molecules
Article
Biodereplication of Antiplasmodial Extracts: Application of the
Amazonian Medicinal Plant Piper coruscans Kunth
Pedro G. Vásquez-Ocmín 1,*,† , Jean-François Gallard 2, Anne-Cécile Van Baelen 1,3, Karine Leblanc 1,
Sandrine Cojean 1, Elisabeth Mouray 4, Philippe Grellier 4, Carlos A. Amasifuén Guerra 5 , Mehdi A. Beniddir 1 ,
Laurent Evanno 1, Bruno Figadère 1 and Alexandre Maciuk 1,*
1 Université Paris-Saclay, CNRS, BioCIS, 91400 Orsay, France
2 Institut de Chimie des Substances Naturelles CNRS UPR 2301, Université Paris-Saclay, 1 Avenue de la
Terrasse, 91198 Gif-sur-Yvette, France
3 Département Médicaments et Technologies pour la Santé (DMTS), CEA, SIMoS, Université Paris-Saclay,
F-91191 Gif-sur-Yvette, France
4 Unité Molécules de Communication et Adaptation des Microorganismes (MCAM, UMR 7245), Muséum
National d’Histoire Naturelle, CNRS, Sorbonne Universités, CP52, 57 Rue Cuvier, 75005 Paris, France
5 Dirección de Recursos Genéticos y Biotecnología (DRGB), Instituto Nacional de Innovación Agraria (INIA),
Avenida La Molina N◦ 1981, La Molina, Lima 15024, Peru
* Correspondence: vasco2224@gmail.com (P.G.V.-O.); alexandre.maciuk@universite-paris-saclay.fr (A.M.)
† Current Address: UMR 152 PharmaDev, Université de Toulouse, IRD, UPS, 31062 Toulouse, France.
Abstract: Improved methodological tools to hasten antimalarial drug discovery remain of inter-
Citation: Vásquez-Ocmín, P.G.; est, especially when considering natural products as a source of drug candidates. We propose a
Gallard, J.-F.; Van Baelen, A.-C.; biodereplication method combining the classical dereplication approach with the early detection of
Leblanc, K.; Cojean, S.; Mouray, E.; potential antiplasmodial compounds in crude extracts. Heme binding is used as a surrogate of the
Grellier, P.; Guerra, C.A.A.; Beniddir, antiplasmodial activity and is monitored by mass spectrometry in a biomimetic assay. Molecular
M.A.; Evanno, L.; et al. networking and automated annotation of targeted mass through data mining were followed by
Biodereplication of Antiplasmodial mass-guided compound isolation by taking advantage of the versatility and finely tunable selectivity
Extracts: Application of the offered by centrifugal partition chromatography. This biodereplication workflow was applied to an
Amazonian Medicinal Plant Piper ethanolic extract of the Amazonian medicinal plant Piper coruscans Kunth (Piperaceae) showing an
coruscans Kunth. Molecules 2022, 27,
IC50 of 1.36 µg/mL on the 3D7 Plasmodium falciparum strain. It resulted in the isolation of twelve
7638. https://doi.org/10.3390/
compounds designated as potential antiplasmodial compounds by the biodereplication workflow.
molecules27217638
Two chalcones, aurentiacin (1) and cardamonin (3), with IC50 values of 2.25 and 5.5 µM, respectively,
Academic Editors: Moses K. Langat, can be considered to bear the antiplasmodial activity of the extract, with the latter not relying on a
Sianne L. Schwikkard and heme-binding mechanism. This biodereplication method constitutes a rapid, efficient, and robust
Derese Solomon technique to identify potential antimalarial compounds in complex extracts such as plant extracts.
Received: 7 October 2022
Accepted: 5 November 2022 Keywords: Piper coruscans Kunth (Piperaceae); biodereplication; heme binding; mass spectrometry;
Published: 7 November 2022 Plasmodium
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1. Introduction
iations.
The ever-growing resistance of Plasmodium spp. to antimalarials continues to make
it a global health problem [1,2]. A major factor that severely hinders the efforts to ‘roll
back malaria’ is the emergence and spread of parasites resistant to affordable antimalar-
Copyright: © 2022 by the authors. ial agents [3]. Research of novel antimalarial molecules usually involves the screening
Licensee MDPI, Basel, Switzerland. of compounds obtained by medicinal or combinatorial chemistry or isolated from natu-
This article is an open access article ral sources, followed by improvement of these molecules by hemisynthesis to enhance
distributed under the terms and the biological activity profile. Finding a hit in a natural extract remains a difficult task;
conditions of the Creative Commons however, owing to the considerable progress made in compound isolation and identifica-
Attribution (CC BY) license (https:// tion techniques during the past decades, a combination of chemical and computational
creativecommons.org/licenses/by/ tools currently allows one to efficiently “dereplicate” an extract to identify the known
4.0/).
Molecules 2022, 27, 7638. https://doi.org/10.3390/molecules27217638 https://www.mdpi.com/journal/molecules
Molecules 2022, 27, 7638 2 of 18
molecules [4,5]. One stunningly performant example of dereplication is molecular net-
working (MN), a computer-based approach allowing one to visualize and organize tandem
mass spectrometry datasets and automate a database search for specialized metabolites
identified within complex mixtures [6–8]. Dereplication approaches strictu senso do not
unequivocally identify compounds but rather annotate them as being identical to a known
compound with a high probability. Such approaches also do not convey any biological
information. We propose the term “biodereplication” as the early detection of a complex
mixture of compounds responsible for the activity before any isolation or formal identifica-
tion. Biodereplication approaches encompass TLC bioautography [9], LC online assays,
and affinity-based methods [10] and require the choice of a relevant biological target to be
implemented.
Heme (ferriprotoporphyrin IX, or Fe(III)PPIX) is freed upon the digestion of hemoglobin
by the parasite. It is a strong oxidizing agent released inside the Plasmodium digestive
vacuole (PDV) after hemoglobin proteolysis [11] and inactivated by the parasite by its
sequestration into hemozoin crystals. The mechanism of action of quinoline drugs such as
chloroquine is based on the inhibition of the crystallization pathway of free heme. The PDV
is a multiphasic mixture of neutral lipids in the form of nanospheres suspended in an aque-
ous solution of proteins, at a pH ranging from 4.8 to 5.5 [12–14]. It is an acidic proteolytic
compartment crucial for the metabolism of the parasite. In this vacuole, amino acids are
released from globin, oxygen radicals are detoxified, drugs are accumulated, and free iron
is generated [15]. Due to the important roles of heme in the elementary cellular processes
of most organisms and especially in Plasmodium, heme metabolism has historically been
a major target for antiparasitic drugs and is still considered a promising target for new
molecules [16–18]. Understanding the heme crystallization process has been a pivotal point
in characterizing the mechanism(s) of action of the major quinoline antimalarial drugs [19]
and is crucial for the development of new drugs able to overcome parasite resistance [20].
The formation of π–π non-covalent bonds is a necessary requirement for the inhibition of
heme crystallization by antimalarial drugs [21,22]. Various chemical and physical factors
modulate hemozoin formation, such as the hydrophobicity of native, surrounding alcohols
and lipids in the digestive vacuole as they impact the solubilization of heme aggregates or
reduce surface tension.
So far, several methodologies have been used for the detection of compounds targeting
the heme detoxification pathway for drug discovery purposes, i.e., spectrophotometric
microassay of heme polymerization (HPIA) [22,23], nuclear magnetic resonance (NMR) [24],
or the detection of heme adducts via mass spectroscopy (MS) [25,26]. Indeed, ESI provides
a rapid, sensitive, and highly selective tool for probing non-covalent interactions [27,28].
