The Potential of Structure-Activity Relationship Studies in Drug Design

Written By Sucharitha Murugu

Structure-activity relationship is very crucial in drug discovery; it basically guides the whole process, right from initial screening to lead optimization. The work of SAR starts by determining whether there is a relationship amongst a set of molecules and their activities, which is followed by the elucidation of details of such relationships.

From this knowledge, one can make informed structural changes with the hope of improving particular properties or activities. Eventually, this will provide a better understanding of the SAR for a given set of molecules, allowing for a reasoned exploration of the chemical space that is otherwise virtually infinite without direction.

[1] Such careful, systematic variation in a molecule and examination of changes in its biological action enable the specification of which portion of the molecule is responsible for its activity. In some instances, it is the whole molecule that greatly contributes to biological activity rather than just some functional groups or parts of the molecule. This holistic view with respect to structure–activity relationships emphasizes that sometimes the overall architecture of the molecule is what is required for appropriate interaction with biological agents. Therefore, detailed knowledge about the conformation, electronic properties, and steric effects of the molecule must be taken into consideration in structure–activity relationship studies for correct prediction of its biological behavior. This is converse to traditional approaches, which have focused on individual components or motifs, underlining the complexity of molecular interactions in drug discovery.

The most significant criteria of drug discovery program is to initiate small, simple changes in the compounds, which optimizes the potency of the drug, its selectivity, its safety profile [2], its solubility [3] and even its permeability into the CNS [4] 

Furthermore, SAR studies provide valuable information on the ADME characteristics of a drug, such as its absorption, distribution, metabolism, and excretion [5] which gives a clue about the behavior compounds will have in an organism.

Due to a better understanding of the interferences occurring between drugs and their targets, thanks to the in-depth view that SAR allows, therapies are more focused. Ultimately, it is the SAR that forms the backbone of modern drug design and thereby serves to connect the chemical structure of a drug with its pharmacodynamic and pharmacokinetic properties.

Through this article, the authors demonstrate the potential of Structure-Activity Relationship studies in drug design by methodologies and their importance in such research for pharmaceutical development.

Emphasis is placed on how systematic variations of a drug molecule's structure are capable of informing its dynamic and kinetic behavior.

The Importance of SAR Studies in Drug Design

 

Potency, Selectivity and Safety: Structure-activity relationship studies, hence, represent one of the most integral parts in drug development, enabling optimizations concerning potency, selectivity, and safety.[6]

In this way, SAR concerns a systematic change in the molecular structure of the drug to enhance its potency by the achievement of the therapeutic effect more efficiently at lower dosages. [7]

In addition, SAR enhances selectivity by outlining the structural attributes that allow the drug to better target biological sites and consequently minimize unwanted interactions with non-target sites and overall limit the magnitude of potential side effects. [8]

Besides perfecting a drug's therapeutic profile, SAR studies are also important towards perfecting its safety profile. They help narrow down the aspects of the structure that, upon administration, may be responsible for adverse effects and hence aid in the needful edits to minimize risks and protect patient safety. [9]

The sum of the message is that SAR studies are an indispensable part of drug development, where effective and targeted therapies must be safe and well-tolerated.

Solubility: The study of the structure-activity relationship has immense importance in enhancement within the drug solubility. By systematic variation in the molecular structure, SAR allows one to find out the structural modifications that would improve the dissolution of a drug within bodily fluids.

Sometimes, an increase in solubility could be very important because it raises the bioavailability of a drug and guarantees that the drug reaches the bloodstream in adequate concentration to become effective. [10]

SAR studies help in optimizing the different physicochemical properties of the drug, such as hydrophobic and hydrophilic interactions that result in superior absorption. [11]

As a matter of fact, this is one key factor for the improvement of medications since it enhances efficacy and rapid onset of action to arrive at more effective and reliable therapeutic results. [12]

Central Nervous System (CNS) Penetration: SAR studies are important in understanding the changes in the drug's ability to penetrate through the CNS. The systematic changes allow for the identification of those changes that enhance the drug's capacity to cross the BBB, crucial in the treatment of CNS disorders.

 

Such studies allow the optimization of properties like lipophilicity, molecular size, and functional groups affecting BBB permeability. Improved CNS penetration ensures effective delivery of the drug at its target in the brain or spinal cord, which is crucial in the development of treatments for neurological and psychiatric disorders.

