Automation plays a key role in enabling new technologies and workflows in the healthcare sector, particularly in the areas of cancer and infectious diseases, such as COVID-19. There is also a need for innovation in rare, hereditary, metabolic and cardiovascular diseases, as well as reproductive health. Many of these workflows are centered around genomics, proteomics, and cell and tissue analysis. Combinations of these workflows are increasingly being used, and leveraging automation is a particularly powerful means to overcome some of the hurdles to adoption in the life sciences and in vitro diagnostic markets.
The COVID-19 pandemic highlighted the need to analyze large numbers of samples very quickly, with fast turnaround times for PCR testing a necessity. Automation supports scalability, enhanced productivity, and standardization, and delivers the reproducibility and precision that are essential for software solutions to work accurately. It also aids robustness at scale and regulatory compliance. Preconfigured, clinical-ready systems registered as Class I medical devices — such as those from Tecan — can smooth the pathway to success, avoiding the costly and time-consuming transition from a research-use-only platform to a regulatory capable and compliant solution later on. Typically, the software will include the necessary functionality to ensure traceability, eliminate human error and restrict access to commands that may alter the workflow, guaranteeing output that meets the highest regulatory standards.
The ability to scale-up testing with an easy-to-use workflow is also becoming more and more important. Modern automation platforms must offer enhanced usability, enabling everyone from lab technicians to experienced analysts to manage complex systems. The availability of democratizing solutions will allow innovation to be adopted by a much wider range of labs and clinics, rather than being concentrated in centers of excellence.
Looking forward, as evermore sensitive technologies enter the marketplace, the need for software to help understand and differentiate the signal from the noise will grow. Previously, assays may have relied on just a few serum biomarkers, whereas today’s tests can use hundreds of data points, making machine learning and artificial intelligence solutions increasingly important. Analysts looking at large genomic or proteomic datasets must learn to distinguish normal from abnormal, and this is where artificial intelligence could come to the fore. Artificial intelligence has a unique capacity to aggregate and interpret data for specific disease questions and is more scalable than employing human analysts.
Improving Cancer Diagnosis and Treatment
The earlier cancer is diagnosed, the better the prognosis. However, as people age, it can become more challenging to diagnose some disorders. For example, the symptoms of early stage cancer can be very nebulous. More accurate tests are necessary to aid diagnosis in the absence of symptomatic presentation, and researchers are now starting to explore multiomics approaches, combining genomics, proteomics, and metabolomics with scalable automation solutions to improve detection methods.
The development of new methods of diagnosis and monitoring needs to be complemented by less invasive sampling methods that will benefit both patients and healthcare economics. Diagnostic companies such as Thrive and Grail are making advances in liquid biopsy approaches, from primary diagnosis of cancers to theranostics and therapy monitoring. Less invasive and more patient friendly, this approach can help to avoid continuation of costly drug regimens that are no longer effective following tumor mutation.
Multiple Myeloma: A Case in Point
The symptoms of multiple myeloma — tiredness, bone pain and repeated infections — are common, making it hard for physicians to identify the disease. Similarly, peripheral blood analysis — electrophoresis and free light chain analysis — is only so sensitive for monitoring multiple myeloma. Bone marrow investigations using genomics and immunophenotyping to assess depth of response are important, but are invasive and expensive, and patients are not keen to undergo frequent biopsies. Initially, the level of plasma cells infiltrating the bone marrow is high and relatively easy to identify, but, as the patient responds to treatment, this decreases and detection becomes harder, potentially leading to a false negative result. These limitations led to diagnostic tools supplier Binding Site collaborating with Tecan to establish a streamlined automated workflow for next generation monoclonal immunoglobulin detection (matrix-assisted laser desorption ionization-time of flight, or MALDI-TOF) for improved patient management.
This novel assay takes advantage of the fact that monoclonal gammopathies produce ideal biomarkers — monoclonal free light chains and monoclonal intact immunoglobulins — as abnormal proteins that can be detected in patients’ blood. These are isolated using highly specific immunoprecipitation reagents that distinguish between IgG, IgA or IgM, or any intact or free kappa or lambda light chains. The entire immunoprecipitation workflow — sample dilutions, incubation with paramagnetic beads, washing, as well as loading the samples onto MALDI plates — is automated on a liquid handling platform. The isolated immunoglobulins are dissociated into their composite heavy and light chains. The light chain — at around 25 KDa — is ideally sized for mass spectrometry analysis without tryptic digestion, making it an ideal substrate for rapid assessment. The operator simply loads the system and starts the run, then transfers the MALDI plates to the mass spectrometer for analysis. Proprietary software is used to analyze the data and evaluate the monoclonal components. The software will also identify the monoclonal immunoglobulin mass, making identification and monitoring of monoclonal patients exceptionally easy.
The clinical relevance of the assay was confirmed by comparison with minimal residual disease (MRD) measurements in bone marrow by next generation flow cytometry, and early investigations suggest that MALDI-TOF mass spectrometry may replace the need for bone marrow investigations in a significant number of patients. The method has a much greater sensitivity for the detection of monoclonal immunoglobulins than existing tests — which can aid more accurate monitoring and earlier identification of relapse, enabling therapy to be restarted at the most opportune moment. Importantly, mass spectrometry can unambiguously distinguish the patient's monoclonal immunoglobulin from the therapeutic monoclonal immunoglobulin. Finally, the laboratory workflow is improved compared to alternative, more laborious technologies used to monitor patients' monoclonal proteins.
Conclusions
Automation can help to support the development of novel workflow concepts with improved sensitivity, precision, reproducibility and accuracy, as well as their translation into the clinical arena. Collaborations between suppliers of automation and diagnostic products can bring significant benefits to healthcare, both in terms of economics and the patient journey, as demonstrated for multiple myeloma. The potential gains are immeasurable.