Antinociceptive Activity of Petiveria alliacea L. Extract via GABAergic and Serotonergic Pathways in Diabetic Neuropathy Model

Abstract

Petiveria alliacea L. (commonly known as “anamu,” “guiné,” “hierba de zorro,” and “tipi”) has been widely used in Mesoamerican traditional medicine to treat pain and inflammation. However, scientific evidence supporting its efficacy in diabetic neuropathy remains limited. This study evaluated the antinociceptive potential of a methanolic leaf extract of P. alliacea in a murine model of alloxan-induced diabetic neuropathy and investigated its possible mechanisms of action. Diabetic CD-1 mice were evaluated for mechanical allodynia and hyperalgesia using the Von Frey test and for tonic pain using the formalin test. Pharmacological antagonists were administered to assess the involvement of opioid, nitric oxide, serotonergic, and GABAergic pathways. Phytochemical profiling was performed by LC-ESI-MS/MS, and potential pharmacological and pharmacokinetic properties of the identified metabolites were predicted using in silico tools (PASS online, SwissTargetPrediction, SwissADME, and pkCSM). The methanolic extract significantly reduced mechanical allodynia and hyperalgesia in diabetic mice and attenuated nociceptive responses in both phases of the formalin test, showing an effect comparable to gabapentin. Antinociceptive activity was not altered by naloxone or L-NAME but was significantly attenuated by methiothepin and bicuculline, suggesting that serotonergic and GABAergic pathways contribute, at least in part, to the observed antinociceptive effects. LC-ESI-MS/MS analysis identified 38 metabolites, including flavonoids, alkaloids, and terpenes, with in silico predictions supporting their potential analgesic and anti-inflammatory activities. The methanolic leaf extract of P. alliacea exhibits significant antinociceptive activity in diabetic neuropathy, partially likely to involve serotonergic and GABAergic mechanisms, supporting its ethnomedicinal use and its potential as a source of novel analgesic agents.

1. Introduction

Painful diabetic neuropathy is the most frequent chronic complication of diabetes mellitus and a leading cause of neuropathic pain worldwide [1,2,3]. Up to half of individuals with diabetes develop some degree of neuropathy, and a substantial proportion experience painful symptoms manifested as spontaneous pain, hyperalgesia, and mechanical allodynia, with major consequences for sleep, mood, and quality of life [4,5,6]. The pathobiology of painful diabetic neuropathy is multifactorial and encompasses both peripheral and central mechanisms, including metabolic and mitochondrial dysfunction, oxidative stress and neuroinflammation, microvascular impairment, and maladaptive plasticity within nociceptive circuits [5,7]. Current pharmacotherapy remains largely symptomatic and provides incomplete relief for many patients. Contemporary guidelines support first-line use of gabapentinoids, serotonin–norepinephrine reuptake inhibitors, tricyclic antidepressants, and selected sodium-channel blockers, while emphasizing the limited role of opioids because of an unfavorable benefit–risk profile in chronic neuropathic pain [8,9,10,11,12]. These limitations underscore the need to identify safer agents with novel mechanisms that are relevant to the neurobiological substrates of painful diabetic neuropathy.

Natural products remain a major source of clinically useful analgesics and continue to provide chemically diverse scaffolds for pain drug discovery. In this context, Petiveria alliacea L. (commonly known as “anamu,” “guiné,” “hierba de zorro,” and “tipi”), a medicinal plant widely used in Mesoamerica and tropical regions of the Americas, is traditionally employed for pain and inflammatory conditions [13,14,15]. Phytochemical investigations have described a complex metabolome enriched in organosulfur compounds, phenolics, and flavonoids, together with other specialized metabolites with potential neuroactive and anti-inflammatory properties [13,16,17,18].

Several experimental studies support the antinociceptive activity of P. alliacea in non-neuropathic paradigms. Early work with crude aqueous root extracts reported inhibition of chemically induced visceral nociception in rodents [19]. Subsequently, a lyophilized root extract showed analgesic effects in a pleurisy model, along with anti-inflammatory activity [20]. Bioactivity-guided studies further demonstrated that distinct root fractions attenuate nociceptive behaviors in acetic acid writhing, hot-plate, and formalin assays [21]. More recently, leaf extracts were evaluated in the formalin test alongside parallel in silico analyses, providing additional evidence of antinociception and identifying candidate metabolites [17]. Despite these advances, the available literature remains dominated by acute or inflammatory pain models and does not address painful diabetic neuropathy, in which hyperglycemia-driven neuroimmune changes and altered neurotransmission can reshape analgesic responsiveness [5,14].

Parallel lines of research suggest that P. alliacea may also modulate diabetes-related biology. Aerial-part preparations have been examined in experimental diabetes models with mixed metabolic outcomes [22], and a recent report described beneficial effects of a methanolic extract in a streptozotocin-based type 2 diabetes setting, with improvement of early diabetic nephropathy features [23]. However, to the best of our knowledge, no study has evaluated whether P. alliacea can attenuate pain-related behaviors in a diabetic neuropathy context, nor has any work integrated mechanism-focused behavioral pharmacology with comprehensive metabolite profiling for this indication. This knowledge gap is particularly relevant because painful diabetic neuropathy involves dysregulation of descending monoaminergic control and spinal inhibitory neurotransmission. Alterations in descending serotonergic/noradrenergic pathways have been documented in experimental painful diabetic neuropathy [24], and serotonergic and GABAergic signaling represent validated therapeutic entry points in neuropathic pain management [8,25]. Although mechanical allodynia and hyperalgesia represent the principal behavioral manifestations of painful diabetic neuropathy, complementary nociceptive assays may provide additional information regarding pain processing under diabetic conditions. Accordingly, the formalin test was incorporated not as an alternative model of diabetic neuropathy, but as a complementary inflammatory nociceptive challenge to determine whether the antinociceptive activity of P. alliacea is maintained when chronic neuropathic sensitization coexists with an acute inflammatory stimulus. This approach enables the evaluation of inflammatory pain processing and central sensitization in addition to disease-relevant mechanical hypersensitivity. Accordingly, to the best of our knowledge, this work is the first to evaluate a P. alliacea leaf extract in a diabetic neuropathic pain model, combining (1) disease-relevant mechanical hypersensitivity endpoints, (2) mechanism-oriented pharmacological antagonism, and (3) non-targeted LC-ESI-MS/MS chemical profiling integrated with in silico target prediction to support hypothesis generation beyond prior acute/inflammatory studies.

