An up-to-date overview of computational polypharmacology in modern drug discovery

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Toward structure-multiple activity relationships (SMARts) using computational approaches: A polypharmacological perspective

In the current era of biological big data, which are rapidly populating the biological chemical space, in silico polypharmacology drug design approaches help to decode structure-multiple activity relationships (SMARts). Current computational methods can predict or categorize multiple properties simultaneously, which aids the generation, identification, curation, prioritization, optimization, and repurposing of molecules. Computational methods have generated opportunities and challenges in medicinal chemistry, pharmacology, food chemistry, toxicology, bioinformatics, and chemoinformatics. It is anticipated that computer-guided SMARts could contribute to the full automatization of drug design and drug repurposing campaigns, facilitating the prediction of new biological targets, side and off-target effects, and drug-drug interactions.

VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.

Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

Virtual screening and molecular dynamics simulation approach for the identification of potential multi-target directed ligands for the treatment of Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial neurological disorder characterized by memory loss and cognitive impairment. The currently available single-targeting drugs have miserably failed in the treatment of AD, and multi-target directed ligands (MTDLs) are being explored as an alternative treatment strategy. Cholinesterase and monoamine oxidase enzymes are reported to play a crucial role in the pathology of AD, and multipotent ligands targeting these two enzymes simultaneously are under various phases of design and development. Recent studies have revealed that computational approaches are robust and trusted tools for identifying novel therapeutics. The current research work is focused on the development of potential multi-target directed ligands that simultaneously inhibit acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) enzymes employing a structure-based virtual screening (SBVS) approach. The ASINEX database was screened after applying pan assay interference and drug-likeness filter to identify novel molecules using three docking precision criteria; High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP). Additionally, binding free energy calculations, ADME, and molecular dynamic simulations were employed to get structural insights into the mechanism of protein-ligand binding and pharmacokinetic properties. Three lead molecules viz. AOP19078710, BAS00314308 and BDD26909696 were successfully identified with binding scores of -10.565, -10.543 & -8.066 kcal/mol against AChE and -11.019, -12.357 & -10.068 kcal/mol against MAO-B, better score as compared to the standard inhibitors. In the near future, these molecules will be synthesized and evaluated through in vitro and in vivo assays for their inhibition potential against AChE and MAO-B enzymes.

IUPHAR Review - Bivalent and bifunctional opioid receptor ligands as novel analgesics

Though efficacious in managing chronic, severe pain, opioid analgesics are accompanied by significant adverse effects including constipation, tolerance, dependence, and respiratory depression. The life-threatening risks associated with µ opioid receptor agonist-based analgesics challenges their use in clinic. A rational approach to combatting these adverse effects is to develop agents that incorporate activity at a second pharmacologic target in addition to µ opioid receptor activation. The promise of such bivalent or bifunctional ligands is the development of an analgesic with an improved side effect profile. In this review, we highlight ongoing efforts in the development of bivalent and bifunctional analgesics that combine µ agonism with efficacy at κ and δ opioid receptors, the nociceptin opioid peptide (NOP) receptor, σ receptors, and cannabinoid receptors. Several examples of bifunctional analgesics in preclinical and clinical development are highlighted, as are strategies being employed toward the rational design of novel agents.

An integrative analysis to predict the active compounds and explore polypharmacological mechanisms of Orthosiphon stamineus Benth

BACKGROUND

Orthosiphon stamineus Benth is a dietary supplement and traditional Chinese herb with widespread clinical applications, but a comprehensive understanding of its active compounds and polypharmacological mechanisms is lacking. This study aimed to systematically investigate the natural compounds and molecular mechanisms of O. stamineus via network pharmacology.

METHODS

Information on compounds from O. stamineus was collected via literature retrieval, while physicochemical properties and drug-likeness were evaluated using SwissADME. Protein targets were screened using SwissTargetPrediction, while the compound-target networks were constructed and analyzed via Cytoscape with CytoHubba for seed compounds and core targets. Enrichment analysis and disease ontology analysis were then carried out, generating target-function and compound-target-disease networks to intuitively explore potential pharmacological mechanisms. Lastly, the relationship between active compounds and targets was confirmed via molecular docking and dynamics simulation.

