elevated in COPD versus controls (log2FC 0.40, adjusted p=0.0390), and inversely correlated with SP-A and SP-D. Serum SP-D was increased in COPD versus healthy ex-smoking volunteers, and predicted COPD status (area under the curve 0.85). Conclusions: Using a multiomics approach, we demonstrate, for the first time, global surfactant dysregulation in COPD that was associated with emphysema, giving new insights into potential mechanisms underlying the cause or consequence of disease.

Design and Preclinical Evaluation of a Novel B7-H4-Directed Antibody-Drug Conjugate, AZD8205, Alone and in Combination with the PARP1-Selective Inhibitor AZD5305.

PURPOSE: We evaluated the activity of AZD8205, a B7-H4-directed antibody-drug conjugate (ADC) bearing a novel topoisomerase I inhibitor (TOP1i) payload, alone and in combination with the PARP1-selective inhibitor AZD5305, in preclinical models. EXPERIMENTAL DESIGN: IHC and deep-learning-based image analysis algorithms were used to assess prevalence and intratumoral heterogeneity of B7-H4 expression in human tumors. Several TOP1i-ADCs, prepared with Val-Ala or Gly-Gly-Phe-Gly peptide linkers, with or without a PEG8 spacer, were compared in biophysical, in vivo efficacy, and rat toxicology studies. AZD8205 mechanism of action and efficacy studies were conducted in human cancer cell line and patient-derived xenograft (PDX) models. RESULTS: Evaluation of IHC-staining density on a per-cell basis revealed a range of heterogeneous B7-H4 expression across patient tumors. This informed selection of bystander-capable Val-Ala-PEG8-TOP1i payload AZ14170133 and development of AZD8205, which demonstrated improved stability, efficacy, and safety compared with other linker-payload ADCs. In a study of 26 PDX tumors, single administration of 3.5 mg/kg AZD8205 provided a 69% overall response rate, according to modified RECIST criteria, which correlated with homologous recombination repair (HRR) deficiency (HRD) and elevated levels of B7-H4 in HRR-proficient models. Addition of AZD5305 sensitized very low B7-H4-expressing tumors to AZD8205 treatment, independent of HRD status and in models representing clinically relevant mechanisms of PARPi resistance. CONCLUSIONS: These data provide evidence for the potential utility of AZD8205 for treatment of B7-H4-expressing tumors and support the rationale for an ongoing phase 1 clinical study (NCT05123482). See related commentary by Pommier and Thomas, p. 991.

A comparison of statistical methods for animal oncology studies.

In pre-clinical oncology studies, tumor-bearing animals are treated and observed over a period of time in order to measure and compare the efficacy of one or more cancer-intervention therapies along with a placebo/standard of care group. A data analysis is typically carried out by modeling and comparing tumor volumes, functions of tumor volumes, or survival. Data analysis on tumor volumes is complicated because animals under observation may be euthanized prior to the end of the study for one or more reasons, such as when an animal's tumor volume exceeds an upper threshold. In such a case, the tumor volume is missing not-at-random for the time remaining in the study. To work around the non-random missingness issue, several statistical methods have been proposed in the literature, including the rate of change in log tumor volume and partial area under the curve. In this work, an examination and comparison of the test size and statistical power of these and other popular methods for the analysis of tumor volume data is performed through realistic Monte Carlo computer simulations. The performance, advantages, and drawbacks of popular statistical methods for animal oncology studies are reported. The recommended methods are applied to a real data set.

Qualification of a Biolayer Interferometry Assay to Support AZD7442 Resistance Monitoring.

