The BioInsights Podcast
The BioInsights Podcast: discussing the challenges of translating novel biologics from bench to clinic to market.
The BioInsights Podcast
Precision by design: engineering site‑specific ADCs with machine learning
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BioInsights
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Season 5
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Episode 2
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Lauren Coyle, Launch Commissioning Editor, Bioconjugate Insights, speaks with Weston Kightlinger, CEO, Ridge Bio, about the challenge of DAR heterogeneity and site specificity in ADC development, Ridge Bio’s ML‑driven enzyme and linker engineering platforms, and what site‑specific conjugation means for the future of ADCs as the field moves beyond oncology.
1. Site specificity has been a persistent challenge in ADC development, with issues such as DAR heterogeneity, unpredictable conjugation, and batch variability. From your position, how significant is this problem in practice and why has it proven so diffi
2. Ridge Bio sits at an unusual intersection of machine learning (ML), cell-free experimentation, and enzyme engineering. Can you walk us through how these three elements work together in practice, and what this combination allows you to do that conventio
3. Ridge's NativeLink and ProTrigger platforms span ADCs, antibody-oligonucleotide conjugates (AOCs), radiotherapies, and beyond. How do you think about the relationship between enzymatic conjugation precision and linker release design as integrated rathe
4. Platform science is one thing, but can you give us a sense of the real problems being solved today, the programs that NativeLink and ProTrigger are being applied to, and what the partnership experience has revealed about where the technology adds the m
5. ML in drug development carries significant promise, but also genuine skepticism, particularly around whether models trained on existing data can navigate the novel chemical and biological spaces that next-generation ADCs require. How do you address tha
6. How does Ridge expect site-specific conjugation to impact the future of ADCs, especially as the field begins to push outside of oncology, and what needs to happen – technically or commercially – to establish ML-driven enzyme engineering as the field's