Statistical Approaches for Establishing Appropriate Immunogenicity Assay Cut Points: Impact of Sample Distribution, Sample Size, and Outlier Removal

Challenging the Standard Immunogenicity Assessment Approach: 1-Tiered ADA Testing Strategy in Clinical Trials

The ADA testing strategy for protein therapeutics was established almost two decades ago when assay methodologies were rudimentary, and serious immunogenicity-related safety issues had recently been observed with some biotherapeutics. The current testing paradigm employs multiple tiers and stringent cut points to minimize false negatives, reflecting a conservative stance towards ADA analysis. The development of highly sensitive ADA assay platforms and technologies such as humanized or fully human monoclonal antibody (mAb) drugs has put the traditional, resource-intensive 3-tiered testing approach under scrutiny. ADA data from clinical studies for three different mAb programs were re-assessed to explore the feasibility of a simplified 1-tiered ADA testing strategy with a 1% false positive cut point versus the traditional 3-tiered approach. The analysis demonstrated moderate to strong correlations between screening results (signal-to-noise, S/N) and those of confirmation and titer results, with the vast majority of samples (~ 97%) across all studies having the same ADA positive/negative classification with either testing approach. Furthermore, at the subject level, over 92% had the same ADA category (pre-existing, treatment-emergent, treatment-boosted) under both testing approaches. The re-categorized subjects had low titer ADA responses with no observed clinical implications on pharmacokinetics, efficacy, or safety. Finally, the treatment-emergent ADA incidences were comparable between the 1-tiered and 3-tiered approaches. The results demonstrate that the 1-tiered testing strategy is suitable for ADA assessment in these programs and is likely more widely applicable. Additionally, the 1-tiered approach could expedite data delivery and reduce resource needs in clinical development without compromising data quality or clinical interpretation.

Investigating the Influence of Heavy Metals and Environmental Factors on Metabolic Syndrome Risk Based on Nutrient Intake: Machine Learning Analysis of Data from the Eighth Korea National Health and Nutrition Examination Survey (KNHANES)

This study delves into the complex interrelations among nutrient intake, environmental exposures (particularly to heavy metals), and metabolic syndrome. Utilizing data from the Korea National Health and Nutrition Examination Survey (KNHANES), machine learning techniques were applied to analyze associations in a cohort of 5719 participants, categorized into four distinct nutrient intake phenotypes. Our findings reveal that different nutrient intake patterns are associated with varying levels of heavy metal exposure and metabolic health outcomes. Key findings include significant variations in metal levels (Pb, Hg, Cd, Ni) across the clusters, with certain clusters showing heightened levels of specific metals. These variations were associated with distinct metabolic health profiles, including differences in obesity, diabetes prevalence, hypertension, and cholesterol levels. Notably, Cluster 3, characterized by high-energy and nutrient-rich diets, showed the highest levels of Pb and Hg exposure and had the most concerning metabolic health indicators. Moreover, the study highlights the significant impact of lifestyle habits, such as smoking and eating out, on nutrient intake phenotypes and associated health risks. Physical activity emerged as a critical factor, with its absence linked to imbalanced nutrient intake in certain clusters. In conclusion, our research underscores the intricate connections among diet, environmental factors, and metabolic health. The findings emphasize the need for tailored health interventions and policies that consider these complex interplays, potentially informing future strategies to combat metabolic syndrome and related health issues.

Strategy and validation of a nonclinical generic plug-and-play antidrug antibody method for human monoclonal antibody biotherapeutics

The measurement of antidrug antibodies (ADA) in nonclinical studies provides limited value because the formation and incidence of nonclinical ADA does not translate to clinical experience. The formation and presence of ADA in nonclinical species can, however, correlate to reduced drug exposure and safety observations including vasculitis and immune complex disease. Generic ADA methods for humanized monoclonal antibody biotherapeutics mitigate the need to develop bespoke ADA methods during nonclinical drug development. A drug-tolerant, sensitive, generic ADA immunoassay has been developed and validated for measuring ADA in cynomolgus monkey serum samples, allowing for immediate qualification of future monoclonal antibody biotherapeutics. This approach allows us to differentiate complexed and free ADA in a rapidly deployable manner when needed.

Immunogenicity of Cemiplimab: Low Incidence of Antidrug Antibodies and Cut-Point Suitability Across Tumor Types

The immunogenicity of cemiplimab, a fully human immunoglobulin G4 monoclonal antibody directed against programmed cell death 1, was assessed in patients across multiple tumor types. The development of antidrug antibodies (ADAs) against cemiplimab was monitored using a validated bridging immunoassay. To identify ADA-positive samples in the assay, statistically determined cut points were established by analyzing baseline clinical study samples from a mixed population of different tumor types, and this validation cut point was used to assess immunogenicity in all subsequent studies. Regulatory guidance requires that ADA assay cut points be verified for appropriateness in different patient populations. Thus, for the cemiplimab ADA assay, we evaluated whether each new oncology population was comparable with the validation population used to set the cut point. Assay responses from 2393 individual serum samples from 8 different tumor types were compared with the validation population, using established statistical methods for cut-point determination and comparison, with no significant differences observed. Across tumor types, the immunogenicity of cemiplimab was low, with an overall treatment-emergent ADA incidence rate of 1.9% and 2.5% at intravenous dose regimens of 3 mg/kg every 2 weeks and 350 mg every 3 weeks, respectively. Moreover, no neutralizing antibodies to cemiplimab were detected in patients with ADA-positive samples, and there was no observed impact of cemiplimab ADAs on pharmacokinetics. Study-specific cut points may be required in some diseases, such as immune and inflammatory diseases; however, based on this analysis, in-study cut points are not required for each new oncology disease indication for cemiplimab.