Cardiovascular toxin-induced hyperglycemic and hypoarousal pathology-associated cognitive impairment: an in silico and in vivo validation : Cardiology Plus

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Cardiovascular toxin-induced hyperglycemic and hypoarousal pathology-associated cognitive impairment: an in silico and in vivo validation

Sundari, S. Karpagam Kumara1,*; Alturki, Mansour2,3; Steinke, Ian2; Deruiter, Jack2; Ramesh, Sindhu2; Govindarajulu, Manoj Y.2; Almaghrabi, Mohammed2; Pathak, Suhrud2; Rassa, A. Mohamed1; Shafeeq, K. A. S. Mohamed1; Lowery, Payton2; Nadar, Rishi M.2; Babu, R. Jayachandra2; Ren, Jun4,5; Rani, K. Reeta Vijaya6; Smith, Forrest2; Moore, Timothy2; Dhanasekaran, Muralikrishnan2,*

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Cardiology Plus 7(4):p 178-185, October-December 2022. | DOI: 10.1097/CP9.0000000000000030
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A valid animal model for studying neurological/neurodegenerative diseases should comprehensively evaluate all the potential neurochemical, and physiological processes contributing to the disease and link them to the endpoints of central nervous system (CNS) pathology and behavioral outcomes. Unfortunately, to date, no single animal model has been developed that can fully characterize the entire range of neuropathology, neurophysiology, neurotransmitters, genes, hormones, proteins (enzyme activity and protein expression), and behavioral effects (general/CNS-related) distinctive to specific neurological/neurodegenerative diseases. However, animal models are designed to mimic a CNS disorder of a particular pathological aspect associated with the disease, which is mainly beneficial for understanding and investigating specific characteristic features of a neurodegenerative disorder.

An ideal animal model for cognitive impairment (chemically induced or genetic model) should accurately represent the gross neuroanatomical morphologic features of the disease, primarily changes in the size and weight of the brain, variation in cortical gyral and sulcal patterns, excessive deposition of proteins (within/outside the neurons), formation of neurofibrillary tangles, changes in the activity of glial cells, and alterations in the structure and function of the synapse leading to specific neuronal (cholinergic, dopaminergic, glutamatergic, GABAergic, serotonergic, and so on) loss in a particular region (basalis meynert, hippocampus, and cortex) of the brain. Furthermore, various biochemical signaling mechanisms associated with neuronal loss may occur because of increased prooxidants, decreased antioxidants (or both), inflammatory cytokine storm (enhanced pro-inflammatory cytokines and decreased anti-inflammatory cytokines), elevated pro-apoptotic markers/decreased anti-apoptotic markers, excitotoxicity (N-methyl-D-aspartate [NMDA] receptor-mediated glutamatergic changes, excessive calcium influx), and insufficient adenosine triphosphate (ATP) formation because of mitochondrial dysfunction. Additionally, neurochemical alterations are usually associated with altered inhibitory and excitatory neurotransmission.

The global prevalence of diabetes mellitus is expected to increase significantly from 463 million in 2019 to 578 million (10.2%) in 2030 for all age groups[1]. Endocrinological alterations (hyperglycemia because of insulin resistance and deficiency), endothelial injury, vascular inflammation, cardiac diseases, and hyperlipidemia can contribute to numerous biochemical and neurochemical changes in diabetes mellitus, affecting molecular signaling pathways and increasing the risk of various comorbidities. The predominant comorbidities included cardiovascular complications (hypertension, stroke, and cardiac failure), kidney failure (end-stage renal disease), respiratory disorders (chronic obstructive pulmonary disease), and neurological problems (cognitive impairment, anxiety, and depression). Interestingly, patients with diabetes mellitus have a significantly higher risk of cardiac disease, anxiety (20%), and depression (two to four times) than those without diabetes mellitus[2,3]. The various anxiety disorders associated with hyperglycemia, including generalized anxiety disorder, social anxiety disorder, specific phobia, and panic disorder, are common anxiety disorders with varying prevalence rates. Drugs that affect GABAergic neurotransmission (benzodiazepines/barbiturates) are commonly used to treat anxiety associated with hyperglycemia[4]. Benzodiazepines/barbiturates induce hypoarousal by modulating the gamma-aminobutyric acid type A (GABA-A) receptor, thereby reducing various types of anxiety. However, the significant adverse reactions include ataxia, loss of movement coordination, binge eating, and cognitive impairment. Millions of hyperglycemic patients are treated with benzodiazepines and barbiturates globally, and there is a lack of scientific literature on their effect on cognition.

