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Virtual screening, pharmacokinetics, and molecular dynamics simulations studies to identify potent approved drugs for Chlamydia trachomatis treatment

Abstract

Background

The most frequent bacterial sexually transmitted disease is Chlamydia trachomatis (STD). In 2010, the Centers for Disease Control and Prevention (CDC) received 1.3 million reports of cases (CDC). Human chlamydial infections are linked to a variety of clinical symptoms. Inclusion (IncA) membranes are a promising drug target for the treatment of Chlamydia trachomatis. In the present study, molecular docking, ADMET, golden triangle, and molecular dynamics (MD) simulation studies were performed on a series of salicylidene acylhydrazides derivatives against Chlamydia trachomatis. Three types of docking software with different algorithms were used to screen the potential candidate against Chlamydia trachomatis.

Results

The results obtained from the docking analysis succeeded in screening nine novel hit compounds with high affinity to IncA membranes. Then, pharmacokinetics properties were calculated to spot out the drug-likeness of the selected compounds. Also, golden triangles were performed on the selected compounds. Compounds outside the golden triangle indicate that they would have clearance problems. Out of the nine novel hits drugs, four compounds pass the golden triangle screening and virtually all the quality assurance tests proposed by the model and were used for further analysis. One-ns molecular dynamics simulations on the docked complex of compound 44 (one of the highly active selected compounds of the dataset) aided in the further exploration of the binding interactions. Some crucial residues such as Ser111, Gln114, Asn107, Leu142, Gly144, Gln143, Lys104, Tyr149, Phe108, Phe145, and Arg146 were identified. Conventional and carbon–hydrogen bond interactions with amino residues Arg146, Asn107, Phe145, and Ser111 were critical for the binding of inclusion (IncA) membranes inhibitors.

Conclusion

Outcomes of the study can further be exploited to develop potent inclusion (IncA) membranes inhibitors.

Background

Chlamydia trachomatis is the world’s most prevalent cause of bacterial sexually transmitted infection and infectious blindness in endemic locations, primarily in Africa and the Middle East infecting approximately a hundred million people each year [1, 2]. Chlamydia trachomatis is a parasite that lives only inside cells. It goes through a replicative developmental growth cycle inside the cells of its host, which leads to cell death [3]. According to the World Health Organization, 92 million new instances of Chlamydia trachomatis infection occur each year [4]. In the USA, it is estimated that over 3 million chlamydial infections occur each year among sexually active adolescents and adults [5, 6]. From 1993 to 2011, the number of chlamydial infections reported to the Centers for Disease Control and Prevention (CDC) increased from 178 to 453.4 cases per 100,000 people [7]. Human illnesses caused by Chlamydia trachomatis include cervicitis, salpingitis, acute urethral syndrome, endometritis, ectopic pregnancy, infertility, and pelvic inflammatory disease (PID) in women; conjunctivitis and pneumonia in neonates; and urethritis, proctitis, and epididymitis in males [8]. Traditional and orthodox treatment of some existing drugs is ineffective and unable to stop the process of a fasting-growing parasitic organelle inside the host cell called "Inclusion," which is poorly understood [9, 10]. The development of novel potential therapies for the treatment of Chlamydia trachomatis represents an important means of extending life span and improving quality of life [11, 12]. Antimicrobial resistance has been listed by the World Health Organization (WHO) as one of the three most serious threats to human health and a severe problem in many parts of the world [13]. Chlamydia trachomatis a Gram-negative bacterium stands as proof of high-level resistance to most categories of antibiotics [14]. Infections caused by multidrug-resistant Chlamydia trachomatis are on the rise in hospitals, especially in the intensive care unit (ICU), and are linked to greater expenses, increased morbidity, and high fatality rates [15]. Increased patient contact with health care personnel, increased severity of illness, overuse of existing antimicrobial agents, underlying conditions, exposure to multiple invasive devices and procedures, and crowding of patients in a small specialized area are all factors that contribute to an increased risk of infection in intensive care unit patients [16]. As a result, finding new strong, safer, and less expensive Chlamydia trachomatis or bacterial inhibitors has become the greatest problem the human race has ever faced. For this reason, the development of new drugs able to fight Chlamydia trachomatis is still in great interest.

