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  • 1
    Language: English
    In: International Journal of Pharmaceutics, 15 October 2012, Vol.436(1-2), pp.711-720
    Description: This study presents regression and classification models to predict human intestinal absorption of 645 drug and drug like compounds using percentage human intestinal values from the published dataset by . The problem with this dataset and other datasets in the literature is there are more highly than poorly absorbed compounds. Any models developed using these datasets will be biased towards highly absorbed compounds and not applicable for use in industry where now more compounds are likely to be poorly absorbed. The study compared two training sets, TS1, a balanced (50:50) distribution of highly and poorly absorbed compounds created by under-sampling the majority high absorption compounds, with TS2, a randomly selected training set with biased distribution towards highly absorbed compounds. The regression results indicate that the best models were those developed using the balanced dataset (TS1). Also for classification, TS1 led to the most accurate models and the highest specificity value of 0.949. In comparison, TS2 led to the highest sensitivity with a value of 0.939. Thus, under-sampling the majority class of the highly absorbed compounds leads to a balanced training set (TS1) that can achieve more applicable in silico regression and classification models for the use in the industry.
    Keywords: Intestinal Absorption ; Qsar ; Oral Absorption ; Training Set ; Regression ; Classification ; Pharmacy, Therapeutics, & Pharmacology
    ISSN: 0378-5173
    E-ISSN: 1873-3476
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  • 2
    Language: English
    In: International Journal of Pharmaceutics, 15 September 2012, Vol.434(1-2), pp.280-291
    Description: methods are commonly used in order to estimate the extent of systemic absorption of chemicals through skin. Due to the wide variability of experimental procedures, types of skin and data analytical methods, the resulting permeation measures varies significantly between laboratories and individuals. Inter-laboratory and inter-individual variations with the measures of skin permeation lead to unreliable extrapolations to situations. This investigation aimed at a comprehensive assessment of the available data and development of validated models for skin flux of chemicals under various experimental and vehicle conditions. Following an exhaustive literature review, the human skin flux data were collated and combined with those from EDETOX database resulting in a dataset of a total of 536 flux reports. Quantitative structure–activity relationship techniques combined with data mining tools were used to develop models incorporating the effects of permeant molecular structure, properties of the vehicle, and the experimental conditions including the membrane thickness, finite/infinite exposure, skin pre-hydration and occlusion. The work resulted in statistically valid models for estimation of the skin flux from varying experimental conditions, including relevant real-world mixture exposure scenarios. The models indicated that the most prominent factors influencing flux values were the donor concentration, lipophilicity, size and polarity of the penetrant, and the melting and boiling points of the vehicle, with skin occlusion playing significant role in a non-linear way. The models will aid assessment of the utility of dermal absorption data collected under different conditions with broad implications on transdermal delivery research.
    Keywords: Qsar ; Skin ; Dermal ; Permeation ; Absorption ; In Vitro ; Decision Tree ; Finite Dosing ; Occlusion ; Hydration ; Pharmacy, Therapeutics, & Pharmacology
    ISSN: 0378-5173
    E-ISSN: 1873-3476
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  • 3
    Language: English
    In: BioImpacts, 01 February 2013, Vol.3(1), pp.21-27
    Description: Introduction: The prediction of plasma protein binding (ppb) is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half life. This study utilized Quantitative Structure – Activity Relationships (QSAR) for the prediction of plasma protein binding. Methods: Protein binding values for 794 compounds were collated from literature. The data was partitioned into a training set of 662 compounds and an external validation set of 132 compounds. Physicochemical and molecular descriptors were calculated for each compound using ACD labs/logD, MOE (Chemical Computing Group) and Symyx QSAR software packages. Several data mining tools were employed for the construction of models. These included stepwise regression analysis, Classification and Regression Trees (CART), Boosted trees and Random Forest. Results: Several predictive models were identified; however, one model in particular produced significantly superior prediction accuracy for the external validation set as measured using mean absolute error and correlation coefficient. The selected model was a boosted regression tree model which had the mean absolute error for training set of 13.25 and for validation set of 14.96. Conclusion: Plasma protein binding can be modeled using simple regression trees or multiple linear regressions with reasonable model accuracies. These interpretable models were able to identify the governing molecular factors for a high ppb that included hydrophobicity, van der Waals surface area parameters, and aromaticity. On the other hand, the more complicated ensemble method of boosted regression trees produced the most accurate ppb estimations for the external validation set.
