Development of Phenoxyacetic Acid Hybrids With COX‐2 Inhibitory Activity as Potential Anti‐Neuroinflammatory Agents

ABSTRACT

This research employs a molecular hybridization strategy to repurpose the pyrazoline scaffold 6a, 6c, 7a‐c, 11a, and 12b, transforming it into a high‐efficiency conjugate designed to tackle the multifaceted pathology of neuroinflammation and epilepsy. By integrating a selective phenoxyacetic acid moiety. Our findings identified compound 7c as a potential lead candidate for the development of novel anticonvulsant agents. In vivo trials demonstrated that 7c offers a superior therapeutic window compared to valproic acid, yielding 90% seizure protection in PTZ models and a remarkable 212.27% delay in seizure onset within the pilocarpine model, alongside 100% survival. Beyond mere symptomatic suppression, 7c re‐engineers the hippocampal environment by slashing glutamate‐driven excitotoxicity by 64.23% and silencing the cytokine‐glial activation axis (TNF‐alpha, IL‐6, GFAP, and Iba‐1). Supported by ADME profile confirming optimal BBB permeability and molecular docking indicating a robust binding affinity of −10.3 kcal/mol, this study positions 7c as a versatile, non‐toxic, and repurposed‐ready hybrid candidate for advanced neuroprotective intervention.

1. Introduction

Despite the maturation of the global antiepileptic drug (AED) market, epilepsy remains a formidable challenge to global health, affecting nearly 1% of the population (de Boer et al. ²⁰⁰⁸). While the disorder—historically surpassed only by stroke in neurological prevalence has seen a surge in diagnostic breakthroughs, therapeutic outcomes have hit a plateau (Karakurt et al. 2010). Current clinical data suggests that nearly one‐third of the 50 million affected individuals worldwide suffer from pharmacoresistance, where seizures remain refractory to even the most aggressive multidrug regimens (Wahab 2010).

The primary clinical strategy remains centered on chronic administration of AEDs to stabilize neuronal membranes and prevent ictogenesis (Engel 2008). Classic agents like phenytoin (I) and carbamazepine (II), while foundational, are often hampered by a narrow therapeutic index and significant systemic toxicity (Malawska et al. 2004; Latini et al. ²⁰⁰⁸). Even the subsequent generation of therapies, including pregabalin (III), and valproic acid (IV), are frequently discontinued due to idiosyncratic adverse effects such as hepatotoxicity and severe metabolic disturbances (Hakami 2021). Beyond these side effects, the lack of target specificity in many current agents underscores the urgent need for a shift in medicinal chemistry toward molecules with better‐defined mechanisms of action (Sills and Rogawski 2020).

Recent pharmacological literature highlights a significant gap in our understanding of the precise mechanisms underlying many established antiepileptic drugs (AEDs). A primary concern is the lack of target specificity; while certain AEDs fail to exhibit high‐affinity receptor binding, others exert their therapeutic effects through pleiotropic or poorly characterized molecular pathways (Kulandasamy et al. 2009). This mechanistic ambiguity is often linked to suboptimal clinical efficacy and a high incidence of off‐target adverse effects. Consequently, the focus of contemporary medicinal chemistry has shifted toward the rational design of site‐specific anticonvulsant agents. There is a critical imperative to develop novel chemotypes that provide superior ictogenic control while circumventing the pharmacodynamic limitations and toxicity profiles associated with legacy therapies.

Modern neurobiology has identified inflammation as a critical mediator in the progression of epilepsy rather than a mere consequence of it (Aghasafari et al. 2019). The central nervous system's inflammatory cascade characterized by the recruitment of leukocytes, increased blood‐brain barrier permeability, and the upregulation of cyclooxygenase (COX) and lipoxygenase creates a pro‐convulsant environment (Abdel‐Lateff et al. ²⁰²⁰). This inflammatory state promotes the release of reactive oxygen species (ROS) and prostaglandins, which further lower the seizure threshold (Sashidhara et al. 2011). Consequently, targeting the neuroinflammatory pathway alongside classical ion‐channel modulation represents a promising frontier for overcoming drug resistance (Minhas et al. 2017).

In response to the multi‐factorial nature of epileptogenesis, molecular hybridization has emerged as a sophisticated drug discovery strategy. This approach fuses distinct pharmacophoric subunits into a single molecular framework to achieve synergistic or poly‐pharmacological profiles. Hydrazones and phenoxyacetic acid derivatives (V–VII) are particularly advantageous scaffolds in this regard, offering high synthetic tractability and a proven history of bioactivity in both inflammatory and neurological context (Ulloora et al. 2013; Elgohary et al. ²⁰²⁵; Zeidan et al. ²⁰²⁵).

