Design, synthesis, and in vitro evaluation of a carbamazepine derivative with antitumor potential in a model of Acute Lymphoblastic Leukemia

Cristian Álvarez-Gómez; Angela V Fonseca-Benítez; James Guevara-Pulido

doi:10.1371/journal.pone.0319415

. 2025 Apr 28;20(4):e0319415. doi: 10.1371/journal.pone.0319415

Design, synthesis, and in vitro evaluation of a carbamazepine derivative with antitumor potential in a model of Acute Lymphoblastic Leukemia

Cristian Álvarez-Gómez ¹, Angela V Fonseca-Benítez ¹, James Guevara-Pulido ^1,^*

Editor: Sapan Kamleshkumar Shah²

PMCID: PMC12036894 PMID: 40293986

Abstract

Acute lymphoblastic leukemia (ALL) is a significant concern in both pediatric and adult demographics. Despite 156 approved cancer therapies based on small molecules, a mere five apply to all types of leukemia. Unfortunately, adherence to these treatments is low due to adverse side effects. Consequently, there is an urgent need to identify more effective treatment options for ALL. This study presents a potential solution. We have designed over fifty analogs of carbamazepine, utilizing a combination of ligand-based and structure-based drug design methodologies. Among these analogs, we identified the CR80 analog, which demonstrated predicted binding values of -8.66 kcal/mol against beta-tubulin, a favorable LogP, and IC₅₀ values suitable for in vitro evaluation. The CR80 compound was synthesized with a yield of 50% and subsequently assessed in vitro against the U-937 cell line. It obtained an IC₅₀ value of 0.8 micromolar to 1 micromolar and a selectivity index of two, thus marking it as a promising candidate for in vivo studies.

Introduction

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer. It is the second most common acute leukemia in adults, with an incidence of over 6500 cases per year in the United States alone [1]. The American Cancer Society estimates that in 2023, the United States will see over 6,500 new cases and almost 1,400 deaths from acute lymphoblastic leukemia (ALL). Sixty percent of cases occur in children, peaking at ages 2–5 and another after age 50 [2].

While the number of approved cancer treatments based on small molecules is 156, only five are for all leukemia types[3]. Among these, 3 are biological drugs, and 5 are small molecules. The prognosis for patients with acute lymphoblastic leukemia (ALL) has dramatically improved due to intensive multimodal treatment strategies, such as chemotherapy, high-dose chemotherapy with stem cell rescue, and radiation therapy when necessary [4]. The treatment of Adult Acute Lymphoblastic Leukemia (ALL) involves complex chemotherapy combinations and schedules typically seen in oncology. Two main chemotherapy regimens are currently used. The Berlin-Frankfurt-Münster protocol features an induction regimen, consolidation regimen, intensification regimen, and maintenance therapy, primarily implemented in European adult ALL clinical trials. Alternatively, the hyper-CVAD regimen, created by MD Anderson Cancer Center researchers, consists of rotating two intensive chemotherapy cycles [5]. However, some treatments do not reach the market due to their toxicity [6]. The current landscape of cancer treatments is marked by their unpleasant and highly toxic side effects [7]. For ALL treatments, patients may experience disorders related to the blood and lymphatic system, eyes, gastrointestinal system, liver, metabolism, musculoskeletal system, respiratory system, and skin, as well as injuries, poisonings, and complications of procedures. This can lead to symptoms such as headaches, weight fluctuations, diarrhea, nausea, and vomiting due to the lack of specificity of these treatments for cancer cells, which can lead to poor treatment adherence [8–11]. The urgency of finding better ALL treatments is apparent. Our study presents a potential solution. The design, synthesis, and in vitro evaluation of potential antitumoral carbamazepine derivatives offer a promising path to a more effective and less toxic ALL treatment.

Recent research aims to develop drugs offering targeted treatments with fewer side effects and improved patient adherence. Computational drug discovery strategies, such as Computer-Aided Drug Design (CADD), are now used to identify, design, and optimize compounds through artificial intelligence and molecular modeling. This multidimensional data integration allows for improved use of time and resources, ultimately enhancing the development of new drugs [12–14]. CADD is a powerful tool that accelerates the repositioning of drugs by identifying potential new uses for existing molecules that have passed safety and toxicity tests and are already on the market. Carbamazepine (CBZ), an anticonvulsant and neuropathic pain medication used for over 20 years, has shown effects on the replication of hematopoietic cells since 1995 [15], leading to a 50% decrease in blood cell count. In more recent studies, Meng et al. (2010) demonstrated that carbamazepine promotes the degradation of the Her-2 protein in breast cancer cells by modulating HDAC6 activity and acetylation of Hsp90 [16].

Additionally, in 2020, Zhao reported that CBZ has an affinity for the Frizzled FZD8 receptor (via Wnt); inhibiting this receptor decreases bone remodeling and promotes the apoptosis of bone cells, which are the origin of some types of leukemia [17]. Based on the information provided, the pharmacophoric core of CBZ has shown intriguing potential for treating ALL. This research utilized CADD strategies, such as structure-based drug design (SBDD) and ligand-based drug design (LBDD), to improve the pharmacodynamics of newly designed carbamazepine analogs. In the LBDD approach, QSAR models were constructed using the INQA-Artificial Neural Network, which our group had previously validated in developing new SSRIs, AKR1C3, and JAK-3 drugs [18–20]. This allowed us to predict the IC₅₀ values and forecast the pharmacokinetic values of the newly designed candidates. Subsequently, these candidates were synthesized and evaluated in vitro, transitioning from in silico design to in vitro testing.

Materials and methods

Computer-aided drug design

To design molecules rationally, we conducted a literature search in public databases such as ChEMBL, DrugBank, and PubChem to find molecules that have shown biological activity against ALL and beta-tubulin. Then, we used two strategies for the chosen molecules.

Structure-based drug design

The binding affinities of drugs that affect beta-tubulin, including their designed analogs and other frequently prescribed medications for treating ALL, were evaluated in kcal/mol utilizing the PDB 6QUS crystal structure [21]. Protein preparation followed the AutoDockTools protocol [22]. The co-crystallized paclitaxel ligand was removed using Samson software [23]. After preparing the crystal, docking was conducted with the known active ligand, vincristine. Following validation of the docking, additional energies were calculated. The structures were mod[eled, and their energies were optimized in Avogadro [24] using the MMFF94s force field. Subsequently, 35 drugs and 58 designed analogs were docked with 6QUS in AutoDock Vina. The grid box was set at 13 × 15 × 25 points with a grid spacing of 0.375 Å, centered at coordinates 2, 23, and 2. Calculations were conducted in triplicate, and the affinity energy of the pose with the lowest RMSD value was averaged for each compound. The interactions and distances were visualized using Discovery Studio Suite®.