    Keywords: Qsar ; Adme ; Distribution ; Protein Binding ; Albumin Binding ; Serum Proteins ; Biology
    ISSN: 2228-5652
    E-ISSN: 2228-5660
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  • 4
    Language: English
    In: The AAPS Journal, 2014, Vol.16(1), pp.65-78
    Description: Biliary excretion is one of the main elimination pathways for drugs and/or their metabolites. Therefore, an insight into the structural profile of cholephilic compounds through accurate modelling of the biliary excretion is important for the estimation of clinical pharmacokinetics in early stages of drug discovery. The aim of this study was to develop quantitative structure-activity relationships as computational tools for the estimation of biliary excretion and identification of the molecular properties controlling this process. The study used percentage of dose excreted intact into bile measured in vivo in rat for a diverse dataset of 217 compounds. Statistical techniques were multiple linear regression analysis, regression trees, random forest and boosted trees. A simple regression tree model generated using the CART algorithm was the most accurate in the estimation of the percentage of bile excretion of compounds, and this outperformed the more sophisticated boosted trees and random forest techniques. Analysis of the outliers indicated that the models perform best when lipophilicity is not too extreme (log P  〈 5.35) and for compounds with molecular weight above 280 Da. Molecular descriptors selected by all these models including the top ten incorporated in boosted trees and random forest indicated a higher biliary excretion for relatively hydrophilic compounds especially if they are anionic or cationic, and have a large molecular size. A statistically validated molecular weight threshold for potentially significant biliary excretion was above 348 Da.
    Keywords: bile ; biliary excretion ; CART ; pharmacokinetic ; QSAR
    E-ISSN: 1550-7416
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  • 5
    Language: English
    In: Xenobiotica, 03 July 2017, Vol.47(7), pp.614-631
    Description: 1. Biliary excretion of compounds is dependant on several transporter proteins for the active uptake of compounds from the blood into the hepatocytes. Organic anion-transporting polypeptides (OATPs) are some of the most abundant transporter proteins in the sinusoidal membrane and have been...
    Keywords: Qsar ; Oatp ; Anion Transporters ; Hepatic Uptake ; Excretion Into Bile ; Pharmacy, Therapeutics, & Pharmacology ; Chemistry
    ISSN: 0049-8254
    E-ISSN: 1366-5928
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  • 6
    In: Sharifi, Mohsen and Ghafourian, Taravat (2014) Estimation of Biliary Excretion of Foreign Compounds Using Properties of Molecular Structure. AAPS journal, 16 (1). pp. 65-78.
    Description: Biliary excretion is one of the main elimination pathways for drugs and/or their metabolites. Therefore, an insight into the structural profile of cholephilic compounds through accurate modelling of the biliary excretion is important for the estimation of clinical pharmacokinetics in early stages of drug discovery. The aim of this study was to develop quantitative structure-activity relationships as computational tools for the estimation of biliary excretion and identification of the molecular properties controlling this process. The study used percentage of dose excreted intact into bile measured in vivo in rat for a diverse dataset of 217 compounds. Statistical techniques were multiple linear regression analysis, regression trees, random forest and boosted trees. A simple regression tree model generated using the CART algorithm was the most accurate in the estimation of the percentage of bile excretion of compounds, and this outperformed the more sophisticated boosted trees and random forest techniques. Analysis of the outliers indicated that the models perform best when lipophilicity is not too extreme (log P 〈 5.35) and for compounds with molecular weight above 280 Da. Molecular descriptors selected by all these models including the top ten incorporated in boosted trees and random forest indicated a higher biliary excretion for relatively hydrophilic compounds especially if they are anionic or cationic, and have a large molecular size. A statistically validated molecular weight threshold for potentially significant biliary excretion was above 348 Da.
    Keywords: QA 76 Software, computer programming, ; QA76.76 Computer software ; QD Chemistry ; RM Therapeutics. Pharmacology ; RS Pharmacy and materia medica
    ISSN: 1550-7416
    Source: University of Kent
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  • 7
    In: Freitas, Alex A. and Limbu, Kriti and Ghafourian, Taravat (2015) Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients. Journal of Cheminformatics, 7 (6).
    Description: Background Volume 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. Results Comparison 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. Conclusions Decision 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.
