Discovery of Novel Liver-Stage Antimalarials through Quantum Similarity

Without quantum theory any understanding of molecular interactions is incomplete. In principal, chemistry, and even biology, can be fully derived from non-relativistic quantum mechanics. In practice, conventional quantum chemical calculations are computationally too intensive and time consuming to be useful for drug discovery on more than a limited basis. A previously described, original, quantum-based computational process for drug discovery and design bridges this gap between theory and practice, and allows the application of quantum methods to large-scale in silico identification of active compounds. Here, we show the results of this quantum-similarity approach applied to the discovery of novel liver-stage antimalarials. Testing of only five of the model-predicted compounds in vitro and in vivo hepatic stage drug inhibition assays with P. berghei identified four novel chemical structures representing three separate quantum classes of liver-stage antimalarials. All four compounds inhibited liver-stage Plasmodium as a single oral dose in the quantitative PCR mouse liver-stage sporozoites-challenge model. One of the newly identified compounds, cethromycin [ABT-773], a macrolide-quinoline hybrid, is a drug with an extensive (over 5,000 people) safety profile warranting its exploitation as a new weapon for the current effort of malaria eradication. The results of our molecular modeling exceed current state-of-the-art computational methods. Drug discovery through quantum similarity is data-driven, agnostic to any particular target or disease process that can evaluate multiple phenotypic, target-specific, or co-crystal structural data. This allows the incorporation of additional pharmacological requirements, as well as rapid exploration of novel chemical spaces for therapeutic applications.     

Pin1 inhibitors: Pitfalls,progress and cellular pharmacology

Compelling data supports the hypothesis that Pin1 inhibitors will be useful for the therapy of cancer: Pin1 deficient mice resist the induction of breast cancers normally evoked by expression of MMTV-driven Ras or Erb2 alleles. While Pin1 poses challenges for drug discovery, several groups have identified potent antagonists by structure based drug design, significant progress has been made designing peptidic inhibitors and a number of natural products have been found that blockade Pin1, notably epigallocatchechin gallate (EGCG), a major flavonoid in green tea. Here we critically discuss the modes of action and likely specificity of these compounds, concluding that a suitable chemical biology tool for probing the function of Pin1 has yet to be found. We conclude by outlining some open questions regarding the target validation of Pin1 and the prospects for identification of improved inhibitors in the future.     

A new strategy for improved secondary screening and lead optimization using high-resolution SPR characterization of compound-target interactions

Biophysical label-free assays such as those based on SPR are essential tools in generating high-quality data on affinity, kinetic, mechanistic and thermodynamic aspects of interactions between target proteins and potential drug candidates. Here we show examples of the integration of SPR with bioinformatic approaches and mutation studies in the early drug discovery process. We call this combination 'structure-based biophysical analysis'. Binding sites are identified on target proteins using information that is either extracted from three-dimensional structural analysis (X-ray crystallography or NMR), or derived from a pharmacore model based on known binders. The binding site information is used for in silico screening of a large substance library (e.g. available chemical directory), providing virtual hits. The three-dimensional structure is also used for the design of mutants where the binding site has been impaired. The wild-type target and the impaired mutant are then immobilized on different spots of the sensor chip and the interactions of compounds with the wild-type and mutant are compared in order to identify selective binders for the binding site of the target protein. This method can be used as a cost-effective alternative to high-throughput screening methods in cases when detailed binding site information is available. Here, we present three examples of how this technique can be applied to provide invaluable data during different phases of the drug discovery process.     

Overview of strategies for addressing BRIs in drug discovery: Impact on optimization and design

The sensitive and specific detection of adducts derived from reactive intermediates during discovery metabolite profiling has been made feasible by advances in LC-MS/MS instrumentation. Many companies employ screens with nucleophilic trapping agents as a routine part of early screening efforts. Although certainly not as straightforward as initial adduct detection, the positives in the profiling experiment can be followed-up with determination of exact adduct structure. This information feeds naturally into drug design efforts as the structural motifs responsible for reactive metabolite formation can be altered to reduce the property. While the process of generation of reactive metabolite data has become more straightforward, the conversion of that data into an optimization paradigm remains challenging. Recent studies have shown a very loose correlation between extent of reactive metabolite formation and observed toxicity, so setting stringent criteria likely leads to discarding compounds that would not have problems. On the other hand, the central role of reactive metabolites in most accepted mechanisms of drug-induced toxicity points to the fact that there is value in minimizing the property. Decision making based on information on reactive metabolite formation remains a difficult process in all phases of drug discovery and development. Decisions on compounds in discovery can be made based on a fixed threshold value or relative to a reference point within a chemical series, but should be made with a firm understanding of the limitation of the data.     

Statistical practice in high-throughput screening data analysis

High-throughput screening is an early critical step in drug discovery. Its aim is to screen a large number of diverse chemical compounds to identify candidate 'hits' rapidly and accurately. Few statistical tools are currently available, however, to detect quality hits with a high degree of confidence. We examine statistical aspects of data preprocessing and hit identification for primary screens. We focus on concerns related to positional effects of wells within plates, choice of hit threshold and the importance of minimizing false-positive and false-negative rates. We argue that replicate measurements are needed to verify assumptions of current methods and to suggest data analysis strategies when assumptions are not met. The integration of replicates with robust statistical methods in primary screens will facilitate the discovery of reliable hits, ultimately improving the sensitivity and specificity of the screening process.     