Ligand-based drug design

We used the INQA-Artificial Neural Network (INQA-ANN) architecture to create a predictive QSAR model [25]. This model correlates molecular descriptors with experimental IC₅₀ values for beta-tubulin and ALL drugs found in the literature. The goal was to predict the IC₅₀ value of 58 designed analogs. Initially, we calculated molecular descriptors using PaDEL-Descriptor v2.20 software [26]. The obtained descriptors were then evaluated through Pearson correlation. As a first step, the descriptors were grouped into subfamilies, and any descriptor with a correlation value between 0.2 and -0.2 was selected. Subsequently, a new Pearson correlation was performed with the filtered descriptors and the experimental IC₅₀ values. The descriptors with correlation values closest to 1 were ultimately chosen. Then, six molecular descriptors were selected as inputs, and 21 IC₅₀ values of the molecules reported in the literature were used as outputs. The number of nodes in the hidden layer was gradually adjusted during predictions, starting with around 100. We selected the model with a coefficient of determination (R²) exceeding 0.7, as calculated by INQA-ANN [27]. Additionally, this model was statistically validated using QSAR validation parameters cited in the literature, including k, R, and the correlation of these values [28].

Chemistry

All reagents used in the experiment were obtained from commercial suppliers and used without further purification. To monitor the progress of the reaction, TLC was performed on aluminum plates coated with silica gel F254 indicator, which was visualized by UV irradiation. Flash chromatography used silica gel 60 (230–240 mesh). For NMR analysis, ¹H and ¹³C spectra were recorded in CDCl₃ using a Bruker Avance NEO 400 MHz spectrometer. The chemical shifts for ¹H and ¹³C were indicated in parts per million (ppm, δ), with tetramethylsilane as the internal reference. The splitting patterns for ¹H NMR were designated as singlet (s), doublet (d), triplet (t), quartet (q), and multiplet (m). The coupling constants and integration were quoted in Hertz (J). Infrared spectra were recorded using a Bruker Alpha-P ATR FTIR with a diamond crystal. High-resolution mass spectrometry was carried out using an Agilent 5973 (80 eV) spectrometer with electrospray ionization (ESI).

The synthesis was carried out using a round-bottom flask connected to a reflux system. 0.5 mmol (118 mg, 99%) of carbamazepine was added and reacted in 5 mL of THF for 3 hours at reflux. A catalyst of 10 mmol% (24 mg) of anhydrous AlCl₃ and one mmol (70 mg, 99%) of 2,3-Dihydrofuran was used. The resulting crude reaction mixture was purified by column chromatography using an AcOEt/Hexane mobile phase, yielding CR80 in a 50% chemical yield.

Solubility and HPLC method to CR80

Ten milligrams of CR80 were added to ten milliliters of phosphate-buffered saline (PBS) at a concentration of 1X and a pH of 7.35. The suspension was sonicated for one hour at room temperature. Subsequently, the resulting suspension was centrifuged at 4000 rpm for 20 min. Then, the resulting solution was filtered, and the solid was discarded. On the other hand, a method was developed: HPLC-RP with a flow of 1 mL/min at 235–250 nm, using a mobile phase of (30/70) water/ACN on a Shimadzu C18 50 × 4.6 mm column. The concentration of CR80 in PBS was established by interpolating the calibration curve of CR80, which exhibits a concentration range of 4–451 micromolar.

Cell lines and culture conditions

The histiocytic lymphoma cell line U937 (ATCC® HTB-22TM) was used to determine the antitumor potential of the CR80. Additionally, the healthy cell line L929 was evaluated as a cytotoxicity control. The cell lines were cultured in DMEM (Dulbecco’s modified Eagle) supplemented with 10% fetal bovine serum (FBS-Gibco, Fischer scientific, Alcobendas Madrid Spain). The cells were incubated in a humidified atmosphere with 5% CO₂ at 37°C. They were provided with fresh culture medium three times a week until they reached confluence. Adherent cells were harvested using a 0.25% trypsin-EDTA solution. On the other hand, for non-adherent U937 cells, RPMI 1640 medium with stable glutamine, 25 mM HEPES, and 10% FBS were used.

Cytotoxicity screening

The Alamar blue Assay was used to determine the effect of CR80 on tumor and healthy cells. Cells in 96-well microplates at a confluence of 10,000 cells per well were seeded. They were treated 24 hours after seeding with four concentrations of CR80 (0.2, 0.4, 0.8, and 1.0 micromolar) and 7,9 micromolar value predicted by the QSAR model. The cytotoxic effect was assessed 24, 48, and 72 hours after treatment. The chemotherapeutic doxorubicin at 25 nM was used as a positive control, and untreated cells were used as a negative control. Briefly, the medium was replaced by 100μL of Alamar blue reagent (40 μM), and the microplates were incubated for 4 hours under standard culture conditions and read the fluorescence in a microplate reader (530–590 nm, Tecan, Infinite® 200 PRO). Finally, the selectivity index (SI) of CR80 was evaluated. The SI was calculated using the formula: SI = (IC₅₀ for normal cell line L-929)/ (IC₅₀ for U-937) [29]. A favorable SI > 1.0 indicates a drug with greater efficacy against tumor cells than toxicity against normal cells. All experiments were assessed in triplicate.

Statistical analysis

Data were expressed as arithmetic mean ± SEM. Statistical analysis and graphical representation of the results were performed using GraphPad Prism software. Multiple comparisons were made between treatment concentrations and untreated cell viability. The Shapiro-Wilk test was conducted to determine data normality and ANOVA for multiple comparisons. A p-value less than 0.05 was considered statistically significant.

Results and discussion

We identified over 150 anticancer molecules, with eight approved for treating acute lymphoblastic leukemia (ALL). Of these, 5 are small molecules: nelarabine (1), vincristine (2), etoposide (3), teniposide (4), and dactinomycin (5) (see Table 1). Additionally, we found 30 molecules specifically targeting beta-tubulin, a promising target because leukemic cells, like those in ALL, divide more rapidly than normal cells. This rapid division can enhance beta-tubulin expression, making these cells more vulnerable to microtubule-interfering agents, such as vincas (vincristine) and taxanes. Therefore, molecules with greater affinity for beta-tubulin will selectively target cells with accelerated division, meaning treatments with a higher affinity for beta-tubulin preferentially affect leukemic cells over healthy cells due to the latter dividing slower [30–31]. We then used these 35 molecules for structure-based drug design (SBDD) and ligand-based drug design (LBDD). We started with LBDD and began by conducting a boxplot analysis, which led us to eliminate fourteen molecules based on their IC₅₀ values and poor selectivity. This left us with 21 molecules for further study. We began by selecting six molecular descriptors for input for the QSAR mathematical model. These descriptors were chosen through a screening process that reduced the initial 1,540 descriptors to 1,100 by eliminating those with zero values. Subsequently, we performed a descriptor vs. descriptor Pearson correlation, retaining only descriptors with correlation values between 0.2 and -0.2. We then conducted a new Pearson descriptor vs. IC₅₀ correlation using the filtered descriptors, aiming for correlation values close to one. From this analysis, we selected the six descriptors used to train the INQA-ANN. We conducted ten training sessions, adjusting the number of nodes until achieving a minimum Neural Network cost of R²=0.7, which provided an accurate prediction function. The results are displayed in Graph 1a, showing an R² = 0.734 and including the model’s cross-validation, demonstrating the validity of the built QSAR model. With a small amount of data but a strict selection, the model’s noise is reduced, obtaining a model with high predictive capacity [32].

