Wang et al. BMC Chemistry(2024) 18:159
https://doi.org/10.1186/s13065-024-01279-z

BMC Chemistry

RESEARCH

Open AccessFused in silico and bioactivity evaluation
method for drug discovery: T001-10027877
was identified as an antiproliferative
agent that targets EGFRT790M/C797S/L858R
and EGFRT790M/L858R
Linxiao Wang1*, Xiaoling Huang1, Shidi Xu1, Yufeng An1, Xinya Lv1, Wufu Zhu1*, Shan Xu1, Yuanbiao Tu2,
Shuhui Chen3*, Qiaoli Lv3 and Pengwu Zheng1
Abstract
Background Facing the significant challenge of overcoming drug resistance in cancer treatment, particularly
resistance caused by mutations in epidermal growth factor receptor (EGFR), the aim of our study was to identify
potent EGFR inhibitors effective against the T790M/C797S/L858R mutant, a key player in resistance mechanisms.
Methods Our integrated in silico approach harnessed machine learning, virtual screening, and activity evaluation
techniques to screen 5105 compounds from three libraries, aiming to find candidates capable of overcoming theresistance conferred by the T790M and C797S mutations within EGFR. This methodical process narrowed the searchdown to six promising compounds for further examination.
Results Kinase assays identified three compounds to which the T790M/C797S/L858R mutant exhibited increased
sensitivity compared to the T790M/L858R mutant, highlighting the potential efficacy of these compounds against
resistance mechanisms. Among them, T001-10027877 exhibited dual inhibitory effects, with IC50 values of 4.34
µM against EGFRT790M/C797S/L858R and 1.27 µM against EGFRT790M/L858R. Further investigations into the antiproliferative
effects in H1975, A549, H460 and Ba/F3-EGFRL858/T790M/C797S cancer cells revealed that T001-10027877 was the mostpotent anticancer agent among the tested compounds. Additionally, the induction of H1975 cell apoptosis and cell
cycle arrest by T001-10027877 were confirmed, elucidating its mechanism of action.
Conclusions This study highlights the efficacy of combining computational techniques with bioactivity assessmentsin the quest for novel antiproliferative agents targeting complex EGFR mutations. In particular, T001-10027877*Correspondence:
Linxiao Wangwanglinxiao542@163.com
Wufu Zhu
zhuwufu-1122@163.com
Shuhui Chen
chenshuhui2008@126.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by-nc-nd/4.0/.

Wang et al. BMC Chemistry(2024) 18:159

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has great potential for overcoming EGFR-mediated resistance and merits further in vivo exploration. Our findings
contribute valuable insights into the development of next-generation anticancer therapies, demonstrating the powerof an integrated drug discovery approach.
Keywords EGFRT790M/C797S/L858R and EGFRT790M/L858R, Machine learning, Virtual screening, Bioactivity evaluation,
Antiproliferative agent

Introduction
Epidermal growth factor receptor (EGFR) is a transmembrane protein in the tyrosine kinase receptor family [1]. When the extracellular domain of EGFR binds to
its ligands (such as EGF or TGF-α), it leads to receptor
dimerization and activation, thereby triggering the intracellular tyrosine kinase activity of EGFR [2]. This activates a series of downstream signalling pathways, such as
the MAPK, PI3K/Akt, and JAK/STAT pathways, which
regulate cell growth, proliferation, migration, and survival [3].
Aberrant activation of the EGFR signalling pathway is
associated with the occurrence, progression, and responsiveness of various cancers to treatment [4]. For example,
genetic mutations or amplifications of EGFR are common in non-small cell lung cancer and are associatedwith EGFR sensitivity to tyrosine kinase inhibitors [3].
In addition, other factors related to the EGFR signalling
pathway, such as miR-20b [5], HOXA3 [6], and JMJD8[7], have been found to play roles in cancer that are
related to the activation of the EGFR signalling pathway.
To inhibit excessive activation of the EGFR signalling
pathway, fourth-generation EGFR inhibitors have beendeveloped (Fig. 1). First-generation EGFR inhibitors (suchas erlotinib and gefitinib) primarily target activated EGFR
mutants, although the T790M mutant exhibits resistanceto these inhibitors [8], and can cause toxic side effects
when targeting wild-type EGFR [9]. Second-generation
EGFR inhibitors (such as afatinib and dacomitinib) aredesigned to target the T790M mutation. However, they
may lead to more side effects [10, 11] (such as rashes andgastrointestinal disorders) due to their inhibition of wildtype EGFR, which has limited their further clinical application [12]. Third-generation EGFR inhibitors (such as
osimertinib) specifically target the T790M mutation andhave weaker inhibitory effects on wild-type EGFR, andtherefore, fewer side effects [13–15]. Fourth-generation
EGFR inhibitors, currently in clinical stages, primarily
target triple mutants, including C797S (such as Del19/
T790M/C797S and L858R/T790M/C797S) [16]. In conclusion, the discovery of novel fourth-generation EGFR
inhibitors has significant research implications for overcoming triple mutants mediated by C797S [17].
In recent years, machine learning techniques have been
widely applied in various fields, such as biology, medicine, and computer science. In the field of drug discovery, machine learning has also been extensively used; forexample, to overcome drug resistance using machinelearning models [18], the broad-spectrum active compound ruthenium(II) N-heterocyclic carbenes [19], diarylurea antibiotics [20] and anticancer drugs [21] havebeen discovered.
This study utilized ROC-guided machine learning, virtual screening, and bioactivity evaluations to discoverpotential compounds targeting EGFRT790M/C797S andEGFRT790M/C797S/L858R. The DUD-E website was used toconstruct training datasets and evaluate three machinelearning models, and the best model was ultimately
selected for potential compound prediction. Receptor
selection, virtual screening, cluster analysis, and binding
analysis were performed, and six promising compounds
were identified. After conducting kinase assays, antiproliferative activity tests, AO (Acridine Orange) staining,cell cycle experiments, qRT–PCR, and haemolytic activity evaluations, Compound T001-10027877 was identified as a novel EGFRT790M/C797S/EGFRT790M/C797S/L858R
inhibitor. The entire framework of the study is illustrated
in Fig. 2.

