Drug-target Binding Kinetics Research Articles

Predicting Protein-Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges.

The prediction of drug-target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

Rapid screening and in vivo target occupancy quantitative evaluation of xanthine oxidase inhibitors based on drug-target binding kinetics research strategy: A case study of Chrysanthemum morifolium Ramat.

Chrysanthemum morifolium Ramat. is a kind of food and drug dual-use traditional Chinese medicine possessing multiple pharmacological and biochemical benefits. In our study, a rapid and high-throughput method based on Surface plasmon resonance (SPR) biosensor technology was developed and verified for screening potential xanthine oxidase (XOD) inhibitors exemplarily in the Chrysanthemum morifolium Ramat. Coupled with ultra-high performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS), 14 XOD-binders were identified. In the SPR-based biosensor and molecular docking analysis, most compounds exhibited a strong affinity and binding kinetic property (association rate constant, Kon and dissociation rate constant, Koff) for XOD and could be regarded as potential inhibitors. More importantly, to further accurately assess target occupancy of candidate compounds in vivo, a mathematical model was established and verified involving three crucial intrinsic kinetic processes (Pharmacokinetics, Binding kinetic and Target kinetic). Overall, the proposed screening and assessment strategy could be proved an effective theoretical basis for further pharmacodynamic evaluation.

Designing Bcl-2 protein binding aptamers using screened Coulomb interaction method & regulate apoptosis.

Cancer cells develop different physiological abnormalities. One of them is overexpression of Bcl-2 protein, an anti-apoptotic biomolecule helping cancer cells to sustain. We planned to regulate the apoptosis by inhibiting the Bcl-2 protein's functions in cancer cells. We used computer aided drug design techniques to make a nucleic acid-based aptamer drug that targets Bcl-2 proteins. We applied a theoretical biophysics technique ‘screened Coulomb interactions approach (SCIA)’ for designing aptamers. This technique was patented (US10916330B1) and found suitable for designing novel aptamers to target biomoleculesin physiological conditions. Physicochemical analysis of Bcl-2 protein helps detect drug binding site(s) and charge distribution inamino acid structures. We made a judicious choice to select amino acid arginine as our target molecule in Bcl-2 structure. We constructed a universal drug design platform by applying SCIA, developed our algorithms and applied in Mathematica 13 to perform numerical computations (NCs) addressing the drug-target binding energetics. The NC results show that nucleotide guanine has the lowest binding energy over other nucleotides while interacting with arginine. Selection of other subsequent nucleotides as aptamer building blocks at various screening order is being made using their preferred SCI binding energetics with arginine. Thus, the discovery of a few aptamers to target arginine (specifically) and Bcl-2 protein (in general) has been made. Based on target binding energetic preferences we rank the aptamers. We are now working on the following two things: (i) perform 100 nanosecond molecular dynamics (MD) simulations to address drug-target binding kinetics and (ii) perform in vitro protein binding assays using top selected aptamers. Thus, we shall be able to finalize a few candidate drugs as optimal therapeutic agents capable of binding with Bcl-2 proteins and thus inhibit the protein functions. This research will lead us to discovering a few aptamer candidates for future chemotherapy applications.

A Predictive Pharmacokinetic Model for Immune Cell-Mediated Uptake and Retention of Nanoparticles in Tumors.

A promise of cancer nanomedicine is the "targeted" delivery of therapeutic agents to tumors by the rational design of nanostructured materials. During the past several decades, a realization that in vitro and in vivo preclinical data are unreliable predictors of successful clinical translation has motivated a reexamination of this approach. Mathematical models of drug pharmacokinetics (PK) and biodistribution (BD) are essential tools for small-molecule drugs development. A key assumption underlying these models is that drug-target binding kinetics dominate blood clearance, hence recognition by host innate immune cells is not explicitly included. Nanoparticles circulating in the blood are conspicuous to phagocytes, and inevitable interactions typically trigger active biological responses to sequester and remove them from circulation. Our recent findings suggest that, instead of referring to nanoparticles as designed for active or passive "tumor targeting", we ought rather to refer to immune cells residing in the tumor microenvironment (TME) as active or passive actors in an essentially "cell-mediated tumor retention" process that competes with active removal by other phagocytes. Indeed, following intravenous injection, nanoparticles induce changes in the immune compartment of the TME because of nanoparticle uptake, irrespective of the nature of tumor targeting moieties. In this study, we propose a 6-compartment PK model as an initial mathematical framework for modeling this tumor-associated immune cell-mediated retention. Published in vivo PK and BD results obtained with bionized nanoferrite® (BNF®) nanoparticles were combined with results from in vitro internalization experiments with murine macrophages to guide simulations. As a preliminary approximation, we assumed that tumor-associated macrophages (TAMs) are solely responsible for active retention in the TME. We model the TAM approximation by relating in vitro macrophage uptake to an effective macrophage avidity term for the BNF® nanoparticles under consideration.

