Drug–Target Association Kinetics in Drug Discovery IJzerman, Adriaan P.; Guo, Dong
Trends in biochemical sciences (Amsterdam. Regular ed.),
October 2019, 2019-10-00, 20191001, Letnik:
44, Številka:
10
Journal Article
Recenzirano
The important role of ligand–receptor binding kinetics in drug design and discovery is increasingly recognized by the drug research community. Over the past decade, accumulating evidence has shown ...that optimizing the ligand’s dissociation rate constant can lead to desirable duration of in vivo target occupancy and, hence, improved pharmacodynamic properties. However, the association rate constant as a pharmacological principle remains less investigated, whereas it can play an equally important role in the selection of drug candidates. This review provides a compilation and discussion of otherwise scarce and dispersed information on this topic, bringing to light the importance of drug−target association in kinetics-directed drug design and discovery.
In contrast to the traditional focus on equilibrium-based assessments of drug–target interactions, drug–target binding kinetics is increasingly recognized as a critical selection criterion in the drug discovery process.The predominant paradigm focuses on the dissociation kinetics of ligand–receptor interaction, largely neglecting the importance of the association kinetics.The association kinetics is not diffusion-limited and, by contrast, can be modulated by medicinal chemistry efforts.The association kinetics has high clinically relevance that is tentatively linked to drug effects, such as the onset of drug action and the duration of in vivo target occupancy.Structure–kinetic relationships, structural biology, and molecular dynamics simulations provide information about the pathway and molecular basis of ligand association to its target.
In the 10 years since our previous International Union of Basic and Clinical Pharmacology report on the nomenclature and classification of adenosine receptors, no developments have led to major ...changes in the recommendations. However, there have been so many other developments that an update is needed. The fact that the structure of one of the adenosine receptors has recently been solved has already led to new ways of in silico screening of ligands. The evidence that adenosine receptors can form homo- and heteromultimers has accumulated, but the functional significance of such complexes remains unclear. The availability of mice with genetic modification of all the adenosine receptors has led to a clarification of the functional roles of adenosine, and to excellent means to study the specificity of drugs. There are also interesting associations between disease and structural variants in one or more of the adenosine receptors. Several new selective agonists and antagonists have become available. They provide improved possibilities for receptor classification. There are also developments hinting at the usefulness of allosteric modulators. Many drugs targeting adenosine receptors are in clinical trials, but the established therapeutic use is still very limited.
Our previous International Union of Basic and Clinical Pharmacology report on the nomenclature and classification of adenosine receptors (2011) contained a number of emerging developments with ...respect to this G protein-coupled receptor subfamily, including protein structure, protein oligomerization, protein diversity, and allosteric modulation by small molecules. Since then, a wealth of new data and results has been added, allowing us to explore novel concepts such as target binding kinetics and biased signaling of adenosine receptors, to examine a multitude of receptor structures and novel ligands, to gauge new pharmacology, and to evaluate clinical trials with adenosine receptor ligands. This review should therefore be considered a further update of our previous reports from 2001 and 2011. SIGNIFICANCE STATEMENT: Adenosine receptors (ARs) are of continuing interest for future treatment of chronic and acute disease conditions, including inflammatory diseases, neurodegenerative afflictions, and cancer. The design of AR agonists ("biased" or not) and antagonists is largely structure based now, thanks to the tremendous progress in AR structural biology. The A
- and A
AR appear to modulate the immune response in tumor biology. Many clinical trials for this indication are ongoing, whereas an A
AR antagonist (istradefylline) has been approved as an anti-Parkinson agent.
Ligand–receptor binding kinetics is an emerging topic in the drug research community. Over the past years, medicinal chemistry approaches from a kinetic perspective have been increasingly applied to ...G protein-coupled receptors including the adenosine receptors (AR), which are involved in a plethora of physiological and pathological conditions. The study of ligand–AR binding kinetics offers room for detailed structure–kinetics relationships next to more traditional structure–activity relationships. Their combination may facilitate the triage of candidate compounds in hit-to-lead campaigns. Furthermore, kinetic studies also help in understanding AR allosterism. Allosteric modulation may yield a change in the activity and conformation of a receptor resulting from the binding of a compound at a site distinct from where the endogenous agonist adenosine binds. Hence, in this Review, we summarize available data and evidence for the binding kinetics of orthosteric and allosteric AR ligands. We hope this Review will raise awareness to consider the kinetic aspects of drug–target interactions on both ARs and other drug targets.
