Next Article in Journal
Tunability of the Superconductivity of NbSe2 Films Grown by Two-Step Vapor Deposition
Next Article in Special Issue
Plant-Derived Compounds and Extracts as Modulators of Plasmin Activity—A Review
Previous Article in Journal
Physicochemical Characterizations, Digestibility, and Lipolysis Inhibitory Effects of Highland Barley Resistant Starches Prepared by Physical and Enzymatic Methods
Previous Article in Special Issue
Glochodpurnoid B from Glochidion puberum Induces Endoplasmic Reticulum Stress-Mediated Apoptosis in Colorectal Cancer Cells
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts

by
Rebeca Gonzalez-Pastor
1,
Saskya E. Carrera-Pacheco
1,
Johana Zúñiga-Miranda
1,
Cristina Rodríguez-Pólit
1,
Arianna Mayorga-Ramos
1,
Linda P. Guamán
1,*,† and
Carlos Barba-Ostria
2,*,†
1
Biomedical Research Center (CENBIO), Eugenio Espejo School of Health Sciences, Universidad UTE, Quito 170527, Ecuador
2
School of Medicine, College of Health Sciences, Universidad San Francisco de Quito (USFQ), Quito 170901, Ecuador
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Molecules 2023, 28(3), 1068; https://doi.org/10.3390/molecules28031068
Submission received: 13 December 2022 / Revised: 10 January 2023 / Accepted: 12 January 2023 / Published: 20 January 2023

Abstract

:
Natural extracts have been and continue to be used to treat a wide range of medical conditions, from infectious diseases to cancer, based on their convenience and therapeutic potential. Natural products derived from microbes, plants, and animals offer a broad variety of molecules and chemical compounds. Natural products are not only one of the most important sources for innovative drug development for animal and human health, but they are also an inspiration for synthetic biology and chemistry scientists towards the discovery of new bioactive compounds and pharmaceuticals. This is particularly relevant in the current context, where antimicrobial resistance has risen as a global health problem. Thus, efforts are being directed toward studying natural compounds’ chemical composition and bioactive potential to generate drugs with better efficacy and lower toxicity than existing molecules. Currently, a wide range of methodologies are used to analyze the in vitro activity of natural extracts to determine their suitability as antimicrobial agents. Despite traditional technologies being the most employed, technological advances have contributed to the implementation of methods able to circumvent issues related to analysis capacity, time, sensitivity, and reproducibility. This review produces an updated analysis of the conventional and current methods to evaluate the antimicrobial activity of natural compounds.

1. Introduction

Antimicrobial resistance (AMR) is a growing concern worldwide [1]. According to a recent study, an estimated 4.95 million people died from diseases associated with AMR in 2019 [2,3]. Furthermore, the accelerated global spread of multi-resistant bacteria is particularly distressing [4]. Hospital strains and those associated with foodborne diseases pose a risk due to the extensive use and misuse of antibiotics for human health and livestock [5,6,7]. Moreover, new antimicrobials that block drug-resistant pathogens are not being developed quickly enough [8,9,10].
In this context, natural products represent an immense source of biologically active components [11,12]. Both primary and secondary metabolites synthesized by mammalian and plant cells, as well as microorganisms, have influenced the development of effective treatments for an array of diseases and health conditions, including infectious diseases, inflammatory processes, and cancer [13,14,15]. Although technological development has enabled improved extraction and characterization techniques of natural compounds [16,17], screening strategies are not always capable of unwrapping the mechanism of the isolated compounds responsible for the combinatory effect [18]. Sometimes the active compound operates at a lower degree compared with the whole extract [19]. It is important to consider the sample solubility; for the extraction of hydrophilic and lipophilic compounds, solvents with a variety of polarity indexes, such as acetone, acetonitrile, dimethyl sulfoxide, ethanol, hexane, methanol, and dichloromethane, are commonly used [20,21,22]. Additionally, the effect of the selected solvent plays an important role in the extraction of total solids, phytochemical composition, and antioxidant potential, affecting the overall extraction efficiency of bioactive compounds [23].
In essence, the complexity of natural product mixtures and the lack of standardization for the procedures somewhat hinder the search for new antimicrobial drugs [24,25]. To generate cost-effective drugs based on natural compounds with better bioactive potential and fewer side effects, it is necessary to set up the proper assays to confirm their activity and establish their ranges and mechanisms of action [26,27,28,29]. In addition, further focus is required on the synergistic action of natural extract mixtures alone and employed in combination with available antimicrobials [30,31].
In terms of methods employed to evaluate antimicrobial activity, traditional technologies are the most widely used [32]. New evaluation techniques have been developed to overcome some of the disadvantages of classic methods, such as low reproducibility and lengthy processing times [11,33]; still, high costs and limited accessibility restrict these systems from frequent analysis, especially in resource-limited regions [34]. This review describes the most common in vitro assays currently used to characterize the antibiotic, antifungal, antiparasitic, and antiviral activities of promising natural compounds. In addition, each section presents the advantages and disadvantages of the discussed methodologies and includes emergent methods and future trends relevant to the field.

2. Antibacterial Activity

Several antibacterial susceptibility testing methods (AST) are available to determine bacterial susceptibility to antimicrobials. The selection of a method is based on many factors, such as practicality, flexibility, automation, cost, reproducibility, accuracy, and whether the results will be used for clinical or research purposes. AST methods must provide reproducible results in day-to-day laboratory analysis to be comparable with an acknowledged “gold standard” reference method [35]. Many authors have focused on plant and microbial metabolites as potential antibacterial agents. However, it is hard to compare these results because of the non-standardized techniques used for inoculum preparation and size, growth medium, incubation conditions, and endpoint determination [32]. The test organisms recommended by the Committee for Clinical Laboratory Standards (CLSI) in the preliminary screening for antibacterial activity are the Gram-positive Enterococcus faecalis (ATCC 29212) and Staphylococcus aureus (ATCC 29213) and the Gram-negative Escherichia coli (ATCC 27853) and Pseudomonas aeruginosa (ATCC 25922) [36].
The European Committee on Antimicrobial Susceptibility Testing (EUCAST) [37] and the CLSI guidelines are available to standardize in vitro AST methods related to clinical testing; these guidelines are also used for natural compounds since there are no standards of their own. However, natural extracts comprise a mixture of molecules that may not perform as expected in the test system. There are different challenges when using clinical guidelines for natural extracts. First, most antibiotics are hydrophilic, so AST methods are optimized for this condition, whereas natural extracts are lipophilic, meaning they are not fully soluble in water [38,39]. Another problem is the absence of the minimum drug concentration of natural compounds expected to be effective against bacteria (the breakpoint). Most of the studies rely on the minimal inhibitory concentration (MIC). MIC values range between 0.01–10 µg/mL for antibiotics, whereas plant extracts are considered antimicrobials if their MICs are between 100–1000 µg/mL [40]. Some authors even consider different cutoffs depending on the compound. For example, a concentration of 1000 µg/mL is considered the breakpoint for a polyphenol. This lack of standardization makes it difficult to have comparable and trusty results [38].
With all these variables, no single method fits all-natural compounds evaluation but rather a combination of methods that consider particular characteristics and are best suited for assessing the sample. No matter which method is chosen, essential considerations must be taken into account to have reliable results, as shown in Figure 1.
This section will discuss the main characteristics of the standard methodologies used to assess the antibacterial activity of natural compounds.

2.1. Disk Well Diffusion

This is the most common qualitative assay for bacteria and fungi; due to its low cost and the fact that it does not require specialized laboratory facilities, it is used to screen many samples. In this technique, a paper disk or a well is filled with the extract, which spreads through the agar plate containing the bacteria of interest; then, the plate is incubated for 24–48 h [12,41]. The concentration is inversely related to the distance from the area or disk/well. The zone without bacterial growth around the disk/well is measured to compare with the antimicrobial used as a control [42,43]. The limitations include the differential diffusion of the extracts, such as essential oils containing terpenoids, which have limited solubility; therefore, they may not reflect the proper antibacterial activity, producing a false-negative result [44,45].

2.2. Agar Dilution Method

Agar dilution is a well-established method for determining antimicrobial susceptibility. The antimicrobial agent is incorporated into a series of agar plates containing the antimicrobial agent to be tested in increasing concentrations [46]. Inoculums of different microorganisms can be rapidly and simultaneously applied to the agar surface and incubated for 24–48 h. The standardized inoculum can be prepared by allowing the growth of the microorganism up to 0.5 turbidity on the McFarland scale (∼1 × 108 colony forming units (CFU) mL1). Growth is measured and compared with the control. One of the limitations is that when this method is used to assess the antibacterial activity of essential oils, it is difficult to make stable emulsions with the agar. Additionally, there is a problem with the concentration of the extract used since the agar may be too diluted to be solidified properly [47]. Mueller Hinton is generally used since it has proved to be the best of all available media for routine susceptibility testing of non-fastidious bacteria. For Helicobacter and Haemophilus species, supplementation with 5% fresh sheep blood is needed [48].

2.3. Broth Dilution Method

This is a more precise method to overcome the challenges regarding hydrophobic compound dilution in agar. Here, the tested compound is added to the culture medium where bacteria are grown; a sample is taken out every 10–20 min and diluted before being plated onto the agar, where colonies are counted after 24–48 h [49]. A variation of this method consists of using microtiter plates. Bacterial growth is measured spectrophotometrically through optical density (600 nm) or using cell viability indicators such as resazurin and methylthiazoldiphenyltetrazolium (MTT) [50,51]. However, when using colored natural extracts such as anthocyanins and carotenoids, they interfere with a correct turbidity measure. Another limitation is that this assay is not appropriate for oxygen-sensitive bacteria since incubation requires 150–200 rpm shaking most of the time [52]. A shared limitation with other methods remains in incorporating hydrophobic compounds into the media [42].

2.4. Thin-Layer Chromatography-Bioautography

Considering that natural extracts are mixtures of molecules, it is essential to have bioassay-guided fractionation methods where separation and biological activity are assessed simultaneously [53,54]. Thin-layer chromatography (TLC) is performed using whole extracts. The plate is sprayed or submerged into the bacterial suspension (direct bioautography) or covered with a layer of agar previously inoculated with the bacteria of interest (overlay bioautography). A positive result is considered when inhibition zones are observed after proper incubation times [53,54,55].
In summary, although various methods exist, the goal of in vitro antimicrobial susceptibility testing is the same: to provide a reliable predictor of how a microorganism is likely to respond to an antimicrobial extract. Therefore, emergent AST methods must include a great capacity to process the vast amount of natural extracts in a simple, cost-effective, reproducible way [56].
Additionally, fluorescent dyes are employed in a variety of methods as a quick way to identify bacterial populations and assess cell viability [57]. This assay can produce a rapid, thorough, and measurable overview of the bacterial population. Although these procedures can be validated to meet the International Organization for Standardization (ISO) standards, flow cytometry needs a significant initial investment and substantial training to calibrate the equipment, set up complex assays, and analyze data and results [58]. Specifically for natural products, a recent publication was used to determine antibacterial activity in citric oil samples using a series of systematic standardization steps to find appropriate conditions for flow cytometry (acquisition of bacteria, fluorochrome concentration, determination of suitable comparison antibiotics, and exposure times) [59].

2.5. Molecular Methods

Other approaches to assess antibacterial activity that are becoming more widely employed do not require culturing bacteria. Due to its lower cost and capacity to produce quick results, quantitative polymerase chain reaction (qPCR) is currently utilized to evaluate the efficacy of antimicrobial substances and methods. qPCR can identify and quantify the amount of target genetic material present in a sample without culturing cells. To differentiate living cells from dead ones, a viability polymerase chain reaction (PCR) assay can be used. This entails incubating samples with a DNA-binding dye, such as propidium monoazide (PMA), which binds to free DNA (including dead cells with broken membranes) and inhibits the PCR amplification, so living cells are the ones detected and amplified [58]. Following the development of molecular techniques such as PCR, sequencing, and metagenomics, many studies have favored these cutting-edge methods at the expense of bacterial culture. Nevertheless, metagenomics presents some drawbacks, notably, a depth bias due to the low sensitivity of some of the primers used [60]. Most current methods only detect DNA and, in some cases, cannot distinguish between DNA from living and dead bacteria and the DNA from the transient bacteria under study. As a result, advanced culture techniques are being developed. In recent years, new culture mediums and conditions have enabled the development of culturomics, a high-throughput culture approach [61]. According to some experts, this demonstrates that culture media continue to be an essential technique for bacteriologists to isolate commensal and harmful bacteria. Therefore, there are still many advancements in bacterial culture that will expand the bacterial repertoire and better comprehend certain diseases [62]. For MIC determination using molecular methods, DNA samples are taken after exposing the cells to treatments with higher concentrations of the natural extracts and using commercial antibiotics as controls [63].

