Identi�cation of Novel Mutant (R132H) Isocitrate Dehydrogenase 1 Inhibitors for Glioma Therapy

Neomorphic transformation in isocitrate dehydrogenase 1 (IDH1) are the key mutations prevalently found in various cancers including glioma. Recently identi�ed mIDH1 speci�c inhibitors such as ivosidenib and Vorasidenib were restricted for use due to its modest brain penetrating potential and dose limiting toxicity respectively. Herein, we elucidate integrated virtual screening strategies to discover persuasive mIDH1 inhibitors from the approved subset of the DrugBank database consisting of 2715 molecules. Initially, structural similarity search identi�ed a total of 1432 lead molecules. The resultant compounds were inspected by molecular docking along with MM-GBSA and ADMET analyses. Altogether, the analyses identi�ed DB12001 (Abemaciclib) as the hit against mIDH1. Notably, Abemaciclib was able to form hydrogen bond interaction with active site residues of mIDH1 protein. In the end, the dynamic behavior of the hit complex was also examined using molecular dynamic (MD) simulation studies. The outcome of the study culminates that the hit complex was stable throughout the simulation period of 100ns. It is worth noting that benzimidazole moiety of Abemaciclib was reported to show inhibitory activity against glioma cells. Overall, these �ndings highlight that DB12001 has the potential as lead molecule against glioma. Indeed the screened hit compound could be further explored for the development of mIDH1 inhibitor with great brain penetrating ability and low toxicity.


Introduction
Gliomas are identi ed as the primary tumor of the central nervous system that derives from glial cells or neural stem cells [1]. It holds the poorest prognosis when compared to other cancer types with increased mortality [2]. Globally, every year 6 per 100,000 individuals were reported with new glioma incidence and the survival rate was found to be at a median of 15 months after conventional cancer treatments [3]. At present the treatment regimes for glioma is restricted to surgical resection along with adjuvant strategies such as chemotherapy and radiotherapy. The ine ciency of the available treatment methods as imposed an emerging necessity to identify new treatment targets and therapeutics against glioma. Recently, mutations in Isocitrate dehydrogenase (IDH) have transformed our understanding of glioma biology and being viewed as an attractive target for the treatment. It catalyzes the conversion of isocitrate to alpha-ketoglutarate (α-KG) through oxidative decarboxylation reaction to generate reduced NADPH from NADP. Further, the generated NADPH is procured for lipid metabolism. In addition, IDH 1 and 2 are key drivers that function at the crossroad of DNA repair, cellular metabolism, redox states and epigenetic regulations. Prevalence of IDH mutations is commonly found in several types of malignancies such as low-grade gliomas, glioblastomas [5], acute myeloid leukemia [6], chondrosarcomas [7] and intrahepatic cholangiocarcinomas [8]. Notably, these somatic mutations appear in the hotspot region of the active site of the enzyme that drives the production of a neomorphic metabolite called D-2-hydroxyglutarate (D-2HG). Shreds of literature evidence highlight that IDH mutation marks the earliest genetic changes that develop during glioma progression [9,10].
The commonly identi ed oncogenic mutations of IDH1 protein are mapped to some key catalytic residues within the active site of the enzymes that are crucial for enzyme binding. For instance, literature evidence have identi ed various mutations of IDH1 which includes R132H, R132C, R132S, R132L and R132G [11].Of note the most prevalent somatic mutation of IDH1 (> 90%) is observed when arginine at 132 positions is substituted with histidine residue (R132H). The signi cance of IDH1 mutation as galvanized attention as a potential molecular target for glioma treatment. Although mutated IDH1 speci c inhibitors were developed, only a few inhibitors were evaluated in clinical trials [12][13][14]. Of note, ivosidenib is the only drug approved by FDA (in 2018) as a potent inhibitor of mIDH1 for the rst-line treatment of Acute Myeloid Leukemia (AML) [15]. However, the use of the ivosidenib was restricted because of its modest blood-brain penetrating capability. Recently, Vorasidenib was identi ed as dual inhibitor of both the isoforms of mutated IDH proteins with appreciable brain penetrating property [16]. However the pipelines of molecules are needed to overcome the drug resistance situation and to provide better treatment option. Thus, we employed virtual screening and molecular dynamic simulation studies to identify active hits against theR132H mutant IDH1protein.
Structure based virtual screening methods have remarkably featured towards the identi cation of anti-glioma compounds that target mIDH1. For instance, the molecular docking and MD based study performed by Zou et al in 2016 identi ed a highly selective mIDH1 inhibitor, FX-03 [17]. In this study about 200,000 molecules were screened by docking against the allosteric site of mIDH1. The cross docking based virtual screening was carried out by Zou et al (2019) to identify novel inhibitor against mIDH1. It is worth mentioning that the study identi ed ZX06 as the potent inhibitor with modest toxicity [18]. Another study by Zheng et al in 2017 recognized clomifene as an effective natural product based inhibitor against mIDH1 [19]. The screening was performed against ZINC Drug database with 2924 molecules. Duan et al employed structure-activity relationship based study along with MD simulation to discover DC_H31 as novel mIDH1 inhibitor [20]. Recently, Wang et al combined in-silico and in-vitro methods and reported 10 molecules with signi cant experimental activity against mIDH1 [21]. Despite the number of available evidence, repurposing the approved drug for identifying mIDH1 (R132H) inhibitor is not reported in the literature. Therefore in the present study approved entities of DrugBank database was explored to identify selective and potent mIDH1 inhibitors.

