Smart Lab Sector Attracts Over $1.4B Investment as Roche, Abbott, Siemens, and Thermo Fisher Lead the Charge

Whether looking back in ten years or fifty, 2021 will undoubtedly remain a significant milestone in the development of the smart laboratory industry, while 2022 will mark a new starting point.

This is because, with the advancement of biotechnology and the normalization of the COVID-19 pandemic, market demand for drug development, biological experiments, and testing has surged, bringing smart laboratories into the spotlight. As the sector gains momentum, numerous laboratory startups are drawing significant attention.

Most notably, Benchling, which develops software tools for scientists and drug R&D organizations, completed its Series E and F financing rounds in 2021, raising a cumulative $300 million. This propelled the company’s valuation from $850 million directly to $6.1 billion, establishing it as a healthcare unicorn. Turning to China, according to incomplete statistics from VCBeat, 2021 was a period of heightened activity for smart laboratories, with eight financing deals closed and total funding exceeding RMB 1 billion—far surpassing the sum of all previous years.

Behind the burgeoning market lies the cumulative strength of China’s smart laboratory infrastructure, the iterative demands of researchers, and the upgrading of new supply chains and technological capabilities. VCBeat broadly categorizes smart laboratories into digital laboratories and automated laboratories, providing separate analyses for each. This article focuses on the digital laboratory segment, aiming to explore the development of laboratory support tools both domestically and internationally and to grow alongside the industry.

According to the global consulting firm Gartner, a digital laboratory is one that leverages digital technologies to transform laboratory operations, optimize business models, and ultimately unlock new revenue streams and value-creation opportunities, while reducing scientists’ non-research work time and alleviating their workload.

Benefiting from a robust economic environment, foreign countries have more resources and conditions to explore the development path of digital laboratories. For instance, pharmaceutical and medical device giants such as Agilent, Thermo Fisher Scientific, Roche, Abbott, Beckman Coulter, and Siemens have already established comprehensive digital laboratory layouts, leveraging their decades or even centuries of accumulated experience.

Li Kang, CEO of Shipu Technologystated that digital laboratories abroad have undergone three stages of development.

First, in the initial phase, companies help scientists conduct experiments efficiently, conveniently, and accurately by providing instruments, equipment, and consumables;

Second,When equipment and consumable supplies fail to meet increasingly complex research demands, manufacturers have introduced professional on-site services.Helping scientists complete non-experimental tasks in the laboratory, such as Thermo Fisher Scientific's Enterprise Services team, which can provide customized comprehensive services for research laboratories, including equipment maintenance, supply chain management, and laboratory operational support;

Third, based on the labor-intensive service model, vendors have begun to leverage information technology tools to process scientific research data., further liberating scientists and improving the overall efficiency of laboratories. Examples include Benchling, which provides ELN services; Science Exchange, which enters the market from the perspectives of laboratory operations and business; and QUARTZY, which focuses on inventory management and supply chain.

Around 2016, industry giants made frequent moves and even extended their reach into China.

In 2016, Thermo Fisher Scientific held the inaugural Thermo Fisher Laboratory Digital Informatics Solutions User Conference in Jiangxi, China, and launched Chromeleon XTR, the latest version of its laboratory digital informatics product at the time; Roche Diagnostics Products (Suzhou) Co., Ltd. was established, becoming the first production base and R&D center set up by Roche Diagnostics’ global operational network in the Asia-Pacific region; Siemens Healthineers announced that its first diagnostic reagent factory in the Asia-Pacific region would be located in Shanghai; and Abbott’s China R&D Center was officially inaugurated.

The moves of industry giants always serve as significant bellwethers, and domestic innovators are beginning to follow suit in their explorations.According to VCBeat’s observations, many innovative enterprises in the smart laboratory sector in China were established during this phase.

From a macro perspective, the emergence of the “Internet Plus” concept and the new reform objective of “making structural reforms on the supply side while moderately expanding aggregate demand” ushered in a major reshuffling of the manufacturing sector. Industries began to recognize shifts in “supply and demand dynamics,” prompting entrepreneurs to pursue technological innovation and achieve a breakthrough from quantity to quality. As the industry transitioned from scale-driven growth to efficiency-oriented development, digital technologies found fertile ground for application.

The increase in the number of laboratories also brings benefits to the laboratory support tools industry.According to data from the “Research Report on the Current Competitive Landscape and Investment Directions of China’s Inspection Agency Industry (2021–2027)” released by Zhiyan Consulting, the number of certification bodies, laboratories, and inspection agencies accredited by the China National Accreditation Service for Conformity Assessment (CNAS) was 7,592 in 2015, rising to 12,381 in 2020. This figure does not include the substantial number of enterprise laboratories and other similar entities.

