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Over the last several years, the landscape of the biopharmaceutical industry has begun to change. Monoclonal antibodies (mAbs) still dominate the pipelines in the biopharmaceutical industry; however, more complex molecules, such as antibody-drug conjugates (ADC), are becoming more predominant. In addition to the complexity of ADCs, one of the fastest growing classes of biopharmaceuticals is cell and gene therapies. Gene therapies, for example, use large viral vectors, such as adeno-associated viruses (AAV), as the preferred vector for performing gene therapy. With the complexity of biopharmaceuticals increasing, especially in their size, new and innovative approaches are needed to address and characterize these molecules. This month's column reviews how charge detection mass spectrometry (CDMS) is being used to characterize some of these larger more complex molecules in the biopharmaceutical industry., Biopharmaceuticals are products that are specifically made by living cells or biotechnological processes. By the nature of these products being manufactured in living cells can lead to variability in the [...]

Although biotherapeutic drugs have the potential of transforming the management of many life‐threatening diseases, their affordability and accessibility remain an issue. This study offers an overview of the global affordability of biotherapeutic products. For this, prices for 10 representative biotherapeutic products were examined in 40 countries, including high‐income countries (HICs), upper middle‐income countries (UMICs), lower middle‐income countries (LMICs), and low‐income countries (LICs). The affordability of these biotherapeutics was calculated based on the World Health Organization/Health Action International (WHO/HAI) method. As expected, affordability was found to be better in HICs, followed by UMICs, LMICs, and finally, LICs. Furthermore, based on the trend of per capita income, we predict that in UMICs and LMICs, the affordability of high molecular weight biologics will worsen by 1.5× and 2× by 2030, respectively, and further by 4× and 6× by 2040. On the other hand, affordability will stay nearly the same for people living in HICs in the coming decades. Our analysis suggests that it is imperative that measures be taken to make this class of products more affordable and accessible. Governments can contribute by creating conducive policies. Global institutions like the WHO can play a significant role as well. Finally, manufacturers need to invest in and implement manufacturing innovations. [ABSTRACT FROM AUTHOR]

The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber–physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs. [ABSTRACT FROM AUTHOR]

While automated peak detection functionalities are available in commercially accessible software, achieving optimal true positive rates frequently necessitates visual inspection and manual adjustments. In the initial phase of this study, hetero‐variants (glycoforms) of a monoclonal antibody were distinguished using liquid chromatography‐mass spectrometry, revealing discernible peaks at the intact level. To comprehensively identify each peak (hetero‐variant) in the intact‐level analysis, a deep learning approach utilizing convolutional neural networks (CNNs) was employed in the subsequent phase of the study. In the current case study, utilizing conventional software for peak identification, five peaks were detected using a 0.5 threshold, whereas seven peaks were identified using the CNN model. The model exhibited strong performance with a probability area under the curve (AUC) of 0.9949, surpassing that of partial least squares discriminant analysis (PLS‐DA) (probability AUC of 0.8041), and locally weighted regression (LWR) (probability AUC of 0.6885) on the data acquired during experimentation in real‐time. The AUC of the receiver operating characteristic curve also illustrated the superior performance of the CNN over PLS‐DA and LWR. [ABSTRACT FROM AUTHOR]

As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near‐infrared (NIR) spectroscopy, are finding increasing use for real‐time quantitative analysis. Yet detection of multiple low‐concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module‐based two‐dimensional (2D) CNN approach (I‐CNN) for calibrating multiple analytes using NIR spectral data. The I‐CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I‐CNN model result statistics depicted an average R2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I‐CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real‐time process monitoring and comparison with offline analytics. The proposed I‐CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy. [ABSTRACT FROM AUTHOR]

Over the last decade or so, biopharmaceuticals have gained market share and become more complex in their composition. Therefore, new analytical workflows are needed to address these advances, and one [...]

The quality of a biotherapeutic is defined by its various biological, immunological, structural, and physicochemical attributes. Some of these are post-translational modifications (PTMs), other product-related heterogeneities, process related impurities, and [...]

With the advances in analytical technology during the past decade, biotherapeutics or biologicals now represent an important and growing class of drugs. They require extensive characterization, due to their large [...]