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. 2023 Oct 30;11(1):32. doi: 10.1007/s40203-023-00165-1

Identification of bioactive compounds from Vaccinium vitis-idaea L. (Lingonberry) as inhibitors for treating KRAS-associated cancer: a computational approach

Ayooluwa Ilesanmi 1,#, Gbenga Dairo 2,✉,#, Sofela Salimat 3, Damilola S Bodun 4, Bibiire Awoyale 5, Toheeb A Balogun 4,6,✉
PMCID: PMC10616029  PMID: 37915613

Abstract

Lung cancer is the cancer of the lung’s epithelial cells typically characterized by difficulty breathing, chest pain, blood-stained coughs, headache, and weight loss. If left unmanaged, lung cancer can spread to other body parts. While several treatment methods exist for managing lung cancer, exploring natural plant sources for developing therapeutics offers great potential in complementing other treatment approaches. In this study, we evaluated the bioactive compounds in Vaccinium vitis-idaea for treating KRAS-associated lung cancer types. In this study, we concentrated on inhibiting the mutated Kirsten rat sarcoma viral oncogene homolog (KRAS) by targeting an associated protein (Phosphodiesterase 6δ) to which KRAS form complexes. We evaluated bioactive compounds from Lingonberry (Vaccinium vitis-idaea L.), adopting computational approaches such as molecular docking, molecular dynamics simulation, molecular mechanics/generalized Born surface area (MM/GBSA) calculations, and pharmacokinetics analysis. A total of 26 out of 39 bioactive compounds of Vaccinium vitis-idaea L. had a higher binding affinity to the target receptor than an approved drug, Sotorasib. Also, further analyses of all lead/top compounds in this study identified (+)—Catechin (Cianidanol), Arbutin, Resveratrol, and Sinapic acid, to be potential drug candidates that could be pursued. In sum, Arbutin, (+)—Catechin, and Sinapic acid are predicted to be the top compound of Vaccinium vitis-idaea L. because of their pharmacokinetic properties and drug-likeness attributes. Also, their stability to the target receptor makes them a potential drug candidate that could be explored for treating KRAS mutation-associated lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00165-1.

Keywords: KRAS, Lingonberry, Sinapic acid, Lung cancer, Molecular docking

Introduction

Among all cancer types, lung cancer is prevalent and accounts for at least 18% of all cancer-linked mortalities (Babar et al. 2020). Cigarette smoking constitutes the major risk factor for the development of lung cancer—with approximately 90% of lung cancer cases linked to cigarette smoking alone (Khuder and Sadik 2001). Consequently, smokers are more prone to developing cancer cells and resisting cancer treatment than non-smokers (Warren et al. 2014). Essentially, lung cancer can be classified into two main types namely, small cell lung cancer (SCLC) and non‐small cell lung cancer (NSCLC), with the latter accounting for approximately 85% of all lung cancer cases (Van Meerbeeck et al. 2011). There have been advances in studying oncogenes and mutation of tumor suppressor genes toward the development of drug candidates for treating lung cancer (Herbst et al. 2018; Robichaux et al. 2018; VanderLaan et al. 2017; Yoda et al. 2019). Among the targeted genes, a rat sarcoma viral oncogene homolog, KRAS is implicated in many lung cancer cases (Shea et al. 2016).

Rat sarcoma viral oncogene (RAS) has three isoforms namely, the Human homolog of RAS, Kirsten rat sarcoma viral oncogene, KRAS, and the Neuroblastoma rat sarcoma viral oncogene, NRAS (Barbacid and Mariano 1987; McBride et al. 1983; Kirsten et al. 1970; Chang et al. 1982). Of the RAS isoforms, the KRAS gene encodes two protein isoforms, KRAS-4B and KRAS-4A, with each consisting of 188 and 189 amino acids, respectively, due to different clipping of the fourth exon (Karnoub and Weinberg 2008). A wild-type KRAS has a glutamine amino acid at position 61 but, when the amino acid at position 61 is substituted by histidine, it becomes a mutated KRAS. This mutation typically occurs at codons 12 and 13. Significant efforts have concentrated on investigating KRAS mutations, especially for the discovery of specific inhibitors that could aid the treatment of KRAS mutation lung cancer (McCarthy et al. 2019; Kwan et al. 2022). Until recently, KRAS had been widely dubbed “undruggable” due to its lack of deep hydrophobic pockets, making it difficult to bind to small molecules (Whaby et al. 2021). However, the Food and Drug Administration (FDA) recently approved a small molecule drug, Sotorasib, which specifically targets the RAS G12C mutation (Skoulidis et al. 2021). Sotorasib is the first approved KRAS inhibition cancer treatment drug that passed FDA approval. Although Sotorasib has a better selectivity to KRAS mutation than other targeted drugs, Sotorasib shows lower efficacy, a higher possibility of experiencing profound side effects, and is prone to resistance (Li et al. 2021).

