Selectivity-activity relationships

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. 

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. 

Historically, the discovery of one effective herbicide has led quickly to the preparation and screening of a family of imitative chemicals (3). Herbicide developers have traditionally used combinations of experience, art-based approaches, and intuitive appHcations of classical stmcture—activity relationships to imitate, increase, or make more selective the activity of the parent compound. This trial-and-error process depends on the costs and availabiUties of appropriate starting materials, ease of synthesis of usually inactive intermediates, and alterations of parent compound chemical properties by stepwise addition of substituents that have been effective in the development of other pesticides, eg, halogens or substituted amino groups. The reason a particular imitative compound works is seldom understood, and other pesticidal appHcations are not readily predictable. Novices in this traditional, quite random, process requite several years of training and experience in order to function productively. 

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. 

The structure activity relationships ( SAR) of newly synthesized analogues of nucleosides, xanthine heterocycles, and nonxanthine heterocycles have been explored at the ARs. Potent and selective AR antagonists have been prepared for all four subtypes [3, 4], and selective agonists are known for three subtypes [1]. Thus, numerous pharmacological tools are available for in vitro and in vivo use (Table 2). Potent and selective A2b AR agonists are yet to be repotted, although several research groups have identified lead compounds. 

Traditionally, in pursuit of their structure-activity relationships, medicinal chemists had focused almost exclusively on finding compounds with greater and greater potency. However, these SARs often ended up with compounds that were unsuitable for development as pharmaceutical products. These compounds would be too insoluble in water, or were not orally bioavailable, or were eliminated too quickly or too slowly from mammalian bodies. Pharmacologists and pharmaceutical development scientists for years had tried to preach the need for medicinal chemists to also think about other factors that determined whether a compound could be a medicine. Table 1.1 lists a number of factors that determine whether a potent compound has what it takes to become a drug. Experimentally, it was difficult to quantitate these other factors. Often, the necessary manpower resources would not be allocated to a compound until it had already been selected for project team status. 

Astles PC, Brealey C, Brown TJ, Facchini V, Handscombe C, Harris NV, McCarthy C, McLay IM, Porter B, Roach AG, Sargent C, Smith C, Walsh RJA. Selective endothelin A receptor antagonists. 3. Discovery and structure-activity relationships of a series of 4-phenoxybutanoic acid derivatives. J Med Ghent 1998 41 2732-44. 

Smith PA, Sorich MJ, McKinnon RA, Miners JO. Pharmacophore and quantitative structure-activity relationship modeling complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity. J Med Chem 2003 46 1617-26. 

Lopez-Rodriguez ML et al. (1996) Synthesis and structure-activity relationships of a new model of arylpiperazines. 1. 2-[[4-(o-Methoxyphenyl)piperazin-l-yl]methyl]-1, 3-dioxoperhydroimidazo[l,5-alpha]pyridine a selective 5-HTlA receptor agonist. J Med Chem 39(22) 4439-4450... 

SB-366791 (18) emerged from screening an in-house library as a potent competitive inhibitor of both hTRPVl and rTRPVl, endowed with superior target selectivity compared to capsazepine [84]. Structure-activity relationships of SB-366791 remain to be reported. 

The first non-peptide oxytocin antagonists, based on a spiropiperidine template, were described by Merck in 1992 [68-70]. The binding affinity data for key compounds from this series are summarised in Table 7.2. The initial screening hit, L-342,643, (23), had modest (4/iM) affinity for rat uterine oxytocin receptors and very little vasopressin selectivity [71]. A structure activity relationship (SAR) study was carried out around this template, focussing on the toluenesulphonamide group. This work led to the identification of bulky lipophilic substitution as key to improved oxytocin potency, while the introduction of a carboxylic acid group led to improved... 

The third step is to optimize the lead molecule through iterative chemical synthesis and biological testing, aiming to obtain molecules with the required potency (typically nanomolar), selectivity, bioavailability, and DMPK (drug metabolism and pharmacokinetics) properties. This step usually requires considerable time and resources usually the synthesis of hundreds of compounds is needed to deduce a robust SAR (structure-activity relationship). Such resources can be considerably reduced and the... 

Hruby VJ, Agnes RS. Conformation-activity relationships of opioid peptides with selective activities at opioid receptors. Biopolymers (Peptide Sci) 1999 51 391-410. 

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