A research team based at MIT and Harvard has engineered a bacterial injection system to precisely deliver proteins to human cells. This work, published online March 29, 2023, in Nature, is important as while more and more molecular therapies are being developed, off-target effects are always a concern and precise targeting of cells and tissues can still be a challenge.

The positively charged nanoparticle polyamidoamine generation 3 (P-G3) can be specifically targeted to either visceral or subcutaneous fat, and affects both types of fat in different ways, researchers from Columbia University reported in two papers recently published. The studies, published online in Nature Nanotechnology on Dec. 1, 2022, and in Biomaterials on Nov. 28, 2022, are both “a conceptual advance” and “quite amenable to translation,” co-corresponding author Kam Leong told BioWorld.

A computational platform that used single-cell RNA sequencing (scRNA-seq) data could quickly predict the best chemical compounds to use to convert cells from one type into another for use in research or cell therapies. The work, published in the Nov. 17, 2022, issue of Stem Cell Reports, was a collaboration between the lab of Hongkui Deng, a professor and director of the Key Laboratory of Cell Proliferation and Differentiation at Peking University in Beijing, and the lab of Antonio del Sol, a professor at the Luxembourg Centre for Systems Biomedicine at the University of Luxembourg.

A new method for controlling naturally magnetized bacteria has improved the prospects of applying them as vehicles for intratumoral delivery of cancer drugs and in hyperthermia therapy. The advance will provide a better way of directing the movement of systemically administered bacteria, using external magnetic fields to target them to tumors sited deep in the body. It also points to a possible route for engineering existing bacteria-based anticancer constructs for better targeting.

Researchers based at the City University of New York (CUNY) have designed a deep learning artificial intelligence (AI) model that can improve preclinical predictions of drug responses in humans. As outlined in the Oct. 17, 2022, online issue of Nature Machine Intelligence, the researchers believe their model – a context-aware deconfounding autoencoder (CODE-AE) – can help improve the quality of early drug response prediction and help reduce subsequent clinical trial failures.