基于片段的药物设计方法研究进展

基于片段的药物设计(FBDD)已经逐渐发展成为一种重要的药物设计新方法。FBDD利用表面等离子共振技术(SPR)、核磁共振技术(NMR)、质谱(MS)和X-射线单晶衍射等方法检测与靶蛋白相互作用的小分子片段,然后以片段为起点来设计先导化合物。与传统的药物设计方法相比,FBDD具有发现活性化合物效率高的特点,且发现的化合物具有活性强、类药性好等优点。该文结合实例综述了基于片段的药物设计方法的研究进展。     

Electrochemical approach of anticancer drugs--DNA interaction

The interaction of drugs with DNA is among the most important aspects of biological studies in drug discovery and pharmaceutical development processes. In recent years there has been a growing interest in the electrochemical investigation of interaction between anticancer drugs and DNA. Observing the pre and post electrochemical signals of DNA or drug interaction provides good evidence for the interaction mechanism to be elucidated. Also this interaction could be used for the quantification of these drugs and for the determination of new drugs targeting DNA. Electrochemical approach can provide new insight into rational drug design and would lead to further understanding of the interaction mechanism between anticancer drugs and DNA.     

The role of structure based virtual screening in the early phase of drug discovery

Identification of a viable lead is a critical step in drug discovery. The qualities of the lead set the stage for subsequent efforts to ameliorate therapeutic efficacy through potency, selectivity, pharmacokinetics, toxicity and side effects. In a retrospective view of drug research the lead identification has been realised mainly by in vivo methodologies. However, limitations of in vivo models were found to be critical factors when analysing attrition rates that prompted research groups to introduce in vitro tests and rational approaches at the frontline of discovery programs. Virtual screening (VS) methods merge in vitro high-throughput (HTS) and rational approaches. The VS methods can be classified as ligand and structure based techniques. Structure based approaches depart from the structural information of the target to identify potential interactions between the ligands and the protein. The advantages and disadvantages and the applicability of the structure based virtual screening approaches constituted the main aim of my studies. The glycogen synthase kinase 3beta (GSK-3beta), the beta-secretase and the c-jun N-terminal kinase 3 (JNK-3) were selected as primary targets for virtual screening. The performance of virtual screens can only be validated in parallel with HTS, therefore a head to head comparative analysis was my next goal.     

The role and significance of unconventional hydrogen bonds in small molecule recognition by biological receptors of pharmaceutical relevance

The discovery and optimization of nonbonded interactions, such as van der Waals interactions, hydrogen bonds, salt bridges and the hydrophobic effect, between small molecule ligands and their receptors is one of the main challenges in rational drug discovery. As the theory of molecular interactions advances more evidence accumulates that nonbonded interactions, such as unconventional hydrogen bonds (X-H...Y interactions, where X can be either C, N or O atom and Y can be either an aromatic ring system O or F atom), contribute to ligand recognition by biological receptors. This review provides an overview of unconventional hydrogen bonds between ligands and their receptors of pharmaceutical relevance by dissecting their structure activity relationships and 3D structural elements. Gaining an understanding of the energetic and the structural properties of unconventional hydrogen bonds in ligand-receptor interactions leads us to the elucidation of their practical significance. Ultimately, this enables us to consciously apply these interactions in hit and lead optimization in rational structure based drug design.     

Triad Therapeutics: integration of NMR structural determinations and smart chemistry to speed drug discovery

Triad Therapeutics’ proprietary technologies enable dramatic increases in both the speed of lead compound generation and the quality (binding affinity and specificity) of the compounds produced. Triad’s approach, called Integrated Object-oriented PharmacoEngineering (IOPE™), focuses on designing inhibitors for entire protein families, such as oxidoreductases and kinases, thereby enabling parallel discovery of inhibitors for multiple targets within a protein family. The technology uses nuclear magnetic resonance (NMR) spectroscopy-based structural information to drive inhibitor design without the need for full structures, therefore significantly expediting the drug discovery process.     

