Between and , Fourier Transform Infrared (FT-IR) spectroscopy has undergone a significant evolution in biopharmaceutical analysis, moving beyond a primarily confirmatory role to become a quantitative, predictive, and process-integrated technology. This shift is driven by advances in instrumentation, data analytics, and hybrid methodologies, expanding the analytical impact of FT-IR across the biopharmaceutical lifecycle.
Biopharmaceutical products present unique analytical challenges due to their molecular complexity, polymorphism, and stringent regulatory requirements. FT-IR spectroscopy, traditionally valued for its rapid molecular insight through vibrational absorption signatures, is now increasingly embedded in quantitative modeling, in-line monitoring, and data-driven decision frameworks. A recent review of ten influential publications from to highlights this transformation.
One key area of advancement is solid-state engineering, specifically in cocrystal design. Researchers at Suryawanshi et al. Demonstrated the use of FT-IR spectroscopy to confirm hydrogen bonding interactions within a quercetagetin–betaine–ethanol cocrystal system. The FT-IR data provided direct evidence of hydroxyl–carboxylate interactions, supporting crystallographic and thermal analyses. This work is significant because it showcases FT-IR’s ability to mechanistically validate crystal engineering strategies aimed at improving the aqueous solubility and in vivo bioavailability of biopharmaceutical substances. It moves FT-IR beyond simple characterization and into a role of rational solid-state design.
Standardization of FT-IR interpretation is also gaining traction. A comprehensive tutorial chapter by Kumar et al. Provides practical case studies involving natural and synthetic bioactive compounds, bridging fundamental vibrational theory with real-world pharmaceutical applications. This resource serves as a unifying reference for practitioners, reinforcing best practices in academic and industrial laboratories.
The integration of FT-IR into real-time bioprocess monitoring is another notable trend. Mishra et al. Reviewed advances in vibrational and fluorescence spectroscopy, positioning FT-IR as a cost-effective Process Analytical Technology (PAT) tool for upstream and fermentation processes. This positions FT-IR as a scalable solution within modern PAT and factory acceptance testing (FAT) frameworks.
Innovations in hardware are further accelerating the adoption of FT-IR in bioprocessing. Christie et al. Evaluated an attenuated total reflectance (ATR) FT-IR platform utilizing disposable internal reflection elements for upstream bioprocess monitoring. Multivariate models enabled accurate quantification of glucose and lactic acid, and differentiation of cellular health states. The use of disposable ATR elements is particularly important, as it addresses concerns about contamination and maintenance, facilitating Good Manufacturing Practice (GMP)-compatible monitoring.
Beyond traditional pharmaceutical applications, FT-IR is finding a role in characterizing green-synthesized nanoparticles. Pasieczna-Patkowska et al. Reviewed the use of FT-IR spectroscopy to identify functional groups responsible for the reduction, capping, and stabilization of these nanoparticles. While metal nanoparticles have limited direct systemic use, the techniques are applicable to lipid nanoparticles, polymeric carriers, and biologically derived nanomaterials increasingly used in biopharmaceutical delivery systems.
The application of FT-IR is also expanding within drug discovery workflows. Kumar et al. Examined the integrated use of FT-IR, Raman, and Near-Infrared (NIR) spectroscopy, emphasizing FT-IR’s role in functional group identification and molecular interaction analysis. This situates FT-IR within high-throughput, data-rich discovery pipelines, reinforcing its relevance in early-stage pharmaceutical and biopharmaceutical development.
A significant development is the convergence of FT-IR spectroscopy with artificial intelligence (AI). Khemchandani et al. Combined FT-IR data with density functional theory (DFT) calculations, molecular dynamics simulations, and machine learning (ML) to design and characterize coamorphous drug systems. FT-IR data were central to identifying intermolecular interactions used as predictive descriptors, elevating the technique from descriptive analysis to a source of predictive features for formulation design and stability modeling.
The analytical power of FT-IR is also being leveraged in clinical diagnostics. Chechekina et al. Demonstrated that FT-IR spectra of blood serum, combined with regression and machine learning models, can accurately predict multiple biochemical parameters. This expands FT-IR’s impact into diagnostics and therapeutic monitoring, highlighting its potential for rapid, minimally invasive biochemical assessment.
Hyphenated techniques, combining FT-IR with other analytical methods, are also experiencing a resurgence. Halko et al. Provided a comprehensive review of high-performance liquid chromatography (HPLC)–FT-IR coupling, addressing interface designs, solvent elimination strategies, and analytical limitations. This highlights FT-IR’s chemical specificity for post-separation identification in complex biopharmaceutical mixtures.
Finally, quantitative polymorph analysis benefits from the combination of FT-IR imaging and chemometrics. Yang et al. Compared powder X-ray diffraction (PXRD), FT-IR, and Raman spectroscopy for quantitative polymorph analysis, demonstrating the effectiveness of FT-IR when paired with multivariate analysis. This confirms that FT-IR spectroscopy can deliver regulatory-relevant quantitative performance in solid-state analysis when combined with appropriate data processing and modeling strategies.
Collectively, these studies demonstrate the transformation of FT-IR spectroscopy into a powerful analytical technology for biopharmaceutical applications. Advances in instrumentation, data analytics, and hybrid methodologies have substantially expanded its analytical impact, supporting modern regulatory and manufacturing expectations.
