Impact, determination and prediction of drug-receptor residence times for GPCRs

The translational value of ligand-receptor binding kinetics in drug discovery

The translation of in vitro potency of a candidate drug, as determined by traditional pharmacology metrics (such as EC50/IC50 and KD/Ki 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.

Pseudoirreversible inhibition elicits persistent efficacy of a sphingosine 1-phosphate receptor 1 antagonist

Sphingosine 1-phosphate receptor 1 (S1PR1), a G protein-coupled receptor, is required for lymphocyte trafficking, and is a promising therapeutic target in inflammatory diseases. Here, we synthesize a competitive S1PR1 antagonist, KSI-6666, that effectively suppresses pathogenic inflammation. Metadynamics simulations suggest that the interaction of KSI-6666 with a methionine residue Met124 in the ligand-binding pocket of S1PR1 may inhibit the dissociation of KSI-6666 from S1PR1. Consistently, in vitro functional and mutational analyses reveal that KSI-6666 causes pseudoirreversible inhibition of S1PR1, dependent on the Met124 of the protein and substituents on the distal benzene ring of KSI-6666. Moreover, in vivo study suggests that this pseudoirreversible inhibition is responsible for the persistent activity of KSI-6666.

Accurate Characterization of Binding Kinetics and Allosteric Mechanisms for the HSP90 Chaperone Inhibitors Using AI-Augmented Integrative Biophysical Studies

The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure-kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate constant (koff), enabling a direct assessment of the drug-target residence time. These models demonstrated good predictive performance, where hydrophobic and hydrogen bond interactions significantly influence the koff prediction. In subsequent applications, our models were used to assist in the discovery of new inhibitors for the N-terminal domain of HSP90α (N-HSP90α), demonstrating predictive capabilities on an experimental validation set with a new scaffold. In X-ray crystallography experiments, the loop-middle conformation of apo N-HSP90α was observed for the first time (previously, the loop-middle conformation had only been observed in holo-N-HSP90α structures). Interestingly, we observed different conformations of apo N-HSP90α simultaneously in an asymmetric unit, which was also observed in a holo-N-HSP90α structure, suggesting an equilibrium of conformations between different states in solution, which could be one of the determinants affecting the binding kinetics of the ligand. Different ligands can undergo conformational selection or alter the equilibrium of conformations, inducing conformational rearrangements and resulting in different effects on binding kinetics. We then used molecular dynamics simulations to describe conformational changes of apo N-HSP90α in different conformational states. In summary, the study of the binding kinetics and molecular mechanisms of N-HSP90α provides valuable information for the development of more targeted therapeutic approaches.

Molecular determinants of antagonist interactions with chemokine receptors CCR2 and CCR5

By driving monocyte chemotaxis, the chemokine receptor CCR2 shapes inflammatory responses and the formation of tumor microenvironments. This makes it a promising target in inflammation and immuno-oncology; however, despite extensive efforts, there are no FDA-approved CCR2-targeting therapeutics. Cited challenges include the redundancy of the chemokine system, suboptimal properties of compound candidates, and species differences that confound the translation of results from animals to humans. Structure-based drug design can rationalize and accelerate the discovery and optimization of CCR2 antagonists to address these challenges. The prerequisites for such efforts include an atomic-level understanding of the molecular determinants of action of existing antagonists. In this study, using molecular docking and artificial-intelligence-powered compound library screening, we uncover the structural principles of small molecule antagonism and selectivity towards CCR2 and its sister receptor CCR5. CCR2 orthosteric inhibitors are shown to universally occupy an inactive-state-specific tunnel between receptor helices 1 and 7; we also discover an unexpected role for an extra-helical groove accessible through this tunnel, suggesting its potential as a new targetable interface for CCR2 and CCR5 modulation. By contrast, only shape complementarity and limited helix 8 hydrogen bonding govern the binding of various chemotypes of allosteric antagonists. CCR2 residues S1012.63 and V2446.36 are implicated as determinants of CCR2/CCR5 and human/mouse orthosteric and allosteric antagonist selectivity, respectively, and the role of S1012.63 is corroborated through experimental gain-of-function mutagenesis. We establish a critical role of induced fit in antagonist recognition, reveal strong chemotype selectivity of existing structures, and demonstrate the high predictive potential of a new deep-learning-based compound scoring function. Finally, this study expands the available CCR2 structural landscape with computationally generated chemotype-specific models well-suited for structure-based antagonist design.

