Hanie Yousefi, PhD, MSc

📢 #Merck Publishes Science Paper on Oral PCSK9 Inhibitor Manufacturing 🔗 Read more: https://lnkd.in/g9vFpv2E #Merck has published a Science paper on scalable manufacturing for oral #PCSK9 inhibitors. The work advances large-scale #biocatalytic drug production. About the Company: • Global #pharmaceutical leader • Strong #cardiovascular pipeline • Advanced #manufacturing capabilities About the Research: • Oral #PCSK9 inhibitor #manufacturing • Scalable #biocatalytic synthesis • Process #innovation focus Key Highlights: • Science paper published • Supports scalable production • Advances oral #cardiovascular therapies "Congratulations to #Merck." 🎉 Rob Davis, Chirfi Guindo, Pamela Craig, Kathy Warden 🤝 “Manufacturing #innovation is often as valuable as the drug itself.” #Pharma #Manufacturing #Cardiovascular #Innovation #Science FDA The National Institutes of Health Centers for Disease Control and Prevention ISO - International Organization for Standardization World Health Organization European Medicines Agency American Chemical Society International Society for Pharmaceutical Engineering American Heart Association European Society of Cardiology || cGxP.wire || cGxP.Directory || cGxP.jobs || cGxP.Tech ||

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The Life Sciences Institute is looking for a drug discovery scientist with expertise in computational methods to partner with the Director of Michigan Drug Discovery in advancing drug discovery research at the University of Michigan (U-M). With its administrative home in the Life Sciences Institute, Michigan Drug Discovery (MDD) is a campus-wide program created to support and fund drug discovery research at the University of Michigan. Learn more by following the post below.

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New polyA and UTR ideas for #mRNA. This paper looked at mixed poly(A) tail (A/G tail) design using 12 repeating units of the AAAAAAAAAAG motif and found it to be more stable in e.coli than BioNTech's segmented polyA. They also confirmed the high potency of the human α-globin 5′ UTR and revealed the potential of the VP6 and SOD 3′ UTRs. 

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If you are following the intersection of AI and biomanufacturing, we recommend this newly published piece from Genetic Engineering & Biotechnology News. In the new article, "Smarter Filings, Smarter Factories" by Mike May, Aizon Co-Founder and CSO Toni Manzano, PhD joins other industry leaders to discuss how AI is moving from experimental pilots to validated, production-grade infrastructure in biopharma. In such a highly regulated industry, AI must earn the trust of both internal teams and regulators. The piece explores how leaders across the drug lifecycle are making this a reality. It is an insightful read on how the industry is converging to build trustworthy AI systems that compress timelines and get safe, effective medicines to patients faster. Read the full article here: https://lnkd.in/eErfVEkv #LifeSciences #Biomanufacturing #Aizon #IntelligentGxPManufacturing

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https://lnkd.in/gSsnjyz3 Excellent summary of how AI is transforming the drug development landscape. One important dimension that often gets overlooked - yet is absolutely critical to accelerating drug discovery - is accurate solvation and molecular interaction modelling. For those of us who have spent years working with 3D-RISM and advanced statistical-mechanical solvation theories, it’s clear that many current AI models still lack the deep physical grounding needed to reliably predict binding, molecular stability, or ADMET behaviour, especially when training data are sparse or biased. This is exactly where physics-based methods shine. 3D-RISM and related solvation frameworks provide: ·        First-principles hydration and solvation thermodynamics ·        Robust predictions in poorly sampled chemical space ·        Mechanistic insight that complements AI-driven screening ·        Transferable results without requiring massive training datasets As AI in pharma moves forward, I see enormous potential in combining physics-based solvation models with LLMs and generative chemistry tools. Integrating rigorous solvent effects and molecular energetics directly into AI workflows would significantly improve molecular design accuracy, reduce false positives, and enhance the reliability of hit-to-lead predictions. AI is reshaping the field, but the future impact will be even greater when we fuse it with decades of molecular theory and computational chemistry. Physically grounded methods like 3D-RISM aren’t being replaced; they are becoming essential components of next-generation drug discovery pipelines. A great discussion and a reminder that true innovation happens when data-driven and physics-driven methods work together.

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Advancing Research through Innovation & Intelligence 🧪🤖 The Department of Chemistry & Biotechnology, in association with Institution’s Innovation Council and Science Club, is organizing an International Seminar on “Machine Learning Applications in Drug Discovery: Predicting Drug-Like Properties and Efficacy.” 🎙️ Resource Person: Dr. Balasubramaniyan Sakthivel Postdoctoral Researcher, Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Japan 🗓️ 04.03.2026 (Wednesday) ⏰ 10.30 AM – 12.00 PM 💻 Online Mode – Google Meet An insightful session for students, faculty, and research scholars to explore how AI and Machine Learning are transforming modern drug discovery. Don’t miss this opportunity to learn from an international researcher! #GCT #GnanamaniCollegeOfTechnology #Chemistry #Biotechnology