3. Antifungal Activity

Infections caused by fungal pathogens are a global threat and affect more than a billion people, resulting in over 300 million people with invasive fungal infection (IFI) and more than 1.7 million deaths annually, which is equivalent to that caused by tuberculosis and more than triple that of malaria [64,65]. IFIs need appropriate antifungal therapy, but only a few antifungal drugs are available [66]. In addition, many have significant drawbacks, such as high cell toxicity, undesirable interactions, poor pharmacokinetics, and a narrow spectrum of activity [67]. Furthermore, in recent decades, the number of patients with risk factors for IFI (severe immunosuppression, exposure to long courses of broad-spectrum antibiotics, and implanted medical devices) has increased [68]. The rising incidence of IFIs [68] poses even more pressure on health systems since acquired resistance has emerged in fungal pathogens, hampering the treatment of severe patients [66,69,70]. In this complex scenario, including changes in patient factors and the lack of effective treatment options, it is critical to identify novel compounds with antifungal activity.
Due to its broad applicability among microorganisms, some basic principles of antibacterial assays, such as dilution and diffusion, are also used to evaluate antifungal activity. Nevertheless, due to the particular features of the fungal lifestyle, some of the modifications listed in Table 1 should be made to properly evaluate the in vitro antifungal activity. Similar to antibacterial tests, the diameter of growth inhibition in solid media is the key parameter for evaluating the antifungal activity of natural compounds or extracts. In the case of microdilution, cells are plated after incubation with different concentrations of the extract/compound (Figure 2). The effect is determined by comparing the growth of treated cells with that of the corresponding untreated control. Although these methods help evaluate the activity of pure compounds or natural extracts, the standard procedure has to be modified to successfully evaluate the activity of natural extracts due to differences between both types of samples (water solubility, purity, and concentration of the bioactive molecule). It is important to consider these factors to correctly interpret the results.
The time-kill method is another simple and effective assay for evaluating the antifungal activity by measuring the effect of pure compounds or natural extracts on fungal growth, measured by changes in cell density (Figure 2). It should be mentioned that time-kill is not designed to exclusively measure antifungal activity; the same approach is commonly used for measuring antibacterial and other antimicrobial activity. This method provides additional details on the type of effect (fungistatic or fungicide) of the analyzed compounds. Additionally, together with the microdilution test, it allows for calculating the minimum inhibitory concentration (MIC). An antifungal activity assay using flow cytometry (FC) or fluorescence-activated cell sorting (FACS) is based on light scattering and fluorescence emission by fluorescently labeled fungal cells passing through a laser beam [68]. Alterations in the fluorescence distribution of the population are interpreted as changes in cell viability and, thus, as an indication of antifungal activity (Figure 2). Although it is not the most common method, FC and FACS can also be used to determine the MIC [68].
Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) is a mass spectrometry ionization technique that uses laser energy to create ions from large molecules, such as proteins. This technique has been mainly used to determine the antifungal activity of pure molecules, but due to its versatility [71,72], it holds potential for evaluating the antifungal activity of natural extracts. Isothermal microcalorimetry (IMC) allows real-time, continuous measurement of the heat flow and the evaluation of heat changes in a sample. However, these two methods are used less frequently due to the need for specialized equipment and the fact that they are more time-consuming or strenuous, even though results are obtained quickly [73]. Table 1 summarizes the key points of the methods and modifications used to evaluate the antifungal activity and other less common but equally effective alternatives.
Mainly because of the threat caused by the worldwide rise of fungal infections, it is highly relevant to develop simpler, faster, more robust, and high-throughput methods that allow the evaluation of multiple extracts/compounds against multiple fungal pathogens in a simple manner.
Table 1. Methods to assess antifungal activity.
Table 1. Methods to assess antifungal activity.
Method/Type of AssayDescriptionMedia and MicroorganismAdvantagesDisadvantagesRef.
Diffusion
methods
Disk/well diffusion
-
Disks or wells containing the extract
-
Diameter of the inhibition halo is measured
-
The larger the diameter, the better the activity
-
Sabouraud-dextrose agar. Tryptone-yeast-glucose agar
-
Agar potato-dextrose agar
-
Yeasts and filamentous fungi
-
Simplicity
-
Low cost
-
Up to 6 extracts per plate can be screened
-
Not appropriate for testing non-polar samples
-
Challenging to run on high-throughput screening systems
[74,75]
Dilution
methods
Broth microdilution
-
Incubation with different concentrations of the extract
-
Plating and counting colonies in agar
-
A lower number of colonies indicates a stronger activity
-
Sabouraud dextrose broth
-
RPMI 1640 growth medium with glucose
-
Filamentous fungi and yeasts
-
Gold standard
-
MIC can be calculated
-
Insoluble samples may interfere with readings
-
Labor-intensive
-
Modification is needed when the fungus is growing as hyphae
[76,77,78]
Time-kill test
-
Sampling of control (cells, no drug) and antimicrobial agent-containing cultures at intervals (usually 0, 4, 8, 10 to 12, and 24 h of incubation)
-
Survivor colony count (CFU per milliliter) determined by spreading onto agar plates
-
Lower number of colonies indicates a stronger activity
-
RPMI 1640 growth medium with glucose
-
Yeast extract Peptone Dextrose
-
Filamentous fungi and yeasts
-
MIC can be calculated
-
Synergistic or antagonistic interactions can be evaluated
-
Modification is needed when the fungus is growing as hyphae
-
Labor-intensive
[73,79,80]
Flow cytometry, or fluorescence-activated cell sorting (FACS)
-
Incubation with different extract concentrations
-
Dilution, staining, and fluorescence measurement by flow cytometer
-
A lower fluorescence indicates better activity
-
RPMI
-
Filamentous fungi and yeasts
-
MIC can be calculated
-
Highly sensitive
-
Quick results
-
Labor-intensive
-
Requires a high level of technician expertise
-
Specialized equipment required
-
Time-consuming.
[68,73,81,82]
CalorimetryIsothermal microcalorimetry (IMC)
-
Serial dilutions of the plant extract with fungal cells
-
Growth is monitored by changes in the total heat
-
Lower total heat indicates better activity
-
RPMI
-
Yeasts
-
Candida
-
Rhizopus
-
Fusarium
-
Compatible with high throughput
-
Routine clinical testing and antifungal drug discovery
-
Specialized equipment required
-
Specialized and trained personnel required
[83]
Mass SpectrometryMALDI-TOF
-
Incubation of fungal cells with the plant extract
-
Growth is monitored by proteome changes (MALDI-TOF) compared with an untreated control
-
RPMI
-
Filamentous fungi and yeasts
-
Elimination of subjectivity present in the visual readout
-
Highly sensitive
-
Expensive
-
Requires specialized equipment and training
[84,85]
Thin-layer chromatography (TLC)Agar overlay bioautography
-
Inoculation of fungal cells in melted agar
-
Mixture applied to TLC plate embedded with the plant extract
-
Incubation and staining of inhibition bands
-
Sabouraud
-
Malt agar
-
Filamentous fungi and yeasts
-
Consistent with spore-producing fungi
-
Inexpensive
-
Simple
-
Qualitative
-
Difficulties in obtaining complete contact between the agar and the plate
[53,86,87]
MALDI-TOF: Matrix-Assisted Laser Desorption/Ionization Time-of-Flight; MS: mass spectrometry; RPMI: Roswell Park Memorial Institute Medium. MIC: Minimum inhibitory concentration; CFU: colony-forming unit.

4. Antiparasitic Activity

Parasitic infections are caused by microorganisms that thrive at the expense of their host, causing significant morbidity and mortality worldwide [88]. Most human parasitic diseases predominate in developing countries and have been considered part of the group of neglected tropical diseases [89]. Given their broad definition, these organisms can range from single–cell eukaryotes to complex multicellular organisms such as endoparasites and ectoparasites [90]. Therefore, the identification of new drugs to treat parasitic diseases is a major priority. Unfortunately, the discovery rate for novel antiparasitic compounds has decreased dramatically in the past couple of decades [89]. Nonetheless, plant extracts have always been described in the literature as a new source of secondary metabolites and potential antiparasitics [91,92]. This section will focus on the methods commonly used to detect antiparasitic activity on endoparasites such as protozoa or helminths, which cause a significant burden in developing countries.

4.1. Antiprotozoal Activity

In the search for novel antimicrobials and antiparasitic drugs, the first screening of all candidate compounds and extracts is usually assessed by a range of classic and standardized assays [93]. More complex evaluations can further assess the test compounds with the best potential (Figure 3). In clinical samples, microscopic examination is the gold standard for unicellular parasite identification [94]. As a result of its simplicity, microscopy can offer preliminary data regarding morphological alterations in parasitic cells after applying the potential antiparasitic compound in the culture. Death by apoptosis can be characterized by a shrinkage of the cell or loss of cellular volume [95]. In necrotic cell death, the parasites could undergo cytolysis, which can be visualized as small fragments or cell debris [96].
Colorimetric assays can also support antiparasitic studies by assessing cell viability and determining the LC50 (lethal dose, 50%) and LC90 (lethal dose, 90%) [97]. These assays are similar to the colorimetric techniques used in mammalian cell cytotoxicity assays, as shown in Table 2. Flow cytometry is another valuable tool that allows the simultaneous multi-parameter analysis of single cells [98]. Subsequently, the confluence of this data can provide valuable information to characterize the mechanism of cell death prompted by the candidate compound. Furthermore, cell proliferation assays allow the adjustment and evaluation of different parameters, such as extract concentration and measurement over multiple time points. Several protozoans undergo their life cycle inside the host cells; therefore, it is also essential to assess the test compound via intracellular parasite proliferation assays [99].
In recent years, novel techniques have gained more notoriety and provided insightful contributions to antiparasitic drug discovery. Current and future trends for antiparasitic drug discovery take advantage of in silico models as a valuable tool for initial screenings before further lab analysis [121,122,123]. Subsequently, the electron microscope has a growing role in characterizing proteins, subcellular targets, and drug action mechanisms in parasitic cells during structure-based drug design [124,125].
In leishmaniasis drug discovery, drugs that target specific parasite–host interactions or vector–host interactions have seen a growing interest [123]. Consequently, their evaluation demands other essays related to the analysis of the immunomodulatory properties of the candidate compounds or their transmission-blocking strategies [126]. Another current method is the adenosine triphosphate (ATP) bioluminescence assay, which is usually used to measure the ability to produce ATP by bacteria or fungi (including yeast and molds) [127,128]. In addition, it is also used to evaluate the influence of biofilms in situ and for medication screening on Leishmania [32].
For Chagas disease, recent advances have taken advantage of next-generation sequencing or high-throughput sequencing for phenotypic-based drug discovery [129]. Moreover, other technological developments show great potential to accelerate initial screenings of multiple compounds [124] quickly. For example, automated microscopy has also significantly influenced drug discovery. It can increase screening throughput by replacing laborious and subjective manual microscopic observation and creating algorithmic systems to identify morphological injury and changes in target parasitic cells [130]. Additionally, omics-based techniques allow the determination of novel targets for parasitic protozoan infections, including Plasmodium spp., Toxoplasma spp., Trypanosoma spp., and Leishmania spp. [131].

4.2. Anthelmintic Activity

Active extracts or pharmaceuticals used against helminths are named anthelmintic; these drugs are used to regulate the uncontrollable reproduction of worms and are expected to eliminate the parasites either by killing them or stimulating their escape from the infected organism [132]. Unlike antiprotozoal activity, microscopy’s observation is the gold standard for identifying anthelmintic activity. This examination relies on the absence of movement (paralysis or death) of larvae or adult worms, the inability of the eggs to hatch, or the larvae to continue with their life cycle [133]. These observations indicate the effectiveness of a given extract or compound in inhibiting parasitic growth through various mechanisms, including alterations in metabolism [134].
Current trends for assessing the anthelmintic activity of natural extracts are based on in vitro physiological assays. At different stages of its life cycle, the parasite is cultured in the presence of the tested compound or extract concentrations, and its viability, motility, and growth are evaluated [135]. A list of the most common tests carried out for anthelmintic activity is described in Table 3.
As mentioned before, these physiology-based assays can be performed at different stages of the parasite life cycle. Figure 4 illustrates the type of tests performed along the parasite life cycle.
Results from current studies suggest that plant extracts have compounds with anthelmintic properties that can be further studied for antimicrobial activity and drug discovery [135,143]. Therefore, it is recommended that anthelmintic tests be combined with other antimicrobial screenings, such as the ones presented in this review.
A new approach to studying anthelmintic activity employs a high-throughput screening platform using the model organism Caenorhabditis elegans; here, new compounds with anthelmintic properties are identified by forward and reverse genetics approaches [144]. The advantage of using C. elegans to uncover new possible anthelmintics is that this organism has a broad genetic toolset, which facilitates the investigation of the mode of action for potential drug candidates [144,145]. Future trends focus on the development of automated imaging and computational frameworks for the analysis of subtle changes in worms’ phenotype induced by test compounds that may be valuable in determining their antiparasitic effects [146].

