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In silico discovery of a novel potential allosteric PI3Kα inhibitor incorporating 2-oxopropyl urea targeting head and neck squamous cell carcinoma

BMC Chemistry volume 19, Article number: 55 (2025) Cite this article

Abstract

Head and neck squamous cell carcinoma (HNSCC) is the most common head and neck cancer and highly aggressive and heterogeneous. Targeted therapy is still the main treatment method used in clinic due to lower side effect and personalized medication. In order to discover novel and effective drugs with low side effect against HNSCC, we analyzed the genes related to HNSCC, and found that PIK3CA was highly expressed in tumor tissues and often experienced mutations, leading to excessive activation of phosphoinositide 3-kinase alpha (PI3Kα), promoting the development of HNSCC. The allosteric PI3Kα inhibitor STX-478 inhibits the growth of tumor with hotspot mutations in PI3Kα and shows prominent efficacy on the treatment of human HNSCC xenografts without displaying the metabolic dysfunction observed in Alpelisib. These mutations open the allosteric site more readily, increasing the selectivity of STX-478 for mutant PI3Kα. STX-478 cleverly avoids the side effect of ATP competitive PI3Kα inhibitors. So, the structure of STX-478 was optimized based on the interaction mechanism between STX-478 and PI3Kα. Then, virtual screening, binding mode research, target verification, physical and chemical properties, pharmacokinetic properties and stabilities of ligand-PI3Kα complexes were evaluated by computer technologies (scaffold hopping, cdocker, SuperPred, SwissTarget prediction, Lipinski’s rule of five, ADMET and MD simulation). Finally, J-53 (2-oxopropyl urea compound) with excellent properties was selected. J-53 not only formed H-bonds with key amino acids, but its unique -C(O)CH3 could also form H-bonds with ILE1019, making it more stably bound to PI3Kα and contributing to its activity. After the SciFinder verification, J-53 with novel structure had the value of further study. This study suggested that J-53 could be used as potential inhibitors of PI3Kα, and provides valuable information for the subsequent drug discovery of allosteric PI3Kα inhibitors.

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Introduction

Head and neck cancer (HNC) is one of the common malignant tumors, including oral cancer, nasopharyngeal cancer, laryngeal cancer, hypopharyngeal cancer and thyroid cancer [1]. Among them, head and neck squamous cell carcinoma (HNSCC) accounts for more than 90% and is a kind of malignant tumor originating from the upper gastrointestinal mucosal epithelium of the oral, maxillofacial, pharyngeal, and laryngeal regions. HNSCC is related to various factors such as smoking, alcohol consumption, chewing betel nut and human papillomavirus infection [2]. HNSCC usually affects the normal physiological functions of patients, such as breathing, swallowing, and language abilities, even reduces quality of life [3].

There are various treatments methods for HNSCC, including surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. The conventional treatment method is surgery combined with radiotherapy and chemotherapy [4]. The difficulty of surgery is high, which may affect the patient's appearance, language and even swallowing function [5]. Radiotherapy and chemotherapy can control the progression of the disease in the short term and not prolong the tumor-bearing survival of patients [6]. The new methods in the treatment of HNSCC are mainly focused on the improvement of comprehensive treatment methods, as well as the application of new therapy models such as immunotherapy and targeted therapy. Molecular targeted drugs aiming at targets related to tumor occurrence and progression, kill tumor cells specifically and reduce toxic and side effects [7].

In the early stage of this study, correlation analysis between genes and diseases revealed that the PIK3CA was overexpressed in HNSCC, which affected the progression of the disease. The TCGA statistical results showed that 97 out of 530 HNSCC patients had mutations of PIK3CA, which encoded PI3Kα [8,9,10]. PI3K includes class I, II, and III. Research has shown that class I PI3K is closely related to tumor progression, including PI3Kα, PI3Kβ, PI3Kδ, and PI3Kγ [11]. The mutations of PIK3CA leading to abnormal activation of the PI3K pathway is one of the most common carcinogenic modes [12]. Mutations of PIK3CA are found in approximately 30% of human cancers. The mutations occur in several common cancers, such as endometrial (28%), breast (29%), head-and-neck (14%) and cervical (17%) cancers [13]. In terms of protein structure, cancer-associated mutation sites in p110α were reported, mainly distributed in four regions: the ABD, C2, helical, and kinase domains. PIK3CA mainly mutates in three hot spots, the helical domain (E542 and E545) and the kinase domain (H1047). E542 and E545 are mutated to Lys on exon 9, forming electrostatic interactions with the nSH2 domain. H1047, which occurs frequently in cancers, is close to the C-terminal and mutated mainly to Arg on exon 20. Alterations of p85α are not as common as p110α in cancer, and most mutations are generally in truncations. In particular, a truncation mutant known as P65α can eliminate all amino acid C-termini to residue 571, leading to abnormal activation of PI3Kα activity [13]. Compared with wild-type PI3Kα, the mutations of PIK3CA are functional acquired mutation [14]. In terms of protein structure, approximately 80% mutated PIK3CA genes in cancer are hot spot mutation [15]. In HNSCC, hotspot mutation sites for PIK3CA mutations are E542K, E545K and H1047R/L, which comprise 90% of PIK3CA mutations identified in HNSCC. Most commonly, the E542 and E545 are substituted with a lysine (K), and H1047 is frequently substituted with arginine (R) [16].

The PI3K/Akt/mTOR signaling pathway is one of the most classic signaling pathways involved by PI3K, which is closely related to tumor occurrence and development [17]. After receiving activation signals, PI3K phosphorylates phosphatidylinositol-4,5-diphosphate (PIP2) to phosphatidylinositol-3,4,5-triphosphate (PIP3), which regulates downstream signaling pathways [18]. In 1993, the pleckstrin homology (PH) domain was discovered in the pleckstrin of platelet protein. It is a loosely conserved domain composed of approximately 120 amino acid residues and is a common effector in class I PI3K [19]. The binding of PIP3 to the PH domain of Akt (also known as PKB) leads to a conformational change in Akt recruited to the cell membrane, increasing the phosphorylation of THR308 and SER473 [20]. PDK1 and mTORC2 are key proteins involved in phosphorylation of THR308 and SER473 [21]. After complete activation, Akt leaves the cell membrane and enters the cytoplasm or nucleus and activates mTORC1 by phosphorylating the two negative regulatory factors (TSC2 and PRAS40) of mTORC1, further phosphorylating p70S6K and 4EBP1, regulating cell proliferation and metabolism (Fig. 1) [22].

