Computational identification of PDL1 inhibitors and their cytotoxic effects with silver and gold nanoparticles

doi:10.21203/rs.3.rs-4704476/v1

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Computational identification of PDL1 inhibitors and their cytotoxic effects with silver and gold nanoparticles

https://doi.org/10.21203/rs.3.rs-4704476/v1

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Background

Immunotherapy is a promising treatment for cancer that aims to boost the immune system's response to cancer cells. This can be achieved by blocking PD1(Programmed cell death 1)/PDL1(Programmed death-ligand 1), which activates T cells. In this work, the aim was to find high-affinity drugs against PDL1 using computational tools and conjugate them with nanoparticles. The cytotoxic activity of the drug-conjugated nanoparticles was then tested.

Methods

The screening of one hundred thousand drugs from the ZINC database and FDA-approved drugs was done computationally. The physicochemical properties and toxicity of the drugs were analyzed using SwissADME and ProTox-II respectively. AgNPs and AuNPs were synthesized using extracts of Catharanthus roseus flowers and Juglans regia shells, respectively. The characterization of AgNPs and AuNPs was performed using UV-Vis spectroscopy, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR). Their conjugation with the drugs Irinotecan, Imatinib, and Methotrexate was also confirmed using UV-Vis, FTIR, and Dynamic light scattering (DLS).

Results

The top screened drugs were ZINC1098661 and three FDA-approved drugs (Irinotecan, Imatinib, and Methotrexate). Docking studies revealed that Irinotecan had the highest binding affinity towards PDL1 when conjugated with silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs). The Irinotecan-PDL1 complex was confirmed as the most stable through molecular dynamics simulations. The result of methylthiazol tetrazolium (MTT) assay showed that conjugated AgNPs and AuNPs with Irinotecan had a high toxic effect on A549 cancer cell line than Imatinib conjugated with AgNPs and AuNPs.

Conclusion:

This study provides a promising avenue for further investigation and development of nanoparticle-drug conjugates as a potential cancer immunotherapy strategy.

Immunotherapy

T cells activation

PD1/PDL1 immune checkpoint

Nanoparticles

Molecular Dynamics Simulations

MTT assay

Cancer is a devastating disease, due to which millions of deaths occur every year worldwide [1]. According to the statistical report by the World Health Organization (WHO), 10 million people died from cancer in 2020, and the most occurring cancer was breast and lung cancer [2]. In the treatment of cancer, chemotherapy is one of the most adopted treatments. However, it has several limitations such as drug resistance issues [3], as well as side effects such as hair loss, constipation [4], and adverse effect on normal cells [5].

Recently, immunotherapy has emerged as a more advanced and promising approach to cancer treatment [6]. In immunotherapy, the immune system is enhanced by modification in immune cells to target cancer cells that are evaded by the immune system [7]. Currently, research is also going on cancer immunotherapy using nanomedicine such as nanoparticles to treat cancer by targeting delivery [8]. Moreover, targeting the PD1/PDL1 immune checkpoint is a widely studied cancer immunotherapy [9]. PDL1, which is also known as CD279 and B7-H1 is a membrane protein that is considered as cancer biomarker. The extracellular region of PDL1 consists of IgV and IgC domains. It belongs to the B7 series and has a 33-kDa type 1 transmembrane glycoprotein with 290 amino acids [10]. There is an overexpression of the PDL1 receptor on cancer cells which binds with PD1. This interaction causes the deactivation of T cells, leading to tumor growth [11]. Targeting PDL1 can inhibit interaction with PD1 to prevent immunosuppression, for example, PDL1 targeted by various nanoparticles and nanoparticles conjugated with drugs [12], [13].

Nanoparticles are materials with dimensions on the nanoscale ranging from 1 to 100 nm [14]. They can be synthesized by physical, chemical, and biological methods [15]. There are several advantages of the biological synthesis of nanoparticles over chemical synthesis such as, low toxicity and high biocompatibility, therefore, it is very significant to use them in healthcare applications [16]. Moreover, biological materials can also provide natural compounds which enhance the efficiency of nanoparticles. According to the WHO, herbal sources possess medicinal properties to treat diseases [17]. For example, Catharanthus roseus plant has seven anticancer compounds which are vincristine, vinblastine, vindogentianine, vindolidine, vindoline, vindolinine, and vindolicine [18]. Additionally, Juglans regia is rich in vitamins E, folate, melatonin, various antioxidative polyphenols, and also ω-3 fatty acids [19]. Moreover, studies have shown that Juglans regia can slow the growth and angiogenesis of colon and renal cancer [20].

Nanoparticles are suitable for drug loading because they have a high surface area due to their size in the nano range. This also improves the stability and hydrophilicity of drugs for drug delivery [21], [22]. For example, AgNPs conjugated with doxorubicin can be effective to kill cancer cells [23], and AuNPs can be conjugated with doxorubicin to target PDL1[24]. For the treatment, nanoparticles conjugated with a drug can be taken through oral, nasal, parenteral, or intraocular routes [25].

These days, in silico drug screening is a very good approach for discovering new drugs, particularly when screening millions of chemical compounds, which is very hard to do in a wet lab [26]. Additionally, molecular dynamics simulations can help to predict the behavior of drugs with protein receptors, which can be useful for analyzing their stability [27].