Table 1. Screening of commercial and experimental drugs for LBDD and SBDD.

CN	Affinity (Kcal/mol)	Log P (o/w)	IC₅₀ Experimental (μM)	IC₅₀ Predicted ANN (μM)	Toxicity^a
CN	Affinity (Kcal/mol)	Log P (o/w)	IC₅₀ Experimental (μM)	IC₅₀ Predicted ANN (μM)	hERG Blockers	Ames Toxicity	Rat Oral Acute toxicity
1	−7.01	−0.097	23.55	7.24	0.054	0.74	0.444
2	−6.25	3.693	0.095	1.11	0.217	0.547	0.317
3	−7.95	1.257	7.61	4.34	0.067	0.995	0.544
4	−8,90	2.801	13.99	5.26	0.088	0.163	0.275
5	−6.11	3.246	13.79	4.61	0.41	0.154	0.482
6	−5.98	3.946	0.066	6.15	0.432	0.101	0.502
7	−6.08	3.557	0.103	5.24	0.249	0.247	0.49
8	−5.98	3.848	0.135	6.10	0.309	0.097	0.608
9	−7.92	3.349	0.92	5.03	0.541	0.747	0.578
10	−7.47	4.575	1	6.44	0.814	0.58	0.813
11	−7.64	4.184	1	4.75	0.641	0.864	0.61
12	−7.64	8.875	1	4.90	0.776	0.895	0.634
13	−7.74	4.459	1	6.16	0.774	0.761	0.564
14	−7.35	3.87	15	5.90	0.346	0.698	0.817
15	−7.92	3.867	1.7	5.23	0.225	0.415	0.593
16	−6.8	3.713	47.75	5.02	0.154	0.522	0.706
17	−7.61	2.307	9.5	4.66	0.63	0.684	0.934
18	−7.08	1.911	4.16	4.79	0.687	0.736	0.871
19	−8.23	4.929	2.5	4.67	0.868	0.472	0.895
20	−6.61	3.072	10	7.99	0.035	0.162	0.339
21	−5.5	3.304	0.0029	8.00	0.054	0.075	0.054

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^aCalculated through software https://admetlab3.scbdd.com

After constructing the QSAR model, we continued with SBDD. To do this, we calculated the affinity energies in kcal/mol for the twenty-one molecules used as a training set. We used the Beta-tubulin structure with the code 6QUS as the molecular target, obtained from the RCSB Protein Data Bank (RCSB PDB). This protein is a microtubule-organizing protein that specifically binds to the minus end of non-centrosome microtubules and regulates their dynamics and organization. Vincristine was used as a validation ligand with demonstrated activity and compared with the designed analogs (entry table 1 [31]. We additionally predict the IC₅₀ values and affinities for the molecular target and calculate the logP and toxicity values of the hit molecules using admelab 3.0 [33]. This helps us establish minimum quality criteria and design molecules that improve all pharmacological attributes evaluated in this set Table 1.

Upon analyzing the structures and results presented in Table 1, it was observed that entries 1–6 exhibit a structure analog to Carbamazepine (CBZ) (SI) [34]. CBZ is an approved drug used for treating epilepsy and pain related to trigeminal neuralgia. Modifying its core structure could enhance its selectivity over beta-tubulin and improve its pharmacodynamic and pharmacokinetic profile compared to other compounds with different structural groupings. To explore this, 57 structural changes were made, including homologous series, bioisosteric changes, and ring replacements in the nucleophilic and electrophilic positions of the CBZ nucleus (see Scheme 1).

The designed molecules’ IC₅₀ value was predicted using the QSAR-ANN model constructed. Additionally, their affinity for the target beta-tubulin in kcal/mol was calculated, and both the logP value and toxicity were determined using AdmeLab software 3.0, as shown in Table 2.

Table 2. LBVS and SBVS results for CBZ and CBZ analogs.

CR	Affinity (Kcal/mol)	Log P (o/w)	IC₅₀ Predicted ANN (μM)	Toxicity^a			CR	Affinity (Kcal/mol)	Log P (o/w)	IC₅₀ Predicted ANN (μM)	Toxicity^a
CR	Affinity (Kcal/mol)	Log P (o/w)	IC₅₀ Predicted ANN (μM)	hERG Blockers	Ames Toxicity	Rat Oral Acute toxicity	CR	Affinity (Kcal/mol)	Log P (o/w)	IC₅₀ Predicted ANN (μM)	hERG Blockers	Ames Toxicity	Rat Oral Acute toxicity
22	−7.04	2.357	8.1	0.244	0.822	0.344	52	−6.97	2.584	8.2	0.185	0.869	0.349
23	−7.56	2.692	7.9	0.268	0.817	0.306	53	−8.14	1.678	7.9	0.239	0.692	0.362
24	−6.94	3.314	7.9	0.326	0.847	0.193	54	−7.58	1.769	8.0	0.145	0.718	0.269
25	−7.09	3.666	8.0	0.498	0.739	0.234	55	−7.43	1.879	8.2	0.201	0.707	0.314
26	−7.16	4.285	8.2	0.671	0.625	0.247	56	−7.1	2.254	7.9	0.204	0.528	0.241
27	−7.12	4.703	7.9	0.806	0.607	0.296	57	−7.03	3.076	8.1	0.262	0.376	0.224
28	−7.62	2.338	7.9	0.378	0.979	0.517	58	−6.9	3.548	8.1	0.364	0.367	0.204
29	−7.13	2.981	8.0	0.382	0.735	0.371	59	−8.95	3.693	8.2	0.567	0.842	0.661
30	−7.04	3.025	8.0	0.283	0.68	0.438	60	−7.81	3.049	7.9	0.4	0.762	0.393
31	−6.8	3.005	8.2	0.712	0.42	0.406	61	−7.94	3.631	8.2	0.378	0.695	0.206
32	−7.11	2.525	7.9	0.333	0.861	0.534	62	−7.35	3.34	8.1	0.342	0.763	0.245
33	−7.55	2.584	8.1	0.196	0.651	0.479	63	−7.31	1.911	8.0	0.419	0.739	0.493
34	−7.62	2.04	8.1	0.328	0.907	0.328	64	−7.45	2.278	8.2	0.26	0.758	0.363
35	−7.45	2.784	8.2	0.351	0.899	0.551	65	−6.75	2.89	7.9	0.163	0.674	0.414
36	−7.43	3.068	7.9	0.383	0.868	0.515	66	−6.86	3.27	8.2	0.199	0.689	0.331
37	−7.29	3.553	8.2	0.498	0.838	0.523	67	−6.68	3.902	8.1	0.285	0.574	0.344
38	−9.54	4.344	8.1	0.814	0.947	0.647	68	−6.75	4.006	7.9	0.199	0.637	0.351
39	−7.91	3.443	8.0	0.624	0.901	0.49	69	−6.83	4.468	8.2	0.269	0.571	0.398
40	−8.33	4.288	8.2	0.567	0.832	0.526	70	−7.78	2.84	8.1	0.065	0.491	0.274
41	−7.37	3.932	8.0	0.552	0.86	0.587	71	−7.87	3.025	8.0	0.186	0.544	0.372
42	−7.41	2.039	8.2	0.623	0.848	0.75	72	−7.91	3.606	7.9	0.188	0.48	0.338
43	−7.63	2.66	7.9	0.466	0.878	0.593	73	−3,60	4.123	8.2	0.268	0.373	0.356
44	−7.15	2.183	8.1	0.22	0.795	0.307	74	−3,59	4.588	8.1	0.346	0.503	0.349
45	−6.9	2.801	8.1	0.247	0.681	0.27	75	−8.83	5.242	8.0	0.521	0.747	0.515
46	−6.8	3.28	8.2	0.284	0.675	0.235	76	−7.87	4.157	8.0	0.393	0.694	0.324
47	−7.1	3.762	7.9	0.361	0.606	0.251	77	−7.86	4.318	7.9	0.29	0.494	0.258
48	−6.97	4.126	8.2	0.391	0.571	0.256	78	−7.47	3.412	8.2	0.213	0.675	0.413
49	−7.56	1.406	8.1	0.109	0.955	0.415	79	−7.6	4.031	8.0	0.275	0.545	0.303
50	−7.3	2.044	8.0	0.217	0.81	0.313	80	−8.66	3.415	7.8	0.377	0.552	0.601
51	−7.39	2.308	8.2	0.125	0.862	0.48