5. Antiviral Activity

Antiviral screening has gained substantial attention since the SARS-CoV-2 pandemic started in 2020. Several emerging and re-emerging viral infections cannot be prevented by vaccination or treated with antiviral drugs or monoclonal antibodies, such as viral diseases caused by the Zika virus, Dengue virus, and Chikungunya virus, among others [147]. In addition, due to the flexibility and adaptability of viruses that result in the development of drug-resistant mutants, there is a constant need to evaluate new inhibitors that can be incorporated into the drug discovery pipeline [148]. This section describes the most common cell-based assays used to evaluate antiviral activity (Figure 5).
Antiviral compounds generally target several key steps of viral life cycles: entry, replication, transcription or retrotranscription, proteolytic processing, and viral particle release [149]. Thus, the goal is to identify compounds that specifically inhibit viral or cellular targets essential for viral replication [150]. In this context, the evaluation of natural products is particularly appealing based on their biological importance and chemical and structural diversity. Plant extracts, triterpenoids, alkaloids, phenols, flavonoids, and honey are among the most analyzed natural extracts with antiviral activity [151,152].
Among the well-accepted methods used to screen for inhibitors aiming at the entire viral life cycle, the yield reduction assay remains a powerful technique [153,154]. The assay involves the infection of target cells in the presence of different concentrations of the test molecule, the collection of supernatants and cells after a cycle of virus replication, and the determination of the virus titers.
The two most commonly used methods to quantify the concentration of replication-competent virions are the plaque assay, which remains the gold standard, and the 50% tissue culture infectious dose assay (TCID50) [155,156,157]. Both assays depend on the capability to evaluate visible cytopathic effects in cell monolayers after the infection of susceptible cell lines. In the plaque assay, a confluent monolayer of host cells is infected with serial dilutions of the virus, and after adsorption, a semi-solid or solid medium is added on top of the monolayer to restrict virus diffusion at the sites of initial infection [158,159]. The local replication of the virus results in zones of cell death, termed “plaques”. Generally, cells are stained to enhance the contrast of the plaques and facilitate counting, and the titer is measured by the number of plaque-forming units per mL (PFU/mL) [160]. In contrast, the TCID50 endpoint dilution assay measures visible cell death inside each well of a 96-well tissue culture plate. The titer is calculated as the dose at which a virus sample is expected to infect a tissue or cell culture 50% of the time and is transformed into virus concentration/mL [161,162].
However, estimation of the relative infectivity by visual inspection can be complicated by variations in plaque size and irregular morphology; it can also be incompatible for non-lytic viruses. For those cases, the focus-forming assay (FFA) allows the identification of single foci by detecting virally encoded proteins expressed by infected cells. The viral titer is expressed in focus-forming units per mL (FFU/mL) [163,164]. Additionally, with the advancement of instrumentation and the high contrast of reagents, counting can be automated for more precision and less subjectivity in a high-throughput setting [163,164,165]. Additionally, other quick and sensitive methods, such as cell-based enzyme-linked immunosorbent assay (ELISA) and quantitative real-time polymerase chain reaction (PCR), are used to determine antiviral activity by measuring the reduction in viral antigen or virus nucleic acid in infected cells in the presence of the test molecule [166,167,168]. Similarly, transmission electron microscopy (TEM) can be used to quantify infectious particles and assess the potential antiviral activity of test compounds [169]. Nonetheless, analysis carried out by these methods may include non-infectious particles that do not contain genetic information, or rather all genetic material present in a sample, including extra genomes not packaged into virions [170].
Instead of aiming at the entire viral life cycle, other cell-based assays have been developed to screen viral inhibitors that target specific steps of the infectious process [171,172,173]. For instance, classic methods to screen inhibitors of viral entry include cell–cell fusion assays and cell–virus fusion assays. These methods use effector cells that express the viral entry protein, or recombinant virions, to facilitate fusion with target cells that express the host-cell receptor and carry a reporter system. The shift in the expression of the reporter corresponds to the efficacy of fusion inhibition [174,175,176]. Apart from blocking virus attachment and entry, other inhibitors typically target the components that are critical for viral genome replication. Due to polymerases being the preferred target for antiviral intervention [177,178], fluorescence-based quantitative real-time PCR and quantitative real-time reverse transcription PCR are the methods of choice to specifically monitor the activity of DNA and RNA polymerases in the presence of inhibitors [179,180,181].
The SARS-CoV-2 pandemic highlighted insufficient preparation for handling certain virus outbreaks. Although there has been extensive development in molecular and cellular biology methods and imaging techniques applied to antiviral testing, the assays developed are often limited by the availability of reagents and equipment that can be used in biosafety containment facilities [147]. Therefore, future trends are focused on the development of accurate, simplified, and fast cell-based assays for sensitive and quick high-throughput screening (HTS) of antivirals [182,183,184,185,186,187]. An additional complication encountered when testing natural products as antiviral agents is the tedious nature of identifying bioactive components, leaving the mechanism of action of the crude extract or the metabolites unknown [151]. In this regard, advances in in silico approaches have revealed a plethora of natural compounds that interact with viral protein targets and can be subjected to in vitro assays [188].

6. Conclusions and Future Perspective

The rising prevalence of drug-resistant diseases urges the use of standardized modern analytical technologies to detect and isolate novel bioactive chemicals from natural sources. Compounds produced by animals, plants, and microbes could offer new and simpler ways to combat harmful microorganisms. Unfortunately, the current range of approved antibacterial compounds from plants does not adequately reflect their potential for future use as antimicrobial agents [42]. Moreover, many promising medicinal plants and active compounds are yet to be studied phytochemically and pharmacologically [189,190].
As described in this article, a wide range of laboratory methodologies are currently used to analyze the antimicrobial activity of natural extracts. Overall, the assessment of most biological activities would benefit from using a combination of multiple assays that can complement or confirm the identification of the antimicrobial mechanisms involved. Although the most popular are the classic, basic methodologies, advanced biotechnological, genomics, proteomics, and metabolomics approaches are now used in natural extract research to assess antimicrobial activity and promote the development of new safe antimicrobial drugs [42,189]. It deserves to be pointed out that natural extracts have a complex range of metabolites, some of which can interfere with the wavelength maxima of the product during spectroscopic measurements because they are often intensely colored. In addition, the opacity of the solutions could interfere with the turbidity of bacterial growth [39,191,192]. In this sense, progress in computational chemistry, the use of public databases, and the implementation of in silico models to accurately assess the efficacy of a promising compound accelerate initial screenings [193,194]. For instance, the antibiotics and Secondary Metabolite Analysis Shell (antiSMASH) tool is a platform that allows the mining of antibiotic biosynthetic gene clusters (BGC) based on similarities to existing instances from plants, fungi, and bacteria [195]. Another new portal mines the genes that code for a natural product’s production and uses that information to see if other strains can manufacture specific critical intermediates of the natural product mining with, a tight emphasis on genes involved in the final few steps of biosynthetic pathways [196]. Furthermore, the implementation of advanced microscopy and high-throughput sequencing enables the analysis of drug action mechanisms and added properties related to the interactions between organisms with lower variability.
In summary, as technology advances and the world becomes increasingly automated in every aspect of life, methods that are regularly utilized, as well as other remarkably rapid and automated testing systems, will become standardized, more available, and more user-friendly. Furthermore, manual methods and automated systems for investigating antimicrobial activity must continue to be improved and updated for this purpose. In the meantime, a combination of manual and semi-automated testing procedures must be used to produce reliable results.

Author Contributions

Conceptualization, C.B.-O. and L.P.G.; writing—original draft preparation, R.G.-P., S.E.C.-P., J.Z.-M., C.R.-P., A.M.-R.; writing—review and editing, R.G.-P.; supervision, L.P.G.; project administration C.B.-O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All tables are created by the authors. All sources of information are adequately referenced. There is no need to obtain copyright permissions.