There are various FDA-approved monoclonal antibody drugs available to inhibit PD1/PDL1, such as Nivolumab and Durvalumab [28]. However, these macromolecular antibody drugs may not be effectively able to penetrate cancerous cells [29]. Moreover, manufacturing monoclonal antibodies is a difficult and expensive process, and storing and transporting them is also very challenging [30]. Therefore, there is a need to discover new small inhibitors that can target PD1/PDL1 immune checkpoint pathway to overcome these limitations. Currently, various small molecule drugs such as JQ1 and CA-170 are under clinical research to evaluate their safety and efficacy in treating cancer [29].

In this study, one hundred thousand compounds retrieved from the ZINC database were screened against PDL1 (accession code 5C3T) in order to identify the compounds with the highest binding affinity with PDL1 to block the interaction between PDL1 and PD1. Further, screened compounds were arranged on the basis of affinity score. Subsequently, seven FDA-approved compounds were again filtered based on low steric hindrance. Then, top drugs Irinotecan, Imatinib, Methotrexate, and ZINC1098661 were filtered out for more analysis based on ADME properties, Lipinski’s rule of five, carcinogenicity, and cytotoxicity. The structural analysis was also done to reveal the binding site and interaction of PDL1 with drugs. Moreover, the binding energy of selected drugs with AuNPs and AgNPs against PDL1 was also analysed. Furthermore, the stability of PDL1 with drugs and their conformational changes were analysed by performing molecular dynamics simulations in a solvent condition for 10 ns and 100 ns, specifically for Irinotecan. In lab, the filtered-out drugs were conjugated with synthesized AuNPs and AgNPs mediated by Catharanthus roseus and Juglans regia respectively. Subsequently, the drugs, nanoparticles, and nanoparticles-conjugated with drugs were tested on the A549 cell line for comparative studies.

2.1 In Silico analysis

2.1.1 Virtual tools and resources

The 3D structure of PDL1 was retrieved from the RCSB Protein Data Bank (PDB) using the accession code 5C3T, and a database containing 100,000 ligand compounds was obtained from the ZINC database. PyRx, Autodock Vina, and 3D QSAR were used for the virtual screening of drugs. The CHARMM-GUI was used for designing nanoparticles and their docking performance was evaluated using Hex 8.0.0. SwissADME and ProTox-II were utilized for analyzing the properties and toxicity of drugs respectively. MD simulation studies were performed using GROMACS on an Asus ROG laptop equipped with a Core i7 processor, 32 GB of RAM, and a 4 GB NVIDIA GTX 1650 Ti graphic card.

2.1.2 Multiple drug docking using PyRx and Autodock Vina

Multiple drug docking was performed using PyRx, utilizing the Random Forest machine learning algorithm [31]. The database was converted into the PDBQT format, and energy minimization was carried out before starting the docking process. The selected drugs were then docked using Autodock Vina, with the use of a genetic algorithm [32]. Heteroatoms were removed, and energy minimization was performed, followed by the addition of polar hydrogen and Kollman charges. The grid parameters were set to 68, 70, 104, centralized at 6, 12, and 8 for the X, Y, and Z coordinates respectively, with a grid spacing of 0.5 Å and an exhaustiveness of 8. The docking was carried out based on high affinity. PyMOL was used to visualize docked structure of protein and ligand complex [33].

2.1.3 3D QSAR (Quantitative structure-activity relationship) for a further selection of drugs

The FDA-approved drugs underwent further analysis using 3D QSAR, in order to pinpoint compounds with higher experimental activity [34]. The drugs were loaded into 3D QSAR server, with IC₅₀ values for each drug being retrieved from ChEMBL database [35]. The 3D QSAR model dataset was created by conducting a conformational analysis of the molecules with the innovative balloon method while aligning the molecules through the use of the RDKit method. Finally, the 3D QSAR model was generated on the basis of higher experimental values that imply lower steric hindrance.

2.1.4 Docking with AgNPs and AuNPs

The selected drugs were docked with silver and gold nanoparticles designed using CHARMM-GUI modulator [36]. The size of the nanoparticles was set at 35nm. The docking process was carried out using Hex 8.0.0, with the protein and conjugated nanoparticles with ligands being loaded into the software [37]. The algorithm used for the process was the Geometric Hashing and Energy Minimization algorithm. The docking process was initiated once these structures were loaded.

2.1.5 Predicting physicochemical properties and toxicity of compounds

The selected compounds were analyzed for their physical and chemical characteristics using the SwissADME, with a particular emphasis on whether they comply with Lipinski's rule of five [38]. In addition, the compounds were assessed for their potential to induce cytotoxicity and carcinogenicity using the ProTox-II tool to ensure their safety [39].

2.1.6 Molecular Dynamics Simulations

The system was prepared for simulation by creating a protein topology file using GROMACS. The PRODRG was then used to generate a topology file for the ligand [40]. The GROMOS96 43a1 force field was chosen to describe the interactions between the atoms in the system [41]. A cubic box was generated to contain the system, and water molecules were added to the box to create an aqueous environment. To neutralize the charge of the system, Na⁺ and Cl⁻ ions were added. A steepest descent method was used to minimize the energy of the system and eliminate any unnecessary steric clashes, for a duration of one nanosecond and a total of 50,000 steps. During both the NPT (constant pressure and temperature) and NVT (constant volume and temperature) equilibration phases, a temperature of 300 K and a pressure of one bar were maintained using periodic boundary conditions throughout the simulation. A 10 ns molecular dynamics simulation was performed using GROMACS to study the interaction between PDL1 and Irinotecan, Imatinib, Methotrexate, and ZINC1098661. Additionally, a 100 ns molecular dynamics simulation was specifically conducted for Irinotecan [42]. The simulation was performed using the Particle Mesh Ewald (PME) method to calculate long-range electrostatic interactions [43]. The resulting trajectories from the simulation were analyzed using the inbuilt utilities of GROMACS and visualized using VMD and XMGRACE software [44], [45].

