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Research Article
ARTICLE IN PRESS
doi:
10.25259/JKSUS_288_2025

Structure-based targeted molecular docking approach towards exploring the bioactive compounds of Lagerstroemia speciosa against black fungus (mucormycosis) and population pharmacokinetic simulation

, , , , ,
aDepartment of Biotechnology, School of Life Sciences, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, 600117, India
bDepartment of Food Science & Biotechnology, Sejong University, 209 Neungdong-ro, Gwangjin District, Seoul, 05006, Republic of Korea
cDepartment of Pharmacy, Kangwon National University, 66-3, Baekryang Ro, Hyoja Dong, Chuncheon Si, Gangwon-Do, Chuncheon, 24341, Republic of Korea
dDepartment of Biotechnology, School of Bio-engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu-603 203, India
eDepartment of Biotechnology, Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Vel Nagar, Avadi, Chennai, 600062, India
fDepartment of Food and Nutrition, College of BioNano Technology, Gachon University, Seongnam 13120, Republic of Korea
gCarbohydrate Bio product Research Center, College of Life Science, Sejong University, Seoul 05006, Republic of Korea

#Authors contributed equally to this work and share co-first authorship.

* Corresponding author: E-mail address: hshin@sejong.ac.kr (Hakdong Shin)

Received: , Accepted: ,
© 2026 Journal of King Saud University – Science - Published by Scientific Scholar
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This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

Abstract

Conventional treatments for Mucormycosis, a comorbidity in immune-compromised patients affecting the brain or respiratory tract, with medicines like posaconazole, isavuconazole, and amphotericin B possess concerning side effects. Hence, an alternative antifungal biocompound Ethyl 11-bromoundecanoate (EBD) from Lagerstroemia speciosa was investigated using in silico molecular docking against antigenic black fungus epitome. The interaction of EBD with two protein targets, 17 β-1,3-glucanosyltransferase (4WTP) and 1,3-β-glucanase (4M80) in black fungus revealed the docking scores of -4.83 and -6.70, respectively, while the positive control isavuconazole scored -8.49 and -9.94 against the respective proteins. Superior hydrogen bond interactions were demonstrated by EBD, despite lower binding affinity. By pharmacokinetic simulations, 5% oral bioavailability was predicted with a 43.13-hour maximum retention time. Rapid clearance (739.87 ml/min/kg) is caused by EBD’s high lipophilicity, which could be addressed by a hydrophilic delivery system. Better therapeutic efficiency at lower doses was indicated by population kinetics, and higher retention was observed in females compared to males. These findings suggest EBD as an alternative treatment for mucormycosis.

Keywords

Lagerstroemia
Molecular docking
Molecular dynamics simulation
Mucormycosis
PBPK modelling
Pharmacokinetics

1. Introduction

Fungal infections are a serious global health concern causing millions of fatalities annually (Bouamrane et al., 2024). Mucormycosis is a rare fungal infection that mainly affects immunocompromised people with COVID-19, diabetes, cancer, or HIV/AIDS by infecting the sinuses, lungs, and brain (Sharma and Goel, 2022). It is caused by black fungi, which is contact through inhaling mucormycete spores (CDC, 2024a). The pathogenic molds infect the lungs, resulting in difficulty breathing, coughing up blood, and chest pain (Islam et al., 2022). It can be fatal in severe conditions or may require excision. Treatment for the disease often includes antifungals such as posaconazole, isavuconazole, and amphotericin B (CDC, 2024b). However, these medications were reported to produce side effects, especially renal injury (Manesh et al., 2023). Herbal remedies have long been used in an oral traditional cure and thus they are being tested as alternative treatments using herbal secondary metabolites against black fungus (Aanouz et al., 2021).

