US Food and Drug Administration Approval Summary: Fam-Trastuzumab Deruxtecan-nxki for Human Epidermal Growth Factor Receptor 2-Low Unresectable or Metastatic Breast Cancer.

PURPOSE: The US Food and Drug Administration approved fam-trastuzumab deruxtecan-nxki (DS-8201a, T-DXd) for the treatment of adult patients with unresectable or metastatic human epidermal growth factor receptor 2 (HER2)-low (immunohistochemistry 1 + or immunohistochemistry 2+/in situ hybridization-) breast cancer who have received a prior chemotherapy in the metastatic setting or developed disease recurrence during or within 6 months of completing adjuvant chemotherapy. PATIENTS AND METHODS: Approval was based on DESTINY-Breast04, a phase III, randomized, open-label, multicenter trial in patients with unresectable or metastatic HER2-low breast cancer, determined at a central laboratory. A total of 557 patients were randomly assigned (2:1) to receive either T-DXd 5.4 mg/kg intravenously once every 3 weeks (n = 373) or physicians' choice of chemotherapy (n = 184). RESULTS: The study met its primary efficacy end point of progression-free survival (PFS) by blinded independent central review assessment in the hormone receptor-positive (HR+) cohort (N = 494) with an estimated hazard ratio (HR) of 0.51(95% CI, 0.40 to 0.64; P < .0001). Key secondary end points were also met, including PFS in the intent-to-treat population with an HR of 0.50 (95% CI, 0.40 to 0.63; P < .0001), overall survival (OS) in the HR+ cohort with an HR of 0.64 (95% CI, 0.48 to 0.86; P = .0028) and OS in the intent-to-treat with an HR of 0.64 (95% CI, 0.49 to 0.84; P = .0010). The safety profile of T-DXd was consistent with previously approved indications, and no new safety signals were observed in this study population. CONCLUSION: The approval of T-DXd in HER2-low metastatic breast cancer was based on statistically significant and clinically meaningful PFS and OS improvements observed in the DESTINY-Breast04 trial and represents the first approved therapy specifically for the treatment of HER2-low metastatic breast cancer.

A Trans-Governmental Collaboration to Independently Evaluate SARS-CoV-2 Serology Assays.

The emergence of SARS-CoV-2 created a crucial need for serology assays to detect anti-SARS-CoV-2 antibodies, which led to many serology assays entering the market. A trans-government collaboration was created in April 2020 to independently evaluate the performance of commercial SARS-CoV-2 serology assays and help inform U.S. Food and Drug Administration (FDA) regulatory decisions. To assess assay performance, three evaluation panels with similar antibody titer distributions were assembled. Each panel consisted of 110 samples with positive (n = 30) serum samples with a wide range of anti-SARS-CoV-2 antibody titers and negative (n = 80) plasma and/or serum samples that were collected before the start of the COVID-19 pandemic. Each sample was characterized for anti-SARS-CoV-2 antibodies against the spike protein using enzyme-linked immunosorbent assays (ELISA). Samples were selected for the panel when there was agreement on seropositivity by laboratories at National Cancer Institute's Frederick National Laboratory for Cancer Research (NCI-FNLCR) and Centers for Disease Control and Prevention (CDC). The sensitivity and specificity of each assay were assessed to determine Emergency Use Authorization (EUA) suitability. As of January 8, 2021, results from 91 evaluations were made publicly available (https://open.fda.gov/apis/device/covid19serology/, and https://www.cdc.gov/coronavirus/2019-ncov/covid-data/serology-surveillance/serology-test-evaluation.html). Sensitivity ranged from 27% to 100% for IgG (n = 81), from 10% to 100% for IgM (n = 74), and from 73% to 100% for total or pan-immunoglobulins (n = 5). The combined specificity ranged from 58% to 100% (n = 91). Approximately one-third (n = 27) of the assays evaluated are now authorized by FDA for emergency use. This collaboration established a framework for assay performance evaluation that could be used for future outbreaks and could serve as a model for other technologies. IMPORTANCE The SARS-CoV-2 pandemic created a crucial need for accurate serology assays to evaluate seroprevalence and antiviral immune responses. The initial flood of serology assays entering the market with inadequate performance emphasized the need for independent evaluation of commercial SARS-CoV-2 antibody assays using performance evaluation panels to determine suitability for use under EUA. Through a government-wide collaborative network, 91 commercial SARS-CoV-2 serology assay evaluations were performed. Three evaluation panels with similar overall antibody titer distributions were assembled to evaluate performance. Nearly one-third of the assays evaluated met acceptable performance recommendations, and two assays had EUAs revoked and were removed from the U.S. market based on inadequate performance. Data for all serology assays evaluated are available at the FDA and CDC websites (https://open.fda.gov/apis/device/covid19serology/, and https://www.cdc.gov/coronavirus/2019-ncov/covid-data/serology-surveillance/serology-test-evaluation.html).

Metrology Standards for Quantitative Imaging Biomarkers.

Although investigators in the imaging community have been active in developing and evaluating quantitative imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo imaging studies.

The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions.

