Nandakumar Kalathil, Country General Manager – India, Agilent Technologies
In an interaction with India Pharma Outlook, Nandakumar Kalathil, Country General Manager – India at Agilent Technologies, discusses how high-resolution mass spectrometry is revolutionizing early disease detection in India by enhancing cancer biomarker sensitivity, addressing neurodegenerative variability, integrating multi-omics, and using automation and AI to accelerate clinical diagnostics and improve throughput. Nandakumar is a seasoned business growth strategist with over 18 years of experience driving strategic growth and customer success. He excels in business development, sales leadership, and fostering strong client partnerships to deliver exceptional enterprise value.
According to him, high-resolution mass spectrometry enhances early disease detection by:
With the increasing adoption of high-resolution mass spectrometry in India, how is it enhancing sensitivity and specificity in early-stage cancer biomarker detection?
Cancer remains a significant public health concern in India, ranking among the top three countries globally in terms of both incidence and mortality rate. This escalating cancer burden highlights the urgent need for advanced diagnostic technologies that facilitate early detection and intervention, crucial for improving patient outcomes.
High-resolution mass spectrometry (HRMS) is emerging as a promising technology in this regard. Its adoption in India is steadily increasing due to its ability to markedly enhance sensitivity and specificity in detecting early-stage cancer biomarkers. HRMS enables detailed and comprehensive analysis of complex biological samples, allowing researchers and clinicians to identify even minute quantities of potential biomarkers amidst a vast array of molecular components. This capability is vital for early-stage cancer detection, where biomarker concentrations are typically very low.
Technological advancements in HRMS have significantly improved its resolution and dynamic range, leading to more accurate identification and quantification of biomolecules. This is particularly beneficial for untargeted analysis, an approach essential for discovering novel biomarkers, especially in cancers where known markers are limited or ineffective. HRMS has already demonstrated its value by identifying novel biomarkers in colorectal cancer, melanoma, and breast cancer.
In the Indian context, while HRMS adoption is growing, its full potential in early cancer detection can be realized only by addressing certain barriers, such as gaps in technical skills, infrastructure, and cost. Investments in these areas are crucial for integrating HRMS more widely across the healthcare and research ecosystem.
HRMS significantly enhances the sensitivity and specificity of early-stage cancer biomarker detection by providing deep analytical insights into complex samples. Its ability to support untargeted discovery makes it a powerful tool in India's fight against cancer, especially as efforts to build capacity and reduce barriers continue.
Given the challenge of biomarker validation, how are Indian labs tackling pre-analytical variability in mass spectrometry-based proteomic analysis for neurodegenerative diseases?
Biomarker validation, especially in the context of neurodegenerative diseases, is a critical yet complex task due to the inherent variability in biological samples and the sensitivity of analytical techniques like mass spectrometry. In neurodegenerative conditions such as Alzheimer’s disease, protein dysregulation in the brain plays a significant role, and proteomics has emerged as a powerful approach for identifying disease progression biomarkers. However, one of the major obstacles in translating proteomics findings into clinical applications is pre-analytical variability, including during sample collection, storage, transport, and initial processing.
Indian laboratories, along with global counterparts, are increasingly focusing on minimizing this variability to ensure accurate, reliable, and reproducible MS-based proteomic data. One of the strategies is to invest considerable effort into the development and adherence to standardized operating procedures (SOPs) for sample handling. These SOPs define precise guidelines for the collection, labeling, storage temperature, duration, and handling of biological specimens to minimize degradation or alteration of proteins and metabolites.
There has also been a rise in training programs and workshops aimed at educating laboratory personnel in best practices for pre-analytical processing. Institutions and industry leaders conduct regular sessions to address sample quality control, aliquoting techniques, and contamination prevention.
In recent years, India has seen an increase in the number of advanced mass spectrometry data analysis centers. These centers are equipped with state-of-the-art instruments and bioinformatics platforms to process and analyze complex proteomic data efficiently.
MS instrument manufacturers of international repute, such as Agilent, partner with Indian labs to provide technical guidance and training. Agilent's integrated proteomics liquid chromatography/mass spectrometry (LC/MS) based workflows offer excellent analytical performance and flexibility, helping labs develop robust sample preparation methods and minimize batch effects and other technical sources of variability.
