The application of quantum mechanics in structure-based drug design

Introduction: Computational chemistry has become an established and valuable component in structure-based drug design. However the chemical complexity of many ligands and active sites challenges the accuracy of the empirical potentials commonly used to describe these systems. Consequently, there is a growing interest in utilizing electronic structure methods for addressing problems in protein–ligand recognition.

Areas covered: In this review, the authors discuss recent progress in the development and application of quantum chemical approaches to modeling protein–ligand interactions. The authors specifically consider the development of quantum mechanics (QM) approaches for studying large molecular systems pertinent to biology, focusing on protein–ligand docking, protein–ligand binding affinities and ligand strain on binding.

Expert opinion: Although computation of binding energies remains a challenging and evolving area, current QM methods can underpin improved docking approaches and offer detailed insights into ligand strain and into the nature and relative strengths of complex active site interactions. The authors envisage that QM will become an increasingly routine and valued tool of the computational medicinal chemist.     

X-ray crystallography over the past decade for novel drug discovery – where are we heading next?

Introduction: Macromolecular X-ray crystallography has been the primary methodology for determining the three-dimensional structures of proteins, nucleic acids and viruses. Structural information has paved the way for structure-guided drug discovery and laid the foundations for structural bioinformatics. However, X-ray crystallography still has a few fundamental limitations, some of which may be overcome and complemented using emerging methods and technologies in other areas of structural biology.

Areas covered: This review describes how structural knowledge gained from X-ray crystallography has been used to advance other biophysical methods for structure determination (and vice versa). This article also covers current practices for integrating data generated by other biochemical and biophysical methods with those obtained from X-ray crystallography. Finally, the authors articulate their vision about how a combination of structural and biochemical/biophysical methods may improve our understanding of biological processes and interactions.

Expert opinion: X-ray crystallography has been, and will continue to serve as, the central source of experimental structural biology data used in the discovery of new drugs. However, other structural biology techniques are useful not only to overcome the major limitation of X-ray crystallography, but also to provide complementary structural data that is useful in drug discovery. The use of recent advancements in biochemical, spectroscopy and bioinformatics methods may revolutionize drug discovery, albeit only when these data are combined and analyzed with effective data management systems. Accurate and complete data management is crucial for developing experimental procedures that are robust and reproducible.     

The future of crystallography in drug discovery

Introduction: X-ray crystallography plays an important role in structure-based drug design (SBDD), and accurate analysis of crystal structures of target macromolecules and macromolecule–ligand complexes is critical at all stages. However, whereas there has been significant progress in improving methods of structural biology, particularly in X-ray crystallography, corresponding progress in the development of computational methods (such as in silico high-throughput screening) is still on the horizon. Crystal structures can be overinterpreted and thus bias hypotheses and follow-up experiments. As in any experimental science, the models of macromolecular structures derived from X-ray diffraction data have their limitations, which need to be critically evaluated and well understood for structure-based drug discovery.

Areas covered: This review describes how the validity, accuracy and precision of a protein or nucleic acid structure determined by X-ray crystallography can be evaluated from three different perspectives: i) the nature of the diffraction experiment; ii) the interpretation of an electron density map; and iii) the interpretation of the structural model in terms of function and mechanism. The strategies to optimally exploit a macromolecular structure are also discussed in the context of ‘Big Data' analysis, biochemical experimental design and structure-based drug discovery.

Expert opinion: Although X-ray crystallography is one of the most detailed ‘microscopes' available today for examining macromolecular structures, the authors would like to re-emphasize that such structures are only simplified models of the target macromolecules. The authors also wish to reinforce the idea that a structure should not be thought of as a set of precise coordinates but rather as a framework for generating hypotheses to be explored. Numerous biochemical and biophysical experiments, including new diffraction experiments, can and should be performed to verify or falsify these hypotheses. X-ray crystallography will find its future application in drug discovery by the development of specific tools that would allow realistic interpretation of the outcome coordinates and/or support testing of these hypotheses.     

Targeted nanotheranostics for personalized cancer therapy

Introduction: The development of nanomedicine, during the last 10 years have given rise to novel delivery systems among which multifunctional platforms called nanotheranostics that are designed to simultaneously diagnose and cure cancer. These systems can be built using the large panel of biocompatible and biodegradable materials. The recent advances of imaging modalities even enable targeted nanotheranostics to probe molecular structures on specific cells opening the doors to personalized cancer therapy.

