Computational Approach for HTS: Q(SAR)
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Posted On :
Jan-02-2012
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Article Word Count :
863
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High throughput screening has become the leading tool in drug discovery. With its fundamentals of sample collection with subsequent screening to find hits to generate optimized leads, HTS is proving to be a useful source of expanding compound libraries.
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Introduction
High throughput screening has become the leading tool in drug discovery. With its fundamentals of sample collection with subsequent screening to find hits to generate optimized leads, HTS is proving to be a useful source of expanding compound libraries. However, the use of computational approach can make this process even more time and cost effective. Structure to activity relationship (SAR) is one of such computational approach which can smooth the dwindling path of hit to lead generation in HTS.
The General Process of HTS and Role of SAR
In high throughput screening successful hits are optimized into leads which are further tested for their selectivity, ADME and druggability in order to use them for practical applications. However, the results of optimization for newly discovered inhibitors (e.g. kinase inhibitors, egfr inhibitors etc) are rarely positive and a lot of problems appear in the lead collection which may render them unfit for human use. This is the main reason for wastage of money and time. However, if structure-activity relationship is studied for optimized leads, it can reveal some useful information which can be used to design analogs for further screening in HTS [1].
What is SAR?
SAR refers to the structure and activity relationship among compounds i.e. similar compounds has similar structures. It requires as few as a single compound. For example, if a compound is known to inhibit a particular target, the structural determinant of this compound is recognized and the structural fragment is mined. This known structure can be encoded into the software and can subsequently be used to design analogs. The greater is the number of compounds with same structural determinant, the more reliable it is for analog designing. SAR is much better for the qualitative endpoint analysis. For instance, SAR describes that a compound is active/inactive, toxic/nontoxic, inhibitor/not inhibitor etc.
What is QSAR?
On the other hand, QSAR refers to the quantitative structure and activity relationship. QSAR is a process in which a chemical structure is quantitatively related to the biological activity. In contrast to SAR for which even a single compound is enough, QSAR needs more compounds (as many as possible) in order to develop a meaningful relationship. There are three important similarities between SAR and QSAR.
• Both SAR and QSAR use numeric data for representation of chemical compounds.
• Both of these approaches can use different types of numeric representations depending on the need of experiment.
• Both SAR and QSAR promise the derivation of relationship between numeric descriptors and the respective activity.
Together, SAR and QSAR are normally named as Q(SAR) in literature. Following table refers to a general data matrix for Q(SAR) approach.
Structural identifiers
Activity to be modeled
Property/Descriptor/Fragment1
Property/Descriptor/Fragment2
The Q(SAR) approach can be very helpful in high throughput screening [2].
It allows successful development of new lead compounds with pharmacological activity.
It saves precious time of researchers and pharmaceutical industries by identifying only desired hits in screening assays.
It helps to eliminates undesired possible compounds at earlier stages of screening process.
It helps to predict the properties of compounds including their toxicity, solubility, bioavailability, possible mechanism of action etc.
It helps to predict the fate and the possible modifications which may take place to the compound after entering human body.
It may also help to design better versions of drugs aginast which resistance have been developed e.g. Imatinib
Conclusions
After a comprehensive overview of the Q(SAR) approach, it can be stated that Q(SAR) can prove to be very a useful approach in HTS. It emphasizes the use of rational approaches in HTS to avoid delays and huge expenses. Details of Q(SAR) will surely be discussed in the upcoming articles on this blog as Q(SAR) is undoubtedly a promising approach in the context of computational approaches for HTS!
References
1. Patani GA, LaVoie EJ. Bioisosterism: A Rational Approach in Drug Design. Chemical Reviews 1996Dec; 96 (8): 3147–3176.
2. Selassie CD. History of quantitative structure-activity relationships. In: Abraham DJ (ed) Burger’s Medicinal Chemistry and Drug Discovery, 6th edn., Volume 1: Drug Discovery. John Wiley and Sons. Inc., New York.
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