2.2 Synthesis of AgNPs and AuNPs

In order to synthesize silver nanoparticles, 20 grams of fresh Catharanthus roseus flowers were washed and added to a flask containing 200 ml of deionized water (dd H₂O). The mixture was boiled to obtain the extract of the flowers, which was then allowed to cool to room temperature. A solution of silver nitrate was made by dissolving 17 mg of silver nitrate powder (Brand RANKEM, Product Code: S0080) in 100 ml of deionized water. The flower extract and silver nitrate solution were then mixed in a 9:1 ratio to initiate the synthesis of silver nanoparticles. Furthermore, to synthesize gold nanoparticles, 20 grams of Juglans regia shells were taken in a separate flask containing 200 ml of deionized water. The flask was boiled to get an extract of shells. A 1.0 M solution of HAuCl₄ (Brand CDH, CAS No. 16961-25-4) was prepared by dissolving 1.0 g of HAuCl₄ in 25 ml of deionized water. The extracts of shells were mixed with 1.0 mM HAuCl₄ solution in separate flasks in a 4.5:0.5 ratio to start the synthesis of gold nanoparticles.

2.3 Characterization of AgNPs and AuNPs

2.3.1 Visible spectral analysis

The absorbance peaks of the AgNPs and AuNPs were measured using a spectrophotometer. Carry 5000 UV-Vis NIR spectrophotometer (Agilent Technologies) was used. The Surface Plasmon Resonance (SPR) absorption of AgNPs is typically observed in the range of 400 to 800 nm, while the SPR absorption of AuNPs is typically observed in the range of 520 to 570 nm [46], [47].

2.3.2 X-ray diffraction (XRD) analysis

The crystal structure and morphology of the AgNPs and AuNPs were confirmed through XRD analysis. The samples were dried on glass slides and prepared for analysis. Rigaku MiniFlex II XRD machine was used to perform XRD. The diffraction pattern of the sample was measured at 2°θ intervals from 30° to 80° [48].

2.3.3 Fourier transform infrared (FTIR) analysis

FTIR spectroscopy was used to identify the functional groups present on the surface of AgNPs & AuNPs. KBr (potassium bromide) was used as a matrix in FTIR spectroscopy, and a small amount of the sample was mixed with it to form a mixture. The mixture was then pressed into a pellet and placed in the sample compartment of an FTIR spectrometer. The PerkinElmer FTIR instrument was utilized to obtain the FTIR spectrum of the AuNPs and AgNPs, with measurements conducted within the wavenumber range of 500–4000 cm^− 1 [49].

2.4 Drug preparation and conjugation with NPs

Irinotecan, Imatinib, and Methotrexate were obtained from the Jawaharlal Nehru Medical College (JNMC) in Aligarh. 100 mg of Imatinib and Methotrexate were weighed and crushed into a powder form. The powdered drugs were then placed in two separate flasks, each containing 100 ml of deionized and autoclaved water. The Whatman filter paper was used to filter the solution. Then, 5 ml of silver nanoparticles and 5 ml of gold nanoparticles were separately mixed with 125 µl (20mg/ml) of Irinotecan to create solutions with a final drug concentration of 0.5 mg/ml for each solution. Additionally, 5 ml of silver nanoparticles and 5 ml of gold nanoparticles were also separately mixed with 5 ml (1mg/ml) of Imatinib and 5 ml (1mg/ml) of Methotrexate respectively, to create solutions for each with a final drug concentration of 0.5 mg/ml. The mixture was stirred and left at room temperature for 48 hours. The solutions were then centrifuged at 6000 rpm for 15 minutes, and the supernatant was discarded. The drugs were loaded again and centrifuged. The supernatant containing unbound drugs was discarded, and the pellet for each drug was collected.

2.5 Characterization of drugs conjugated with AgNPs and AuNPs

2.5.1 Visible spectral analysis

After drug conjugation, the optical densities (ODs) of the drug-conjugated nanoparticles were measured using a spectrophotometer. Slight variations in the maximum absorbance peaks were observed in both AgNPs and AuNPs which also confirmed that the nanoparticles had not undergone any changes in shape or structure after conjugation. The measurements were carried out utilizing an Agilent Technologies Instrument Carry 5000 UV-Vis NIR spectrophotometer [50].

2.5.2 Fourier transform infrared (FTIR) analysis

FTIR analysis was performed to examine the conjugation between drugs with AgNPs and AuNPs. The liquid samples were prepared for FTIR analysis using a previously described method. To obtain the fourier transform infrared (FTIR) spectrum of the nanoparticle conjugated with the drug, the PerkinElmer instrument was utilized. The measurement was conducted within the wavenumber range of 500–4000 cm^− 1 [51].

2.5.3 Dynamic light scattering (DLS) analysis

Dynamic light scattering was used to determine the size distribution of AgNPs and AuNPs before and after they were conjugated with tested drugs. The average size (d.nm) of the solutions were measured [52].

