Thermodynamic studies for drug design and screening

INTRODUCTION: A key part of drug design and development is the optimization of molecular interactions between an engineered drug candidate and its binding target. Thermodynamic characterization provides information about the balance of energetic forces driving binding interactions and is essential for understanding and optimizing molecular interactions. AREAS COVERED: This review discusses the information that can be obtained from thermodynamic measurements and how this can be applied to the drug development process. Current approaches for the measurement and optimization of thermodynamic parameters are presented, specifically higher throughput and calorimetric methods. Relevant literature for this review was identified in part by bibliographic searches for the period 2004 - 2011 using the Science Citation Index and PUBMED and the keywords listed below. EXPERT OPINION: The most effective drug design and development platform comes from an integrated process utilizing all available information from structural, thermodynamic and biological studies. Continuing evolution in our understanding of the energetic basis of molecular interactions and advances in thermodynamic methods for widespread application are essential to realize the goal of thermodynamically driven drug design. Comprehensive thermodynamic evaluation is vital early in the drug development process to speed drug development toward an optimal energetic interaction profile while retaining good pharmacological properties. Practical thermodynamic approaches, such as enthalpic optimization, thermodynamic optimization plots and the enthalpic efficiency index, have now matured to provide proven utility in the design process. Improved throughput in calorimetric methods remains essential for even greater integration of thermodynamics into drug design.     

Some binding-related drug properties are dependent on thermodynamic signature

The binding affinity is determined by the Gibbs energy of binding (ΔG) which is the sum of enthalpic (ΔH) and entropic (?TΔS) contributions. Because the enthalpy and entropy contribute in an additive way to the binding energy, the same binding affinity can be achieved by many different combinations of enthalpic and entropic contributions; however, do compounds with similar binding affinities but different thermodynamic signatures (i.e., different ΔH, ?TΔS combinations) exhibit the same functional effects? Are there characteristics of compounds that can be modulated by modifying their thermodynamic signatures? In this paper, we consider the minimization of unwanted conformational effects arising during the development of CD4/gp120 inhibitors, a new class of HIV‐1 cell entry inhibitors. Competitive inhibitors of protein/protein interactions run the risk of triggering the very same signals that they are supposed to inhibit. Here, we show that for CD4/gp120 inhibitors, the magnitude of those unwanted effects is related to the proportion in which the enthalpy and entropy changes contribute to the binding affinity. The thermodynamic optimization plot (TOP) previously proposed to optimize binding affinity can also be used to obtain appropriate enthalpy/entropy combinations for drug candidates.     

The development of label-free cellular assays for drug discovery

Introduction: The need to improve drug research and development productivity continues to drive innovation in pharmacological assays. Technologies that can leverage the advantages of both molecular and phenotypic assays would hold great promise for discovery of new medicines.

Areas covered: This article briefly reviews current label-free platforms for cell-based assays and is primarily focused on fundamental aspects of these assays using dynamic mass redistribution technology as an example. The article also presents strategies for relating label-free profiles to molecular modes of actions of drugs.

Expert opinion: Emerging evidence suggests that label-free cellular assays are phenotypic in nature, yet permit molecular mechanistic deconvolution. Together with unique competency in throughput, sensitivity and pathway coverages, label-free cellular assays allow users to screen drugs against endogenous receptors in native cells (including disease relevant primary cells) and determine the molecular modes of action of drug molecules. However, there are challenges for label-free in both basic research and drug discovery: the deconvolution of the cellular and molecular mechanisms for the biosensor signatures of receptor–drug interactions, new methodologies for data analysis and the development of new biosensor technologies. These challenges will need to be met for the wide adoption of these assays in drug discovery.     

Approaching Target Selectivity by De Novo Drug Design

ABSTRACT

Introduction: The development of drug candidates with a defined selectivity profile and a unique molecular structure is of fundamental interest for drug discovery. In contrast to the costly screening of large substance libraries, the targeted de novo design of a drug by using structural information of either the biological target and/or structure–activity relationship data of active modulators offers an efficient and intellectually appealing alternative.