Structural Re nement
The crude structures of the mIDH1 protein were procured from Protein Data Bank (PDB). The repository contains 19 PDB codes that correspond to the mutated IDH1 protein structure speci cally with R132H mutation. The list of PDB IDs along with their resolution was enlisted in Table S1. Here, the protein structure with the ID 6VEI was used to carry out virtual screeningas it possessed appreciable resolution alongside bound Vorasidenib molecule. The raw structures of the mIDH1 protein and small molecules were subjected for preprocessing and minimization using the 'Protein preparation wizard' and 'LigPrep' module of Schrödinger software respectively [22]. The approved subset of the DrugBank database with approximately 2715 compounds was utilized for virtual screening process. The schematic work ow of our study is highlighted in Fig. 1.

Tanimoto Coe cient
The molecular similarity search method assesses a compound's likelihood of being active against a therapeutic target according to its structural similarity to known active compounds. [23]. In the present study, the similarity between the compounds is measured in terms of the Tanimoto coe cient (Tc). The Tanimoto coe cient is measured using the following equation.
Tc = N C / N A + N B -N C (1) Where N A denotes the number of ngerprints in structure A, N B denotes the number of ngerprints in structure B and N C denotes the number of ngerprints common in both structures A and B.
The DataStructs subpackage from RDKit was utilized to calculate the Tanimoto coe cient of all the compounds from the approved subsets of the DrugBank database [24].

Virtual Screening
The compounds with appreciable Tanimoto coe cient were screened and all the compounds were further subjected for molecular docking using the Glide module of the Schrödinger suite.The Glide docking is a grid-based technique that identi es the best binding position of the ligand, based on their a nity towards each other. Prior to molecular docking, the grid was generated around the catalytic site of the mIDH1 protein identi ed from the literature [25]. A cubic grid of 1nm was constructed identifying the centroid of its active site. Additionally, the partial charge cut-off and van der Waals radius scaling factor were set as 0.25 and 1 respectively to lower the receptor's non-polar parts potential [26]. After the grid generation, coordinates of the grid were used as an input for the molecular docking process. The advantage of using the Glide module is the three-level hierarchical lters that classify the docking modes into high-throughput virtual screening (HTVS), standard precision (SP) and extra precision (XP).The XP docking mode uses an anchor and grow algorithm that re nes the predicted docking pose using an extensive scoring function which identi es and rewards the signi cant structural entities important for binding [27]. Thus, it enhances the overall accuracy of the docking process.

Prime MM-GBSA Analysis
The binding a nity of all the screened molecules from XP docking was then rescored using Prime Molecular Mechanics Generalized Born Surface Area (MM-GBSA) analysis. Prime MM-GBSA analysis estimates the relative binding free energy of ligand receptor complex using the following equation.

