Interactions In Drug Discovery Research Articles

Assessing Trends in Cytokine-CYP Drug Interactions and Relevance to Drug Dosing.

The regulation of drug-metabolizing enzymes and transporters by cytokines has been extensively studied in vitro and in clinic. Cytokine-mediated suppression of cytochrome P450 (CYP) or drug transporters may increase or decrease the systemic clearance of drug substrates that are primarily cleared via these pathways; neutralization of cytokines by therapeutic proteins may thereby alter systemic exposures of such drug substrates. The Food and Drug Administration recommends evaluating such clinical drug interactions during clinical development and has provided labeling recommendations for therapeutic proteins. To determine the clinical relevance of these drug interactions to dose adjustments, trends in steady-state exposures of CYP-sensitive substrates coadministered with cytokine modulators as reported in the University of Washington Drug Interaction Database were extracted and examined for each of the CYPs. Coadministration of cytochrome P450 family 3 subfamily A (CYP3A) (midazolam/simvastatin), cytochrome P450 subfamily 2C19 (omeprazole), or cytochrome P450 subfamily 1A2 (caffeine/tizanidine) substrates with anti-interleukin-6 and with anti-interleukin-23 therapeutics led to changes in systemic exposures of CYP substrates ranging from ∼ -58% to ∼35%; no significant trends were observed for cytochrome P450 subfamily 2D6 (dextromethorphan) and cytochrome P450 subfamily 2C9 (warfarin) substrates. Although none of these changes in systemic exposures have been reported as clinically meaningful, dose adjustment of midazolam for optimal sedation in acute care settings has been reported. Simulated concentration-time profiles of midazolam under conditions of elevated cytokine levels when coadministered with tocilizumab, suggest a ∼six- to sevenfold increase in midazolam clearance, suggesting potential implications of cytokine-CYP drug interactions on dose adjustments of sensitive CYP3A substrates in acute care settings. Additionally, this article also provides a brief overview of nonclinical and clinical assessments of cytokine-CYP drug interactions in drug discovery and development. SIGNIFICANCE STATEMENT: There has been significant progress in understanding cytokine-mediated drug interactions for CYP-sensitive substrates. This article provides an overview of the progress in this field, including a trend analysis of systemic exposures of CYP-sensitive substrates coadministered with anti-interleukin therapeutics. In addition, the review also provides a perspective of current methods used to assess these drug interactions during drug development and a focus on individualized medicine, particularly in acute care settings.

Recent advances in the development of small molecule inhibitors for targeting protein-protein interactions in drug discovery

Recent advances in the development of small molecule inhibitors for targeting protein-protein interactions in drug discovery

Biosensor Assays Types and Their Roles Toward Ligand-Receptor Interactions in Drug Discovery.

Ligand-receptor interactions (LRIs) are the basis for all the biological processes taking place in living cells and have been exploited to develop and implement in medical field a number of highly sensitive biosensors for the detection of various biomarkers in complex biological fluids. Drug-target interactions, one of the LRIs, are important to understand the biological processes that further help in developing new and better therapeutic molecules. Biosensors based on these interactions give us an idea for the need of modification of existing drugs or to develop new drugs. Common approach to develop biosensors requires the labeling; however, label-free systems provide advantages in avoiding the chances of conformational changes, off-site labeling, and labeling-based hindrances, thus saving time and effort toward assay development. Preliminary drug screening assays are carried out in two-dimensional (2D) models, followed by animal models, which require huge capital investment to reach from bench-top to clinical trials, where only 21% of new compounds make way to phase-1 clinical trials. Three-dimensional culture or organoid culture or organ-on-chip technology has made way for predictive and complex in vitro approach that recapitulates human physiology and represents more similar in vivo behavior than 2D. Multiplexing and nanotechnology have remarkably enhanced the efficacy of biosensors and might lead to a generation of miniaturized biosensors and more than just point-of-care kits. This review provides in-depth analysis of different types of biosensor assays based on drug-target interactions, their advantages, and limitations based on cost, sensitivity, and selectivity and industrial applications.

Detection of Protein-Ligand Interactions by 19F Nuclear Magnetic Resonance Using Hyperpolarized Water.

