Anita Venkitaraman

For Master’s or PhD students in Immunology and related programs, J&J is offering co-ops in the Greater Philadelphia region in Cell Therapy Process Development, Cell & Gene Therapy, and Cell Therapy. https://lnkd.in/eiT_fVN7

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A colleague recently reached out and asked, “Where should I start if I want to better understand the FDA process for drugs and devices?” It’s a great question—and one I hear often from folks working in research and development who’ve learned regulatory "by osmosis" rather than formal training. So, I compiled a curated list of resources and 🔗 to help demystify FDA processes and provide a solid foundation, whether you’re just beginning or looking to fill in the gaps: 📘 Introductory Reading - FDA Basics for Industry: https://lnkd.in/em3DPAPh - FDA 101: An Overview of FDA's Regulatory Review and Research Activities: https://lnkd.in/ePf9Rz_c - The FDA's Drug Review Process: https://lnkd.in/ehsqyTpt - Overview of Device Regulation: https://lnkd.in/eUEs-zkB - How to Study and Market Your Device: https://lnkd.in/eyKVkuMP 📄 Policies, Procedures & Guidance - CDER MAPP Library: https://lnkd.in/et4zgDba - CBER SOPPs for Biologics: https://lnkd.in/ejxnSNSw iologics/guidance-compliance-regulatory-information-biologics/biologics-procedures-sopps - FDA Guidance Docs: https://lnkd.in/eZ8tgssA 🎥 Recorded Workshops, Webinars, & Trainings - REdI Conference Series: https://lnkd.in/eSwxHxFu - CDRH Learn – Devices: https://lnkd.in/eQ76gUi7 - FDA Workshops & Meetings: https://lnkd.in/eGkk32AH 🎓 Courses & Professional Development - RAPS: https://lnkd.in/e68ENPA7 - DIA: https://lnkd.in/etNX6Gp8 If you’re looking to build your regulatory knowledge—or just want to finally connect the dots—I hope this helps. 💬 Have other go-to resources you recommend? Feel free to share in the comments! #RegulatoryAffairs #FDA #DrugDevelopment #MedicalDevices #ClinicalResearch #Biotech #MedTech #ProfessionalDevelopment #LifelongLearning

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𝐔𝐒 𝐣𝐨𝐢𝐧𝐬 𝐄𝐔 & 𝐂𝐚𝐧𝐚𝐝𝐚 𝐢𝐧 𝐝𝐫𝐨𝐩𝐩𝐢𝐧𝐠 𝐏𝐡𝐚𝐬𝐞 3 𝐟𝐨𝐫 𝐦𝐀𝐛 𝐛𝐢𝐨𝐬𝐢𝐦𝐢𝐥𝐚𝐫𝐬 Working so many years in biosimilar analytics, I remember how inevitable Phase 3 trials once felt—expensive, time-consuming, and scientifically questionable. Now the FDA has followed EMA and Health Canada in waiving Phase 3 efficacy studies for monoclonal antibody biosimilars as they recently anounced for Stelara. Instead, robust analytical, PK, and immunogenicity data are considered sufficient to prove biosimilarity. This change could: • Cut development costs by ~90% • Accelerate approvals by ~70% • Open the door for smaller companies to enter the field • Speed up patient access to affordable biologics But beyond biosimilars, there’s a bigger lesson that I am really happy about: when science proves it can answer the key questions, regulation adapts. Legacy requirements fall away. In the end, science wins—and that’s good news not only for biosimilars, but for the future of drug development as a whole.