Areas covered: This review provides an overview on the different techniques of de novo drug design (ligand-based drug design, structure-based drug design, and fragment-based drug design) and highlights successful examples of this targeted approach toward selective modulators of therapeutically relevant targets.

Expert opinion: De novo drug design has established itself as a very efficient method for the development of potent and selective modulators for a variety of different biological target classes. The ever-growing wealth of structural data on therapeutic targets will certainly further enhance the importance of de novo design for the drug discovery process in the future. However, a consistent use of the terminology of de novo drug design in the scientific literature should be sought.     

Affinity-based screening techniques: their impact and benefit to increase the number of high quality leads

Importance of the field: The generation of new chemical leads as a starting point for drug development is a critical step in pharmaceutical drug discovery. High-throughput screening and the attached processes have rapidly evolved over the past few years to become one of the main sources for new leads by testing large compound libraries for activity against a target of interest in biochemical in vitro tests using the recombinant protein or cell-based assays. Very recently, the traditional functional assay read-out technologies are being complemented by biophysical methods which directly measure the physical interaction (affinity) between a low molecular weight compound and a target protein. These technologies are receiving increasing attention and application for affinity screening and increasingly complement and augment the more classical activity screens. Today, such biophysical techniques are applied in hit identification as well as later stages such as hit validation, optimization and lead optimization phase.

Areas covered in this review: This review focuses on the principle and application of selected affinity-based screening technologies, especially those which increasingly have been used in different phases of the lead finding process over the past few years. Furthermore, we highlight how throughput, robustness and information content of the discussed methods guide and determine their impact in lead finding and how to make the best use of them.

What the reader will gain: The reader will gain an insight into the very broad spectrum of biophysical affinity screening methods and its high potential to support the generation of new leads. As a consequence, the reader will be able to judge which affinity method is of advantage at a certain lead discovery phase.

Take home message: Biophysical methods are very powerful tools to identify new hits and/or validate/optimize a hit to a lead. Those technologies often offer novel ways of screening complementing available classical screening technologies. An integrated, holistic approach using the combination of functional read-out technologies with different biophysical methods enables a project team to efficiently promote and progress the most promising chemotypes.     

3D in vitro tissue models and their potential for drug screening

Introduction: The development of one standard, simplified in vitro three-dimensional tissue model suitable to biological and pathological investigation and drug-discovery may not yet be feasible, but standardized models for individual tissues or organs are a possibility. Tissue bioengineering, while concerned with finding methods of restoring functionality in disease, is developing technology that can be miniaturized for high throughput screening (HTS) of putative drugs. Through collaboration between biologists, physicists and engineers, cell-based assays are expanding into the realm of tissue analysis. Accordingly, three-dimensional (3D) micro-organoid systems will play an increasing role in drug testing and therapeutics over the next decade. Nevertheless, important hurdles remain before these models are fully developed for HTS.

Areas covered: We highlight advances in the field of tissue bioengineering aimed at enhancing the success of drug candidates through pre-clinical optimization. We discuss models that are most amenable to high throughput screening with emphasis on detection platforms and data modeling.

Expert opinion: Modeling 3D tissues to mimic in-vivo architecture remains a major challenge. As technology advances to provide novel methods of HTS analysis, so do potential pitfalls associated with such models and methods. We remain hopeful that integration of biofabrication with HTS will significantly reduce attrition rates in drug development.     

