Quantitative structure-activity relationships bioavailability

R., Khaledi, M. G. Quantitative structure-activity relationships studies with micellar electrokinetic chromatography. Influence of surfactant type and mixed micelles on estimation of hydrophobicity and bioavailability. J. Chromatogr. A 1996, 727, 323-335. 

There are several properties of a chemical that are related to exposure potential or overall reactivity for which structure-based predictive models are available. The relevant properties discussed here are bioaccumulation, oral, dermal, and inhalation bioavailability and reactivity. These prediction methods are based on a combination of in vitro assays and quantitative structure-activity relationships (QSARs) [3]. QSARs are simple, usually linear, mathematical models that use chemical structure descriptors to predict first-order physicochemical properties, such as water solubility. Other, similar models can then be constructed that use the first-order physicochemical properties to predict more complex properties, including those of interest here. Chemical descriptors are properties that can be calculated directly from a chemical structure graph and can include abstract quantities, such as connectivity indices, or more intuitive properties, such as dipole moment or total surface area. QSAR models are parameterized using training data from sets of chemicals for which both structure and chemical properties are known, and are validated against other (independent) sets of chemicals. 

Yoshida F, Topliss JG (2000) QSAR Model for drug human oral bioavailability. J Med Chem 43 2575-2585 Zhao YH, Le J, Abraham MH et al. (2001) Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J Pharm Sci 90 749-784... 

Yang, S.Y., Bumgarner, J.G., Kruk, L.F. and Khaledi, M.G. (1996). Quantitative Structure-Activity Relationships Studies with Micellar Electrokinetic Chromatography Influence of Surfactant Type and Mixed Micelles on Estimation of Hydrophobicity and Bioavailability. J.Chro-mat, 721A, 323-335. 

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. 

Absorption, Distribution, Metabolism, and Elimination. All of the marketed sulfonyl-ureas are nearly completely absorbed, and the class has excellent oral bioavailability. A quantitative structure-activity relationship (QSAR) model for human drug oral bioavailability (84) has recently been developed, in which the descriptors A logD (logHe.s - log... 

QSAR (quantitative structure activity relationship) assessment involves an elegant comparison of structural elements within known substances with similar elements in the substance under evaluation, and predicts with fair success in most substances their toxicological/ecotoxicological profile. Where QSAR can fail, however, is that it does not take into consideration the mega-insolubility in H PPs referred to in Section 25.3.1 above, and assumes that the pigment is always bioavailable, which is not the case. 

Traditionally, in pursuit of their structure-activity relationships, medicinal chemists had focused almost exclusively on finding compounds with greater and greater potency. However, these SARs often ended up with compounds that were unsuitable for development as pharmaceutical products. These compounds would be too insoluble in water, or were not orally bioavailable, or were eliminated too quickly or too slowly from mammalian bodies. Pharmacologists and pharmaceutical development scientists for years had tried to preach the need for medicinal chemists to also think about other factors that determined whether a compound could be a medicine. Table 1.1 lists a number of factors that determine whether a potent compound has what it takes to become a drug. Experimentally, it was difficult to quantitate these other factors. Often, the necessary manpower resources would not be allocated to a compound until it had already been selected for project team status. 

Accordingly, sorption has received a tremendous amount of attention and any method or modeling technique which can reliably predict the sorption of a solute will be of great importance to scientists, environmental engineers, and decision makers (references herein and in Chaps. 2 and 3). The present chapter is an attempt to introduce an advanced modeling approach which combines the physical and chemical properties of pollutants, quantitative structure-activity, and structure-property relationships (i. e., QSARs and QSPRs, respectively), and the multicomponent joint toxic effect in order to predict the sorption/desorp-tion coefficients, and to determine the bioavailable fraction and the action of various organic pollutants at the aqueous-solid phase interface. 

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