by Sudhir Dahal, PhD, Product Manager- Raman Spectroscopy, Thermo Fisher Scientific
Modern technologies touch nearly all aspects of our lives, offering connectivity and productivity at an unprecedented scale. Across industries from mining to biopharmaceutical research and development, technological advancements allow for new levels of efficiency and throughput with solutions that streamline workflows, alleviate data silos and provide real-time results for quality control. In biopharma, technology has been instrumental in accelerating both upstream and downstream manufacturing workflows to help the industry achieve its goal of getting life-saving therapies to the patients who need them most. In fact, according to Accenture, the convergence of life sciences research and technology can offer new discoveries, sustainable growth and profound patient impact.
As new therapies for often hard-to-treat diseases, such as cancer or rare diseases, are discovered and developed, biopharma companies must continue to adopt technologies that allow them to rapidly commercialize their pipelines and accelerate time to market for new drugs that can make the global population healthier and safer. Biopharma manufacturing requires precise monitoring and control throughout the process to ensure product quality, yield and efficiency, and companies must use innovative analytical technologies for real-time quality control and, ultimately, to enhance speed to market.
Raman spectroscopy, a non-destructive and non-invasive analytical technique, has emerged as a versatile tool in bioprocessing operations. The technique involves using a laser to probe molecular vibrations, and as a result, produces scattered photons – known as a Raman scattering – which are unique to each molecule. When scientists analyze a Raman spectrum, it provides unique and reliable molecular data, or a molecular fingerprint, that scientists use to make qualitative and quantitative analyses. By implementing Raman as an in-line process analytical technology (PAT), biopharma companies can enable continuous analysis and obtain real-time data that allows for quick decision-making, consistent quality and reproducible results.
Upgrading upstream processing
During the early stages of drug discovery, Raman spectroscopy can be used to quickly and accurately identify and qualify raw materials which can lead to new drug candidates. By comparing Raman spectra with a control group, scientists can obtain critical data to determine the presence and quality of raw materials, active pharmaceutical ingredients (APIs) and excipients.
Raman spectroscopy can also enable real-time monitoring throughout upstream bioprocessing, such as during cell life development, cell culture and harvesting, so that scientists can quickly identify deviations from desired conditions and adjust upstream workflows to get the desired results. By optimizing the workflow with Raman spectroscopy, biopharma labs can reduce the time and cost associated with drug development.
Increasing successful downstream outputs
Raman spectroscopy also offers benefits of real-time metabolite monitoring and quality control in downstream processing. The technique enables scientists to facilitate continuous real-time protein and impurities quantification in a flow cell arrangement. The resulting Raman spectra can be used to determine metabolite levels when compared to established models. This data allows manufacturers to make timely adjustments and best gauge optimal process performance.
Raman spectroscopy can also be utilized to monitor the quality attributes of bioprocess outputs. This can include protein products, during downstream processing, for instance. By analyzing Raman spectra, significant quality parameters such as protein secondary structure, aggregation, and glycosylation can be assessed. In this way, the production of high-quality biopharmaceuticals can be ensured.
Enabling a more efficient future of biopharma manufacturing
Utilizing Raman spectroscopy as a PAT allows for a real-time, non-destructive, and non-invasive approach for in-line monitoring of critical process parameters, enabling effective control and optimization of bioprocessing operations. It also allows for a reduced reliance on off-line sampling and laboratory analysis, significantly reducing human errors and contributing to time and cost savings. As the biopharma industry continues to prioritize innovation, adopting advanced analytical technologies like Raman spectroscopy will be key component of meeting the demand for safe and effective new therapies.
Technology manufacturers are prioritizing innovation, too. By integrating next-generation technologies, such as artificial intelligence (AI), biopharma researchers may be able to automate monitoring and control processes for more efficient upstream and downstream workflows. For example, AI could interact directly with in-line instruments via a close loop in real time. This would automatically trigger changes in how, for instance, cells might be fed.
As the industry continues to move forward, so will the applications of Raman spectroscopy. However, additional research, cross-industry collaboration and regulatory frameworks and support will be critical for the wider adoption of the technique as a PAT. This is important as worldwide drug pipeline continues to fill with more funding going toward biopharma R&D than ever before. We will see it continue to evolve as an integral component of biopharmaceutical manufacturing PAT frameworks, contributing to improved process efficiency, product quality and regulatory compliance, allowing biopharma companies to speed up the journey from molecule to medicine to market.
About the author: Sudhir Dahal is Product Manager of Research Raman Products at Thermo Fisher Scientific. The products include Raman microscopes and benchtop Raman spectrometers. He has worked with several spectroscopy techniques in the industry and has over 7 years of experience. He has a PhD from University of Maryland Baltimore County (UMBC), where he researched and collaborated on developing novel spectroscopy-based technique for brain tumor cell detection.