Identi cation of Drug-like Candidate using In-silico Screening
The pharmacological liabilities of the lead compounds serve as one of the salient features in lead optimization. The growing literature evidence highlights that the increasing failure rate of screened molecules in clinical trials was ascribed majorly due to deviation from Absorption, Distribution, Metabolism, Excretion (ADME) and toxicity properties [29]. Therefore, the QikProp module of Maestro was employed to analyze the drug-likeness of screened molecules. The descriptors such as CNS, stars, blood-brain barrier (logBB) were taken into consideration for the selection of lead compounds. Since mIDH1 protein is predominantly found in glioma, the drug molecule should penetrate the central nervous system. Therefore, the compounds with active CNS scores of 0 and above alongside positive valued log BB were identi ed as crucial threshold for the hit molecules. The potential acute toxicity endpoints associated with the chemical structure of screened lead molecules were assessed using Protox-II algorithm. The server evaluates various levels of toxicological endpoints such as oral toxicity, toxicity targets and organ toxicity [30]. This in-silico prediction platform is believed to enhance the process of hit selection and optimization and it also provides additional insights into the mechanism of toxicity. Towards the end, the biological activity of the screened molecules was measured in terms of P a and P i values obtained from the PASS prediction server [31]. The software predicts the pharmacological effect and biological activity spectrum of the chemical compound based on the structural formula.

Molecular Dynamic Simulation
The molecular dynamics simulation study of the mIDH1 protein and ligand complex was performed using GROMACS 5.1.2. Firstly, the topology les for the screened small molecules were generated using the PRODRG server in the framework of the GROMOS96 43a1 force eld. The docked protein-ligand complex was solvated using a simple point charge (SPC) water solvation model enclosed within the dodecahedron box of 10 Å. The electrostatic energy calculation was computed using the Particle Mesh Ewald method and linear constraint solver algorithm (LINCS) was utilized for covalent bond constraints [32,33]. Prior to minimization, the neutralization step was carried out by adding 3 sodium ions to the system. Further, the energy minimization was performed by using the steepest descent approach. Berendsen coupling and Parrinello-Rahman method were employed to regulate temperature (NVT) and pressure (NPT) at 300k and 1 bar respectively inside the box. SHAKE algorithm was used to constrain the length of the hydrogen bond [34]. Ultimately, 100 nanosecond simulations were performed for the reference and the hit complexes. Various inbuilt gromacs utilities such as gmx rms, gmx hbond, gmx gyrate, gmx sasa, gmx covar and gmx anaeig were exploited to compute the outcomes of MD simulation and the resultant trajectories were visualized using xmgrace.

Tanimoto Coe cient
The similarity search algorithm based on molecular ngerprint is a signi cant tool usually employed in retrospective benchmarking studies. According to the similarity-property principle, increased chemical similarity correlates with a greater possibility that two molecules shares similar activity [35]. Therefore the degree of resemblance between the known inhibitor and the small molecules from the approved subset of the DrugBank database were measured using the Tanimoto coe cient (Tc). The overall structural similarity of all the compounds in our study ranged from 0.16 to 0.52and thus the mean value of the tanimoto co-e cient was used as selection criteria for screening compounds. So, compounds with the threshold value of 0.2 and above were considered for further molecular docking. A total of 1432 compounds possessed a structural similarity of 0.2 and above were used for further analyses.

Virtual Screening
The approved set of compounds with Tc ≥ 0.2 was retrieved and they were preprocessed using the LigPrep module of the Schrödinger suite. Subsequently, molecular docking was performed for all the compounds to identify the potential lead molecules against mIDH1 protein. Initially, all the compounds (1432 compounds) were subjected to the HTVS mode of docking. As the HTVS method indulges in faster prediction of the appropriate binding mode and ranks the ligand-based on its empirical scoring functions [27]. The top-scoring ligands were selected from HTVS level and used for SP docking. Towards the end, XP docking process was initiated with the high scoring compounds respectively. For instance, 299 molecules were subjected for XP docking and the binding score of each compound was compared with the reference compound Vorasidenib that had a binding score of -5.403 kcal/mol. In comparison, about 59 molecules showed increased binding a nity towards mIDH1 protein. The binding score of the screened compound was ranging from − 5.433 kcal/mol to − 9.964 kcal/mol. The Glide XP GScore of the docked complexes was depicted in Table S2.