Methods and materials
Data preparation
Dataset preparation for machine learning

First, 100 known active compounds (Table S1) wereobtained from Selleck (https://www.selleck.cn/) and used
as a reference to ensure the quality of the datasets used
for machine learning. Then, based on the SMILES files of
the active compounds, decoys were obtained according
to the DUD-E website [22] (https://dude.docking.org/).
The DUD-E database processed these files and returned
a pool of decoy molecules. From this pool, we methodically selected one molecule out of every seven, ultimately
forming a set of 220 decoys. The active dataset (100 compounds) and the decoy dataset (220 compounds) were
amalgamated to create a dataset, encompassing a totalof 320 compounds (Table S2). This consolidated dataset
was subsequently converted into an SDF file to facilitate
advanced analysis of its physicochemical properties.
Moreover, the physicochemical properties of the active
and inactive molecules were predicted using KNIME [23](https://www.KNIME.com/). This process (Figure S1)
utilized modules such as SDF Reader, RDKit From Molecule, RDKit Descriptor Calculation, and CSV Writer topredict the physicochemical properties of the 320 compounds. Notably, before the predictions were made, aWang et al. BMC Chemistry(2024) 18:159

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Fig. 1 Features of fourth-generation EGFR inhibitors

Fig. 2 The machine learning, virtual screening and bioactivity evaluation framework“class” row was added for classification, where the active
molecules were given a label of 1 and inactive molecules
were given the label 0. This step is crucial for ensuring
the accuracy and reliability of both data processing andthe resultant predictions.

preparation for machine learning).” These physicochemical properties were then used as features for further
machine learning to predict potential compounds.

Compound library preparation

Machine learning and the prediction of potential active
compounds
Selecting the best-performing machine learning modelFirst, three compound libraries (including the FDA, Bioactivity and Specs libraries; over 220000 compounds)
were uploaded into the cheminformatics software
KNIME to remove the same molecules and then to predict the physicochemical properties of those remaining (a similar method was described in section “Dataset

Using the data mining and analysis software Orange3[24], we evaluated five machine learning models (KNN,
SVM, random forest, neural network and gradient boosting) on the training datasets in section “Dataset preparation for machine learning”. The machine learningparameters were set as follows: (1) 70% of the datasets

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were used for training, and 30% were used for testing; (2)the number of cross-validations was set to 10-fold; and(3) the other parameters were kept at their default values. We considered AUC, CA, FA, precision, and recall
to select the best-performing machine learning model for
predicting active compounds.
Predicting potential active compounds with the random
forest model

the sensitivity of Vina towards these three proteins.
Finally, the complex with the best RMSD and binding
affinity was selected as the docking acceptor for the virtual screen. The docking parameters are listed in Table 1.
The affinity, determined from the docking results, andthe aligned model were used as rules to select suitable
docking receptors.
Compound normalization and preparation

For this section, we utilized the top-performing machinelearning model, random forest, to forecast the percompound “class” values for the compounds in the FDA
library and Specs library. It is crucial to clarify thatthese scores, referred to as “class” values, do not carryany units. These class values represent the likelihood of
a compound being classified within a specific category,with scores ranging from 0 to 1. Our primary goal was topinpoint compounds whose “class” values were close to 1
or exceeded 0.5, which indicated a higher probability of
relevance to our research objectives. Compounds meeting these criteria were subsequently identified as significant findings for the forthcoming phase of our virtual
screening study.

In section “Compound library preparation”, we detailed
the normalization process for hit compounds identified through machine learning using KNIME software.
This workflow (Figure S2) includes several modules: File
Reader, RDKit Canon SMILES, Duplicate Row Filter,
RDKit Add Hydrogens, RDKit Generate Coordinates,
RDKit Optimize Geometry, and SDF Writer. These steps
systematically prepare the hit compounds by addinghydrogens, generating molecular coordinates, optimizing
geometries, and ultimately saving the data in SDF format.
This meticulous preparation culminates in converting thecompound library into PDBQT files, making them readyfor subsequent applications.