Building on this rationale, the present work details the design and synthesis of a novel series of hybrid molecules. By merging the anti‐inflammatory potential of phenoxyacetic acid with the pharmacologically versatile hydrazone moiety, we aim to develop dual‐action anticonvulsants. This study evaluates the efficacy of these novel constructs in mitigating both seizure activity and the underlying inflammatory components of the disease (Figure 1).

Figure 1.

Diagram of marketed compounds I‐IV, and previously reported compounds V‐VII, as antiepileptic candidates.

2. Rational and Design

The present study explores the therapeutic repurposing of a series of previously synthesized hybrid compounds, including 6a, 6c, 7a‐c, 11a, and 12b, which were originally designed as selective COX inhibitors based on the integration of diaryl pyrazoline and phenoxyacetic acid pharmacophores. Unlike our previous work, which focused primarily on their anti‐inflammatory activity, the novelty of the current investigation resides in evaluating these structurally diverse hybrids as potential antiepileptic agents. This repurposing strategy was motivated by the growing evidence linking neuroinflammation, particularly COX‐2‐mediated signaling, to the pathogenesis and progression of epilepsy. Accordingly, the selected compounds were systematically investigated for their anticonvulsant potential through in vivo pharmacological evaluation, supported by mechanistic and computational studies. This approach expands the therapeutic scope of these previously reported molecules and provides new insights into their potential application as multifunctional agents for epilepsy management (Figure 2).

Figure 2.

Design Strategy for Dual Antiepileptic and Anti‐Inflammatory Agents Based on Compounds 6a, 6c, 7a‐c, 11a, and 12b.

To optimize binding interactions within the ant‐epileptic active site, systematic structural modifications were introduced at the N1‐position of the pyrazoline scaffold. In this regard, N1‐carboamide derivatives 7a‐c were designed to promote additional hydrogen‐bonding interactions within the enzyme binding pocket. In parallel, further structural refinement was achieved through scaffold simplification, as exemplified by compound 11a, in which a carbothioamide moiety was directly linked to the phenoxyacetic acid fragment. In addition, thiazolidinone‐based hybrid 12b was synthesized via incorporation of a hydrazone linker connecting the thiazolidinone and phenoxyacetic acid moieties, as illustrated in Figure 2.

3. Pathophysiology of Neuroinflammation

Neuroinflammation plays a central role in the pathophysiology of neurological disorders, including epilepsy, as illustrated in Figure 3. It is initiated by the activation of microglia and astrocytes in response to neuronal injury or excessive neuronal firing, leading to a sustained inflammatory response within the central nervous system. Activated glial cells release a range of pro‐inflammatory mediators, including tumor necrosis factor‐α (TNF‐α), interleukin‐1β (IL‐1β), and interleukin‐6 (IL‐6), which contribute to the amplification of inflammatory signaling. This process is closely associated with increased production of reactive oxygen species (ROS) and reactive nitrogen species (RNS), resulting in oxidative stress, mitochondrial dysfunction, and lipid peroxidation. In parallel, disruption of the blood–brain barrier (BBB) further exacerbates neuroinflammation by facilitating peripheral immune cell infiltration into the brain. Collectively, these interconnected events establish a self‐perpetuating cycle of inflammation and oxidative stress that enhances neuronal excitability and contributes to seizure generation and progression (Polascheck et al. 2010; Baik et al. ¹⁹⁹⁹; Trandafir et al. ²⁰¹⁵).

Figure 3.

Mechanistic Cascades of Neuroinflammation and Oxidative Stress in Epilepsy.

Note: (This figure was generated with the assistance of [AI Tool Name, e.g., Google Gemini 2.0 (https://gemini.google.com)].

4. Chemistry (Elgohary et al. ^2024a)

The target compounds 6a, c, 7a‐c, 11a, and 12b were synthesized following the established synthetic pathways and experimental procedures previously reported by our group. Full experimental details are provided in the Supporting Material. (Scheme 1, 2).

Scheme 1.

Reagents and conditions: (i) DMF/K₂CO₃/KI, stir, RT, 12 h; (ii) 10% NaOH, MeOH, reflux 6 h, then add dil. HCl, dropwise; (iii) EtOH, 10% NaOH, stir 0°C, stir 6–8 h, then add dil. HCl, dropwise (iv) N₂H₄.H₂O/PhNHNH₂, EtOH, reflux 6–8 h, (v) NH₂CXNHNH_2, EtOH, NaOH, reflux 6–8 h.

Scheme 2.

Reagents and conditions: (i) ClCH₂COOH, DMF/K₂CO₃, KI, stir, RT, 12 h; (ii) NH₂CSNHNH_2, EtOH‐CH₃COOH, reflux 4–6 h (iii) ClCH₂COOH, EtOH/sodium acetate, reflux 4–6 h.

