Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example

Virtual screening of large libraries of organic compounds combined with pharmacological high throughput screening is widely used for drug discovery in the pharmaceutical industry. Our aim was to explore the efficiency of using a biased 3D database comprising secondary metabolites from antiinflammatory medicinal plants as a source for the virtual screening. For this study pharmacophore models of cyclooxygenase I and II (COX-1, COX-2), key enzymes in the inflammation process, were generated with structure-based as well as common feature based modeling, resulting in three COX hypotheses. Four different multiconfomational 3D databases limited in molecular weight between 300 and 700 Da were applied to the screening in order to compare and analyze the obtained hit rates. Two of them were created in-house (DIOS, NPD). The database DIOS consists of 2752 compounds from phytochemical reports of antiinflammatory medicinal plants described by the ethnopharmacological source 'De material medica' of Pedanius Dioscorides, whereas NPD contains almost 80,000 compounds gathered arbitrarily from natural sources. In addition, two available multiconformational 3D libraries comprising marketed and development drug substances (DWI and NCI), mainly originating from synthesis, were used for comparison. As a test of the pharmacophore models' capability in natural sources, the models were used to search for known COX inhibitory natural products. This was achieved with some exceptions, which are discussed in the paper. Depending on the hypothesis used, DWI and NCI library searches produced hit rates in the range of 6.6% to 13.7%. A slight increase of the number of molecules assessed for binding was achieved with the database of natural products (NPD). Using the biased 3D database DIOS, however, the average increase of efficiency reached 77% to 133% compared to the hit rates resulting from WDI and NCI. The statistical benefit of a combination of an ethnopharmacological approach with the potential of computer aided drug discovery by in silico screening was demonstrated exemplified on the applied targets COX-1 and COX-2.     

Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning

Phosphine-borane complexes are novel chemical entities with preclinical efficacy in neuronal and ophthalmic disease models. In vitro and in vivo studies showed that the metabolites of these compounds are capable of cleaving disulfide bonds implicated in the downstream effects of axonal injury. A difficulty in using standard in silico methods for studying these drugs is that most computational tools are not designed for borane-containing compounds. Using in silico and machine learning methodologies, the absorption-distribution properties of these unique compounds were assessed. Features examined with in silico methods included cellular permeability, octanol-water partition coefficient, blood-brain barrier permeability, oral absorption and serum protein binding. The resultant neural networks demonstrated an appropriate level of accuracy and were comparable to existing in silico methodologies. Specifically, they were able to reliably predict pharmacokinetic features of known boron-containing compounds. These methods predicted that phosphine-borane compounds and their metabolites meet the necessary pharmacokinetic features for orally active drug candidates. This study showed that the combination of standard in silico predictive and machine learning models with neural networks is effective in predicting pharmacokinetic features of novel boron-containing compounds as neuroprotective drugs.     

Going Retro,Going Viral: Experiences and Lessons in Drug Discovery from COVID-19

The severity of the COVID-19 pandemic and the pace of its global spread have motivated researchers to opt for repurposing existing drugs against SARS-CoV-2 rather than discover or develop novel ones. For reasons of speed, throughput, and cost-effectiveness, virtual screening campaigns, relying heavily on in silico docking, have dominated published reports. A particular focus as a drug target has been the principal active site (i.e., RNA synthesis) of RNA-dependent RNA polymerase (RdRp), despite the existence of a second, and also indispensable, active site in the same enzyme. Here we report the results of our experimental interrogation of several small-molecule inhibitors, including natural products proposed to be effective by in silico studies. Notably, we find that two antibiotics in clinical use, fidaxomicin and rifabutin, inhibit RNA synthesis by SARS-CoV-2 RdRp in vitro and inhibit viral replication in cell culture. However, our mutagenesis studies contradict the binding sites predicted computationally. We discuss the implications of these and other findings for computational studies predicting the binding of ligands to large and flexible protein complexes and therefore for drug discovery or repurposing efforts utilizing such studies. Finally, we suggest several improvements on such efforts ongoing against SARS-CoV-2 and future pathogens as they arise.     