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^aCalculated through software https://admetlab3.scbdd.com

The criteria for selecting the designed molecules were as follows: the IC₅₀ values should be less than ten micromolar, the affinity for the molecular target should be more negative than -8 kcal/mol (keeping in mind that the CBZ nucleus presents a value of −7 kcal/mol), and the logP values should fall within the range of 2–4, as this range has experimentally shown promising results. Ultimately, the toxicity profile must match or surpass that of current alternatives drugs.

When analyzing the predicted values, 90% of the candidates exceed the IC₅₀ filter of less than ten micromolar Table 2. This result can be attributed to the fact that the CBZ nucleus is conserved in all the designed analogs, and the modifications made are auxophoric. This means the changes do not significantly vary the biological activity value but make them good candidates. However, these auxophoric modifications substantially impact the candidate’s pharmacodynamics. The results obtained from calculating the affinity in kcal/mol against the beta-tubulin target found a range from −6.5 kcal/mol to −9.5 kcal/mol, demonstrating a significant difference. This variance indicates that the hydrophobic interactions between beta-tubulin and the analogs increase the affinity. Only six candidates designed exceed the -8 kcal/mol affinity value established as a quality criterion. Of these six candidates (entries 38, 40, 53, 59, 75, and 80 Table 2), three have LogP values in the promising range of logP 2–4 (entries 53, 59, 80, Table 2), While the remaining three are outside the established range, these candidates have security profiles equivalent to CBZ and commercial drugs. However, candidate 80 has the best toxicity profile, better than the three selected candidates so that it will proceed to the synthesis stage Scheme 2.

Synthesis of N-(2-tetrahydrofuran)-Carbamazepine and characterization CR80

Scheme 3 describes the reaction between 2,3-Dihydrofuran (DHF) 2 and CBZ 1, with aluminum trichloride as the catalyst. Some researchers have proposed methods for obtaining N-substituted amides [35–37], but we have our approach. We optimized the reaction conditions by experimenting with different solvents, catalyst proportions, and stoichiometric ratios. The best conditions we found were using ten mol% of anhydrous aluminum trichloride, a 1:2 CBZ to DHF ratio, and refluxing in THF for three hours. This resulted in a 50% yield of CR80 (N-(2-tetrahydrofuran)-5H-dibenzo[b,f]azepine-5-carboxamide). We purified the compound using a mixture of ethyl acetate and hexane in a 2:1 ratio. ¹H-NMR (400 MHz, Chloroform-d) δ 7.39 (ddt, J = 39.1, 12.3, 7.9 Hz, 8H), 6.92 (d, J = 1.9 Hz, 2H), 5.62–5.52 (m, 1H), 4.72 (d, J = 8.4 Hz, 1H), 3.76 (dd, J = 28.6, 7.3 Hz, 2H), 2.11–2.04 (m, 1H), 1.79 (dt, J = 13.1, 6.8 Hz, 2H), 1.48 (d, J = 7.0 Hz, 1H). ¹³C-NMR (100 MHz, Chloroform-d) 156.8, 137.4 137.2 132, 129.4, 129.3 128.4, 128.2, 127.5, 127,4, 118.1 118.0, 68, 67,9, 26, 25,9 FT-IR (neat) u(cm⁻¹): 3342, 3062, 3020, 2955, 2928, 1708, 1345, 1290, 1133, 1101. HRMS (ESI): C₁₉H₁₉N₂O₂ [M + H⁺]: calc. 307.1447, found. 307.14529. DRX characterization (S7)

The biological activity of the synthesized CR80 compound was evaluated in vitro. A calibration curve was constructed using CR80 dissolved in ethyl acetate. The area under the curve of a CR80 solution prepared in PBS (1X pH 7.35) was interpolated, yielding a concentration of 10 micromolar. We analyzed the cytotoxic effect on the human lymphoma-derived tumor cell line U-937 and the healthy fibroblast line L-929 to assess the selectivity of the synthesized drug, the cytotoxic effect of CR80 on the U937 and L929 cell lines was evaluated at a concentration of 7.9 micromolar, as predicted by the neuronal network (refer to Table 2, entry 80). This concentration caused cytotoxicity in the U937 and healthy L929 lines, resulting in a non-viable selectivity index (data excluded). It is crucial to understand that the developed prediction function does not support forecasting the SI selectivity index, as it relies solely on the IC₅₀ values of cancer cells; this represents a methodological limitation we must consider. Although CR80 showed promising activity, its low safety profile led us to conduct dilutions in the nanomolar range, concentrating on four levels: 0.2, 0.4, 0.8, and 1 micromolar. Doxorubicin was used as a positive control at 25 nM. Cytotoxicity was assessed at 24, 48, and 72 hours, but at 24 hours, the cytotoxic effect was not apparent (data not shown). However, at 48 hours (Fig 1A), statistically significant differences were observed at 0.8 and 1 micromolar compared to the untreated control, showing a better cytotoxic effect and more excellent safety in the healthy cell line evaluated.

