Target–drug interactions: first principles and their application to drug discovery

On the 'micro'-pharmacodynamic and pharmacokinetic mechanisms that contribute to long-lasting drug action

INTRODUCTION

Optimal drug therapy often requires continuing high levels of target occupancy. Besides the traditional pharmacokinetic (PK) contribution thereto, drug-target interactions that comprise successive 'microscopic' steps as well as the intervention of the cell membrane and other 'micro'-anatomical structures nearby may help attaining this objective.

AREAS COVERED

This article reviews the 'micro'-pharmacodynamic (PD) and PK mechanisms that may increase a drug's residence time. Special focus is on induced-fit- and bivalent ligand binding models as well as on the ability of the plasma membrane surrounding the target to act as a repository for the drug (e.g., microkinetic model), to actively participate in the binding process (e.g., exosite model) and, along with microanatomical elements like synapses and interstitial spaces, to act on the drug's diffusion properties (reduction in dimensionality and drug-rebinding models).

EXPERT OPINION

The PK profile, as well as the target dissociation kinetics of a drug, may fail to account for its long-lasting efficiency in intact tissues and in vivo. This lacuna could potentially be alleviated by incorporating some of the enumerated 'microscopic' mechanisms and, to unveil them, dedicated experiments on sufficiently physiologically relevant biological material like cell monolayers can already be implemented early on in the lead optimization process.

'Partial' competition of heterobivalent ligand binding may be mistaken for allosteric interactions: a comparison of different target interaction models

BACKGROUND AND PURPOSE

Non-competitive drugs that confer allosteric modulation of orthosteric ligand binding are of increasing interest as therapeutic agents. Sought-after advantages include a ceiling level to drug effect and greater receptor-subtype selectivity. It is thus important to determine the mode of interaction of newly identified receptor ligands early in the drug discovery process and binding studies with labelled orthosteric ligands constitute a traditional approach for this. According to the general allosteric ternary complex model, allosteric ligands that exhibit negative cooperativity may generate distinctive 'competition' curves: they will not reach baseline levels and their nadir will increase in par with the orthosteric ligand concentration. This behaviour is often considered a key hallmark of allosteric interactions.

EXPERIMENTAL APPROACH

The present study is based on differential equation-based simulations.

KEY RESULTS

The differential equation-based simulations revealed that the same 'competition binding' pattern was also obtained when a monovalent ligand binds to one of the target sites of a heterobivalent ligand, even if this process is exempt of allosteric interactions. This pattern was not strictly reciprocal when the binding of each of the ligands was recorded. The prominence of this phenomenon may vary from one heterobivalent ligand to another and we suggest that this phenomenon may take place with ligands that have been proposed to bind according to 'two-domain' and 'charnière' models.

CONCLUSIONS AND IMPLICATIONS

The present findings indicate a familiar experimental situation where bivalency may give rise to observations that could inadvertently be interpreted as allosteric binding. Yet, both mechanisms could be differentiated based on alternative experiments and structural considerations.

Study of the interaction of trastuzumab and SKOV3 epithelial cancer cells using a quartz crystal microbalance sensor

Analytical methods founded upon whole cell-based assays are of importance in early stage drug development and in fundamental studies of biomolecular recognition. Here we have studied the binding of the monoclonal antibody trastuzumab to human epidermal growth factor receptor 2 (HER2) on human ovary adenocarcinoma epithelial cancer cells (SKOV3) using quartz crystal microbalance (QCM) technology. An optimized procedure for immobilizing the cells on the chip surface was established with respect to fixation procedure and seeding density. Trastuzumab binding to the cell decorated sensor surface was studied, revealing a mean dissociation constant, K_D, value of 7 ± 1 nM (standard error of the mean). This study provides a new perspective on the affinity of the antibody-receptor complex presented a more natural context compared to purified receptors. These results demonstrate the potential for using whole cell-based QCM assay in drug development, the screening of HER2 selective antibody-based drug candidates, and for the study of biomolecular recognition. This real time, label free approach for studying interactions with target receptors present in their natural environment afforded sensitive and detailed kinetic information about the binding of the analyte to the target.