RESULTS

A total of 22 key active compounds and 65 targets were identified and the main polypharmacological mechanisms of O. stamineus were addressed. The molecular docking results suggested that nearly all core compounds and their targets possess good binding affinity. In addition, the separation of receptor and ligands was not observed in all dynamics simulation processes, whereas complexes of orthosiphol Z-AR and Y-AR performed best in simulations of molecular dynamics.

CONCLUSION

This study successfully identified the polypharmacological mechanisms of the main compounds in O. stamineus, and predicted five seed compounds along with 10 core targets. Moreover, orthosiphol Z, orthosiphol Y, and their derivatives can be utilized as lead compounds for further research and development. The findings here provide improved guidance for subsequent experiments, and we identified potential active compounds for drug discovery or health promotion.

Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature

INTRODUCTION

Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations.

AREAS COVERED

The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process.

EXPERT OPINION

Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.

Polypharmacology: promises and new drugs in 2022

Polypharmacology is an emerging strategy of design, synthesis, and clinical implementation of pharmaceutical agents that act on multiple targets simultaneously. It should not be mixed up with polytherapy, which is based on the use of multiple selective drugs and is considered a cornerstone of current clinical practice. However, this 'classic' approach, when facing urgent medical challenges, such as multifactorial diseases, increasing resistance to pharmacotherapy, and multimorbidity, seems to be insufficient. The 'novel' polypharmacology concept leads to a more predictable pharmacokinetic profile of multi-target-directed ligands (MTDLs), giving a chance to avoid drug-drug interactions and improve patient compliance due to the simplification of dosing regimens. Plenty of recently marketed drugs interact with multiple biological targets or disease pathways. Many offer a significant additional benefit compared to the standard treatment regimens. In this paper, we will briefly outline the genesis of polypharmacology and its differences to polytherapy. We will also present leading concepts for obtaining MTDLs. Subsequently, we will describe some successfully marketed drugs, the mechanisms of action of which are based on the interaction with multiple targets. To get an idea, of whether MTDLs are indeed important in contemporary pharmacology, we also carefully analyzed drugs approved in 2022 in Germany: 10 out of them were found multi-targeting, including 7 antitumor agents, 1 antidepressant, 1 hypnotic, and 1 drug indicated for eye disease.

Development and Characterization of Folic Acid-Conjugated Amodiaquine-Loaded Nanoparticles-Efficacy in Cancer Treatment

The objective of this study was to construct amodiaquine-loaded, folic acid-conjugated polymeric nanoparticles (FA-AQ NPs) to treat cancer that could be scaled to commercial production. In this study, folic acid (FA) was conjugated with a PLGA polymer followed by the formulation of drug-loaded NPs. The results of the conjugation efficiency confirmed the conjugation of FA with PLGA. The developed folic acid-conjugated nanoparticles demonstrated uniform particle size distributions and had visible spherical shapes under transmission electron microscopy. The cellular uptake results suggested that FA modification could enhance the cellular internalization of nanoparticulate systems in non-small cell lung cancer, cervical, and breast cancer cell types. Furthermore, cytotoxicity studies showed the superior efficacy of FA-AQ NPs in different cancer cells such as MDAMB-231 and HeLA. FA-AQ NPs had better anti-tumor abilities demonstrated via 3D spheroid cell culture studies. Therefore, FA-AQ NPs could be a promising drug delivery system for cancer therapy.

Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Explaining Accurate Predictions of Multitarget Compounds with Machine Learning Models Derived for Individual Targets

In drug discovery, compounds with well-defined activity against multiple targets (multitarget compounds, MT-CPDs) provide the basis for polypharmacology and are thus of high interest. Typically, MT-CPDs for polypharmacology have been discovered serendipitously. Therefore, over the past decade, computational approaches have also been adapted for the design of MT-CPDs or their identification via computational screening. Such approaches continue to be under development and are far from being routine. Recently, different machine learning (ML) models have been derived to distinguish between MT-CPDs and corresponding compounds with activity against the individual targets (single-target compounds, ST-CPDs). When evaluating alternative models for predicting MT-CPDs, we discovered that MT-CPDs could also be accurately predicted with models derived for corresponding ST-CPDs; this was an unexpected finding that we further investigated using explainable ML. The analysis revealed that accurate predictions of ST-CPDs were determined by subsets of structural features of MT-CPDs required for their prediction. These findings provided a chemically intuitive rationale for the successful prediction of MT-CPDs using different ML models and uncovered general-feature subset relationships between MT- and ST-CPDs with activities against different targets.

