Data in Biotech

In this episode of Data In Biotech, Ross Katz interviews Naren Tallapragada, CEO and Co-founder of Tessel Bio, about his background in electrical engineering and physics and how personal circumstances led to him pivoting his focus to enter biotech and start Tessel Bio. Naren breaks down Tessel Bio’s unique approach to drug discovery, which involves "reverse engineering" chronic diseases. Instead of starting with a potential drug and testing its effects, they begin with a clear picture of the disease itself—specifically, how it appears and behaves in the body. A key part of their method is using human organoids—tiny, lab-grown versions of human tissues—to closely replicate real diseases and see how different treatments interact with them. This “small data” approach is made substantially more efficient with the addition of active learning. Join us for a fascinating conversation about Tessel Bio’s approach to finding cures for chronic diseases that impact hundreds of thousands of people every day.  ​​What You'll Learn in This Episode: Naren’s journey into biotech and personal motivationUnderstanding Tessel Bio's approach to drug discoveryThe role of organoids in disease modelingData gathering and target identification strategiesActive learning in drug discovery at Tessel Bio Connect with Our Guest: Sponsor: CorrDyn, a data consultancyFind out more about Tessel Bio Connect with Naren on LinkedIn Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode!

Alexander Junge, Co-founder and CTO of amass, brings a wealth of bioinformatics experience from Novo Nordisk and Cordy AI. He discusses how amass leverages AI to help researchers navigate the complexities of scientific data. Alex dives into the challenges of information retrieval and emphasizes the importance of design and user experience in scientific tools. He shares insights on collaboration between AI and human expertise, and predicts the transformative impact of AI on scientific discovery in the coming decade.

Dipen Sangurdekar, VP of Data Sciences at KSQ Therapeutics, dives into the innovative fusion of data science with gene and cell therapy. He shares his transition from engineering to oncology, highlighting the integration of computational techniques in therapeutic design. The discussion covers advancements in CRISPR technology, emphasizing its potential for personalized medicine and the challenges of patient variability. Dipen stresses the importance of collaboration between data scientists and biologists to refine therapeutic models, underscoring the critical role of data in drug development.

This week on Data in Biotech, we welcome Timothy Jenkins, the Head of Data Science and Associate Professor at DTU Bioengineering, a leading scientific community dedicated to advancing areas of biotechnology, food technology, and health through innovative teaching and research.  Timothy starts the conversation by walking us through his background and early career beginnings, from the first time he expressed interest in zoology and venomous snakes to now leading a research group focused on AI-guided drug discovery for snake antivenom. He and our host, Ross Katz, dive into one of DTU’s most exciting recent publications about "de novo" designed proteins to neutralize lethal snake venom toxins.  Inspired by Nobel Prize winner David Baker’s groundbreaking paper and in collaboration with the Baker Lab on computational design methodology, this project holds great promise in therapeutic discovery and drug development. Tim explains how computational protein design and protein structure prediction are revolutionizing his field, highlighting compelling examples and milestones from his research on antivenom. He also provides an overview of the process used to discover new antivenoms, including the sourcing of biological data, model training, and integration of experimental feedback. Finally, we get Tim’s perspective on the future of AI-powered therapeutic discovery, and his take on the integration of quantum computing into protein design. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Useful Links Nature - De novo designed proteins neutralize lethal snake venom toxins Timothy’s LinkedIn DTU Bioengineering Website UW Institute for Protein Design “de novo” designed proteins project Information on Nobel Prize winner David Baker

Fred Manby, Co-Founder and CTO of Iambic Therapeutics, shares fascinating insights into how AI is transforming drug discovery. He highlights the impressive capabilities of the Enchant multi-modal transformer model and its edge over traditional methods. The conversation also touches on integrating diverse data types, the significance of user-friendly interfaces for scientists, and the role of data-driven strategies in oncology drug development. Fred emphasizes the exciting future these innovations hold for the biotech landscape.

In this episode of Data in Biotech, Ross Katz explores the transformative role of AI in microscopic imaging and life sciences with Ilya Goldberg, Chief Science Officer, and Reese Findley, an AI Data Scientist at ViQi.  They discuss the cutting-edge applications of AI in automating high-content screening (HCS), enabling more efficient drug discovery, and unraveling complex biological processes. From streamlining time-course analysis to detecting off-target effects in drug compounds, ViQi’s tools are revolutionizing how scientists approach image-based data. Key Highlights: How AI-powered "eyes" analyze thousands of microscopy images to identify phenotypic changes.The benefits of using brightfield imaging and live-cell dyes to capture time-course data efficiently.Real-world applications of AutoHCS for high-content screening, including drug efficacy studies, stem cell differentiation tracking, and phenotypic clustering for toxicity detection.Insights into how automation empowers researchers by reducing repetitive tasks and enhancing focus on innovation.The broader implications of AI in medicine, from radiology to pathology, and how it's redefining workflows for specialists. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Learn more about who was featured on the podcast: CorrDyn, an enterprise data specialist that enables excellent companies to make smarter strategic decisions, at www.corrdyn.com ViQi – helping virologists accelerate, automate and simplify infectivity assays:  www.viqiai.com  Connect with Reese and Ilya on LinkedIn

Reflecting on a year of insights, the discussion highlights the importance of predictive models grounded in real-world experimentation. It tackles biases in model evaluation and the need for balancing computational methods with experimental validation. Looking ahead to 2025, the potential democratization of biotech data is explored, envisioning decentralized collaboration for disease research. The impact of emerging technologies like foundation models and advanced imaging on drug discovery is examined. Lastly, the hosts emphasize curiosity and community engagement as essential for growth in biotech.