While the advances could help design more broad-spectrum therapeutics, the mutation considered in the development of the drug is only found in low cases of lung cancer (~ 13% of lung adenocarcinoma) (AACR 2017). Also, there have been reports of treatment resistance (Awad et al. 2021; Koga et al. 2021). Hence, there is a need for continuous discovery of drug candidates for treating KRAS mutation lung cancer.

Before the discovery of Sotorasib, efforts to directly target KRAS mutations have historically achieved little success due to the complexity associated with KRAS mutations (Whaby et al. 2021). Since KRAS interacts with several proteins and is involved in many regulatory processes, including cell growth and differentiation (Korzeniecki and Priefer 2021), the inhibition of specific upstream or downstream signaling pathways, membrane localization, and protein interactions, can provide an alternative pathway for discovering effective inhibitors for treating KRAS mutation lung cancer. An important protein that KRAS interacts with is phosphodiesterase 6δ (PDE6D)—termed the trafficking chaperone of prenylated proteins (Yadav and Artemyev 2017). Phosphodiesterase 6δ (PDE6D) has a beta-sandwich immunoglobulin fold which contains a hydrophobic pocket capable of binding to the farnesyl group (Ismail et al. 2011). Schmick et al. (2014) showed that PDE6D binds to and sequesters the lipid of cytoplasmic RAS. Also, Zimmermann et al. showed that the manipulation of PDE6D directly affects the localization and spatial organization of KRAS, which implies RAS signaling. Since targeting KRAS directly might appear elusive, a focus on the KRAS: PDE6D complex by targeting PDE6D can be an effective approach to developing drug candidates that could treat KRAS-mutation-associated lung cancer. Moreover, there have been reports on indirect inhibitor discovery against PDE6D (Papke et al. 2016; Zimmermann et al. 2013).

This study focuses on the exploration of Lingonberry (Vaccinium vitis-idaea L.), a shrub primarily found in Central Europe, Russia, and Canada, due to its rich reservoir of phytochemical compounds known for their therapeutic properties. Various research studies (Li and Weng 2017; Gustavsson 2001; Kowalska 2021; Ștefănescu et al. 2020) have highlighted Lingonberry’s potential in containing beneficial phenolic compounds.

Notably, McDougall et al. (2008) demonstrated that berry extracts, including those from Lingonberry, possess anti-proliferative properties against cervical and colon cancer cells. Additionally, recent research by Zhu et al. (2022) revealed Lingonberry extracts' ability to inhibit the growth of hepatoma cells (HepG2).

This study's primary objective is to identify small-molecule inhibitors within Lingonberry against PDE6D, which is intricately linked to KRAS. To achieve this goal, we employed a combined approach involving structural bioinformatics and molecular modeling to analyze the interactions between ligands and proteins. We aim to develop potential drug candidates for the treatment of KRAS-mutated lung cancer.

Methodology

Ligand and protein preparation

36 (Out of the original 37 identified compounds) bioactive compounds assessed in this study were obtained from a study (Raudone et al. 2019) which had evaluated phenolic compounds of Lingonberry (Vaccinium vitis-idaea L.). The bioactive compounds from the study, together with an FDA-approved drug (Sotorasib), were screened virtually using the PyRx software (Python Prescription 0.8). The bioactive compounds and Sotorasib 3D conformers in special data format (SDF) were retrieved from the PubChem database, while that of the protein (4JV8) was retrieved from the Protein Data Bank (PDB). Polar hydrogen atoms were added to refine the target protein using the Biovia Discovery Studio software (v.21.1.0.20298) before docking.