In Vitro and In Silico Analysis of the Residence Time of Serotonin 5-HT7 Receptor Ligands with Arylpiperazine Structure: A Structure-Kinetics Relationship Study

During the last decade, the kinetics of drug-target interaction has received increasing attention as an important pharmacological parameter in the drug development process. Several studies have suggested that the lipophilicity of a molecule can play an important role. To date, this aspect has been studied for several G protein-coupled receptors (GPCRs) ligands but not for the 5-HT₇ receptor (5-HT₇R), a GPCR proposed as a valid therapeutic target in neurodevelopmental and neuropsychiatric disorders associated with abnormal neuronal connectivity. In this study, we report on structure-kinetics relationships of a set of arylpiperazine-based 5-HT₇R ligands. We found that it is not the overall lipophilicity of the molecule that influences drug-target interaction kinetics but rather the position of polar groups within the molecule. Next, we performed a combination of molecular docking studies and molecular dynamics simulations to gain insights into structure-kinetics relationships. These studies did not suggest specific contact patterns between the ligands and the receptor-binding site as determinants for compounds kinetics. Finally, we compared the abilities of two 5-HT₇R agonists with similar receptor-binding affinities and different residence times to stimulate the 5-HT₇R-mediated neurite outgrowth in mouse neuronal primary cultures and found that the compounds induced the effect with different timing. This study provides the first insights into the binding kinetics of arylpiperazine-based 5-HT₇R ligands that can be helpful to design new 5-HT₇R ligands with fine-tuning of the kinetic profile.

Discovery and Characterization of BAY 1214784, an Orally Available Spiroindoline Derivative Acting as a Potent and Selective Antagonist of the Human Gonadotropin-Releasing Hormone Receptor as Proven in a First-In-Human Study in Postmenopausal Women

The growth of uterine fibroids is sex hormone-dependent and commonly associated with highly incapacitating symptoms. Most treatment options consist of the control of these hormonal effects, ultimately blocking proliferative estrogen signaling (i.e., oral contraceptives/antagonization of human gonadotropin-releasing hormone receptor [hGnRH-R] activity). Full hGnRH-R blockade, however, results in menopausal symptoms and affects bone mineralization, thus limiting treatment duration or demanding estrogen add-back approaches. To overcome such issues, we aimed to identify novel, small-molecule hGnRH-R antagonists. This led to the discovery of compound BAY 1214784, an orally available, potent, and selective hGnRH-R antagonist. Altering the geminal dimethylindoline core of the initial hit compound to a spiroindoline system significantly improved GnRH-R antagonist potencies across several species, mandatory for a successful compound optimization in vivo. In a first-in-human study in postmenopausal women, once daily treatment with BAY 1214784 effectively lowered plasma luteinizing hormone levels by up to 49%, at the same time being associated with low pharmacokinetic variability and good tolerability.

Determining the unbinding events and conserved motions associated with the pyrazinamide release due to resistance mutations of Mycobacterium tuberculosis pyrazinamidase

Pyrazinamide (PZA) is the only first-line antitubercular drug active against latent Mycobacterium tuberculosis (Mtb). It is activated to pyrazinoic acid by the pncA-encoded pyrazinamidase enzyme (PZase). Despite the emergence of PZA drug resistance, the underlying mechanisms of resistance remain unclear. This study investigated part of these mechanisms by modelling a PZA-bound wild type and 82 mutant PZase structures before applying molecular dynamics (MD) with an accurate Fe2+ cofactor coordination geometry. After observing nanosecond-scale PZA unbinding from several PZase mutants, an algorithm was developed to systematically detect ligand release via centre of mass distances (COM) and ligand average speed calculations, before applying the statistically guided network analysis (SGNA) method to investigate conserved protein motions associated with ligand unbinding. Ligand and cofactor perspectives were also investigated. A conserved pair of lid-destabilising motions was found. These consisted of (1) antiparallel lid and side flap motions; (2) the contractions of a flanking region within the same flap and residue 74 towards the core. Mutations affecting the hinge residues (H51 and H71), nearby residues or L19 were found to destabilise the lid. Additionally, other metal binding site (MBS) mutations delocalised the Fe2+ cofactor, also facilitating lid opening. In the early stages of unbinding, a wider variety of PZA poses were observed, suggesting multiple exit pathways. These findings provide insights into the late events preceding PZA unbinding, which we found to occur in some resistant PZase mutants. Further, the algorithm developed here to identify unbinding events coupled with SGNA can be applicable to other similar problems.