2.6 In vitro analysis

2.6.1 Cell culture

A549, a lung cancer cell line, was purchased from the National Centre for Cell Science (NCCS) in Pune. The medium used for cell culture was Dulbecco's Modified Eagle's Medium (DMEM, AL007S-Himedia), which was supplemented with 10% fetal bovine serum (FBS, RM10434-Himedia) and 1% penicillin-streptomycin (Pen-Strep, A004-Himedia). The cells were cultured in a T15 flask containing 5 mL of media in an incubator with 5% CO₂, 95% humidity, and a temperature of 37°C. The medium was refreshed on every other day.

2.6.2 Cytotoxicity assay

Twelve samples were analyzed using the methylthiazol tetrazolium (MTT) assay (Promega CellTiter 96® Aqueous, G3582), a colorimetric assay, to evaluate the effectiveness of AgNPs, AuNPs, drugs (1.0 mg/ml), and drugs conjugated with AgNPs and AuNPs nanoparticles (0.5 mg/ml). The MTT assay used a solution of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide dye to assess the samples. A T15 flask containing 80% confluent A549 cells (with a cell number of 1.12 x 10⁶) was used for performing the MTT assay. The medium was removed and the cells were washed with phosphate buffer saline (PBS). The cells were trypsinized using 0.5 ml of EDTA trypsin and incubated for 2 minutes in an incubator at 37°C to detach them from the surface of the T15 flask. The cells with EDTA trypsin were centrifuged at 2000 rpm for 2 minutes, and the supernatant was discarded. The obtained pellet was resuspended in 1 ml of media. Subsequently, 0.7 ml of the cell suspension containing 7.84 x 10⁵ cells were carefully drawn from the 1 ml resuspended solution and mixed with 9.6 ml of fresh media. Finally, 100 µl of the cell suspension containing 7840 cells was delicately seeded into each well of a 96-well plate. Then, 100 µl of each of the test samples was seeded in triplicate wells. In well A, untreated cells were used as a control, and three drugs (Irinotecan, Imatinib, and Methotrexate) were also included. In well B, AgNPs and their drug conjugates were seeded, and in well C, AuNPs were seeded along with their drug conjugates. The 96-well plate was incubated at 37°C for 24 hours in a humidified, 5% CO₂ atmosphere. After 24 hours, 20 µl of MTT Reagent was added to each well that contained samples. The culture plates were then incubated at 37°C in a humidified, 5% CO₂ atmosphere for 3 hours. After incubation, a 96-well plate ELISA Plate reader was used to record the absorbance at 490nm.

3.1 PyRx & Autodock Vina docking analysis

The PyRx docking tool was used to select the top 10 hits chemical compounds with high affinity for the PDL1 protein from a database of one hundred thousand drugs. These compounds had an affinity ranging from − 10.3 to -10.7 kcal/mol (Table 1). Next, the primary lead compound, ZINC1098661 was selected from the 10 chemical compounds based on its higher affinity of -7.7 kcal/mol, as determined by AutoDock Vina (Table 2).

In addition, the top 28 FDA-approved drugs with high affinity for PDL1 protein were also selected using the PyRx docking tool, which had affinities ranging from − 6.4 to -10.8 kcal/mol (Table 3). Then, among these FDA-approved drugs, the seven drugs with higher affinities in the range of -7.1 to -8.4 kcal/mol were selected using AutoDock Vina (Table 4).

Table 3: List of the 28 anticancer drugs obtained through interaction analysis using PyRx.

Table 4: List of the 7 top-hit anticancer drugs using Autodock Vina.

Table 5: List of the selected top 3 anticancer drugs using 3D QSAR analysis.

3.2 3D QSAR analysis

The 7 FDA-approved anticancer drugs, which were previously selected by AutoDock Vina, were further filtered using 3D QSAR analysis. The resulting three drugs, Irinotecan, Imatinib, and Methotrexate, were selected based on higher experimental values, which depend on steric hindrance between cellular components and the drugs. Drugs with lower steric hindrance have higher experimental values. (Table 5).

3.3 Analysis of physicochemical properties and toxicity of drugs

Irinotecan, Imatinib, Methotrexate, and ZINC1098661 have passed the Lipinski’s rule of five, indicating that they are likely to be effective and safe for use (Table 6). The logP values for all the drugs in this group are less than 5, indicating that they are soluble in water. Additionally, tests for cytotoxicity and carcinogenicity showed that these drugs had low toxicity (Table 7). The gastrointestinal (GI) absorption and bioavailability scores for Irinotecan, Imatinib, and ZINC1098661 were higher than Methotrexate.

Table 7: Toxicity, GI absorption, and Bioavailability Score of the selected Compounds.

3.4 Analysis of drugs conjugated with PDL1 receptor

The Irinotecan, Imatinib, Methotrexate, and ZINC1098661 hydrophobic interaction with the PDL1 domain are shown in (Figs. 1A, 1B, 1C, and 1D). Specifically, the residues Pro24, Lys25, and Lys41 of the PDL1 protein are found to interact with Irinotecan, while Lys25, Asp26, Tyr28, and Val30 are the residues that interact with Imatinib. Methotrexate interacts with Val23 and Lys124, and ZINC1098661 interacts with Thr22 and Lys41 residues of the protein.

