Next Article in Journal
Dalbergia odorifera Volatile Oil Alleviates Microsphere-Induced Myocardial Microcirculatory Dysfunction via Inhibiting Neutrophil Extracellular Traps Formation
Previous Article in Journal
Pharmacogenomic Stratification for Oncology Drug Repurposing: An Exposure Target Context Eligibility Framework
Previous Article in Special Issue
The Extract of Salvia miltiorrhiza ‘Hongdan’ Attenuates Inflammation in LPS-Activated BV2 Microglia via ERK1/2, JNK, and p38 MAPK Signaling Inhibition
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pharmaceuticals
Volume 19
Issue 6
10.3390/ph19060958
pharmaceuticals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

In Vitro, In Silico, and In Vivo Evaluation of Antiplasmodial Activity of Ursodeoxycholic Acid Following GNPS Dereplication of an Active Streptomyces sp. Fraction

by
Nanang R. Ariefta
1,
Baldorj Pagmadulam
1,2,
Takako Aboshi
3 and
Yoshifumi Nishikawa
1,*
1
National Research Center for Protozoan Diseases, Obihiro University of Agriculture and Veterinary Medicine, Inadacho, Obihiro 080-8555, Japan
2
Laboratory of Microbial Synthesis, Institute of Biology, Mongolian Academy of Sciences, Bayanzurkh District, 12 Khoroo, Ulaanbaatar 13270, Mongolia
3
Department of Life, Food, and Environmental Sciences, Faculty of Agriculture, Yamagata University, Wakaba-machi 1-23, Tsuruoka 997-8555, Japan
*
Author to whom correspondence should be addressed.
Pharmaceuticals 2026, 19(6), 958; https://doi.org/10.3390/ph19060958 (registering DOI)
Submission received: 30 April 2026 / Revised: 13 June 2026 / Accepted: 18 June 2026 / Published: 20 June 2026
(This article belongs to the Special Issue Bioactive Natural Extracts: Advances in Pharmacological and Therapeutic Research)
Browse Figures
Versions Notes

Abstract

Background/Objectives: The emergence of drug-resistant Plasmodium falciparum highlights the need for new antiplasmodial compounds with distinct mechanisms of action. Microbial secondary metabolites, particularly from Streptomyces species, remain important sources of bioactive molecules. This study aimed to evaluate antiplasmodial metabolites associated with a Mongolian Streptomyces isolate. Methods: Streptomyces sp. strain D10 was isolated from Mongolian soil samples and extracted with ethyl acetate. Bioassay-guided fractionation was performed, followed by LC–HRMS analysis and GNPS-based spectral dereplication. Antiplasmodial activity was evaluated against P. falciparum 3D7, K1, and Dd2 strains using a SYBR Green I assay. Cytotoxicity was assessed in HSF cells. Stage-specific susceptibility assays were conducted using synchronized 3D7 parasites. Comparative docking analyses against β-hematin and the chloroquine resistance transporter (PfCRT), together with target prediction and molecular docking analyses, were performed to explore potential mechanisms. In vivo efficacy was evaluated using a Plasmodium yoelii 17XNL mouse model. Results: Fractionation yielded an active fraction (C2), and LC–HRMS and GNPS-based dereplication suggested a bile acid-like metabolite, with ursodeoxycholic acid (UDCA) returned as a putative spectral library candidate associated with fraction C2. Fraction C2 and UDCA showed comparable antiplasmodial activity against P. falciparum 3D7 (IC50 = 6.55 ± 3.00 and 4.68 ± 0. 65 µg/mL, respectively) without detectable cytotoxicity up to 200 µg/mL. Activity was retained against multidrug-resistant K1 and Dd2 strains. Stage-specific assays demonstrated inhibitory activity across ring, trophozoite, and schizont stages without significant stage-dependent differences. Comparative docking analyses suggested interaction profiles distinct from chloroquine in β-hematin and PfCRT models. Additional docking analyses identified PfGluPho, PfMAPK, and PfPFT-β as potential targets. In vivo, UDCA reduced parasitemia in a dose-dependent manner without significant toxicity. Conclusions: UDCA exhibited moderate antiplasmodial activity across in vitro, in silico, and in vivo evaluations with a favorable selectivity profile, supporting further investigation of bile acid-like metabolites as potential antimalarial scaffolds.

1. Introduction

Malaria remains a major global health problem, particularly due to the emergence and spread of drug-resistant Plasmodium falciparum strains [1]. The reduced effectiveness of current antimalarial drugs, including reports of delayed artemisinin response, highlights the need for new compounds with different structures and modes of action. Natural products continue to be an important source of antiparasitic agents, especially those derived from microorganisms [2]. Among these, actinomycetes, particularly Streptomyces species, are well known for producing a wide range of bioactive secondary metabolites [3]. Soil-derived Streptomyces from less-explored environments are considered promising sources of new compounds [4]. In this context, Mongolian soils have been reported to contain diverse actinomycetes capable of producing structurally vast bioactive metabolites, as demonstrated in previous studies [5,6,7]. However, the chemical characterization of these metabolites and their relevance to antimalarial drug discovery remain limited. In addition, recent studies have highlighted the potential of multi-target or phenotypic approaches in antimalarial drug discovery, as single-target drugs are more prone to resistance development [8,9]. Compounds with broader biological activities, including those affecting redox balance [10], membrane integrity [11], and signaling pathways [11,12], may provide alternative strategies for parasite control.
In this study, Streptomyces strains were isolated from soil samples collected in Mongolia and cultivated under standard fermentation conditions. The resulting crude extracts were fractionated and analyzed using LC–HRMS to identify potential bioactive compounds. Among the detected metabolites, Global Natural Products Social Molecular Networking (GNPS)-based spectral dereplication [13] yielded a tentative bile acid-related annotation, with UDCA returned as the closest spectral library match associated with the active fraction. The antiplasmodial activity of the extracts and a commercial UDCA standard, selected based on this annotation, was evaluated against P. falciparum strains, including drug-sensitive (3D7) and multidrug-resistant strains (K1 and Dd2), using a SYBR Green I-based assay. Cytotoxicity was assessed in mammalian cell lines (HSF cells) to determine selectivity, and in vivo efficacy was evaluated using a P. yoelii 17XNL mouse model. In addition, comparative target simulation, target prediction, and molecular docking were performed to explore potential mechanisms. The results demonstrated that standard UDCA exhibits moderate antiplasmodial activity in vitro and in vivo and suggest a possible multi-target mode of action. These findings support the potential of microbial-derived bile acid–like compounds as candidates for further investigation in antimalarial drug discovery.

2. Results

2.1. Isolation and Identification of Actinomycetes

In this study, an actinomycete strain (D10) was isolated from soil samples collected in Selenge Province, Mongolia. Colonies were obtained on agar media following incubation at 28 °C for 7–14 days. Colonies exhibiting typical actinomycete morphology were selected and purified through repeated streaking. Genomic DNA was extracted, and the 16S rRNA gene was sequenced using a 3130xl Genetic Analyzer. BLAST (v.2.17.0) analysis against the NCBI database showed that the strain shared 93.5% sequence identity with its closest relative, Streptomyces sp. This relatively low sequence similarity suggests that the isolate may represent a phylogenetically distinct Streptomyces-related strain (Supplementary Figure S1). Therefore, additional taxonomic analyses, including extended 16S rRNA gene sequence comparison [14], multi-locus sequence analysis (MLSA) [15], and comprehensive phylogenetic characterization, will be necessary to resolve its species-level classification.

2.2. In Vitro Antiplasmodial Activities of Crude Extract and Fraction from Streptomyces sp. Strain D10

The crude extract of Streptomyces sp. D10 showed inhibitory activity against P. falciparum 3D7, with 82.3% inhibition at 100 µg/mL. Following chromatographic fractionation, two major fractions (C1 and C2) were obtained. Fraction C1 exhibited moderate antiplasmodial activity with an IC50 value of 23.80 µg/mL against P. falciparum 3D7, whereas fraction C2 showed higher potency with an IC50 value of 6.55 µg/mL (Supplementary Figures S2 and S3). LC–HRMS analysis revealed that fraction C1 contained a complex mixture of multiple components, while fraction C2 showed a simplified profile dominated by a single major compound (Figure 1A; Supplementary Figure S3). Due to its higher potency and improved purity, fraction C2 was selected for further biological evaluation and chemical characterization. Fraction C2 also exhibited antiplasmodial activity against P. falciparum strains K1 and Dd2, with IC50 values of 24.50 µg/mL and 37.76 µg/mL, respectively (Table 1). No cytotoxicity was observed in HSF cells up to 200 µg/mL (CC50 > 200 µg/mL), resulting in selectivity indices (SI) greater than 30.53 for 3D7, 8.16 for K1, and 5.30 for Dd2. The resistant indices were calculated as 3.74 for K1 and 5.76 for Dd2. These results indicate that fraction C2 retained antiplasmodial activity with moderate potency and acceptable selectivity.