Acknowledgments

Figures were created with BioRender.com.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Larsson, D.G.J.; Flach, C.-F. Antibiotic resistance in the environment. Nat. Rev. Microbiol. 2022, 20, 257–269. [Google Scholar] [CrossRef] [PubMed]
  2. Laxminarayan, R. The overlooked pandemic of antimicrobial resistance. Lancet 2022, 399, 606–607. [Google Scholar] [CrossRef] [PubMed]
  3. Nadimpalli, M.L.; Chan, C.W.; Doron, S. Antibiotic resistance: A call to action to prevent the next epidemic of inequality. Nat. Med. 2021, 27, 187–188. [Google Scholar] [CrossRef] [PubMed]
  4. WHO. Global Antimicrobial Resistance Surveillance System (GLASS) Report: Early Implementation 2020; WHO: Geneva, Switzerland, 2020. [Google Scholar]
  5. Cohen, T. The Next Pandemic: A Pragmatic and Ethical Discussion About the Looming Threat of Antibiotic Resistance. Voices Bioethics 2022, 8. [Google Scholar] [CrossRef]
  6. Vaughn, V.M.; Gandhi, T.N.; Petty, L.A.; Patel, P.K.; Prescott, H.C.; Malani, A.N.; Ratz, D.; McLaughlin, E.; Chopra, V.; Flanders, S.A. Empiric Antibacterial Therapy and Community-onset Bacterial Coinfection in Patients Hospitalized With Coronavirus Disease 2019 (COVID-19): A Multi-hospital Cohort Study. Clin. Infect. Dis. 2021, 72, e533–e541. [Google Scholar] [CrossRef]
  7. Ghimpețeanu, O.M.; Pogurschi, E.N.; Popa, D.C.; Dragomir, N.; Drăgotoiu, T.; Mihai, O.D.; Petcu, C.D. Antibiotic Use in Livestock and Residues in Food-A Public Health Threat: A Review. Foods 2022, 11, 1430. [Google Scholar] [CrossRef]
  8. Plackett, B. Why big pharma has abandoned antibiotics. Nature 2020, 586, S50–S52. [Google Scholar] [CrossRef]
  9. Klug, D.M.; Idiris, F.I.M.; Blaskovich, M.A.T.; von Delft, F.; Dowson, C.G.; Kirchhelle, C.; Roberts, A.P.; Singer, A.C.; Todd, M.H. There is no market for new antibiotics: This allows an open approach to research and development. Wellcome Open Res. 2021, 6, 146. [Google Scholar] [CrossRef]
  10. Baker, S.J.; Payne, D.J.; Rappuoli, R.; De Gregorio, E. Technologies to address antimicrobial resistance. Proc. Natl. Acad. Sci. USA 2018, 115, 12887–12895. [Google Scholar] [CrossRef] [Green Version]
  11. Thomford, N.E.; Senthebane, D.A.; Rowe, A.; Munro, D.; Seele, P.; Maroyi, A.; Dzobo, K. Natural products for drug discovery in the 21st century: Innovations for novel drug discovery. Int. J. Mol. Sci. 2018, 19, 1578. [Google Scholar] [CrossRef]
  12. Lautié, E.; Russo, O.; Ducrot, P.; Boutin, J.A. Unraveling plant natural chemical diversity for drug discovery purposes. Front. Pharmacol. 2020, 11, 397. [Google Scholar] [CrossRef] [PubMed]
  13. Omokhefe Bruce, S. Secondary Metabolites from Natural Products. In Secondary Metabolites [Working Title]; IntechOpen: Rijeka, Croatia, 2022. [Google Scholar]
  14. Stan, D.; Enciu, A.-M.; Mateescu, A.L.; Ion, A.C.; Brezeanu, A.C.; Stan, D.; Tanase, C. Natural compounds with antimicrobial and antiviral effect and nanocarriers used for their transportation. Front. Pharmacol. 2021, 12, 723233. [Google Scholar] [CrossRef] [PubMed]
  15. Dejani, N.N.; Elshabrawy, H.A.; Bezerra Filho, C.d.S.M.; de Sousa, D.P. Anticoronavirus and immunomodulatory phenolic compounds: Opportunities and pharmacotherapeutic perspectives. Biomolecules 2021, 11, 1254. [Google Scholar] [CrossRef] [PubMed]
  16. Zhang, Q.-W.; Lin, L.-G.; Ye, W.-C. Techniques for extraction and isolation of natural products: A comprehensive review. Chin. Med. 2018, 13, 20. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Bucar, F.; Wube, A.; Schmid, M. Natural product isolation--how to get from biological material to pure compounds. Nat. Prod. Rep. 2013, 30, 525–545. [Google Scholar] [CrossRef] [Green Version]
  18. Long, F.; Yang, H.; Xu, Y.; Hao, H.; Li, P. A strategy for the identification of combinatorial bioactive compounds contributing to the holistic effect of herbal medicines. Sci. Rep. 2015, 5, 12361. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Wagner, H.; Ulrich-Merzenich, G. Synergy research: Approaching a new generation of phytopharmaceuticals. Phytomedicine 2009, 16, 97–110. [Google Scholar] [CrossRef]
  20. Borges, A.; José, H.; Homem, V.; Simões, M. Comparison of Techniques and Solvents on the Antimicrobial and Antioxidant Potential of Extracts from Acacia dealbata and Olea europaea. Antibiotics 2020, 9, 48. [Google Scholar] [CrossRef]
  21. Sosa-Hernández, J.E.; Escobedo-Avellaneda, Z.; Iqbal, H.M.N.; Welti-Chanes, J. State-of-the-Art Extraction Methodologies for Bioactive Compounds from Algal Biome to Meet Bio-Economy Challenges and Opportunities. Molecules 2018, 23, 2953. [Google Scholar] [CrossRef] [Green Version]
  22. Gullón, P.; Gullón, B.; Romaní, A.; Rocchetti, G.; Lorenzo, J.M. Smart advanced solvents for bioactive compounds recovery from agri-food by-products: A review. Trends Food Sci. Technol. 2020, 101, 182–197. [Google Scholar] [CrossRef]
  23. Ngo, T.V.; Scarlett, C.J.; Bowyer, M.C.; Ngo, P.D.; Vuong, Q.V. Impact of Different Extraction Solvents on Bioactive Compounds and Antioxidant Capacity from the Root of Salacia chinensis L. J. Food Qual. 2017, 2017, 1–8. [Google Scholar] [CrossRef] [Green Version]
  24. Caesar, L.K.; Cech, N.B. Synergy and antagonism in natural product extracts: When 1 + 1 does not equal 2. Nat. Prod. Rep. 2019, 36, 869–888. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Egan, J.M.; van Santen, J.A.; Liu, D.Y.; Linington, R.G. Development of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures. J. Nat. Prod. 2021, 84, 1044–1055. [Google Scholar] [CrossRef] [PubMed]
  26. Mickymaray, S. Efficacy and Mechanism of Traditional Medicinal Plants and Bioactive Compounds against Clinically Important Pathogens. Antibiotics 2019, 8, 257. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Li, J.; Xie, S.; Ahmed, S.; Wang, F.; Gu, Y.; Zhang, C.; Chai, X.; Wu, Y.; Cai, J.; Cheng, G. Antimicrobial activity and resistance: Influencing factors. Front. Pharmacol. 2017, 8, 364. [Google Scholar] [CrossRef] [Green Version]
  28. de Melo, A.L.F.; Rossato, L.; Barbosa, M.D.S.; Palozi, R.A.C.; Alfredo, T.M.; Antunes, K.A.; Eduvirgem, J.; Ribeiro, S.M.; Simionatto, S. From the environment to the hospital: How plants can help to fight bacteria biofilm. Microbiol. Res. 2022, 261, 127074. [Google Scholar] [CrossRef]
  29. Moloney, M.G. Natural products as a source for novel antibiotics. Trends Pharmacol. Sci. 2016, 37, 689–701. [Google Scholar] [CrossRef]
  30. Sharma, K.; Guleria, S.; Razdan, V.K.; Babu, V. Synergistic antioxidant and antimicrobial activities of essential oils of some selected medicinal plants in combination and with synthetic compounds. Ind. Crops Prod. 2020, 154, 112569. [Google Scholar] [CrossRef]
  31. Chusri, S.; Siriyong, T.; Na-Phatthalung, P.; Voravuthikunchai, S.P. Synergistic effects of ethnomedicinal plants of Apocynaceae family and antibiotics against clinical isolates of Acinetobacter baumannii. Asian Pac. J. Trop. Med. 2014, 7, 456–461. [Google Scholar] [CrossRef] [Green Version]
  32. Balouiri, M.; Sadiki, M.; Ibnsouda, S.K. Methods for in vitro evaluating antimicrobial activity: A review. J. Pharm. Anal. 2016, 6, 71–79. [Google Scholar] [CrossRef]
  33. Masota, N.E.; Vogg, G.; Ohlsen, K.; Holzgrabe, U. Reproducibility challenges in the search for antibacterial compounds from nature. PLoS ONE 2021, 16, e0255437. [Google Scholar] [CrossRef] [PubMed]
  34. Khan, Z.A.; Siddiqui, M.F.; Park, S. Current and emerging methods of antibiotic susceptibility testing. Diagnostics 2019, 9, 49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Temmerman, R.; Goethals, K.; Garmyn, A.; Vanantwerpen, G.; Vanrobaeys, M.; Haesebrouck, F.; Antonissen, G.; Devreese, M. Agreement of Quantitative and Qualitative Antimicrobial Susceptibility Testing Methodologies: The Case of Enrofloxacin and Avian Pathogenic Escherichia coli. Front. Microbiol. 2020, 11, 570975. [Google Scholar] [CrossRef] [PubMed]
  36. Humphries, R.M.; Ambler, J.; Mitchell, S.L.; Castanheira, M.; Dingle, T.; Hindler, J.A.; Koeth, L.; Sei, K. CLSI Methods Development and Standardization Working Group of the Subcommittee on Antimicrobial Susceptibility Testing CLSI methods development and standardization working group best practices for evaluation of antimicrobial susceptibility tests. J. Clin. Microbiol. 2018, 56, e01934-17. [Google Scholar] [CrossRef] [Green Version]
  37. CLSI Dilution AST for Aerobically Grown Bacteria—CLSI. Available online: https://clsi.org/standards/products/microbiology/documents/m07/ (accessed on 26 May 2022).
  38. Bubonja-Šonje, M.; Knežević, S.; Abram, M. Challenges to antimicrobial susceptibility testing of plant-derived polyphenolic compounds. Arh. Hig. Rada Toksikol. 2020, 71, 300–311. [Google Scholar] [CrossRef]
  39. Tan, J.B.L.; Lim, Y.Y. Critical analysis of current methods for assessing the in vitro antioxidant and antibacterial activity of plant extracts. Food Chem. 2015, 172, 814–822. [Google Scholar] [CrossRef]
  40. Silva, A.C.O.; Santana, E.F.; Saraiva, A.M.; Coutinho, F.N.; Castro, R.H.A.; Pisciottano, M.N.C.; Amorim, E.L.C.; Albuquerque, U.P. Which approach is more effective in the selection of plants with antimicrobial activity? Evid. Based Complement. Alternat. Med. 2013, 2013, 308980. [Google Scholar] [CrossRef] [Green Version]
  41. Jorgensen, J.H.; Turnidge, J.D. Susceptibility test methods: Dilution and disk diffusion methods*. In Manual of Clinical Microbiology, 11th ed.; Pfaller, M.A., Richter, S.S., Funke, G., Jorgensen, J.H., Landry, M.L., Carroll, K.C., Warnock, D.W., Eds.; American Society of Microbiology: Washington, DC, USA, 2015; pp. 1253–1273. ISBN 9781555817374. [Google Scholar]
  42. Vaou, N.; Stavropoulou, E.; Voidarou, C.; Tsigalou, C.; Bezirtzoglou, E. Towards advances in medicinal plant antimicrobial activity: A review study on challenges and future perspectives. Microorganisms 2021, 9, 41. [Google Scholar] [CrossRef]
  43. Mamedov, N. Medicinal plants studies: History, challenges and prospective. Med. Aromat. Plants 2012, 1, 133. [Google Scholar] [CrossRef] [Green Version]
  44. Guimarães, A.C.; Meireles, L.M.; Lemos, M.F.; Guimarães, M.C.C.; Endringer, D.C.; Fronza, M.; Scherer, R. Antibacterial activity of terpenes and terpenoids present in essential oils. Molecules 2019, 24, 2471. [Google Scholar] [CrossRef]
  45. Horváth, G.; Bencsik, T.; Ács, K.; Kocsis, B. Sensitivity of ESBL-Producing Gram-Negative Bacteria to Essential Oils, Plant Extracts, and Their Isolated Compounds. In Antibiotic Resistance; Elsevier: Amsterdam, The Netherlands, 2016; pp. 239–269. ISBN 9780128036426. [Google Scholar]
  46. Sadd, M.H. Encyclopedia of Microbiology, 4th ed.; Elsevier S & T: Amsterdam, The Netherlands, 2019; p. 1. ISBN 978-0-12-811737-8. [Google Scholar]
  47. Massoud, R.; Saffari, H.; Massoud, A.; Moteian, M.Y. Screening methods for assessment of antibacterial activity in nature. In Screening Methods for Assessment of Antibacterial Activity in Nature; University of Brussel: Brussel, Belgium, 2020. [Google Scholar]
  48. Christenson, J.C.; Korgenski, E.K.; Relich, R.F. Laboratory diagnosis of infection due to bacteria, fungi, parasites, and rickettsiae. In Principles and Practice of Pediatric Infectious Diseases; Elsevier: Amsterdam, The Netherlands, 2018; pp. 1422–1434.e3. ISBN 9780323401814. [Google Scholar]
  49. 20776–1; Susceptibility Testing of Infectious Agents and Evaluation of Performance of Antimicrobial Susceptibility Test Devices–Part 1: Broth Micro-Dilution Reference Method for Testing the In Vitro Activity of Antimicrobial Agents against Rapidly Growing Aerobic Bacteria Involved in Infectious Diseases. International Organization for Standardization: Geneva, Switzerland, 2019.
  50. Foerster, S.; Desilvestro, V.; Hathaway, L.J.; Althaus, C.L.; Unemo, M. A new rapid resazurin-based microdilution assay for antimicrobial susceptibility testing of Neisseria gonorrhoeae. J. Antimicrob. Chemother. 2017, 72, 1961–1968. [Google Scholar] [CrossRef] [PubMed]
  51. Kowalska-Krochmal, B.; Dudek-Wicher, R. The minimum inhibitory concentration of antibiotics: Methods, interpretation, clinical relevance. Pathogens 2021, 10, 165. [Google Scholar] [CrossRef] [PubMed]
  52. Schumacher, A.; Vranken, T.; Malhotra, A.; Arts, J.J.C.; Habibovic, P. In vitro antimicrobial susceptibility testing methods: Agar dilution to 3D tissue-engineered models. Eur. J. Clin. Microbiol. Infect. Dis. 2018, 37, 187–208. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Dewanjee, S.; Gangopadhyay, M.; Bhattacharya, N.; Khanra, R.; Dua, T.K. Bioautography and its scope in the field of natural product chemistry. J. Pharm. Anal. 2015, 5, 75–84. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Wang, M.; Zhang, Y.; Wang, R.; Wang, Z.; Yang, B.; Kuang, H. An Evolving Technology That Integrates Classical Methods with Continuous Technological Developments: Thin-Layer Chromatography Bioautography. Molecules 2021, 26, 647. [Google Scholar] [CrossRef]
  55. Suleimana, M.M.; McGaw, L.J.; Naidoo, V.; Eloff, J.N. Detection of antimicrobial compounds by bioautography of different extracts of leaves of selected South African tree species. Afr. J. Tradit. Complement. Altern. Med. 2009, 7, 64–78. [Google Scholar] [CrossRef]