5. In Silico Studies

5.1. Bioinformatics Study

To explore the potential anti‐epileptic effects of the studied compounds, a network pharmacology approach was performed by integrating predicted targets of compounds 6a, c, 7a‐c, 11a, and 12b with epilepsy‐associated genes retrieved from GeneCards. A total of 1853 epilepsy‐related genes were identified and intersected with the top predicted targets of each compound group to determine shared molecular targets and potential therapeutic relevance.

Venn diagram analysis revealed that compounds 6a, c shared 7 common targets with epilepsy‐related genes (Figure 4A), whereas compounds 7a‐c exhibited 12 overlapping genes (Figure 5A). In addition, compounds 11a, and 12b showed 18 shared targets with epilepsy‐associated genes (Figure 6A). These findings suggest that all three compound groups may interact with epilepsy‐related molecular networks, although with varying degrees of target coverage.

Figure 4.

Overlap and functional enrichment analysis of compounds 6a, 6c with anti‐epilepsy–associated targets. (A) Venn diagram showing the intersection between predicted targets of compounds 6a, c and epilepsy‐related genes, identifying 13 common targets. B. Gene Ontology (GO) biological process enrichment analysis of the overlapping genes. C. GO molecular function enrichment analysis highlighting key functional activities associated with the identified targets. Dot size represents the number of genes involved in each pathway, while dot color indicates statistical significance expressed as –log10(FDR), with higher values corresponding to more significant enrichment. The x‐axis represents fold enrichment. Statistical significance was determined using Benjamini–Hochberg FDR correction (FDR < 0.05).

Figure 5.

Overlap and functional enrichment analysis of compounds 7a‐c with anti‐epilepsy–associated targets. (A) Venn diagram showing the intersection between predicted targets of compounds 7a‐c and epilepsy‐related genes, identifying 15 common targets. B. Gene Ontology (GO) biological process enrichment analysis of the overlapping genes. C. GO molecular function enrichment analysis highlighting key functional activities associated with the identified targets. Dot color indicates statistical significance expressed as –log10(FDR), with higher values corresponding to more significant enrichment. The x‐axis represents fold enrichment. Statistical significance was determined using Benjamini–Hochberg FDR correction (FDR < 0.05).

Figure 6.

Overlap and functional enrichment analysis of compounds 11a, and 12b with anti‐epilepsy–associated targets. (A) Venn diagram showing the intersection between predicted targets of compounds 11a, 12b and epilepsy‐related genes, identifying 7 common targets. B. Gene Ontology (GO) biological process enrichment analysis of the overlapping genes. C. GO molecular function enrichment analysis highlighting key functional activities associated with the identified targets. Dot color indicates statistical significance expressed as –log10(FDR), with higher values corresponding to more significant enrichment. The x‐axis represents fold enrichment. Statistical significance was determined using Benjamini–Hochberg FDR correction (FDR < 0.05).

To further investigate the functional relevance of these overlapping targets, Gene Ontology (GO) enrichment analysis was performed. For compounds 6a, c, biological process analysis (Figure 4B) revealed significant enrichment in metabolic and cellular response‐related processes, including cellular response to steroid hormones, lipid metabolic processes, and response to chemical stimuli. Molecular function analysis (Figure 4C) demonstrated enrichment in enzyme‐related activities, particularly oxidoreductase and dehydrogenase activities, as well as receptor‐related functions such as epidermal growth factor receptor activity. Binding‐related functions, including fatty acid and organic acid binding, were also observed.

For compounds 7a‐c, GO analysis indicated significant enrichment in biological processes related to lipid metabolism, response to steroid hormones, and cellular response to chemical and endogenous stimuli (Figure 5B). Additional processes related to signal transduction, cell communication, and wound healing were also identified. Molecular function analysis (Figure 5C) revealed enrichment in monoamine oxidase, amine oxidase, and oxidoreductase activities, along with binding‐related functions such as fatty acid binding, nuclear receptor activity, and transcription coactivator binding.

For compounds 11a and 12b, GO enrichment analysis revealed significant involvement in metabolic processes, including drug metabolism, glycoside metabolism, and small molecule metabolic pathways (Figure 6B). In addition, hormone response and insulin receptor signaling pathways were identified. Molecular function analysis (Figure 6C) demonstrated enrichment in receptor‐related and enzyme activities, including insulin receptor activity, insulin binding, oxidoreductase activity, and dehydrogenase activity, as well as growth factor binding and steroid‐related enzyme functions.

KEGG pathway enrichment analysis of compounds 6a, c revealed significant enrichment in metabolic and regulatory pathways, with folate biosynthesis showing the highest enrichment, followed by arachidonic acid metabolism. The PPAR signaling pathway was also significantly represented (Table 1).

Table 1.

KEGG pathway enrichment analysis of overlapping targets between compounds 6a, c and anti‐epilepsy–associated genes.

