Duplicate analysis has been a conventional practice in the industry for ligand-binding assays (LBA), particularly for plate-based platforms like Enzyme-linked immunosorbent assay (ELISA) and Meso Scale Discovery (MSD) assays. Recent whitepapers and guidance have opened a door to exploring the implementation of single-well (singlicate) analysis approach for LBAs. Although the bioanalytical industry has actively investigated the suitability of singlicate analysis, applications in supporting regulated LBA bioanalysis are limited. The primary reason for this limitation is the absence of appropriate strategy to facilitate the transition from duplicate to singlicate analysis. In this paper we present the first case study with our data-driven approach to implement singlicate analysis in a clinical pharmacokinetics (PK) plate based LBA assay with ISR data. The central aspect of this strategy is a head-to-head comparison with Precision and Accuracy assessment in both duplicate and singlicate formats as the initial stage of assay validation. Subsequently, statistical analysis is conducted to evaluate method variability in both precision and accuracy. The results of our study indicated that there was no impactful difference between duplicate vs singlicate, affirming the suitability of singlicate analysis for the remaining steps of PK assay validation. The validation results obtained through singlicate analysis demonstrated acceptable assay performance characteristics across all validation parameters, aligning with regulatory guidance. The validated PK assay in singlicate has been employed to support a Phase I study. The appropriateness of singlicate analyses is further supported by initial Incurred Sample Reanalysis (ISR) data in which 90.1% of ISR samples fall within the acceptable criteria.

Biological networks are characterized by diverse interactions and dynamics in time and space. Many regulatory modules operate in parallel and are interconnected with each other. Some pathways are functionally known and annotated accordingly, e.g., endocytosis, migration, or cytoskeletal rearrangement. However, many interactions are not so well characterized. For reconstructing the biological complexity in cellular networks, we combine here existing experimentally confirmed and analyzed interactions with a protein-interaction inference framework using as basis experimentally confirmed interactions from other organisms. Prediction scoring includes sequence similarity, evolutionary conservation of interactions, the coexistence of interactions in the same pathway, orthology as well as structure similarity to rank and compare inferred interactions. We exemplify our inference method by studying host-pathogen interactions during infection of Mus musculus (phagolysosomes in alveolar macrophages) with Aspergillus fumigatus (conidia, airborne, asexual spores). Three of nine predicted critical host-pathogen interactions could even be confirmed by direct experiments. Moreover, we suggest drugs that manipulate the host-pathogen interaction.

The bioanalytical strategy for monoclonal antibody therapeutics, intended for multiple oncology indications, includes multiple integrated measurements of pharmacologically relevant therapeutics from discovery through development. Three ligand binding assays were cohesively developed and validated, as applicable, using the Gyrolab microfluidic system for the measurement of a free monoclonal antibody BMS-986207. Accuracy and precision demonstrate %bias from -6.3 to 4.4%, percent coefficient of variation (%CV) from 2.6 to 9.8%, and total error from 4.2 to 13.4% in the nonclinical assay; %bias from -0.3 to 3.3%, %CV from 3.5 to 18.2%, and total error from 6.1 to 19.7% in the clinical assay; and >97% of the sample meeting incurred sample reanalysis criteria. The clinical assay was validated using singlicate wells after gaining significant data in the early phase studies to support this cost-effective and efficient strategy. Each assay met fit-for-purpose and/or regulated bioanalytical method validation criteria including stability, selectivity, dilutional linearity, carryover, and specificity criteria with no interference from co-administered monoclonal antibody.

The Gyrolab™ xP is a microfluidic platform for conducting ligand binding assays (LBAs) and is recognized for its utility in discovery bioanalysis. However, few reports have focused on the technology for regulated bioanalysis. This technology has the advantage of low reagent consumption, low sample volume, and automated ligand binding methods. To improve bioanalysis testing timelines and increase the speed at which biotherapeutics are delivered to patients, we evaluated the technology for its potential to deliver high-quality data at reduced testing timelines for regulated bioanalysis. Six LBA methods were validated to support bioanalysis for GLP toxicokinetic or clinical pharmacokinetic studies. Validation, sample analysis, and method transfer are described. In total, approximately 4000 samples have been tested for regulated bioanalysis to support 6 GLP toxicology studies and approximately 1000 samples to support 2 clinical studies. Gyrolab™ xP had high run pass rates (≥83%) and high incurred sample reanalysis (ISR) pass rates (>94%). The maximum total error observed across all QC levels for a given assay was <30% for all six LBAs. High instrument response precision (CV ≤5%) was observed across compact discs (CDs), and methods were validated to use a single standard curve across multiple CDs within a Gyrolab™ xP run. Reduced bioanalysis timelines were achieved compared to standard manual plate-based methods, and methods were successfully transferred across testing labs, paving the way for this platform for use in late-stage clinical development.