Molecular docking and molecular mechanics/generalized Born surface area calculation

First, the binding pocket scoring coordinates of PDE6D were determined by adopting the grid generation module of Schrödinger Maestro 11.5. Next, the prepared ligands were docked into the generated active site in the PyRx software (Python Prescription 0.8). The centers of the x, y, and z generated grid were 26.1752, − 12.4132, and − 6.5578, respectively, while the dimensions (Angstrom) of the x, y, and z grid were 27.7133, 29.2607, and 12.6735, respectively. The advanced quantum mechanics calculation was adopted via molecular mechanics generalized Born surface area (MM/GBSA) to remove the false-positive values obtained from molecular docking (Omoboyowa et al. 2022a). The relative free energy for each PDE6D-ligand complex and the reference complex was computed using the Maestro–Schrödinger suite under default parameters (Prime). The mathematical equation adopted herein was shown

ΔGbind=GcomplexX-Gprotein+GLigand

where ΔGbind is the Protein–Ligand complex’s binding free energy. Gcomplex is the free energy of the complex, Gprotein is the free energy of the protein in the absence of the ligand, and Gligand is the free energy of the ligand in the absence of the protein.

ADME and pharmacokinetics prediction

Selected compounds, including a standard drug, were analyzed for their ADME properties (Absorption, Distribution, Metabolism, and Excretion) using the QikProp module, admetSAR server (http://lmmd.ecust.edu.cn/admetsar2/) and SwissADME web server (http://www.swissadme.ch/). These processes play a crucial role in drug development and were determined to be important for predicting the effectiveness of the compounds (Omoboyowa et al. 2023). Then these predictions were visualized using R studio (Bodun et al. 2023).

Molecular dynamics and post-molecular dynamics simulation calculation

To investigate the stability of the selected compounds to the target receptor, four compounds with the highest binding affinity and promising pharmacokinetic properties were subjected to molecular dynamics simulation for 100 ns (ns). The Schrödinger suite’s Desmond program was used to adopt energy-minimized receptor-ligand complexes, and the explicit solvent system was employed to set up the molecular dynamics model (Shaw Research). The selected model was created in a periodic box called a transferable intermolecular potential-4 point (TIP4P), which permits a 10 Å buffer region equidistance between protein atoms and box sides (Omoboyowa et al. 2022b). It was then upgraded with 0.15 M to reflect physiological conditions. At a temperature and pressure of 300 K and 1.01325 bar, respectively, an appropriate quantity of counter sodium (Na+) and chloride (Cl−) ions were utilized to neutralize the complete simulation model system. The entire system was reduced by the minimization tool in the Desmond Maestro interface using the default values of 1.0 kcal/mol, a 2000-iteration maximum, and a convergence threshold. Lastly, the selected docked complexes were subjected to molecular simulation at 100 ns with default settings using the OPLS-2005 force field (Schrodinger).

Furthermore, the simulation interaction diagram module of the Desmond program was used to conduct more research on the root means square deviation (RMSD), root means square fluctuation (RMSF), ligand torsion, and protein–ligand interactions profiling (Maestro). Thus, the trajectory's root means square deviation (RMSD) was determined for each frame. The equation for RMSD for frame x was as represented below:

RMSDx=1N∑i=1Nr1′tx-ritref2

where N denotes the number of atoms included in the atom selection; tref denotes the reference time, and r′ denotes the site of the atoms selected in frame x after superimposing on the reference frame, where frame x is recorded at time tx. For every frame in the simulation trajectory, the process is repeated. The equation for root means square fluctuation for atom I is represented below:

RMSFi=1T∑i=1Nr1′tx-ritref2

where T denotes the trajectory time over which the RMSF is calculated, tref denotes the reference time; r denotes the site of atom I in the reference at time tref, and r′ is the position of atom I at time t after superposition on the reference frame.

Result and discussion

Structure-based and Lipinski-based virtual screening

We utilized the selection of 36 bioactive compounds, to identify compounds suitable for oral administration. These compounds underwent a structure-based screening based on docking scores, however, compounds that violated more than the Lipinski rule of 5 were filtered out in this report. These criteria encompass factors such as Molecular Weight (MW) not exceeding 500, a Hydrogen Bond Donor count (HBD) of no more than 5, a Hydrogen Bond Acceptor count (HBA) less than or equal to 10, Lipophilicity (iLOGP) not exceeding 5, and a Molar Refractivity (MR) within the range of 40–130 (Lipinski 2004). This screening procedure resulted in the identification of 20 compounds that meet the criteria for oral bioavailability where they violated none or just one of the rules.