G Protein-Coupled Receptor-Ligand Dissociation Rates and Mechanisms from τRAMD Simulations.

There is a growing appreciation of the importance of drug-target binding kinetics for lead optimization. For G protein-coupled receptors (GPCRs), which mediate signaling over a wide range of time scales, the drug dissociation rate is often a better predictor of in vivo efficacy than binding affinity, although it is more challenging to compute. Here, we assess the ability of the τ-Random Acceleration Molecular Dynamics (τRAMD) approach to reproduce relative residence times and reveal dissociation mechanisms and the effects of allosteric modulation for two important membrane-embedded drug targets: the β2-adrenergic receptor and the muscarinic acetylcholine receptor M2. The dissociation mechanisms observed in the relatively short RAMD simulations (in which molecular dynamics (MD) simulations are performed using an additional force with an adaptively assigned random orientation applied to the ligand) are in general agreement with much more computationally intensive conventional MD and metadynamics simulations. Remarkably, although decreasing the magnitude of the random force generally reduces the number of egress routes observed, the ranking of ligands by dissociation rate is hardly affected and agrees well with experiment. The simulations also reproduce changes in residence time due to allosteric modulation and reveal associated changes in ligand dissociation pathways.

Prediction of the Drug–Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (koff) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of koff values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.

Study on drug-target binding kinetics profiles of flavonoids in Chrysanthemum morifolium and xanthine oxidase

Based on the target occupancy mathematical model, the binding kinetic process of potential active ingredients of lowering uric acid in Chrysanthemum morifolium with xanthine oxidase(XOD) was evaluated. The potential active ingredients of lowering uric acid in Ch. morifolium were screened by UPLC-Q-Exactivems MS technology, reference substance identification and in vitro enzymatic kinetics experiments. The binding kinetic parameters of xanthine oxidase and potential inhibitor in Ch. morifolium were determined by surface plasma resonance(SPR). The verified mathematical model of the XOD target occupancy evaluated the kinetic binding process of inhibitors and xanthine oxidase in vivo. According to UPLC-Q-Exactive MS and reference substance identification, 39 potential uric acid-lowering active ingredients in Ch. morifolium extracts were identified and the inhibitory activities of 23 compounds were determined. Three potential xanthine oxidase inhibitors were screened, namely genistein, luteolin, and apigenin. whose IC_(50 )were 1.23, 1.47 and 1.59 μmol·L~(-1), respectively. And the binding rate constants(K_(on)) were 1.26×10~6, 5.23×10~5 and 6.36×10~5 mol·L~(-1)·s~(-1), respectively. The dissociation rate constants(K_(off)) were 10.93×10~(-2), 1.59×10~(-2), and 5.3×10~(-2 )s~(-1), respectively. After evaluation by different administration methods, the three selected compounds can perform rapid and sustained inhibition of xanthine oxidase in vivo under combined administration. This study comprehensively evaluated the target occupancy process of three effective components in different ways of administration in vivo by UPLC-MS, concentration-response method, SPR technology and xanthine oxidase target occupancy model, which would provide a new research idea and method for screening active ingredients in traditional Chinese medicine.

Encounter complexes and hidden poses of kinase-inhibitor binding on the free-energy landscape

Modern drug discovery increasingly focuses on the drug-target binding kinetics which depend on drug (un)binding pathways. The conventional molecular dynamics simulation can observe only a few binding events even using the fastest supercomputer. Here, we develop 2D gREST/REUS simulation with enhanced flexibility of the ligand and the protein binding site. Simulation (43 μs in total) applied to an inhibitor binding to c-Src kinase covers 100 binding and unbinding events. On the statistically converged free-energy landscapes, we succeed in predicting the X-ray binding structure, including water positions. Furthermore, we characterize hidden semibound poses and transient encounter complexes on the free-energy landscapes. Regulatory residues distant from the catalytic core are responsible for the initial inhibitor uptake and regulation of subsequent bindings, which was unresolved by experiments. Stabilizing/blocking of either the semibound poses or the encounter complexes can be an effective strategy to optimize drug-target residence time.