Binding Free energy Analysis
The accuracy of docking was further validated by binding free energy analysis using MM-GBSA algorithm. The reference molecule Vorasidenib exhibited binding free energy (ΔG bind ) of -56.44kcal/mol. Therefore, ΔG bind of Vorasidenib was used as the threshold to screen molecules with an enhanced binding a nity towards the target protein mIDH1. A total of 16 molecules from the approved set of DrugBank database showed lower binding energies than the reference compound. The results of the compounds were represented in Table 1. It is evident from the table that the binding energies of the compounds vary from − 57.42 kcal/mol to -77.33 kcal/mol. Further the major contributions to the enhanced binding of the hits are due to the exceptionally strong lipophilic and van der Waals interaction. For instance, shreds of literature evidence also highlight the signi cance of Coulomb, lipophilic energy, electrostatic solvation and van der Waals interactions in the effective binding of protein-ligand complex [36,37]. Therefore, in addition to ΔG bind , the above mentioned parameters were also included in selection criteria. Notably, only 10 molecules namely, DB01076, DB13751, DB04868, DB00229, DB01698, DB06769, DB12001, DB00183, DB06590 and DB00492 possessed appropriate energy values. Other screened molecules such as DB01326, DB05039, DB03310, DB13874, DB00293 and DB12789 were eliminated because of its disfavored Coulomb, van der Waals and solvation energies with the values less than − 7.97 kcal/mol, -27.84 kcal/mol, and 11.88 kcal/mol respectively. These selected 10 molecules were then examined for its drug-likeliness screening.

In-silico drug bioavailability screening
In search of an e cient drug candidate for glioma treatment, the resultant compounds with high binding a nity and binding energies were analyzed for their drug-likeliness and pharmacokinetic properties using the QikProp module of the Schrödinger suite. QikProp module is equipped with a varying range of descriptors that broadly categorizes the absorption, distribution, metabolism and excretion properties of the chemical structure. The inclusion criteria considered for the screening of desired small molecules include the blood-brain barrier (log BB), stars, human oral absorption (HOA) and CNS. The FDA-approved small molecule ivosidenib was restricted from use only due to its poor blood-brain penetrating ability with the logBB value of -1.112 [38]. Thus, the screened molecules with increased logBB value were taken into account. The star descriptor identi es a number of physiochemical features each screened molecule failed to abide within the acceptable limits. The compounds with less than 5 star values were selected as hit compounds. The HOA values of reference and the hits were analyzed and compounds with an HOA value of 3 were scrutinized.The CNS value of the molecules lies between the range of -2 to 2. In glioma the neoplastic cells are predominantly present in the neuronal cells; therefore an effective drug molecule should penetrate into the brain cells to inhibit the activity of tumor cells. Hence, an active CNS score of zero and above was considered for screening. The PK/PD properties of all the screened molecules are shown in Table 2. Amongst the 10 hit molecules, only DB12001 was found to have satisfactory QPlogBB, stars, HOA and CNS properties with the value of 0.14, 1, 3 and 1 respectively. Further, the biological activity of the hit compound was predicted using the PASS server [31]. The server predicts the biological activity by calculating the probability of active and inactive. The potent hit molecule should possess a higher probability of active (P a ) score when compared with the probability of inactivity (P i ). Interestingly, the identi ed hit was found to have anti-cancerous activity with a P a value of 0.5 (Table S3).
Finally, the toxicological endpoints of the Vorasidenib and DB12001 were examined using the ProTox-II server [30]. From Table 3 we infer that the reference compound was found to exhibit organ toxicity, especially hepatotoxicity. Interestingly, DB12001 was predicted to have a lethal dose level of 2000 mg/kg and categorized under class IV toxicity. Further, the molecular insights of the reference and the hit were inspected through the interaction pattern and structural scaffolds.

Interactions of the Protein-ligand complex
Initially, the binding scheme of all the available PDB codes of mIDH1 protein along with their respective ligand was analyzed using PLIP [39]. Figure 2 highlights the important interacting residues of mIDH1 protein along with their PDB codes. It is evident from the Fig. 2 that most of the mIDH1 proteins were forming hydrophobic interactions with residues such as TRP124, VAL255 which are highlighted in red color. Predominantly, hydrogen bond interactions were formed by the residues GLN277, ASP279, SER280. Similarly, the binding pattern of the Vorasidenib and DB12001 was analyzed and key interactions were identi ed. The atomic interactions of the hit and the reference molecules were highlighted in different color representations. The interaction of Vorasidenib and DB12001 is highlighted in Fig. 3. The blue color line depicts the existence of hydrogen bond interactions. From the results, we infer that the Vorasidenib was capable of forming two hydrogen bond interactions with the residue GLN277.
Furthermore, Vorasidenib was able to form halogen bond interaction between ASP273 residue and chloro-pyridine moiety which is highlighted in cyan color. Comparably, DB12001 was also observed to create two hydrogen bond interactions with the residue GLN277. In addition, DB12001 was forming hydrophobic interactions (dashed line) with some of the conserved residues present in the catalytical site of mIDH1 protein such as TRP124, ASP252, VAL276 and SER280 [16]. Overall, from the above results, we deduce that the additional hydrophobic interaction might contribute to the increased binding a nity of the hit molecule against the mIDH1 protein.