Molecular docking and molecular mechanics/generalized Born surface (MM/GBSA)

The top compounds reported in this study are Catechin (Cianidanol), Arbutin, Resveratrol, and Sinapic acid (Supplementary Table S1). Also, it can be inferred that the selected compounds can bind better to PDE6D and interact with the amino residues, including, Ile129, Iles109, Arg61, Trp90, and Gln78 of the target receptor (Fig. 1). To get a better representation of how the ligands interact at the active site of PDE6D, the top compounds in complex with PDE6D were visualized in the Ligand Interaction Diagram of Maestro.

Fig. 1.

Fig. 1

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Post-molecular docking 3D profile of all simulated ligands with 4JV8

Catechin displayed a strong binding energy of − 8.1 kcal/mol. It created two hydrogen bond connections, one with CYS 56 and another with GLN 78. Additionally, it interacted with various hydrophobic amino acids, including ILE 53, ILE 54, CYS 56, ALA 58, VAL 59, VAL 49, MET 117, TYR 149, LEU 147, LEU 22, ILE 129, ALA 111, ILE 109, and TRP 90 (Fig. 2).

Fig. 2.

Fig. 2

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2D molecular contacts profiling of top compounds with amino acid residues at the active site of PDE6D: a catechin (cianidanol), b resveratrol, c arbutin, and d sinapic acid

Resveratrol achieved a docking score of − 7.9 kcal/mol. It established two hydrogen bonds with ILE 53 and GLN 78. Furthermore, it engaged in hydrophobic interactions with several amino acids, such as ILE 53, LEU 54, CYS 56, ALA 58, VAL 59, MET 117, ALA 111, ILE 109, TRP 90, LEU 22, MET 20, VAL 49, LEU 147, VAL 145, LEU 38, and ILE 129.

Arbutin, with a docking score of − 6.6 kcal/mol, formed three hydrogen bonds, two with ARG 61 and one with GLN 78. It also displayed hydrophobic contacts with various amino acids, including MET 117, ILE 53, LEU 54, TYR 149, TRP 90, LEU 147, VAL 145, ILE 129, VAL 49, ALA 47, LEU 30, LEU 22, MET 20, LEU 63, ILE 109, VAL 109, and VAL 59. Additionally, there was a unique interaction involving a Pi-Pi stacking bond with TRP 90.

On the other hand, Sinapic acid, which had a docking score of − 6.5 kcal/mol, formed two hydrogen bond interactions, one with GLN 78 and the other with ARG 61. It established hydrophobic contacts with several amino acids, including TRP 90, PHE 92, ILE 109, VAL 59, LEU 76, LEU 63, MET 20, LEU 22, ILE 129, TRP 32, LEU 38, VAL 145, VAL 49, ALA 47, and LEU 147.

To better ascertain the structural stability of the selected compounds due to lack of accuracy in molecular docking, MMGBSA calculation was adopted to determine the net receptor-ligand interaction. From this study, (+)—Catechin (Cianidanol) had the highest value (− 55.8 kcal/mol) following MMGBSA calculation, which highlights its stronger binding energy to 4JV8 compared to other compounds of Vaccinium vitis-idaea L. (Table 1).

Table 1.

Molecular docking result of the selected compounds and MM/GBSA values

Compound PubChem ID Binding Affinity (Kcal/mol) MMGBSA (Kcal/mol)
(+)—Catechin (Cianidanol) 9064  − 8.1  − 55.8
Resveratrol 445,154  − 7.9  − 52.5
Arbutin 440,936  − 6.6  − 51.6
Sinapic acid 637,775  − 6.5  − 42.4
Sotorasib 137,278,711  − 5.6  − 46.2
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Molecular dynamics and post-molecular dynamics calculation

The selected bioactive compounds, which conformed to the Lipinski rule of five (Fig. 3), were subjected to molecular dynamics (MD) simulation for 100 ns in the Desmond package (Fig. 1).

Fig. 3.

