Quantitative Structure—Activity Relationships

The first use of QSARs to rationalise biological activity is usually attributed to Harisch [Hansch 1969]. He developed equations which related biological activity to a molecule s electronic characteristics and hydrophobicity For example  

The Hammett substituent parameter was used by Hansch as a concise measure of the electronic characteristics of the molecules. Hammett and others (such as Taft) showed that 

Another type of parameter that often appears in published QSAR equations is an indicator variable. Indicator variables are used to extend a QSAR equation over a variety of different types of molecule and so make the equation more generally applicable. For example, Hansch and colleagues derived the following equation for the bindmg constants of sulphonamides (X-C6H4-SO2NH2) to human carbonic anhydrase [Hansch et al. 1985]  

1] takes the value 1 for meta substituents (0 for others) and J2 is 1 for ortho substituents (0 for others). 

The derivation of a QSAR equation involves a number of distinct stages. First, it is obviously necessary to synthesise the compounds and determine their biological activities. When planning which compounds to synthesise, it is important to cover the range of properties that may affect the activity. This means applying the data-checking and -manipulation procedures discussed earlier. For example, it would be unwise to make a series of compounds with almost identical partition coefficients if this is believed to be an important property. 

In structure-based design, new drugs are developed on the basis of the known structure of the receptor site of a known target. However, in many cases a number of so-called lead compounds are known to have some biological activity but Uttle 

The first stage of the QSAR method consists of compiling molecular descriptors for a very large number of lead compounds. Descriptors such is molar mass, molecular dimensions and volume, and relative solubihty in water and nonpolar solvents are available from routine experimental procedures. Quantum mechanical descriptors determined by calculations of the type described in Chapter 10 include bond orders and HOMO and LUMO energies. 

In the second stage of the process, biologicd activity is expressed is a function of the molecular descriptors. An example of a QSAR equation is  

In the final stage of the QSAR process, the activity of a drug candidate can be estimated from its molecular descriptors and the QSAR equation either by interpolation or extrapolation of the data. The predictions are more reliable when a large number of lead compounds and molecular descriptors are used to generate the QSAR equation. 

MALDI-TOF mass spectrometry is a technique for the determination of molar masses in which a sample is ionized in the gas phase and the mass-to-charge number ratios of aU ions are measured. 

The work by Hammett and Taft in the 1950s had been dedicated to the separation and quantification of steric and electronic influences on chemical reactivity. Building on this, from 1964 onwards Hansch started to quantify the steric, electrostatic, and hydrophobic effects and their influences on a variety of properties, not least on the biological activity of drugs. In 1964, the Free-Wilson analysis was introduced to relate biological activity to the presence or absence of certain substructures in a molecule. 

In the late 1960s, Langridge and co-workers developed methods, first at Princeton, then at UC San Francisco, to visualize 3D molecular models on the screens of cathode-ray tubes. At the same time Marshall, at Washington University St. Louis, MO, USA, started visuaHzing protein structures on graphics screens. 

As stated, the development of complexes has been dictated by systematic changes based on empirical observations — the d5-Pt(am)2 unit works. A more quantitative approach takes into account a mathematical equation with electronic and steric factors and such parameters as lipophilicity. The idea that biological response was a function of chemical composition was first advanced in 1869 and a review of these developments is available in Albert s book (Chapter 1, Ref. 1). An account of the quantitative approach which correlates concentration to obtain a given response with electronic factors (e.g. Hammett constants) and lipophilicity (partition coefficients) has been given by Hansch [85]. Few QSAR relationships have been applied to the platinum complexes. 

In the series [PtCl2(am)2] the most active complexes are indeed the most stable, where displacement is unlikely and the antitumour activity is not related to amine loss. 

The relationship between chemical structure, lipophilicity, and its disposition in vivo has been extensively studied. These include solubility, absorption potential, membrane permeability, plasma protein binding, volume of distribution, and renal and hepatic clearance. Activities used in quantitative structure-activity relationships (QSAR) include chemical measurements and biological assays. QSAR currently are applied in many disciplines, with many pertaining to drug design and environmental risk assessment. 

QSAR studies date back to the nineteenth century. In 1863, A. F. A. Cros at the University of Strasbourg observed that toxicity of alcohols to mammals increased as the water solubility of the alcohols decreased. In the 1890s, Hans Horst Meyer of the University of Marburg and Charles Ernest Overton of the University of Zurich, working independently, noted that the toxicity of organic compounds depended on their lipophilicity. 