Multiple anti-inflammatory mechanisms of Zedoary Turmeric Oil Injection against lipopolysaccharides-induced acute lung injury in rats elucidated by network pharmacology combined with transcriptomics

BACKGROUND

Prospects for the drug treatment of acute lung injury (ALI) is unpromising. Managing inflammation can prevent ALI from progressing and minimize further deterioration. Zedoary turmeric oil injection (ZTOI), a patented traditional Chinese medicine (TCM) that has been used against ALI, has shown significant anti-inflammatory effects. However, the mechanisms underlying these effects remain unclear.

PURPOSE

Elucidate the anti-inflammatory mechanism by which ZTOI acts against ALI in rats using an ingredients-targets-pathways (I-T-P) interaction network.

STUDY DESIGN AND METHODS

The key ingredients of ZTOI were characterized using UPLC-MS/MS combined with literature mining. The target profiles of each ingredient were established using drug-target databases. The anti-inflammatory activity of ZTOI against lipopolysaccharides (LPS)-induced rat ALI was validated using histopathology and inflammatory factor assessments. The therapeutic targets of ZTOI were screened by integrating transcriptomic results of lung tissues with protein-protein interaction (PPI) expansion. Using KEGG pathway enrichment, an I-T-P network was established to determine the essential interactions among ingredients, targets, and pathways of ZTOI against lung inflammation in ALI. Molecular docking and immunofluorescence staining were utilized to confirm the accuracy of the I-T-P network.

RESULTS

A total of 11 sesquiterpenes, whose target profiles may characterize the potential function of ZTOI, were identified as key ingredients. In the ALI rat model, ZTOI can alleviate lung inflammation by decreasing the levels of C-reactive protein, interleukin-6, interleukin-1β, and tumor necrosis factor α both in serum and lung tissues. Based on our biological samples, transcriptomics, PPI network expansion, and KEGG pathway enrichment, 11 ingredients, 174 targets, and 8 signaling pathways were linked in the I-T-P networks. From these results, ZTOI could be inferred to exert multiple anti-inflammatory effects against ALI through Toll-like receptor, NF-kappa B, RIG-I-like receptor, TNF, NOD-like receptor, IL-17, MAPK, and the Toll and Imd signaling pathways. In addition, two significantly regulated targets in the transcriptome, Usp18 and Map3k7, could be the essential anti-inflammatory targets of ZTOI.

CONCLUSION

By integrating network pharmacology with ingredient identification and transcriptomics, we show the multiple anti-inflammatory mechanisms by which ZTOI acts against ALI on an I-T-P level. This work also provides a methodological reference for related research into TCM.

Dual Inhibitors of AChE and BACE-1 for Reducing Aβ in Alzheimer's Disease: From In Silico to In Vivo

Alzheimer's disease (AD) is a complex and widespread condition, still not fully understood and with no cure yet. Amyloid beta (Aβ) peptide is suspected to be a major cause of AD, and therefore, simultaneously blocking its formation and aggregation by inhibition of the enzymes BACE-1 (β-secretase) and AChE (acetylcholinesterase) by a single inhibitor may be an effective therapeutic approach, as compared to blocking one of these targets or by combining two drugs, one for each of these targets. We used our ISE algorithm to model each of the AChE peripheral site inhibitors and BACE-1 inhibitors, on the basis of published data, and constructed classification models for each. Subsequently, we screened large molecular databases with both models. Top scored molecules were docked into AChE and BACE-1 crystal structures, and 36 Molecules with the best weighted scores (based on ISE indexes and docking results) were sent for inhibition studies on the two enzymes. Two of them inhibited both AChE (IC₅₀ between 4-7 μM) and BACE-1 (IC₅₀ between 50-65 μM). Two additional molecules inhibited only AChE, and another two molecules inhibited only BACE-1. Preliminary testing of inhibition by F681-0222 (molecule 2) on APPswe/PS1dE9 transgenic mice shows a reduction in brain tissue of soluble Aβ42.