Scaffold Analysis
Shreds of literature evidence report that amino triazine and chloropyridine moiety of Vorasidenib enhances its binding a nity towards mIDH1 protein [16,38]. Similarly, the structure of DB12001 was analyzed to identify the important structural moiety that contributes to the increased binding a nity. The benzimidazole group of DB12001 was reported to exhibit anti-cancerous activity especially against glioma [40]. The 2dimensional structure of both the reference and the hit are depicted in Fig. 4. The generic name of DB12001 is Abemaciclib. It is an anti-cancerous agent that is reported to dually inhibit cyclin-dependent kinase 4 and 6 (CDK 4 and CDK 6). Abemaciclib is an FDA-approved drug used for the treatment of HR-positive and HER-negative metastatic breast cancer in combination with Fulvestrant [41]. Further, the drug has been used in various other trials including for the treatment of melanoma, lymphoma and solid tumors [42,43]. All these literature evidence highlights that Abemaciclib could also be repurposed for the inhibition of mIDH1 in glioma treatment.Thus, the structural dynamics of Vorasidenib and Abemaciclib were investigated using molecular dynamics simulation studies to enrich the prediction accuracy.

Root Mean Square Deviation (RMSD)
The conformational stability of the protein-ligand complexes were evaluated using MD simulation. The gmx rms utility of gromacs was employed to quantitatively estimate the conformational changes and the stability of the system that occur within the stipulated time boundaries [44]. In our study, the average root mean square deviation (RMSD) of the reference and the hit complexes were calculated for backbone atoms of the protein. Figure 5 highlights the RMSD plots of mIDH1-Vorasidenib (reference) and mIDH1-Abemaciclib (hit) complex. From Fig. 5, it is evident that the reference complex exhibited least deviation from 0 to 27ns. However, the average RMSD of the reference complex increased from ~ 0.41 to ~ 0.65 nm between 30 to 100ns. Although the hit complex showed increased deviation from 0 to 60 ns with the RMSD value of ~ 0.49 nm, the complex attained the state of equilibrium within 70 ns time frame. Interestingly, at the end of the 100ns simulation the complex showed the average RMSD value of ~ 0.48 nm. Thus, from RMSD plot we infer that the mIDH1-Abemaciclib complex proclaimed lesser backbone deviation when compared to mIDH1-Vorasidenib complex.

Hydrogen bond
The gmx hbond was employed to ascertain the speci c inter-molecular interactions between protein-ligand complexes. The stability of the H-bond created between reference and the hit complex was deduced by extracting the time dependent hydrogen bond pattern observed throughout 100ns simulation. The results from the trajectory (Fig. 7) revealed that the reference complex formed an average of ~ 6 hydrogen bonds during the simulation. Whilst the hit complex was capable of forming 3 hydrogen bond interaction with the target protein. From interaction studies, we understand that the hit complex was actively forming hydrophobic interactions with the crucial binding site residues of mIDH1 protein (Fig. 3).

Radius of Gyration
The structural compactness of the reference and hit complex were analyzed using gmx gyrate. The inbuilt gyrate (Rg) tool of gromacs calculates the weighted root mean square distance of collective Cα atoms from the center of mass. Thus, Rg imparts insights on the overall dimensions and the folding state of the target protein [45]. Increased uctuation in Rg value highlights the unfolding of the target protein. Figure 8 illustrates  SASA estimates the interacting surface area of target protein along with its solvent molecules [47]. The gmx sasa tool was employed to measure the average SASA value of mIDH1-Vorasidenib and mIDH1-Abemaciclib complex throughout the time period of 100ns. The results from SASA plot ( Fig. 9) illustrates that the mean SASA value of Vorasidenib and Abemaciclib was 197.39 nm 2 and 200.71 nm 2 respectively. The increased SASA value of the hit complex signi es that the internal residues of the mIDH1-Abemaciclib complex are disclosed to the solvent molecules for interactions.The free energy of solvation for hit complex is similar to that of the reference. Thus, the results from SASA analysis highlight the stable binding of the hit complex.

