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Dr. Norman Putzki, MD — Novartis — Gene Therapy And A New Era Of Neuroscience

Gene Therapy And A New Era Of Neuroscience — Dr. Norman Putzki, MD — SVP, Global Clinical Development Head, and U.S. Development Site Head, Novartis.

Dr. Norman Putzki, MD is Senior Vice President, Global Clinical Development Head, and U.S. Development Site Head at Novartis (https://www.novartis.com/) where he oversees global teams working on next-generation gene therapies, RNA-based medicines, targeted biologics, and innovative small molecules.

Dr. Putzki most recently served as Global Head of Development for Neuroscience and Gene Therapy at Novartis, where he oversaw one of the world’s most ambitious pipelines aimed at transforming the lives of patients with neurological, neuromuscular, and rare genetic diseases.

A physician–scientist by training, with an MD from University of Duisburg Essen, Dr. Putzki has built a career at the intersection of clinical medicine, translational research, and large-scale drug development.

Before joining Novartis, Dr. Putzki led programs across multiple therapeutic areas at Biogen Idec and has played key roles in advancing clinical treatments for conditions long considered intractable including MS and Parkison’s disease.

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How to build a genome: Scientists release troubleshooting manual for synthetic life

Leading synthetic biologists have shared hard-won lessons from their decade-long quest to build the world’s first synthetic eukaryotic genome in a Nature Biotechnology paper. Their insights could accelerate development of the next generation of engineered organisms, from climate-resilient crops to custom-built cell factories.

“We’ve assembled a comprehensive overview of the literature on how to build a lifeform where we review what went right—but also what went wrong,” says Dr. Paige Erpf, lead author of the paper and postdoctoral researcher at Macquarie University’s School of Natural Sciences and the Australian Research Council (ARC) Center of Excellence in Synthetic Biology.

The Synthetic Yeast Genome Project (Sc2.0) involved a large, evolving global consortium of 200-plus researchers from more than ten institutions, who jointly set out to redesign and chemically synthesize all 16 chromosomes of baker’s yeast from scratch. Macquarie University contributed to the synthesis of two of these chromosomes, comprising around 12% of the project overall.

Biphenomycin biosynthetic pathway decoded, opening door to new antibiotic development

Biphenomycins, natural products derived from bacteria, show excellent antimicrobial activity, but have long remained out of reach for drug development. The main obstacle was the limited understanding of how these compounds are produced by their microbial hosts.

A research team led by Tobias Gulder, department head at the Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), has now deciphered the biosynthetic pathway of the biphenomycins, establishing the foundation for their pharmaceutical advancement. The team published its findings in the journal Angewandte Chemie International Edition.

Chip-scale magnetometer uses light for high-precision magnetic sensing

Researchers have developed a precision magnetometer based on a special material that changes optical properties in response to a magnetic field. The device, which is integrated onto a chip, could benefit space missions, navigation and biomedical applications.

High-precision magnetometers are used to measure the strength and direction of magnetic fields for various applications. However, many of today’s magnetometers must operate at extremely low temperatures—close to 0 kelvin—or require relatively large and heavy apparatus, which significantly restricts their practicality.

“Our device operates at room temperature and can be fully integrated onto a chip,” said Paolo Pintus from the University of California, Santa Barbara (UCSB) and the University of Cagliari, Italy, co-principal investigator for the study. “The light weight and low power consumption of this magnetometer make it ideal for use on small satellites, where it could enable studies of the magnetic areas around planets or aid in characterizing foreign metallic objects in space.”

AI tool can detect missed Alzheimer’s diagnoses while reducing disparities

Researchers at UCLA have developed an artificial intelligence tool that can use electronic health records to identify patients with undiagnosed Alzheimer’s disease, addressing a critical gap in Alzheimer’s care: significant underdiagnosis, particularly among underrepresented communities.

The study appears in the journal npj Digital Medicine.

Scientists uncover key driver of treatment-resistant cancer

University of California San Diego researchers have discovered the enzyme responsible for chromothripsis, a process in which a single chromosome is shattered into pieces and rearranged in a scrambled order, allowing cancer cells to rapidly evolve and become resistant to treatment.

Since its discovery more than a decade ago, chromothripsis has emerged as a major driver of cancer progression and treatment resistance, but scientists haven’t learned what causes it. Now, UC San Diego scientists have solved this longstanding mystery in cancer biology, opening up new possibilities for treating the most aggressive cancers. The results are published in Science.

New sensor technology can detect life-threatening complications after intestinal surgery at an earlier stage

An interdisciplinary research team from Dresden University of Technology (TUD), Rostock University Medical Center (UMR) and Dresden University Hospital has developed an innovative, implantable and fully absorbable sensor film. For the first time, it enables reliable early detection of circulatory disorders in intestinal anastomoses—one of the riskiest surgical procedures in the abdominal cavity. The results have now been presented in the journal Advanced Science.

Intestinal anastomoses, which is the surgical connection of two sections of the intestine after the removal of diseased tissue, carry a considerable risk of post-operative complications. In particular, circulatory disorders or immunological reactions can lead to serious consequential damage or even death within a short period of time. However, direct monitoring of the suture site has not been possible until now, which often entails corresponding risks for patients as well as considerable costs due to follow-up operations and long hospital stays.

Based on this specific medical need, the interdisciplinary network of the Else Kröner Fresenius Center (EKFZ) for Digital Health at TUD and Dresden University Hospital brought together key experts from Dresden and Rostock.

Squashing ‘fantastic bugs’ hidden in AI benchmarks

After reviewing thousands of benchmarks used in AI development, a Stanford team found that 5% could have serious flaws with far-reaching ramifications.

Each time an AI researcher trains a new model to understand language, recognize images, or solve a medical riddle, one big question remains: Is this model better than what went before? To answer that question, AI researchers rely on batteries of benchmarks, or tests to measure and assess a new model’s capabilities. Benchmark scores can make or break a model.

But there are tens of thousands of benchmarks spread across several datasets. Which one should developers use, and are all of equal worth?

Fungal allies arm plant roots against disease by rewriting the rules of infection

Scientists have discovered that beneficial root-dwelling fungi boost plant resilience to disease by remodeling the plant cell membrane at pathogen infection sites—offering critical new insights into how plants coordinate defenses in complex natural environments.

This new research reveals that the membrane interface between plant cells and invading pathogen microbes is not fixed. Instead, it can be reshaped by co-colonizing symbionts, fundamentally altering how plants interact with pathogens and potentially improving resistance to disease.

The study is published in the journal Cell Reports.

Destructured Drug Discovery: How Sequence-Based AI Speeds and Expands the Search for New Therapeutics

Predictive computational methods for drug discovery have typically relied on models that incorporate three-dimensional information about protein structure. But these modeling methods face limitations due to high computational costs, expensive training data, and inability to fully capture protein dynamics.

Ainnocence develops predictive AI models based on target protein sequence. By bypassing 3D structural information entirely, sequence-based AI models can screen billions of drug candidates in hours or days. Ainnocence uses amino acid sequence data from target proteins and wet lab data to predict drug binding and other biological effects. They have demonstrated success in discovering COVID-19 antibodies and their platform can be used to discover other biomolecules, small molecules, cell therapies, and mRNA vaccines.

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