AZD7442, a combination of two long-acting monoclonal antibodies (tixagevimab [AZD8895] and cilgavimab [AZD1061]), has been authorized for the prevention and treatment of coronavirus disease 2019 (COVID-19). The rapid emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants requires methods capable of quickly characterizing resistance to AZD7442. To support AZD7442 resistance monitoring, a biolayer interferometry (BLI) assay was developed to screen the binding of tixagevimab and cilgavimab to SARS-CoV-2 spike proteins to reduce the number of viral variants for neutralization susceptibility verification. Six spike variants were chosen to assess the assay's performance: four with decreased affinity for tixagevimab (F486S:D614G and F486W:D614G proteins) or cilgavimab (S494L:D614G and K444R:D614G proteins) and two reference proteins (wild-type HexaPro and D614G protein). Equilibrium dissociation constant (KD) values from each spike protein were used to determine shifts in binding affinity. The assay's precision, range, linearity, and limits of quantitation were established. Qualification acceptance criteria determined whether the assay was fit for purpose. By bypassing protein purification, the BLI assay provided increased screening throughput. Although limited correlation between pseudotype neutralization and BLI data (50% inhibitory concentration versus KD) was observed for full immunoglobulins (IgGs), the correlations for antibody fragments (Fabs) were stronger and reflected a better comparison of antibody binding kinetics with neutralization potency. Therefore, despite strong assay performance characteristics, the use of full IgGs limited the screening utility of the assay; however, the Fab approach warrants further exploration as a rapid, high-throughput variant-screening method for future resistance-monitoring programs. IMPORTANCE SARS-CoV-2 variants harbor multiple substitutions in their spike trimers, potentially leading to breakthrough infections and clinical resistance to immune therapies. For this reason, a BLI assay was developed and qualified to evaluate the reliability of screening SARS-CoV-2 spike trimer variants against anti-SARS-CoV-2 monoclonal antibodies (MAbs) tixagevimab and cilgavimab, the components of AZD7442, prior to in vitro pseudovirus neutralization susceptibility verification testing. The assay bypasses protein purification with rapid assessment of the binding affinity of each MAb for each recombinant protein, potentially providing an efficient preliminary selection step, thus allowing a reduced testing burden in the more technically complex viral neutralization assays. Despite precise and specific measures, an avidity effect associated with MAb binding to the trimer confounded correlation with neutralization potency, negating the assay's utility as a surrogate for neutralizing antibody potency. Improved correlation with Fabs suggests that assay optimization could overcome any avidity limitation, warranting further exploration to support future resistance-monitoring programs.

Engraftment of Bacteria after Fecal Microbiota Transplantation Is Dependent on Both Frequency of Dosing and Duration of Preparative Antibiotic Regimen.

The gut microbiota has emerged as a key mediator of human physiology, and germ-free mice have been essential in demonstrating a role for the microbiome in disease. Preclinical models using conventional mice offer the advantage of working with a mature immune system. However, optimal protocols for fecal microbiota transplant (FMT) engraftment in conventional mice are yet to be established. Conventional BALB/c mice were randomized to receive 3-day (3d) or 3-week (3w) antibiotic (ABX) regimen in their drinking water followed by 1 or 5-daily FMTs from a human donor. Fecal samples were collected longitudinally and characterized using 16S ribosomal RNA (rRNA) sequencing. Semi-targeted metabolomic profiling of fecal samples was also done with liquid chromatography-mass spectrometry (LC-MS). Lastly, we sought to confirm our findings in BKS mice. Recovery of baseline diversity scores were greatest in the 3d groups, driven by re-emergence of mouse commensal microbiota, whereas the most resemblance to donor microbiota was seen in the 3w + 5-FMT group. Amplicon sequence variants (ASVs) that were linked to the input material (human ASVs) engrafted to a significantly greater extent when compared to mouse ASVs in the 3-week groups but not the 3-day groups. Lastly, comparison of metabolomic profiles revealed distinct functional profiles by ABX regimen. These results indicate successful model optimization and emphasize the importance of ABX duration and frequency of FMT dosing; the most stable and reliable colonization by donor ASVs was seen in the 3wk + 5-FMT group.

Blocking endothelial lipase with monoclonal antibody MEDI5884 durably increases high density lipoprotein in nonhuman primates and in a phase 1 trial.

