
==== Front
Curr Genomics
Curr Genomics
CG
Current Genomics
1389-2029
1875-5488
Bentham Science Publishers

CG-25-323
10.2174/0113892029281602240422052210
Life Sciences, Genetics & Genomics, Genetics & Heredity
Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines
Ahlawat Vaishali 12
Sura Kiran 2
Singh Bharat 3
Dangi Mehak 2*
Chhillar Anil Kumar 1*
1 Centre for Biotechnology, M.D. University, Rohtak, Haryana, India;
2 Centre for Bioinformatics, M.D. University, Rohtak, Haryana, India;
3 Department of Biotechnology and Central Research Cell, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana-133207, India
* Address correspondence to these authors at the Centre for Biotechnology, M.D. University, Rohtak, Haryana, India; E-mail: anil.chhillar@gmail.com and Centre for Bioinformatics, M.D. University, Rohtak, Haryana, India; E-mail: mehakdangi2007@gmail.com
16 5 2024
2024
25 5 323333
11 9 2023
31 12 2023
11 3 2024
© 2024 The Author(s). Published by Bentham Science Publishers
2024
The Author(s)
https://creativecommons.org/licenses/by/4.0/ © 2024 The Author(s). Published by Bentham Science Publishers. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode.
Fungal infections are considered a great threat to human life and are associated with high mortality and morbidity, especially in immunocompromised individuals. Fungal pathogens employ various defense mechanisms to evade the host immune system, which causes severe infections. The available repertoire of drugs for the treatment of fungal infections includes azoles, allylamines, polyenes, echinocandins, and antimetabolites. However, the development of multidrug and pandrug resistance to available antimycotic drugs increases the need to develop better treatment approaches. In this new era of -omics, bioinformatics has expanded options for treating fungal infections. This review emphasizes how bioinformatics complements the emerging strategies, including advancements in drug delivery systems, combination therapies, drug repurposing, epitope-based vaccine design, RNA-based therapeutics, and the role of gut-microbiome interactions to combat anti-fungal resistance. In particular, we focused on computational methods that can be useful to obtain potent hits, and that too in a short period.

Keywords

Antifungal resistance
drug repurposing
reverse vaccinology
pharmacomicrobiomics
multidrug resistance
pandrug resistance
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pmc1 INTRODUCTION

About 3-5 million fungal species have been estimated to exist on our planet, of which about 300 species are pathogenic to humans [1]. Aspergillus, Cryptococcus, Pneumocystis, and Candida are the four major genera that cause lethal infections [2]. Additionally, Candida and Aspergillus have been identified to interact synergistically with the COVID-19 virus [3]. Treatment for mycotic infections relies on five major classes of drugs: echinocandins, polyenes, azoles, allylamines, and antimetabolites [4]. Although these drugs are effective in many cases, their therapeutic efficacy is limited because of their high toxicity and frequent development of resistance to therapeutics [5]. Despite the advancements in the mega science of mycology, the development of novel antifungal therapeutics remains a challenging, time- consuming, expensive, and inefficient process. This review emphasizes how the integration of bioinformatics and multi-omics approaches complement the development of new therapeutic products.

The fungal pathogens develop strategies to escape the host immune system and confer resistance to protective antifungal response. One such strategy is shielding the pathogen-associated molecular patterns (PAMPs) with different molecules, such as β-1,3-glucan, by the outer mannan layer, preventing its interaction with dectin-1. Dectin-1 is a pattern recognition receptor (PRR) on host immune cells and mediates antifungal cellular responses [6]. In dimorphic fungi, β-1,3-glucans (more immunogenic) are converted into α -1,3 glucans (less immunogenic) during a change in morphology from filamentous form to yeast form. Eng1 protein secreted by Histoplasma caspulatum has glucanase activity, which reduces as β-1,3-glucan on yeast cell wall [7].

Numerous mechanisms related to the development of antifungal resistance have been identified, including alteration or overexpression of antifungal targets, reduction in the intracellular drug concentration due to upregulation of multidrug transporters, biofilm formation, and activation of stress responses [8]. These processes are influenced by multiple factors like drug misuse, lack of strong regulatory measures, improper sewage disposal, and low-quality medicine and non-specific medications, causing the emergence of drug-resistant microbes [9]. The long-term use of antifungals can result in serious adverse drug effects (ADEs). The triazoles (itraconazole, posaconazole, voriconazole, fluconazole, and isavuconazole) has recently gained attention for its ADEs. The use of posaconazole causes an elevation in liver enzymes leading to hepatotoxicity, while voriconazole and isavuconazole have the highest risk of nervous disorders. The echinocandins consist of three approved drugs: micafungin, caspofungin, and anidulafungin. Furthermore, all three drugs are similar in their chemical structure, but it was found that micafungin and caspofungin have the highest incidences of subcutaneous tissue and skin disorders [10]. Amphotericin B, belonging to class polyenes, was the first antifungal drug approved by the FDA for the treatment of mycosis. The major drawback of Amphotericin B reported was its toxicity, notably nephrotoxicity, which causes kidney damage [11]. The other ADEs related to amphotericin B include subcutaneous tissue and skin disorders, a decrease in potassium levels, and respiratory and gastrointestinal disorders [10].

Antifungal vaccines could be an alternative treatment to eradicate fungal infections. Conventional methods of vaccine development rely on the growth of the pathogen in laboratories and the purification of antigenic proteins that can serve as potential vaccine candidates. These traditional approaches, besides being time-consuming and of low yield, have failed in several instances, such as in the cases of non-culturable/cultivatable pathogens [12]. Advances in bioinformatics facilitate the birth of a rationalized strategy known as reverse vaccinology (RV) [13]. The RV approach exploits the whole genome of the pathogen and searches for putative immunogenic targets. The basic idea behind this approach was B and T cell receptors recognize the predicted antigenic determinants and evoke both humoral and cell-mediated immune responses [14].

The restricted scope of currently available antifungals calls for finding new therapeutic approaches. Integrated genomics, transcriptomics, proteomics, and bioinformatics aid in the advancement of therapeutics for various infectious diseases. Various computational strategies have been employed to investigate how drug candidates interact with target proteins and elicit a therapeutic response impacting biological pathways and functions. The development of better diagnostic tools and strategies that allow targeted use of antifungals is essential to promote drug effectiveness. This article provides insight into the use of bioinformatics and computational approaches for novel therapeutic discoveries.