Similarly, after the evaluation at 72 hours (Fig 1B), a decrease in the percentage of cell viability close to 50% was evident at all concentrations, maintaining a safety profile at 0.2, 0.4, and 0.8 micromolar compared to the untreated control with most statistics differences. Additionally, it showed better results in reducing cell viability than chemotherapeutic Doxorubicin treatment. The SI obtained was equal to 2, showing a highly favorable result regarding the selectivity of the synthesized molecule against the evaluated tumor line, instilling optimism about its potential.

We confirmed that the in vitro values predicted by the QSAR model developed with the INQA-ANN align when assessing the U -937 cell line. However, its impact on healthy cells (L929) shows toxicity at 10 micromolar. To improve the selectivity index (SI), we performed dilutions that yielded excellent SI results. This demonstrates that the synergistic approach combining LBDD and SBDD can lead to identifying a promising new drug for in vivo trials. Furthermore, we are developing new QSAR models to predict the activity against cancer cells and the selection index, which will serve as a crucial criterion for new lead compounds.

Conclusions

A new candidate has been developed rationally for potentially treating Acute Lymphoblastic Leukemia. It was designed through the synergy between ligand-based and structure-based drug design. The starting point was the pharmacophoric core of carbamazepine, which underwent more than fifty modifications to obtain an analog with greater affinity for the target beta-tubulin (−8.66 Kcal/mol). This candidate has shown a promising IC₅₀ 0.8–1 micromolar value in vitro on the U-937 cell line and an SI of 2, making it a promising candidate for in vivo trials. This development continues to prove the importance of rational computer-assisted drug design.

Supporting information

S1 and S2 Fig. Pearson Correlation.

(DOCX)

pone.0319415.s001.docx^{(415.5KB, docx)}

S2 File. CR80 characterization.

(DOCX)

pone.0319415.s002.docx^{(1.1MB, docx)}

S3 Table. Table 1SI and Table 2SI.

(DOCX)

pone.0319415.s003.docx^{(220.4KB, docx)}

Acknowledgments

I want to thank the INQA group and the pharmaceutical chemistry program for their support at Universidad El Bosque in Bogotá, Colombia.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. 2025 Apr 28;20(4):e0319415. doi: 10.1371/journal.pone.0319415.r001

Author response to Decision Letter 0

6 Sep 2024

PLoS One. doi: 10.1371/journal.pone.0319415.r002

Decision Letter 0

Sapan Shah

15 Oct 2024

PONE-D-24-38959Design, Synthesis, and In Vitro Evaluation of a Carbamazepine Derivative with Antitumor Potential in a Model of Acute Lymphoblastic LeukemiaPLOS ONE

Dear Dr. Guevara Pulido,

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Additional Editor Comments:

Abstract need to be refurbish. Many grammatical mistakes are provided in abstract.

At least 5 key words should be added by authors.

The statement “The affinity of ALL and beta-tubulin drugs and their designed analogs was evaluated in kcal/mol using the PDB 6QUS crystal structure” not justified?

Sentence never start with value eg. “10 mg of CR80 was added to 10 mL of (PBS, 1X pH 7.35)” grammatically incorrect.

What is meaning of this “CR80 dissolved in Ethyl Acetate 451.2;121.2; y 41.01 µM.??”

Overall, authors utilized only 21 molecules for QSAR model building. These no. is too less for building statistically robust QSAR. Further authors have not validated QSAR model according to OECD guidelines? So, applicability of build model for screening external dataset is doubtful and meaningless as authors have not even performed splitting of dataset, internal validation and Applicability domain studies?

Authors have performed molecular docking studies on selected 21 molecules. However, they have not performed any cross validation studies of docking results even with standard inhibitors of beta tubulin or native inhibitors of 6QUS taxol? How authors will confirmed surety of docking studies? Howe molecular docking results are helpful for authors they have not mentioned any significance of it? Authors used both approach LBDD and SBDD but not correlated both approached design?

Authors have not given detail account on design of 57 compounds using various methods. Authors must give details and elaborate on this design? Is selected 6 descriptors in QSAR models, Molecular docking interaction how contribute for these 57 compound design?

Authors have designed 57 compounds and selected those compound having IC50 value less that 10 micro molar. What is the logic for selecting such large value? Generally compound with less than 1 micromolar value are rational to select?

Authors claim that CR80 less toxic. However, as per my knowledge compounds with statistical values in table above 0.5 for HERG, AMES and RAT TOXICITY are not good and in table CR80 has AMES 0.552 and RAT toxicity 0.601 which is quite high? Authors claim CR80 has best toxicity profile, however, one can see from table 2 compounds 57,58,70, 73, 77 and even 79 have better profile over CR80?

Authors have predicted IC50 value of compound CR80 to be 8.1 mircomolar. However, while performing invitro analysis authors used concentration only upto 1000 nanomolar? What its rationale for these dosage?

Authors have provided so many studies but relevancy, validation and accuracy of data is not justified. I would suggest major revision to authors for above points including reviewer comments.

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: The manuscript is well written. Authors use In-silico approaches for selection and designing of molecule, Also, synthesis and in vitro experiments were performed. However, some additional detail are needed to enhance the its impact

In introduction, brief overview of the current therapies for acute lymphoblastic leukemia should be include. Also, rationale for selecting carbamazepine as a lead compound should be explain.

Some more details on the molecular docking and software used for binding affinity predictions would strengthen the methodology section.

In discussion, how findings of studies correlate with potential mechanisms of CR80 through beta-tubulin should include.

Reviewer #2: 1. Please elaborate on the role of target selected i.e. beta tubulin in ALL

2. It is not stated anywhere how the data is recorded for in vitro test... duplicate or triplicate ?

3. There is no mention of probability value for toxicity value output... so it not clear

4. Rationale behind the selection of CR80 is not satisfactory as the similar values are observed for other derivatives

**********

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PLoS One. 2025 Apr 28;20(4):e0319415. doi: 10.1371/journal.pone.0319415.r003

Author response to Decision Letter 1

30 Oct 2024

Additional Editor Comments:

Abstract need to be refurbish. Many grammatical mistakes are provided in abstract.

• Response: Thanks for the correction. All grammatical errors were corrected.

Acute lymphoblastic leukemia (ALL) is a significant concern in both pediatric and adult demographics. Despite 156 approved cancer therapies based on small molecules, a mere five apply to all types of leukemia. Unfortunately, adherence to these treatments is low due to adverse side effects. Consequently, there is an urgent need to identify more effective treatment options for ALL. This study presents a potential solution. We have designed over fifty analogs of carbamazepine, utilizing a combination of ligand-based and structure-based drug design methodologies. Among these analogs, we identified the CR80 analog, which demonstrated predicted binding values of -8.66 kcal/mol against beta-tubulin, a favorable LogP, and IC50 values suitable for in vitro evaluation. The CR80 compound was synthesized with a yield of 50% and subsequently assessed in vitro against the U-937 cell line. It obtained an IC50 value of 800 nM to 1000 nM and a selectivity index of two, thus marking it as a promising candidate for in vivo studies.

