Welcome to Talking Techniques! In this Podcast BioTechniques Digital Editor Tristan Free, interviews researchers at the forefront of their fields about the latest breakthroughs, controversies and conversations in the life sciences. From CRISPR to COVID-19, organoids to the microbiome, this podcast will explore the latest developments in the lab and interesting applications of techniques, while trying to determine how we can drive science forward in progressive and inventive ways.<hr><p style='color:grey; font-size:0.75em;'> Hosted on Acast. See <a style='color:grey;' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for more information.</p>
Talking Techniques
Claim This Podcastby BioTechniques
5.0(1 reviews)
68 episodes
Updated Weekly
Accepts GuestsHas SponsorsLocation 🇬🇧
56
Podcast Authority
BetaFairBased on show quality, social media presence, reviews, charts, and more
Quality79
Social0
YouTube0
Engagement81
Podcast Overview
Welcome to Talking Techniques! In this Podcast BioTechniques Digital Editor Tristan Free, interviews researchers at the forefront of their fields about the latest breakthroughs, controversies and conversations in the life sciences. From CRISPR to COVID-19, organoids to the microbiome, this podcast will explore the latest developments in the lab and interesting applications of techniques, while trying to determine how we can drive science forward in progressive and inventive ways.<hr><p style='color:grey; font-size:0.75em;'> Hosted on Acast. See <a style='color:grey;' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for more information.</p>
Language
🇺🇲
Publishing Since
8/7/2020
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56
Podcast Authority
BetaFairBased on show quality, social media presence, reviews, charts, and more
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Engagement81
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Recent Episodes
June 22, 2026
AI & Antibodies miniseries | Designing smart antibodies in the age of AI
<p>In this episode, the fourth in our miniseries covering the mAbs journal article collection on artificial intelligence and machine learning in antibody development, we speak to Andrew Buchanan, Senior Vice President of Discovery at a biotech company currently in stealth mode, and former Principle Scientist at AstraZeneca, about his paper in the collection: How to think about designing smart antibodies in the age of GenAI: integrating biology, technology, and experience.</p><br><p>Andrew provides a holistic overview of how AI and machine learning are transforming the design of smart antibodies – the more complex evolution of monoclonal antibodies that can bind multiple receptors and utilize different mechanisms of action. Together, we explore the critical role of establishing robust candidate drug target profiles (CDTPs), the current capabilities and limitations of AI in structural antibody design, and how the simultaneous rise of multi-specificity and AI-driven approaches is reshaping the field.</p><p><br></p><h2><strong>Contents</strong></h2><p>[02:10] Exploring the simultaneous rise of AI and multi-specificity in therapeutic antibody design</p><p>[04:20] Establishing a candidate drug target profile with AI</p><p>[06:50] Limitations of AI in the development of a CDTP</p><p>[08:20] AI in practical therapeutic antibody design</p><p>[10:45] How industry and academia can work together to overcome current limitations in the use of AI in antibody therapeutic design</p><p>[13:45] Exciting recent applications of AI in antibody design</p><p>[16:32] Predictions for the next 5 years of AI in antibody design</p><p>[18:10] If I could grant you a wish to improve the abilities of AI in antibody development, what would it be?</p><hr><p style='color:grey; font-size:0.75em;'> Hosted on Acast. See <a style='color:grey;' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for more information.</p>
June 15, 2026
AI & Antibodies miniseries | Reducing antibody viscosity to improve subcutaneous delivery
<p>In Episode 3 of our AI in Antibodies mini-series, we speak to Peter Tessier, the Albert M. Mattocks Professor of Pharmaceutical Sciences and Chemical Engineering at the University of Michigan, about his groundbreaking work in the application of machine learning to predict and improve the formulation and delivery of therapeutic antibodies. </p><br><p>Subcutaneous delivery of antibody therapeutics offers patients the convenience of at-home administration, but high-concentration formulations create viscosity challenges that limit practical delivery. Here, Peter explains how his team developed machine learning models that predict antibody viscosity and self-association, reveals surprising discoveries about formulation properties and shares practical guidance for antibody developers.</p><p><br></p><h2>Contents:</h2><p>[01:00] Pros and cons of subcutaneous delivery </p><p>[03:30] Favorable antibody characteristics for subcutaneous delivery</p><p>[05:20] Predicting viscosity</p><p>[10:10] Zooming in on self-association for antibodies</p><p>[15:10] Predicting sell-association based on antibody characteristics and external factors and formulation properties.</p><p>[22:00] Advice for antibody therapeutics developers</p><p>[24:45] Closing remarks and final requests</p><hr><p style='color:grey; font-size:0.75em;'> Hosted on Acast. See <a style='color:grey;' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for more information.</p>
June 9, 2026
AI & Antibodies mini-series | Balancing binding affinity and therapeutic practicality
<p>In episode two of our AI & Antibodies mini-series, we speak to Ryan Emerson, Senior Vice President of Data Science at A-Alpha Bio, to discuss AlphaBind, A-Alpha Bio’s antibody-antigen binding-affinity prediction model.</p><p>We discuss how this model was trained, how it operates and how it has enabled researchers to test mutations designed to optimize an antibody candidate for critical quality attributes computationally, assessing their likely impact on binding affinity, before returning to the wet lab. The conversation also explores the future of AI in antibody engineering and the critical role of high-quality data in advancing the field.</p><p><br></p><h2>Contents</h2><p>[02:20] Current challenges in antibody sequence design </p><p>[04:20] Presenting AlphaBind</p><p>[08:40] Demonstrating AlphaBind’s effectiveness</p><p>[11:40] Benefits of AlphaBind and it’s applications</p><p>[16:05] How to make the most of AlphaBind</p><p>[19:25] Current use of AlphaBind </p><p>[23:00] Predictions for the impact of AI in antibody engineering</p><p>[25:40] A brief detour into the uses of AI in drug design (<a href="https://www.the-scientist.com/chatgpt-and-alphafold-help-design-personalized-vaccine-for-dog-with-cancer-74227" rel="noopener noreferrer" target="_blank">See this story for more detail</a>)</p><p>[26:45] What wish could be granted to improve AI in antibody design? </p><hr><p style='color:grey; font-size:0.75em;'> Hosted on Acast. See <a style='color:grey;' target='_blank' rel='noopener noreferrer' href='https://acast.com/privacy'>acast.com/privacy</a> for more information.</p>
68 total episodes available
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Ritwika Biswas
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