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12 Experimental Assays: Chemical Properties, Biochemical and... 357
nzymatic assay
rotein sequence
Expression/purificatio
Half-maximal inhibitory concentration (IC50)
rotein in solutio
rystallograph
D modelin
Detergent, BSA
Enzymatic inhibition
K
, K
cat,Kinact
I
ocking analys
simulation
Reversibility
hineLearnin
Fig. 12.2 Attaining enzymatic assays: Requirements and possibilities for experimental determination in vitro and simulations in silico. A given protein sequence may be selected as a target for drug
design and discovery and follow two distinct paths toward inhibitory activity determination. On one
side, performing and achieving expression and purification may result in a stable protein in solution,
which can follow crystallography studies. On the other end, a protein sequence can be modeled to a
three-dimensional (3D) structure to perform computational simulations, such as docking, molecular
dynamics (MD) simulations, and machine learning (ML) techniques, including comparative quantitative structure-activity relationship (QSAR) analysis. Ultimately, various enzymatic assays can be
performed to experimentally validate the target, considering inhibitory activity to determine the
values, reversibility, and K
IC
50
, K
cat
and Kiassays, including the possibility of disregarding
inact
unspecific aggregative compounds (e.g., detergent, or bovine serum albumin (BSA))
cellular staining for nonviable cells (e.g., trypan blue) [154], as well as the evaluation
of the metabolic function with pH and redox indicators, or fluorescence (e.g.,
resazurin and alamarBlue™)[155] are broadly employed in research. Viability
assays in vitro can be simple and less time-consuming methods that allow for
evaluation in microplates, but may depend on the purpose of the assay as well as
the availability of laboratory equipment and reagents [156, 157].
Overall, an assay should consider cost and sensitivity, while being rapid, safe,
reliable, and reproducible without interfering with the test compounds
[149, 157]. For instance, the crystal violet (CV) staining method (triphenylmethane
dye (4-[(4-dimethyl amino phenyl)-phenyl-methyl]-N,Nimethyl-aniline) is an alternative or complementary approach to indirect tests such as MTT and alamarBlue™.
Staining by CV depends on the viability of the cell monolayer in culture, that is,
more CV is taken when there is a higher abundance of viable cells. This simple assay
is a direct approach [158] useful for assessing the density of a cell monolayer while
allowing for observation of morphological changes, and also allowing to storage the
microplates for further analysis or future reference [159, 160]. Despite being simple
and useful, CV and other assays may not offer substantial information for
358 M. Sá Magalhães Serafim et al.
simulations in specific predictions, such as assessing synergism or antagonism
effects [161], and may even not match predictions and comparisons between different methods for assessing specific molecules (e.g., nanoparticles) [162].
In this sense, some distinctions and specificities may be required for experimental
validation, in which certain cell lineages or phenotype assays can be helpf ul. For
instance, phenotypic assays, that is, experiments that assess alterations in the phenotype of a given cell or organism [163], may display a predicted compound potency
and selectivity to a proposed cell target while also being able to evaluate potential
adverse effects in vitro [164]. This can be especially important in drug discovery
approaches that aim for cytotoxic compounds, as in the search for anticancer drugs,
where a phenotypic approach can account for the diversity and plasticity of molecular mechanisms and phenotypes in mutated cells, which could be indistinguishable
from a general or indirect cell viability assay [165]. These phenotype assays can also
benefit from different toxicity predictions in silico [166] and even function as a
validation method for assessing the adverse toxic effects of a compound in vitro
[167], ultimately providing a more realistic disease model for translating simulations
to biological determinations [168].