2 BIOINFORMATICS IN THE IDENTIFICATION OF POTENTIAL THERAPEUTIC TARGETS

2.1 Mechanistic Targets for Available Antifungals and Resistance Mechanism

Based on the action mechanisms, the existing classes of drugs have three main targets: (i) inhibition of ergosterol biosynthesis, (ii) disruption of the fungal membrane, and (iii) inhibition of macromolecule synthesis [15]. Azoles block the activity of 14-sterol demethylase, an enzyme belonging to the Cytochrome P-450 family, that plays a role in ergosterol production. This causes the depletion of ergosterol and the accumulation of toxic sterol intermediates that lead to the loss of membrane integrity, as ergosterol is the main component of the fungal cell membrane, which results in cell death [16]. The prevalence of resistance to azoles happens due to mutations in the target gene ERG11 and overexpression of efflux transporters that cause azole molecules to escape outside the cell [8]. Polyenes, another class of antifungal drugs, interact with the fungal membrane and target a vital molecule-ergosterol [17]. The binding of polyenes to ergosterol facilitates the leakage of intracellular ions that disrupt the membrane potential and active transport mechanism. Amphotericin B belongs to the class polyenes, and it was the first antifungal discovered. The major drawback of Amphotericin B reported was its toxicity, notably nephrotoxicity, which causes kidney damage [11]. Flucytosine (5-fluorocytosine) is an antimetabolite that targets DNA and RNA synthesis in fungi. Once it enters the fungal cell, it is metabolized into 5-fluorouracil, a pyrimidine analog that can be disincorporated into DNA and RNA. Two common side effects attributed to flucytosine are hepatotoxicity and hematological toxicity [18]. Another major antifungal category is Echinocandins, which block the activity of 1,3- β-d-glucan (BDG) synthase. BDG synthase is an enzyme responsible for the synthesis of 1,3- β-d-glucan, which is one of the main structural elements of the cell wall in most fungi but is absent in mammalian cells, therefore making it a perfect target for antifungals [19]. However, mutations in the gene FKS1, which encodes a catalytic subunit of glucan synthase, serve as the site of drug resistance [20].

2.2 Computer-aided Target Discovery

The currently available antifungals either target cell wall synthesis, ergosterol synthesis, or ergosterol itself. However, these targets constitute a minor fraction of potential therapeutic targets encoded by the fungal genome [21]. To improve therapeutic success, pharmacological molecules interact with specific targets, therefore, identification of new targets and target validation are of utmost importance for the development of new therapeutic molecules. There are two basic criteria for a gene to function as a therapeutic target. First, pathogen survival and growth must rely on that gene. Second, the homolog of the candidate gene must not be present in mammals. Traditionally, target identification relies on wet lab experiments and is cost-ineffective, time-consuming, and has low accuracy. In the multi-omics era, computer-aided target identification is overcoming the limitations of conventional methods. Bioinformatics has created a paradigm shift in the identification of novel drug targets and facilitates the process of drug discovery. Novel targets can be uncovered using two methods: A) Comparative genomics, which includes a comparison of host and pathogen metabolic pathways. B) Network-based approach, is useful for constructing the endogenous signaling, regulatory, and metabolic pathway with which the novel drug target can interact [22, 23].

2.2.1 Comparative Genomics Approach

The comparative genomics approach is based on the fact that candidate drug targets are key components in metabolic pathways and are crucial for pathogen survival. Moreover, the comparative genomics approach, combined with metabolic pathways analysis, yields the proteins specified to the pathogen and is followed by the subtractive proteomics approach, which narrows the selection for target identification.

Firstly, all the existing metabolic pathways from both the host and pathogen are collected and compared using databases like KEGG or MetaCyc. In addition, common metabolic pathways are removed, and genes as well as enzymes belonging to unique metabolic pathways are identified. In the second step, protein sequences for the enzymes involved in pathogen-specific pathways are retrieved in FASTA format from the UniProt database and subjected to homology searches using the BLAPSTp tool. Non-homologous pathogen enzymes are identified using BLAST results that have no hits with host enzymes [23]. Finally, resultant proteins are prioritized using several parameters, including 1) the selection of essential proteins, virulent proteins, and resistance proteins, 2) Subcellular localization (cytoplasmic proteins are more suitable as drug targets), 3) the removal of proteins already existing as drug targets for novelty, and 4) Drug ability and toxicity analysis. The novel/potential drug targets identified using the above-mentioned techniques need to be further subjected to structure generation and validation, which the present article does not factor into its scope. Fig. (1) shows a workflow for in-silico identification of novel targets using a Comparative Genomics approach.

2.2.2 Network-based Approach

The concept of network graph theory explores the biological network by mapping all the relevant data through data mining. This helps to identify the functional concept in the network and identify the potential targets [24]. The integration of such huge biological datasets requires system biology tools and computational algorithms together with the use of functional genomic and network analysis databases. Cytoscape [25] and Gephi [26] are the two popularly used tools for complex network analysis. GeneMANIA is a web-based tool for analyzing gene lists [27]. These tools identify the sub-networks and regions of similarity and dissimilarity to interpret the interactions within the network. Paolini et al. first developed the drug-target network based on 200,000 compounds with more than 500,000 bioactivities by linking proteins through chemical spaces [24].

The functional component in the biological network is depicted as a node, and any connection between the nodes, which can be physical or functional interactions is termed as edge. Different types of networks include gene interaction networks, protein-protein interaction networks, mi-RNA-mRNA interaction networks, signal transduction networks, metabolic networks, and genetics interaction networks. Table 1 tabulates the parameters for analyzing the general structure of biological networks [28, 29]. In general, network-based approaches require an in-depth knowledge of the interaction network and, therefore, require pathway enrichment analysis to identify the potential drug target. Fig. (2) describes the workflow of the network-based approach.

Recently, Robin et al. proposed three promising therapeutic targets against Cryptococcus gattii using comparative genomics and subtractive approach that are- Mitochondrial distribution and morphology protein 10 (MDM10), osmolarity two-component system, phosphorelay intermediate protein YPD1 (YPD1), and mitochondrial distribution and morphology protein 34 (MDM34, MMM2) [30]. However, the study is completely based on computational analysis and still needs to be confirmed experimentally. Rrp9 (U3 small nucleolar ribonucleoprotein associated protein) is a promising drug target against Candida albicans based on in-silico studies. Docking studies revealed that it shows binding affinity with dicyclomine, which targets signal transduction genes and inhibits virulence factors in C. albicans [31]. Computational studies revealed that 5 protein-coding genes, namely His6, PabaA, FasA, FtmA, and erg6, can act as putative drug targets against Aspergillus fumigatus [32]. However, elucidation of the 3D structure of these targets is lacking till now.

3 RECENT THERAPEUTIC APPROACHES FOR COMBATING ANTIFUNGAL RESISTANCE

3.1 Advancements in the Drug Delivery System

Despite the available antifungal therapeutics, the prevalence of fungal infections is still increasing due to the development of multidrug resistance to existing antifungal drugs, and the foremost reason for the development of drug resistance is found to be associated with suboptimal drug concentration and non-specific cell targeting [33, 34]. Furthermore, novel antifungal therapies show less efficacy due to the insufficiency of the suggested route of administration, lack of controlled clinical trials, or high cost of production compared to conventional antifungals [35]. The development of the drug delivery system based on nano-formulations provides insight to overcome these limitations. However, most of the available drugs are hydrophobic, which reduces their solubility and bioavailability, causing pharmacokinetic problems. However, the pharmacokinetic profile can be improvised by the covalent conjugation of drugs with the polymers [36]. Efforts have been made to optimize the compatibility between drugs and nanoparticles using the in-silico approach, which is time-effective and augments drug loading, retention, and stability. Molecular simulations are the ideal technique to improve the design of drug delivery devices and are driven by long-range non-covalent interactions. Simulations serve as a “computational microscope,” which provides information that is difficult to get experimentally, such as the influence of molecular interactions on crucial parameters like release rate, drug delivery device's responsiveness to external stimuli, and interactions between nanoparticles and biological material [37]. Several Databases/Tools are available to design nanoparticle-based drug delivery systems (Table 2). However, so far, there is no currently stored information regarding the 3D structure of nanomaterials and their correlation with physicochemical properties and toxicity, which brings about the need to build a database regarding such information.