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Congratulations to Jonathan Nguyen and Abhishek Singharoy on their recent publication, “Transient protein structure guides surface diffusion pathways for electron transport in membrane supercomplexes,” in Nature Communications. This study examines how mitochondrial “supercomplexes,” or groups, support efficient electron transfer within cells. They found that this negatively charged “hinge” region helps guide electron-carrying molecules along the membrane, promoting fast and directional movement. Rather than hindering the process, this structural flexibility enhances it. The study proposes that a “refolding-guided diffusion” mechanism can increase energy conversion efficiency by nearly 30%. The findings highlight how the movement of these protein structures can potentially play an active role in cellular processes. 🔗 Read the publication: https://lnkd.in/gvd6jGwS

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𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈) 𝐝𝐫𝐢𝐯𝐞𝐧 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐛𝐢𝐨𝐦𝐚𝐫𝐤𝐞𝐫 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐭𝐫𝐢𝐚𝐥 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬 𝐢𝐧 𝐜𝐚𝐧𝐜𝐞𝐫 𝐩𝐚𝐭𝐢𝐞𝐧𝐭𝐬. In this paper “AI-driven predictive biomarker discovery with contrastive learning to improve clinical trial outcomes”, Dr. Gustavo Arango-Argoty and team summarize the role of AI-driven predictive biomarker discovery with contrastive learning in improving clinical trial outcomes. This paper is freely available at the following URL (https://lnkd.in/ekqYzJcx) The promise of precision medicine is simple: give each patient the treatment that works best for their specific disease. A key part of this approach is the use of predictive biomarkers. These are measurable signals, such as genes or proteins that help identify which patients are more likely to benefit from a particular treatment. Clinical trials that select patients based on these biomarkers are about twice as likely to lead to an approved medicine. The Predictive Biomarker Modeling Framework (PBMF) uses artificial intelligence to discover new biomarkers. The role of AI-driven predictive biomarkers in improving clinical trial outcomes in cancer patients is explained in the following video link:. https://lnkd.in/eMkNKTPj 𝘈𝘵 𝘐𝘺𝘭𝘰𝘯 𝘗𝘳𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘖𝘯𝘤𝘰𝘭𝘰𝘨𝘺 (www.iylon.com), 𝘸𝘦 𝘩𝘢𝘳𝘯𝘦𝘴𝘴 𝘨𝘦𝘯𝘦𝘵𝘪𝘤 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘵𝘰 𝘱𝘳𝘰𝘷𝘪𝘥𝘦 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭𝘪𝘻𝘦𝘥, 𝘦𝘷𝘪𝘥𝘦𝘯𝘤𝘦-𝘣𝘢𝘴𝘦𝘥 𝘳𝘦𝘤𝘰𝘮𝘮𝘦𝘯𝘥𝘢𝘵𝘪𝘰𝘯𝘴, 𝘩𝘦𝘭𝘱𝘪𝘯𝘨 𝘨𝘶𝘪𝘥𝘦 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘮𝘦𝘥𝘪𝘤𝘪𝘯𝘦, 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘱𝘢𝘵𝘪𝘦𝘯𝘵, 𝘢𝘵 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘥𝘰𝘴𝘦, 𝘢𝘵 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘵𝘪𝘮𝘦, 𝘢𝘯𝘥 𝘢𝘪𝘮𝘦𝘥 𝘢𝘵 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘵𝘢𝘳𝘨𝘦𝘵. Sendurai Mani John Tarantino Gregory Mercurio Jr Christine Mcginn Matthew Hadfield, DO Poornima Bhat

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🔬 The regulatory landscape for drug development just shifted, and most organizations haven't caught up yet. In March 2026, the 27th NIH Tissue Chip Consortium Meeting met jointly with the CIVM Qualification Framework Public Workshop. We were there across both days. Here's what it means for biotech, pharma, and investors: ✅ FDA issued its draft guidance on New Approach Methodologies (NAMs), giving tissue chips a concrete regulatory framework for the first time. ✅ ISTAND is now a permanent qualification program. ✅ Organ-on-chip platforms are moving from "interesting experiments" to decision-grade regulatory tools. The biggest takeaway? The bottleneck has shifted. It's no longer about invention. It's about evidentiary strategy, reproducibility, data standardization, and regulatory framing. The conclusion is clear: success will belong to organizations that align five elements simultaneously biologically credible models, a tightly scoped context of use, reproducibility, structured data, and an explicit regulatory strategy from day one. The field has crossed a threshold. The question is no longer IF tissue chips will enter regulatory decision-making, it's how fast and under what evidentiary bar. 👇 Read our full analysis on the blog (link in comments): https://lnkd.in/e4nXy8YB #OrganOnChip #DrugDevelopment #RegulatoryScience #NAMs #Biotech #MicrophysiologicalSystems #FDA #NIH #CIVM #ClinicalTrials #PharmaInnovation #BioSolutionsConsulting