Areas covered: This review presents the different requirements nanotheranostics should fulfill to achieve an optimized anticancer therapy. It focuses on two imaging modalities: MRI and ultrasonography used to visualize drug delivery, release, and efficacy. The advantages and limitations of these two methods are considered. The review will enable the readers to virtually tune a nanotheranostic system according to the nature of the targeting tissue and the availability of imaging modality.

Expert opinion: Despite great perspectives, described for nanotheranostic systems in personalized cancer therapy, the imaging techniques still face technological issues, such as high sensitivity and good spatial and temporal resolutions. Active targeting should consider better specificity and low immunogenicity of the ligand selected, to be more efficient.     

Testing for drug-human serum albumin binding using fluorescent probes and other methods

ABSTRACT

Introduction: Drug plasma protein binding remains highly relevant to research and drug development, making the assessment and profiling of compound affinity to plasma proteins essential to drug discovery efforts. Although there are a number of fully-characterized methods, they lack the throughput to handle large numbers of compounds. As the evaluation of adsorption, distribution, metabolism, and excretion is addressed earlier in the drug development timeline, the need for higher-throughput methods has grown.

Areas Covered: This review will highlight recent developments on methods for profiling drug plasma binding, with an emphasis on fluorescent probes and emerging high-throughput methodologies.

Expert Opinion: There have been a number of high-throughput assays developed in recent years to meet the scaled up demands for compound profiling. Ultimately, the selection of assay technology relies on a number of factors, such as capabilities of the laboratory and the breadth and amount of data required. Fluorescent probe displacement assays are highly flexible and amenable to high-throughput screening, easily scaling up to handle large compound libraries. Recent developments in fluorescence technologies, such as homogenous time-resolved fluorescence and probes utilizing the aggregation-induced emission effect, have improved the sensitivity of these assays. Other technologies, such as microscale thermophoresis and quantitative structure-activity relationship modeling, are gaining popularity as alternative techniques for drug plasma protein binding characterization.     

Using virtual reality for drug discovery: a promising new outlet for novel leads

ABSTRACT

Introduction: Virtual reality (VR) environments are increasingly being used by researchers in various fields in addition to being increasingly integrated into various areas of human life, ranging from videogames to different industrial uses. VR can be used to create interactive and multimodal sensory stimuli and thus offers unique advantages over other computer-based approaches for scientific research and molecular-level applications. Consequently, VR is starting to be used in novel drug development, such as in drug discovery, and rational drug design.

Areas covered: In this review, the authors discuss the basic development of VR technology, including the available hardware and software. The latest advances of VR technology in novel drug development are then detailed, and the VR programs that can be applied in relevant studies are highlighted.

Expert opinion: VR will lead to a revolution in pharmaceutical development. However, there are still obstacles to the successful and extensive application of VR to drug development, including the demand for further improvements to the available hardware and software and the various limitations described with regard to accuracy and precision. As technology continues to improve, the barriers to the widespread adoption of VR will diminish and VR technologies will play an increasingly important role in novel drug development.     

Opportunities to minimise risk in drug discovery and development

ABSTRACT

Introduction: Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.

Areas covered: In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.

Expert opinion: The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.     

Macromolecular target prediction by self-organizing feature maps

Introduction: Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the ‘de-orphaning’ of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes.

Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique.

Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.     

Future challenges with DNA-encoded chemical libraries in the drug discovery domain

ABSTRACT

Introduction: DNA-encoded chemical libraries (DELs) have come of age and emerged to become a powerful technology platform for ligand discovery in biomedical research and drug discovery. Today, DELs have been widely adopted in the pharmaceutical industry and employed in drug discovery programs worldwide. DELs are capable of interrogating drug targets with an extremely large number of compounds highly efficiently.

Area covered: In this review, the authors introduce the history of DELs and provide an overview of the major technological components, including encoding methods, library synthesis, chemistry, selection methods, hit deconvolution strategy, and post-selection data analysis. A brief update on the hit compounds recently discovered from DEL selections against drug targets is also provided. Finally, the authors discuss their views on the present challenges and future directions for the development and application of DELs in drug discovery.