A fluorescence polarization assay for inhibitors of Hsp90

Hsp90 encodes a ubiquitous molecular chaperone protein conserved among species which acts on multiple substrates, many of which are important cell-signaling proteins. Inhibition of Hsp90 function has been promoted as a mechanism to degrade client proteins involved in tumorigenesis and disease progression. Several assays to monitor inhibition of Hsp90 function currently exist but are limited in their use for a drug discovery campaign. Using data from the crystal structure of an initial hit compound, we have developed a fluorescence polarization assay to monitor binding of compounds to the ATP-binding site of Hsp90. This assay is very robust (Z' > 0.9) and can detect affinity of compounds with IC50s to 40 nM. We have used this assay in conjunction with cocrystal structures of small molecules to drive a structure-based design program aimed at the discovery and optimization of a novel class of potent Hsp90 inhibitors.     

High-throughput siRNA-based functional target validation

The drug discovery process pursued by major pharmaceutical companies for many years starts with target identification followed by high-throughput screening (HTS) with the goal of identifying lead compounds. To accomplish this goal, significant resources are invested into automation of the screening process or HTS. Robotic systems capable of handling thousands of data points per day are implemented across the pharmaceutical sector. Many of these systems are amenable to handling cell-based screening protocols as well. On the other hand, as companies strive to develop innovative products based on novel mechanisms of action(s), one of the current bottlenecks of the industry is the target validation process. Traditionally, bioinformatics and HTS groups operate separately at different stages of the drug discovery process. The authors describe the convergence and integration of HTS and bioinformatics to perform high-throughput target functional identification and validation. As an example of this approach, they initiated a project with a functional cell-based screen for a biological process of interest using libraries of small interfering RNA (siRNA) molecules. In this protocol, siRNAs function as potent gene-specific inhibitors. siRNA-mediated knockdown of the target genes is confirmed by TaqMan analysis, and genes with impacts on biological functions of interest are selected for further analysis. Once the genes are confirmed and further validated, they may be used for HTS to yield lead compounds.     

Structural chemoproteomics and drug discovery

Our laboratories have developed several technologies to accelerate drug discovery process on the basis of structural chemoproteomics. They include SPS technology for the efficient determination of protein structures, SCP technology for the rapid lead generation and SDF technology for the productive lead optimization. Using these technologies, we could determine many 3D structures of target proteins bound with biologically active chemicals including the structure of phosphodiesterase 5/Viagra complex and obtain highly potent compounds in animal models of obesity, diabetes, cancer and inflammation. In this paper, we will discuss concepts and applications of structural chemoproteomics for drug discovery.     

Toward better drug discovery with knowledge graph

Drug discovery is the process of new drug identification. This process is driven by the increasing data from existing chemical libraries and data banks. The knowledge graph is introduced to the domain of drug discovery for imposing an explicit structure to integrate heterogeneous biomedical data. The graph can provide structured relations among multiple entities and unstructured semantic relations associated with entities. In this review, we summarize knowledge graph-based works that implement drug repurposing and adverse drug reaction prediction for drug discovery. As knowledge representation learning is a common way to explore knowledge graphs for prediction problems, we introduce several representative embedding models to provide a comprehensive understanding of knowledge representation learning.     

Phenocopy--a strategy to qualify chemical compounds during hit-to-lead and/or lead optimization

A phenocopy is defined as an environmentally induced phenotype of one individual which is identical to the genotype-determined phenotype of another individual. The phenocopy phenomenon has been translated to the drug discovery process as phenotypes produced by the treatment of biological systems with new chemical entities (NCE) may resemble environmentally induced phenotypic modifications. Various new chemical entities exerting inhibition of the kinase activity of Transforming Growth Factor β Receptor I (TGF-βR1) were qualified by high-throughput RNA expression profiling. This chemical genomics approach resulted in a precise time-dependent insight to the TGF-β biology and allowed furthermore a comprehensive analysis of each NCE's off-target effects. The evaluation of off-target effects by the phenocopy approach allows a more accurate and integrated view on optimized compounds, supplementing classical biological evaluation parameters such as potency and selectivity. It has therefore the potential to become a novel method for ranking compounds during various drug discovery phases.     

3D pharmacophore based virtual screening of T-type calcium channel blockers

Virtual screening of the commercial databases was done by using a three dimensional pharmacophore previously developed for T-type calcium channel blockers using CATALYSTtrade mark program. Biological evaluation of 25 selected virtual hits resulted in the discovery of a highly potent compound VH04 with IC(50) value of 0.10 microM, eight times as potent as the known selective T-type calcium channel blocker, mibefradil. Search for similar compounds yielded several hits with micro-molar IC(50) values and high T-type calcium channel selectivity. Based on the structure of the virtual hits, small molecule libraries with novel scaffolds were designed, synthesis and biological evaluation of which are currently in progress. This result shows a successful example of ligand based drug discovery of potent T-type calcium channel blockers.     