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Drugs like Wegovy and Ozempic continue to astonish with their wide ranging effects on health. They are also great examples of serendipity - weight loss was first noticed as a side effect. There's a lesson here about data too - never underestimate what gems may lay hidden in your data that you haven't noticed. Invest in data and data scientists - the more people you have looking at data, historical and new, the more likely you will make valuable new discoveries. https://lnkd.in/eJykA7_p

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Recently at LatchBio x Greylock Partners, I shared some thoughts on scaling "virtual cell" perturbation models to 1B+ cells (18T tokens)! Venture funds have been putting huge capital into the space for models that can reliably predict drug treatment effects or clinical trial outcomes. Why? 1. As organizations like Arc Institute, Ultima Genomics, 10x Genomics, Tahoe Therapeutics or Xaira Therapeutics invest capital into assay development, the cost and time of generating training data drops exponentially. 2. One can hope this data is open sourced for the academic community. 🙂 Regardless, the assay breakthroughs lead to orders of magnitude more data. See yesterday's datasets of ~100k cells vs today's datasets of ~100M cells. 3. The training data unlocks new model capabilities and, critically, helps researchers differentiate which models are better. For example, the STATE model is slightly better than linear baselines on small datasets, but significantly outperforms on huge datasets. 4. These improved model capabilities will enable in silico therapeutics design, scaling the role of computation in the drug discovery pipeline. This fuels the flywheel, incentivizing venture and others to invest more money. The state of perturbation models today feels similar to the state of NLP before BERT - bespoke models trained for specific tasks. I'm extremely bullish and excited on tomorrow's generalist models in this space, and what they will enable.

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The (True) Current State and Roadmap for AI in Cancer Treatment Decisions A few months ago, Isaac Kohane asked me to write an opinionated review about "The Use of Artificial Intelligence for Cancer Therapeutic Decision-Making" for NEJM AI. I was fortunate to have colleagues Sean Khozin and Cora Sternberg join me in this effort, and today I'm pleased to share that it's been published: https://lnkd.in/eW_nMNf8 Let's be honest: despite the AI hype cycle, we're still far from having AI truly transform cancer treatment decisions. While diagnostic applications in radiology and pathology are showing promising maturity, the broader implementation in treatment selection just isn't there yet. Current AI models, especially LLMs, struggle with consistent adherence to clinical guidelines and most critically, lack validation through properly designed clinical trials. We remain convinced, however, that AI has the potential to dramatically improve cancer care outcomes if we take the right approach. We need robust multicenter datasets that reflect real-world complexity, practical validation frameworks, and most importantly, a fundamental shift toward rigorously testing these systems like we would any other medical intervention. The gap between AI's promise and current reality in oncology is substantial, but with the right roadmap, we can bridge it. Whether incremental or transformative, the path forward must be built on evidence, not hype.

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"Notable spinouts from local research institutions include companies like Juno Therapeutics. It was at the forefront of the cutting-edge technology called cell therapy, a type of biologics where human cells are engineered or modified and transplanted as medicine. Founded in Seattle in 2013, Juno was acquired by Celgene in 2018 for $9 billion. Subsequently, Lawrenceville, New Jersey-based Bristol Myers Squibb bought Celgene less than two years later. But many Juno veterans went on to start or fill leadership positions at other biotechs with Seattle-area ties. Among them, Tune Therapeutics, which announced an over $175 million Series B round in January, and Indapta Therapeutics, which extended its cash runway with a $22.5 million dollar round in December." #seattle #lifesciences #biotech #fundinguncertainty

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BIG Moves in Cell Therapy: CAR T for Solid Tumors 👇 Dispatch Bio just raised $216M to tackle solid tumors - the toughest challenge in CAR-T therapy. VCs tell me, “No need for more CAR-Ts in liquid tumors.” They’re right. That space is crowded. Unless there's room for more Ex Vivo BCMA CD19s... But Dispatch is different. They use a virus to put a synthetic “flare” antigen on any solid tumor cell, creating a universal target for CAR-T. Plus, they reprogram the tumor environment so CAR-T cells stay active and multiply -solving the biggest barrier for solid tumors. 👉 Led by Jeff, CAR-T pioneer Carl June, along with top scientists like Chris Garcia (Stanford expert in protein engineering), Andy Minn (MSKCC specialist in tumour immunology), and Kole Roybal (UCSF leader in synthetic biology). On the route to clinical testing. Congrats to Dispatch on the fundraise! Do you think now is correct time to 'Dispatch' solid tumors? #celltherapy #oncology #cellandgenetherapy #CGTweekly

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