On the other hand, SAR studies are able to identify structural modifications that prevent unwanted entry into the CNS; hence, they help avoid potential side effects. [13]

 

It works both ways: to tailor drugs to intended CNS effects with minimum adverse reactions. [14]

Absorption, Distribution, Metabolism, and Excretion (ADME): SAR studies provide the basis on which further refinement in drug design is possible for improving its ADME characteristics: Absorption, Distribution, Metabolism, and Excretion. [15]

The SAR approach is to systematically change the structure of a molecule and improve many facets of drug performance. It improves the rate of absorption by the optimization of solubility and permeability, thus increasing the uptakes via the gastrointestinal route. [16]

It refines the distribution such that a drug may reach certain tissues more properly and cross critical barriers like the blood-brain barrier. [17] From the metabolic point of view, SAR helps design such compounds that have a favorable metabolic profile, avoiding quick degradation and reducing the risk of toxic metabolites.

Furthermore, the estimation of SAR gives support for the optimization of excretion routes, ensuring effective removal from the body and preventing accumulation that may lead to toxicity. [18]

In general, studies on SAR are highly required during drug development in creating drugs with balanced ADME profiles for maximum therapeutic efficacy while ensuring safety with minimal side effects.

Methodologies in SAR Studies

SAR studies employ various methodologies to analyze and optimize drug candidates:

Methodologies in Structure-Activity Relationship (SAR) studies are crucial for understanding how molecular changes impact a drug's biological activity. Here are some key methodologies:

  1. Experimental SAR and Biological Testing: Sometimes called chemical modifications and biological testing, it is a strategy that includes the synthesis of a series of related compounds with systematic structural variations to experimentally validate hypotheses of SAR in in-vitro and in-vivo assays to confirm the predicted effects of structural changes on biological activity. [19]

  2. Molecular Modeling: This involves various techniques in molecular docking, molecular dynamics, and 3D-QSAR to identify and predict how changes in molecular structure affect its interaction with a biological target. [20]

  3. Quantitative SAR (QSAR): This approach uses statistical models that relate chemical structure to biological activity. The QSAR models can thus be used to predict the effects of structural modifications and determine important features for optimal activity. [21]

  4. High-Throughput Screening: HTS could be used to quickly screen large libraries of related compounds for biological activity. A vast data set would result from which the structural features related to the desired activity could be determined. This approach could be combined with the experimental SAR or chemical modifications method. [22]

  5. Chemoinformatics: Chemoinformatics involves a wide range of computational tools and databases, which are presently used for mining, analysis, and interpretation of the SAR data. These help to identify patterns and relationships between the structures of molecules and their biological activities.[23]

  6. Structure-Based Drug Design and In Silico Predictions: This putative method uses computation to predict the effect of structural changes to the biological activity, a theory which is bound by an understanding of three-dimensional structure of biological targets, hence pertains to structure-activity relationships, or SAR. Knowing the target protein's three-dimensional format, it is possible to deduce the type of modification in chemical structure a compound would effect and, by that mediation, interact with the protein hence generally affecting its outcome in a biological process. This approach is taken first to screen for most likely alternatives of modification of molecules before they are even made. Thus, it saves their cost and time in the process. However, this method can be utilized only in cases when the three-dimensional structure of the target protein is known. [24]

These techniques, when used individually or in combination, can let us design and develop more effective, selective, and safe drugs.

Case Study: SAR Studies in the Development of Statins

Among the greatest successes of SAR studies in drug design are drugs of the statin class, used in lowering cholesterol.

In the development of statins, extensive SAR studies were required to arrive at an optimum inhibitory activity of the drug against HMG-CoA reductase, the enzyme responsible for cholesterol synthesis.

Structure-activity relationship studies conducted on mevastatin and lovastatin have been important in developing an understanding of statins, which are necessary in cholesterol management and reduction of cardiovascular risk. [25]

Here’s a summary of their SAR studies:

Mevastatin

Mevastatin was one of the first statins ever discovered and, until now, has been used as a precursor in the development of more effective statins. [26]

SAR on mevastatin focused mainly on the lactone ring and its importance in binding to the enzyme HMG-CoA reductase for the purpose of inhibiting cholesterol synthesis. Modifications were considered in the lactone ring and its side chains in the hope of achieving higher potency and selectivity.[27]

It was found that the structural changes in its ring system brought significant variation in its HMG-CoA reductase inhibition. The presence of a certain side chain was found critical for the drug activity and thus its development led to the discovery of more potent statin lovastatin. [28][29]

Lovastatin

Lovastatin is a semi-synthetic analog of mevastatin. It was developed from the original compound to enhance its efficacy and pharmacokinetic properties. [30]

SAR studies on lovastatin examined changes that would increase the bioavailability and enhance the selectivity for HMG-CoA reductase. Both the side chain and the lactone ring were important in identifying changes.