Little additional development of QSAR occurred until the work of Louis Hammett (1894-1987), who correlated electronic properties of organic acids and bases with their equilibrium constants and reactivity. Consider the dissociation of benzoic acid  

Hammett observed that adding substituents to the aromatic ring of benzoic acid had an orderly and quantitative effect on the dissociation constant. For example. 

The equilibrium constant is even larger than for the nitro group in the meta position, indicating even greater withdrawal of electrons. Now, consider the case in which an ethyl group is in the para position  

The pharmaceutical industry was one of the first to employ this technique to reduce the cost of developing and testing new drugs, which can cost thousands to hundreds of thousands of dollars depending on the tests and time involved. Another common use of QSARs is in the area of environmental risk assessments. 

During the past few decades, the use of organotins has increased dramatically, most likely due to their diverse biocidal properties. In fact, organotins have a higher commercial usage than any other organometallic system. In turn, this has led to an increased concern about the fate of the compounds and their degradation 

The toxicity for a series of di- and triorganotins was also found to correlate well with the hydrophobic characteristics (log P or Hansch jt) of the compounds against two mammalian cell lines (BALB/c mouse fibroblast 3T3 and mouse neuroblastoma Naa cells). The sequence of the cytotoxicity for the organotins was similar to those observed in earlier studies. Another study by Babich and Borenfreund using bluegill sunfish BF-2 cell fines showed that there was a direct linear correlation between the cytotoxicity of a series of diorganotins and the lipophilicity of the compounds, with a correlation coefficient of 0.958. 

A QSAR approach was used to determine the anti-tumor activities of a host of organotins. Several classes of diorganotins were screened against P-388 lymphocytic leukaemia in mice. The study showed 

Descriptors and/or QSARs that previously required mainframe computer time to calculate, or were not possible at all, can now be routinely done on a desktop computer. A common descriptor that is easily calculable is the total surface area (TSA), since a broad database of bond distances, angles, and van der Waals radii is readily available in the literature. Using literature values, Brinckman et were able 

The goal of cheminformatics and materials informatics (Rodgers and Cebon 2006) is to catalog, store, manipulate, and analyze the vast databases of materials and molecules. The tasks can be 

Molecular Modeling for the Design of Novel Performance Chemicals and Materials 

Substituted pyridazinones can be categorized into five groups by cluster analysis, of which one is of analogues which inhibit phytoene desaturase. A comparison of seven different phenylpyridazinones found that an m-trifluoro substituent at the phenyl ring and a methyl- 

QSAR data on substituted 2-phenylpyridazinones have shown that the electronic properties of substituents strongly influence activity but can be countered by lipophilicity. Data from whole cells and cell extracts of Aphanocapsa correlated well, with the in vitro results showing a linear relationship between lipophilicity and inhibition of phytoene desaturase in accordance with the QSAR equation for intact cells.  

Studies with phenoxynicotinamide analogues have been reported. It should be remembered that the lower potency of the commercial herbicide under laboratory conditions may be outweighed by other considerations such as selectivity, toxicity, and persistence in the soil, factors which can only be assessed by field trials. 

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A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Besides the aforementioned descriptors, grid-based methods are frequently used in the field of QSAR quantitative structure-activity relationships) [50]. A molecule is placed in a box and for an orthogonal grid of points the interaction energy values between this molecule and another small molecule, such as water, are calculated. The grid map thus obtained characterizes the molecular shape, charge distribution, and hydrophobicity. 

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). 

The fundamental assumption of SAR and QSAR (Structure-Activity Relationships and Quantitative Structure-Activity Relationships) is that the activity of a compound is related to its structural and/or physicochemical properties. In a classic article Corwin Hansch formulated Eq. (15) as a linear frcc-cncrgy related model for the biological activity (e.g.. toxicity) of a group of congeneric chemicals [37, in which the inverse of C, the concentration effect of the toxicant, is related to a hy-drophobidty term, FI, an electronic term, a (the Hammett substituent constant). Stcric terms can be added to this equation (typically Taft s steric parameter, E,). 

Neural networks have been proposed as an alternative way to generate quantitative structure-activity relationships [Andrea and Kalayeh 1991]. A commonly used type of neural net contains layers of units with connections between all pairs of units in adjacent layers (Figure 12.38). Each unit is in a state represented by a real value between 0 and 1. The state of a unit is determined by the states of the units in the previous layer to which it is connected and the strengths of the weights on these connections. A neural net must first be trained to perform the desired task. To do this, the network is presented with a... 