In this work, we investigated the antinociceptive potential of a methanolic leaf extract of P. alliacea in an alloxan-induced painful diabetic neuropathy mouse model and interrogated pathway involvement using targeted antagonism focused on serotonergic and GABAergic mechanisms. In parallel, we performed non-targeted LC-ESI-MS/MS profiling and in silico target prediction to support hypothesis generation on candidate metabolites and pathways that may contribute to activity. This integrated experimental design aims to provide a rigorous preclinical framework for evaluating P. alliacea metabolites for the treatment of painful diabetic neuropathy while generating mechanistic hypotheses to guide future pharmacological investigations.

2. Materials and Methods

2.1. Collection of Plant Material

Leaves of P. alliacea were collected from randomly selected individuals at the same phenological stage in Ocozocoautla de Espinosa, Chiapas, Mexico (16°45′07.4″ N, 93°22′22.6″ W). Botanical identification was performed at the Herbarium of the Dr. Faustino Miranda Botanical Garden, under the registration number 54016. The scientific name P. alliacea L. was verified using The Plant List database (http://www.theplantlist.org). Locally, the species is known in Spanish as “hierba de zorro,” and in English it is commonly referred to as “guinea hen weed”. Detailed information on the collection site, environmental conditions, and morphological traits of P. alliacea leaves is provided in Supplementary Table S1.

2.2. Extraction of Plant Material

Leaves of P. alliacea were dried in an oven (Ecoshel, Model 9023A, Shanghai, China) at 45 °C and manually ground to obtain a fine powder. The methanolic extract was obtained by macerating the powdered material with methanol at a 1:10 ratio, followed by sonication for 2 h at 4 °C. The mixture was then filtered and centrifuged at 5000 rpm for 15 min. The resulting extract was lyophilized at −40 °C under a vacuum of 0.035 mBar and resuspended in saline solution for subsequent evaluation [17].

2.3. Chemical Characterization by LC-ESI-MS/MS Analysis

The chemical characterization of P. alliacea extracts was performed using non-targeted liquid chromatography coupled to electrospray ionization tandem mass spectrometry (LC–ESI–MS/MS) for qualitative metabolite profiling. Analyses were conducted on a Waters 600 analytical HPLC system (Agilent Technologies, Santa Clara, CA, USA) equipped with a Synergi Polar-RP column (150 mm × 2.0 mm, 4 μm particle size), coupled to a Bruker Esquire 6000 ion trap mass spectrometer (Agilent Technologies, Santa Clara, CA, USA).

Extract samples were dissolved in 500 μL of HPLC-grade methanol (Sigma-Aldrich^®, St. Louis, MO, USA) and filtered through a 0.45 μm nylon syringe filter (Agilent Technologies, Santa Clara, CA, USA). Chromatographic separation was achieved using a gradient elution with solvent A (0.1% formic acid in H₂O) and solvent B (0.1% formic acid in CH₃CN/MeOH, 1:1, v/v). The gradient program was as follows: 95% A for 5 min, 95–90% A over 10 min, 90–50% A over 55 min, 50–95% A over 65 min, and re-equilibration at 95% A up to 70 min. The injection volume was 10 μL, and the flow rate was set at 0.6 mL/min, with a 1:1 split prior to the mass spectrometer interface [26].

Mass spectra were acquired in positive ion mode over an m/z range of 150–1000, with a scan cycle time of 2 s. Electrospray ionization source parameters were as follows: drying gas (N₂), capillary temperature 350 °C, spray voltage 40.0 V, auxiliary gas pressure 30 psi, and dry gas flow rate 10 L/min. All samples were analyzed in triplicate to ensure analytical reproducibility.

The LC–ESI–MS/MS method was validated for qualitative, non-targeted metabolite profiling, focusing on analytical reliability and reproducibility rather than quantitative determination. Validation criteria included consistent chromatographic performance, retention time stability, reproducible mass spectral features across replicate injections, and stable MS/MS fragmentation patterns. Tentative compound identification was achieved by comparing experimental MS/MS spectra with reference spectra from public databases, including the MassBank of North America (MoNA, 2025) (https://mona.fiehnlab.ucdavis.edu/) and the MassBank High Quality Mass Spectral Database (MassBank, 2025) (https://msbi.ipb-halle.de/MassBank/, accessed on 28 June 2026).

2.4. In Silico Analysis of Components in P. alliacea Extracts

The secondary metabolites identified in this study were encoded as SMILES (Simplified Molecular Input Line Entry System) strings obtained from the PubChem database. In silico analyses were performed using PASS online [27], SwissADME [28], SwissTargetPrediction [29], and pkCSM [30] to predict biological and toxicological activities, including pharmacological effects and potential interactions with enzymes and metabolic transporters involved in nociception. Each platform calculates physicochemical and structural properties, which were subsequently compared with their respective databases. Results were expressed as probabilities (P) ranging from 0 to 1, where 1 indicates a high likelihood of the event occurring in vivo, and 0 indicates a negligible likelihood.

2.5. Drugs and Reagents

The following drugs and reagents were used: alloxan monohydrate, naloxone, bicuculline, L-NAME, methiothepin (Sigma-Aldrich^®, St. Louis, MO, USA), citrate buffer (Merck Millipore^®, Darmstadt, Germany), gabapentin (Pharmalife^®, Milan, Italy), formaldehyde (Reactivos LABESSA^®, Mexico City, Mexico), Tween 80, methanol (Meyer^®, Mexicali, Mexico), and saline solution (PiSA^®, Guadalajara, Mexico).

2.6. Animal Model

All experimental procedures approved by the Institutional Committee of the National Technological Institute of Mexico in Tuxtla Gutiérrez (protocol No. 09-2022/ITTG). Male CD-1 mice (30–40 g) were used and housed under controlled humidity and a 12 h light/dark cycle, with ad libitum access to food and water; except for a 6 h fasting period before experimentation. All procedures complied with international ethical guidelines for animal research [31] and the Mexican Official Standard NOM-062-ZOO-1999 [32], ensuring minimization of suffering and the use of the fewest animals necessary.

2.7. Alloxan-Induced Diabetes Model

Diabetes was induced in male mice (35 g; 8–12 weeks old) via two intraperitoneal injections of alloxan monohydrate dissolved in 0.1 M citrate buffer (pH 4.5). Doses of 150 mg/kg and 100 mg/kg were administered 48 h interval, following the protocol described by Bromme et al. [33]. After the first dose, animals received a 10% sucrose solution for five days to prevent hypoglycemia, which was replaced with drinking water on day six. On day seven, mice with fasting blood glucose levels > 200 mg/dL were classified as diabetic [34]. Body weight was recorded every 3 days throughout the experimental period as an indicator of diabetes progression and general animal health. The corresponding data are provided in the Supplementary Material (Figure S3) and blood glucose were monitored on days 0 (2 h before induction), 3, 7, and 15 post-induction using capillary blood samples collected from the tail and measured with a portable glucometer (True Metrix, Trividia Health, Fort Lauderdale, FL, USA) [35].