3.2 Combination Therapy

Combination therapy using multiple drugs, is a promising therapeutic strategy, improving the combined molecules' efficacy, reducing toxicity, and combating antimicrobial resistance [38]. Clinical studies have demonstrated the effectiveness of combination therapy in various instances. Shaban et al. demonstrated that Carvacrol, a monoterpene phenol, shows both additive and synergistic effects when combined with antifungal drugs: nystatin fluconazole, caspofungin, and amphotericin B against C. auris [39]. Terbinafine (TEF) and azoles show synergistic effects; azoles target the plasma membrane, increasing TEF absorption [40]. Various combinations of plant natural products and existing antifungal drugs have been investigated for combating antifungal resistance, such as Brazilian Red Propolis and A. Sellowiana in combination with fluconazole act synergistically against C. parapsilosis and C. glabrata. Propolis acts on the cell wall and facilitates the penetration of fluconazole inside the cells [41]. Recently, it has been reported that ribavirin works synergetically with caspofungin against C. albicans and can be effective in treating C. albicans infections [42].

There will be millions of combinations for the thousands of FDA-approved drugs, and the systematic high-throughput screening of all possible drug combinations is time-consuming and challenging, therefore, there is delimited knowledge of effective drug combinations [43]. There are many unanswered questions, like which two molecules in combination would be optimal or at what concentration they will work as a better therapeutic agent? What endpoint is relevant? What percentage of populations are likely to get leverage? How can combination therapy be justified over monotherapy? How to counterbalance the production cost and the chances of potentially increased toxicity of this approach?

Computational strategies enable in silico screening of combination effects. The network-based approach is one such strategy that offers novel insight to explore the “multiple-drug, multiple targets” paradigm aiming at modifying multiple disease proteins within the same disease module while minimizing toxicity profiles [44].

The pharmacokinetic and pharmacodynamic properties of drugs and the appropriate drug dosage in combination can be quantified using mathematical modeling [45, 46]. The binding effect of each drug involved in combination therapy is determined using the MD simulations, which also suggest the possible outcome of the allosteric binding of other drugs, whether the drugs in combination show synergism or antagonism [47].

Various databases have been generated on combination therapy, such as the Drug combination database (DCDB) [48], drug-drug interaction (DDI) [49], Antifungal synergistic drug combination database (ASDCD) [50], DrugComb (DB) [51]. Some freely available software and tools that have been developed for analyzing combination data based on machine learning techniques are Combenefit [52], SynergyFinder [53], Synergy [54], and SynToxProfiler [55]. However, the lack of available input data is still considered a major limitation for the computational design of combination therapies that demands attention for bringing in better results of combination therapies.

3.3 Drug Discovery

3.3.1 De Novo Drug Development

Developing novel drugs with required pharmacological activity is crucial for maintaining the development pipeline. Computer-Aided Drug Design is an efficient tool to expedite the drug discovery process and relies on information regarding the receptor (target) and its binding ligand. The two different approaches in CADD are Structure-Based Computer-Aided Drug Design (SB-CADD) and Ligand-Based Computer-Aided Drug Design (LB-CADD) [56]. SB-CADD approach includes the following steps: (1) Mining data: Various databases have been developed to extract information about protein structural data, drug interactions, side effects, metabolic pathways, protein-protein interactions/networks, drug targets, etc. (2) Protein structure prediction: 3D structures of proteins can be predicted computationally using methods such as homology modeling and de novo modeling. However, the former is the best-suited method and the most accurate [57]. Other methods for the detection of 3D structures of protein include X-ray crystallography, NMR, and Electron microscopy [58]. (3) Molecular docking and Molecular dynamic (MD) simulations: Molecular docking allows the prediction of interaction between a drug candidate and target protein (receptor) to make a stable complex [59]. Various docking programs have been developed till now, and among all software, Autodock Vina, MOE-Dock, and GOLD give the best scores with their algorithms [60]. Molecular docking is insufficient for understanding the behavior of a drug in the actual physical system, as proteins are dynamic and exist in different conformational states. MD simulations are an advantageous technique to overcome the shortcomings of molecular docking [61]. LB-CADD: A ligand-based approach is implemented when the 3D structure of the protein or target molecule is unavailable [62]. This approach elucidates the relationship between the structural and physicochemical properties of the compound and its biological activity [63]. The two most widely used computational strategies in the LB-CADD approach are Quantitative structure-activity relationships (QSAR) and pharmacophore modeling [64]. While the 3D QSAR pharmacophore approach incorporates the chemical properties of both the most active and inactive compounds together with their biological activity, pharmacophore modeling solely makes use of the common chemical features found in the most active compounds. DrugRep is a web server for re-profiling drugs that achieves its task using both receptor-based screening and ligand-based screening. The cavity detection approach detects the possible binding pockets of receptors and performs batch docking using AutoDock Vina [65]. However, discussing the details of these approaches is not in the scope of this article.

3.3.2 Drug Repurposing

The re-profiling of existing drugs, as compared to the traditional drug discovery approach or new drug designing is cost-effective and time-saving with additional benefitssuch as lower chances of failure in the later stages of clinical trials [66]. Multi-omics era and bioinformatics analysis provide insight into drug repurposing [62]. Antifungal effects of various non-antifungals (antitumor and antimicrobial agents) can be uncovered using the drug repurposing strategy. Various anti-bacterial drugs such as aminoglycosides, macrolides, tetracyclines, quinolone peptides, and others, including rifampicin and linezolid, are also known to possess antifungal activities [67]. It has been recently reported that atorvastatin, an inhibitor of HMG-CoA reductase, a lipid-lowering drug, is confirmed to have antifungal activity in fluconazole-resistant Candida albicans [68].

Computational drug repurposing have been classified into drug-centric (similar drugs have similar pharmacological effects) and disease-centric (similar disease needs the same therapies). Current in-silico approaches that have been developed in the context of drug repurposing are of three types: (1) target-driven repurposing, (2) genome-wide repurposing, and (3) Literature-driven repurposing [69].

3.3.2.1 Target-driven Repurposing

The affinity of drug molecules to more than one target is the key notion behind target-driven repurposing [70]. Target-driven repurposing exploits the drug libraries available for high-throughput screening, followed by virtual screening such as docking or ligand-based screening. This approach can screen nearly all drug compounds with known chemical structures [71]. Based on protein targets, new indications are identified by linking a drug to a specific disease [69].

3.3.2.2 Genome-wide Repurposing

The advancement in genome-wide metrics has made it possible to repurpose FDA-approved drugs for treating heterogeneous diseases [72]. The Online Mendelian Inheritance in Man (OMIM) and the Gene Expression Omnibus (GEO) are two publicly available repositories that enable a systemic survey of disease similarity within the framework of the genome. The drug-target interaction networks represent another domain of genome-wide repurposing. It exploits the disease omics data because disease pathways can be constructed using network analysis [69].

