When analoging is not enough: scaffold discovery in medicinal chemistry

Importance of the field: As an integral part of lead generation and optimization, scaffold discovery has broad implications in drug discovery. Currently available chemical scaffolds might be inadequate to provide drug-like ligands for new targets such as phosphatases and protein-protein interactions and therapeutically useful chemical space needs to be continuously explored. New scaffolds are often desired to overcome major hurdles (e.g., potency plateau, selectivity, pharmacokinetics, etc.) in lead generation and optimization. Timely discovery of proof-of-concept compounds facilitates target validation, diversifies clinical candidates and improves the overall success rate of drug discovery. Areas covered in this review: This analysis discusses the strategies involved in finding new scaffolds (i.e., fragment-, ligand- and structure-based design) and their applications (e.g., improve potency/selectivity, multiple ligand design, protein-protein interactions, etc.) in drug discovery. What the reader will gain: The readers will learn the strategies involved in scaffold design and the problems that they solve. They will also gain the understanding of the circumstances suitable for using scaffold design. Take home message: Scaffold is defined by the authors as a biological target dependent concept. Therapeutically useful scaffolds are limited and the identification of new scaffolds is sometimes required to overcome major optimization hurdles. However, depending on the promiscuity of the binding pocket of the target and the validity of the optimization protocol, finding better scaffolds can be a challenging task. Several strategies in scaffold discovery have emerged or matured owing to recent trends such as pursuit of targets from new proteomic families, lack of validated targets, advances in synthesis and biological assays and adoption of in vitro activity-driven screening paradigms.     

The hunt for antimitotic agents: an overview of structure-based design strategies

ABSTRACT

Introduction: Structure-based drug discovery offers a rational approach for the design and development of novel anti-mitotic agents which target specific proteins involved in mitosis. This strategy has paved the way for development of a new generation of chemotypes which selectively interfere with the target proteins. The interference of these anti-mitotic targets implicated in diverse stages of mitotic cell cycle progression culminates in cancer cell apoptosis.

Areas covered: This review covers the various mitotic inhibitors developed against validated mitotic checkpoint protein targets using structure-based design and optimization strategies. The protein-ligand interactions and the insights gained from these studies, culminating in the development of more potent and selective inhibitors, have been presented.

Expert opinion: The advent of structure-based drug design coupled with advances in X-ray crystallography has revolutionized the discovery of candidate lead molecules. The structural insights gleaned from the co-complex protein-drug interactions have provided a new dimension in the design of anti-mitotic molecules to develop drugs with a higher selectivity and specificity profile. Targeting non-catalytic domains has provided an alternate approach to address cross-reactivity and broad selectivity among kinase inhibitors. The elucidation of structures of emerging mitotic drug targets has opened avenues for the design of inhibitors that target cancer.     

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.     

De novo design: balancing novelty and confined chemical space

Importance of the field: De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

Areas covered in this review: This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

What the reader will gain: The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

Take home message: De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.     

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.     

Molecular modeling of vasopressin receptor and in silico screening of V1b receptor antagonists

Introduction: G protein-coupled receptors (GPCRs) are integral membrane proteins which contain seven-transmembrane-spanning alpha-helices. GPCR-mediated signaling has been associated with various human diseases, positioning GPCRs as attractive targets in the drug discovery field. Recently, through advances in protein engineering and crystallography, the number of resolved GPCR structures has increased dramatically. This growing availability of GPCR structures has greatly accelerated structure-based drug design (SBDD) and in silico screening for GPCR-targeted drug discovery.

Areas covered: The authors introduce the current status of X-ray crystallography of GPCRs and what has been revealed from the resolved crystal structures. They also review the recent advances in SBDD and in silico screening for GPCR-targeted drug discovery and discuss a docking study, using homology modeling, with the discovery of potent antagonists of the vasopressin 1b receptor.

Expert opinion: Several innovative protein engineering techniques and crystallographic methods have greatly accelerated SBDD, not only for already-resolved GPCRs but also for those structures which remain unclear. These technological advances are expected to enable the determination of GPCR-fragment complexes, making it practical to perform fragment-based drug discovery. This paves the way for a new era of GPCR-targeted drug discovery.     

New opportunities in drug design of metalloproteinase inhibitors: combination between structure–function experimental approaches and systems biology

Introduction: MMPs (matrix metalloproteinases) and ADAMs (a disintegrin and metalloproteinases) are endopeptidases central to the degradation and remodeling of the extracellular matrix. These proteases also exhibit regulatory activity in cell signaling pathways and thus tissue homeostasis under normal conditions and in many diseases. Consequently, individual members of the MMP and ADAM protein families were identified as important therapeutic targets. However, designing effective inhibitors in vivo for this class of enzymes appears to be extremely challenging. This is attributed to the broad structural similarity of their active sites and to the dynamic functional interconnectivity of MMPs with other proteases, their inhibitors, and substrates (the so-called degradome) in healthy and disease tissues.