These methods are based on the assumption that a molecule binding to heme would
prevent its crystallization and increase the amount of free heme in the digestive vacuole,
hence leading to parasite death. Stable non-covalent complexes can be formed between
Fe(III)-heme and antimalarial agents, i.e., quinine and artemisinin and its derivatives
dihydroartemisinin, α- and β-artemether, and arteether [11]. Relative binding strengths
between drugs and Fe (III)-heme can also be measured. This approach has previously
been shown to be useful in determining the structure–activity relationship of antimalarial
agents, namely, terpene isonitriles and neocryptolepine derivatives [29], or in assessing the
strength of π–π non-covalent binding [25,26,30].
In this work, we propose an integrated workflow applicable to natural extracts and
dedicated to isolating compounds of biological relevance. We implement several tools to
screen an extract for heme-binding compounds and annotate and isolate them in an inte-
grated workflow. This allows the early detection of potential antiplasmodial compounds in
crude extracts. Heme binding used as a surrogate of antiplasmodial activity is monitored by
mass spectrometry under biomimetic conditions. Automated annotation of targeted mass
through molecular networking and data mining was followed by mass-guided compound
isolation by taking advantage of the versatility and finely tunable selectivity offered by
centrifugal partition chromatography. This biodereplication workflow was applied to an
Molecules 2022, 27, 7638 3 of 18
extract of Piper coruscans Kunth (Piperaceae), a plant traditionally used in the Peruvian
Amazonia as a medicinal plant to fight malaria and whose aerial part ethanolic extract
showed promising in vitro activity on P. falciparum (IC50 = 1.36 µg/mL) [31,32].
2. Results and Discussion
2.1. Biodereplication as a New Strategy of Antiplasmodial Drug Discovery
In this study, we describe a biomimetic, miniaturized, automated, MS-based heme-
binding assay and data-mining workflow, which improves on the work described by
Muñoz-Durango et al. [25]. It reduces false positives and allows for a high-throughput
screening format. The overall workflow is presented in Figure 1. Compared to the previous
version of the assay, based on aqueous basic conditions, it benefits from a biomimetic
approach by mimicking the pH (pH 5.2) and multiphasic nature (aqueous–lipid interface)
of the PDV [33]. Heme is insoluble in acidic water as the protonation of its propionic
residues decreases aqueous solubility and increases intermolecular hydrogen bonding [34].
The value of pH may also indirectly affect the formation of π–π complexes [22]. Conse-
quently, the lower the pH, the lower the solubility but the higher the proportion of dimers.
Additionally, recent reports suggest that hemozoin formation may occur at the interface
of aqueous and lipidic regions in the PDV [34–36]. Data from X-ray tomography of the
digestive vacuole indicate that, while lipid nanospheres are important for hemozoin nucle-
ation, crystal growth may occur in the aqueous phase [37,38]. Regarding the PDV biological
lipid mixture being solid at room temperature (it is liquid at 37 ◦C), N-octanol has been
suggested as a more convenient biomimetic model than the glycerides mixture [39–41].
The solubility of water is lower in N-octanol than in a glycerides mixture, but it is still
sufficient for hydrogen bond formation and hematin crystal formation [35]. The organic
phase of the citric-buffer-saturated octanol system (CBSO) was found to properly solubilize
heme [33,42]. Octanol mimics the lipid nanospheres, while the solubilized citric buffer
imitates the acidic environment of the PDV [13,33]. After having screened different condi-
tions (pH 4.8, 5, 5.2, and 10 ad substitution of citric acid with formic acid or ammonia) and
comparing the effects on hemin solubilization and the intensity of adducts signals, we used
a modified CBSO solution of pH 5.2, allowing the intensities of the adduct signals to be
equal or superior to those observed in aqueous basic conditions.
Due to the nature of the solution composed of octanol, DMSO, and water, param-
eters of the ionization source were optimized to allow evaporation, ionization, and low
background noise (temperature and gas flow of 325 ◦C and 11 L/min, respectively). To
ease the formation of the micellar interface, we added a small concentration of Tween 20
detergent [43]. The MS analyses of such a mixture of heme with a complex extract show
numerous peaks (Figure 2). Deciphering such signals to detect adducts can be performed by
several methods. One can compare the spectrum from the extract alone with the spectrum
of the mixture of the extract plus heme (m/z 616) and visually detect whether new ions
of m/z > 616 appear in the second case. This approach is definitely time-consuming and
prone to mistakes and failures. As an alternative, a program script could be designed
to analyze data and automatically detect such discrepancies. Another method consists
of using a triple-quadrupole instrument in precursor ion mode. In such an implementa-
tion, with the second quadrupole set to detect m/z 616, any adduct analyzed by the first
quadrupole and able to produce heme as a fragment upon dissociation would be high-
lighted. Unfortunately, such instruments do not provide high m/z accuracy and are not
able to give any information on the molecular formula of unknown compounds. In contrast,
a Q-ToF instrument provides high-accuracy readings but is not able to work in precursor
ion mode. Indeed, it needs to gather ions in a “pulse” before flight-time measurement and
thus cancels the “separation in time” of precursor ions by the two first quadrupoles. A
Q-ToF instrument can indicate when a target fragment ion is produced but is unable to
indicate to which precursor it correlates. This limitation can be artificially minimized when
running chromatographic separation, which is not applicable in our case since the injection
is performed in infusion mode (see below). To maintain the advantage of QToF’s high
Molecules 2022, 27, 7638 4 of 18
resolution and still be able to deconvoluate the adduct data into compound information,
we applied a molecular networking (MN) approach. In such an approach, a network is
computerized by the GNPS platform [8] or equivalent software to detect ions producing
heme fragments upon dissociation. In such an analysis, parameters can be set up so every
species producing m/z 616 as a fragment is plotted as a node in the same network cluster.
Molecules 2022N, 2o7,d xe FsOiRn PaEEcRlu RsEtVeIrEWar e connected following their cosine values. The cosine value approaches 4 of 19 
 1 when fragmentation routes are very similar [7].
 
Figure 1. BioderepFliigcuarteio 1n. wBiodrkerfleopwlic.aSticohn ewmoartkifzloawtio. nScohfemthaetizmateiochn aonf isthme mofeachcatinoinsmo fofa nacttiimona loafr ianltimalarial 
drugs based on thedirnuhgisb bitaisoend oofn tthhee cinrhyisbtiatilolinz aotfi tohne pcraytshtwallaizyaotifonfr peeathhwemaye o(fF fer(eIeI Ih)PemPIeX ()Fe(A(II)I.)PMPiIxXt)u (rAe). Mixture 
of heme and a natuofr ahlemexet raancdt ain naatubrioalm eixmtraectti cinP ala bsimomodimiuemtic dPilagsemstoivdieumva cduigoelsetiv(Pe DvaVc)usoolel u(PtiDoVn)( sBo)l.ution (B). 
Heme-binding assay by mass spectrometry (MS) using a QToF instrument in infusion mode (C). 