3.5 Hex docking result analysis

The docking results of AgNPs & AuNPs with Irinotecan, Imatinib, Methotrexate and ZINC1098661 targeting PDL1 receptor protein were analyzed (Table 8). The highest energy was found with AgNPs and AuNPs with Irinotecan to target PDL1. Higher energy values indicate greater stability. The interaction of AgNPs and AuNPs with these drugs to target PDL1 is shown in (Fig. 2).

3.6 Molecular Dynamics Simulation analysis

A 10 ns molecular dynamics simulation was performed for PDL1 with Irinotecan, Imatinib, Methotrexate, and ZINC1098661 respectively. The parameters used to determine the stability of the simulated system are Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), and Solvent Accessible Surface Area (SASA). PDL1 conjugated with Irinotecan, Imatinib, and Methotrexate have a higher RMSD value than only PDL1 as shown in (Figs. 3A, 4A, 5A, and 6A). The RMSD value for Irinotecan with PDL1 is the most stable with low fluctuation as compared to the RMSD value of Imatinib, Methotrexate and Zinc1098661.The complex PDL1 with Irinotecan becomes more stable after 2500 (ps) and then, a little fluctuation was observed until it reached 10000 (ps). The overall RMSF values were similar to PDL1 and PDL1 with Irinotecan, Imatinib Methotrexate, and ZINC1098661 as shown in (Figs. 3B, 4B, 5B, and 6B). The only region with high structural fluctuation is between atoms 1200 and 1300 which can be the site of the interaction of drugs with PDL1. The Rg value of Irinotecan, Imatinib, Methotrexate and ZINC1098661 are shown in (Figs. 3C, 4C, 5C, and 6C). The Rg value of Irinotecan and ZINC1098661 with PDL1 decreased over time which indicated that the stability of the complex had increased. The SASA of PDL1 with Irinotecan, Imatinib, Methotrexate and ZINC1098661 are shown in (Fig. 3D, 4D, 5D, and 6D). The SASA of PDL1 is approximately 71 (nm²) at 10000(ps), while PDL1 with Irinotecan and Imatinib, SASA increased to 75 (nm²) and 77 (nm²) respectively. This showed that the area of PDL1 increased after drug conjugation and also confirmed the conformational changes in the protein structure.

The stability of Irinotecan with PDL1 was further confirmed by molecular dynamics simulation for 100 ns. The analysis of RMSD value is depicted in (Fig. 7A) from 95000(ps) to 100000(ps). RMSD peaks of PDL1 with Irinotecan are approximately between 0.45 nm and 0.7 nm which indicates that there was low fluctuation and the complex was stable. The high fluctuations in RMSF value were seen approximately at residue number 140 as shown in (Fig. 7B), which may be the region of protein and drug interaction. The Rg value between the time period 95000(ps) to 100000(ps) is shown in (Fig. 7C). The Rg value of the protein and ligand complex was decreased which confirms the stability of the complex. The SASA value of PDL1 and Irinotecan complex had increased due to the conformational changes by protein and drug interaction (Fig. 7D).

3.7 Characterization of AgNPs and AuNPs

3.7.1 UV-Vis Analysis

The visible spectral analysis results showed that the AgNPs and AuNPs have a maximum absorbance peak of around 430 nm and 550 nm respectively as shown in (Figs. 8A and 8B).

3.7.2 XRD analysis

XRD results confirmed the crystal structure of AgNPs and AuNPs. The miller indices (hkl) align with diffraction peaks of the AgNPs and AuNPs at 111, 200, 220, and 311, representing face-centered cubic (fcc) structure as shown in (Figs. 9A and 9B).

3.7.3 FTIR analysis

In order to determine the structure of the AgNPs and AuNPs, FTIR analysis was used. In the FTIR analysis of AgNPs, as shown in (Fig. 10A), the peak at a wave number 3400 cm^− 1 indicates the presence of the OH group, and its wideness was due to the acidic OH group. The spectrum at a wave number 2100 cm^− 1 is associated with the C ≡ C stretching of the alkyne molecule. The peak at wave number 1650 cm^− 1 represents the C = O stretching of the carbonyl group. The peak available at 1100 cm^− 1 corresponds to the deformation vibration of the OH group. The peak available at 700 cm^− 1 is associated with CH out-of-plane bending vibrations. On analysis of FTIR of AuNPs, as shown in (Fig. 10B), the wide peak at a wave number 3400 cm^− 1 indicates the presence of the OH group that is not involved in hydrogen bonding. The spectrum available at wave number 1650 cm^− 1 represents the C = O stretching of the carbonyl group. The peaks at 1200 cm^− 1 and at 600 cm^− 1 represent ester carbonyl and C-Cl, respectively.

3.8 Characterization of drugs conjugated with AgNPs and AuNPs

3.8.1 UV-Vis analysis

Three different drugs (Irinotecan, Imatinib, and Methotrexate) were conjugated with AgNPs and AuNPs and analysed using a spectrophotometer. The result demonstrated slight variation in the wavelength after conjugating AgNPs and AuNPs with these drugs as shown in (Figs. 11A,11B, and 11C) and (Figs. 12A,12B, and 12C).

