Prediction of Pharmacokinetic Parameters

Мадан, А. К.; Dureja, Harish

doi:10.1007/978-1-62703-050-2_14

Cited by 8 publications

(3 citation statements)

References 97 publications

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“…In this study, the drug metabolism mentioned here referred to the metabolism at pharmacokinetic level. C max was a main parameter reflecting the absorption degree of the drug, T max made known the speed to absorb the drug, so the short T max meant high absorption rate (Madan & Dureja, 2012). The C max of ENR at LD 50 in the brain, liver, kidney, and muscle was 719.69, 3307.29, 1153.69, and 433.94 μg/g, respectively.…”

Section: Metabolism and Residue Differences Of Enr Due To Tissue And ...mentioning

confidence: 99%

Metabolism and residue differences of Enrofloxacin between the brain and peripheral tissues and the resulting brain damages in crucian carp (Carassius auratus var. Pengze)

Wan

Zhang

Wang

et al. 2022

Vet Pharm & Therapeutics

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This study aimed to explore the metabolism and residue differences of Enrofloxacin (ENR) at two doses between the brain and peripheral tissues (liver, kidney, and muscle) along with the brain damages caused by ENR in crucian carp (Carassius auratus var. Pengze). The concentrations of ENR in tissues were determined using a validated high‐performance liquid chromatography (HPLC) analysis. Relying on the hematoxylin–eosin (HE) staining method, brain damages caused by the drug were evaluated by the section of pathological tissue. Metabolism and residue results showed that ENR could be detected in the brain throughout the experiment both at median lethal dose (LD50 at 96 h, 1949.84 mg/kg) and safe dose (SD, 194.98 mg/kg), as well as in the three peripheral tissues. The maximum residue at LD50 followed the decreasing order of liver >kidney > brain > muscle. Although the Cmax of ENR at SD in the brain was significantly lower than that in other peripheral tissues (p < .05), it still reached 41.91 μg/g. The T1/2 of ENR in brain tissue at the same dose was both shorter than that in peripheral tissues. At LD50, the amount of ENR residues in brain was lower than that in peripheral tissues on the whole, except that it had been higher than in the muscle for the first 3 h. At SD, the drug residue in brain tissue was lower than that in peripheral tissues from 12 h to 960 h, but it exceeded the muscle and kidney at 1 h and 6 h, respectively. At 960 h, the residual amount of ENR at SD in the brain was 0.09 μg/g, while it was up to 0.15 μg/g following the oral administration at LD50. Demonstrated by the HE staining, there were pathological lesions caused by ENR in the brain at LD50, which were characterized by sparse neural network and increased staining of glial cells. The present results indicated that metabolism and residue of ENR in crucian carp were affected by the tissue type and drug dosage, and the ENR could also bring about histopathological changes in the brain.

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Section: Metabolism and Residue Differences Of Enr Due To Tissue And ...mentioning

confidence: 99%

Metabolism and residue differences of Enrofloxacin between the brain and peripheral tissues and the resulting brain damages in crucian carp (Carassius auratus var. Pengze)

Wan

Zhang

Wang

et al. 2022

Vet Pharm & Therapeutics

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“…The use of computational models to predict ADME parameters has grown rapidly during drug discovery because of the immense benefits in throughput and early application for drug design. − Several in silico models using statistical and machine learning approaches have been reported for human clearance prediction. − In these reports, machine learning has shown the potential to compete with the bottom-up approach. However, most published studies have applied relatively few computational approaches for creating in silico models.…”

Section: Introductionmentioning

confidence: 99%

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay

Kosugi

Hosea

2020

Mol. Pharmaceutics

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The in vitro–in vivo extrapolation (IVIVE) approach for predicting total plasma clearance (CLtot) has been widely used to rank order compounds early in discovery. More recently, a computational machine learning approach utilizing physicochemical descriptors and fingerprints calculated from chemical structure information has emerged, enabling virtual predictions even earlier in discovery. Previously, this approach focused more on in vitro intrinsic clearance (CLint) prediction. Herein, we directly compare these two approaches for predicting CLtot in rats. A structurally diverse set of 1114 compounds with known in vivo CLtot, in vitro CLint, and plasma protein binding was used as the basis for this evaluation. The machine learning models were assessed by validation approaches using the time- and cluster-split training and test sets, and five-fold cross validation. Assessed by five-fold validation, the random forest regression (RF) and radial basis function (RBF) models demonstrated better prediction performance in eight attempted machine learning models. The CLtot values predicted by the RF and RBF models were within two-fold of the observed values for 67.7 and 71.9% of cluster-split test set compounds, respectively, while the predictivity was worse in the time-split dataset. The predictivity of both models tended to be improved by incorporating in vitro parameters, unbound fraction in plasma (f u,p), and CLint. CLtot prediction utilizing in vitro CLint and the well-stirred model, correcting for the fraction unbound in blood, was substantially worse compared to machine learning approaches for the same cluster-split test set. The reason that CLtot is underestimated by IVIVE is not fully explained by considering the calculated microsomal unbound fraction (cfu,mic), extended clearance classification system (ECCS), and omitting high clearance compounds in excess of hepatic blood flow. The analysis suggests that in silico machine learning models may have the power to reduce reliance on or replace in vitro and in vivo studies for chemical structure optimization in early drug discovery.

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“…This work is based on the in silico approach, using data mining (or machine learning) methods to build models that predict the Vss using the properties of molecular structures of chemical compounds as the model features. Such a modeling approach is generally known as Quantitative Structure-Activity Relationship (QSAR) approach, with the special QSAR case here being the Quantitative Structure-Pharmacokinetic Relationship (QSPkR) modeling [ 11 , 12 ]. More precisely, we use two types of data mining methods – mainly decision tree-based regression methods, but also a feature selection method (see Methods section) – to produce QSPkR models that predict the Vss of chemical compounds.…”

Section: Introductionmentioning

confidence: 99%

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

2015

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BackgroundVolume of distribution is an important pharmacokinetic property that indicates the extent of a drug’s distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds’ molecular descriptors and the compounds’ tissue:plasma partition coefficients (Kt:p) – often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds’ molecular descriptors but also (a subset of) their predicted Kt:p values.ResultsComparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied.ConclusionsDecision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models.Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0054-x) contains supplementary material, which is available to authorized users.

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Prediction of Pharmacokinetic Parameters

Cited by 8 publications

References 97 publications

Metabolism and residue differences of Enrofloxacin between the brain and peripheral tissues and the resulting brain damages in crucian carp (Carassius auratus var. Pengze)

Metabolism and residue differences of Enrofloxacin between the brain and peripheral tissues and the resulting brain damages in crucian carp (Carassius auratus var. Pengze)

Direct Comparison of Total Clearance Prediction: Computational Machine Learning Model versus Bottom-Up Approach Using In Vitro Assay

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

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