Advanced Biotech Research Techniques

Our team just published our latest work in Nature revealing how patients' own antibodies can make or break their response to checkpoint immunotherapy. The Question: Why do some cancer patients experience dramatic tumor shrinkage when they received immunotherapy while others see no benefit? Our Approach: Using REAP (Rapid Extracellular Antigen Profiling), we screened blood samples from 374 cancer patients for autoantibodies against 6,000+ proteins. Key Findings: · Cancer patients have an extraordinarily diverse “autoantibody reactome.” We detected ~3,000 unique autoantibody reactivities and clearly had not achieved saturation. · Patients with anti-interferon antibodies were up to 40x more likely to respond to treatment. This is a complete reversal from COVID-19 where these same antibodies increase mortality by 20-200 fold. · Novel finding: Anti-TL1A antibodies enhance treatment by preventing T cell apoptosis in the TME · Red flag: 10% of non-responders had antibodies against BMP receptors, revealing a previously unknown barrier to treatment success Conclusions: Treatment-modifying autoantibodies act as a roadmap for developing better therapies. We can now design drugs that mimic beneficial antibodies or counteract harmful ones, potentially improving outcomes for any patient who receives immunotherapy. This work was only possible through incredible collaboration between the Fred Hutchinson Cancer Center, the Yale Cancer Center, and my company Seranova Bio. Special recognition to lead author Yile Dai and the entire team who made this vision a reality. Read the full paper here: https://lnkd.in/dRxYd4bC

GENERATIVE BIOLOGY AI just wrote genetic instructions that cells actually followed – a breakthrough that turns biology into a programming language. For the first time ever, researchers at the Center for Genomic Regulation created AI-generated DNA sequences that successfully controlled gene expression in healthy mammalian cells. Think of it as writing software, but for living organisms. Why this matters: → The AI can design custom 250-letter DNA fragments with specific instructions like "activate this gene in stem cells becoming red blood cells but not platelets" → These synthetic enhancers worked EXACTLY as predicted when tested in mouse blood cells → Unlike previous efforts focused on cancer cells, this team worked with healthy cells, uncovering subtle mechanisms that shape our immune system → The researchers built a library of 64,000+ synthetic enhancers tested across seven stages of blood cell development Most fascinating was discovering "negative synergy" - where two factors that individually activate genes can completely shut them down when combined. This unlocks precision we never had before. The implications are enormous for gene therapy. Instead of being limited to DNA sequences evolution produced, we can now design ultra-selective gene switches customized to specific cells and tissues - potentially making treatments more effective with fewer side effects. Full paper: https://lnkd.in/en3bGZP9 Follow-up with @EricTopol's post about curing rare diseases with the existing genomic technology stack https://lnkd.in/eGCYMjGJ