The structural modifications again enabled lovastatin to bind more effectively to the enzyme and thus to exhibit higher activity compared with mevastatin. The methyl ester moiety in lovastatin was important for its enhanced potency and stability.

Most Common SAR Approaches in HMG-CoA inhibitors

Lactone Ring: A lactone ring presents in the structure of both mevastatin and lovastatin; it is a necessary attribute to bind HMG-CoA reductase, for any change in this ring dramatically affects inhibitory activity. [31]

Side Chains: The optimizations in pharmacokinetics and the efficacy of these statins have considerably come through changes within the side chains. [32]

The bioavailability thereof: It has been possible to improve both the solubility and absorption of lovastatin compared with mevastatin by conducting SAR studies on the limitations found within the original compound. [33]

Generally speaking, SAR studies in both mevastatin and lovastatin provided the important insights that allowed for optimization of statin drugs toward much stronger and specific inhibitors such as simvastatin and atorvastatin for more effective therapies on cholesterol management. This has pointed out that structural modifications are very crucial in a drug to enhance its potency and selectivity, thereby making it more therapeutically effective.

Challenges in SAR Studies

The complexity of biological systems adds significant challenges to SAR studies because the relationship between a chemical structure and its biological activity is often influenced by multiple dynamic and interconnected factors:

  • Complexity of Biological Systems: The relationship between chemical structure and biological activity can be complex and not always straightforward.

a.     Protein Dynamics: Receptors or enzymes that are the target are in continuous motion. Proteins change their conformation continuously; these changes in conformation may affect how small molecules interact with them. A compound that perfectly fits into one conformation of a protein may become less active, or completely inactive when the protein switches to another conformation. [34]

b.     Allosteric Effects: Molecules can also bind to sites on the protein other than the active site, called allosteric sites, which act to modulate the function of the protein through indirect means. These allosteric effects can vary widely with very minor chemical changes, and thus it is difficult to predict SAR based on active site interactions alone. [35]

c.     Cellular and Tissue Environment: Biological activity is also often determined by the cellular or tissue environment, where pH, ionic strength, and the presence of cofactors or competing molecules may affect how a compound behaves. Such compounds may display quite different activities when studied in cell cultures, animal models, or human systems. [36]

d.     Multitarget interaction: Many bioactive compounds interact with more than one target within a biological system, giving rise to the phenomenon described as off-target effects. This could be problematic in terms of the SAR analysis, because in general one is not sure whether a detected biological effect is due to the desired target interaction or rather an off-target effect.[37]

e.     Complexity of interactions: The bioactivity of a molecule in a living system often emanates from complex interactions and feedback in a signal transduction pathway involving hundreds of proteins. Even with strong binding affinity for a target, the downstream effects on cellular pathways can be unpredictable, complicating clear SAR conclusions.[38]

f.      Compensatory mechanisms: All biological systems possess compensatory mechanisms, which accommodate the presence of an exogenous compound. Thus, if a drug is an inhibitor of some enzyme, it may upregulate other pathways to compensate for the enzyme inhibition, which therefore would have an impact on the overall activity of the compound. [39]

g.     Genetic and Epigenetic Differences: Genetic or epigenetic variability across cell lines, animal models, or patient populations may affect how the test compound interacts with its target; this can result in differential output from SAR studies. A compound that is effective in one genetic background can be less effective or even toxic in another. [40]

The complexity of biological systems elevates SAR analysis beyond simple structure-to-activity mapping. Such subtlety in basic biology understanding is a must for appreciation, and it often resorts to sophisticated techniques like system biology or high-throughput screening.

  • Data Quality and Quantity: High-quality and voluminous biological data are demanded for the construction of accurate SAR models. In any study on a Structure-Activity Relationship, the quality and quantity of data are very important to ensure accuracy in modeling and reliable insights. [41] Following are some of the common challenges associated with each:

    • Data Quality Challenges:

      1. Inconsistent Data: Data from SAR studies are most often compiled from a variety of sources, including experimental results and databases. Inconsistent procedures, the use of different measurement units, and differences in reporting standards may affect the reliability of data. [42]

      2. Noisy Biological Assay Signals: Biological assays often show variability due to noise, such as inconsistencies in cell viability measurements or enzyme inhibition results. These factors can introduce experimental errors, making it difficult to directly correlate molecular structure with biological activity. [43]

