Structure/Activity relationships

3 Structure-activity relationships 5.3.1 Receptor cells generalists and specialists 

Fish have receptor cells specific for certain compounds. Examples are the reception of the fish toxins tetrodotoxin (TTX) and saxitoxin (STX) by rainbow trout, Salmo gairdneri, and Arctic chart, Salvelinus alpinus. Both toxins are extremely potent taste stimuli. Not only are the receptors extremely sensitive 

How specific are the responses to certain compounds Will slight changes in the size, shape, or functional groups of the molecule render it unrecognizable for a certain response  

The methods to investigate the specificity of behaviorally active compounds include spontaneous responses of untrained animals and discrimination tests, where a discriminated stimulus is substituted by another closely related compound to detect the degree of generalization from one stimulus to another. In field studies, the first is the method of choice. 

An example for stimulus generalization are responses of rats to stress-inducing odors. Laboratoiy rats of the Wistar strain respond to predator odors, specifically mercapto compounds in fox droppings, with stress reactions, for example avoidance behavior such as freezing and increased plasma corticosterone concentrations (Vemet-Mauiy et ah, 1984). The rats were trained to avoid water scented with a mercapto odorant that contained both a keto- and a sulfhydryl group (4-mercapto-4-methyl-2-pentanone). As the animals licked a waterspout, a mild electric shock was applied to their tongue. When different compounds were tested thereafter, the rats avoided compounds with similar 

The Q is put into QSAR by describing the structure of a compound in a quantitative way, the simplest examples of quantitative descriptors being the mass of the compound or the number of atoms present. When the compound is described using physical, as opposed to structural, properties the relationship becomes a PAR. Correlations of this type have been used in the perfumery industry to describe and predict the substantivity and retention of fragrance ingredients that is, the ability of a compound to stick to and remain bound to surfaces such as hair, skin or cloth (see Chapter 11 for more details). 

Extensive structure-activity relationships for NO release from oximes do not currently exist. Structural changes do influence the rate of NO release from the nitro-containing oximes, such as FK409 (9, ti/2 = 46 min, Fig. 7.2) [46, 59]. Changing the alkyl group attached to the carbon of the nitro group can increase (12, -CH2OCH3, ti/2 = 2.6 min, Fig. 7.2) or decrease (13, -tertBu, ti/2 = 2900 min, Fig. 7.2) the rate of NO release from 

The underlying principle behind any S AR is that the molecular structure of an organic compound determines the properties of that compound 

The data in Table 7.3 suggest that substituents R R, R, and in the general structure of alkylphosphonate lo had great influence on the inhibitory potency when H as R was kept unchanged. On the scalfold of the alkylphosphonates lo with H as R , the effects of substituents R R, R, and Y on the inhibitory potency can be summarized as follows  

R and R in the phosphonates were also essential for inhibitory potency against plant PDHc. When 2,4-dichloro as Y on the phenoxy-benzene ring was kept constant, inhibitory potency against plant PDHc could be greatly enhanced by the modifications of R and R.  

When Yn, R R, and R were kept constant, R bound to a-carbon also had great influence on the inhibitory potency. The introduction of Me as R seems to have a favorable effect on inhibitory potency. 

IIB-2 with Me as R, as the monosodium salt of IC-22 exhibited the best inhibitory potency against rice PDHc among compounds in Table 7.3. However, monosodium salt IID-10 with fur-2-yl as R showed weaker activity against rice PDHc than those of compounds with H or alkyl groups as R, such as IIB-1, IIB-2, IIB-3, and IIB-4. 

2 Relationships between Inhibitory Potency Against Plant PDHc and Herbicidal Activity 

Is it possible to say something about the structure and activity of pheromones  

Most of them are esters. They may also be alcohols, carboxylic acids, lactones, aldehydes, ketones, and hydrocarbons (Silverstein, 1984). Crucial properties of releaser pheromones are their volatility, their stability, and, of course, the degree of specificity possible to build into a relatively small molecule. The molecular weight tends to be between 80 and 300. On the basis of evaporation and diffusion rates, it can be predicted that long-distance sex pheromones would have a molecular weight between 200 and 300. A pheromone may be one chemical, but usually it is a mixture of chemicals, each of which is a component of the pheromone. Mixtures increase the specificity, a property important especially for the sex pheromones. 

On the basis of empirical structure-activity relationships, it is possible to develop a three-dimensional model of the CBl-receptor. Important for a high binding affinity are the phenolic hydroxy group at C-1, the alkyl residue at C-3, the (l -configuration at both stereogenic centres, as well as the substitution pattern at C-9 and C-11 (Fig. 5.69 and Tab. 5.7). 