Previous studies on anti-Chlamydia trachomatis compounds have been published; one is on the synthesis of substituted ring-fused 2-pyridines and evaluation for the ability to attenuate Chlamydia trachomatis infectivity [17]. In 2014, Abdelsayed and his colleagues design and synthesize a 3-isoxazolidone derivative and also evaluate the paths by which these new compounds affect Chlamydia trachomatis serovar L2 development in HeLa cells, in the presence or absence of exogenously added iron [1]. Edache and his co-workers conducted quantitative structure–activity relationship (2D-QSAR), comparative molecular field analysis (CoMFA), and molecular dynamics simulations on novel thiazoline 2-pyridone amide derivatives [2]. In recent years, the use of molecular modeling has produced very impressive results in the drug discovery process [18].

In this place, we report the molecular modeling of salicylidene acylhydrazides derivatives to continue our ongoing research on Chlamydia trachomatis inhibitor molecular modeling. The docking simulation, molecular dynamics simulations, and ADMET filtering were applied to detect new potential candidates for Chlamydia trachomatis treatment.

Methods

In the present study, we have selected 58 salicylidene acylhydrazides derivatives from the PubChem database (www.ncbi.nlm.nih.gov/pubchem) with accession number AID 473049/473050 that have been already validated to have antimicrobial activity. All compounds were tested using the same experimental procedure, and a wide range of Chlamydia trachomatis inhibition activities were covered. Structures of the selected prevailing forms were optimized with the semiempirical PM3 method using the Spartan’14 software (www.wavefun.com) (Table 1). The optimized structures were saved for further analysis.

Table 1 PubChem_CID number and corresponding activity pIC50 of salicylidene acylhydrazides derivatives

Virtual screening using multiple molecular docking tools

The strength of association or binding affinity between two molecules can be predicted by molecular docking techniques [19]. Molecular docking simulations were calculated from three different algorithms; therefore, it is assumed that the value represents a real condition. We chose 58 phytochemical substances that have been considered to have potential antibacterial activity for this study. The three-dimensional crystal structure of the IncA soluble domain was retrieved from RCSB (www.rcsb.org/pdb) protein data bank (PDB Code: 6e7e). The whole structure of the receptors was targeted for our molecular docking study except for heteroatoms that were detected from the receptors. Primary docking analysis was performed using iGemdock [20], which uses empirical scoring function and Generic Evolutionary Method for molecular docking. It comes with a graphical user interface that detects pharmacological interactions and does virtual screening. The following parameters for docking have been chosen: population size = 200, number of generations = 70, and number of solutions = 3. To further increase the docking accuracy, by using a more complex scoring function, Molegro Virtual Docker (MVD) was used [21]. The imported structures must be properly prepared, that is, the atom connectivity and bond orders are correct, and partial atomic charges are assigned. PDB files often have the poor or missing assignment of explicit hydrogen, and the PDB file format cannot accommodate bond order information. The utilities included in MVD were used to do all necessary valency tests and H-atom additions. The binding location is the area where the docking procedure will look for promising poses. The docking simulation results were then validated through AutoDock Vina [22] (PyRx software) with the following setup: exhaustiveness = 8; center_x = − 2.3621; center_y = 17.7247; center_z = 36.6066; size_x = 71.1413702774; size_y = 32.6226404202; size_z = 50.6705288601, and the interaction of all the docking approach was visualized through Discovery studio software.

ADMET, drug-likeness evaluation

Candidates for drugs should have positive ADME qualities and, ideally, be non-toxic. Using the DataWarrior software, the developed compounds were tested for their ADME profile, which included drug-likeness, partition coefficient, solubility, and several other metrics. In addition, the toxicity of the chosen drug was predicted.

Molecular dynamic (MD) simulation

The chosen complex was then simulated for 1 ns with VMD-NAMD software [23, 24]. To construct receptor molecular topology and configuration files, the Charmm-gui Web site was utilized. By adding 0.15 M KCl to the simulated system, the system was neutralized. TIP3PBOX, a water molecules box, was then submerged in the neutralized system. Each system was equilibrated with NVT (constant number of particles, volume, and temperature) as well as NPT (constant number of particles, pressure, and temperature) before the production phase to guarantee an even distribution of solvent and ions surrounding the protein–ligand complex.

Results

Docking evaluations

Tables 2 and 3 reveal the numerical value of the total energy, van der Waals, hydrogen bond, and electrostatic of the standard drugs and the 58 salicylidene acylhydrazides derivatives with iGemDock v2.1 software. The docking interactions of the standard drugs and the best interacting ligand are reflected in Figs. 1 and 2.