Cardiovascular disease (CVD) is the leading global cause of death, and treatments that further reduce CV risk remain an unmet medical need. Epidemiological studies have consistently identified low high-density lipoprotein cholesterol (HDL-C) as an independent risk factor for CVD, making HDL elevation a potential clinical target for improved CVD resolution. Endothelial lipase (EL) is a circulating enzyme that regulates HDL turnover by hydrolyzing HDL phospholipids and driving HDL particle clearance. Using MEDI5884, a first-in-class, EL-neutralizing, monoclonal antibody, we tested the hypothesis that pharmacological inhibition of EL would increase HDL-C by enhancing HDL stability. In nonhuman primates, MEDI5884 treatment resulted in lasting, dose-dependent elevations in HDL-C and circulating phospholipids, confirming the mechanism of EL action. We then showed that a favorable lipoprotein profile of elevated HDL-C and reduced low-density lipoprotein cholesterol (LDL-C) could be achieved by combining MEDI5884 with a PCSK9 inhibitor. Last, when tested in healthy human volunteers, MEDI5884 not only raised HDL-C but also increased HDL particle numbers and average HDL size while enhancing HDL functionality, reinforcing EL neutralization as a viable clinical approach aimed at reducing CV risk.

A Bayesian Statistical Approach to Continuous Qualification of a Bioassay.

A validated bioassay is used to measure the potency of commercial lots, and as such, must be accurate, precise, and fit for its intended purpose. Regulatory expectations for a bioassay include a characterization of features, such as accuracy, precision, linearity, and range. The journey of a bioassay typically starts in a development lab, where it is initially qualified and used to support the release and stability testing of clinical lots. As a program moves through the different clinical phases, it may be optimized further, used to support process development, or transferred to new laboratories, with each activity generating additional bioassay data. Finally, the bioassay is fully validated as part of the transfer to the commercial quality control testing laboratories. In this work, rather than capturing the data from each study as a separate, independent report, it is proposed that, beginning with the qualification study, the accuracy and precision of the bioassay be continuously characterized, with each subsequent study result building upon the preceding ones. We call this approach continuous qualification Such a proposition is naturally carried out using Bayesian statistical methods in which the historical study data is used to construct prior knowledge that is blended with the current study data. By doing so, the bioassay accuracy and precision may be assessed with high confidence well ahead of commercial manufacturing. Further, by following the total-variance approach, the method also allows for a robust construction of system suitability and control limits for potency.

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.

Mean comparisons and power calculations to ensure reproducibility in preclinical drug discovery.

In the pharmaceutical industry, in vivo animal experiments are conducted to test the effects of novel preclinical drug compounds. Well-planned animal studies involve a sample size and statistical power analysis to provide a basis for the number of animals allocated into comparator arms of a future study. These calculations require approximate values for the parameters of a statistical model that will be applied to the future data and used to test for differences via statistical hypotheses. If the prestudy parameter estimates are nearly correct, the power analysis guarantees that a difference will be detected from the study data, up to a prespecified probability. Traditional power computations, however, are not calculated with reproducibility in mind. In this work, the issue of reproducibility in drug discovery is tackled from the point of view that study-to-study variability is not included in a typical sample size and power analysis. Three proposed methods that yield a reproducible mean-comparison analysis are derived and compared.

Preclinical Characterization of an Antibody-Drug Conjugate Targeting CS-1 and the Identification of Uncharacterized Populations of CS-1-Positive Cells.

Multiple myeloma is a hematologic cancer that disrupts normal bone marrow function and has multiple lines of therapeutic options, but is incurable as patients ultimately relapse. We developed a novel antibody-drug conjugate (ADC) targeting CS-1, a protein that is highly expressed on multiple myeloma tumor cells. The anti-CS-1 mAb specifically bound to cells expressing CS-1 and, when conjugated to a cytotoxic pyrrolobenzodiazepine payload, reduced the viability of multiple myeloma cell lines in vitro In mouse models of multiple myeloma, a single administration of the CS-1 ADC caused durable regressions in disseminated models and complete regression in a subcutaneous model. In an exploratory study in cynomolgus monkeys, the CS-1 ADC demonstrated a half-life of 3 to 6 days; however, no highest nonseverely toxic dose was achieved, as bone marrow toxicity was dose limiting. Bone marrow from dosed monkeys showed reductions in progenitor cells as compared with normal marrow. In vitro cell killing assays demonstrated that the CS-1 ADC substantially reduced the number of progenitor cells in healthy bone marrow, leading us to identify previously unreported CS-1 expression on a small population of progenitor cells in the myeloid-erythroid lineage. This finding suggests that bone marrow toxicity is the result of both on-target and off-target killing by the ADC.