2.3. GNPS-Based Dereplication of Chemical Components in the Active Fraction C2

The active fraction C2 was analyzed using LC–HRMS to determine its chemical composition. The chromatographic profile (TIC) showed a dominant peak at a retention time of 9.77 min, indicating the presence of a major compound in the fraction (Figure 1A). MS/MS data obtained from fraction C2 were subjected to GNPS spectral library dereplication analysis. Among the returned annotations, a bile acid-related metabolite was suggested, with ursodeoxycholic acid (UDCA; C24H40O4; Figure 1B) returned as the closest spectral library match (Table S1). However, because the precursor ion detected in the experimental data differed from the library precursor ion of UDCA and no authentic standard comparison or spectroscopic confirmation was performed, the annotation was considered tentative. Therefore, the GNPS result was treated as a hypothesis-generating observation, and commercial UDCA was selected as a representative bile acid-related compound for subsequent biological evaluation.

2.4. In Vitro Antiplasmodial Activity of UDCA

Following GNPS-based annotation of a putative UDCA in fraction C2, a commercially available UDCA was evaluated to investigate whether a bile acid candidate with similar structural features could account for the observed antiplasmodial activity. UDCA showed antiplasmodial activity against all tested P. falciparum strains, with IC50 values of 4.68 ± 0.65 µg/mL (3D7), 12.89 ± 6.02 µg/mL (K1), and 21.13 ± 8.61 µg/mL (Dd2). These values are comparable to those of fraction C2 (6.55 ± 3.00, 24.50 ± 7.39, and 37.76 ± 4.17 µg/mL, respectively), indicating that UDCA may account, at least in part, for the activity observed in the fraction. Both UDCA and fraction C2 showed no cytotoxicity against HSF cells at concentrations up to 200 µg/mL (CC50 > 200 µg/mL). UDCA exhibited slightly higher selectivity indices (>42.74, >15.52, >9.47) compared to fraction C2 (>30.53, >8.16, >5.30), although both remained within a similar range. The resistant indices of UDCA (2.75 for K1 and 4.51 for Dd2) were comparable to those of fraction C2 (3.74 and 5.76, respectively). Reference compounds chloroquine (CQ) and artemisinin (ART) showed substantially lower IC50 values, consistent with their known potency. However, chloroquine displayed higher resistance indices, whereas UDCA maintained moderate resistance values. These results indicate that UDCA exhibits antiplasmodial activity at a level comparable to fraction C2 and likely contributes to its bioactivity.
To further evaluate stage-dependent susceptibility, synchronized P. falciparum 3D7 parasites were exposed to UDCA and fraction C2 during ring, trophozoite, and schizont stages (Figure 2). No statistically significant differences were observed among the tested developmental stages for either UDCA, fraction C2, or the reference control drugs based on two-way ANOVA followed by Tukey’s multiple comparison test (p > 0.05). These findings suggest that both UDCA and fraction C2 exhibit inhibitory activity across multiple intraerythrocytic developmental stages.

2.5. In Silico Drug Target Prediction and Docking Simulations of UDCA

To investigate whether UDCA may act through mechanisms distinct from classical quinoline antimalarial drugs, comparative docking analyses against β-hematin and the P. falciparum chloroquine resistance transporter (PfCRT) were performed using chloroquine (CQ) as a reference compound (Figure 3; Table 2). In the β-hematin model, both UDCA and CQ showed favorable predicted binding affinities and convolutional neural network (CNN) scores; however, their binding poses differed markedly. CQ localized within the canonical quinoline-associated groove region of the β-hematin crystal surface, whereas UDCA occupied a distinct surface-exposed interaction region (Figure 3A). These findings suggest that UDCA may interact with β-hematin through a binding mode different from the classical CQ-associated heme detoxification inhibition mechanism. Similarly, docking analysis against PfCRT demonstrated that UDCA localized within a cavity region partially overlapping with the CQ-binding region; however, the predicted molecular orientation and interaction profile differed from those of CQ (Figure 3B). Although both compounds interacted within the central PfCRT cavity and showed favorable docking scores (Table 2), distinct molecular poses and residue-contact patterns were observed between UDCA and CQ, suggesting that UDCA may not be strongly influenced by classical CQ resistance-associated transport mechanisms. Because UDCA retained antiplasmodial activity against the multidrug-resistant K1 and Dd2 strains, these findings support the possibility that UDCA may act through pathways partially distinct from conventional quinoline antimalarial drugs. Based on these observations, additional protein target prediction and docking analyses were subsequently performed to further investigate alternative mechanisms associated with UDCA antiplasmodial activity.
To identify potential molecular targets of UDCA, malaria-associated genes from GeneCards were compared with predicted targets for UDCA from SwissTargetPrediction. A total of 5263 genes were obtained from GeneCards and 106 predicted targets from SwissTargetPrediction, with 40 overlapping targets (0.7%). These 40 overlapping targets were subsequently analyzed using STRING to explore protein–protein interactions and to identify corresponding P. falciparum orthologs. This analysis resulted in 11 mapped P. falciparum proteins with varying levels of sequence homology and network connectivity (Table 3). Among these, three targets were selected for further study based on their relatively higher sequence homology and functional relevance: glucose-6-phosphate dehydrogenase/6-phosphogluconolactonase (PfGluPho; Q8IKU0, 39.65%), mitogen-activated protein kinase (PfMAPK; Q8ILF0, 39.49%), and protein farnesyltransferase subunit beta (PfPFT-β; Q8IHP6, 33.18%). These proteins are associated with key metabolic and signaling pathways and showed moderate node degrees within the interaction network. Other identified targets showed lower sequence homology or less relevance for parasite-specific processes and were therefore not prioritized. Based on this selection, PfGluPho, PfMAPK, and PfPFT-β were chosen as representative targets for subsequent molecular docking analysis with UDCA.
To investigate the potential binding of UDCA to the selected targets, molecular docking was performed against PfGluPho, PfMAPK, and PfPFT-β. The docking results showed that UDCA exhibited favorable binding affinities across all three proteins (Table 4; Figure 4), with the strongest interaction observed for PfGluPho (−8.04 kcal/mol), followed by PfPFT-β (−6.53 kcal/mol) and PfMAPK (−5.46 kcal/mol). The corresponding convolutional neural network (CNN) scores were 0.9450, 0.8384, and 0.8251, respectively, indicating reliable docking poses. For PfGluPho, UDCA formed multiple hydrogen bond interactions with key residues, including Ser347, Asp349, Leu350, Arg379, and Thr380. In addition, hydrophobic interactions were observed with Leu350, Arg379, Ile523, and Tyr626, suggesting stable binding within the active region. The relatively strong binding affinity and high CNN score support PfGluPho as a potential target. In the case of PfMAPK, UDCA formed hydrogen bonds with Thr232 and Met233, along with hydrophobic interactions involving Val180, Ile182, Thr232, Phe258, and Ile262. Although the binding affinity was lower than that of PfGluPho, the interaction profile indicates a possible binding mode within the kinase domain. For PfPFT-β, UDCA formed through hydrogen bonds with Arg10 and Phe214, and hydrophobic contacts with Met1, Leu9, Arg10, Arg13, and Val213. The binding affinity (−6.53 kcal/mol) and interaction pattern suggest moderate binding stability. Comparison with reference inhibitors (provided in Supplementary Figure S4) showed that UDCA exhibited comparable binding affinity trends, although slightly lower than the known inhibitors for each respective target. Overall, these results indicate that UDCA can interact with multiple parasite proteins, with the strongest predicted binding toward PfGluPho, supporting its potential multi-target mode of action.

2.6. Effects of UDCA on P. yoelii 17XNL-Infected Mice

The in vivo antiplasmodial effect of UDCA was evaluated in P. yoelii 17XNL-infected mice by monitoring parasitemia, area under the parasitemia–time curve (AUC), body weight, and hematocrit during and after the 7-day oral treatment period. At 100 mg/kg (Figure 5A), UDCA produced a moderate reduction in parasitemia compared with the untreated control. In the control group, parasitemia gradually increased and reached a peak of approximately 24–25% around 19–20 dpi, whereas the UDCA-treated group reached a lower peak of about 20–21%. The overall parasite burden was also reduced, as reflected by a lower AUC value in the treated group (342.9) compared with the control (414.6). The difference was most apparent during the ascending phase of infection and around the peak parasitemia period, where several time points showed statistical significance. After the peak, parasitemia declined in both groups and reached similarly low levels by the end of the observation period.
At 450 mg/kg (Figure 5B), the reduction in parasitemia was more evident. While the control group again showed a progressive increase in parasitemia with a peak of approximately 25–26%, the UDCA-treated group remained clearly lower throughout most of the infection course and peaked at around 15–16%. This effect was also supported by the AUC analysis, which showed a marked reduction from 443.6 in the control group to 246.4 in the UDCA-treated group. Significant differences were observed across multiple days, indicating a stronger suppressive effect at 450 mg/kg than at 100 mg/kg.
Changes in body weight were minimal in both treatment groups. At 100 mg/kg, body weight remained close to the starting value throughout the experiment and was comparable to the control group. A similar pattern was observed at 450 mg/kg, although a slight transient decrease was seen during the treatment period. Overall, no marked body weight loss was detected. Likewise, hematocrit values remained generally stable in both control and UDCA-treated mice. In the 100 mg/kg group, a temporary decrease was observed around the middle of infection, but the values recovered and remained comparable to those of the control group thereafter. In the 450 mg/kg group, hematocrit remained nearly unchanged throughout the study and did not differ significantly from the control.