Dave Johnson, CEO and co-founder of Dash Bio, has a stellar background, previously serving as Chief Data and AI Officer at Moderna. He shares insights from his time at Moderna during the rapid development of mRNA technology for COVID-19 vaccines. Dave highlights significant inefficiencies in drug development, advocating for automation and standardization. He emphasizes the need to shift perceptions in lab automation and discusses Dash Bio's mission to optimize clinical bioanalysis for a more efficient drug development process.

This week, Chitrang Dave, Global Head of Enterprise Data & Analytics at Edwards Lifesciences, joins us to discuss the transformative power of real-time data, AI, and collaboration in medical device manufacturing and support.  He and host, Ross Katz, dive into how real-time data from IoT devices is reshaping quality assurance in medtech and what the future holds for medtech as big tech players like Apple and Meta enter the healthcare arena.  Together, they discuss everything from AI-powered patient identification to the integration of consumer wearables with FDA-approved medical devices. Tune in to hear how collaboration, innovation, and cutting-edge technology are improving patient outcomes and revolutionizing healthcare. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Chapter Markers [01:36] Chitrang shares the experience that led him to work at leading data and analytics organizations and what work there is to be done [04:09] Chitrang highlights the role of IoT devices in medical device manufacturing, where real-time data can drive automation and improve quality assurance [06:25] What is driving innovation right now in research and development, and how companies like Apple are disrupting the medical device space  [09:23] Chitrang talks about how connectivity in devices and the expectation of the user to be able to use an intuitive interface are evolving into more real-time medical device technology  [11:47] The importance of keeping patient data private between the patient and the practitioner while using anonymized data to create solutions and identify patterns in health  [13:25] Using data to create a complete picture of the patient in order to make their life easier  [14:20] Chitrang discusses the challenge of manufacturing medical devices when there are issues with raw materials  [16:30] Chitrang discusses the potential for automation for real-time data in manufacturing [19:17] Ross and Chitrang discuss the value of having comprehensive data to personalize treatments and ensure timely responses, especially for scenarios where early detection of Alzheimer’s can save trillions of dollars [21:27] Chitrang mentions significant collaborations, such as the Cancer AI Alliance, where tech giants like AWS, Microsoft, NVIDIA, and Deloitte are working together to address critical problems in healthcare [27:10] How real-time data from medical devices could improve patient outcomes, stakeholder coordination and future trends  [28:29] Closing thoughts and where to find Chitrang Dave online  Download CorrDyn’s latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.” Find the white paper online at: https://connect.corrdyn.com/biotech-ml

This week on Data in Biotech, we’re joined by Martin Permin, the co-founder of Invert, a company that builds software that automates bioprocessing. Martin talks us through his own unique journey into biotech - starting from a role at Airbnb - through to co-founding Invert. Invert helps users grab data from their instruments, map out their individual processes, clean up the data for analysis, and look for ways to speed up the “mundane” data cleaning tasks that often take up the majority of one’s time.  With our host, Ross Katz, Martin tells us the statistical problems Invert works to solve for their different types of clients: biologic development labs, full-scale manufacturers, and CDMOs. While they all approach data cleaning and analysis from different directions, Invert can see how clients use the system and look for ways to automate repeated processes to help them save time. They discuss implementing Invert into the Design, Build, Test, Learn Loop and why Invert is invested in reducing how many times one has to go around that loop. Martin explains how his company looks to reduce the risk in tech transfer in both directions, in terms of time and labor.  Then, the conversation moves to ML/AI, where Martin tells us how a lot of his customers are finding that the bottlenecks in their processes aren’t where they thought they were, thanks to using Invert for process automation.  Finally, Martin gives us his opinions on the future trends around the corner for the biotech industry - and how Invert is preparing themselves and their customers.  Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Chapter Markers [1:29] Introduction to Martin and his journey into biotech [4:10] Introduction to Invert - the what and why [6:47] How Invert is implemented into a customer’s workflow [11:36] The problems Invert can solve [16:16] Design > build > test > learn… and how Invert facilitates that [20:00] CDMOs and contractors - how Invert works with their different customers [22:15] The use of ML/AI in bio-processing [33:40] Trends in Biotech that will influence Invert over the long-term