Expert opinion: DELs have provided great opportunities for lead compound discovery at an unprecedented scale and efficiency in drug discovery. The key to the future success of DELs as true discovery modalities, rather than just ‘a way to make many compounds,’ is to go beyond physical binding to functional or even phenotypic assays with the capability to probe the biological system.     

Recent advances in protein–protein interaction prediction: experimental and computational methods

Introduction: Proteins within the cell act as part of complex networks, which allow pathways and processes to function. Therefore, understanding how proteins interact is a significant area of current research.

Areas covered: This review aims to present an overview of key experimental techniques (yeast two-hybrid, tandem affinity purification and protein microarrays) used to discover protein–protein interactions (PPIs), as well as to briefly discuss certain computational methods for predicting protein interactions based on gene localization, phylogenetic information, 3D structural modeling or primary protein sequence data. Due to the large-scale applicability of primary sequence-based methods, the authors have chosen to focus on this strategy for our review. There is an emphasis on a recent algorithm called Protein Interaction Prediction Engine (PIPE) that can predict global PPIs. The readers will discover recent advances both in the practical determination of protein interaction and the strategies that are available to attempt to anticipate interactions without the time and costs of experimental work.

Expert opinion: Global PPI maps can help understand the biology of complex diseases and facilitate the identification of novel drug target sites. This study describes different techniques used for PPI prediction that we believe will significantly impact the development of the field in a new future. We expect to see a growing number of similar techniques capable of large-scale PPI predictions.     

Medical devices modified at the surface by γ-ray grafting for drug loading and delivery

Importance of the field: Medical devices with the capability of hosting drugs are being sought for prophylaxis and treatment of inflammatory response and microbial colonization and proliferation that are associated with their use.

Areas covered in this review: This review analyzes the interest of γ-ray irradiation for providing medical devices with surfaces able to load drugs and to deliver them in a controlled way. The papers published in the last 20 years on the subject of γ-ray irradiation methods for surface functionalization of polymers and their application for developing medicated medical devices are discussed.

What the reader will gain: The information reported may help to gain insight to the state-of-the-art of γ-ray irradiation approaches and their current advantages/limitations for tailoring the surface of medical devices to fit preventive and curative demands.

Take home message: Grafting of polymer chains able to establish specific interactions with the drug, grafting of stimuli-responsive networks that regulate drug diffusion through the hydrogel-type surface as a function of the surrounding conditions, and grafting of cyclodextrins that control uptake and delivery through the affinity constant of inclusion complexes have been revealed as efficient approaches for endowing medical devices with the capability of also acting as drug delivery systems.     

Selecting oral bioavailability enhancing formulations during drug discovery and development

Introduction: The role of chemical structure, lipophilicity, physico-chemical, absorption, distribution, metabolism, excretion, toxicity (ADMET) and biopharmaceutical properties of compounds including bioavailability are critical in drug discovery and drug dosage forms design.

Areas covered: The authors discuss a number of parameters including computational approaches used for selected chemical structures with biological activity for lead optimization and chemogenomics and preclinical studies for ADMET process development of ligand properties. The authors also look at a number of other parameters including: early drug product formulations with method selection based on the biopharmaceutical classification system (BCS); in vitro–in vivo correlation (IVIVC) and different formulation strategies to enhance solubility; dissolution rate and permeability; bioavailability evaluation and quality by design as an opportunity to develop ‘safe space' regions, where bioavailability is unaffected by pharmaceutical variations.

Expert opinion: The biopharmaceutical requirements for absorption are solubility and permeability. Both are influenced by lipophilicity, but in the opposite way. The genomic methodology, coupled with combinatorial chemistry, high-throughput screening, structure-based design and in silico ADMET would yield parameters as a starting point for the biopharmaceutical properties determination in further preclinical and clinical studies. Consecutive stages in drug discovery and development are irreplaceable, but pharmacokinetics is the critical step. Selection of drug formulations based on the BCS, IVIVC are the principal aspects to enhance the solubility and dissolution rate, while a rationale management of pharmaceutical and technological factors will enhance the bioavailability.     