Principal Component Analysis (PCA)
Essential dynamics / PCA aids in identifying the most dominant and probable conformational changes that occurs in the target protein at the time ligand binding. This study allows us to quantify the effect of functionally critical movement upon ligand binding [48]. The important conformational subspace with crucial amount of collective motions of the reference and hit complex are con ned within rst few eigenvectors. Notably, the rst two principal components (PCs) were used for detailed study. Each point on the subspace illustrates a speci c conformation of the protein ligand complex. The exibility of mIDH1-Vorasidenib and mIDH1-Abemaciclib complex was evaluated using the trace value. The trace value for the reference and the hit complex was found to be 26.08 nm 2 and 14.12 nm 2 respectively. The higher trace value of the reference compound suggests that there is an increased exibility and expansion in the collective motion than the hit complex. The 2d projection of the MD trajectories was depicted in Fig. 10 (a) illustrates the concreted motion of system in the phase space spanned by the rst two principal components of the reference and hit complex. Interestingly, the results revealed that the Abemaciclib complex occupied smaller phase space along the PC when compared to the reference complex implicating higher exibility of the reference compound [49]. Notably, the results of 2d projection were found to be in agreement with trace values of the covariance matrix.
Furthermore, covariance calculation also aids to correlate the collective motion of the protein. For instance, when two atoms moves unidirectional they are termed as positively correlated and if the atoms moves in opposite directions they are termed as anti-correlated motion. The positively correlated motion of atoms is highlighted in red regions and the negatively correlated motion in blue region. From Fig. 10, we conclude that both the complexes showed net anti-correlated motion. Overall, less trace value and smaller conformational phase space of the hit molecule suggest its stable binding than the reference compound.

Free Energy Landscape (FEL)
Finally, the conformational states of the PCs were examined in terms of free energy landscape to gain insights on the protein folding. The FEL is merely post-processing of PCs where the difference between free energy is evaluated from probability of energy state occupancy [50]. The conformational states of each complex are represented in different color codes. For instance, the global energy minima are represented in blue and the meta-stable states are depicted in green and cyan. For mIDH1-Vorasidenib, a single narrow energy minima basin was identi ed, while mIDH1-Abemaciclib complex showed a broader free energy minima basin. Figure 11 highlights the FEL plot of reference and hit complex. From the FEL analysis, we infer that the Abemaciclib complex is thermodynamically stable than mIDH1-reference complex.

Conclusion
The present study focused on exploring plausible mIDH1 protein inhibitors using ligand similarity-based repurposing strategy. The compounds with the similarity coe cient of 0.2 and above were identi ed from the approved subset of the DrugBank database and used for the molecular docking process. After the hierarchical docking process, about 229 small molecules were screened and their respective binding a nities were compared with the reference compound. The binding energies of the resultant compounds were examined using MM-GBSA analysis. About 16 small molecules with optimum binding a nity were scrutinized and their physicochemical properties such as CNS, QPlogBB, Ro5 and Ro3 were also investigated. Collectively, our analysis resulted DB12001 (Abemaciclib) as a potent lead molecule with binding energy of -69.26 kcal/mol, QlogBB of 0.14 together with active CNS score of 1. The higher Pa value of the Abemaciclib compound highlights its anti-cancerous activity. Interestingly, Abemaciclib exhibits similar binding pattern to that of Vorasidenib with the active site of mIDH1. From the scaffold analysis, we hypothesize that the increased activity of Abemaciclib is mainly due to the existence of benzimidazole moiety in its structure. Our study correlates well with the literature evidence that the benzimidazole scaffold reported to have antineoplastic activity against glioma.In essence, the MD simulation studies also highlights that the Abemaciclib complex maintained a stable conformation throughout the simulation period of 100 ns than the reference molecule.Indeed, we believe that DB12001 (Abemaciclib) portrays desirable attributes to be an effective anti-cancer drug, that could be repurposed for glioma treatment in the impending years.

Declarations
Ethics approval and consent to participate -Not applicable Consent for publication -Not applicable Availability of data and materials-Not applicable The interaction pattern of the available mIDH1 Protein-ligand complex from PDB as heatmap The interaction patterns of (a) Vorasidenib and (b) DB12001 in the binding pocket of mIDH.