Advanced Biotech Research Techniques

In an advancement in cancer research, a team led by Assistant Professor Balaji Panchapakesan at the University of Delaware has engineered an approach to oncological therapy called nano-bombs. This technology targets cancer cells whilst minimizing damage to surrounding healthy tissues. 🔬 𝐇𝐨𝐰 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬 - Nano-Engineering: Researchers utilize carbon nanotubes known for their unique thermal properties. - Targeted Therapy: These nanotubes are engineered to bind specifically to cancer cells. - Activation by Light: Upon exposure to a certain light wavelength, these nanotubes heat up rapidly, causing a micro-explosion that directly targets and destroys cancer cells. 🛡️ 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐒𝐚𝐟𝐞𝐭𝐲 The beauty of this technology lies in its precision. The nano-bombs can differentiate between healthy cells and cancer cells, ensuring that only the harmful cells are destroyed. This method promises a significant reduction in the side effects typically associated with traditional cancer treatments like chemotherapy and radiation. 🌟 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐂𝐚𝐧𝐜𝐞𝐫 𝐓𝐫𝐞𝐚𝐭𝐦𝐞𝐧𝐭 This innovative approach opens new avenues for treating cancer more effectively while preserving healthy cells, leading to quicker patient recovery and fewer side effects. It represents a significant step forward in the pursuit of targeted cancer therapies that offer patients not just more life, but a better quality of life. 🤔 What impact do you think such targeted treatments will have on the future of cancer therapy? Could this be the key to turning the tide against one of the biggest health challenges worldwide? #innovation #technology #future #management #startups

Big breakthrough: A few months my lab at MIT introduced SPARKS, our autonomous scientific discovery model. Since then we have demonstrated applicability to broad problem spaces across domains from proteins, bio-inspired materials to inorganic materials. SPARKS learns by doing, thinks by critiquing itself & creates knowledge through recursive interaction; not just with data, but with the physical & logical consequences of its own ideas. It closes the entire scientific loop - hypothesis generation, data retrieval, coding, simulation, critique, refinement, & detailed manuscript drafting - without prompts, manual tuning, or human oversight. SPARKS is fundamentally different from frontier models. While models like o3-pro and o3 deep research can produce summaries, they stop short of full discovery. SPARKS conducts the entire scientific process autonomously, generating & validating falsifiable hypotheses, interpreting results & refining its approach until a reproducible, fully validated evidence-based discovery emerges. This is the first time we've seen AI discover new science. SPARKS is orders of magnitude more capable than frontier models & even when comparing just the writing, SPARKS still outperforms: in our benchmark evaluation, it scored 1.6× higher than o3-pro and over 2.5× higher than o3 deep research - not because it writes more, but because it writes with purpose, grounded in original, validated compositional reasoning from start to finish. We benchmarked SPARKS on several case studies, where it uncovered two previously unknown protein design rules: 1⃣ Length-dependent mechanical crossover β-sheet-rich peptides outperform α-helices—but only once chains exceed ~80 amino acids. Below that, helices dominate. No prior systematic study had exposed this crossover, leaving protein designers without a quantitative rule for sizing sheet-rich materials. This discovery resolves a long-standing ambiguity in molecular design and provides a principle to guide the structural tuning of biomaterials and protein-based nanodevices based on mechanical strength. 2⃣ A stability “frustration zone” At intermediate lengths (~50- 70 residues) with balanced α/β content, peptide stability becomes highly variable. Sparks mapped this volatile region and explained its cause: competing folding nuclei and exposed edge strands that destabilize structure. This insight pinpoints a failure regime in protein design where instability arises not from randomness, but from well-defined physical constraints, giving designers new levers to avoid brittle configurations or engineer around them. This gives engineers and biologists a roadmap for avoiding stability traps in de novo design - especially when exploring hybrid motifs. Stay tuned for more updates & examples, papers and more details.