3.8.2 FTIR analysis

The conjugation of drugs with AgNPs and AuNPs was confirmed using FTIR. In (Figs. 13A and 14A), the peaks observed in the Irinotecan spectrum were analyzed. The peak at 850 cm^− 1 is associated with C-Cl stretching of a halo group. The peak available at 1150 cm^− 1 is associated with ester carbonyl, while the peak at 1400 cm^− 1 represents the CH₂ band. The peak at 1650 cm^− 1 corresponds to C = O stretching of the carbonyl group. The peaks at 2650 and 2950 cm^− 1 correspond to C-H stretching with the presence of an aldehyde and alkane group respectively. The peak at 3400 cm^− 1 indicates the presence of an OH group. Moreover, Irinotecan peaks were also present in AgNPs and AuNPs conjugated with Irinotecan. Furthermore, a peak at 1750 cm^− 1 was observed in AuNPs conjugated with Irinotecan, which represents the C = C stretching of the ester group.

Imatinib FTIR spectra is demonstrated in (Figs. 13B and 14B). The peak at wave number 1100 cm^− 1 corresponds to the deformation vibration of OH. The peak at 1250 cm^− 1 is associated with the C-N stretching of the amine group, while the peak at 1650 cm^− 1 is associated with the N-H bending of the amine group. The peak available at 2950 cm^− 1 corresponds to the C-H stretching of the alkane group. The peak at 3400 cm^− 1 indicates the presence of an OH group. These peaks were also observed in the FTIR spectra of AgNPs and AuNPs conjugated with Imatinib. In addition, in the AgNPs conjugated with Imatinib, a peak at 1350 cm^− 1 was observed, which belongs to the C-N stretching vibration of aromatic amines. Furthermore, a peak at 1400 cm^− 1, indicative of the CH₂ band, was observed in AuNPs conjugated with Imatinib.

Methotrexate FTIR analysis is shown in (Figs. 13C and 14C). The peak at 850 cm^− 1 is corresponded to C-Cl stretching of a halo compound. The peak at 1100 cm^− 1 represents the deformation vibration of OH group, while the peak at 1350 cm^− 1 indicates the C-N stretching vibration of aromatic amines. The peak at 1650 cm^− 1 represents the C = O stretching of the carbonyl group. The peak available at 2950 cm^− 1 corresponds to the C-H stretching of the alkane group. The peak at 3400 cm^− 1 indicates the presence of an OH group. These peaks were also observed in the FTIR spectra of AgNPs and AuNPs conjugated with Methotrexate.

3.8.3 Dynamic light scattering (DLS) analysis

The results obtained from Dynamic Light Scattering (DLS) indicate that the size of AgNPs and AuNPs has changed after conjugating with drugs. In particular, the average size of AgNPs was found to be 78.57 d.nm before drug conjugation (Fig. 15A). However, after conjugation with Irinotecan, Imatinib, and Methotrexate, the average size increased to 168.9, 293.2, and 220.2 d.nm, respectively, as shown in (Figs. 15B, 15C, and 15D). Similarly, the average size of AuNPs was found to be 81.93 d.nm before drug conjugation, as demonstrated in (Fig. 16A). After conjugation with Irinotecan, Imatinib, and Methotrexate, the average size increased to 392.8, 566.9, and 562.7 d.nm, respectively, as shown in (Figs. 16B, 16C, and 16D). This significant increase in size indicates successful conjugation of drugs with AgNPs and AuNPs.

3.9 In Vitro analysis

The three drugs (Irinotecan, Imatinib, and Methotrexate), nanoparticles (AgNPs and AuNPs), and drugs conjugated with nanoparticles, were tested on A549 cells. The MTT assay result was carried out in triplicate, the result of the MTT assay is shown in (Figs. 17A, 17B, and 17C). Imatinib showed high efficacy results in killing cells, with cell viability of only 11%. Irinotecan killed approximately 40% of the cells, while Methotrexate killed 35% of the cells. The cell viability of Irinotecan and Methotrexate was 59.35% and 65% respectively. The cell viability of AgNPs was 55.7%. When AgNPs were conjugated with Irinotecan and Imatinib, the cell killing was enhanced with cell viabilities of 41.9% and 42.5%, respectively. However, when AgNPs were conjugated with Methotrexate, the cell viability increased to 65.3%. The cell viability of AuNPs was 74.3%. Conjugation of AuNPs with Irinotecan enhances cell killing rate. The cell viability of Irinotecan with AuNPs was 35.4%. In the case of AuNPs conjugated with Imatinib, the cell viability increased to 78.7%. Furthermore, AuNPs conjugation with Methotrexate showed a negative result in cell killing and the cell viability reached 100%.

Targeting PD1/PDL1 immune checkpoint is an excellent approach in immunotherapy, which leads to the activation of T Cells [53], [54].

Therefore, the aim of this work was to find a molecule that targets PDL1, which can be used with nanoparticles to inhibit the PD1/PDL1 interaction.

In the drug screening process, PyRx was used firstly for multiple drug screening, followed by the utilization of Autodock Vina for further filtration. It has been reported that Abdellah El Aissouq et.al also utilized PyRx and Autodock Vina to search for an inhibitor against SARS-COV-2 [55].

Irinotecan, Imatinib, Methotrexate, and ZINC1098661 were selected after drug screening and 3D QSAR analysis [56]. The Irinotecan had a higher affinity against PDL1, and a high experimental value. The higher experimental value was dependent on lower steric hindrance between cellular components and drugs. Moreover, ZINC1098661 with AgNPs and AuNPs had a lower affinity against PDL1, therefore, it was excluded from the wet lab experiment. Irinotecan had one violation of Lipinski’s rule of five due to its weight being greater than 500 Da, but it is still considered a viable drug candidate. The GI absorption and bioavailability scores of these drugs were high, except for Methotrexate. A bioavailability score of 0.55 is considered high, as demonstrated by Joanna Bojarska et al., and the majority of compounds come close to this value [57]. In another study, Priyanka Sharma et.al reported that a bioavailability score of 0.11 is considered a poor score [58].