Heme-binding assTaoytably iomn acussrrsepnte c(TtrIoCm) cehtrroym(aMtoSg)raumsi ndigspalaQyiTnogF mianssstersu pmroevnitdiinngi nadfudsuicotsn inm poodseiti(vCe )i.onization 
Total ion current (TmICod)ec (hDro).m Ina Dto, gar, abm, c, dainsdp dla =y icnogmpmoausnsdes prreosevnidt iin gthae dexdturacctts. Minopleocsuiltairv neeitownoirzkaitnigo nof infusion 
mode (D). In (D),ma,odbe, sch,oawnidngd ad=dcuoctms opfo tuarngdestedp rmesaessn, ti.ein., [tah +e 6e1x6t]r+.a Mcto. leMcuolaler cnueltawronrkeitnwg oisr kalisnog uosefd to iden-
infusion mode shotwifyi ntagrgaedteddu mctassos fint aLrCg–eMteSd dmataas fsr,oim.e .t,h[ea N+P 6e1x6tr]a+c.t (ME)o. lCeocmulparrehneentwsivoer kaninngotiastioanls oof targeted 
mass using MS-Dial and MS-Finder guided by dedicated databases (F). Compound isolation of tar-
used to identify targgeetteedd mmaasss susininLg Cce–nMtriSfudgaatla pfarrotimtiotnh cehNroPmeaxtotrgarcatp(hEy) (.CCPoCm) apnredh pernepsiavreataivnen HotPaLtiCo n(Go)f. Chemical 
targeted mass usingchMarSa-cDteiraizl aatniodnM ofS -iFsoinladteedr gcoumidpeodubnydsd (eHd)i.c aPtheadrmdaatcaobloagsiecsal( Fv)a.lCidoamtiopno uofn disoislaotleadt ioconmpounds: 
of targeted mass uHseinmge-cbeindtrinifgu sgtarelnpgathr t(ietxiopnrescsherdo ams aDtVog50r) apndh yin( vCitProC b) iaonlodgicparle apcatirvaityiv (exHprPesLsCed (aGs )I.C50). NP = 
Chemical characterNizaatutiroanl porfodisuoclta; t*e [dhecmome]+p =o 6u1n6d (Is).( H). Pharmacological validation of isolated com-
pounds: Heme-binding strength (expressed as DV ) and in vitro biological activity (expressed as
Due to the natu+re of the solu
5t0ion composed of octanol, DMSO, and water, parameters 
IC50). NP = Naturaolfp trhoed iuocnt;iz*a[thioenm seo]ur=ce6 1w6e(rIe). optimized to allow evaporation, ionization, and low back-
ground noise (temperature and gas flow of 325 °C and 11 L/min, respectively). To ease the 
formation of the micellar interface, we added a small concentration of Tween 20 detergent 
[43]. The MS analyses of such a mixture of heme with a complex extract show numerous 
peaks (Figure 2). Deciphering such signals to detect adducts can be performed by several 
methods. One can compare the spectrum from the extract alone with the spectrum of the 
mixture of the extract plus heme (m/z 616) and visually detect whether new ions of m/z > 
 
Molecules 2022, 27, x FOR PEER REVIEW 6 of 19 
 Molecules 2022, 27, 7638 5 of 18
 
Figure 2. ValidatioFnig oufr eth2e.  Vbailoidaetrieopnloicf athtieobnio wdeorrekpfllicoawtio. n(Aw)o Vrkisfluowal.iz(Aat)iVoins ubayli zMatSio annbdy MmSoalencdumlaorl enceutl-ar network
work of heme addouf chtesm feora dad suectt soffo raantsiemt oaflaarnitaiml dalraurgiasl. dSrpugesc.trSupmec toruf meaocfhe adcrhudgr–uhge–mheem me mixitxuturere ddiiss-played the
played the m/z of dmr/uzgo f[Mdr+uHg ][+M, h+Hem]+e,  h[6em16e]+[,6 1a6n]d+, danrudgd–rhuegm–hee madedadudctu c[dt [rdurgu g+ +hhememee]]++. .AAddddiittiioonnaallllyy, ,M N for all
MN for all drugs idsr suhgos wisnsh ionw tnhein stahme sea mclue sctleurs.t e(rB. )(B V) iVsiusaulailzizaattiioonn bbyy MSSa annddm moloecleuclaurlanre tnweotrwkoorfkh eomf e adducts
heme adducts in ainn aalnkaallkoaidlo iedxetrxatrcat cftrformom CC. .ppuubbeesscceennss.. 
2.2. Scientific ValidatioTnw oof stahme Tprleasdiwtieorneaul sUesde aosf aPv. acolirduastcioannsp arso caend Aurnet.imAnaleaqriuailm Roelmaredmyi x of eight struc-
Using Biodereplicatutiroanll y diverse antimalarial drugs known to bind to heme was used as a positive control
to validate the ability of the workflow to detect heme ligands. An alkaloid extract of
The biodereCpinlicchaotinoanp uwboerskcefnloswVa whla(sR uabpipacleieade) tcoo natani neitnhgaqnuoilniicn eex(atrhaecmt eofli gleanavd)esa loonf gPw. ith other
coruscans (PCE), haenm Aem-bianzdoinngianan pdlannotn t-rhaedmitei-obninadlliyn gusaelkda alogiadisnwsta ms ualsaerdiat o[3v1a]l.i dMaSte atnhaelyabsiilsi ty of the
of the PCE–hemwe omrkixfltouwret oshdoiswcreimd ifnoauter bmin/zd ionfg ianntderneosnt -(bFiingduinreg 3coAm, pleofut npdasnienl)a: c9o1m4.p2l9e3x8e,x tract. In
900.2814, 886.532a7ll, canseds ,8a7lo1n.5g5w79i.t hAhse mexepaencdtehde,m heemdime edrism, heerms ewferareg malesnot siwdeenretiifdiedn tifine dthaet m/z 557
same spectrum a(Fnidgu49r8e c3oArr,e srpigohntd ipnagnteol)t.h eInlo tshseo fmthoeleficrustlaarn dnestewcoonrdk c(aFribgouxryem 3eBth),y lngordoeusp s, [heme
+ +
within the adduc−t cCluHs2tCerO aOrHe ]relaantded[h teom seat−isf(aCcHto2rCyO cOoHsi)n2e]  viaolnuse, sre rsapnegctiinvgel fyro[4m4] .0A.7l ltoth 0e.9an6.t imalarial
In parallel, LC–MdrSu gdsastah oowf ePdCaEd dwuecrtes  (aFnigaulyreze2dA )tow aitnhnhoetmatee- Fteh(eII It)a.rAgeftteerdM mN/zd uatsainpgr odcaestasi-ng, every
bases (Figure 3Cd).r uTghiosf atnhealmysix of antimalarial drugs was detected in thedrugs do not biesl ocnognftiormtheeds atmhaet cthheem aidcadlufacmt ailty m. T/zh e87h1e.m55
sa (mCe HclusNter, although thesee ad1d6 uc1t7 clOus2t)e crofrro- m the al-
responds to a mkinaolori dcoemxtrpaoctu(nFdig uatr em2/Bz) 2sh5o6w.1e3d [M +all+cHom]  p(oFuignudrsek 3nDow1,nDt2o)b. iTnhdet oadhedmuect(ss eoebF-igure S1),
served at m/z 914.28 (C18H18O4), 900.28 (C17H16O4), and 886.53 (C16H14O4) correspond to 
compounds in the extract at nominal m/z 299, 285, and 271, respectively, which appeared 
to encompass several isobaric species present in the extract (Figure 3D3–D5). All the an-
notated compounds and their details are shown in Table 1. Although isomers are distin-
guished on the LC–MS data (Figure 3D1–D5), this is not possible in the heme-binding 
assay, which is performed in infusion mode. Applying a chromatographic separation to 
 
Molecules 2022, 27, 7638 6 of 18
with some of the adducts (e.g., m/z 956) possibly resulting from the binding of heme with
quinoline artifacts [45,46]. Noteworthily, a non-covalent bis-adduct was observed for
quinine [(2heme-Fe(III) – H) + quinine]+ at m/z 1556.539 (see Figure S2). Docking studies
on hemozoin-quinoline drugs also reported such a sandwich adduct with quinine and
chloroquine [47]. Nevertheless, in our experiment, this adduct was not deconvoluted by
molecular networking into a heme-binding compound. This may be explained by the fact
that these adducts are dissociated at very high collision energies. Lastly, under acidic and
reducing conditions (obtained by the adjunction of glutathione), several artemisinin-heme-
Fe(II) adducts were detected at m/z 838, 898, and 1180 (see Figure S3). The adduct at m/z 838
[heme + artemisinin -CH2-COOH + H]+ was shown to be of a covalent nature by MS–MS
analyses, as reported in the literature [24]. The reductive activation of endoperoxide by
Fe(II) heme produces homolytic cleavage of the endoperoxide bond and the subsequent
formation of the artemisinin-heme adduct is able to alkylate heme or other proteins [25].
