Recent progress in the computational prediction of aqueous solubility and absorption

Johnson, Stephen R.; Zheng, Weifan

doi:10.1208/aapsj080104

Cited by 86 publications

(49 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, more sophisticated QSPR models such as those using nonlinear statistical techniques have been proposed that rely solely on calculated molecular descriptors, without the need for the experimentally determined melting point (Palmer et al, 2007;Johnson and Zheng, 2006;Schroeter et al, 2007;Zhou et al, 2008;Huuskonen et al, 1998;Cheng and Merz, 2003;Wassvik et al, 2006;Hou et al, 2004;Bergström, 2005). Melting point is a measure of the crystal lattice energy that needs to be overcome during dissolution, hence the significance in solubility models.…”

Section: Introductionmentioning

confidence: 99%

Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

Ghafourian

Bozorgi

2010

European Journal of Pharmaceutical Sciences

View full text Add to dashboard Cite

With the aim of solubility estimation in water, polyethylene glycol 400 (PEG) and their binary mixtures, Quantitative Structure -Property Relationships (QSPRs) were investigated to relate the solubility of a large number of compounds to the descriptors of the molecular structures. The relationships were quantified using linear regression analysis (with descriptors selected by stepwise regression) and Formal Inference -based Recursive Modeling (FIRM). The models were compared in terms of the solubility prediction accuracy for the validation set. The resulting regression and FIRM models employed a diverse set of molecular descriptors explaining crystal lattice energy, molecular size, and solute -solvent interactions. Significance of molecular shape in compound's solubility was evident from several shape descriptors being selected by FIRM and stepwise regression analysis. Some of these influential structural features, e.g. connectivity indexes and Balaban topological index, were found to be related to the crystal lattice energy. The results showed that regression models outperformed most FIRM models and produced higher prediction accuracy. However, the most accurate estimation was achieved by the use of a combination of FIRM and regression models. The results also showed that the use of melting point in regression models improves the estimation accuracy especially for solubility in higher concentrations of PEG. Aqueous or PEG/water solubilities can be estimated by these models with root mean square error of below 0.70.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of drug solubility in water, PEG 400 and their binary mixtures using the molecular structures of solutes

Ghafourian

Bozorgi

2010

European Journal of Pharmaceutical Sciences

View full text Add to dashboard Cite

show abstract

“…In silico models for solubility take into account log P , melting point, crystal packing, intrinsic solubility, and ionization effects [56][57][58][59][60]. From the simple general solubility equation, to more complex models based on Monte Carlo simulations using 2D or 3D properties, and fragmental or atom-based or quantum-chemical methods have all been used to predict aqueous as well as Dimethyl sulfoxide (DMSO) solubility of compounds with fair accuracy.…”

Section: In Silicomentioning

confidence: 99%

ADMEProfiling in Drug Discovery and Development: An Overview

Bohnert

Prakash

2012

Encyclopedia of Drug Metabolism and Interactions

View full text Add to dashboard Cite

The applications of parallel synthesis and combinatory chemistry to expedite lead finding and lead optimization processes have shifted the chemical libraries toward poorer biopharmaceutical, ADME (absorption, distribution, metabolism, and excretion), and pharmacokinetic (PK) properties. A drug candidate should have desirable biopharmaceutical properties such as good solubility and good permeability, as well as optimal and ADME/PK properties. The early knowledge of liability of biopharmaceutical, ADME/PK, and DDI properties is very valuable in drug candidate selection process. In the last one decade, multiple in silico, in vitro, and in vivo ADME studies have been developed and implemented in the drug discovery and development process to alert chemists and drug metabolism scientists of the potential ADME, PK and DDI issues in the clinic. In this chapter, we attempt to discuss these various ADME profiling approaches in drug discovery and development process and the latest technologies of the selected assays.

show abstract

“…For instance, using QSPR approaches and 17 diverse drug compounds, the following equation was proposed (see reviews such as [20,136,137]):…”

Section: Pk Amentioning

confidence: 99%

“…For all these factors, ionization of the molecules tends to play an important role, but this state is difficult to predict in silico [50]. Many methods have been developed and reviewed to predict solubility [137,141,142,148], permeability [144,149], and/or absorption [43,84,[150][151][152][153] (Table 2.3). In 2002, Veber et al reported the findings from a study of rat availability data, acquired by GlaxoSmithKline, for 1100 drug candidates [112].…”

Section: Pk Amentioning

confidence: 99%