Dataset Information

Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients.

ABSTRACT: Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quantify the yield of reactions by HPLC without needing to isolate the product(s) by combining a machine learning model for molar extinction coefficient estimation, and both UV-vis absorption and mass spectra. We demonstrate the method for a variety of reactions important in medicinal and process chemistry, including amide couplings, palladium catalyzed cross-couplings, nucleophilic aromatic substitutions, aminations, and heterocycle syntheses. The reactions were all performed using an automated synthesis and isolation platform. Calibration-free methods such as the presented approach are necessary for such automated platforms to be able to discover, characterize, and optimize reactions automatically.

SUBMITTER: McDonald MA

PROVIDER: S-EPMC11220585 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients.

McDonald Matthew A MA Koscher Brent A BA Canty Richard B RB Jensen Klavs F KF

Chemical science 20240529 26

Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quanti ...[more]

PMID: 38966367

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Project description:Extinction learning refers to the phenomenon that a previously learned response to an environmental stimulus, for example the expression of an aversive behavior upon exposure to a specific context, is reduced when the stimulus is repeatedly presented in the absence of a previously paired aversive event. Extinction of fear memories has been implicated with the treatment of anxiety disease but the molecular processes that underlie fear extinctionare only beginning to emerge. Here we show that fear extinction initiates up-regulation of hippocampal insulin-growth factor 2 (Igf2) and down-regulation of insulin-growth factor binding protein 7 (Igfbp7). In line with this observation we demonstrate that IGF2 facilitates fear extinction, while IGFBP7 impairs fear extinction in an IGF2-dependent manner. Furthermore, we identify one cellular substrate of altered IGF2-signaling during fear extinction. To this end we show that fear extinction-induced IGF2/IGFBP7-signaling promotes the survival of 17-19 day-old newborn hippocampal neurons. In conclusion, our data suggests that therapeutic strategies that enhance IGF2-signaling and adult neurogenesis might be suitable to treat disease linked to excessive fear memory. We employed mice to investigate fear extinction in the hippocampus-dependent contextual fear conditioning paradigm. To this end, male C57BL/6J mice were exposed to the fear conditioning box (context) followed by an electric foot-shock which elicits the acquisition of conditioned contextual fear. For extinction training animals were repeatedly reexposed to the conditioned context on consecutive days (24h interval) without receiving the footshockagain (extinction trial, E). This procedure eventually results in the decline of the aversive freezing behavior. Mice that were exposed to the conditioning context without receiving fear conditioning training served as control groups. To gain a better understanding of the molecular processes underlying fear extinction we performed a genome-wide analysis of the hippocampal transcriptome during fear extinction. In the employed paradigm fear extinction is a gradual process. To capture the longitudinal course of fear extinction we decided to perform hippocampal microarray analysis at two time points: (1) After the first extinction trial (E1) when animals display high levels of aversive freezing behavior and (2) at the extinction trial on which the freezing behavior was significantly reduced when compared to E1. This extinction trial, in the case of this experiment E5, we termed “extinction trial low freezing” (ELF). Mice that were exposed to the conditioning context without receiving fear conditioning training served as control groups (3). For all three groups we hybridized 5 samples (biological replicates).

Dataset Information

Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients.

Publications

Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients.

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