      3. Outdated or Incorrect Data: Chemical structure identification or reporting errors, activity values of outdated material, or stereochemistry inaccuracies have the potential to mislead SAR studies. [44]

      4. Preprocessing Issues: Preprocessing or cleaning of data before being fed to the models might highly impact the result. Any incompleteness or improper handling of missing values can introduce bias.[45]

      5. Poor or low numbers of experimental results in general can often be the causes of overfitting in a SAR model, reducing its predictive power. [46]

    • Data Quantity Challenges:

      1. Data Sparsity for Complex Systems: For example, in some therapeutic areas, such as complex biological systems or multi-target drugs, data linking molecular changes to biological effects may be sparse.[47]

      2. The generation of data is another expensive and time-consuming activity. In the context of screening large chemical libraries through high-throughput methodology, the generation of large-scale experimental data required for SAR studies is time-consuming and often expensive. [48]

      3. Imbalanced Data: The biggest problem, shared by the majority of SAR datasets, is that some chemical scaffolds are very well represented, whereas others have few data points; this in turn limits the ability of models to generalize across different chemotypes [49]

    • Mitigating Strategies:

      1. Standardization of Data: There is also a need to implement standardized protocols in the collection and processing of data to increase consistency. [50]

      2. Public Database Utilization: Data addition from public databases like ChEMBL or PubChem allows for quantity but can give rise to possible quality control problems. [51]

      3. Machine Learning for Sparse Data: If data is sparse, transfer learning or semi-supervised learning algorithms may help in making the SAR prediction. [52]

Addressing these challenges requires careful curation, integration, and application of data science techniques to enhance both the quality and quantity of data for SAR studies. Limited or noisy data can lead to incorrect conclusions and hinder the optimization process.

  1. Resource Intensiveness: Structure-Activity Relationship (SAR) studies can be resource-intensive due to several challenges:

    1. High Computation Demands:

      1. Data Processing: It includes the analysis of massive datasets involving molecular structures, biological activities, and physicochemical properties. The needed computational resources can be high for such large data, and at times, machine learning or deep learning techniques are utilized. [53]

      2. Model complexity: As SAR studies progress from simple 2D to more complex 3D or multi-dimensional models, including quantum mechanics or molecular dynamics, the computational power and simulation times increase exponentially. Many of such cases therefore require access to high-performance computing infrastructure.Dagmar et. al. and Ashraf, et al. highlights the computational infrastructure investment required to carry out multiple and complex calculations involved in a meaningful SAR study.[54]

    2. Experimental Resource Demands:

      1. High-Throughput Screening (HTS): Most of the SAR studies involve experimental validation of large-sized chemical libraries. Though useful, HTS methods are resource-draining in terms of time, manpower, and financial investment. [55]

      2. Compound Synthesis: To synthesize and test analogs requires quite a lot from the laboratory point of view, in order to establish SAR trends: raw materials, lab equipment, and specialized personnel. [56]

      3. In Vivo Testing: Biological assays become increasingly involved in systems like higher-order animal models, adding another layer of resource intensiveness by requiring very precise conditions and controls with extensive validation. [57]

    3. Skilled Labor Requirements:

      1. Cross-disciplinary expertise: Effective SAR study requires several skills across multidisciplinary fields such as medicinal chemistry, bioinformatics, pharmacology, and machine learning. This demands a multidisciplinary team and further increases resource burdens in terms of training, collaboration efforts, and labor costs. [58]

      2. Resource-intensive iterations: The process for SAR is cyclic in nature; model building and validation and refinement must be iterated several times. Substantial mental and physical input is required to start from data curation to experimental revalidation of promising compounds for each cycle. [59]

    4. Cost of Failure or Uncertainty:

      1. Unpredictable Outcomes: Even with investment in the study, SAR studies may not always result in predictable or clear outcomes. Thus, failures in the prediction of correct biological activity or off-target effects lead to wasted resources. [60]

      2. Regulatory and Compliance Costs: For certain industries such as pharmaceuticals, creating quality SAR data requires additional steps for compliance and quality assurance. Indeed, fulfilling regulatory requirements of reproducibility and traceability places higher demands on both time and cost. [61]

    5. Mitigation Strategies:

      1. Automation: The use of automated systems synthesizing and testing compounds will minimize manually intensive labor and time.[62]

      2. Data Sharing and Collaboration: It can share resources across the load when collaborating across institutions or industries, making the studies of SAR more efficient. [63]

      3. Efficient Model Selection: This can be achieved by reducing the resource burden. Predictive modeling techniques, such as QSAR or transfer learning, improve the accuracy of predictions early in the study. [64]

The resource-intensive nature of SAR studies arises from their inherently complex computational and experimental workflows, which require careful planning and efficient resource allocation to execute.

