Automatic real-time calibration, assessment, and maintenance of generic Raman models for online monitoring of cell culture processes.

Raman spectroscopy is a multipurpose analytical technology that has found great utility in real-time monitoring and control of critical performance parameters of cell culture processes. As a process analytical technology (PAT) tool, the performance of Raman spectroscopy relies on chemometric models that correlate Raman signals to the parameters of interest. The current calibration techniques yield highly specific models that are reliable only on the operating conditions they are calibrated in. Furthermore, once models are calibrated, it is typical for the model performance to degrade over time due to various recipe changes, raw material variability, and process drifts. Maintaining the performance of industrial Raman models is further complicated due to the lack of a systematic approach to assessing the performance of Raman models. In this article, we propose a real-time just-in-time learning (RT-JITL) framework for automatic calibration, assessment, and maintenance of industrial Raman models. Unlike traditional models, RT-JITL calibrates generic models that can be reliably deployed in cell culture experiments involving different modalities, cell lines, media compositions, and operating conditions. RT-JITL is a first fully integrated and fully autonomous platform offering a self-learning approach for calibrating and maintaining industrial Raman models. The efficacy of RT-JITL is demonstrated on experimental studies involving real-time predictions of various cell culture performance parameters, such as metabolite concentrations, viability, and viable cell density. RT-JITL framework introduces a paradigm shift in the way industrial Raman models are calibrated, assessed, and maintained, which to the best of authors' knowledge, have not been done before.

A machine-learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes.

The manufacture of biotherapeutic proteins consists of complex upstream unit operations requiring multiple raw materials, analytical techniques, and control strategies to produce safe and consistent products for patients. Raman spectroscopy is a ubiquitous multipurpose analytical technique in biopharmaceutical manufacturing for real-time predictions of critical parameters in cell culture processes. The accuracy of Raman spectroscopy relies on chemometric models that need to be carefully calibrated. The existing calibration procedure is nontrivial to implement as it necessitates executing multiple carefully designed experiments for generating relevant calibration sets. Further, existing procedure yields calibration models that are reliable only in operating conditions they were calibrated in. This creates a unique challenge in clinical manufacturing where products have limited production history. In this paper, a novel machine-learning procedure based on just-in-time learning (JITL) is proposed to calibrate Raman models. Unlike traditional techniques, JITL-based generic Raman models can be reliably used for different modalities, cell lines, culture media, and operating conditions. The accuracy of JITL-based generic models is demonstrated on several validation studies involving real-time predictions of critical cell culture performance parameters, such as glucose, glutamate, glutamine, ammonium, lactate, sodium, calcium, viability, and viable cell density. The proposed JITL framework introduces a paradigm shift in the way industrial Raman models are calibrated, which to the best of authors' knowledge have not been done before.

Discovery of a novel small-molecule targeting selective clearance of mutant huntingtin fragments.

CAG-triplet repeat extension, translated into polyglutamines within the coding frame of otherwise unrelated gene products, causes 9 incurable neurodegenerative disorders, including Huntington's disease. Although an expansion in the CAG repeat length is the autosomal dominant mutation that causes the fully penetrant neurological phenotypes, the repeat length is inversely correlated with the age of onset. The precise molecular mechanism(s) of neurodegeneration remains elusive, but compelling evidence implicates the protein or its proteolytic fragments as the cause for the gain of novel pathological function(s). The authors sought to identify small molecules that target the selective clearance of polypeptides containing pathological polyglutamine extension. In a high-throughput chemical screen, they identified compounds that facilitate the clearance of a small huntingtin fragment with extended polyglutamines fused to green fluorescent protein reporter. Identified hits were validated in dose-response and toxicity tests. Compounds have been further tested in an assay for clearance of a larger huntingtin fragment, containing either pathological or normal polyglutamine repeats. In this assay, the authors identified compounds selectively targeting the clearance of mutant but not normal huntingtin fragments. These compounds were subjected to a functional assay, which yielded a lead compound that rescues cells from induced mutant polyglutamine toxicity.