  56. Shakeri, A.; Sharifi, M.J.; Fazly Bazzaz, B.S.; Emami, A.; Soheili, V.; Sahebkar, A.; Asili, J. Bioautography Detection of Antimicrobial Compounds from the Essential Oil of Salvia Pachystachys. Curr. Bioact. Compd. 2018, 14, 80–85. [Google Scholar] [CrossRef]
  57. Berney, M.; Hammes, F.; Bosshard, F.; Weilenmann, H.-U.; Egli, T. Assessment and interpretation of bacterial viability by using the LIVE/DEAD BacLight Kit in combination with flow cytometry. Appl. Environ. Microbiol. 2007, 73, 3283–3290. [Google Scholar] [CrossRef] [Green Version]
  58. Bankier, C.; Cheong, Y.; Mahalingam, S.; Edirisinghe, M.; Ren, G.; Cloutman-Green, E.; Ciric, L. A comparison of methods to assess the antimicrobial activity of nanoparticle combinations on bacterial cells. PLoS ONE 2018, 13, e0192093. [Google Scholar] [CrossRef] [Green Version]
  59. Nieto-Velázquez, N.G.; Gomez-Valdez, A.A.; González-Ávila, M.; Sánchez-Navarrete, J.; Toscano-Garibay, J.D.; Ruiz-Pérez, N.J. Preliminary Study on Citrus Oils Antibacterial Activity Measured by Flow Cytometry: A Step-by-Step Development. Antibiotics 2021, 10, 218. [Google Scholar] [CrossRef]
  60. Lagier, J.-C.; Hugon, P.; Khelaifia, S.; Fournier, P.-E.; La Scola, B.; Raoult, D. The rebirth of culture in microbiology through the example of culturomics to study human gut microbiota. Clin. Microbiol. Rev. 2015, 28, 237–264. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Diakite, A.; Dubourg, G.; Dione, N.; Afouda, P.; Bellali, S.; Ngom, I.I.; Valles, C.; Tall, M.L.; Lagier, J.-C.; Raoult, D. Optimization and standardization of the culturomics technique for human microbiome exploration. Sci. Rep. 2020, 10, 9674. [Google Scholar] [CrossRef] [PubMed]
  62. Bonnet, M.; Lagier, J.C.; Raoult, D.; Khelaifia, S. Bacterial culture through selective and non-selective conditions: The evolution of culture media in clinical microbiology. New Microbes New Infect. 2020, 34, 100622. [Google Scholar] [CrossRef] [PubMed]
  63. Aloni-Grinstein, R.; Shifman, O.; Lazar, S.; Steinberger-Levy, I.; Maoz, S.; Ber, R. A rapid real-time quantitative PCR assay to determine the minimal inhibitory extracellular concentration of antibiotics against an intracellular Francisella tularensis Live Vaccine Strain. Front. Microbiol. 2015, 6, 1213. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Kainz, K.; Bauer, M.A.; Madeo, F.; Carmona-Gutierrez, D. Fungal infections in humans: The silent crisis. Microb. Cell 2020, 7, 143–145. [Google Scholar] [CrossRef]
  65. Bongomin, F.; Gago, S.; Oladele, R.O.; Denning, D.W. Global and Multi-National Prevalence of Fungal Diseases-Estimate Precision. J. Fungi 2017, 3, 57. [Google Scholar] [CrossRef]
  66. Robbins, N.; Caplan, T.; Cowen, L.E. Molecular evolution of antifungal drug resistance. Annu. Rev. Microbiol. 2017, 71, 753–775. [Google Scholar] [CrossRef] [Green Version]
  67. Aldholmi, M.; Marchand, P.; Ourliac-Garnier, I.; Le Pape, P.; Ganesan, A. A Decade of Antifungal Leads from Natural Products: 2010-2019. Pharmaceuticals 2019, 12, 182. [Google Scholar] [CrossRef] [Green Version]
  68. Berkow, E.L.; Lockhart, S.R.; Ostrosky-Zeichner, L. Antifungal susceptibility testing: Current approaches. Clin. Microbiol. Rev. 2020, 33, e00069-19. [Google Scholar] [CrossRef]
  69. Fisher, M.C.; Alastruey-Izquierdo, A.; Berman, J.; Bicanic, T.; Bignell, E.M.; Bowyer, P.; Bromley, M.; Brüggemann, R.; Garber, G.; Cornely, O.A.; et al. Tackling the emerging threat of antifungal resistance to human health. Nat. Rev. Microbiol. 2022, 20, 557–571. [Google Scholar] [CrossRef]
  70. Perlin, D.S.; Rautemaa-Richardson, R.; Alastruey-Izquierdo, A. The global problem of antifungal resistance: Prevalence, mechanisms, and management. Lancet Infect. Dis. 2017, 17, e383–e392. [Google Scholar] [CrossRef] [PubMed]
  71. Posteraro, B.; De Carolis, E.; Vella, A.; Sanguinetti, M. MALDI-TOF mass spectrometry in the clinical mycology laboratory: Identification of fungi and beyond. Expert Rev. Proteom. 2013, 10, 151–164. [Google Scholar] [CrossRef] [PubMed]
  72. Singhal, N.; Kumar, M.; Kanaujia, P.K.; Virdi, J.S. MALDI-TOF mass spectrometry: An emerging technology for microbial identification and diagnosis. Front. Microbiol. 2015, 6, 791. [Google Scholar] [CrossRef] [Green Version]
  73. Sanchez Armengol, E.; Harmanci, M.; Laffleur, F. Current strategies to determine antifungal and antimicrobial activity of natural compounds. Microbiol. Res. 2021, 252, 126867. [Google Scholar] [CrossRef] [PubMed]
  74. Scorzoni, L.; Benaducci, T.; Almeida, A.M.F.; Silva, D.H.S.; Bolzani, V.d.S.; Gianinni, M.J.S.M. The use of standard methodology for determination of antifungal activity of natural products against medical yeasts Candida sp and Cryptococcus sp. Braz. J. Microbiol. 2007, 38, 391–397. [Google Scholar] [CrossRef] [Green Version]
  75. Hadacek, F.; Greger, H. Testing of antifungal natural products: Methodologies, comparability of results and assay choice. Phytochem. Anal. 2000, 11, 137–147. [Google Scholar] [CrossRef]
  76. CLSI M27Ed4: Broth Dilution Antifungal Susceptibility, Yeasts. Available online: https://clsi.org/standards/products/microbiology/documents/m27/ (accessed on 30 May 2022).
  77. Rodriguez-Tudela, J.L.; Arendrup, M.C.; Barchiesi, F.; Bille, J.; Chryssanthou, E.; Cuenca-Estrella, M.; Dannaoui, E.; Denning, D.W.; Donnelly, J.P.; Dromer, F.; et al. Subcommittee on Antifungal Susceptibility Testing (AFST) of the ESCMID European Committee for Antimicrobial Susceptibility Testing (EUCAST) EUCAST definitive document EDef 7.1: Method for the determination of broth dilution MICs of antifungal agents for fermentative yeasts. Clin. Microbiol. Infect. 2008, 14, 398–405. [Google Scholar] [CrossRef]
  78. Alexander, B.D. Reference Method For Broth Dilution Antifungal Susceptibility Testing Of Filamentous Fungi; Clinical and Laboratory Standards Institute: Wayne, PA, USA, 2017; p. 50. ISBN 1-56238-831-2. [Google Scholar]
  79. Arikan, S. Current status of antifungal susceptibility testing methods. Med. Mycol. 2007, 45, 569–587. [Google Scholar] [CrossRef] [Green Version]
  80. Pfaller, M.A.; Sheehan, D.J.; Rex, J.H. Determination of fungicidal activities against yeasts and molds: Lessons learned from bactericidal testing and the need for standardization. Clin. Microbiol. Rev. 2004, 17, 268–280. [Google Scholar] [CrossRef] [Green Version]
  81. Vale-Silva, L.A.; Buchta, V. Antifungal susceptibility testing by flow cytometry: Is it the future? Mycoses 2006, 49, 261–273. [Google Scholar] [CrossRef]
  82. Bleichrodt, R.-J.; Read, N.D. Flow cytometry and FACS applied to filamentous fungi. Fungal Biol. Rev. 2019, 33, 1–15. [Google Scholar] [CrossRef]
  83. Covarrubias-Rivera, L.; López-Cruz, R.; Ragazzo-Sánchez, J.A.; Iñiguez-Moreno, M.; Calderón-Santoyo, M. Determination by isothermal microcalorimetry of the sensitivity of phytopathogenic fungi of tropical fruits against an ethanolic extract of jackfruit leaf (Artocarpus heterophyllus Lam.). J. Microbiol. Methods 2022, 195, 106457. [Google Scholar] [CrossRef] [PubMed]
  84. Marinach, C.; Alanio, A.; Palous, M.; Kwasek, S.; Fekkar, A.; Brossas, J.-Y.; Brun, S.; Snounou, G.; Hennequin, C.; Sanglard, D.; et al. MALDI-TOF MS-based drug susceptibility testing of pathogens: The example of Candida albicans and fluconazole. Proteomics 2009, 9, 4627–4631. [Google Scholar] [CrossRef] [PubMed]
  85. Durand, C.; Maubon, D.; Cornet, M.; Wang, Y.; Aldebert, D.; Garnaud, C. Can we improve antifungal susceptibility testing? Front. Cell. Infect. Microbiol. 2021, 11, 720609. [Google Scholar] [CrossRef]
  86. Nuthan, B.R.; Rakshith, D.; Marulasiddaswamy, K.M.; Rao, H.C.Y.; Ramesha, K.P.; Mohana, N.C.; Siddappa, S.; Darshan, D.; Kumara, K.K.S.; Satish, S. Application of Optimized and Validated Agar Overlay TLC-Bioautography Assay for Detecting the Antimicrobial Metabolites of Pharmaceutical Interest. J. Chromatogr. Sci. 2020, 58, 737–746. [Google Scholar] [CrossRef] [PubMed]
  87. Marston, A. Thin-layer chromatography with biological detection in phytochemistry. J. Chromatogr. A 2011, 1218, 2676–2683. [Google Scholar] [CrossRef]
  88. Pisarski, K. The global burden of disease of zoonotic parasitic diseases: Top 5 contenders for priority consideration. Trop. Med. Infect. Dis. 2019, 4, 44. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Goupil, L.S.; McKerrow, J.H. Introduction: Drug discovery and development for neglected diseases. Chem. Rev. 2014, 114, 11131–11137. [Google Scholar] [CrossRef]
  90. Campbell, S.; Soman-Faulkner, K. Antiparasitic Drugs. In StatPearls; StatPearls Publishing: Treasure Island, FL, USA, 2022. [Google Scholar]
  91. Wink, M. Medicinal plants: A source of anti-parasitic secondary metabolites. Molecules 2012, 17, 12771–12791. [Google Scholar] [CrossRef] [Green Version]
  92. Tagboto, S.; Townson, S. Antiparasitic properties of medicinal plants and other naturally occurring products. Adv. Parasitol. 2001, 50, 199–295. [Google Scholar] [CrossRef]
  93. Das, K.; Tiwari, R.K.; Shrivastava, D.K. Techniques for evaluation of medicinal plant products as antimicrobial agents: Current methods and future trends. J. Med. Plants Res. 2010, 4, 104–111. [Google Scholar]
  94. McHardy, I.H.; Wu, M.; Shimizu-Cohen, R.; Couturier, M.R.; Humphries, R.M. Detection of intestinal protozoa in the clinical laboratory. J. Clin. Microbiol. 2014, 52, 712–720. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Doonan, F.; Cotter, T.G. Morphological assessment of apoptosis. Methods 2008, 44, 200–204. [Google Scholar] [CrossRef] [PubMed]
  96. Henry, C.M.; Hollville, E.; Martin, S.J. Measuring apoptosis by microscopy and flow cytometry. Methods 2013, 61, 90–97. [Google Scholar] [CrossRef] [PubMed]
  97. Riss, T.L.; Moravec, R.A.; Niles, A.L.; Benink, H.A.; Worzella, T.J.; Minor, L. Cell Viability Assays. In Assay Guidance Manual; Sittampalam, G.S., Coussens, N.P., Nelson, H., Arkin, M., Auld, D., Austin, C., Bejcek, B., Glicksman, M., Inglese, J., Iversen, P.W., et al., Eds.; Eli Lilly & Company and the National Center for Advancing Translational Sciences: Bethesda, MD, USA, 2004. [Google Scholar]
  98. Adan, A.; Alizada, G.; Kiraz, Y.; Baran, Y.; Nalbant, A. Flow cytometry: Basic principles and applications. Crit. Rev. Biotechnol. 2017, 37, 163–176. [Google Scholar] [CrossRef] [PubMed]
  99. Soeiro, M.N.C.; Werbovetz, K.; Boykin, D.W.; Wilson, W.D.; Wang, M.Z.; Hemphill, A. Novel amidines and analogues as promising agents against intracellular parasites: A systematic review. Parasitology 2013, 140, 929–951. [Google Scholar] [CrossRef] [Green Version]
  100. Aslantürk, Ö.S. In vitro cytotoxicity and cell viability assays: Principles, advantages, and disadvantages. In Genotoxicity—A Predictable Risk to Our Actual World; Larramendy, M.L., Soloneski, S., Eds.; InTech: London, UK, 2018; ISBN 978-1-78923-418-3. [Google Scholar]
  101. Präbst, K.; Engelhardt, H.; Ringgeler, S.; Hübner, H. Basic colorimetric proliferation assays: MTT, WST, and resazurin. Methods Mol. Biol. 2017, 1601, 1–17. [Google Scholar] [CrossRef]
  102. García Díaz, J.; Tuenter, E.; Escalona Arranz, J.C.; Llauradó Maury, G.; Cos, P.; Pieters, L. Antimicrobial activity of leaf extracts and isolated constituents of Croton linearis. J. Ethnopharmacol. 2019, 236, 250–257. [Google Scholar] [CrossRef]
  103. Henriques, C.; Moreira, T.L.B.; Maia-Brigagão, C.; Henriques-Pons, A.; Carvalho, T.M.U.; de Souza, W. Tetrazolium salt based methods for high-throughput evaluation of anti-parasite chemotherapy. Anal. Methods 2011, 3, 2148. [Google Scholar] [CrossRef]
  104. Ilaghi, M.; Sharifi, I.; Sharififar, F.; Sharifi, F.; Oliaee, R.T.; Babaei, Z.; Meimamandi, M.S.; Keyhani, A.; Bamorovat, M. The potential role and apoptotic profile of three medicinal plant extracts on Leishmania tropica by MTT assay, macrophage model and flow cytometry analysis. Parasite Epidemiol. Control 2021, 12, e00201. [Google Scholar] [CrossRef]
  105. Barrio, G.; Grueiro, M.; Montero, D.; Nogal, J.J.; Escario, J.A.; Muelas, S.; Fernández, C.; Vega, C.; Rolón, M.; Fernández, M.; et al. In Vitro Antiparasitic Activity of Plant Extracts from Panama. Pharm. Biol. 2004, 42, 332–337. [Google Scholar] [CrossRef]
  106. Jiménez-Ruiz, A.; Alzate, J.F.; Macleod, E.T.; Lüder, C.G.K.; Fasel, N.; Hurd, H. Apoptotic markers in protozoan parasites. Parasit. Vectors 2010, 3, 104. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Díaz-Godínez, C.; Ontiveros-Rodríguez, J.C.; Ríos-Valencia, D.G.; Herbert-Pucheta, J.E.; Zepeda-Vallejo, L.G.; Carrero, J.C. Anti-amoebic Activity of Leaf Extracts and Aporphine Alkaloids Obtained from Annona purpurea. Planta Med. 2020, 86, 425–433. [Google Scholar] [CrossRef] [PubMed]
  108. Kim, K.H.; Sederstrom, J.M. Assaying cell cycle status using flow cytometry. Curr. Protoc. Mol. Biol. 2015, 111, 28.6.1–28.6.11. [Google Scholar] [CrossRef] [Green Version]
  109. Mukherjee, D.; Singh, C.B.; Dey, S.; Mandal, S.; Ghosh, J.; Mallick, S.; Hussain, A.; Swapana, N.; Ross, S.A.; Pal, C. Induction of apoptosis by zerumbone isolated from Zingiber zerumbet (L.) Smith in protozoan parasite Leishmania donovani due to oxidative stress. Braz. J. Infect. Dis. 2016, 20, 48–55. [Google Scholar] [CrossRef] [Green Version]
