Phase I Study of Pazopanib in Patients with Advanced Solid Tumors and Hepatic Dysfunction: A National Cancer Institute Organ Dysfunction Working Group Study

Pazopanib is a potent, multi-targeted receptor tyrosine kinase inhibitor; however, there is limited information regarding the effects of liver function on pazopanib metabolism and pharmacokinetics (PK). The objective of this study was to establish the maximum tolerated dose (MTD) and PK profile of pazopanib in patients with varying degrees of hepatic dysfunction

Data science-guided investigations of synthetic methodologies

Machine learning (ML) has become indispensable in organic chemistry for optimizing synthetic processes, elucidating reaction mechanisms, and predicting reactivity. The ability of ML to analyze obscure patterns and generate predictive models, coupled with the large-scale dataset generation that can be accomplished via high-throughput experimentation (HTE), can prove valuable for gaining a deeper understanding of chemical reactivity. Despite its potential, several key challenges remain for application of ML in organic chemistry, including: modeling of mechanistically ambiguous or substrate-dependent reactions, for which traditional mechanistic studies may be challenging; and modeling of reactions with strong interaction effects, wherein different substrates exhibit varying sensitivities to changes in reaction conditions.To explore the first challenge, we investigated the highly substrate-dependent Chan-Evans-Lam (CEL) coupling. Through the design and application of an unsupervised learning workflow, we systematically selected diverse substrates for high-throughput data collection and modeling, resulting in a dataset of 3,552 reactions. This diverse dataset allowed for the identification of broadly applicable conditions for the CEL coupling of primary sulfonamides. We found that larger datasets or different featurization techniques may be necessary to achieve high accuracy in yield regression modeling. Nevertheless, a regression model was successfully able to predict the yield of out-of-sample substrates with errors within experimental uncertainty; close inspection of poorly predicted substrates allowed us to put forth hypotheses for the model’s shortcomings. We also explored the challenge of modeling interaction effects. Study of a simulated high-throughput experimentation dataset revealed that irrelevant features pose a significant obstacle to learning interaction effects with common ML algorithms. To overcome this challenge, we proposed a two-part statistical modeling approach: classical analysis of variance to identify systematic effects that impact yield, followed by regression of individual effects using chemistry-informed features. Applying this methodology to a published alcohol deoxyfluorination dataset enhanced our understanding of interaction dynamics and ultimately resulted in a more accurate and generalizable model. Taken together, these studies offer insights into the CEL coupling of primary sulfonamides and alcohol deoxyfluorination with sulfonyl fluorides. Furthermore, they offer valuable data science tools for modeling organic chemistry datasets and guidelines for dataset design in future studies

Vinyl-fluorene Molecular Wires for Voltage Imaging with Enhanced Sensitivity and Reduced Phototoxicity

Fluorescent voltage indicators are an attractive alternative for studying the electrical activity of excitable cells; however, the development of indicators that are both highly sensitive and low in toxicity over long-term experiments remains a challenge. Previously, we reported a fluorene-based voltage-sensitive fluorophore that exhibits much lower phototoxicity than previous voltage indicators in cardiomyocyte monolayers, but suffers from low sensitivity to membrane potential changes. Here, we report that the addition of a single vinyl spacer in the fluorene molecular wire scaffold improves the voltage sensitivity 1.5- to 3.5-fold over fluorene-based voltage probes. Furthermore, we demonstrate the improved ability of the new vinyl-fluorene VoltageFluors (v-fVFs) to monitor action potential kinetics in both mammalian neurons and human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Addition of the vinyl spacer between the aniline donor and fluorene monomer results in indicators that are significantly less phototoxic in cardiomyocyte monolayers. These results demonstrate how structural modification to the voltage sensing domain have a large effect on improving the overall properties of molecular wire-based voltage indicators. </p

A machine learning approach to model interaction effects: development and application to alcohol deoxyfluorination

