Lifeboat Foundation

Targeted protein degradation (TPD) is an emerging therapeutic modality and has attracted great attention from academia and industry^1,2. The prototypical TPD agents, molecular glues (MGs) and proteolysis targeting chimeras (PROTACs), can lead to temporal proteasomal degradation of the protein-of-interest (POI). PROTACs are small heterobifunctional molecules integrating an E3-ligase binder and a POI binding moiety through a synthetic linker construct. The PROTACs technology has been applied to degrade numerous pathological proteins and a rich pipeline is currently progressing into preclinical and early clinical trials^3,4,5. However, overcoming PK/PD issues towards clinical compounds is demanding due to the intrinsically high molecular weight and related physicochemical properties⁶. On the other hand, MGs are small molecules with beneficial ‘drug-like’ physicochemical properties binding to an E3 ligase, and, similarly to PROTACs, leading to neosubstrate proteasomal degradation. Their mechanism of action is however less predictable; their often hydrophobic surface-exposed portions of the MGs seem to change the hydrophobic surface area of the E3 ligase and thereby leading to neosubstrate ubiquitination and degradation^7,8. MGs have already proven their validity as marketed drugs, as there are several approved drugs or clinical compounds working by an MG mechanism (Fig. 1A), for example, the IKZF1/3 degrader thalidomide and its analogs pomalidomide and lenalidomide⁸, and the RBM39 degrader indisulam⁹. Thalidomide analogs induce selective ubiquitination and degradation of two lymphoid transcription factors, IKZF1 and IKZF3, by the CRBN-CRL4 ubiquitin ligase¹⁰. Additionally, CSNK1A1 (CK1α) was recently discovered as a lenalidomide-specific neo-substrate¹¹. Interestingly, modification of pomalidomide or lenalidomide can have a profound impact on the degradation potency and degradation profiles. For example, CC-220 (Fig. 1A) showed 10-fold more potency in the cells than lenalidomide, and CC-885 (Fig. 1A) was found to induce degradation of the substrate GSPT1^12,13. Both MGs and PROTACs are emerging drug modalities providing interesting features over classical pharmacology-driven drugs by their ability to drive the destruction of proteins that have multiple functions, thereby potentially overcoming resistance mechanisms and providing new pharmacology. While PROTACs can be developed highly rationally, MGs are discovered rather serendipitously requiring synthesis and testing of large series of compounds^14,15. Additionally, the discovery of MGs and PROTACs is done in a sequential, often mmol scale synthesis which is time-consuming and expensive.

In this work, to address current shortcomings in MGs discovery, we use the direct-to-biology (D2B) approach and combined the automated, high throughput miniaturized synthesis with cell-based phenotypic screening (Fig. 1B). The I.DOT (Immediate Drop on Demand Technology, a pressure-based nano dispensing technology) is employed to accelerate the synthesis of diverse MGs libraries on nano scale^{16,17,18,19,20,21}. In a subsequent cell-based phenotypic screening cascade, the compounds are tested in the thalidomide and analog sensitive MM.1S multiple myeloma cell line which reportedly is used for MGs screening²². In this D2B screening platform, the crude compounds are directly screened on cells without further chromatographic purification or clean up. Then, the 19 best compounds are selected for re-synthesis on mmol scale followed by purification and fully characterized.

A biohybrid, leaf-spring design of DNA origami functions as a pulsating nanoengine that exploits the DNA-templated RNA transcription mechanism while consuming nucleoside triphosphates as fuel. The nanoengine also drives a nanomechanical follower structure.

Scientists may have found a more efficient water to desalinate water using solar power, according to new research, offering a solution for global water scarcity through the use of renewable energy.

Researchers at Nankai University in Tianjin, China, developed the concept of a solar-powered desalination system that produces fresh water by using smart DNA hydrogels that does not consume additional energy, compared to conventional desalination strategies currently in use, such as reverse osmosis, which use copious amounts of energy, according to a paper published in the journal Science Advances on Thursday.

The same process can be used simultaneously to extract uranium from seawater or treat uranyl containing nuclear wastewater, the researchers said.