Virtual screening for hit compounds
Docking software and protein selection

First, the protein was processed in Discovery Studio 3.0
to remove water molecules, ions, and the original ligand
and to correct the residues. Subsequently, the PDBQT
formats of both the protein and molecules were utilized
as input according to the parameters specified in theconfiguration file outlined in section “Docking softwareand protein selection”. Next, the compounds with thehighest affinity were preserved for cluster analysis using
Discovery Studio 3.0. Ultimately, the potential molecules
were chosen based on analysis of both the cluster libraryand binding model. Detailed methodological details of
the virtual screening can be found in our previous work
[33–35].

Recent research [25] has shown that Vina boasts comparable performance to other open source and academic
software programs, including LeDock [26], Gold [27],Moe [28], and Glide [29]. Furthermore, Vina has provento be a valuable and potent tool in numerous docking
research projects aimed at identifying new active compounds. As a result, Vina was chosen as the docking software for this virtual screening.
Our primary aim was to identify compounds capable oftargeting EGFR mutants, specifically those with dual or
triple mutations. This objective necessitates the carefulselection of proteins for virtual screening to ensure theidentification of highly specific and effective inhibitors.
Therefore, three proteins, 5ZWJ (EGFRT790M/C797S/V948R
mutant), 6JRJ (EGFRT790M/C797S mutant) and 6LUD [30–32] (EGFRL858R/T790M/C797S mutant), were evaluated to
select suitable proteins for virtual screening. Using thesethree targets, the original protein ligands were extracted
so that they could be docked into the active site again.
The RMSD and alignment model were used to evaluate
Table 1 Docking parameters of the proteins ZWJ, 6JRJ and 6LUD
Protein ID Central coordinates (x, y, z)
5ZWJ
6JRJ
6LUD

Grid box
Size (x, y, z)

6.77, 21.42, 12.09
30, 30, 30-51.36, 0.69, -22.60
30, 30, 30-49.73, -0.039, -18.22 30, 30, 30

Num
modes
9
9
9

Other
parameters
Default
Default
Default

Virtual screening

EGFR kinase assay

The kinase inhibition assays were conducted by Heyan
Biopharmaceutical Technology Co., Ltd. (Wuhan). The
compounds were tested for their inhibitory activity
against EGFRT790M/C797S/L858R and EGFRT790M/L858R using
the ADP-GLO and Lance Ultra methods. Detailed methodological details can be found in our previous work [36].
Antitumour evaluation

To initiate the experiment, four cell lines (A549, H1975,
H460 and Ba/F3-EGFRL858/T790M/C797S) in the logarithmic phase of growth were carefully placed into 96-wellplates and allowed to incubate for 24 h (the A549, H1975,
H460 and Ba/F3-EGFRL858/T790M/C797S cells used in ourstudy were obtained from Heyan Biopharmaceutical
Technology Co., Ltd. (Wuhan), as per the guidelines andWang et al. BMC Chemistry(2024) 18:159

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protocols approved for research). Subsequently, 20 µL
of compound-containing medium was evenly distributed into each well, with five different concentrations ofeach compound and three replicates for each concentration. The final concentrations of the six compounds,
D008-10022050, D008-10206173, D008-10099272, D00810135583, T001-10026427, and T001-10027877, were100.0, 33.3, 11.1, 3.7, and 1.2 µM. After 72 h of incubation, MTT solution (5 mg/ml) was added to each well,and the plates were allowed to incubate for four hours.
Once the incubation period ended, the MTT solution
was removed, and DMSO (150 µl) was added to eachwell. Finally, an enzyme marker was used to measure theoptical density (OD) of each well at 492 nm.
To determine the antiproliferative effects of the compounds on tumour cells, the measured optical density
values were used. The inhibition rate (%) was calculatedusing the following formula: (OD control - OD treatment)/(OD control - OD blank) × 100%. Using SPSS
software, we calculated the half-maximal inhibitory
concentration (IC50) for each of the compounds on thetumour cells.

Migration experiment

AO staining analysis

Using 1 µM AZD9291 as a positive control, fluorescencequantitative PCR was utilized to investigate the concentration-dependent inhibition of the substances on EGFR
and mTOR in H1975 cells. The concentrations of T00110027877 used were 0.5 and 1 µM. The procedure wasdescribed by Sun et al. [38].