Moreover, considering the influence of various targets in vitro, the use of
chemical probes as reagents for improvi ng experimental validation [169] of targets
modified by diseases can also benefit cytotoxicity e valuation, facilitating the investigation of target function and safety [170]. Chemogenomic and the use of chemical
probes may also be helpful in drug predictions [171], as they are often more selective
and differ in their chemical properties, allowing for comparing selectivity from small
compounds evaluated for their cytotoxicity [170, 172]. Additionally, other reagents
can be used for assessing target interactions and potential modifications, such as
intracellular signaling, ligand binding, and receptor interactions [173]. For example,
by the improved luciferase assays with furimazine (NanoLuc) [174] and bioluminescence resonance energy transfer (BRET) in nanoBRET assays [175]. These
assays exploit the energy donor from an enzyme (luciferase) to an acceptor
(fluorophore), followed by the oxidation of a given substrate, which results in a
quantifiable signal that translates interactions between molecules and proteins by
proximity [173]. Herein, these assays can be used for experimental validation of
cellular target simulations, as in the prediction of protein structures and stabilizing
amino acids [174]. However, one should also account for potential interferences,
such as inconsistent reagent concentrations and compounds acting as luciferase
inhibitors, which could potentially impair expected results [175].
The interference of assay reagents in testing compounds accounts for one of the
issues in translating predicted compounds to be assessed in cellular-based assays
in vitro (e.g., promoting spurious results) [176]. For instance, compounds with
antioxidant properties may generate false negative results by causing the reduction
of substrate reagents, such as resazurin [176]. In addition, compounds can also be
directly interferents in a fluorescent assay, either quenching, that is, directly absorbing light that would be necessary for the
fluorescent probe, or emitting light that
would overlap the fluorophore (i.e., presenting self-fluorescence) [139]. This fluorescence property was suggested for more than 5% (>70,000 tested compounds) of
12 Experimental Assays: Chemical Properties, Biochemical and... 359
the PubChem libraries in 2008 [177]. Similarly, ~12% (43,885 chemicals) of the
bioactive compounds found in the NIH Molecular Libraries Small Molecule Repository in 2012 inhibited the firefly luci ferase to a certain level, displaying various
luminescence changes [178]. Therefore, choosing a suitable method is pivotal to
properly evaluate hits selected from simulations in drug discovery, as these behaviors may also impact different experimental validation methods, including the
enzymatic inhibition assays and impact corresponding targets (e.g., proteases)
[179], as aforementioned.
A suitable fluorescence-based method is also important for HTS platforms in drug
discovery and toxicity screenings, where false readings could drive erroneous results
for in vitro assessments and falsely corroborate previous negative or positive predictions [139]. Regarding HTS, the Z-factor coefficient is a measurement that can
help assure quality in a screening assessment [180], as it is a dimensionless statistical
parameter that incorporates the means and errors associated with a given positive
control and a negative reference, useful for comparison purposes as well as optimization and validation [181]. In addition to assuring experimental determination
quality, the success of simulations also needs to account for variations in the
biological phenomena of cell cultures, the range and/or filter of absorbance, the
availability of equipment, the ability to detect, and specificity of reagents used (e.g.,
fluorophores) [157, 182]. Herein, in silico predictions have successfully identified
particularly problematic chemical structures and substructures that could interfere in
such biological assays, such as quinones capable of forming covalent adducts with
thiols [183] and the presence of compounds that might exhibit autofluorescence and
interfere with fluorophores [184].
However, pan assay interference compounds (PAINS), that is, chemical structures that present a non-selective activity [185] or multiple behaviors that interfere
with assays [186], are usually false positive hits from computational predictions.
PAINS can interfere with various assays, such as cell viability and enzymatic inhibition, displaying nonspecific cytotoxicity and promiscuous target interference
[138]. They may not be easily or properly predicted in simulations, thus requiring
specific screening or filtering of substructural features to be identified and avoided
[185]. Here MD-based protocols may help simulate the propensity of a given
compound effect as interferent (e.g. , lipid membrane perturbation) [186], but
assessing libraries in a large-scale would require formulated and appropriate filters
to recognize different PAINS among thousands of screened compounds to exclude
them from further analysis [138].