Here are additional challenges commonly encountered in SAR (Structure-Activity Relationship) studies:

1. Nonlinear and Multidimensional SAR:

SAR relationships are often nonlinear, meaning a small change in the chemical structure might result in a disproportionately large or unexpected change in biological activity. This makes prediction difficult. Additionally, activity may be influenced by multiple variables simultaneously (multidimensional SAR), requiring sophisticated models to capture these interactions.  [65]

2. Context-Dependent Activity:

A.    Target selectivity: Many compounds may interact with multiple targets in the body, leading to off-target effects. This complicates the isolation of SAR for a specific target. [66]

B.    Environmental factors: The activity of a compound can vary depending on the experimental conditions (e.g., pH, temperature, co-factors), further complicating SAR interpretation. [67]

3. Protein-Ligand Binding Kinetics:

The kinetics of how a ligand binds to and dissociates from a target protein can influence biological activity. Compounds may have similar structures but differing binding kinetics, leading to different activity profiles. [68]

4. Metabolic Stability and ADME Properties:

In vivo SAR depends not only on target binding but also on Absorption, Distribution, Metabolism, and Excretion (ADME) properties. A compound may show promising in vitro activity but be rapidly metabolized or poorly absorbed in vivo, complicating SAR analysis. [69]

5. Stereochemistry and 3D Conformational Effects:

Stereochemistry plays a significant role in drug-target interactions. Even small stereoisomeric differences can lead to drastic changes in activity. Understanding how 3D molecular conformations influence activity is critical but can be challenging to model. [70]

6. Solubility and Permeability Issues:

Poor solubility or permeability of compounds can limit biological testing and mask the true SAR. Structural changes aimed at improving these physicochemical properties can also affect biological activity, requiring trade-offs during optimization. [71]

7. Chemical Space Exploration:

Exploring the full chemical space around a hit compound can be resource-intensive. It can be difficult to identify which parts of the molecule are truly important for activity (pharmacophores) without exploring a large number of structural analogs. [72]

8. Interplay Between Toxicity and Efficacy:

The optimization of SAR must balance efficacy with safety. Structural changes that increase activity may also increase toxicity or decrease therapeutic index, which can make achieving an optimal balance between the two challenging. [73]

9. Computational Challenges in SAR:

While quantitative SAR (QSAR) and AI-based SAR models are valuable tools, they rely on robust datasets. Incomplete or biased datasets can lead to models with poor predictive power, which may miss critical SAR insights or generate false leads. [74]

10. Lead Optimization Bottlenecks:

Once SAR has identified potential leads, optimizing these compounds to improve potency, selectivity, and ADME properties can be a lengthy and iterative process, requiring multiple rounds of synthesis and testing. This makes lead optimization a significant bottleneck in drug discovery. [75]

These additional challenges underscore the complexity of SAR studies, requiring multidisciplinary expertise in chemistry, biology, and computational modeling to overcome the hurdles.

High potency alone cannot make a molecule a drug. There are numerous important factors, like selectivity, metabolic stability, solubility, and toxicity, involved in drug development. Structure-activity relationship studies balance potency with other pharmacokinetic and pharmacodynamic properties to optimize such properties. So, fine-tuning with bioavailability and off-target effects through SAR gives rise to a candidate suitable for use therapeutically. Therefore, SAR is responsible for the translation of a potent molecule into an effective drug.

About SmaBio Labs

SmaBio Labs is a leading Contract Development and Manufacturing Organization (CDMO) focused on drug discovery, product development, and analytical services. With advanced facilities and a team of experts, we provide high-quality solutions throughout the pharmaceutical development process, including specialized services in structure-activity relationship (SAR) studies.

Our key offerings include:

Comprehensive SAR Services: We deliver complete SAR study solutions, such as chemical modifications, QSAR modeling, molecular docking, and bioisosterism.

Route Optimization Services: Our cutting-edge technology ensures robust, scalable processes while maintaining the highest quality standards.

Regulatory Expertise: With extensive regulatory experience, we help clients navigate complex regulations, ensuring their products meet all necessary compliance requirements.

At SmaBio Labs, we recognize the crucial role SAR studies play in drug design. Our integrated approach and dedication to excellence enable our clients to develop safe, effective, and high-quality products for the market.


 

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Sucharitha Murugu