Acridine orange (AO) is a dye that interacts with DNA
and RNA by intercalation or electrostatic attraction [37].
In living cells, AO primarily stains the nucleus green; indead or damaged cells, AO can penetrate the cell andbind to DNA, staining the nuclei of dead cells orangeor red and thus differentiating between living and deadcells. For this assay, T001-10027877 was administered atconcentrations of 0.5, 1, and 2 µM, with 1 µM AZD9291
serving as the positive control and untreated cells serving
as the blank control. The entire experimental method has
been described in our previous research [38].
Cell cycle

H1975 cells were seeded at a density of 4.0 × 105 cells per
flask and cultured at 37 °C in a 5% CO2 incubator for
24 h. The drugs were dissolved in 20 µL of DMSO andthen diluted in culture medium to the desired concentrations (AZD9291: 1 µM; and T001-10027877: 0.5 µM
or 1 µM) before being added to the culture flasks. Afterincubation, the cells were then digested with trypsin,centrifuged at 1000 rpm for 5 minutes to remove thesupernatant, and washed twice with PBS. Next, 10 mL of
70% ethanol was added at 4 °C to fix the cells for morethan 24 h. The fixed cells were centrifuged at 2000 rpm
for 5 minutes, the supernatant was discarded, and thecells were washed twice with PBS before 300 µL of PI
staining solution was added. After incubation in the dark
at 4 °C for 30 min, the cell cycle distribution was analysedusing flow cytometry, and key data were retained.

To conduct the cell scratch assay, log-phase H1975 cells
were seeded into a six-well plate, with each well receiving approximately 5 × 105 cells. Before seeding, paralleland equidistant lines were marked on the underside of
the plate to serve as guides for the scratch. After 24 h,
the cells had reached confluence, and a pipette tip wasused to create a precise scratch along the premarked
lines. Subsequently, the plate was rinsed twice with PBS
to remove any detached cells, and fresh culture medium
was added. At this stage, the cells were treated with
T001-10027877 at concentrations of 0.5, 1, and 2 µM,
and a control group was treated with 1 µM AZD9291.
Initial observations and photographs of the scratchedarea were taken under a microscope to establish baseline
data (0 h). Culture was then continued in the incubator for 24 h. After the plate was retrieved, the cells wereobserved and photographed under a microscope again to
document the migration and healing process, with particular attention given to the effects of T001-10027877
and AZD9291 on cell migration.
Real-time PCR

Haemolytic activity assay

A haemolytic activity assay was conducted to evaluate
the potential haemolytic effects of T001-10027877. The
concentrations tested for T001-10027877 included 0 (asa control), 16, 32, 64, 128, and 256 µg/mL. In this assay,
1% Triton X-100, which is known to cause 100% haemolysis of sheep blood erythrocytes, was used as a positive control to indicate complete cell lysis. Saline solution
served as a negative control, representing no haemolytic
activity. The specific experimental method was based on
our previous research [38].

Results
Machine learning to predict potential compounds
Selection of the machine learning model

To select the best machine learning model, the ROC
method was used. First, five machine learning models(KNN, SVM, random forest, neural network and gradient boosting) were built with Orange3 to evaluate thetraining set to identify the best model, and the results aredescribed in Table 2; Fig. 3. The AUC values can be calculated from Table S3. According to Table 2; Fig. 3, theAUCs of the random forest (0.98) and SVM (0.98) models
were better than those of the other three models (KNN

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Table 2 Performance of the five machine learning models
Model
KNN
SVM
Random Forest
Neural Network
Gradient Boosting

AUC
0.93
0.98
0.98
0.97
0.97

CA
0.88
0.95
0.97
0.95
0.93

F1
0.88
0.95
0.97
0.95
0.93

Precision
0.89
0.95
0.97
0.95
0.93

Recall
0.88
0.95
0.97
0.95
0.93

(0.93), neural network (0.97) and gradient boosting(0.97)). Among the random forest and SVM models, therandom forest model also performed better in terms ofCA, F1, precision and recall. Based on these results, therandom forest model was selected as the machine learning model in this study.

Using the random forest model to predict potential
compounds

After the physicochemical properties of the compounds
in the library were predicted, the data mining software
Orange3 was used to construct a random forest model topredict the probability of target inhibition. Compoundswith a positive rate greater than 0.5 were retained, which
resulted in approximately 5105 compounds (Table S4).
Then, these compounds were processed by KNIME to
add hydrogen atoms, generate coordinates and optimize
the geometry and saved as an SDF file. Finally, the SDF
file was converted into PDBQT by Open Babel 3.0 for
further study. The workflow is shown in Fig. 4.

Fig. 3 ROC curves of five machine learning models: KNN (A), SVM (B), random forest (C), neural network (D) and gradient boosting (E). The AUCs of thefive machine learning methods are summarized in Fig. 3F

Fig. 4 Workflow of the random forest model prediction and form conversion of the compound library

Wang et al. BMC Chemistry(2024) 18:159

Table 3 RMSDs after redocking the original ligand relative tothat of the original protein
Protein
5ZWJ
6JRJ
6LUD

Affinity (kcal/mol)
-8.7
-11.4
-8.0

RMSD of the aligned original ligand
0.35
0.18
0.07

Virtual screening
Selecting suitable proteins for virtual screening

To select suitable virtual screening software, the threemutant proteins were selected as receptors, and the original ligand of each was redocked into the active pocket toevaluate the RMSD. Thus, the three proteins were used
as receptors, and AutoDock Vina was used to dock theoriginal ligand into the active pocket to evaluate the sensitivity of Vina towards the three proteins. As shown in
Table 3; Fig. 5, the original ligand in 6JRJ showed better
affinity (affinity value: -11.4 kcal/mol) than those in 5ZWJ
(affinity value: -8.7 kcal/mol) and 6LUD (affinity value:-8.0 kcal/mol). In addition, among the three ligands, theRMSD of the aligned original ligand of 6JRJ (RMSD: 0.18)
was better than that of 5ZWJ (RMSD: 0.35) but close tothat of 6LUD (RMSD: 0.07). Taking these results into
consideration, clearly, protein 6JRJ was more suitable forvirtual screening than 5ZWJ and 6LUD.
3.2.2 Virtual screening and hit compound selection

