To qualify a mechanism-of-action (MoA)–reflective reporter gene assay (RGA) for measuring the biological activity of adalimumab (Humira) and its biosimilars, supporting assessment of product quality, comparability, and functional consistency across the product lifecycle. The assay evaluates TNF-α neutralization by monitoring inhibition of NF-κB signaling in a reporter system. Qualification focused on key performance attributes, including system suitability, working range, reproducibility, and intermediate precision, to confirm fitness for routine use. The RGA yielded MoA-relevant readouts of NF-κB pathway inhibition in the presence of adalimumab, demonstrating strong system suitability, a broad working range, high reproducibility, and consistent intermediate precision across repeated measures. These characteristics support reliable measurement of functional activity among adalimumab products and biosimilars. The qualified, MoA-reflective RGA provides a robust tool for lifecycle management of adalimumab products, enabling quality assessment, comparability exercises, and monitoring of functional consistency across indications in which adalimumab is broadly used (e.g., rheumatoid arthritis, Crohn’s disease, psoriasis).
Glycosylation is a critical quality attribute of certain therapeutic proteins, influencing efficacy, safety, and pharmacokinetics. This study analyzed glycan characterization data and drug substance release specifications from 209 Biologics License Applications (BLAs) approved by the U.S. Food and Drug Administration (FDA) through May 2025. Ten predominant Fc N-glycans were identified across IgG antibodies expressed by CHO, NS0, and Sp2/0 cell lines, with six glycans common to all systems. Five low-abundance afucosylated glycans (< 10
Externally controlled trials (ECTs) are increasingly used when randomized controls are infeasible, unethical, or insufficient, including applications in rare diseases, oncology, pediatrics, and post-approval effectiveness research. Although methodological work has expanded rapidly across causal inference, Bayesian dynamic borrowing, and hybrid trial designs, the literature remains fragmented. We adopt a six-step scientific roadmap to organize modern ECT methodology in two primary settings: (i) single-arm trials that evaluate efficacy through comparison with external controls, and (ii) hybrid controlled trials that augment the internal control arm with external controls drawn from real-world data or historical studies. The roadmap clarifies causal estimands, identifiability assumptions, and how statistical parameters arise from identification, and shows how modeling and borrowing strategies trade off efficiency and robustness, especially under covariate shift and outcome drift. Within this framework, we synthesize and evaluate recent Bayesian and frequentist developments, compare their strengths, limitations, operating characteristics, and available software, and emphasize the role of sensitivity analysis. By re-framing ECT methodology through a causal lens, this work establishes a coherent foundation for integrating external data into regulatory and clinical decision-making and highlights core challenges and opportunities for future research.
COVID-19 disease outcomes can vary considerably among infected patients. Most studies have focused on patients with severe COVID-19. However, investigations of asymptomatic infection can provide insights into patient-specific immunological features that protect patients from COVID-19 symptoms. Recent studies have shown an association between common human leukocyte antigen (HLA) alleles and asymptomatic COVID-19 infections. Here we utilize machine learning in conjunction with explainable AI (XAI) to identify alleles in five HLA loci that can be either protective or put the patient at risk for symptomatic COVID-19. Data from the public online HLA-COVID database (1946 samples) was used for training and validating multiple ML classification models to identify the top performing model. The model was then further processed with XAI via SHAP (SHapley Additive exPlanations) to identify the protective and high-risk HLA alleles. This study provides a proof-of-concept study for utilizing machine learning to provide valuable insights for COVID-19 patients. These findings can be translated into clinical algorithms to help physicians personalize COVID-19 treatments and achieve better clinical outcomes.