    Keywords: Q335 Artificial intelligence
    ISSN: 1758-2946
    Source: University of Kent
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  • 8
    Language: English
    In: International Journal of Pharmaceutics, 2006, Vol.319(1), pp.82-97
    Description: An estimate of volume of distribution ( ) is of paramount importance both in drug choice as well as maintenance and loading dose calculations in therapeutics. It can also be used in the prediction of drug biological half life. This study employs quantitative structure–pharmacokinetic relationship (QSPR) techniques for the prediction of volume of distribution. Values of for 129 drugs were collated from the literature. Structural descriptors consisted of partitioning, quantum mechanical, molecular mechanical, and connectivity parameters calculated by specialized software and p values obtained from ACD labs/log database. Genetic algorithm and stepwise regression analyses were used for variable selection and model development. Models were validated using a leave-many-out procedure. QSPR analyses resulted in a number of significant models for acidic and basic drugs separately, and for all the drugs. Validation studies showed that mean fold error of predictions for the selected models were between 1.79 and 2.17. Although separate QSPR models for acids and bases resulted in lower prediction errors than models for all the drugs, the external validation study showed a limited applicability for the equation obtained for acids. Therefore, the universal model that requires only calculated structural descriptors was recommended. The QSPR model is able to predict the volume of distribution of drugs belonging to different chemical classes with a prediction error similar to that of the other more complicated prediction methods including the commonly practiced interspecies scaling. The structural descriptors in the model can be interpreted based on the known mechanisms of distribution and the molecular structures of the drugs.
    Keywords: Volume of Distribution ; Qspr ; Pharmacokinetics ; In Silico ; Prediction ; Pharmacy, Therapeutics, & Pharmacology
    ISSN: 0378-5173
    E-ISSN: 1873-3476
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  • 9
    Language: English
    In: European Journal of Medicinal Chemistry, Jan 27, 2015, Vol.90, p.751(15)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejmech.2014.12.006 Byline: Danielle Newby, Alex A. Freitas, Taravat Ghafourian Abstract: Oral absorption of compounds depends on many physiological, physiochemical and formulation factors. Two important properties that govern oral absorption are in vitro permeability and solubility, which are commonly used as indicators of human intestinal absorption. Despite this, the nature and exact characteristics of the relationship between these parameters are not well understood. In this study a large dataset of human intestinal absorption was collated along with in vitro permeability, aqueous solubility, melting point, and maximum dose for the same compounds. The dataset allowed a permeability threshold to be established objectively to predict high or low intestinal absorption. Using this permeability threshold, classification decision trees incorporating a solubility-related parameter such as experimental or predicted solubility, or the melting point based absorption potential (MPbAP), along with structural molecular descriptors were developed and validated to predict oral absorption class. The decision trees were able to determine the individual roles of permeability and solubility in oral absorption process. Poorly permeable compounds with high solubility show low intestinal absorption, whereas poorly water soluble compounds with high or low permeability may have high intestinal absorption provided that they have certain molecular characteristics such as a small polar surface or specific topology. Author Affiliation: (a) Medway School of Pharmacy, Universities of Kent and Greenwich, Chatham, Kent ME4 4TB, UK (b) School of Computing, University of Kent, Canterbury, Kent CT2 7NF, UK (c) Drug Applied Research Centre and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran Article History: Received 24 April 2014; Revised 2 December 2014; Accepted 3 December 2014
    Keywords: Permeability ; Drugstores ; Pharmacy
    ISSN: 0223-5234
    Source: Cengage Learning, Inc.
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  • 10
    In: Journal of Cheminformatics, 2016, Vol.8
    Description: The ability to define the regions of chemical space where a predictive model can be safely used is a necessary condition to assure the reliability of new predictions. This implies that reliability must be determined across chemical space in the attempt to localize “safe” and “unsafe” regions for prediction. As a result we devised an applicability domain technique that addresses the data locally instead of handling it as a whole—the reliability-density neighbourhood (RDN). The main novelty aspect of this method is that it characterizes each single training instance according to the density of its neighbourhood in the training set, as well as its individual bias and precision. By scanning through the chemical space (by iteratively increasing the applicability domain area), it was observed that new test compounds are successively included into the applicability domain region in such a manner that strongly correlates to their predictive performance. This allows the mapping of local reliability across different locations in the training set space, and thus allows identifying regions where the model has low reliability. This method also showed matching profiles between two external sets, which is an indication that it performs robustly with new data. Another novel aspect in this technique is that it is paired with a specific feature selection algorithm. As a result, the impact of the feature set used was studied from which the top 20 features selected by ReliefF yielded the best results, as opposed to using the model’s features or the entire feature set as commonly done. As the third novel aspect, in this work we propose a new scoring function to help evaluate the quality of an applicability domain profile (i.e., the curve of accuracy vs the applicability domain measure in question). Overall, the RDN showed to be a promising method that can correctly sort new instances according to predictive performance. As a result, this technique can be received by an end-user as proof of concept for the performance of a QSAR model in new data, thus promoting the user’s trust on the QSAR output. Graphical abstract . Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0182-y) contains supplementary material, which is available to authorized users.
    Keywords: Methodology ; Qsar ; Applicability Domain ; P-Gp ; Prediction Reliability ; K-Nearest Neighbour ; Dk-Nn ; Kernel Density Estimation ; P-Glycoprotein
    E-ISSN: 1758-2946
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