CORAL model for predicting human plasma protein binding (square-root of fraction unbound) for CS1 compounds.
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ABSTRACT:
SUBMITTER: Domenico Gadaleta
PROVIDER: S-TOXR903 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Large-scale initiatives for obtaining spatial protein structures by experimental or computational means have accentuated the need for the critical assessment of protein structure determination and prediction methods. These include blind test projects such as the critical assessment of protein structure prediction (CASP) and the critical assessment of protein structure determination by nuclear magnetic resonance (CASD-NMR). An important aim is to establish structure validation criteria that can reliably assess the accuracy of a new protein structure. Various quality measures derived from the coordinates have been proposed. A universal structural quality assessment method should combine multiple individual scores in a meaningful way, which is challenging because of their different measurement units. Here, we present a method based on a generalized linear model (GLM) that combines diverse protein structure quality scores into a single quantity with intuitive meaning, namely the predicted coordinate root-mean-square deviation (RMSD) value between the present structure and the (unavailable) "true" structure (GLM-RMSD). For two sets of structural models from the CASD-NMR and CASP projects, this GLM-RMSD value was compared with the actual accuracy given by the RMSD value to the corresponding, experimentally determined reference structure from the Protein Data Bank (PDB). The correlation coefficients between actual (model vs. reference from PDB) and predicted (model vs. "true") heavy-atom RMSDs were 0.69 and 0.76, for the two datasets from CASD-NMR and CASP, respectively, which is considerably higher than those for the individual scores (-0.24 to 0.68). The GLM-RMSD can thus predict the accuracy of protein structures more reliably than individual coordinate-based quality scores.
| S-EPMC3324767 | biostudies-literature
Project description:ObjectivesTo describe the unbound and total flucloxacillin pharmacokinetics in critically ill patients and to define optimal dosing strategies.Patients and methodsObservational multicentre study including a total of 33 adult ICU patients receiving flucloxacillin, given as intermittent or continuous infusion. Pharmacokinetic sampling was performed on two occasions on two different days. Total and unbound flucloxacillin concentrations were measured and analysed using non-linear mixed-effects modelling. Serum albumin was added as covariate on the maximum binding capacity and endogenous creatinine clearance (CLCR) as covariate for renal function. Monte Carlo simulations were performed to predict the unbound flucloxacillin concentrations for different dosing strategies and different categories of endogenous CLCR.ResultsThe measured unbound concentrations ranged from 0.2 to 110 mg/L and the observed unbound fraction varied between 7.0% and 71.7%. An integral two-compartmental linear pharmacokinetic model based on total and unbound concentrations was developed. A dose of 12 g/24 h was sufficient for 99.9% of the population to achieve a concentration of >2.5 mg/L (100% fT>5×MIC, MIC = 0.5 mg/L).ConclusionsCritically ill patients show higher unbound flucloxacillin fractions and concentrations than previously thought. Consequently, the risk of subtherapeutic exposure is low.
| S-EPMC8598283 | biostudies-literature
Project description:We determined for the first time the profiles of the nine most abundant unbound FFAs (FFAus) in human plasma. Profiles were determined for a standard reference plasma of pooled healthy adults for which the Lipid MAPSMAPS Consortium had determined the total FFA profiles. Measurements were performed by using 20 different acrylodan-labeled fatty acid binding protein mutants (probes), which have complementary specificities for the nine FFAs that comprise more than 96% of long-chain plasma FFA. The acrylodan fluorescence emission for each probe changes upon binding a FFAu. The plasma concentrations of each of the nine FFAus were determined by combining the measured fluorescence ratios of the 20 probes. The total molar FFAu concentration accounted for <10-5 of the total FFA concentration, and the mole fractions of the FFAu profiles were substantially different than the total FFA profiles. Myristic acid, for example, comprises 22% of the unbound versus 2.8% of the total. The most surprising difference is our finding of zero unbound cis-9-palmitoleic acid (POA), whereas the total POA was 7.2%. An unidentified plasma component appears to specifically prevent the release of POA. FFAus are the physiologically active FFAs, and plasma FFAu profiles may provide novel information about human health.