On January 1, 2022, the newly revised Law on Progress in Science and Technology officially came into effect. Notably, national laboratories were mentioned for the first time as a key component of China’s strategic scientific and technological capabilities, ranking first among such entities. According to statistics from Qingta Network, nearly 100 “provincial laboratories” across China had either been officially unveiled or had initiated preparatory work by April 2022. A vast new blue ocean is quietly beginning to ripple outward.

Challenges are universal within the same industry. At an event hosted by Agilent in 2020^[1]At the event, Wang Liju, Director of Enterprise Service Sales for Agilent’s Greater China region, identified the following challenges in laboratory settings:First, the risk of non-compliance in laboratory management; second, the stability and career development paths for laboratory personnel; and last but not least, improving laboratory efficiency.。

Zhu Yingxin, Director of the Sales Expansion Team for Agilent’s Greater China region, further delineated these challenges by tailoring them to the specific needs of customers across different industry sectors:For Research Laboratory Clients, the greatest challenge is how to achieve unique research-oriented outcomes;For corporate laboratories,, the challenge lies in how to control costs while improving laboratory efficiency;Government Laboratoryplaces greater emphasis on stability, reliability, and regulatory compliance;Third-Party Laboratoryis even more distinctive, facing challenges related to rapid capital flows, efficiency improvements, and the need for flexible business operations.

According toLi KangIntroduction: After surveying the heads of R&D center laboratories at China-based operations of the world’s top 10 biopharmaceutical companies, he found that scientists currently spend an average of more than two hours per day on manual recording and data compilation, meaning that 25% of their eight-hour workday is consumed by paper-based manual documentation.

Li Kang stated that laboratory settings are subject to stringent compliance requirements, with extensive specifications governing data integrity and traceability.such as record traceability, signatures, audit trails, and archival backups.Ultimately, digital technologies aim to enhance experimental efficiency while ensuring safety and compliance.

Jia Shuxin, Founder of Zebrafish Digital IntelligenceIt is believed that the core issue to be addressed in laboratory digitalization is “data” processing. Data exportability, integrity, and traceability are common concerns among research users, and fully leveraging data value can more fundamentally enhance laboratory efficiency.However, at present, the use of laboratory data faces three challenges:Data is fragmented and non-digitized, making it inaccessible; data is unstructured and unlabeled, rendering it unusable; and complex data ownership hinders effective sharing.

Furthermore, the actualLaboratories also face other challenges, such as project collaboration, sample management, and tool utilization., all of which are critical to establishing a robust data closed-loop. The adoption of digital tools will inevitably alter laboratory work practices, and these factors must not be overlooked in the digitalization process.

In traditional laboratory settings, researchers commonly use paper notebooks and reference charts to track and record experimental data and results. However, data in paper-based systems are difficult to share, search, and standardize, which can compromise the integrity and accuracy of experiments.

With the emergence and development of digital technologies, paper-based systems are being replaced by digital systems. Leveraging digital laboratory information systems and connected devices, scientists can gain clearer insights into the correlations between data, thereby enabling more accurate and effective decision-making based on data analysis to achieve research outcomes.

Furthermore, digital technologies enable laboratories to employ the appropriate skills, tools, and processes to prevent, report, manage, and respond to data breaches, thereby ensuring secure data exchange and sharing, while improving the efficiency of resource utilization for laboratory assets, reagents, consumables, and supplies.

Overall,Laboratory digitalization has improved data management practices, streamlined workflows, and enhanced the efficiency of research and development. Furthermore, the rapid advancements in machine learning and artificial intelligence are continuously driving the digital transformation of laboratories. As digital applications in laboratory settings become increasingly diverse, data from the research environment will become an indispensable component of every laboratory.

In the 1960s, some U.S. universities, research institutes, and chemical companies began to study and use computers and local area network systems to process laboratory data, marking the emergence of Laboratory Information Management Systems (LIMS). LIMS has become an excellent tool for managing structured information generated in laboratories, searching for specific test results, or collecting sample and research information.

Since the turn of the 21st century, IT companies and laboratories in China have successively developed and implemented Laboratory Information Management Systems (LIMS), expanding from the field of chemistry to various sectors including food safety, pharmaceutical testing, epidemic control, disease prevention and control, and third-party testing.

However, most of the information recorded during drug discovery and early development is typically unstructured, such as documents, notebook pages, sequences, molecular models, spectra, and images. These datasets are often unsuitable for structured LIMS databases. Furthermore, the managed workflow functionalities of many LIMS platforms are often too rigid, making them unsuitable for research institutions focused on early-stage discovery.

Driven by the need to protect, preserve, and share R&D knowledge, the Electronic Laboratory Notebook (hereinafter referred to as ELN) has emerged.

ELN emerged as an alternative to paper laboratory notebooks, but its functionality extends far beyond that, encompassing support for complex workflows, web services, digital and electronic signatures, as well as analytical and reporting capabilities.The implementation of these features is designed to enhance laboratory efficiency, leverage institutional knowledge, and strengthen the protection of intellectual property (IP).