Lagerstroemia speciosa, commonly known as Banaba or Giant crepe myrtle tree, is native to Southeast Asia, has shown antifungal activity and is used in traditional medicine for controlling diabetes and obesity (Guo et al., 2020). Methanolic extract of L. speciosa has been reported to exhibit antifungal activity against Candida albicans, Aspergillus fumigatus, and Aspergillus flavus (Nurcahyanti et al., 2024). One of the major bioactive constituents of L. speciosa, Ethyl 11-bromoundecanoate (EBD), has been reported to be responsible for its antifungal activity (Yasa et al., 2017). Hence, we evaluated the antifungal potential of EBD at the molecular level against black fungus using molecular docking. The molecular docking suggests the probability of interaction between therapeutic compound with pathogenic protein targets (Elangovan et al., 2025b, 2025a; Lisha et al., 2025).

Our in silico molecular docking analysis identified 17 β-1,3-glucanosyltransferase (PDB ID: 4WTP) and 1,3-β-glucanase (PDB ID: 4M80) as target proteins for EBD. The selected proteins, 4WTP and 4M80, are critical in fungal cell wall synthesis and maintenance and are unique to fungi and not present in human cells. Therefore, they were chosen as the ideal therapeutic targets in this study. Their crystal structures are well-characterized, enabling reliable molecular docking studies. Our in silico data indicated that EBD from L. speciosa extracts could be a potent alternative to antifungals for treating mucormycosis.

We estimated the dose-dependent fate of EBD in the human system following oral and intravenous administration using non-compartmental pharmacokinetics (PK) simulations on the East Asian population. The non-compartmental physiology-based pharmacokinetic (PBPK) modeling is generally performed with a drug’s in vitro release and metabolism data (Kumar et al., 2024). However, in this in silico study, we simulated the pharmacokinetics (PK) of EBD and predicted its probable ADME fate in the human system. These data can support future in vitro and in vivo studies aimed at developing alternative therapeutic formulations and delivery systems against mucormycosis.

2. Materials and Methods

2.1 Plant selection and database preparation

Plants containing antifungal phytochemicals were identified using Dr. Duke’s Phytochemical Dictionary, (Duke, 2016) IMPPAT-Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT, 2024), and Super Natural Database. Crepe myrtle was selected for its fungal resistance. Phytochemical content was analyzed through GCMS data and literature review using Molecular Operating Environment software.

2.2 Ligand and target preparation

Virtual screening identified compounds with biological activity using Swiss ADME. The structure of 1,3-β-glucanase was modeled using SWISS-MODEL. Three-dimensional coordinates were generated using LigPrep and Epik. Target proteins were prepared using Chimera software and AutoDockTools (The UniProt Consortium, 2023).

2.3 Molecular docking and dynamics

Molecular docking used AutoDockVina software with grid box dimensions of 60 along X, Y, and Z axes. Molecular dynamics simulations used GROMACS 2020.2, including energy minimization and system relaxation (Dinakarkumar et al., 2021).

2.4 Pharmacokinetic simulation

PK-Sim version 11 simulated EBD PK in East Asian humans. The building blocks included compound properties, physiological parameters, formulation, and administration protocol (Table 1). Non-compartmental models were developed for both healthy and renal-impaired populations(Kalishwaralal et al., 2024).

Table 1. Properties of EBD and physiology parameters fed in for simulation in PK-Sim.
Parameter Value Reference
Molecular weight (g/mol) 293.24 PubChem (PubChem, n.d.)
Lipophilicity (log Units) 5
Fraction unbound (fu) (%) 0.017 (Lobell and Sivarajah, 2003)
Aqueous Solubility (mg/L) 2.00 E-3 PubChem
Anatomic parameters
Mean body weight (Kg) 60.03 PK-Sim Default
BMI 20.78
Age (years) 30
Blood flow rate 0.26 L/min (Edginton et al., 2006)

2.4.1 Non-compartmental model development: PK simulation

PK-Sim version 11-build 150 (Bayer Technology Services, Biophysics, 42096 Wuppertal, Germany) was used to simulate the models and assess the PK of EBD in East Asian humans. The building blocks, including the compound properties, physiological parameters, formulation, and administration protocol, are built using PK-Sim as listed in Table 1. As it is a non-compartmental model, no metabolism was included in the modeling. Administration routes were IV and Oral, and the formulation was assumed to be dissolved considering that the phytochemicals would be given in the form of extracts or decoction.