However, in vitro models are unsystematically valid for evaluating cognitive impairment. Therefore, animal models are crucial to substantiate the CNSs functional characteristics of attention, cognition, learning, and memory. Novel and multitarget-based toxicological in vivo models of cognitive impairment are required to assess the etiopathology, markers of disease progression, neurocognitive insult from environmental neurotoxicants and drugs of abuse, and efficacy of potentially therapeutic drugs. Furthermore, translational research should use patterns and paradigms that permit the extrapolation of disease states from animal models to humans. However, few studies have evaluated the translational paradigms of hyperglycemia and hypoarousal-based validation of cognitive impairment. Hence, in this study, chemically induced hyperglycemic and hypoarousal environments were induced to assess their effects on the cognition of rodents.


Chemicals and reagents

Alloxan and phenytoin were purchased from Sigma Aldrich.

In silico computational analysis

To investigate the absorption, distribution, metabolism, and elimination (ADME) properties of alloxan and phenytoin, we used Schrödinger release 2021–1: (QikProp, Schrödinger, LLC, New York, NY, USA). QikProp was used to estimate various pharmacokinetic and pharmacodynamic properties (solvent accessible surface area [SASA], a hydrophobic component of SASA [FOSA], a hydrophilic component of SASA [FISA], carbon and attached hydrogen) component of the SASA [PISA–π], number of metabolites [#], CNS distribution, predicted brain/blood partition coefficient [QPlog BB], estimated number of donor hydrogen bonds [Donor HB], estimated number of accepted hydrogen bonds [Accept HB], logP, and % human oral absorption [percentage])[5].

Molecular docking

Schrodinger software, a computational methodology intended to quantify ligand binding free energy and predict the orientation and conformation of the ligand within the target binding site, was used to evaluate molecular docking[6] (Table 1).

Table 1 - Docking scores and binding energies (reported in kcal/mol)
Compound ID GABA-A, PDB = 6 × 40 Insulin Kinase Receptor, PDB = 4IBM
Docking score Binding affinity Docking score Binding affinity
GABA –7.43 –37.89 –4.16 –10.31
Streptozotocin –5.44 –51.13 –6.29 –39.23
Phenytoin –3.83 –27.74 –6.03 –12.37
Eszopiclone –4.23 –33.79 –5.04 –45.07
Bicuculline –3.60 –45.85 –4.84 –41.11
BMS-754807 –3.07 –45.69 –6.91 –39.57
Rutaecarpine –5.01 –24.48 –8.82 –38.71
Alloxan –5.09 –16.18 –6.29 –11.82
4548-G05 –5.28 –21.3 –5.33 –33.70
GABA: Gamma-aminobutyric acid; PDB: Protein Data Bank.

Protein crystal structure preparation

This study employed crystal structures: GABA-A (Protein Data Bank [PDB] = 6 × 40, resolution 2.86 Å) bound to GABA-A plus picrotoxin and insulin receptor kinase (PDB = 4IBM: a Crystal structure of insulin receptor kinase domain in complex with an inhibitor Irfin-1, resolution 1.8 Å) complex with an inhibitor. The protein preparation wizard from the Schrodinger software was used to formulate proteins for molecular studies by performing suitable modifications, such as adding missing hydrogen atoms, partial charges, and missing side chains, removing water molecules, and filling loop regions. All necessary modifications, refinement, optimization, and energy minimization for the target protein were performed using the optimized potentials for liquid simulations (OPLS3e) option.