2.8. Treatments

Experiments were conducted using groups of eight animals each. Treatments were administered orally (1 mL/100 g body weight) 30 min before testing. Experimental groups received Petiveria alliacea extract (100 mg/kg), selected from prior dose–response studies (10–316 mg/kg, p.o.) as the minimal dose producing maximal antinociceptive effect [17]. The positive control group received gabapentin (100 mg/kg), and the negative control received sterile distilled water containing Tween 80.

2.9. Von Frey Test

Mechanical allodynia and hyperalgesia were evaluated using calibrated Von Frey filaments (3.22–5.46 mN). Each filament was applied to the plantar surface of the right hind paw. The withdrawal threshold was defined as the minimal force that evoked paw withdrawal in at least three out of five consecutive applications, with 1–2 s intervals between stimuli, following the protocol described by Kim et al. [36]. The sensitivity (S%) was calculated using Equation 1, where N_w is the number of paw withdrawals observed, and N_t is the total number of stimulations applied (five consecutive applications). A complete withdrawal response in all stimulations corresponded to 100%, whereas the absence of withdrawal corresponded to 0%.

$$S (\%)=\left({\displaystyle \frac{N_w}{N_t}}\right)\times 100$$

(1)

2.10. Formalin Test

The formalin test was performed as a complementary nociceptive assay to evaluate whether diabetic neuropathy modifies inflammatory pain processing and to determine whether the extract retains antinociceptive activity under conditions combining neuropathic hypersensitivity with an acute inflammatory stimulus. For nociceptive evaluation, nociception was induced by subcutaneous injection of 20 μL of 2% formalin into the dorsal surface of the right hind paw. Nociceptive behavior was quantified by recording the number of paw flinches for 1 min every 5 min over a 60 min observation period, according to established protocols [37,38]. The response was analyzed in two phases: the neurogenic phase (1–10 min) and the inflammatory phase (15–60 min). Responses were expressed as the area under the curve (AUC), calculated by the trapezoidal method. Antinociception (A%) was determined as the percentage relative to control groups using Equation (2), where AUC_HG represents the nociceptive response of the hyperglycemic (HG) control group, and AUC_Treat corresponds to that of the treated groups.

$$A (\%)=\left({\displaystyle \frac{{AUC}_{HG}-{AUC}_{Trat}}{{AUC}_{HG}}}\right)\times 100$$

(2)

2.11. Evaluation of Mechanisms of Action Using Antagonists

The involvement of different pathways was evaluated in mice pretreated with specific antagonists: naloxone (2 mg/kg, intraperitoneally [i.p.], [39]) for the opioid pathway, bicuculline (1 mg/kg, i.p., [39]) for GABA receptors, L-Nitroarginine methyl ester (L-NAME, 10 mg/kg, i.p., [40]) for the nitric oxide pathway, and methiothepin (1 mg/kg, i.p., [41]) for serotonin receptors. 15 min after pretreatment, animals received P. alliacea extract (100 mg/kg) or vehicle (sterile distilled water). Nociceptive behaviors were then evaluated using the formalin test and the Von Frey test for mechanical allodynia and hyperalgesia [42].

2.12. Statistical Analysis

Statistical analyses were performed on data from all experimental groups. Results are expressed as mean ± standard error of the mean (SEM). Time-course experiments were analyzed using two-way ANOVA with Bonferroni’s post hoc test. Single time-point comparisons, including area under the curve (AUC) values from the formalin test and percentages of antinociception (A%), were analyzed using one-way ANOVA followed by Tukey’s post hoc test. To ensure consistency across datasets, this ANOVA-based framework was also retained in panels involving two-group comparisons (e.g., NG vs. HG), where statistical significance is indicated by different alphabetic letters (p < 0.05). A value of p < 0.05 was considered statistically significant.

3. Results

3.1. Chemical Profile Identified by LC-ESI-MS/MS

The methanolic leaf extract of P. alliacea was characterized by high-performance liquid chromatography coupled to tandem mass spectrometry (LC–ESI–MS/MS). The chromatographic fingerprint (Figure 1) revealed a complex profile composed of multiple peaks distributed throughout the retention time range, confirming the presence of a rich diversity of secondary metabolites.

A total of 38 compounds were tentatively identified in the methanolic extract of P. alliacea by LC–ESI–MS/MS. Fragmentation spectra and structural annotations are provided in Supplementary Figures S1 and S2, and the complete list of identified metabolites is reported in Supplementary Table S2. Compounds were annotated based on their retention time (RT), mass-to-charge ratio (m/z), molecular mass, relative abundance (%), structure and spectral matching with the MassBank and MoNA databases using a similarity threshold >0.80, providing a high level of confidence for non-targeted metabolite profiling.

Based on relative abundance and analytical reproducibility, the six most abundant metabolites were selected as major marker compounds and are summarized in

Table 1. Most abundant secondary metabolites identified in P. alliacea extract by LC–ESI–MS/MS.

No.	RT	Tentative Compound	m/z	Relative Abundance (%)	Molecular Mass	Chemical Classification	Structure
1	0.5	5-Methoxy-3-indoleacetic acid.	160.0986	18.08	205.0739	Heterocyclic aromatic organic acid
3	6	Xanthone	197.1193	1.34	196.0525	Oxygenated aromatic tricyclic
13	11.9	Sclareol	331.2512	1.47	308.27153	Diterpene
16	13.3	Methyl 2-hydroxydodecanoate	231.1617	3.09	230.1881	Fatty hydroxyester
17	14	Lauric isopropanolamide	258.2822	4.06	258.2822	Amide
35	20.1	Diosmin	609.2776	7.60	608.17412	Flavonoid glycoside

Retention time (RT), experimental mass-to-charge ratio (m/z), calculated m/z, and main fragment ions are listed for each compound. Identification was achieved by comparing fragmentation patterns with spectral data available in the MassBank of North America (MoNA) and MassBank High Quality databases.

. These compounds consistently exhibited the highest signal intensities across replicate analyses and were therefore considered representative of the dominant chemical features of the extract. The selection of these marker compounds was based on relative abundance rather than absolute quantification, in accordance with the non-targeted nature of the analytical approach.