Kinetics of protein-ligand unbinding: Predicting pathways, rates, and rate-limiting steps

The ability to predict the mechanisms and the associated rate constants of protein-ligand unbinding is of great practical importance in drug design. In this work we demonstrate how a recently introduced metadynamics-based approach allows exploration of the unbinding pathways, estimation of the rates, and determination of the rate-limiting steps in the paradigmatic case of the trypsin-benzamidine system. Protein, ligand, and solvent are described with full atomic resolution. Using metadynamics, multiple unbinding trajectories that start with the ligand in the crystallographic binding pose and end with the ligand in the fully solvated state are generated. The unbinding rate k off is computed from the mean residence time of the ligand. Using our previously computed binding affinity we also obtain the binding rate k on. Both rates are in agreement with reported experimental values. We uncover the complex pathways of unbinding trajectories and describe the critical rate-limiting steps with unprecedented detail. Our findings illuminate the role played by the coupling between subtle protein backbone fluctuations and the solvation by water molecules that enter the binding pocket and assist in the breaking of the shielded hydrogen bonds. We expect our approach to be useful in calculating rates for general protein-ligand systems and a valid support for drug design.

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features

Drugtarget interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drugtarget interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drugtarget interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68 % and 84.72 % for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.

NMR approaches in structure-based lead discovery: recent developments and new frontiers for targeting multi-protein complexes

Nuclear magnetic resonance (NMR) spectroscopy is a pivotal method for structure-based and fragment-based lead discovery because it is one of the most robust techniques to provide information on protein structure, dynamics and interaction at an atomic level in solution. Nowadays, in most ligand screening cascades, NMR-based methods are applied to identify and structurally validate small molecule binding. These can be high-throughput and are often used synergistically with other biophysical assays. Here, we describe current state-of-the-art in the portfolio of available NMR-based experiments that are used to aid early-stage lead discovery. We then focus on multi-protein complexes as targets and how NMR spectroscopy allows studying of interactions within the high molecular weight assemblies that make up a vast fraction of the yet untargeted proteome. Finally, we give our perspective on how currently available methods could build an improved strategy for drug discovery against such challenging targets.

Structure-activity relationships (SAR) and structure-kinetic relationships (SKR) of bicyclic heteroaromatic acetic acids as potent CRTh2 antagonists III: the role of a hydrogen-bond acceptor in long receptor residence times

The correct positioning and orientation of an hydrogen bond acceptor (HBA) in the tail portion of the biaryl series of CRTh2 antagonists is a requirement for long receptor residence time. The HBA in combination with a small steric substituent in the core section (R(core) ≠ H) gives access to compounds with dissociation half-lives of ⩾ 24h.

Applications of SPR for the characterization of molecules important in the pathogenesis and treatment of neurodegenerative diseases

Characterization of binding kinetics and affinity between a potential drug and its receptor are key steps in the development of new drugs. Among the techniques available to determine binding affinities, surface plasmon resonance has emerged as the gold standard because it can measure binding and dissociation rates in real-time in a label-free fashion. Surface plasmon resonance is now finding applications in the characterization of molecules for treatment of neurodegenerative diseases, characterization of molecules associated with pathogenesis of neurodegenerative diseases and detection of neurodegenerative disease biomarkers. In addition it has been used in the characterization of a new class of natural autoantibodies that have therapeutic potential in a number of neurologic diseases. In this review we will introduce surface plasmon resonance and describe some applications of the technique that pertain to neurodegenerative disorders and their treatment.