An interpretable machine learning model for selectivity of small molecules against homologous protein family

Aim: In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side effects. Methods: We have developed a multitask deep learning model for predicting the selectivity on closely related homologs of the target protein. The model has been tested on the Janus-activated kinase and dopamine receptor families of proteins. Results & conclusion: The feature-based representation (extended connectivity fingerprint 4) with Extreme Gradient Boosting performed better when compared with deep neural network models in most of the evaluation metrics. Both the Extreme Gradient Boosting and deep neural network models outperformed the graph-based models. Furthermore, to decipher the model decision on selectivity, the important fragments associated with each homologous protein were identified.

De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference

Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug–drug–target network constructed from protein–ligand interaction annotations and drug–drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning.

Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity

The reductionist concept, based on the ligand–receptor interaction, is not a suitable model for adaptogens, and herbal preparations affect multiple physiological functions, revealing polyvalent pharmacological activities, and are traditionally used in many conditions. This review, for the first time, provides a rationale for the pleiotropic therapeutic efficacy of adaptogens based on evidence from recent gene expression studies in target cells and where the network pharmacology and systems biology approaches were applied. The specific molecular targets and adaptive stress response signaling mechanisms involved in nonspecific modes of action of adaptogens are identified.

Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm

Jamu is an Indonesian traditional herbal medicine that has been practiced for generations. Jamu is made from various medicinal plants. Each plant has several compounds directly related to the target protein that are directly associated with a disease. A pharmacological graph can form relationships between plants, compounds, and target proteins. Research related to the prediction of Jamu formulas for some diseases has been carried out, but there are problems in finding combinations or compositions of Jamu formulas because of the increase in search space size. Some studies adopted the drug–target interaction (DTI) implemented using machine learning or deep learning to predict the DTI for discovering the Jamu formula. However, this approach raises important issues, such as imbalanced and high-dimensional dataset, overfitting, and the need for more procedures to trace compounds to their plants. This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant–protein bipartite graph. The branch and bound technique is implemented using the search strategy of breadth first search (BrFS), Depth First Search, and Best First Search. To show the performance of the proposed method, we compared our method with a complete search algorithm, searching all nodes in the tree without pruning. In this study, we specialize in applying the proposed method to search for the Jamu formula for type II diabetes mellitus (T2DM). The result shows that the bipartite graph search with the branch and bound algorithm reduces computation time up to 40 times faster than the complete search strategy to search for a composition of plants. The binary branching strategy is the best choice, whereas the BrFS strategy is the best option in this research. In addition, the the proposed method can suggest the composition of one to four plants for the T2DM Jamu formula. For a combination of four plants, we obtain Angelica Sinensis, Citrus aurantium, Glycyrrhiza uralensis, and Mangifera indica. This approach is expected to be an alternative way to discover the Jamu formula more accurately.

Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature Analysis

Protein kinases are major drug targets. Most kinase inhibitors are directed against the adenosine triphosphate (ATP) cofactor binding site, which is largely conserved across the human kinome. Hence, such kinase inhibitors are often thought to be promiscuous. However, experimental evidence and activity data for publicly available kinase inhibitors indicate that this is not generally the case. We have investigated whether inhibitors of closely related human kinases with single- or multi-kinase activity can be differentiated on the basis of chemical structure. Therefore, a test system consisting of two distinct kinase triplets has been devised for which inhibitors with reported triple-kinase activities and corresponding single-kinase activities were assembled. Machine learning models derived on the basis of chemical structure distinguished between these multi- and single-kinase inhibitors with high accuracy. A model-independent explanatory approach was applied to identify structural features determining accurate predictions. For both kinase triplets, the analysis revealed decisive features contained in multi-kinase inhibitors. These features were found to be absent in corresponding single-kinase inhibitors, thus providing a rationale for successful machine learning. Mapping of features determining accurate predictions revealed that they formed coherent and chemically meaningful substructures that were characteristic of multi-kinase inhibitors compared with single-kinase inhibitors.