The LC–ESI–MS/MS analysis revealed a broad phytochemical diversity encompassing multiple structural classes with potential pharmacological relevance. Among the identified metabolites (Supplementary Table S2), alkaloids (e.g., reserpine, hordenine, tetrandrine, sparteine, and solanidine), flavonoids and flavonoid glycosides (e.g., tricin, diosmin, fortunellin, isorhamnetin-3-O-rutinoside, pectolinarin, and acacetin-7-O-rutinoside), polyphenols, phenolic lactones, lipids, terpenoids, and plant steroids were detected.

Additional compounds included polyphenolic and aromatic derivatives such as xanthone and 4-methylumbelliferone, as well as diverse lipid species (monoacylglycerols, triacylglycerols, and phosphatidylcholines), which may influence membrane interactions and bioavailability. Oxygenated terpenes (e.g., sclareol and 3,7-epoxycaryophyllan-6-ol), plant steroids such as digoxigenin, and oxygenated carotenoids including β,β-carotene-2,2′-diol were also identified, highlighting the chemical complexity of the extract.

Overall, this chemically diverse profile supports the presence of a multicomponent phytochemical system, in which the six major marker compounds (Table 1), together with less abundant constituents (Supplementary Table S2), may contribute collectively to the biological activity observed. These findings provide the chemical basis for the subsequent pharmacological and in silico analyses, without implying direct causal relationships between individual metabolites and the observed antinociceptive effects.

3.2. In Silico Prediction of the Biological Activity of P. alliacea Components

The in silico evaluation of secondary metabolites identified by LC–ESI–MS/MS in the methanolic extract of P. alliacea was conducted to explore their predicted pharmacological activities, toxicokinetic properties, and potential molecular targets using PASS Online, SwissTargetPrediction, SwissADME, and pkCSM. The complete set of compounds subjected to computational analysis is reported in Supplementary Table S3.

Based on relative abundance and analytical reproducibility, the six major marker compounds selected in the chemical profiling analysis were prioritized for detailed in silico evaluation and are summarized in

Table 2. In silico prediction of pharmacological and toxicological properties of selected abundant secondary metabolites from P. alliacea.

No.	Pass Online				Swiss Target Prediction	Swiss ADME								pkCSM
	Antinociceptive		Antiinflamatory		Target	Pharmacokinetics							Druglikeness	Toxicology
	Pa	Pi	Pa	Pi		GIa	BHE	CA2i	CC19i	CC9i	CD6i	CA4i	Lipinski	A	H1	H2	HP
1	0.338	0.119	0.412	0.09	Serotonin receptor Cyclooxygenase-2	H	YES	NO	NO	NO	NO	NO	YES	NO	NO	YES	NO
3	0.463	0.061	0.465	0.068	Serotonin receptor	H	YES	YES	NO	NO	NO	NO	YES	NO	NO	YES	NO
13	0.429	0.087	0.563	0.040	GABA and Vanilloid receptor	H	YES	NO	NO	NO	YES	NO	YES	NO	NO	NO	NO
16	0.453	0.068	0.768	0.002	Caspase-1,3,6 and 7	H	YES	NO	NO	NO	NO	NO	YES	NO	NO	NO	NO
17	0.489	0.043	-	-	GABA and Vanilloid receptor	H	YES	YES	NO	NO	YES	NO	YES	NO	NO	NO	NO
35	0.400	0.109	0.692	0.017	TNF-alpha Interleukin-2	L	NO	NO	NO	NO	NO	NO	YES	NO	NO	YES	NO

Pa: probability to be active; Pi: probability to be inactive; GIa: gastrointestinal absorption (H: high; L: low); BBB: blood–brain barrier permeant; CA2i: CYP1A2 inhibitor; CC19i: CYP2C19 inhibitor; CC9i: CYP2C9 inhibitor; CD6i: CYP2D6 inhibitor; CA4i: CYP3A4 inhibitor; A: Ames mutagenicity; H1: hERG I inhibitor; H2: hERG II inhibitor; HP: hepatotoxicity.

. These compounds were chosen to represent the dominant chemical features of the extract, and the resulting predictions are presented as supportive, hypothesis-generating evidence, rather than experimental validation of specific mechanisms.

Among the marker compounds (Table 2), several metabolites exhibited moderate to high probabilities of antinociceptive activity, including solanidine (Pa = 0.679) and hordenine (Pa = 0.587), both alkaloids previously reported to exert neuromodulatory effects. Other marker compounds displayed predicted affinities for serotonergic and GABAergic targets, consistent with the partial attenuation of antinociceptive effects observed following pharmacological antagonism in the behavioral assays. These convergent predictions suggest potential involvement of inhibitory and descending modulatory pathways, although direct molecular interactions remain to be experimentally confirmed.

Regarding anti-inflammatory activity, selected marker compounds, including diosmin, showed predicted interactions with pro-inflammatory mediators such as TNF-α, IL-2, and COX-2. These predictions are consistent with the attenuation of nociceptive behavior observed during the inflammatory phase of the formalin test and support a potential peripheral anti-inflammatory contribution to the overall antinociceptive profile. In addition, methyl 2-hydroxydodecanoate, one of the abundant constituents, exhibited predicted affinity for caspases 1, 3, 6, and 7, enzymes implicated in inflammatory and apoptotic signaling cascades relevant to neuropathic pain.

Predictive pharmacokinetic analyses indicated that most marker compounds exhibit favorable gastrointestinal absorption, and some such as solanidine and tetrandrine were predicted to cross the blood–brain barrier, supporting the plausibility of both peripheral and central sites of action. Most of the evaluated compounds complied with Lipinski’s criteria and did not display major toxicological alerts, including hepatotoxicity, mutagenicity, or hERG channel inhibition, suggesting an overall acceptable predicted safety profile.

The broader in silico analysis of all identified metabolites (Supplementary Table S3) revealed a wide range of predicted molecular targets, including serotonergic, GABAergic, cannabinoid, and vanilloid receptors, as well as cytokines, growth factor receptors, and caspase-related pathways. This diversity supports a multicomponent and multifactorial mode of action, characteristic of complex plant extracts. Compounds exceeding conventional oral bioavailability thresholds were retained in the analysis, as such metabolites may still contribute indirectly through synergistic interactions or by influencing the pharmacokinetic behavior of co-occurring constituents.

Overall, the in silico findings provide a computational framework that complements the chemical and behavioral data, supporting biologically plausible hypotheses regarding the mechanisms underlying the antinociceptive effects of the P. alliacea extract, without implying definitive molecular causality.