Structure-Kinetic Profiling of Haloperidol Analogues at the Human Dopamine D2 Receptor

Haloperidol is a typical antipsychotic drug (APD) associated with an increased risk of extrapyramidal side effects (EPSs) and hyperprolactinemia relative to atypical APDs such as clozapine. Both drugs are dopamine D₂ receptor (D₂R) antagonists, with contrasting kinetic profiles. Haloperidol displays fast association/slow dissociation at the D₂R, whereas clozapine exhibits relatively slow association/fast dissociation. Recently, we have provided evidence that slow dissociation from the D₂R predicts hyperprolactinemia, whereas fast association predicts EPS. Unfortunately, clozapine can cause severe side effects independent of its D₂R action. Our results suggest an optimal kinetic profile for D₂R antagonist APDs that avoids EPS. To begin exploring this hypothesis, we conducted a structure-kinetic relationship study of haloperidol and revealed that subtle structural modifications dramatically change binding kinetic rate constants, affording compounds with a clozapine-like kinetic profile. Thus, optimization of these kinetic parameters may allow development of novel APDs based on the haloperidol scaffold with improved side-effect profiles.

Probing the CB1 Cannabinoid Receptor Binding Pocket with AM6538, a High-Affinity Irreversible Antagonist

Cannabinoid receptor 1 (CB1) is a potential therapeutic target for the treatment of pain, obesity and obesity-related metabolic disorders, and addiction. The crystal structure of human CB1 has been determined in complex with the stabilizing antagonist AM6538. In the present study, we characterize AM6538 as a tight-binding/irreversible antagonist of CB1, as well as two derivatives of AM6538 (AM4112 and AM6542) as slowly dissociating CB1 antagonists across binding simulations and cellular signaling assays. The long-lasting nature of AM6538 was explored in vivo wherein AM6538 continues to block CP55,940-mediated behaviors in mice up to 5 days after a single injection. In contrast, the effects of SR141716A abate in mice 2 days after injection. These studies demonstrate the functional outcome of CB1 antagonist modification and open the path for development of long-lasting CB1 antagonists.

Brownian Dynamics Simulations of Biological Molecules

Brownian dynamics (BD) is a technique for carrying out computer simulations of physical systems that are driven by thermal fluctuations. Biological systems at the macromolecular and cellular level, while falling in the gap between well-established atomic-level models and continuum models, are especially suitable for such simulations. We present a brief history, examples of important biological processes that are driven by thermal motion, and those that have been profitably studied by BD. We also present some of the challenges facing developers of algorithms and software, especially in the attempt to simulate larger systems more accurately and for longer times.

Ensemble-Based Steered Molecular Dynamics Predicts Relative Residence Time of A2A Receptor Binders

Drug-target residence time, the length of time for which a small molecule stays bound to its receptor target, has increasingly become a key property for optimization in drug discovery programs. However, its in silico prediction has proven difficult. Here we describe a method, using atomistic ensemble-based steered molecular dynamics (SMD), to observe the dissociation of ligands from their target G protein-coupled receptor in a time scale suitable for drug discovery. These dissociation simulations accurately, precisely, and reproducibly identify ligand-residue interactions and quantify the change in ligand energy values for both protein and water. The method has been applied to 17 ligands of the A_2A adenosine receptor, all with published experimental kinetic binding data. The residues that interact with the ligand as it dissociates are known experimentally to have an effect on binding affinities and residence times. There is a good correlation ( R² = 0.79) between the computationally calculated change in water-ligand interaction energy and experimentally determined residence time. Our results indicate that ensemble-based SMD is a rapid, novel, and accurate semi-empirical method for the determination of drug-target relative residence time.