Table 2 The results of the reference drugs with iGemDock v2.1
Table 3 The results of the 58 salicylidene acylhydrazides derivatives with iGemDock v2.1
Fig. 1
figure 1

Interactions of the reference drugs: (A) ampicillin, (B) oxytetracycline, (C) chlortetracycline, and (D) ceftriaxone with the crystal structure of IncA soluble domain

Fig. 2
figure 2

Interaction of compound 44 with the crystal structure of IncA soluble domain

With Molegro Virtual Docker (MVD) and PyRx software, all the standard drugs and ligands were docked with the protein structure and the best-docked molecules were listed depending on their MolDock score, Rerank score, hydrogen bond, and binding affinity as given in Tables 4 and 5. Figures 3 and 4 show detailed interaction of the standard drugs and the best four ligands with the MVD program, while Figs. 5 and 6 give in detail the docking pose of the reference or standard drugs and the four best ligands with PyRx software, respectively.

Table 4 The results of the standard drugs with MVD software
Table 5 The MolDock scores and AutoDock Vina binding affinity of the docked compounds
Fig. 3
figure 3

2D diagram of reference drugs and the crystal structure of IncA soluble domain interactions

Fig. 4
figure 4

2D diagram of ligands and the crystal structure of IncA soluble domain interactions

Fig. 5
figure 5

Docking pose of the reference drugs into the active site of IncA membranes receptor

Fig. 6
figure 6

Docking pose of the ligands into the active site of IncA membranes receptor

Pharmacokinetic evaluation

Lipinski’s rule of 5 and Veber’s rule of 2 of both the standard drugs and the selected compounds are reflected in Table 6. Table 7 shows the lipophilicity of the selected drugs, while Table 8 presents the toxicity risk factors of the standard drugs and the selected compounds.

Table 6 Physicochemical properties of selected compounds in comparison with standard drugs
Table 7 Lipophilicity analysis of the selected compounds in comparison with standard drugs
Table 8 Toxicity risk of selected compounds in comparison with standard drugs

Golden triangle and MD simulations

Figure 8A–C shows the variation in kinetic energy, total energy, potential energy versus time in 1-ns MD simulations, while Fig. 8D shows the binding interaction of the simulated complex.

Discussion

Docking screening of compounds with iGemdock software

Four anti-Chlamydia trachomatis drugs—ampicillin, oxytetracycline, chlortetracycline, and ceftriaxone, were used as a control for the following study. iGemdock calculated the total energy, van der Waals energy, electrostatic, and hydrogen bond of the four standard drugs when docking them into the enzymes. K-means and hierarchal clustering approaches are used by iGemdock's post-analysis tools. Table 2 shows the summary of docking results, and detailed interactions with the enzyme are shown in Fig. 1. Binding energies of the receptor–ligand interactions are very important to report how fit the drug binds to the target macromolecule. It can be calculated that according to iGemdock ampicillin is the best binding receptor because of its total energy of − 92.5875 kcal/mol. A higher fitness score suggests better docking interaction between protein and ligand and will be acceptable of the compound as a drug.

The total energy of compounds 4, 38, 43, 44, and 48 was found to be − 78.3402, − 76.561, − 78.9421, − 87.3827, and − 77.9572 kcal/mol, respectively (Table 3). From the results, a good number of standards (Table 2) displayed activity comparable to those selected compounds. Compound 44 has the best score and the most outstanding inhibitory activity from the selected ligands, amino acids involved in its van der Waals interaction with receptor molecules are His249, His250, His253, Ser154, Thr238, Arg245, and Lys246, while those in hydrophobic interactions include Ile241 and Ala242 and one pi-donor hydrogen bond with His249 (Fig. 2).

Docking screening of compounds with Molegro Virtual Docker (MVD)

The standards docked drugs were listed depending on their MolDock score as given in Table 4, and the nature of interaction with the protein structure is shown in Fig. 3. The best pose of the docked molecules was analyzed based on the lowest MolDock score. Docking results showed that ceftriaxone was best docked with the protein structure. Ceftriaxone with the protein was found to be the most active with the best MolDock value as (− 124.659) as compared to all the other poses as shown in Table 4. The result showed that ceftriaxone perfectly binds with the active regions of the target protein by forming hydrophobic interactions with Gln114, Lys115, and Phe108 as shown in Fig. 3. Oxytetracycline makes other side interactions (unfavorable bump and unfavorable donor–donor) with Gly144, Phe145, and Ser111 amino residue, as such can lead to side effects.