Route to Prolonged Residence Time at the Histamine H1 Receptor: Growing from Desloratadine to Rupatadine.

Drug–target binding kinetics are an important predictor of in vivo drug efficacy, yet the relationship between ligand structures and their binding kinetics is often poorly understood. We show that both rupatadine (1) and desloratadine (2) have a long residence time at the histamine H1 receptor (H1R). Through development of a [3H]levocetirizine radiolabel, we find that the residence time of 1 exceeds that of 2 more than 10-fold. This was further explored with 22 synthesized rupatadine and desloratadine analogues. Methylene-linked cycloaliphatic or β-branched substitutions of desloratadine increase the residence time at the H1R, conveying a longer duration of receptor antagonism. However, cycloaliphatic substituents directly attached to the piperidine amine (i.e., lacking the spacer) have decreased binding affinity and residence time compared to their methylene-linked structural analogues. Guided by docking studies, steric constraints within the binding pocket are hypothesized to explain the observed differences in affinity and binding kinetics between analogues.

Probe dependency in the determination of ligand binding kinetics at a prototypical G protein-coupled receptor

Drug-target binding kinetics are suggested to be important parameters for the prediction of in vivo drug-efficacy. For G protein-coupled receptors (GPCRs), the binding kinetics of ligands are typically determined using association binding experiments in competition with radiolabelled probes, followed by analysis with the widely used competitive binding kinetics theory developed by Motulsky and Mahan. Despite this, the influence of the radioligand binding kinetics on the kinetic parameters derived for the ligands tested is often overlooked. To address this, binding rate constants for a series of histamine H1 receptor (H1R) antagonists were determined using radioligands with either slow (low koff) or fast (high koff) dissociation characteristics. A correlation was observed between the probe-specific datasets for the kinetic binding affinities, association rate constants and dissociation rate constants. However, the magnitude and accuracy of the binding rate constant-values was highly dependent on the used radioligand probe. Further analysis using recently developed fluorescent binding methods corroborates the finding that the Motulsky-Mahan methodology is limited by the employed assay conditions. The presented data suggest that kinetic parameters of GPCR ligands depend largely on the characteristics of the probe used and results should therefore be viewed within the experimental context and limitations of the applied methodology.

Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics

Computational approaches currently assist medicinal chemistry through the entire drug discovery pipeline. However, while several computational tools and strategies are available to predict binding affinity, predicting the drug-target binding kinetics is still a matter of ongoing research. Here, we challenge scaled molecular dynamics simulations to assess the off-rates for a series of structurally diverse inhibitors of the heat shock protein 90 (Hsp90) covering 3 orders of magnitude in their experimental residence times. The derived computational predictions are in overall good agreement with experimental data. Aside from the estimation of exit times, unbinding pathways were assessed through dimensionality reduction techniques. The data analysis framework proposed in this work could lead to better understanding of the mechanistic aspects related to the observed kinetic behavior.

Prediction of Drug-Target Binding Kinetics by Comparative Binding Energy Analysis.

A growing consensus is emerging that optimizing the drug–target affinity alone under equilibrium conditions does not necessarily translate into higher potency in vivo and that instead binding kinetic parameters should be optimized to ensure better efficacy. Therefore, in silico methods are needed to predict the kinetic parameters and the mechanistic determinants of drug–protein binding. Here we demonstrate the application of COMparative BINding Energy (COMBINE) analysis to derive quantitative structure–kinetics relationships (QSKRs) for the dissociation rate constants (koff) of inhibitors of heat shock protein 90 (HSP90) and HIV-1 protease. We derived protein-specific scoring functions by correlating koff rate constants with a subset of weighted interaction energy components determined from the energy-minimized structures of drug–protein complexes. As the QSKRs derived for these sets of chemically diverse compounds have good predictive ability and provide insights into important drug–protein interactions for optimizing koff, COMBINE analysis offers a promising approach for binding kinetics-guided lead optimization.

