A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases

Effects of the G-quadruplex-binding drugs quarfloxin and CX-5461 on the malaria parasite Plasmodium falciparum

Plasmodium falciparum is the deadliest causative agent of human malaria. This parasite has historically developed resistance to most drugs, including the current frontline treatments, so new therapeutic targets are needed. Our previous work on guanine quadruplexes (G4s) in the parasite's DNA and RNA has highlighted their influence on parasite biology, and revealed G4 stabilising compounds as promising candidates for repositioning. In particular, quarfloxin, a former anticancer agent, kills blood-stage parasites at all developmental stages, with fast rates of kill and nanomolar potency. Here we explored the molecular mechanism of quarfloxin and its related derivative CX-5461. In vitro, both compounds bound to P. falciparum-encoded G4 sequences. In cellulo, quarfloxin was more potent than CX-5461, and could prevent establishment of blood-stage malaria in vivo in a murine model. CX-5461 showed clear DNA damaging activity, as reported in human cells, while quarfloxin caused weaker signatures of DNA damage. Both compounds caused transcriptional dysregulation in the parasite, but the affected genes were largely different, again suggesting different modes of action. Therefore, CX-5461 may act primarily as a DNA damaging agent in both Plasmodium parasites and mammalian cells, whereas the complete antimalarial mode of action of quarfloxin may be parasite-specific and remains somewhat elusive.

A publication-wide association study (PWAS), historical language models to prioritise novel therapeutic drug targets

Most biomedical knowledge is published as text, making it challenging to analyse using traditional statistical methods. In contrast, machine-interpretable data primarily comes from structured property databases, which represent only a fraction of the knowledge present in the biomedical literature. Crucial insights and inferences can be drawn from these publications by the scientific community. We trained language models on literature from different time periods to evaluate their ranking of prospective gene-disease associations and protein-protein interactions. Using 28 distinct historical text corpora of abstracts published between 1995 and 2022, we trained independent Word2Vec models to prioritise associations that were likely to be reported in future years. This study demonstrates that biomedical knowledge can be encoded as word embeddings without the need for human labelling or supervision. Language models effectively capture drug discovery concepts such as clinical tractability, disease associations, and biochemical pathways. Additionally, these models can prioritise hypotheses years before their initial reporting. Our findings underscore the potential for extracting yet-to-be-discovered relationships through data-driven approaches, leading to generalised biomedical literature mining for potential therapeutic drug targets. The Publication-Wide Association Study (PWAS) enables the prioritisation of under-explored targets and provides a scalable system for accelerating early-stage target ranking, irrespective of the specific disease of interest.

Bestatin analogs-4-quinolinone hybrids as antileishmanial hits: Design, repurposing rational, synthesis, in vitro and in silico studies

Amongst different forms of leishmaniasis, visceral leishmaniasis caused by L. donovani is highly mortal. Identification of new hit compounds might afford new starting points to develop novel therapeutics. In this lieu, a rationally designed small library of bestatin analogs-4-quinolone hybrids were prepared and evaluated. Analysis of SAR unveiled distinct profiles for hybrids type 1 and type 2, which might arise from their different molecular targets. Amongst type 1 bestatin analog-4-quinolone hybrids, hybrid 1e was identified as potential hit inhibiting growth of L. donovani promastigotes by 91 and 53% at 50 and 25 μM concentrations, respectively. Meanwhile, hybrid 2j was identified amongst type 2 bestatin analog-4-quinolone hybrids as potential hit compound inhibiting growth of L. donovani promastigotes by 50 and 38% at 50 and 25 μM concentrations, respectively. Preliminary safety evaluation of the promising hit compounds showed that they are 50-100 folds safer against human derived monocytic THP-1 cells relative to the drug erufosine. In silico study was conducted to predict the possible binding of hybrid 1e with methionine aminopeptidases 1 and 2 of L. donovani. Molecular dynamic simulations verified the predicted binding modes and provide more in depth understanding of the impact of hybrid 1e on LdMetAP-1 and LdMetAP-2.