After selecting suitable docking software and proteins,approximately 5105 compounds were docked into theactive site of the protein 6JRJ, and after setting a threshold affinity of less than − 9.5 kcal/mol, 284 compounds
remained (Table S5). Then, cluster analysis was performed to obtain 45 compounds based on the diversityof the skeletons. Finally, based on the binding modelcompared with the original ligand of 6JRJ, 6 compounds
were selected as hit compounds, as displayed in Table 4.
In addition, the 6 selected compounds were aligned withthe original ligand of 6JRJ, and nearly all showed conformations more similar to the original ligand than did

Fig. 5 The original ligands (red) aligned with the redocked ligands

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the 39 unselected compounds, as displayed in Fig. 6.
These results indicated that the selected compounds
may inhibit EGFRT790M/L858R and EGFRT790M/C797S/L858R.
Finally, all selected compounds were purchased fromTargetMol (US).
EGFRT790M/C797S/L858R and EGFRT790M/L858R
inhibition assays and binding model analysis

To prove our suspicions, kinase assays were performed
to evaluate whether the hit compounds were on target. Table 5 shows that EGFRT790M/C797S/L858R exhibited increased sensitivity to nearly all the compounds.
For example, three compounds (D008-10099272,
D008-10135583 and T001-10027877) inhibited
EGFRT790M/C797S/L858R with IC50 values of 3.26, 3.25 and4.32 µM, respectively, and only one compound inhibited
EGFRT790M/L858R (T001-10027877: 1.27 µM). Additionally, compared to AZD9291, which served as a positive
control with IC50 values of 0.64 and 0.012 µM against
EGFRT790M/C797S/L858R and EGFRT790M/L858R, respectively,
T001-10027877 clearly demonstrated less potent kinase
inhibition. However, the unique structural architectureof T001-10027877 offers significant potential for future
structural modifications. Therefore, T001-10027877
emerged as a promising lead compound for devisingstrategies to counteract EGFR-induced resistance, especially by targeting the T790M and C797S mutations.
In addition, T001-10027877 was selected for binding analysis, using D008-10206173 as the negative control, as shown in Fig. 7. According to recent research,
the residues M790, L858, S797 and M793 are importantfor EGFR inhibitors to overcome resistance mediated by
T790M or C797S. Figure 7A shows the robust inhibitory
activity of AZD9291 against EGFR resistance, as evidenced by the formation of four hydrogen bonds withthe crucial amino acids K745, S797, and M793 and theestablishment of five pi-cation interactions with otheramino acids. This demonstrates its high efficacy in targeting the EGFR protein. On the other hand, Fig. 7B shows

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Table 4 Docking information forthe 6 selected compounds

that T001-10027877 exhibits a diverse set of interactions, including hydrogen bonding, hydrophobic, andpi-cation interactions, notably enhancing its binding to
the S797 residue via additional interactions with M790,
S796, and M793. Despite this broad interaction spectrum, the potency of T001-10027877 did not reach thatof AZD9291.
Further analysis of D008-10206173 (presented inFig. 7C) shows that this compound forms primarilyhydrophobic and pi-stacking interactions, which are

inherently weaker than hydrogen bonds. This comparison revealed that T001-10027877 demonstrates greater
activity than D008-10206173 but remains less potent
than AZD9291. The similar interaction patterns of T00110027877 and AZD9291 with the protein, particularly
in overcoming EGFR resistance caused by the T790M or
C797S mutation, suggest that T001-10027877, despite
its reduced potency compared to that of AZD9291, stillholds potential as a candidate for overcoming resistancemediated by specific EGFR mutants. Such integrated

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Fig. 6 (A) The original ligand of protein 6JRJ aligned with the 39 unselected compounds. (B) The original ligand of protein 6JRJ aligned with the 6 selected compounds. It is important to highlight that these compounds were chosen from among 45 candidates identified through a clustering process

Table 5 Kinase inhibitory activity of six selected compounds
against EGFRT790M/C797S/L858R and EGFRT790M/L858R
Compd.
D008-10022050
D008-10206173
D008-10099272
D008-10135583
T001-10026427
T001-10027877
AZD9291aa

IC50 (µM)
EGFRT790M/C797S/L858R
>10.0
>10.0
3.26
3.35
>10.0
4.34
0.64

EGFRT790M/L858R
>10.0
>10.0
>10.0
>10.0
>10.0
1.27
0.012

Used as the positive control

analysis emphasizes the nuanced efficacy of these compounds in targeting EGFR mutations, highlighting theability of T001-10027877 to serve as an alternative forcombating EGFR resistance.