Fig. 1
figure 1

Schematic diagram of the PI3K/Akt/mTOR signaling pathway. p85 regulatory subunit and p110 catalytic subunit can form p85-p110 complexes causing p110 to be in a resting state. SH2 domains of p85 permit complex to combine with phosphorylated tyrosine residues, which recover activity of p110. Gβγ subunits can activate PI3Kγ/β. Class I PI3Ks phosphorylate PIP2 to produce PIP3, which recruits Akt by binding to the pH domain. Then, Akt is phosphorylated at THR308 and SER473. Subsequently, downstream effectors are phosphorylated, leading to the cascade amplification of PI3K pathway

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Currently, there are some ATP competitive PI3Kα inhibitors exhibit excellent anti-tumor activity in HNSCC cell lines carrying PIK3CA mutations, xenograft models, and patients in vivo [23]. Alpelisib is an oral PI3Kα selective inhibitor [24]. The treatment effect of Alpelisib in solid tumor patients with PIK3CA mutation (HNSCC patients with PIK3CA mutation account for 14.2%) was significantly better than that of wild type patients. The disease control rate (DCR) of HNSCC patients reaches 68.4% (NCT01219699) [25]. Alpelisib has been approved by FDA to treat breast cancer patients with PIK3CA mutation. Multiple clinical studies have shown that HNSCC patients with PIK3CA mutations can benefit from Alpelisib (NCT02282371, NCT03292250, NCT03601507, NCT02145312) [26]. Alpelisib is an inhibitor of wild-type and all mutant PI3Kα. The most frequent treatment-related AE was hyperglycemia, an on-target effect of PI3K inhibition, reported in 69 (51.5%) patients across both dosing regimens (400 mg once daily and 150 mg twice daily) and 32 [23.9%] patients at grade 3 or 4. Hyperglycemia was increased in a dose-dependent manner. In normal tissue, the PI3K/Akt signal transduction pathway is essential for insulin-dependent glucose uptake, particularly in skeletal muscle and adipose tissues that are largely responsible for systemic glucose homeostasis. Inhibiting WT PI3Kα signaling causes acute insulin resistance and promotes hyperglycemia, which limits the clinical utility of Alpelisib [27].

Inavolisib is a powerful PI3Kα inhibitor and degradation agent of mutant PI3Kα and used in patients with locally advanced or metastatic solid tumors with PIK3CA mutations (NCT03006172) [28]. CYH33 is a highly selective PI3Kα inhibitor and the objective response rate (ORR) for the treatment of solid tumors with PIK3CA mutations (including HNSCC) is 14.3% (NCT03544905) [29]. In addition, PI3K inhibitors such as Copanlisib, Pictilisib, GDC-0084, PKI-402, Buparlisib and NVP-BEZ235 have shown strong therapeutic effects on solid tumors (HNSCC, breast cancer, colon cancer and so on) with PIK3CA mutations [30]. The structures of some PI3K inhibitors against PIK3CA mutant HNSCC were shown in Fig. 2.

Fig. 2
figure 2

Reported PI3K inhibitors against PIK3CA mutant HNSCC

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In addition, HNSCC patients with PIK3CA mutations have already benefited from the allosteric PI3Kα inhibitors. There are 3 allosteric PI3Kα inhibitors enter clinical trials [31]. STX-478, which is an allosteric PI3Kα inhibitor and inhibits the growth of tumor with hotspot mutations in PI3Kα, shows robust efficacy in cell line derived xenograft tumor models (CDX), patient derived xenograft tumor models (PDX) and human HNSCC xenografts. STX-478 was found to be a potent and selective inhibitor of all kinase-domain mutant of PI3Kα in cancer. STX-478 led to robust efficacy without WT toxicities in PI3Kα mutant tumors in vivo. It did not display the severe metabolic dysfunction observed with Alpelisib. It has obvious advantages in safety. The evaluation of STX-478 in participants with advanced solid tumors has entered clinical study (NCT03601507) [27]. RLY-2608 has a stronger inhibitory effect on mutated PI3Kα than the wild-type. A phase I ReDiscover trial confirmed sustained target inhibition in PIK3CA mutated solid tumor patients. It has minimal impact on glucose homeostasis and acceptable safety and anti-tumor activity [32]. LOXO-783 shows highly selective in PI3Kα with PIK3CA mutations and displays good therapeutic effect on breast cancer with PIK3CA mutation (NCT05307705). The structures of some PI3K inhibitors against PIK3CA mutant HNSCC were shown in Fig. 2.

Differences and similarities between allosteric PI3Kα inhibitors such as STX-478 and ATP-competitive PI3Kα inhibitors such as Alpelisib in terms of therapeutic efficacy in tumors, binding affinity, and safety profiles are described below: (1) The therapeutic efficacy in tumors. 1) The significant increases in serum insulin were observed in animals dosed with Alpelisib 50 mg/kg 1 h after dose, whereas STX-478 100 mg/kg was not associated with elevated insulin [27]. 2) STX-478 monotherapy was studied in a panel of 10 CDX and PDX tumors comprising primarily breast and HNSCC tumors, with one example each for colon and lung cancers, representing prevalent PI3Kα-mutated cancer types. STX-478 100 mg/kg q.d. demonstrated robust efficacy in xenograft models that was similar or superior to high-dose Alpelisib, giving a mouse Alpelisib exposure that exceeded patient exposure by approximately twofold [27]. (2) The binding affinity. STX-478 was found to be a potent and selective inhibitor of all kinase-domain mutant of PI3Kα in cancer, including the most common variant H1047R (IC50 = 9.4 nmol/L), with 14-fold selectivity over WT PI3Kα (IC50 = 131 nmol/L). Under these same assay conditions, STX-478 was less potent against E542K (IC50 = 113 nmol/L) and E545K (IC50 = 71 nmol/L) helical-domain mutants, whereas Alpelisib showed no mutant selectivity as previously reported [27]. (3) The safety profiles. 1) Clinical studies. The first in human phase Ia study of single-agent Alpelisib (NCT01219699) in 134 patients with PIK3CA-altered advanced solid tumors (such as 27% breast, 26% colorectal and 14% head and neck cancer) was implemented to evaluate efficacy and tolerability. The MTDs of oral Alpelisib were 400 mg once daily and 150 mg twice daily. Hyperglycemia (52%) was one of the most frequent treatment-related adverse events (AEs, all-grade) [27]. A phase 1/2 study conducted for the first-in-human evaluated the efficacy of STX-478 alone or in combination for the treatment of advanced PI3Kα solid tumors patients. As of June 21st 2024, 61 patients (29 HR+/HER2− BC, 32 other solid tumors) were treated at STX-478 doses of 20 mg to 160 mg daily. STX-478 was well-tolerated with a MTD of 100 mg daily. Treatment-related adverse events (TRAEs) of 15% included: fatigue (30%), hyperglycemia (23%), nausea (20%), and diarrhea (15%). No patient discontinued due to an AE [33]. The repeat doses of STX-478 at 100 mg/kg q.d. were well tolerated without metabolic dysregulation, whereas Alpelisib caused overt insulin resistance at 50 mg/kg dose levels [33]. 2) Preclinical studies. The repeat doses of STX-478 at 100 mg/kg q.d. were well tolerated without metabolic dysregulation, whereas Alpelisib caused overt insulin resistance at 50 mg/kg dose levels [33].