3. Discussion

This study identified an antiplasmodial active fraction from Streptomyces sp. D10, for which GNPS dereplication returned UDCA as the closest library match among the detected metabolites. The activity of the fraction C2 and the comparable efficacy of the commercial UDCA standard suggest that UDCA and/or structurally related bile acid derivatives may contribute, at least in part, to the observed activity, while additional components or interactions within the fraction cannot be excluded. Although the active metabolite present in fraction C2 remains unconfirmed, the putative annotation is also consistent with previous reports showing that bile acid–like compounds can be produced by Streptomyces species [16]. Several studies have described microbial transformation [17] or de novo biosynthesis [16] of bile acid derivatives by actinomycetes, suggesting that such metabolites are not restricted to mammalian systems. In actinomycetes, structurally diverse secondary metabolites are often associated with environmental adaptation, chemical competition, and stress-response regulation [18,19]. Therefore, bile acid-like metabolites produced or transformed by Streptomyces species may contribute to ecological fitness and microbial survival.
UDCA is known for its anti-inflammatory, antioxidant, and cytoprotective properties and is clinically used for the treatment of biliary tract diseases [20,21]. Its biological effects are associated with multiple mechanisms, including modulation of cell signaling, maintenance of mitochondrial integrity, and regulation of cellular stress responses [22]. In Plasmodium, redox balance, mitochondrial function, and membrane integrity are critical for parasite survival, as the parasite is continuously exposed to oxidative stress during intraerythrocytic development and relies on tightly regulated antioxidant systems for viability [23]. Disruption of these processes, including NADPH-dependent redox pathways and mitochondrial metabolism, can impair parasite growth and represents a validated strategy for antimalarial intervention [24]. Therefore, bile acid–like molecules such as UDCA may represent a relevant chemical class for further investigation in antiparasitic drug discovery.
In vitro, UDCA showed moderate activity against P. falciparum 3D7 and retained activity against multidrug-resistant strains K1 and Dd2, with relatively low resistance indices compared to CQ. In addition, stage-specific susceptibility assays demonstrated that both fraction C2 and UDCA inhibited parasite growth across ring, trophozoite, and schizont stages without significant stage-dependent differences, indicating activity throughout multiple intraerythrocytic developmental stages. While the potency of UDCA was lower than that of standard antimalarials such as CQ and ART, its selectivity profile was favorable, with no cytotoxicity observed up to 200 µg/mL. This suggests that UDCA may represent a chemically distinct scaffold with a different mechanism of action, rather than a direct competitor to existing high-potency drugs.
Comparative docking analyses against β-hematin and PfCRT using CQ as a reference compound suggested that UDCA may interact with parasite targets through mechanisms partially distinct from classical quinoline antimalarial drugs. Although UDCA showed favorable predicted interactions with both β-hematin and PfCRT, its binding poses differed from those observed for CQ. In the β-hematin model, UDCA occupied a distinct surface-exposed crystal interaction region rather than the canonical CQ-associated groove region. This mode of interaction differs from established models in which quinoline antimalarials inhibit β-hematin growth by adsorbing to specific crystal faces or growth steps, thereby blocking further crystal elongation [25]. Similarly, within the PfCRT cavity, UDCA adopted a different molecular orientation and interaction profile compared with CQ. These observations suggest that the retained activity of UDCA against the multidrug-resistant K1 and Dd2 strains may not primarily depend on classical chloroquine-associated resistance pathways, supporting the possibility that UDCA may exert antiplasmodial effects through alternative molecular targets and biological pathways.
Target prediction and docking analysis suggest that UDCA may interact with multiple parasite proteins, including PfGluPho, PfMAPK, and PfPFT-β. Among these, PfGluPho showed the strongest predicted binding affinity and highest CNN score, supported by multiple hydrogen bonds and hydrophobic interactions. PfGluPho is a bifunctional enzyme involved in the pentose phosphate pathway, which plays a central role in maintaining intracellular redox balance through NADPH production [26]. In Plasmodium, this pathway is essential for protecting the parasite from oxidative stress generated during hemoglobin digestion and host immune responses. Inhibition of PfGluPho can disrupt NADPH supply, leading to accumulation of reactive oxygen species and impaired parasite survival [26]. Therefore, the interaction of UDCA with PfGluPho provides a plausible explanation for its antiplasmodial activity, particularly under conditions in which oxidative stress is critical for parasite viability.
PfMAPK (mitogen-activated protein kinase) is associated with signaling pathways that regulate parasite development, differentiation, and cell cycle progression. Although the MAPK pathways in Plasmodium are less complex than in higher eukaryotes, they remain important for coordinating responses to environmental changes during the intraerythrocytic cycle. Inhibition of PfMAPK may interfere with parasite growth and stage progression, potentially leading to delayed development or reduced replication [27]. The moderate binding affinity of UDCA to PfMAPK suggests that it may contribute to parasite growth suppression through partial disruption of signaling pathways.
PfPFT-β is involved in post-translational modification of proteins through farnesylation, a process required for the proper localization and function of several essential proteins, including small GTPases [28]. In Plasmodium, farnesylation is critical for membrane trafficking, signal transduction, and parasite survival. Inhibition of PfPFT-β can disrupt these processes, leading to impaired protein targeting and reduced parasite viability [29]. The interaction of UDCA with PfPFT-β, although moderate in binding affinity, suggests a possible contribution to its multi-target activity. Taken together, these findings indicate that UDCA may exert its antiplasmodial effects through a multi-target mechanism involving disruption of redox homeostasis (PfGluPho), signaling pathways (PfMAPK), and protein modification processes (PfPFT-β). This multi-target profile may partly explain its activity against drug-resistant strains and could be advantageous in reducing the likelihood of resistance development. However, the potential targeting of G6PD-related pathways requires careful consideration. In humans, glucose-6-phosphate dehydrogenase (G6PD) deficiency is a common genetic condition that affects redox homeostasis in erythrocytes. Drugs that interfere with this pathway can induce hemolysis in G6PD-deficient individuals [30]. Although UDCA is an FDA-approved drug with a well-established safety profile for liver-related disorders [31], its potential interaction with parasite G6PD-like enzymes raises the need to evaluate its effects in the context of human G6PD deficiency. The low hemolysis rate in vitro and stable hematocrit levels in vivo are encouraging, but more specific studies are required to confirm safety in this population.
The in vivo results using the P. yoelii 17XNL model further support the biological relevance of UDCA. A dose-dependent reduction in parasitemia was observed, particularly at 450 mg/kg, where both peak parasitemia and overall parasite burden (AUC) were clearly reduced. Importantly, this effect was achieved without significant changes in body weight or hematocrit, suggesting that UDCA was well tolerated under the tested conditions. However, parasite clearance was not achieved, indicating that UDCA alone may not be sufficient as a standalone therapy but could be considered as part of a combination strategy. Because the present study used the non-lethal P. yoelii 17XNL in C57BL/6J mice model, which allows extended monitoring of parasitemia progression and treatment responses, additional studies using more virulent rodent malaria strains, such as P. yoelii 17XL, or other experimental malaria models will be valuable to further evaluate the strain dependency and broader in vivo antimalarial potential of UDCA.
Another important aspect of this study is that GNPS dereplication of the active fraction suggested a bile acid-related metabolite, which prompted investigation of bile acid-like compounds as potential antiplasmodial agents. The biological activity observed for commercial UDCA supports the possibility that bile acid-related metabolites may contribute to the antiplasmodial activity associated with the active fraction. This also highlights the possibility that microbial-derived metabolites with known pharmacological properties may have additional, previously unrecognized biological activities. Previous studies have demonstrated that bile acid-related compounds can be generated through microbial biotransformation or biosynthetic processes in actinomycetes and other microorganisms [16,17]. Therefore, optimization of fermentation conditions, exploration of alternative microbial hosts, or metabolic engineering approaches may improve UDCA production yield and potentially enable the generation of structurally related bile acid derivatives with enhanced antiparasitic properties. Overall, the results indicate that UDCA exhibits moderate antiplasmodial activity in vitro and in vivo, with a favorable safety profile under the tested conditions. Its activity against resistant strains and predicted multi-target interactions suggest potential as a lead compound or as part of combination therapy. Further studies are needed to clarify its mechanism of action, evaluate its activity in combination with existing antimalarial drugs, definitively identify the active metabolite(s) present in fraction C2, and assess its safety in the context of G6PD deficiency.