Areas covered: The article covers the progress in designing metalloproteinase inhibitors, based on recent advancements in our understanding of enzyme structures and their function as master regulators. It also discusses the potential of utilizing structure-based drug design strategies in conjunction with systems biology experimental approaches for designing potent and therapeutically effective metalloproteinase inhibitors.

Expert opinion: We highlight the use of protein-based drug design strategies, for example, antibodies and protein scaffolds, targeting extracatalytic domains, which are central to proteolytic and non-proteolytic enzyme functions. Such rationally designed function-blocking inhibitors may create new opportunities in disease management and in emerging therapies that require control of dysregulated MMP activity without causing severe side effects. Importantly, the lessons learned from studying these protein-based inhibitors can be implemented to design new and effective small or medium sized synthetic antagonists.     

Designing drugs with multi-target activity: the next step in the treatment of neurodegenerative disorders

Introduction: Neurodegenerative diseases have had devastating effects on patients' quality of life. These complex diseases have several pathways that are affected to initiate cell death. Current therapies, designed to address only a single target, fall short in mitigating or preventing disease progression, and disease-modifying drugs are desperately needed. Over the past several years, a new paradigm has emerged which has as a goal the targeting of multiple disease etiological pathways. Such “multi-targeted designed drugs” (MTDD) have shown great promise in preclinical studies as neuroprotective agents, as well as being able to afford symptomatic relief to blunt the day-to-day burden of these illnesses.

Areas covered: In this review, the authors evaluate the use of chemical scaffolds that led themselves exquisitely to the development of MTDDs in central nervous system disorders. Some of the examples discussed have also transitioned into the clinic, which underscores the importance of pursuing drug discovery programs within the multifunctional arena.

Expert opinion: Currently, very little can be done to slow the progress of neurodegeneration. The multifaceted profile of neurodegeneration necessitates a change in paradigm toward the design of compounds that address several drug targets simultaneously. With successful compounds in clinical trials as well as compounds moving into the clinic, support is growing and the feasibility of this approach is now becoming recognized. This review shows that several small molecule scaffolds can be successfully utilized to design MTDD compounds with good CNS pharmacokinetics.     

Multitarget ligands in antibacterial research: progress and opportunities

Introduction: Resistance to current antibacterial therapies is an inevitability that represents a significant global health concern. Bacteria have the capacity to render all current drug treatments ineffective, which places a demand on the drug discovery community to constantly develop new antibacterial agents. Compounds that inhibit multiple biological targets, often referred to as multitarget ligands, are an inviting prospect in antibacterial research because, although they will not solve the issue of resistance, they might help to delay the onset.

Areas covered: This review covers some of the recent progress in identifying new ligands that deliberately interact with more than one essential biological target in bacteria. The two principal areas covered are inhibitors of DNA replication and cell wall biosynthesis.

Expert opinion: Antibacterial programs for the design of multitarget ligands present an important opportunity for production of antibacterial agents. Their longevity, due to slow development of resistance, is comparable to that seen with other successful agents – but is much improved over single-targeted agents for which resistance can appear in vitro overnight. The preclinical development of these agents will have to overcome the standard problems of antibacterial discovery. Such problems include optimization of characteristics favoring cell entry and particularly the demonstration of selectivity of inhibition of the desired multiple targets without inhibition of other bacterial or any mammalian functions.     

New design strategies for antidepressant drugs

Introduction: In spite of research efforts spanning six decades, the most prominent antidepressant drugs to date still carry several adverse effects, often serious enough to warrant discontinuation of the drug. Molecular mechanisms of depression are now better understood such that some of the specific receptors responsible can be targeted for activation or inhibition. This advance, coupled with the recent availability of crystal structures of relevant drug targets or their homologs, has opened the door for new antidepressant therapeutic compounds.

Areas covered: The authors review the evolution of monoamine-based antidepressant drugs, up to the selective serotonin reuptake inhibitors (SSRIs). The authors discuss classic and contemporary antidepressant drug design strategies, with a focus on virtual screening and fragment-based drug design methods. Furthermore, they discuss the recent advancements in the understanding of the serotonin transporter (SERT) structure/function relationship in the context of recognition of SSRIs and outline a strategy for the use of computational approaches in producing new SSRI lead compounds.

Expert opinion: The authors suggest that given the long-awaited availability of credible three-dimensional structures for the SERT and related monoamine transporter proteins, cutting-edge computational methods should be the linchpin of future drug discovery efforts regarding monoamine-based antidepressant lead compounds. Because these transporter inhibitors cause a ubiquitous increase in extraneuronal neurotransmitter levels leading to side and adverse therapeutic effects, the drug discovery should extend to appropriate manipulation of the ‘downstream' receptors affected by the neurotransmitter boost. Efficient use of new computational strategies will accelerate the drug discovery process and reduce its economic burden.     