Synthetic biology is - quite literally - our future. A goundbreaking new biological foundation model Evo2 achieves state-of-the-art prediction of genetic variation impacts and generates coherent genome sequences, spanning all domains of life. A diverse team from leading research institutions including Arc Institute Stanford University NVIDIA University of California, Berkeley trained the model on 9.3 trillion DNA base pairs and has fully shared all code, parameters, and data. A few highlights from the paper (link in comments) 🔬 Zero-shot prediction achieves state-of-the-art accuracy in genetic variant interpretation. Evo 2 can predict the functional consequences of genetic mutations across all domains of life without specialized training. It surpasses existing models in assessing the pathogenicity of both coding and noncoding variants, including BRCA1 cancer-linked mutations. This generalist capability suggests Evo 2 could revolutionize genetic disease research, reducing reliance on expensive, manually curated datasets. 🛠 Genome-scale generation paves the way for synthetic life design. Evo 2 can generate full-length genome sequences with realistic structure and function, including mitochondrial genomes, bacterial chromosomes, and yeast DNA. Unlike prior models, Evo 2 ensures natural sequence coherence, improving synthetic biology applications like engineered microbes or artificial organelles. This sets the stage for programmable biology at an unprecedented scale. 🧬 Unprecedented long-context understanding revolutionizes genomic analysis. Evo 2 operates with a context window of up to 1 million nucleotides—far beyond the capabilities of previous models—allowing it to analyze genomic features across vast distances. This ability enables it to accurately identify regulatory elements, exon-intron boundaries, and structural components critical for understanding genome function. Its long-context recall is a major breakthrough for interpreting complex biological sequences. 🎛 Inference-time search enables controllable epigenomic design. Evo 2’s generative abilities extend beyond raw DNA sequence to epigenomic features, allowing researchers to design sequences with specific chromatin accessibility patterns. This approach successfully encoded Morse code messages into synthetic epigenomes, demonstrating a new method for controlling gene regulation via AI. This could lead to breakthroughs in gene therapy and epigenetic engineering. 🔮 Future potential: Toward AI-driven biological design and virtual cell modeling. Evo 2 represents a major leap toward AI-powered genomic engineering. Future iterations could integrate additional biological layers—such as transcriptomics and proteomics—to create virtual cell models that simulate complex cellular behaviors. This could revolutionize drug discovery, genetic therapy, and even synthetic life creation.

New Winning Drugs in ER+ Breast Cancer? #medicine The treatment landscape for advanced estrogen receptor (ER)–positive, HER2-negative breast cancer is evolving with novel oral selective estrogen receptor degraders (SERDs). Two such agents, vepdegestrant and camizestrant, have been evaluated in clinical trials, offering insights into their potential to replace the standard intramuscular SERD, fulvestrant. Hot off the press #ASCO25! In the VERITAC-2 Phase 3 trial, published in NEJM yesterday, vepdegestrant was compared to fulvestrant in patients who had progressed on prior endocrine therapy and a CDK4/6 inhibitor. In the overall population, vepdegestrant did not significantly improve PFS (3.8 vs. 3.6 months; HR, 0.86; 95% CI, 0.70–1.06; P=0.16). However, in patients with ESR1 mutations, vepdegestrant extended median PFS to 5.0 months versus 2.0 months for fulvestrant (HR, 0.60; 95% CI, 0.43–0.83; P=0.002). Meanwhile, camizestrant has shown broader efficacy in the SERENA-6 trial published in NEJM today. This Phase 3 study involved patients on first-line aromatase inhibitor plus CDK4/6 inhibitor therapy. ESR1 mutations were tracked via ctDNA, and patients with these mutations were randomized to switch to camizestrant plus the same CDK4/6 inhibitor or continue standard therapy. Median PFS was 16.0 months for camizestrant versus 9.2 months for continued standard therapy, a 56% reduction in risk (HR, 0.44; 95% CI, 0.32–0.60; P<0.001). Camizestrant plus CDK4/6 inhibitors was well-tolerated with low discontinuation rates. Compared to fulvestrant, which is limited by its intramuscular administration and median PFS of 3.6 months, vepdegestrant (oral) offers targeted benefit in ESR1-mutant disease with PFS of 5.0 months. Camizestrant, also oral, demonstrated broader efficacy with a median PFS of 16.0 months in patients with ESR1 mutations detected through liquid biopsy while on first-line therapy. Both oral SERDs represent a major advance, offering convenient administration and potential to overcome resistance mechanisms. Vepdegestrant’s activity in ESR1-mutant disease highlights its targeted promise, while camizestrant’s robust efficacy and proactive treatment strategy may establish it as a new standard of care. These findings suggest a dynamic shift in the endocrine therapy landscape, with new options poised to replace fulvestrant and improve outcomes for patients with advanced ER-positive, HER2-negative breast cancer. Really very exciting for patients! References in comments. Follow Chris De Savi or ring the 🔔 icon to be notified of all his posts #healthcare #pharmaceuticals