The natural product curcumin, for example, a PAINS that has displayed various
biological and inhibitory activities [ 187], could be associated with intercalation in
the lipid bilayer of mammalian cells [188], as well as changing the structure of
bacterial membranes [189] or viral envelopes [190], thus being associated with a
nonspecific toxicity [63]. This potential disturbance or intercalation of lipid membranes could be assessed by zeta potential assays in vitro, which can measure the
electrokinetic potential of colloidal suspensions, that is, the behavior of a colloidal
slipping movement under an electric field (e.g., lipid bilayer) [191]. The zeta
potential can be simulated in silico, for example, using nonequilibrium MD
360 M. Sá Magalhães Serafim et al.
simulations, which can predict temperature and ion influences and differences in
diverse surfaces [192]. However, even though it may be accurately predicted [193],
an observed decrease in cell viability may not always be positively correlated with
zeta potential data, as the assay demonstrates the enhancement of membrane permeabilities and not direct disruption [63, 194], as those observed for curcumin
itself [195].
Moreover, compounds’ solubility should also be considered for experimental
validations, as in enzymatic and cell viability assays. Some simulations can be
employed as qualitative and quantitative approaches aiming to predict solubility in
water-based systems [196]. For example, a quantitative structure–property relationships (QSPR) model may allow for the prediction of solubility [136] providing the
quantified numeric values with an uncertainty measured by a root mean square error
(RMSE) of the model, helping in a direct comparison between two chemical
structures [197]. In addition, similar properties can also be quantitatively predicted,
which benefit from structural analogs and common scaffolds, but can also face
difficulties when assessing large datasets and structurally diverse compo unds
retrieved from diffe rent chemical libraries [136]. However, solubility issues such
as saturation and precipitation may be troublesome for many in vitro assays,
potentially promoting unwanted effects, such as variations in permeation
[198]. These may result in false negatives or false positives even in high concentrations of dimethyl sulfoxide (DMSO) [137], which is usually a universal solvent
option that can dissolve compounds presenting a wide range of physicochemical
properties [199].
Physicochemical properties themselves may ultimately impact in PK or ADMET
parameters. To one’s advantage, these parameters can be simulated in compu tational
predictions [18], including a qualitative classification of being active or inactive
(e.g., hepatotoxicity) [200], and positive or negative (e.g., intestinal absorption)
[201]. While there is no ideal PK parameter for any drug candidate, as it depends
on each target, various tools are mostly freely available as integrated platforms on
web servers aiming to accurately and comprehensively predict ADMET and/or PK
properties of assessed c ompounds [202]. For instance, they can target specific
receptors (e.g., Pred-hERG [203]) or a broader set of predictions in a single simulation (ADMETlab [204]), which may have been updated and/or upgraded
(ADMETlab 2.0 [205]). However, experimentally validating ADMET and PK
simulations from these web servers would require specific evaluation in vitro or
in vivo (e.g., toxicity in specific cells or animal models), which could be limited to
laboratory structure, expertise, and resources while not being cost-effective. In this
sense, the major recommendation for these predictors is to be used in combination as
consensus predictors to increase individual accuracy (usually >70%) and approximate a comparative evaluation, potentially improving the success of drug
candidates [18].
12 Experimental Assays: Chemical Properties, Biochemical and... 361
4 Antiviral Assays in Experimental Validation
The drug design, discovery, and development of new antiviral drugs are one of the
main focuses in medicinal chemistry, especially when considering that various
known and new viral diseases are yet to come, with the emergence and reemergence
of different viruses [73]. In the search for new antivirals, various computational
approaches (e.g., VS) have played a key role and were successful in o btaining lead
candidates that later were licensed as antiviral drugs [206]. For instance, one can cite
successful cases in the antiretroviral therapy of human immunodeficiency virus
(HIV), especially protease inhibitors [207], which benefited from CADD approaches
in the initial design of lead candidates and led to the approval of drugs targeting
proteases [208], such as of ritonavir [209] and saquinavir [210]. Other examples of
success include oseltamivir and zanamivir as therapeutic options for influenza virus
[211, 212], the viral protease inhibitor boceprevir for hepatitis C virus (HCV) [213],
and more recently the SARS-CoV-2 M
diseases are considered a public health concern worldwide, and can progress from
outbreaks to endemic, epidemic, and pandemic scenarios [214]. Still in 2019, the
World Health Organization (WHO) declared different viruses as the top 10 global
threats to be faced in the near future [215], months before the emergence of SARSCoV-2 [216], yet unknown at the time as a novel coronavirus (2019-nCoV) [217].