Targeting trypanosomes: how chemogenomics and artificial intelligence can guide drug discovery

Trypanosomatids are protozoan parasites that cause human and animal neglected diseases. Despite global efforts, effective treatments are still much needed. Phenotypic screens have provided several chemical leads for drug discovery, but the mechanism of action for many of these chemicals is currently unknown. Recently, chemogenomic screens assessing the susceptibility or resistance of parasites carrying genome-wide modifications started to define the mechanism of action of drugs at large scale. In this review, we discuss how genomics is being used for drug discovery in trypanosomatids, how integration of chemical and genomics data from these and other organisms has guided prioritisations of candidate therapeutic targets and additional chemical starting points, and how these data can fuel the expansion of drug discovery pipelines into the era of artificial intelligence.

Computational methods directed towards drug repurposing for COVID-19: advantages and limitations

Novel coronavirus disease 2019 (COVID-19) has significantly altered the socio-economic status of countries. Although vaccines are now available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent for COVID-19, it continues to transmit and newer variants of concern have been consistently emerging world-wide. Computational strategies involving drug repurposing offer a viable opportunity to choose a medication from a rundown of affirmed drugs against distinct diseases including COVID-19. While pandemics impede the healthcare systems, drug repurposing or repositioning represents a hopeful approach in which existing drugs can be remodeled and employed to treat newer diseases. In this review, we summarize the diverse computational approaches attempted for developing drugs through drug repurposing or repositioning against COVID-19 and discuss their advantages and limitations. To this end, we have outlined studies that utilized computational techniques such as molecular docking, molecular dynamic simulation, disease-disease association, drug-drug interaction, integrated biological network, artificial intelligence, machine learning and network medicine to accelerate creation of smart and safe drugs against COVID-19.

Heterogeneous information network and its application to human health and disease

The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.

A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma

Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.

Identification of Trypanosoma cruzi Growth Inhibitors with Activity In Vivo within a Collection of Licensed Drugs

Chagas disease, caused by the parasite Trypanosoma cruzi (T. cruzi), affects more than six million people worldwide, with its greatest burden in Latin America. Available treatments present frequent toxicity and variable efficacy at the chronic phase of the infection, when the disease is usually diagnosed. Hence, development of new therapeutic strategies is urgent. Repositioning of licensed drugs stands as an attractive fast-track low-cost approach for the identification of safer and more effective chemotherapies. With this purpose we screened 32 licensed drugs for different indications against T. cruzi. We used a primary in vitro assay of Vero cells infection by T. cruzi. Five drugs showed potent activity rates against it (IC₅₀ < 4 µmol L⁻¹), which were also specific (selectivity index >15) with respect to host cells. T. cruzi inhibitory activity of four of them was confirmed by a secondary anti-parasitic assay based on NIH-3T3 cells. Then, we assessed toxicity to human HepG2 cells and anti-amastigote specific activity of those drugs progressed. Ultimately, atovaquone-proguanil, miltefosine, and verapamil were tested in a mouse model of acute T. cruzi infection. Miltefosine performance in vitro and in vivo encourages further investigating its use against T. cruzi.

Of Drugs and Trypanosomatids: New Tools and Knowledge to Reduce Bottlenecks in Drug Discovery

Leishmaniasis (Leishmania species), sleeping sickness (Trypanosoma brucei), and Chagas disease (Trypanosoma cruzi) are devastating and globally spread diseases caused by trypanosomatid parasites. At present, drugs for treating trypanosomatid diseases are far from ideal due to host toxicity, elevated cost, limited access, and increasing rates of drug resistance. Technological advances in parasitology, chemistry, and genomics have unlocked new possibilities for novel drug concepts and compound screening technologies that were previously inaccessible. In this perspective, we discuss current models used in drug-discovery cascades targeting trypanosomatids (from in vitro to in vivo approaches), their use and limitations in a biological context, as well as different examples of recently discovered lead compounds.