4. Materials and Methods

4.1. Sampling and Isolation of Actinomycetes

Soil samples were collected from Tujiin Nars National Park, Altanbulag soum, Selenge Province, Mongolia (GPS: 50.192862° N, 106.444618° E), following removal of surface debris and sampling at a depth of 5–10 cm. Selenge Province was selected because it represents one of the major forested regions of Mongolia and is considered a potential source of diverse microbial communities and bioactive actinomycetes [6]. The samples were subsequently air-dried, and serial dilutions ranging from 10−1 to 10−8 were prepared using sterile 0.9% NaCl solution (Wako, Osaka, Japan). A volume of 0.1 mL from suitable dilutions was spread onto Gauze No.1 medium (comprising 20 g starch, 1 g/L KNO3, 0.5 g/L NaCl, 0.5 g/L K2HPO4, 0.5 g/L MgSO4, 0.01 g/L FeSO4, and 10 g/L agar; Wako, Osaka, Japan) and YM agar (containing 3 g/L yeast extract, 3 g/L malt extract, 5 g/L peptone, 10 g/L glucose, and 15–20 g/L agar; Wako, Osaka, Japan), both supplemented with cycloheximide (50 mg/L; Wako, Osaka, Japan) to suppress fungal contamination. Plates were incubated at 28 °C for 7–14 days to allow colony development and facilitate isolation of pure cultures. Colonies exhibiting actinomycete-like morphology, including dry and powdery colony surfaces with filamentous growth characteristics, were selected and purified through repeated streaking on agar media. Genomic DNA of four independent actinomycete isolates was extracted from the purified actinomycete isolates using a commercial kit (Guangzhou Dongsheng Biotech Co., Ltd., Guangzhou, China). DNA quality and concentration were evaluated using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The extracted genomic DNA showed concentrations ranging from 20.9 to 29.5 ng/μL, with purity ratios within acceptable ranges for downstream PCR amplification and sequencing. The 16S rRNA gene was amplified by PCR using primers 8F (Forward; 5′-AGA GTT TGA TCC TGG CTC AG-3′) and 1492R (Reverse; 5′-TAC GGC TAC CTT GTT ACG ACT T-3′) [32]. The resulting PCR products were purified with a Qiagen gel extraction kit (Qiagen, Hilden, Germany) and sequenced using an ABI 3130xl Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). Sequencing analysis was successfully obtained for all isolates. The resulting sequences consistently indicated affiliation with the genus Streptomyces (Supplementary Figure S1). For strain D10, repeated sequencing was performed to improve sequence quality, and the final sequence data used for analysis were obtained after at least three sequencing attempts. Sequence data were analyzed using BioEdit version 7.2 and MEGA version 12 software.

4.2. Fermentation and Extraction

The D10 strain was cultured in ISP 2 (International Streptomyces Project-2; BD Difco, Sparks, MD, USA) broth at 28 °C for 7 days to produce crude metabolites. Fermentation was performed in 500 mL Erlenmeyer flasks containing 250 mL of ISP 2 medium, incubated at 28 °C with agitation at 200 rpm for 7 days. After cultivation, the culture broth was centrifuged (2236× g, 10 min) to separate the supernatant from the mycelial biomass. The supernatant was then extracted with an equal volume of ethyl acetate, and the organic phase was concentrated using a Ren-1000 rotary evaporator at 50 °C (IWAKI, Shizuoka, Japan). Crude extract was subsequently obtained (2 g) and used for antiprotozoal evaluation.

4.3. Fractionation and Compound Identification

Compound separation was performed through a two-step chromatographic approach involving column chromatography using silica gel 60 (Kanto Chemical Co., Inc., Tokyo, Japan), followed by thin-layer chromatography (TLC) on precoated silica gel 60 F254 plates (Merck, Darmstadt, Germany). The crude extract was chromatographed on a silica gel column using 10% stepwise of n-hexane-EtOAc (100:0–0:100, each 100 mL), then a mixture of EtOAc-MeOH (50:50, 100 mL), and finally MeOH (100 mL) to give 13 fractions (Fr. 1–1 to 1–13). Fractions 1–5 to 1–7 were combined and further chromatographed on a silica gel column using 10% stepwise of CHCl3-EtOAc (100:0–0:100, each 50 mL) to afford 11 fractions (Fr. 2–1 to 2–11). Fractions 2–4 and 2–5 were combined and were further subjected to preparative thin-layer chromatography with a chloroform–methanol mixture (9:1, v/v) as the mobile phase for fractionation. After TLC separation (Supplementary Figure S2), the desired bands were scraped from the plates and extracted with ethyl acetate. The silica residues were subsequently removed by centrifugation. The resulting purified fractions C1 (90 mg) and C2 (50 mg) were then analyzed by liquid chromatography–high resolution mass spectrometry (LC-HRMS) for purity check and compound identification (Supplementary Figure S3).

4.4. LC-HRMS Analysis

The chemical composition of fractions C1 and C2 was characterized using ultra-performance liquid chromatography (UPLC) coupled with a Synapt G2 HDMS mass spectrometer (Waters, Milford, MA, USA). Mass spectrometric detection was carried out in positive electrospray ionization (ESI) mode. The operating parameters included a capillary voltage of 2.0 kV, cone voltage of 30 V, desolvation temperature of 550 °C, and source temperature of 120 °C. The desolvation gas flow was maintained at 90 L/h, and ions were monitored within an m/z range of 150–800. Leucine enkephalin was used as an internal standard for mass calibration in the Waters LC–HRMS system. Chromatographic separation was performed on a Mightysil RP-18 GP II column (50 × 2.0 mm; Kanto Chemical, Tokyo, Japan). A gradient elution system was applied using water containing 0.1% formic acid (solvent A) and acetonitrile containing 0.1% formic acid (solvent B). The gradient program was set as follows: 0–1 min, 1% B; 1–6 min, increased linearly to 90% B; 7–12 min, further increased to 99% B; 12–16 min, held at 99% B; 16–17 min, decreased to 1% B; and 17–20 min, maintained at 1% B. The acquired MS data for C2 was subsequently analyzed using GNPS-based metabolomic tools to annotate and identify the major constituents.

4.5. Chemicals

Standard ursodeoxycholic acid (UDCA) was purchased from Adipogen Life Sciences (San Diego, CA, USA). Chloroquine diphosphate (CQ) and artemisinin (ART) were obtained from Sigma-Aldrich (St. Louis, MO, USA) and used as reference compounds. Dimethyl sulfoxide (DMSO; Wako, Osaka, Japan) was used as the vehicle control.

4.6. In Vitro Antiplasmodial Activity

Cultures of P. falciparum 3D7 (CQ-sensitive), K1 (CQ- and pyrimethamine resistant), and Dd2 (multidrug-resistant; including CQ, mefloquine, and pyrimethamine) strains were maintained in human erythrocytes at 2% hematocrit using RPMI 1640 medium (Sigma-Aldrich). Parasites were synchronized to the ring stage using sorbitol treatment, achieving a synchronization level above 90%. For stage-specific susceptibility assays, sorbitol-synchronized P. falciparum 3D7 parasites were exposed to UDCA during ring (0–2 h), trophozoite (8–10 h), or schizont (24–26 h) stages. Crude extracts, fractions, or compounds at the required concentrations were dispensed into 96-well plates (50 μL per well), followed by the addition of infected erythrocytes (50 μL per well; parasitemia 0.5%, hematocrit 2%). Crude extracts, fractions, and UDCA were tested at concentrations ranging from 100 to 0.78 μg/mL using eight serial two-fold dilutions, while the positive control drug was tested at concentrations ranging from 0.1 to 0.00078 μg/mL. The selected concentration ranges were designed to ensure appropriate coverage for accurate IC50 determination and to obtain reliable sigmoidal dose–response curves across samples with varying antiplasmodial potencies. Plates were incubated at 37 °C for 72 h.
Parasite proliferation was assessed by adding 100 μL of lysis buffer containing SYBR Green I nucleic acid stain (10,000×; Lonza Rockland, Rockland, ME, USA). Fluorescence intensity was measured using a SpectraMax iD5 (Molecular Devices, San Jose, CA, USA) with excitation and emission wavelengths of 485 nm and 518 nm, respectively. The percentage of growth inhibition was calculated by comparing fluorescence signals between treated and untreated control wells. Background signals from uninfected erythrocytes and compound-related fluorescence were subtracted prior to analysis.

4.7. In Vitro Cytotoxicity and Hemolysis Assays

In this study, human skin fibroblast cells (HSF; NB1RGB, RCB0222, RIKEN BRC, Ibaraki, Japan) were cultured in DMEM supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin (Wako). Cell suspensions (1 × 105 cells/mL) were seeded into 96-well plates and incubated at 37 °C in a 5% CO2 incubator for 48 h. The tested samples, including compounds, fractions, and crude extracts, were dissolved in DMSO and applied at a maximum final concentration of 200 μg/mL and serially 2× diluted to final concentrations ranging from 200 to 1.56 μg/mL (eight concentrations). The selected concentration range was designed to provide sufficient coverage for accurate CC50 determination and reliable sigmoidal dose–response curve fitting across samples, while maintaining consistency with the concentration ranges used in the antiplasmodial assays. After 72 h of treatment, cell viability was evaluated by adding Cell Counting Kit-8 (CCK-8; Dojindo, Kumamoto, Japan), and absorbance was measured at 450 nm using a SpectraMax iD5. Hemolysis assay against human red blood cells was evaluated at 100 μg/mL for each fraction or compound as described previously [33].