Antiproliferative activity

Furthermore, the antiproliferative activity of the six
selected compounds was evaluated by the MTT methodwith four cancer cell lines (A549, H1975, H460 and Ba/
F3-EGFRL858/T790M/C797S). These cell lines were chosen toencompass a broad spectrum of EGFR mutation statuses:
A549 and H460 as wild-type EGFR controls, H1975 representing the EGFRT790M/L858R mutations associated withacquired resistance to first-generation EGFR tyrosine
kinase inhibitors, and Ba/F3-EGFRL858/T790M/C797S tomodel compound efficacy against the complex scenario
involving the T790M, L858R, and C797S mutations. As
shown in Table 6, all the compounds inhibited the growthof A549, H1975, H460 and Ba/F3-EGFRL858/T790M/C797S
cells to different degrees, with IC50 values ranging from
1 to 50 µM. Moreover, nearly all the compounds were

Fig. 7 Analysis of the molecular interactions of AZD9291-6JRJ, T001-10027877-6JRJ and D008-10206173-6JRJ

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Table 6 The antiproliferative activities of the six selectedcompounds in A549, H460 and Ba/F3-EGFRL858/T790M/C797S cells
Compd.

D00810022050
D00810206173
D00810099272
D00810135583
T00110026427
T00110027877
AZD9291aa1.55 ± 0.13

3.51 ± 0.36

4.46 ± 0.46

than the C797S mutant. Consequently, the H1975 cellline was chosen for assessing the antiproliferative effects
using the AO staining assay at AO concentrations of 0.5,
1, and 2 µM (Fig. 8). As shown in Fig. 8, the H1975 cells in
the control group exhibited a normal morphology, emitting circular and dense green fluorescence. Notably, boththe positive control (AZD9291) and T001-10027877
induced significant shrinkage of the cell membrane andthe appearance of sharply defined cell edges, indicating
the induction of apoptosis. At a concentration of 2 µM,
T001-10027877 induced apoptosis to the same extent
as AZD9291. Moreover, as the concentration of T00110027877 increased, its ability to induce H1975 cell
apoptosis also increased.

4.951 ± 0.51 4.47 ± 0.45

5.38 ± 0.49

4.34 ± 0.34

Effects of T001-10027877 on H1975 cell cycle progression

IC50 (µM ± SD)
H1975
A549

H460

38.17 ± 3.56 > 50

> 50

Ba/F3-EGFRL858/T790M/C797S
> 50

> 50

> 50

> 50

> 50

41.97 ± 4.69 > 50

> 50

> 50

39.92 ± 4.05 15.90 ± 2.73 4.68 ± 0.41

> 50

43.14 ± 4.68 34.68 ± 3.61 44.22 ± 4.68 > 50
1.70 ± 0.12

Used as the positive control

more sensitive to the H1975 cell line, which implies
that these selected compounds could be used as potential compounds for overcoming resistance. Notably,
the T001-10027877 showed 1- to 3-fold greater activity than the positive control compound AZD9291 (4.95,
1.55, 5.38 and 4.34 µM) against A549, H1975, H460
and Ba/F3-EGFRL858/T790M/C797S cells, with IC50 valuesof 1.7, 1.55, 3.51 and 4.46 µM, respectively. Notably,
only T001-10027877 and AZD9291 inhibited the Ba/
F3-EGFRL858/T790M/C797S cell line, with IC50 values of 4.46
µM and 4.34 µM, respectively. These data indicate thatcompared to AZD9291, T001-10027877 shows similaror even superior inhibition of Ba/F3-EGFRL858/T790M/C797S
cells harbouring the triple mutation, as well as the double-mutant H1975 cells. This finding highlights thepotential of T001-10027877 inhibiting the proliferationof cancer cells with the T790M and C797S mutations in
EGFR.
AO staining assays

Our goal was to identify a potential compound to overcome the resistance induced by the EGFR mutations
T790M and C797S. Moreover, T001-10027877 demonstrated greater inhibition of the EGFR T790M mutant

To explore the impact of Compound T001-10027877
on cell proliferation, cell cycle analysis was conductedin H1975 cells. As shown in Fig. 9, Compound T00110027877 induced cell cycle arrest at the G0/G1 checkpoint in a dose-dependent manner compared to that in
the control group. As the concentration of Compound
T001-10027877 increased, the S phase population
increased from 17.01 to 28.84%, while the G2/M phase
population decreased from 29.02 to 11.54%. The proportion of cells in G0/G1 phase did not significantly change.
Additionally, at a concentration of 1 µM, Compound
T001-10027877 caused cell cycle arrest at the S checkpoint at a rate of 28.84%, which is similar to that of thepositive control AZD9291 (29.10%).
Inhibitory effects of T001-10027877 on H1975 cell
migration