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We report new work from our group in collaboration with Google's genomics team. A challenge with genetic discovery for heart failure is that its classification within large scale biobanks is extremely vague. This has led to the surprising situation where, in 2026, there are only 2 validated GWAS loci for heart failure with preserved ejection fraction, a sub-group that accounts for HALF of the total heart failure patients, heart failure being the single most common admitting diagnosis in patients over the age of 65 (more than 30 million people globally). We need to do better. One approach is to use machine learning to develop a precision phenotype (for this you need rich health care system data from many patients, not biobank data). Then, you can take that probabilistic framework and apply it to biobanks which, while less enriched for patients, provide the power of large scale and deep genomics and proteomics. This was the idea that Jack W O'Sullivan MD, PhD and a large collaborating team sought to pursue in this work. The findings went beyond all our expectations. First, the team increased the number of loci confidently associated with HFpEF almost 50 fold! We report 99 loci, up from 2. Second, by integrating multi-omics in a causal inference framework, we prioritize gene-protein pathways as candidates for therapeutic development while de-prioritizing non-causal targets, reclassifying them as biomarkers. How could we tell we were on track? We noted first that three clinical trials for one of our deprioritized targets (MPO) were negative (genomics really can save money in drug discovery). We also validated one of our prioritized targets in a novel clinical study. In conclusion, we hope this work accelerates our understanding of heart failure. We believe this approach to precision phenotyping can extend the reach of our global biobanks in many directions. Finally, we believe incorporating causal inference in a multiomic framework can directly inform investment decisions in drug discovery pipelines. Congratulations to the entire team and special thanks to our Google collaborators who are the co-leaders of this work. Taedong (Ted) Yun Cory McLean Andrew Carroll Paper: https://lnkd.in/gamstJQM

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**🚀🌟 𝐀𝐝𝐯𝐚𝐧𝐜𝐢𝐧𝐠 𝐇𝐢𝐠𝐡-𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭 𝐂𝐡𝐚𝐫𝐚𝐜𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐏𝐫𝐨𝐭𝐞𝐢𝐧–𝐋𝐢𝐠𝐚𝐧𝐝 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬🌟🚀** 🎯Overview: Understanding biophysical parameters such as the equilibrium dissociation constant (Kd) and maximum binding capacity (Bmax) is fundamental to modern drug discovery. Rapid and reliable determination of these values enables better insights into binding affinity and capacity—critical factors in the design of new therapeutic molecules. Adepoju et al previously showcased the ability of IR-MALDESI-MS to detect noncovalent protein–ligand complexes. Here Adepoju et al presents Kd and Bmax using IR-MALDESI-MS through a ligand titration approach. 🎯 Summary: Unlike conventional ESI-MS, IR-MALDESI-MS delivers analysis in under 13 seconds per ligand concentration, offering a powerful combination of speed, automation, and sensitivity. Using carbonic anhydrase II (CAH) and its known inhibitor sulfanilamide (SLFA), Adepoju et al demonstrated accurate extraction of key binding parameters under native conditions. 🧪Why This Work Matters These findings, together with other recent studies, highlight the strong potential of IR-MALDESI-MS as a high-throughput screening (HTS) platform for rapid and precise characterization of protein–ligand interactions. 🔗 Check out the full publication below: 📌 https://lnkd.in/etpseHNT #massspec #chemistry #drugdiscovery #massspectrometry

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🚨 Just published in Nature Medicine An international team of researchers from various institutions, including GenBio AI Co-Founder and Chief Scientist Eric Xing, Senior Scientific Fellow Eran Segal, the MBZUAI (Mohamed bin Zayed University of Artificial Intelligence), and others, has published a landmark study as part of the Human Phenotype Project (HPP) in Nature Medicine, part of the Nature Portfolio. This work shows how deep phenotyping combined with advanced AI can transform healthcare, from early disease detection to personalized interventions at population scale. 🔍 Key insights: • Phenotypic variation across age and ethnicity • Biological aging vs. chronological age • Disease-associated microbiome signatures • Individualized hypotheses for disease pathogenesis • Quantified impact of lifestyle on health outcomes • Foundation AI models for multimodal prediction This is a major step toward proactive, personalized, and universal healthcare powered by AI. 📄 Read the full paper: https://lnkd.in/efNKDNy2 🎥 Watch the video below. Video courtesy of MBZUAI (Mohamed bin Zayed University of Artificial Intelligence).

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Yesterday our co-founder and CSO Babak Alipanahi presented at the Bay Area Biotech-Pharma Statistics Workshop (BBSW) about the powerful combination of AI and cell-free RNA to detect early-stage lung cancer in the blood. Thank you to BBSW for this opportunity to share how AI outperforms traditional machine learning methods and will play a pivotal role in the future of liquid biopsy. To learn more, check out our Nature Communications publication: https://lnkd.in/gFbrywag

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AAPS Open has an open call for papers on "Innovations in Enabling Formulations and Characterization of Small Molecules for Drug Development," dedicated to the latest theoretical and experimental research in enabling formulation development for small molecules. Refer to the call for papers for full details. The submission deadline is May 31, 2026. The guest editors for this special issue are Ahmed Elkhabaz, Abu Zayed Badruddoza, Ph.D., MBA, Deliang Zhou, Yihong Qiu, Dana Moseson-Tarrh, PhD, and Marina A. Solomos, Ph.D.. https://lnkd.in/gB_r7QHj

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