The Oracle's Guide to High-Throughput Screening
Unraveling the Matrix of Biopharma to Accelerate Life-saving Medicines
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What’s inside:
High-throughput screening (HTS) is a method used by biopharma companies to rapidly test numerous compounds on biological targets to find potential drug candidates.
Three-Eyed Ravens are real. Oracles like Ricardo Macarron, Lorenz Mayr, David Swinney, and Andrew Hopkins accurately predicted the integration of HTS with computational techniques, miniaturization, and automation to transform drug discovery.
Current challenges in HTS include scattered data, manual data processes, and a lack of traceability, leading to delays, errors, and wasted resources.
Purpose-built scientific data clouds like TetraScience, designed specifically for scientific research and biopharma industries, address unique challenges and requirements of scientific data management compared to general-purpose data cloud services.
The future of HTS and biopharma will see increased adoption of purpose-built scientific data clouds, leading to automation, improved quality, and enhanced analytics, accelerating the discovery of life-saving medicines.
Unveiling the Past, Present, and Future of Drug Discovery & Development
Imagine a world where life-saving medicines are discovered in 3-5 years instead of today’s industry standard of 10, leaving no time for disease to claim its victims. This isn't a distant utopia, but a reality waiting to unfold. The biopharma industry is on the cusp of a revolution, one driven by the power of high-throughput screening (HTS) and scientific data. HTS is like a voracious reader, scanning through libraries of molecules to identify the one that holds the answer to our most pressing medical challenges.
High-throughput screening is an industrious method employed by biopharma companies to rapidly test the impact of countless compounds on biological targets, with the ultimate goal of finding potential drug candidates. Imagine a colossal molecular speed-dating event, where the most fruitful connections have the potential to change lives.
Over the past two decades, the field of HTS has made leaps and bounds. Three-Eyed Ravens like Ricardo Macarron (2011), Lorenz Mayr (2009), David Swinney (2011), and Andrew Hopkins (2004) saw the potential of integrating HTS with computational techniques, miniaturization, and automation to transform drug discovery. These real life oracles predicted that the use of ligand efficiency metrics and phenotypic screening would be crucial for identifying the next generation of life-saving drugs.
Yet, even the most extraordinary innovations have their hurdles and create new problems for researchers. Biopharma companies are currently wrestling with the challenges of scattered data, big data getting bigger, manual data processes, and a lack of traceability. Here are the facts:
Nearly 50% of scientific data today is neither FAIR nor sufficiently prepared and available for analysis and data science.
50+% of data scientists’ and scientists’ time is wasted on manual extraction and transformation tasks — impeding their ability to do higher-value AI/ML and advanced analysis to help bring new life-saving therapeutics to market.
1/3 of all data produced is in healthcare — From 2000 to 2020, the volume of information/data created, captured, copied, and consumed annually grew to 64.2 Zettabytes (ZB). Industry experts expect that number to grow to 181 ZB by 2025. To appreciate how much data that is, imagine 1 Megabyte (MB) being equal to the size of a Lego brick.
With 64.2 Zettabytes of Lego bricks, you could build 612,245 Empire State Buildings.
With 181 Zettabytes of Lego bricks, you could build 1,727,348 Empire State Buildings.
Biopharmas are cloud laggards and their HTS workflows are not equipped to handle the tsunami of information that’s heading their direction.
Today’s labs in biopharma look less like Tony Stark’s workshop, and much more closely resemble your college’s lab and library, with stacks of paper and harried researchers poring over details of their experiments by hand.
The outcome? Delays, errors, and squandered resources.
But the future is within reach, and the biopharma industry is poised to embrace the next evolution of HTS by looking at cloud technologies that are able to more efficiently process and make sense of experiment data. The fastest moving global biopharmas and biotechs are adopting purpose-built scientific data clouds (SDCs) with intentionally designed applications that catapult their drug discovery efforts into a new era of productivity. These early adopters benefit from automation, improved quality, and analytics converge to create a whirlwind of scientific progress.
Automation enables increased screening throughput, harmonizing data across diverse formats, and eradicating the need for error-prone manual processes. Picture a lab filled with digital assistants working tirelessly to ensure efficiency and precision.
Improved quality translates to the identification of high-quality leads, reduced false positives and negatives, and metadata enrichment that supplies essential scientific context. Imagine the wealth of a global biopharma’s collective knowledge accessible with a few keystrokes, empowering scientists to solve the mysteries of disease and therapy — like the Sherlock Holmes of drug discovery, effortlessly sifting through thousands of clues (molecules) to find the one that holds the answer (a potential drug candidate).
ELNs, LIMs, data science tools, and analytics software are starving for useable data. Reliable data pipelines connecting lab instrumentation to downstream applications empowers data scientists and scientists to spend their precious time making sense of experiment results instead of preparing data for analysis.
So what is the result of biopharma companies centralizing their scientific data in the cloud, making it FAIR (findable, accessible, interoperable, and reusable)?
A boost in experimental design and efficiency, accelerating the pace of discovery. Enabling drug discovery, development, quality control, and AI initiatives through having a vast, interconnected network of databases that provide real-time insights and predictions to fuel innovation.
The biopharma industry has a unique opportunity to significantly increase the value and impact of their scientific data. By harnessing the power of purpose-built cloud technology, we can expedite drug discovery and deliver life-saving medicines to patients with unprecedented speed. The race against disease is on, and it's time to claim victory.
What is a Purpose-built Scientific Data Cloud?