A cell scratch assay was used to investigate the effect ofT001-10027877 on the migratory capacity of H1975
cells. The cells were treated with 0.5, 1 or 2 µM T00110027877 for 24 h and then photographed under amicroscope. As shown in Fig. 10, after 24 h of culture,
the cells in the control group significantly migrated
towards the centre. The cells treated with T00110027877 migrated less than those in the control group,and this inhibitory effect was more potent at 2 µM
T001-10027877 than at 0.5 µM T001-10027877. The

Fig. 8 Cell morphology observed by fluorescence microscopy after treatment with T001-10027877

Wang et al. BMC Chemistry(2024) 18:159

Page 11 of 14

Fig. 9 Compound T001-10027877 induces H1975 cell cycle arrest in a dose-dependent mannerFig. 10 Dose-dependent inhibition of H1975 cell migration by Compound T001-10027877: A comparative study with AZD9291

inhibitory effect of T001-10027877 was similar to that of
AZD9291 at a concentration of 1 µM. These results demonstrated that T001-10027877 had a dose-dependent
inhibitory effect on the migratory capacity of H1975 cells
and exhibited efficacy similar to that of the positive control AZD9291.
Quantitative reverse transcription polymerase chain
reaction (qRT–PCR) analysis after treatment with T00110027877

To determine the mechanism of action of Compound
T001-10027877 in tumour cells, fluorescence quantitative PCR was used to examine the EGFR and mTOR
expression levels in H1975 cells. In untreated H1975 cells,
the expression levels of EGFR and mTOR were at baseline levels, as shown in Fig. 11. Treatment with 0.5 µM
T001-10027877 effectively reduced the relative EGFR
expression to 0.51 and the relative mTOR expression to
0.64. Increasing the concentration of T001-10027877 to
1 µM further decreased the relative EGFR expression andmTOR expression to 0.35 and 0.53, respectively, demonstrating dose-dependent inhibition. At 1 µM, the positive
control AZD9291 reduced the relative EGFR expression
to 0.28 and that of mTOR to 0.52, slightly surpassing theeffects of T001-10027877 in terms of EGFR suppression

but comparably affecting mTOR expression. These results
highlight the potent, dose-responsive inhibitory effectof T001-10027877 on the EGFR and mTOR signalling
pathways, as T001-10027877 shows marked selectivity
for EGFR at relatively high concentrations. Furthermore,
T001-10027877 demonstrated similar selectivity andefficacy towards EGFR as the positive control AZD9291,
indicating comparable levels of expression and activity.
Haemolytic activity of T001-10027877 at various
concentrations

Moreover, haemolytic activity was also considered. As
shown in Fig. 12, the haemolytic activity values were 0,
1.69, 3.78, 7.13, 7.84 and 8.76 for T001-10027877 at 0,
16, 32, 64, 128 and 256 µg/mL, respectively. All of thehaemolytic activity values at various concentrations wereless than 9%, which means that compared with the control, 1% Triton X, T001-10027877 was safe in terms of
haemolytic activity for the treatment of cancer cells.

Discussion
Herein, we emphasize the multifaceted evaluation of
T001-10027877, which spans from machine learningmodel selection to detailed molecular interaction analysis, highlighting the potential of this compound as a novel

Wang et al. BMC Chemistry(2024) 18:159

Page 12 of 14

Fig. 11 Quantitative RT‒PCR analysis of EGFR and mTOR expression in H1975 cells treated with T001-10027877

Fig. 12 Evaluation of the haemolytic safety of Compound T00110027877: A dose‒response study

agent for overcoming EGFR-mediated resistance in cancer therapy.
Initially, the selection of the random forest model via
the ROC method, as documented in Table 2; Fig. 3, provided a solid foundation for computational screening.
This model outperformed the others, including SVM,
KNN, neural network, and gradient boosting, by demonstrating superior accuracy in predicting compounds withpotential inhibitory activity against EGFR mutants. Such
precision led to the identification of approximately 5105
compounds, a testament to the model’s efficacy in navigating the complex chemical space of potential inhibitors.
Redocking studies further refined our search, pinpointing protein 6JRJ as the most suitable receptor for virtual

screening due to its affinity for and sensitivity towards
the ligands, as shown in Table 3; Fig. 5. This specificity
in target selection was crucial for identifying 284 compounds with significant affinity, which were further
narrowed down to six hit compounds through cluster
analysis, emphasizing the importance of structural diversity in the selection process.
A comparative analysis of these compounds revealed
that T001-10027877 had notable inhibitory activity, particularly against the EGFRT790M/L858R and
EGFRT790M/C797S/L858R mutants, showing efficacy similar
to or superior to that of the positive control, AZD9291.
This is supported by molecular interaction studies(Fig. 7A and B), where T001-10027877 exhibited a comprehensive interaction profile with key EGFR residues,suggesting its ability to effectively inhibit EGFR-driven
cell proliferation.
Further experimental validation via kinase assays
and antiproliferative activity tests across multiple cancer cell lines (A549, H1975, H460, and Ba/
F3-EGFRL858/T790M/C797S) confirmed the potent activityof T001-10027877. Notably, T001-10027877 demonstrated 1- to 3-fold greater activity than did AZD9291
in certain assays, highlighting its potential to serve as amore effective therapeutic agent.
The antimigratory effects of T001-10027877, as evidenced by the cell scratch assay, and its ability to induce
cell cycle arrest, as demonstrated via cell cycle analysis, further substantiated the efficacy of this novel compound. These results, together with the fluorescencequantitative PCR findings, revealed that T001-10027877
dose-dependently inhibited EGFR and mTOR expression,
revealing its targeted mechanisms of action against cancer cell proliferation.
Finally, the evaluation of haemolytic activity confirmed
the safety of T001-10027877, indicating that it is a viable

