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Optimization of an ammonia assay based on transmembrane pH-gradient polymersomes.

ABSTRACT: Reliable ammonia quantification assays are essential for monitoring ammonemia in patients with liver diseases. In this study, we describe the development process of a microplate-based assay for accurate, precise, and robust ammonia quantification in biological fluids, following regulatory guidelines on bioanalytical method validation. The assay is based on transmembrane pH-gradient polymersomes that encapsulate a pH-sensitive ratiometric fluorophore, the fluorescence signal of which correlates with the ammonia concentration in the sample. Using a four-parameter logistic regression, the assay had a large quantification range (30-800 μM ammonia). As for selectivity, the presence of amino acids or pyruvate (up to clinically relevant concentrations) showed no assay interference. In samples with low bilirubin levels, polymersomes containing the fluorophore pyranine provided accurate ammonia quantification. In samples with high bilirubin concentrations, billirubin's optical interference was alleviated when replacing pyranine with a close to near-infrared hemicyanine fluorophore. Finally, the assay could correctly retrieve the ammonia concentration in ammonia-spiked human plasma samples, which was confirmed by comparing our measurements with the data obtained using a commercially available point-of-care device for ammonia.

SUBMITTER: Spyrogianni A

PROVIDER: S-EPMC8586157 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional-step-restricted second-order truncated expansion-molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In this paper, we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect, the GEK procedure will be used to mimic the behavior of a conventional second-order scheme but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimizations to facilitate restricted-variance optimizations. A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two: the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, full Baker-TS, and S22 test suites, at the density functional theory and second-order Møller-Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected.

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Optimization of an ammonia assay based on transmembrane pH-gradient polymersomes.

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