In vitro and in silico analysis of the effects of D2 receptor antagonist target binding kinetics on the cellular response to fluctuating dopamine concentrations

AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology

Identifying ligands targeting G protein coupled receptors (GPCRs) with novel chemotypes other than the physiological ligands is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinson's disease and cancer. The identified antagonists were tested here in a radio ligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310 ± 23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range.

Current Approaches of Building Mechanistic Pharmacodynamic Drug-Target Binding Models

Mechanistic pharmacodynamic models that incorporate the binding kinetics of drug-target interactions have several advantages in understanding target engagement and the efficacy of a drug dose. However, guidelines on how to build and interpret mechanistic pharmacodynamic drug-target binding models considering both biological and computational factors are still missing in the literature. In this chapter, current approaches of building mechanistic PD models and their advantages are discussed. We also present a methodology on how to select a suitable model considering both biological and computational perspectives, as well as summarize the challenges of current mechanistic PD models.

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.

Photoactivatable Dopamine and Sulpiride to Explore the Function of Dopaminergic Neurons and Circuits

Kinetic analysis of dopamine receptor activation and inactivation and the study of dopamine-dependent signaling requires precise simulation of the presynaptic release of the neurotransmitter dopamine and tight temporal control over the release of dopamine receptor antagonists. The 8-cyano-7-hydroxyquinolinyl (CyHQ) photoremovable protecting group was conjugated to dopamine and the dopamine receptor antagonist sulpiride to generate "caged" versions of these neuromodulators (CyHQ-O-DA and CyHQ-sulpiride, respectively) that could release their payloads with 365 or 405 nm light or through 2-photon excitation (2PE) at 740 nm. These compounds are stable under physiological conditions in the dark, yet photolyze rapidly and cleanly to yield dopamine or sulpiride and the caging remnant CyHQ-OH. CyHQ-O-DA mediated the light activation of dopamine-1 (D1) receptors on the breast cancer cell line MDA-MB-231 in culture. In mouse brain slice from the substantia nigra pars compacta, localized flash photolysis of CyHQ-O-DA accurately mimicked the natural presynaptic release of dopamine and activation of dopamine-2 (D2) receptors, causing a robust, concentration-dependent, and repeatable G protein-coupled inwardly rectifying potassium channel-mediated outward current in whole-cell voltage clamp recordings that was amplified by cocaine and blocked by sulpiride. Photolysis of CyHQ-sulpiride rapidly blocked synaptic activity, enabling measurement of the unbinding rates of dopamine and quinpirole, a D2 receptor agonist. These tools will enable more detailed study of dopamine receptors, their interactions with other GPCRs, and the physiology of dopamine signaling in the brain.

Icariside II, a phosphodiesterase 5 inhibitor, attenuates cerebral ischaemia/reperfusion injury by inhibiting glycogen synthase kinase-3β-mediated activation of autophagy

Background and Purpose

Cerebral ischaemia/reperfusion causes exacerbated neuronal damage involving excessive autophagy and neuronal loss. The present study was designed to investigate the effect of icariside II, one of main active ingredients of Herba Epimedii on this loss and whether this is related to its PDE 5 inhibitory action.

Experimental Approach

Focal cerebral ischaemia was induced in the rat by transient middle cerebral artery occlusion over 2 hr, followed by reperfusion with icariside II, 3‐methylamphetamine or rapamycin. The effect of icariside II was determined measuring behaviour changes and the size of the infarction. The expressions of PDE 5, autophagy‐related proteins and the level of phosphorylation of glycogen synthase kinase‐3β (GSK‐3β) were determined. Cultured primary cortical neurons were subjected to oxygen and glucose deprivation followed by reoxygenation in the presence and absence of icariside II. A surface plasmon resonance assay and molecular docking were used to explore the interactions of icariside II with PDE 5 or GSK‐3β.

Key Results

Icariside II not only protected against induced ischaemic reperfusion injury in rats but also attenuated such injury in primary cortical neurons. The neuroprotective effects of icariside II on such injury were attributed to interfering with the PKG/GSK‐3β/autophagy axis by directly bounding to PDE 5 and GSK‐3β.

Conclusions and Implications

These findings indicate that icariside II attenuates cerebral I/R‐induced injury via interfering with PKG/GSK‐3β/autophagy axis. This study raises the possibility that icariside II and other PDE 5 inhibitors maybe effective in the treatment ischaemia stroke.

Multi-scale modeling of drug binding kinetics to predict drug efficacy

Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.