Pharmacological responses of G protein-coupled receptors (GPCRs) can be fine-tuned by allosteric modulators. Structural studies of such effects have been limited due to the medium resolution of GPCR ...structures. We reengineered the human A 2A adenosine receptor by replacing its third intracellular loop with apocytochrome b⁵⁶² RIL and solved the structure at 1.8 angstrom resolution. The high-resolution structure allowed us to identify 57 ordered water molecules inside the receptor comprising three major clusters. The central cluster harbors a putative sodium ion bound to the highly conserved aspartate residue Asp 2.50 . Additionally, two cholesterols stabilize the conformation of helix VI, and one of 23 ordered lipids intercalates inside the ligand-binding pocket. These high-resolution details shed light on the potential role of structured water molecules, sodium ions, and lipids/cholesterol in GPCR stabilization and function.
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Prof. Geoffrey Burnstock originated the concept of purinergic signaling. He demonstrated the interactions and biological roles of ionotropic P2X and metabotropic P2Y receptors. This ...review paper traces the historical origins of many currently used antagonists and agonists for P2 receptors, as well as adenosine receptors, in early attempts to identify ligands for these receptors – prior to the use of chemical libraries for screening. Rather than presenting a general review of current purinergic ligands, we focus on common chemical scaffolds (privileged scaffolds) that can be adapted for multiple receptor targets. By carefully analyzing the structure activity relationships, one can direct the selectivity of these scaffolds toward different receptor subtypes. For example, the weak and non-selective P2 antagonist reactive blue 2 (RB-2) was derivatized using combinatorial synthetic approaches, leading to the identification of selective P2Y2, P2Y4, P2Y12 or P2X2 receptor antagonists.
A P2X4 antagonist NC-2600 is in a clinical trial, and A3 adenosine agonists show promise, for chronic pain. P2X7 antagonists have been in clinical trials for depression (JNJ-54175446), inflammatory bowel disease (IBD), Crohn’s disease, rheumatoid arthritis, inflammatory pain and chronic obstructive pulmonary disease (COPD). P2X3 antagonists are in clinical trials for chronic cough, and an antagonist named after Burnstock, gefapixant, is expected to be the first P2X3 antagonist filed for approval. We are seeing that the vision of Prof. Burnstock to use purinergic signaling modulators, most recently at P2XRs, for treating disease is coming to fruition.
Drug discovery is time- and resource-consuming. To this end, computational approaches that are applied in de novo drug design play an important role to improve the efficiency and decrease costs to ...develop novel drugs. Over several decades, a variety of methods have been proposed and applied in practice. Traditionally, drug design problems are always taken as combinational optimization in discrete chemical space. Hence optimization methods were exploited to search for new drug molecules to meet multiple objectives. With the accumulation of data and the development of machine learning methods, computational drug design methods have gradually shifted to a new paradigm. There has been particular interest in the potential application of deep learning methods to drug design. In this chapter, we will give a brief description of these two different de novo methods, compare their application scopes and discuss their possible development in the future.
The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few ...years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution. Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method (‘DNN_PCM’) performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi-task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized ‘DNN_PCM’). Here, a standardized set to test and evaluate different machine learning algorithms in the context of multi-task learning is offered by providing the data and the protocols.
Graphical Abstract
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The association and dissociation kinetics of ligands binding to proteins vary considerably, but the mechanisms behind this variability are poorly understood, limiting their utilization for drug ...discovery. This is particularly so for G protein-coupled receptors (GPCRs) where high resolution structural information is only beginning to emerge. Engineering the human A2A adenosine receptor has allowed structures to be solved in complex with the reference compound ZM241385 and four related ligands at high resolution. Differences between the structures are limited, with the most pronounced being the interaction of each ligand with a salt bridge on the extracellular side of the receptor. Mutagenesis experiments confirm the role of this salt bridge in controlling the dissociation kinetics of the ligands from the receptor, while molecular dynamics simulations demonstrate the ability of ligands to modulate salt bridge stability. These results shed light on a structural determinant of ligand dissociation kinetics and identify a means by which this property may be optimized.
Abstract
The translation of in vitro potency of a candidate drug, as determined by traditional pharmacology metrics (such as EC
50
/IC
50
and K
D
/K
i
values), to in vivo efficacy and safety is ...challenging. Residence time, which represents the duration of drug‐target interaction, can be part of a more comprehensive understanding of the dynamic nature of drug‐target interactions in vivo, thereby enabling better prediction of drug efficacy and safety. As a consequence, a prolonged residence time may help in achieving sustained pharmacological activity, while transient interactions with shorter residence times may be favourable for targets associated with side effects. Therefore, integration of residence time into the early stages of drug discovery and development has yielded a number of clinical candidates with promising in vivo efficacy and safety profiles. Insights from residence time research thus contribute to the translation of in vitro potency to in vivo efficacy and safety. Further research and advances in measuring and optimizing residence time will bring a much‐needed addition to the drug discovery process and the development of safer and more effective drugs. In this review, we summarize recent research progress on residence time, highlighting its importance from a translational perspective.
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