The application of machine learning (ML) techniques to model high-throughput experimentation (HTE) datasets has seen a recent rise in popularity. Nevertheless, the ability to model the interplay between reaction components, known as interaction effects, with ML remains an outstanding challenge. Using a simulated HTE dataset, we find that the presence of irrelevant features poses a strong obstacle to learning interaction effects with common ML algorithms. To address this problem, we propose a two-part statistical modeling approach for HTE datasets: classical analysis of variance (ANOVA) of the experiment to identify systematic effects that impact reaction yield across the experiment, followed by regression of individual effects using chemistry-informed features. To illustrate this methodology, we use our previously published alcohol deoxyfluorination dataset comprising 740 reactions to build compact, interpretable regression models that account for each significant effect observed in the dataset. We achieve a sizeable performance boost compared to our previously published Random Forest model, reducing mean absolute error (MAE) from 18.1% to 13.4% and root mean squared error (RMSE) from 21.7% to 16.5% on a newly generated test set. Finally, we demonstrate that this approach can facilitate the generation of new mechanistic hypotheses which, when probed experimentally, can lead to a deeper understanding of chemical reactivity

New Molecular Scaffolds for Fluorescent Voltage Indicators

The ability to non-invasively monitor membrane potential dynamics in excitable cells like neurons and cardiomyocytes promises to revolutionize our understanding of the physiology and pathology of the brain and heart. Here, we report the design, synthesis, and application of a new class of fluorescent voltage indicator that makes use of a fluorene-based molecular wire as a voltage sensing domain to provide fast and sensitive measurements of membrane potential in both mammalian neurons and human-derived cardiomyocytes. We show that the best of the new probes, fluorene VoltageFluor 2 (fVF 2) readily reports on action potentials in mammalian neurons, detects perturbations to cardiac action potential waveform in human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes, shows a substantial decrease in phototoxicity compared to existing molecular wire-based indicators, and can monitor cardiac action potentials for extended periods of time. Together, our results demonstrate the generalizability of a molecular wire approach to voltage sensing and highlights the utility of fVF 2 for interrogating membrane potential dynamics. </div

Using Data Science to Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)–C(sp3) bond formation. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because non-standardized sets of aryl bromides are used in scope evaluation. Herein we report a Ni/photoredox-catalyzed alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. We describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing the scope examples from published Ni/photoredox methods on this chemical space, we identify areas of sparse coverage and high/low yields, enabling comparisons between prior art and this method. We demonstrate that the systematically-selected scope of aryl bromides can be used to quantify population-wide reactivity trends with supervised ML

Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)-C(sp3) bond formation. While many of these methods typically employ aryl bromides as the C(sp2) coupling partner, a variety of aliphatic radical sources have been investigated. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because nonstandardized sets of aryl bromides are used in scope evaluation. Herein, we report a Ni/photoredox-catalyzed (deutero)methylation and alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. Reaction development, mechanistic studies, and late-stage derivatization of a biologically relevant aryl chloride, fenofibrate, are presented. Then, we describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing scope examples from published Ni/photoredox methods on this same chemical space, we identify areas of sparse coverage and high versus low average yields, enabling comparisons between prior art and this new method. Additionally, we demonstrate that the systematically selected scope of aryl bromides can be used to quantify population-wide reactivity trends and reveal sources of possible functional group incompatibility with supervised machine learning

Photoredox Cyanomethylation of Indoles: Catalyst Modification and Mechanism

The direct cyanomethylation of indoles at the 2- or 3-position was achieved via photoredox catalysis. The versatile nitrile synthon is introduced as a radical generated from bromoacetonitrile, a photocatalyst, and blue LED as a light source. The mechanism of the reaction is explored by determination of the Stern-Volmer quenching constants. By combining photophysical data and mass spectrometry to follow the catalyst decomposition, the catalyst ligands were tuned to enable synthetically useful yields of radical coupling products. A range of indole substrates with alkyl, aryl, halogen, ester, and ether functional groups participate in the reaction, affording products in 16-90% yields. The reaction allows the rapid construction of synthetically useful cyanomethylindoles, products that otherwise require several synthetic steps