Leveraging technology that manages underlying electronic records, ELNs provide access security, version control, record authentication, and automatic timestamping—features unattainable with paper-based records. ELNs serve as the ultimate repository for data and information supporting patents and protecting intellectual property, thereby complementing the traditional role of LIMS in research and development.

Although there are still major areas of overlap between ELN and LIMS, the differences can be summarized as follows:LIMS is better suited for managing structured information, while ELN excels at handling more flexible and dispersed unstructured information.It can be said that ELN, LIMS, SDMS, Enterprise Content Management (ECM), or any other technology category is not the sole optimal solution for addressing all data management challenges in the laboratory.Based on the specific conditions and operational characteristics of each laboratory, every technology has its appropriate application.

In China,Saiyin Information, Biolink, Zebrafish, Eagle Valley Information, Mingdu Intelligence, Qingruan Qingzhi Softwareand other enterprises that focus on or include ELN business.

Zebrafish, which completed its angel round of financing in March 2022Focusing on Building an R&D Data Platform for Life Sciences: Products CompletedYilan Notes features core modules such as project management, electronic lab notebooks, a registry center, and inventory management, covering the vast majority of use cases in biological laboratories. Its underlying data interoperability enables users to effectively achieve a closed-loop data management workflow within the system.Each module of the system supports multiple interaction modes and allows users to configure them according to their specific needs, offering a user-friendly and flexible experience that can meet the requirements of various R&D projects.

Laboratory digitalization systems can also streamline experimental procedures and enhance accuracy through machine learning and artificial intelligence technologies, connecting tools within the laboratory environment with researchers to create an intelligent and efficient research ecosystem. As these systems take over repetitive tasks, researchers are able to devote more time and energy to core research activities.

By accessing large datasets, scientific texts, images, and videos, AI may enable comparative studies, making it easier for scientists to formulate hypotheses and design experimental protocols or follow-up studies. Scientists will be able to leverage the advantages of AI to streamline their workflows and accelerate research progress.

On February 19, 2022, Agilent acquired ACIES, an advanced AI technology developed by Virtual Control. ACIES automates labor-intensive tasks in gas chromatography/mass spectrometry data analysis, enhancing the efficiency of laboratory workflows from sampling to reporting.

Agilent will integrate this technology into its MassHunter software suite for liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC/MS) instruments to enhance the productivity, efficiency, and accuracy of high-throughput laboratories served by the company globally.

Virtual Control is an artificial intelligence and machine learning software developer that creates innovative analytical solutions for laboratory testing. Through this acquisition, Agilent has acquired intellectual property and other assets related to ACIES. As part of the transaction, core members of the ACIES team have also become employees of Agilent.

This acquisition represents Agilent’s latest investment in digital technologies to enhance laboratory productivity. Building on the company’s existing investments and innovations, it advances analytical laboratories and transforms their capabilities through new technologies, improved instrument and data integration, and more efficient laboratory workflows.

The application of the Internet of Things (IoT) enables real-time monitoring of laboratory assets, equipment, data platforms, and environmental controls, providing real-time insights into asset utilization and inventory.IoT laboratories can be remotely controlled by researchers, connecting to cloud and local server devices for access anytime, anywhere. Furthermore, by linking experimental equipment to backend systems via sensors, scientists can conduct experiments more efficiently and reduce the risk of human operational errors.

The Internet of Things (IoT) can also integrate laboratory equipment into a unified workflow, thereby automatically capturing the complete end-to-end process and data. This further reduces costs and waste while enabling future data reuse.

Meanwhile, the IoT platform can be extended to multiple laboratories, enabling remote experimentation, data collection, and cloud-based uploading for collaboration and advanced sharing, thereby ensuring that laboratories remain secure, compliant, and operate efficiently. In China,ShiPu Technology, BianWuJi, Shiwan TechnologySuch enterprises have entered the digital laboratory sector through the Internet of Things (IoT).

Li Kang, Founder and CEO of Shipu TechnologyIntroduced at VCBeat’s “Smart Laboratory” series of events, Shipu Technology focuses on the laboratory sector,Leveraging AIoT, the IoT Data Intelligence Platform, and artificial intelligence technologies to build a comprehensive digital-intelligent laboratory solution for clients—the “Shiyanbao” Laboratory Digital-Intelligence Management Platform, helping clients achieve automated and intelligent management across the five key areas of personnel, equipment, materials, methods, and environment, thereby comprehensively enhancing operational efficiency.

The essence of innovation lies in value creation, with all innovation ultimately anchored in human needs. Technology is merely a means, not an end; only by combining “humanity + technology” can we deliver people-centric services of excellence. This process will undoubtedly be challenging. But shouldn’t doing the right thing be difficult? After all, the arduous path is also the accessible path.

[1] https://www.instrument.com.cn/news/20201216/567853.shtml Agilent’s “Qiangqiang Dialogue” Roundtable: Digital Laboratory Management Is an Irreversible Trend, with a Focus on Building a WeChat AI Platform