INTRODUCTION:
QSAR approach endeavors to identify and quantify the physiochemical properties of a drug and to observe whether they have an effect on the biological activity of the drug. By quantifying physicochemical properties, it could be possible to calculate well in advance what the biological activity of a novel analogue or lead compound might be. In the classic QSAR studies, affinities of ligands to their binding sites, rate constants, inhibition constants and other biological end points are correlated with molecular properties such as lipophilicity, polarizability, electronic and steric parameters (Hansch analysis) or Free-Wilson analysis.1 We can put efforts on analogues which could have improved activity and cut down the number of analogues that has to be made. If an analogue is discovered which does not fit the equation, it implies some other characteristics is important and provides a lead for further development.
History of QSAR:
Modern QSAR studies are expected to have begun from the 1960s. The scientists have been making predictions based on the knowledge regarding the physical and chemical properties of the compounds until 1816. Investigations regarding the correlation of biological activities with physicochemical properties like molecular weight and aqueous solubility were done around 1841, almost 60 years before the work of Overton and Meyer that linked the aquatic toxicity to lipid-water partitioning. Almost throughout the 20th century QSAR progressed, although many lean years were present. In 1962 Corwin Hansch and co-workers came up the seminal works, which stimulated a huge interest in the prediction of biological activities.3
The QSAR was initiated by Corwin Hansch which led to the paradigm of various new methods. The concept gradually evolved from 2D to 3D QSAR and other dimensions were added thereon.4
Initially the interest was focused largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, paving way for QSAR modelling on environmental toxicities, especially once the regulatory authorities began to be involved. QSAR has continued to expand since then, with around 1400 publications made annually from 2011.
More than a century ago, Crum-Brown and Fraser pronounced the idea that the physiological action of a substance was a function of its chemical composition and constitution. A few decades later, in 1893, Richet expressed that the cytotoxicity of a diverse set of simple organic molecules were inversely related to their corresponding water solubilities. After the 20th century, Meyer and Overton independently suggested that the narcotic (depressant) action of a group of organic compounds paralleled their olive oil/water partition coefficients. In 1939 Ferguson established a thermodynamic generalization to the correlation of depressant action with the relative saturation of volatile compounds in the vehicle with which they were administered. The extensive work of Albert, Bell and Roblin established the significance of ionization of bases and weak acids in bacteriostatic activity. Meanwhile on the physical organic front, significant pace was being made in the description of substituent effects on organic reactions, established by the influential work of Hammett, which gave rise to the "sigma-rho" culture. Taft conceived a way for separating polar, steric, and resonance effects and introducing the first steric parameter, Es. The contributions of Hammett and Taft together made the basis for the development of the QSAR paradigm by Hansch and Fujita. In 1962 Hansch and Muir published their ingenious analytical study on the structure-activity relationships of various plant growth regulators and their dependency on Hammett constants and hydrophobicity. 2
Physicochemical properties:
Several physical, chemical, and structural properties have been analysed by the QSAR approach, but the most common parameters are hydrophobic, electronic and steric properties, since these possible to be quantified. Medicines have many predictable properties like water solubility, melting point, boiling point and so on. These are related to the molecular structure of the medicine. These molecular structures can be represented as graph theory graph structures. Hydrophobic properties can be easily quantified for complete molecules or for individual substances. On the other hand, it is hard to quantify the electronic and stearic properties for complete molecules, and this is only feasible for individual substances. The three more widely studied physicochemical properties are discussed in this article.
Hydrophobicity:
More than a hundred years ago, Meyer and Overton made their pioneering discovery on the correlation between oil/water partition coefficients and the narcotic potencies of small organic molecules. Molecular recognition strongly depends on the hydrophobic interactions between ligands and receptors. Hydrophobic character of a drug is crucial to determine how easily the drug crosses the cell membrane and to study about the receptor interactions.
Hydrophobicity of a drug is measured experimentally by testing the drug’s relative distribution, known as partition coefficient. Partition coefficient is a measure of the relative affinity of a molecule for the lipid and aqueous phase in the absence of ionization. Hansch put forward the lipophilicity measurement in terms of partition coefficient “P”.