2.2. Scientific Validation of the Traditional Use of P. coruscans as an Antimalarial Remedy Using
Biodereplication
The biodereplication workflow was applied to an ethanolic extract of leaves of P. cor-
uscans (PCE), an Amazonian plant traditionally used against malaria [31]. MS analysis
of the PCE–heme mixture showed four m/z of interest (Figure 3A, left panel): 914.2938,
900.2814, 886.5327, and 871.5579. As expected, heme dimers were also identified in the
same spectrum (Figure 3A, right panel). In the molecular network (Figure 3B), nodes
within the adduct cluster are related to satisfactory cosine values ranging from 0.7 to
0.96. In parallel, LC–MS data of PCE were analyzed to annotate the targeted m/z using
databases (Figure 3C). This analysis confirmed that the adduct at m/z 871.55 (C16H17NO2)
corresponds to a minor compound at m/z 256.13 [M+H]+ (Figure 3D1,D2). The adducts
observed at m/z 914.28 (C18H18O4), 900.28 (C17H16O4), and 886.53 (C16H14O4) correspond
to compounds in the extract at nominal m/z 299, 285, and 271, respectively, which ap-
peared to encompass several isobaric species present in the extract (Figure 3D3–D5). All
the annotated compounds and their details are shown in Table 1. Although isomers are
distinguished on the LC–MS data (Figure 3D1–D5), this is not possible in the heme-binding
assay, which is performed in infusion mode. Applying a chromatographic separation to the
heme–extract mixture would pose additional problems: Among others, it may displace the
equilibrium of the non-covalent association or lead to an absence of separation of the differ-
ent adducts. Injection after rapid mixing and short incubation guarantees the persistence of
non-covalent bounds even of low strength. Furthermore, the retention time of adducts with
different isomers would likely not correlate with the retention times of the isomers alone,
so the issue of attributing m/z to a given eluted peak would be displaced and not solved.
Hence, we did not address these questions, as our aim is rapid, high-throughput format
visualization of non-covalent binding without chromatographic separation. The molecular
network based on LC–MS of the ethyl acetate fraction (PCA) (see Figure S4) allowed us to
determine three main clusters of the phytochemical superclass: Compounds 2, 3, 6, 7, 8,
and 9 (flavonoids), 11 (alkaloid derivative), and 12 (naphthalene derivative).
Target compounds identified by the MS-binding assay from P. coruscans (Figure 3E)
were displayed at m/z 299 (compounds 1, 5, and 9), m/z 285 (compounds 4, 6, 2, 10, and
8), m/z 271 (compounds 3 ad 7), and m/z 256 (compound 11). Annotation resulted in the
identification of the chalcones aurentiacin (1), stercurensin (2), and cardamonin (3); the
flavanones strobopinin 7-methyl ether (4), 5-hydroxy-7-methoxy-6,8-dimethyl flavanone (5),
desmethoxymatteucinol (6), alpinetin (7), pinocembrin (8), and dimethyl cryptostrobin (9);
and an alkylamide, N-benzoyltyrarnine methyl ether (11). Among the ten targeted com-
pounds annotated, three were annotated at the “genus” annotation level (i.e., picked in the
database of compounds described in the literature as identified in the Piper genus). Carda-
monin (3), 5-hydroxy-7-methoxy-6,8-dimethyl flavanone (5), and alpinetin (7) were previ-
ously isolated from Piper aduncum, P. hispidum, and P. hostmannianum, respectively [50,51].
Compound 10 was not annotated because it is described as resulting from degradation
Molecules 2022, 27, x FOR PEER REVIEW 7 of 19 
 
Molecules 2022, 27, 7638 the heme–extract mixture would pose additional problems: Among others, it7 omf 1a8y dis-
place the equilibrium of the non-covalent association or lead to an absence of separation 
of the different adducts. Injection after rapid mixing and short incubation guarantees the 
perinsitshtenMceS osof unrocne.-cAovsuaglegnets tbeoduMndS s oeuvrecne odfe clomwp sotrseitniognthp.a Fthuwrtahyerfomrothreis, tchoem rpeotuentdioisn time 
of pardodpuocsetsd winitFhi gduirfefeSr5e-n1t. iSspoemcierssf rwomoutlhde lPikipeelyra nceoate cfoarmreilyat(e .wg.,itPhi ptehrea nredtePnetpieornom tiam) es of 
thea riseoamroemrsa tailcopnlea,n stos  uthseed isisnuter aodfi atitotnriablumtiendgic min/ez itno tar ogpiviceanl  aenludtesudb ptreoapki cwaloruelgdio bnes d[5is2p].laced 
anAd lknaolto isdoslvoerda.m Hideenscseu, cwh ea sdpidip enroint ea(dbdeirnegssn tehuerosep rqouteecsttivioen) s[5, 3a]s,  poiuprla ratiimne i(ss hroawpiidng, high-
thraonutigchanpcuetr fporrompeartt iveissuanadlizaanttiiopnar oafs intiocna-gcaoinvsatleSncht ibstionsdominagm wanitshonoiu) t[ 5c4h,5r5o]m, aadtuongcraamphidice sepa-
rat(iboenin. gThane tmibaoclteecruiallara gnaeintwstoBrakc ibllaussesdu btilis and Mderivatives (being antiplasmodial againosnt LP.Cfa–lMcipS
i corof ctohcecu s luteus) [56], or benzoic acidarum) [57e]tharyel oancleytaatefe fwraecxtiaomnp (lPesCoAf ) (see 
Figthuerea cSt4iv)e aclolomwpeodu nudss ttoh adtevtaelridmaitneet htehsreeue smesaoifnP cipluerstsepresc ioefs .tCheo mpphoyutoncdhseimsoilactaeld sfurpomerclass: 
CotmhepPoiupenrdgse n2u, s3,m 6a, y7,a l8s,o ainndcl u9d (eflparvoopnenoyidlpsh),e 1n1o l(sa, llkiganlaonids, dneeorilivgantaivnse,),t earnpden 1e2s, (sntearpohidtsh,alene 
derkiavwaatipvyer)o. nes, piperolides, flavones, chalcones, and dihydrochalcones [58].
 
FigFuigreu r3e. 3B.iBodioedreerpeplilciacatitoionn workffllow bbyyM MSSf rformomP .Pco. rcuosrcuasncsalnesa vleasveexstr eaxct.raMcSt. sMpeSc tsrpuemctorfuhmem oef –heme–
exterxatcrta cmt imxtixutruer eshshoowwiningg tthhee region ooffa addduuctcsts(l e(flet,fdt,o dttoetdtesdq usaqrue)aaren)d atnhde  rtehgei ornegoifohne mofe hdeimer sdimers 
(rig(rhigt,h dt,odttoetdte dsqsuquaraere) )((AA)).. Networrkk sshhoowwininggt atragregtedteadd daducdtuscgtrso ugproeudpinedt hien stahmee scalmusete crl(ulesftter (left 
squsaqruea)r ea)nadn dhehmeme eddimimeerrss (riight ssqquuaarree) )( B(B).)A. Annnontaottiaotnioonf coofm cpoomupnodusni desn itdifieendtiufiseindg uMsiSn-gD iMalS-Dial 
andan Md SM-FSi-nFidnedre r(C(C).) .LLCC-D-DAAD ((280 nm)) cchhrorommataotgorgarmamof oeft heatnhoalnicoleixct reaxcttrafrcotm frPo.mco rPu.s caonrus s(cDa1n)s. (D1). 