Bioinformatics approaches for new drug discovery: a review

Prolonged antibiotic therapy for the bacterial infections has resulted in high levels of antibiotic resistance. Initially, bacteria are susceptible to the antibiotics, but can gradually develop resistance. Treating such drug-resistant bacteria remains difficult or even impossible. Hence, there is a need to develop effective drugs against bacterial pathogens. The drug discovery process is time-consuming, expensive and laborious. The traditionally available drug discovery process initiates with the identification of target as well as the most promising drug molecule, followed by the optimization of this, in-vitro, in-vivo and in pre-clinical studies to decide whether the compound has the potential to be developed as a drug molecule. Drug discovery, drug development and commercialization are complicated processes. To overcome some of these problems, there are many computational tools available for new drug discovery, which could be cost effective and less time-consuming. In-silico approaches can reduce the number of potential compounds from hundreds of thousands to the tens of thousands which could be studied for drug discovery and this results in savings of time, money and human resources. Our review is on the various computational methods employed in new drug discovery processes.     

AlphaFold2 protein structure prediction: Implications for drug discovery

The drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intelligence methods compile the three-dimensional structure of proteins has made protein targets more accessible to the drug design process. Here, we present our perspective of the significance of accurate protein structure prediction on various stages of the small molecule drug discovery life cycle focusing on current capabilities and assessing how further evolution of such predictive procedures can have a more decisive impact in the discovery of new medicines.     

后基因组时代微生物天然产物高效发掘体系设计与展望

微生物天然产物具有丰富的化学结构多样性和诱人的生物活性,持续启迪着创新医药和农药的发现。近年来,随着高通量测序技术的快速发展,巨大的微生物基因组数据揭示了多样生物合成和新颖天然产物的潜能远高于以前的认知。然而,如何高效地激活隐性的生物合成基因簇 (BGCs) 并识别相应的化合物,以及避免已知代谢产物的重复发现等挑战依然严峻。本文描述了面对这些问题时基因组学、生物信息学、机器学习、代谢组学、基因编辑和合成生物学等新技术在发现药用先导化合物过程中提供的机遇;总结并论述了在潜力菌株优选、BGCs的生物信息学预测、沉默 BGCs的高效激活以及目标产物的识别和跟踪方面的新见解;提出了基于潜力菌株选择和多组学挖掘技术从微生物天然产物中高效发现先导结构的系统线路 (SPLSD),并讨论了未来天然产物药用先导发现的机遇和挑战。     

Recent advances in target identification of bioactive natural products

Natural products are a tremendous source of tool discovery for basic science and drug discovery for clinical uses. In contrast to the large number of compounds isolated from nature, however, the number of compounds whose target molecules have been identified so far is fairly limited. Elucidation of the mechanism of how bioactive small molecules act in cells to induce biological activity (mode of action) is an attractive but challenging field of basic biology. At the same time, this is the major bottleneck for drug development of compounds identified in cell-based and phenotype-based screening. Although researchers’ experience and inspiration have been crucial for successful target identification, recent advancements in genomics, proteomics, and chemical genomics have made this challenging task possible in a systematic fashion.     

Chalcone derivatives and their antibacterial activities: Current development

The increase in antibiotic resistance due to various factors has encouraged the look for novel compounds which are active against multidrug-resistant pathogens. In this framework, chalcone-based compounds showed a diversity of pharmacological properties, and its derivatives possess a high degree of structural diversity, and it is helpful for the discovery of new therapeutic agents. The growing resistance to antibiotics worldwide has endangered their efficacy. This has led to a surging interest in the discovery of new antibacterial agents. Thus, there is an urgent need for new antibacterial drug candidates with increased strength, new targets, low cost, superior pharmacokinetic properties, and minimum side effects. The present review concluded and focuses on the recent developments in the area of medicinal chemistry to explore the diverse chemical structures of potent antibacterial agents and also describes its structure-activity relationships studies. The various synthetic structures leading to this class of neutral protective compound is common and additional structural optimization is promising for potential drug discovery and development.     

Novel pyrazinone mono-amides as potent and reversible caspase-3 inhibitors

The iterative process for the discovery of a series of pyrazinone mono-amides as potent, selective and reversible non-peptide caspase-3 inhibitors (e.g., M826 and M867) is reported. These compounds display potent anti apoptotic activities in a number of cell based systems in vitro as well as in several animal models in vivo.     

Antitumor potential of natural products from Mediterranean ascidians

Ascidians, invertebrates belonging to the subphylum Urochordata (Tunicata), are renowned for their great chemical diversity, and during the last 25 years, they have been shown to produce an array of cytotoxic molecules. Among the first six marine-derived compounds that have reached clinical trials as antitumor agents, three are derived from ascidians, as evidence of the high potential of these organisms as a new source of antitumor compounds. Reported in this communication are some recent results on the chemistry of Mediterranean ascidians; a number of new molecules with different structural features but all endowed with antiproliferative or cytotoxic activity are discussed. These results strongly evidence the highly significant role that Mediterranean ascidians natural products could play in anticancer drug discovery and development process.