Many viruses, including coronaviruses, are considered as threatening pathogens
that can cause human diseases with high dissemination, morbidity, or mortality, such
as the acquired immunodeficiency syndrome (AIDS), COVID-19, Ebola, influenza,
and dengue and Zika fever [214, 218], some of which still do not have antiviral drugs
as therapeutic options [219]. The urge of SARS-CoV-2 and the COVID-19 pandemic, for example, promptly required collective efforts and resources to tackle the
scenario, including strategies focusing in the discovery of new or repurposed
antivirals [220, 221]. In support of this notion, between January 1981 and September
2019, 81 small molecules were approved by the Food and Drug Administration
(FDA) as antiviral drugs, accounting for 44% of approvals aside from peptides,
proteins, and vaccines [222]. As antivirals are usually designed to target a single
microorganism, they can be direct-acting antiviral agents (DAA) used in a combined
therapy (e.g., antiretroviral therapy against HIV), or aim for an induced cell response
[223]. On the other hand, aiming for broad-spectrum activity usually does not
display favorable outcomes due to the target specificity in different viruses
[224, 225], which may result in a lower antiviral activity or high cytotoxicity
[226]. Despite that, multi-virus or multi-target activity could be still predicted in
simulations and employed to urgent scenarios, or in the preparedness for future
emerging or reemerging viruses [221], as was the case of CADD approaches against
different coronaviruses [227].
Initially, to validate multi-virus or multiple targets activity, one should consider a
compound ability to permeate cell membranes and reach a given virus target (e.g.,
protease), or block viral entry in ho st cells, as viruses are obligate intracellular
microorganisms [228]. Here, certain challenges are expected, such as a target
pro
inhibitor, ensitrelvir [29]. Most viral
362 M. Sá Magalhães Serafim et al.
specificity and consequent lower cell or host toxicity, and the necessity of intracellular availability and efficacy [226, 229]. Aside from predicting a given compound
permeability, one should also consider that a virus will have a cell, tissue, or host
tropism, that is, the preference to infect a certain cellular type [230]. In addition, a
cell must be permissive to the virus, that is, support the infection by having receptors
that allow for viral attachment and entry, as well as allowing for intracellular
replication, thus successfully resulting in viral progeny or subsequent infections
[231]. This is important when defining which cell platform will be assessed in vitro,
but also to design specific predictive models toward a certain cell, cell receptor, or
protein of interest, such as models assessing inhibitors of the angiotensin converting
enzyme 2 (ACE2) receptor for blocking the SARS-CoV-2 variants entry in mammalian cells [232]. Furthermore, some viruses require a biosafety level 3 (BSL3)
facility to be assessed in experimental validations, which can also be a limiting factor
for the development of antiviral compounds [233], and potentially require an
adaptation for BSL2 models in vitro [234] and in vivo [235].
Even if able to successfully move a computational simulation to an experimental
validation, one should also account for fast replication or mutation rates of different
viruses in vitro, which can impact the antiviral activity and result in a lead drug candidate failure [236], especially for viruses that cause chronic infections, such as
hepatitis B virus (HBV), HCV, HIV, and that might include drug resistance [73, 229,
237]. In addition, by requiring specific cell lineages and culture conditions add to the
reasons why the design and discovery of antivirals may have a slower rate [238],
especially when compared to antibacterials or antifungals [73, 239]. Additionally,
standard protocols for propagation, purification, and titration of viral strains may be
needed [240, 241], as well as standardizing viral adsorption and incubation periods,
and determining the multiplicity of infection (MOI), that is, the number of viral
particles calculated to infect cells in culture. This number is crucial for some antiviral
experiments, as the higher the MOI, the faster the cell viability reduces due to the
cytopathic effects (CPE) caused by a higher number of viruses. In this sense,
one-step and growth curves can contribute to evaluating the best MOI of work,
while observing a more suitable log-phase in viral propagation to elaborate an
optimal strategy of the study [240, 241].