P = [C] Octanol/[C] Water
It is called “Distribution coefficient”.
Partition coefficient can be calculated by knowing the contribution of various substituent, is known as substituent hydrophobicity constant (π). With the help of partition co-efficient we can determine the π of substituted and unsubstituted compounds. Formula is,
= log PX- log PH
Hydrophobicity is a parameter which is of great importance in medicinal chemistry. On the molecular level it gives information about the inter and intramolecular forces affecting the exchange of drugs through the lipid membranes and the interactions they exhibit with the target protein. 5
Electronic parameters:
The extent to which a given reaction responds to electronic apprehension constitutes a measure of the electronic demands of that reaction, which is determined by its mechanism. The overall mechanism is described by the introduction of substituent groups into the framework and the subsequent alteration of reaction rates. The electronic effect of various substituents will clearly have an effect on drug ionization and polarity.
It influences how easily drug can pass through the cell membrane or how it can interact with the binding site. Hammett substituent constant (σ) is used as far as substituents on an aromatic ring is concerned. It is a measure of the size of the electronic effect i.e., whether an electron withdrawing or electron donating for a given substituent and represents a measure of electronic charge distribution in the benzene nucleus.
σx = log KX – log KH or log (KX/KH) = - Pkx + pKH
If the substituent X is an electron donating group (I+), then the aromatic ring is less able to stabilize the carboxylate ion. The equilibrium shifts to the left and weaker acid is obtained with a smaller kx value. If the substituent X is an electron withdrawing group(I-), smaller Kx value than the benzoic acid itself and hence the value of σx for an electron withdrawing substituent will be positive.
Hammett constant takes into account both resonance and inductive effects; and the value depends on the substituent, whether it is para or meta substituted. Ortho is not measured due to steric effects.
Steric parameters:
The bulk, size, and shape of the drug influences the interaction of the drug with the receptor binding site. Steric properties are difficult to quantify than hydrophobic and electronic parameters. Taft’s steric factor has been used to quantify the steric features of substituents. This is restricted to those substituents that interact sterically with tetrahedral transition state of reaction and not by resonance or internal hydrogen binding. Molecular refractivity is another measure of steric factor. Sterimol, computer program is used in measuring steric factors by calculating Verloop steric parameters from standard bond angles, Vander Waals radii, bond lengths and conformation for the substituents.
Hansch analysis:
Hansch equation relates biological activity to a number of different parameters, the most commonly used physicochemical parameters are log P or π, σ and steric factor. If the range of hydrophobicity values is limited to a small range than the equation is linear as follows
Log (1/C) = k1 log P + k2 σ + k3E3 + k4
If the log P values are spread over large range than the equation is parabolic, as follows
Log (1/C) = k1 (log P)2 + k2 log P + k3 σ + k4E4 + k5
Free Wilson approach:
In the Free-Wilson approach to QSAR, the biological activity of a parent structure is measured and then compared with the activities of a wide range of substituted analogues. Since the approach considers the overall effect of the substituents to biological activity rather than its physicochemical properties there is no need for physicochemical constants and tables. 6
Activity = k1X1 + k2X2 + ……knXn + Z
Mixed approach:
Mixed approach was developed based on the theoretical and numerical equivalence of Hansch's linear multiple regression model and the modified Free-Wilson model. Since the mixed approach is a combination of both models it makes use of the advantages of each model and widens the applicability of Hansch and Free-Wilson analysis.
3D QSAR:
QSAR is used for lead optimization while 3D-QSAR is applied to variety of data sets of enzymes and target sites.3D-QSAR formalisms, like comparative molecular field analysis (CoMFA), use a set of compounds to generate 3D descriptors for building partial least squares (PLS) models, and provide relevant information for developing ligand-based drug design.
Two 3D-QSAR methods, CoMFA and GRID, have evolved almost simultaneously in the mid-to late-1980s. Since its introduction, the CoMFA approach has become one of the most popular methods of QSAR studies. Over the recent years, this approach has been applied to a wide variety of receptor and enzyme ligands. CoMFA methodology is based on the assumption that the drug-receptor interactions are noncovalent, the changes in the biological activities or binding affinities of sample compounds correlate with changes in the steric and electrostatic fields of these molecules. 7
The steric and electrostatic fields surrounding a molecule can be measured and given using the grid and probe method. Defining the steric and electrostatic fields of a series of molecules is performed automatically by the software program. The next step is to relate these properties to the biological activity of the molecules.