3.3.2.3 Literature Driven Repurposing

The literature-driven repurposing, or “text mining,” leverages the huge scientific literature on drugs and disease [71]. Bioinformatics and chemoinformatics tools combined with the text mining approach led to novel discoveries systemically. Several information sources or databases are available for indication discovery such as PubMed and OMIM [69].

3.4 Drug-microbiome Interactions

The gut microbiome (GM) and drug interactions share a reciprocal relationship. GM can interfere with drug metabolism and, hence, can increase, decrease, or toxify the drug efficacy to a clinically significant level. On the contrary, drug intake may also alter the composition of gut microbiota, which, in turn, may affect the individual's health and other drug responses [73]. Drug metabolism by GM of over 180 orally administered drugs has already been reported, and non-oral administered drugs are under research [74]. Human GM shows inter-individual variation and acts as unique fingerprints [75]. A new field, “Pharmacomicrobiomics,” has been proposed, to investigate the interplay between GM variation and drug pharmacodynamics and pharmacokinetics [76]. Drug absorption, distribution in the body, metabolism, and elimination (ADME) are the four fundamental processes studied in the field of pharmacokinetics. GM interaction with antifungal drugs has been reported in the literature. Fluconazole administration is the most widely used antifungal, which impacts the gut microbiome composition [77]. Hence, the study of the drug-microbiome interactions can prove to be a milestone in antifungal treatment.

Table 3 depicts some available databases that contain information about drug-microbiome interactions. DrugBug is the only tool available to predict the susceptibility of the drug to get metabolized by the GM. In addition, DrugBug was developed using a machine learning technique based on the structural similarity of drugs, as drugs with certain functional groups are more prone to metabolism by the GM [78]. Currently, the use of the in-silico approach in understanding drug-microbiome interactions lags behind other areas. The emergence of next-generation sequencing and advancements in the characterization of GM provide a lot of the latest information to create datasets and develop novel computational pipelines using these datasets. Moreover, the composition of GM varies in every individual due to numerous factors like population difference, age, diet, genome, presence of disease, lifestyle, and gender [79]. Hence, the universalization of these studies is still far from being achieved.

3.5 RNA-based Therapeutics

RNA molecules are used as a drug or a vaccine to generate a therapeutic response in experimental organisms which brings RNA-based therapeutics to the forefront as an emerging source of treatment option for different fungal infections. The core concept of RNA therapeutics is the manipulation of protein function and/or production. This can be accomplished by either directly targeting proteins, interfering with the RNAs that encode the necessary proteins, or supplying the genetic instructions for protein synthesis [83]. RNA- mediated gene silencing is a well-conserved phenomenon and has been investigated in a diverse group of fungi. There used to be so many limitations on the practical implications of RNA-based methods, such as rapid therapeutic deterioration, specificity to inhibit fungal pathways only, crossing the cell envelope barrier, and challenges in facilitating RNA escape from the endosome [84]. Some frequently used strategies to overcome the challenges felt in RNA-based therapeutics are nanoparticle-based delivery [85], chemical modification to prevent deterioration and decrease immunogenicity [86], and the use of aptamer as a delivery carrier to increase specificity [87]. Based on machine learning, various bioinformatics tools and servers have been developed which facilitate the designing of RNA-based therapeutics and are discussed in the next section.

In Aspergillus nidulans, the siRNA shows an in-vitro gene silencing effect targeting ornithine decarboxylase (ODC), a fungal polyamine gene essential for fungal growth and development [88]. RNAi mechanism was successfully tested in Aspergillus fumigatus against ALB1/PKSP and FKS1 genes [89].

imRNA is a server for designing immunomodulatory single-stranded RNA to develop RNA-based therapeutics. In addition to it, this server may also identify minimum mutations required to decrease the immunomodulatory potential of a given RNA sequence. Computer-aided designing of siRNA can also be done using this server [90]. AptaBlocks is a computational approach that aids in designing RNA complexes and improvising RNA-based drug delivery systems [91]. PFRED is another computational platform for designing antisense oligonucleotides and siRNA [92]. Si-Fi (siRNA Target Finder) is yet another tool available online for designing siRNA [93]. Various link prediction models have been developed, such as GKLOMLI for inferring miRNA–lncRNA interactions [94], SPRDA predicts piRNA associated with diseases [95], and AMDECDA predicts circRNA-disease association [96].

4 MODERN VACCINE DEVELOPMENT

Traditional methods of developing vaccines come at a huge cost in terms of time and money [97]. Artificial intelligence-driven immunology research has led to the emergence of immunoinformatics as the field of study [98]. The use of immunoinformatics tools in designing vaccines nowadays has also facilitated a rationalized strategy known as “Reverse Vaccinology (RV). The basic premise of the RV approach is to search for immune-dominant epitopes that can be recognized by B and T cell receptors, known as B cell epitopes (BCEs) and T cell epitopes (TCEs), respectively, which evoke both humoral and cellular immune response [14]. The most widely used bioinformatics tools available for the prediction of epitopes are tabulated in Table 4. Promiscuous antigenic proteins are filtered using the subtractive proteomics approach [99], and the epitope selection list is narrowed down based on antigenicity, allergenicity, immunogenicity, druggability, virulence, self-tolerance, immune boosting potential, toxicity, conservancy, etc [100]. Since the single-epitope-based vaccine has low immunogenicity and antigenicity, hence the multi-epitope vaccine construct was favored. The epitopes and adjuvant can be fused using appropriate linker selection, and the final vaccine construct can be simulated and evaluated before the experimental validation [14]. RV approach has been successfully applied to build effective subunit vaccine candidates against emerging strains of mycobacterium, peptide vaccines based on essential genes and virulent genes against bacterial infections, subunit vaccines against the Zika virus, and many others against coronaviruses [101].

CONCLUSION

Despite advancements in antifungal treatment, the prevalence of infections is still increasing, resistance to the existing antifungal drugs remains a major concern, and the goal of achieving control over fungal diseases is constantly pushed further. Of course, there has been tremendous progress in the development of novel antifungal drugs, but it may take many years from discovery to clinical use. For this reason, it is important to optimize existing molecules and develop novel combinations and alternative therapies to prevent and treat mycosis. Computer-aided drug designing, nano-modeling tools, drug repurposing, and immunoinformatics approaches can change the paradigm of therapeutic development. Moreover, the interactions between the microbiome and drug metabolism need to be explored further to improve drug efficiency. To conclude, it can be said that bioinformatics or computational approaches have the immense potential to accelerate the process of identification of more efficient drug/vaccine candidates and facilitate the development of antifungal therapeutics.

AUTHORS’ CONTRIBUTIONS

The authors confirm contribution to the paper as follows: V.A., K.S., B.S., M.D. and A.K.C. contributed to the research design and implementation, as well as the data analysis and manuscript writing.

ACKNOWLEDGEMENTS

Declared none.

