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 motivation
• Understanding Tessel Bio's approach to drug discovery
• The role of organoids in disease modeling
• Data gathering and target identification strategies
• Active learning in drug discovery at Tessel Bio

Connect with Our Guest:

• Sponsor: CorrDyn, a data consultancy
• Find 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!

In this episode of the Data and Biotech Podcast, host Ross Katz sits down with Alexander Junge, Co-Founder and CTO of amass, to break down how AI is reshaping scientific research in life sciences. 

Alex walks us through how his platform is creating professional tools for researchers, biotech companies, and venture firms to navigate the (rapidly!) growing breadth and depth of scientific knowledge by leveraging artificial intelligence - and what this will look like in the future. 

Alex also shares insights into his company’s work with Nordic Bio Ventures and how amass delivers reliability and builds trust in its platform.

​​What You'll Learn in This Episode:

• What challenges researchers face in formulating questions and retrieving relevant answers from the breadth of scientific knowledge.
• The importance of design and user experience in life sciences, and how the company adopts a rigorous approach to information retrieval.
• How amass helps life sciences professionals navigate complex scientific domains by building trust in answers generated by AI.
• A real-world case study of how amass is assisting Nordic BioVentures a venture firm to build a biotech company.
• Alex’s predictions for AI's impact on scientific discovery over the next decade.

Connect with Our Guest:

• Sponsor: CorrDyn, a data consultancy
• Find out more about amass
• Connect with Alex 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!

In this episode of Data in Biotech, Ross sits down with Dipen Sangurdekar, VP of Data Sciences at KSQ Therapeutics, to discuss the role of data-driven approaches in therapeutic design and development. The conversation explores the intersection of computational biology, machine learning, and bioinformatics in advancing personalized medicine and improving patient outcomes.

Dipen shares his journey in the industry, emphasizing the importance of integrating data science with biological research and the challenges associated with working in the rapidly evolving field of cell therapies. From hypothesis-driven research to leveraging multimodal data for actionable insights, this episode explores the nuances of using statistical methods and AI to enhance drug development.

Key Takeaways:

• Successful data science in therapeutics requires a deep understanding of both statistical methods and biological processes.
• High-dimensional but low-sample-size data demands a guided hypothesis-driven approach to avoid false positives.
• Data integration and collaboration between computational and biological teams are critical for generating meaningful insights.
• Emerging AI and machine learning tools are enhancing productivity but must be carefully applied in therapeutic research.
• Picking a problem you’re passionate about and going deep into it is crucial for long-term success in the field.

Connect with Our Guest:

• Sponsor: CorrDyn, a data consultancy
• Find out more about KSQ Therapeutics 
• Connect with Dipen 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!

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

In this episode, Ross Katz sits down with Fred Manby, Co-Founder and Chief Technology Officer of Iambic Therapeutics, to explore how cutting-edge AI technologies are reshaping the landscape of drug discovery. From building advanced machine learning platforms to designing user-friendly interfaces for scientists, Fred shares insights into the company’s approach to tackling some of the biggest challenges in biotech.

Fred dives into the unique capabilities of Iambic’s Enchant multi-modal transformer model, its differentiation from other biological foundation models, and the importance of aligning model architecture with data creation and acquisition in modern drug discovery. We also discuss Iambic’s data-driven approach to developing oncology drugs, the exciting possibilities of incorporating new modalities like imaging, and the recent breakthroughs in protein-ligand structure prediction with NeuralPLexer3. 

Highlights:

• Enchant Multi-Modal Transformer Model: How it systematically surpasses state-of-the-art molecular AI technologies.
• Uncertainty Quantification: The role of probabilities and predictions in streamlining drug discovery.

Interface Design for Scientists: Iambic’s approach to integration between AI platforms and experimental workflows.

Connect with Our Guest:

• Sponsor: CorrDyn, a data consultancy
• Find out more about Iambic’s work on their website 

Connect with Fred Manby on LinkedIn

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

In this episode of Data in Biotech, Ross Katz reflects on what he’s learned from one year of hosting the podcast. Diving deep into the intersection of data science and biotechnology, this episode covers topics like:

• The need for predictive models in biotech that are grounded in real-world experimentation.
• The challenges of biases in model evaluation and designing experiments that maximize collecting information for iterative improvements.
• The balance between leveraging computational methods and validating insights through experimental data. 

As we look to 2025, Ross shares his vision of the emerging democratization of the biotech data ecosystem by making domain knowledge, datasets and tools more accessible. He discusses the possibility of a future where decentralized collaboration, akin to open-source software projects, can tackle specific diseases through computational pipelines and cloud labs, enabling experiments without the need for costly infrastructure. Or where emerging trends like foundation models and ensemble modeling in drug discovery, cell and gene therapy, and the role of new data from advanced imaging and assay technologies can be unlocked to create novel insights.

Finally, he invites regular listeners to contribute ideas, guest suggestions and resources as we build community and embrace more curiosity and openness. 

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

In this episode of Data in Biotech, Ross Katz sits down with Dave Johnson, CEO and co-founder of Dash Bio, a next-gen drug development services company with a mission to revolutionize clinical bioanalysis and streamline drug development.

Dave begins the episode by taking us back to the early research days in Moderna, where he helped lay the groundwork for mRNA technology, which later enabled the development of a vaccine for COVID-19 at unprecedented speed. As he explains, this automated work and pre-built systems ultimately played a central role in responding to urgent health challenges.

He also shares his firsthand experience of working in a rapidly scaling pharma company, discussing the potential challenges that arose along the way and the lessons he learned to overcome them.

Dave then proceeds to highlight the most significant insufficiencies in drug development—particularly the lack of industrialization and standardization. He explains how Dash Bio aims to address these issues, focusing on clinical bioanalysis now and expanding to broader standardization later. The goal is ultimately to develop a more efficient, high-quality end-to-end system and improve the overall efficacy of the drug development process.

Finally, Dave and Ross discuss the misconceptions surrounding lab automation and emphasize the need for a shift of perspective within the drug development space. They also touch upon Dave’s vision for the future of Dash Bio, plus his advice for aspiring biotech data leaders eager to contribute to industry transformation. 

Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences.

Chapter Markers:

[1:38] Introduction to Dave Johnson and his career journey from Moderna to founding a next-gen drug development company

[2:57] Establishing mRNA technology groundwork in Moderna

[4:36] The challenges of scaling up COVID-19 vaccine development

[7:55] How rapid company growth impacts the organizational structure and engaging models

[11:03] The role of AI, automation, and machine learning in drug development

[12:45] Addressing the most significant insufficiencies in drug development and potential solutions

[16:31] The need for standardization and automation in drug development

[18:04] Current focus of Dash Bio on clinical bioanalysis

[19:37] The misconceptions surrounding lab automation and the need for a shift of perspective within the drug development space

[22:33] Dave’s vision for the future of Dash Bio and streamlining drug development

[25:16] The current state of lab automation

[27:41] The role of experimentation in Dash Bio's approach

[29:47] Advice for aspiring data scientists and leaders in the biotech sector

Useful Links

Dave Johnson LinkedIn

Dash Bio Website

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