3.3. Alloxan Model

Administration of alloxan successfully induced diabetes in mice, as illustrated in Figure 2. The time course of blood glucose concentrations (Figure 2A) demonstrated that normoglycemic (NG) animals maintained stable glycemia around ~100 mg/dL throughout the 15-day period. In contrast, hyperglycemic (HG) animals exhibited a progressive and sustained increase in blood glucose, surpassing 200 mg/dL by day 7 and reaching >400 mg/dL by day 15 (p < 0.05 vs. NG). Consistent with this diabetic phenotype, HG mice also exhibited a progressive reduction in body weight throughout the experimental period compared with the normoglycemic group (Supplementary Figure S3). Neither the methanolic extract of P. alliacea nor gabapentin significantly altered diabetes-associated weight loss, indicating that the observed antinociceptive effects were unlikely to result from improvements in glycemic control or overall metabolic status.

The oral glucose tolerance test (OGTT) (Figure 2B) further differentiated the groups. NG mice showed a typical physiological response characterized by a transient rise in glycemia after glucose administration followed by a rapid return to baseline, reflecting efficient insulin secretion and glucose utilization. Conversely, HG animals displayed a pronounced elevation in glucose concentrations with delayed clearance, indicating impaired pancreatic β-cell function and altered glucose homeostasis. The area under the curve (AUC) analysis (Figure 2C) provided a quantitative measure of glucose tolerance, revealing significantly higher values in HG compared with NG mice. This metric consolidates the evidence that HG animals not only develop chronic hyperglycemia but also present defective glucose handling. Collectively, these results validate the alloxan-induced model as a robust and reliable representation of experimental diabetes, suitable for evaluating the potential antinociceptive and pharmacological effects of P. alliacea extract.

3.4. Mechanical Nociceptive Sensitivity Assessed by the Von Frey Test

Mechanical sensitivity was assessed with Von Frey filaments ranging from 3.22 to 5.46 mN (Figure 3), revealing marked differences between normoglycemic (NG) and hyperglycemic (HG) mice.

Each bar represents the percentage of positive responses (paw withdrawal) following five consecutive applications of the mechanical stimulus. A full response (five withdrawals) corresponds to 100%, whereas a complete lack of response (zero withdrawals) corresponds to 0%. Error bars indicate the standard deviation calculated from the eight animals evaluated in each group.

At the lowest forces (3.22 and 3.61 mN), NG animals showed almost no response, reflecting a normal mechanical sensitivity threshold. In contrast, HG mice already exhibited 12–20% responses with stimuli of only 3.61–4.17 mN, indicating mechanical allodynia: the perception of normally non-noxious stimuli as painful. At 4.08 mN, the HG group reached approximately 30% responses, whereas NG mice remained below 5%.

Using higher-force filaments (4.31–4.56 mN), HG mice exhibited a sharp increase in mechanical sensitivity: approximately 50% withdrawal with the 4.31 mN filament and up to 90–100% with the 4.56 mN filament, compared to moderate responses (15–25%) in NG mice. These differences also indicate mechanical hyperalgesia, as the more intense stimuli elicited exaggerated reactions in the HG group. Above 4.56 mN, NG mice began to show increasing sensitivity, although their responses did not reach the levels observed in HG mice at the same force threshold. Based on these results, the 4.17 mN filament was selected to evaluate the effect of the extract on allodynia, and the 4.56 mN filament to assess effects on hyperalgesia.

The effect of the methanolic P. alliacea extract on altered mechanical sensitivity in alloxan-induced diabetic neuropathy mice is shown in Figure 4. Responses to the allodynic stimulus (4.17 mN filament, Figure 4A) and to the hyperalgesic stimulus (4.56 mN filament, Figure 4B) were analyzed. Both tests were conducted under controlled conditions using NG and HG groups, with treatments including the plant extract (E) or gabapentin (G), the latter serving as the reference drug.

In Figure 4A, NG animals showed no response to the low-intensity stimulus, as expected, since the 4.17 mN filament is below the basal nociceptive threshold. In contrast, HG animals exhibited increased sensitivity, with approximately 50% positive responses, indicating the presence of mechanical allodynia. Administration of the plant extract resulted in a significant reduction in this allodynic response, an effect also observed in the gabapentin-treated group. Although gabapentin completely abolished the response, and the extract did not achieve the same magnitude, no statistically significant differences were found between the two treatments, suggesting comparable efficacy under the experimental conditions employed.

In Figure 4B, the response to the hyperalgesic stimulus (4.56 mN) followed a similar pattern. NG animals displayed a limited response (~20%), whereas HG mice exhibited exaggerated sensitivity (>80%), confirming the presence of mechanical hyperalgesia. As observed with allodynia, both the extract and gabapentin significantly reduced the percentage of nociceptive responses. Although gabapentin showed a more pronounced reduction, no statistically significant differences were observed between the extract and gabapentin treatments, indicating that both interventions restored mechanical sensitivity to levels comparable to those of the NG group.

3.5. Formalin-Induced Nociceptive Responses

The nociceptive response of NG and HG mice to subcutaneous injection of 2% formalin was analyzed, using this model to evaluate biphasic tonic pain (Figure 5). This model is widely employed to study the antinociceptive activity of drugs and plant extracts, as it reproduces two distinct phases of pain: a neurogenic phase (0–10 min) associated with direct activation of peripheral C fibers, and an inflammatory phase (15–60 min) involving local mediators and central sensitization.

In Figure 5A, the time course of the number of pain responses (hind paw flinches) over 60 min is shown. Hyperglycemic animals exhibited a higher frequency of nociceptive responses in both phases compared to normoglycemic mice, indicating a generalized increase in pain sensitivity under chronic hyperglycemia. Figure 5B quantifies the response during the neurogenic phase using the area under the curve (AUC), showing a significant increase in the HG group relative to the NG group. This difference reflects greater primary nociceptor activation in diabetic animals. Figure 5C shows the AUC corresponding to the inflammatory phase, where the difference between NG and HG is even more pronounced, suggesting an exacerbation of the nociceptive inflammatory process in the context of sustained hyperglycemia.

The time course of the nociceptive response in untreated hyperglycemic animals showed a high and sustained profile during both phases, indicative of a hyperalgesic state characteristic of diabetic neuropathy. In contrast, groups treated with P. alliacea and gabapentin exhibited a marked reduction in the number of pain responses throughout the test, particularly during the inflammatory phase, suggesting a sustained antinociceptive effect (Figure 6A).