2.4.2 Sensitivity analysis

After simulating the PK-curve, the physiology and EBD properties parameters that impact the PK were estimated by sensitivity analysis. All the physiology parameters were tested for the sensitivity analysis. The mathematical equation employed in PK-Sim to run the sensitivity analysis is given as in Eq. (1)

(1)
Si,j =  ΔPKj ΔPi * Pi PKj

where Pi is the input parameter whose change (ΔPi) will reflect a change in pharmacokinetic output (PKj). The sensitivity analysis was performed with the default 2 number of steps and variation range of 0.1.

2.4.3 PK in renal impairment

Chronic kidney disease (CKD) can impact the clearance of drugs due to their faulty glomerular filtration rate (GFR) (Behal et al., 2023). To model the renal-impaired human model, only the GFR was reduced from 93.94 ml/min to 58.39 ml/min. Other physiological parameters from the healthy individual were retained. By reducing the GFR, the estimated GFR (eGFR) was reduced from 96.53 ml/min/1.73m2 to 60.00 ml/min/1.73m2 when normalized with the Body Surface Area (BSA) of the specified human model.

2.4.4 Population model

The population model was generated with the specified human model specifications. Two population models with 1:1 gender ratio were defined for healthy and renal-impaired East Asian models. The weight and age distributions were 40 to 80 Kg and 20 to 60 years, respectively in the normal BMI range (18-22). The population model was assessed to find the correlation of different covariates such as age, gender, weight, and BMI on the PK of EBD.

3. Results and Discussion

3.1 Plant selection and database preparation

The phytochemical compounds, identified from existing literature based on GC-MS reports and collected from various databases, were used to establish a primary database for the study (Mobin et al., 2019). The two-dimensional structures of the compounds were obtained from Pubchem and were compiled into a single structural database

3.2 Virtual screening of phytochemicals ligands

The virtual screening of ligands was conducted using the Swiss ADME server, where parameters such as Lipinski’s rule of five and “drug likeliness” were used as filters to narrow down the number of compounds for subsequent docking. Initially, 51 of 168 total compounds present in the database were filtered based on having fewer than three violations. Then these compounds were subjected to the same Lipinski filter (without any violation) and assessed for “drug likeliness”. Finally, 25 compounds were found to have nil violations in the Lipinski rule and zero to one violation for “drug likeliness”, suggesting them as lead target molecules for docking studies. From the assorted phytochemical database, EBD was found to possess high anti-fungal activity (Gandhi et al., 2023) and thus it was selected for this study.

3.3 Molecular docking of compounds

Molecular docking analysis of EBD with target proteins (Fig. 1) revealed docking scores of -4.83 (4WTP) and -6.70 (4M80), compared to isavuconazole’s scores of -8.49 and -9.94, respectively (Tables 2 and 3). Electrostatic force interactions were observed between EBD and 6-15 amino acid residues through hydrogen bonds (Bulusu and Desiraju, 2020). Key amino acid residues involved in hydrogen binding included PHE(154), TRP(157), and TYR(102) of 17 β-1,3-glucanosyltransferase (Palanisamy et al., 2020) and ASN(146), TYR(29), and LEU(304) of 1,3-β-glucanase (Mohanty et al., 2023). The binding parameters indicated high specificity towards the methyl‐CoM reductase binding site, suggesting potential anti-methanogen inhibitory activity (Dinakarkumar et al., 2021). The compound demonstrated positive BBB and Caco-2 values, indicating favorable membrane permeability for targeting fungal enzymes.