Accordingly, all the 58 salicylidene acylhydrazides derivatives were docked into the binding pocket of the protein. The MolDock score, rerank score, and hydrogen bond values of the five most active compounds from the 58 datasets are provided in Table 5. It was observed (Table 4) that ceftriaxone, in particular, showed a superior binding affinity with a MolDock score of -124.659, in comparison with compounds 16, 23, 32, 44, and 51 with a MolDock score of -102.325, -105.42, -103.832, -101.187, and -104.935, respectively. Hydrogen bond, electrostatic, and hydrophobic interactions of compounds 16, 23, 32, and 51 with the protein are illustrated in Fig. 4. Likewise, docking pose analysis of four best-performing derivatives (compounds 16, 23, 32, and 51) showed the similarity of binding mode compared to the standard drugs. The similarity is observed in the urea moiety of the compounds, which is with Lys160, Asn161, Ser162, Glu158, Met166, Glu248, His252, His251, His249, Thr163, Arg245, and Asp157, respectively. The MolDock score of compounds 16, 23, 32, 44, and 51 is far better than the rest of the standard drugs (ampicillin, oxytetracycline, and chlortetracycline).

Docking screening of compounds with AutoDock Vina (PyRx software)

Based on AutoDock Vina (Table 5), out of 58 compounds used in the present study, compounds 36, 37, and 39 exhibited the lowest binding affinity with the enzyme (-6.9 kcal/mol) active sites, followed by compounds 44 and 50 (− 6.8 kcal/mol) and compounds 11 and 20 (− 6.7 kcal/mol), respectively. Compounds 36, 37, and 39 display the highest docking scores when compared with the standard drugs (Table 4); furthermore, chlortetracycline (standard drug) showed the highest binding energy of − 6.8 kcal/mol and exhibited two binding interactions;:van der Waals interaction with His249, His252, Thr163, Glu248, and Tyr159 and four hydrogen-bonding interactions with Glu158, Arg245, Asp157, and Asn161 (Fig. 5). Ampicillin, oxytetracycline, and ceftriaxone showed binding affinities of − 6.0, − 6.5, and − 6.1 kcal/mol, respectively. These compounds were bound at the van der Waals, conventional and carbon–hydrogen bond, ampicillin has one hydrophobic interaction with Lys235, and ceftriaxone has unfavorable donor–donor with Arg146 as shown in Fig. 5.

The selected compounds (36, 37, 39, and 44) all have a higher binding affinity than the standard drugs except compounds 44 and 50 that have their binding energy equal to chlortetracycline of the standard drug. Compounds 36 and 39 were found to have the highest binding affinity as such are more compatible with the receptor than compounds 37 and 44 with unfavorable bumps (Fig. 6). As illustrated in Fig. 6, there are a variety of interactions between these chemicals, and the target protein revealed that compound 36 interacts with one hydrogen bond with Glu215 and one carbon–hydrogen bond with Phe191. In addition to the binding interactions of compound 36 are hydrophobic (Ile219, Phe191, and Pro194), van der Waals (Thr218, Leu195, and Glu198), and Halogen (Glu188) interaction between amino acid residues, respectively. Compound 39 has three conventional hydrogen bonds with His204, Arg200, and Glu197, one carbon–hydrogen bond with Phe124, and hydrophobic interactions with Arg201, His204, His204, Arg200, and Phe124, respectively.

It was concluded from the docking simulation that compound 36 and compound 39 were strongly bonded through van der Waals, hydrophobic, and conventional hydrogen bond interactions and stabilized into the active site of the target protein.

