Other in vitro Data

HSV-thymidine kinase [721], uridine phosphorylase [669], and xanthine oxidase [668, 722] 

Specific and nonspecific binding of drugs to proteins (other than enzymes) show significant differences. While the serum albumin binding of miscellaneous neutral compounds can be explained by their lipophilicity (eq. 148) but not by their polarizability (log 1/C vs. MR r = 0.307) [18, 723], the highly specific, polar binding of phenyl P-D-glucosides to concanavalin A is described much better by a polarizability term MR (eq. 149) than by their lipophilicity (log M50 us. re r = 0.664) [724]. 

Structure-activity relationships in immunochemistry [21, 335, 396, 725 — 728] reveal the importance of steric interactions in the QSAR of hapten-antibody (32) interactions (eq. 150) [396, 725] eq. 151 could be derived for the 50% inhibition of complement by benzylpyridinium ions (33) [727]. 

Systematic investigations have been performed for more than one decade on muscarinic receptor ligands [729 — 732]. Examples of QSAR equations for a combined set of rigid and flexible agonists (eq. 152) [729] and for a group of structurally different antagonists (eq. 153) [730] are given below. 

A QSAR equation for the acetylcholine receptor affinity of quaternary ammonium compounds was used to predict the potency of a new, structurally different analog (34) [733]. The compound was synthesized and tested however, the prediction turned out to be completely wrong [734] observed and predicted affinities differed by 6 log units ( ), once again showing the risk of predictions for structurally dissimilar compounds which are too far outside the included parameter range. A reanalysis of the data led to an equation which gave a much better prediction of the affinity of this compound (error 1.4 log units) [403]. 

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The type of data resulting from in vitro methods is often very diverse. Some data or combinations of data are considered as 100 % alternative data to the in vivo data and predict a similar endpoint. Other in vitro data are considered as more supplementary data. For instance, such data have been generated in the pharmaceutical sector to substantiate specific cases where only in vivo data were available. More and more new use-cases for in vitro data are being identified and are not merely an alternative to something that already exists at the regulatory level. A good example is the growing interest in in vitro data to substantiate read-across for chemicals [1]. 

This model integrates existing in vitro data, such as Caco-2 permeability (Papp) and metabolic stability in liver S9 or microsomes, to estimate bioavailability as being either low, medium, or high. Oral bioavailability predictions for not only humans but also other species can be made by using the metabolic stability values of drugs in liver microsomal enzyme preparations from that species. A premise of this model is that metabolic clearance is more important than renal or biliary clearance in determining bioavailability. However, despite the lack of in vitro renal... 

As in any field, extrapolation of in vitro data to the in vivo context warrants caution. Nevertheless, as revealed in this study, experimentally demonstrated down-regulation of ET-1 gene expression by HU corroborates the in vivo findings where ET-1 levels correlate with the clinical stage of SCA. Similarly, the balance between soluble and membrane-bound adhesive receptors of endothelial and other cells may dictate the triggering of acute clinical events. Individualized therapies for... 

If animal testing is required, a full-scale Draize test may not be necessary given the background established in the beginning of the tier approach. For instance, the compound could be tested in a single sentinel animal to obtain confirmation of in vitro data. In addition, other modifications could be used, such as the administration of appropriate anesthetics to the test animals, or the use of the low-volume Draize modification (Falahee et al., 1982 Freeberg et al., 1984 Griffith, 1987). 

BioPrint consists of a large database and a set of tools with which both the data and the models generated from the data can be accessed. The database contains structural information, in vivo and in vitro data on most of the marketed pharmaceuticals and a variety of other reference compounds. The in vitro data generated consist of panels of pharmacology and early ADME assays. The in vivo data consist of ADR data extracted from drug labels, mechanisms of action, associated therapeutic areas, pharmacokinetic (PK) data and route of administration data. 

The role of other ABC-efHux transporters (ATP-binding cassette transporters) such as MRPs and BCRP was also intensively investigated over the past decade. However, most studies investigating the importance of ABC transporters for drug disposition were performed with different cell lines and animal models. In comparison to P-glycoprotein, only limited human data are available about the role of ABC transporters on drug disposition. In this chapter mainly human data will be discussed. For more detailed description and in vitro data about MRPs see Section 15.3. 

Evidence of the formation of mono-M-octylphthalatc and phthalate ester metabolites has been shown in in vitro studies. The appearance of mono-w-octylphthalatc was observed with preparations of human small intestine, rat liver and intestine, ferret liver and intestine, and baboon liver and intestine (Lake et al. 1977). However, the amount of phthalic acid and other metabolites in these preparations was either minimal or not detected. The study authors concluded that di-ra-octylphthalate is probably absorbed primarily as mono- -octylphthalate (Lake et al. 1977). An in vitro study reported the formation of five keto acids and two diols when metabolic oxidation of the alkyl groups of di-ra-octylphthalate was simulated abiotically (Brodsky et al. 1986). Therefore, the in vivo and in vitro data indicate that major oxidation may occur in the remaining alkyl chain after di-ra-octylphthalate has been hydrolyzed to the... 

The cellular uptake of AS-ODN is an energy-dependent process and takes place in a saturable and sequence-independent manner [120,121]. The exact mechanism of uptake remains controversial. From in vitro experiments, some authors have proposed that the uptake is endocytic and mediated by membrane receptor proteins. The receptor responsible for the cellular uptake of AS-ODNs was reported to consist of both a 30-kDa protein [122] and an 80-kDa membrane protein [121]. However, other workers have argued that AS-ODN binding to membrane proteins is relatively non-specific and is mostly charge associated, consistent with adsorptive endocytosis or fluid-phase pinocytosis [101]. As a result of these conflicting reports, it is unlikely that in vitro data can be safely extrapolated to what occurs in the intact organism. 

In order to use PBTK modeling in the assessment of mixtures, Cassee et al. (1998) suggest that one of the components is first modeled and regarded as the prime toxicant being modified by the other components. Based on in vitro data on the other components, effects of, e.g., inhibition or induction of specific biotransformation isoenzymes can be incorporated in the model. Effects of competition between chemicals in a mixture for the same biotransformation enzymes may also be incorporated by translating the effects into effects on the Michaelis-Menten parameters that are then incorporated into the model. 

Lion Biosciences is the supplier of the iDEA Metabolism software package as well as other ADME/T services (289). The iDEA software simulates metabolism and predicts a compound s metabolic behavior in humans. The Metabolism Module consists of a data expert module to perform data fitting and analysis of collected in vitro data and the physiological metabolism model. The physiological metabolism model is constructed from proprietary database of 64 clinically tested compounds. Additionally, the metabolism module automatically calculates the Michaelis-Menten constants Km and VjIiax for the kinetic analysis of metabolism turnover (289). 

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