The pharmaceutical industry depends on the continual discovery of novel chemicals for designing new drugs for effective therapeutics for patients in the future. Two of the common features of drug development are chemical modification of drug molecules and analyzing the resulting pharmacological activities. Selectivity is an important aspect of drug designing as it defines the effect and side effect profiles of the active ingredients. This article focuses on how researchers have improved selectivity in drug design.
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Although selectivity is an important aspect of drug development, it is not often suitably followed during the discovery process. One of the reasons is that high-throughput screening is a cost-intensive approach that involves the use of compound libraries of natural and synthetic origin from research laboratories, commercial vendors, and in-house inventories. Selectivity is based on two approaches, i.e., specific to a single target or effective against a panel of desired targets.
De novo Drug Design Approaches
Scientists have designed drugs based on novel chemicals with desirable activity profiles against a specific target biomolecule. They primarily use structure-based and ligand-based de novo designs. They obtain the three-dimensional structure of the target biomolecule via electron microscopy, X-ray crystallography, or NMR analysis, which is the first step in drug design. In the recent past, computational methods have gained popularity for discovering novel compounds but drug designing is not solely based on machine-made proposals.
Detailed information about the structural features of the biological target, i.e., identification of lipophilic or polar groups in the biological structure, is important to establish molecular interactions, such as hydrophobic (e.g., van der Waals forces) or polar interactions (e.g., dipole-dipole or hydrogen bonds). Generally, when three-dimensional data of target are available, homology models help select effective drug compounds.
However, when three-dimensional structures are not available and only active binding sites are known, a ligand-based approach is used in designing a drug. In this method, an active structure can be superimposed on the three-dimension structure of the biomolecule, and accordingly, the active ingredient against the target biomolecule can be selected.
Rational Selectivity in Drug Design
The key principles of rational selectivity are discussed below:
The shape complementarity between ligands and receptors is pivotal for molecular recognition. Researchers have indicated that the shape of a molecule is an important factor in rational design for selective inhibitors. Previous studies have reported that molecular shape can be determined via computational methods or ligand-based methods.
For instance, software, such as the rapid overlay of chemical structures (ROCS) and phase shape, enable the superimposition of unknown molecules onto the actual conformation of a known active compound. This method is popular as it can retrieve molecules with a similar three-dimensional structure to active molecules and it will appropriately fit the target site. Scientists have applied this method for designing an antibacterial drug, i.e., small molecule inhibitors of the ZipA–FtsZ protein-protein interaction.
The electrostatic force of attraction deals with interaction among neutral polar groups, charged groups, and solvents. Electrostatic complementarity is a more complex selection process compared to shape complementarity because of the introduction of the desolvation penalty while traveling from an aqueous environment in the unbound state to a partially or fully desolvated one in the bound state. This means, when a ligand binds to a receptor, the dielectric environments of both the ligand and the receptor are changed, which commences the desolvation penalty.
The desolvation penalty results in alteration in the solvent-mediated intramolecular interactions in the protein and the ligand. However, in favorable conditions, the desolvation penalty is outweighed by the interaction between charged or polar groups and ultimately results in a net gain in binding affinity. Hence, it is important to select the most complimentary group for a particular site which will help improve the binding affinity and that involves a fine balance between enhancing favorable interactions and minimizing desolvation penalty.
Previous studies have shown that electrostatics complementarity has played a pivotal role in selectivity for protein tyrosine phosphatases (PTPs) which provides a novel platform for drug discovery.
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Protein flexibility is another important aspect for improving selectivity in drug design. This is because the molecular property of flexibility can vary significantly between proteins with similar binding sites. Therefore, it is important to understand the plasticity of the decoy (a computer-generated protein structure) structure as it predicts a mechanism for gaining selectivity, especially, when the target is flexible and the decoy is rigid.
Selectivity based on protein flexibility has been observed for many classes of proteins. For instance, docking studies and crystal structure analysis have revealed that selectivity between different species of thymidylate synthase (TS), a protein that is targeted for cancer chemotherapy, can be attributed to protein flexibility.
Water Molecule Bound at Target Sites and Allosteric Modulation:
Scientists have stated that differences in the location and thermodynamics of binding-site water molecules play an important role in the selectivity of drug design. However, more improvements associated with the modeling of explicit water molecules are required for significant contribution in selectivity design. Some of the recently developed computational models can predict the effect of water molecules binding on the target site.
Determination of the thermodynamic stability of water molecules in a given environment could be carried out using free energy perturbation and thermodynamic integration.
Allosteric modulation of the target biomolecule is used to gain selectivity when the decoy does not possess an allosteric site. In many proteins, allosteric sites have been identified, which help gain selectivity.
Selectivity helps in the determination of active ingredients for a targeted biomolecule with minimum side effects. Various computational and analytical tools are available which ensure effective drug design and development. Advancements in these processes will facilitate the discovery of novel drugs with optimal biological properties.
- Albanese, K. S. et al. (2020) Is Structure-Based Drug Design Ready for Selectivity Optimization? Journal of Chemical Information and Modelling. 60(12). pp. 6211–6227. https://doi.org/10.1021/acs.jcim.0c00815
- Fischer, T. et al. (2019) Approaching Target Selectivity by De Novo Drug Design. Expert Opinion on Drug Discovery. https://doi.org/10.1080/17460441.2019.1615435
- Huggins, J. D. et al. (2012) Rational Approaches to Improving Selectivity in Drug Design.Journal of Medical Chemistry. 55(4). pp. 1424–1444. https://doi.org/10.1021/jm2010332