Fig. 3

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The 2D structures of the selected bioactive compounds from Vaccinium vitis-idaea L. and control: A (+)—catechin (cianidanol, B arbutin, C resveratrol, D sinapic acid, E sotorasib

This helps analyze the conformational stability, intermolecular interaction profiling, and binding site occupancy of the compounds, which is critical in understanding the protein inhibition mechanism based on docking data. During the simulation, the root mean square deviation (RMSD), root mean square fluctuation (RMSF), protein–ligand contacts, and protein/ligand torsion were computed to understand the conformational stability of the PDE6D ligand complex better. At the atomistic level, this study showed an intermolecular contact formation between (+)—Catechin (Cianidanol), Arbutin, Resveratrol, Sinapic acid, and the amino acid residues of PDE6D. The result suggests a hydrogen bond interaction with TYR 149, ILE 109, CYS 56, GLN 78, ARG 61, ALA 112, GLU 110, SER 115, and GLN 116 during the simulation (Fig. 4).

Fig. 4.

Fig. 4

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Structural view of ligand atom interactions with the protein residues: A (+)—catechin (cianidanol), B arbutin, C resveratrol, D sinapic acid

In this study, the RMSD values for all four ligands are stable and < 4 Å during the 100 ns simulation trajectories. (+)—Catechin (Cianidanol) displayed a stable value of 0.4–1.6 Å at 0 ns to 75 ns, followed by a slight elevation which became stable at 85 ns to 100 ns. Arbutin revealed stability from 0.45 to 2.8 Å from 0 to 100 ns with a short-lived fluctuation to 3.3 Å (Fig. 5). Resveratrol displays from 0.45 to 1.8 Å from 0 to 25 ns, fluctuated and became stable at 1–1.65 Å from 25 to 100 ns. Sinapic acid shows stability from 1.2 to 2.4 Å from 20 to 100 ns. Essentially, the four ligands showed better RMSD values than Sotorasib, as they were more stable and had lesser fluctuations. The Cα atoms in the PDE6D docked with the ligands in this study showed a mean deviation < 4 Å which is acceptable for small globular proteins.

Fig. 5.

Fig. 5

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Protein–Ligand RMSD of the compounds selected for molecular dynamics simulation. A (+)—catechin (cianidanol), B arbutin, C sinapic acid, D sotorasib, E resveratrol

The structural changes that occurred per complex because of ligand binding were further investigated. Thus, the root means square fluctuation (RMSF) was adopted to calculate the residues' dynamic mobility structures following docking. According to the RMSF trajectory plot (Fig. 6), the protein amino acid residues of the docked complexes are moderately similar in their fluctuation pattern during the simulation. On this plot, peaks indicate areas of the protein that fluctuate the most during the simulation. Particularly, the tails of protein chains (i.e., N- and C-terminal) tend to fluctuate more while observing overall protein parts. In contrast, the secondary structure elements, which include alpha helices and beta strands, fluctuate less than the loop regions because they are more rigid than the unstructured parts of the protein.

Fig. 6.

Fig. 6

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Protein–Ligand RMSF of the compounds selected for molecular dynamics simulation. A (+)—catechin (cianidanol), B arbutin, C resveratrol, D sinapic acid, E sotorasib

The stability of docked PDE6D-ligand complexes was investigated to determine intermolecular protein–ligand contacts, which are hydrogen bonds, ionic interactions, hydrophobic contacts, and water bridges (Fig. 7). Interestingly, during the simulation, selected docked ligands exhibited good interactions with amino acid residues in the selective pocket of the PDE6D crystal structure.

Fig. 7.

Fig. 7

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Stability of docked 4JV8-ligand complexes for hydrogen bonds, ionic interactions, hydrophobic contacts, and water bridges

ADMET prediction and drug-likeness

The SwissADME web server (http://www.swissadme.ch) was used to predict the drug-likeness of the selected compounds following the Lipinski rule of five. The rule states that a compound must not violate > 2 rules to be considered an oral bioavailable drug (Bakchi et al. 2022). The rules include Molecular Weight (MW) ≤ 500, Hydrogen Bond Donor (HBD) ≤ 5, Hydrogen Bond Acceptor (HBA) ≤ 10, Lipophilicity (iLOGP) ≤ 5, and Molar Refractivity (MR) between 40 and 130 (Lipinski 2004). This is reflected in the top compounds: (+)—Catechin, Arbutin, Resveratrol, and Sinapic acid (Table 2). A more detailed table that contains the predicted Drug likeness properties of all the identified compounds is shown in Supplementary Table S2. Drug bioavailability measures how effectively a drug is absorbed and what portion of the administered dose reaches the bloodstream unchanged (Indu et al. 2020). The bioavailability can differ between oral and intravenous administration due to factors like first-pass drug metabolism. To achieve a significant pharmacological response and reach an optimal concentration in the bloodstream, a drug typically needs a higher bioavailability score. In this study, the top compounds were predicted to have bioavailability scores of around 0.55 to slightly above 0.56, which falls within the desired range (Kehinde et al. 2019).

