GEN-MKT-18-7897-A
Aug 31, 2023 | Blogs, SCIEX OS software, Technology | 0 comments
Read time: 4 minutes
Introduction In today’s rapidly evolving technological landscape, businesses heavily rely on software and IT systems to drive their operations. However, the pursuit of efficiency and speed often leads to the accumulation of what is known as technical debt. Technical debt refers to the implied cost incurred when businesses choose quick but limited solutions over better approaches that may take more time to implement. This blog post will delve into the concept of technical debt, its implications for businesses and how to avoid falling victim to its detrimental effects.
Defining technical debt Technical debt can be understood as the consequence of postponing necessary improvements or fixes to software systems, processes or infrastructure. It arises when shortcuts or suboptimal solutions are employed to meet immediate needs, resulting in compromises that can cause problems to worsen over time. Just as financial debt incurs interest over time, technical debt accumulates and becomes more costly to rectify the longer it persists.
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Consider SCIEX mass spectrometry software, for instance. Analyst software offered powerful analytics for its time, but SCIEX OS software surpasses its capabilities and continues to evolve to bring more powerful acquisition and processing techniques across a broader range of SCIEX products than Analyst software could ever provide. Continued reliance on Analyst software, which is no longer updated, increases technical debt as more beneficial functionality is added to SCIEX OS software.
The consequences of accruing technical debt Growing technical debt can have a variety of consequences, including:
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Avoiding technical debt To prevent the detrimental consequences of technical debt, businesses can adopt the following strategies:
Conclusion Technical debt is a hidden challenge that businesses must address to maintain efficiency, productivity and competitiveness. By understanding the concept, recognizing the consequences of accumulating technical debt and taking proactive measures to avoid it, businesses can help ensure they are on a path of sustainable growth. Embracing up-to-date software, leveraging automation and staying informed about industry trends empowers organizations to tackle technical debt head-on and paves the way for a successful future. SCIEX OS software represents a contribution to that effort to continuously improve and close the gap on technical debt.
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As an analytical strategy, middle-down mass spectrometry (MS) workflows characterize biotherapeutic proteins by analyzing large, digested protein fragments or defined subunits, rather than fully intact proteins (top-down) or digested peptides (bottom-up). A middle-down strategy combines the strengths of top-down and bottom-up approaches by delivering high sequence coverage and structural specificity while maintaining relatively simple sample preparation. In practice, middle-down analysis enables accurate mass measurement, rapid sequence confirmation, and localization of key post-translational modifications (PTMs) on protein subunits that are directly relevant to product quality.
In biopharmaceutical development, sequence variants (SV) are considered an inherent risk of producing complex proteins in living systems. Sequence variants are unintended changes to the amino acid sequence of a biotherapeutic and can be caused by errors in transcription or translation in the host cell, or cell culture and process conditions. Detailed analysis of SVs is important in process and product development to ensure the drug’s safety and efficacy. Even low‑level sequence variants can have significant implications for product quality, safety, and efficacy, making their accurate detection and characterization a critical requirement across development, process optimization, and regulatory submission.
CE‑SDS remains a cornerstone assay for characterizing fragmentation, aggregation, and product‑related impurities in therapeutic proteins. UV detection has been the long‑standing standard. However, it frequently struggles with baseline noise, limited sensitivity for minor fragments, and subjective integration.
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