When it comes to delivering drugs to the body, a major challenge is ensuring that they remain in the area they’re treating and continuing to deliver their payload accurately. While major strides have been made in delivering drugs, monitoring them is a challenge that often requires invasive procedures like biopsies.

Researchers at NYU Tandon led by Jin Kim Montclare, Professor of Chemical and Biomolecular Engineering, have developed proteins that can assemble themselves into fibers to be used as therapeutic agents for the potential treatments of multiple diseases.

These biomaterials can encapsulate and deliver therapeutics for a host of diseases. But while Montclare’s lab has long worked on producing these materials, there was once a challenge that was hard to overcome—how to make sure that these proteins continued to deliver their therapeutics at the correct location in the body for the necessary amount of time.

The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to limited understanding of how nanoparticle (NP) properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by using various predictive models based on collection of large data sets of 403 data points incorporating both numerical and categorical features. Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain. Furthermore, the physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve. The analysis employed various linear models, with a particular emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional accuracy. The model was validated via the preparation and administration of two distinct NP formulations via the intranasal and intravenous routes. Among the various modeling approaches, LMEMs exhibited superior performance in capturing underlying patterns. Factors such as the release rate and molecular weight had a negative impact on brain targeting. The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate.

Optogenetics has revolutionized neuroscience understanding by allowing spatiotemporal control over cell-type specific neurons in neural circuits. However, the sluggish development of noninvasive photon delivery in the brain has limited the clinical application of optogenetics. Focused ultrasound (FUS)-derived mechanoluminescence has emerged as a promising tool for in situ photon emission, but there is not yet a biocompatible liquid-phase mechanoluminescence system for spatiotemporal optogenetics. To achieve noninvasive optogenetics with a high temporal resolution and desirable biocompatibility, we have developed liposome (Lipo@IR780/L012) nanoparticles for FUS-triggered mechanoluminescence in brain photon delivery. Synchronized and stable blue light emission was generated in solution under FUS irradiation due to the cascade reactions in liposomes.

The fusion of biological principles with technological innovation has resulted in significant advancements in artificial intelligence (AI) through the development of Brainoware. Developed by researchers at Indiana University, Bloomington, this innovative system leverages clusters of lab-raised brain cells to achieve elementary speech recognition and solve mathematical problems.

The crux of this technological leap lies in the cultivation of specialized stem cells that mature into neurons—the fundamental units of the brain. While a typical human brain comprises a staggering 86 billion neurons interconnected extensively, the team managed to engineer a minute organoid, merely a nanometer wide. This tiny but powerful structure was connected to a circuit board through an array of electrodes, allowing machine-learning algorithms to decode responses from the brain tissue.

Termed Brainoware, this amalgamation of biological neurons and computational circuits exhibited remarkable capabilities after a brief training period. It was discerned between eight subjects based on their diverse pronunciation of vowels with an accuracy rate of 78%. Impressively, Brainoware outperformed artificial networks in predicting the Henon map, a complex mathematical construct within chaotic dynamics.

For instance, the pegRNA molecules used in prime editing are difficult and expensive to chemically synthesise or laborious to clone, which hampers the crucial optimisation of prime-editing efficiency. Additionally, the reverse transcriptase (RT) enzymes used in prime editing are relatively error-prone and have low processivity, which may limit the precision and size of edits that can be introduced. Furthermore, RTs have a low affinity for dNTPs, which can impact prime-editing efficiency in non-dividing and differentiated cells.

To address these issues, two research groups led by Dr. Ben Kleinstiver at Mass General Hospital (MGH) & Harvard Medical School, and Dr. Erik Sontheimer at the RNA Therapeutics Institute (UMass Chan Medical School) have independently developed new approaches that build upon prime editing by replacing RT with another type of enzyme, namely a DNA-dependent DNA polymerase. This change permits the use of DNA instead of RNA as a template for editing, potentially addressing some of the main limitations of prime editing by allowing higher efficiency and adaptability.

Signal processing is key to communications and video image processing for astronomy, medical diagnosis, autonomous driving, big data and AI. Menxi Tan and colleagues report a photonic processor operating at 17Tb/s for ultrafast robotic vision and machine learning.