Drug discovery strategies for acute radiation syndrome

Introduction: There are at the minimum two major, quite different approaches to advance drug discovery. The first being the target-based drug discovery (TBDD) approach that is commonly referred to as the molecular approach. The second approach is the phenotype-based drug discovery (PBDD), also known as physiology-based drug discovery or empirical approach.

Area covered: The authors discuss, herein, the need for developing radiation countermeasure agents for various sub-syndromes of acute radiation syndromes (ARS) following TBDD and PBDD approaches. With time and continuous advances in radiation countermeasure drug development research, the expectation is to have multiple radiation countermeasure agents for each sub-syndrome made available to radiation exposed victims.

Expert opinion: The majority of the countermeasures currently being developed for ARS employ the PBDD approach, while the TBDD approach is clearly under-utilized. In the future, an improved drug development strategy might be a ‘hybrid’ strategy that is more reliant on TBDD for the initial drug discovery via large-scale screening of potential candidate agents, while utilizing PBDD for secondary screening of those candidates, followed by tertiary analytics phase in order to pinpoint efficacious candidates that target the specific sub-syndromes of ARS.     

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations.

Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors.

Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor–ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS’s position in pharmaceutical research.     

Homology modeling in drug discovery: Overview,current applications,and future perspectives

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.     

Fragment‐ and negative image‐based screening of phosphodiesterase 10A inhibitors

A novel virtual screening methodology called fragment‐ and negative image‐based (F‐NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A‐specific small‐molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F‐NiB combines features from both fragment‐based drug discovery and negative image‐based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein‐bound ligand(s) are seamlessly combined with the negative image of the target's ligand‐binding cavity. This cavity‐ and fragment‐based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F‐NiB methodology, 3D quantitative structure–activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F‐NiB provides a flexible way to incorporate small‐molecule fragments into the drug discovery.     

Urinary peptidomics in kidney disease and drug research

Introduction: Due to its close connection with the renal system, urine is considered a valuable source of information in kidney disease research. Peptidomics methods focus on the discovery of endogenous peptides, given their wide range of biological functions and diagnostic and therapeutic potential. Representing a non-invasive and sensitive method, technological prospects of urinary peptidomics should be evaluated in the context of drug discovery and research.

Areas covered: This review describes urinary peptidomics with focus on its application in drug research in the field of kidney diseases. The authors provide an overview of current achievements and potential future applications.

Expert opinion: The urinary peptidome is a dynamically changing source of information, able to reflect sudden and long-term changes affecting the renal system. Studies utilizing urinary peptidomics techniques have demonstrated their value in patient stratification and detection of early pathological changes in kidney disease. Serving as a liquid biopsy, urinary peptides are an invaluable tool for drug response monitoring. Nevertheless, peptidomics is largely underexplored in drug research in general, as evidenced by the scarce number of scientific publications on this topic. Further progress will be driven by the successful validation of current discoveries and continued efforts to improve the translation of results into therapeutic applications.     

The predictive utility of the plant phylogeny in identifying sources of cardiovascular drugs

Context: Cardiovascular disease (CVD) is the number one cause of death globally, responsible for over 17 million (31%) deaths in the world. Novel pharmacological interventions may be needed given the high prevalence of CVD.

Objective: In this study, we aimed to find potential new sources of cardiovascular (CV) drugs from phylogenetic and pharmacological analyses of plant species that have experimental and traditional CV applications in the literature.

Materials and methods: We reconstructed the molecular phylogeny of these plant species and mapped their pharmacological mechanisms of action on the phylogeny.

Results: Out of 139 plant species in 71 plant families, seven plant families with 45 species emerged as phylogenetically important exhibiting common CV mechanisms of action within the family, as would be expected given their common ancestry: Apiaceae, Brassicaceae, Fabaceae, Lamiaceae, Malvaceae, Rosaceae and Zingiberaceae. Apiaceae and Brassicaceae promoted diuresis and hypotension; Fabaceae and Lamiaceae had anticoagulant/thrombolytic effects; Apiaceae and Zingiberaceae were calcium channel blockers. Moreover, Apiaceae, Lamiaceae, Malvaceae, Rosaceae and Zingiberaceae species were found to possess anti-atherosclerotic properties.

Discussion and conclusions: The phylogeny identified certain plant families with disproportionately more species, highlighting their importance as sources of natural products for CV drug discovery. Though there were some species that did not show the same mechanism within the family, the phylogeny predicts that these species may contain undiscovered phytochemistry, and potentially, the same bioactivity. Evolutionary pharmacology, as applied here, may guide and expedite our efforts in discovering sources of new CV drugs.     

Investigating Mammalian Tyrosine Phosphatase Inhibitors as Potential ‘Piggyback’ Leads to Target Trypanosoma brucei Transmission

African trypanosomiasis is a neglected tropical disease affecting humans and animals across 36 sub‐Saharan African countries. We have investigated the potential to exploit a ‘piggyback’ approach to inhibit Trypanosoma brucei transmission by targeting the key developmental regulator of transmission, T. brucei protein tyrosine phosphatase 1. This strategy took advantage of the extensive investment in inhibitors for human protein tyrosine phosphatase 1B, a key target for pharmaceutical companies for the treatment of obesity and diabetes. Structural predictions for human and trypanosome tyrosine phosphatases revealed the overall conservation of important functional motifs, validating the potential for exploiting cross specific compounds. Thereafter, nineteen inhibitors were evaluated; seventeen from a protein tyrosine phosphatase 1B‐targeted inhibitor library and two from literature analysis – oleanolic acid and suramin, the latter of which is a front line drug against African trypanosomiasis. The compounds tested displayed similar inhibitory activities against the human and trypanosome enzymes, mostly behaving as noncompetitive inhibitors. However, their activity against T. brucei in culture was low, necessitating further chemical modification to improve their efficacy and specificity. Nonetheless, the results validate the potential to explore a ‘piggyback’ strategy targeting T. brucei protein tyrosine phosphatase 1 through exploiting the large pharmacological investment in therapies for obesity targeting protein tyrosine phosphatase 1B.     

土家医药半结构化数据的功效挖掘及可视化研究＊

目的 利用数据挖掘技术对半结构化数据的土家族医药进行品种规范和常用功效的可视化发现。方法 收集整理《中国民族药辞典》、《土家医方剂学》等土家族代表性医药文献，建立土家族医药原始数据库，包含药物拉丁学名、药用部位、性味、毒性、功效、主治疾病等。借助Visual FoxPro 9.0软件对土家族医药特性进行频次统计和品种规范，主要以法定标准收录；借助TCMISS (V2.5)、Cytoscape3.6.1、Gephi0.9.2等软件对土家药“药物-功效-主治”间的语义关系，进行功效挖掘和可视化展示。结果 筛选土家药共1453味，方剂共771首，其中土家药主要以“寒（凉）、平”性偏多，苦味药频次最高，其次是辛味药、甘味药；清热败毒、赶风除湿等功效出现频次最高；皮肤疮癣、妇科疾病等是关联程度较大的主治病症。结论 土家族医药的品种规范和“药性-药味-功效-主治”隐形关系可视化分析，可为完善土家药信息化研究奠定基础。