Molecular prosthetics for CFTR designed for anion selectivity outperform amphotericin B in cultured cystic fibrosis airway epithelia.

The ion channel-forming natural product amphotericin B (AmB) can serve as a molecular prosthetic for the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel and thereby restore host defenses in cultured cystic fibrosis (CF) airway epithelia. This is despite the fact that the permeability of AmB-based channels favors cations, and these channels lose their capacity to increase airway surface liquid (ASL) pH in CF airway epithelia at high concentrations. We hypothesize that modifying such channels to favor anion permeability would make them more CFTR-like and thus increase their potential therapeutic effects compared to AmB.

Molecular prosthetics and CFTR modulators additively increase secretory HCO flux in cystic fibrosis airway epithelia.

Cystic Fibrosis (CF) is caused by loss-of-function mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR), an anion channel predominantly expressed on the apical membrane of epithelial cells. Reduced Cl and HCO secretion due to dysfunctional CFTR results in a decrease in lung function and is the leading cause of morbidity in individuals with CF. Recent therapies, known as highly effective CFTR modulator therapy (HEMT), help improve the lung function in individuals with specific CF-causing mutations by enhancing the folding, trafficking, and gating of CFTR.

Automated Iterative N─C and C─C Bond Formation.

Small molecule solutions to many contemporary societal challenges await discovery, but the artisanal and manual process via which this class of chemical matter is typically accessed limits the discovery of new functions. Automated assembly of (N-methyl iminodiacetic acid) MIDA or (tetramethyl N-methyl iminodiacetic acid) TIDA boronate building blocks via iterative C─C bond formation, an approach we call "block chemistry", alternatively enables generalized and automated preparation of many different types of small molecules in a modular fashion. But in its current form, this engine cannot also leverage nitrogen atoms as iteration handles.

Catalytic allylation of native hexoses and pentoses in water with indium.

Carbohydrates are an abundant, inexpensive and renewable biomass feedstock that could be a cornerstone for sustainable chemical manufacturing, but scalable and environmentally friendly methods that leverage these feedstocks are lacking. For example, 1-allyl sorbitol is the foundational building block for the polypropylene clarifying agent Millad NX 8000, which is produced on the multi-metric ton scale annually, but the manufacturing process at present requires superstoichiometric amounts of tin. The NX 8000 additives dominate about 80% of the global clarified polypropylene market and are used in concentrations of 0.

Closed-loop transfer enables artificial intelligence to yield chemical knowledge.

Artificial intelligence-guided closed-loop experimentation has emerged as a promising method for optimization of objective functions, but the substantial potential of this traditionally black-box approach to uncovering new chemical knowledge has remained largely untapped. Here we report the integration of closed-loop experiments with physics-based feature selection and supervised learning, denoted as closed-loop transfer (CLT), to yield chemical insights in parallel with optimization of objective functions. CLT was used to examine the factors dictating the photostability in solution of light-harvesting donor-acceptor molecules used in a variety of organic electronics applications, and showed fundamental insights including the importance of high-energy regions of the triplet state manifold.

Delocalized, asynchronous, closed-loop discovery of organic laser emitters.

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics.

Minimizing higher-order aggregation maximizes iron mobilization by small molecules.

The natural product hinokitiol mobilizes iron across lipid bilayers at low concentrations and restores hemoglobinization in iron transporter protein-deficient systems. But hinokitiol fails to similarly mobilize iron at higher concentrations, limiting its uses in chemical biology and medicine. Here we show that at higher concentrations, hinokitiol:Fe(III) complexes form large, higher-order aggregates, leading to loss of transmembrane iron mobilization.

Computational prediction of complex cationic rearrangement outcomes.

Recent years have seen revived interest in computer-assisted organic synthesis. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field, including examples leading to advanced natural products. Such methods typically operate on full, literature-derived 'substrate(s)-to-product' reaction rules and cannot be easily extended to the analysis of reaction mechanisms.

MIDA- and TIDA-Boronates Stabilize α-Radicals Through B-N Hyperconjugation.