  110. Souza, R.O.d.S.; Sousa, P.L.; de Menezes, R.R.P.P.B.; Sampaio, T.L.; Tessarolo, L.D.; Silva, F.C.O.; Pereira, M.G.; Martins, A.M.C. Trypanocidal activity of polysaccharide extract from Genipa americana leaves. J. Ethnopharmacol. 2018, 210, 311–317. [Google Scholar] [CrossRef]
  111. Vermes, I.; Haanen, C.; Steffens-Nakken, H.; Reutelingsperger, C. A novel assay for apoptosis. Flow cytometric detection of phosphatidylserine expression on early apoptotic cells using fluorescein labelled Annexin V. J. Immunol. Methods 1995, 184, 39–51. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  112. Crowley, L.C.; Marfell, B.J.; Scott, A.P.; Waterhouse, N.J. Quantitation of apoptosis and necrosis by annexin V binding, propidium iodide uptake, and flow cytometry. Cold Spring Harb. Protoc. 2016, 2016, pdb-prot087288. [Google Scholar] [CrossRef]
  113. Khademvatan, S.; Saki, J.; Gharavi, M.J.; Rahim, F. Allium sativum extract induces apoptosis in Leishmania major (MRHO/IR/75/ER) promastigotes. J. Med. Plants Res. 2011, 5, 3725–3732. [Google Scholar]
  114. Proto, W.R.; Coombs, G.H.; Mottram, J.C. Cell death in parasitic protozoa: Regulated or incidental? Nat. Rev. Microbiol. 2013, 11, 58–66. [Google Scholar] [CrossRef]
  115. Moreira, A.L.; Scariot, D.B.; Pelegrini, B.L.; Pessini, G.L.; Ueda-Nakamura, T.; Nakamura, C.V.; Ferreira, I.C.P. Acyclic Sesquiterpenes from the Fruit Pericarp of Sapindus saponaria Induce Ultrastructural Alterations and Cell Death in Leishmania amazonensis. Evid. Based Complement. Alternat. Med. 2017, 2017, 5620693. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Albuquerque, R.D.D.G.; Oliveira, A.P.; Ferreira, C.; Passos, C.L.A.; Fialho, E.; Soares, D.C.; Amaral, V.F.; Bezerra, G.B.; Esteves, R.S.; Santos, M.G.; et al. Anti-Leishmania amazonensis activity of the terpenoid fraction from Eugenia pruniformis leaves. An. Acad. Bras. Cienc. 2020, 92, e20201181. [Google Scholar] [CrossRef] [PubMed]
  117. Dayakar, A.; Chandrasekaran, S.; Veronica, J.; Sundar, S.; Maurya, R. In vitro and in vivo evaluation of anti-leishmanial and immunomodulatory activity of Neem leaf extract in Leishmania donovani infection. Exp. Parasitol. 2015, 153, 45–54. [Google Scholar] [CrossRef] [PubMed]
  118. Shinjyo, N.; Nakayama, H.; Ishimaru, K.; Hikosaka, K.; Mi-Ichi, F.; Norose, K.; Yoshida, H. Hypericum erectum alcoholic extract inhibits Toxoplasma growth and Entamoeba encystation: An exploratory study on the anti-protozoan potential. J. Nat. Med. 2020, 74, 294–305. [Google Scholar] [CrossRef]
  119. Hendrickx, S.; Caljon, G.; Maes, L. In Vitro Growth Inhibition Assays of Leishmania spp. Methods Mol. Biol. 2020, 2116, 791–800. [Google Scholar] [CrossRef]
  120. García, M.; Monzote, L.; Montalvo, A.M.; Scull, R. Screening of medicinal plants against Leishmania amazonensis. Pharm. Biol. 2010, 48, 1053–1058. [Google Scholar] [CrossRef]
  121. Kashif, M.; Hira, S.K.; Upadhyaya, A.; Gupta, U.; Singh, R.; Paladhi, A.; Khan, F.I.; Rub, A.; Manna, P.P. In silico studies and evaluation of antiparasitic role of a novel pyruvate phosphate dikinase inhibitor in Leishmania donovani infected macrophages. Int. J. Antimicrob. Agents 2019, 53, 508–514. [Google Scholar] [CrossRef]
  122. Wolf, K.; Dormeyer, M. Information-based methods in the development of antiparasitic drugs. Parasitol. Res. 2003, 90 (Suppl. 2), S91–S96. [Google Scholar] [CrossRef]
  123. Ogungbe, I.V.; Setzer, W.N. In-silico Leishmania target selectivity of antiparasitic terpenoids. Molecules 2013, 18, 7761–7847. [Google Scholar] [CrossRef] [Green Version]
  124. Johnson, R.M.; Rawson, S.; McPhillie, M.J.; Fishwick, C.W.G.; Muench, S.P. The Growing Role of Electron Microscopy in Anti-parasitic Drug Discovery. Curr. Med. Chem. 2018, 25, 5279–5290. [Google Scholar] [CrossRef]
  125. Vannier-Santos, M.A.; De Castro, S.L. Electron microscopy in antiparasitic chemotherapy: A (close) view to a kill. Curr. Drug Targets 2009, 10, 246–260. [Google Scholar] [CrossRef] [PubMed]
  126. Hendrickx, S.; Caljon, G.; Maes, L. Need for sustainable approaches in antileishmanial drug discovery. Parasitol. Res. 2019, 118, 2743–2752. [Google Scholar] [CrossRef] [PubMed]
  127. Finger, S.; Wiegand, C.; Buschmann, H.-J.; Hipler, U.-C. Antibacterial properties of cyclodextrin-antiseptics-complexes determined by microplate laser nephelometry and ATP bioluminescence assay. Int. J. Pharm. 2013, 452, 188–193. [Google Scholar] [CrossRef] [PubMed]
  128. Kapoor, R.; Yadav, J.S. Development of a rapid ATP bioluminescence assay for biocidal susceptibility testing of rapidly growing mycobacteria. J. Clin. Microbiol. 2010, 48, 3725–3728. [Google Scholar] [CrossRef] [Green Version]
  129. Villalta, F.; Rachakonda, G. Advances in preclinical approaches to Chagas disease drug discovery. Expert Opin. Drug Discov. 2019, 14, 1161–1174. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  130. Weng, H.-B.; Chen, H.-X.; Wang, M.-W. Innovation in neglected tropical disease drug discovery and development. Infect. Dis. Poverty 2018, 7, 67. [Google Scholar] [CrossRef] [Green Version]
  131. Cowell, A.N.; Winzeler, E.A. Advances in omics-based methods to identify novel targets for malaria and other parasitic protozoan infections. Genome Med. 2019, 11, 63. [Google Scholar] [CrossRef] [Green Version]
  132. Munguía, B.; Saldaña, J.; Nieves, M.; Melian, M.E.; Ferrer, M.; Teixeira, R.; Porcal, W.; Manta, E.; Domínguez, L. Sensitivity of Haemonchus contortus to anthelmintics using different in vitro screening assays: A comparative study. Parasit. Vectors 2022, 15, 129. [Google Scholar] [CrossRef]
  133. Zenebe, S.; Feyera, T.; Assefa, S. In Vitro Anthelmintic Activity of Crude Extracts of Aerial Parts of Cissus quadrangularis L. and Leaves of Schinus molle L. against Haemonchus contortus. Biomed Res. Int. 2017, 2017, 1905987. [Google Scholar] [CrossRef] [Green Version]
  134. Garbin, V.P.; Munguía, B.; Saldaña, J.C.; Deschamps, C.; Cipriano, R.R.; Molento, M.B. Chemical characterization and in vitro anthelmintic activity of Citrus bergamia Risso and Citrus X paradisii Macfad essential oil against Haemonchus contortus Kirby isolate. Acta Trop. 2021, 217, 105869. [Google Scholar] [CrossRef]
  135. Jayawardene, K.L.T.D.; Palombo, E.A.; Boag, P.R. Natural products are a promising source for anthelmintic drug discovery. Biomolecules 2021, 11, 457. [Google Scholar] [CrossRef] [PubMed]
  136. Vijayakumar, S.; Malaikozhundan, B.; Saravanakumar, K.; Durán-Lara, E.F.; Wang, M.-H.; Vaseeharan, B. Garlic clove extract assisted silver nanoparticle—Antibacterial, antibiofilm, antihelminthic, anti-inflammatory, anticancer and ecotoxicity assessment. J. Photochem. Photobiol. B Biol. 2019, 198, 111558. [Google Scholar] [CrossRef] [PubMed]
  137. Athanasiadou, S.; Almvik, M.; Hellström, J.; Madland, E.; Simic, N.; Steinshamn, H. Chemical Analysis and Anthelmintic Activity Against Teladorsagia Circumcincta of Nordic Bark Extracts In vitro. Front. Vet. Sci. 2021, 8, 666924. [Google Scholar] [CrossRef]
  138. Eguale, T.; Tadesse, D.; Giday, M. In vitro anthelmintic activity of crude extracts of five medicinal plants against egg-hatching and larval development of Haemonchus contortus. J. Ethnopharmacol. 2011, 137, 108–113. [Google Scholar] [CrossRef]
  139. Giovanelli, F.; Mattellini, M.; Fichi, G.; Flamini, G.; Perrucci, S. In Vitro Anthelmintic Activity of Four Plant-Derived Compounds against Sheep Gastrointestinal Nematodes. Vet. Sci. 2018, 5, 78. [Google Scholar] [CrossRef] [Green Version]
  140. Carvalho, C.O.; Chagas, A.C.S.; Cotinguiba, F.; Furlan, M.; Brito, L.G.; Chaves, F.C.M.; Stephan, M.P.; Bizzo, H.R.; Amarante, A.F.T. The anthelmintic effect of plant extracts on Haemonchus contortus and Strongyloides venezuelensis. Vet. Parasitol. 2012, 183, 260–268. [Google Scholar] [CrossRef] [Green Version]
  141. Ferreira, L.E.; Benincasa, B.I.; Fachin, A.L.; Contini, S.H.T.; França, S.C.; Chagas, A.C.S.; Beleboni, R.O. Essential oils of Citrus aurantifolia, Anthemis nobile and Lavandula officinalis: In vitro anthelmintic activities against Haemonchus contortus. Parasit. Vectors 2018, 11, 269. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  142. Maestrini, M.; Tava, A.; Mancini, S.; Tedesco, D.; Perrucci, S. In Vitro Anthelmintic Activity of Saponins from Medicago spp. Against Sheep Gastrointestinal Nematodes. Molecules 2020, 25, 242. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  143. Khan, A.; Tak, H.; Nazir, R.; Lone, B.A. In vitro and in vivo anthelmintic activities of Iris kashmiriana Linn. J. Saudi Soc. Agric. Sci. 2016, 17, 235–240. [Google Scholar] [CrossRef] [Green Version]
  144. Mathew, M.D.; Mathew, N.D.; Miller, A.; Simpson, M.; Au, V.; Garland, S.; Gestin, M.; Edgley, M.L.; Flibotte, S.; Balgi, A.; et al. Elegans Forward and Reverse Genetics to Identify New Compounds with Anthelmintic Activity. PLoS Negl. Trop. Dis. 2016, 10, e0005058. [Google Scholar] [CrossRef]
  145. Corsi, A.K.; Wightman, B.; Chalfie, M. A Transparent Window into Biology: A Primer on Caenorhabditis elegans. Genetics 2015, 200, 387–407. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  146. Zamanian, M.; Chan, J.D. High-content approaches to anthelmintic drug screening. Trends Parasitol. 2021, 37, 780–789. [Google Scholar] [CrossRef] [PubMed]
  147. Adamson, C.S.; Chibale, K.; Goss, R.J.M.; Jaspars, M.; Newman, D.J.; Dorrington, R.A. Antiviral drug discovery: Preparing for the next pandemic. Chem. Soc. Rev. 2021, 50, 3647–3655. [Google Scholar] [CrossRef] [PubMed]
  148. Irwin, K.K.; Renzette, N.; Kowalik, T.F.; Jensen, J.D. Antiviral drug resistance as an adaptive process. Virus Evol. 2016, 2, vew014. [Google Scholar] [CrossRef] [Green Version]
  149. De Clercq, E.; Li, G. Approved Antiviral Drugs over the Past 50 Years. Clin. Microbiol. Rev. 2016, 29, 695–747. [Google Scholar] [CrossRef] [Green Version]
  150. Li, G.; De Clercq, E. Chapter 1. overview of antiviral drug discovery and development: Viral versus host targets. In Antiviral Discovery for Highly Pathogenic Emerging Viruses; Muñoz-Fontela, C., Delgado, R., Eds.; Drug Discovery; Royal Society of Chemistry: Cambridge, UK, 2021; pp. 1–27. ISBN 978-1-78801-564-6. [Google Scholar]
  151. Owen, L.; Laird, K.; Shivkumar, M. Antiviral plant-derived natural products to combat RNA viruses: Targets throughout the viral life cycle. Lett. Appl. Microbiol. 2021, 75, 476–499. [Google Scholar] [CrossRef]
  152. Musarra-Pizzo, M.; Pennisi, R.; Ben-Amor, I.; Mandalari, G.; Sciortino, M.T. Antiviral Activity Exerted by Natural Products against Human Viruses. Viruses 2021, 13, 828. [Google Scholar] [CrossRef]
  153. Prichard, M.N.; Turk, S.R.; Coleman, L.A.; Engelhardt, S.L.; Shipman, C.; Drach, J.C. A microtiter virus yield reduction assay for the evaluation of antiviral compounds against human cytomegalovirus and herpes simplex virus. J. Virol. Methods 1990, 28, 101–106. [Google Scholar] [CrossRef] [Green Version]
  154. Cirne-Santos, C.C.; Barros, C.d.S.; Gomes, M.W.L.; Gomes, R.; Cavalcanti, D.N.; Obando, J.M.C.; Ramos, C.J.B.; Villaça, R.C.; Teixeira, V.L.; Paixão, I.C.N.d.P. In Vitro Antiviral Activity Against Zika Virus From a Natural Product of the Brazilian Brown Seaweed Dictyota menstrualis. Nat. Prod. Commun. 2019, 14, 1934578X1985912. [Google Scholar] [CrossRef] [Green Version]
  155. Gu, L.; Schneller, S.W.; Li, Q. Assays for the identification of novel antivirals against bluetongue virus. J. Vis. Exp. 2013, 80, e50820. [Google Scholar] [CrossRef] [Green Version]
  156. Chiamenti, L.; da Silva, F.P.; Schallemberger, K.; Demoliner, M.; Rigotto, C.; Fleck, J.D. Cytotoxicity and antiviral activity evaluation of Cymbopogon spp hydroethanolic extracts. Braz. J. Pharm. Sci. 2019, 55, e18063. [Google Scholar] [CrossRef] [Green Version]
  157. Smither, S.J.; Lear-Rooney, C.; Biggins, J.; Pettitt, J.; Lever, M.S.; Olinger, G.G. Comparison of the plaque assay and 50% tissue culture infectious dose assay as methods for measuring filovirus infectivity. J. Virol. Methods 2013, 193, 565–571. [Google Scholar] [CrossRef] [PubMed]
  158. Baer, A.; Kehn-Hall, K. Viral concentration determination through plaque assays: Using traditional and novel overlay systems. J. Vis. Exp. 2014, 93, e52065. [Google Scholar] [CrossRef] [PubMed]
  159. Visintini Jaime, M.F.; Redko, F.; Muschietti, L.V.; Campos, R.H.; Martino, V.S.; Cavallaro, L.V. In vitro antiviral activity of plant extracts from Asteraceae medicinal plants. Virol. J. 2013, 10, 245. [Google Scholar] [CrossRef] [Green Version]
  160. Mendoza, E.J.; Manguiat, K.; Wood, H.; Drebot, M. Two Detailed Plaque Assay Protocols for the Quantification of Infectious SARS-CoV-2. Curr. Protoc. Microbiol. 2020, 57, ecpmc105. [Google Scholar] [CrossRef]
  161. Cresta, D.; Warren, D.C.; Quirouette, C.; Smith, A.P.; Lane, L.C.; Smith, A.M.; Beauchemin, C.A.A. Time to revisit the endpoint dilution assay and to replace the TCID50 as a measure of a virus sample’s infection concentration. PLoS Comput. Biol. 2021, 17, e1009480. [Google Scholar] [CrossRef]
  162. Bullen, C.K.; Davis, S.L.; Looney, M.M. Quantification of Infectious SARS-CoV-2 by the 50% Tissue Culture Infectious Dose Endpoint Dilution Assay. Methods Mol. Biol. 2022, 2452, 131–146. [Google Scholar] [CrossRef]