Molecular docking analysis of EBD and Isavuconazole with black fungus enzymes. (a) EBD with 17 β-1,3-glucanosyltransferase, (b) Isavuconazole with 17 β-1,3-glucanosyltransferase, (c) EBD with 1,3-β-glucanase, and (d) Isavuconazole with 1,3-β-glucanase.
Fig. 1. Molecular docking analysis of EBD and Isavuconazole with black fungus enzymes. (a) EBD with 17 β-1,3-glucanosyltransferase, (b) Isavuconazole with 17 β-1,3-glucanosyltransferase, (c) EBD with 1,3-β-glucanase, and (d) Isavuconazole with 1,3-β-glucanase.
Table 2. 17 β-1, 3-GLUCANOSYLTRANSFERASE (PDB ID: 4WTP).
S.No Compounds Docking score Glide energy Hydrogen bond interactions Distances (Å)
1. EBD -4.83 -33.93 (PHE154)N-HO2 2.97
2.

Isavuconazole

(Drug)

 -8.49 -64.62 O1-HO(TRP157) 2.98
(TYR102)OH-HN3 2.98
Table 3. 1, 3-β-GLUCANASE (PDB ID: 4M80).
S.No Compounds Docking score Glide energy Hydrogen bond interactions Distances (Å)
1. EBD -6.70 -33.26 (ASN146)ND2-HO2 3.11
(TYR29)OH-HO2 2.91
2.

Isavuconazole

(Drug)

 -9.94 -54.93 O1-HO(LEU304) 2.91

3.4 Molecular docking

3.4.1 Root mean square deviation (RMSD)

The structural stability analysis showed protein RMSD values of 1.0-2.7 Å for compound C-4M80 complex and 0.9-2.6 Å for compound D-4M80 complex. The C-4M80 system remained stable until 18 ns, showed divergence until 82 ns, and stabilized thereafter. The D-4M80 system showed deviations until 22 ns, followed by stability up to 100 ns (Fig. S1).

Figure S1

3.4.2 Root mean square fluctuation (RMSF)

The C-4M80 complex showed high fluctuation (4.5 Å) in the 302-310 region and mild fluctuations (2.5-4.4 Å) in regions 30-40, 220-230, 270-280, and 310-320. The D-4M80 complex exhibited 4.5 Å RMSF value with enhanced fluctuation at 30-40, and values of 3.0 Å and 2.8 Å in regions 90-100 and 310-320, respectively. Ligand binding significantly influenced residue fluctuations (Fatriansyah et al., 2022).

3.4.3 Occupancy of H-bond

The C-4M80 complex formed hydrogen bonds with TRP363 (4%) and TYR255 (1.5%) residues. The D-4M80 complex showed hydrogen bonding with SER292 (65%), TYR255 (13%), ASN146 (1%), and TRP363 (1%). Additional amino acid, hydrophobic, and water bridge interactions were observed (Fig. S1). These bonds facilitate EBD’s electrostatic interaction, inhibiting anti-methanogenic activity and fungal growth.

3.5 Simulated PK

Pharmacokinetic simulations utilized physiological parameters (Table 1) with plasma fraction unbound (fup) calculated via QIVIVE tools (Korzekwa et al., 2022; “QIVIVE tools,” n.d.) using molecular weight (293.24 g/mol), neutral charge, and logP (5). PK-Sim generated 24-hour oral and IV administration curves (Willmann et al., 2005), showing linear PK profiles due to absent clearance data (Fig. 2). Analysis revealed low oral bioavailability (6.14%) due to EBD’s high lipophilicity (Daniel-Mwambete et al., 2004), with clearance increasing from 739.87 to 2017.05 ml/min/Kg between 1-3 mpk doses (Table 4). Lower oral versus IV pharmacokinetic parameters indicated the need for an enhanced delivery system.