In silico tools to study molecular targets of neglected diseases: inhibition of TcSir2rp3, an epigenetic enzyme of Trypanosoma cruzi

There is a growing interest to study and address neglected tropical diseases (NTD). To this end, in silico methods can serve as the bridge that connects academy and industry, encouraging the development of future treatments against these diseases. This chapter discusses current challenges in the development of new therapies, available computational methods and successful cases in computer-aided design with particular focus on human trypanosomiasis. Novel targets are also discussed. As a case study, we identify amentoflavone as a potential inhibitor of TcSir2rp3 (sirtuine) from Trypanosoma cruzi (20.03 μM) with a workflow that integrates chemoinformatic approaches, molecular modeling, and theoretical affinity calculations, as well as in vitro assays.

Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework

Complexity of cascading interrelations between molecular cell components at different levels from genome to metabolome ordains a massive difficulty in comprehending biological happenings. However, considering these complications in the systematic modelings will result in realistic and reliable outputs. The multilayer networks approach is a relatively innovative concept that could be applied for multiple omics datasets as an integrative methodology to overcome heterogeneity difficulties. Herein, we employed the multilayer framework to rehabilitate colon adenocarcinoma network by observing co-expression correlations, regulatory relations, and physical binding interactions. Hub nodes in this three-layer network were selected using a heterogeneous random walk with random jump procedure. We exploited local composite modules around the hub nodes having high overlay with cancer-specific pathways, and investigated their genes showing a different expressional pattern in the tumor progression. These genes were examined for survival effects on the patient's lifespan, and those with significant impacts were selected as potential candidate biomarkers. Results suggest that identified genes indicate noteworthy importance in the carcinogenesis of the colon.

Searching for drugs for Chagas disease, leishmaniasis and schistosomiasis: a review

Chagas disease, leishmaniasis and schistosomiasis are neglected diseases (NDs) and are a considerable global challenge. Despite the huge number of people infected, NDs do not create interest from pharmaceutical companies because the associated revenue is generally low. Most of the research on these diseases has been conducted in academic institutions. The chemotherapeutic armamentarium for NDs is scarce and inefficient and better drugs are needed. Researchers have found some promising potential drug candidates using medicinal chemistry and computational approaches. Most of these compounds are synthetic but some are from natural sources or are semi-synthetic. Drug repurposing or repositioning has also been greatly stimulated for NDs. This review considers some potential drug candidates and provides details of their design, discovery and activity.

TDR Targets 6: driving drug discovery for human pathogens through intensive chemogenomic data integration

The volume of biological, chemical and functional data deposited in the public domain is growing rapidly, thanks to next generation sequencing and highly-automated screening technologies. These datasets represent invaluable resources for drug discovery, particularly for less studied neglected disease pathogens. To leverage these datasets, smart and intensive data integration is required to guide computational inferences across diverse organisms. The TDR Targets chemogenomics resource integrates genomic data from human pathogens and model organisms along with information on bioactive compounds and their annotated activities. This report highlights the latest updates on the available data and functionality in TDR Targets 6. Based on chemogenomic network models providing links between inhibitors and targets, the database now incorporates network-driven target prioritizations, and novel visualizations of network subgraphs displaying chemical- and target-similarity neighborhoods along with associated target-compound bioactivity links. Available data can be browsed and queried through a new user interface, that allow users to perform prioritizations of protein targets and chemical inhibitors. As such, TDR Targets now facilitates the investigation of drug repurposing against pathogen targets, which can potentially help in identifying candidate targets for bioactive compounds with previously unknown targets. TDR Targets is available at https://tdrtargets.org.

The Thiol-polyamine Metabolism of Trypanosoma cruzi: Molecular Targets and Drug Repurposing Strategies