Cheminformatics in anti-infective agents discovery

The existing chemical data such as those created by high throughput screening (HTS), structure-activity relationship (SAR) studies are converted into information as a result of storage and registration. Accessibility, manipulation, and data mining of such information make up the knowledge for drug development. Cheminformatics, exploiting the combination of chemical structural knowledge, biological screening, and data mining approaches is used to guide drug discovery and development and would assist by integrating complex series of rational selection of designed compounds with drug-like properties, building smarter focused libraries. This paper presents cheminformatics approaches and tools for designing and data mining of chemical databases and information. Many examples of success in lead identification and optimization in the area of anti-infective therapy have been discussed.     

Use and value of metabolism databases

A key objective during drug discovery is to ensure selection of lead compounds for development that have the optimum pharmacodynamic profile without undesirable toxicity. Metabolism plays a key role in determining the biological activity of compounds in vivo, and is therefore an important factor to consider during the discovery phase. The ability to predict the biotransformation pathways for specific chemical structures provides the opportunity to implement structural modifications that might advantageously modify these processes. Potentially valuable tools include knowledge databases that contain searchable information on the known metabolites of existing compounds and databases that are designed to predict the metabolites.     

Fragment-based lead discovery on G-protein-coupled receptors

Introduction: G-protein-coupled receptors (GPCRs) form one of the largest groups of potential targets for novel medications. Low druggability of many GPCR targets and inefficient sampling of chemical space in high-throughput screening expertise however often hinder discovery of drug discovery leads for GPCRs. Fragment-based drug discovery is an alternative approach to the conventional strategy and has proven its efficiency on several enzyme targets. Based on developments in biophysical screening techniques, receptor stabilization and in vitro assays, virtual and experimental fragment screening and fragment-based lead discovery recently became applicable for GPCR targets.

Areas covered: This article provides a review of the biophysical as well as biological detection techniques suitable to study GPCRs together with their applications to screen fragment libraries and identify fragment-size ligands of cell surface receptors. The article presents several recent examples including both virtual and experimental protocols for fragment hit discovery and early hit to lead progress.

Expert opinion: With the recent progress in biophysical detection techniques, the advantages of fragment-based drug discovery could be exploited for GPCR targets. Structural information on GPCRs will be more abundantly available for early stages of drug discovery projects, providing information on the binding process and efficiently supporting the progression of fragment hit to lead. In silico approaches in combination with biological assays can be used to address structurally challenging GPCRs and confirm biological relevance of interaction early in the drug discovery project.     

Targeting disease, not disease targets: Innovative approaches in tackling neurodegenerative disorders

Preclinical drug investigation entails identifying and optimizing drug candidates to yield effective therapeutics with an acceptable level of adverse side effects. Inevitably, this investigation phase is bound to using model systems that mimic crucial aspects of disease biology in order to assess drug efficacy. The quality or predictability of these disease models is therefore of utmost importance to the development of successful drugs. Models should also be cost-effective and, from a biological point of view, sufficiently simple to enable molecules that act specifically (ie, that modulate a single, pre-defined target) to be identified easily and to allow for HTS. To meet these demands, typical drug discovery approaches rely heavily on biochemical assays in which the activity of a pre-defined target is reconstituted artificially. However, such a rational reductionist approach may compromise the predictability of a model because targets are assessed in an artificial environment that is deprived of any relevant biological context. Moreover, given the pre-established limits on target space and mode of action in a model, efficient and innovative drug discovery programs may be hampered. This feature article considers alternative or complementary approaches that advocate the introduction of biological context early in the drug discovery process. A case study of how NV reMYND has implemented 'biology-driven' drug discovery is presented.     