AI-enabled drug discovery reaches clinical milestone My piece in @NatureMedicine on exciting progress in our field https://rdcu.be/eugUu Few AI-designed drug candidates have gone beyond in silico benchmarks. Now, a study in Nature Portfolio Medicine reports a successful phase 2a trial of rentosertib, an AI-discovered drug and target combination for idiopathic pulmonary fibrosis What distinguishes this study (in addition to clinical data) is the upstream innovation pipeline This trial marks a turning point: it affirms a potential for AI to do more than generate molecules faster and cheaper; guide discovery, de-risk development and potentially reshape how we develop medicines A pertinent question is: why did this AI-generated drug candidate advance to clinical testing when so many others have not? 🎯 Cross-disease target discovery and 'time-machine' setup: AI models trained on past data predicted therapeutic targets years ahead of traditional methods, pinpointing TNIK as a promising target 🔬 Robust biological validation: Integrated multi-omic analyses, network biology, and extensive literature mining rapidly validated TNIK’s biological relevance for fibrosis ⚙️ Chemistry design: Generative AI models designed molecules targeting novel binding sites, prioritized drug-likeness and synthetic feasibility, and proactively optimized pharmacokinetics and potency from early stages Alex Zhavoronkov Insilico Medicine Harvard Medical School Department of Biomedical Informatics Harvard University Harvard Medical School Harvard Data Science Initiative Kempner Institute at Harvard University Broad Institute of MIT and Harvard

This new AI tool can design antibody drugs 100x faster than traditional methods - without trial and error. It’s called Chai-2. And it might quietly change how we discover drugs. Here’s why: Normally, designing an antibody drug takes months of testing in the lab - just to find one molecule that works. But Chai-2, an AI model built by Chai Discovery, skips that entire process. It watched hours of real-world data and learned to generate antibody candidates from scratch - with atomic-level precision. This new AI tool can design antibody drugs 100x faster - and with zero trial-and-error. In early tests on 52 new antigens, it produced viable hits for 50% of them - in just two weeks. That too without any screening or manual lab trials. The team calls it “Photoshop for molecules.” And the analogy makes sense - it lets researchers design antibodies with programmable control, rather than waiting for randomness to deliver results. This matters for two big reasons: ▶ 1. It’s faster and cheaper Fewer experiments means lower R&D costs and faster GTM. Especially powerful for early-stage biotechs running on tight timelines and capital. ▶ 2. It’s not just about antibodies Chai-2 can design miniproteins, explore new formats, and expand the kinds of molecules we can even consider therapeutically. As a funding coach and investor in healthtech, I see a signal here: → Founders who can compress the drug discovery loop - even slightly - will unlock investor confidence faster. Would you bet on a drug designed by AI if it meant saving months (and millions)? #entrepreneurship #healthtech #innovation

Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins. Cellular indexing of transcriptomes and epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell-type annotation requires a classifier that integrates multimodal data. Here, we describe multimodal classifier hierarchy (MMoCHi), a marker-based approach for accurate cell-type classification across multiple single-cell modalities that does not rely on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities. Interesting single cell method for the analysis of immune cell states: https://lnkd.in/gTRQQAxM