Population Pharmacokinetics of an Anti-PD-L1 Antibody, Durvalumab in Patients with Hematologic Malignancies.

BACKGROUND AND OBJECTIVES: Durvalumab, a human monoclonal antibody targeting programmed cell death ligand 1, has been approved for urothelial carcinoma and stage III non-small cell lung cancer by the US Food and Drug Administration and is being evaluated in various malignancies. The objective of this study was to develop a population-pharmacokinetic model of durvalumab in patients with various hematologic malignancies and to investigate the effects of demographic and disease factors on the pharmacokinetics in this population. METHODS: A total of 1812 concentrations from 267 patients with myelodysplastic syndromes, acute myeloid leukemia, multiple myeloma, non-Hodgkin lymphoma, or Hodgkin lymphoma were included in the analysis. RESULTS: The pharmacokinetics of durvalumab was adequately described by a two-compartment model with first-order elimination. A decrease in durvalumab clearance over time was mainly explained by incorporation of time-dependent changes in albumin (in all patients) and immunoglobulin G (in patients with multiple myeloma) into the model. For multiple myeloma, patients with immunoglobulin G ≥ 20 g/L showed a 30% lower area under the concentration-time curve at cycle 1 compared with patients with immunoglobulin G < 20 g/L. The impact of any baseline covariates on durvalumab pharmacokinetics did not appear to be clinically relevant. The pharmacokinetics of durvalumab in hematologic malignancies was generally consistent with previously reported pharmacokinetics in solid tumors. CONCLUSIONS: These results support the same dosing regimen (1500 mg every 4 weeks) for both solid tumors and hematologic malignancies from the perspective of adequate exposure. Additionally, total immunoglobulin G level could be a critical covariate for the pharmacokinetics of monoclonal antibodies in patients with multiple myeloma.

Comparisons of outlier tests for potency bioassays.

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 USP<1032> recommends 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 USP<1032> outlier guidance. As a follow-up, several outlier detection methods, including those proposed by the USP<1010>, 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.

Detecting bliss synergy in in vivo combination studies with a tumor kinetic model.

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.

A fast and reliable test for parallelism in bioassay.

Parallelism in bioassay is a synonym of similarity between two concentration-response curves. Before the determination of relative potency in bioassays, it is necessary to test for and claim parallelism between the pair of concentration-response curves of reference standard and test sample. Methods for parallelism testing include p-value-based significance tests and interval-based equivalence tests. Most of the latter approaches make statistical inference about the equivalence of parameters of the concentration-response curve models. An apparent drawback of such methods is that equivalence in model parameters does not guarantee similarity between the reference and test sample. In contrast, a Bayesian method was recently proposed that directly tests the parallelism hypothesis that the concentration-response curve of the test sample is a horizontal shift of that of the reference. In other words, the testing sample is a dilution or concentration of the reference standard. The Bayesian approach is shown to protect against type I error and provides sufficient statistical power for parallelism testing. In practice, however, it is challenging to implement the method as it requires both specialized Bayesian software and a relatively long run time. In this paper, we propose a frequentist version of the test with split-second run time. The empirical properties of the frequentist parallelism test method are evaluated and compared with the original Bayesian method. It is demonstrated that the frequentist method is both fast and reliable for parallelism testing for a variety of concentration-response models.

Effect of a statistical outlier in potency bioassays.

The USP<1032> guidelines 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 USP<1032> guidance 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 USP<1032> recommendation and illustrate the impact on the RP calculation when application of outlier removal procedures are discounted.