Explainable deep learning enhances robust and reliable real-time monitoring of a chromatographic protein A capture step

Current PAT Landscape in the Downstream Processing of Biopharmaceuticals

Protein-based therapeutics have revolutionized modern medicine, addressing complex diseases with unprecedented specificity and efficacy. The rising demand for biologics has driven the evolution of biomanufacturing practices to ensure consistent quality and operational efficiency. Traditional batch testing, with its inherent limitations, is being replaced by quality by design (QbD) frameworks and process analytical technology (PAT). PAT facilitates real-time monitoring and control by integrating advanced analytical tools and data-driven methodologies to optimize downstream processing (DSP). This review highlights the recent advancements in PAT tools, including spectroscopy, chromatography and biosensors. Spectroscopic techniques provide rapid, non-invasive measurements, while biosensors offer high specificity for monitoring critical quality attributes. Additionally, the integration of chemometric modelling and digital twins enables predictive analytics and enhances process control, paving the way for real-time release (RTR) of the product. Despite challenges in regulatory compliance and technology integration, innovations in automation and machine learning are bridging these gaps, accelerating the transition to intelligent manufacturing systems. This article provides a comprehensive evaluation of emerging analytical technologies and strategic insights into their integration, aiming to support the biopharmaceutical industry's shift towards robust, continuous and adaptive manufacturing paradigms.

Prediction of pH Gradient Elution of Ion Exchange Chromatography for Antibody Charge Variants Separation Based on Salt Gradient Elution Experiments

Mechanistic modeling of ion exchange chromatography (IEC) is a promising technique to improve process development. However, when considering the pH influence, model prediction becomes challenging due to the multiple pH-dependent parameters and complex interactions. In order to more effectively predict the pH gradient elution behavior, a two-step model calibration strategy was proposed for the pH-dependent steric mass action (SMA) model with the empirical correlations of characteristic charge ν and equilibrium coefficient keq. The strategy was verified through a case study of monoclonal antibody charge variants purification with IEC. All nine calibration experiments were conducted using linear salt gradient elution at three fixed pH values. The average root mean square error (RMSE) was 14.28% between the model calculation and experiments. Both ν and ln(keq) exhibited good linear correlations with pH (R2 > 0.99). Then, the well-calibrated pH-dependent SMA model showed a satisfactory capability for predicting the pH gradient elution behaviors with an RMSE of 16.18%. Moreover, the model was used for process optimization under different elution modes, including salt gradient, pH gradient, and salt-pH dual gradient, improving the yield from 70.07% to 74.91%. The optimized linear pH gradient elution was verified by experiment (RMSE = 8.30%). Finally, a methodological framework for utilizing the simplified pH-dependent SMA model developed in this work was summarized to explore its practical applications. The two-step calibration strategy proposed significantly alleviates the workload for the pH-dependent IEC modeling. The model-based process optimization effectively enables faster pH-dependent process development with minimal experiments.

Interpretable and explainable predictive machine learning models for data-driven protein engineering

Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing and enhancing proteins with desired properties. The integration of artificial intelligence methods has further accelerated protein engineering process by enabling the development of predictive models based on data-driven strategies. However, the lack of interpretability and transparency in these models limits their trustworthiness and applicability in real-world scenarios. Explainable Artificial Intelligence addresses these challenges by providing insights into the decision-making processes of machine learning models, enhancing their reliability and interpretability. Explainable strategies has been successfully applied in various biotechnology fields, including drug discovery, genomics, and medicine, yet its application in protein engineering remains underexplored. The incorporation of explainable strategies in protein engineering holds significant potential, as it can guide protein design by revealing how predictive models function, benefiting approaches such as machine learning-assisted directed evolution. This perspective work explores the principles and methodologies of explainable artificial intelligence, highlighting its relevance in biotechnology and its potential to enhance protein design. Additionally, three theoretical pipelines integrating predictive models with explainable strategies are proposed, focusing on their advantages, disadvantages, and technical requirements. Finally, the remaining challenges of explainable artificial intelligence in protein engineering and future directions for its development as a support tool for traditional protein engineering methodologies are discussed.

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.