At least 5 key words should be added by authors.

• Response: Beta-tubulin, drug discovery, and in vitro were added as keywords

The statement “The affinity of ALL and beta-tubulin drugs and their designed analogs was evaluated in kcal/mol using the PDB 6QUS crystal structure” not justified?

• Response: thank you for the comment. Indeed, we had not explained this statement in the best way. It was corrected as follows.

“The affinity of drugs with pharmacological activity against beta-tubulin, their designed analogs, and other drugs commonly used for the treatment of ALL was evaluated in kcal/mol using the PDB 6QUS crystal structure.

Sentence never start with value eg. “10 mg of CR80 was added to 10 mL of (PBS, 1X pH 7.35)” grammatically incorrect.

• Response: Thank you for the correction.

“Ten milligrams of CR80 were added to ten milliliters of phosphate-buffered saline (PBS) at a concentration of 1X and a pH of 7.35.”

What is meaning of this “CR80 dissolved in Ethyl Acetate 451.2;121.2; y 41.01 µM.??”

• Response: We wanted to say that the concentration of CR80 in PBS was established by interpolating the calibration curve of CR80, which exhibits a concentration range of 41 to 451 micromolar. This was corrected in the manuscript.

• Response: In agreement with the comments, we allow ourselves to give the following explanation. In the most recent review published in July 2024 [1] the authors describe how a small number of data with a correct choice can offer a robust QSAR model “Despite the need for large training datasets being the 'Achilles' heel' of deep learning in drug discovery, several advances allow neural networks to be – paradoxically – powerful tools in low-data scenarios. An increasing body of literature shows how strategies like the ones discussed in this minireview can lead to high-performing deep learning models, even with little data.” In our QSAR model we reduce the noise of the model by carrying out an in-depth bibliographic review and grouping molecules with similar electronic and steric properties with a data normalization that allows a robust model verified by the internal validation of the model R2=0.734 and an external validation that exceeds the parameters described in the literature as described in graph 1.

For greater clarity, the following paragraph has been added in the document.

The results are displayed in graph 1a, showing an R² = 0.734 and including the model's cross-validation, demonstrating the validity of the built QSAR model. With a small amount of data but a strict selection, the model's noise is reduced, obtaining a model with high predictive capacity [2].

[1]Tilborg, D., Brinkmann, H., Criscuolo, E., Rossen, L., Özçelik, R., & Grisoni, F. (2024). Deep learning for low-data drug discovery: hurdles and opportunities. Current Opinion in Structural Biology, 86, 102818.

[2] Golbraikh, A., & Tropsha, A. (2000). Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Molecular diversity, 5, 231-243.

• Response: According to your question the docking was validated by docking with the ligand with activity demonstrated by beta tubulin vincristine [3] the energy was evaluated, and based on it the comparison was made as described in table 1.

The document was clarified for readers.

[3] Reichle, A., Diddens, H., Altmayr, F., Rastetter, J., & Andreesen, R. (1995). Beta-tubulin and P-glycoprotein: major determinants of vincristine accumulation in B-CLL cells. Leukemia research, 19(11), 823-829.

Authors used both approach LBDD and SBDD but not correlated both approached design?

• Response. According to your question, we would like to clarify that of course, we correlate both models; the selection criteria for our candidates are the following as described in the manuscript,

1. Affinity by Docking

2. IC50 By QSAR model

3. ADME evaluation

We additionally predict the IC50 values and affinities for the molecular target and calculate the logP and toxicity values of the hit molecules using admelab 3.0 [26]. This helps us establish minimum quality criteria and design molecules that improve all pharmacological attributes evaluated in this set Table 1

The criteria for selecting the designed molecules were as follows: the IC50 values should be less than ten micromolar, the affinity for the molecular target should be more negative than -8 kcal/mol (keeping in mind that the CBZ nucleus presents a value of -7 kcal/mol), and the logP values should fall within the range of 2 to 4, as this range has experimentally shown promising results. Finally, the toxicity profile should be equivalent to or better than that of existing drugs.

The authors have not given a detailed accounts of the design of 57 compounds using various methods. Authors must give details and elaborate on this design?

• Response: According to your question, in this paragraph described in the manuscript we establish the criteria for the design of the molecules.

“To explore this, 57 structural changes were made, including homologous series, bioisosteric changes, and ring replacements in the nucleophilic and electrophilic positions of the CBZ nucleus.”

This is done by seeking to optimize the subsequent synthesis process using a commercial building block such as CBZ for subsequent optimal scaling.

Is selected 6 descriptors in QSAR models, Molecular docking interaction how contribute for these 57 compound design?

• Response: Thank you for your question. The descriptors contribute to the pharmacophoric core of carbamazepine. Our 57 changes were made to the auxophores of carbamazepine, which kept the pharmacophoric grouping intact, ensuring biological activity but improving its pharmacophoric and dynamic attributes. It was added to the text pharmacophoric and dynamic attributes.

Authors have designed 57 compounds and selected those compound having IC50 value less that 10 micro molar. What is the logic for selecting such a large value? Generally compound with less than 1 micromolar value are rational to select?

• Response: Thanks for the question. If we are clear, values of 1 micromolar are ideal for rational design. However, the auxophoric changes generated predictions close to 8 micromolar, but since our only criterion is not the IC50 value, we consider that a synergy between the logP affinity and the activity value made the candidate promising, as was demonstrated in the in-house tests. vitro.

The authors claim that CR80 is less toxic. However, to my knowledge, compounds with statistical values above 0.5 for HERG, AMES, and RAT TOXICITY are not good. In table CR80, AMES is 0.552, and RAT toxicity is 0.601, which is quite high. The authors claim that CR80 has the best toxicity profile. However, one can see from table 2 that compounds 57, 58, 70, 73, 77, and even 79 have better profiles than CR80.

• Response: Thank you for your comment, we would like to clarify why the selection of CR80

The choice of the candidate did not depend exclusively on one or another criterion, our proposal is that with respect to Hit CBZ the pharmacodynamic and pharmacokinetic attributes would be improved; in this case, candidates 57, 58, 73, and 79 have log values greater than 4 lo which makes them disposable and 70 does not exceed the affinity, for that reason in this coma we do not select the mentioned candidates.

For readers' clarity, the paragraph was written as follows: While the remaining three are outside the established range, these candidates have security profiles equivalent to CBZ and commercial drugs. However, candidate 80 has the best toxicity profile, better than the three selected candidates, so it will proceed to the synthesis stage.