Enrichment FDR	nGenes	Pathway Genes	Fold enrichment	Pathways
1.1E‐03	2	28	237.3	Folate biosynthesis
3.3E‐03	2	62	107.2	Arachidonic acid metabolism
3.8E‐03	2	76	87.4	PPAR signaling pathway

Note: The table presents significantly enriched pathways ranked by false discovery rate (FDR), number of involved genes (nGenes), pathway gene counts, and fold enrichment.

For compounds 7a‐c, KEGG analysis identified several significantly enriched pathways related to neuronal signaling and metabolism. Notably, neurotransmitter‐related pathways such as serotonergic and dopaminergic synapses were prominently enriched. In addition, key signaling pathways including VEGF, PPAR, Rap1, and EGFR signaling were identified, along with amino acid metabolism pathways (Table 2).

Table 2.

KEGG pathway enrichment analysis of overlapping targets between compounds 7a‐c and anti‐epilepsy–associated genes.

Enrichment FDR	nGenes	Pathway genes	Fold enrichment	Pathways
2.0E‐04	3	59	98.6	VEGF signaling pathway
2.4E‐04	3	76	76.5	PPAR signaling pathway
2.4E‐04	3	79	73.6	EGFR tyrosine kinase inhibitor resistance
5.3E‐04	3	111	52.4	Serotonergic synapse
6.2E‐04	3	131	44.4	Dopaminergic synapse
6.8E‐04	3	153	38	Oxytocin signaling pathway
2.0E‐04	4	211	36.7	Rap1 signaling pathway
1.2E‐03	2	40	96.9	Glycine serine and threonine metabolism
1.2E‐03	2	42	92.3	Tryptophan metabolism
5.7E‐04	2	16	242.3	Phenylalanine metabolism
6.8E‐04	2	22	176.2	Histidine metabolism
1.1E‐03	2	36	107.7	Tyrosine metabolism

Note: The table presents significantly enriched pathways ranked by false discovery rate (FDR), number of involved genes (nGenes), pathway gene counts, and fold enrichment.

KEGG pathway enrichment analysis of compounds 11a, and 12b revealed significant enrichment in metabolic and intracellular signaling pathways. The most enriched pathway was nitrogen metabolism, followed by EGFR resistance and adherens junction pathways. Additional pathways included ErbB, IL‐17, mTOR, MAPK, and PI3K‐Akt signaling pathways, as well as broader metabolic pathways (Table 3).

Table 3.

KEGG pathway enrichment analysis of overlapping targets between compounds 11a, 12b and anti‐epilepsy–associated genes.

Enrichment FDR	nGenes	Pathway genes	Fold enrichment	Pathways
1.6E‐10	5	17	380	Nitrogen metabolism
2.8E‐05	4	79	65.4	EGFR tyrosine kinase inhibitor resistance
2.8E‐05	4	93	55.6	Adherens junction
3.5E‐04	3	84	46.1	ErbB signaling pathway
4.3E‐04	3	93	41.7	IL‐17 signaling pathway
9.4E‐05	4	156	33.1	MTOR signaling pathway
5.4E‐05	5	300	21.5	MAPK signaling pathway
9.9E‐05	5	362	17.8	PI3K‐Akt signaling pathway
2.8E‐05	9	1556	7.5	Metabolic pathways

Note: The table presents significantly enriched pathways ranked by false discovery rate (FDR), number of involved genes (nGenes), pathway gene counts, and fold enrichment.

Overall, compounds 6a, c were primarily associated with oxidative stress and metabolic regulation, compounds 7a‐c were mainly involved in neurotransmitter‐related pathways and signal transduction, while compounds 11a, and 12b were linked to metabolic and regulatory signaling networks.

5.2. Predicted Toxicological Profile

In silico ADMET predictions generated using the ADMETlab platform indicated that compounds 6a, c, 7a‐c, 11a, and 12b are classified within favorable toxicity categories, implying a low risk of acute oral toxicity and an acceptable therapeutic safety margin. Furthermore, comprehensive toxicity reports for all evaluated compounds, as provided in the Supporting Material, consistently corroborated the minimal toxicological liability of the designed molecules (Figures 7 and S1).

Figure 7.

Bioavailability radar of the designed compounds predicted using the ProTox‐3.0 software 6a (A), 6c (B), 7a (C), 7b (D), 7c (E), 11a (F) and 12b (G).

6. Results and Discussion

6.1. Anti‐Inflammatory Profile

6.1.1. In‐Vitro COX‐2 Assay

All synthesized compounds 6a, c, 7a‐c, 11a, and 12b displayed strong inhibition (IC₅₀ = 0.03–0.06 µM), with efficacy comparable to or matching that of the reference inhibitor celecoxib (IC₅₀ = 0.04 µM). (Table 4).