The allosteric PI3Kα inhibitors such as STX-478 and ATP-competitive PI3Kα inhibitors such as Alpelisib can bind to the kinase domain of PI3Kα, with the difference being in the mechanism of action at the molecular level. The details are presented below: (1) STX-478 is a mutant-selective and allosteric PI3Kα inhibitor. It can make several specific contacts within the allosteric site, which forms due to a major conformational shift in residues 936–940, along with other smaller, local rearrangements (PHE937 and LEU938). The allosteric site occupied by STX-478 is more accessible in mutant forms of the enzyme. The benzofuran and trifluoromethyl moieties of STX-478 position at the deepest point of the pocket, anchoring against hydrophobic and aromatic residues. The urea present in STX-478 forms a bifurcated hydrogen bond with the LEU911 backbone carbonyl and a suboptimal hydrogen bond with the GLY912 carbonyl. This hydrogen bonding secures the inhibitor’s central linker with the pyrimidine positioned closer to the protein’s surface. The LYS941 side chain forms van der Waals contacts across the pyrimidine ring in an orientation roughly 180 degrees from the typical WT conformation of this side chain. The LYS941 side-chain nitrogen is positioned within hydrogen-bonding distance of the ASP1018 backbone carbonyl, stabilizing the protein in this conformation. A small shift in the TYR1021 side chain relative to the apo conformation stabilizes the opposite side of the STX-478 pyrimidine [27]. (2) ATP-competitive PI3Kα inhibitors bind to the ATP binding site. The ATP binding pocket is located in a cleft between the two lobes of the kinase domain, and there is a hinge valine residue at the end of the cleft. This valine residue is conservative in all class I PI3K isoforms and can form an H-bond with the purine ring during the binding process with ATP. Most PI3K inhibitors that have been identified as ATP competitive inhibitors can form an H-bond with this valine residue. Alpelisib is a PI3Kα inhibitor approved for marketing by the FDA. It can form contacts with PI3Kα ATP pocket. Most residues that contact Alpelisib in PI3Kα are conserved, and only five residues (852RNSH855 and Q859) in the hinge region are variable. It can form hydrogen bonds with VAL851, GLN859 and SER854. The CF3 group is near the charged terminal amine of LYS802, and GLN859 in the hinge region forms dual hydrogen bonds with Alpelisib, which are very important for PI3Kα isoform selectivity [13].

Computer-aided drug design (CADD) technology can accelerate the drug discovery process, reduce costs of research and development and the probability of failure. CADD is an interdisciplinary field that combines bioinformatics, cheminformatics, and computational science to dramatically accelerate the discovery and development of new drugs. CADD utilizes computer algorithms and models to help researchers screen candidate drug molecules, optimize drug structures, and understand molecular-level interactions [34]. CADD includes a variety of methods, such as scaffold hopping, molecular docking, molecular dynamics, virtual screening, and pharmacophore modeling [35,36,37,38]. Among these, scaffold hopping, molecular docking, and molecular dynamics are the most widely used drug screening methods. Scaffold hopping can help researchers quickly discover drug structures that break through patent protection. Molecular docking can help determine the binding mode of the compound to the target. Molecular dynamics assess the stability of receptor-ligand binding [39]. The influence of CADD in preclinical drug development is profound.

At present, there are relatively few PI3Kα inhibitors used for the treatment of HNSCC. Most of them are ATP competitive inhibitors, which have side effects such as metabolic dysfunction [40]. PIK3CA mutation is closely related to the progression of HNSCC [41]. In order to develop PI3K inhibitors with novel structures, low side effects, and good therapeutic effects, structural optimization of STX-478 was executed based on the interaction mechanism between STX-478 and PI3Kα. By utilizing computer technologies. we found potential allosteric PI3Kα inhibitors. In this work, we report the discovery of new allosteric PI3Kα inhibitors through scaffold hopping in combination with cdocker, SuperPred, SwissTarget prediction, ADMET (Discovery studio), Swiss ADME, ADMETlab 2.0 and molecular dynamics simulation (Fig. 3).

Fig. 3
figure 3

The flow chart of identification of allosteric PI3Kα inhibitors

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Materials and methods

Correlation analysis between genes and HNSCC

We searched for HNSCC keywords in the GeneCards database (https://www.genecards.org/) to identify genes related to HNSCC. We calculated the median of relevance score and further selected genes. We analyzed the data and determine highly correlated genes. The relevance score of the PIK3CA gene was 8.97, which was the sixth gene affecting disease progression in HNSCC patients. Analyzed the expression of PIK3CA mutations on cancers through COSMIC database (https://cancer.sanger.ac.uk/cosmic). Further analysis was conducted on the expression of PIK3CA in tumor and normal tissues by TCGA database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga).

Scaffold hopping of STX-478

Leonard Buckbinder et al. studied the binding of PI3Kα allosteric pocket to PI3Kα inhibitors STX-478 and made the conclusion: (1) The benzofuran and trifluoromethyl moieties position at the deepest point of the pocket, anchoring against hydrophobic and aromatic residues; (2) The urea present in STX-478 formed a bifurcated hydrogen bond (H-bond) with the LEU911 and a suboptimal H-bond with the GLY912 [27]. In order to discover novel and highly selective allosteric PI3Kα inhibitors without the side effect of metabolic dysfunction, we modified STX-478 by scaffold hopping strategy [42]. It introduce suitable groups making that compound can form more interactions and fully occupy allosteric sites at the same time. STX-478 will be optimized by scaffold hopping in Discovery studio 3.5. The process of scaffold hopping was as follows: (1) Building a high-quality fragment database. (2) Selecting the modification points. (3) Searching the fragment database to obtain compound [43].

Virtual screening based on molecular docking

As an effective tool, virtual screening is used in the discovery of drug. In this study, Discovery studio 3.5 software was used for virtual screening based on molecular docking. The allosteric binding site of PI3Kα (PDB ID: 8TGD) was defined through the “Define and Edit Binding Sites”. The “From Current Selection” module was used to construct binding pockets around the key residues LEU911, GLY912, LYS941, ARG949, GLU950, ASP1018, ILE1019, and ILE1022 at the allosteric binding site, which was showed as a sphere with a radius of 8.22, and its coordinates were X = 19.22, Y = − 28.87, and Z = 92.63. The RMSD of the STX-478 in the screening model file after overlapping with the STX-478 in the crystal is less than 1 Å. The CDOCKER method was used to further study the -cdocker energy and affinity between the screened compounds and the allosteric site. Finally, compounds with high docking scores and strong binding affinity were obtained [44].

Target prediction of compounds by SuperPred and SwissTarget prediction web server

The SuperPred web server (https://prediction.charite.de/index.php) connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. It offers both ATC and target prediction services for user-provided molecular structures. The stronger the targeting ability of the compound, the smaller the side effects [45].

Swiss Target Prediction web server (http://www.swisstargetprediction.ch/) predicts the target of a compound based on its similarity to the 2D and 3D structures of known compounds. The prediction can be made in three different species: humans, rats, and mice. Existing interactions come from the 16th edition of the ChEMBL database (https://www.ebi.ac.uk/chembl). Swiss Target Prediction provides a score for each predicted target to evaluate the druggability of accurate predictions [46].

Lipinski’s filter and ADMET study

Structure properties of drugs determine physicochemical and biochemical properties, which ultimately determine pharmacokinetics and toxicity. This section utilizes the ADMET algorithm module in Discovery studio 3.5 to predict the pharmacokinetics and toxicity of compounds [47]. The content of the evaluation is as follows: aqueous solubility, CYP2D6 binding, intestinal absorption, plasma protein binding (PPB), 2D polar surface area (PSA2D) and ALogP. Evaluate the molecular weight (MW), number of rotatable bonds (nRot), number of hydrogen bond acceptors (nHA) and number of hydrogen bond donors (nHD) using Swiss ADME (http://www.swissadme.ch/index.php). After screening potential PI3Kα inhibitors through the above experiments, the druggability of the compound was validated again using ADMET lab 2.0 (https://admetmesh.scbdd.com/), including MW, nHA, nHD, topological polar surface area (TPSA) and so on [48].