Analgesic efficacy during the neurogenic (Figure 6B) and inflammatory (Figure 6C) phases was assessed by calculating the percentage of antinociception based on the AUC. Both treatments significantly reduced nociceptive responses compared to the untreated HG group. In the neurogenic phase, both the extract and gabapentin achieved similar levels of pain inhibition (~60%), with no statistically significant differences between them. During the inflammatory phase, the plant extract also demonstrated efficacy comparable to gabapentin, significantly reducing nociceptive activity.

3.6. Mechanistic Evaluation Using Pharmacological Antagonists

To determine potential mechanisms of action, antagonistic drugs were administered prior to evaluating the antinociceptive activity of the methanolic P. alliacea extract in hyperglycemic mice, using both the formalin test and the Von Frey assay. The antagonists employed were naloxone (opioid receptor antagonist), L-NAME (nitric oxide synthase inhibitor), methiothepin (serotonin receptor antagonist), and bicuculline (GABA_A receptor antagonist).

In Figure 7A,B, corresponding to the neurogenic and inflammatory phases of the formalin model, the P. alliacea extract administered without antagonists significantly reduced nociceptive responses (~60% antinociception) compared to the untreated HG group. Notably, pretreatment with any of the antagonists did not significantly alter this antinociceptive activity, suggesting that the opioid and nitric oxide pathways are not substantially involved in the extract’s effect, at least in this chemical pain model.

However, in Figure 7 C,D, corresponding to the evaluation of mechanical pain via allodynia (4.17 mN filament) and hyperalgesia (4.56 mN filament) using the Von Frey test, critical differences were observed. Pretreatment with methiothepin and bicuculline significantly reduced the efficacy of the extract in attenuating both types of hypersensitivity. Under allodynic conditions, pretreatment with the serotonin (M) and GABA (B) antagonists increased the percentage of mechanical responses, partially reversing the protective effect of the extract. The same pattern was observed under hyperalgesic conditions, suggesting that serotonergic and GABAergic pathways may contribute to the observed antinociceptive activity.

4. Discussion

The present study provides integrative pharmacological, phytochemical, and computational evidence supporting the antinociceptive potential of the methanolic leaf extract of Petiveria alliacea in a murine model of diabetic neuropathy. By combining behavioral assays relevant to neuropathic and tonic pain, pharmacological antagonism, non-targeted LC-ESI-MS/MS profiling, and in silico predictions, this work offers a systems-level framework to contextualize the observed analgesic effects. Importantly, the mechanistic interpretations proposed herein should be regarded as hypothesis-generating rather than definitive molecular explanations, as the metabolites identified were not isolated, quantitatively validated using analytical standards, nor experimentally confirmed through receptor-binding or functional assays [27,29,30].

Oral administration of the P. alliacea methanolic extract produced a robust and reproducible reduction of mechanical allodynia and hyperalgesia in alloxan-induced diabetic mice, together with a marked attenuation of both phases of the formalin test. The magnitude and consistency of these effects were comparable to those of gabapentin across multiple behavioral endpoints, underscoring the potential therapeutic relevance of the extract in a disease-relevant model of neuropathic pain [12]. These findings extend previous reports describing antinociceptive or anti-inflammatory effects of P. alliacea in acute or inflammatory pain models toward a more complex and clinically relevant neuropathic context [14,17,20,21]. The inclusion of the formalin assay should not be interpreted as an attempt to establish an additional model of diabetic neuropathy. Rather, it was incorporated as a complementary nociceptive paradigm to determine whether the antinociceptive activity of the extract was preserved when chronic diabetic neuropathy was challenged by an acute inflammatory stimulus. This experimental approach extends the evaluation beyond disease-relevant mechanical hypersensitivity by providing complementary information on inflammatory nociceptive processing and central sensitization, both of which contribute to the complex pathophysiology of painful diabetic neuropathy. Consequently, the combined use of the Von Frey and formalin tests offers a broader pharmacological characterization of the extract than either assay alone.

4.1. Functional Relevance of GABAergic and Serotonergic Pathways in Diabetic Neuropathy

Diabetic neuropathy is characterized by sustained hyperglycemia-induced alterations in nociceptive processing at both peripheral and central levels, involving oxidative stress, neuroinflammation, peripheral nerve damage, and dysregulation of inhibitory and descending modulatory neurotransmitter systems [5,43,44]. Impairment of spinal GABAergic inhibitory tone and alterations in descending serotonergic pathways have been directly linked to central sensitization, mechanical allodynia, and hyperalgesia, which represent hallmark features of neuropathic pain under diabetic conditions [34,45,46]. In this context, the significant reversal of mechanical allodynia and hyperalgesia observed in the Von Frey test indicates that the P. alliacea extract modulates mechanisms specifically associated with neuropathic pain rather than producing a nonspecific suppression of nociceptive behavior. This interpretation is reinforced by pharmacological antagonism experiments, in which pretreatment with bicuculline and methiothepin partially attenuated the antinociceptive effects of the extract. Restoration or enhancement of inhibitory neurotransmission has been consistently shown to alleviate neuropathic pain symptoms in diabetic models [4,47]. Notably, in silico target prediction and PASS Online analysis revealed that several metabolites present in the extract display moderate-to-high probabilities of antinociceptive activity and converge on serotonergic and GABAergic targets. The recurrence of these targets across chemically distinct metabolites supports a network-level modulation of inhibitory neurotransmission rather than a single ligand–receptor interaction [27]. This convergence between behavioral antagonism and computational predictions supports the hypothesis that modulation of descending serotonergic pathways and spinal GABAergic neurotransmission may contribute, at least in part, to the antinociceptive profile of the extract.

4.2. Differential Modulation of Neuropathic and Inflammatory Nociception

A key observation of the present study is the differential contribution of neurotransmitter pathways across nociceptive paradigms. Although the extract significantly reduced nociceptive behavior in both phases of the formalin test, pretreatment with the tested antagonists did not significantly modify its efficacy in this assay. In contrast, blockade of GABAergic and serotonergic signaling selectively reduced antinociceptive activity in the Von Frey test. This distinction aligns with the mechanistic differences between tonic chemical pain and neuropathic mechanical pain models. The formalin test comprises an early neurogenic phase driven by direct activation of peripheral C fibers and a late inflammatory phase mediated by peripheral inflammation and central sensitization [35,36]. Conversely, mechanical hypersensitivity in diabetic neuropathy is closely associated with dysfunctional inhibitory control and impaired descending modulation of nociceptive transmission [44,45]. It should also be considered that nociceptive responses in experimental diabetic neuropathy vary according to the stage of disease progression. While advanced stages are frequently associated with degeneration of peripheral sensory fibers and the development of hypoalgesia, earlier stages are characterized by enhanced nociceptive sensitivity resulting from neuroinflammation, peripheral sensitization, and central sensitization [46,47,48,49]. In the present study, behavioral evaluations, including the formalin test, were performed 15 days after alloxan administration, corresponding to an early stage of experimental diabetic neuropathy in which persistent hyperglycemia and mechanical hypersensitivity had already been established. Therefore, the increased nociceptive responses observed in untreated diabetic mice are consistent with an early hyperalgesic phenotype rather than with the hypoalgesia reported during more advanced stages of disease progression. This temporal context supports the interpretation that the extract was evaluated under conditions of active neuropathic sensitization, in which modulation of inhibitory neurotransmission is expected to play a more prominent role. The selective involvement of GABAergic and serotonergic pathways in mechanical allodynia and hyperalgesia therefore supports the hypothesis that the P. alliacea extract preferentially targets neuropathic pain mechanisms.