ExtErxatcrta icotnio cnhcrhormomataotgograramm (E(EIICC)) ccorrrrespondiinnggt otom m//zz2 25566( D(D2)2,)m, m/z/z2 27711( D(D3)3, )m, m/z/z2 82585(D (D4),4a),n adnd m/z 
299m (/Dz52)9.9 Is(Dol5a)t.eIdso tlartegdett acrogmetpcomunpdosu nfrdosmfr oPm. cPo.rcuosrcuasncasn (sE()E. ).
  
 
Molecules 2022, 27, 7638 8 of 18
Table 1. Details on the annotation of targeted compounds. Using NPClassifire and ClassyFire [48,49],
the major classes and subclasses of compounds following their biosynthetic pathway were identified.
Cpd = compounds; NA = compounds not annotated; RT = retention time; * compound not targeted
with the heme-binding assay; ** see Section 3.5.
Classyfire
Cpd RT m/z Compound Level[M + H]+ Formula Annotated cLogP Annotation ** (Superclass/Class)
1 26.51 299.123 C18H18O4 aurentiacin 4.47 generic flavonoid/chalcone
2′, 4′-dihydroxy-6′-
2 23.02 285.110 C17H16O4 methoxy-3′- 4.08 generic flavonoid/chalcone
methylchalcone
3 21.61 271.094 C16H14O4 cardamomin 3.62 genus flavonoid/chalcone
4 25.25 285.111 C H O strobopinin 7-methyl17 16 4 ether 4.57 generic flavonoid/flavanones
5-hydroxy-7-methoxy-
5 25.98 299.127 C18H18O4 6,8-dimethyl 5.03 genus flavonoid/flavanones
flavanone
6 23.49 285.110 C17H16O4 desmethoxymatteucinol 4.85 generic flavonoid/flavanones
7 16.75 271.495 C16H14O4 alpinetin 3.71 genus flavonoid/flavanones
8 19.74 285.109 C17H16O4 pinocembrin 3.40 generic flavonoid/flavanones
9 21.48 299.127 C18H18O4 dimethyl cryptostrobin 3.86 generic flavonoid/flavanones
Phenylpropanoi/
10 17.80 285.111 C17H16O4 NA 2.44 NA cinnamic acid and
derivative
11 18.23 256.133 C16H17NO
N-Benzoyltyrarnine
2 methyl ether 2.79 generic alkaloid/alkylamide
naphthalenes/
12 * 16.01 131.048 C9H6O 1H-inden-1-one 2.21 generic naphthoquinone
(indanone)
Once m/z values of target compounds were annotated (Table 1), we performed mass-
guided isolation. Target species were detected principally in the ethyl acetate fraction,
except for the non-polar compounds (1, 4, and 5), which were detected in the cyclohexane
fraction. We isolated these molecules by CPC using a mixture of five solvents specifically
optimized for this purpose. The choice of a CPC solvent system is guided by the solubility
of the analytes, as well as their partition coefficient. In the preliminary optimization step,
the rational selection of suitable stationary and mobile phases is essential [59]. This particu-
lar solvent mixture (heptane/ethyl acetate/butanol/methanol/water) was selected as it
provided a good retention factor and selectivity for the targeted molecules, namely, a parti-
tion coefficient of nearly 1 for the targeted analytes and different from 1 for other analytes.
Two solvent systems were designed, the first system suited to non-polar compounds with
heptane/ethyl acetate/butanol/methanol/water (12:14:14:23:37) and another system suited
to moderately polar compounds with heptane/ethyl acetate/butanol/methanol/water
(8:16:16:18:42) with increased proportions of ethyl acetate and butanol in the organic phase.
Non-polar compounds 1, 4, and 5, were isolated by CPC, as can be seen in the CPC
fractograms (See Figure S6). For polar compounds, LC–MS analysis of both phases of
several biphasic systems allowed us to determine that the solvent system heptane/ethyl
acetate/butanol/methanol/water (20:10:10:30:30) showed satisfactory selectivity targeted
on flavonoid compounds such as flavanones or chalcones. This system is a variant of the
Oka range in which hexane was replaced by heptane [59]. The use of this system allowed
us to obtain compounds 1, 4, and 5 in only one step (injection of the fraction into the CPC
system and the collection of pure compounds). Other compounds required a final step by
Molecules 2022, 27, 7638 9 of 18
preparative HPLC. More information on the identification of the compounds is given in
Supplementary Materials, i.e., 1D and 2D NMR (see Figures S7 and S8, and Table S1).
The antiplasmodial activity of PCE was promising (IC50 = 1.36 µg/mL) [31]. The
chalcones aurentiacin (1) and cardamonin (3) showed the best antiplasmodial activity with
IC50 = 2.25 and 5.5 µM, respectively. Aurentiacin has the lowest cytotoxicity on fibroblasts
and the highest selectivity index, suggesting it is the most interesting structure (Table 2).
Considering the low DV50 value for these two compounds, namely, 5.15 and 5.60 eV, re-
spectively, their antiplasmodial activity is likely not related to a heme-binding mechanism,
corroborating hypotheses from other authors [60]. Indeed, DV50 is the fragmentor voltage
at which half of the adduct is dissociated (see Figure 4). We previously demonstrated
that it can be interpreted as proportional to the binding strength between the heme and
the compound [25,26]. As the isolation of compounds was mass-driven, isomers could
not be distinguished, and compounds not binding to heme were also isolated. Antiplas-
modial activity may still be detected for these compounds, albeit not mediated by heme
binding. Cardamonin was reported to have significant activity against Trypanosoma brucei
(IC50 = 1.8 µM), a parasite that does not rely on hemoglobin for its metabolism [61]. Studies
also showed that cardamonin binds with a high affinity to site II of HSA (human serum al-
bumin) [62]. Chalcones are abundant in edible plants and are precursors of flavonoids and
isoflavonoids. The presence of a double bond conjugated with the carbonyl is believed to be
responsible for the biological activities of chalcones as the removal of the double bound has
an inactivating effect [63]. The antimalarial activity of chalcones has triggered great interest,
and several natural and synthetic chalcones have been described to possess antimalarial
effects [64]. Their antimalarial activity is supposed to be a result of a Michael addition of cel-
lular nucleophilic sites to the activated double bond [65]. Indeed, the Michael acceptor site
of chalcones can readily form covalent bonds with the sulfhydryl of cysteines or other thiols
to obtain a Michael adduct, which may play an important role in the biological activities of
chalcones [66]. Covalent heme-chalcone adducts via the Michael addition pathway have
not been reported so far. Nevertheless, the asymmetric Michael addition of nitroalkanes to
chalcones has been reported [64]. Licochalcone A is an example of a chalcone inhibiting the
in vitro growth of both chloroquine-sensitive (3D7) and chloroquine-resistant (Dd2) strains
of P. falciparum. It was shown to have multiple targets in the P. falciparum mitochondrion.