Molecular dynamics simulation

The molecular dynamics (MD) simulations were carried out by GROMACS 2020.3 software. The amber99sb-ildn force field and the general Amber force field (GAFF) were used to generate the parameter and topology of proteins and ligands, respectively. The operation steps are as follows. (1) The simulation box size was optimized with the distance between each atom of the protein and the box greater than 1.0 nm. (2) Fill the box with water molecules based on a density of 1. (3) The water molecules were replaced with Cl− and Na+ ions to make the simulation system electrically neutral. (4) Reduce the unreasonable contact or atom overlap in the entire system by the steepest descent method, which energy optimization of 5.0 × 104 steps were performed to minimize the energy consumption of the entire system. (5) After energy minimization, first-phase equilibration was performed with the NVT ensemble at 300 K for 100 ps to stabilize the temperature of the system. Second-phase equilibration was simulated with the NPT ensemble at 1 bar and 100 ps. (6) MD simulations were performed for 500 ns. The system was running with 300 K and 1 atmosphere [49].

Results

Correlation analysis between genes and HNSCC

We searched the GeneCards for HNSCC keywords and found 2083 genes related to HNSCC. After downloading the search results, we analyzed the relevance score data. The higher the relevance score in the Genecards, the closer the correlation between the gene and the disease. Using the median score (0.38) as the screening criterion, 1094 genes, which had highly correlated with HNSCC were identified. Among them, the PIK3CA gene had a relevance score of 8.97, which was significantly higher than the median and was the sixth largest gene affecting disease progression in HNSCC patients (Fig. 4A). We analyzed the mutation of amino acids in PI3Kα by COSMIC. Figure 4B shown that there were two regions with high mutation frequencies, located in the kinase (a) and helical (b) regions, respectively. Figure 4C showed that the mutations in the PI3Kα were mainly missense mutations. The amino acids with higher mutation frequencies were ranked in descending order: H1047 (kinase region), E545 (helical region) and E542 (helical region). Among them, histidine (H) at position 1047 was most likely to mutate into arginine (R), and glutamic acid (E) at position 545 was most likely to mutate into lysine (K).

Fig. 4
figure 4

A The top 10 genes had high correlation with HNSCC disease. B Mutated amino acids in PI3Kα. The darker the color, the higher the frequency of mutation. C The mutations in PI3Kα

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In addition, we analyzed the PIK3CA expression in bodymaps. From Fig. S1A, it could be seen that the PIK3CA was expressed in various tissues, such as the tongue, brain, and breast. Transcripts Per Million (TPM) could be used to analyze the expression of different genes in cancer tissue and adjacent normal tissues. From Fig. S1B and S1C, it could be seen that the expression levels of PIK3CA in HNSCC, ESCA, LGG, and LUSC were significantly higher than that in normal tissues. Based on the above analysis, HNSCC was highly associated with the PIK3CA, and mutations could lead to abnormal activation of PI3Kα. It is necessary to study novel PI3Kα inhibitors for the treatment of HNSCC.

Scaffold hopping of STX-478

STX-478 is a structurally novel, orally effective, allosteric PI3Kα inhibitors, which can occupy the allosteric site. STX-478 can cause tumor regression. It can form important H-bonds with the key amino acids LEU911 and GLY912. This H-bonds secures the STX-478’s central linker with the pyrimidine positioned closer to the PI3Kα’s surface. In addition, the interaction between LYS941 and STX-478 also contributes to the tight binding of the compound with allosteric site. At present, only 3 allosteric PI3Kα inhibitors (LOXO-783, RLY-2608, STX-478) without the side effect of metabolic dysfunction have entered clinical trials to evaluate their therapeutic effects in patients with solid tumors. In order to discover novel allosteric PI3Kα inhibitors without the side effect of metabolic dysfunction, structural optimization of STX-478 was executed based on the interaction mechanism between STX-478 and PI3Kα. In this study, we carried out scaffold hopping strategy to discover potential allosteric PI3Kα inhibitors. In general, interactions, including hydrogen bonding, hydrophobic, and electrostatic interactions, play an important role in the stability of these receptor-ligand complexes. Among them, hydrogen bond interactions are more critical to evaluate the binding affinity and stable conformations. So, searching for compounds with strong hydrogen bonding ability and good druggability may lead to the discovery of novel allosteric PI3Kα inhibitors. The urea present in STX-478 forms H-bond with LEU911 and GLY912. So, urea is a key pharmacophore that determines the ability of STX-478 to target allosteric sites. Based on this, we replaced the substituents at positions R1, R2, and R3 on structures A and B in Fig. S2, hoping to obtain compounds with diverse structures and targeting PI3Kα that have not been reported. In order to obtain a batch of new compounds that have strong binding ability to PI3Kα, we replaced urea and tried to find better functional groups (Fig. S2C and Fig. S2D). By structural optimization of STX-478, 17,834 new compounds were obtained for further study (Fig. S2).

Virtual screening

By structural optimization of STX-478, 17,834 compounds were obtained. CDOCKER method were performed to analyze the binding modes of compounds-PI3Kα. Based on the results, 12 molecules were screened out (the -cdocker energies of the screened compounds were higher than STX-478). Table S1 showed the structural formulas and -cdocker energies of the screened compounds.

Through virtual screening, it could be seen that the -cdocker energies of 12 compounds were higher than STX-478. The -cdocker energies of J-16, J-76, and J-53 were 74.7335, 72.9519, and 72.5405 kcal/mol, respectively, and higher than STX-478 (66.7128 kcal/mol). Figure 5 showed the binding modes of J-16, J-76, J-53, and STX-478 with allosteric sites. From Fig. 5, it could be seen that J-16 (Fig. 5A), J-76 (Fig. 5B), J-53 (Fig. 5C) and STX-478 (Fig. 5D) could form H-bond interactions with LEU911, GLY912, LYS941, ARG949, GLU950, and ASP1018. J-16 could also form H-bonds with THR813 (Fig. 5A). J-76 could also form H-bonds with GLN809 (Fig. 5B), and J-53 could also form H-bonds with ILE1019 (Fig. 5C). This might be the reason why the docking results of J-16, J-76, and J-53 with PI3Kα were better than those of STX-478.

Fig. 5
figure 5

Two-dimensional (2D) diagrams of ligand-PI3Kα interactions in allosteric PI3Kα pockets. A The 2D diagrams of J-16-PI3Kα interactions. B The 2D diagrams of J-76-PI3Kα interactions. C The 2D diagrams of J-53-PI3Kα interactions. D The 2D diagrams of STX-478-PI3Kα interactions

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Target prediction of the top 12 compounds by SuperPred web server

From the prediction results, PI3Kα was chosen as a target with high probability and model accuracy. The possibilities of targeting PI3Kα of compounds were higher than that of STX-478 (Fig. 6 and Table S2). Among them, there were 7 compounds with a probability of targeting PI3Kα greater than 90%. The probabilities of J-16, J-53, and J-32 were 93.14%, 92.42%, and 94.82% respectively, and higher than STX-478 (88.15%).