The ProTox-II analysis showed that cytotoxicity and carcinogenicity were nil with these drugs, implying that they are safe for use. Manisha D.R et al. reported that AgNPs mediated by Catharanthus roseus flower extracts have a size range of 11nm to 15nm [59]. Additionally, M. Salandari Rabori et al. study demonstrated that AuNPs mediated by Juglans Regia have a size range of 10nm to 50nm [60]. Therefore, for in silico analysis of nanoparticles, AgNPs and AuNPs were designed arbitrarily with an average size of 35nm.

A 10 ns molecular dynamics simulation was performed to analyze the parameters, such as RMSD, RMSF, Rg, and SASA to determine the stability of the drugs with PDL1. The higher RMSD value of PDL1 with drugs than only PDL1 indicates that the drugs induce large conformational changes in the protein structure. Overall, all the drugs were found to be stable with PDL1, but Irinotecan was found to be the most stable. Therefore, a 100 ns molecular dynamics simulation was performed specifically for Irinotecan, even though a 10 ns molecular dynamics simulation was sufficient, as demonstrated by Yu. V. Kordonskaya and his team in their research work on the behavior of tetragonal lysozyme dimer using molecular dynamics simulation for 10 nanoseconds [61].

In UV-Vis analysis, the maximum absorbance peaks of AgNPs and AuNPs were found to be 430nm and 550nm, respectively, due to their SPR properties. The crystal structure of both AgNPs and AuNPs was confirmed by XRD. The FTIR analysis showed functional groups present in them. Since AgNPs and AuNPs were synthesized using a biological route, they had natural protein capping. Therefore, there was no need for an additional linker for drug conjugation, which is generally required in chemically synthesized NPs.

AgNPs and AuNPs conjugated with drugs were characterized using UV-Vis spectroscopy, FTIR, and DLS. The UV-Vis analysis showed a slight shift in the absorbance peak, indicating that drugs were conjugated [62]. Furthermore, FTIR also confirmed that AgNPs and AuNPs were conjugated with drugs [63]. DLS analysis revealed an increase in the overall size of the mixture, which also showed that the AgNPs and AuNPs were successfully conjugated with drugs [64].

The MTT assay was performed to test the effect of drugs (Irinotecan, Imatinib, and Methotrexate), as well as AgNPs & AuNPs, and drug-conjugated AgNPs & AuNPs on A549 cells [65]. The MTT assay result showed that all the three drugs have a toxic effect on A549 cells, among which Imatinib showed the best result. Moreover, in others studies, the toxic effect of Irinotecan, Imatinib, and Methotrexate on MCF7 cells are also reported by Saeedeh Keyvani-Ghamsari et.al, Seyed Ataollah Sadat Shandiz et al., and Javad Farzanfar et.al., respectively [66–68].

AgNPs were found to be more toxic than AuNPs on A549 cells. This may be due to the presence of additional anti-cancer alkaloids in AgNPs derived from Catharanthus roseus flowers. Yury Shkryl et al. conducted a comparative study of AgNPs and AuNPs mediated by Lithospermum erythrorhizon, and result showed that the cytotoxicity of AgNPs was higher than that of AuNPs on NIH3T3 cells [69]. In other studies, the cytotoxicity of AgNPs mediated by E. procera and AuNPs mediated by Lactobacillus acidophilus were tested on MCF7 cells by Seyed Ataollah Sadat Shandiz et al and Elmer Casley Repotente Jr. et al., respectively. Upon comparing their results, AgNPs were found to be more toxic than AuNPs [67], [70].

The conjugation of AgNPs with Irinotecan and Imatinib increased the toxic effect of AgNPs, whereas the effect of Methotrexate was the same with or without AgNPs. Moreover, AuNPs conjugated with Irinotecan enhanced the toxic effect of AuNPs. After conjugation, the size of NPs increased, which implies that cell uptake would decrease. However, despite the increase in size, the efficiency of cell toxicity increased. This indicates a higher probability of the drug-conjugated NPs binding to the cell surface receptor (PDL1).

However, the toxicity of Imatinib decreased after conjugation with AuNPs, and AuNPs conjugated with Methotrexate were deactivated and had no effect on the A549 cells. This deactivation may be due to distortion of Methotrexate structure which altered pharmacokinetic properties that occur after the conjugation process of Methotrexate with AuNPs [71]. This may also be due to the contrary effect of AuNPs, leading to an increase in the number of cells. The study conducted by Chen Li et al. demonstrated the proliferative effect of AuNPs on Human periodontal ligament cells (hPDLCs) [72].

Based on our current knowledge, there have been no reports regarding the targeting of PDL1 using AgNPs and AuNPs conjugated with Irinotecan, Imatinib, and Methotrexate. However, Fakhrossadat Emami et al. reported that AuNPs conjugated with doxorubicin and anti-PDL1 antibody were used to target PDL1 [24]. Our result showed that Irinotecan conjugated with AgNPs and AuNPs had a high toxic effect which confirmed the in-silico prediction for Irinotecan in which the high affinity against PDL1 was found with AgNPs and AuNPs.