Table 4.

In Vitro COX‐2 enzyme inhibition by designed compounds 6a, c, 7a‐c, 11a, and 12b.

Compounds	R 1	R 2	COX‐2 IC 50 (µM)	SI
Celecoxib	‐‐‐‐‐‐	‐‐‐‐‐‐	0.04 ± 0.003	715.9
6a	H	H	0.03 ± 0.0014a	365.4
6c	‐OCH3	H	0.03 ± 0.0017a	196.9
7a	H	CONH2	0.06 ± 0.0048a	201.5
7b	Cl	CONH2	0.06 ± 0.0040a	234.5
7c	‐OCH3	CONH2	0.04 ± 0.0037a	383.5
11a	‐‐‐‐‐‐	‐‐‐‐‐‐	0.06 ± 0.0005a	227.2
12b	‐‐‐‐‐‐	‐‐‐‐‐‐	0.06 ± 0.0006a	169.1

Note: Data expressed as mean ± SEM (n = 3) (a) is statistically significant with celecoxib at P < 0.05 using one‐way ANOVA followed by Tukey as a post hoc test.

6.2. Anti‐Epileptic Profile

6.2.1. Anticonvulsant Activity in the PTZ‐Induced Seizure Model

The PTZ assay provided an initial ranking of the current compounds according to seizure protection and short‐term survival. As summarized in Table 5, PTZ alone induced generalized convulsions in all animals and a 24 h mortality rate of 70%. Sodium valproate conferred 70% protection and reduced mortality to 20%, confirming the suitability of the model for detecting anticonvulsant activity (Ahuja and Siddiqui 2014; Marzouk et al. ²⁰²⁰).

Table 5.

Protective efficacy, relative protection, and mortality rates in the PTZ‐induced ‎seizure model.

	Protection (%)	Relative Protection (%)	24 h mortality (%)
PTZ	0%		70%
VAL	70%		20%
6a	60%	85.71%	20%
6c	30%	42.86%	60%
7a	70%	100.00%	10%
7b	70%	100.00%	20%
7c	90%	128.57%	0%
11a	50%	71.43%	40%
12b	50%	71.43%	50%

Note: Data show the percentage of animals protected from PTZ‐induced tonic‐clonic seizures, the relative protection calculated in comparison with the valproic acid group, and the 24 h mortality rate after PTZ challenge. Values are expressed as percentages (n  = 10 mice/group).

Among the tested analogues, compound 7c showed the strongest efficacy. It protected 90% of animals from PTZ‐induced tonic‐clonic seizures and completely prevented mortality within 24 h. Relative to valproic acid, 7c produced a 28.57% improvement in protective efficacy, while mortality was abolished compared with the PTZ group. Compounds 7a and 7b each provided 70% protection, matching the reference drug; however, 7a displayed a more favorable survival outcome, with mortality reduced to 10%, whereas 7b matched valproic acid with 20% mortality. Compound 6a produced moderate anticonvulsant activity, protecting 60% of animals, corresponding to a 14.29% lower protection than valproic acid, while maintaining a mortality rate comparable to the reference group.

The remaining compounds were less effective. Compounds 11a and 12b each protected 50% of animals, corresponding to a 28.57% lower protective efficacy than valproic acid, with mortality rates of 40% and 50%, respectively. Compound 6c was the weakest member of the series in this model, providing only 30% protection and showing 60% mortality. Taken together, the PTZ efficacy order was 7c > 7a ≈ 7b > 6a > 11a ≈ 12b > 6c. The superior effect of 7c in this GABA‐A antagonist model suggests a stronger ability to suppress acute neuronal hyperexcitability than the other tested analogues (Samokhina and Samokhin 2018; Taspinar et al. ²⁰²¹).

6.2.2. Evaluation of Seizure Onset, Severity, and Survival in the Pilocarpine‐Induced Convulsion Model

The compounds were subsequently evaluated in the pilocarpine model, which provides a more demanding platform for assessing seizure initiation, severity progression, and survival under conditions resembling temporal lobe seizure pathology (Balaha et al. 2023). In the pilocarpine group, seizures developed rapidly after 4.89 ± 0.71 min and progressed to a final Racine score of 4.78 ± 0.44 at 120 min. Only 60% of animals survived the 24 h period. Valproic acid significantly delayed seizure onset to 10.12 ± 0.83 min, representing a 106.95% increase compared with pilocarpine, and reduced seizure severity by 28.95%, 31.71%, 40.91%, and 53.97% at 30, 60, 90, and 120 min, respectively. Survival increased to 70% (Table 6).

Table 6.

Effects of valproic acid and test compounds on seizure onset, seizure severity, and ‎survival in the ‎pilocarpine model.