4.8. In Silico Target Prediction and Molecular Docking

Malaria-associated genes were obtained from GeneCards, where these genes are defined as those functionally linked to Plasmodium biology, parasite survival, or antimalarial relevance based on curated annotations and literature. The UDCA predicted targets were identified using SwissTargetPrediction [34]. These targets were then matched with P. falciparum orthologs using UniProt and PlasmoDB to ensure parasite-specific identification. The overlapping targets were further analyzed using STRING (Search Tool for the Retrieval of Interacting Genes/Proteins; species: Homo sapiens, confidence ≥ 0.4) to infer corresponding P. falciparum homologs [35]. The top three predicted targets, based on their percent homology, were selected for molecular docking studies.
For molecular docking, conformers of UDCA were generated using RDKit with the ETKDGv3 algorithm (Experimental Torsion Knowledge Distance Geometry, version 3) [36] and subsequently optimized using the MMFF (Merck Molecular Force Field). Conformational clustering was performed using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) [37,38], resulting in 14 clusters from which representative structures were selected for docking. Each conformer was docked using Gnina [39,40,41,42], which combines traditional scoring functions with a convolutional neural network (CNN)-based scoring system. The CNN score (ranging from 0 to 1) reflects the predicted likelihood of a correct binding pose, with higher values indicating greater confidence in the docking result. Comparative docking analyses against β-hematin and the P. falciparum chloroquine resistance transporter (PfCRT) were performed to evaluate whether UDCA may interact through mechanisms distinct from classical quinoline antimalarial drugs. The β-hematin crystal structure was obtained from the Cambridge Crystallographic Data Centre (CCDC 162267) [43] and expanded into a 2 × 2 × 2 hemozoin supercell using VESTA version 3 software [44]. The PfCRT structure was retrieved from the RCSB Protein Data Bank (PDB ID: 6UKJ), corresponding to the chloroquine-resistant 7G8 isoform [45]. Docking grids were defined to encompass the entire receptor structure. Four predicted UDCA protein target structures were retrieved from the AlphaFold database, including P. falciparum glucose-6-phosphate dehydrogenase/6-phosphogluconolactonase (PfGluPho; AF-Q8IKU0), mitogen-activated protein kinase (PfMAPK; AF-Q8ILF0), and protein farnesyltransferase subunit beta (PfPFT-β; AF-Q8IHP6). The docking centers were defined as follows: PfGluPho (x, y, z: 7.555, −16.756, −12.839), PfMAPK (x, y, z: 4.335, 5.077, 20.264), and PfPFT-β (x, y, z: −24.747, 30.206, −3.371), with a box size of 20 Å in each dimension. Protein–ligand interactions were analyzed using PLIP (Protein–Ligand Interaction Profiler) [46]. Known inhibitors, SBI-0797750 (PfGluPho) [47], SB203580 (PfMAPK) [48], and BMS-386914 (PfPFT-β) [49], were included in the simulations as references. All molecular visualizations were generated using PyMOL version 3.1.6.1.

4.9. Mice and In Vivo Infection

In vivo experiments were conducted using twelve-week-old male C57BL/6J mice (average body weight ~30 g; Japan CLEA, Tokyo, Japan). Animals were housed in groups of six per cage under controlled environmental conditions (24 °C, 50% relative humidity, 12 h light–dark cycle) with ad libitum access to water and a standard rodent diet (CLEA Rodent Diet CE-2). All procedures were approved by the institutional animal ethics committee and performed in accordance with relevant guidelines.
The non-lethal rodent malaria parasite P. yoelii 17XNL was revived from cryopreserved infected erythrocytes by passage in mice. For infection, each mouse received an intraperitoneal injection of 1 × 107 infected erythrocytes under isoflurane anesthesia, defined as day 0 post-infection (dpi). The experimental unit was an individual mouse. Mice were randomly assigned to control and treatment groups (n = 6 per group; total n = 12 per experiment), and the study was conducted in two independent experiments. Randomization was performed without a specific sequence generation method. No blinding was applied during allocation, conduct, or outcome assessment.
Parasitemia was assessed using Giemsa-stained thin blood smears prepared from 2 μL of tail blood. Treatment was initiated when parasitemia reached approximately 1%. Mice received oral administration of vehicle (1× PBS) or UDCA at doses of 100 or 450 mg/kg once daily for 7 consecutive days (0–6 dpi). The selected UDCA dose range was based on previously published reports demonstrating that oral administration of UDCA at doses up to 450 mg/kg/day in mice was generally well tolerated without severe toxicity [50]. Parasitemia and survival were monitored daily up to 30 dpi, and hematocrit levels were measured every other day.
No predefined inclusion or exclusion criteria were applied, and all animals were included in the analysis. No animals were excluded during the experiment. The sample size was determined based on commonly used group sizes in rodent malaria studies and ethical considerations to minimize animal use; no a priori power calculation was performed.

4.10. Statistical Analysis

Half-maximal inhibitory (IC50) and cytotoxic (CC50) values were determined from three independent experiments by applying nonlinear regression analysis, plotting the logarithm of compound concentration against the percentage inhibition of parasite growth or cell viability. Calculations were performed using GraphPad Prism 10 (GraphPad Software, Inc., La Jolla, CA, USA). Statistical comparisons between groups were carried out using two-way ANOVA, followed by Tukey’s or Sidak’s multiple comparison test. A p-value < 0.05 was considered statistically significant and is indicated by an asterisk along with the corresponding statistical test.

5. Conclusions

GNPS dereplication of an antiplasmodial active fraction from Streptomyces sp. D10 returned UDCA as the closest spectral library match, providing a hypothesis-generating basis for biological evaluation. Commercial UDCA exhibited moderate antiplasmodial activity in vitro and in vivo with low cytotoxicity and retained activity against multidrug-resistant P. falciparum strains. In silico analyses further suggested a potential multi-target mode of action involving PfGluPho, PfMAPK, and PfPFT-β, with interaction profiles distinct from chloroquine in β-hematin and PfCRT models. While the active metabolite present in fraction C2 remains unconfirmed, the results suggest that bile acid-related compounds may contribute to the observed bioactivity and merit further investigation as potential antimalarial scaffolds.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ph19060958/s1: Figure S1. Taxonomic characterization of strain D10 based on 16S rRNA gene analysis; Figure S2. Bioassay-guided fractionation of the crude extract from Streptomyces sp. D10; Figure S3. LC–HRMS total ion chromatograms (TIC) of fractions C1 and C2; Figure S4. Binding modes and predicted receptor–ligand interaction of UDCA and respective reference control for each receptor; Table S1: GNPS-based spectral annotation of major compounds detected in fraction C2.

Author Contributions

Conceptualization, Y.N.; methodology, N.R.A., B.P., T.A., and Y.N.; software, N.R.A.; validation, Y.N.; formal analysis, N.R.A., B.P., and T.A.; investigation, N.R.A., B.P., and T.A.; resources, B.P.; data curation, N.R.A., B.P., and T.A.; writing—original draft preparation, N.R.A.; writing—review and editing, Y.N.; visualization, N.R.A.; supervision, Y.N.; project administration, Y.N.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.

Funding

This study was partially supported by KAKENHI grants from the Japan Society for the Promotion of Science (Grant No. 20F20402 [Y.N.]) and by the Research Program on Emerging and Re-emerging Infectious Diseases of the Japan Agency for Medical Research and Development (AMED) (Grant No. 23fk0108682s0501, 26fk0108739s0601 [Y.N.]).

Institutional Review Board Statement

The study protocol for P. falciparum culture using human RBC was approved by the Institutional Ethics Committee of Obihiro University of Agriculture and Veterinary Medicine (OUAVM approval number 2023-07, approved on 14 November 2023). All animal experiments were authorized by the OUAVM ethics committee (Permit No. 24-38, approved on 1 April 2024) and conducted in accordance with ARRIVE guidelines.

Informed Consent Statement

Human erythrocytes were supplied by the Japanese Red Cross Society (Hokkaido Branch) as anonymized blood materials. The provider handled donor consent, and no identifying information was available to the authors; therefore, additional informed consent was not required.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

P. falciparum strains 3D7 and K1 were kindly provided by Shin-ichiro Kawazu (National Research Center for Protozoan Diseases, Obihiro University of Agriculture and Veterinary Medicine, Japan). The Dd2 strain (MRA-156) was obtained from BEI Resources (NIAID, NIH, USA) and originally contributed by Thomas E. Wellems. Human erythrocytes used for parasite culture were supplied by the Japanese Red Cross Society (Hokkaido Branch).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ARTArtemisinin
CC5050% cytotoxic concentration
CNNConvolutional neural network
CQChloroquine
DBSCANdensity-based spatial clustering of applications with noise
DMSODimethyl sulfoxide
dpiDay post-infection
ETKDGExperimental torsion knowledge distance geometry
FBSFetal bovine serum
FDAFood and Drug Administration
G6PDGlucose-6-phosphate dehydrogenase deficiency
GNPSGlobal Natural Products Social Molecular Networking
HSFHuman skin fibroblast cells
IC5050% inhibitory concentration
MMFFMerck molecular force field
MWMolecular weight
PfGluPhoPlasmodium falciparum glucose-6-phosphate dehydrogenase/6-phosphogluconolactonase
PfMAPKPlasmodium falciparum mitogen-activated protein kinase
PfPFT-βPlasmodium falciparum protein farnesyltransferase subunit beta
PLIPProtein–ligand interaction profiler
RIResistance index
SISelectivity index
STRINGSearch tool for the retrieval of interacting genes/proteins
UDCAUrsodeoxycholic acid