It inhibited ubiquinol cytochrome c reductase (bc1 complex) and succinate ubiquinone
reductase (complex II) [60]. It is known that chalcones bearing a Cl group in position 4 of
ring A and methoxy (position-3) and allyloxy (position-4) on ring B are active against 3D7
P. falciparum with average IC50 values of 2.5 µM [67,68].
The N-benzoyltyrarnine methyl ether (11) compound has the highest DV50 value
(Figure 4) but, surprisingly, showed no antiplasmodial activity, suggesting that DV50 values
should only be considered as relevant when they are above the DV50 value of reference
drugs, e.g., chloroquine. All the isolated flavanones (4–9) showed moderate antiplasmodial
activity, ranging from 85 to 33 µM (Table 1). This activity can be partly explained by their
cytotoxicity, as their selectivity indexes are very low. Their DV50 values were in accordance
with this moderate activity, as these compounds would not bind or only bind loosely
(DV50 in the range of 8–9) while chloroquine had a DV50 above 14. The DV50 value of the
artemisinin covalent adduct is similar to that of chloroquine but the profile of the stability
curve is not sigmoidal (Figure 4), with this observation being explained by the fact that
the observed effect is not a dissociation but an actual fragmentation with covalent-bound
breakage. Adducts at m/z 898 [heme + artemisinin]+ and 1180 [heme + 2 artemisinin]+ were
identified for the first time by MS as being non-covalent adducts as they would dissociate
very quickly and have a DV50 < 2 (data not shown).
Molecules 2022, 27, 7638 10 of 18
Table 2. Antiplasmodial activity, cytotoxicity, and dissociation voltage of isolated compounds.
NT = not tested; NA = no adduct; CQ = Chloroquine; ART = Artemisinin. * Adduct not detected.
Cpd = compounds. Selectivity index was calculated with CC50 on AB943 Primary human fibroblast
line cells and IC50 for antiplasmodial activity.
m/z Adduct m/z 3D7 Sensitive
AB943 Selectivity
Cpd [M + H]+ Expected with Strain
Primary Human
Fibroblast Index DV50 (eV)heme-Fe(III) IC50 (µM) CC ( M) CC50/IC50 µ 50
1 299 915 2.25 68.5 30.4 5.15
2 285 900 51 >100 1.9 5.93
3 271 886 5.5 58 10.5 5.60
4 285 900 60 >100 <1 NA
Molecules 2022, 27, x FOR PEER REVIEW 10 of 19 
 5 299 915 33.2 23 <1 NA
6 285 900 71 >100 1.4 NA
7 271 cysteine8s8 o6r other thiols to ob7t2ain a Michael add>u1c0t0, which may play1. 4an important8 r.4o0le in 
8 285 the biolo9g0i0cal activities of cha7l8cones [66]. Covalen>t1 0h0eme-chalcone ad1d.3ucts via the M8.i6c8hael 
9 299 addition9 1p5athway have not b8e5en reported so fa>r1. 0N0evertheless, the1 .a2symmetric M7.i6c8hael 
addition of nitroalkanes to chalcones has been reported [64]. Licochalcone A is an example 
10 285 of a chal9c0o0ne inhibiting the >in1 0v0itro growth of bo>t1h0 0chloroquine-sens1itive (3D7) andN Achlo-
11 256 roquine-8r7e1sistant (Dd2) stra>in10s0 of P. falciparum. >It1 0w0as shown to hav1e multiple tar9g.4e5ts in 
12 131 the P. fa7l4c6ip*arum mitochond>ri1o0n0. It inhibited ubi>q1u0i0nol cytochrome c1 reductase (bcN1 Acom-
Crude extract plex) and succinate u1b.3i6qu±in0.o0n6eµ gr/edmuLctase (c3o7m.5 pµgle/xm ILI) [60]. It is 2k7n.6own that chaNlcTones 
bearing a Cl group in position± 4 of ring A and methoxy (position-3) and allyloxy (position-CQ 320 4) on rin9g3 5B are active aga0i.n04st 3D3.275 P. falciparum wNitTh average IC NT 14.2750 values of 2.5 µM [67,68]. 
ART 283 898 (heme-Fe(II)) 0.004 ± 0.1 [69] NT NT 15.93
 
Figure 4. Dissociation curves for the more significant compounds isolated from P. coruscans compared
Figure 4. Dissociation curves for the more significant compounds isolated from P. coruscans com-
pawreitdh wchitlho rcohqluoirnoequ(CinQe) (aCnQd) aarntedm airstienminis(iAniRnT ()A. RT). 
3. Materials and Methods
Ta3b.1le.  B2.i oAlnogtiipclaalsMmoatdeiraila alctivity, cytotoxicity, and dissociation voltage of isolated compounds. NT= 
not tested; NA = no adduct; CQ = Chloroquine; ART = Artemisinin. * Adduct not detected. Cpd = 
compouLnedasv. eSseloefctPiviiptyer incodreuxs wcaanss cKalucunltahte(dP wipietrha cCeCae50) owne AreB9c4o3l lePcritmedarbyy huDmr.anP efidbrroobVlaásts qliunee z-
ceOllsc manídn IiCn50D foecr eamntbipelras2m01o6diianl Lacutpivuitnya. (Zona II) village, in the province of Maynas, located in
the region of Loreto, Peru (18M 0704173, UTM 9585656). The plant was identified by Dr.
/ Adduct mm z Ca/zrl os A3mDa7s iSfueénnsiatnivded eposited in thAeBH9e4r3b arium AmazonSeenlescetoivfitthye National University
Cpd [M + H]  Expected owfitthhe PeruviSatnraAinm azon (APrMimAaZr)y, IHquuimtoasn,  PFeirbur,owbliathstt he voucher number 0D3V98504 9(e.VL) + eavesheme-Fe (III)  IC  (µM) CC  (µM) Index CC50/IC50 50 50
1 299 915 2.25 68.5 30.4 5.15 
2 285 900 51 >100 1.9 5.93 
3 271 886 5.5 58 10.5 5.60 
4 285 900 60 >100 <1 NA 
5 299 915 33.2 23 <1 NA 
6 285 900 71 >100 1.4 NA 
7 271 886 72 >100 1.4 8.40 
8 285 900 78 >100 1.3 8.68 
9 299 915 85 >100 1.2 7.68 
10 285 900 >100 >100 1 NA 
11 256 871 >100 >100 1 9.45 
12 131 746 * >100 >100 1 NA 
Crude   1.36 ± 0.06 µg/mL 37.5 µg/mL 27.6 NT 
extract 
 
Molecules 2022, 27, 7638 11 of 18
were dried and ground (600 g), soaked for 24 h twice in 5 L of ethanol 96%, and filtrated,
and the extract was evaporated under reduced pressure at below 40 ◦C.
3.2. Heme-Binding Assay by Mass Spectrometry (MS)
Hemin (iron-containing porphyrin with chlorine counterion) and the crude ethanolic
extract were dissolved in DMSO at 5 mM and 10 mg/mL, respectively. Pure compounds
were dissolved in DMSO at the concentration of 10 mM, except for Tween 20, which was
dissolved in water at 10 µM. Citric-buffer-saturated octanol (CBSO) [70] was prepared
by mixing 5 mL of citric acid, 50 mM and pH 5.2, and 15 mL of N-octanol (anhydrous,
≥99%) and letting it settle at 23 ◦C for 30 min. The upper phase of the CBSO biphasic
system was used. Autosampler 1260 infinity G1367E 1260 Hip ALS (Agilent Technologies,
Santa Clara, CA, USA) was used to perform the automatic mixing of aliquots in 384-well
plates. The incubation mixture was as follows: Compound 5 µL + heme 5 µL + Tween
5 µL + organic phase of CBSO 85 µL. Samples were injected in infusion mode (direct
injection) in a 6530 Accurate-Mass QToF LC–MS instrument (Agilent Technologies) with
the following settings: Positive ESI mode and 2 GHz acquisition rate. Ionization source
conditions were a drying gas temperature of 325 ◦C, a drying gas flow rate of 11 L/min,
a nebulizer of 35 psig, a fragmentor of 175 V, and a skimmer of 65 V. The range of m/z
was 200–1700. In positive-ion mode, purine ion C5H4N +4 [M + H] (m/z 121.050873) and
the hexakis (1H,1H,3H-tetrafluoropropoxy)-phosphazene ion C +18H18F24N3O6P3 [M+H]
(m/z 922.009798) were used as internal lock masses. Full scans were acquired at a resolution
of ca 11 000 (at m/z 922).