🧪 Exploring an Alternative Pathway in Targeted Protein Degradation: ByeTACs In a recent study by Loy et al. (2025), a new class of bifunctional molecules called ByeTACs (Bypassing E3 ligase Targeting Chimeras) was introduced. Unlike traditional PROTACs, which rely on E3 ligases and ubiquitination to tag proteins for degradation, ByeTACs offer a different strategy. Instead of tagging proteins for destruction, ByeTACs directly recruit the protein of interest to the 26S proteasome by binding the Rpn-13 subunit, a nonessential ubiquitin receptor. This allows for degradation independent of the ubiquitination cascade — a potentially useful option when ligase expression is limited or when ubiquitination is inefficient. 🧬 Highlights: ✅ Demonstrated degradation of engineered (HaloTag-GSK3β) and endogenous proteins (BRD4, BTK) ✅ Effective in multiple cell types at low μM to nM concentrations ✅ Degradation is proteasome-dependent but ubiquitin-independent, confirmed by E1 inhibition (TAK-243) and Rpn-13 knockdown While not a replacement for existing degrader technologies, ByeTACs offer a complementary approach with potential to expand the scope of degradable targets. 📄 Read the paper: J. Med. Chem. 2025, https://lnkd.in/gRjutRtr #TargetedProteinDegradation #ByeTACs #TPD #PROTAC #DrugDiscovery #Biotech #Rpn13 #MedicinalChemistry

The lab of David Baker keeps pushing the AI frontier in biology. The newest advance: A team of 21 scientists describing how they made enzymes from scratch using AI models that perform some fairly complicated chemical feats. There is vast potential for where this technology goes from here. It could allow scientists to design enzymes in new ways — from building better gene editors to new proteases to a library of plastic-degrading enzymes. “The bigger picture is we can now use deep learning, ML diffusion methods, to make really active enzymes,” Baker told me, adding his lab is now working on making nucleases, or enzymes that cut nucleic acids, and thinking about base editors, another type of gene editing. For my latest at Endpoints News, I talked with Baker and three of the leading researchers on this Science paper: Anna Lauko, Sam Pellock, and Kiera Sumida:

⚙️ AI in Biotech – Day 22: Bioprocess Optimization — Making Biotech More Efficient, One Cell at a Time Biotech breakthroughs don’t end in the lab. To bring therapies, enzymes, vaccines, and cell-based products to the world, we need something just as critical: bioprocessing. That’s where AI is stepping up — helping biotech companies fine-tune the way we grow cells, purify proteins, and scale up production without compromising quality. Here’s how AI is transforming bioprocess optimization: 🧫 1. Smarter Cell Culture Management AI can continuously monitor and adjust bioreactor conditions — like pH, temperature, dissolved oxygen, and nutrient supply — in real time. Cytiva’s Ambr® systems integrate AI to predict cell growth and product yield, adjusting media and feeds automatically. MilliporeSigma’s Bio4C® suite uses AI to make cell culture processes more predictable and reproducible. 🧪 2. Faster Process Development Traditionally, optimizing a new process takes weeks or months. AI accelerates this by modeling thousands of variables — and predicting ideal parameters. Novo Nordisk uses AI to reduce time-to-clinic by predicting the best fermentation setups for insulin analogues. Ginkgo Bioworks leverages machine learning to refine microbial fermentation for large-scale biomolecule production. 🧼 3. Predictive Maintenance & Quality Control AI can monitor equipment health and flag anomalies before they cause failures — minimizing downtime and maintaining product integrity. GE Healthcare’s AI-powered bioprocess systems track pump behavior and filtration pressure in real time. Sanofi uses AI-driven dashboards to detect early signs of contamination or batch variability. 💡 4. Sustainable Biomanufacturing By reducing material waste, energy use, and failed batches, AI contributes to a greener and more cost-effective biotech industry. Biogen uses AI to optimize upstream and downstream processing, cutting down on water and raw material usage. 📊 The bottom line? AI isn’t just about discovery — it’s about delivery. Smarter bioprocessing means lower costs, better scalability, fewer batch failures, and faster access to life-saving innovations. Further reads for the Geeks: 🔗Bioprocessing Warms to Artificial Intelligence Bioprocessing Warms to Artificial Intelligence https://lnkd.in/gF8JFUSK 🔗Artificial intelligence technologies in bioprocess: Opportunities and challenges - ScienceDirect https://lnkd.in/gFF2We2E 🔗Artificial Intelligence to Advance Bioprocessing | Frontiers Research Topic https://lnkd.in/g3yf4DiJ 🔗 DeCYPher innovating Bioprocess with microbes and AI https://lnkd.in/gT5MTrm8 🔔Follow me for Day 23: #AIinBiotech #Bioprocessing #Biomanufacturing #SmartLabs #FermentationTech #CellCulture #GreenBiotech #WomenInSTEM #LinkedInSeries