Oral bioavailability analysis of the selected compounds and standards

These findings prompted us to evaluate the physicochemical properties of our selected lead compounds from the docking tools, one from iGemDock (compound 44), five from MVD (compounds 16, 23, 32, 44, and 51), and five from AutoDock Vina with PyRx (compounds 36, 37, 39, 44, and 50), respectively (Table 6), to identify compounds with optimal oral absorption properties and to guide future structural modifications to improve ADMET properties. The oral bioavailability analysis of selected compounds was predicted using DataWarrior v5.5.0 and is presented in Table 6; following Lipinski's rule of five [25] and Veber’s rules of two [26], any compound violating more than one of these rules could have problems related to its bioavailability. From the table (Table 6), all the compounds can easily pass through the cell membrane since their molecular weight is less than 500 Da, except chlortetracycline that has a molecular weight of more than 500 Da. If the size increases, it will create barriers such as the prevention of passive diffusion through the tight aliphatic side chains of the bilayer membrane [27]. According to the Lipinski rule, cLogP should be less than or equal to 5, all the selected compounds have positive cLogP (affinity for lipids), so they have a good permeability through a biological membrane. Compound 36 is more than the threshold value of 5, while the standard drugs have a negative value of cLogP, which means they are hydrophilic (strong affinity for water) [28]. A drug molecule is expected to be in an aqueous solubility, range of − 1 to − 5 [29], and the cLogS values of all the selected compounds and standards fall within the range, indicating that the compounds have good absorption and distribution potential except compound 36 with cLogS value of − 6.523. The number of hydrogen bond donors and acceptors of the selected compounds conforms to the rule of 5 (such as H–A ≤ 10; H–D ≤ 5); this shows that the compounds can influence absorption and permeation and also tend to have an increased chance of reaching the market except for the standards (Std2, Std3, and Std4) as shown in Table 6. The two descriptors (1) rotatable bonds ≤ 10 and (2) total polar surface area ≤ 140 Å2 are identified as Veber’s rule [26] concerning the oral bioavailability of the drug. Compound 44 and the standards (Std2, Std3, and Std4) violate the total polar surface area as shown in Table 6 with their values more than the standard range.

Drug-likeness and lipophilicity analysis of the selected compounds and standards

From Table 7, all the selected compounds having their ligand efficiency (LE) values greater than 0.3 (LE recommended values ≥ 3) are qualified as a hit, while compound 23 is the most potent of all the selected compounds. For other bioactivity parameters such as ligand lipophilicity efficiency (LLE) and ligand-efficiency-dependent lipophilicity (LELP), their recommended values for a hit are ≥ 5 and − 10 to 10, respectively [30,31,32]. The LLE values were observed for all the selected compounds, and compounds 16, 44, and 51 are within the recommended range. All the selected compounds obey (LELP) recommended value except compound 36 and compound 37 with LELP values of 20.177 and 14.208, respectively (Table 7).

Toxicity analysis of the selected compounds and standards

The toxicity, total surface area, and relative polar surface area analysis of the selected compounds was performed using the DataWarrior package. All the selected compounds have no toxicity risk, except compound 16 which has high mutagenic and low irritant, while compound 32 toxicity risk factors were high in tumorigenic, reproductive effects, and irritant (Table 8).

The overall theoretical ADMET properties justify that compounds 23, 44, and 51 showed potential lead-like features and can be used for further assessment for Chlamydia trachomatis treatment.

Clearances and computational data designed

The golden triangle [33] is a visualization tool that helps medicinal chemists find metabolically stable, permeable, and potent drug candidates by combining in vitro permeability, in vitro clearance, and computational data. Moving the design properties into a region with a baseline of clogP = − 2 to 5 at MW = 200 and a peak of 450 gives a triangular shape termed the golden triangle, which increases the probability of success in maximizing potency, stability, and permeability. For our selected compounds and standard drugs, it is apparent that compounds 16, 23, 44, and 51 have good permeability and low clearance because they are concentrated within the golden triangle area (Fig. 7). These compounds (16, 23, 44, 51) have the highest probability of success in maximizing potency, stability, and permeability.

Fig. 7
figure 7

Golden triangle of the reference drugs and the selected compounds

Because they are concentrated within the golden triangle area, compounds 16, 23, 44, and 51 have strong permeability and low clearance for our selected compounds and standard medications (Fig. 7).