  163. Stewart, H.; Bartlett, C.; Ross-Thriepland, D.; Shaw, J.; Griffin, S.; Harris, M. A novel method for the measurement of hepatitis C virus infectious titres using the IncuCyte ZOOM and its application to antiviral screening. J. Virol. Methods 2015, 218, 59–65. [Google Scholar] [CrossRef] [Green Version]
  164. Singh, P.; Singh, G.; Karsky, J.; Nelson, E.; Ramamoorthy, S. A convenient colorimetric assay for the quantification of porcine epidemic diarrhea virus and neutralizing antibodies. J. Virol. Methods 2018, 262, 32–37. [Google Scholar] [CrossRef]
  165. Bolívar-Marin, S.; Bosch, I.; Narváez, C.F. Combination of the Focus-Forming Assay and Digital Automated Imaging Analysis for the Detection of Dengue and Zika Viral Loads in Cultures and Acute Disease. J. Trop. Med. 2022, 2022, 2177183. [Google Scholar] [CrossRef]
  166. Coimbra, L.D.; Borin, A.; Fontoura, M.; Gravina, H.D.; Nagai, A.; Shimizu, J.F.; Bispo-dos-Santos, K.; Granja, F.; Oliveira, P.S.L.; Franchini, K.G.; et al. Identification of Compounds With Antiviral Activity Against SARS-CoV-2 in the MMV Pathogen Box Using a Phenotypic High-Throughput Screening Assay. Front.Virol. 2022, 2, 854363. [Google Scholar] [CrossRef]
  167. Koishi, A.C.; Zanello, P.R.; Bianco, É.M.; Bordignon, J.; Nunes Duarte dos Santos, C. Screening of Dengue virus antiviral activity of marine seaweeds by an in situ enzyme-linked immunosorbent assay. PLoS ONE 2012, 7, e51089. [Google Scholar] [CrossRef] [PubMed]
  168. Srinivasan, V.; Brognaro, H.; Prabhu, P.R.; de Souza, E.E.; Günther, S.; Reinke, P.Y.A.; Lane, T.J.; Ginn, H.; Han, H.; Ewert, W.; et al. Antiviral activity of natural phenolic compounds in complex at an allosteric site of SARS-CoV-2 papain-like protease. Commun. Biol. 2022, 5, 805. [Google Scholar] [CrossRef] [PubMed]
  169. Sachse, M.; Tenorio, R.; Fernández de Castro, I.; Muñoz-Basagoiti, J.; Perez-Zsolt, D.; Raïch-Regué, D.; Rodon, J.; Losada, A.; Avilés, P.; Cuevas, C.; et al. Unraveling the antiviral activity of plitidepsin against SARS-CoV-2 by subcellular and morphological analysis. Antivir. Res. 2022, 200, 105270. [Google Scholar] [CrossRef] [PubMed]
  170. Dolskiy, A.A.; Grishchenko, I.V.; Yudkin, D.V. Cell cultures for virology: Usability, advantages, and prospects. Int. J. Mol. Sci. 2020, 21, 978. [Google Scholar] [CrossRef]
  171. Rumlová, M.; Ruml, T. In vitro methods for testing antiviral drugs. Biotechnol. Adv. 2018, 36, 557–576. [Google Scholar] [CrossRef]
  172. Aliabadi, N.; Jamalidoust, M.; Pouladfar, G.; Ziyaeyan, A.; Ziyaeyan, M. Antiviral activity of triptolide on herpes simplex virus in vitro. Immun. Inflamm. Dis. 2022, 10, e667. [Google Scholar] [CrossRef] [PubMed]
  173. Sureram, S.; Arduino, I.; Ueoka, R.; Rittà, M.; Francese, R.; Srivibool, R.; Darshana, D.; Piel, J.; Ruchirawat, S.; Muratori, L.; et al. The Peptide A-3302-B Isolated from a Marine Bacterium Micromonospora sp. Inhibits HSV-2 Infection by Preventing the Viral Egress from Host Cells. Int. J. Mol. Sci. 2022, 23, 947. [Google Scholar] [CrossRef]
  174. Marin, M.; Du, Y.; Giroud, C.; Kim, J.H.; Qui, M.; Fu, H.; Melikyan, G.B. High-Throughput HIV-Cell Fusion Assay for Discovery of Virus Entry Inhibitors. Assay Drug Dev. Technol. 2015, 13, 155–166. [Google Scholar] [CrossRef] [Green Version]
  175. Ho, H.P.T.; Vo, D.N.K.; Lin, T.-Y.; Hung, J.-N.; Chiu, Y.-H.; Tsai, M.-H. Ganoderma microsporum immunomodulatory protein acts as a multifunctional broad-spectrum antiviral against SARS-CoV-2 by interfering virus binding to the host cells and spike-mediated cell fusion. Biomed. Pharmacother. 2022, 155, 113766. [Google Scholar] [CrossRef]
  176. Meunier, T.; Desmarets, L.; Bordage, S.; Bamba, M.; Hervouet, K.; Rouillé, Y.; François, N.; Decossas, M.; Sencio, V.; Trottein, F.; et al. A Photoactivable Natural Product with Broad Antiviral Activity against Enveloped Viruses, Including Highly Pathogenic Coronaviruses. Antimicrob. Agents Chemother. 2022, 66, e0158121. [Google Scholar] [CrossRef] [PubMed]
  177. Kausar, S.; Said Khan, F.; Ishaq Mujeeb Ur Rehman, M.; Akram, M.; Riaz, M.; Rasool, G.; Hamid Khan, A.; Saleem, I.; Shamim, S.; Malik, A. A review: Mechanism of action of antiviral drugs. Int. J. Immunopathol. Pharmacol. 2021, 35, 20587384211002620. [Google Scholar] [CrossRef] [PubMed]
  178. Peng, S.; Wang, H.; Wang, Z.; Wang, Q. Progression of antiviral agents targeting viral polymerases. Molecules 2022, 27, 370. [Google Scholar] [CrossRef] [PubMed]
  179. Gabaglio, S.; Alvarenga, N.; Cantero-González, G.; Degen, R.; Ferro, E.A.; Langjahr, P.; Chnaiderman, J.; Sotelo, P.H. A quantitative PCR assay for antiviral activity screening of medicinal plants against Herpes simplex 1. Nat. Prod. Res. 2021, 35, 2926–2930. [Google Scholar] [CrossRef] [PubMed]
  180. Sáez-Álvarez, Y.; Arias, A.; Del Águila, C.; Agudo, R. Development of a fluorescence-based method for the rapid determination of Zika virus polymerase activity and the screening of antiviral drugs. Sci. Rep. 2019, 9, 5397. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  181. Beadle, J.R.; Valiaeva, N.; Yang, G.; Yu, J.-H.; Broker, T.R.; Aldern, K.A.; Harden, E.A.; Keith, K.A.; Prichard, M.N.; Hartman, T.; et al. Synthesis and Antiviral Evaluation of Octadecyloxyethyl Benzyl 9-[(2-Phosphonomethoxy)ethyl]guanine (ODE-Bn-PMEG), a Potent Inhibitor of Transient HPV DNA Amplification. J. Med. Chem. 2016, 59, 10470–10478. [Google Scholar] [CrossRef]
  182. Vicenti, I.; Dragoni, F.; Giannini, A.; Giammarino, F.; Spinicci, M.; Saladini, F.; Boccuto, A.; Zazzi, M. Development of a Cell-Based Immunodetection Assay for Simultaneous Screening of Antiviral Compounds Inhibiting Zika and Dengue Virus Replication. SLAS Discov. 2020, 25, 506–514. [Google Scholar] [CrossRef]
  183. Case, J.B.; Bailey, A.L.; Kim, A.S.; Chen, R.E.; Diamond, M.S. Growth, detection, quantification, and inactivation of SARS-CoV-2. Virology 2020, 548, 39–48. [Google Scholar] [CrossRef]
  184. Liu, T.; Li, Y.; Koydemir, H.C.; Zhang, Y.; Yang, E.; Wang, H.; Li, J.; Bai, B.; Ozcan, A. Stain-free, rapid, and quantitative viral plaque assay using deep learning and holography. arXiv 2022, arXiv:2207.00089. [Google Scholar]
  185. Theuerkauf, S.A.; Michels, A.; Riechert, V.; Maier, T.J.; Flory, E.; Cichutek, K.; Buchholz, C.J. Quantitative assays reveal cell fusion at minimal levels of SARS-CoV-2 spike protein and fusion from without. iScience 2021, 24, 102170. [Google Scholar] [CrossRef]
  186. Chan, S.-W. Fusion assays for screening of fusion inhibitors targeting SARS-CoV-2 entry and syncytia formation. Front. Pharmacol. 2022, 13, 1007527. [Google Scholar] [CrossRef]
  187. Hochdorfer, D.; Businger, R.; Hotter, D.; Seifried, C.; Solzin, J. Automated, label-free TCID50 assay to determine the infectious titer of virus-based therapeutics. J. Virol. Methods 2022, 299, 114318. [Google Scholar] [CrossRef] [PubMed]
  188. Romano, J.D.; Tatonetti, N.P. Informatics and computational methods in natural product drug discovery: A review and perspectives. Front. Genet. 2019, 10, 368. [Google Scholar] [CrossRef]
  189. Gomaa, H.; Elshoubaky, G. Antiviral Activity of Sulfated Polysaccharides Carrageenan from Some Marine Seaweeds. Int. J. Curr. Pharm. Rev. Res. 2015, 7, 34–42. [Google Scholar]
  190. Padmanabhan, P.; Khaleefathullah, S.; Kaveri, K.; Palani, G.; Ramanathan, G.; Thennarasu, S.; Tirichurapalli Sivagnanam, U. Antiviral activity of Thiosemicarbazones derived from α-amino acids against Dengue virus. J. Med. Virol. 2017, 89, 546–552. [Google Scholar] [CrossRef] [PubMed]
  191. Golus, J.; Sawicki, R.; Widelski, J.; Ginalska, G. The agar microdilution method—A new method for antimicrobial susceptibility testing for essential oils and plant extracts. J. Appl. Microbiol. 2016, 121, 1291–1299. [Google Scholar] [CrossRef] [Green Version]
  192. Lu, W.; Su, X.; Klein, M.S.; Lewis, I.A.; Fiehn, O.; Rabinowitz, J.D. Metabolite measurement: Pitfalls to avoid and practices to follow. Annu. Rev. Biochem. 2017, 86, 277–304. [Google Scholar] [CrossRef]
  193. Periwal, V.; Bassler, S.; Andrejev, S.; Gabrielli, N.; Patil, K.R.; Typas, A.; Patil, K.R. Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs. PLoS Comput. Biol. 2022, 18, e1010029. [Google Scholar] [CrossRef]
  194. Zhang, R.; Li, X.; Zhang, X.; Qin, H.; Xiao, W. Machine learning approaches for elucidating the biological effects of natural products. Nat. Prod. Rep. 2021, 38, 346–361. [Google Scholar] [CrossRef]
  195. Weber, T.; Blin, K.; Duddela, S.; Krug, D.; Kim, H.U.; Bruccoleri, R.; Lee, S.Y.; Fischbach, M.A.; Müller, R.; Wohlleben, W.; et al. antiSMASH 3.0-a comprehensive resource for the genome mining of biosynthetic gene clusters. Nucleic Acids Res. 2015, 43, W237–W243. [Google Scholar] [CrossRef] [Green Version]
  196. Mantravadi, P.K.; Kalesh, K.A.; Dobson, R.C.J.; Hudson, A.O.; Parthasarathy, A. The quest for novel antimicrobial compounds: Emerging trends in research, development, and technologies. Antibiotics 2019, 8, 8. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Key considerations to assess Antibacterial Susceptibility Tests (AST) in natural compounds; BHI: Bain heart infusion, TBS: Tryptic soy broth, MH: Mueller Hinton, ATCC: American Type Culture Collection, DMSO: Dimethyl sulfoxide.
Figure 1. Key considerations to assess Antibacterial Susceptibility Tests (AST) in natural compounds; BHI: Bain heart infusion, TBS: Tryptic soy broth, MH: Mueller Hinton, ATCC: American Type Culture Collection, DMSO: Dimethyl sulfoxide.
Molecules 28 01068 g001
Figure 2. Summary of the principal methods used to evaluate antifungal activity; MALDI-TOF: matrix-assisted laser desorption/ionization-time of flight; OD: optical density.
Figure 2. Summary of the principal methods used to evaluate antifungal activity; MALDI-TOF: matrix-assisted laser desorption/ionization-time of flight; OD: optical density.
Molecules 28 01068 g002
Figure 3. Process of in vitro evaluation for potential antiprotozoal activity; LC: Lethal concentration, CC50: 50% cytotoxicity concentration, FSC: Forward Scattered FSC, SSC: Side Scatter, PI: Propidium iodide, 7-AAD: 7-aminoactinomycin D, AnnV: Annexin V, PS: phosphatidylserine, Rho123: Rhodamine 123, DiOC5(3): 3,3′- dipentyloxacarbocyanine iodide, SubG0 and G1: cell cycle phases. In Cell growth inhibition assay, A and B represent the untreated and treated samples of the assay.
Figure 3. Process of in vitro evaluation for potential antiprotozoal activity; LC: Lethal concentration, CC50: 50% cytotoxicity concentration, FSC: Forward Scattered FSC, SSC: Side Scatter, PI: Propidium iodide, 7-AAD: 7-aminoactinomycin D, AnnV: Annexin V, PS: phosphatidylserine, Rho123: Rhodamine 123, DiOC5(3): 3,3′- dipentyloxacarbocyanine iodide, SubG0 and G1: cell cycle phases. In Cell growth inhibition assay, A and B represent the untreated and treated samples of the assay.
Molecules 28 01068 g003
Figure 4. Anthelmintic evaluation throughout the parasite life cycle. L1 to L5 refer to larval stages. EHT/EHIA: Eggs Hatching Test/Eggs Hatching Inhibition Assay, LTD: Larval Development Test, LMT: Larval Motility Test, AMA: Adult Motility Assay.
Figure 4. Anthelmintic evaluation throughout the parasite life cycle. L1 to L5 refer to larval stages. EHT/EHIA: Eggs Hatching Test/Eggs Hatching Inhibition Assay, LTD: Larval Development Test, LMT: Larval Motility Test, AMA: Adult Motility Assay.
Molecules 28 01068 g004
Figure 5. Common cell-based in vitro assays for direct and indirect antiviral activity determination of natural molecules. TCID50: 50% tissue culture infectious dose assay; ELISA: enzyme-linked immunosorbent assay; PCR: real-time polymerase chain reaction; hν: emitted energy (fluorescence, luminescence).
Figure 5. Common cell-based in vitro assays for direct and indirect antiviral activity determination of natural molecules. TCID50: 50% tissue culture infectious dose assay; ELISA: enzyme-linked immunosorbent assay; PCR: real-time polymerase chain reaction; hν: emitted energy (fluorescence, luminescence).
Molecules 28 01068 g005
Table 2. Methods of evaluation for antiparasitic activity in unicellular parasites (protozoa).
Table 2. Methods of evaluation for antiparasitic activity in unicellular parasites (protozoa).
Type of AssayDescription
Detection/Equipment
Activity Under EvaluationAdvantagesDisadvantagesRef.
Dye Exclusion
ResazurinColorimetric/M
Determine the viability of cells by measuring the chromogenic reaction concentration of colored compounds in a solution
Metabolic activity.
Mitochondrial dehydrogenase and oxidoreductase activity
-
Cell-to-cell count (quantitative)
-
Simple
-
Inexpensive
-
Fast
-
Counting errors
-
Difficulty in processing large numbers of samples
[100,101,102]
Tetrazolium salts (MTT, XTT, MTS, WST)
-
Easy to use
-
Safe
-
High reproducibility
-
Possible high background (interference with reagent/media)
-
Time-consuming
-
Record additional data about progressive cytotoxic effects)
[103,104,105]
Flow Cytometry
FSC vs SSCFC
Variations in Forward Scattered (FSC) light determine volume changes and variations in Side Scatter (SSC) can determine internal composition
Cell size and volume
-
Fully automated
-
High throughput data acquisition (data virtually stored)
-
Allows re-analysis
-
Use of specific equipment
-
Cell lysis can create debris which may interfere with the results
[106,107]
Viability dyes
PI, 7-AAD