• Response: Thank you for the question. It clarified why the reported results are lower than those predicted by the neural network. Therefore, we allow ourselves to clarify the following in the document.

We analyzed the cytotoxic effect on the human lymphoma-derived tumor cell line U-937 and the healthy fibroblast line L-929 to assess the selectivity of the synthesized drug, the cytotoxic effect of CR80 on the U937 and L929 cell lines was evaluated at a concentration of 7.9 micromolar, as predicted by the neuronal network (refer to Table 2, entry 80). This concentration caused cytotoxicity in both the U937 and healthy L929 lines, resulting in a non-viable selectivity index (data not included). It is crucial to understand that the developed prediction function does not support forecasting the SI selectivity index, as it relies solely on the IC50 values of cancer cells; this represents a methodological limitation we must consider. Although CR80 showed promising activity, its low safety profile led us to conduct dilutions in the nanomolar range, concentrating on four levels: 200, 400, 800, and 1000 nM. Doxorubicin was used as a positive control at 25 nM.

We confirmed that the in vitro values predicted by the QSAR model developed with the INQA-ANN align when assessing the U -937 cell line. However, its impact on healthy cells, such as L929, shows toxicity at 10 micromolar. To improve the selectivity index (SI), we performed dilutions that yielded excellent SI results. This demonstrates that the synergistic approach combining LBDD and SBDD can lead to the identification of a promising new drug for in vivo trials. Furthermore, we are developing new QSAR models aimed at predicting both the activity against cancer cells and the selection index, which will serve as a crucial criterion for new lead compounds.

In introduction, brief overview of the current therapies for acute lymphoblastic leukemia should be include. Also, rationale for selecting carbamazepine as a lead compound should be explain.

• Response: Thank you for your suggestion. Current therapies for the treatment of children and adults with ALL were included, and the manuscript was updated with ALL were included and the manuscript was updated.

The prognosis for patients with acute lymphoblastic leukemia (ALL) has greatly improved due to intensive multimodal treatment strategies, such as chemotherapy, high-dose chemotherapy with stem cell rescue, and radiation therapy when necessary [3]. The treatment of Adult Acute Lymphoblastic Leukemia (ALL) involves complex chemotherapy combinations and schedules typically seen in oncology. Two main chemotherapy regimens are currently used. The Berlin-Frankfurt-Münster protocol features an induction regimen, consolidation regimen, reintensification regimen, and maintenance therapy, primarily implemented in European adult ALL clinical trials. Alternatively, the hyper-CVAD regimen, created by MD Anderson Cancer Center researchers, consists of rotating two different intensive chemotherapy cycles [4].

3 Chang, J. H. C., Poppe, M. M., Hua, C. H., Marcus, K. J., & Esiashvili, N. (2021). Acute lymphoblastic leukemia. Pediatric Blood & Cancer, 68, e28371.

4 Imai, K. (2017). Acute lymphoblastic leukemia: pathophysiology and current therapy. [Rinsho Ketsueki] The Japanese Journal of Clinical Hematology, 58(5), 460-470.

Some more details on the molecular docking and software used for binding affinity predictions would strengthen the methodology section.

• Response: Thank you for your suggestion

The methodology was expanded in the manuscript as follows “The binding affinities of drugs that affect beta-tubulin, including their designed analogs and other frequently prescribed medications for treating ALL, were evaluated in kcal/mol utilizing the PDB 6QUS crystal structure [18]. Protein preparation followed the AutoDockTools protocol [19]. The co-crystallized ligand, paclitaxel, was removed using Samson software. After preparing the crystal, docking was conducted with the known active ligand, vincristine. Following validation of the docking, additional energies were calculated. The structures were modeled, and their energies were optimized in Avogadro [20] using the MMFF94s force field. Subsequently, 35 drugs and 58 designed analogs were docked with 6QUS. The grid box was set at 13 × 15 × 25 points with a grid spacing of 0.375 Å, centered at coordinates 2, 23, and 2. Calculations were conducted in triplicate, and the affinity energy of the pose with the lowest RMSD value was averaged for each compound. The interactions and distances were visualized using Discovery Studio Suite®.”

In discussion, how findings of studies correlate with potential mechanisms of CR80 through beta-tubulin should include.

• Response Thank you for your comments, in the manuscript we expand the role of ALL.

Additionally, we found 30 molecules specifically targeting beta-tubulin, a promising target because leukemic cells, like those in ALL, divide more rapidly than normal cells. This rapid division can enhance beta-tubulin expression, making these cells more vulnerable to microtubule-interfering agents, such as vincas (vincristine) and taxanes. Therefore, molecules with greater affinity for beta-tubulin will selectively target cells with accelerated division, meaning treatments with a higher affinity for beta-tubulin preferentially affect leukemic cells over healthy cells due to the latter dividing slower [X]

González-García, J. R., et al. (2019). "Targeting microtubules in cancer therapy." Cancer Treatment Reviews, 80, 101895.

Wang, X., Gigant, B., Zheng, X., & Chen, Q. (2023). Microtubule‐targeting agents for cancer treatment: Seven binding sites and three strategies. MedComm–Oncology, 2(3), e46.

Reviewer #2:

1. Please elaborate on the role of target selected i.e. beta tubulin in ALL

Response Thank you for your comments, in the manuscript we expand the role of ALL

Attachment

Submitted filename: Response Reviewers .docx

pone.0319415.s007.docx^{(34.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0319415.r004

Decision Letter 1

Sapan Shah

14 Jan 2025

PONE-D-24-38959R1Design, Synthesis, and In Vitro Evaluation of a Carbamazepine Derivative with Antitumor Potential in a Model of Acute Lymphoblastic LeukemiaPLOS ONE

Dear Dr. Guevara Pulido,

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Academic Editor

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Manuscript is ready to accept and accepted after some minor corrections.

1. As author said in the abstract that 156 approved cancer therapies based on small molecules are there so they should give reference for this.

2. The IC 50 values are in nanomolar and some in millimolar, authors should explain why they are different. Authors must use only one unit for all the compounds.

3. Authors should give numbering to the structures of scheme.

4. The word in vivo, in vitro, in silico must be in italics.

5. Authors must give graphs of IR, NMR and Mass of CR80.

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Reviewers' comments:

Reviewer's Responses to Questions

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1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Yes

Reviewer #4: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: No

**********

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Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: Yes

Reviewer #4: No

**********

6. Review Comments to the Author

Reviewer #3: All comments have been addressed successfully by the authors.

The manuscript may be accepted for publication in the journal.

Reviewer #4: 1. As author said in the abstract that 156 approved cancer therapies based on small molecules are there so they should give reference for this.

2. Rationale behind the selection of CR80 is not satisfactory, Authors must give an explanation

3. The IC 50 values are in nanomolar and some in millimolar, authors should explain why they are different. Authors must use only one unit for all the compounds.