LIST OF ABBREVIATIONS

ADEs Adverse Drug Effects

ASDCD Antifungal Synergistic Drug Combination Database

BCEs B Cell Epitopes

DCDB Drug Combination Database

DDI Drug-drug Interaction

GEO Gene Expression Omnibus

GM Gut Microbiome

LB-CADD Ligand-Based Computer-Aided Drug Design

MASI Microbiota- Active Substance Interaction Database

MDAD Microbe Drug Association Database

ODC Ornithine Decarboxylase

OMIM Online Mendelian Inheritance in Man

PAMPs Pathogen-associated Molecular Patterns

PRR Pattern Recognition Receptor

QSAR Quantitative Structure-activity Relationships

RV Reverse Vaccinology

SB-CADD Structure-Based Computer-Aided Drug Design

TCEs T Cell Epitopes

CONSENT FOR PUBLICATION

Not applicable.

FUNDING

None.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

Fig. (1) The computational workflow for the identification of novel drug targets using comparative genomics.

Fig. (2) The computational workflow for the identification of novel drug targets using a network-based approach.

Table 1 Description of the parameters used in biological network analysis.

Parameters	Description	
Network density	Maximum number of edges connecting each node to each other.	
Degree	The property of the node to interact with other neighboring nodes.	
Betweenness	The frequency with which the distance between any pair of nodes passes through that node.	
Distance	The shortest path length between two nodes.	
Clustering coefficient	It measures the interconnectivity of its neighbors.	
Connectivity	Minimum number of elements that need to be removed to disconnect the leftover nodes from each other.	
Assortativity	Measures the correlation coefficient of degree between pairs of linked nodes.	
Eigen value	It is the measure of the influence of nodes in a network.	

Table 2 Databases/tools available to design nanoparticle-based drug delivery systems.

Name	Type	Description	URL	
Nanowerk	Database	Currently available nanomaterials (about 4500)	http://www.nanowerk.com/	
ENanoMapper	Database	Provides toxicology data	http://data.enanomapper.net/	
NBI knowledge base	Repository	Data on nanomaterial characterization, biological interactions, and synthesis methods	http://nbi.oregonstate.edu/	
Nanomaterial Registry (NR)	Repository	Physiochemical properties of nanomaterials and their biological interactions	http://naomaterialregistry.net/	
PubVINAS	Tool	Nano-modeling tool	http://www.pubvinas.com/	

Table 3 Drug-microbiome interaction databases.

Name	Description	References	
Microbe Drug Association Database (MDAD)	Contains experimentally supported information about drug-microbe interaction	[80]	
PharmacoMicrobiomics database	Classify drug-microbe interaction based on microbial taxa and body site	[81]	
Microbiota- Active Substance Interaction Database (MASI)	Provide information about the abundance of GM, drug impact on GM, and vice-versa	[82]	

Table 4 The most widely used bioinformatics tools available for the prediction of epitopes.

MHC-I Binding Prediction Tools	MHC- II Binding Prediction Tools	Linear B-cell Epitope Prediction Tools	Conformational B-cell Epitopes Prediction Tools	
IEDB
NetCTL
MHCpred NetMHC nHLAPred
CTL-Pred
SVMHC	RANKPE
BIMAS
MAPPP
ProPred SYFPEITIPREDEP
MHCPEP	IEDB
NetMHC-II MHCpred
MetaMHC MetaSVMP, Propred-II
RANKPEP PREDIVACEpiDOCK Consensus
	SYFPEITH,
BIMAS CTL-pred
EpiTOP
MHCPEP EpiVax,
PREDEPP TEPITOPE
EPIPREDITEpiMatrix
	Bepipred BCpred
ABCpred
Pcipep BCEpred
Igpred	BepiTope
PrediTop PEOPLE LBtope
SVMTrip
COBEproEPMLR	Discotope
Ellipro
CBTope
Epitope
BEPro
CEP
SEPPA	CED
EPITOME
MAPOTODEEPCES
EPSVR EPMETA
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REFERENCES