The Impact of Software Used and the Type of Target Protein on Molecular Docking Accuracy

The modern development of computer technology and different in silico methods have had an increasing impact on the discovery and development of new drugs. Different molecular docking techniques most widely used in silico methods in drug discovery. Currently, the time and financial costs for the initial hit identification can be significantly reduced due to the ability to perform high-throughput virtual screening of large compound libraries in a short time. However, the selection of potential hit compounds still remains more of a random process, because there is still no consensus on what the binding energy and ligand efficiency (LE) of a potentially active compound should be. In the best cases, only 20–30% of compounds identified by molecular docking are active in biological tests. In this work, we evaluated the impact of the docking software used as well as the type of the target protein on the molecular docking results and their accuracy using an example of the three most popular programs and five target proteins related to neurodegenerative diseases. In addition, we attempted to determine the “reliable range” of the binding energy and LE that would allow selecting compounds with biological activity in the desired concentration range.     

Tracking antiangiogenic components from Glycyrrhiza uralensis Fisch. based on zebrafish assays using high-speed countercurrent chromatography

Natural products are some of the most important sources of lead compounds for drug discovery. The advanced isolation technique of lead compounds of natural origin using therapeutically relevant bioassays is capable of enhancing work efficiency from complex multiconstituent extracts. In the present study, a bioassay-guided isolation strategy combined with bioactivity screening was used to identify novel angiogenesis inhibitors from licorice (Glycyrrhiza uralensis Fisch.) based on the zebrafish model and rapid preparative separation by high-speed countercurrent chromatography. Zebrafish embryos at 24 h postfertilization were chosen as the angiogenesis inhibition model for bioactivity screening. A solvent system (n-hexane-ethyl acetate-methanol-water) with different ratios was optimized and applied in the high-speed countercurrent chromatography separation of two fractions, Fr5 and Fr6, from the ethyl acetate extract of licorice. Blood circulation and vascular outgrowth in intersegmental vessels were found to be simultaneously inhibited by isoliquiritigenin and isolicoflavonol in a dose-dependent manner. Thus, these two compounds were identified and considered as active inhibitors against angiogenesis. These experimental results indicate that zebrafish bioassays combined with high-speed countercurrent chromatography may provide an alternative pathway for the rapid isolation of bioactive natural products.     

A Method for the Rapid Discovery of Naturally Occurring Products by Proteins Immobilized on Magnetic Beads and Reverse Affinity Chromatography

A highly efficient screening method for naturally occurring products that bind to a specific target protein was demonstrated by using hVDR magnetic beads. The native ligand 1α,25(OH)₂ VD₃ ( 1 ) was selectively bound by hVDR magnetic beads when present in a mixture of natural compounds. Furthermore, this method was shown to be applicable to the identification of natural products that interact with a specific protein immobilized on the beads from an extract of a natural resource. Two new natural compounds were isolated by this method. This approach will be helpful for the discovery of novel, naturally occurring products that bind to specific target proteins. This method has the further advantages that it can identify the HPLC peak corresponding to the target compound for isolation, as well as provide important UV, CD, or MS profile information.     

Investigation of innovative synthesis of biologically active compounds on the basis of newly developed reactions

Synthesis of biologically active compounds, including natural products and pharmaceutical agents, is an important and interesting research area since the large structural diversity and complexity of bioactive compounds make them an important source of leads and scaffolds in drug discovery and development. Many structurally and also biologically interesting compounds, including marine natural products, have been isolated from nature and have also been prepared on the basis of a computational design for the purpose of developing medicinal chemistry. In order to obtain a wide variety of derivatives of biologically active compounds from the viewpoint of medicinal chemistry, it is essential to establish efficient synthetic procedures for desired targets. Newly developed reactions should also be used for efficient synthesis of desired compounds. Thus, recent progress in the synthesis of biologically active compounds by focusing on the development of new reactions is summarized in this review article.     

Modern drug discovery technologies: opportunities and challenges in lead discovery

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.     

