In Silico ADME Prediction: Data, Models, Facts and Myths

Lombardo, Franco; Gifford, Eric M.; Shalaeva, Marina

doi:10.2174/1389557033487629

Cited by 118 publications

(76 citation statements)

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“…As solubility prediction has been the focus of several reviews over recent years, [4][5][6][7][8][9][10][11] we will focus on work from only the last few years, with a particular focus on related methodologies that may positively affect solubility prediction in the future. We turn the reader ' s attention to an excellent review of solubility prediction by Delaney,7 who discusses some of the major obstacles faced in empirical solubility modeling.…”

Section: Solubility Predictionmentioning

confidence: 99%

Recent progress in the computational prediction of aqueous solubility and absorption

Johnson

Zheng

2006

AAPS J

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The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such an in silico counterpart to the biopharmaceutical classifi cation system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We fi nd that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefl y discuss aspects of emerging scientifi c understanding that may lead to breakthroughs in the computational modeling of these properties.

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Section: Solubility Predictionmentioning

confidence: 99%

Recent progress in the computational prediction of aqueous solubility and absorption

Johnson

Zheng

2006

AAPS J

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“…The issue can be addressed by an early ADME profiling to better assess the developability of a drug (3). Such a profiling is nowadays not only conducted by means of in vitro experiments, but also by computational methods (4,5). The well known Lipinski rules (6) can help in anticipating drug absorption hurdles and more recently, some quantitative structure bioavailability relationships (QSBR) were proposed leading to a more refined estimate of the drug absorption (7)(8)(9)(10)(11)(12)(13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

Drug Absorption Modeling as a Tool to Define the Strategy in Clinical Formulation Development

Kuentz

2008

AAPS J

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Abstract. The purpose of this mini review is to discuss the use of physiologically-based drug absorption modeling to guide the formulation development. Following an introduction to drug absorption modeling, this article focuses on the preclinical formulation development. Case studies are presented, where the emphasis is not only the prediction of absolute exposure values, but also their change with altered input values. Sensitivity analysis of technologically relevant parameters, like the drug's particle size, dose and solubility, is presented as the basis to define the clinical formulation strategy. Taking the concept even one step further, the article shows how the entire design space for drug absorption can be constructed. This most accurate prediction level is mainly foreseen once clinical data is available and an example is provided using mefenamic acid as a model drug. Physiologically-based modeling is expected to be more often used by formulators in the future. It has the potential to become an indispensable tool to guide the formulation development of challenging drugs, which will help minimize both risks and costs of formulation development.

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“…Because unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates, key properties such as absorption, distribution, Fig. 8 HQSAR pharmacokinetic model generation and integrated ADME property prediction metabolism and excretion (ADME) have been recently considered in early phases of the drug discovery process [55,56]. This new paradigm of research has driven the need for largescale screening methods.…”

Section: Hqsar: From Correlation To Property Predictionmentioning

confidence: 99%

“…In vitro and in vivo ADME assays are lengthy, complex, and relatively expensive in terms of resources, reagents, and detection techniques. In this regard, a variety of useful in silico ADME models has been developed with different levels of complexity for the screening of large data sets of compounds, creating tools that are faster, simpler, and more cost-effective than traditional experimental procedures [56].…”

Section: Hqsar: From Correlation To Property Predictionmentioning

confidence: 99%

Fragment-based QSAR: perspectives in drug design

Salum

Andricopulo

2009

Mol Divers

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Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

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In Silico ADME Prediction: Data, Models, Facts and Myths

Cited by 118 publications

References 0 publications

Recent progress in the computational prediction of aqueous solubility and absorption

Recent progress in the computational prediction of aqueous solubility and absorption

Drug Absorption Modeling as a Tool to Define the Strategy in Clinical Formulation Development

Fragment-based QSAR: perspectives in drug design

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