Dunn W J III, S Wold, U Edlund, S Hellberg and J Gasteiger 1984. Multivariate Structure-Activib Relationships Between Data from a Battery of Biological Tests and an Ensemble of Structur Descriptors The PLS Method. Quantitative Structure-Activity Relationships 3 131-137. 

K and G M Crippen 1986. Atomic Physicochemical Parameters for Three-dimensional Struc-directed Quantitative Structure-Activity Relationships. I. Partition Coefficients as a Measure ydrophobicity. Journal of Computational Chemistry 7 565-577. 

Z, ] McClarin, T Klein and R Langridge 1985. A Quantitative Structure-Activity Relationship and ecular Graphics Study of Carbonic Anhydrase Inhibitors. Molecular Pharmacology 27 493-498. 

Holiday J D, S R Ranade and P Willett 1995. A Fast Algorithm For Selecting Sets Of Dissimilar Molecule From Large Chemical Databases. Quantitative Structure-Activity Relationships 14 501-506. 

Hudson B D, R M Hyde, E Rahr, J Wood and J Osman 1996. Parameter Based Methods for Compoun Selection from Chemical Databases. Quantitative Structure-Activity Relationships 15 285-289. 

Poso A, R Juvonen and J Gynther 1995. Comparative Molecular Field Analysis of Compounds wii CYP2A5 Binding Affinity. Quantitative Structure-Activity Relationships 14 507-511. 

When the property being described is a physical property, such as the boiling point, this is referred to as a quantitative structure-property relationship (QSPR). When the property being described is a type of biological activity, such as drug activity, this is referred to as a quantitative structure-activity relationship (QSAR). Our discussion will first address QSPR. All the points covered in the QSPR section are also applicable to QSAR, which is discussed next. 

Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology W. Karcher, J. Devillers, Eds., Kluwer, Dordrecht (1990). 

PW91 (Perdew, Wang 1991) a gradient corrected DFT method QCI (quadratic conhguration interaction) a correlated ah initio method QMC (quantum Monte Carlo) an explicitly correlated ah initio method QM/MM a technique in which orbital-based calculations and molecular mechanics calculations are combined into one calculation QSAR (quantitative structure-activity relationship) a technique for computing chemical properties, particularly as applied to biological activity QSPR (quantitative structure-property relationship) a technique for computing chemical properties... 

Quantitative Structure—Activity Relationships (QSAR). Quantitative Stmcture—Activity Relationships (QSAR) is the name given to a broad spectmm of modeling methods which attempt to relate the biological activities of molecules to specific stmctural features, and do so in a quantitative manner (see Enzyme INHIBITORS). The method has been extensively appHed. The concepts involved in QSAR studies and a brief overview of the methodology and appHcations are given here. 

Quantitative Structure—Activity Relationships. Many quantitative stmcture—activity relationship (QSAR) studies of progestins have appeared in the Hterature and an extensive review of this work is available (174). QSAR studies attempt to correlate electronic, steric, and/or hydrophobic properties to progestational activity or receptor binding affinity. A review focusing on the problems associated with QSAR of steroids has been pubUshed (175). 

The QSAR (quantitative structure-activity relationship) approach has been considered for the identification of toxicants that bind to steroid and aryl... 

GR Marshall, CD Barry, HE Bosshard, RA Dammkoehler, DA Dunn. The conformational parameter m drug design The active analog approach. ACS Symp Ser 112 205-226, 1979. JL Fauchere, ed. QSAR Quantitative Structure-Activity Relationships m Drug Design. New York Alan R Liss, 1989, pp 177-181. 

S Hellberg, M Sjostrom, B Skagerberg, S Wold. Peptide quantitative structure-activity relationships, a multivariate approach. I Med Chem 30 1126-1135, 1987. 

GM Crippen. Quantitative structure-activity relationships by distance geometry Systematic analysis of dihydrofolate reductase inhibitors. I Med Chem 23 599-606, 1980. 

JM Sutter, SL Dixon, PC Jurs. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. I Chem Inf Comput Sci 35(I) 77-84, 1995. 

BT Luke. Evolutionary programming applied to the development of quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34(6) I279-1287, 1994. 

SS So, M Karplus. Evolutionary optimization in quantitative structure-activity relationship An application of genetic neural networks. J Med Chem 39 1521-1530, 1996. 

TA Andrea, H Kalayeh. Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors. J Med Chem 34 2824-2836, 1991. 

Among others, 11 was included in a series of drugs to study quantitative structure-activity relationships (96KFZ(6)29, 98MI7, 99BMC2437). A statistically significant CoMFA model was developed for describing the... 

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