Bioproduction and Synthetic Biology Bioanalysis

bioproduction and synthetic biology synbiobeta2024
bioproduction and synthetic biology synbiobeta2024

Simplifying bioproduction and synthetic biology assay development and testing with Quantum electrochemical Spectroscopy (QES)

In this Synbiobeta poster, we demonstrate the use QES to measure fermentation biochemistry in a broad-spectrum manner.

The measurement data can be re-queried to get specific intelligence on microbe health, product concentration and end-product consumption, all from one measurement and with one instrument 

Synbiobeta 2024 QES Poster

Synbiobeta 2024

Quantum Electrochemical Spectroscopy (QES): A Novel AI-driven Spectroscopic Approach to Simplify
Bioanalytical Development in Biomanufacturing and Synthetic Biology

The manufacture of biopharmaceutical products, such as proteins, oligonucleotides, engineered cells, vaccines, and peptides, requires demonstration of process control from raw material inputs through finished product release testing to ensure reproducible, safe products. Typically, hundreds of chemical, biochemical, in vitro and in vivo assays are conducted with a diverse set of analytical techniques, such as HPLC, NMR, mass spectrometry, infrared spectrometry, ELISA, and potency assays, each of which requires extensive development, bespoke reagents, complex sample preparation, high cost capital and highly trained staff. Quantum electrochemical spectroscopy (QES), a practical application of inelastic electron tunnelling spectroscopy, offers broad opportunity to simplify biopharmaceutical testing as well as early detection of process anomalies that may lead to batch failure. The QES benchtop instrument (~8.5 x 12.8 x 17 cm) measures molecular vibrations, generating a digital fingerprint, a digital twin for each sample for use in real time or years from now to monitor process shifts. Typically, the sample size is a single pipetting of less than 10uL, does not require sample preparation or additional reagents, with results obtained in about an hour. Sample complexity ranges from pure samples in solution to crude cell culture. In this manufacturing application the output is a digital representation of all molecules in the sample at a given point in the process. The QES mathematical model representing the mixture also allows longitudinal process monitoring, with real time classification of the manufacturing run as to it producing a passing or a failing product. Further, using authentic samples, a model is created to quantify selected individual components in the sample, or the structural homogeneity or degradation state of an analyte of interest, for example. QES has demonstrated broad sensitivity and specificity in differentiating mass isotopes, structural isomers1, disease state, proinflammatory biomarkers and markers of liver toxicity, for example. In this work, we expand the assessment to showcase the differentiation and quantitation of two very similar proteins, long-acting (Toujeo/ glargine) and short-acting (Humalog/ lispro) insulin in a mixture of the two molecules; detect and quantify low-abundance inflammatory proteins (e.g. IL-6) in high complexity samples; detect and quantify short oligonucleotide pharmaceutical products from background impurities and demonstrate the use of QES to predict the sanctioning manufacturing runs generating microbial fermentation products while also qualifying the state of microbial health within the fermentation matrix. Developing and using bioanalytical assays in bioproduction is an intensive and expensive process. The cost is commonly driven by the extensive validation required by reagent-centric methods (e.g. Immunoassays), the extensive sample preparation required (e.g. LC-MS), the cost of capital and infrastructure, and the need for highly trained staff to run the assays. QES does not require reagents, nor sample preparation; , requires one pipetting step, and approximately 2 linear feet of lab bench space, thereby dramatically reducing the complexity and the cost of method creation and validation. Despite being a simple and straightforward analytical method, QES does not compromise the sensitivity or specificity of the analyte detection, as demonstrated in this work by the very low level of insulin isoforms identified and quantified (fg/mL) in a simpler background matrix. We also demonstrate the ability of QES to quantify a bigger protein in a more complex matrix resembling cell culture supernatants. In summary, QES has been used to classify complex samples (pass/fail; disease/healthy; toxic/non-toxic) using uL’s of complex samples as-collected (cell culture, human blood); quantify selected analytes in those samples over large dynamic ranges; and differentiate structural homologs of proteins, peptides, and small molecules. Because QES does not require a laboratory infrastructure and many of the sample handling steps of other quantitative analytical methods, systematic errors associated with sample transport, handling and preparation are eliminated. The digital model of the sample resides either on premises or in the cloud, thereby allowing rapid sanctioning of the results against historic trends. Together, QES affords more information in less time at a lower cost and facile technology transfer than many analytical methods used to characterize manufacturing processes. 1) Gupta, Chaitanya, et al. “Quantum tunneling currents in a nanoengineered electrochemical system.”

The Journal of Physical Chemistry C 121.28 (2017): 15085-15105.

Juan Cuevas, Chaitanya Gupta, Dhangur Singh, Jeremy Hui , John Baldoni, Juan Cruz Cuevas, and Emmanuel Quevy1

1 Probius, Fremont, CA

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