Fig. 6
figure 6

The prediction of target probability and model accuracy of the top 12 compounds against PI3Kα by the SuperPred target prediction web server

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Swiss target prediction for the top 12 compounds

The SwissTarget prediction report in Table S3 was depicting. Table S3 suggested that the probabilities of PI3Kα being the target of J-16, J-53, J-11, J-256, J-60, J-28, J-263, and J-532 were consistent with STX-478.

Lipinski’s filter and ADMET prediction

A large number of drugs are failed due to poor efficacy and toxicity. Linpiski’s rule of five is the rule which describes molecular properties important for a drug’s pharmacokinetics in the human body. In this study, we further studied the physical and chemical properties and absorption, distribution, metabolism, excretion, and toxicity characteristics of the top 12 screened compounds, and selected potential novel allosteric PI3Kα inhibitors with druggability. The Linpiski’s rule, including MW, nHD, nHA, nRot, and AlogP were showed in radar chart in Fig. S3. It could be seen from the radar chart that the most of molecules followed the Lipinski’s rule of five, except J-16 and J-47.

ADMET characteristics such as human intestinal absorption, aqueous solubility, PPB, CYP2D6, PSA2D, rodent carcinogenicity, mutagenicity (ames test), skin irritancy, and developmental toxicity potential (DTP prediction), were studied for STX-478 and top 12 compounds. The results were shown in Table 1. Human intestinal absorption and solubility are two key factors that affect oral bioavailability. The solubility levels of STX-478 and the top 12 compounds were 1, 2 and 4, respectively, indicating that the solubility of all 12 compounds was better than STX-478. The absorption levels of STX-478 and 12 compounds were 0, 1 and 3, respectively, indicating that most compounds (except J-16) had moderate to high intestinal absorption. The inhibition of CYP2D6 by drugs constitutes the majority of cases of drug-drug interaction. It could be found that none compounds might inhibit the CYP2D6. Besides, the predicting results of toxicity (mouse female NTP, mouse male NTP, DTP prediction, ames prediction and skin irritancy) of STX-478 as well as 12 molecules showed that studied compounds had no risk of carcinogen, mutagenicity, and skin irritation (Table S4). The DTP prediction results indicated that compounds (except for J-32, J-47 and J-263) and STX-478 had no risk of developmental toxicity potential (Table S4). So, the physicochemical and ADMET properties of 9 compounds (except J-16, J-32, J-47 and J-263) were within an acceptable range. Among them, the ability of intestinal absorption of J-76 was not as good as J-53. We further evaluated the drug-like of J-53 by ADMET lab 2.0, including MW, logP, LogD and so on (Fig. S4). From the results, it could be seen that the physicochemical properties of J-53 meet the requirements of druggability.

Table 1 The ADME of the top 12 compounds with docking score
Full size table

Based on the results of virtual screening, target prediction of compounds by SuperPred and SwissTarget prediction web server, Linpiski’s rule of five and ADMET analysis, J-53 was selected as the lead compound for futher evaluation.

Here we analyzed the binding mode of J-53-PI3Kα, and the results were shown in the Fig. S5. The following conclusions could be obtained from Fig. 5 and Fig. S5. (1) STX-478 was an allosteric PI3Kα inhibitor, binding in the allosteric binding site (around the key residues LEU911, GLY912, LYS941, ARG949, GLU950, ASP1018, ILE1019, and ILE1022, Fig. S5A). J-53 could bind to this allosteric binding site, indicating that J-53 might be an allosteric PI3Kα inhibitor (Fig. 5C and Fig. S5B). (2) The ability of a compound to form H-bonds with key amino acids LEU911 and GLY912 in the allosteric binding site affects its selectivity towards the PI3Kα. The urea of J-53 and STX-478 could form H-bonds with LEU911, GLY912 and LYS941. The aminopyrimidine of J-53 and STX-478 generated H-bonds with ARG949 and GLU950. In addition, J-53 and STX-478 could also form H-bonds with ASP1018, and ILE1022. The H-bond between J-53 and ILE1019 in the pocket might be the reason why the -cdocker energy of J-53 was better than STX-478 (Fig. 5C, D, and Fig. S5B, C). (3) J-53 could bind to allosteric sites, and its locations were basically consistent with STX-478 (Fig. S5D and S5E).

Through the above analysis, we found that J-53 was a potential allosteric PI3Kα inhibitor. The stability of J-53-PI3Kα system needs to be further studied by molecular dynamics simulation.

Molecular dynamics simulation of PI3Kα-STX-478/J-53system

The root mean square deviation (RMSD), which measures the coordinate deviation of a specific atom relative to a reference structure, is often used to evaluate whether a simulation system has reached stability [50]. A stable RMSD means that the corresponding atoms become stable. As shown in Fig. 7A, the PI3Kα, PI3Kα-STX-478, PI3Kα-J-53 reached equilibrium after 130 ns, indicating that the entire simulation process was stable and reliable.

Fig. 7
figure 7

A The RMSD trajectories of PI3Kα-ligand complexes during 500 ns simulations. B The RMSF maps of PI3Kα-ligands complexes during simulations. C The variation curve of SASA during 500 ns simulations. D The variation curve of Rg during 500 ns simulations

Full size image

Root mean square fluctuation (RMSF) calculates the fluctuations of each atom relative to its average position, characterizes the average effect of structural changes on time, and provides a characterization of the flexibility of various regions of the protein [51]. From Fig. 7B, we could see that the RMSF values of PI3Kα, PI3Kα-STX-478, and PI3Kα-J-53 were 0.1347 ± 0.0107 nm, 0.1189 ± 0.0196 nm, and 0.1042 ± 0.0197 nm, respectively. The results showed that the RMSF in the PI3Kα-J-53 complex system was the lowest, indicating J-53 could make PI3Kα more stable.

The solvent accessible surface area (SASA) is calculated by the interaction between Vander Waals forces and solvent molecules, and the lower the SASA value, the more stable the simulation system [52]. As shown in Fig. 7C, the SASA values of PI3Kα in PI3Kα-STX-478/J-53 complexes showed a decreasing trend during simulation processes. The SASA average of the PI3Kα, PI3Kα-STX-478 and PI3Kα-J-53 system were 577.7756 ± 3.9836 nm2, 562.5435 ± 3.9994 nm2 and 561.5331 ± 4.3737 nm2, respectively. PI3Kα-J-53 system was more stable compared to the PI3Kα-STX-478 system, which was consistent with the results of RMSF.

The radius of gyration (Rg) is used to demonstrate the protein structural density, which is the distance between the centroids of all atoms and their ends within a specific time interval and helps to deepen a detailed understanding of all dimensions of the simulation system [53]. Figure 7D showed that the Rg value of the PI3Kα and PI3Kα-STX-478/J-53. Overall, the Rg average of the PI3Kα, PI3Kα-STX-478 and PI3Kα-J-53 system were 3.5045 ± 0.0120 nm, 3.4776 ± 0.0124 nm and 3.4686 ± 0.0119 nm, respectively. The results showed that the PI3Kα-J-53 system was more stable compared to the PI3Kα-STX-478 system during the simulation, which was consistent with the results of RMSF and SASA.