References

  1. World Health Organization (WHO). World Malaria Report 2025: Addressing the Threat of Antimalarial Drug Resistance; World Health Organization: Geneva, Switzerland, 2025. [Google Scholar]
  2. Shang, X.; Dai, L.; Cao, X.; Ma, Y.; Gulnaz, I.; Miao, X.; Li, X.; Yang, X. Natural Products in Antiparasitic Drug Discovery: Advances, Opportunities and Challenges. Nat. Prod. Rep. 2025, 42, 1419–1458. [Google Scholar] [CrossRef] [PubMed]
  3. Alam, K.; Mazumder, A.; Sikdar, S.; Zhao, Y.-M.; Hao, J.; Song, C.; Wang, Y.; Sarkar, R.; Islam, S.; Zhang, Y.; et al. Streptomyces: The Biofactory of Secondary Metabolites. Front. Microbiol. 2022, 13, 968053. [Google Scholar] [CrossRef] [PubMed]
  4. Sivalingam, P.; Hong, K.; Pote, J.; Prabakar, K. Extreme Environment Streptomyces: Potential Sources for New Antibacterial and Anticancer Drug Leads? Int. J. Microbiol. 2019, 2019, 5283948. [Google Scholar] [CrossRef] [PubMed]
  5. Pagmadulam, B.; Tserendulam, D.; Rentsenkhand, T.; Igarashi, M.; Sawa, R.; Nihei, C.-I.; Nishikawa, Y. Isolation and Characterization of Antiprotozoal Compound-Producing Streptomyces Species from Mongolian Soils. Parasitol. Int. 2020, 74, 101961. [Google Scholar] [CrossRef] [PubMed]
  6. Baljinova, T. Diversity and Biotechnological Potential of Actinomycetes in Arid Lands of Mongolia. In Actinomycetes in Marine and Extreme Environments: Unexhausted Sources for Microbial Biotechnology; Taylor and Francis: Oxfordshire, UK, 2024; pp. 290–322. [Google Scholar]
  7. Norovsuren, Z.; Oborotov, G.V.; Zenova, G.M.; Aliev, R.A.; Zvyagintsev, D.G. Haloalkaliphilic Actinomycetes in Soils of Mongolian Desert Steppes. Biol. Bull. 2007, 34, 417–422. [Google Scholar] [CrossRef]
  8. Coulson, L.B.; Chibale, K. Multi-target Antimalarials as a Strategy to Reduce Resistance Risk. In Polypharmacology; Wiley: Hoboken, NJ, USA, 2025; pp. 423–436. [Google Scholar]
  9. Tse, E.G.; Korsik, M.; Todd, M.H. The Past, Present and Future of Anti-Malarial Medicines. Malar. J. 2019, 18, 93. [Google Scholar] [CrossRef] [PubMed]
  10. Pal, C. Redox Modulating Small Molecules Having Antimalarial Efficacy. Biochem. Pharmacol. 2023, 218, 115927. [Google Scholar] [CrossRef] [PubMed]
  11. Soni, R.; Sharma, D.; Rai, P.; Sharma, B.; Bhatt, T.K. Signaling Strategies of Malaria Parasite for Its Survival, Proliferation, and Infection during Erythrocytic Stage. Front. Immunol. 2017, 8, 349. [Google Scholar] [CrossRef] [PubMed]
  12. Sanyaolu, A.; Marinkovic, A.; Prakash, S.; Balendra, V.; Shazley, O.; Gardellini, T.; Jan, A.; Younis, K.; Okorie, C.; Izurieta, R. Emerging Molecular Mechanisms in Malaria Pathogenesis and Novel Therapeutic Approaches: A Focus on P. falciparum Malaria. Biomolecules 2025, 15, 1038. [Google Scholar] [CrossRef] [PubMed]
  13. Wang, M.; Carver, J.J.; Phelan, V.V.; Sanchez, L.M.; Garg, N.; Peng, Y.; Nguyen, D.D.; Watrous, J.; Kapono, C.A.; Luzzatto-Knaan, T.; et al. Sharing and Community Curation of Mass Spectrometry Data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 2016, 34, 828–837. [Google Scholar] [CrossRef] [PubMed]
  14. Campodónico, V.L.; Ruelle, J.; Fitzgerald, A.; Bergman, Y.; Osborne, B.; Bourdas, D.; Lu, J.; Carroll, K.C.; Simner, P.J. Evaluation of Long-Read 16S RRNA next-Generation Sequencing for Identification of Bacterial Isolates in a Clinical Diagnostic Laboratory. J. Clin. Microbiol. 2025, 63, e0167024. [Google Scholar] [CrossRef] [PubMed]
  15. Glaeser, S.P.; Kämpfer, P. Multilocus Sequence Analysis (MLSA) in Prokaryotic Taxonomy. Syst. Appl. Microbiol. 2015, 38, 237–245. [Google Scholar] [CrossRef] [PubMed]
  16. Kim, D.; Lee, J.S.; Kim, J.; Kang, S.-J.; Yoon, J.-H.; Kim, W.G.; Lee, C.H. Biosynthesis of Bile Acids in a Variety of Marine Bacterial Taxa. J. Microbiol. Biotechnol. 2007, 17, 403–407. [Google Scholar] [PubMed]
  17. Mukai, A.; Yazawa, K.; Mikami, Y.; Harada, K.I.; Gräfe, U. Biotransformation of Bile Acids by Pathogenic Actinomycetes Nocardia otitidiscaviarum and Amycolatopsis sp. Strains. J. Antibiot. 2005, 58, 356–360. [Google Scholar] [CrossRef] [PubMed]
  18. van der Meij, A.; Worsley, S.F.; Hutchings, M.I.; van Wezel, G.P. Chemical Ecology of Antibiotic Production by Actinomycetes. FEMS Microbiol. Rev. 2017, 41, 392–416. [Google Scholar] [CrossRef] [PubMed]
  19. Selim, M.S.M.; Abdelhamid, S.A.; Mohamed, S.S. Secondary Metabolites and Biodiversity of Actinomycetes. J. Genet. Eng. Biotechnol. 2021, 19, 72. [Google Scholar] [CrossRef] [PubMed]
  20. Goossens, J.-F.; Bailly, C. Ursodeoxycholic Acid and Cancer: From Chemoprevention to Chemotherapy. Pharmacol. Ther. 2019, 203, 107396. [Google Scholar] [CrossRef] [PubMed]
  21. Onofrio, F.Q.; Hirschfield, G.M.; Gulamhusein, A.F. A Practical Review of Primary Biliary Cholangitis for the Gastroenterologist. Gastroenterol. Hepatol. 2019, 15, 145–154. [Google Scholar]
  22. Lazaridis, K.N.; Gores, G.J.; Lindor, K.D. Ursodeoxycholic Acid “Mechanisms of Action and Clinical Use in Hepatobiliary Disorders”. J. Hepatol. 2001, 35, 134–146. [Google Scholar] [CrossRef] [PubMed]
  23. Kehr, S.; Sturm, N.; Rahlfs, S.; Przyborski, J.M.; Becker, K. Compartmentation of Redox Metabolism in Malaria Parasites. PLoS Pathog. 2010, 6, e1001242. [Google Scholar] [CrossRef] [PubMed]
  24. Müller, S. Role and Regulation of Glutathione Metabolism in Plasmodium falciparum. Molecules 2015, 20, 10511–10534. [Google Scholar] [CrossRef] [PubMed]
  25. Olafson, K.N.; Nguyen, T.Q.; Rimer, J.D.; Vekilov, P.G. Antimalarials Inhibit Hematin Crystallization by Unique Drug-Surface Site Interactions. Proc. Natl. Acad. Sci. USA 2017, 114, 7531–7536. [Google Scholar] [CrossRef] [PubMed]
  26. Allen, S.M.; Lim, E.E.; Jortzik, E.; Preuss, J.; Chua, H.H.; MacRae, J.I.; Rahlfs, S.; Haeussler, K.; Downton, M.T.; McConville, M.J.; et al. Plasmodium falciparum Glucose-6-Phosphate Dehydrogenase 6-Phosphogluconolactonase Is a Potential Drug Target. FEBS J. 2015, 282, 3808–3823. [Google Scholar] [CrossRef] [PubMed]
  27. Campbell, C.O.; Santiago, D.N.; Guida, W.C.; Manetsch, R.; Adams, J.H. In Silico Characterization of an Atypical MAPK Phosphatase of Plasmodium falciparum as a Suitable Target for Drug Discovery. Chem. Biol. Drug Des. 2014, 84, 158–168. [Google Scholar] [CrossRef] [PubMed]
  28. Berndt, N.; Sebti, S.M. Measurement of Protein Farnesylation and Geranylgeranylation In Vitro, in Cultured Cells and in Biopsies, and the Effects of Prenyl Transferase Inhibitors. Nat. Protoc. 2011, 6, 1775–1791. [Google Scholar] [CrossRef] [PubMed]
  29. Fletcher, S.; Cummings, C.G.; Rivas, K.; Katt, W.P.; Hornéy, C.; Buckner, F.S.; Chakrabarti, D.; Sebti, S.M.; Gelb, M.H.; Van Voorhis, W.C.; et al. Potent, Plasmodium-Selective Farnesyltransferase Inhibitors That Arrest the Growth of Malaria Parasites: Structure-Activity Relationships of Ethylenediamine-Analogue Scaffolds and Homology Model Validation. J. Med. Chem. 2008, 51, 5176–5197. [Google Scholar] [CrossRef] [PubMed]
  30. Shah, S.S.; Stone, E.F.; Francis, R.O.; Karafin, M.S. The Global Role of G6PD in Infection and Immunity. Front. Immunol. 2024, 15, 1393213. [Google Scholar] [CrossRef] [PubMed]
  31. Guarino, M.P.L.; Cocca, S.; Altomare, A.; Emerenziani, S.; Cicala, M. Ursodeoxycholic Acid Therapy in Gallbladder Disease, a Story Not yet Completed. World J. Gastroenterol. 2013, 19, 5029–5034. [Google Scholar] [CrossRef] [PubMed]
  32. Lane, D.J. 16S/23S RRNA Sequencing. In Nucleic Acid Techniques in Bacterial Systematics; Stackebrandt, E., Goodfellow, M., Eds.; John Wiley and Sons: New York, NY, USA, 1991; pp. 115–175. [Google Scholar]
  33. Evans, B.C.; Nelson, C.E.; Yu, S.S.; Beavers, K.R.; Kim, A.J.; Li, H.; Nelson, H.M.; Giorgio, T.D.; Duvall, C.L. Ex Vivo Red Blood Cell Hemolysis Assay for the Evaluation of PH-Responsive Endosomolytic Agents for Cytosolic Delivery of Biomacromolecular Drugs. J. Vis. Exp. 2013, 73, e50166. [Google Scholar] [CrossRef]
  34. Daina, A.; Michielin, O.; Zoete, V. SwissTargetPrediction: Updated Data and New Features for Efficient Prediction of Protein Targets of Small Molecules. Nucleic Acids Res. 2019, 47, W357–W364. [Google Scholar] [CrossRef] [PubMed]
  35. Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; et al. The STRING Database in 2021: Customizable Protein-Protein Networks, and Functional Characterization of User-Uploaded Gene/Measurement Sets. Nucleic Acids Res. 2021, 49, D605–D612. [Google Scholar] [CrossRef] [PubMed]
  36. Landrum, G.; Tosco, P.; Kelley, B.; sriniker; Ric; gedeck; Vianello, R.; NadineSchneider; Dalke, A.; N, D.; et al. Rdkit/Rdkit: 2020_09_5 (Q3 2020) Release; Zenodo: Geneva, Switzerland, 2021. [Google Scholar] [CrossRef]
  37. Ester, M.; Kriegel, H.-P.; Sander, J.; Xu, X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA, 2–4 August 1996; pp. 226–231. [Google Scholar]
  38. Schubert, E.; Sander, J.; Ester, M.; Kriegel, H.P.; Xu, X. DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. ACM Trans. Database Syst. 2017, 42, 1–21. [Google Scholar] [CrossRef]
  39. Koes, D.R.; Baumgartner, M.P.; Camacho, C.J. Lessons Learned in Empirical Scoring with Smina from the CSAR 2011 Benchmarking Exercise. J. Chem. Inf. Model. 2013, 53, 1893–1904. [Google Scholar] [CrossRef] [PubMed]
  40. Eberhardt, J.; Santos-Martins, D.; Tillack, A.F.; Forli, S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J. Chem. Inf. Model. 2021, 61, 3891–3898. [Google Scholar] [CrossRef] [PubMed]
  41. Trott, O.; Olson, A.J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. J. Comput. Chem. 2010, 31, 455–461. [Google Scholar] [CrossRef] [PubMed]
  42. McNutt, A.T.; Francoeur, P.; Aggarwal, R.; Masuda, T.; Meli, R.; Ragoza, M.; Sunseri, J.; Koes, D.R. GNINA 1.0: Molecular Docking with Deep Learning. J. Cheminform. 2021, 13, 43. [Google Scholar] [CrossRef] [PubMed]
  43. Pagola, S.; Stephens, P.W.; Bohle, D.S.; Kosar, A.D.; Madsen, S.K. The Structure of Malaria Pigment β-Haematin. Nature 2000, 404, 307–310. [Google Scholar] [CrossRef] [PubMed]
  44. Momma, K.; Izumi, F. VESTA 3 for Three-Dimensional Visualization of Crystal, Volumetric and Morphology Data. J. Appl. Crystallogr. 2011, 44, 1272–1276. [Google Scholar] [CrossRef]