Chagas´ disease continues to be a challenging and neglected public health problem in many American countries. The etiologic agent, Trypanosoma cruzi, develops intracellularly in the mammalian host, which hinders treatment efficacy. Progress in the knowledge of parasite biology and host-pathogen interaction has not been paralleled by the development of novel, safe and effective therapeutic options. It is then urgent to seek for novel therapeutic candidates and to implement drug discovery strategies that may accelerate the discovery process. The most appealing targets for pharmacological intervention are those essential for the pathogen and, whenever possible, absent or significantly different from the host homolog. The thiol-polyamine metabolism of T. cruzi offers interesting candidates for a rational design of selective drugs. In this respect, here we critically review the state of the art of the thiolpolyamine metabolism of T. cruzi and the pharmacological potential of its components. On the other hand, drug repurposing emerged as a valid strategy to identify new biological activities for drugs in clinical use, while significantly shortening the long time and high cost associated with de novo drug discovery approaches. Thus, we also discuss the different drug repurposing strategies available with a special emphasis in their applications to the identification of drug candidates targeting essential components of the thiol-polyamine metabolism of T. cruzi.

A paradigm shift in medicine: A comprehensive review of network-based approaches

Network medicine is a rapidly evolving new field of medical research, which combines principles and approaches of systems biology and network science, holding the promise to uncovering the causes and to revolutionize the diagnosis and treatments of human diseases. This new paradigm reflects the fact that human diseases are not caused by single molecular defects, but driven by complex interactions among a variety of molecular mediators. The complexity of these interactions embraces different types of information: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression and regulation, to metabolic and disease pathways up to drug-disease relationships. The analysis of these complex networks can reveal new disease genes and/or disease pathways and identify possible targets for new drug development, as well as new uses for existing drugs. In this review, we offer a comprehensive overview of network types and algorithms used in the framework of network medicine. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Target-based Screening of the Chagas Box: Setting Up Enzymatic Assays to Discover Specific Inhibitors Across Bioactive Compounds

Chagas disease is a neglected tropical illness caused by the protozoan parasite Trypanosoma cruzi. The disease is endemic in Latin America with about 6 million people infected and many more being at risk. Only two drugs are available for treatment, Nifurtimox and Benznidazole, but they have a number of side effects and are not effective in all cases. This makes urgently necessary the development of new drugs, more efficient, less toxic and affordable to the poor people, who are most of the infected population. In this review we will summarize the current strategies used for drug discovery considering drug repositioning, phenotyping screenings and target-based approaches. In addition, we will describe in detail the considerations for setting up robust enzymatic assays aimed at identifying and validating small molecule inhibitors in high throughput screenings.

Systems biology in inflammatory bowel diseases: on the way to precision medicine

Inflammatory bowel diseases (IBD) are chronic and recurrent inflammatory disorders of the gastrointestinal tract. The elucidation of their etiopathology requires complex and multiple approaches. Systems biology has come to fulfill this need in approaching the pathogenetic mechanisms of IBD and its etiopathology, in a comprehensive way, by combining data from different scientific sources. In combination with bioinformatics and network medicine, it uses principles from computer science, mathematics, physics, chemistry, biology, medicine and computational tools to achieve its purposes. Systems biology utilizes scientific sources that provide data from omics studies (e.g., genomics, transcriptomics, etc.) and clinical observations, whose combined analysis leads to network formation and ultimately to a more integrative image of disease etiopathogenesis. In this review, we analyze the current literature on the methods and the tools utilized by systems biology in order to cover an innovative and exciting field: IBD-omics.

A review of network-based approaches to drug repositioning

Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.

Drug repurposing for antimicrobial discovery

Antimicrobial resistance continues to be a public threat on a global scale. The ongoing need to develop new antimicrobial drugs that are effective against multi-drug-resistant pathogens has spurred the research community to invest in various drug discovery strategies, one of which is drug repurposing-the process of finding new uses for existing drugs. While still nascent in the antimicrobial field, the approach is gaining traction in both the public and private sector. While the approach has particular promise in fast-tracking compounds into clinical studies, it nevertheless has substantial obstacles to success. This Review covers the art of repurposing existing drugs for antimicrobial purposes. We discuss enabling screening platforms for antimicrobial discovery and present encouraging findings of novel antimicrobial therapeutic strategies. Also covered are general advantages of repurposing over de novo drug development and challenges of the strategy, including scientific, intellectual property and regulatory issues.