The transfer of nuclear spin hyperpolarization from water to ligand 19F spins results in a transient signal change that is indicative of protein-ligand interaction. The 19F nucleus allows for background-free detection of these signals, which are modulated by polarization transfer via pathways similar to those in a hyperpolarized 1H water LOGSY experiment. Quantification of the apparent heteronuclear cross-relaxation rates is facilitated by a simultaneous dual-channel detection of 1H and 19F signals. Calculated cross-relaxation rates for the 1H-19F transfer step indicate that these rates are sensitive to binding to medium- and large-sized proteins. The heteronuclear observation of hyperpolarization transfer from water may be used to screen protein-ligand interactions in drug discovery and other applications.

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.

In vitro and in vivo methods to assess pharmacokinetic drug- drug interactions in drug discovery and development.

Drug-drug interactions (DDIs) caused by the co-administration of multiple drugs are major safety concerns in the clinic. Several drugs have been withdrawn from the market due to perpetrator or victim DDIs. Strategies have been developed to assess DDI risks early in drug discovery to reduce DDI liabilities. High-to-medium throughput assays are available to identify undesirable scaffolds and to guide structural modifications to minimize DDIs. Definitive methods are used at later stages of drug discovery and development to provide a more accurate measurement of DDI parameters and to enable clinical translations. Physiologically based pharmacokinetic modeling and simulations are powerful tools to accurately predict DDIs and to assess risks in the clinic. Although significant advances have been made over the years, many challenges remain for clinical DDI translations. This includes DDIs involving non-cytochrome P450 enzymes, transporters, enzyme-transporter interplay, indirect effects from biologics, and pharmacodynamic based DDI. This review focuses on methods that are used to assess hepatic DDIs caused by enzyme inhibition and induction.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery.

In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process. Novel designing and screening tactics are necessary to substantiate a potent and efficient lead compounds. Such methods are emphasized and portrayed in the current review targeting protein-ligand and protein-protein interactions involved in various diseases with potential applications.

Molecular dynamics to enhance structure-based virtual screening on cathepsin B.

Molecular dynamics (MD) and molecular docking are commonly used to study molecular interactions in drug discovery. Most docking approaches consider proteins as rigid, which can decrease the accuracy of predicted docked poses. Therefore MD simulations can be used prior to docking to add flexibility to proteins. We evaluated the contribution of using MD together with docking in a docking study on human cathepsin B, a well-studied protein involved in numerous pathological processes. Using CHARMM biomolecular simulation program and AutoDock Vina molecular docking program, we found, that short MD simulations significantly improved molecular docking. Our results, expressed with the area under the receiver operating characteristic curves, show an increase in discriminatory power i.e. the ability to discriminate active from inactive compounds of molecular docking, when docking is performed to selected snapshots from MD simulations.

Fragment-based drug discovery and protein–protein interactions

Fragment-based drug discovery and protein–protein interactions Andrew P Turnbull,1 Susan M Boyd,2 Björn Walse31CRT Discovery Laboratories, Department of Biological Sciences, Birkbeck, University of London, London, UK; 2IOTA Pharmaceuticals Ltd, Cambridge, UK; 3SARomics Biostructures AB, Lund, SwedenAbstract: Protein–protein interactions (PPIs) are involved in many biological processes, with an estimated 400,000 PPIs within the human proteome. There is significant interest in exploiting the relatively unexplored potential of these interactions in drug discovery, driven by the need to find new therapeutic targets. Compared with classical drug discovery against targets with well-defined binding sites, developing small-molecule inhibitors against PPIs where the contact surfaces are frequently more extensive and comparatively flat, with most of the binding energy localized in “hot spots”, has proven far more challenging. However, despite the difficulties associated with targeting PPIs, important progress has been made in recent years with fragment-based drug discovery playing a pivotal role in improving their tractability. Computational and empirical approaches can be used to identify hot-spot regions and assess the druggability and ligandability of new targets, whilst fragment screening campaigns can detect low-affinity fragments that either directly or indirectly perturb the PPI. Once fragment hits have been identified and confirmed using biochemical and biophysical approaches, three-dimensional structural data derived from nuclear magnetic resonance or X-ray crystallography can be used to drive medicinal chemistry efforts towards the development of more potent inhibitors. A small-scale comparison presented in this review of “standard” fragments with those targeting PPIs has revealed that the latter tend to be larger, be more lipophilic, and contain more polar (acid/base) functionality, whereas three-dimensional descriptor data indicate that there is little difference in their three-dimensional character. These physiochemical properties can potentially be exploited in the rational design of PPI-specific fragment libraries and correlate well with optimized PPI inhibitors, which tend to have properties outside currently accepted guidelines for drug-likeness. Several examples of small-molecule PPI inhibitors derived from fragment-based drug discovery now exist and are described in this review, including navitoclax, a novel Bcl-2 family inhibitor which has entered Phase II clinical trials in patients with small-cell lung cancer and chronic lymphocytic leukemia.Keywords: hot spot, druggability, ligandability

Protein–Protein Interactions in Drug Discovery. Edited by Alexander Dömling.