Simulated PK curve of EBD administered orally and intravenously at three doses (1 mg/Kg-solid line; 2 mg/Kg-dashed line; 3 mg/Kg-dotted line). The data was simulated for the EBD concentration in venous blood plasma for 24 hours.
Fig. 2. Simulated PK curve of EBD administered orally and intravenously at three doses (1 mg/Kg-solid line; 2 mg/Kg-dashed line; 3 mg/Kg-dotted line). The data was simulated for the EBD concentration in venous blood plasma for 24 hours.
Table 4. Predicted pharmacokinetic values of EBD administered to East Asian Human.
Parameter 1 mpk
 2 mpk
 3 mpk
IV Oral IV Oral IV Oral
AUC∞ [µg*min/ml] 26.63 1.35 53.25 1.44 79.88 1.49
AUCt.End [µg*min/ml] 13.75 0.65 27.49 0.71 41.24 0.74
Cmax [µg/ml] - 0.001 - 0.001 - 0.001
Ct.End [µg/ml] 0.00 0.00 0.01 0.00 0.01 0.00
Total Body Clearance [ml/min/kg] 37.56 739.87 37.56 1390.98 37.56 2017.50
% AUC (tlast-∞) 0.48 0.52 0.48 0.51 0.48 0.51
MRT [h] 39.83 43.13 39.83 41.80 39.83 41.12
tmax [h] - 2.50 - 2.75 - 3
Half-Life [h] 34.46 35.26 34.46 34.14 34.46 33.56
Vd (plasma)/F [ml/kg] 11.20E4 22.58E5 11.20E4 41.11E5 11.20E4 58.61E5
Vss (plasma)/F [ml/kg] 89.76E3 19.14E5 89.76E3 34.89E5 89.76E3 49.78E5
Bioavailability (%) 6.14 2.79 1.91

Vd- Volume of Distribution; Vss - Volume of Distribution in Steady Sate; MRT – Maximum Retention Time

3.6 Sensitivity analysis

The sensitivity analysis (Fig. 3) revealed that membrane affinity (Log MA), plasma protein binding, and fraction unbound plasma (fup) were primary factors affecting EBD’s area under the curve (AUC). High lipophilicity led to negative impacts of fat volume and blood flow rate on plasma exposure. These findings suggest poor absorption and bioavailability, indicating the need for an improved delivery system (Mahendiran et al., 2022; Nazeer et al., 2019).

Sensitivity analysis illustrating the extent of influential physiology parameters that can impact the AUC∞ of EBD in intravenous plasma of East Asian Humans.
Fig. 3. Sensitivity analysis illustrating the extent of influential physiology parameters that can impact the AUC∞ of EBD in intravenous plasma of East Asian Humans.

3.7 Healthy and renal impaired population model kinetics

Population kinetics analysis (Fig. 4, Table 5) revealed reduced PK parameters (Cmax, AUC, MRT, half-life) in both routes. While intravenous EBD exposure decreased in renal-impaired populations, oral administration showed increased exposure due to intestinal absorption (Vinarov et al., 2021). Higher bioavailability in renal-impaired populations resulted from poor glomerular filtration (Fradette et al., 2016), though bioavailability decreased with increasing dosage. Among covariates, only body weight showed non-linear influence on PK parameters (Fig. 5), with females exhibiting higher half-life but lower AUC than males.

Simulated PK curve on aggregated population (n = 100) of (a) healthy males (b) healthy females (c) males with CKD and (d) females with CKD for EBD administered orally and intravenously at three doses (1 mg/Kg-solid line; 2 mg/Kg-dashed line; 3 mg/Kg-dotted line) The data was simulated for the EBD concentration in venous blood plasma for 24 hours.
Fig. 4. Simulated PK curve on aggregated population (n = 100) of (a) healthy males (b) healthy females (c) males with CKD and (d) females with CKD for EBD administered orally and intravenously at three doses (1 mg/Kg-solid line; 2 mg/Kg-dashed line; 3 mg/Kg-dotted line) The data was simulated for the EBD concentration in venous blood plasma for 24 hours.
Table 5. Predicted pharmacokinetic values of EBD administered to the East Asian Human population with normal and impaired renal functioning.
Parameter 1 mpk
 2 mpk
 3 mpk
IV Oral IV Oral IV Oral
Healthy AUC∞ [µg*min/ml] 24.40 1.52 48.80 1.62 73.20 1.68
AUCt.End [µg*min/ml] 11.72 0.70 23.44 0.76 35.15 0.80
Cmax [µg/ml] - 0.001 - 0.001 - 0.001
Ct.End [µg/ml] 0.00 0.00 0.01 0.00 0.01 0.00
% AUC (tlast-∞) 0.52 0.54 0.52 0.53 0.52 0.53
MRT [h] 48.11 49.80 48.11 47.70 48.11 46.52
tmax [h] - 2.75 - 3 - 3.25
Half-Life [h] 42.69 41.84 42.69 40.11 42.69 39.12
Bioavailability (%) 5.93 3.11 2.13
Renal impaired AUC∞ [µg*min/ml] 21.95 1.58 43.90 1.69 65.85 1.76
AUCt.End [µg*min/ml] 10.72 0.78 21.44 0.85 32.16 0.90
Cmax [µg/ml] - 0.00 - 0.00 - 0.001
Ct.End [µg/ml] 0.00 0.00 0.01 0.00 0.01 0.00
% AUC (tlast-∞) 0.51 0.51 0.51 0.50 0.51 0.49
MRT [h] 46.00 42.81 46.00 40.79 46.01 39.67
tmax [h] - 3.25 - 3.50 - 3.75
Half-Life [h] 40.59 35.65 40.59 33.84 40.59 32.82
Bioavailability (%) 6.57 3.46 2.37

