Investigating the Influence of Tracer Kinetics on Competition-Kinetic Association Binding Assays: Identifying the Optimal Conditions for Assessing the Kinetics of Low-Affinity Compounds

High-throughput kinetics in drug discovery

The importance of a drug's kinetic profile and interplay of structure-kinetic activity with PK/PD has long been appreciated in drug discovery. However, technical challenges have often limited detailed kinetic characterization of compounds to the latter stages of projects. This review highlights the advances that have been made in recent years in techniques, instrumentation, and data analysis to increase the throughput of detailed kinetic and mechanistic characterization, enabling its application earlier in the drug discovery process.

Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory

Mathematical modelling has become a key tool in pharmacological analysis, towards understanding dynamics of cell signalling and quantifying ligand-receptor interactions. Ordinary differential equation (ODE) models in receptor theory may be used to parameterise such interactions using timecourse data, but attention needs to be paid to the theoretical identifiability of the parameters of interest. Identifiability analysis is an often overlooked step in many bio-modelling works. In this paper we introduce structural identifiability analysis (SIA) to the field of receptor theory by applying three classical SIA methods (transfer function, Taylor Series and similarity transformation) to ligand-receptor binding models of biological importance (single ligand and Motulsky-Mahan competition binding at monomers, and a recently presented model of a single ligand binding at receptor dimers). New results are obtained which indicate the identifiable parameters for a single timecourse for Motulsky-Mahan binding and dimerised receptor binding. Importantly, we further consider combinations of experiments which may be performed to overcome issues of non-identifiability, to ensure the practical applicability of the work. The three SIA methods are demonstrated through a tutorial-style approach, using detailed calculations, which show the methods to be tractable for the low-dimensional ODE models.

Design, Synthesis, and Application of Fluorescent Ligands Targeting the Intracellular Allosteric Binding Site of the CXC Chemokine Receptor 2

The inhibition of CXC chemokine receptor 2 (CXCR2), a key inflammatory mediator, is a potential strategy in the treatment of several pulmonary diseases and cancers. The complexity of endogenous chemokine interaction with the orthosteric binding site has led to the development of CXCR2 negative allosteric modulators (NAMs) targeting an intracellular pocket near the G protein binding site. Our understanding of NAM binding and mode of action has been limited by the availability of suitable tracer ligands for competition studies, allowing direct ligand binding measurements. Here, we report the rational design, synthesis, and pharmacological evaluation of a series of fluorescent NAMs, based on navarixin (2), which display high affinity and preferential binding for CXCR2 over CXCR1. We demonstrate their application in fluorescence imaging and NanoBRET binding assays, in whole cells or membranes, capable of kinetic and equilibrium analysis of NAM binding, providing a platform to screen for alternative chemophores targeting these receptors.

A multiscale predictive digital twin for neurocardiac modulation

Cardiac function is tightly regulated by the autonomic nervous system (ANS). Activation of the sympathetic nervous system increases cardiac output by increasing heart rate and stroke volume, while parasympathetic nerve stimulation instantly slows heart rate. Importantly, imbalance in autonomic control of the heart has been implicated in the development of arrhythmias and heart failure. Understanding of the mechanisms and effects of autonomic stimulation is a major challenge because synapses in different regions of the heart result in multiple changes to heart function. For example, nerve synapses on the sinoatrial node (SAN) impact pacemaking, while synapses on contractile cells alter contraction and arrhythmia vulnerability. Here, we present a multiscale neurocardiac modelling and simulator tool that predicts the effect of efferent stimulation of the sympathetic and parasympathetic branches of the ANS on the cardiac SAN and ventricular myocardium. The model includes a layered representation of the ANS and reproduces firing properties measured experimentally. Model parameters are derived from experiments and atomistic simulations. The model is a first prototype of a digital twin that is applied to make predictions across all system scales, from subcellular signalling to pacemaker frequency to tissue level responses. We predict conditions under which autonomic imbalance induces proarrhythmia and can be modified to prevent or inhibit arrhythmia. In summary, the multiscale model constitutes a predictive digital twin framework to test and guide high-throughput prediction of novel neuromodulatory therapy. KEY POINTS: A multi-layered model representation of the autonomic nervous system that includes sympathetic and parasympathetic branches, each with sparse random intralayer connectivity, synaptic dynamics and conductance based integrate-and-fire neurons generates firing patterns in close agreement with experiment. A key feature of the neurocardiac computational model is the connection between the autonomic nervous system and both pacemaker and contractile cells, where modification to pacemaker frequency drives initiation of electrical signals in the contractile cells. We utilized atomic-scale molecular dynamics simulations to predict the association and dissociation rates of noradrenaline with the β-adrenergic receptor. Multiscale predictions demonstrate how autonomic imbalance may increase proclivity to arrhythmias or be used to terminate arrhythmias. The model serves as a first step towards a digital twin for predicting neuromodulation to prevent or reduce disease.