Binding kinetics of ligands acting at GPCRs

The influence of drug-receptor binding kinetics has often been overlooked during the development of new therapeutics that target G protein-coupled receptors (GPCRs). Over the last decade there has been a growing understanding that an in-depth knowledge of binding kinetics at GPCRs is required to successfully target this class of proteins. Ligand binding to a GPCR is often not a simple single step process with ligand freely diffusing in solution. This review will discuss the experiments and equations that are commonly used to measure binding kinetics and how factors such as allosteric regulation, rebinding and ligand interaction with the plasma membrane may influence these measurements. We will then consider the molecular characteristics of a ligand and if these can be linked to association and dissociation rates.

A live cell NanoBRET binding assay allows the study of ligand-binding kinetics to the adenosine A3 receptor

There is a growing interest in understanding the binding kinetics of compounds that bind to G protein-coupled receptors prior to progressing a lead compound into clinical trials. The widely expressed adenosine A₃ receptor (A₃AR) has been implicated in a range of diseases including immune conditions, and compounds that aim to selectively target this receptor are currently under development for arthritis. Kinetic studies at the A₃AR have been performed using a radiolabelled antagonist, but due to the kinetics of this probe, they have been carried out at 10 °C in membrane preparations. In this study, we have developed a live cell NanoBRET ligand binding assay using fluorescent A₃AR antagonists to measure kinetic parameters of labelled and unlabelled compounds at the A₃AR at physiological temperatures. The kinetic profiles of four fluorescent antagonists were determined in kinetic association assays, and it was found that XAC-ser-tyr-X-BY630 had the longest residence time (RT = 288 ± 62 min) at the A₃AR. The association and dissociation rate constants of three antagonists PSB-11, compound 5, and LUF7565 were also determined using two fluorescent ligands (XAC-ser-tyr-X-BY630 or AV039, RT = 6.8 ± 0.8 min) as the labelled probe and compared to those obtained using a radiolabelled antagonist ([³H]PSB-11, RT = 44.6 ± 3.9 min). There was close agreement in the kinetic parameters measured with AV039 and [³H]PSB-11 but significant differences to those obtained using XAC-S-ser-S-tyr-X-BY630. These data indicate that selecting a probe with the appropriate kinetics is important to accurately determine the kinetics of unlabelled ligands with markedly different kinetic profiles.

Enhancing Drug Residence Time by Shielding of Intra-Protein Hydrogen Bonds: A Case Study on CCR2 Antagonists

The target residence time (RT) for a given ligand is one of the important parameters that have to be optimized during drug design. It is well established that shielding the receptor-ligand hydrogen bond (H-bond) interactions from water has been one of the factors in increasing ligand RT. Building on this foundation, here we report that shielding an intra-protein H-bond, which confers rigidity to the binding pocket and which is not directly involved in drug-receptor interactions, can strongly influence RT for CCR2 antagonists. Based on our recently solved CCR2 structure with MK-0812 and molecular dynamics (MD) simulations, we show that the RT for this and structurally related ligands is directly dependent on the shielding of the Tyr120-Glu291 H-bond from the water. If solvated this H-bond is often broken, making the binding pocket flexible and leading to shorter RT.

Chaperome heterogeneity and its implications for cancer study and treatment

The chaperome is the collection of proteins in the cell that carry out molecular chaperoning functions. Changes in the interaction strength between chaperome proteins lead to an assembly that is functionally and structurally distinct from each constituent member. In this review, we discuss the epichaperome, the cellular network that forms when the chaperome components of distinct chaperome machineries come together as stable, functionally integrated, multimeric complexes. In tumors, maintenance of the epichaperome network is vital for tumor survival, rendering them vulnerable to therapeutic interventions that target critical epichaperome network components. We discuss how the epichaperome empowers an approach for precision medicine cancer trials where a new target, biomarker, and relevant drug candidates can be correlated and integrated. We introduce chemical biology methods to investigate the heterogeneity of the chaperome in a given cellular context. Lastly, we discuss how ligand-protein binding kinetics are more appropriate than equilibrium binding parameters to characterize and unravel chaperome targeting in cancer and to gauge the selectivity of ligands for specific tumor-associated chaperome pools.

