In Silico Human and Rat <i>V</i><sub>ss</sub> Quantitative Structure−Activity Relationship Models

Gleeson, M. Paul; Waters, Nigel J.; Paine, Stuart W.; Davis, A. M.

doi:10.1021/jm0510070

Cited by 73 publications

(78 citation statements)

References 15 publications

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“…These polarity descriptors, to some extent inversely related to lipophilicity, demonstrate that VD ss cannot be explained by logP and charge type alone and highlight the utility of multivariate in silico approaches to VD ss prediction (Gleeson et al, 2006;Lombardo et al, 2006). The magnitude of this data set may now allow exploration of specific functional moieties or substituents that play an integral role in tissue distribution and VD ss .…”

Section: Obach Et Almentioning

confidence: 93%

“…In particular, the exploration of the predictability of clearance and half-life using in silico, in vitro-in vivo, or in vivo correlative methods could be attempted using this large and accurate data set of human data. Volume of distribution, as an example, may be more related to overall physicochemical properties, which would explain the previous successes in model building for this parameter (Gleeson et al, 2006;Lombardo et al, 2006), whereas clearance may have a greater dependence on specific chemical substituents.…”

Section: Obach Et Almentioning

confidence: 99%

“…Previous reports have focused on prediction methods that utilize animal pharmacokinetic data (Caldwell et al, 2004;Ward and Smith, 2004a,b;Jolivette and Ward, 2005;Evans et al, 2006;Mahmood et al, 2006;Martinez et al, 2006;Tang and Mayersohn, 2006;Fagerholm, 2007;McGinnity et al, 2007) and in vitro data (Obach et al, 1997;Lombardo et al, 2002Lombardo et al, , 2004Nestorov et al, 2002;Riley et al, 2005;Grime and Riley, 2006). Recently, the availability of computational chemistry methodologies has increased, and these have been applied to the prediction of human pharmacokinetics and/or general absorption-distribution-metabolism-excretion-toxicology properties (Cruciani et al, 2005;Ghafourian et al, 2006;Gleeson et al, 2006;Lombardo et al, 2006;Gleeson, 2007;Gunturi and Narayanan, 2007;Norinder and Bergstroem, 2007). The construction of effective models not only requires sound computational tools but, very importantly, databases that have been carefully assembled.…”

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confidence: 99%

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Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds

Obach

Lombardo²,

Waters³

2008

Drug Metab Dispos

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362

304

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ABSTRACT:We present herein a compilation and trend analysis of human i.v. pharmacokinetic data on 670 drugs representing, to our knowledge, the largest publicly available set of human clinical pharmacokinetic data. This data set provides the drug metabolism scientist with a robust and accurate resource suitable for a number of applications, including in silico modeling, in vitro-in vivo scaling, and physiologically based pharmacokinetic approaches. Clearance, volume of distribution at steady state, mean residence time, and terminal phase half-life were obtained or derived from original references exclusively from studies utilizing i.v. administration. Plasma protein binding data were collected from other sources to supplement these pharmacokinetic data. These parameters were analyzed concurrently with a range of simple physicochemical descriptors, and resultant trends and patterns within the data are presented. Our findings with this much expanded data set were consistent with earlier described notions of trends between physicochemical properties and pharmacokinetic behavior. These observations and analyses, along with the large database of human pharmacokinetic data, should enable future efforts aimed toward developing quantitative structure-pharmacokinetic relationships and improving our understanding of the relationship between fundamental chemical characteristics and drug disposition.The prediction of human pharmacokinetics for new compounds has become an important process in drug research. Previous reports have focused on prediction methods that utilize animal pharmacokinetic data (Caldwell et al

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Section: Obach Et Almentioning

confidence: 93%

Section: Obach Et Almentioning

confidence: 99%

mentioning

confidence: 99%

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Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds

Obach

Lombardo²,

Waters³

2008

Drug Metab Dispos

Self Cite

362

304

View full text Add to dashboard Cite

show abstract

“…Several quantitative structure-activity relationship (QSAR) in silico models have been developed to predict human and rat VD ss using various computed descriptors based solely on inputs of molecular structure (Ghafourian et al, 2004;Gleeson et al, 2006;Lombardo et al, 2006;Berellini et al, 2009). The assumptions are that binding to tissues is nonspecific and that models rely heavily on physicochemical properties.…”

Section: Predicting Volume Of Distributionmentioning

confidence: 99%

A Perspective on the Prediction of Drug Pharmacokinetics and Disposition in Drug Research and Development

Feng

Goosen

et al. 2013

Drug Metab Dispos

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Prediction of human pharmacokinetics of new drugs, as well as other disposition attributes, has become a routine practice in drug research and development. Prior to the 1990s, drug disposition science was used in a mostly descriptive manner in the drug development phase. With the advent of in vitro methods and availability of human-derived reagents for in vitro studies, drugdisposition scientists became engaged in the compound design phase of drug discovery to optimize and predict human disposition properties prior to nomination of candidate compounds into the drug development phase. This has reaped benefits in that the attrition rate of new drug candidates in drug development for reasons of unacceptable pharmacokinetics has greatly decreased. Attributes that are predicted include clearance, volume of distribution, halflife, absorption, and drug-drug interactions. In this article, we offer our experience-based perspectives on the tools and methods of predicting human drug disposition using in vitro and animal data.

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“…The models are derived by different statistical and machine learning methods as artificial neural networks (ANN) (7,9,13), multiple linear regression (MLR) (8,10,12,14,15,18,19), partial least squares (PLS) (10 -12, 16, 18, 20), Bayesian neural networks (BNN) (16), classification and regression trees (CART) (16), mixed determinant analysis -random forest (MDA -RF) (17), recursive partitioning classification (RPC) (20).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure – Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution

2015

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-Purpose. The early prediction of pharmacokinetic behavior is of paramount importance for saving time and resources and for increasing the success of new drug candidates. The steady-state volume of distribution (VD ss ) is one of the key pharmacokinetic parameters required for the design of a suitable dosage regimen. The aim of the study is to propose a quantitative structure -pharmacokinetics relationships (QSPkR) for VD ss of basic drugs. Methods: The data set consists of 216 basic drugs, divided to a modeling (n = 180) and external validation set (n = 36). 179 structural and physicochemical descriptors are calculated using validated commercial software. Genetic algorithm, stepwise regression and multiple linear regression are applied for variable selection and model development. The models are validated by internal and external test sets. Results: A number of significant QSPkRs are developed. The most frequently emerged descriptors are used to derive the final consensus model for VD ss with good explanatory (r 2 0.663) and predictive ability (q 2 LOO-CV 0.606 and r 2 pred 0.593). The model reveals clear structural features determining VD ss of basic drugs which are summarized in a short list of criteria for rapid discrimination between drugs with a large and small VD ss . Conclusions: Descriptors like lipophilicity, fraction ionized as a base at pH 7.4, number of cycles and fused aromatic rings, presence of Cl and F atoms contribute positively to VD ss , while polarity and presence of strong electrophiles have a negative effect.

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In Silico Human and Rat V_ss Quantitative Structure−Activity Relationship Models

Cited by 73 publications

References 15 publications

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds

A Perspective on the Prediction of Drug Pharmacokinetics and Disposition in Drug Research and Development

Quantitative Structure – Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution

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In Silico Human and Rat Vss Quantitative Structure−Activity Relationship Models

Cited by 73 publications

References 15 publications

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds

Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds

A Perspective on the Prediction of Drug Pharmacokinetics and Disposition in Drug Research and Development

Quantitative Structure – Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution

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Product

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In Silico Human and Rat V_ss Quantitative Structure−Activity Relationship Models