	Onset (mins)	Seizure severity				24 h. survival (%)
		30 min	60 min	90 min	120 min
Pilocarpine	4.89 ± 0.71	3.8 ± 0.63	4.1 ± 0.57	4.4 ± 0.52	4.78 ± 0.44	60%
Valproic acid	10.12 ± 0.83a	2.7 ± 0.67a	2.8 ± 0.79a	2.6 ± 0.52a	2.2 ± 0.63a	70%
6a	10.02 ± 0.77a	3.2 ± 0.42	3.3 ± 0.67	3 ± 0.94a	2.6 ± 0.70a	70%
6c	7.41 ± 0.81ab	3.7 ± 0.48	4 ± 0.82b	3.8 ± 0.63b	3.4 ± 0.52ab	50%
7a	13.29 ± 0.74ab	2.6 ± 0.52a	2.8 ± 0.42a	2.6 ± 0.52a	2.1 ± 0.74a	90%
7b	11.54 ± 0.71ab	2.7 ± 0.48a	3 ± 0.47a	2.9 ± 0.57a	2.5 ± 0.53a	70%
7c	15.27 ± 1.04ab	2.4 ± 0.52a	2.2 ± 0.63a	1.8 ± 0.42a	1.1 ± 0.74ab	100%
11a	9.69 ± 0.83a	3.3 ± 0.48	3.4 ± 0.52	3.4 ± 0.52	3.1 ± 0.57a	60%
12b	8.68 ± 0.90ab	3.5 ± 0.53	3.6 ± 0.52	3.4 ± 0.52	3.2 ± 0.63a	60%

Note: Values are expressed as mean ± SD or percentages. Seizure onset was analyzed using one‐way ANOVA followed by Tukey's post hoc test, whereas seizure severity over time was analyzed using two‐way ANOVA followed by Tukey's multiple‐comparisons test. a: p  < 0.05 versus pilocarpine group; b: p  < 0.05 versus valproic acid group.

Consistent with the PTZ results, compound 7c showed the most prominent anticonvulsant profile in the pilocarpine model. It prolonged seizure onset to 15.27 ± 1.04 min, corresponding to a 212.27% delay relative to the pilocarpine group, which represented the longest latency among all tested compounds. In addition, 7c reduced Racine scores by 36.84%, 46.34%, 59.09%, and 76.99% at 30, 60, 90, and 120 min, respectively, versus the pilocarpine group. The late‐stage protection was especially notable at 120 min, where the final severity score was 50% lower than that observed with valproic acid. This strong behavioral effect was accompanied by complete survival protection, with 100% of animals surviving 24 h after pilocarpine challenge (Table 6).

Compound 7a exhibited the next most favorable profile. It delayed seizure onset by 171.78% relative to pilocarpine and decreased the final Racine score by 56.07%, while improving survival to 90%. Compound 7b also displayed clear activity, increasing seizure latency by 135.99% and reducing final severity by 47.70%, with 70% survival. Compound 6a delayed seizure onset by 104.91% and reduced the final seizure score by 45.61%, also with 70% survival. These findings place 7a, 7b, and 6a in the active range of the series, but their overall behavioral profiles remained below that of 7c (Table 6).

Compounds 11a, 12b, and 6c were less effective. Compound 11a increased seizure onset by 98.16% and decreased the final severity score by 35.15%, but survival remained unchanged from the pilocarpine group. Compound 12b produced a 77.51% increase in seizure latency and a 33.05% reduction in final severity, again with no survival advantage. Compound 6c displayed the weakest profile in the pilocarpine assay, producing only a 51.53% increase in seizure latency, a 28.87% reduction in final severity, and the lowest survival rate among the treatment groups (50%). Overall, the behavioral ranking in this model followed the order 7c > 7a > 7b > 6a > 11a > 12b > 6c. The consistency between the PTZ and pilocarpine results supports the selection of 7c as the leading compound across mechanistically distinct seizure model. Because pilocarpine‐induced seizures are strongly linked to excitotoxic and inflammatory hippocampal injury, the robust behavioral activity of 7c warranted further biochemical evaluation (Singh and Singh 2023).

6.2.3. Modulation of Pilocarpine‐Induced Hippocampal Alterations by Compound 7c

Compound 7c was advanced to hippocampal biochemical assessment because it showed the strongest behavioral efficacy in both seizure models. The selected markers reflect three major pathological components of seizure‐associated hippocampal injury: cytokine‐driven neuroinflammation, glutamate‐mediated excitotoxicity, and glial activation. These mechanisms interact closely during epileptic injury and contribute to the amplification of neuronal dysfunction and seizure propagation (Vezzani et al. 2019; Coulter and Eid ²⁰¹²; Rana and Musto ²⁰¹⁸; Vishwakarma et al. ²⁰²²).