Computational modeling approaches to quantitative structure-binding kinetics relationships in drug discovery

Simple comparative correlation analyses and quantitative structure-kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug-target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling.

Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.

New approaches for computing ligand-receptor binding kinetics

The recent and growing evidence that the efficacy of a drug can be correlated to target binding kinetics has seeded the development of a multitude of novel methods aimed at computing rate constants for receptor-ligand binding processes, as well as gaining an understanding of the binding and unbinding pathways and the determinants of structure-kinetic relationships. These new approaches include various types of enhanced sampling molecular dynamics simulations and the combination of energy-based models with chemometric analysis. We assess these approaches in the light of the varying levels of complexity of protein-ligand binding processes.

Potent Prearranged Positive Allosteric Modulators of the Glucagon-like Peptide-1 Receptor

Drugs that allosterically modulate G protein-coupled receptor (GPCR) activity display higher specificity and may improve disease treatment. However, the rational design of compounds that target the allosteric site is difficult, as conformations required for receptor activation are poorly understood. Guided by photopharmacology, a set of prearranged positive allosteric modulators (PAMs) with restricted degrees of freedom was designed and tested against the glucagon-like peptide-1 receptor (GLP-1R), a GPCR involved in glucose homeostasis. Compounds incorporating a trans-stilbene comprehensively outperformed those with a cis-stilbene, as well as the benchmark BETP, as GLP-1R PAMs. We also identified major effects of ligand conformation on GLP-1R binding kinetics and signal bias. Thus, we describe a photopharmacology-directed approach for rational drug design, and introduce a new class of stilbene-containing PAM for the specific regulation of GPCR activity.

Binding Kinetics and Pathways of Ligands to GPCRs

Previously, drugs were developed focusing on target affinity and selectivity. However, it is becoming evident that the drug-target residence time, related to the off-rate, is an important parameter for successful drug development. The residence time influences both the on-rate and overall effectiveness of drugs. Furthermore, ligand binding is now appreciated to be a multistep process because metastable and/or intermediate binding sites in the extracellular region have been identified. In this review, we summarize experimental ligand-binding data for G-protein-coupled receptors (GPCRs), and their binding pathways, analyzed by molecular dynamics (MD). The kinetics of drug binding to GPCRs are complex and depend on several factors, including charge distribution on the receptor surface, ligand-receptor interactions in the binding channel and the binding site, or solvation.

How and when does an anticancer drug leave its binding site?

Obtaining atomistic resolution of drug unbinding from a protein is a much sought-after experimental and computational challenge. We report the unbinding dynamics of the anticancer drug dasatinib from c-Src kinase in full atomistic resolution using enhanced sampling molecular dynamics simulations. We obtain multiple unbinding trajectories and determine a residence time in agreement with experiments. We observe coupled protein-water movement through multiple metastable intermediates. The water molecules form a hydrogen bond bridge, elongating a specific, evolutionarily preserved salt bridge and enabling conformation changes essential to ligand unbinding. This water insertion in the salt bridge acts as a molecular switch that controls unbinding. Our findings provide a mechanistic rationale for why it might be difficult to engineer drugs targeting certain specific c-Src kinase conformations to have longer residence times.

Estimation of kinetic and thermodynamic ligand-binding parameters using computational strategies

Kinetic and thermodynamic ligand–protein binding parameters are gaining growing importance as key information to consider in drug discovery. The determination of the molecular structures, using particularly x-ray and NMR techniques, is crucial for understanding how a ligand recognizes its target in the final binding complex. However, for a better understanding of the recognition processes, experimental studies of ligand–protein interactions are needed. Even though several techniques can be used to investigate both thermodynamic and kinetic profiles for a ligand–protein complex, these procedures are very often laborious, time consuming and expensive. In the last 10 years, computational approaches have enormous potential in providing insights into each of the above effects and in parsing their contributions to the changes in both kinetic and thermodynamic binding parameters. The main purpose of this review is to summarize the state of the art of computational strategies for estimating the kinetic and thermodynamic parameters of a ligand–protein binding.