Bioanalysis最新文献

Background: Risdiplam has been used to treat spinal muscular atrophy for 3 years. There are limited number of papers devoted to its analytics. Until now, risdiplam and its metabolites have only been analyzed using a C18 column, while the sample preparation method involved protein precipitation.

Research design and methods: Risdiplam was analyzed using reversed-phase UHPLC. The experiment was designed to compare the retention of risdiplam on five columns using various mobile phases. The protein precipitation was used as the sample preparation method.

Results: Risdiplam shows greater retention on phenyl columns, where π-π interactions take part in retention. The increase of mobile phase pH caused increased risdiplam retention, while salt concentration had no significant effect. An octadecyl column with pentafluorophenyl groups was selected with a mobile phase containing 10 mM ammonium formate (pH 4) and acetonitrile. The method was characterized by good linearity, repeatability, and short analysis time. It was applied to risdiplam analysis in serum samples after protein precipitation with different solvents. Finally, proteins were effectively precipitated using 10% TFA solution, providing 90% recovery.

Conclusions: The developed procedure of extraction and determination of risdiplam is simple, fast and reliable. It may find application for routine monitoring of risdiplam or for quality control.

Objectives: Disruption of bile acid homeostasis is implicated in the pathogenesis and progress of several liver diseases, including drug-induced liver injury (DILI). A rapid liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for quantifying 30 bile acids in human serum was developed and applied to analyze serum bile acid profile in voriconazole administered patients with invasive fungal infections.

Methods: The stable isotope-coded bile acids were used as internal standards. The analytes in serum samples (50 μL) were extracted by protein precipitation and isolated by a Kinetex EVO C₁₈ column (50 × 2.1 mm, 2.6 μm). The detection was performed using multiple reaction monitoring (MRM) in electrospray negative ionization mode.

Results: All of the 30 bile acids were sufficiently separated within 10 min with good linearity (r > 0.99) over tested calibration ranges. The accuracy and precision values were in the range of 85.42-114.3% and 1.8-13.8%, respectively. The matrix effect, recovery, stability, and dilution integrity all met the acceptance criteria. Serum bile acid profile changed in patients with supratherapeutic voriconazole concentration (>10 μg/mL).

Conclusions: This LC-MS/MS method for the quantification of 30 bile acids in human serum might apply a new analytical approach for liver function evaluation.

Background: Immunogenicity assessments are critical for evaluating biological therapeutics. Preexisting immune reactivity can affect clinical safety and efficacy and confound anti-drug antibody (ADA) assay cut point determination, complicating identification of treatment-emergent ADA. In the ALXN-X program, approximately 50% of naïve human serum samples showed preexisting reactivity, with some signals exceeding background by more than 100-fold, challenging ADA method development (cut points, sensitivity, drug tolerance).

Methods: Competitive inhibition experiments were conducted to map the binding site of the preexisting reactivity and depletion with protein A/G agarose was performed to further characterize the preexisting reactivity. A modified ADA assay with an engineered drug molecule was developed to mitigate its impact on assay performance.

Results: This preexisting reactivity binds to the C-terminal conserved framework sequence of the drug molecule. Characterization indicated that the preexisting reactivity is likely attributable to immunoglobulins. The modified ADA assay can effectively mitigate the impact of the preexisting reactivity on the assay performance, while maintaining the ability to detect ADA response specific to the unique epitopes of ALXN-X.

Conclusions: Preexisting, immunoglobulin-mediated reactivity targeting ALXN-X's Cterminal framework sequence complicates ADA assay performance. A modified ADA assay mitigates this interference while preserving detection of ALXN-X-specific ADA, supporting more reliable immunogenicity assessment.

Pre-existing anti-drug antibodies (pre-ADA) pose a significant challenge in the development of biotherapeutics. This article underscores the importance of early pre-ADA screening in mitigating adverse immune responses and optimizing therapeutic efficacy. Drawing on collaborative insights from three pharmaceutical companies, the paper presents a comparative analysis of pre-ADA assessment strategies, including timing, assay formats, and risk thresholds. It details how each company integrates pre-ADA data into immunogenicity risk evaluation, emphasizing the shift toward earlier screening during lead candidate selection. The article also explores assay-specific considerations, such as detection sensitivity and isotype coverage, and outlines criteria for ranking molecular candidates based on pre-ADA reactivity. By characterizing positive pre-ADA responses and proposing standardized practices, the paper offers actionable guidance for incorporating pre-ADA screening into preclinical workflows. This work highlights the growing relevance of pre-ADA assessments in an increasingly complex biopharmaceutical landscape and advocates for harmonized methodologies to support the development of safe and effective medicines.