Multifunctional organoboron compounds increasingly enable the simple generation of complex, Csp -rich small molecules. The ability of boron-containing functional groups to modify the reactivity of α-radicals has also enabled a myriad of chemical reactions. Boronic esters with vacant p-orbitals have a significant stabilizing effect on α-radicals due to delocalization of spin density into the empty orbital.

Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling.

General conditions for organic reactions are important but rare, and efforts to identify them usually consider only narrow regions of chemical space. Discovering more general reaction conditions requires considering vast regions of chemical space derived from a large matrix of substrates crossed with a high-dimensional matrix of reaction conditions, rendering exhaustive experimentation impractical. Here, we report a simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions.

Targeting fungal membrane homeostasis with imidazopyrazoindoles impairs azole resistance and biofilm formation.

Fungal infections cause more than 1.5 million deaths annually. With an increase in immune-deficient susceptible populations and the emergence of antifungal drug resistance, there is an urgent need for novel strategies to combat these life-threatening infections.

A small molecule redistributes iron in ferroportin-deficient mice and patient-derived primary macrophages.

Deficiencies of the transmembrane iron-transporting protein ferroportin (FPN1) cause the iron misdistribution that underlies ferroportin disease, anemia of inflammation, and several other human diseases and conditions. A small molecule natural product, hinokitiol, was recently shown to serve as a surrogate transmembrane iron transporter that can restore hemoglobinization in zebrafish deficient in other iron transporting proteins and can increase gut iron absorption in FPN1-deficient flatiron mice. However, whether hinokitiol can restore normal iron physiology in FPN1-deficient animals or primary cells from patients and the mechanisms underlying such targeted activities remain unknown.

Validation of a molecular assay to detect SARS-CoV-2 in saliva.

Aim: To validate a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) assay to detect SARS-CoV-2 in saliva in two independent Aotearoa New Zealand laboratories.

Methods: An RT-qPCR assay developed at University of Illinois Urbana-Champaign, USA, was validated in two New Zealand laboratories. Analytical measures, such as limit of detection (LOD) and cross-reactivity, were performed.

Mitigation of SARS-CoV-2 transmission at a large public university.

In Fall 2020, universities saw extensive transmission of SARS-CoV-2 among their populations, threatening health of the university and surrounding communities, and viability of in-person instruction. Here we report a case study at the University of Illinois at Urbana-Champaign, where a multimodal "SHIELD: Target, Test, and Tell" program, with other non-pharmaceutical interventions, was employed to keep classrooms and laboratories open. The program included epidemiological modeling and surveillance, fast/frequent testing using a novel low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD, and digital tools for communication and compliance.

Using automated synthesis to understand the role of side chains on molecular charge transport.

The development of next-generation organic electronic materials critically relies on understanding structure-function relationships in conjugated polymers. However, unlocking the full potential of organic materials requires access to their vast chemical space while efficiently managing the large synthetic workload to survey new materials. In this work, we use automated synthesis to prepare a library of conjugated oligomers with systematically varied side chain composition followed by single-molecule characterization of charge transport.

Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki-Miyaura Coupling.

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki-Miyaura coupling with heterocyclic building blocks─and a carefully selected database of >10,000 literature examples─we show that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases.

Digitizing Chemical Synthesis in 3D Printed Reactionware.

Chemistry digitization requires an unambiguous link between experiments and the code used to generate the experimental conditions and outcomes, yet this process is not standardized, limiting the portability of any chemical code. What is needed is a universal approach to aid this process using a well-defined standard that is composed of syntheses that are employed in modular hardware. Herein we present a new approach to the digitization of organic synthesis that combines process chemistry principles with 3D printed reactionware.

Automated iterative Csp-C bond formation.

Fully automated synthetic chemistry would substantially change the field by providing broad on-demand access to small molecules. However, the reactions that can be run autonomously are still limited. Automating the stereospecific assembly of Csp-C bonds would expand access to many important types of functional organic molecules.