In utero delivery of targeted ionizable lipid nanoparticles facilitates in vivo gene editing of hematopoietic stem cells.

Monogenic blood diseases are among the most common genetic disorders worldwide. These diseases result in significant pediatric and adult morbidity, and some can result in death prior to birth. Novel ex vivo hematopoietic stem cell (HSC) gene editing therapies hold tremendous promise to alter the therapeutic landscape but are not without potential limitations. In vivo gene editing therapies offer a potentially safer and more accessible treatment for these diseases but are hindered by a lack of delivery vectors targeting HSCs, which reside in the difficult-to-access bone marrow niche. Here, we propose that this biological barrier can be overcome by taking advantage of HSC residence in the easily accessible liver during fetal development. To facilitate the delivery of gene editing cargo to fetal HSCs, we developed an ionizable lipid nanoparticle (LNP) platform targeting the CD45 receptor on the surface of HSCs. After validating that targeted LNPs improved messenger ribonucleic acid (mRNA) delivery to hematopoietic lineage cells via a CD45-specific mechanism in vitro, we demonstrated that this platform mediated safe, potent, and long-term gene modulation of HSCs in vivo in multiple mouse models. We further optimized this LNP platform in vitro to encapsulate and deliver CRISPR-based nucleic acid cargos. Finally, we showed that optimized and targeted LNPs enhanced gene editing at a proof-of-concept locus in fetal HSCs after a single in utero intravenous injection. By targeting HSCs in vivo during fetal development, our Systematically optimized Targeted Editing Machinery (STEM) LNPs may provide a translatable strategy to treat monogenic blood diseases before birth.

Catalytic Light-Driven Strategy for Transforming Oximes to Carbonyls.

Oxime and carbonyl functional groups serve as powerful chemical hubs for constructing complex synthetic targets and valuable molecular scaffolds. In furthering this value, we report a photopromoted catalytic deoximation protocol for converting oximes and their derivatives to carbonyl functional groups. This strategic approach benefits from the use of renewable light energy input and ambient air conditions, in addition to demonstrating good substrate scope, functional group tolerance, and product yields. In offering, insights into these reactivity mechanistic studies are communicated, and the value of this protocol is further shown through one-pot operations.

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia.

Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either "high risk" or "low risk" in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.

A Boron Scan of Ethyl Acetoacetate Leads to Versatile Building Blocks.

Discovered in the 19th century, ethyl acetoacetate has been central to the development of organic chemistry, including its pedagogy and applications. In this study, we present borylated derivatives of this venerable molecule. A boron handle has been installed at either α ${{\rm \alpha }}$ - or ß ${\beta }$ -position of acetoacetate by homologation of acyl-MIDA (N-methyliminodiacetic acid) boronates with diazoacetates. Either alkyl or boryl groups were found to migrate with regiochemistry being a function of the steric bulk of the diazo species. Boryl ß ${{\rm \beta }}$ -ketoesters can be further modified into borylated pyrazolones and oximes, thereby expanding the synthetic toolkit and offering opportunities for additional modifications.