A Multiscale Simulation Approach to Modeling Drug-Protein Binding Kinetics.

Drug-target binding kinetics has recently emerged as a sometimes critical determinant of in vivo efficacy and toxicity. Its rational optimization to improve potency or reduce side effects of drugs is, however, extremely difficult. Molecular simulations can play a crucial role in identifying features and properties of small ligands and their protein targets affecting the binding kinetics, but significant challenges include the long time scales involved in (un)binding events and the limited accuracy of empirical atomistic force fields (lacking, e.g., changes in electronic polarization). In an effort to overcome these hurdles, we propose a method that combines state-of-the-art enhanced sampling simulations and quantum mechanics/molecular mechanics (QM/MM) calculations at the BLYP/VDZ level to compute association free energy profiles and characterize the binding kinetics in terms of structure and dynamics of the transition state ensemble. We test our combined approach on the binding of the anticancer drug Imatinib to Src kinase, a well-characterized target for cancer therapy with a complex binding mechanism involving significant conformational changes. The results indicate significant changes in polarization along the binding pathways, which affect the predicted binding kinetics. This is likely to be of widespread importance in binding of ligands to protein targets.

The long duration of action of the second generation antihistamine bilastine coincides with its long residence time at the histamine H1 receptor

Drug-target binding kinetics has recently attracted considerable interest in view of the potential predictive power for in vivo drug efficacy. The recently introduced antihistamine bilastine has a long duration of in vivo drug action, which outlasts pharmacological active bilastine concentrations in blood. To provide a molecular basis for the long duration of action, we explored the kinetics of bilastine binding to the human histamine H1 receptor using [3H]mepyramine binding studies and compared its pharmacodynamics properties to the reference compounds fexofenadine and diphenhydramine, which have a long (60 ± 20 min) and short (0.41 ± 0.1 min) residence time, respectively. Bilastine shows a long drug-target residence time at the H1 receptor (73 ± 5 min) and this results in a prolonged H1 receptor antagonism in vitro (Ca2+ mobilization in Fluo-4 loaded HeLa cells), following a washout of unbound antagonist. Hence, the long residence time of bilastine can explain the observed long duration of drug action in vivo.

A binding kinetics study of human adenosine A3 receptor agonists

The human adenosine A3 (hA3) receptor has been suggested as a viable drug target in inflammatory diseases and in cancer. So far, a number of selective hA3 receptor agonists (e.g. IB-MECA and 2-Cl-IB-MECA) inducing anti-inflammatory or anticancer effects are under clinical investigation. Drug-target binding kinetics is increasingly recognized as another pharmacological parameter, next to affinity, for compound triage in the early phases of drug discovery. However, such a kinetics-driven analysis has not yet been performed for the hA3 receptor. In this study, we first validated a competition association assay for adenosine A3 receptor agonists to determine the target interaction kinetics. Affinities and Kinetic Rate Index (KRI) values of 11 ribofurano and 10 methanocarba nucleosides were determined in radioligand binding assays. Afterwards, 15 analogues were further selected (KRI <0.70 or KRI >1.35) for full kinetics characterization. The structure-kinetics relationships (SKR) were derived and longer residence times were associated with methanocarba and enlarged adenine N6 and C2 substitutions. In addition, from a kon-koff-KD kinetic map we divided the agonists into three subgroups. A residence time “cliff” was observed, which might be relevant to (N)-methanocarba derivatives’ rigid C2-arylalkynyl substitutions. Our findings provide substantial evidence that, next to affinity, additional knowledge of binding kinetics is useful for developing and selecting new hA3R agonists in the early phase of the drug discovery process.

From receptor binding kinetics to signal transduction; a missing link in predicting in vivo drug-action

An important question in drug discovery is how to overcome the significant challenge of high drug attrition rates due to lack of efficacy and safety. A missing link in the understanding of determinants for drug efficacy is the relation between drug-target binding kinetics and signal transduction, particularly in the physiological context of (multiple) endogenous ligands. We hypothesized that the kinetic binding parameters of both drug and endogenous ligand play a crucial role in determining cellular responses, using the NK1 receptor as a model system. We demonstrated that the binding kinetics of both antagonists (DFA and aprepitant) and endogenous agonists (NKA and SP) have significantly different effects on signal transduction profiles, i.e. potency values, in vitro efficacy values and onset rate of signal transduction. The antagonistic effects were most efficacious with slowly dissociating aprepitant and slowly associating NKA while the combination of rapidly dissociating DFA and rapidly associating SP had less significant effects on the signal transduction profiles. These results were consistent throughout different kinetic assays and cellular backgrounds. We conclude that knowledge of the relationship between in vitro drug-target binding kinetics and cellular responses is important to ultimately improve the understanding of drug efficacy in vivo.