Latest approaches for efficient protein production in drug discovery

Introduction: Pharmaceutical research looks to discover and develop new compounds which influence the function of disease-associated proteins or respective protein–protein interactions. Various scientific methods are available to discover those compounds, such as high-throughput screening of a library comprising chemical or natural compounds and computational rational drug design. The goal of these methods is to identify the seed compounds of future pharmaceuticals through the use of these technologies and laborious experiments. For every drug discovery effort made, the possession of accurate functional and structural information of the disease-associated proteins helps to assist drug development. Therefore, the investigation of the tertiary structure of disease-associated proteins and respective protein–protein interactions at the atomic level are of crucial importance for successful drug discovery.

Areas covered: In this review article, the authors broadly outline current techniques utilized for recombinant protein production. In particular, the authors focus on bacterial expression systems using Escherichia coli as the living bioreactor.

Expert opinion: The recently developed pCold-glutathione S-transferase (GST) system is one of the best systems for soluble protein expression in E. coli. Where the pCold-GST system does not succeed, it is preferable to change the host from E. coli to higher organisms such as yeast expression systems like Pichia pastoris and Kluyveromyces lactis. The selection of an appropriate expression system for each desired protein and the optimization of experimental conditions significantly contribute toward the successful outcome of any drug discovery study.     

Strategies of multi-objective optimization in drug discovery and development

Introduction: Drug discovery and development is a typical multi-objective problem and its successes or failures depend on the simultaneous control of numerous, often conflicting, molecular and pharmacological properties. Multi-objective optimization strategies represent a new approach to capture the occurrence of varying optimal solutions based on trade-offs among the objectives taken into account. In view of this, multi-objective optimization aims to discover a set of satisfactory compromises that may in turn be used to find the global optimal solution by optimizing numerous dependent properties simultaneously.

Areas covered: The authors review the potential of multi-objective strategies in a number of fields including: drug library design; substructure mining; the derivation of quantitative structure–activity relationship models; ranking of docking poses. The authors also discuss the potential of multi-objective strategies in controlling competing properties for absorption, distribution, metabolism and elimination/toxicity optimization.

Expert opinion: It is very clear to those who work in drug discovery and development that the success of rational drug design is largely dependent on the control of a number of, often conflicting, objectives. Therefore, multi-objective optimization methods, which have recently been introduced to the field of molecular discovery, represent the ultimate frontier in chemoinformatics. The widespread use of these multi-objective techniques has provided new opportunities in medicinal chemistry as seen through its use in a number of applications for chemoinformatics both within academia and the pharmaceutical industry.     

Mathematical modeling for novel cancer drug discovery and development

Introduction: Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments.

Areas covered: This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment.

Expert opinion: Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.     

3D-QSAR and molecular docking for the discovery of ketolide derivatives

Introduction: There is an urgent need to discover novel antibiotics to overcome the growing problem of antibiotic resistance, which has become a serious concern in current medicine. Ketolides, the third generation of macrolide antibiotics, have shown promising effect against macrolide-resistant pathogens in respiratory diseases. Currently, a number of ketolide derivatives with excellent antibacterial activities have been reported, while their structure–activity relationships (SARs) were rarely explored systematically. Computer-aided drug design (CADD) such as 3D-QSAR and molecular docking are useful tools to study drug SARs in medicinal chemistry. Using these technologies, ketolide derivatives were systemically analyzed revealing important useful information about their SARs, providing useful information which can guide new drug design and optimization.

Areas covered: The authors provide an overview of the currently reported 3D-QSAR models of ketolide derivatives. The authors present a comprehensive SAR model obtained from in-depth 3D-QSAR and molecular docking analysis for all kinds of ketolide derivatives.

Expert opinion: 3D-QSAR has been shown to be a reliable tool that had successfully assisted the design of several new antibiotics with improved activity and reduced toxicity. By applying 3D-QSAR and molecular docking, a comprehensive and systematic SAR model for ketolide derivative discovery was formed, which is important to guide future drug design for the discovery of better ketolides with lower toxicity.     

Hot-spot analysis for drug discovery targeting protein-protein interactions

Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions.

Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions.

Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.     

