Genmab Inc.

In EPCORE® NHL-1 (NCT03625037), the CD3×CD20 bispecific antibody epcoritamab demonstrated promising efficacy and manageable safety in patients with relapsed/refractory (R/R) follicular lymphoma (FL) after ≥2 prior lines of therapy. Using the clonoSEQ® assay, we evaluated minimal residual disease (MRD) status using peripheral blood mononuclear cells (PBMCs) and/or circulating tumor DNA (ctDNA) at prespecified time points and investigated correlation with clinical outcomes. Epcoritamab induced rapid conversion to MRD-negativity, with most MRD-evaluable patients reaching MRD-negativity by cycle (C) 3 day (D) 1 as measured by either analyte. MRD-negativity at C3D1 landmark by either analyte correlated with prolonged progression-free survival (PFS; median not reached) irrespective of radiographic response status. PFS was comparable among patients with overall MRD-negativity by PBMCs or ctDNA, regardless of challenging-to-treat disease features. By C3D1 and C5D1 landmarks, patients with complete or partial response as assessed by positron emission tomography/computed tomography (PET/CT) who were MRD-positive had a shorter PFS compared with those who were MRD-negative. In multivariable analyses at week 12 and week 18 PET/CT landmarks, MRD-negative responders by PBMC assessment (week 12) and by both PBMC and ctDNA assessment (week 18) had significantly improved PFS when adjusted for baseline clinical risk factors. These analyses demonstrate that MRD-negativity is associated with rapid molecular responses to epcoritamab and prolonged PFS in patients with R/R FL, underscoring the value of MRD analysis to complement conventional response assessment. These findings may aid future clinical trial design or clinical practices in FL.

The development of computational models addressing therapeutic antibodies faces significant challenges. Particularly, the prediction of binding affinity across a diverse set of measurements, due to the scarcity of data. A critical data element is the set of antibody-antigen interaction pairs associated with sequences. To address this issue, we developed the Antigen Specific Antibody Database (ASD, https://naturalantibody.com/agab/), a database aggregating antibody-antigen interaction data from multiple studies with standardized formatting and annotations. Our dataset compilation strategy resulted in data from 15 distinct sources, resulting in 1,097,946 unique antibody-antigen interactions (with 9575 unique antigens). The ASD captures diverse affinity measures and qualitative binding assessment, along with metadata including UniProt and PDB identifiers, target protein names, confidence levels, and experimental conditions such as type of measured affinity, source organism, and germline genes. Through this integration drive, we make available an ample resource of interaction data gathered from the public domain to act as a foundation for model development and further data generation.

Many circulating biomarkers are assessed at different time intervals during clinical studies. Despite of the success of standard joint models in predicting clinical outcomes using low-dimensional longitudinal data (1-2 biomarkers), significant computational challenges are encountered when applying these techniques to high-dimensional biomarker datasets. Modern machine- or deep-learning models show potential for multiple biomarker processes, but systematic evaluations and applications to high-dimensional data in the clinical settings have yet to be reported. We aimed to enhance the scalability of joint modeling and provide guidance on optimal approaches for high-dimensional biomarker data and outcomes. We evaluated multiple deep-learning and machine-learning models using 24 clinical biomarkers and survival data from the SQUIRE trial, a phase 3 randomized clinical trial investigating necitumumab and standard gemcitabine/cisplatin treatment in patients with squamous non-small-cell lung cancer (NSCLC). Overall, we confirmed that longitudinal models enabled more accurate prediction of patients’ survival compared to those solely based on baseline information. Coupling multivariate functional principal component analysis (MFPCA) with Cox regression (MFPCA-Cox) provided the highest predictive discrimination and accuracy for the NSCLC patients with AUC values of 0.7 - >0.8 at various landmark time points and prediction timeframes, outperforming recent advanced Transformer and convolutional neural network deep-learning algorithms (TransformerJM and Match-Net, respectively). In conclusion, we identified that MFPCA-Cox represents a robust and versatile joint modeling algorithm for high-dimensional biomarker longitudinal data with irregular and missing data, capturing complex relationships within the data, yielding accurate predictions for both longitudinal biomarkers and survival outcomes, and gaining insights into the underlying dynamics.

RATIONALE AND OBJECTIVES:Diagnostic safety in radiology may be influenced by occupational conditions beyond the point of image interpretation. This study examined whether workload was associated with diagnostic safety-related events directly or through sequential effects on recovery impairment and psychological strain. MATERIALS AND METHODS: In a nationwide cross-sectional survey of 3729 radiology residents from 407 training institutions in mainland China, we evaluated structural pathways linking workload, recovery impairment, psychological strain, and recent self-reported diagnostic safety-related events. Workload, recovery impairment, and psychological strain were operationalized as standardized composite scores. Generalized structural equation modeling with a weighted least squares mean- and variance-adjusted estimator was used to estimate direct and indirect associations. RESULTS:Workload was positively associated with recovery impairment (β = 0.45, P < .001), and recovery impairment was strongly associated with psychological strain (β = 1.63, P < .001). Psychological strain was associated with 1-month diagnostic safety-related events (β = 0.39, P = .03), whereas direct paths from workload and recovery impairment to events were not statistically significant. The serial indirect pathway from workload through recovery impairment and psychological strain to safety-related events was statistically significant (P = .03). In the 3-month sensitivity analysis, estimates followed the same direction as the primary model but were smaller in magnitude and did not reach statistical significance. Findings were consistent across individual workload indicators and training stages. CONCLUSION:Among radiology residents in China, workload was associated with recent self-reported diagnostic safety-related events primarily through impaired recovery and psychological strain rather than through a direct workload effect. These findings support a recovery-strain framework for understanding diagnostic vulnerability during training and suggest that protecting restorative capacity may be relevant to diagnostic safety interventions.

Quantitative systems pharmacology (QSP) models are increasingly applied to inform decision making across drug development and to support regulatory interactions within model informed drug development (MIDD). QSP supports a broad range of applications across drug development and can be tailored to specific therapeutic areas, mechanisms of action, and contexts of use (CoU). While this diversity is a core strength of QSP, it also presents challenges for reporting for regulatory use. Despite the growing impact of QSP models, there is currently no established guidance on how QSP analyses should be documented and reported for regulatory purposes. This white paper, developed by the International Society of Pharmacometrics (ISoP) QSP Special Interest Group Working Group on Credibility Assessment of QSP for Regulatory Use, seeks to address this gap by proposing best practices for QSP model reporting in regulatory settings. The recommendations are grounded in collective real world experience from regulatory interactions and are aligned with reporting guidance established for physiologically based pharmacokinetic (PBPK) modeling and reporting principles outlined in ICH M15. Rather than prescribing a rigid, one size fits all template, this work proposes a flexible, tiered reporting framework that accounts for development phase and model impact. The proposed framework is intended to facilitate regulatory review and enhance transparency while accommodating the inherent diversity of QSP modeling.