Avidity and positive allosteric modulation/cooperativity act hand in hand to increase the residence time of bivalent receptor ligands

Bivalent ligands bear two target-binding pharmacophores. Their simultaneous binding increases their affinity (avidity) and residence time. They become 'bitopic' when the binding sites at the target permit the pharmacophores the exert allosteric modulation of each other's affinity and/or activity. Present simulations reveal that positive cooperativity exacerbates these phenomena, whereas negative cooperativity curtails them, irrespective of whether the association or dissociation rates of the individual pharmacophores are affected. Positive cooperativity delays the attainment of equilibrium binding, yielding 'hemi-equilibrium' conditions and only apparent affinity constants under usual experimental conditions. Monovalent ligands that bind to one of the target sites decrease the bitopic ligand's residence time concentration-wise; their potency depends on their association rate and thereon acting cooperativity rather than on affinity. This stems from the repetitive, very fast reformation of fully bound bitopic ligand-target complexes by rebinding of freshly dissociated pharmacophores. These studies deal with kinetic binding properties (of increasing interest in pharmacology) of bitopic ligands (a promising avenue in medicinal chemistry).

Conformational restriction approach to β-secretase (BACE1) inhibitors III: effective investigation of the binding mode by combinational use of X-ray analysis, isothermal titration calorimetry and theoretical calculations

For further investigation of BACE1 inhibitors using conformational restriction with sp(3) hybridized carbon, we applied this approach to 6-substituted aminopyrimidone derivatives 3 to improve the inhibitory activity by reducing the entropic energy loss upon binding to BACE1. Among eight stereoisomers synthesized, [trans-(1'R,2'R),6S] isomer 6 exhibited the best BACE1 inhibitory activity, which was statistically superior to that of the corresponding ethylene linker compound (R)-3. Combinational examinations of the binding mode of 6 were performed, which included isothermal titration calorimetry (ITC), X-ray crystallographic structure analysis and theoretical calculations, to clarify the effect of our conformational restriction approach. From the ITC measurement, the binding entropy of 6 was found to be ∼0.5kcal larger than that of (R)-3, which is considered to be affected by conformational restriction with a cyclopropane ring.

Large-scale prediction of human kinase-inhibitor interactions using protein sequences and molecular topological structures

The kinase family is one of the largest target families in the human genome. The family's key function in signal transduction for all organisms makes it a very attractive target class for the therapeutic interventions in many diseases states such as cancer, diabetes, inflammation and arthritis. A first step toward accelerating kinase drug discovery process is to fast identify whether a chemical and a kinase interact or not. Experimentally, these interactions can be identified by in vitro binding assay - an expensive and laborious procedure that is not applicable on a large scale. Therefore, there is an urgent need to develop statistically efficient approaches for identifying kinase-inhibitor interactions. For the first time, the quantitative binding affinities of kinase-inhibitor pairs are differentiated as a measurement to define if an inhibitor interacts with a kinase, and then a chemogenomics framework using an unbiased set of general integrated features (drug descriptors and protein descriptors) and random forest (RF) is employed to construct a predictive model which can accurately classify kinase-inhibitor pairs. Our results show that RF with integrated features gave prediction accuracy of 93.76%, sensitivity of 92.26%, and specificity of 95.27%, respectively. The results are superior to those by only considering two separated spaces (chemical space and protein space), demonstrating that these integrated features contribute cooperatively. Based on the constructed model, we provided a high confidence list of drug-target associations for subsequent experimental investigation guidance at a low false discovery rate.

Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.

Troubleshooting and deconvoluting label-free cell phenotypic assays in drug discovery

INTRODUCTION

Central to drug discovery and development is to comprehend the target(s), potency, efficacy and safety of drug molecules using pharmacological assays. Owing to their ability to provide a holistic view of drug actions in native cells, label-free biosensor-enabled cell phenotypic assays have been emerging as new generation phenotypic assays for drug discovery. Despite the benefits associated with wide pathway coverage, high sensitivity, high information content, non-invasiveness and real-time kinetics, label-free cell phenotypic assays are often viewed to be a blackbox in the era of target-centric drug discovery.

