Signal Processing Analysis of Different Interferon-α Expression Variability for Optimized Pharmaceutical Manufacturing
Seah (Selina) Lee
Publication date: July 19, 2026
Publication date: July 19, 2026
DOI: http://doi.org/10.34614/JIYRC2026I42
ABSTRACT
Protein manufacturing often experiences variability in yield and product quality due to folding instability, inclusion body formation, and environmental sensitivity. Interferon-α (IFN-α), an immunotherapeutic cytokine, displays differences in structural stability and expression behavior for each subtype. This study applies signal processing analysis to model expression variability among IFN-α subtypes expressed in Escherichia coli. Four human IFN-α subtypes (IFN-α2, IFN-α5, IFN-α8, and IFN-α13) were evaluated using computational predictions of disorder scores, instability index, and GC content as a proxy for codon optimization. MATLAB simulations modeled time-dependent protein expression using a kinetic growth equation, while environmental effects of pH and temperature were modeled using Gaussian scaling functions. Results indicate that structurally stable subtypes exhibit lower variability in expression, whereas higher disorder and instability are associated with greater fluctuations. These findings suggest that structural properties play a key role in determining expression consistency. This approach helps identify optimal harvest timing and environmental conditions to maximize recombinant IFN-α yield while minimizing production variability for more efficient pharmaceutical manufacturing processes.
Protein manufacturing often experiences variability in yield and product quality due to folding instability, inclusion body formation, and environmental sensitivity. Interferon-α (IFN-α), an immunotherapeutic cytokine, displays differences in structural stability and expression behavior for each subtype. This study applies signal processing analysis to model expression variability among IFN-α subtypes expressed in Escherichia coli. Four human IFN-α subtypes (IFN-α2, IFN-α5, IFN-α8, and IFN-α13) were evaluated using computational predictions of disorder scores, instability index, and GC content as a proxy for codon optimization. MATLAB simulations modeled time-dependent protein expression using a kinetic growth equation, while environmental effects of pH and temperature were modeled using Gaussian scaling functions. Results indicate that structurally stable subtypes exhibit lower variability in expression, whereas higher disorder and instability are associated with greater fluctuations. These findings suggest that structural properties play a key role in determining expression consistency. This approach helps identify optimal harvest timing and environmental conditions to maximize recombinant IFN-α yield while minimizing production variability for more efficient pharmaceutical manufacturing processes.