The Kinetic Component in Drug Discovery: Using the Most Basic Pharmacological Concepts to Advance in Selecting Drugs to Combat CNS Diseases

To reach the central nervous system (CNS), drugs must cross the brain-blood barrier and have appropriate pharmacokinetic/dynamic properties. However, in early drug discovery steps, the selection of lead compounds, for example, those targeting G-protein-coupled receptors (GPCRs), is made according to i) affinity, which is calculated in in vitro equilibrium conditions, and ii) potency, a signal transduction-related parameter, usually quantified at a fixed time-point in a heterologous expression system. This paper argues that kinetics must be considered in the early steps of lead compound selection. While affinity calculation requires the establishment of a ligand-receptor equilibrium, the signal transduction starts as soon as the receptor senses the agonist. Taking cAMP production as an example, the in vitro-measured cytoplasmic levels of this cyclic nucleotide do not depend on equilibrium dissociation constant, KD. Signaling occurs far from the equilibrium and correlates more with the binding rate (kon) than with KD. Furthermore, residence time, a parameter to consider in lead optimization, may significantly vary from in vitro to in vivo conditions. The results are discussed from the perspective of dopaminergic neurotransmission and dopaminereceptor- based drug discovery.

Considerations for improved performance of competition association assays analysed with the Motulsky-Mahan's "kinetics of competitive binding" model

Background and Purpose

Target engagement dynamics can influence drugs' pharmacological effects. Kinetic parameters for drug:target interactions are often quantified by evaluating competition association experiments—measuring simultaneous protein binding of labelled tracers and unlabelled test compounds over time—with Motulsky–Mahan's “kinetics of competitive binding” model. Despite recent technical improvements, the current assay formats impose practical limitations to this approach. This study aims at the characterisation, understanding and prevention of these experimental constraints, and associated analytical challenges.

Experimental Approach

Monte Carlo simulations were used to run virtual kinetic and equilibrium tracer binding and competition experiments in both normal and perturbed assay conditions. Data were fitted to standard equations derived from the mass action law (including Motulsky–Mahan's) and to extended versions aiming to cope with frequently observed deviations of the canonical traces. Results were compared to assess the precision and accuracy of these models and identify experimental factors influencing their performance.

Key Results

Key factors influencing the precision and accuracy of the Motulsky–Mahan model are the interplay between compound dissociation rates, measurement time and interval frequency, tracer concentration and binding kinetics and the relative abundance of equilibrium complexes in vehicle controls. Experimental results produced recommendations for better design of tracer characterisation experiments and new strategies to deal with systematic signal decay.

Conclusions and Implications

Our data advances our comprehension of the Motulsky–Mahan kinetics of competitive binding models and provides experimental design recommendations, data analysis tools, and general guidelines for its practical application to in vitro pharmacology and drug screening.

Analysis of Biased Agonism

Agonists and most natural ligands bind to receptors in their inactive state and quickly induce an active receptor conformation that initiates cell signaling. The active receptor state initiates signaling because of its structural complementariness with coupling proteins that activate signaling pathways, such as G proteins and G protein-coupled receptor kinases. Agonist bias refers to the propensity of an agonist to direct receptor signaling through one pathway relative to another. Thus, if the agonist exhibits much higher affinity for active state 1 compared to active state 2, it will cause a robust activation of receptor coupling protein 1 but not 2, and ultimately, a preferential stimulation of signaling pathway 1. Biased agonists are potentially more selective therapeutic agents because there are numerous cases where the therapeutic and adverse effects of an agonist are mediated by distinct pathways involving G proteins and β-arrestin. Given the mechanism for agonist bias, the most straightforward approach for quantifying bias involves the estimation of agonist affinity for the inactive receptor state and the active receptor states involved in signaling through different pathways. The approach provides quantitative estimates of the sensitivities of different signaling pathways, enabling one to determine to what extent the observed selectivity is caused by agonist or system bias. In addition, the approach is a powerful adjunct to in silico docking studies and can be applied to in vivo assays, structure-activity relationships, and the analysis of published agonist concentration-response curves.

In vitro and in silico analysis of the effects of D2 receptor antagonist target binding kinetics on the cellular response to fluctuating dopamine concentrations

Background and Purpose

Target binding kinetics influence the time course of the drug effect (pharmacodynamics) both (i) directly, by affecting the time course of target occupancy, driven by the pharmacokinetics of the drug, competition with endogenous ligands and target turnover, and (ii) indirectly, by affecting signal transduction and homeostatic feedback. For dopamine D₂ receptor antagonists, it has been hypothesized that fast receptor binding kinetics cause fewer side effects, because part of the dynamics of the dopaminergic system is preserved by displacement of these antagonists.

Experimental Approach

Target binding kinetics of D₂ receptor antagonists and signal transduction after dopamine and D₂ receptor antagonist exposure were measured in vitro. These data were integrated by mechanistic modelling, taking into account competitive binding of endogenous dopamine and the antagonist, the turnover of the second messenger cAMP and negative feedback by PDE turnover.

Key Results

The proposed signal transduction model successfully described the cellular cAMP response for 17 D₂ receptor antagonists with widely different binding kinetics. Simulation of the response to fluctuating dopamine concentrations revealed that a significant effect of the target binding kinetics on the dynamics of the signalling only occurs at endogenous dopamine concentration fluctuations with frequencies below 1 min⁻¹.

Conclusions and Implications

Signal transduction and feedback are important determinants of the time course of drug effects. The effect of the D₂ receptor antagonist dissociation rate constant (k_off) is limited to the maximal rate of fluctuations in dopamine signalling as determined by the dopamine k_off and the cAMP turnover.