Episode 21: The Future Lab — Innovations in Digital TransformationDiscover 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 DevelopmentIn 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 DetectionWhen 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 MedicinePrecision 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, insitroCopyright © 2022 Bio-Rad Laboratories, Inc.All rights reserved.

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, UCBCopyright © 2022 Bio-Rad Laboratories, Inc. All rights reserved

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 SinaiCopyright © 2022 Bio-Rad Laboratories, Inc. All rights reserved.

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.

Synthetic biology is the design, testing, and construction of new, standardized biological parts and devices, and programming them to do something productive. Significant medical breakthroughs are already happening as a result of advances in synthetic biology. From antiviral treatment and immune cell engineering to biomonitoring, the applications of synthetic biology are endless, creating new opportunities for innovative informatics. In this episode, we discuss revolutionary applications of synthetic biology in biopharma. Guest: Mark Charbonneau, PhD, Director and Head of Quantitative Biology, Synlogic

Over the past three decades, since the first monoclonal antibody was approved for clinical use in the U.S., antibody techniques and technologies have advanced radically. However, the supporting informatics have evolved at a much slower pace. There are significant informatic challenges throughout the research process, from concept to regulatory submission. How can a holistic informatics strategy support gaining clearer insights into scientific operations, make smarter decisions about their campaigns, and ultimately bring therapeutics to the clinic faster? Guest: Colby Souders, PhD, Chief Scientific Officer, Abveris

Data liquidity, the ability of data to flow easily and securely, provides tremendous value for biopharma researchers. Open access is paving the way for increased collaboration and discovery. However, additional initiatives are needed to further promote the management and sharing of scientific data and ensure that information is quickly available to researchers. This episode highlights the needs, solutions, and challenges of ensuring interoperability of research data throughout the data lifecycle.

Increasingly, pharma is looking at partnerships and collaborations to increase the velocity of drug discovery and development. Common data sharing platforms are not built to meet lab-specific requirements. In addition to discussing the necessity of simple and secure collaboration, listeners will learn about the dangers of not using proper tools.

As biopharma research and development continues to transform digitally, the industry as a whole struggles with guidance on how data and relevant metadata are captured, managed, and shared. Typically experimental data are siloed,

Sequencing is used to identify novel drug targets and genetically stratifying clinical trial patients, improving the power of precision medicine. However, the global disparity of genomic technologies is increasing. How will this affect drug development, particularly for non-European ethnicities? Why is it important to have a global context for targeted therapies?

Life science companies are looking to modernize their research environments and build the Lab of the Future. From early discovery to preclinical research, how can technology help automate the lab and accelerate discoveries?

What are the theories, algorithmic principles, and limitations of active learning? What are some current applications of active learning within BioPharma drug screening and optimization? How can active learning accelerate multi-dimensional drug discove...

What is the vision of interactive data exploration beyond enriching visualizations and facilitating the integration of data? Within the context of research, what are the challenges and opportunities in building next-generation visual analytics?

Artificial Intelligence (AI) and Machine Learning (ML) are big topics getting lots of attention within BioPharma. What is the role of AI/ML in genomics? How can we separate the hype of AI/ML from reality? Listen in to find out this and more.