| S-EPMC5335587 | biostudies-literature
Project description:We present the application of seven binding-site prediction algorithms to a meticulously curated dataset of ligand-bound and ligand-free crystal structures for 304 unique protein sequences (2528 crystal structures). We probe the influence of starting protein structures on the results of binding-site prediction, so the dataset contains a minimum of two ligand-bound and two ligand-free structures for each protein. We use this dataset in a brief survey of five geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITEcsc, Fpocket, Depth, AutoSite, and Kalasanty. Distributions of the F scores and Matthew's correlation coefficients for ligand-bound versus ligand-free structure performance show no statistically significant difference in structure type versus performance for most methods. Only Fpocket showed a statistically significant but low magnitude enhancement in performance for holo structures. Lastly, we found that most methods will succeed on some crystal structures and fail on others within the same protein family, despite all structures being relatively high-quality structures with low structural variation. We expected better consistency across varying protein conformations of the same sequence. Interestingly, the success or failure of a given structure cannot be predicted by quality metrics such as resolution, Cruickshank Diffraction Precision index, or unresolved residues. Cryptic sites were also examined.
| S-EPMC7522209 | biostudies-literature
Project description:The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge, in the form of a prior distribution on an uncertainty class of feature-label distributions to which the true, but unknown, feature-distribution belongs, can facilitate accurate error estimation (in the mean-square sense) in circumstances where accurate completely model-free error estimation is impossible. This paper provides analytic asymptotically exact finite-sample approximations for various performance metrics of the resulting Bayesian Minimum Mean-Square-Error (MMSE) error estimator in the case of linear discriminant analysis (LDA) in the multivariate Gaussian model. These performance metrics include the first, second, and cross moments of the Bayesian MMSE error estimator with the true error of LDA, and therefore, the Root-Mean-Square (RMS) error of the estimator. We lay down the theoretical groundwork for Kolmogorov double-asymptotics in a Bayesian setting, which enables us to derive asymptotic expressions of the desired performance metrics. From these we produce analytic finite-sample approximations and demonstrate their accuracy via numerical examples. Various examples illustrate the behavior of these approximations and their use in determining the necessary sample size to achieve a desired RMS. The Supplementary Material contains derivations for some equations and added figures.
| S-EPMC3979595 | biostudies-literature
Project description: Not available
| S-TOXR915 | biostudies-other
Project description:Background and objectiveTherapeutic drug monitoring of tacrolimus whole-blood concentrations is standard care in thoracic organ transplantation. Nevertheless, toxicity may appear with alleged therapeutic concentrations possibly related to variability in unbound concentrations. However, pharmacokinetic data on unbound concentrations are not available. The objective of this study was to quantify the pharmacokinetics of whole-blood, total, and unbound plasma tacrolimus in patients early after heart and lung transplantation.MethodsTwelve-hour tacrolimus whole-blood, total, and unbound plasma concentrations of 30 thoracic organ recipients were analyzed with high-performance liquid chromatography-tandem mass spectrometry directly after transplantation. Pharmacokinetic modeling was performed using non-linear mixed-effects modeling.ResultsPlasma concentration was < 1% of the whole-blood concentration. Maximum binding capacity of erythrocytes was directly proportional to hematocrit and estimated at 2700 pg/mL (95% confidence interval 1750-3835) with a dissociation constant of 0.142 pg/mL (95% confidence interval 0.087-0.195). The inter-individual variability in the binding constants was considerable (27% maximum binding capacity, and 29% for the linear binding constant of plasma).ConclusionsTacrolimus association with erythrocytes was high and suggested a non-linear distribution at high concentrations. Monitoring hematocrit-corrected whole-blood tacrolimus concentrations might improve clinical outcomes in clinically unstable thoracic organ transplants.Clinical trial registrationNTR 3912/EudraCT 2012-001909-24.
| S-EPMC7292814 | biostudies-literature
Project description:Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.
| S-EPMC6906481 | biostudies-literature