In order to study the interaction between proteins and ligands, we conducted H-bond analysis on the PI3Kα-ligand complexes. After equilibrium, the averages of H-bonds number between PI3Kα-STX-478 and PI3Kα-J-53 were 2.8101 ± 0.0289 and 4.9779 ± 0.0272, respectively, indicating the existence of H-bonds between PI3Kα and ligands (Fig. S6).

The binding energy of PI3Kα-STX-478/J-53 complexes was calculate during the equilibrium stage. In the MMPBSA method, the total binding energy was decomposed into four independent parts (electrostatic interaction, van der Waals interaction, and polar and non-polar solvation interactions). The results were showed in Table S5. In the PI3Kα-STX-478/J-53 complex system, the binding free energies of PI3Kα-STX-478 and PI3Kα-J-53 were -125.280 ± 5.66 and -134.416 ± 4.32 kJ/mol, respectively. The PI3Kα-J-53 system was more stable and the main interactions were electrostatic and van der Waals interaction.

In order to further understand the interaction between allosteric PI3Kα pocket and ligands, we evaluated the contribution energy of each residue. From Fig. S7A, we could see that LEU911, PHE937, LEU938, LYS941, PHE1002, ILE1019 and TYR1021 were the main residues involved in the interaction between the PI3Kα and STX-478. From Fig. S7B, we could see that LEU911, PHE937, LEU938, PHE1002, IEU1013, ILE1019 and TYR1021 were the main residues involved in PI3Kα and J-53 interactions.

Conclusions

PIK3CA was highly expressed in HNSCC tumor tissues and often experienced mutations, leading to excessive activation of PI3Kα, promoting the occurrence and development of HNSCC. HNSCC patients with PIK3CA mutations have already benefited from the PI3Kα inhibitors. There are only 6 PI3K inhibitors which belong to ATP competitive inhibitors approved for market by the FDA. Among them, the PI3Kα inhibitor Alpelisib has entered the clinical research stage for the treatment of HNSCC, but it can bring side effects such as hyperglycemia. The emergence of allosteric PI3Kα inhibitors has broken this dilemma. At present, there are 3 allosteric PI3Kα inhibitors that have entered clinical study. STX-478 cleverly avoids the side effect (metabolic dysfunction) of ATP competitive PI3Kα inhibitors while effectively treating HNSCC.

In order to discover more effective drugs for the treatment of HNSCC, we adopt the method of scaffold hopping to modify the structure of STX-478 based on the interaction mechanism between STX-478 and PI3Kα. Cdocker, SuperPred, SwissTarget prediction, ADMET (Discovery studio), Swiss ADME, ADMETlab 2.0 and molecular dynamics simulation were used to analysis the binding mode, pharmacokinetic properties and stabilities of ligand–protein complexes. J-53 (2-oxopropyl urea compound) was discovered with reasonable structure and excellent properties. The urea present in J-53 formed a bifurcated hydrogen bond with the key amino acid LEU911 backbone carbonyl and a suboptimal hydrogen bond with the key amino acid GLY912 carbonyl. The carbonyl group present in J-53 could form hydrogen bond with ILE1019, making it more stably bound to PI3Kα and contributing to its activity. Multiple results showed that J-53 binded more stably to the allosteric site and had a strong ability to PI3Kα. It might not produce side effects such as hyperglycemia like STX-478. The existing study results also indicated that J-53 and STX-478 had no risk of carcinogen, mutagenicity, and skin irritation, indicating that J-53 had good safety. The structure of J-53 was novel by SciFinder verification with important value for further study.

The PI3Kα inhibitors currently approved for market by the FDA were Alpelisib and Inavolisib. They were approved for the treatment of breast cancer [54, 55]. Alpelisib plus fulvestrant has been proposed as treatment of many indications, such as postmenopausal women and men with HR+/HER2− ABC or MBC in the US [24, 56]. Inavolisib is used in combination with the CDK4/6 inhibitor palbociclib and fulvestrant to treat adult patients with tumors carrying PIK3CA mutations, endocrine resistance, HR+, HER2−, locally advanced or metastatic breast cancer (LA/MBC). In addition, there are some PI3K inhibitors, such as Serabelisib (NCT03193853), CYH33 [57], CH5132799 [58], Taselisib [59], which are under clinical research and mainly used to treat breast cancer. The study on STX-478 for the treatment of breast cancer, gynecological cancer, and HNSCC has entered phase 1/2 studies (NCT05768139). So, J-53 might apply to other cancers where PI3Kα mutations are prevalent, such as breast cancer.

This work enriched the data of well-investigated area of discovering potential allosteric PI3Kα inhibitors and promoted the development of targeted drugs for the treatment of HNSCC. At the same time, it provides valuable information for the further evaluation direction of J-53.

Data availability

Data is provided within the manuscript or supplementary information files.

References

  1. Ruffin AT, Li H, Vujanovic L, Zandberg DP, Ferris RL, Bruno TC. Improving head and neck cancer therapies by immunomodulation of the tumour microenvironment. Nat Rev Cancer. 2023;23(3):173–88.

    Article  CAS  PubMed  Google Scholar 

  2. Li Q, Tie Y, Alu A, Ma X, Shi H. Targeted therapy for head and neck cancer: signaling pathways and clinical studies. Signal Transduct Target Ther. 2023;8(1):31.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chen ZG, Saba NF, Teng Y. The diverse functions of FAT1 in cancer progression: good, bad, or ugly? J Exp Clin Cancer Res. 2022;41(1):248.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wang H, Zheng Z, Zhang Y, Bian C, Bao J, Xin Y, Jiang X. Locally advanced head and neck squamous cell carcinoma treatment efficacy and safety: a systematic review and network meta-analysis. Front Pharmacol. 2023;14:1269863.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Perry A, Lee SH, Cotton S, Kennedy C. Therapeutic exercises for affecting post-treatment swallowing in people treated for advanced-stage head and neck cancers. Cochrane Database Syst Rev. 2016;2016(8):Cd011112.

    PubMed  PubMed Central  Google Scholar 

  6. Su YC, Lee WC, Wang CC, Yeh SA, Chen WH, Chen PJ. Targeting PI3K/AKT/mTOR signaling pathway as a radiosensitization in head and neck squamous cell carcinomas. Int J Mol Sci. 2022;23(24):15749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Guan J, Xu X, Qiu G, He C, Lu X, Wang K, Liu X, et al. Cellular hierarchy framework based on single-cell/multi-patient sample sequencing reveals metabolic biomarker PYGL as a therapeutic target for HNSCC. J Exp Clin Cancer Res. 2023;42(1):162.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Patel VB, Zhabyeyev P, Chen X, Wang F, Paul M, Fan D, McLean BA, et al. PI3Kα-regulated gelsolin activity is a critical determinant of cardiac cytoskeletal remodeling and heart disease. Nat Commun. 2018;9(1):5390.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Swaney DL, Ramms DJ, Wang Z, Park J, Goto Y, Soucheray M, Bhola N, et al. A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity. Science. 2021;374(6563):eabf2911.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wang X, Li JP, Yang Y, Ding J, Meng LH. A pharmacological model reveals biased dependency on PI3K isoforms for tumor cell growth. Acta Pharmacol Sin. 2013;34(9):1201–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sun P, Meng LH. Emerging roles of class I PI3K inhibitors in modulating tumor microenvironment and immunity. Acta Pharmacol Sin. 2020;41(11):1395–402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jin N, Keam B, Cho J, Lee MJ, Kim HR, Torosyan H, Jura N, et al. Therapeutic implications of activating noncanonical PIK3CA mutations in head and neck squamous cell carcinoma. J Clin Invest. 2021;131(22): e150335.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jia W, Luo S, Guo H, Kong D. Development of PI3Kα inhibitors for tumor therapy. J Biomol Struct Dyn. 2023;41(17):8587–604.