Moreover, purification protocols may require specific centrifugation or ultracentrifugation protocols before testing [242], all aiming to establish the best conditions
to assess compounds against certain viruses in vitro and subsequently in vivo. These
may be challenging to translate from computational simulations, either requiring
individual approaches for each parameter to be tested (e.g., cell permeability) [243],
or not being able to be predicted (e.g., purification conditions). Thus, even after
predicting a given compound, experimental validati on should consider different
antiviral assays (Fig. 12.3). For example: (i) pre-treatment, aiming to identify
potential compound-induced cellular antiviral responses; (ii) virucidal activity,
with direct action of compounds against viral particles before infection; and (iii)
neutralization or reduction assays, assessing a direct effect of a given compound in a
step of the virus replication cycle, such as viral entry, uncoating, replication, protein
synthesis, morphogenesis/assembly, or release [244, 245].
12 Experimental Assays: Chemical Properties, Biochemical and... 363
Cellular-based assays
Cytotoxicity Antiviral
Successful approach
Fig. 12.3 Considerations for computational simulations translating into cellular-based assays
(cytotoxicity and antiviral). For a given compound toxicity assessment against cell lineages
in vitro, one should consider potential precipitation, as well as the interference of pan assay
interference compounds (PAINS) in assays, before following absorption, distribution, metabolism,
excretion, and toxicity (ADMET) or pharmacokinetics (PK) simulations (white box). Ultimately,
compounds may be disregarded or follow in vivo studies. In parallel, computational simulations
may support cellular or viral propagation conditions, acknowledging potential limitations regarding
temperature and incubation periods in cell infection, or purification predictions of a viral particle (light gray box). These may help in the prediction of an antiviral compound. Lastly, if a
biosafety laboratory level is not a restriction, and a permissive cell lineage is available, cytopathic
effects (CPE) can be validated in vitro and follow various assays for determining a compound
antiviral activity, such as plaque neutralization or reduction assays (dark gray box). On the other
end, chronic infection models and high mutation rates may impair experimental validation, while
potential inhibitors may be inactive, or require specific viral target validation. Bold lines represent a
successful experimental path, while regular lines represent issues or obstacles
Furthermore, various targets were considered and assessed for the design of
potential antiviral candidates against SARS-CoV-2 proteins, including structural
(e.g., spike [246, 247]), and non-structural associated with viral replication (e.g.,
viral proteases [79, 248] and RNA polymerase [249]). As for protein-pr otein interactions (PPI) from the viral attachment protein to host cell receptors, various
predictive ML models [250, 251] and tools (e.g., DeepViral [252] and MP-VHPPI
[253]) have been proposed, including DL models [254]. These can benefit the design
of target specific compounds as antiviral inhibitors, for example, in approaches that
may benefit from the availabil ity of chemical structures [255] and previous predictive ML models [254]. Among these possibilities, predictive or classification ML
could help in training DL models from specific antiviral databases as sources of
different chemical structures, such as experimentally determined peptides (AVPdb
[256]) and those classified by ML models, such as AVP-IC
[257], a viral annotated
50
antiviral activity database from ChEMBL [258]. In addition, virus-targeted databases, such as the Influenza Research Database [259] and the Coronavirus Antiviral
Research Database (CoV-RDB) [260], could also be used for feeding models.
364 M. Sá Magalhães Serafim et al.
Hereupon, with the large availability of SARS-CoV-2 experimental data from the
search of potential antiviral drugs in the recent years, developing new focused
databases with experimental validation data (e.g., CoronaDB-AI [261]) could also
improve the success rate in the discovery of new lead candidates against other
viruses [262, 263] and other viral targets [264–266], such as those of HCV and
HIV [267], and even open possibilities to other microorganisms [268], such as
bacteria and fungi.