Molecular dynamics simulations

The molecular dynamics simulations are widely used to investigate the behavioral and dynamical characteristics of the protein–ligand complex. The MD simulations of the proposed docked ligand were explored for 1 ns. Several parameters such as kinetic energy, total energy, and potential energy were obtained from the MD simulation trajectory. Each of the parameters explains the stability of the complex in dynamics states. Compound 44 came first with iGemdock with a total energy of − 87.383, fifth with MVD with MolDock score of − -101.325, and second with AutoDock Vina with PyRx with a binding affinity value of − 6.8 kcal/mol. These compounds also fit the Lipinski rule of 5 and also Veber rules. Even though compound 44 did not fit with one criterion in Veber’s rules (TPSA), it has no mutagenic, tumorigenic, irritant, and developmental toxicity properties. It also passes lipophilicity indices (LE, LLE, and LELP) [34] and also falls within the golden triangle that was used for 1-ns MD simulations. Figure 8A shows the variation in kinetic energy versus time in 1 ns of MD simulations. It shows a fluctuation at about 0.89 ns of the MD simulations. In Fig. 8B and C, the total energy and the potential energy versus time in 1-ns MD simulations show a fluctuation at about 0.8-ns MD simulations. After 500,000 runs (1 ns) using the NAMD package, the kinetic, total, and potential energy values are 51,926.64, − 234,493.11, and − 286,419.79 kcal/mol, respectively. These proofs show that energy conservation was satisfied in MD simulations. The binding site residues of compound 44 predicted by MD simulations yield 11 amino acid residues in the binding pocket, namely Ser111, Gln114, Asn107, Leu142, Gly144, Gln143, Lys104, Tyr149, Phe108, Phe145, and Arg146 (Fig. 8D). After a close look at compound 44 complex during MD simulations with the initial molecular docking analysis (Fig. 6), the simulated compound 44 complex adopts a similar binding pocket with the docking complex. The conventional hydrogen bonds, the van der Waals interactions, and the carbon–hydrogen bond observed in the docking complex were monitored. It was observed that the binding interactions of the docking complex (Fig. 6) were reproduced in MD simulations (Fig. 8D). Compound 44 showed four conventional hydrogen bonds, two with Arg146, one each with Asn107 and Phe145, respectively, and one carbon–hydrogen bond with Ser111. The complex stabilizes its interactions with the binding pocket forming van der Waals interactions with the non-polar surfaces of the active site receptor with the amino acid residues (e.g., Gln114, Gln143, Leu142, Gly144, Lys104, Tyr149, and Phe108).

Fig. 8
figure 8

Interaction of the simulated complex

Conclusions

Herein, molecular docking, ADMET, golden triangle, and MD simulation studies were performed on a series of salicylidene acylhydrazides derivatives against Chlamydia trachomatis inhibitors. Three types of powerful docking software (namely MVD, iGemDock, and PyRx) with different algorithms were used to screen the most potent compounds. Docking studies confirm the potential of the nine compounds as novel inhibitors of the Chlamydia trachomatis receptor. Besides that, investigated compounds were subjected to the drug-likeness evaluation and golden triangle analysis. The findings revealed that the majority of the compounds would not be orally bioavailable rather would have clearance issues. Compound 44 passes all the quality assurance tests proposed by the model. For further investigation of binding modes of compound 44 and to explain the effects of the compound binding on protein conformation, 1-ns MD simulations were executed. At the end of the MD simulations, a similar position and orientation of the compound in the binding pocket were observed. This significant finding indicated that using MD simulations following ligand docking was beneficial. The nine anticipated salicylidene acylhydrazides inhibitors were predicted through computational techniques; hence, additional experimental validation would bring fresh insight on these compounds toward designing inhibitory drug molecules against common pathogens of Chlamydia trachomatis.

Availability of data and materials

Not applicable.

Abbreviations

STD:

Sexually transmitted disease

CDC:

Centers for Disease Control and Prevention

MD:

Molecular dynamics

VMD:

Van der Waals

WHO:

World Health Organization

ADMET:

Absorption, distribution, metabolism, elimination, and toxicity

MW:

Molecular weight

cLogP:

Calculated LogP

sLogS:

Solubility

H–A:

Hydrogen bond acceptor

H–D:

Hydrogen bond donor

TPSA:

Total polar surface area

R-Bond:

Rotatable bond

DL:

Drug-likeness

LE:

Ligand efficiency

LLE:

Ligand lipophilic efficiency

LELP:

Ligand efficiency-dependent lipophilicity

S-index:

Shape index

MolFex:

Molecular flexibility

Mol.com:

Molecular complexity

Rep-Eff:

Reproductive effective

TSA:

Total surface area

RPSA:

Relative polar surface area

Std1:

Ampicillin

Std2:

Oxytetracycline

Std3:

Chlortetracycline

Std4:

Ceftriaxone

References

  1. Abdelsayed S, Ha Duong NT, Hai J, Hémadi M, El Hage Chahine JM, Verbeke P, Serradji N (2014) Design and synthesis of 3-isoxazolidone derivatives as new Chlamydia trachomatis inhibitors. Bioorg Med Chem Lett 24:3854–3860. https://doi.org/10.1016/j.bmcl.2014.06.056

    CAS  Article  PubMed  Google Scholar 

  2. Edache EI, Uzairu A, Mamza PA, Shallangwa GA (2020) Computational modeling and molecular dynamics simulations of thiazolino 2-pyridone amide analog compounds as Chlamydia trachomatis inhibitor. J Chem Lett 1:123–138