Work on the relationship between chemical structure and pharmacological activity of morphinans to 1966 has been reviewed by Hellerbach et al. s) and morphinans with antagonist properties were reviewed in 1973.(158) As is the case for 4,5-epoxymorphinans (Chapter 2) and benzomorphans (Chapter 4), molecular geometry is the major structure-biological activity influence, although the nature of the N-substituent imparts significant qualitative and quantitative variations in morphinan pharmacology. 

The (—)-morphinans (i.e., those configurationally related to morphine) are responsible for all the antinociceptive properties of the racemate. In addition, such compounds tend to have parallel respiratory depression levels and PDCs. In (+)-series (e.g., dextromethorphan), clinically useful antitussive properties are encountered, again following the separation of biological activities noted in 4,5-epoxymorphinan optical antipodes. 

Isomorphinans possess a B/C-trans ring fusion and in this they differ from B/C cis morphine geometry. Such geometry, however, may be related stereochemically to endo-ethanotetrahydrooripavines. Opioid activity tends to be increased in B/C trans series, and a compound such as 3-hydroxy-N-methylisomorphinan is several times more potent than morphine in rat analgesic tests. 

Aromatic substitution elsewhere in the ring is of little or no benefit. 

Substituents elsewhere in the C-ring may also endow morphinans with interesting pharmacological properties. 7-Alkylisomorphinans tend to be more 

Key fragment contributions to activity/tolerance and structure-activity relationships in the aromatic moiety as well as in the hydrazine region can be summarized as follows  

6-Diethyl-4-methyl is the preferred aromatic substitution pattern. The 

methoxv ethvnvl ethvi ethenvl bromo methyl propyl OH CF3. CN Rj phenyl methyl ethyl H. halogen Rj ethyl ethvnvl methyl 

Many studies on the bioactivities of organotin compounds present work on structure-activity relationships. A recent review article summarizes some of the results of these investigations.  

In general, compounds with alkyl groups are more toxic than compounds with aryl groups. In RjSnX derivatives, X itself can be biologically active or can assist the transport of the molecule to the active site. 

One way to reduce the number of organotin derivatives discharged into the environment, and to reduce the cost of developing more effective tin-based biocides, is to design more effective compounds. Quantitative structure-activity relationship (QS AR) studies appear to be an interesting strategy to address this problem. 

Once the structure of a biologically active compound is known, the medicinal chemist is ready to move on to study the structure-activity relationships of the compound. 

The aim of such a study is to discover which parts of the molecule are important to biological activity and which are not. The chemist makes a selected number of compounds, which vary slightly from the original molecule, and studies what effect that has on the biological activity. 

As far as a drug is concerned, the weapons and armour are the various chemical functional groups present in the structure, which can bind to the receptor or enzyme. We have to be able to recognize these functional groups and determine which ones are important. 

Let us imagine that we have isolated a natural product with the structure shown in Fig. 7.3. We shall name it Glipine. There are a variety of groups present in the structure and the diagram shows the potential bonding interactions which are possible with a receptor. 

It is unlikely that all of these interactions take place, so we have to identify those which do. By synthesizing compounds (such as the examples shown in Fig. 7.4) where 

However, most of these processes and the whys of the actions depending on chemical structure are still unknown. Thus, structure-activity relationships can be established for phenoxy compounds only in synthesis laboratories by the comparison of activity measurements of compounds in vitro and in vivo, prepared in series, on the basis of structural analogies. 

Several in vitro tests are suitable for the determination of the growth-regulating activity of hormone-type herbicides, among them the straight growth test of wheat or oat, and the pea curvature test. Both methods yield quantitative results of good reproducibility. The Avena test elaborated by Went cannot be used in this case, but the root growth inhibition test is suitable (Audus, 1949 1951). 

Activity values obtained by the tests mentioned or by other known methods cannot be compared with each other, because they yield results differing by orders of magnitude. It is recommended that several test methods be used simultaneously for comparative evaluation. With a view to future practical considerations, tests are carried out on several kinds of intact plants, to obtain at the same time information on selectivity. 

The first detailed investigation of plant growth-regulating activity is linked with the names of Koepfli et al. (1938). On the basis of their experiments with indole and arylalkanoic acids, they established five structural requirements necessary for the biological activity of the molecule. 

Accordingly, to have the growth hormone effect the molecule must contain a ring system in which at least one double bond is present, it must contain a side chain with 

The first PC compound was prepared using the 2-methanesulfonylpyrimidine (OMSP) 6 and the salicylic acid ester in DMF. The sulfonyl compound 6 was a very efficient intermediate to synthesize 2-substituted pyrimidines [8]. The meth-anesulfonyl group in 6 is easily replaced by nucleophilic reagents like 7 and 8 (Fig. 2.5.5) [7, 9, 10]. This method was generally employed to synthesize numerous analogues aiming at new herbicides, not only with a high potency but also with an enhanced crop safety. Structural modifications were first made with the skeletal structure 9 (Fig. 2.5.6) [3]. 