Fluorescent/FC
Loss of the integrity of the membrane can be determined by using “viability dies”
Cell cycle arrest
Apoptosis/Necrosis
Evaluation of membrane integrity
-
Single-cell quantification of stained DNA
-
Endpoint (fixed-permeabilized cells)
-
High throughput data acquisition (data virtually stored)
[108,109,110]
Annexin V (AnnV) conjugated to fluorochromes (FITC, PE, APC, etc.)Fluorescent/FC
Apoptotic cells show the migration of PS from the inner layer of the plasma membrane towards the outer layer (become exposed)
Translocation of phosphatidylserine (PS)
Evaluation of membrane integrity
-
Fully automated
-
High sensitivity
-
Difficult to differentiate apoptotic from necrotic cells
[111,112,113]
Permeable cationic lipophilic fluorochrome
-(Rho 123) and DiOC5(3)

Fluorescent/FC
Apoptotic cells show alterations in the synthesis of ATP and the mitochondrial electron transport chain in the mitochondrial membrane
Mitochondrial membrane Potential
-
Fully automated
-
Extremely low background
-
Immediate readout
-
Detection in mixed cell culture model
-
Endpoint, need for cell engineering, decrease in luminescent signal with increased cell death,
-
Challenging to observe small changes in the number of dead cells.
[114,115]
Sub-G0/G1Fluorescent/M
Percentage quantification of cells with fragmented DNA by analyzing the “sub-G0/G1” peak in a DNA histogram
DNA fragmentation (strand breaks)
-
Rapid
-
Detect cumulative apoptosis
-
Applicable to all cell types
-
Low detection rate:
-
If cells enter apoptosis from the S or G2/M phase of the cell cycle
-
If there is an aneuploid population undergoing apoptosis
[116,117]
In Vitro culture
Growth Inhibition AssayColorimetric/M
The candidate drug is added to the culture, and the parasite grows during a determined time.
Variables: drug concentration and time collection points
Cell viability/proliferation
-
Live cell analysis
-
Long experiments
-
Several options to visualize the results by different detection methods.
-
Requires specific equipment
-
Expensive reagents
-
May require damage to the cells for quantification (toxicity from the reagents or cell fixation)
-
Limited in intracellular parasites
[118]
Intracellular Parasite Growth
Inhibition Assay
Colorimetric/M
Eukaryotic cells are infected with intracellular parasites in vitro. Then, test compounds are added, and the culture grows during a determined time
Cell viability/proliferation inside host
-
Cell-to-cell count and estimated number of viable cells (quantitative)
-
Simple
-
Inexpensive
-
Requires the proper growth of the host eukaryotic cells each time
-
Affected reproducibility between results/replicates
[119,120]
M: microscopy, FC: flow cytometry, APC: Allophycocyanin, FITC: Fluorescein isothiocyanate PI: Propidium iodide, 7-AAD: 7-aminoactinomycin D, MTT: 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide), XTT: (2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-5-carboxanilide-2H-tetrazolium), MTS: 3-(4,5-dimethylthiazol-2-yl)-5(3-carboxymethonyphenol)-2-(4-sulfophenyl)-2H-tetrazolium, PE: Phycoerythrin, WST: Water-soluble tetrazolium salts, Rho 123: Rhodamine 123 and DiOC5(3): 3,3′- dipentyloxacarbocyanine iodide, Sub-G0, G1, S, G2 and M: cell cycle phases.
Table 3. Most common methods for in vitro determination of anthelmintic activity.
Table 3. Most common methods for in vitro determination of anthelmintic activity.
AssayDescriptionActivity Under EvaluationAdvantagesDisadvantagesRef.
Adult Motility Assay (AMA)
-
Adult worms are subjected to different concentrations of the extract
-
The time of paralysis (lack of movement) and the time of death are measured
Inhibition of adult worm motility, which can indicate mortality or paralysis
-
Simple
-
Low cost
-
Earthworms can be used instead of gastrointestinal parasites due to their anatomical and physiological similarities
-
Cannot tell the drug’s mode of action
-
Can take several days to track the recovery or death of the worms
[133,134,136]
Larval development test (LDT)
-
L1 larvae are subjected to different concentrations of the extract and allow to grow several days
-
Larvae that reach L3 stage or survive are counted under the microscope and registered
Development of L1 to infective L3 larvae
-
Simple
-
Low cost
-
Anthelmintic resistance can be measured with specific equipment
-
Fresh L1 larvae have to be grown from eggs
-
Requires time to track the development of the larvae from L1 to L3
[137,138]
Larval mortality/paralysis test (LMT)
-
L3 larvae are subjected to different concentrations of the extract and allow to grow 24 h
-
Motionless larvae L3 are counted under the microscope and registered
Inhibition of L3 larvae motility
-
Simple
-
Low cost
Motile L3 larvae have to be grown prior testing[139]
Egg hatch test (EHT) or Egg hatch inhibition assay (EHIA)
-
Parasite eggs are isolated from freshly collected feces
-
Eggs are subjected to different concentrations of the extract and allowed to hatch for 48 h
-
The addition of helminthological iodine stops hatching
-
Eggs and L1 larvae are counted under the microscope
Inhibition of eggs hatching
-
Low cost
-
Can be performed in microtiter plates
Eggs have to be isolated from infected samples or adult females[133,138,140,141,142]
L1 to L3 refer to larval stages.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Gonzalez-Pastor, R.; Carrera-Pacheco, S.E.; Zúñiga-Miranda, J.; Rodríguez-Pólit, C.; Mayorga-Ramos, A.; Guamán, L.P.; Barba-Ostria, C. Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts. Molecules 2023, 28, 1068. https://doi.org/10.3390/molecules28031068

AMA Style

Gonzalez-Pastor R, Carrera-Pacheco SE, Zúñiga-Miranda J, Rodríguez-Pólit C, Mayorga-Ramos A, Guamán LP, Barba-Ostria C. Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts. Molecules. 2023; 28(3):1068. https://doi.org/10.3390/molecules28031068

Chicago/Turabian Style

Gonzalez-Pastor, Rebeca, Saskya E. Carrera-Pacheco, Johana Zúñiga-Miranda, Cristina Rodríguez-Pólit, Arianna Mayorga-Ramos, Linda P. Guamán, and Carlos Barba-Ostria. 2023. "Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts" Molecules 28, no. 3: 1068. https://doi.org/10.3390/molecules28031068

APA Style

Gonzalez-Pastor, R., Carrera-Pacheco, S. E., Zúñiga-Miranda, J., Rodríguez-Pólit, C., Mayorga-Ramos, A., Guamán, L. P., & Barba-Ostria, C. (2023). Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts. Molecules, 28(3), 1068. https://doi.org/10.3390/molecules28031068

Article Metrics

Back to TopTop