Correction: Applying Linear and Non-Linear Methods for Parallel Prediction of Volume of Distribution and Fraction of Unbound Drug.
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ABSTRACT:
SUBMITTER: Del Amo EM
PROVIDER: S-EPMC4624968 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Volume of distribution and fraction unbound are two key parameters in pharmacokinetics. The fraction unbound describes the portion of free drug in plasma that may extravasate, while volume of distribution describes the tissue access and binding of a drug. Reliable in silico predictions of these pharmacokinetic parameters would benefit the early stages of drug discovery, as experimental measuring is not feasible for screening purposes. We have applied linear and nonlinear multivariate approaches to predict these parameters: linear partial least square regression and non-linear recursive partitioning classification. The volume of distribution and fraction of unbound drug in plasma are predicted in parallel within the model, since the two are expected to be affected by similar physicochemical drug properties. Predictive models for both parameters were built and the performance of the linear models compared to models included in the commercial software Volsurf+. Our models performed better in predicting the unbound fraction (Q(2) 0.54 for test set compared to 0.38 with Volsurf+ model), but prediction accuracy of the volume of distribution was comparable to the Volsurf+ model (Q(2) of 0.70 for test set compared to 0.71 with Volsurf+ model). The nonlinear classification models were able to identify compounds with a high or low volume of distribution (sensitivity 0.81 and 0.71, respectively, for test set), while classification of fraction unbound was less successful. The interrelationship between the volume of distribution and fraction unbound is investigated and described in terms of physicochemical descriptors. Lipophilicity and solubility descriptors were found to have a high influence on both volume of distribution and fraction unbound, but with an inverse relationship.
| S-EPMC3792104 | biostudies-literature
Project description:Sex differences in 116 local gray matter volumes (GMVOL) were assessed in 444 males and 444 females without correcting for total intracranial volume (TIV) or after adjusting the data with the scaling, proportions, power-corrected proportions (PCP), and residuals methods. The results confirmed that only the residuals and PCP methods completely eliminate TIV-variation and result in sex-differences that are "small" (∣d∣ < 0.3). Moreover, as assessed using a totally independent sample, sex differences in PCP and residuals adjusted-data showed higher replicability ([Formula: see text] 93%) than scaling and proportions adjusted-data [Formula: see text] 68%) or raw data ([Formula: see text] 45%). The replicated effects were meta-analyzed together and confirmed that, when TIV-variation is adequately controlled, volumetric sex differences become "small" (∣d∣ < 0.3 in all cases). Finally, we assessed the utility of TIV-corrected/ TIV-uncorrected GMVOL features in predicting individuals' sex with 12 different machine learning classifiers. Sex could be reliably predicted (> 80%) when using raw local GMVOL, but also when using scaling or proportions adjusted-data or TIV as a single predictor. Conversely, after properly controlling TIV variation with the PCP and residuals' methods, prediction accuracy dropped to [Formula: see text] 60%. It is concluded that gross morphological differences account for most of the univariate and multivariate sex differences in GMVOL.
| S-EPMC7395772 | biostudies-literature
Project description:PurposeThe goal was to assess, for lipophilic drugs, the impact of logP on human volume of distribution at steady-state (VDss) predictions, including intermediate fut and Kp values, from six methods: Oie-Tozer, Rodgers-Rowland (tissue-specific Kp and only muscle Kp), GastroPlus, Korzekwa-Nagar, and TCM-New.MethodA sensitivity analysis with focus on logP was conducted by keeping pKa and fup constant for each of four drugs, while varying logP. VDss was also calculated for the specific literature logP values. Error prediction analysis was conducted by analyzing prediction errors by source of logP values, drug, and overall values.ResultsThe Rodgers-Rowland methods were highly sensitive to logP values, followed by GastroPlus and Korzekwa-Nagar. The Oie-Tozer and TCM-New methods were only modestly sensitive to logP. Hence, the relative performance of these methods depended upon the source of logP value. As logP values increased, TCM-New and Oie-Tozer were the most accurate methods. TCM-New was the only method that was accurate regardless of logP value source. Oie-Tozer provided accurate predictions for griseofulvin, posaconazole, and isavuconazole; GastroPlus for itraconazole and isavuconazole; Korzekwa-Nagar for posaconazole; and TCM-New for griseofulvin, posaconazole, and isavuconazole. Both Rodgers-Rowland methods provided inaccurate predictions due to the overprediction of VDss.ConclusionsTCM-New was the most accurate prediction of human VDss across four drugs and three logP sources, followed by Oie-Tozer. TCM-New showed to be the best method for VDss prediction of highly lipophilic drugs, suggesting BPR as a favorable surrogate for drug partitioning in the tissues, and which avoids the use of fup.
| S-EPMC11196289 | 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:BackgroundB-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes.ResultsAfter data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available.ConclusionsWe applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.