The most common method for the preparation of PCPP is the macromolecular substitution of the polydichlorophosphazene precursor [37] (see Section 1.1). This requires the use of protecting group chemistry, as it is well known that free acid groups lead to skeletal breakdown reactions. Propylparaben, the n-propyl ester of p-hydroxybenzoic acid has been most widely used for this, with subsequent deprotection of the propyl ester by hydrolysis under basic conditions. The requirement for complete macromolecular reactions has been addressed and structurally homogeneous, fully deprotected PCPP can be attained with the correct structure on relatively large (2 kg) scales [21, 26, 38, 39]. 

The ability to produce such libraries from the same polyphosphazene backbone facilitates the direct comparison and, although more work is required, it is an extremely interesting approach toward the targeted design and preparation of conjugates with optimised solubility, binding and activation. The ability to produce polymers 

SARs have been used for decades by medicinal chemists in the design of highly efficacious drug substances [47] and by the US Environmental Protection Agency (EPA) for assessing the toxicity of new, untested commercial chemicals prior to commercialization [i.e., chemicals submitted to the EPA in the form of Premanufacture Notifications (PMNs)] [48]. However, despite the structure-activity data available for many classes of commercial chemical substances, the use of SARs has been given much less attention by chemists as a rational approach for designing new, less toxic, commercial chemical substances. 

The remainder of this section discusses how SAR data can be used by chemists as a powerful tool for designing safer chemical substances. Unless indicated otherwise, the word activity refers to toxicity. Chapter 13 provides more detailed discussions on the use of SARs, and demonstrates the usefulness of this approach with respect to the design of safer aromatic amines. 

With qualitative structure-activity relationships (SARs), the correlation of toxic effect with structure is made by visual comparison of the structures of the chemicals in a series of congeneric substances and the corresponding effects their structural differences have on toxic potency, for example, as represented by their LD50 values. From qualitative examination of structure-activity data the chemist may be able to see a relationship between structure and toxicity, and identify the least toxic members of the class as possible commercial alternatives to the more toxic members. 

In addition, the chemist may infer the structural characteristics that reduce toxic potency, thereby providing a rational basis to design new, less toxic analogs. Typically, in larger data sets the relationship between structure and activity is more apparent, but small data sets can nonetheless be fairly useful. The application of SARs for the design of safer chemicals is demonstrated below using aliphatic carboxylic acids and organonitriles, two classes of important commercial chemical substances. 

The related hormone PYY binds to the Y1 receptor as well as to the Y2 receptor as strongly as NPY, although only 70% of the amino acids are identical (Wieland et al., 

By contrast, neither PP nor NPY-OH, an analogue in which the C-terminal tyrosine-amide has been substituted by tyrosine, were recognized by these two receptor types. The feeding receptor recognizes PYY as well, while the Y3 receptor does not bind PYY (Table 1). The PP1 /Y4 receptor binds PYY better than NPY, however, both lack affinity compared to PP (Gehlert and Hipskind, 1995). 

The results from the investigations on [Ahx5 24]NPY (Beck-Sickinger etal., 1990a) are only partially in agreement with the alanine-scan of full-length NPY (Beck- 

In both investigations the sensitivity of residues 35 and 36 has been confirmed. An affinity loss of 104-fold indicates a direct interaction of Arg35-Tyr36-NH2 with the human Y2 receptor. Since, however, NPY 32-36-NH2 is inactive (Fuhlendorff et al., 1990b), additional amino-acid residues or mimetics are required in order to stabilize the active conformation of the C-terminal part of NPY. 

THC refers to tetrahydrocannabinol, and A refers to the position of the double bond. Various numbering systems are used, so the following equivalences should be noted A THC = A 3,4-trans-THC = A THC and A THC = A THC = A THC = A -3, 

It should be noted that recent testing has indicated that a 

Substituting N, O, or S atoms at various places or saturating the double bond to produce hexahydrocannabinol probably retains activity. fSeeCA 74,125667(1971) for S analogs.] Alkoxy side chains at 5 retain activity. Unsaturated side chains arc as active as saturated ones. Ether moieties at the 5 position, but not as the 3, retain activity. Activity is retained if an additional alkyl is placed at 4 but lost if placed at 6. Activity is greatly decreased or lost if the H at the 4 or 6 positions is replaced by carboxyl, carbomethoxyl, acetyl or acetoxyl if the hydroxyl is replaced by H if the OH is at 5 and the side chain at 4. Methyl and/or ethyl at 1 and 5 retains activity, as does removal of the methyl at 1. An hydroxyl in the side chain is active, but not on 