5 Determining Antibacterial and Antifungal Bioactive
Compounds In Vitro
In the field of drug discovery, the development of new antimicrobials facing
infectious diseases also consider, along with antivirals, antibacterial and antifungal
compounds [269]. These have been known for over a century and have also
influenced the current knowledge for drug design and discovery [270]. The increasing demand for novel antimicrobials against the global spread of multidrug-resistant
(MDR) pathogens, for example, may be faced by various sectors of research and
funding, aiming for a bridge between academic, governmental, industrial, and
pharmaceutical efforts that can translate expertise and data from computational
and experimental studies into a successful drug candidate [271].
Regarding MDR bacteria, there is a lack of newly approve d antibiotics [272],
contrasted with estimates that overcome more than $1.7 billion in increased
healthcare costs per year related only to methicillin-resistant Staphylococcus aureus
(MRSA) [273], which approximates the costs to develop a single new drug [1]. Further, back in 2019, AMR was considered one of the top 10 global threats to the future
by WHO [215], which reflected the critical current scenario of ~700,000 deaths
yearly due to MDR infections [274], and estimates of over 10 million by 2050
[275]. For instance, deaths by MRSA infections surpassed those from HIV/AIDS
and tuberculosis combined in the US [276], which is also considered a serious threat
by the Centers of Disease Control and Prevention (CDC) [273], demanding a high
priority for the search and development of new drugs [277].
On the other hand, fungal diseases reflect the concern for primary or
co-infections, such as those caused by the emer ging Candida auris, which has
drawn attention since it was first identified in 2009 and is rapidly rising to a global
risk in healthcare clinical settings as an MDR pathogen with mortality rates ranging
from 30% to 60% [278]. After being declared an urgent health threat by the CDC in
2019 [273], a warning on C. auris was issued in early 2023, summarizing clinical
cases (present infection) increasing from 476 in 2019 to 1471 in 2021 and 2377 in
2022, in addition to screening cases (i.e., detected but not causing infection) that
tripled in the US [279]. In the meantime, as the COVID-19 pandemic may have
impacted invasive fungal infections in public health and antifungal drugs usage
[280, 281], including COVID-19-associated C. auris outbreaks (mortality rates up to
12 Experimental Assays: Chemical Properties, Biochemical and... 365
83% [282]), MDR candidemia cases are spreading and increasing in hospital settings
worldwide [283], with the ability to form multilayer biofilms also resisting various
niche skin conditions (e.g., desiccation) [284].
In this sense, computational methods may provide reliable and cost-effective
strategies at different stages of drug design and discovery of new antibiotics [271],
as in efforts for safer and more effective drug candidates needed for MDR infections
[285]. For instance, predictive ML techniques may help in the screen ing of
antibacterial and antifungal compounds, for example, feeding models for selecting
natural products, semisynthetic derivatives, or synthetic compounds, as was the
discovery of halicin, a potential antibacterial drug candidate discovered by a DL
model (for more details on machine learning and neural networks, see Chap. 4)
[286]. This compound, structurally different from many antibiotics, successfully
supported a computational model’s ability to predict potential drug candidates based
solely on other chemical stru ctures [286]. As mentioned for antivirals [73], predictive ML models can not only provide improvements for other potential drug
candidates [60], such as antibacterial [270] and antifungal bioactive compounds
[287], but also for the search for potential antimicrobial targets [288] and drug
resistance [289]. For instance, ML methods can be applied in simulations to verify
the accuracy of a designed model against experimentally evaluated peptides [290].