In 3D QSAR, the variables for each molecule are the calculated steric and electronic interactions at several thousand lattice points. Once a formula is devised, the formula is tested against the structure which was left out. This tests how well the formula predicts the biological property for the molecule which was left out, called the cross validation. 8,9
For example, in silico approach was used to study structure of compounds and their interaction within the binding cavity amino acid residues of selected proteins for production of pyrazoline derivatives as antimalarial agents. The 3D-QSAR model provides possible modifications in the structures for designing more potent pyrazoline derivatives.
Figure 1: 3D QSAR model
For example, Diabetes mellitus is one of the prominent metabolic diseases with high prevalence rates requiring novel effective therapeutic agents with minimum side effects and toxicity for the treatment. Several natural products such as flavonoids, flavanols, terpenoids as α- glucosidase inhibitors are suspected to treat hyperglycaemia. Thus, in the present research work comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods are used in evaluating the structural constraints that maybe required to make an anti-diabetic drug from these substances.10
Role of QSAR in drug design:
Drug discovery and development aims to device safe and effective medications to improve the quality of life and to reduce suffering. However, the process is very tedious, time consuming, and resource intensive, requiring multi-disciplinary expertise and innovative attitude. Recent improvement in the technology has caused a drastic change in the healthcare system over the past few years. In recent times the drug discovery and designing process has been simplified by the computational application in combining the biological and chemical attributes of drug discovery. The drug design has been classified into two: Structure based drug design (SBDD) and Ligand based drug design (LBDD). In SBDD the structure of the biological target is considered for developing drug design, while LBDD is used in the absence of information regarding the biological target. Quantitative structure – activity relationship (QSAR) is an essential tool in drug design which is used in ascertaining or predicting the biological activities of various compounds based on the physicochemical properties. 11
In drug discovery, to identify the chemical structures with good inhibitory effects on binding sites and with low toxicity levels QSAR plays a major role. Xanthine oxidase inhibitory flavylium salts is a QSAR model which was implemented to predict the inhibitory potency of anthocyanidins as a function of their molecular properties.
A three-dimensional QSAR study has been applied to study epothilones – tubulin depolymerization inhibitors.
QSAR has been applied extensively to design predictive models for activity of bioactive agents. It has also been applied to areas related to discovery and subsequent development of bioactive agents: distinguishing drug like molecules from non-drug like molecules, resistance, toxicity prediction, physicochemical properties prediction, gastrointestinal absorption, activity of peptides, data mining, drug metabolism and prediction of other pharmacokinetic and ADME properties.
Applications:
In the last 40 years, the surfeit in scientific information has resulted in the development of numerous equations pertaining to structure-activity relationships in biological systems. In its original definition, the Hansch equation was devised to explain the drug-receptor interactions involving electronic, steric, and hydrophobic contributions.
a) Chemical application of QSAR
QSAR applications is to forecast the boiling points. There is a relationship between number of carbons in alkane and their boiling points. The increase in the boiling points is found to be in trend with the increase in number of carbons. Thus, this provides a mean for predicting the boiling points of higher alkenes. Various other interesting applications include Hammett equation, Taft equation and pKa application.
b) Biological applications of QSAR
Drug discovery often involves the use of QSAR to determine the chemical structures that could have good inhibitory effects on specific targets and have low toxicity.
c) For risk management
QSAR models have been used in risk management. The commonly used QSAR assessment software such as DEREK or MCASE is used to determine genotoxicity of impurity according to ICH M7. QSAR has been extensively used over decades to find possible models for activity of bioactive agents.
d) In the field of drug design
· Information from the intercept values:
Intercepts in QSAR equation is used to obtain valuable information regarding the compounds. Intercept displays the activity of unsubstituted compound. The activity increases or decreases based upon the substitution which is described by the slope or regression coefficient. If the intercept is very high and slope is low in a regression equation, it indicates that the basic nucleus or the parent compound has high activity and the contribution of the substituents is not significant. Intercept measures the intrinsic activity.
