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Episode 21: The Future Lab — Innovations in Digital Transformation

Discover how the future biopharma lab will enable seamless collaborations and data-driven insights, catalyzing research advancements. Equipped to integrate data streams, automate repetitive tasks, and accelerate experimentation, the future lab promises transformative impact. Technologies — from AI-driven analytics to high-throughput screening platforms — will revolutionize how scientists explore drug candidates and unravel disease mechanisms faster and more efficiently. Listen to this episode to learn how the future lab will facilitate digital innovations and transform biopharma R&D. 

Guest: Asha D'Souza, PhD, CEO, Ashtrix, Inc.

Data silos and challenges around data fragmentation and interoperability hinder holistic insights and innovation in research and development. Specifically, these challenges include retrieving, parsing, and cleansing data from various sources and formats, ensuring data security and privacy. However, at the center of data interoperability is the role of the patient. The patient brings data together and provides potential for transformative insights and accelerated innovation through a unified approach to data management. With a comprehensive strategy that integrates genomic, phenotypic, clinical, and device data, enhancing drug discovery and fostering innovation can be expedited, effectively bridging the existing data divide.

Guest: Ardy Arianpour, CEO & Cofounder, SEQSTER

Episode 19: Reimagining Cancer Drug Development

In this episode, we discuss the way cancer drug development can be reimagined using AI and machine learning. Traditional drug development faces a high failure rate, highlighting the importance of understanding a drug's mechanism of action to improve success rates. AI and computational models that analyze data from various modalities, such as genomics and clinical information, can identify novel targets and enhance precision medicine. Advancements in data availability, especially in the clinic, and a deeper biological understanding are fundamental to revolutionize future drug discovery.

Guest: David Li, Co-Founder and CEO, Meliora Therapeutics, Inc.

Microbial drug discovery holds immense promise in revolutionizing healthcare. Although fungi and other microbes have long been a source of natural compounds with therapeutic potential, advancements in technology have made the search for new candidates easier than ever. By harnessing the power of artificial intelligence and machine learning, researchers can rapidly explore the rich genetic diversity of fungi and predict therapeutic potential. This convergence of science and technology will undoubtedly drive the future of microbial drug discovery, leading to the development of groundbreaking treatments for various diseases.

Guest: Karen Wong, PhD, Computational Biologist, Hexagon Bio

Episode 17: Biomarker Identification for Early Disease Detection

When diseases such as cancers are detected in their early stages, before they have spread, the overall survival rate is significantly higher than when diagnosed in later stages. Developing biomarker tests that detect early-stage tumors is complex and difficult, but quite rewarding, leading to improved characterization, treatment, and management of cancer. Determining which types of data (genomic, proteomic, etc.) are most informative is a major challenge requiring extensive research and discovery. In this episode, we discuss the need for early detection tools, methods for developing them, and some of the informatics challenges, including algorithm and model development, handling large volumes of data from multiple sources, and data integration.

Guest: Peter Meintjes, PhD, CEO, Pacific Edge, Ltd. 

Episode 16: Digital Precision Medicine

Precision medicine is a multifaceted approach to designing and delivering individualized patient treatments. Using multi-omic, environmental, lifestyle, and other data, researchers aim to more accurately develop treatment and prevention strategies tailored to each unique patient. The promise of precision medicine is huge, but it also creates new challenges around data capture, storage, computation, and creation of algorithms for prediction and analysis. In this episode, we discuss the evolution of precision medicine and some ways these challenges are and can be addressed, including blockchain, remote patient monitoring, and strategic data integration.

Guest: Kumar Bala, Former Head of Digitalomics Strategy, Oracle

Traditionally, drug targets are found by scouring scientific publications for insights into molecular pathways or known causative genetic variants, linked to disease. The failure rate of drug candidates in the clinic, even in relatively late-stage clinical trials, is quite high and is extremely costly. Fundamentally, finding better targets will lead to development of better medicines. In this episode, we discuss how artificial intelligence (AI) and the increasing availability of complex biological datasets can be leveraged to identify molecular targets. Machine learning models trained on large amounts of data allow researchers to differentiate between states or conditions more specifically to predict disease-relevant targets.

Guest: Avantika Lal, PhD, Senior Genomic Data Scientist, insitro

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Digital therapeutics are software-driven, evidence-based products used to prevent, manage, or treat a medical disease or disorder. Digital therapeutics enable patients to be more aware of and play a more active role in managing their health and can improve access for patients for whom visiting a clinician is challenging. This significantly improves health outcomes and reduces healthcare demands as compared to more traditional interventions alone. Digital therapeutics are expected to grow dramatically over the next few years, but significant challenges around regulation and adoption remain. Listen in as we discuss the current state of the industry and where this technology may take us in the future.

Guest: Emily Lewis, MS, CCRP, Global Digital Transformation Lead, Neurology, UCB

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Personalized medicine is critical to the future of our healthcare and wellness. Using vast sets of clinical, genomic, proteomic, and other patient data, potentially lifechanging treatments can be discovered and delivered to patients. However, implementation of personalized medicine faces unique challenges, partly because it is focused on individuals, each with their own unique genetics and history, whereas clinical trials tend to average across populations. What changes are needed for personalized medicine to truly become a reality? In this episode, we discuss some of the biggest obstacles and what is needed to catalyze advances in personalized medicine.

Guest: Rong Chen, PhD, Assistant Professor, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai

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High-throughput technologies have triggered a surge in scientific data generation and consumption, but managing these volumes of data is challenging. Listen as we discuss data fabric, an emerging solution that connects data sources in one environment.