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Results for “toxicokinetics”

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2 recordsLinked to original sources
PubMed2026

The Gyrolab platform for immunogenicity assessment and biotherapeutic and biomarker analysis: technical advances and bioanalytical applications.

Gyrolab is an automated ligand‑binding assay platform designed for the quantification of proteins with high sensitivity and broad dynamic range. Its low minimum required dilution and microfluidic disc format enable efficient sample processing while maintaining assay robustness, making it a valuable tool across drug‑development stages. This review summarizes published applications of Gyrolab for pharmacokinetic and toxicokinetic analysis and immunogenicity assessment through anti‑drug antibody detection. In addition, the platform's high-volume assay discs have facilitated its use in biomarker studies, allowing quantification of low‑abundance analytes. Representative examples from the literature are presented together with key assay elements, including analytes, matrices, dynamic ranges, and critical reagents. Because assay replicates remain an important consideration for Gyrolab workflows, recent publications addressing singlet versus duplicate strategies and their impact on data interpretation are also discussed. Finally, we provide real analytical data visualized using a three‑dimensional Gyrolab viewer to illustrate variability in duplicate measurements and to highlight future opportunities for improving assay reliability.

Humans↗
PubMed2026

Exploiting Omic Data to Advance Predictive Ecotoxicology.

Predicting species-specific chemical sensitivity using in silico approaches has the potential to transform environmental risk assessment, conservation, and biomonitoring, while reducing, and ultimately replacing, animal testing. Genomic and transcriptomic data capture extensive sensitivity-relevant variation, including differences in molecular targets, xenobiotic metabolism, and damage mitigation pathways. Large-scale sequencing initiatives therefore offer an unprecedented opportunity to address ecotoxicology's "too many species" problem. Although existing omic-based predictive tools provide proof of concept, they have so far been applied to a narrow set of relatively straightforward prediction scenarios. To achieve broader applicability, current and future tools must be firmly grounded in the diverse molecular mechanisms underlying differential chemical responses. Here, we critically evaluate the emerging field of predicting species sensitivity using molecular variation inferred from omic data. We analyze the strengths and limitations of current omic-based approaches and identify major sequence and ecotoxicological data gaps, as well as critical bioinformatic challenges. We then review the current knowledge of how molecular biology underlies differential chemical sensitivity, outlining research paths to allow the next generation of sensitivity prediction tools to exploit ever expanding omic data.

Ecotoxicology↗