Cell Surface Transporters and Novel Drug Developments

Despite the numerous scientific and technological advances made within the last decade the attrition rates for new drug discovery remain as high as 95% for anticancer drugs. Recent drug development has been in part guided by Lipinski’s Rule of 5 (Ro5) even though many approved drugs do not comply to these rules. With Covid-19 vaccine development strategy dramatically accelerating drug development perhaps it is timely to question the generic drug development process itself to find a more efficient, cost effective, and successful approach. It is widely believed that drugs permeate cells via two methods: phospholipid bilayer diffusion and carrier mediated transporters. However, emerging evidence suggests that carrier mediated transport may be the primary mechanism of drug uptake and not diffusion as long believed. Computational biology increasingly assists drug design to achieve desirable absorption, distribution, metabolism, elimination and toxicity (ADMET) properties. Perfecting drug entry into target cells as a prerequisite to intracellular drug action is a logical and compelling route and is expected to reduce drug attrition rates, particularly gaining favour amongst chronic lifelong therapeutics. Novel drug development is rapidly expanding from the utilisation of beyond the rule of five (bRo5) to pulsatile drug delivery systems and fragment based drug design. Utilising transporters as drug targets and advocating bRo5 molecules may be the solution to increasing drug specificity, reducing dosage and toxicity and thus revolutionising drug development. This review explores the development of cell surface transporter exploitation in drug development and the relationship with improved therapeutic index.

Alvarez KL, Aguilar-Pineda JA, Vera-Lopez KJ, Lino Cardenas CL. In Silico Analysis of Metabolites from Peruvian Native Plants as Potential Therapeutics against Alzheimer's Disease

Background: Despite research on the molecular bases of Alzheimer’s disease (AD), effective therapies against its progression are still needed. Recent studies have shown direct links between AD progression and neurovascular dysfunction, highlighting it as a potential target for new therapeutics development. In this work, we screened and evaluated the inhibitory effect of natural compounds from native Peruvian plants against tau protein, amyloid beta, and angiotensin II type 1 receptor (AT1R) pathologic AD markers. Methods: We applied in silico analysis, such as virtual screening, molecular docking, molecular dynamics simulation (MD), and MM/GBSA estimation, to identify metabolites from Peruvian plants with inhibitory properties, and compared them to nicotinamide, telmisartan, and grapeseed extract drugs in clinical trials. Results: Our results demonstrated the increased bioactivity of three plants’ metabolites against tau protein, amyloid beta, and AT1R. The MD simulations indicated the stability of the AT1R:floribundic acid, amyloid beta:rutin, and tau:brassicasterol systems. A polypharmaceutical potential was observed for rutin due to its high affinity to AT1R, amyloid beta, and tau. The metabolite floribundic acid showed bioactivity against the AT1R and tau, and the metabolite brassicasterol showed bioactivity against the amyloid beta and tau. Conclusions: This study has identified molecules from native Peruvian plants that have the potential to bind three pathologic markers of AD.

Polypharmacology: The science of multi-targeting molecules

Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.

Discovery of a Novel Template, 7-Substituted 7-Deaza-4'-Thioadenosine Derivatives as Multi-Kinase Inhibitors

The development of anticancer drugs remains challenging owing to the potential for drug resistance. The simultaneous inhibition of multiple targets involved in cancer could overcome resistance, and these agents would exhibit higher potency than single-target inhibitors. Protein kinases represent a promising target for the development of anticancer agents. As most multi-kinase inhibitors are heterocycles occupying only the hinge and hydrophobic region in the ATP binding site, we aimed to design multi-kinase inhibitors that would occupy the ribose pocket, along with the hinge and hydrophobic region, based on ATP-kinase interactions. Herein, we report the discovery of a novel 4'-thionucleoside template as a multi-kinase inhibitor with potent anticancer activity. The in vitro evaluation revealed a lead 1g (7-acetylene-7-deaza-4'-thioadenosine) with potent anticancer activity, and marked inhibition of TRKA, CK1δ, and DYRK1A/1B kinases in the kinome scan assay. We believe that these findings will pave the way for developing anticancer drugs.