In recent years, the study of extracellular vesicles (EVs) has gained increasing attention due to their pivotal role in intercellular communication and their potential as noninvasive biomarkers and innovative therapeutic tools. Their molecular cargo, shaped by the source cell and its microenvironment, offers an accurate mirror of cellular states and pathophysiological dynamics. Characterizing this content provides critical insights into disease mechanisms, while paving the way for novel diagnostic, prognostic and therapeutic strategies. Among their components, proteins and metabolites stand out for their functional relevance and biomarker potential. In this context, proteomics and metabolomics have emerged as key approaches to unravel the molecular complexity of EVs and clarify their biological functions, thereby advancing their translation into clinical applications. This review highlights the contributions of these disciplines, outlines associated analytical workflows, and discusses major technical challenges. A systematic literature search was conducted in PubMed, Web of Science, Scopus, and Google Scholar for studies published between 2012 and 2025, focusing on proteomic and metabolomic analyses of EVs. Emphasis is placed on the need for methodological consensus to standardize protocols, improve inter-laboratory reproducibility, and support the successful clinical application of EV-based strategies.

Background: As the synthetic abused drug with high addictive potential, methamphetamine (METH) and its major metabolite amphetamine (AMP) are chiral compounds. The S-enantiomer of METH is primarily abused because of its potent psychoactive effects, whereas the R-enantiomer may originate from the metabolism of selegiline, a prescription medication for Parkinson's disease.

Research design and methods: This research aimed to develop a robust and reliable analytical method to distinguish illicit METH abuse from legal selegiline therapy. A novel, simplified chiral stationary phase liquid chromatography-tandem mass spectrometry (CSP-LC-MS/MS) method was developed and validated for the rapid determination of R- and S-enantiomers of METH and AMP in human hair, eliminating the need for derivatization pretreatment.

Results: Employing an Agilent Chiral-V column under isocratic conditions, the developed CSP-LC-MS/MS method achieved efficient baseline separation (resolution ≥2) and rapid quantification of the R/S enantiomers of METH and AMP within 10 min. Analysis of hair samples from three METH abusers revealed a predominance of the S-enantiomers. Conversely, only the R-enantiomer was detected in the hair of a selegiline user.

Conclusions: This research enables precise enantiomer differentiation, offering critical insights into drug metabolism and forensic discrimination between illicit METH abuse and legitimate selegiline treatment.

Bioanalytical (BA) data are foundational to clinical trial integrity and regulatory success. Yet, circumstances such as regulatory noncompliance, operational disruptions, resource limitations, or evolving analytical needs can necessitate a BA laboratory transition during a clinical trial. Through the lens of a real-world case study and supported by broader industry experience, this manuscript explores the operational, scientific, and regulatory challenges of BA laboratory transitions. We examine four potential scenarios that trigger such changes, the risks to data continuity and compliance, and the significant implications for sponsors. The manuscript outlines actionable, multidisciplinary strategies for managing transitions: from vendor qualification and project planning to Corrective and Preventive Action (CAPA) implementation, method transfer, and ongoing performance assessment. When guided by early risk recognition, transparent communication, and organizational learning, laboratory transitions though challenging, can be successfully navigated to protect trial outcomes and strengthen the resilience of clinical programs.

Aim: To develop an anti-drug antibody (ADA) assay for a bispecific antibody (bsAb) we will refer to as Drug-1, and to quantify the ADAs against each specific fragment antigen-binding (Fab) site of Drug-1.

Materials and methods: Here we use two additional unlabeled monoclonal antibodies (mAbs) Drug-2 & Drug-3 in an ADA confirmation assay format. Drugs-2 & -3 have the same targets as the two Fabs of Drug-1, facilitating our aim to measure and quantify the anti-drug Abs against each specific Fab of Drug-1 by signal inhibition.

Results and conclusion: By using the combination of a screening and a dual confirmation assay format, we were able to accurately quantify the ADAs against each specific Fab of the bsAb Drug-1.

Immunogenicity undermines the safety and effectiveness of biologics and emerging modalities. Routine workflows are constrained by drug/target interference in binding assays, matrix and dilution biases in functional assays, fragile cellular/innate readouts, and the difficulty of translating titers into patient-level risk. Advances in artificial intelligence (AI) and real-world evidence (RWE) offer practical remedies. This narrative review searched five search engines and databases and included evidence from published original research, reviews, and regulatory data, and reports between 2022 and 2025. It identifies common failure locations and links them to practical AI and RWE solutions. We outline the regulatory and data underpinnings for decision-grade RWE, provide a closed-loop blueprint, and summarize recent developments in T- and B-cell epitope prediction, neoantigen prioritization, and pharmacovigilance/NLP pipelines for case deduplication and normalization: Define laboratory panels and computable phenotypes, map multi-source data to a common model, deduplicate, train transparent internally validated models, externally validate across distributed networks, route outputs to clinic/lab actions, and continuously monitor for drift with auditable updates. AI-augmented RWE can replace assay-centric snapshots in immunogenicity assessment with a learning system that focusses on clinically significant ADA/NAb effects, targets limited laboratory resources, speeds up signal verification, and enhances post-market vigilance.