🔬 A new era of biology is coming—not just single-cell, but cell–cell. When flow cytometry first became widely adopted in the 1980s, it revolutionized immunology. Suddenly, we could dissect the immune system one cell at a time, revealing T cell subsets, memory phenotypes, activation states, and more. Entire fields flourished because we could see and sort what was previously invisible. Now imagine doing that—not with one-dimensional fluorescence signals—but with full images of each cell as it's flowing by at thousands per second. And not just of single cells, but of cell pairs, clusters, and interactions. That’s the promise of image-activated cell sorting (IACS). Our recent review in Nature Bioengineering explores how IACS is poised to drive a new biological revolution: 📄 https://lnkd.in/guMkSxqJ At its core, IACS combines high-throughput microscopy, real-time image processing, and precision microfluidic sorting, opening the door to analyze and isolate cells based on morphology, subcellular localization, cell-cell contact, cell secretions and more. 💡 At UCLA Henry Samueli School of Engineering and Applied Science, I’ve had the privilege of watching and contributing to many of these advances emerge—from our collaborations with Keisuke Goda, Bahram Jalali and Kevin Tsia on STEAM to the early FIRE imaging system (Eric Diebold, Ph.D.) that now powers BD’s FACSDiscover CellView, to participating in the "Serendipiter" developed by Keisuke Goda's ImPACT program, to Deepcell (founded by my former PhD student Maddison Masaeli), and now through our work on nanovials (Joe de Rutte, Partillion Bioscience), which serve as test tubes for probing cell-cell communication. We are no longer limited to what a cell expresses in isolation, but can now ask how it behaves, who it talks to, and how it responds. Just as early flow cytometry revealed the immune system's complexity, these tools will help uncover the dynamic networks that govern multicellular biology, development, and disease. Providing the massive data needed to fuel predictive AI models that link cells to tissues to organisms—and perturbations that transform health to disease. 🔁 The future is moving beyond single-cell to interaction-level biology. And the tools are finally here. #CellBiology #SingleCell #ImageActivatedCellSorting #Nanovials #microfluidics #FlowCytometry #IACS #UCLA #Bioengineering #NatureBioengineering

Top 10 Technologies Revolutionizing Biotech Manufacturing: Paving the Way for Innovation Here are ten groundbreaking technologies that are redefining efficiency, precision, and scalability in biomanufacturing. These advancements are opening opportunities for costs reduction, increased product quality, and innovative therapeutic solutions. 1. Optogenetics: Precision Redefined. Optogenetics employs light to control gene expression within bioreactors, allowing for unparalleled precision in biological processes. Startups like Ningaloo Biosystems are at the forefront, utilizing blue and red light to enhance production efficiency and product consistency. 2. Artificial Intelligence and Machine Learning The integration of AI and ML algorithms into manufacturing will optimize processes, predict maintenance needs, and ensure stringent quality control. This results in reduced downtime and operational costs, accelerating the path from product R&D to market. 3. 3D Bioprinting 3D bioprinting technology enables the manufacturing of complex biological structures, such as tissues and organs. This innovation can enable on-demand production of biological materials. 4. Continuous Bioprocessing Transitioning from traditional batch processes to continuous bioprocessing allows for a more consistent and efficient production line. This approach helps scalability and reduces production costs. 5. CRISPR-Based Gene Editing CRISPR technology facilitates precise editing of genetic material, revolutionizing the development of therapies and the manufacturing of biologics. Applications include improvements in cell lines to create more effective therapeutic proteins. 6. Single-Use Unit Operations The adoption of single-use systems (e.g. bioreactors) increase flexibility, reduces contamination risks, and lowers cleaning costs. Multi-product facilities can improve segregation by using single-use systems. 7. Blockchain Technology Implementing blockchain in the supply chain enhances transparency, traceability, and security across the manufacturing and distribution process. 8. Nanotechnology Nanotechnology introduces materials at the nanoscale to improve drug delivery systems and diagnostic tools. 9. Digital Twins Creating digital replicas of physical bioprocesses allows for simulation and optimization without disrupting actual production. This approach can reduce experimentation costs to accelerate R&D timelines. 10. Robotics and Automation The integration of robotics and automation in increases precision, reduces human error, and enhances throughput. These technological advancements are setting new standards in biotech manufacturing. As the industry continues to evolve, embracing these technologies will be crucial to stay at the forefront of biotechnology. #BiotechInnovation #Biotechnology #Biomanufacturing #AIInBiotech #3DPrinting #GeneEditing #CRISPR #Nanotechnology #DigitalTwins #Automation #ATMP #calidad #quality #HealthcareInnovation