On the spectral window, heme appeared at m/z 616.16; adducts at m/z 616.16 + “X”
and 2heme + “X”; heme + DMSO at m/z 694.19; heme dimers at m/z 1231.32 [2heme − H]+;
m/z 1253.30 [2heme− 2H + Na]+; m/z 1267.26 [2heme + 35Cl]+ and 1269.24 [2heme + 37Cl]+.
Chloroquine (chloroquine diphosphate, Sigma-Aldrich, St. Louis, MO, USA) and artemisinin
(Sigma-Aldrich) of 5 mM in water were used as positive control. To obtain heme-Fe(II)
from hemin (heme-Fe(III)), glutathione (50 mM in water) was mixed with hemin (5 mM in
DMSO) in a ratio of 1:1 and left to incubate for 40 min. This solution was used as the heme
solution, as described above.
3.3. Collision-Induced Dissociation
Dissociation in the collision chamber was caused by increasing collision energies from
0 to 56 eV. The MS–MS targeted mode, an isolation width of m/z 1.3, and the flow injection
analysis mode (FIA) were applied. MS–MS parameters were the same as described above.
The results were plotted, and a sigmoidal regression curve allowed us to calculate the
energy necessary to dissociate 50% of adducts (DV50) using GraphPrism software version
5.00 (San Diego, CA, USA) or a Python script [25].
3.4. Visualization of Adduct Fragmentation via Molecular Networking (MN)
Ethanolic extract and hemin (heme-Fe(III)) were mixed and injected using infusion
mode for the MS–MS experiment. Three collision energies were used: 30, 50, and 70 eV.
The ten most intense ions (top 10) per cycle were selected. MS–MS acquisition parameters
were as follows: m/z range of 100–1700, default charge of 1, a minimum intensity of
5000 counts, 3 spectra/s, and isolation width of 1.3 m/z. A list of compounds was generated
by the “find by auto MSMS” algorithm of MassHunter software (Qualitative Analysis
B.07.00, Agilent Technologies). The “extract average MS–MS spectrum” mode was used to
summarize and average the results of the 3 collision energies. Exportation of generated
spectra was performed in MGF format using the option “entire data file”. The information
generated was uploaded onto the online platform GNPS (global natural product social
network) [71]. Parameters used for the algorithm were as follows: Precursor ion mass
tolerance = 1; fragment ion mass tolerance = 0.3; min pairs cos = 0.7; minimum matched
fragment ions = 3; and minimum cluster size = 1. From the data, a network was calculated
measuring correlations and was plotted using Cytoscape 3.5.1. The m/z ratio of each
Molecules 2022, 27, 7638 12 of 18
adduct [heme + “X”]+ was observed as an individual node in the same cluster, related to
other nodes in agreement with the loss of a similar fragment (heme at m/z 616.16). An
alkaloid extract from Cinchona pubescens Vahl (Rubiaceae) and eight antiplasmodial drugs
(chloroquine, quinine, amodiaquine, ketoconazole, miconazole, mefloquine, praziquantel,
and sulfadoxine) were used as examples of the visualization of adduct fragments by MN.
The methodology and conditions were the same as described above.
3.5. LC–MS of Extract and Compounds Annotation
The ethanolic extract from P. coruscans (PCE) at 5 mg/mL in DMSO was analyzed by
liquid chromatography performed on an Agilent 1260 series HPLC coupled to a 6530 QToF
(Agilent Technologies). Chromatography separations were performed on a Sunfire C18
column, 2.1 × 150 mm−3.5 µm (Waters). The mobile phase comprised water (0.1% formic
acid) (A) and acetonitrile (B). A linear gradient of 5–100% B in 20 min at a constant flow
rate of 0.2 mL/min was used to elute the extract, followed by 5 min of conditioning and
10 min of equilibration. Analyses of the samples (1 µL injected) were performed in an
optimized DDA setting (280 nm) followed by MS–MS scans for the three most intense ions
(Top 3). Mass spectrometer settings were as follows: Positive ESI mode, 50–3200 mass
range calibration, and a 2 GHz acquisition rate. The ionization source conditions were as
described above. MS–MS acquisitions were performed using three collision energies: 10, 20,
and 30 eV. MS–MS acquisition parameters were defined as follows: m/z range of 100–1700,
default charge of 1, a minimum intensity of 5000 counts, rate/time = 3 spectra/s, and
isolation width of 1.3 u [32]. The resolution of the MS instrument was ca 5000 at m/z 118
and 10,000 at m/z 922. LC–MS data were processed with MS-DIAL version 4.80 [72]. MS1
and MS2 tolerances were set to 0.01 and 0.05 Da, respectively, in centroid mode for each
dataset. Peaks were aligned with an RT tolerance of 0.2 min, a mass tolerance of 0.015 Da,
and a minimum peak height detection at 1 × 104. MS-DIAL data were deconvoluted
with MS-CleanR [73] by selecting all filters with a minimum blank ratio set to 0.8 and
a maximum relative standard deviation (RSD) set to 40 as previously reported [74,75].
Two peaks were kept in each cluster for further database requests and the retained features
were annotated with MS-FINDER version 3.52 [76]. The MS1 and MS2 tolerances were set
to 5 and 15 ppm, respectively. The formula finder was exclusively processed with C, H, O,
and N atoms. Two pathways were followed for the compound’s annotation by comparing
data to 2 dedicated compounds databases: (i) Compounds identified in the literature for
both the Piper genus and the Piperaceae family (Dictionary of Natural Products version 28.2,
CRC press) mined by MS Finder based on exact mass and in silico fragmentation (“genus”
annotation level); and (ii) generic databases included in MS-FINDER such as PlantCyc,
ChEBI, NANPDB, COCONUT, and KNApSAcK (“generic” annotation level).
3.6. Isolation of Targeted Compounds
Molecule isolation was performed using centrifugal partition chromatography (CPC)
(Sanki LLB-M, 200 mL capacity), preparative HPLC, and LC–MS. Successive liquid–liquid
extraction was carried out on 60 g of PCE using cyclohexane/water with a cyclohexane
fraction (PCC) yielding 45 g, an ethyl acetate/water with ethyl acetate fraction (PCA)
yielding 10 g, and water (PCAq) yielding 11 g. Targeted molecules were localized by
LC–MS analysis in the cyclohexane and ethyl acetate extracts (as described in 2.5). CPC in
descending mode was performed on 1.8 g of the PCC fraction using the biphasic solvent
system heptane/ethyl acetate/butanol/methanol/water (20:10:10:30:30) determined by
testing several biphasic systems and analyzing both phases by LC–MS (data not shown).