    Article  CAS  PubMed  Google Scholar 

  14. Amzel LM, Huang CH, Mandelker D, Lengauer C, Gabelli SB, Vogelstein B. Structural comparisons of class I phosphoinositide 3-kinases. Nat Rev Cancer. 2008;8(9):665–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Rekhi B, Upadhyay P, Ramteke MP, Dutt A. MYOD1 (L122R) mutations are associated with spindle cell and sclerosing rhabdomyosarcomas with aggressive clinical outcomes. Mod Pathol. 2016;29(12):1532–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Giudice FS, Squarize CH. The determinants of head and neck cancer: unmasking the PI3K pathway mutations. J Carcinog Mutagen. 2013;5:003.

    Google Scholar 

  17. Xiao H, Sun X, Lin Z, Yang Y, Zhang M, Xu Z, Liu P, et al. Gentiopicroside targets PAQR3 to activate the PI3K/AKT signaling pathway and ameliorate disordered glucose and lipid metabolism. Acta Pharm Sin B. 2022;12(6):2887–904.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Insall RH, Weiner OD. PIP3, PIP2, and cell movement-similar messages, different meanings? Dev Cell. 2001;1(6):743–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Powis G, Meuillet EJ, Indarte M, Booher G, Kirkpatrick L. Pleckstrin homology [PH] domain, structure, mechanism, and contribution to human disease. Biomed Pharmacother. 2023;165: 115024.

    Article  CAS  PubMed  Google Scholar 

  20. Zhang S, Gui X, Ding Y, Tong H, Ju W, Li Y, Li Z, et al. Matrine impairs platelet function and thrombosis and inhibits ROS production. Front Pharmacol. 2021;12: 717725.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen M, Choi S, Wen T, Chen C, Thapa N, Lee JH, Cryns VL, et al. A p53-phosphoinositide signalosome regulates nuclear AKT activation. Nat Cell Biol. 2022;24(7):1099–113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bruning U, Morales-Rodriguez F, Kalucka J, Goveia J, Taverna F, Queiroz KCS, Dubois C, et al. Impairment of angiogenesis by fatty acid synthase inhibition involves mTOR malonylation. Cell Metab. 2018;28(6):866-880.e815.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lee RH, Johnson DE, Grandis JR. To tip or not to tip: a new combination for precision medicine in head and neck cancer. Cancer Res. 2023;83(19):3162–4.

    Article  PubMed  PubMed Central  Google Scholar 

  24. André F, Ciruelos EM, Juric D, Loibl S, Campone M, Mayer IA, Rubovszky G, et al. Alpelisib plus fulvestrant for PIK3CA-mutated, hormone receptor-positive, human epidermal growth factor receptor-2-negative advanced breast cancer: final overall survival results from SOLAR-1. Ann Oncol. 2021;32(2):208–17.

    Article  PubMed  Google Scholar 

  25. Juric D, Rodon J, Tabernero J, Janku F, Burris HA, Schellens JHM, Middleton MR, et al. Phosphatidylinositol 3-kinase α-selective inhibition with Alpelisib (BYL719) in PIK3CA-altered solid tumors: results from the First-in-human Study. J Clin Oncol. 2018;36(13):1291–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kim DH, Lim ST, Kim HR, Kang EJ, Ahn HK, Lee YG, Sun S, et al. Impact of PIK3CA and cell cycle pathway genetic alterations on durvalumab efficacy in patients with head and neck squamous cell carcinoma: post hoc analysis of TRIUMPH study. Oral Oncol. 2024;151: 106739.

    Article  CAS  PubMed  Google Scholar 

  27. Buckbinder L, St Jean DJ, Jr., Tieu T, Ladd B, Hilbert B, Wang W, Alltucker JT, et al. STX-478, a mutant-selective, allosteric PI3Kα inhibitor spares metabolic dysfunction and improves therapeutic response in PI3Kα-mutant xenografts. Cancer Discov. 2023; 13(11): 2432-2447

  28. Song KW, Edgar KA, Hanan EJ, Hafner M, Oeh J, Merchant M, Sampath D, et al. RTK-dependent inducible degradation of mutant PI3Kα drives GDC-0077 (Inavolisib) efficacy. Cancer Discov. 2022;12(1):204–19.

    Article  CAS  PubMed  Google Scholar 

  29. Wei XL, Liu FR, Liu JH, Zhao HY, Zhang Y, Wang ZQ, Qiu MZ, et al. First-in-human phase ia study of the PI3Kα inhibitor CYH33 in patients with solid tumors. Nat Commun. 2022;13(1):7012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Chaudagar K, Hieromnimon HM, Khurana R, Labadie B, Hirz T, Mei S, Hasan R, et al. Reversal of lactate and PD-1-mediated macrophage immunosuppression controls growth of PTEN/p53-deficient prostate cancer. Clin Cancer Res. 2023;29(10):1952–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang M, Jang H, Nussinov R. PI3K inhibitors: review and new strategies. Chem Sci. 2020;11(23):5855–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Varkaris A, Pazolli E, Gunaydin H, Wang Q, Pierce L, Boezio AA, Bulku A, et al. Discovery and clinical proof-of-concept of RLY-2608, a first-in-class mutant-selective allosteric PI3Kα inhibitor that decouples antitumor activity from hyperinsulinemia. Cancer Discov. 2024;14(2):240–57.

    Article  CAS  PubMed  Google Scholar 

  33. Montero AJ, Giordano A, Jhaveri K, Munster P, Rodon J, Lorusso P, Orr D, et al. LBA27 first-in-human results of STX-478, a mutant-selective PI3Ka inhibitor, in advanced solid tumor patients. Ann Oncol. 2024;35:1220.

    Article  Google Scholar 

  34. Rajasekaran R, George Priya Doss C, Sudandiradoss C, Ramanathan K, Purohit R, Sethumadhavan R. Effect of deleterious nsSNP on the HER2 receptor based on stability and binding affinity with herceptin: a computational approach. C R Biol. 2008;331(6):409–17.

    Article  CAS  PubMed  Google Scholar 

  35. Singh R, Bhardwaj VK, Sharma J, Das P, Purohit R. Identification of selective cyclin-dependent kinase 2 inhibitor from the library of pyrrolone-fused benzosuberene compounds: an in silico exploration. J Biomol Struct Dyn. 2022;40(17):7693–701.