A rational approach to maximize success rate in target discovery

To overcome the problem of high attrition rates in the drug discovery process, an efficient strategy how to identify, select, characterize and validate the most suitable drug targets before embarking on the resource-intense steps of lead discovery and lead optimization is mandatory. We have implemented such an efficient strategy consisting of (i) Target Selection based on gene expression analyses of drugable target genes in clinical samples and relevant in vitro model systems, to identify candidate targets with a specific tissue distribution and presence in human disease; (ii) Target Assessment exploiting the three-dimensional structure of proteins for detailed binding site analysis, to estimate the drugability of the protein for small-molecule inhibitor binding as well as selectivity profiles; and (iii) Target Validation providing evidence for a functional role in in vitro model systems, thus corroborating the biological hypothesis underlying the therapeutic concept. This rational approach has led to the discovery of drug targets for Lead Discovery, maximizing the likelihood for achieving target-selective inhibition by small-molecule inhibitors with minimal in vivo side effects and a therapeutic effect based on a sound biological hypothesis.     

Harnessing the cyclization strategy for new drug discovery

The design of new ligands with high affinity and specificity against the targets of interest has been a central focus in drug discovery. As one of the most commonly used methods in drug discovery, the cyclization represents a feasible strategy to identify new lead compounds by increasing structural novelty, scaffold diversity and complexity. Such strategy could also be potentially used for the follow-on drug discovery without patent infringement. In recent years, the cyclization strategy has witnessed great success in the discovery of new lead compounds against different targets for treating various diseases. Herein, we first briefly summarize the use of the cyclization strategy in the discovery of new small-molecule lead compounds, including the proteolysis targeting chimeras (PROTAC) molecules. Particularly, we focus on four main strategies including fused ring cyclization, chain cyclization, spirocyclization and macrocyclization and highlight the use of the cyclization strategy in lead generation. Finally, the challenges including the synthetic intractability, relatively poor pharmacokinetics (PK) profiles and the absence of the structural information for rational structure-based cyclization are also briefly discussed. We hope this review, not exhaustive, could provide a timely overview on the cyclization strategy for the discovery of new lead compounds.     

NMR-based approaches for lead discovery

NMR methods have long been used for studying molecular interactions. In the last few years, various NMR approaches have been developed to aid lead discovery. These involve different NMR screening methods to identify initial compounds, which often bind only weakly (in the micro- to millimolar range) to the drug target. Intelligent and focused follow-up strategies enable the development of these compounds into potent, submicromolar drug-like inhibitors for use as leads in drug discovery projects. NMR can be used as both a remarkably reliable screening tool and a structural tool; thus, this technique has unique opportunities for lead discovery.     

Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions

Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based docking experiments. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein-ligand binding interactions. Key to this approach is the generation of an interaction fingerprint that translates 3D structural binding information from a protein-ligand complex into a one-dimensional binary string. Each fingerprint represents the "structural interaction profile" of the complex that can be used to organize, analyze, and visualize the rich amount of information encoded in ligand-receptor complexes and also to assist database mining. We have applied SIFt to tackle three common tasks in structure-based drug design. The first involved the analysis and organization of a typical set of results generated from a docking study. Using SIFt, docking poses with similar binding modes were identified, clustered, and subsequently compared with conventional scoring function information. A second application of SIFt was to analyze approximately 90 known X-ray crystal structures of protein kinase-inhibitor complexes obtained from the Protein Databank. Using SIFt, we were able to organize the structures and reveal striking similarities and diversity between their small molecule binding interactions. Finally, we have shown how SIFt can be used as an effective molecular filter during the virtual chemical library screening process to select molecules with desirable binding mode(s) and/or desirable interaction patterns with the protein target. In summary, SIFt shows promise to fully leverage the wealth of information being generated in rational drug design.     

Target-based drug discovery: is something wrong?

For the past decade the pharmaceutical industry has experienced a steady decline in productivity and a striking observation is that the decline coincided with the introduction of target-based drug discovery. The target-based approach can very effectively develop novel treatments for a validated target, but the process of target validation is complex and associated with a high degree of uncertainty. The purpose of this paper is to analyse these aspects in detail to determine if weaknesses in this part of the drug discovery path might explain why this paradigm has not resulted in increased productivity over the traditional in vivo approach, considering its superiority in screening capacity and its ability to define rational drug discovery programs.     