Wang et al. BMC Chemistry(2024) 18:159

candidate for further development. The minimal haemolytic effects of this compound, even at high concentrations, align with the requirements for a therapeuticallyeffective yet safe anticancer agent.
In summary, the comprehensive assessment of T00110027877, from computational predictions to experimental validation, showcases its promise as a novel
therapeutic option for overcoming EGFR-mediatedresistance. Its potent efficacy, coupled with its favourable
safety profile, makes it a compelling candidate for further
investigation and potential clinical application in cancer
therapy.

Conclusion
In this study, an in silico fusion method, which included
machine learning, virtual screening and clustering,combined with activity evaluations was used to identify
active EGFR inhibitors targeting EGFRT790M/C797S/L858R.
First, ROC-guided machine learning was used to identify
nearly 5105 compounds from three compound libraries (over 200000 compounds). Second, virtual screening
and binding model analysis were performed to obtainsix potential compounds. In addition, the kinase assay
showed that EGFRT790M/C797S/L858R exhibited greater sensitivity to these six compounds than EGFRT790M/L858R.
Among them, Compound T001-10027877 inhibited
both EGFRT790M/C797S/L858R and EGFRT790M/L858R, with
IC50 values of 4.34 and 1.27 µM, respectively, whichmeans that T001-10027877 could overcome the EGFR
resistance mediated by the T790M or C797S mutations.
Moreover, the antiproliferative effects of the six hit compounds were also investigated, and T001-10027877
showed the best anticancer activity in the H1975, A549,
H460 and Ba/F3-EGFRL858/T790M/C797S cell lines, with IC50
values of 1.7, 1.5, 3.5 and 4.46 µM, respectively. Furthermore, AO staining and cell cycle experiments revealed
that Compound T001-10027877 could induce H1975
cell apoptosis in a concentration-dependent manner.
Moreover, the haemolytic activity evaluation suggested
that the Compound T001-10027877 is safe for further invivo study.
Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s13065-024-01279-z.
Supplementary Material 1
Author contributions
LW: Conceptualization, Investigation, Methodology, Formal analysis, Project
administration, Data curation, Software, Validation, Writing – original draft,
Writing – review & editing, Resources, Funding acquisition. XH: Investigation,
Methodology, Visualization, Resources, Data curation, Software, Validation.
SX: Visualization, Data curation, Validation. YA: Investigation, Visualization,
Validation. Xl: Investigation, Visualization, Validation. WZ: Investigation,
Methodology, Formal analysis, Project administration, Supervision,

Page 13 of 14

Resources, Funding acquisition. SX: Visualization, Data curation, Validation.
YT: Investigation, Methodology, Formal analysis, Project administration,
Data curation, Validation, Resources. SC: Conceptualization, Investigation,
Methodology, Formal analysis, Project administration, Supervision, Writing –review & editing, Resources, Funding acquisition. QL: Methodology, Formal
analysis, Funding acquisition. PZ: Methodology, Formal analysis.
Funding
This work was financially supported by the Jiangxi Province Natural Science
Foundation for Youths (20224BAB216112), the Science and Technology
Research Foundation of Jiangxi Education Department (GJJ21113), the Ph.D.
Startup Foundation of Jiangxi Science and Technology Normal University(2021BSQD25), the “Five-level Progressive” talent cultivation project of Jiangxi
Cancer Hospital & Institute (WCDJ2024YQ03) and the Key R&D plan of Jiangxi
Province (2021BBG71006).
Data availability
All data generated or analyzed during this study are included in the
manuscript. The compound libraries used in this study are accessible via
the following links: FDA Library (https://file.selleckchem.com/downloads/
library/20240801-L1300-FDA-approved-Drug-Library.SDF), Bioactivity Library(https://file.selleckchem.com/downloads/library/20240801-L1700-BioactiveCompound-Library-I.SDF and https://file.selleckchem.com/downloads/
library/20210610-L1700-II-Bioactive-Compound-Library-II-(Provided-by-Pfizer)96well-com.sdf ), and Specs Library (https://www.specs.net/).

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Jiangxi Provincial Key Laboratory of Drug Design and Evaluation, Schoolof Pharmacy, Jiangxi Science & Technology Normal University,
Nanchang 330013, China
2
Cancer Research Center, Jiangxi University of Traditional Chinese
Medicine, Nanchang 330004, China
3
Jiangxi Provincial Cancer Hospital, Nanchang, Jiangxi 330029, P. R. China
Received: 1 October 2023 / Accepted: 22 August 2024

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