Since 0 or 1 and perhaps 2 double bonds anywhere in the lefthand ring above, as well as changes in the size and position of the alkyl groups will probably all produce compounds with THC activity, many compounds similar to menthadieneol, men-thatriene, verbenol, epoxycarene, pulegone and 4-carbethoxy-l-Me-3-cyclohexanone can be used in the methods below to get active THC analogs (e.g., isopiperitinol will work [TL 945 

For new information on the structure-activity relationships of cannabinoids see JMC 16,1200(1973), Arzneim, Forsch 22, 1995(1972), and Chem. Revs. 76,75(1976). 

The availability of synthetic vitamin D metabohtes and of analogs systematically modified at specific sites has made possible a rou definition of the relative im- 

Morphine, the prototypic opioid, is a phenan-threne alkaloid with a benzylisoquinoline backbone. It is composed of five fused rings A-E, 

Examples of some drugs and endogenous peptides showing 

Receptors preferential binding Primary general effect Clinical effects 

R Morphine Heroin Oxymorphone Hydrocodone Fentanyl, etc. Endomorphins Central depression Anrilgesia euphoria respiratory depression bradycardia hypothermia constipation physical dependence 

K Nalbuphine Butorphanol Pentazocine Dynorphins Sedation Sedation analgesia dysphoria dissociative, deliriant and hallucinogenic effects 

Some researchers have used the tehaiodoplatinum dianion because of the more powerful trans effect of the iodme amon compared to the chloride ion. 

While most active platinum compoimds have these characteristics, there exist active platinum compoimds that do not conform to these general requhements. Farrell and coworkers have described three classes of complexes of the general form PtCl2LL. The structme of one of these where L = L = pyridine is given as 

These trans complexes displayed good antiproliferative activity. In such complexes a plane can be envisioned through both pyridine rings that might allow for more effective DNA base intercalation than would be achievable with corresponding cis isomers. 

Farrell and coworkers also synthesized products of the form trans-[PtCl2(het)(R,RiSO)], where het = iV-heterocycle, R, Ri = alkyfaryl. When het = quinoline and R,Ri are methyl, the complex shows activity equivalent to that of cisplatin.  

In another study, Coluccia et al. found that Irans complexes with iminoether ligands, 9, were more active than the corresponding cis complexes. 

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This is the domain of establishing Structure-Property or Structure-Activity Relationships (SPR or SAR), or even of finding such relationships in a quantitative manner (QSPR or QSAR). 

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,). 

The reliability of the in silico models will be improved and their scope for predictions will be broader as soon as more reliable experimental data are available. However, there is the paradox of predictivity versus diversity. The greater the chemical diversity in a data set, the more difficult is the establishment of a predictive structure-activity relationship. Otherwise, a model developed based on compounds representing only a small subspace of the chemical space has no predictivity for compounds beyond its boundaries. 

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... 

Kubinyi H 1995. The Quantitative Analysis of Structure-Activity Relationships. In Wolff M E (Editor) Burger s Medicinal Chemistry and Drug Discovery. 5th Edition, Volume 1. New York, John Wiley Sons, pp. 497-571. 

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. 

B Mohney and L B Kier 1991. The Electrotopological State An Atom Index for QSAR. ntitative Structure-Activity Relationships 10 43-51. 

A Quantitative Approach to Biochemical Structure-Activity Relationships. Accounts of nical Research 2 232-239. 

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. 

Rogers D and A J Hopfinger 1994. Application of Genetic Function Approximation to Quantitatir Structure-Activity Relationships and Quantitative Structure-Property Relationships. Journal Chemical Information and Computer Science 34 854-866. 

Completely ah initio predictions can be more accurate than any experimental result currently available. This is only true of properties that depend on the behavior of isolated molecules. Colligative properties, which are due to the interaction between molecules, can be computed more reliably with methods based on thermodynamics, statistical mechanics, structure-activity relationships, or completely empirical group additivity methods. 

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). 

An area of great interest in the polymer chemistry field is structure-activity relationships. In the simplest form, these can be qualitative descriptions, such as the observation that branched polymers are more biodegradable than straight-chain polymers. Computational simulations are more often directed toward the quantitative prediction of properties, such as the tensile strength of the bulk material. 

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. 

M. G. J. Beets, SAR Structure—Activity Relationships in Human Chemoreception, AppHed Science Pubhshers, London, 1978. 

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