Pharmacokinetics QSAR model

QSAR and neural network approaches in combination with physiologicaUy-based pharmacokinetic (PBPK) modelling hold promise in becoming a powerful tool in drug discovery [45]. Below we briefly discuss some of these studies. 

The ATSDR use of QSAR and models to predict toxicity is well described by El-Masri et al. (2002). In 1998, the ATSDR established a computational toxicology laboratory and initiated efforts to use Physiologically Based PharmacoKinetic (PBPK) models, BenchMark Dose (BMD) models, and QSARs. PBPK models are used by the ATSDR to ... 

Quantitative structure-activity/pharmacokinetic relationships (QSAR/ QSPKR) for a series of synthesized DHPs and pyridines as Pgp (type I (100) II (101)) inhibitors was generated by 3D molecular modelling using SYBYL and KowWin programs. A multivariate statistical technique, partial least square (PLS) regression, was applied to derive a QSAR model for Pgp inhibition and QSPKR models. Cross-validation using the leave-one-out method was performed to evaluate the predictive performance of models. For Pgp reversal, the model obtained by PLS could account for most of the variation in Pgp inhibition (R2 = 0.76) with fair predictive performance (Q2 = 0.62). Nine structurally related 1,4-DHPs drugs were used for QSPKR analysis. The models could explain the majority of the variation in clearance (R2 = 0.90), and cross-validation confirmed the prediction ability (Q2 = 0.69) [ 129]. 

Moreover, a final 3D-QSAR model vahdation was done using a prospective study with an external test set. The 82 compounds from the data set were used in a lead optimization project. A CoMFA model gave an (cross validated) value of 0.698 for four relevant PLS components and a conventional of 0.938 were obtained for those 82 compounds. The steric descriptors contributed 54% to the total variance, whereas the electrostatic field explained 46%. The CoMSIA model led to an (cross vahdated) value of 0.660 for five PLS components and a conventional of 0.933. The contributions for steric, electrostatic, and hydrophobic fields were 25, 44, and 31%. As a result, it was proved that the basic S4-directed substituents should be replaced against more hydrophobic building blocks to improve pharmacokinetic properties. The structural and chemical interpretation of CoMFA and CoMSIA contour maps directly pointed to those regions in the Factor Xa binding site, where steric, electronic, or hydrophobic effects play a dominant role in ligand-receptor interactions. 

This chapter reviews some of the in silico attempts to predict oral bioavailability. However, bioavailability is a complex property, and various pros and cons of current quantitative structure-activity relationship (QSAR) based approaches will be discussed here. As an alternative, physiologically-based pharmacokinetic (PBPK) modeling is discussed as a promising approach to predict and simulate pharmacokinetics (PK), including estimating bioavailability. 

Cuba, W. and Cruciani, G. Molecular field-derived descriptors for the multivariate modelling of pharmacokinetic data, in Mdecular Modelling and Prediction of Bioactivity, Proceedings of the 12th European Symposium on Quantitative Structure-Activity Relationships (QSAR 98), Gundertofte, K. and Jorgensen, F.S. (Eds). Plenum Press, New York, 2000, 89-95. 

As a possible alternative to in vitro metabolism studies, QSAR and molecular modelling may play an increasing role. Quantitative stracture-pharmacokinetic relationships (QSPR) have been studied for nearly three decades [42,45-52]. These are often based on classical QSAR approaches based on multiple linear regression. In its most simple form, the relationship between PK properties and lipophilicity has been discussed by various workers in the field [36, 49, 50]. 

There are a remarkable number and diversity of activities that have been modeled successfully. The activity to be modeled may be a toxicity to an environmental organism or to man, the fate of a pollutant in an ecosystem, or the pharmacokinetic properties of a xenobiotic in man. To model any of these activities, relevant biological data for the endpoint are required. Chapter 2 describes how toxicological and fate information for chemicals may be obtained from external sources such as the open literature, databases, and the Internet. QSAR developers may also have their own data to model. 

Over the last 40 years, the scientific literature has abounded with examples of the application of quantitative structure-activity relationships (QSARs) and molecular modeling techniques to the problem of predicting biological activity. The application of these techniques to the prediction of pharmacokinetic or toxicokinetic parameters has been, until recently, less intensely researched. 

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