By adopting these multifaceted approaches, Indian laboratories are steadily overcoming the challenges posed by pre-analytical variability. These efforts are instrumental in improving the reproducibility and translational potential of proteomics research in neurodegenerative diseases.
With growing emphasis on multi-omics approaches, how is mass spectrometry integrating with genomics and metabolomics to refine early disease detection strategies in India?
Multi-omics approaches are proving to be highly effective in identifying disease biomarkers. By integrating transcriptomics, metabolomics, and proteomics, researchers can obtain comprehensive pathway-level insights that are crucial for understanding disease progression and developing targeted therapies. These integrated workflows are not only pivotal for biomarker discovery but also for devising clinical diagnostic strategies, particularly in diseases such as cancer.
In India, multi-omics research is steadily gaining momentum, especially within academic and research institutions. A notable increase in publications involving mass spectrometry in multi-omics studies highlights its dual utility in identifying both proteins and metabolites. This reflects a broader trend toward adopting advanced analytical platforms for disease research.
The cancer research and omics landscape in India is expanding rapidly, propelled by increased funding, collaborative initiatives, and a growing focus on innovative therapeutic solutions. Between 2003 and 2022, India recorded the highest number of cancer research publications in Southeast Asia, underscoring its rising prominence in the field. The launch of India’s first Cancer Multi-Omics Data Portal in 2022 marked a significant milestone, offering a comprehensive repository of multi-omics data.
Leading institutions in India are actively engaged in applying multi-omics analyses across various cancers and diseases. India is emerging as a key player in the global effort to harness mass spectrometry and integrated omics for early and precise disease detection.
What advancements in automation and AI-driven spectral analysis are addressing throughput limitations in mass spectrometry workflows for biomarker discovery?
Advancements in automation and AI-driven spectral analysis are significantly addressing the throughput limitations in mass spectrometry (MS) workflows, particularly in the context of biomarker discovery. These technologies are enabling faster, more accurate and scalable analyses, helping to overcome traditional bottlenecks in sample preparation, data analysis, and interpretation.
High-throughput automation systems, such as Rapidfire, support automated sample preparation platforms, while robotic liquid handling systems like Bravo reduce the time and labor involved in preparing large numbers of samples for mass spectrometry. Automation of tasks like sample dilution, extraction, filtration, and labeling ensures that samples are processed consistently and efficiently, thus enhancing throughput.
LC-MS systems now integrate robotic arms or automated liquid chromatography systems (e.g., LC-MS/MS setups) to inject samples into the mass spectrometer automatically. These systems can handle hundreds or thousands of samples in a day, increasing throughput while maintaining accuracy and reproducibility.
The mass spectrometry data generated is often complex, with overlapping peaks, noise, and interference. AI algorithms are now being used to automate the detection of peaks, the deconvolution of overlapping signals, and the extraction of relevant biomarkers from the data. These AI models can analyze large datasets at speeds far beyond human capabilities, improving throughput. AI systems can extract relevant features (such as m/z ratios, ion intensities, and isotope distributions) from mass spectrometry results to identify potential biomarkers without the need for manual intervention.
AI-driven software like MassHunter Professional integrates data from different omics technologies (such as proteomics, metabolomics, lipidomics, and genomics) to provide a more holistic view of the biomarkers under study. These platforms come with powerful data visualization capabilities that help researchers interact with complex datasets. Using tools like principal component analysis (PCA) or t-test maps, researchers can visually explore mass spectrometry data in ways that are more intuitive, accelerating the process of biomarker identification.
The capability of Laboratory Information Management Systems (LIMS) like SLIMS, along with barcode scanning options in LC systems, enhances productivity and reduces manual errors in sample handling. This contributes to more confident results and streamlined workflows.
Advancements in automation and AI-driven spectral analysis are dramatically increasing throughput and efficiency in mass spectrometry workflows, especially in the context of biomarker discovery. These technologies are not only speeding up the process of data acquisition and analysis but also helping researchers handle the increasing complexity of mass spectrometry data. With the continued integration of AI and automation, mass spectrometry workflows are becoming more streamlined, scalable, and accessible, opening new avenues for personalized medicine, early disease detection, and targeted therapies. These advancements will play a pivotal role in accelerating biomarker discovery and transforming clinical diagnostics.
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