4.3. Independence from Opioid and Nitric Oxide–Dependent Mechanisms

At the minimal maximally effective dose selected for mechanistic testing (100 mg/kg, p.o.), pretreatment with naloxone or L-NAME did not significantly modify the extract’s antinociceptive effects, indicating that opioid receptor activation and nitric oxide–dependent mechanisms are not required to sustain efficacy under the conditions examined. Importantly, we interpret these data as evidence against a major opioid/NO contribution at this dose, rather than as definitive proof of complete independence. This conclusion is compatible with the limited clinical efficacy of opioid-based strategies in diabetic neuropathic pain [12,50], but our inference is driven primarily by the pharmacological antagonism results.

In silico PASS/target-prediction analyses suggested that certain metabolites may interact with opioid receptors; however, these predicted interactions appear to be non-dominant and not functionally necessary at the tested exposure, given the absence of naloxone sensitivity in vivo. We therefore cannot exclude a subtle, context- or dose-dependent opioid component, which should be explored in future studies using additional dose levels and/or fractionated constituents. From a translational perspective, a mechanism that does not rely predominantly on opioid signaling may be advantageous by lowering the potential for tolerance and dependence.

4.4. Integration of Phytochemical Profiling and In Silico Analyses for Hypothesis Generation

Non-targeted LC-ESI-MS/MS analysis revealed a chemically diverse phytochemical profile, with 38 metabolites tentatively identified, including alkaloids, flavonoids, flavonoid glycosides, terpenoids, lipids, and phenolic compounds. Many of these classes have been independently associated with neuromodulator, anti-inflammatory, and antioxidant effects relevant to chronic pain and neuropathic conditions [20,21,51,52,53,54,55,56,57]. The integration of PASS Online, SwissTargetPrediction, SwissADME, and pkCSM analyses was intended to complement the phytochemical characterization by generating hypotheses regarding potential metabolite–target interactions. These computational approaches do not establish the contribution of individual constituents to the observed pharmacological activity but instead identify candidate metabolites and molecular pathways that warrant future experimental validation. Within this exploratory framework, several tentatively identified metabolites exhibited Pa values exceeding Pi for antinociceptive and anti-inflammatory activities, whereas target prediction suggested possible interactions with serotonergic, GABAergic, vanilloid, cannabinoid, and inflammatory pathways. Because the extract was evaluated as a complex multicomponent mixture, these computational predictions should not be interpreted as evidence that any individual metabolite is responsible for the observed antinociceptive effects. Instead, they provide a rational basis for prioritizing candidate constituents for future bioactivity-guided isolation and functional studies. Vanilloid and cannabinoid receptor interactions are particularly relevant to peripheral nociceptor sensitization, whereas predicted interactions with COX-2, TNF-α, interleukin-2, and caspase-related pathways support a role in inflammatory modulation [37,44]. Pharmacokinetic predictions further suggest a dual-site mechanism of action. Compounds predicted to cross the blood–brain barrier were predominantly associated with serotonergic and GABAergic targets, supporting central modulation of nociceptive processing. In contrast, metabolites exceeding classical drug-likeness thresholds were primarily associated with inflammatory mediators, consistent with a peripheral anti-inflammatory contribution [29]. Toxicological predictions indicated an overall acceptable safety profile, with limited predicted hepatotoxicity or cytochrome P450 inhibition.

Representative metabolites were selected for detailed discussion based on their relative abundance, predicted pharmacological relevance, chemical diversity, and availability of supporting literature. Additional abundant metabolites with limited mechanistic evidence are briefly discussed to provide a more comprehensive overview of the phytochemical profile. The following examples illustrate how chemically distinct constituents may converge on complementary molecular targets involved in nociceptive processing and inflammatory modulation. Additional metabolites are discussed solely to support a phytochemical network perspective and are not intended to be considered formal marker compounds. Among the identified compounds (Table 1 and Table 2), 5-methoxy-3-indoleacetic acid was one of the most abundant constituents (18.08%) and was predicted to interact with serotonin receptors and cyclooxygenase-2 (COX-2). Structurally related indole derivatives have been extensively reported as modulators of serotonergic signaling, a pathway critically involved in descending inhibitory control of nociception, particularly under neuropathic pain conditions [58,59]. In parallel, indole-based metabolites and synthetic derivatives have demonstrated the ability to suppress COX-2 expression and activity, thereby contributing to anti-inflammatory and analgesic effects in both acute and chronic pain models [58,59]. The predicted interaction of 5-methoxy-3-indoleacetic acid with COX-2 therefore represents a plausible hypothesis that may contribute to explaining the attenuation of tonic nociception observed during the inflammatory phase of the formalin test, although direct functional validation remains necessary [58,59]. Xanthone, detected at lower relative abundance, was also associated with serotonergic targets [60,61,62]. Xanthone derivatives have been reported for their neuromodulator and anti-inflammatory properties, including modulation of monoaminergic transmission and suppression of pro-inflammatory mediators such as prostaglandins and cytokines [63,64]. Even at modest concentrations, such compounds may contribute synergistically within a phytochemical network, reinforcing serotonergic modulation, as supported by the partial reversal of antinociception observed following serotonergic antagonism. Sclareol and lauric isopropanolamide, both predicted to interact with GABAergic and vanilloid receptors, provide further mechanistic plausibility for the involvement of inhibitory and sensory pathways. GABA_A-mediated neurotransmission plays a fundamental role in maintaining spinal nociceptive thresholds, and its dysfunction is a hallmark of neuropathic pain [65]. Recent evidence suggests that diterpenes such as sclareol can modulate GABA receptor activity, supporting a potential contribution to inhibitory reinforcement [66,67]. In addition, vanilloid receptors, particularly TRPV1, are key mediators of peripheral nociceptor activation and sensitization in inflammatory and neuropathic pain states [68,69]. Compounds capable of modulating TRPV1 signaling have been reported to reduce mechanical allodynia and hyperalgesia in preclinical models [70], consistent with the behavioral outcomes observed in the Von Frey test. Methyl 2-hydroxydodecanoate was predicted to interact with caspases-1, -3, -6, and -7, enzymes involved in apoptotic and inflammatory signaling cascades. Increasing evidence implicates caspase activation, particularly caspase-1–dependent inflammasome signaling, in neuroinflammation, peripheral nerve damage, and the maintenance of chronic pain, including diabetic neuropathy [71]. Thus, modulation of caspase-related pathways may contribute to limiting inflammatory and neurodegenerative processes that sustain nociceptive sensitization, providing a plausible link between the extract’s chemical profile and its sustained antinociceptive effects. Diosmin, a flavonoid glycoside detected at relatively high abundance (7.60%), was predicted to interact with pro-inflammatory cytokines such as TNF-α and interleukin-2 [72,73]. Diosmin and structurally related flavonoids have been extensively documented to suppress cytokine production, inhibit NF-κB activation, and attenuate inflammatory responses across multiple experimental models [74,75,76,77]. These properties are consistent with the marked inhibition of nociceptive behavior during the inflammatory phase of the formalin test and support a peripheral anti-inflammatory contribution to the extract’s overall analgesic profile [78,79,80].