However, when considering the prediction ability of ML models to discover
potential bioactive compounds, the true positive rate of a given model can be
dependent on the availability of a varied set of compounds (e.g., small, large, similar,
and diverse libraries) and their biological information (e.g., experimental determination). For instance, Deep-AntiFP, a tool for prediction of antifungal peptides with
deep neural networks, reached the highest accuracy of 94.23% when using the
training dataset to validate the models, but achieved 91.02% and 89.08%, when
assessing an alternative and an independent dataset, respectively. Despite an overall
reasonable accuracy (~90%), even when increasing the number of iterations in the
models, the predictive error would tend to remain stable, indicating the need for a
more diverse dataset as input, thus consequently requiring more experimentally
validated compounds to increase the model’s accuracy [291]. In addition, even
novel approaches that pre-trained a predictor model achieved only comparable
performance to other available computational methods [292], as in a consensus
QSAR-screening approach [293], which also corroborated the need for a larger
and more diverse set of experimentally determined compounds for increasing
predictive accuracy.
This would stand in contrast to the discussion of finding an equilibrium between
the cost of experimental testing and enough supporting evidence before selecting a
target or compound to be tested in drug discovery approaches [294]. Building the
most accurate model for each approach would be ideal, but one should consider the
fact that it is improbable to generate or deploy models with 100% accuracy, and that
adding compounds or data that “do not belong” would not be beneficial to a model.
In addition, it is unlikely to have all the chemical or biological information necessary
to achieve high accuracy models, even when using previously solved studies, which
also depend on the amount of information available [295]. Hereupon, virtually
366 M. Sá Magalhães Serafim et al.
screening compounds could still be the major approach to discovering new compounds, regardless of accuracy, as shown for halicin [286]. Thus, as aforementioned,
the consensus approaches that combine VS with AI could increase success and
reduce false positive hits predicted with single computational methods [ 206].
Identifying a compound predicted to have a given biological activity, such as
antibacterial or antifungal, would require further characterization in vitro. For
example, starting with a minimal inhibitory concentration (MIC) determination
and further assessing a proposed mechanism, such as bactericidal activity with
minimal bactericidal concentration (MBC) determination, might help progress to
in vivo evaluation [296]. In this sense, initial screening assays, such as broth
microdilution assays in 96-well microplates [297], may be an easy, fast, and low
cost method [298] to be used in early stages for antimicrobial activity. Finding if a
given compou nd is active or not may even require further investigation of potential
mechanisms (e.g., association with a specific infection or microorganism), or focus
solely on bioactive compound identification [ 299]. However, validation assays
should also account for difficulties in culturing a targeted microorganism, such as
slow-growing mycobacteria or intracellular bacteria [300], as well as the existence of
non-culturable microbes with pathogenic potential [301], and the possibility of some
microorganisms undergoing a viable but non culturable state, as some Candida
species [302].
Pretomanid, for example, a drug for the treatment of MDR Mycobacterium
tuberculosis, was first identified in 2000 from a series of 100 compounds assessed
for their antimycobacterial activity [303], which were repurposed from an original
investigation as radiosensitizers for chemotherapy [304]. Empirically or randomly
testing compounds, despite being known as usually not cost-effective [305], can still
be an alternative when it is difficult to sustain experimental studies based on
simulations from computational tools (e.g., validating a target) [306] or when
assessing a relatively small number of compounds from small chemical libraries in
96-well microplate assays [294](e.g., MIC assays). These could lead to the identi-
fication of compounds with activity against various bacteria but also result in cellular
cytotoxicity due to nonspecific mechanisms [307], such as in cell membrane disturbance, structural changes, damage, and potential disruption [63].
However, with a large amount of biological information and experimental data
available for some pathogens, such as M. tuberculosis and S. aureus [73], some
methods can provide reasonable results for translating simulations into biological
assays, despite the fact that they could still lack the ability to predict novel resistance
mechanisms [308]. Chemical similarity methods could be of interest when simulating novel antibacterial compounds based on existing antibiotic drugs, as shown in
the identification of crizotinib as a potential repurposing antibacterial candidate
[63
]. However, these and other methods focusing on repurposing candidates may
be limited by finite experimental resources, that is, a finite amount of available
licensed drugs [309]. Lastly, the alternative of searching for repurposing candidates
also falls under the recommendation of employing consensus approaches that
maximize the number of available options for testing, usually providing consistent