AUC- Area under curve; Cmax- Maximum concentration; MRT- Maximum retention time

Scatter analysis of the influence of body weight on the AUC0-24h and half-life for healthy and renal-impaired patients. The comparison displays the contrast between the scatter of PK parameters in the male (black square) and female (red circle) population.
Fig. 5. Scatter analysis of the influence of body weight on the AUC0-24h and half-life for healthy and renal-impaired patients. The comparison displays the contrast between the scatter of PK parameters in the male (black square) and female (red circle) population.

4. Conclusions

EBD from Lagerstroemia speciosa demonstrated significant binding with 17 β-1,3-glucanosyltransferase and 1,3-β-glucanase proteins in black fungus. Although docking scores (-4.83 and -6.70) were lower than isavuconazole, EBD showed effective hydrogen bond interactions sufficient to inhibit fungal growth. Pharmacokinetic analysis revealed a half-life exceeding 24 hours but poor bioavailability (6.14%) due to high lipophilicity. Population kinetics indicated three key findings: better bioavailability at lower doses, increased drug accumulation in renal-impaired patients, and higher retention in females compared to males. The molecular dynamics simulation further confirmed EBD’s stability through consistent RMSD values and specific residue interactions during the 100ns trajectory. Drug-protein interaction analysis and sensitivity studies suggest optimizing the delivery mechanism could enhance EBD’s therapeutic potential against mucormycosis. These findings suggest EBD is a promising therapeutic alternate for mucorpmycosis, but requires a hydrophilic drug delivery system. The future research can be performed in vivo with EBD and drug delivery system to test the therapeutic efficiency.

Acknowledgement

Authors are thankful to Sejong University, SRM Institute of Science and Technology, and Vels Institute of Science, Technology, and Advanced Studies for providing the infrastructure and support to conduct this research

CRediT authorship contribution statement

Yuvaraj Dinakarkumar: Writing – original draft, validation, methodology, investigation; Selvaraj Arokiyaraj: Writing – review & editing, visualization, validation; Nazeer Abdul Azeez: Writing – original draft, visualization, validation, software, resources, data curation; Shalini Shivakumar: Visualization, software, resources; Seongok Kim: Writing – review & editing, resources, formal analysis, data curation; Hakdong Shin: Supervision, project administration, funding acquisition, conceptualization.

Declaration of competing 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.

Data availability

The data presented in this study are available on request from the corresponding author.

Declaration of generative AI and AI-assisted technologies in the writing process

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.

Funding

This research was supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education (2023R1A6C101A045), and the NRF funded by the Ministry of Education (2022R1A6A1A03055869). This research was also supported by the “Regional Innovation System & Education (RISE)” through the Seoul RISE Center, funded by the Ministry of Education (MOE) and the Seoul Metropolitan Government (2026-RISE-01-019-04).

Supplementary data

Supplementary material to this article can be found online at https://dx.doi.org/10.25259/JKSUS_288_2025.

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