1 León-Buitimea A. Garza-Cervantes J.A. Gallegos-Alvarado D.Y. Osorio-Concepción M. Morones-Ramírez J.R. Nanomaterial-based antifungal therapies to combat fungal diseases aspergillosis, Coccidioidomycosis, Mucormycosis, and candidiasis. Pathogens 2021 10 10 1303 10.3390/pathogens10101303 34684252
2 Heard S.C. Wu G. Winter J.M. Antifungal natural products. Curr. Opin. Biotechnol. 2021 69 232 241 10.1016/j.copbio.2021.02.001 33640596
3 Chen N. Zhou M. Dong X. Qu J. Gong F. Han Y. Qiu Y. Wang J. Liu Y. Wei Y. Xia J. Yu T. Zhang X. Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020 395 10223 507 513 10.1016/S0140-6736(20)30211-7 32007143
4 Gintjee T.J. Donnelley M.A. Thompson G.R. III Aspiring antifungals: review of current antifungal pipeline developments. J. Fungi 2020 6 1 28 10.3390/jof6010028 32106450
5 Fisher M.C. Hawkins N.J. Sanglard D. Gurr S.J. Worldwide emergence of resistance to antifungal drugs challenges human health and food security. Science 2018 360 6390 739 742 10.1126/science.aap7999 29773744
6 Hernández-Chávez M. Pérez-García L. Niño-Vega G. Mora-Montes H. Fungal strategies to evade the host immune recognition. J. Fungi 2017 3 4 51 10.3390/jof3040051 29371567
7 Marcos C.M. de Oliveira H.C. de Melo W.C.M.A. da Silva J.F. Assato P.A. Scorzoni L. Rossi S.A. de Paula e Silva A.C.A. Mendes-Giannini M.J.S. Fusco-Almeida A.M. Anti-immune strategies of pathogenic fungi. Front. Cell. Infect. Microbiol. 2016 6 142 10.3389/fcimb.2016.00142 27896220
8 Revie N.M. Iyer K.R. Robbins N. Cowen L.E. Antifungal drug resistance: evolution, mechanisms and impact. Curr. Opin. Microbiol. 2018 45 70 76 10.1016/j.mib.2018.02.005 29547801
9 Imchen M. Moopantakath J. Kumavath R. Barh D. Tiwari S. Ghosh P. Azevedo V. Current trends in experimental and computational approaches to combat antimicrobial resistance. Front. Genet. 2020 11 563975 10.3389/fgene.2020.563975 33240317
10 Yang Y.L. Xiang Z.J. Yang J.H. Wang W.J. Xu Z.C. Xiang R.L. Adverse effects associated with currently commonly used antifungal agents: a network meta-analysis and systematic review. Front. Pharmacol. 2021 12 697330 10.3389/fphar.2021.697330 34776941
11 Wall G. Lopez-Ribot J.L. Current antimycotics, new prospects, and future approaches to antifungal therapy. Antibiotics 2020 9 8 445 10.3390/antibiotics9080445 32722455
12 Aqib A.I. Anjum A.A. Islam M.A. Murtaza A. Rehman A. Recent global trends in vaccinology, advances and challenges. Vaccines 2023 11 3 520 10.3390/vaccines11030520 36992104
13 Heinson A.I. Woelk C.H. Newell M.L. The promise of reverse vaccinology. Int. Health 2015 7 2 85 89 10.1093/inthealth/ihv002 25733557
14 Parvizpour S. Pourseif M.M. Razmara J. Rafi M.A. Omidi Y. Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. Drug Discov. Today 2020 25 6 1034 1042 10.1016/j.drudis.2020.03.006 32205198
15 Ghannoum M.A. Rice L.B. Antifungal agents: mode of action, mechanisms of resistance, and correlation of these mechanisms with bacterial resistance. Clin. Microbiol. Rev. 1999 12 4 501 517 10.1128/CMR.12.4.501 10515900
16 Whaley S.G. Berkow E.L. Rybak J.M. Nishimoto A.T. Barker K.S. Rogers P.D. Azole antifungal resistance in Candida albicans and emerging non-albicans Candida species. Front. Microbiol. 2017 7 2173 10.3389/fmicb.2016.02173 28127295
17 Carolus H. Pierson S. Lagrou K. Van Dijck P. Amphotericin B and other polyenes—Discovery, clinical use, mode of action and drug resistance. J. Fungi 2020 6 4 321 10.3390/jof6040321 33261213
18 Viviani M.A. Flucytosine—what is its future? J. Antimicrob. Chemother. 1995 35 2 241 244 10.1093/jac/35.2.241 7759388
19 Mota Fernandes C. Dasilva D. Haranahalli K. McCarthy J.B. Mallamo J. Ojima I. Del Poeta M. The future of antifungal drug therapy: novel compounds and targets. Antimicrob. Agents Chemother. 2021 65 2 e01719-20 10.1128/AAC.01719-20 33229427
20 Scorzoni L. de Paula e Silva A.C. Marcos C.M. Assato P.A. de Melo W.C. de Oliveira H.C. Costa-Orlandi C.B Mendes-Giannini M.J. Fusco-Almeida A.M. Antifungal therapy: new advances in the understanding and treatment of mycosis. Front. Microbiol. 2017 8 242257
21 Andriole V.T. Current and future antifungal therapy: new targets for antifungal therapy. Int. J. Antimicrob. Agents 2000 16 3 317 321 10.1016/S0924-8579(00)00258-2 11091055
22 Jiang Z. Zhou Y. Using gene networks to drug target identification. J. Integr. Bioinform. 2005 2 1 48 57 10.1515/jib-2005-14
23 Zhang X. Wu F. Yang N. Zhan X. Liao J. Mai S. Huang Z. In silico methods for identification of potential therapeutic targets. Interdiscip. Sci. 2022 14 2 285 310 10.1007/s12539-021-00491-y 34826045
24 Paolini G.V. Shapland R.H. van Hoorn W.P. Mason J.S. Hopkins A.L. Global mapping of pharmacological space. Nat. Biotechnol. 2006 24 7 805 815 10.1038/nbt1228 16841068
25 Smoot M.E. Ono K. Ruscheinski J. Wang P.L. Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011 27 3 431 432 10.1093/bioinformatics/btq675 21149340
26 Bastian M. Heymann S. Jacomy M. Gephi: an open source software for exploring and manipulating networks. Proceedings of the third international AAAI conference on web and social media vol. 3, no. 1, pp. 361-362, Mar. 2009. 10.1609/icwsm.v3i1.13937
27 Mostafavi S. Ray D. Warde-Farley D. Grouios C. Morris Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 2008 9 Suppl 1 Suppl. 1 S4 10.1186/gb-2008-9-s1-s4 18613948
28 Zhu X. Gerstein M. Snyder M. Getting connected: analysis and principles of biological networks. Genes Dev. 2007 21 9 1010 1024 10.1101/gad.1528707 17473168
29 Agamah F.E. Mazandu G.K. Hassan R. Bope C.D. Thomford N.E. Ghansah A. Chimusa E.R. Computational/in silico methods in drug target and lead prediction. Brief. Bioinform. 2020 21 5 1663 1675 10.1093/bib/bbz103 31711157
30 Robin T.B. Rani N.A. Ahmed N. Prome A.A. Bappy M.N.I. Ahmed F. Identification of novel drug targets and screening potential drugs against Cryptococcus gattii: An in silico approach. Inform. Med. Unlocked 2023 38 101222 10.1016/j.imu.2023.101222
31 Ali A. Wakharde A. Karuppayil S.M. Rrp9 as a potential novel antifungal target in candida albicans: Evidences from in silico studies. Med. Mycol. Open Access 2017 3 2 1 5 10.21767/2471-8521.100026
32 Gupta R. Rai C.S. Identification of novel drug targets in pathogenic aspergillus fumigatus: An in Silico approach. Commun. Comput. Inf. Sci. 2020 1229 151 160 10.1007/978-981-15-5827-6_13
33 Vandeputte P. Ferrari S. Coste A.T. Antifungal resistance and new strategies to control fungal infections. Int. J. Microbiol. 2012 2012 1 26 10.1155/2012/713687 22187560
34 Pradhan D. Biswasroy P. Goyal A. Ghosh G. Rath G. Recent advancement in nanotechnology-based drug delivery system against viral infections. AAPS Pharm Sci Tech 2021 22 1 47 10.1208/s12249-020-01908-5 33447909