A purpose-built scientific data cloud like TetraScience differs from general-purpose data cloud services such as AWS, Azure, Databricks, Oracle, and Snowflake in several key ways. While both types of platforms offer cloud-based data storage and processing capabilities, they cater to different needs and industries.
Focus on specific use cases and industries: Purpose-built scientific data clouds, like TetraScience, are designed specifically for the scientific research and biopharma industries. They address unique challenges and requirements inherent in scientific data management, such as complex data formats, metadata, and domain-specific data processing. In contrast, general-purpose data cloud services like AWS, Azure, Oracle, and Snowflake cater to a broad range of industries and applications, without a specific focus on scientific research.
Domain-specific applications and tools: TetraScience provides applications and tools specifically designed for scientific workflows, such as high-throughput screening, protein purification, batch release and stability, and cell culture fermentation. These applications enable automation, data harmonization, and analytics tailored to scientific research. General-purpose data cloud services offer a wide range of applications and tools for various industries, but they may not have the same level of domain-specific features and capabilities as a purpose-built scientific data cloud.
Data management and interoperability: Scientific data clouds like TetraScience are built to handle the unique challenges of scientific data management, such as handling diverse and complex data formats, ensuring data interoperability, and maintaining metadata and scientific context. General-purpose data cloud services, while versatile, may not have the same level of built-in support for scientific data standards and formats, requiring additional customization and integration efforts.
Compliance and regulatory requirements: Purpose-built scientific data clouds are designed with compliance and regulatory requirements specific to the biopharma and scientific research industries in mind, such as FDA 21 CFR Part 11, Good Laboratory Practices (GLP), and Good Manufacturing Practices (GMP). General-purpose data cloud services may offer various compliance certifications, but they might not be tailored to the specific needs of the scientific research domain.
Community and ecosystem: A purpose-built scientific data cloud like TetraScience typically has a community and ecosystem of users, partners, and developers focused on the biopharma and scientific research industries. This creates a collaborative environment where domain-specific knowledge, best practices, and integrations are shared among stakeholders. General-purpose data cloud services, on the other hand, cater to a diverse range of industries and use cases, with a more heterogeneous ecosystem.
Scientific data clouds are specifically designed to address the unique challenges and requirements of the scientific research and biopharma industries, while general-purpose data cloud services offer more versatile solutions that cater to a broader range of industries and applications.
Biotech’s Still Breathing — Funding Announcements
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Aer Therapeutics operates a biopharmaceutical company that focuses on treating respiratory diseases with significant unmet needs, such as COPD, enabling researchers to advance the treatment paradigm for mucus-mediated lung diseases.
View on: LinkedIn.com
New Funding Raised: $36M, Series A
Round Investors: Hatteras Venture Partners, Canaan Partners, OrbiMed
Growth Focus: Advance the development of a novel drug to help COPD patients breathe better.
Press: PR Newswire, Silicon Republic
HQ: San Francisco, CA
Industry: Pharmaceutical Manufacturing
Employee Count: 8
CTRL Therapeutics is a biotechnology company that develops a next-generation cell therapy platform with the potential to unlock a new frontier in cell therapy for solid tumors, enabling doctors to evaluate protein expression on tumor cells with non-invasive procedures.
View on: LinkedIn.com
New Funding Raised: $10M, Seed
Round Investors: General Catalyst, FACIT (lead), Intermountain Healthcare
Growth Focus: Enable further optimization and validation of its proprietary technology platform; expand its team to support its mission to deliver curative therapies for all individuals living with cancer.
Press: BioSpace, PR Newswire
HQ: Chicago, IL + Toronto, Canada
Industry: Biotechnology Research
Employee Count: 9
Tally Health operates a consumer biotechnology company that provides proprietary diagnostic tests and individualized interventions that give members science-backed tools toward longevity, aiming to improve healthspan and extend longevity at the cellular level with TallyAge epigenetic age tests, and personalized lifestyle recommendations.
View on: LinkedIn.com
New Funding Raised: $10M, Seed
Round Investors: Forerunner Ventures (lead), L Catterto, G9 Ventures, Second Sight Ventures
Growth Focus: Expand research and development of new products; launch additional features; support new technology integrations to allow users to integrate their TallyAge feedback.
Press: PR Newswire, FinSME
HQ: New York, NY
Industry: Biotechnology Research
Employee Count: 17 (55% increase in last 6 months)
HeartFlow has developed a cardiac diagnostic platform that uses data from standard CT scans to create a personalized 3D model of arteries and analyzes the impact of blockages on blood flow, enabling clinicians to treat patients with significant coronary artery disease while reducing unnecessary invasive testing.
View on: LinkedIn.com
New Funding Raised: $215M, Series F
Round Investors: Bain Capital Life Sciences (lead), Baillie Gifford, Janus Henderson Investors, Hayfin Capital Management
Growth Focus: Extend the demand for its commercial products; support clinical evidence and advance its comprehensive product portfolio.
Press: FinSMEs, HeartFlow Press
HQ: Mountain View, CA
Industry: Medical Equipment Manufacturing
Employee Count: 477
Lookin’ Around?
Dream Jobs in Science & Companies Hiring:
Gilead — Executive Director, Head of US Digital Strategy and Operations (View Job Post)
Promega — Data Engineer (View Here)
Plexium — Associate Director, Bioinformatics (View Here)
Lonza — Head of Operations mRNA (View Here)
TetraScience — Chief Data Architect (View Here)
Share your company’s job openings with our community of 2000+ life science managers, directors, VPs, and executives by linking your career page in the comments section. We’ll feature your open positions in next week’s letter.1
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