    Google Scholar 

  3. Gaydos C, Essig A (2015) Chlamydiaceae. In: Jorgensen JH, Carroll KC, Funke G, Pfaller MA, Landry ML, Richter SS, Warnock DW (eds) Manual of clinical microbiology, 11th edn. John Wiley & Sons, Inc., pp 1106–1121

    Chapter  Google Scholar 

  4. WHO (2001) Global prevalence and incidence of selected curable sexually transmitted infections: overview and estimates. World Health Organization, Geneva

    Google Scholar 

  5. Workowski KA, Berman SM (2011) Centers for disease control and prevention sexually transmitted disease treatment guidelines. Clin Infect Dis 53(suppl_3):S59–S63

    Article  Google Scholar 

  6. Cordova D, Mendoza Lua F, Muñoz-Velázquez J, Street K, Bauermeister JA, Fessler K et al (2019) A multilevel mHealth drug abuse and STI/HIV preventive intervention for clinic settings in the United States: a feasibility and acceptability study. PLoS ONE 14(8):e0221508

    CAS  Article  Google Scholar 

  7. CDC (2011) Sexually transmitted disease surveillance, centers for disease control and prevention. US Department of Health and Human Services, Atlanta, pp 1–155

    Google Scholar 

  8. Longo DL, Fauci AS, Kasper DL, Hauser SL, Jameson JL, Loscalzo J (2014) Harrison’s principles of internal medicine, 19th edn. McGraw-Hill Medical, New York

    Google Scholar 

  9. Lutter E, Martens C, Hackstadt T (2012) Evolution and conservation of predicted inclusion membrane proteins in chlamydiae. Comp Funct Genom 2012:362104

    Article  Google Scholar 

  10. Cingolani G, McCauley M, Lobley A, Bryer AJ, Wesolowski J, Greco DL et al (2019) Structural basis for the homotypic fusion of chlamydial inclusions by the SNARE-like protein IncA. Nat Commun 10(1):1–12. https://doi.org/10.1038/s41467-019-10806-9

    CAS  Article  Google Scholar 

  11. Madonna R, Novo G, Balistreri CR (2016) Cellular and molecular basis of the imbalance between vascular damage and repair in aging and age-related diseases: as biomarkers and targets for new treatments. Mech Ageing Dev 159:22–30. https://doi.org/10.1016/j.mad.2016.03.005

    CAS  Article  PubMed  Google Scholar 

  12. Baig MH, Ahmad K, Rabbani G, Danishuddin M, Choi I (2018) Computer-aided drug design and its application to the development of potential drugs for neurodegenerative disorders. Curr Neuropharmacol 16:740–748. https://doi.org/10.2174/1570159X15666171016163510

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. Bassetti M, Ginocchio F, Mikulska M (2011) New treatment options against gram-negative organisms. Crit Care 15:215. https://doi.org/10.1186/cc9997

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zabawa TP, Pucci MJ, Parr TR Jr, Lister T (2016) Treatment of gram-negative bacterial infections by potentiation of antibiotics. Curr Opin Microbiol 33:7–12

    CAS  Article  Google Scholar 

  15. Chelazzi C, Pettini E, Villa G, Gaudio ARD (2015) Epidemiology, associated factors and outcomes of ICU-acquired infections caused by gram-negative bacteria in critically ill patients: an observational and retrospective study. BMC Anesthesiol 15:125

    Article  Google Scholar 

  16. Ivády B, Kenesei É, Tóth-Heyn P, Kertész G, Tárkányi K, Kassa C et al (2016) Factors influencing antimicrobial resistance and outcome of Gram-negative bloodstream infections in children. Infection 44(3):309–321. https://doi.org/10.1007/s15010-015-0857-8

    CAS  Article  PubMed  Google Scholar 

  17. Kulén M, Núñez-Otero C, Cairns AG, Silver J, Lindgren AE, Wede E et al (2019) Methyl sulfonamide substituents improve the pharmacokinetic properties of bicyclic 2-pyridone based Chlamydia trachomatis inhibitors. MedChemComm 10(11):1966–1987. https://doi.org/10.1039/c9md00405j

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Hadni H, Elhallaoui M (2020) 2D and 3D-QSAR, molecular docking and ADMET properties in silico studies of azaaurones as antimalarial agents. New J Chem. https://doi.org/10.1039/c9nj05767f