Combinatorial biosynthesis in plants: a (p)review on its potential and future exploitation

Combinatorial biochemistry, also called combinatorial biosynthesis, comprises a series of methods that establish novel enzyme-substrate combinations in vivo and, in turn, lead to the biosynthesis of new, natural product-derived compounds that can be used in drug discovery programs. Plants are an extremely rich source of bioactive natural products and continue to possess a huge potential for drug discovery. In this review, we discuss the state-of-the-art in combinatorial biosynthesis methods to generate novel molecules from plants. We debate on the progress and potential in biotransformation, mutasynthesis, combinatorial metabolism in hybrids, activation of silent plant metabolism and synthetic biology in plants to create opportunities for the combinatorial biosynthesis of plant-derived natural products, and, ultimately, for drug discovery. The therapeutic value of two classes of natural products, the terpenoid indole alkaloids and the triterpene saponins, is particularly highlighted.     

Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation

c-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention. Based on the above background, this research aims to combine emerging Artificial Intelligence technologies with traditional Computer-Aided Drug Design methods to find natural products with JNK1 inhibitory activity. First, we constructed three machine learning models (Support Vector Machine, Random Forest, and Artificial Neural Network) and performed model fusion based on Voting and Stacking strategies. The integrated models with better performance (AUC of 0.906 and 0.908, respectively) were then employed for the virtual screening of 4112 natural products in the ZINC database. After further drug-likeness filtering, we calculated the binding free energy of 22 screened compounds using molecular docking and performed a consensus analysis of the two methodologies. Subsequently, we identified the three most promising candidates (Lariciresinol, Tricin, and 4′-Demethylepipodophyllotoxin) according to the obtained probability values and relevant reports, while their binding characteristics were preliminarily explored by molecular dynamics simulations. Finally, we performed in vitro biological validation of these three compounds, and the results showed that Tricin exhibited an acceptable inhibitory activity against JNK1 (IC₅₀ = 17.68 μM). This natural product can be used as a template molecule for the design of novel JNK1 inhibitors.     

Determination of the absolute configuration of peptide natural products by using stable isotope labeling and mass spectrometry

Structure elucidation of natural products including the absolute configuration is a complex task that involves different analytical methods like mass spectrometry, NMR spectroscopy, and chemical derivation, which are usually performed after the isolation of the compound of interest. Here, a combination of stable isotope labeling of Photorhabdus and Xenorhabdus strains and their transaminase mutants followed by detailed MS analysis enabled the structure elucidation of novel cyclopeptides named GameXPeptides including their absolute configuration in crude extracts without their actual isolation.     

In silico analysis of a few dietary phytochemicals as potential tumor chemo-sensitizers

P-glycoprotein (P-gp) is a membrane ATP-binding cassette (ABC) transporter that extrudes different xenobiotics out of cells. Besides its tissue protection role, overexpression of P-gp on the surface of many neoplastic cells restricts the cell entry of many anti-cancer drugs, the phenomenon which is known as multidrug resistance (MDR). It has been demonstrated that MDR cells can be sensitized toward anti-cancer agents when treated with P-gp inhibitors/modulators known as chemo-sensitizers. Due to the clinical significance and also considering the fact that many P-gp inhibitors are transported by P-gp, the search for more potent and low toxic non-transported chemo-sensitizers is an active area of research. Regarding this, several naturally occurring compounds were reported as MDR reversal agents, a category which is generally referred to as “fourth-generation P-gp inhibitors.” Dietary supplements containing natural products are widely used, and it is possible that they interact with co-administered pharmaceutical substances that are P-gp substrates, leading to altered pharmacokinetic profile. In silico approaches for quantitative and quantitative prediction of binding mechanism of dietary natural products to P-gp may be regarded as appropriate strategy in the early phase of drug discovery projects since they describe structural features of various phytochemicals for interaction with P-gp and pave the way toward alternative and novel anti-MDR scaffolds. In the present contribution, some phytochemicals of turmeric, black pepper, and green tea as commonly consumed dietary sources were subjected to systematic combined in silico analysis including molecular docking and amino acid decomposition analysis through B3LYP functional in association with 6-31G basis set. On the basis of major identified drug binding sites within P-gp internal pocket, modeled natural compounds were categorized as substrate, inhibitor, or modulator while structure binding relationship of each category was developed and elucidated.     