Interaction profiles of protein kinase-inhibitor complexes and their application to virtual screening

A major challenge facing structure-based drug discovery efforts is how to leverage the massive amount of experimental (X-ray and NMR) and virtual structural information generated from drug discovery projects. Many important drug targets have large numbers of protein-inhibitor complexes, necessitating tools to compare and contrast their similarities and differences. This information would be valuable for understanding potency and selectivity of inhibitors and could be used to define target constraints to assist virtual screening. We describe a profile-based approach that enables us to capture the conservation of interactions between a set of protein-ligand receptor complexes. The use of profiles provides a sensitive means to compare multiple inhibitors binding to a drug target. We demonstrate the utility of profile-based analysis of small molecule complexes from the protein-kinase family to identify similarities and differences in binding of ATP, p38, and CDK2 compounds to kinases and how these profiles can be applied to differentiate the selectivity of these inhibitors. Importantly, our virtual screening results demonstrate superior enrichment of kinase inhibitors using profile-based methods relative to traditional scoring functions. Interaction-based analysis should provide a valuable tool for understanding inhibitor binding to other important drug targets.     

The future of drug discovery: enabling technologies for enhancing lead characterization and profiling therapeutic potential

Technology often serves as a handmaiden and catalyst of invention. The discovery of safe, effective medications depends critically upon experimental approaches capable of providing high-impact information on the biological effects of drug candidates early in the discovery pipeline. This information can enable reliable lead identification, pharmacological compound differentiation and successful translation of research output into clinically useful therapeutics. The shallow preclinical profiling of candidate compounds promulgates a minimalistic understanding of their biological effects and undermines the level of value creation necessary for finding quality leads worth moving forward within the development pipeline with efficiency and prognostic reliability sufficient to help remediate the current pharma-industry productivity drought. Three specific technologies discussed herein, in addition to experimental areas intimately associated with contemporary drug discovery, appear to hold particular promise for strengthening the preclinical valuation of drug candidates by deepening lead characterization. These are: i) hydrogen–deuterium exchange mass spectrometry for characterizing structural and ligand-interaction dynamics of disease-relevant proteins; ii) activity-based chemoproteomics for profiling the functional diversity of mammalian proteomes; and iii) nuclease-mediated precision gene editing for developing more translatable cellular and in vivo models of human diseases. When applied in an informed manner congruent with the clinical understanding of disease processes, technologies such as these that span levels of biological organization can serve as valuable enablers of drug discovery and potentially contribute to reducing the current, unacceptably high rates of compound clinical failure.     

Second-site NMR screening and linker design

One of the prime merits of NMR as a tool for lead finding in drug discovery research is its sensitivity and robustness to detect weak protein-ligand interactions. This sensitivity allows to build up ligands for a given target in a modular way, by a fragment-based approach. In this approach, two ligands are seperately identified which bind to the target protein generally weakly, but at adjacent binding sites. In a next step, they are chemically linked to produce a high-affinity ligand. This review discusses methods to detect "second-site" ligands that bind to a protein in the presence of a "first-site" ligand, and methods to elucidate structural details on the spatial orientation of both ligands, so that chemical linkage is based on a large piece of experimental information. Published examples from second-site screening and linker design are summarized, and are complemented by previously unpublished in-house examples.     

NMR fragment screening: tackling protein-protein interaction targets

High-throughput screening of libraries containing compounds of 'drug-like' molecular weight has frequently resulted in no or poor drug candidates, especially when screening against demanding drug targets such as protein-protein interactions. Fragment-based lead discovery and optimization has evolved as a promising solution to this problem by combining the universal adaptability of low-molecular-weight fragments with immediate structural information on fragment binding modes. This review focuses on nuclear magnetic resonance (NMR) fragment screening techniques, which provide a unique combination of medium-throughput, direct binding site information and broad applicability. The utility and exemplary data of chemical shift-detected NMR fragment screening applied to the challenging protein-protein interaction target PDZ domains are summarized.