New Delhi: The emergence of closed loop automation systems, digital workflow management practices and IVD regulatory compliance protocols are driving the industry towards a new paradigm of connected labs.
Frost & Sullivan forecasts the global life science instrumentation and research tools market to hit $82.57 billion by 2023 due to the increased spending by biopharmaceutical customers as they seek higher efficiencies throughout the clinical development lifecycle. Genomics research is taking the center stage and replacing older methods, especially in drug discovery. This segment has now overtaken proteomics arena and is likely to exhibit a faster growth rate over the next five years.
Global Life Science Instrumentation and Research Tools Market – Forecast to 2023, evaluates and discusses market projections, key trends, competitors, growth opportunities for existing companies, new entrants, and impact of digital transformation. The research scope includes instruments, equipment, reagents, chemicals, consumables, suppliers and IT solutions used research labs.
“The industry has witnessed a number of mergers and acquisitions (M&As) over the last 10 years as large companies were looking to aggressively expand their product portfolio and customer base and we expect that trend to continue,” noted Nitin Naik, Life Sciences Global Vice President at Frost & Sullivan. “It is imperative that established players refocus to capture customer lifecycle value rather than just instrument or product sales. Furthermore, players reframing business model to implement turnkey digital workflow solutions will emerge as market leaders in the long term.”
Further new growth opportunity trends include:
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Analytics based-solutions: While integration as a theme has been in existence in the industry for a long time now, applications of laboratory information management system and electronic laboratory notebook (LIMS/ELN) systems will move over tipping point during the forecast period.
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CRISPR based solutions: This platform which has broad applicability (both in vivo and ex vivo) holds immense potential to leverage machine learning based tools to automate sgRNA identification from databases and integrate results with LIMS/ELN.