Targeted drug delivery remains a major challenge in cancer therapy, often limiting both efficacy and safety. Although microRNA sponges and short-hairpin RNAs show potential for gene-based cancer treatment, their clinical use is restricted by delivery inefficiency, off-target effects, cytotoxicity, and instability. Viral vectors offer high efficiency but are associated with issues such as immune responses, insertional mutagenesis, and limited cargo capacity. Non-viral carriers are safer and more affordable but suffer from poor transfection efficiency, instability, and inadequate endosomal escape. These limitations hinder the clinical application of RNA therapeutics. The Vir-inspired Biotechnical Vector (VIBV) is a novel hybrid platform that combines viral and non-viral elements with nanotechnology to enable personalized, tumor-specific gene therapy. Engineered with a spindle-shaped nanocore and a polyethylene glycolylated liposomal shell, VIBV ensures immune evasion, prolonged circulation, and controlled therapeutic release triggered by tumor microenvironmental cues such as acidity, hypoxia, and elevated glutathione levels. It delivers oncogenic microRNA sponges, short-hairpin RNAs, tumor-specific antigens, and cyclin-targeting RNAs to enhance gene silencing, immune activation, and tumor suppression. This review examines the limitations of current delivery systems and presents VIBV as a promising next-generation strategy with improved biocompatibility, targeting precision, and potential for cost-effective, personalized cancer therapy, while also addressing its remaining challenges and prospects.

During National Postdoc Appreciation Week, the Blavatnik Family Foundation and The New York Academy of Sciences are proud to announce the three Laureates and six Finalists of the 2025 Blavatnik Regional Awards for Young Scientists, the Blavatnik Awards’ flagship prize that honors outstanding postdoctoral scientists from academic research institutions across New York, New Jersey, and Connecticut. 

The 2025 Blavatnik Regional Awards Laureates are:

 

Life Sciences: Veena Padmanaban, PhD, nominated by The Rockefeller University

Recognized for discovering a molecular mechanism allowing sensory neurons to communicate with breast cancer cells to drive metastasis and uncovering novel actionable therapeutic targets.

Physical Sciences & Engineering: Valentin Crépel, PhD, nominated by the Flatiron Institute

Recognized for advancing theories describing stacked, single-atom-thick materials, enabling easier control of their behaviors for applications in quantum technology and opening up new avenues for developing materials useful in novel quantum computing platforms.

 

Chemical Sciences: Xiao Xie, PhD, nominated by Princeton University

Recognized for pioneering chemical biology tools to map protein phase separation and copper signaling, which uncover molecular mechanisms underlying cancer and neurodegenerative diseases. Xie is a postdoctoral fellow in the lab of 2015 Blavatnik National Awards Laureate, Christopher Chang, PhD.

The Finalists are:

LIFE SCIENCES

Maria Cecilia Campos Canesso, PhD, nominated by The Rockefeller University

Recognized for advancing our understanding of food allergies and inflammatory bowel disease by developing new tools to uncover how immune cells communicate in the intestine, laying the foundation for more effective treatments.

 

Ipshita Zutshi, PhD, nominated by New York University

Recognized for discovering how the brain integrates dynamic goals with sensory inputs (sound, vision) to guide memory and decision-making — providing a framework for understanding cognitive dysfunction in psychiatric disorders.

 

PHYSICAL SCIENCES & ENGINEERING

Ore Gottlieb, PhD, nominated by the Flatiron Institute

Recognized for shifting the paradigm describing neutron star mergers, giving scientists a roadmap for finding and studying these rare events.

 

Viraj Pandya, PhD, nominated by Columbia University

Recognized for groundbreaking discoveries on early galaxy evolution, reshaping our understanding of how galaxies formed and challenging longstanding theories of the early universe.

 

CHEMICAL SCIENCES

Lucien Dupuy, PhD, nominated by Rutgers University, Newark

Recognized for developing quantum-classical and machine learning simulations that elucidate how molecules respond to light, crucial to understanding diverse topics like DNA stability, photocatalysis, and energy conversion. Dupuy is the first Blavatnik Awards honoree from Rutgers University, Newark, NJ.

 

Yunjia Lai, PhD, nominated by Columbia University

Recognized for pioneering mass spectrometry innovations to map and screen environmental exposures and biological changes that drive neurodegenerative diseases including Parkinson’s and Alzheimer’s.

Some of our biggest threats can come in the tiniest forms—viruses and bacteria. Thankfully, we are born with a built-in defense system, our innate immune system that protects us in our youth but can turn against us as we age. New research from the Stowers Institute for Medical Research is revealing why this tradeoff exists, identifying a common power source driving many immune system responses to pathogens. The findings may potentially lead to new ways to combat inflammation and diseases associated with aging like Alzheimer’s, Parkinson’s, and cancer.

A team from Sun Yat-sen University Sun Yat-sen Memorial Hospital has identified a novel mechanism underlying platinum resistance in triple-negative breast cancer (TNBC): the circular RNA circSCAP encodes a 129-amino-acid protein (SCAP-129aa) that activates the PI3K/AKT pathway by stabilizing PIK3R2. Silencing circSCAP or combining platinum therapy with the PIK3R2 inhibitor  significantly improved treatment efficacy in preclinical models, highlighting circSCAP and SCAP-129aa as potential biomarkers and therapeutic targets.

Researchers at The University of Osaka and Kanazawa University have developed a novel method for analyzing cancer metabolism, revealing new insights into cancer's inefficient energy process. This breakthrough, published in Metabolic Engineering, combines biological experiments with advanced information science techniques to uncover the role of cancer-specific inefficient metabolism.

A new study published in JCO Clinical Cancer Informatics demonstrates that machine learning models incorporating patient-reported outcomes and wearable sensor data can predict which patients with non–small cell lung cancer are most at risk of needing urgent care during treatment. The study was led by researchers and clinicians at Moffitt Cancer Center.