In silico prediction of human serum albumin binding for drug leads

Introduction: Binding of drugs to human serum albumin (HSA) strongly influences their pharmacokinetic behavior and is associated with drug safety issues, low clearance, low brain penetration, as well as drug-drug interactions. Thus, in silico prediction of HSA binding contributes significantly to the discovery of new drug candidates.

Areas covered: The authors provide a short overview on the principles of HSA binding and the crystal structure of HSA, as well as discussing and analyzing the recent structure- and ligand-based HSA binding models. The authors also present the advantages and limitations of each methodology to construct efficient local or global models and outline the critical structural features contributing to HSA.

Expert opinion: The in silico estimation of drug binding to HSA in early drug discovery contributes to the lead optimization process. Local models are useful for the design of new compounds with reduced HSA binding for a particular target receptor, while real-time quantitative structure-activity relationships or global models combining structure- and ligand-based approaches serve for compound libraries screening. However, research efforts on other important plasma proteins should be strengthened in the perspective to enable predictions of total plasma protein binding for clinical candidates.     

Identification of protein binding partners of small molecules using label-free methods

ABSTRACT

Introduction: Drug discovery efforts across the globe are chasing new drug targets and novel mechanisms of action. To support the identification of novel mechanisms of action, phenotype-based drug screening has significantly increased over the last decade. Along with the rise in phenotypic screening, methods and technologies that can help to identify drug targets of phenotypically screened ‘hits’ have also evolved significantly.

Areas covered: This article provides an overview of successful examples, limitations and advances in small-molecule target identification methodologies. Primarily, the methods are described, where small-molecules without derivatization are used as test-molecules for identifying their direct binding protein partners, the targets, in detail. A brief discussion of other affinity chromatography coupled mass-spectrometry based target identification methods are also presented for comparative appreciation of label-free methods.

Expert opinion: Label-free methods do not require (a) extensive structure activity analysis of phenotypically screened ‘hits’ and (b) preparation of tool compounds or target capturing probes for target identification. These methods are significantly shortening the time required for the identification and the downstream validation of targets and hence are gaining popularity as the method of choice for target identification.     

An outlook on using serial femtosecond crystallography in drug discovery

ABSTRACT

Introduction: X-ray crystallography has made important contributions to modern drug development but its application to many important drug targets has been extremely challenging. The recent emergence of X-ray free electron lasers (XFELs) and advancements in serial femtosecond crystallography (SFX) have offered new opportunities to overcome limitations of traditional crystallography to accelerate the structure-based drug discovery (SBDD) process.

Areas covered: In this review, the authors describe the general principles of X-ray generation and the main properties of XFEL beams, outline details of SFX data collection and processing, and summarize the progress in the development of associated instrumentation for sample delivery and X-ray detection. An overview of the SFX applications to various important drug targets such as membrane proteins is also provided.

Expert opinion: While SFX has already made clear advancements toward the understanding of the structure and dynamics of several major drug targets, its robust application in SBDD still needs further developments of new high-throughput techniques for sample production, automation of crystal delivery and data collection, as well as for processing and storage of large amounts of data. The expansion of the available XFEL beamtime is a key to the success of SFX in SBDD.     

The application of absolute quantitative 1H NMR spectroscopy in drug discovery and development

ABSTRACT

Introduction: The identification of a drug candidate and its structural determination is the most important step in the process of the drug discovery and for this, nuclear magnetic resonance (NMR) is one of the most selective analytical techniques.

Area covered: The present review illustrates the various perspectives of absolute quantitative ¹H NMR spectroscopy in drug discovery and development. It deals with the fundamentals of quantitative NMR (qNMR), the physiochemical properties affecting qNMR, and the latest referencing techniques used for quantification. The precise application of qNMR during various stages of drug discovery and development, namely natural product research, drug quantitation in dosage forms, drug metabolism studies, impurity profiling and solubility measurements is elaborated. To achieve this, the authors explore the literature of NMR in drug discovery and development between 1963 and 2015. It also takes into account several other reviews on the subject.

Expert opinion: qNMR experiments are used for drug discovery and development processes as it is a non-destructive, versatile and robust technique with high intra and interpersonal variability. However, there are several limitations also. qNMR of complex biological samples is incorporated with peak overlap and a low limit of quantification and this can be overcome by using hyphenated chromatographic techniques in addition to NMR.