  45. Kim, J.; Tan, Y.Z.; Wicht, K.J.; Erramilli, S.K.; Dhingra, S.K.; Okombo, J.; Vendome, J.; Hagenah, L.M.; Giacometti, S.I.; Warren, A.L.; et al. Structure and Drug Resistance of the Plasmodium falciparum Transporter PfCRT. Nature 2019, 576, 315–320. [Google Scholar] [CrossRef] [PubMed]
  46. Adasme, M.F.; Linnemann, K.L.; Bolz, S.N.; Kaiser, F.; Salentin, S.; Haupt, V.J.; Schroeder, M. PLIP 2021: Expanding the Scope of the Protein-Ligand Interaction Profiler to DNA and RNA. Nucleic Acids Res. 2021, 49, W530–W534. [Google Scholar] [CrossRef] [PubMed]
  47. Berneburg, I.; Peddibhotla, S.; Heimsch, K.C.; Haeussler, K.; Maloney, P.; Gosalia, P.; Preuss, J.; Rahbari, M.; Skorokhod, O.; Valente, E.; et al. An Optimized Dihydrodibenzothiazepine Lead Compound (SBI-0797750) as a Potent and Selective Inhibitor of Plasmodium falciparum and P. vivax Glucose 6-Phosphate Dehydrogenase 6-Phosphogluconolactonase. Antimicrob. Agents Chemother. 2022, 66, e0210921. [Google Scholar] [CrossRef] [PubMed]
  48. Brumlik, M.J.; Nkhoma, S.; Kious, M.J.; Thompson, G.R.; Patterson, T.F.; Siekierka, J.J.; Anderson, T.J.C.; Curiel, T.J. Human P38 Mitogen-Activated Protein Kinase Inhibitor Drugs Inhibit Plasmodium falciparum Replication. Exp. Parasitol. 2011, 128, 170–175. [Google Scholar] [CrossRef] [PubMed]
  49. Nallan, L.; Bauer, K.D.; Bendale, P.; Rivas, K.; Yokoyama, K.; Hornéy, C.P.; Pendyala, P.R.; Floyd, D.; Lombardo, L.J.; Williams, D.K.; et al. Protein Farnesyltransferase Inhibitors Exhibit Potent Antimalarial Activity. J. Med. Chem. 2005, 48, 3704–3713. [Google Scholar] [CrossRef] [PubMed]
  50. Winston, J.A.; Rivera, A.; Cai, J.; Patterson, A.D.; Theriot, C.M. Secondary Bile Acid Ursodeoxycholic Acid Alters Weight, the Gut Microbiota, and the Bile Acid Pool in Conventional Mice. PLoS ONE 2021, 16, e0246161. [Google Scholar] [CrossRef] [PubMed]
Pharmaceuticals 19 00958 g001
Figure 1. LC–HRMS analysis of the active fraction C2 and candidate compound selected for biological evaluation. (A) Total ion chromatogram (TIC) of fraction C2 showing a dominant peak at RT 9.77 min. (B) Chemical structure of commercial ursodeoxycholic acid (UDCA), which was selected for subsequent biological evaluation based on GNPS spectral dereplication results.
Figure 1. LC–HRMS analysis of the active fraction C2 and candidate compound selected for biological evaluation. (A) Total ion chromatogram (TIC) of fraction C2 showing a dominant peak at RT 9.77 min. (B) Chemical structure of commercial ursodeoxycholic acid (UDCA), which was selected for subsequent biological evaluation based on GNPS spectral dereplication results.
Pharmaceuticals 19 00958 g001
Pharmaceuticals 19 00958 g002
Figure 2. Stage-specific antiplasmodial activity of fraction C2 and ursodeoxycholic acid (UDCA) against P. falciparum 3D7 parasites. Synchronized parasites at ring, trophozoite, and schizont stages were exposed to fraction C2, UDCA, or reference control drugs for 72 h, followed by growth evaluation using the SYBR Green I assay. No statistically significant differences were observed among developmental stages for fraction C2, UDCA, or the reference drugs based on two-way ANOVA followed by Tukey’s multiple comparison test (p > 0.05). The numbers shown and graph data are presented as average IC50 ± SD from three independent experiments. Chloroquine (CQ); artemisinin (ART); half maximal inhibitory concentration (IC50).
Figure 2. Stage-specific antiplasmodial activity of fraction C2 and ursodeoxycholic acid (UDCA) against P. falciparum 3D7 parasites. Synchronized parasites at ring, trophozoite, and schizont stages were exposed to fraction C2, UDCA, or reference control drugs for 72 h, followed by growth evaluation using the SYBR Green I assay. No statistically significant differences were observed among developmental stages for fraction C2, UDCA, or the reference drugs based on two-way ANOVA followed by Tukey’s multiple comparison test (p > 0.05). The numbers shown and graph data are presented as average IC50 ± SD from three independent experiments. Chloroquine (CQ); artemisinin (ART); half maximal inhibitory concentration (IC50).
Pharmaceuticals 19 00958 g002
Pharmaceuticals 19 00958 g003
Figure 3. Comparative docking analysis of ursodeoxycholic acid (UDCA) and chloroquine (CQ) against β-hematin and PfCRT. (A) Docking visualization of UDCA (magenta) and CQ (cyan) on the β-hematin crystal surface. (B) Comparative docking analysis within the P. falciparum chloroquine-resistant transporter (PfCRT) cavity; the approximate membrane boundaries are indicated by horizontal bars.
Figure 3. Comparative docking analysis of ursodeoxycholic acid (UDCA) and chloroquine (CQ) against β-hematin and PfCRT. (A) Docking visualization of UDCA (magenta) and CQ (cyan) on the β-hematin crystal surface. (B) Comparative docking analysis within the P. falciparum chloroquine-resistant transporter (PfCRT) cavity; the approximate membrane boundaries are indicated by horizontal bars.
Pharmaceuticals 19 00958 g003
Pharmaceuticals 19 00958 g004
Figure 4. Binding modes and predicted receptor–ligand interactions within 4 Å of ursodeoxycholic acid (UDCA) for (A) P. falciparum glucose-6-phosphate dehydrogenase/6-phosphogluconolactonase (PfGluPho; AlphaFold ID: AF-Q8IKU0), (B) P. falciparum mitogen-activated protein kinase (PfMAPK; AlphaFold ID: AF-Q8ILF0), and (C) P. falciparum protein farnesyltransferase subunit beta (PfPFT-β; AlphaFold ID: AF-Q8IHP6). Receptor structures are shown in gray and UDCA is colored magenta.
Figure 4. Binding modes and predicted receptor–ligand interactions within 4 Å of ursodeoxycholic acid (UDCA) for (A) P. falciparum glucose-6-phosphate dehydrogenase/6-phosphogluconolactonase (PfGluPho; AlphaFold ID: AF-Q8IKU0), (B) P. falciparum mitogen-activated protein kinase (PfMAPK; AlphaFold ID: AF-Q8ILF0), and (C) P. falciparum protein farnesyltransferase subunit beta (PfPFT-β; AlphaFold ID: AF-Q8IHP6). Receptor structures are shown in gray and UDCA is colored magenta.
Pharmaceuticals 19 00958 g004
Pharmaceuticals 19 00958 g005
Figure 5. Effects of 7-day treatment with ursodeoxycholic acid (UDCA) on parasitemia levels in P. yoelii 17XNL-infected C57BL/6 mice at doses of (A) 100 mg/kg and (B) 450 mg/kg. Parasitemia progression, AUC, body weight (BW), and hematocrit are shown. Statistically significant differences relative to the untreated control are indicated by asterisks (* p < 0.05). Data are presented as mean ± SD and were analyzed using two-way ANOVA followed by Sidak’s multiple comparisons test. AUC, area under the curve.
Figure 5. Effects of 7-day treatment with ursodeoxycholic acid (UDCA) on parasitemia levels in P. yoelii 17XNL-infected C57BL/6 mice at doses of (A) 100 mg/kg and (B) 450 mg/kg. Parasitemia progression, AUC, body weight (BW), and hematocrit are shown. Statistically significant differences relative to the untreated control are indicated by asterisks (* p < 0.05). Data are presented as mean ± SD and were analyzed using two-way ANOVA followed by Sidak’s multiple comparisons test. AUC, area under the curve.
Pharmaceuticals 19 00958 g005
Table 1. In vitro activities of the fraction and compounds tested in this study against P. falciparum, HSF cells, and RBC.
Table 1. In vitro activities of the fraction and compounds tested in this study against P. falciparum, HSF cells, and RBC.
SampleIC50 P. falciparum (μg/mL)
[Hill Slope]		CC50 HSF Cells (μg/mL)RISIRBC Hemolysis Rate at 100 μg/mL (%)
3D7K1Dd2K1Dd23D7K1Dd2
C26.55 ± 3.00
[1.69 ± 0.34]		24.50 ± 7.39
[1.21 ± 0.05]		37.76 ± 4.17
[1.09 ± 0.61]		>2003.745.76>30.53>8.16>5.301.642 ± 0.362
UDCA4.68 ± 0.65
[2.89 ± 0.20]		12.89 ± 6.02
[1.32 ± 0.13]		21.13 ± 8.61
[0.92 ± 0.24] 		>2002.754.51>42.74>15.52>9.475.022 ± 0.527
Chloroquine0.014 ± 0.002
[3.64 ± 0.79]		0.524 ± 0.300
[3.78 ± 1.16]		0.461 ± 0.020
[1.04 ± 0.06]		10.68 ± 3.5137.4332.93762.8620.3823.171.262 ± 0.553
Artemisinin0.006 ± 0.004
[2.10 ± 1.11]		0.007 ± 0.004
[1.79 ± 0.27]		0.013 ± 0.002
[1.57 ± 0.05]		43.20 ± 8.681.172.177200.006171.433323.080.586 ± 0.369
Values are presented as the average ± SD of at least three independent experiments. IC50, half-maximal inhibitory concentration. UDCA, ursodeoxycholic acid; CC50, half-maximal cytotoxic concentration; HSF, human skin fibroblast; RBC, red blood cell; RI, resistant index (ratio between IC50 of 3D7 with IC50 of K1 or Dd2); SI, selectivity index (ratio between IC50 and CC50).
Table 2. Interactions of chloroquine (CQ) and ursodeoxycholic acid (UDCA) from comparative docking simulations.
Table 2. Interactions of chloroquine (CQ) and ursodeoxycholic acid (UDCA) from comparative docking simulations.
ReceptorLigand
CQUDCA
β-hematin
Binding affinity (kcal/mol)−7.50−10.00
CNN score0.94510.8951
PfCRT (6UKJ)
Binding affinity (kcal/mol)−5.93−7.01
CNN score0.80530.9231
Hydrogen bond interactionsSer140, Ser220, Gln253Gln352, Gly353
Hydrophobic interactionsVal141, Leu160, Leu217, Leu221Gln156, Val 159, Leu160, Leu221
Table 3. Predicted potential target genes of ursodeoxycholic acid (UDCA) from protein–protein interaction (PPI) network analysis.
Table 3. Predicted potential target genes of ursodeoxycholic acid (UDCA) from protein–protein interaction (PPI) network analysis.
UniProt CodeHuman GeneUniProt CodeP. falciparum 3D7 Gene% HomologyNode Degree in PPI
P11413G6PDQ8IKU0PF14_051139.651
Q16539MAPK14Q8ILF0PF3D7_143150039.493
P49356FNTBQ8IHP6PF3D7_114750033.182
Q9UBT2UBA2Q8I553PF3D7_123700032.241
Q9UBE0SAE1Q8IHS2PF3D7_114450027.211
O75907DGAT1O97295PF3D7_032230026.622
P12931SRCQ8IEG4PF3D7_131510025.0018
P35610SOAT1O97295PF3D7_032230023.351
P61964WDR5C0H4D3PF3D7_051080022.772
P49354FNTAQ8I503PF3D7_124260022.192
P00533EGFRO96197PF3D7_021170019.6314
Table 4. Interactions of ursodeoxycholic acid (UDCA) from docking simulations.
Table 4. Interactions of ursodeoxycholic acid (UDCA) from docking simulations.
ParameterReceptor
PfGluPho
PF14_0511
(AF-Q8IKU0)		PfMAPK
PF3D7_1431500
(AF-Q8ILF0)		PfPFT-β
PF3D7_1147500
(AF-Q8IHP6)
Binding affinity−8.04 kcal/mol−5.46 kcal/mol−6.53 kcal/mol
CNN score0.94500.82510.8384
Hydrogen bond interactionsSer347, Asp 349, Leu350, Arg379, Thr380, Thr232, Met233Arg10, Phe 214
Hydrophobic interactionsLeu350, Arg379, Ile523, Tyr626Val180, Ile182, Thr232, Phe258, Ile262Met1, Leu9, Arg10, Arg13, Val213
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