Protein–Protein Interactions in Drug Discovery. Edited by Alexander Dömling.

Protein-Protein Interactions in Drug Discovery. (Series: Methods and Principles in Medicinal Chemistry, Vol. 56) Edited by Alexander Dömling.

Protein-Protein Interactions in Drug Discovery. (Series: Methods and Principles in Medicinal Chemistry, Vol. 56) Edited by Alexander Dömling.

The Significance of Quantum Chemical Interactions for Medicinal Science and Design of β-Secretase Inhibitors

This review discusses the importance of quantum chemical interactions in biomolecules for medicinal science and their relevance to the author's β-secretase (BACE1) inhibitor drug discovery research. Although molecular mechanics/dynamics (MM/MD) methods are available in many in silico design tools used for drug discovery, they cannot accurately evaluate quantum effects between biomolecules and drugs. The key roles of biomolecular quantum chemical interactions in drug discovery are discussed using the arginine side chain as an example. Arginine is recognized as a charged amino acid in commonly used drug design software, unlike other amino acids with π-electron orbitals, such as phenylalanine, tyrosine, and tryptophan. Quantum chemical interactions via the arginine side chain are crucial for molecular recognition, and are found in many X-ray crystal structures, such as protein-protein, protein homodimer, RNA aptamer-protein, and enzyme-inhibitor complexes. This review describes the essential role of quantum chemical interactions via the arginine side chain in the mechanism of BACE1 inhibition, and proposes an "electron donor/acceptor bioisostere" concept for medicinal science based on quantum chemical interactions. Several potent BACE1 inhibitors, as well as the first peptides with BACE1 inhibiting activity were designed and synthesized based on studies of quantum chemical interactions via arginine side chain and the "electron donor bioisostere" concept.

Editorial: Lucky Number 8!

Editorial: Lucky Number 8!

Nuclear Magnetic Resonance of Hyperpolarized Fluorine for Characterization of Protein–Ligand Interactions

Fluorine NMR spectroscopy is widely used for detection of protein-ligand interactions in drug discovery because of the simplicity of fluorine spectra combined with a relatively high likelihood for a drug molecule to include at least one fluorine atom. In general, an important limitation of NMR spectroscopy in drug discovery is its sensitivity, which results in the need for unphysiologically high protein concentrations and large ligand:protein ratios. An enhancement in the (19)F signal of several thousand fold by dynamic nuclear polarization allows for the detection of submicromolar concentrations of fluorinated small molecules. Techniques for exploiting this gain in signal to detect ligands in the strong-, intermediate-, and weak-binding regimes are presented. Similar to conventional NMR analysis, dissociation constants are determined. However, the ability to use a low ligand concentration permits the detection of ligands in slow exchange that are not easily amenable to drug screening by traditional NMR methods. The relative speed and additional information gained may make the hyperpolarization-based approach an interesting alternative for use in drug discovery.

Integrated Ligand Based Pharmacophore Model Derived from Diverse FAAH Covalent Ligand Classes

3D pharmacophore modeling is an important computational methodology for ligand-enzyme binding interactions in drug discovery. More specifically, a consensus pharmacophore model derived from diverse ligands is a key determinant upon which the prediction power of computational models is based for designing novel ligands. In this work, by merging the important pharmacophore features based on four classes of covalent FAAH ligands, and then integrating the exclusion volume spheres derived from the crystal structure, we created for the first time an integrated FAAH pharmacophore model to describe the ligand-enzyme binding interactions. This new integrated FAAH pharmacophore model can correctly predict the covalent ligand binding mode, which correlates with the SAR data. The study is expected to provide insights into novel covalent ligand-FAAH binding interactions, and facilitate the design of covalent ligands against FAAH.

