Jim McNally , CSO Sword Bio

CRO Selection as a Long-Term Scientific Partnership

Choosing a contract research organization (CRO) is one of the earliest—and most consequential—decisions a biotech company makes. While CRO selection is often framed around immediate study needs, timelines, or cost, this short-term view can create downstream challenges as programs advance. In reality, the right CRO functions not as a vendor, but as a long-term scientific…

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Building Scalable Bioanalytical Workflows for Rapid Biotech Growth

Scalable Organizational Structure in the CRO Environment Rapid growth challenges not only bioanalytical workflows, but also the organizational structures that support them. For sponsors, a common concern when working with a fast-growing CRO is whether scientific rigor, data quality, and regulatory discipline can be maintained as teams expand and programs multiply. A scalable CRO organization…

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Preparing for GLP and GCLP Compliance in Bioanalytical Studies

Bioanalytical laboratories sit at a critical juncture in drug development, supporting non-clinical toxicology/PK studies and human clinical trials. Ensuring compliance with both 21 CFR Part 58 (“GLP”) for non-clinical work and with Good Clinical Laboratory Practice (GCLP) frameworks for clinical sample analysis builds data integrity, regulatory trust and operational robustness. In this article we review…

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Strategies for Immunogenicity Mitigation During Clinical Development

Immunogenicity—the immune system’s ability to recognize and respond to a therapeutic protein—remains one of the greatest challenges in biologics development. Unwanted immune activation can generate anti-drug antibodies (ADAs) that neutralize efficacy, accelerate drug clearance, or provoke hypersensitivity reactions. Even when responses are subclinical, they can compromise pharmacokinetics and increase interpatient variability. Because immunogenicity can arise…

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The Future of Immunogenicity Testing with AI & Machine Learning – Part 1: Intended Immunogenicity

Immunogenicity testing—assessing whether a vaccine antigen or vector will provoke an immune response—has long relied on experimental assays (in vitro T cell and B cell assays, ELISpot, cytokine readouts) and ultimately clinical data. As datasets expand and computational tools advance, **machine learning (ML)** and **artificial intelligence (AI)** are redefining how we predict, optimize, and understand…

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Overcoming Matrix Interference in Plate-Based Immunoassays

In bioanalytical testing, few challenges are as persistent as matrix interference — the unwanted influence of sample components on assay performance. For plate-based immunoassays like ELISA and MSD (Meso Scale Discovery) platforms, these interferences can obscure true analyte signals, introduce bias, and compromise reproducibility. Understanding the sources and solutions for matrix effects is essential for…

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