Fortunellin, another highly abundant flavonoid glycoside (7.48%), also exhibited favorable PASS predictions for both antinociceptive and anti-inflammatory activities and was predicted to interact with TNF-α, interleukin-2, and cyclooxygenase-2. Because flavonoid glycosides frequently share antioxidant and anti-inflammatory properties, Fortunellin may complement the actions proposed for Diosmin through modulation of inflammatory signaling. However, its contribution to the overall pharmacological activity remains hypothetical and requires experimental confirmation. β-, β-Carotene-2,2′-diol, the second most abundant metabolite identified in the extract (16.44%), also showed favorable PASS predictions for antinociceptive and anti-inflammatory activities. Although SwissTargetPrediction did not identify specific pain-related molecular targets for this compound, oxygenated carotenoids have been associated with antioxidant and cytoprotective activities that may indirectly influence neuroinflammatory processes involved in chronic pain [81]. Therefore, this metabolite may contribute to the overall pharmacological profile of the extract through complementary mechanisms that were not specifically addressed in the present study. Digoxigenin, another relatively abundant constituent (5.68%), exhibited a favorable PASS prediction for antinociceptive activity, whereas SwissTargetPrediction primarily suggested ATP1A2 as its potential molecular target. Although this target is not directly associated with the nociceptive pathways investigated here, modulation of ion transport and membrane excitability could indirectly influence neuronal function [82]. Consequently, the potential contribution of Digoxigenin to the observed antinociceptive effects remains speculative and should be evaluated in future mechanistic studies.

Collectively, the representative metabolites discussed above, together with the additional abundant constituents considered herein, illustrate how chemically distinct compounds may converge on complementary molecular targets involved in nociceptive transmission, inhibitory neurotransmission, inflammatory signaling, and cellular protection. Although relative abundance does not necessarily imply pharmacological dominance, the convergence of these metabolites on key nociceptive and inflammatory pathways provides biological plausibility for the extract’s multicomponent antinociceptive effects. Rather than acting through a single dominant compound, the extract appears to exert its activity through a coordinated and potentially synergistic mechanism, in which serotonergic modulation, reinforcement of GABAergic inhibition, attenuation of vanilloid-mediated nociception, and suppression of inflammatory mediators may collectively contribute to the observed antinociceptive effects. These associations should be interpreted as biologically plausible hypotheses that guide future bioactivity-guided fractionation and functional validation studies, rather than as definitive evidence of direct molecular mechanisms. Overall, the present findings are consistent with a phytochemical network model in which compounds with complementary molecular targets and pharmacokinetic properties may act synergistically to modulate neuropathic and inflammatory pain. This framework provides a rationale for future bioactivity-guided fractionation and functional validation studies aimed at identifying the metabolites responsible for the observed biological activity [9].

4.5. Novelty, Limitations, and Future Perspectives

Previous studies on P. alliacea have primarily focused on hypoglycemic or acute anti-inflammatory effects [20,21,83]. In contrast, the present study is, to the best of our knowledge, the first to evaluate whether a P. alliacea extract attenuates pain-related behaviors in an experimental model of diabetic neuropathy characterized by mechanical allodynia and hyperalgesia, supported by complementary pharmacological and computational analyses. Nevertheless, several limitations should be acknowledged. The extract was evaluated as a complex multicomponent mixture; therefore, the contribution of individual metabolites to the observed antinociceptive activity could not be experimentally established. Likewise, the in silico analyses were intended exclusively to generate mechanistic hypotheses and do not constitute direct evidence of receptor interactions or molecular mechanisms.

In addition, receptor-binding studies, functional assays, and the evaluation of complementary parameters such as motor performance, paw edema, and inflammatory biomarkers were beyond the scope of the present work and should be addressed in future investigations. Accordingly, future studies should focus on bioactivity-guided fractionation, quantitative characterization of the active constituents, and molecular and pharmacological validation to confirm the mechanisms proposed herein and identify the compounds primarily responsible for the observed biological activity [83,84].

5. Conclusions

The present study provides integrative evidence supporting the antinociceptive potential of methanolic P. alliacea leaf extract in a murine model of diabetic neuropathy. Oral administration attenuated mechanical allodynia and hyperalgesia and reduced nociceptive behavior in both phases of the formalin test, with efficacy comparable to gabapentin. Pharmacological antagonism suggests involvement of serotonergic and GABAergic pathways, while opioid- and nitric oxide–dependent mechanisms appear minimal. LC-ESI-MS/MS tentative metabolite profiling revealed a chemically diverse extract, while in silico target predictions identified candidate metabolite–target interactions that support hypothesis generation regarding potential serotonergic, GABAergic, vanilloid, cannabinoid, and inflammatory pathways. The pharmacological activity of the extract is therefore more likely to result from additive or synergistic interactions among multiple constituents than from a single dominant compound, although the contribution of individual metabolites remains to be experimentally established. Collectively, the behavioral, pharmacological, phytochemical, and computational findings provide a biologically plausible framework that supports further investigation of P. alliacea as a potential multitarget candidate for the management of diabetic neuropathic pain, while emphasizing the need for bioactivity-guided fractionation, receptor-level validation, and functional studies to identify the metabolites responsible for the observed biological activity.