    Article  CAS  PubMed  Google Scholar 

  36. Kamaraj B, Rajendran V, Sethumadhavan R, Kumar CV, Purohit R. Mutational analysis of FUS gene and its structural and functional role in amyotrophic lateral sclerosis 6. J Biomol Struct Dyn. 2015;33(4):834–44.

    Article  CAS  PubMed  Google Scholar 

  37. Bhardwaj V, Singh R, Singh P, Purohit R, Kumar S. Elimination of bitter-off taste of stevioside through structure modification and computational interventions. J Theor Biol. 2020;486: 110094.

    Article  CAS  PubMed  Google Scholar 

  38. Singh R, Manna S, Nandanwar H, Purohit R. Bioactives from medicinal herb against bedaquiline resistant tuberculosis: removing the dark clouds from the horizon. Microbes Infect. 2024;26(3): 105279.

    Article  CAS  PubMed  Google Scholar 

  39. Gupta A, Purohit R. Identification of potent BRD4-BD1 inhibitors using classical and steered molecular dynamics based free energy analysis. J Cell Biochem. 2024;125(3): e30532.

    Article  CAS  PubMed  Google Scholar 

  40. Guo LW, Li XG, Yang YS, Lu XX, Han XC, Lang GT, Chen L, et al. Large-scale genomic sequencing reveals adaptive opportunity of targeting mutated-PI3Kα in early and advanced HER2-positive breast cancer. Clin Transl Med. 2021;11(11): e589.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Moret M, Pachon Angona I, Cotos L, Yan S, Atz K, Brunner C, Baumgartner M, et al. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat Commun. 2023;14(1):114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Callis TB, Garrett TR, Montgomery AP, Danon JJ, Kassiou M. Recent scaffold hopping applications in central nervous system drug discovery. J Med Chem. 2022;65(20):13483–504.

    Article  CAS  PubMed  Google Scholar 

  43. Riyaphan J, Pham DC, Leong MK, Weng CF. In silico approaches to identify polyphenol compounds as α-glucosidase and α-amylase inhibitors against Type-II diabetes. Biomolecules. 2021;11(12):1877.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Wu B, Zhang Z, Dou G, Lv X, Ge J, Wang H, Xie H, et al. Novel natural inhibitors targeting B-RAF(V600E) by computational study. Bioengineered. 2021;12(1):2970–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gallo K, Goede A, Preissner R, Gohlke BO. SuperPred 30: drug classification and target prediction-a machine learning approach. Nucleic Acids Res. 2022;50(W1):W726-w731.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Shang L, Wang Y, Li J, Zhou F, Xiao K, Liu Y, Zhang M, et al. Mechanism of sijunzi decoction in the treatment of colorectal cancer based on network pharmacology and experimental validation. J Ethnopharmacol. 2023;302(Pt A): 115876.

    Article  CAS  PubMed  Google Scholar 

  47. Zhu J, Li K, Xu L, Cai Y, Chen Y, Zhao X, Li H, et al. Discovery of novel selective PI3Kγ inhibitors through combining machine learning-based virtual screening with multiple protein structures and bio-evaluation. J Adv Res. 2022;36:1–13.

    Article  PubMed  Google Scholar 

  48. Xiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C, Yin M, et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021;49(W1):W5-w14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Nam KH. Molecular dynamics-from small molecules to macromolecules. Int J Mol Sci. 2021;22(7):3761.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Chen C, Zhu T, Liu X, Zhu D, Zhang Y, Wu S, Han C, et al. Identification of a novel PHGDH covalent inhibitor by chemical proteomics and phenotypic profiling. Acta Pharm Sin B. 2022;12(1):246–61.

    Article  CAS  PubMed  Google Scholar 

  51. Lv Q, Xing Y, Liu J, Dong D, Liu Y, Qiao H, Zhang Y, et al. Lonicerin targets EZH2 to alleviate ulcerative colitis by autophagy-mediated NLRP3 inflammasome inactivation. Acta Pharm Sin B. 2021;11(9):2880–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Gong H, Rose GD. Assessing the solvent-dependent surface area of unfolded proteins using an ensemble model. Proc Natl Acad Sci U S A. 2008;105(9):3321–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Egorov SA. Depletion interactions between nanoparticles: the effect of the polymeric depletant stiffness. Polymers (Basel). 2022;14(24):5398.

    Article  CAS  PubMed  Google Scholar 

  54. Rugo HS, André F, Yamashita T, Cerda H, Toledano I, Stemmer SM, Jurado JC, et al. Time course and management of key adverse events during the randomized phase III SOLAR-1 study of PI3K inhibitor Alpelisib plus fulvestrant in patients with HR-positive advanced breast cancer. Ann Oncol. 2020;31(8):1001–10.

    Article  CAS  PubMed  Google Scholar 

  55. Turner NC, Im SA, Saura C, Juric D, Loibl S, Kalinsky K, Schmid P, et al. Inavolisib-based therapy in PIK3CA-mutated advanced breast cancer. N Engl J Med. 2024;391(17):1584–96.

    Article  CAS  PubMed  Google Scholar 

  56. Turner S, Chia S, Kanakamedala H, Hsu WC, Park J, Chandiwana D, Ridolfi A, et al. Effectiveness of Alpelisib + fulvestrant compared with real-world standard treatment among patients with HR+, HER2-, PIK3CA-mutated breast cancer. Oncologist. 2021;26(7):e1133–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Liu XL, Xu YC, Wang YX, Chen Y, Wang BB, Wang Y, Chen YH, et al. Decrease in phosphorylated ERK indicates the therapeutic efficacy of a clinical PI3Kα-selective inhibitor CYH33 in breast cancer. Cancer Lett. 2018;433:273–82.

    Article  CAS  PubMed  Google Scholar 

  58. Tanaka H, Yoshida M, Tanimura H, Fujii T, Sakata K, Tachibana Y, Ohwada J, et al. The selective class I PI3K inhibitor CH5132799 targets human cancers harboring oncogenic PIK3CA mutations. Clin Cancer Res. 2011;17(10):3272–81.

    Article  CAS  PubMed  Google Scholar 

  59. Hutchinson KE, Chen JW, Savage HM, Stout TJ, Schimmoller F, Cortés J, Dent S, et al. Multiple PIK3CA mutation clonality correlates with outcomes in taselisib + fulvestrant-treated ER+/HER2-, PIK3CA-mutated breast cancers. Genome Med. 2023;15(1):28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. School of Chemistry and Chemical Engineering, Qilu Normal University, Jinan, China

    Wenqing Jia, Ruijie Zhang & Yukui Ma

  2. School of Bioengineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Guangzhuang Li

  3. Tianjin Key Laboratory On Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, China

    Xianchao Cheng

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W.J. wrote the main manuscript text and prepared figures and tables. G.L., R.Z. and X.C. wrote the main manuscript text, prepared figures, and tables and supervised and reviewed the manuscript. W.J. and Y.M. reviewed the manuscript.

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Correspondence to Wenqing Jia or Yukui Ma.

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Jia, W., Li, G., Cheng, X. et al. In silico discovery of a novel potential allosteric PI3Kα inhibitor incorporating 2-oxopropyl urea targeting head and neck squamous cell carcinoma. BMC Chemistry 19, 55 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13065-025-01420-6

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13065-025-01420-6

Keywords

BMC Chemistry

ISSN: 2661-801X

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