Table 2.

Drug-likeness prediction of top-ranked compounds

Compound MW (g/mol) HBD HBA iLOGP MR TPSA (Å2) BA Score Pains Lipinski violation
(+)—Catechin (cianidanol) 290.27 5 6 1.33 74.33 110.38 0.55 1 0
Arbutin 272.25 5 7 1.64 62.61 119.61 0.55 0 0
Resveratrol 228.24 3 3 1.71 67.88 60.69 0.55 0 0
Sinapic acid 224.21 2 5 1.63 58.12 75.99 0.56 0 0
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In this study, we examined the ADME properties (Absorption, Distribution, Metabolism, and Excretion) of the chosen compounds to predict their pharmacokinetic potential as bioactive molecules. The ADME analysis (Fig. 8) indicated that all the primary compounds except Arbutin derived from Vaccinium vitis-idaea L. in this study are readily absorbed by the body. Furthermore, all compound, excluding the standard drug, are non-substrates of P-glycoprotein, a cellular transporter that removes drugs (Kumar et al. 2023). However, except for the standard, all compounds are predicted to act as non-inhibitors of P-glycoprotein.

Fig. 8.

Fig. 8

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ADMET profile of top compounds and standard

Regarding distribution analysis, only Arbutin and the standard drug exhibited the ability to permeate the blood–brain barrier, indicating their potential to enter the brain. In terms of metabolism, it was observed that none of the compounds inhibited CYP2D6, a critical liver enzyme system. However, except Resveratrol and the standard, all compounds were predicted as non-inhibitors of CYP3A4.

Finally, the assessment of toxicity revealed that, apart from the standard drug, all compounds were determined to be non-carcinogenic, non-harmful to the human liver except the standard, Sotorasib.

Conclusion

In summary, this study explored the potential use of bioactive compounds from Vaccinium vitis-idaea L. as drugs against PDE6D in the KRAS-PDE6D complex found in human epithelial lung cancer cells. Various aspects of these compounds, including their binding affinity, MM/GBSA analysis, protein–ligand interactions, pharmacokinetic properties, and drug-likeness, were evaluated and compared to the FDA-approved drug Sotorasib.

After conducting docking studies, it was observed that 27 out of 36 bioactive compounds exhibited higher binding affinities to the target receptor compared to Sotorasib. Among these compounds, Arbutin, (+)—Catechin, and Sinapic acid emerged as promising candidates from Vaccinium vitis-idaea L. for the treatment of lung cancer, particularly those associated with KRAS mutations. However, further in vivo and in vitro studies are necessary to validate the effectiveness of these selected compounds in combating lung cancer linked to KRAS mutations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (DOCX 19 KB) (19.3KB, docx)

Acknowledgements

We appreciate Dotun Olaoye’s helpful input during the manuscript preparation.

Abbreviations

KRAS

Kirsten rat sarcoma viral oncogene homolog

PDE6D

Phosphodiesterase 6δ

RMSD

Root mean square deviation

RMSF

Root mean square fluctuation

MW

Molecular weight

HBD

Hydrogen bond donor (HBD)

HBA

Hydrogen bond acceptor (HBA)

iLOGP

Lipophilicity (iLOGP)

MR

Molar refractivity

Author contributions

GD and TAB designed the study. AI, SS, DSB, and BA performed virtual screening and chemical library curation. AI, GD, SS, DSB, and TAB performed molecular docking, pharmacokinetic analysis, molecular dynamics simulation, and post-simulation analysis. AI wrote the first draft. GD and TAB critically revised the manuscript. All authors read and approved the final manuscript.

Funding

This study received no funding.

Availability of data and materials

Available upon request.

Declarations

Conflict of interest

The authors declare no conflict of interest.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ayooluwa Ilesanmi and Gbenga Dairo: First author.

Contributor Information

Gbenga Dairo, Email: gs-dairo@wiu.edu.

Toheeb A. Balogun, Email: tbalogun@ucsd.edu

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