35 Sousa F. Ferreira D. Reis S. Costa P. Current insights on antifungal therapy: Novel nanotechnology approaches for drug delivery systems and new drugs from natural sources. Pharmaceuticals 2020 13 9 248 10.3390/ph13090248 32942693
36 Pacheco C. Baião A. Ding T. Cui W. Sarmento B. Recent advances in long-acting drug delivery systems for anticancer drug. Adv. Drug Deliv. Rev. 2023 194 114724 10.1016/j.addr.2023.114724 36746307
37 Casalini T. Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations. J. Control. Release 2021 332 390 417 10.1016/j.jconrel.2021.03.005 33675875
38 Kontoyiannis D.P. Lewis R.E. Toward more effective antifungal therapy: the prospects of combination therapy. Br. J. Haematol. 2004 126 2 165 175 10.1111/j.1365-2141.2004.05007.x 15238137
39 Shaban S. Patel M. Ahmad A. Improved efficacy of antifungal drugs in combination with monoterpene phenols against Candida auris. Sci. Rep. 2020 10 1 1162 10.1038/s41598-020-58203-3 31980703
40 Campitelli M. Zeineddine N. Samaha G. Maslak S. Combination antifungal therapy: a review of current data. J. Clin. Med. Res. 2017 9 6 451 456 10.14740/jocmr2992w 28496543
41 Pippi B. Lana A.J.D. Moraes R.C. Güez C.M. Machado M. de Oliveira L.F.S. Lino von Poser G. Fuentefria A.M. In vitro evaluation of the acquisition of resistance, antifungal activity and synergism of Brazilian red propolis with antifungal drugs on Candida spp. J. Appl. Microbiol. 2015 118 4 839 850 10.1111/jam.12746 25565139
42 Wang Y. Yan H. Li J. Zhang Y. Wang Z. Sun S. Antifungal activity and potential mechanism of action of caspofungin in combination with ribavirin against Candida albicans. Int. J. Antimicrob. Agents 2023 61 3 106709 10.1016/j.ijantimicag.2023.106709 36640848
43 Güvenç Paltun B. Kaski S. Mamitsuka H. Machine learning approaches for drug combination therapies. Brief. Bioinform. 2021 22 6 bbab293 10.1093/bib/bbab293 34368832
44 Cheng F. Kovács I.A. Barabási A.L. Network-based prediction of drug combinations. Nat. Commun. 2019 10 1 1197 10.1038/s41467-019-09186-x 30867426
45 Pearson R.A. Wicha S.G. Okour M. Drug combination modeling: methods and applications in drug development. J. Clin. Pharmacol. 2023 63 2 151 165 10.1002/jcph.2128 36088583
46 Vakil V. Trappe W. Drug combinations: mathematical modeling and networking methods. Pharmaceutics 2019 11 5 208 10.3390/pharmaceutics11050208 31052580
47 Abdel-Halim H. Hajar M. Hasouneh L. Jnr Abdelmalek S.M.A. Identification of drug combination therapies for sars-cov-2: A molecular dynamics simulations approach. Drug Des. Devel. Ther. 2022 16 2995 3013 10.2147/DDDT.S366423 36110398
48 Liu Y. Hu B. Fu C. Chen X. DCDB: Drug combination database. Bioinformatics 2010 26 4 587 588 10.1093/bioinformatics/btp697 20031966
49 Niazi-Ali S. Atherton G.T. Walczak M. Denning D.W. Drug– drug interaction database for safe prescribing of systemic antifungal agents. Ther. Adv. Infect. Dis. 2021 8 10.1177/20499361211010605 33996073
50 Chen X. Ren B. Chen M. Liu M.X. Ren W. Wang Q.X. Zhang L.X. Yan G.Y. ASDCD: antifungal synergistic drug combination database. PLoS One 2014 9 1 e86499 10.1371/journal.pone.0086499 24475134
51 Zagidullin B. Aldahdooh J. Zheng S. Wang W. Wang Y. Saad J. Malyutina A. Jafari M. Tanoli Z. Pessia A. Tang J. DrugComb: an integrative cancer drug combination data portal. Nucleic Acids Res. 2019 47 W1 W43 W51 10.1093/nar/gkz337 31066443
52 Di Veroli G.Y. Fornari C. Wang D. Mollard S. Bramhall J.L. Richards F.M. Jodrell D.I. Combenefit: an interactive platform for the analysis and visualization of drug combinations. Bioinformatics 2016 32 18 2866 2868 10.1093/bioinformatics/btw230 27153664
53 Ianevski A. He L. Aittokallio T. Tang J. SynergyFinder: a web application for analyzing drug combination dose–response matrix data. Bioinformatics 2017 33 15 2413 2415 10.1093/bioinformatics/btx162 28379339
54 Wooten D.J. Albert R. Synergy: a Python library for calculating, analyzing and visualizing drug combination synergy. Bioinformatics 2021 37 10 1473 1474 10.1093/bioinformatics/btaa826 32960970
55 Ianevski A. Timonen S. Kononov A. Aittokallio T. Giri A.K. SynToxProfiler: An interactive analysis of drug combination synergy, toxicity and efficacy. PLOS Comput. Biol. 2020 16 2 e1007604 10.1371/journal.pcbi.1007604 32012154
56 Mouchlis V.D. Afantitis A. Serra A. Fratello M. Papadiamantis A.G. Aidinis V. Lynch I. Greco D. Melagraki G. Advances in de novo drug design: from conventional to machine learning methods. Int. J. Mol. Sci. 2021 22 4 1676 10.3390/ijms22041676 33562347
57 Schmidt T. Bergner A. Schwede T. Modelling three-dimensional protein structures for applications in drug design. Drug Discov. Today 2014 19 7 890 897 10.1016/j.drudis.2013.10.027 24216321
58 Jisna V.A. Jayaraj P.B. Protein structure prediction: conventional and deep learning perspectives. Protein J. 2021 40 4 522 544 10.1007/s10930-021-10003-y 34050498
59 Pinzi L. Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int. J. Mol. Sci. 2019 20 18 4331 10.3390/ijms20184331 31487867
60 Pagadala N.S. Syed K. Tuszynski J. Software for molecular docking: a review. Biophys. Rev. 2017 9 2 91 102 10.1007/s12551-016-0247-1 28510083
61 Salo-Ahen O.M.H. Alanko I. Bhadane R. Bonvin A.M.J.J. Honorato R.V. Hossain S. Juffer A.H. Kabedev A. Lahtela-Kakkonen M. Larsen A.S. Lescrinier E. Marimuthu P. Mirza M.U. Mustafa G. Nunes-Alves A. Pantsar T. Saadabadi A. Singaravelu K. Vanmeert M. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes 2020 9 1 71 10.3390/pr9010071
62 Ou-Yang S. Lu J. Kong X. Liang Z. Luo C. Jiang H. Computational drug discovery. Acta Pharmacol. Sin. 2012 33 9 1131 1140 10.1038/aps.2012.109 22922346
63 Shim J. MacKerell A.D. Jr Computational ligand-based rational design: role of conformational sampling and force fields in model development. MedChemComm 2011 2 5 356 370 10.1039/c1md00044f 21716805
64 Aparoy P. Kumar Reddy K. Reddanna P. Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors. Curr. Med. Chem. 2012 19 22 3763 3778 10.2174/092986712801661112 22680930
65 Gan J. Liu J. Liu Y. Chen S. Dai W. Xiao Z.X. Cao Y. DrugRep: an automatic virtual screening server for drug repurposing. Acta Pharmacol. Sin. 2023 44 4 888 896 10.1038/s41401-022-00996-2 36216900
66 Cha Y. Erez T. Reynolds I.J. Kumar D. Ross J. Koytiger G. Kusko R. Zeskind B. Risso S. Kagan E. Papapetropoulos S. Grossman I. Laifenfeld D. Drug repurposing from the perspective of pharmaceutical companies. Br. J. Pharmacol. 2018 175 2 168 180 10.1111/bph.13798 28369768
67 Zhang Q. Liu F. Zeng M. Mao Y. Song Z. Drug repurposing strategies in the development of potential antifungal agents. Appl. Microbiol. Biotechnol. 2021 105 13 5259 5279 10.1007/s00253-021-11407-7 34151414