In future, a larger database of drugs can be used to discover new chemical compounds to target PDL1 receptors. Moreover, various nanoparticles such as silica shell gold core NPs, Ag-Au alloy NPs, and their conjugation with drugs can be tested in various cell lines, including 3D cell lines, and also in mouse models.

The computational approach helped to predict the lead compound to target PDL1. One hundred thousand compounds were screened in silico against PDL1, and the filtered drugs were Irinotecan, Imatinib, Methotrexate, and ZINC1098661. Among these drugs, Irinotecan had a high affinity with AuNPs and AgNPs against PDL1. A molecular dynamics simulation was also performed to analyze the stability of these drugs with PDL1. Based on RMSD, RMSF, Rg, and SASA, Irinotecan with PDL1 was found to be the most stable. Furthermore, these drugs were also toxic to the A549 cells. The biological method of synthesizing AgNPs and AuNPs were simple and less expensive. Moreover, the biologically synthesized AgNPs and AuNPs had the ability to conjugate with drugs without any additional linker. The size of AgNPs and AuNPs were increased after drug conjugation. Due to their larger size, drug-conjugated nanoparticles had lower cellular penetration than nanoparticles alone. This reveals that the AgNPs and AuNPs conjugated with drugs were bound to the cell surface. The AgNPs and AuNPs also showed a toxic effect on the cell line, and the efficacy of killing cancer cells with AgNPs and AuNPs was enhanced with Irinotecan. This result suggests that Irinotecan conjugated with AuNPs can be used to target PDL1 to treat cancer. In the future, various nanoparticles such as silica shell gold core NPs and Ag-Au alloy NPs can be used to conjugate with Irinotecan and test on cell lines and mouse models.

PDL1, programmed death-ligand 1; PD1, programmed cell death 1; AuNPs, gold nanoparticles; AgNPs, silver nanoparticles; UV-Vis spectroscopy, ultraviolet visible spectroscopy; XRD, X-ray diffraction; FTIR, fourier transform infrared spectroscopy; DLS, dynamic light scattering, TEM, transmission electron microscopy; MTT, methylthiazol tetrazolium; PDB, Protein data bank; MD simulation, molecular dynamics simulation; RMSD, root mean square deviation, RMSF, root mean square fluctuation; Rg, radius of gyration; SASA, solvent accessible surface area.

Data Availability Statement

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

Additional Requirements

ORIGINAL RESEARCH article

Author Contributions

MAA conceived the study, planned experiments, provided resources, discussed results, analyzed data, and helped write the manuscript. SHA did experimental work and wrote the manuscript draft. HA analyzed data and helped in manuscript writing and discussion. All authors contributed to the article and approved the submitted version.

Acknowledgments

We would like to express our gratitude Prof. Mohammad Owais (Interdisciplinary Biotechnology Unit, AMU) for helping in MTT assay, Dr. Snober S. Mir (Department of Bioengineering, Integral University) for providing cell line. We acknowledge the laboratory assistance provided by Mr. Khateeb Ahmad (INC), Mr. Ashraf Ali (INC), and Mr. Syed Danish Ali (Department of Applied physics, AMU) in the characterization of samples. We thank Drs. Suhail Akhtar and Abdul Malik for their valuable suggestions and reviewing the contents.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This work was supported and funded by Aligarh Muslim University, Aligarh, India.

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No competing interests reported.

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Computational identification of PDL1 inhibitors and their cytotoxic effects with silver and gold nanoparticles

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion:

Figures

1. Introduction

2. Materials and methods

2.1 In Silico analysis

2.1.1 Virtual tools and resources

2.1.2 Multiple drug docking using PyRx and Autodock Vina

2.1.3 3D QSAR (Quantitative structure-activity relationship) for a further selection of drugs

2.1.4 Docking with AgNPs and AuNPs

2.1.5 Predicting physicochemical properties and toxicity of compounds

2.1.6 Molecular Dynamics Simulations

2.2 Synthesis of AgNPs and AuNPs

2.3 Characterization of AgNPs and AuNPs

2.3.1 Visible spectral analysis

2.3.2 X-ray diffraction (XRD) analysis

2.3.3 Fourier transform infrared (FTIR) analysis

2.4 Drug preparation and conjugation with NPs

2.5 Characterization of drugs conjugated with AgNPs and AuNPs

2.5.1 Visible spectral analysis

2.5.2 Fourier transform infrared (FTIR) analysis

2.5.3 Dynamic light scattering (DLS) analysis

2.6 In vitro analysis

2.6.1 Cell culture

2.6.2 Cytotoxicity assay

3. Results

3.1 PyRx & Autodock Vina docking analysis

3.2 3D QSAR analysis

3.3 Analysis of physicochemical properties and toxicity of drugs

3.4 Analysis of drugs conjugated with PDL1 receptor

3.5 Hex docking result analysis

3.6 Molecular Dynamics Simulation analysis

3.7 Characterization of AgNPs and AuNPs

3.7.1 UV-Vis Analysis

3.7.2 XRD analysis

3.7.3 FTIR analysis

3.8 Characterization of drugs conjugated with AgNPs and AuNPs

3.8.1 UV-Vis analysis

3.8.2 FTIR analysis

3.8.3 Dynamic light scattering (DLS) analysis

3.9 In Vitro analysis

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1