    Article  Google Scholar 

  19. Edache EI, Uzairu A, Mamza PA, Shallangwa GA (2021) Docking Simulations and Virtual Screening to find Novel Ligands for T3S in Yersinia pseudotuberculosis YPIII, A drug target for type III secretion (T3S) in the Gram-negative pathogen Yersinia pseudotuberculosis. Chem Rev Lett 4(3):13–144

    Google Scholar 

  20. Hsu KC, Chen YF, Lin SR, Yang JM (2011) iGemDock: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis. BMC Bioinformatics 2(1):1–11

    Google Scholar 

  21. Kusumaningrum S, Budianto E, Kosela S, Sumaryono W, Juniarti F (2014) The molecular docking of 1,4-naphthoquinone derivatives as inhibitors of Polo-like kinase 1 using Molegro virtual docker. J App Pharm Sci 4(11):047–053

    Google Scholar 

  22. Trott O, Olson AJ (2009) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. https://doi.org/10.1002/jcc.21334

    CAS  Article  Google Scholar 

  23. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38

    CAS  Article  Google Scholar 

  24. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802

    CAS  Article  Google Scholar 

  25. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46(1–3):3–26

    CAS  Article  Google Scholar 

  26. Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45(12):2615–2623

    CAS  Article  Google Scholar 

  27. Fadel FZ, Tchouar N, Belaidi S, Soualmia F, Oukil O, Ouadah K (2021) Computational screening and QSAR study on a series of theophylline derivatives as aldh1a1 inhibitors. J Fundam Appl Sci 13(2):942–964. https://doi.org/10.4314/jfas.v13i2.17

    Article  Google Scholar 

  28. Edache EI, Uzairu A, Mamza PA, Shallangwa GA (2020) Molecular docking, molecular dynamics simulations, and ADME study to identify inhibitors of Crimean-Congo Hemorrhagic Fever (CCHF) viral ovarian tumor domain protease (vOTU). Chem Res J 5(5):16–30

    CAS  Google Scholar 

  29. Bergenhem N (2011) Preclinical candidate nomination and development. In: Tsaioun K, Kate SA (eds) ADMET for medicinal chemists. Wiley, Singapore, pp 399–415

    Google Scholar 

  30. Schultes S, De Graaf C, Haaksma EEJ, De Esch IJP, Leurs R, Kramer O (2010) Ligand efficiency as a guide in fragment hit selection and optimization. Drug Discov Today Technol 7:e157–e162. https://doi.org/10.1016/j.ddtec.2010.11.003

    CAS  Article  Google Scholar 

  31. Hopkins AL, Keserü GM, Leeson PD, Rees DC, Reynolds CH (2014) The role of ligand efficiency metrics in drug discovery. Nat Rev Drug Discov 13:105–121. https://doi.org/10.1038/nrd4163

    CAS  Article  PubMed  Google Scholar 

  32. Smida KB, Belaidi S, Benbrahim I, Boughdiri S (2015) Theoretical studies of structure/activity relationships applied to flavone derivate for drug discovery. Res J Pharm Biol Chem Sci 6:874–885

    Google Scholar 

  33. Johnson TW, Dress KR, Edwards M (2009) Using the golden triangle to optimize clearance and oral absorption. Bioorg Med Chem Lett 19:5560–5564. https://doi.org/10.1016/j.bmcl.2009.08.045

    CAS  Article  PubMed  Google Scholar 

  34. Keseru GM, Makara GM (2009) The influence of lead discovery strategies on the properties of drug candidates. Nat Rev Drug Discov 3:203–212. https://doi.org/10.1038/nrd2796

    CAS  Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the technical effort of Adawara Samuel Ndaghiya, Department of Pure and Applied Chemistry, University of Maiduguri, Borno State, Nigeria.

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EIE designed and wrote the manuscript, and AU, GAS, and PAM supervised and carried out the statistical analysis. All authors read and approved the final manuscript.

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Correspondence to Emmanuel Israel Edache.

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Edache, E.I., Uzairu, A., Shallangwa, G.A. et al. Virtual screening, pharmacokinetics, and molecular dynamics simulations studies to identify potent approved drugs for Chlamydia trachomatis treatment. Futur J Pharm Sci 7, 220 (2021). https://doi.org/10.1186/s43094-021-00367-4

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Keywords

  • Molecular docking
  • iGemDock
  • MVD
  • AutoDock Vina
  • ADMET
  • Golden triangle
  • Chlamydia trachomatis
  • Salicylidene acylhydrazides derivatives
  • MDs simulations