For example, the average intercepts for antifungal data sets were similar to those of the antibacterial agent that disturbs the membranes. Thus, QSAR equation suggested that these antifungal agents also act through membrane distortion.
Figure 2: Plot of intercept value
Many bis (aryl amino) pyrimidines inhibit the growth of various bacteria and fungi. The QSAR studies showed that the intercept for these pyrimidines is very high compared to other antifungal agents that exert action through membrane mechanism.
· Importance of log Po concept:
Log P0 which is the optimum partition coefficient is based on the parabolic relationship between the partition coefficient and biological activity. This plays a significant role in drug design.
For example, barbiturates and other hypnotics the logP0 was about 2.0. The logP0 value of many CNS drug was found to be around 2.0. For example, chlordiazepoxide = 2.44, diphenylhydantoin = 2.47, diazepam =2.82, etc. Thus, these results indicate that for a compound to have CNS activity, it must have logP0 around 2.0. Therefore, this information can be very useful in designing a new CNS activity compound.
· Bio-isoterism:
Bio-isoterism has been applied in drug design since many years successfully. The concept is qualitative and intuitive. Thus, using QSAR, one can now measure the similarities. These can be classified based on the physicochemical properties. It includes replacement of one functional group with other having similar properties both qualitatively and quantitatively. One of the applications of bio-isosterism includes discovery of cyanoguanidine as a bio isostere of thiourea, resulting in the development of H2 receptor antagonist.12
Figure 3: Hydrophobicity of the molecule
· Enzyme inhibition:
Dihydrofolate reductase (DHFR) is a most extensively analysed enzyme. DHFR inhibitors are therapeutically important as they are highly selective in Antibacterial, Antimalarial and Antitumor agents.
Example: - Replacement of one methoxy group of trimethoprim by an acidic side chain or carboxylate group, increases the inhibitory activities but selectivity and membrane permeability are significantly decreased. This information has been used for designing drugs.
· Information of receptor site:
QSAR studies have enhanced the knowledge regarding the receptor sites. One of the most widely analysed study is the inhibition of dihydrofolate reductase (DHFR) by benzyl pyrimidines (trimethoprim type).
QSAR studies give important information regarding the surfaces of enzymes or receptors. This is widely employed in the drug designing.
· Importance in drug research:
QSAR has appropriately predicted the activity of a large number of compounds before their synthesis. QSAR is used to get precise information for synthesis of more active or less toxic compounds. Several modifications have been made and QSAR studies done on Colchicine as an anti - cancer drug to reduce toxicity.12
DISCUSSION:
It is highly momentous to introduce computer-aided drug design (CADD) approach to accelerate the time-consuming process of conventional drug discovery. Quantitative structure activity relationships (QSAR) and molecular docking are two useful methods of CADD for drug design and prediction of drug activity. In QSAR large number of compounds are usually analysed resulting in models that can devise the potency or activity of new or even non-synthesized compounds.
When the three-dimensional structure of the target protein is available or can be modelled, docking is often used for screening of the lead libraries. Virtual screening is used as a very sharp tool for screening large array of lead libraries with the desired properties. Molecular docking prophesies the conformation of a protein-ligand complex and evaluates the binding affinity and various protein–ligand interactions.13
Drugs which act on multiple targets have better protection, curative influence and resistance. Computational methods can play a major role in designing novel drugs with a preferred biological activity. Various cheminformatics methods and structure-based approaches act as a good tool in extracting required information.14
Recent advances in the QSAR have enhanced the scope of rational drug design and the search for mechanism of actions of various drugs. AIDS and HIV being a great concern worldwide, QSAR analysis are of great importance in establishing relationship between biological activities of N, N- diphenyl urea derivatives and their physiochemical properties. This establishes the action of the drug as CCR5 receptor antagonist.