METHODS

This article first reviews the biochemical and biological complexity of drug-target interactions, and then discusses the key characteristics of label-free cell phenotypic assays and presents a five-step strategy to troubleshooting and deconvoluting the label-free cell phenotypic profiles of drugs.

RESULTS

Drug-target interactions are intrinsically complicated. Label-free cell phenotypic signatures of drugs mirror the innate complexity of drug-target interactions, and can be effectively deconvoluted using the five-step strategy.

DISCUSSION

The past decades have witnessed dramatic expansion of pharmacological assays ranging from molecular to phenotypic assays, which is coincident with the realization of the innate complexity of drug-target interactions. The clinical features of a drug are defined by how it operates at the system level and by its distinct polypharmacology, ontarget, phenotypic and network pharmacology. Approaches to examine the biochemical, cellular and molecular mechanisms of action of drugs are essential to increase the efficiency of drug discovery and development. Label-free cell phenotypic assays and the troubleshooting and deconvoluting approach presented here may hold great promise in drug discovery and development.

Dissociation kinetics of the streptavidin-biotin interaction measured using direct electrospray ionization mass spectrometry analysis

Dissociation rate constants (k (off)) for the model high affinity interaction between biotin (B) and the homotetramer of natural core streptavidin (S(4)) were measured at pH 7 and temperatures ranging from 15 to 45 °C using electrospray ionization mass spectrometry (ESI-MS). Two different approaches to data analysis were employed, one based on the initial rate of dissociation of the (S(4) + 4B) complex, the other involving nonlinear fitting of the time-dependent relative abundances of the (S(4) + iB) species. The two methods were found to yield k (off) values that are in good agreement, within a factor of two. The Arrhenius parameters for the dissociation of the biotin-streptavidin interaction in solution were established from the k (off) values determined by ESI-MS and compared with values measured using a radiolabeled biotin assay. Importantly, the dissociation activation energies determined by ESI-MS agree, within 1 kcal mol(-1), with the reported value. In addition to providing a quantitative measure of k (off), the results of the ESI-MS measurements revealed that the apparent cooperative distribution of (S(4) + iB) species observed at short reaction times is of kinetic origin and that sequential binding of B to S(4) occurs in a noncooperative fashion with the four ligand binding sites being kinetically and thermodynamically equivalent and independent.

Dual-Point Competition Association Assay

The concept of ligand-receptor binding kinetics is emerging as an important parameter in the early phase of drug discovery. Since the currently used kinetic assays are laborious and low throughput, we developed a method that enables fast and large format screening. It is a so-called dual-point competition association assay, which measures radioligand binding at two different time points in the absence or presence of unlabeled competitors. Specifically, this assay yields the kinetic rate index (KRI), which is a measure for the binding kinetics of the unlabeled ligands screened. As a prototypical drug target, the adenosine A1 receptor (A1R) was chosen for assay validation and optimization. A screen with 35 high-affinity A1R antagonists yielded seven compounds with a KRI value above 1.0, which indicated a relatively slow dissociation from the target. All other compounds had a KRI value below or equal to 1.0, predicting a relatively fast dissociation rate. Several compounds were selected for follow-up kinetic quantifications in classical kinetic assays and were shown to have kinetic rates that corresponded to their KRI values. The dual-point assay and KRI value may have general applicability at other G-protein-coupled receptors, as well as at drug targets from other protein families.

Ligand-receptor interaction platforms and their applications for drug discovery

INTRODUCTION

The study of drug-target interactions is essential for the understanding of biological processes and for the efforts to develop new therapeutic molecules. Increased ligand-binding assays have coincided with the advances in reagents, detection and instrumentation technologies, the expansion in therapeutic targets of interest, and the increasingly recognized importance of biochemical aspects of drug-target interactions in determining the clinical performance of drug molecules. Nowadays, ligand-binding assays can determine every aspect of many drug-target interactions.