Molecular dynamics simulations and novel drug discovery

Introduction: Molecular dynamics (MD) simulations can provide not only plentiful dynamical structural information on biomacromolecules but also a wealth of energetic information about protein and ligand interactions. Such information is very important to understanding the structure-function relationship of the target and the essence of protein–ligand interactions and to guiding the drug discovery and design process. Thus, MD simulations have been applied widely and successfully in each step of modern drug discovery.

Areas covered: In this review, the authors review the applications of MD simulations in novel drug discovery, including the pathogenic mechanisms of amyloidosis diseases, virtual screening and the interaction mechanisms between drugs and targets.

Expert opinion: MD simulations have been used widely in investigating the pathogenic mechanisms of diseases caused by protein misfolding, in virtual screening, and in investigating drug resistance mechanisms caused by mutations of the target. These issues are very difficult to solve by experimental methods alone. Thus, in the future, MD simulations will have wider application with the further improvement of computational capacity and the development of better sampling methods and more accurate force fields together with more efficient analysis methods.     

Combinatorial design of biomaterials for drug delivery: opportunities and challenges

Background: The rational design of biodegradable polymeric devices for controlled drug delivery and tissue engineering is an important area of research for advancing new therapies for cancer, diabetes and immune-related disorders. In an era of escalating costs for discovery-based research, there is an urgent need to develop new and rapid methods to design drug delivery systems. Objective/methods: By merging this field of study with rapid and high throughput methods of design, optimization and development, researchers have been able to accelerate the discovery and design processes for these devices. Combinatorial research enables the rapid identification of key regions of interest. Conclusion: This review focuses on the opportunities and challenges in the area of combinatorial biomaterials design for drug delivery, as there has been a great deal of significant progress over the past decade to propel this approach for the rational design of biomaterials.     

Study of Binding Thermodynamics in the Optimization of BH3 Mimetics

The use of small molecule B‐cell lymphoma 2 homology domain 3 mimetics to neutralize the B‐cell lymphoma 2 protein is an attractive strategy for cancer treatment due to its ability to cause targeted cell apoptosis. We have previously reported the design and optimization of a series of B‐cell lymphoma 2 homology domain 3‐mimetics, called compounds 1 – 6 . In this study, we evaluated the optimization of B‐cell lymphoma 2 homology domain 3‐mimetics from a thermodynamic perspective. Understanding the thermodynamic parameters of B‐cell lymphoma 2 homology domain 3‐mimetics plays a critical role in the development of B‐cell lymphoma 2 small‐molecule inhibitors. The thermodynamic parameters for the interactions of these compounds with the myeloid cell leukemia sequence 1 protein were obtained using isothermal titration calorimetry. Owing to compounds 1 – 6 overcoming enthalpy–entropy compensation, the affinities of them improved gradually. Toward binding to the myeloid cell leukemia sequence 1 protein, compound 6 was deemed optimal with an obtained K_d value of 238 nm , which is a 10⁴‐fold improvement compared with 1 . Analysis of the enthalpy and ?TΔS efficiencies showed that ligand efficiencies with respect to molecular size are correlated with the enthalpic efficiencies. Notably, an enthalpy gain of 4.65 kcal/mol identified that an additional hydrogen bond is formed by 2 with myeloid cell leukemia sequence 1 compared with compound 1 . For the first time, hydrogen bonding between a small‐molecule inhibitor of B‐cell lymphoma 2 was demonstrated experimentally.     

Ariadne's ChemEffect and Pathway Studio knowledge base

Importance of the field: Drug discovery and development is a very complex and costly process. Understanding the detailed molecular mechanisms of a disease and drug actions can make it more efficient not only for new target discovery but also for lead prioritization, drug repositioning and development of biomarkers for drug efficacy and safety. Access to formalized knowledge about functions of proteins and small molecules is crucial for rationalization of the drug development process, and scientific publications are the main source of this knowledge. Protein knowledge networks capturing protein functions, protein–protein relations and organization of proteins in complex cellular sub-systems are making their way into modern drug discovery. Chemical networks representing multiple aspects of chemical functional information integrated into a protein systems biology network is even more advanced and promising paradigm.