68 Nour E.M. El-Habashy S.E. Shehat M.G. Essawy M.M. El-Moslemany R.M. Khalafallah N.M. Atorvastatin liposomes in a 3D-printed polymer film: a repurposing approach for local treatment of oral candidiasis. Drug Deliv. Transl. Res. 2023 13 11 2847 2868 10.1007/s13346-023-01353-4 37184748
69 Liu Z. Fang H. Reagan K. Xu X. Mendrick D.L. Slikker W. Jr Tong W. In silico drug repositioning – what we need to know. Drug Discov. Today 2013 18 3-4 110 115 10.1016/j.drudis.2012.08.005 22935104
70 Parvathaneni V. Kulkarni N.S. Muth A. Gupta V. Drug repurposing: a promising tool to accelerate the drug discovery process. Drug Discov. Today 2019 24 10 2076 2085 10.1016/j.drudis.2019.06.014 31238113
71 Park K. A review of computational drug repurposing. Transl. Clin. Pharmacol. 2019 27 2 59 63 10.12793/tcp.2019.27.2.59 32055582
72 Cheng F. Lu W. Liu C. Fang J. Hou Y. Handy D.E. Wang R. Zhao Y. Yang Y. Huang J. Hill D.E. Vidal M. Eng C. Loscalzo J. A genome-wide positioning systems network algorithm for in silico drug repurposing. Nat. Commun. 2019 10 1 3476 10.1038/s41467-019-10744-6 31375661
73 Weersma R.K. Zhernakova A. Fu J. Interaction between drugs and the gut microbiome. Gut 2020 69 8 1510 1519 10.1136/gutjnl-2019-320204 32409589
74 McCoubrey L.E. Elbadawi M. Orlu M. Gaisford S. Basit A.W. Harnessing machine learning for development of microbiome therapeutics. Gut Microbes 2021 13 1 1872323 10.1080/19490976.2021.1872323 33522391
75 Gilbert J.A. Our unique microbial identity. Genome Biol. 2015 16 1 97 10.1186/s13059-015-0664-7 25971745
76 Doestzada M. Vila A.V. Zhernakova A. Koonen D.P.Y. Weersma R.K. Touw D.J. Pharmacomicrobiomics: a novel route towards personalized medicine Protein cell 2018 9 5 432 445 10.1007/s13238-018-0547-2 29705929
77 Heng X. Jiang Y. Chu W. Influence of fluconazole administration on gut microbiome, intestinal barrier, and immune response in mice. Antimicrob. Agents Chemother. 2021 65 6 e02552-20 10.1128/AAC.02552-20 33722893
78 Sharma A.K. Jaiswal S.K. Chaudhary N. Sharma V.K. A novel approach for the prediction of species-specific biotransformation of xenobiotic/drug molecules by the human gut microbiota. Sci. Rep. 2017 7 1 9751 10.1038/s41598-017-10203-6 28852076
79 McCoubrey L.E. Gaisford S. Orlu M. Basit A.W. Predicting drug-microbiome interactions with machine learning. Biotechnol. Adv. 2022 54 107797 10.1016/j.biotechadv.2021.107797 34260950
80 Sun Y.Z. Zhang D.H. Cai S.B. Ming Z. Li J.Q. Chen X. MDAD: a special resource for microbe-drug associations. Front. Cell. Infect. Microbiol. 2018 8 424 10.3389/fcimb.2018.00424 30581775
81 Rizkallah R. Rizkallah R.M. Gamal-Eldin S. Saad R. K Aziz R. The pharmacomicrobiomics portal: a database for drug-microbiome interactions. Curr. Pharmacogenomics Person. Med. 2012 10 3 195 203 10.2174/187569212802510030
82 Zeng X. Yang X. Fan J. Tan Y. Ju L. Shen W. Wang Y. Wang X. Chen W. Ju D. Chen Y.Z. MASI: microbiota—active substance interactions database. Nucleic Acids Res. 2021 49 D1 D776 D782 10.1093/nar/gkaa924 33125077
83 Curreri A. Sankholkar D. Mitragotri S. Zhao Z. RNA therapeutics in the clinic. Bioeng. Transl. Med. 2023 8 1 e10374 10.1002/btm2.10374 36684099
84 Bruch A. Kelani A.A. Blango M.G. RNA-based therapeutics to treat human fungal infections. Trends Microbiol. 2022 30 5 411 420 10.1016/j.tim.2021.09.007 34635448
85 Dammes N. Peer D. Paving the road for RNA therapeutics. Trends Pharmacol. Sci. 2020 41 10 755 775 10.1016/j.tips.2020.08.004 32893005
86 Selvam C. Mutisya D. Prakash S. Ranganna K. Thilagavathi R. Therapeutic potential of chemically modified si RNA : Recent trends. Chem. Biol. Drug Des. 2017 90 5 665 678 10.1111/cbdd.12993 28378934
87 Esposito C. Catuogno S. Condorelli G. Ungaro P. de Franciscis V. Aptamer chimeras for therapeutic delivery: the challenging perspectives. Genes (Basel) 2018 9 11 529 10.3390/genes9110529 30384431
88 Khatri M. Rajam M.V. Targeting polyamines of Aspergillus nidulans by siRNA specific to fungal ornithine decarboxylase gene. Med. Mycol. 2007 45 3 211 220 10.1080/13693780601158779 17464842
89 Mouyna I. Henry C. Doering T.L. Latgé J.P. Gene silencing with RNA interference in the human pathogenic fungus Aspergillus fumigatus. FEMS Microbiol. Lett. 2004 237 2 317 324 10.1111/j.1574-6968.2004.tb09713.x 15321679
90 Nagpal G. Chaudhary K. Dhanda S.K. Raghava G.P.S. Computational prediction of the immunomodulatory potential of RNA sequences. RNA Nanostruct.: Meth. Proto. 2017 1632 75 90 10.1007/978-1-4939-7138-1_5 28730433
91 Hoinka J. Wang Y. Przytycka T.M. AptaBlocks online: A web-based toolkit for the in silico design of oligonucleotide sticky bridges. J. Comput. Biol. 2020 27 3 356 360 10.1089/cmb.2019.0470 32160038
92 Sciabola S. Xi H. Cruz D. Cao Q. Lawrence C. Zhang T. Rotstein S. Hughes J.D. Caffrey D.R. Stanton R.V. PFRED: A computational platform for siRNA and antisense oligonucleotides design. PLoS One 2021 16 1 e0238753 10.1371/journal.pone.0238753 33481821
93 Lück S. Kreszies T. Strickert M. Schweizer P. Kuhlmann M. Douchkov D. siRNA-Finder (si-Fi) software for RNAi-target design and off-target prediction. Front. Plant Sci. 2019 10 1023 10.3389/fpls.2019.01023 31475020
94 Wong L. Wang L. You Z.H. Yuan C.A. Huang Y.A. Cao M.Y. GKLOMLI: a link prediction model for inferring miRNA–lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm. BMC Bioinformatics 2023 24 1 188 10.1186/s12859-023-05309-w 37158823
95 Zheng K. Zhang X.L. Wang L. You Z.H. Ji B.Y. Liang X. Li Z.W. SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs. Brief. Bioinform. 2023 24 1 bbac498 10.1093/bib/bbac498 36445194
96 Wang L. Wong L. You Z.H. Huang D.S. AMDECDA: attention mechanism combined with data ensemble strategy for predicting CircRNA-disease association. IEEE Trans. Big Data 2023
97 Dhanda S.K. Usmani S.S. Agrawal P. Nagpal G. Gautam A. Raghava G.P. Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief Bioinform 2017 18 3 467 468 10.1093/bib/bbw025 27016393
98 Thomas S. Abraham A. Baldwin J. Piplani S. Petrovsky N. Artificial intelligence in vaccine and drug design. Methods Mol Biol 2022 2410 131 146 10.1007/978-1-0716-1884-4_6 34914045
99 Solanki V. Tiwari V. Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii. Sci. Rep. 2018 8 1 9044 10.1038/s41598-018-26689-7 29899345
100 Jalal K. Abu-Izneid T. Khan K. Abbas M. Hayat A. Bawazeer S. Uddin R. Identification of vaccine and drug targets in Shigella dysenteriae sd197 using reverse vaccinology approach. Sci. Rep. 2022 12 1 251 10.1038/s41598-021-03988-0 34997046
101 Lathwal A. Kumar R. Raghava G.P.S. In-silico identification of subunit vaccine candidates against lung cancer-associated oncogenic viruses. Comput. Biol. Med. 2021 130 104215 10.1016/j.compbiomed.2021.104215 33465550