The development and implementation of quantitative imaging biomarkers has been hampered by the inconsistent and often incorrect use of terminology related to these markers. Sponsored by the Radiological Society of North America, an interdisciplinary group of radiologists, statisticians, physicists, and other researchers worked to develop a comprehensive terminology to serve as a foundation for quantitative imaging biomarker claims. Where possible, this working group adapted existing definitions derived from national or international standards bodies rather than invent new definitions for these terms. This terminology also serves as a foundation for the design of studies that evaluate the technical performance of quantitative imaging biomarkers and for studies of algorithms that generate the quantitative imaging biomarkers from clinical scans. This paper provides examples of research studies and quantitative imaging biomarker claims that use terminology consistent with these definitions as well as examples of the rampant confusion in this emerging field. We provide recommendations for appropriate use of quantitative imaging biomarker terminological concepts. It is hoped that this document will assist researchers and regulatory reviewers who examine quantitative imaging biomarkers and will also inform regulatory guidance. More consistent and correct use of terminology could advance regulatory science, improve clinical research, and provide better care for patients who undergo imaging studies.

Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment.

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined.

Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies.

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

US FDA and personalized medicine: in vitro diagnostic regulatory perspective.

Personalized medicine has captured the attention of the public, including patients, healthcare providers, scientists, medical product manufacturers and many others. The US FDA will evaluate many of the products that will allow personalized medicine to be successfully implemented in the USA. This article addresses the FDA's approach to regulation of one component of personalized medicine, in vitro diagnostic devices. It also describes the FDA's efforts to integrate the various medical product regulatory authorities provided by Congress in the Federal Food, Drug and Cosmetic Act to develop effective mechanisms for oversight of medical products used to personalize treatment. Finally, it presents some of the current challenges in in vitro diagnostics oversight that may be of interest for personalized medicine.

Biomarkers to improve the benefit/risk balance for approved therapeutics: a US FDA perspective on personalized medicine.

This article highlights a current US FDA perspective concerning the use of biomarker-based diagnostics for personalized medicine. Specifically, current biomarkers that have application for improving the benefit/risk profile of already approved drugs are discussed. The success of biomarkers for use in personalized medicine depends on many factors, including proper evaluation of the usefulness of the biomarker for assessing the event of interest, and the safety and effectiveness of the diagnostic device used to measure the biomarker, which includes appropriate analytical and clinical validation. These points along with the many regulatory concerns regarding co-labeling of drugs and devices and future aspects, such as co-development, will be discussed in this regulatory science focus.

Protein-based multiplex assays: mock presubmissions to the US Food and Drug Administration.

As a part of ongoing efforts of the NCI-FDA Interagency Oncology Task Force subcommittee on molecular diagnostics, members of the Clinical Proteomic Technology Assessment for Cancer program of the National Cancer Institute have submitted 2 protein-based multiplex assay descriptions to the Office of In Vitro Diagnostic Device Evaluation and Safety, US Food and Drug Administration. The objective was to evaluate the analytical measurement criteria and studies needed to validate protein-based multiplex assays. Each submission described a different protein-based platform: a multiplex immunoaffinity mass spectrometry platform for protein quantification, and an immunological array platform quantifying glycoprotein isoforms. Submissions provided a mutually beneficial way for members of the proteomics and regulatory communities to identify the analytical issues that the field should address when developing protein-based multiplex clinical assays.

Comparing two medical tests when results of reference standard are unavailable for those negative via both tests.

In studies for comparing the diagnostic accuracy of two qualitative tests, very often the reference standard to confirm the disease status is not applied to all study subjects. We considered a situation when all subjects with a positive result by at least one of the tests had a verified disease status and none of the subjects with both tests negative results had a verification of disease status. In this paper, we discuss whether the information about the ratio of true positive rates and the ratio of false positive rates of two qualitative tests, T(New) and T(Old), is sufficient to draw a conclusion about effectiveness of the T(New). We show that if there is a statistically significant increase in true positive rates and the increase in true positive rates is statistically larger than increase in false positive rates, then a conclusion about effectiveness of test T(New) can be made and this does not require application of the reference standard to the subjects with negative results by both tests. An application of ratio of true positive rates and ratio of false positive rates to post-market studies is also presented.

Partly nonparametric approach for determining the limit of detection.

BACKGROUND: According to recent International Organization for Standardization (ISO) standards, the limit of detection (LoD) of an assay should be estimated taking both type I (alpha) and II (beta) errors into account. The suggested procedure, however, supposes gaussian distributions of both blank and sample measurements and a linear calibration curve. In clinical chemistry, asymmetric, nongaussian blank distributions are common, and the calibration curve may be nonlinear. We present a partly nonparametric procedure that takes these aspects into account. METHODS: Using theoretical distribution models and simulation studies, we developed a LoD estimation procedure suitable for the field of clinical chemistry that is partly based on nonparametric statistics. RESULTS: For sample size n, the nonparametrically determined 95th percentile of the blank measurements obtained as the value of the [n(95/100) + 0.5]th ordered observation defines the limit for results significantly exceeding zero [limit of blank (LoB)]. The LoD is the lowest value that is likely to yield a result exceeding the LoB. LoD is estimated as: LoB + cbeta x SDS, where SDS is the analytical SD of a sample with a low concentration; cbeta = z(1 - beta)/[1 - 1/(4 x f)]; z(1 - beta) is the standard normal deviate; and f is the number of degrees of freedom for estimation of SD(S). c(beta) is approximately equal to 1.65 for a type II error of 5%. Approaches and needed tabular values for calculation of confidence limits are presented as well as sample size. Worked examples are given to illustrate estimation and verification of the limit of detection. Simulation results are used to document performance. CONCLUSION: The proposed procedure appears useful for application in the field of clinical chemistry and promotes a standardized approach for estimating LoDs of clinical chemistry assays.