The interleukin-1 receptor associated kinases (IRAKs) are serine/threonine kinases involved in mediating cellular signalling downstream of the IL-1, IL-18 and a number of Toll-like receptors. IRAK-4 is very essential for the activation of the intracellular signalling cascades of pathways, such as the NFКB and MAPK pathways, which are essential for the production of the inflammatory cytokines. It has been shown that mice lacking IRAK-4 are viable and show complete abrogation of inflammatory cytokine production in response to IL-1 and IL-18. The people lacking this IRAK-4 are highly immunocompromised and do not show response to the cytokines. Thus, the inhibitors of these IRAK-4 are a great target for development of drugs using QSAR statistical models. The quantification of the inhibition activity of chemicals is difficult to be done experimentally, since it is expensive and time-consuming. Thus, a great deal of effort has been put into attempting the estimation of activity through statistical modelling.15
A series of 2,4,5-trisubstituted imidazole derivatives can been designed with better antibacterial activity, antifungal activity, anti-tubercular activity and anti-inflammatory activity using the QSAR methodologies.16
Many oxadiazole derivatives have been reported which tend to possess biological activities like antimicrobial, anti-inflammatory and antifungal activities. QSAR studies were performed for the molecular descriptors and synthesized compounds were characterized by IR, NMR and elemental analysis. Molecular descriptors play a crucial role in the fields of QSAR as well as in quantitative structure-property relationship studies. Quantitative structure–activity relationship (QSAR) analysis is an effectual method in research and ensuing rational drug design and the mechanism of drug actions.
The success of a QSAR study depends on choosing sturdy statistical methods for obtaining the possible model and also the relevant structural parameters for expressing the essential features within those chemical structures. In a QSAR study the model must be validated for its prophetical value before it can be used to conjecture the response of additional chemicals. Validating QSAR with external data, although demanding, is a best method for validation.
The designing newly potent, safer and cheaper drugs is needed for the treatment of a parasitic disease caused by protozoan parasites of the genus Leishmania. A promising strategy for discovering new therapeutic leads is to study various classes of compounds that are potentially bioactive or old active compounds for alternative uses. The process of drug discovery and development of newer antileishmanial could be of benefit. Numerous trials have been carried out in the past few years on number of nitrogen heterocycles such as quinolines, pyrimidines, acridines, phenothiazines, indolequinones, in general, and particularly thiadiazole derivatives, as well as their reduced derivatives, for the antileishmanial activity. QSAR studies based on principal components analysis (PCA), multiple linear regression (MLR), nonlinear regression (RNLM), and artificial neural network (ANN) calculations were executed on a series of 36 of (5-nitroheteroaryl-1,3,4-thiadiazole-2-yl) piperazinyl derivatives, in order to judge the important structural features that are required to design new potent lead candidate of this class.17,18
Thus, QSAR has helped in the development of mathematical relationships linking chemical structures and pharmacological activity or the biological activity in the quantitative manner of series of compound.
CONCLUSION:
Quantitative structure-activity relationship (QSAR) modelling is one of the most prominent computer-aided tools that is used for drug discovery and lead optimization. It is a puissant tool in the absence of 3D structures of specific drug targets. QSAR can be intentionally used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. QSAR is a Fragment-based drug discovery, it could be applied further and have a significant role in dealing with problems where a large number of experimentally determined structures are available, but these cannot be acquired easily. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly significant.
Therefore, it is clear that QSAR has a number of potential applications in the structure-property modelling. An understanding regarding the basic concepts of QSAR is needed for people involved in the designing of bioactive molecules. In the recent times, a number of molecular descriptors and different statistical regression methodologies are being proposed and extensively applied in the development of new drugs.
Thus, QSAR methods have been proven to be of significant importance not only in the reliable prediction of physical and specific biological properties of new compounds, but it also helps to elucidate the possible molecular mechanism of the receptor-ligand interactions.19
QSAR has played a crucial role in enhancing the understanding of the fundamental processes and phenomena in medicinal chemistry and drug design.
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Received on 22.11.2022 Modified on 30.04.2023
Accepted on 26.09.2023 ©AJRC All right reserved
Asian J. Research Chem. 2023; 16(6):459-466.
DOI: 10.52711/0974-4150.2023.00076