| S-EPMC7371472 | biostudies-literature
Project description:The intracellular unbound inhibitor concentration ([I]unbound,cell) is the most relevant concentration for predicting the inhibition of hepatic efflux transporters. However, the intracellular unbound fraction of inhibitor in hepatocytes (fu,cell,inhibitor) is not routinely determined. Studies are needed to evaluate the benefit of measuring fu,cell,inhibitor and using [I]unbound,cell versus intracellular total inhibitor concentration ([I]total,cell) when predicting inhibitory effects. This study examined the benefit of using [I]unbound,cell to predict hepatocellular bile acid disposition. Cellular total concentrations of taurocholate ([TCA]total,cell), a prototypical bile acid, were simulated using pharmacokinetic parameters estimated from sandwich-cultured human hepatocytes. The effect of various theoretical inhibitors was simulated by varying ([I]total,cell/ half maximal inhibitory concentration [IC50]) values. In addition, the fold change was calculated as the simulated [TCA]total,cell when fu,cell,inhibitor = 1 divided by the simulated [TCA]total,cell when fu,cell,inhibitor = 0.5-0.01. The lowest ([I]total,cell/IC50) value leading to a >2-fold change in [TCA]total,cell was chosen as a cutoff, and a framework was developed to categorize risk inhibitors for which the measurement of fu,cell,inhibitor is optimal. Fifteen compounds were categorized, 5 of which were compared with experimental observations. Future work is needed to evaluate this framework based on additional experimental data. In conclusion, the benefit of measuring fu,cell,inhibitor to predict hepatic efflux transporter-mediated drug-bile acid interactions can be determined a priori.
| S-EPMC5617730 | biostudies-literature
Project description:Interventions: PET scanning, kinetic analysis, respiratory motion correction. This involves 60 additional minutes of PET scanning (after the routine PET tracer injection up until the usual time of commencement of the routine PET scan at 60 minutes post injection). The information for kinetic analysis and respiratory motion correction are obtained during this 60 minute scan.
Primary outcome(s): Number of F18-Flourodexoyglucose (FDG) avid cancer secondaries detected in the liver with the modified method compared with standard FDG PET imaging.[Baseline only]
Study Design: Purpose: Diagnosis; Allocation: Non-randomised trial; Masking: Open (masking not used);Assignment: Single group
| 2456122 | ecrin-mdr-crc
Project description:The objective of this work was to establish that unbound maximum concentrations may be reasonably predicted from a combination of computed molecular properties assuming subcutaneous (SQ) dosing. Additionally, we show that the maximum unbound plasma and brain concentrations may be projected from a mixture of in vitro absorption, distribution, metabolism, excretion experimental parameters in combination with computed properties (volume of distribution, fraction unbound in microsomes). Finally, we demonstrate the utility of the underlying equations by showing that the maximum total plasma concentrations can be projected from the experimental parameters for a set of compounds with data collected from clinical research.
| S-EPMC4025848 | biostudies-other
Project description:IntroductionCardiopulmonary exercise testing (CPET) plays an important role in properly phenotyping signs and symptoms of heart failure with preserved ejection fraction (HFpEF). The prognostic value of CPET is strengthened when accompanied by cardiac hemodynamic measurements. Although recognized as the "gold" standard, cardiac catheterization is impractical for routine CPET. Thus, advancing the scientific/methodologic understanding of noninvasive techniques for exercise cardiac hemodynamic assessment is clinically impactful in HFpEF. This study tested the concurrent validity of noninvasive acetylene gas (C2H2) uptake, echocardiography (ECHO), and oxygen pulse (O2pulse) for measuring/predicting exercise stroke volume (SV) in HFpEF.MethodsEighteen white HFpEF and 18 age-/sex-matched healthy controls participated in upright CPET (ages, 69 ± 9 yr vs 63 ± 9 yr). At rest, 20 W, and peak exercise, SV was measured at steady-state via C2H2 rebreathe (SVACET) and ECHO (SVECHO), whereas O2pulse was derived (=V˙O2/HR).ResultsResting relationships between SVACET and SVECHO, SVECHO and O2pulse, or SVACET and O2pulse were significant in HFpEF (R = 0.30, 0.36, 0.67), but not controls (R = 0.07, 0.01, 0.09), respectively. Resting relationships persisted to 20 W in HFpEF (R = 0.70, 0.53, 0.70) and controls (R = 0.05, 0.07, 0.21), respectively. Peak exercise relationships were significant in HFpEF (R = 0.62, 0.24, 0.64), but only for SVACET versus O2pulse in controls (R = 0.07, 0.04, 0.33), respectively. Standardized standard error of estimate between techniques was strongest in HFpEF at 20 W: SVACET versus SVECHO = 0.57 ± 0.22; SVECHO versus O2pulse = 0.71 ± 0.28; SVACET versus O2pulse = 0.56 ± 0.22.ConclusionsConstituting a clinically impactful step towards construct validation testing, these data suggest SVACET, SVECHO, and O2pulse demonstrate moderate-to-strong concurrent validity for measuring/predicting exercise SV in HFpEF.
| S-EPMC5561476 | 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