Development of a chemogenomics library for phenotypic screening

With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.

From Lock-In to Transformation: A Path-Centric Theory of Emerging Technology and Organizing

We offer a path-centric theory of emerging technology and organizing that addresses a basic question. When does emerging technology lead to transformative change? A path-centric perspective on technology focuses on the patterns of actions afforded by technology in use. We identify performing and patterning as self-reinforcing mechanisms that shape patterns of action in the domain of emerging technology and organizing. We use a dynamic simulation to show that performing and patterning can lead to a wide range of trajectories, from lock-in to transformation, depending on how emerging technology in use influences the pattern of action. When emerging technologies afford new actions that can be flexibly recombined to generate new paths, decisive transformative effects are more likely. By themselves, new affordances are not likely to generate transformation. We illustrate this theory with examples from the practice of pharmaceutical drug discovery. The path-centric perspective offers a new way to think about generativity and the role of affordances in organizing.

Strategies to identify candidate repurposable drugs: COVID-19 treatment as a case example

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.

Uncovering the anti-angiogenic effect of semisynthetic triterpenoid CDDO-Im on HUVECs by an integrated network pharmacology approach

AIM

To reveal the molecular mechanism of anti-angiogenic activity of semisynthetic triterpenoid CDDO-Im.

MATERIALS AND METHODS

Using re-analysis of cDNA microarray data of CDDO-Im-treated human vascular endothelial cells (HUVECs) (GSE71622), functional annotation of revealed differentially expressed genes (DEGs) and analysis of their co-expression, the key processes induced by CDDO-Im in HUVECs were identified. Venn diagram analysis was further performed to reveal the common DEGs, i.e. genes both susceptible to CDDO-Im and involved in the regulation of angiogenesis. A list of probable protein targets of CDDO-Im was prepared based on Connectivity Map/cheminformatics analysis and chemical proteomics data, among which the proteins that were most associated with the angiogenesis-related regulome were identified. Finally, identified targets were validated by molecular docking and text mining approaches.

KEY FINDINGS

The effect of CDDO-Im in HUVECs can be divided into two main phases: the short early phase (0.5-3 h) with an acute FOXD1/CEBPA/JUNB-regulated pro-angiogenic response induced by xenobiotic stress, and the second anti-angiogenic step (6-24 h) with massive suppression of various angiogenesis-related processes, accompanied by the activation of cytoprotective mechanisms. Our analysis showed that the anti-angiogenic activity of CDDO-Im is mediated by its inhibition of the expression of PLAT, ETS1, A2M, SPAG9, RASGRP3, FBXO32, GCNT1 and HDGFRP3 and its direct interactions with EGFR, mTOR, NOS2, HSP90AA1, MDM2, SYK, IRF3, ATR and KIF14.

SIGNIFICANCE

Our findings provide valuable insights into the understanding of the molecular mechanisms of the anti-angiogenic activity of cyano enone-bearing triterpenoids and revealed a range of novel promising therapeutic targets to control pathological neovascularization.

Connecting chemistry and biology through molecular descriptors

Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Desirable drug-drug interactions or when a matter of concern becomes a renewed therapeutic strategy

Drug-drug interactions are sometimes considered to be detrimental and responsible for adverse effects. In some cases, however, some are stakeholders of the efficiency of the treatment and this combinatorial strategy is exploited by some drug associations, including levodopa (L-Dopa) and dopadecarboxylase inhibitors, β-lactam antibiotics and clavulanic acid, 5-fluorouracil (5-FU) and folinic acid, and penicillin and probenecid. More recently, some drug-drug combinations have been integrated in modern drug design strategies, aiming to enhance the efficiency of already marketed drugs with new compounds acting not only as synergistic associations, but also as real boosters of activity. In this review, we provide an update of examples of such strategies, with a special focus on microbiology and oncology.