New approaches for pediatric rhabdomyosarcoma drug discovery: targeting combinatorial signaling

Introduction: Rhabdomyosarcomas (RMS) are rare heterogeneous pediatric tumors that are treated by surgery, chemotherapy and irradiation. New therapeutic approaches are needed, especially in the advanced stages to target the pro-oncogenic signals. Exploring the molecular interactions of the regulatory signals and their roles in the developmental aspects of different subtypes of RMS is essential to identify potential targets and develop new therapeutic drugs.

Areas covered: Insights into different drug discovery approaches are discussed with specific emphasis on gene expression profiling, fusion protein, role of small interfering RNA (siRNA)- and microRNA (miRNA)-based discovery approaches, targeting cancer stem cells, and in vitro and in vivo model systems. Targeting some overexpressed signals along with the possibilities of combination therapy of validated drug targets is discussed. Additionally, methods to overcome the limitations of discovery-based research are briefly discussed.

Expert opinion: Due to drug resistance, ineffective therapy in advanced stages and relapse, there is a demand to explore new drug targets and discovery approaches. Implementing miRNA-based profiling would reveal the extent of miR-based regulation, various biomarkers and potential targets in RMS. A suitable combination of innovative techniques and the use of model systems might assist the identification and validation of novel targets and drug discovery methods. Combining specific drugs along with type-specific target inhibition of overexpressed mRNAs through siRNA approaches would enable the development of personalized therapy.     

How can high-throughput screening deliver drugs to treat atherosclerosis?

Importance of the field: Atherosclerosis is a progressive disease that is characterized by the accumulation of lipid-rich plaques within the artery walls. Despite the past 3 decades witnessing the most significant advances in the pharmacotherapy of atherosclerosis with statins, atherosclerosis is still one of the leading causes of mortality in industrialized and developing nations. The applications of high-throughput screening (HTS) have retrieved hits and lead compounds which may be further developed to new promising therapeutics to achieve more effective reductions in the risk of cardiovascular morbidity and mortality.

Areas covered in this review: The review provides a summary of potential drug targets other than HMG-CoA reductase (primary target of statins) and their application in biochemical or cell-based HTS assays used by pharmaceutical companies and academic laboratories for anti-atherosclerotic drug discovery.

What the reader will gain: The reader will gain an overview of the HTS strategies currently used in the development of anti-atherosclerotic agents. The reader is also provided with some abortive examples in anti-atherosclerotic drug discovery as well as the associated limitations and challenges of the process that HTS delivers new drugs to treat atherosclerosis.

Take home message: HTS can assist in the efficient discovery of new drugs towards the potential targets involved in the progress of atherosclerosis.     

Discovery of potential Toxoplasma gondii CDPK1 inhibitors with new scaffolds based on the combination of QSAR and scaffold‐hopping method with in vitro validation

To discover drugs for toxoplasmosis with less side‐effects and less probability to get drug resistance is eagerly appealed for pregnant women, infant or immunocompromised patients. In this work, using TgCDPK1 as drug target, we design a method to discover new inhibitors for CDPK1 as potential drug lead for toxoplasmosis with novel scaffolds based on the combination of 2D/3D‐QSAR and scaffold‐hopping methods. All the binding sites of the potential inhibitors were checked by docking method, and only the ones that docked to the most conserved sites of TgCDPK1, which make them have less probability to get drug resistance, were remained. As a result, 10 potential inhibitors within two new scaffolds were discovered for TgCDPK1 with experimentally verified inhibitory activities in micromole level. The discovery of these inhibitors may contribute to the drug development for toxoplasmosis. Besides, the pipeline which is composed in this work as the combination of QSAR and scaffold‐hopping is simple, easy to repeat for researchers without need of in‐depth knowledge of pharmacology to get inhibitors with novel scaffolds, which will accelerate the procedure of drug discovery and contribute to the drug repurposing study.     

Progress in structure-based drug design against influenza A virus

Introduction: The 2009-H1N1 influenza pandemic has prompted new global efforts to develop new drugs and drug design techniques to combat influenza viruses. While there have been a number of attempts to provide drugs to treat influenza, drug resistance has been a major problem with only four drugs currently approved by the FDA for its treatment.

Areas covered: In this review, the drug-resistant problem of influenza A viruses is discussed and summarized. The article also introduces the experimental and computational structures of drug targeting proteins, neuraminidases, and of the M2 proton channel. Furthermore, the article illustrates the latest drug candidates and techniques of computer-aided drug design with examples of their application, including virtual in silico screening and scoring, AutoDock and evolutionary technique AutoGrow.

Expert opinion: Structure-based drug design is the inventive process for finding new drugs based on the structural knowledge of the biological target. Computer-aided drug design strategies and techniques will make drug discovery more effective and economical. It is anticipated that the recent advances in structure-based drug design techniques will greatly help scientists to develop more powerful and specific drugs to fight the next generation of influenza viruses.     

Computational modeling in melanoma for novel drug discovery

ABSTRACT

Introduction: There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches.

Areas covered: This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials.

Expert opinion: Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.     

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.