Quantitative structure-activity relationships experimental data

The prediction of the properties of molecules from a knowledge of their structure (quantitative structure-property relationships [QSPRs] or quantitative structure-activity relationships [QSARs]). ANNs can be used to determine QSPRs or QSARs from experimental data and, hence, predict the properties of a molecule, such as its toxicity in humans, from its structure. 

Quantitative Structure-Activity Relationship studies search for a relationship between the activity/toxicity of chemicals and the numerical representation of their structure and/or features. The overall task is not easy. For instance, several environmental properties are relatively easy to model, but some toxicity endpoints are quite difficult, because the toxicity is the result of many processes, involving different mechanisms. Toxicity data are also affected by experimental errors and their availability is limited because experiments are expensive. A 3D-QSAR model reflects the characteristics of... 

Recently, hologram quantitative structure-activity relationship (HQSAR) was conducted by Moda et al. [63] on a series of structurally diverse molecules with known PPB. The best statistical model (n — 250, r2 = 0.91, and q2 — 0.72) was used to predict the PPB of 62 test set compounds, and the predicted values were in good agreement with the experimental results (ntest —62, q2 — 0.86, RMSEtest = 12%). It is indicated that this model used a much smaller data set than the VolSurf and Wang7s models. 

Although little structural information is available for the CYP enzymes, the amount of experimental data on the substrates and inhibitors is growing rapidly. As a result, ligand-based analyses, quantitative structure-activity relationship... 

In the assessment of the uptake of a chemical after dermal exposure, for instance, the dermal permeability of the skin is often estimated using the Potts-Guy quantitative structure-activity relationship (Guy Potts, 1992), which was derived from an experimental data set of in vitro measured steady-state skin permeations (Wilschut et al., 1995). Uncertainty in the use of a value for the skin permeation obtained this way comes from questions of how well a regression model based on Kow and molecular weight predicts the skin permeability of a chemical that was not in the original data set, and how representative the steady-state permeability measured in vitro is for a (possibly) non-steady-state permeability in vivo (see also IPCS, 2006b). 

The electronic properties of amino acid side chains are summarized in Table 3, and they represent a wide spectrum of measures. The NMR data are derived experimentally (37). The dipole (38), C mull, inductive, field, and resonance effects were derived from QM calculations (15). The VHSE5 (39) and Z3 (25) scales were developed for use in quantitative structure-activity relationship analysis of the biologic activity of natural and synthetic peptides. Both were derived from principal components analysis of assorted physico-chemical properties, which included NMR chemical shift data, electron-ion interaction potentials, charges, and isoelectric points. Therefore, these scales are composites rather than primary measures of electronic effects. The validity of these measures is indicated by their lack of overlap with hydrophobicity and steric parameters and by their ability to predict biologic activity of synthetic peptide analogs (25, 39). Finally, coefficients of electrostatic screening by amino acid side chains (ylocal and Ynon-local) were derived from an empirical data set (40), and they represent a composite of electronic effects. 

While experimentally derived test data are preferred, where no experimental data are available, validated Quantitative Structure Activity Relationships (QSARs) for aquatic toxicity and log Kqw may be used in the classification process. Such validated QSARs may be used without modification to the agreed criteria, if restricted to chemicals for which their mode of action and applicability are well characterized. Reliable calculated toxicity and log Kow values should be valuable in the safety net context. QSARs for predicting ready biodegradation are not yet sufficiently accurate to predict rapid degradation. 

A9.5.2.4.1 For organic substances experimentally derived high-quality Kow values, or values which are evaluated in reviews and assigned as the recommended values , are preferred over other determinations of Kow. When no experimental data of high quality are available, validated Quantitative Structure Activity Relationships (QSARs) for log Kow may be used in the classification process. Such validated QSARs may be used without modification to the agreed criteria if they are restricted to chemicals for which their applicability is well characterized. For substances like strong acids and bases, substances which react with the eluent, or surface-active substances, a QSAR estimated value of Kow or an estimate based on individual -octanol and water solubilities should be provided instead of an analytical determination of Kow (EEC A.8., 1992 OECD 117, 1989). Measurements should be taken on ionizable substances in their non-ionized form (free acid or free base) only by using an appropriate buffer with pH below pK for free acid or above the pK for free base. 

When working with pseudo-receptors, and in general with quantitative structure-activity relationships (QSAR) of any dimension, a word of caution is necessary with respect to the biological data that is used. These should preferably constitute binding affinities from a single laboratory, a prerequisite which is also true for all QSAR studies. Since the receptor models simulate interaction events (AH) in a highly simplihed manner, the experimental data which are combined with them in a correlation analysis mnst be as close to the molecular level as possible. It is therefore nonsense to correlate the calculated interaction energies... 

According to RIP 1, the studied compounds II-IV with short R = Me, Et more selectively inhibit AChE they are not hazardous as delayed neurotoxicants. For all series of compounds, anti-NTE (log for NTE) and selectivity for NTE (log [ i(NTE)/ i(AChE)] = log RIP) increased with increasing hydrophobicity. This result agrees with experimental data [3,9,26,58,59,67] and a recent quantitative structure-activity relationship (QSAR) study [68] on other OP molecules. 

Three major approaches to the prediction of aqueous solubility of organic chemicals using Quantitative Structure Activity Relationship (QSAR) techniques arc reviewed. The rationale behind six QSAR models derived from these three approaches, and the quality of their fit to the experimental data are summarized. Their utility and predictive ability are examined and compared on a common basis. Three of the models employed octanol-water partition coefficient as the primary descriptor, while two others used the solvatochromic parameters. The sixth model utilized a combination of connectivity indexes and a modified polarizability parameter. Considering the case of usage, predictive ability, and the range of applicability, the model derived from the connectivity- polarizability approach appears to have greater utility value. 

In recent years there has been a notable increase in research on structure-activity relationships (SARs), also called quantitative structure-activity relationships (QSARs), used to assess the toxicity of substances for which there are few experimental data. This approach involves establishing mathematical relationships derived from computer modeling, based on known toxicity data of similar (or dissimilar) types of compounds, octanol-water partition coefficients, molar connectivity index values, and other parameters. A detailed discussion on this subject is beyond the scope of this book. 

QSARs for Pand B. Since experimental data for persistence and bioaccumulation (as well as ecotoxicity) are often unavailable, quantitative structure activity relationship (QSAR) models are commonly used by Environment Canada, the US EPA, and other government agencies to predict values for these hazards. For the purposes of assigning levels of concern in the Green Screen for persistence and bioaccumulation, when measurable data are absent, QSARs are considered acceptable (for further discussion of the use and limits of QSARs to fill data gaps see section 4.4). 

Experimental ecotoxicity data for metabolites are generally scarce. Therefore, the model rehes on a large number of assumptions while using any experimental evidence available. If there are no toxicity data available at all, they are estimated by quantitative structure activity relationships (QSAR) as described below. 

The conclusion of Schumacher and Hpiland was not accepted by Richard et al. (305a) for reasons not mentioned in their paper. Using a method of quantitative structure-activity relationship determination, the above authors tried to evaluate the contribution of hydroxyl substituents to the toxicity of orellanine. The results appeared to be in total contradiction to the experimental data [e.g., calculated LD50 5 g/kg versus an experimentally determined LD50 of -4.9-12.5 mg/kg (276)] and led the authors to the conclusion that the proposal of the exact structure of orellanine may be questioned. It must be mentioned, however, that neither paraquat nor diquat were taken into account in the calculations. Three years later, more substantial arguments were made by Richard et al. 305b) as the result of their studies of the electrochemical behavior of orellanine which was shown to be different from diquat and paraquat. 

---