Accurate and cost-effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor

Highly efficient strategy of lipopolysaccharide (LPS) decontamination from rHBsAg: synergistic effect of enhanced magnetic nanoparticles (MNPs) as an LPS affinity adsorbent (LAA) and surfactant as a dissociation factor

The interaction of lipopolysaccharide with a recombinant protein is a serious bottleneck, particularly in the purification step of bioprocessing. Recombinant hepatitis B surface antigen (rHBsAg), the active ingredient of the hepatitis B vaccine, is probably contaminated by extrinsic LPS like other biopharmaceuticals. This research intends to eliminate LPS from its mixture with rHBsAg efficiently. Immobilized polymyxin B on magnetic nanoparticles (PMB-MNPs) was synthesized and implemented as an enhanced LPS affinity adsorbent (LAA). The 20-80 EU/dose binary samples with and without surfactant were applied to PMB-MNPs. Formerly, dynamic light scattering (DLS) and transmission electron microscopy (TEM) were examined on the samples to qualitatively show the dissociation effect of the surfactant. Considering the high potential interaction of LPS with HBsAg, the dissociation effects of 0.5 and 1.5% Tween 20 on the binary samples were assessed using immunoaffinity chromatography (IAC) as a quantification tool. The dissociation effect of Tween 20 substantially diminished the interaction, leading to a proportional increase of free LPS up to 66%. The synergetic effect of Tween 20 and privileged LAA was highly effective in eliminating more than 80% of LPS with a remarkable LPS clearance factor of 5.8 and a substantial protein recovery rate of 97%.

Artificial intelligence and machine learning applications in biopharmaceutical manufacturing

Artificial intelligence and machine learning (AI-ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI-ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurring the rise of integrated process platforms and continuous processes that require intelligent, automated supervision. This review summarizes AI-ML applications in biopharmaceutical manufacturing, with a focus on the most used AI-ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of AI-ML applications in the area and the challenges of implementing these techniques at manufacturing scale are also presented.

Review on machine learning-based bioprocess optimization, monitoring, and control systems

Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.