The need for a data-driven culture in life sciences: What you don’t know can hurt you

Preventing sudden failures and unexpected downtime in life sciences organizations starts with promoting a data-driven culture across the enterprise.

[Image courtesy of Oleksii/Adobe Stock]

Promoting a data-driven culture across the enterprise is what makes today’s most successful life sciences organizations stand out. Efficient operations are only possible when the processes underlying those activities are functional, and this type of reliable operation depends on acute visibility. That visibility is, in turn, reliant on clear, comprehensive maintenance and equipment reliability data. When equipment is down due to an unexpected failure, processes grind to a halt and costs skyrocket. 

For this reason, today’s most successful life sciences organizations are expanding their focus as they now place the same value on their maintenance and reliability data as their operational data. These teams are bui…

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Opentrons Flex flexes the power of AI in lab automation

Opentrons Flex [Image courtesy of Opentrons]

Queens-based lab automation company Opentrons has debuted its new Flex robot, with the aim of broadening access to advanced lab automation for life scientists. In a recent email interview, Opentrons CEO, Jonathan Brennan-Badal, highlighted that the Opentrons Flex robot is designed to offer a technically sophisticated, yet economically accessible automation platform for life scientists. The robot is designed to execute a variety of experiments, including those designed by large language models (LLMs) such as ChatGPT, he noted.

AI-generated automated protocols for life sciences

“Without wanting to be hyperbolic, LLMs will revolutionize how we design automated protocols in the life sciences,” Brennan-Badal explained. Opentrons’ compatibility with LLMs makes it possible to create “AI-generated automated protocols for every well-documented manual experiment in the scientif…

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Ready, set, analyze: Amazon Omics unveils new Ready2Run workflows

Amazon Web Services (AWS) recently announced a significant expansion of Amazon Omics at the annual AWS Life Sciences Executive Symposium in Boston. Amazon Omics, which the company introduced last year, helps life science organizations to store, query and analyze genomic, transcriptomic and other omics data.

Other similar tools include Qlucore Omics Explorer, Genospace, StrandOmics, Signals Translational and the publicly-funded academic-developed platform Galaxy.

Omics data encompass genomics, transcriptomics and other related fields, providing a comprehensive understanding of the genetic, transcriptional and functional elements of biological systems. Such data, instrumental for researchers studying biological systems, plays a vital role in modern drug discovery and development.

Amazon Omics introduces Ready2Run workflows

AWS aims to distinguish Amazon Omics in the marketplace through its comprehensive managed service approach and unique features such as …

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Unraveling the impact of FDORA and PREVENT Pandemics Acts on the life sciences

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As the world continues to grapple with global health challenges, the role of science and biotech law has taken center stage in shaping public health policy and innovation. The FDORA and PREVENT Pandemics Acts are poised to help shape the landscape.

In a recent interview, life sciences attorney Barbara Binzak Blumenfeld offers insights into significant recent legislative, judicial, and regulatory developments, including the planned end to the COVID-19 Public Health Emergency (PHE), the enactment of the Food and Drug Omnibus Reform Act (FDORA), and the Prepare for and Respond to Existing Viruses, Emerging New Threats, and Pandemics Act (PREVENT Pandemics Act).

With a Ph.D. in molecular biology, Blumenfeld is well-versed in the scientific, ethical, and legal issues that are pivotal in the life sciences. In a recent interview, she touched on the expiration of the COVID-19 Public Health…

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The ‘Iron man suit’ for QA Managers: Quality management software taps power of ChatGPT

Gone are the days when Microsoft’s animated paperclip, Clippy, popped up on your screen with helpful — and sometimes annoying — tips and tricks for your Word document. Now, the Israeli eQMS compliance provider Dot Compliance has launched what it hails as an industry-first electronic Quality Management System (eQMS) for life sciences with a prominent generative AI-enhanced digital assistant that could take intelligent assistance to new heights. “It’s not just ChatGPT. It’s many other algorithms that are evolving,” said Doron Sitbon, founder and CEO of the company.

Transforming quality assurance with AI-driven quality management software

Rather than Clippy, Sitbon uses another metaphor to illustrate the company’s ChatGPT-enhanced “Dottie” capabilities for its QMS Xpress quality management software. “I think of it as an Iron Man suit for a [quality assurance] manager,” Sitbon quipped. The Dottie digital assistan…

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Nvidia launches BioNeMo Cloud as a breakthrough AI service for drug discovery research

[Image courtesy of Nvidia]

During Nvidia’s (Nasdaq:NVDA) GTC event, the company introduced BioNeMo Cloud, a new component to their AI Foundations suite. This service, designed to streamline life sciences research, drug discovery and protein engineering, provides researchers with access to pretrained AI models, allowing for customization with proprietary data. Offered as a cloud service, BioNeMo Cloud enables accelerated drug discovery workflows, with multinational biotech company Amgen (Nasdaq:AMGN) and several startups already utilizing the platform.

BioNeMo Cloud features pretrained AI models for molecular biology, chemistry and molecular dynamics. The platform allows researchers to fine-tune models with proprietary data, executing AI model inference via web browsers or new cloud APIs. This accelerates drug discovery, molecular identification and protein 3D structure prediction.

Featured models in…

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What’s driving the natural language processing revolution in pharma and life sciences

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Pharmaceutical and life sciences companies are faced with a constant stream of new data flowing into often siloed information systems. About 80% of that information exists in unstructured text that is difficult to extract and use, despite its paramount importance in driving clinical and commercial outcomes.

As a result, these organizations find themselves increasingly overwhelmed with volumes of inaccessible data. At the same time, researchers and data scientists lack effective search tools to find the right information in this “big data” tsunami, causing them to miss opportunities to enhance patient safety, improve clinical trial design, identify previously undetected biomarkers and better understand the voice of the customer.

To overcome the limitations of time-consuming, manual searches through mountains of data, pharma and life sciences companies are looking to artificial…

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