Data in Biotech

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

This week on Data in Biotech, we’re joined by Mo Jain, the Founder and CEO of Sapient, a biomarker discovery organization that enables biopharma sponsors to go beyond the genome to accelerate precision drug development.  Mo talks us through his personal journey into the world of science, from school to working in academia to founding his business, Sapient. He explains how and why Sapient first started and the evolution of the high-throughput mass-spectrometry service it provides to the biopharmaceutical sector.  Together with our host Ross, they explore the technology that’s allowed scientists to explore one's medical history like never before via metabolome, lipidome, and proteome analysis. They look at how the technology developed to allow data testing to go from running twenty tests per blood sample to twenty thousand. How have Sapient built themselves up to such a renowned status in biopharmaceuticals for large-scale data projects? They discuss Sapient’s process when working with clients on genome projects. We learn about Sapient’s relationship with their clients, how they understand the targets and aims of each project, why they put so much importance on proprietary database management and quality control, and Sapient’s three pillars for high quality data discovery. Finally, Mo takes the opportunity to give us his insights on the future of biomarker discovery and mass-spectrometry technology - and how AI and Machine Learning are leading to enhanced data quality and quantity.  Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Chapter Markers [1:33] Introduction to Mo Jain, his journey, Genomics, and Sapient’s use of Genomics data to accelerate Medicine and Drug Development  [6:50] The types of data generated at Sapient via metabolome, lipidome & proteome, and why that data is generated [12:30] How Sapient generates this data at scale, via specialist mass-spectrometry technology  [14:48] The problems Sapient can solve for pharma and biotech companies with this data [21:03] Sapient as a service company: the questions they’re asked by pharmaceutical businesses, why they come to Sapient, and Sapient’s process for answering those questions.  [26:23] computational frameworks and data handling side of things, and how the team interact with the client [29:59] Proprietary database development and quality control  [35:27] The future of biomarker discovery and mass-spectrometry technology, and how AI and Machine Learning are leading the way at Sapient

This week on Data in Biotech, we are joined by Parul Bordia Doshi, Chief Data Officer at Cellarity, a company that is leveraging data science to challenge traditional approaches to drug discovery.  Parul kicks off the conversation by explaining Cellarity’s mission and how it is using generative AI and single-cell multiomics to design therapies that target the entire cellular system, rather than focusing on single molecular targets. She gives insight into the functionality of Cellarity Maps, the company’s cutting-edge visualization tool that maps the progression of disease states and bridges the gap between biologists and computational scientists.  Along with host Ross Katz, Parul walks through some of the big challenges facing Chief Data Officers, particularly for biotech organizations with data-centric propositions. She emphasizes the importance of robust data frameworks for validating and standardizing complex data sets, and looks at some of the practical approaches that ensure data scientists can derive the maximum amount of value from all available data.  They discuss what data science teams look like within Cellarity, including the unique way the company incorporates human intervention into its processes. Parul also emphasizes the benefits that come through hiring multilingual, multidisciplinary teams and putting a strong focus on collaboration.  Finally, we get Parul’s take on the future of data science for drug discovery, plus a look at Cellarity’s ongoing collaboration with Novo Nordisk on the development of novel therapeutics.  Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Chapter Markers [1:45] Introduction to Parul, her career journey, and Cellarity’s approach to drug discovery. [5:47] The life cycle of data at Cellarity from collection to how it is used by the organization.  [7:45] How the Cellarity Maps visualization tool is used to show the progression of disease states [9:05] The role of a Chief Data Officer in aligning an organization’s data strategy with its company mission.   [11:46] The benefits of collaboration and multidisciplinary, cross-functional teams to drive innovation.  [14:53] Cellarity's end-to-end discovery process; including how it uses generative AI, contrastive learning techniques, and visualization tools.  [19:42] The role of humans vs the role of machines in scientific processes.  [23:05] Developing and validating models, including goal setting, benchmarking, and the need for collaboration between data teams and ML scientists. [30:58] Generating and managing massive amounts of data, ensuring quality, and maximizing the value extracted. [37:08] The future of data science for drug discovery, including Cellarity’s collaboration with Novo Nordisk to discover and develop a novel treatment for MASH.

Ryan Mork, Director of Data Science at Evozyne, shares insights on using generative AI for therapeutic protein design. He discusses how Evozyne combines large language models with evolutionary history to create new biomolecules. Ryan highlights the challenges of model evaluation and the critical collaboration between wet lab and data science teams. He also envisions the future of AI in biotechnology, emphasizing the role of human insight in advancing drug development and the ongoing innovative projects at Evozyne.

This week on Data in Biotech, Ross is joined by Jonathan Eads, VP of Engineering at genomics intelligence company Genomenon, to discuss how his work supports the company’s mission to make genomic evidence actionable. Jonathan explains his current role leading the teams focused on clinical engineering, curation engineering, platform development and overseeing Genomenon’s data science and AI efforts. He gives insight into how Genomenon’s software works to scan genomics literature and index genetic variants, providing critical evidence-based guidance for those working across biotech, pharmaceutical, and medical disciplines. Jonathan outlines the issues with inconsistent genetic data, variant nomenclature and extracting genetic variants from unstructured text, before explaining how human curators are essential to ensure accuracy of output. Jonathan and Ross discuss the opportunities and limitations that come with using AI and natural language processing (NLP) techniques for genetic variant analysis. Jonathan lays out the process of developing robust validation datasets and fine-tuning AI models to handle issues like syntax anomalies and outlines the need to balance the short-term need for data quality with the long-term goal of advancing the platform’s AI and automation capabilities. We hear notable success stories of how Genomenon’s platform is being used to accelerate variant interpretation, disease diagnosis, and precision medicine development. Finally, Ross gets Jonathan’s take on the future of genomics intelligence, including the potential of end-to-end linkage of information from variants all the way out to patient populations. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Chapter Markers [1:50] Introduction to Jonathan and his academic and career background. [5:14] What Genomenon’s mission to ‘make genomic evidence actionable’ looks like in practice. [14:48] The limitations of how scientists and doctors have historically been able to use literature to understand genetic variants. [16:08] Challenges with nomenclature and indexing and how this impacts on access to information.  [18:11] Extracting genetic variants from scientific publications into a structured, searchable index. [22:04] Using a combination of software processes and human curation for accurate research outputs. [24:57] Building high functionality, complex, and accurate software processes to analyze genomic literature. [29:45] Dealing with the challenges of AI and the role of human curators for the accuracy of genetic variant classification.   [34:37] Managing the trade-off between short-term needs for improved data and long-term goals for automation and AI development. [38:39] Success stories using the Genomenon platform including making an FDA case and diagnosing rare disease.  [41:55] Predictions for future advancements in literature search for genetic variant analysis. [43:21] The potential impact of Genomenon’s acquisition of Jack's Clinical Knowledge Base.

Amy Gamber, VP of Manufacturing at Atara Biotherapeutics, dives into the world of allogeneic T-cell immunotherapy, highlighting the groundbreaking Tabulecosel treatment. She discusses the complexities of scaling manufacturing, addressing donor variability, and efficient inventory management. The conversation reveals the vital role of data analytics in enhancing product quality, continuous quality monitoring, and predictive modeling. Gamber also explores the future of cell therapy manufacturing, emphasizing the potential of AI and the importance of standardizing data for improved patient outcomes.