Want to accelerate your advanced therapy's path to patients AND attract more investment?  💯 From preclinical development to commercial manufacturing, every decision biotech teams make is driven by data. But if that data is fragmented, incomplete, or reliant on paper-based records, it can slow everything down - regulatory approvals, investor confidence, and ultimately, patient access. In the latest article in my Blueprint for Breakthroughs newsletter, I've teamed up with Emmanuel Casasola, Anand Srinivasan, Ph.D., and Joe Higdon, to explore why early adoption of electronic batch records (eBR) is becoming a competitive advantage in biotech. Unfortunately, many companies wait until late-stage clinical trials or commercialization to implement digital systems, but the reality is: ⚠️ Deferring eBR leads to costly rework, slow approvals, and data integrity concerns. ✅ Starting early builds a seamless, scalable foundation that accelerates progress. At BBG Advanced Therapies and BioBridge Global, we designed our digital infrastructure with this in mind, and our customers benefit greatly. By integrating a fully compliant eBR system early in development, our partners gain: 🔹 Regulatory-Ready Data – Structured, traceable records support IND/BLA filings from day one. 🔹 Process Maturity – Batch records evolve with the therapy, ensuring continuity from preclinical to commercial scale. 🔹 Investor Confidence – A well-documented, GMP-aligned process signals operational readiness and reduces perceived risk. The question isn’t whether to adopt eBR, but WHEN. Companies that embrace early data maturity will stand out in an increasingly competitive funding landscape. 📖 Read the full article below. What’s been your experience with digital manufacturing systems? Let’s discuss how biotech teams can future-proof development and move therapies forward faster. #AdvancedTherapies #eBR #DataIntegrity #CellTherapy #GeneTherapy #ManufacturingInnovation #BlueprintForBreakthroughs #CGT #ATMP #ISPE #ISCT #ASCGT #AABB Blood Centers of America | Foundation for the Accreditation of Cellular Therapy | AABB | ISPE | MasterControl

Imagine gene therapy treatments costing $100,000 instead of $2 million per dose. A new review shows this isn't just wishful thinking – continuous bioprocessing could reduce manufacturing costs by up to 80%, potentially transforming patient access to these life-changing treatments. A exciting review paper by Lorek et al. reveals how the shift from traditional batch processing to continuous manufacturing may revolutionize gene therapy production. The innovation lies in running multiple production steps simultaneously with constant material flow, enabled by multi-column chromatography systems and advanced process analytic technology (PAT). What makes this particularly exciting is how continuous processing addresses the core challenges of gene therapy manufacturing. Traditional batch processing requires larger facilities, faces significant downtime between batches, and struggles with consistency. In contrast, continuous processing achieves higher productivity at a smaller scale while improving product quality – critical factors for reducing those astronomical million-dollar-plus treatment costs. The technology behind this transformation is fascinating. Multi-column chromatography systems now enable continuous capture and purification of viral vectors, improving productivity nearly threefold while maintaining yields above 82%. Even more impressive is the integration of real-time monitoring through process analytical technologies. These systems use in -line spectroscopic sensors, dynamic light scattering, and rapid analytics to track critical quality attributes in real-time, ensuring consistent product quality while dramatically reducing manufacturing time and costs. The implications for patient care are profound. By reducing facility footprint, increasing productivity, and improving product quality, continuous processing could help transform gene therapies from last-resort options into more widely accessible treatments. Early studies suggest manufacturing costs could drop by 60-80% compared to traditional batch processing – a game-changing reduction that could dramatically expand patient access. What excites me most is how these advances are converging with artificial intelligence and automation. Real-time monitoring systems coupled with advanced process controls are enabling unprecedented precision in manufacturing, ensuring every batch meets the highest quality standards while maximizing efficiency. We're witnessing a fundamental shift in how gene therapies are manufactured. The question isn't just about cost reduction – it's about reimagining production to make these transformative treatments accessible to everyone who needs them. What are your thoughts on these developments? How do you see these manufacturing innovations reshaping the future of genetic medicine? #GeneTherapy #Biotechnology #ContinuousProcessing #Healthcare #Innovation #PatientAccess