4. Authors should give numbering to the structures of scheme.

5. Authors should incorporate 3D representation of the docking results.

6. Authors must compare the binding affinity of the compounds with the standard drug.

7. The word in vivo, in vitro, in silico must be in italics.

8. The graphical representation is missing.

9. Authors must give graphs of IR, NMR and Mass of CR80.

10. Authors must follow the referencing style given by journal.

**********

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Reviewer #3: Yes

Reviewer #4: No

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Attachment

Submitted filename: Reviewers Comments.doc

pone.0319415.s006.doc^{(23KB, doc)}

PLoS One. 2025 Apr 28;20(4):e0319415. doi: 10.1371/journal.pone.0319415.r005

Author response to Decision Letter 2

30 Jan 2025

Sapan Kamleshkumar Shah, Ph.D., M.Pharm.

Academic Editor

PLOS ONE

Additional Editor Comments and Reviewer 4

Thank you for your comments, which enable us to present an improved version of the manuscript. Below, we provide a point-by-point response to the comments from the editor and referee 4.

The manuscript is ready to accept and accepted after some minor corrections.

1. As the author said in the abstract, 156 approved cancer therapies based on small molecules are available, so they should give a reference for this.

Response: reference was added.

3. National Cancer Institute. Cancer Drugs - National Cancer Institute [Internet]. www.cancer.gov. 2012. Available from: https://www.cancer.gov/about-cancer/treatment/drugs.

2. The rationale behind the selection of CR80 is not satisfactory; authors must give an explanation

Response: Thank you for your comment; we would like to clarify why the selection of CR80

We want to clarify the reasons for selecting CR80. The candidate's selection was not solely based on one criterion. Our proposal suggests that, regarding Hit CBZ, the pharmacodynamic and pharmacokinetic attributes would see improvement. Candidates 57, 58, 73, and 79 have log values greater than 4, making them acceptable, whereas candidate 70 does not demonstrate sufficient affinity; therefore, we did not select the mentioned candidates in this instance.

Additionally, the criteria for choosing the designed molecules included the following: the IC50 values should be less than ten micromolar, the affinity for the molecular target must be more negative than -8 kcal/mol (considering that the CBZ nucleus has a value of -7 kcal/mol), and the logP values should be within the range of 2 to 4, as this range has been shown to yield promising experimental results. Lastly, the toxicity profile should be comparable to or better than that of existing drugs. Consequently, any candidates that had a more promising toxicity profile but did not satisfy the three primary criteria—affinity, IC50, and logP—were excluded from consideration.

3. The IC 50 values are in nanomolar and some in millimolar. The authors should explain why they are different. They must use only one unit for all the compounds.

Response: Thanks for the suggestion; the change of units referred to a way of representing a low concentration, but all the units and the graph have already been converted to micromolar.

4. Authors should give numbering to the structures of the scheme.

Response: this was corrected

5. The words in vivo, in vitro, and silico must be in italics.

Response: this was corrected in the manuscript.

6. Authors must provide graphs of IR, NMR, and Mass of CR80.

Response In the SI, IR, and MS-HR were added. NMR was already in the SI

7. Authors should incorporate a 3D representation of the docking results.

Scheme 2. Non-covalent interactions CR80 and 3D representation

8. Authors must compare the binding affinity of the compounds with the standard drug.

Response: The manuscript in the following paragraph is compared to the standard drug described.

“We used the Beta-tubulin structure with the code 6QUS as the molecular target, obtained from the RCSB Protein Data Bank (RCSB PDB). This protein is a microtubule-organizing protein that specifically binds to the minus end of non-centrosome microtubules and regulates their dynamics and organization. Vincristine was used as a validation ligand with demonstrated activity and compared with the designed analogs (entry 2 table 1)[31].”

9. The graphical representation is missing.

This information can be found in an additional file titled "Figures and Schemes."

10. Authors must follow the referencing style given by the journal.

The referencing style has been corrected.

James Guevara Pulido

Corresponding author

joguevara@unbosque.edu.co

Attachment

Submitted filename: Response to reviewers R2.docx

pone.0319415.s008.docx^{(270.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0319415.r006

Decision Letter 2

Sapan Shah

2 Feb 2025

Design, Synthesis, and In Vitro Evaluation of a Carbamazepine Derivative with Antitumor Potential in a Model of Acute Lymphoblastic Leukemia

PONE-D-24-38959R2

Dear Dr. Guevara Pulido,

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PLOS ONE

Additional Editor Comments (optional):

Authors have implemented all suggestions by reviewers and recommend for further publication.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0319415.r007

Acceptance letter

Sapan Shah

PONE-D-24-38959R2

PLOS ONE

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 and S2 Fig. Pearson Correlation.

(DOCX)

pone.0319415.s001.docx^{(415.5KB, docx)}

S2 File. CR80 characterization.

(DOCX)

pone.0319415.s002.docx^{(1.1MB, docx)}

S3 Table. Table 1SI and Table 2SI.

(DOCX)

pone.0319415.s003.docx^{(220.4KB, docx)}

Attachment

Submitted filename: reviewer comments.docx

pone.0319415.s004.docx^{(11.7KB, docx)}

Attachment

Submitted filename: Response Reviewers .docx

pone.0319415.s007.docx^{(34.3KB, docx)}

Attachment

Submitted filename: Reviewers Comments.doc

pone.0319415.s006.doc^{(23KB, doc)}

Attachment

Submitted filename: Response to reviewers R2.docx

pone.0319415.s008.docx^{(270.5KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

Design, synthesis, and in vitro evaluation of a carbamazepine derivative with antitumor potential in a model of Acute Lymphoblastic Leukemia

Cristian Álvarez-Gómez

Angela V Fonseca-Benítez

James Guevara-Pulido

Roles

Abstract

Introduction

Materials and methods

Computer-aided drug design

Structure-based drug design

Ligand-based drug design

Chemistry

Solubility and HPLC method to CR80

Cell lines and culture conditions

Cytotoxicity screening

Statistical analysis

Results and discussion

Table 1. Screening of commercial and experimental drugs for LBDD and SBDD.

Graph 1. a) R2 ANN model, b) external cross-validation.

Scheme 1. Eighty structural changes to the CBZ nucleus.

Table 2. LBVS and SBVS results for CBZ and CBZ analogs.

Scheme 2. Non-covalent interactions CR80 and 3D representation.

Synthesis of N-(2-tetrahydrofuran)-Carbamazepine and characterization CR80

Scheme 3. Reaction equation for the synthesis of CR80.

Fig 1. Cytotoxicity concentration-response curves for CR80.

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Author response to Decision Letter 0

Decision Letter 0

Sapan Shah

Roles

Author response to Decision Letter 1

Decision Letter 1

Sapan Shah

Roles

Author response to Decision Letter 2

Decision Letter 2

Sapan Shah

Roles

Acceptance letter

Sapan Shah

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Graph 1. a) R² ANN model, b) external cross-validation.