Ariefta, N.R.; Pagmadulam, B.; Aboshi, T.; Nishikawa, Y. In Vitro, In Silico, and In Vivo Evaluation of Antiplasmodial Activity of Ursodeoxycholic Acid Following GNPS Dereplication of an Active Streptomyces sp. Fraction. Pharmaceuticals 2026, 19, 958. https://doi.org/10.3390/ph19060958

AMA Style

Ariefta NR, Pagmadulam B, Aboshi T, Nishikawa Y. In Vitro, In Silico, and In Vivo Evaluation of Antiplasmodial Activity of Ursodeoxycholic Acid Following GNPS Dereplication of an Active Streptomyces sp. Fraction. Pharmaceuticals. 2026; 19(6):958. https://doi.org/10.3390/ph19060958

Chicago/Turabian Style

Ariefta, Nanang R., Baldorj Pagmadulam, Takako Aboshi, and Yoshifumi Nishikawa. 2026. "In Vitro, In Silico, and In Vivo Evaluation of Antiplasmodial Activity of Ursodeoxycholic Acid Following GNPS Dereplication of an Active Streptomyces sp. Fraction" Pharmaceuticals 19, no. 6: 958. https://doi.org/10.3390/ph19060958

APA Style

Ariefta, N. R., Pagmadulam, B., Aboshi, T., & Nishikawa, Y. (2026). In Vitro, In Silico, and In Vivo Evaluation of Antiplasmodial Activity of Ursodeoxycholic Acid Following GNPS Dereplication of an Active Streptomyces sp. Fraction. Pharmaceuticals, 19(6), 958. https://doi.org/10.3390/ph19060958

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop