Quantitative structure-activity relationship validation

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

Kraker, J. J., Hawkins, D. M., Basak, S. C., Natarajan, R., Mills, D. Quantitative structure-activity relationship (QSAR) modebng of juvenile hormone activity Comparison of validation procedures. Chemometr. Intell. Lab. Syst. 2007, 87, 33M2. 

The chemometric basic tools may be divided into the following typologies of study data exploration, modelling, prediction and validation, design of experiments (DOE), process analytical technology (PAT), quantitative structure-activity relationship (QSAR). Details and relevant literature are reported in the following paragraphs. 

REACH is an extraordinarily ambitious program. There are discussions underway regarding proposals to limit the numbers of chemicals to be subjected to these requirements. The potential for toxicological testing on a massive scale raises questions about the availability of facilities to carry out such tests, and runs counter to the objective of reducing the numbers of animals used for such purposes. The need to accomplish REACH objectives without the overuse of laboratory animals has promoted discussion and research regarding the use of alternative methods to collect the necessary data tools such as in vitro tests and quantitative structure-activity relationships (QSARs) are being promoted, and this has led to substantial research efforts to test their predictive validity. Time will tell where all of this activity leads us. 

OECD. 2007b. Guidance document on the validation of (quantitative) structure-activity relationships [(Q) SAR] models. ENV/JM/MONO(2007)2. 15 February 2007. Paris OECD http //www.oecd.org/ searchResult/0,2665,en 2649 201185 l l l l L00.html OECD. 2007c. Report on the regulatory uses and applications in OECD member countries of (quantitative) structure activity relationship (Q)SAR models in the assessment of new and existing chemicals. ENV/JM/MONO(2006)25. 15 February 2007. Paris OECD http //www.oecd.org/searchResult/ 0,2665,en 2649 201185 l l l l L00.html... 

The validity of a model is always limited to a certain domain in the parameter space. For example, if a quantitative structure-activity relationships (QSAR) model is specified for nonpolar organic chemicals in the log range from 2 to 6 and has a molecular weight below 700, then an application to substances outside this range is an improper extrapolation. Note that the parameter space may be difficult to discern for example, combinations of low values for one variable and high values for another could constitute an extrapolation if such combinations had been missing in the validation or specification of the model. Exceedence of model boundaries introduces additional uncertainty at best, but can also lead to completely incorrect outcomes. 

ECVAM is the leading international center for alternative test method validation. Hartung et al. (29) summarized the modular steps necessary to accomplish stage 3 (test validation). The seven modular steps are (I) test definition, (2) within-laboratory variability, (3) transferability, (4) between-laboratory variability, (5) predictive capacity, (6) applicability domain, and (7) performance standards (29). Steps 2-4 evaluate the test s reliability steps 5 and 6 evaluate the relevance of the test. Successful completion of all seven steps is necessary to proceed to stage 4 (independent assessment or peer review). This modular approach allows flexibility for the validation process where information on the test method can be gathered either prospectively or retrospectively. The approach is applicable not only to in vitro test methods but also to in silico approaches (e.g., computer-based approaches such as quantitative structure-activity relationships or QSAR) and pattern-based systems (e.g., genomics and proteomics). 

Waller CL McKinney JD (1995) Three-dimensional quantitative structure activity relationships of dioxins and dioxin-like compounds Model validation and Ah receptor characterization. Chem Res Toxicol, 8 847-858. 

Peterson, Y. K., Wang, X. S., Casey, P. J., Tropsha, A. (2009) Discovery of geranylgeranyltransferase-I inhibitors with novel scaffolds by the means of quantitative structure-activity relationship modeling, virtual screening, and experimental validation. J Med Chem 52, 4210-4220. 

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. 

E. Benfenati et al., Validation of the Models, in Quantitative Structure—Activity Relationships (QSAR) for Pesticide Regulatory Purposes, ed. by E. Benfenati (Elsevier, Amsterdam, 2007), pp. 185-199... 

OECD. OECD Principles for the Validation, for Regulatory Purposes, of (Quantitative) Structure-Activity Relationship Models. Paris, France, http //www.oecd.org/document/23/ 0,3343,en 2649 34379 33957015 l l l l,00.html... 

Guidance Document on the Validation of (Quantitative) Structure Activity Relationship [(Q) SAR] Models, No. 69 OECD Series on Testing and Assessment Organisation of Economic Cooperation and Development Paris, France, 2007. http //www.oecd.org. Accessed May 2008... 

Organisation for Economic Co-operation and Development [OECD]. 2005a. OECD principles for the validation, for regulatory purposes, of (quantitative) structure-activity relationship models. http //www.oecd.Org/document/23/0,2340,en 2649 34365 33957015 l l l l,00.html (accessed December 15, 2005). 

This book intends to provide a starting point for those interested in the prediction of the toxicity and fate of chemicals to humans and the environment. SARs and, more frequently, quantitative structure-activity relationships (QS ARs) provide methods to predict these endpoints. A brief history of the area, the driving forces, and basis of the topic is provided in this chapter. Further chapters (2 to 7) describe the methods to develop predictive models the application of models to human health endpoints (Chapters 8 to 11) their application to environmental toxicity and fate (Chapters 12 to 17) and the use of predictive models (Chapter 19), adoption by the regulatory authorities (Chapter 19), and validation (Chapter 20). 

As the uses of toxicological-based quantitative structure-activity relationships (QSARs) move into the arenas of priority setting, risk assessment, and chemical classification and labeling the demands for a better understanding of the foundations of these QSARs are increasing. Specifically, issues of quality, transparency, domain identification, and validation have been recognized as topics of particular interest (Schultz and Cronin, 2003). 

Sadler, B.R., Cho, S.J., Ishaq, K.S., Chae, K., and Korach, K.S., Three-dimensional quantitative structure-activity relationship study of nonsteroidal estrogen receptor ligands using the comparative molecular field analysis cross-validated r2-guided region selection approach, J. Med. Chem., 41, 2261-2267, 1998. 

The principles for establishing the status of development and validation of (quantitative) structure-activity relationships [(Q)SARs]. OECD document ENV/JM/ TG(2004)27... 

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. 

A9.3.6.2.4 In the absence of empirical test data, validated Quantitative Structure Activity Relationships... 

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. 

Figure 3. Plot of the logarithm of experimentally determined rate constants (iccat, min ) against energy barriers calculated with a QM/MM method for hydroxylation of severalparahydroxybenzoate derivatives by the enzymepara-hydroxybenzoate hydroxylase (PHBH), showing a linear correlation (r=0.96) between the calculated and experimental results [49,50]. This correlation supports the proposed mechanistic scheme, and the identification of the hydroxylation step as rate-limiting within it. It also validates the QM/MM method for this application, and shows that QM/MM results can be predictive and will be useful in the development of quantitative structure-activity relationships (QSAR). (Adapted from ref. 49, with thanks to Dr. L. Ridder).

The actual evaluation of the possible hazards of chemicals and the risk to humans handling such chemicals is based on data obtained from animal studies. This approach is constantly under discussion in terms of the ethical use of animals and some difficulties in adapting animal data to humans. Thanks to years of research, a huge amount of data on chemicals already exists, and the availability of data banks means that it is easy to access. Nevertheless, many chemicals are still unclassified for safety, and much research still needs to be done. Over the last 3 or 4 years, some industry associations have launched programs focused on testing chemicals to cover the lack of safety information, namely ICCA and HPV initiatives. Furthermore, some theoretical new tools such as the family approach and the quantitative structure-activity relationship (QSAR) are now available. These approaches are now under validation processes, which hopefully will lead to their use for regulatory purposes. 

So, S.-S. and Karplus, M. (1997a). Three-Dimensional Quantitative Structure-Activity Relationships from Molecular Similarity Matrices and Genetic Neural Networks. 1. Method and Validations. J.Med.Chem., 40,4347-4359. 

Waller, C.L. and Bradley, M.P. (1999). Development and Validation of a Novel Variable Selection Technique with Application to Multidimensional Quantitative Structure-Activity Relationship Studies. J.Chem.lnfComput.ScL, 39,345-355. 

Chemical Classes Included in the RSTS The chemical and physical forms of the substances included must be consistent with the stated prediction model. For example, if the prediction model indicates that the alternative method is valid for assessing the eye irritation potential of mild, moderate, and severely irritating liquid, surfactant-based formulations, then the RSTS should contain liquid surfactant-based substances of the relevant class that cover a range of toxicity from mild to severe. Quantitative structure-activity relationships may be useful in helping selection of relevant test chemicals. 

In studies of quantitative structure activity relationships (QSAR), the relative potencies of a series of drugs are subjected to analysis with the hope that biological potency will be described by a mathematical equation. QSAR is an actuarial or statistical method in which only objective data are used with no intrusion of models or mechanistic hypotheses. The equation that is obtained not only accounts for the relative potencies of the compounds, but from it are deduced predictions of the potencies of untested compounds if the equation is valid, the predictions are ineluctable. The method thus has the capacity of yielding new (structurally related) drugs with desired potency, perhaps drugs with enhanced selectivity or fewer side effects. 

The impressive success of the Hammett equation in correlating literally hundreds of observed properties (17) (e.g., rate and equilibrium constants, spectroscopic properties, etc.) may be attributable to the multitude of interaction mechanisms that is implicitly embedded in the values of a. The validity of the separability and additivity axioms used in the derivation of extra-thermodynamic relationships is confirmed by the ability to separate experimentally multiple interaction mechanisms (e.g., inductive and resonance (19, 20, 21), polar and steric (10), enthalpic and entropic (22)). This separation fostered significant progress in the application of quantitative structure-activity relationships to the study of chemical mechanisms. For these relationships can now be expressed in terms of more basic properties of the molecules under study. 

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