Areas covered in this review: This review describes utilization of literature-derived protein and chemical functional knowledge bases in drug development.

What the reader will gain: Readers will gain an understanding of how integrated protein and chemical knowledge networks can be used for understanding and building the models of cellular events, disease mechanisms, and drug actions, finding biomarkers of drug efficacy and safety, as well as interpretation of high-throughput gene expression, proteomic and metabolomic experiments.

Take home message: Integrated literature-derived protein and chemical knowledge bases can rationalize many aspects of drug development process including drug repositioning and biomarker design.     

How Much Binding Affinity Can be Gained by Filling a Cavity?

Binding affinity optimization is critical during drug development. Here, we evaluate the thermodynamic consequences of filling a binding cavity with functionalities of increasing van der Waals radii (–H, –F, –Cl, and CH₃) that improve the geometric fit without participating in hydrogen bonding or other specific interactions. We observe a binding affinity increase of two orders of magnitude. There appears to be three phases in the process. The first phase is associated with the formation of stable van der Waals interactions. This phase is characterized by a gain in binding enthalpy and a loss in binding entropy, attributed to a loss of conformational degrees of freedom. For the specific case presented in this article, the enthalpy gain amounts to − 1.5 kcal/mol while the entropic losses amount to +0.9 kcal/mol resulting in a net 3.5-fold affinity gain. The second phase is characterized by simultaneous enthalpic and entropic gains. This phase improves the binding affinity 25-fold. The third phase represents the collapse of the trend and is triggered by the introduction of chemical functionalities larger than the binding cavity itself [CH(CH₃)₂]. It is characterized by large enthalpy and affinity losses. The thermodynamic signatures associated with each phase provide guidelines for lead optimization.     

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.     

Focusing on shared subpockets – new developments in fragment-based drug discovery

Introduction: Protein–protein interactions (PPIs) are important targets for understanding fundamental biology and for the development of therapeutic agents. Based on different physicochemical properties, numerous pieces of software (e.g., POCKETQUERY, ANCHORQUERY and FTMap) have been reported to find pockets on protein surfaces and have applications in facilitating the design and discovery of small-molecular-weight compounds that bind to these pockets.

Areas covered: The authors discuss a pocket-centric method of analyzing PPI interfaces, which prioritize their pockets for small-molecule drug discovery and the importance of multicomponent reaction chemistry as starting points for undruggable targets. The authors also provide their perspectives on the field.

Expert opinion: Only the tight interplay of efficient computational methods capable of screening a large chemical space and fast synthetic chemistry will lead to progress in the rational design of PPI antagonists in the future. Early drug discovery platforms will also benefit from efficient rapid feedback loops from early clinical research back to molecular design and the medicinal chemistry bench.     

Molecular topology as a novel approach for drug discovery

Introduction: Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. One key part of MT is that, in the process of drug design/discovery, there is no need for an explicit knowledge of a drug's mechanism of action unlike other drug discovery methods.

Areas covered: In this review, the authors introduce the topic by explaining briefly the most common methodology used today in drug design/discovery and address the most important concepts of MT and the methodology followed (QSAR equations, LDA, etc.). Furthermore, the significant results achieved, from this approach, are outlined and discussed.

Expert opinion: The results outlined herein can be explained by considering that MT represents a new paradigm in the field of drug design. This means that it is not only an alternative method to the conventional methods, but it is also independent, that is, it represents a pathway to connect directly molecular structure with the experimental properties of the compounds (particularly drugs). Moreover, the process can be realized also in the reverse pathway, that is, designing new molecules from their topological pattern, what opens almost limitless expectations in new drugs development, given that the virtual universe of molecules is much greater than that of the existing ones.