Reliable High-throughput Method for Inhibition Assay of 8 Cytochrome P450 Isoforms Using Cocktail of Probe Substrates and Stable Isotope-labeled Internal Standards

A significant number of new chemical entities (NCEs) disappear due to cytochrome P450 (CYP)-mediated clinical drug-drug interactions in drug discovery. Therefore, a high throughput assay of CYP activities is necessary in order to evaluate the inhibitory or inducible potencies of CYP isoforms with NCEs in early drug discovery. Here, we developed and validated a high-throughput assay to simultaneously monitor the in vitro activities of 8 CYP isoforms. A cocktail of 9 probe substrates for the 8 major CYPs (CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4/5) was incubated with human liver microsomes. Each substrate-derived metabolite was simultaneously analyzed by multiple reactions monitoring with a single ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) run using stable isotope-labeled internal standards. The ultra-fast UPLC gradient allowed each metabolite to be separated within 1 min, providing quantitative linearity of over 2 orders of magnitude. CYP inhibition by 8 well-known inhibitors was confirmed by comparing single substrates with the substrate cocktail. The inhibition curve profiles and IC₅₀ values for all CYPs in the cocktail substrate were similar to those of single substrates. UPLC-MS/MS using a CYP substrate cocktail is a reliable and robust high-throughput method to accurately assess CYP inhibition potencies of newly developed drugs.

Evaluation of Drug Transporter Interactions in Drug Discovery and Development

Drug transporters play an important role in the absorption, distribution, excretion and toxicity of both endogenous and exogenous compounds. Transporters may act as physiological 'gatekeepers' in the regulation of the pharmacological and/or toxicological effects of drugs by limiting distribution to tissues responsible for their effect and/or toxicity. This review will first provide a brief outline of the characteristics of membrane bound drug transporter families and their respective roles in regulating drug pharmacokinetics. This background then provides the context for a discussion of the characterization of a drug candidate as a substrate, inhibitor and/or inducer of drug transporter(s), followed by an assessment of the in vitro and in vivo preclinical methods used in drug discovery and development for screening molecules to identify potential transporter interactions. Finally, specific examples of the translation of in vitro findings to the in vivo effects are discussed to link the current understanding of the impact of drug transporters to clinical pharmacology. Thus, the goal is to provide the drug discovery scientist with a cadre of concepts, strategies, and tools for ultimately making rational decisions in drug design and delivery resulting in the optimization of drug concentrations at the target of pharmacology.

Structure–pharmacokinetic relationship of in vivo rat biliary excretion

Accurately measuring and predicting biliary excretion would be extremely valuable in evaluating the contribution of biliary excretion to the total systemic clearance, understanding potential mechanisms of hepatobiliary toxicity as well as potentials for drug-drug interactions in drug discovery. In this study, in vivo rat biliary excretion of drug-like molecules was measured using bile duct cannulated rats. Literature biliary excretion data with similar experimental conditions were collected. A predictive quantitative structure-pharmacokinetic relationship (QSPR) model was developed using genetic algorithm guided principal component regression analysis and 2D molecular descriptors. In the derived model, hydrophobicity expressed with calculated distribution coefficients (cLogD) is the most important molecular property correlating biliary excretion. The derived model has been validated using literature data, and should be useful in estimating biliary excretion potentials of molecules in drug discovery.

Biophysical methods for monitoring cell-substrate interactions in drug discovery.

Cell-substrate interactions are implicated in a number of relevant pathways for drug targets such as angiogenesis, arteriosclerosis, chronic inflammatory diseases, and carcinogenesis. Moreover, cell adhesion and cytoskeletal activity have served as valuable indicators for cytotoxicity, cell density, and cell morphology. This review focuses on impedance, capacitance, resonant frequency, and refractive index measurements for monitoring cell adhesion in real time and without the use of cell labeling. ECIS, QCM, and OWLS deliver information about the cell-substrate interactions, cell-cell contact, and the strength of cell adhesion. Because of high sensitivity of these assays, events down to the single cell level have been observed, and resolutions at the nanometer level of cell-substrate distances have been achieved. The physical principles, including assay sensitivity and selectivity, are discussed in the context of cellular pathways of cell adhesion and migration. With the miniaturization of these types of sensors, cell migration and adhesion measurements in combination with specific fluorescent assays might thus deliver a high-content platform for drug development.

LC/MS/MS warfarin assay − An emerging tool for the early detection of cytochrome P450-associated drug−drug interactions in drug discovery

The LC/MS/MS warfarin assay, combining stereo- and regioselective cytochrome P450 (CYP) form-specific warfarin hydroxylation with sensitive and specific LC/MS/MS detection technique, is emerging to be a promising tool for the study of CYP-associated drug−drug interactions for new chemical entities (NCEs) during drug discovery process.