A Novel Selective Inverse Agonist of the CB2 Receptor as a Radiolabeled Tool Compound for Kinetic Binding Studies.

The endocannabinoid system, and in particular the cannabinoid type 2 receptor (CB2R), raised the interest of many medicinal chemistry programs for its therapeutic relevance in several (patho)physiologic processes. However, the physico-chemical properties of tool compounds for CB2R (e.g., the radioligand [3H]CP55,940) are not optimal, despite the research efforts in developing effective drugs to target this system. At the same time, the importance of drug-target binding kinetics is growing since the kinetic binding profile of a ligand may provide important insights for the resulting in vivo efficacy. In this context we synthesized and characterized [3H]RO6957022, a highly selective CB2R inverse agonist, as a radiolabeled tool compound. In equilibrium and kinetic binding experiments [3H]RO6957022 showed high affinity for human CB2R with fast association (kon) and moderate dissociation (koff) kinetics. To demonstrate the robustness of [3H]RO6957022 binding, affinity studies were carried out for a wide range of CB2R reference ligands, spanning the range of full, partial, and inverse agonists. Finally, we used [3H]RO6957022 to study the kinetic binding profiles (i.e., kon and koff values) of selected synthetic and endogenous (i.e., 2-arachidonoylglycerol, anandamide, and noladin ether) CB2R ligands by competition association experiments. All tested ligands, and in particular the endocannabinoids, displayed distinct kinetic profiles, shedding more light on their mechanism of action and the importance of association rates in the determination of CB2R affinity. Altogether, this study shows that the use of a novel tool compound, i.e., [3H]RO6957022, can support the development of novel ligands with a repertoire of kinetic binding profiles for CB2R.

The influence of drug distribution and drug-target binding on target occupancy: The rate-limiting step approximation

The influence of drug-target binding kinetics on target occupancy can be influenced by drug distribution and diffusion around the target, often referred to as “rebinding” or “diffusion-limited binding”. This gives rise to a decreased decline of the drug-target complex concentration as a result of a locally higher drug concentration that arises around the target, which leads to prolonged target exposure to the drug. This phenomenon has been approximated by the steady-state approximation, assuming a steady-state concentration around the target. Recently, a rate-limiting step approximation of drug distribution and drug-target binding has been published. However, a comparison between both approaches has not been made so far.In this study, the rate-limiting step approximation has been rewritten into the same mathematical format as the steady-state approximation in order to compare the performance of both approaches for the investigation of the influence of drug-target binding kinetics on target occupancy.While both approximations clearly indicated the importance of kon and high target concentrations, it was shown that the rate-limiting step approximation is more accurate than the steady-state approximation, especially when dissociation is fast compared to association and distribution out of the binding compartment.It is therefore concluded that the new rate-limiting step approximation is to be preferred for assessing the influence of binding kinetics on local target site concentrations and target occupancy.

A quantitative mechanistic PK/PD model directly connects Btk target engagement and in vivo efficacy.

Correlating target engagement with in vivo drug activity remains a central challenge in efforts to improve the efficiency of drug discovery. Previously we described a mechanistic pharmacokinetic-pharmacodynamic (PK/PD) model that used drug-target binding kinetics to successfully predict the in vivo efficacy of antibacterial compounds in models of Pseudomonas aeruginosa and Staphylococcus aureus infection. In the present work we extend this model to quantitatively correlate the engagement of Bruton's tyrosine kinase (Btk) by the covalent inhibitor CC-292 with the ability of this compound to reduce ankle swelling in an animal model of arthritis. The modeling studies include the rate of Btk turnover and reveal the vulnerability of Btk to engagement by CC-292.

ChemInform Abstract: Public‐Private Partnerships in Lead Discovery: Overview and Case Studies

AbstractReview: 25 refs.