Pilocarpine markedly disturbed hippocampal biochemical homeostasis. Compared with the normal control group, TNF‐α increased by 298.25%, IL‐6 by 257.72%, glutamate by 543.37%, GFAP by 594.55%, and Iba‐1 by 109.46%. These changes confirm substantial activation of inflammatory, excitotoxic, astroglial, and microglial pathways. Valproic acid significantly attenuated these abnormalities, reducing TNF‐α, IL‐6, glutamate, GFAP, and Iba‐1 by 36.73%, 35.17%, 47.57%, 65.97%, and 31.96%, respectively, relative to the pilocarpine group (Figure 8).

Figure 8.

Effect of compound 7c and valproic acid on pilocarpine‐induced hippocampal disturbances related to ‎neuroinflammation, excitotoxicity, and glial activation. Hippocampal homogenates were analyzed for pro‐inflammatory cytokines (TNF‐α (A), IL‐6 (B)), excitotoxicity marker (glutamate) (C), and glial activation indices (GFAP (D) and Iba‐1 (E)) in the following groups: normal control (CON), pilocarpine control (PIL), valproic acid (VPA), and compound 7c. Bars represent mean ± SD and values were normalized to total protein content. Statistical analysis was performed using one‐way ANOVA followed by Tukey's multiple comparisons test.

Compound 7c produced a stronger normalization of all measured biomarkers. ‎Relative to the pilocarpine group, 7c significantly reduced TNF‐α by 48%, IL‐6 by 46.39%, ‎glutamate by 64.23%, GFAP by 73.04%, and Iba‐1 by 45.66%. Importantly, TNF‐α, IL‐6, ‎glutamate, and Iba‐1 were also significantly lower than those measured in the valproic acid ‎group, with additional reductions of 17.81%, 17.31%, 31.79%, and 20.13%, respectively. ‎GFAP showed a further 20.77% numerical decrease compared with valproic acid, but this ‎comparison did not reach statistical significance. This pattern indicates that the protective ‎profile of 7c extends beyond behavioral seizure suppression to include meaningful ‎attenuation of hippocampal inflammatory, excitotoxic, and glial‐associated injury (Figure 8).

The reduction in glutamate is mechanistically important because excessive glutamatergic transmission is a central contributor to excitotoxic neuronal damage and persistent hyperexcitability in epilepsy (Coulter and Eid 2012; Madireddy and Madireddy ²⁰²³). The stronger glutamate‐lowering effect of 7c compared with valproic acid suggests a pronounced impact on the excitotoxic component of pilocarpine‐induced injury. Likewise, the marked decreases in TNF‐α and IL‐6 indicate effective suppression of proinflammatory signaling. These cytokines are strongly implicated in blood‐brain barrier disruption, altered synaptic excitability, and the maintenance of glial activation during seizure pathology (Devinsky et al. 2013).

The glial markers further support the action of 7c. The pilocarpine‐induced elevation of GFAP and Iba‐1 indicates astrocytic and microglial activation, respectively. These glial responses contribute to sustained neuroinflammation, impaired glutamate buffering, and progressive neuronal dysfunction (Devinsky and Lai 2008; Kaur et al. ²⁰¹⁵; Rosciszewski et al. ²⁰¹⁹; Pohlentz et al. ²⁰²²). Treatment with 7c significantly reduced both markers, with a particularly strong reduction in GFAP. Together, these results indicate that 7c combines strong anticonvulsant activity with suppression of cytokine release, excitotoxic neurotransmitter accumulation, and glial reactivity.

6.2.4. Evaluation of Safety and Systemic Tolerability of Compound 7c

To evaluate the short‐term safety of compound 7c at a dose exceeding its anticonvulsant testing dose, mice received a supratherapeutic oral dose of 100 mg/kg and were monitored for 48 h. No mortality, abnormal behavior, or visible signs of toxicity were observed during the monitoring period.

Serum biochemical markers were then examined to assess hepatic, renal, and cardiac function. As summarized in Table 7, the 7c‐treated group did not show statistically significant differences from the control group in ALT, AST, or ALP. Serum urea and creatinine also remained statistically comparable to control values, indicating preserved renal function. Likewise, CK‐MB did not differ significantly from the control group, suggesting no detectable cardiac injury under the tested conditions.

Table 7.

Biochemical safety profile of compound 7c.

	ALT (IU/L)	AST (IU/L)	ALP (IU/L)	Serum urea (mg/dL)	Serum creatinine (mg/dL)	CK‐MB (U/L)
Control	31.43 ± 3.12	36.08 ± 11.24	137.26 ± 12.15	38.20 ± 3.46	0.69 ± 0.16	43.33 ± 7.72
7c	32.59 ± 2.63	40.74 ± 4.90	131.81 ± 15.20	38.38 ± 3.48	0.66 ± 0.13	46.08 ± 8.01

Note: Data are expressed as mean ± SD. Unpaired Student's t‐test showed no statistically significant differences between groups.