Competition in drug binding and … the race to equilibrium

Binding kinetics has become a popular topic in pharmacology due to its potential contribution to the selectivity and duration of drug action. Yet, the overall kinetic aspects of complex binding mechanisms are still merely described in terms of elaborate algebraic equations. Interestingly, it has been recommended some 10 years ago to examine such mechanisms in terms of binding fluxes instead of the conventional rate constants. Alike the velocity of product formation in enzymology, those fluxes refer to the velocity by which one target species converts into another one. Novel binding flux-based approaches are utilized to get a better visual insight into the "competition" between two drugs/ligands for a single target as well as between induced fit- and conformational selection pathways for a single ligand within a thermodynamic cycle. The present data were obtained by differential equation-based simulations. Early on, the ligand-binding steps "race" to equilibrium (i.e., when their forward and reverse fluxes are equal) at their individual pace. The overall/global equilibrium is only reached later on. For the competition association assays, this parting might produce a transient "overshoot" of one of the bound target species. A similar overshoot may also show up within a thermodynamic cycle and, at first glance, suggest that the induced fit pathway dominates. Yet, present findings show that under certain circumstances, it could rather be the other way round. Novel binding flux-based approaches offer visually attractive insights into crucial aspects of "complex" binding mechanisms under non-equilibrium conditions.

The Problems of Applying Classical Pharmacology Analysis to Modern In Vitro Drug Discovery Assays: Slow Binding Kinetics and High Target Concentration

The analysis framework used to quantify drug potency in vitro (e.g., K_d or K_i) was initially developed for classical pharmacology bioassays, for example, organ bath experiments testing moderate-affinity natural products. Modern drug discovery can infringe the assumptions of the classical pharmacology analysis equations, owing to the reduction of assay volume in miniaturization, target overexpression, and the increase of compound-target affinity in medicinal chemistry. These assumptions are that (1) the compound concentration greatly exceeds the target concentration (i.e., minimal ligand depletion), and (2) the compound is at equilibrium with the receptor (i.e., rapid ligand binding kinetics). Unappreciated infringement of these assumptions can lead to substantial underestimation of compound affinity, which negatively impacts the drug discovery process, from early-stage lead optimization to prediction of human dosing. This study evaluates the real-world impact of these factors on the target interaction assays used in drug discovery using literature examples, database searches, and simulations. The ranges of compound affinity and the assay types that are prone to depletion and equilibration artifacts are identified. Importantly, the highest-affinity compounds, usually the highest value chemical matter in drug discovery, are the most affected. Methods and simulation tools are provided to enable investigators to evaluate, manage, and minimize depletion or equilibration artifacts. This study enables the correct application of pharmacological data analysis to accurately quantify affinity using modern drug discovery assay technology.

Perspective: Implications of Ligand-Receptor Binding Kinetics for Therapeutic Targeting of G Protein-Coupled Receptors

The concept of ligand-receptor binding kinetics has been broadly applied in drug development pipelines focusing on G protein-coupled receptors (GPCRs). The ligand residence time (RT) for a receptor describes how long a ligand-receptor complex exists, and is defined as the reciprocal of the dissociation rate constant (k _off). RT has turned out to be a valuable parameter for GPCR researchers focusing on drug development as a good predictor of in vivo efficacy. The positive correlation between RT and in vivo efficacy has been established for several drugs targeting class A GPCRs (e.g., the neurokinin-1 receptor (NK₁R), the β₂ adrenergic receptor (β₂AR), and the muscarinic 3 receptor (M₃R)) and for drugs targeting class B1 (e.g., the glucagon-like peptide 1 receptor (GLP-1R)). Recently, the association rate constant (k _on) has gained similar attention as another parameter affecting in vivo efficacy. In the current perspective, we address the importance of studying ligand-receptor binding kinetics for therapeutic targeting of GPCRs, with an emphasis on how binding kinetics can be altered by subtle molecular changes in the ligands and/or the receptors and how such changes affect treatment outcome. Moreover, we speculate on the impact of binding kinetic parameters for functional selectivity and sustained receptor signaling from endosomal compartments; phenomena that have gained increasing interest in attempts to improve therapeutic targeting of GPCRs.

Fluorescent ligands: Bringing light to emerging GPCR paradigms

In recent years, several novel aspects of GPCR pharmacology have been described, which are thought to play a role in determining the in vivo efficacy of a compound. Fluorescent ligands have been used to study many of these, which have also required the development of new experimental approaches. Fluorescent ligands offer the potential to use the same fluorescent probe to perform a broad range of experiments, from single-molecule microscopy to in vivo BRET. This review provides an overview of the in vitro use of fluorescent ligands in further understanding emerging pharmacological paradigms within the GPCR field, including ligand-binding kinetics, allosterism and intracellular signalling, along with the use of fluorescent ligands to study physiologically relevant therapeutic agents.