Modern Approaches in the Discovery and Development of Plant-Based Natural Products and Their Analogues as Potential Therapeutic Agents

Natural products represents an important source of new lead compounds in drug discovery research. Several drugs currently used as therapeutic agents have been developed from natural sources; plant sources are specifically important. In the past few decades, pharmaceutical companies demonstrated insignificant attention towards natural product drug discovery, mainly due to its intrinsic complexity. Recently, technological advancements greatly helped to address the challenges and resulted in the revived scientific interest in drug discovery from natural sources. This review provides a comprehensive overview of various approaches used in the selection, authentication, extraction/isolation, biological screening, and analogue development through the application of modern drug-development principles of plant-based natural products. Main focus is given to the bioactivity-guided fractionation approach along with associated challenges and major advancements. A brief outline of historical development in natural product drug discovery and a snapshot of the prominent natural drugs developed in the last few decades are also presented. The researcher’s opinions indicated that an integrated interdisciplinary approach utilizing technological advances is necessary for the successful development of natural products. These involve the application of efficient selection method, well-designed extraction/isolation procedure, advanced structure elucidation techniques, and bioassays with a high-throughput capacity to establish druggability and patentability of phyto-compounds. A number of modern approaches including molecular modeling, virtual screening, natural product library, and database mining are being used for improving natural product drug discovery research. Renewed scientific interest and recent research trends in natural product drug discovery clearly indicated that natural products will play important role in the future development of new therapeutic drugs and it is also anticipated that efficient application of new approaches will further improve the drug discovery campaign.     

Machine Learning Methods in Drug Discovery

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.     

Crystalline‐Sponge‐Based Structural Analysis of Crude Natural Product Extracts

Characterization of complex natural product mixtures to the absolute structural level of their components often requires significant amounts of starting materials and lengthy purification process, followed by arduous structure elucidation efforts. The crystalline sponge (CS) method has demonstrated utility in the absolute structure elucidation of isolated organic compounds at miniscule quantities compared to conventional methods. In this work, we developed a new CS‐based workflow that greatly expedites the in‐depth structural analysis of crude natural product extracts. Using a crude extract of the red alga Laurencia pacifica, we showed that CS affinity screening prior to compound isolation enables prioritization of analytes present in the extract, and we subsequently resolved the molecular structures of six sesquiterpenes with stereochemical clarity from around 10 mg crude extract. This study demonstrates a new chemotyping workflow that can greatly accelerate natural product discovery from complex samples.     

Natural Products Extracted from Fungal Species as New Potential Anti-Cancer Drugs: A Structure-Based Drug Repurposing Approach Targeting HDAC7

Mushrooms can be considered a valuable source of natural bioactive compounds with potential polypharmacological effects due to their proven antimicrobial, antiviral, antitumor, and antioxidant activities. In order to identify new potential anticancer compounds, an in-house chemical database of molecules extracted from both edible and non-edible fungal species was employed in a virtual screening against the isoform 7 of the Histone deacetylase (HDAC). This target is known to be implicated in different cancer processes, and in particular in both breast and ovarian tumors. In this work, we proposed the ibotenic acid as lead compound for the development of novel HDAC7 inhibitors, due to its antiproliferative activity in human breast cancer cells (MCF-7). These promising results represent the starting point for the discovery and the optimization of new HDAC7 inhibitors and highlight the interesting opportunity to apply the “drug repositioning” paradigm also to natural compounds deriving from mushrooms.     

Rapid Identification of Potential Drug Candidates from Multi-Million Compounds’ Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds’ databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.     

An in silico ensemble method for lead discovery: decision forest

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.     

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.     

In Silico Identification of Tripeptides as Lead Compounds for the Design of KOR Ligands

The kappa opioid receptor (KOR) represents an attractive target for the development of drugs as potential antidepressants, anxiolytics and analgesics. A robust computational approach may guarantee a reduction in costs in the initial stages of drug discovery, novelty and accurate results. In this work, a virtual screening workflow of a library consisting of ~6 million molecules was set up, with the aim to find potential lead compounds that could manifest activity on the KOR. This in silico study provides a significant contribution in the identification of compounds capable of interacting with a specific molecular target. The main computational techniques adopted in this experimental work include: (i) virtual screening; (ii) drug design and leads optimization; (iii) molecular dynamics. The best hits are tripeptides prepared via solution phase peptide synthesis. These were tested in vivo, revealing a good antinociceptive effect after subcutaneous administration. However, further work is due to delineate their full pharmacological profile, in order to verify the features predicted by the in silico outcomes.