Fifteen sub-fractions were obtained from PCC (PCC-1 to PCC-15). Fractions of 0.5 mL
were collected, analyzed by LC–MS (as described in 4.6) to verify these fractions, and
grouped into 15 sub-fractions. Isolated compounds were aurentiacin (1) (20 mg) from
PCC-3; strobopinin 7-methyl ether (4) (18 mg); and 5-hydroxy-7-methoxy-6,8-dimethyl
flavanone (5) (7 mg) from PCC-5 and PCC-8-10, respectively. Then, CPC in descending
mode was performed on 8 g of the PCA fraction using the biphasic solvent system hep-
Molecules 2022, 27, 7638 13 of 18
tane/ethyl acetate/butanol/methanol/water (12:14:14:23:37) determined by testing several
biphasic systems and analyzing both phases by LC–MS, with conditions similar to those
described above. Six sub-fractions were obtained (PCA-1-to PCA-6), including PCA-2
(yielding 840 mg). A second CPC in descending mode was performed on 800 mg of PCA-2
with heptane/ethyl acetate/butanol/methanol/water (8:16:16:18:42), producing 13 sub-
fractions (PCA-2-1 to PCA-2-13). Three preparative HPLC were performed on a Sunfire
column (150 × 19 mm I.D., 5 µm (Waters) with eluent A (water + 0.1% formic acid) and
eluent B (acetonitrile), from 30 to 70% B in 20 min from PCA-2-4 (26 mg), PCA-2-6 (65 mg),
and PCA-2-13 (120 mg). Eight compounds were isolated: Desmethoxymatteucinol (6)
(1.2 mg), 2′, 4′-dihydroxy-6′-methoxy-3′-methylchalcone (2) (0.8 mg), and cardamomin (3)
(3.7 mg) from PCA-2-13; alpinetin (7) (1.1 mg) and compound (10) (1.3 mg) from PCA-2-4;
and pinocembrin (8) (2.6 mg), dimethyl cryptostrobin (9) (0.6 mg), and N-benzoyltyramine
methyl ether (11) (1.9 mg) from PCA-2-6. Along with these targeted compounds, one
untargeted compound was isolated: 1H-inden-1-one (12) (3 mg) from PCA-2-4. Details of
the compound identification by LC–MS (see Figure S9) and NMR spectroscopic data are
given in Supplementary Information (see Figures S7 and S8, and Table S1).
3.7. Antiplasmodial Activity
3.7.1. Plasmodium falciparum Culture
The chloroquine-sensitive 3D7 P. falciparum strain (clone of the NF54) was obtained
from the Malaria French National Reference Center (CNR Paludisme, Hôpital Bichat Claude
Bernard, Paris). The strain was maintained in O+ human erythrocytes in an albumin-
supplemented RPMI medium under continuous culture using the candle-jar method [77].
The parasites were synchronized to the ring stage via repeated sorbitol treatment [78].
3.7.2. In Vitro Antiplasmodial Activity on Plasmodium falciparum
A 2.5% (v/v) erythrocytes suspension with 1% parasitemia (number of infected red
blood cells per 100 red blood cells) was incubated in duplicate with the compounds at
concentrations ranging from 48.5 nM to 100 µM, obtained by serial dilution. After 44 h of
incubation at 37 ◦C, the plates were subjected to 3 freeze–thaw cycles to achieve complete
hemolysis. The parasite lysis suspension was diluted 1:5 in lysis buffer. In vitro susceptibil-
ity is expressed as the concentration inhibiting 50% of the parasite’s growth (IC50). Parasite
growth was determined using SYBR® Green I, a dye with strong fluorescence enhancement
upon contact with DNA. Incorporation of the SYBR® Green I (Fisher Scientific, Illkirch-
Graffenstaden, France) in parasite DNA was measured using the Master epRealplex cycler®
(Eppendorf, Montesson, France) according to the following program to increase the SYBR®
Green I incorporation: 90 ◦C (1 min) and a decrease to 10 ◦C over 5 min followed by fluores-
cence reading. Untreated infected and uninfected erythrocytes were used as controls and
chloroquine diphosphate (Sigma, Lezennes, France) was used as a reference drug [31,32,79].
IC50 was calculated by IC-estimator online software (www.antimalarial-icestimator.net,
accessed on 18 January 2018).
3.8. Cytotoxicity Studies
Cellular cytotoxicity was evaluated using the AB943 primary human dermal fibroblast
cell line maintained at 37 ◦C in the RPMI 1640 medium supplemented with 10% (v/v)
fetal calf serum. Cells were seeded in 96-well plates (20,000 cells/mL) and incubated
for 24 h, then treated with the drug for 72 h. After incubation, the cell growth medium
was replaced by 100 µL of RPMI 1640 containing 20% (v/v) Alamar Blue (Thermo-Fisher,
Illkirch-Graffenstaden, France). Fluorescent viable cells were monitored after 5 h of incu-
bation at 37 ◦C at a wavelength of 530 nm for excitation and 590 nm for emission in an
FL600 luminescence spectrometer (Biotek, Colmar, France). CC50, corresponding to drug
concentrations causing 50% AB943 cell proliferation inhibition, were calculated from the
drug concentration–response curves [80].
Molecules 2022, 27, 7638 14 of 18
3.9. ADMET Prediction
ChemSketch (ACD/Labs Products) software was used to predict the logP of all com-
pounds, using the SK2 format as the file input format [81].
4. Conclusions
Crystallization of heme is the principal pathway used by Plasmodium to detoxify
hemoglobin digestion by-products. Interruption of this process remains a promising target
for antimalarial drug discovery as it does not lead to resistance per se. Our biodereplication
methodology contributes to the automated detection of compounds binding to heme in
complex mixtures without the need for maintaining a parasite culture. Its miniaturized
format accounts for minimal reagent consumption and is amenable to the high-throughput
format (up to 480 extracts analyzed per day). Applying a molecular networking approach
combined with bio-informatic data-mining tools is an efficient strategy to analyze data
and annotate and designate compounds binding non-covalently to heme. The approach
was applied to a Piper extract, highlighting chalcones as the main compounds responsible
for the antiplasmodial activity. The versatility and finely tunable selectivity of CPC was
illustrated in the isolation process. This integrated biodereplication workflow able to detect
and designate antiplasmodial compounds in an extract in 3 min represents a significant
input to antimalarial drug discovery. We propose an integrated workflow involving
the non-orthodox use of molecular networking to make such biodereplication readily
implementable in every lab equipped with HRMS–MS spectrometry. The biodereplication
method allows a rapid, efficient, and robust technique to identify potential antimalarial
compounds in complex extracts such as plant extracts.
Supplementary Materials: The following supporting information can be downloaded at https://
www.mdpi.com/article/10.3390/molecules27217638/s1. References [25,50,51,61,82–92] are cited in the
Supplementary Materials.
Author Contributions: Conceptualization, P.G.V.-O., B.F. and A.M.; data curation, P.G.V.-O., J.-
F.G., A.-C.V.B., K.L., S.C., C.A.A.G., L.E. and A.M.; formal analysis, P.G.V.-O., S.C., E.M. and A.M.;
investigation, P.G.V.-O., A.-C.V.B., S.C., E.M., P.G., C.A.A.G., M.A.B., L.E. and B.F.; methodology,
P.G.V.-O. and A.M.; writing—original draft, P.G.V.-O.; writing—review and editing, J.-F.G., A.-C.V.B.,
S.C., E.M., P.G., C.A.A.G., M.A.B., L.E., B.F. and A.M. 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: Not applicable.
Acknowledgments: This research was funded by Consejo de Ciencia y Tecnología del Perú (CONCYTEC)
(Council for science and technology of Peru) (299-2014-FONDECYT) for the Ph.D. scholarship (PGVO).
We also appreciate the contribution of A. Fox, G. Bernadat (Université Paris Saclay), B. Cabanillas, K.
Mejia, E. Rengifo (Instituto de Investigaciones de la Amazonía Peruana), J.-M. Nuzillard, A. Martinez
(Université de Reims Champagne-Ardenne), Guillaume Marti (Laboratoire de Recherche en Sciences
Végétales, UMR 5546, CNRS, Université de Toulouse, MetaToul-Agromix platform, Toulouse, France),
and Rizwana Zaffaroullah (CNR du Paludisme, AP-HP, Hôpital Bichat, Paris).
Conflicts of Interest: The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported in this paper.
Molecules 2022, 27, 7638 15 of 18
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