Network-Based Drug Repositioning: Approaches, Resources, and Research Directions

The wealth of knowledge and omic data available in drug research allowed the rising of several computational methods in drug discovery field yielding a novel and exciting application called drug repositioning. Several computational methods try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter we present an in-depth review of data resources and computational models for drug repositioning.

Developing Network Models of Multiscale Host Responses Involved in Infections and Diseases

Complex interactions involved in host response to infections and diseases require advanced analytical tools to infer drivers of the response in order to develop strategies for intervention. This chapter discusses approaches to assemble interactions ranging from molecular to cellular levels and their analysis to investigate the cross talk between immune pathways. Particularly, construction of immune networks by either data-driven or literature-driven methods is explained. Next, graph theoretic approaches for probing static network properties as well as visualization of networks are discussed. Finally, development of Boolean models for simulation of network dynamics to investigate cross talk and emergent properties are considered along with Boolean-like models that may compensate for some of the limitations encountered in Boolean simulations. In conclusion, the chapter will allow readers to construct and analyze multiscale networks involved in immune responses.

Synthesis and in vitro evaluation of Ca2+ channel blockers 1,4-dihydropyridines analogues against Trypanosoma cruzi and Leishmania amazonensis: SAR analysis

Drugs containing the1,4-dihydropyridine (DHP) core have recently attracted attention concerning their antiparasitic effect against various species of Leishmania and Trypanosoma. This approach named drugs repositioning led to interesting results, which have prompted us to prepare 21 DHP's analogues. The 1,4-DHP scaffold was decorated with different function groups at tree points including the nitrogen atom (NH and N-phenyl), the aryl group attached to C-4 (various substituted aryl residues) and the carbon atoms 2 and 6 (bearing Ph or Me groups). Moreover, the products were evaluated for their cytotoxicity on three cancer and a non-tumoral cell lines. Only 6 of them were antiproliferative and their weak effect (CC₅₀ comprised between 27 and 98μM) suggested these DHPs as good candidates against the intracellular amastigote forms of L. amazonensis and T. cruzi. L. amazonensis was sensitive to DHPs 5, 11 and 15 (IC₅₀ values at 15.11, 45.70 and 53.13μM, respectively) while 12 of them displayed significant to moderate trypanocidal activities against T. cruzi. The best trypanocidal activities were obtained with compounds 2, 18 and 21 showing IC₅₀ values at 4.95, 5.44, and 6.64μM, respectively. A part of the N-phenylated DHPs showed a better selectivity than their NH analogues towards THP-1 cells. 4-Chlorophenyl, 4-nitrophenyl and 3-nitrophenyl residues attached to the carbon atom 4 turned to be important sub-structures for the antitrypanosomal activity.

Laying in silico pipelines for drug repositioning: a paradigm in ensemble analysis for neurodegenerative diseases

When faced with time- and money-consuming problems, new practices in pharmaceutical R&D arose when trying to alleviate them. Drug repositioning has great promise and when combined with today's computational power and intelligence it becomes more precise and potent. This work showcases current approaches of creating a computational pipeline for drug repositioning, along with an extensive example of how researchers can influence therapeutic approaches and further understanding, through either single or multiple disease studies. This paradigm is based on three neurodegenerative diseases with pathophysiological similarities. It is our goal to provide the readers with all the information needed to enrich their research and note expectations along the way.

Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/.

The emergence of multi-resistant bacterial strains and the existing void in the discovery and development of new classes of antibiotics is a growing concern. Indeed, some bacterial strains are now resistant to last-line antibiotics and considered untreatable. Drug repositioning has been suggested as a strategy to minimize time and cost expenses until the drug reaches the market, compared to traditional drug design. Drug-target interactions (DTIs) are the basis of rational drug design and thus, we proposed a computational approach to predict DTIs solely based on the primary sequence of the protein and the simplified molecular-input line-entry system of the ligand. In addition, network metrics are used to identify vital putative drug-targets in bacteria. Molecular docking experiments were performed to compare the binding affinities between a given ligand and a putative drug-target, as well as with their original targets. According to the docking results, the predicted DTIs have better or similar binding activities than the ligand and their real target, indicating the validity of the proposed model.