Scientists Create Rapid CRISPR Therapeutics Therapy to Save Infant With Rare Genetic Disease: 🧬 A baby was born with CPS1 deficiency, a rare and life-threatening disorder that causes toxic ammonia buildup in the blood 🧬 Within just six months, scientists developed and delivered a personalized Crispr treatment tailored to his specific genetic mutations 🧬 The therapy used base editing, a precise form of Crispr that makes single-letter DNA changes, delivered via lipid nanoparticles in an IV 🧬 After just three doses, the baby began hitting developmental milestones, eating more normally, and needing less medication 🧬 It’s not considered a cure, but doctors believe it turned a severe condition into a more manageable one 🧬 This case shows custom gene-editing therapies for ultra-rare diseases can be developed in months, not years, and in urgent cases, even fast-tracked through FDA approval in just a week #DigitalHealth #HealthTech

🔬 “Therapy for One” vs. “One-Size-Fits-All” Base editing is quietly becoming one of the most investable technologies in biotech, not because it’s flashy, but because it’s fundamentally changing how we treat disease. Unlike traditional CRISPR, base editing doesn’t cut DNA. It rewrites it, one letter at a time, with surgical precision. That nuance matters. It means fewer off-target effects, better safety profiles, and the ability to correct mutations that were previously untouchable. But what makes this space truly compelling isn’t just the science, it’s the strategic bifurcation in how it’s being applied. On one side, we have “therapy for one”: ultra-personalized interventions for patients with rare, often fatal mutations. These are high-cost, high-impact therapies that won’t scale in volume, but they scale in influence. They validate platforms, unlock regulatory innovation, and build trust with clinicians and regulators. The CPS1 case at CHOP is a perfect example: a bespoke therapy designed for a single child, delivered successfully. That’s not just a medical milestone, it’s a signal that N-of-1 therapies are no longer theoretical. On the other side, we have “one-size-fits-all” applications base editing deployed at scale for common diseases. Beam Therapeutics is pushing forward with BEAM-101 for sickle cell disease, while Verve Therapeutics is targeting PCSK9 to lower LDL cholesterol with a one-time in vivo therapy. These programs are built for mass adoption, and they’re already showing signs of clinical viability. The delivery systems are improving, the regulatory momentum is real, and the economics are attractive. So where does smart capital go? In the near term, the edge lies with companies that have clinical-stage programs and proprietary delivery platforms, especially those targeting liver and hematologic diseases. These are the firms closest to regulatory inflection points and early revenue. But the deeper opportunity is in the infrastructure layer, bioinformatics platforms that accelerate variant interpretation, decentralized manufacturing models for personalized therapies, and regulatory processes that understand how to navigate N-of-1 approvals. - Longer term, the real upside will come from platform convergence. The companies that integrate base editing with AI-driven target discovery, RNA editing, and epigenetic modulation will be the ones that define the next generation of precision medicine. Base editing is not a niche. It’s the nucleus of a new therapeutic paradigm. Let’s talk about where this is headed, share your comments below. #CGT #geneediting #invest #market #investor #VC #PE