These markers are widely used to screen for systemic organ toxicity. ALT, AST, and ALP are established indicators of hepatocellular integrity and are frequently elevated in drug‐induced liver injury (Sokar et al. 2022; Alkabbani et al. ²⁰²⁴; Sweilam et al. ²⁰²⁴; Elsisi et al. ²⁰²⁵). Serum urea and creatinine reflect renal excretory function and glomerular filtration capacity (Griffin et al. 2019; Inker and Titan ²⁰²¹; Elkady et al. ²⁰²⁶), while CK‐MB is commonly used as a marker of myocardial injury (Danese and Montagnana 2016; Ghareb et al. ²⁰¹⁷; Elmorsi et al. ²⁰²³). The absence of significant changes across these biochemical indices supports the preliminary systemic tolerability of 7c and strengthens its selection as the lead compound of the current series.

7. Molecular Docking Study

Molecular docking studies were performed to investigate the binding interactions of the synthesized compounds within the active site of reduced human cytosolic branched‐chain aminotransferase (hBCATc) complexed with gabapentin in the Protein Data Bank under ID 2COJ (PDB ID: 2COJ). The docking grid was centered at coordinates x = −36.14, y = −6.293, and z = −17.866 to encompass the ligand‐binding pocket. Docking simulations were carried out using the AutoDock Vina scoring function. To validate the docking protocol, the co‐crystallized ligand, gabapentin, was extracted from the protein structure and subsequently re‐docked into the binding site under the same docking conditions. The re‐docked pose showed complete superimposition with the crystallographic conformation, yielding a root‐mean‐square deviation (RMSD) of 0.0 Å (Figure S2).

To detail the precise interaction profile of compound 7c, the docking poses were analyzed to identify specific molecular anchors within the VGCC binding pocket. The carboxylate moiety acts as a primary stabilizing element, establishing a dense network of hydrogen bonds with Val289, Arg119, and Thr290. The aromatic architecture of the hybrid further secures the molecule through a pi‐cation interaction between the phenoxy ring and Lys222, while the pyrazoline core and the methoxy group are anchored via ‐alkyl interactions with Trp227 and Leu231, respectively. Finally, the structural stability is reinforced by an additional hydrogen bond between the carbonyl group and Arg163. (Figure 9).

Figure 9.

2D and 3D schematic representations of compound 7c bound within the active sites of (hBCATc).

8. Conclusion

In conclusion, compound 7c, designed through a rational molecular hybridization strategy, represents a promising multi‐target antiepileptic candidate. It integrates a pyrazoline scaffold with a phenoxyacetic acid moiety, resulting in significantly improved pharmacological activity compared to the standard drug valproic acid. Experimentally, it showed strong anticonvulsant effects with near‐complete seizure suppression and 100% survival in different seizure models, indicating broad and reliable efficacy. Mechanistically, compound 7c acts on multiple pathological pathways involved in epilepsy. It reduces excessive glutamate levels, thereby protecting neurons from excitotoxic damage, and it also suppresses neuroinflammation by downregulating glial activation markers such as GFAP and Iba‐1. This dual action helps break the harmful cycle of neuronal injury and inflammatory response, which contributes to disease progression. In silico studies further supported these findings by showing that compound 7c has suitable properties for crossing the blood brain barrier and exhibits strong binding affinity toward its target protein, indicating effective central nervous system activity. Additionally, toxicity assessments revealed no significant adverse effects on the liver, kidney, or heart, even at higher doses, confirming a good safety profile. In addition, in silico studies including ADME prediction and molecular docking confirmed favorable pharmacokinetic properties, efficient blood–brain barrier penetration, and strong binding affinity toward the target protein. Overall, compound 7c combines strong anticonvulsant efficacy, neuroprotective and anti‐inflammatory actions, good brain availability, and excellent safety, making it a strong lead candidate for further development as a potential disease modifying therapy for epilepsy.

9. Experimental

9.1. Synthesis of Pyrazole Sulfonamide 6a, c, 7a‐c, 11a, and 12b (Elgohary et al. ^2024a)

The complete synthetic methodology and schematic representations of the synthetic routes are presented in the Supporting Material.

9.2. In silico and Biological Studies

Technical specifics regarding molecular docking simulations (Elkotamy et al. ^2024a, ^2024b; Elgohary et al. ^2024b; Mostafa et al. ²⁰²³; Elsonbaty et al. ²⁰²¹), bioinformatics analyses (Elgohary et al. 2025, ²⁰²⁶), and the experimental frameworks for in vivo seizure models, ELISA quantification, and toxicological assessments are detailed in the Supporting Material.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File

Data Availability Statement

The data that supports the findings of this study are available in the supplementary material of this article.