AREAS COVERED

Given that ligand-target interactions are very diverse, the author has decided to focus on the binding of small molecules to protein targets. This article first reviews the key biochemical aspects of drug-target interactions, and then discusses the detection principles of various ligand-binding techniques in the context of their primary applications for drug discovery and development.

EXPERT OPINION

Equilibrium-binding affinity should not be used as a solo indicator for the in vivo pharmacology of drugs. The clinical relevance of drug-binding kinetics demands high throughput kinetics early in drug discovery. The dependence of ligand binding and function on the conformation of targets necessitates solution-based and whole cell-based ligand-binding assays. The increasing need to examine ligand binding at the proteome level, driven by the clinical importance of the polypharmacology of ligands, has started to make the structure-based in silico binding screen an indispensable technique for drug discovery and development. Integration of different ligand-binding assays is important to improve the efficiency of the drug discovery and development process.

High-affinity binding of remyelinating natural autoantibodies to myelin-mimicking lipid bilayers revealed by nanohole surface plasmon resonance

Multiple sclerosis is a progressive neurological disorder that results in the degradation of myelin sheaths that insulate axons in the central nervous system. Therefore promotion of myelin repair is a major thrust of multiple sclerosis treatment research. Two mouse monoclonal natural autoantibodies, O1 and O4, promote myelin repair in several mouse models of multiple sclerosis. Natural autoantibodies are generally polyreactive and predominantly of the IgM isotype. The prevailing paradigm is that because they are polyreactive, these antibodies bind antigens with low affinities. Despite their wide use in neuroscience and glial cell research, however, the affinities and kinetic constants of O1 and O4 antibodies have not been measured to date. In this work, we developed a membrane biosensing platform based on surface plasmon resonance in gold nanohole arrays with a series of surface modification techniques to form myelin-mimicking lipid bilayer membranes to measure both the association and dissociation rate constants for O1 and O4 antibodies binding to their myelin lipid antigens. The ratio of rate constants shows that O1 and O4 bind to galactocerebroside and sulfated galactocerebroside, respectively, with unusually small apparent dissociation constants (K(D) ≈ 0.9 nM) for natural autoantibodies. This is approximately one to 2 orders of magnitude lower than typically observed for the highest affinity natural autoantibodies. We propose that the unusually high affinity of O1 and O4 to their targets in myelin contributes to the mechanism by which they signal oligodendrocytes and induce central nervous system repair.

Structure-activity relationships and mechanism of action of Eph-ephrin antagonists: interaction of cholanic acid with the EphA2 receptor

The Eph-ephrin system, including the EphA2 receptor and the ephrinA1 ligand, plays a critical role in tumor and vascular functions during carcinogenesis. We previously identified (3α,5β)-3-hydroxycholan-24-oic acid (lithocholic acid) as an Eph-ephrin antagonist that is able to inhibit EphA2 receptor activation; it is therefore potentially useful as a novel EphA2 receptor-targeting agent. Herein we explore the structure-activity relationships of a focused set of lithocholic acid derivatives based on molecular modeling investigations and displacement binding assays. Our exploration shows that while the 3-α-hydroxy group of lithocholic acid has a negligible role in recognition of the EphA2 receptor, its carboxylate group is critical for disrupting the binding of ephrinA1 to EphA2. As a result of our investigation, we identified (5β)-cholan-24-oic acid (cholanic acid) as a novel compound that competitively inhibits the EphA2-ephrinA1 interaction with higher potency than lithocholic acid. Surface plasmon resonance analysis indicates that cholanic acid binds specifically and reversibly to the ligand binding domain of EphA2, with a steady-state dissociation constant (K(D) ) in the low micromolar range. Furthermore, cholanic acid blocks the phosphorylation of EphA2 as well as cell retraction and rounding in PC3 prostate cancer cells, two effects that depend on EphA2 activation by the ephrinA1 ligand. These findings suggest that cholanic acid can be used as a template structure for the design of effective EphA2 antagonists, and may have potential impact in the elucidation of the role played by this receptor in pathological conditions.