Preparation of ligands

Alloxan, streptozotocin (STZ), phenytoin, eszopiclone, bicuculline, BMS-754807, rutaecarpine, GABA, and 4548-G05 were used for this analysis. All proposed compounds were generated in 3D coordinates using the LigPrep (a comprehensive set of tools for creating high-quality, all-atom 3D structures of several drug-like compounds) option (OPLS force field), considering ionization states and energy minimization to optimize ligand geometry using Epik at pH 7.0 + 2.

Active site identification

Grid generation (Schrödinger software) was used to identify the 3D coordinates of the proteins’ active sites. This tool was used to highlight the active sites of receptors for suitable interactions with ligands. All grids were generated based on the ligand interaction with the critical amino acid residues because the active sites of the proteins were complexed with ligands. For example, the selection of amino acids for the GABA-A receptor was based on GABA ligand interactions (arginine [ARG] 67, glutamic acid [GLU] 155, serine [SER] 156, and tryptophan [TYP] 205)[7]. For insulin receptor kinase, the essential amino acids were highlighted based on Irfin 1 inhibitor interactions (Glu 1077, methionine [Met] 1079, aspartic acid [Asp] 1083, and Asp 1150).

Virtual screening (docking and free binding energies)

Glide docking (Schrödinger software) protocols were applied. In this docking program, the flexibility of the ligands was considered, while the protein was deemed a rigid structure. The 3D coordinates of the active sites were identified using grid generation. Standard precision was selected, and all other parameters were maintained at their default settings. Subsequently, the ligands were subjected to binding free energy analysis (ΔGbind in kcal/mol) using MM/GBSA (Schrödinger). The pose view file was retrieved from the glide docking scores and applied to the OPLS3e force field. The binding free energy of MMGBSA was predicted for each ligand-protein complex as follows: ΔGbind = Gcomplex − Gprotein − Gligand, where ΔGbind is the binding free energy and Gcomplex, Gprotein, and Gligand are the free energies of the complex, protein, and ligand, respectively.


The experimental protocols for this study were approved by the Institutional Animal Ethics Committee and conducted according to the guidelines of the Indian National Sciences Academy for the use and care of experimental animals (approval number PCP/IAEC/002/2010; July 27, 2012). Mice were purchased from Sree Venkateshwara Enterprises, Bengaluru. Pharmacological studies were conducted at the Department of Pharmacology, Periyar College of Pharmaceutical Sciences, Tiruchirappalli, Tamil Nadu, India. The Committee for the Purpose of Control and Supervision of Experiments on Animals approved the animal facility of this institute. The animals were maintained in a well-ventilated, temperature-controlled 30°C ± 1°C animal room for 7 days before the experimental period and provided a Himalaya pellet diet and water ad libitum. The animals were acclimatized to laboratory conditions before the test.

Animal treatment

Swiss albino mice weighing 25–35 g were used in this study. Control mice were treated with phosphate-buffered saline. Additionally, alloxan monohydrate (150 mg/kg, i.p.) and phenytoin (20 mg/kg, p.o.) were administered concurrently to induce hyperglycemia and hypoarousal in an animal model[8,9].

General behavioral assessment

Control and alloxan + phenytoin-treated mice were monitored regularly for various behavioral parameters (Table 2), including allergic reactions (redness of the skin or eye), anaphylactic shock/death, diarrhea, drooling, fighting (aggressive behavior), grooming, hair coat erection, hind limb abduction, hyperactivity (excessive jumping), licking of genitals, mortality, penile erection (stimulatory behavior), seizure, stool color, straub tail, and tremor. Animals were monitored by two trained behavioral observers using a well-established protocol[10,11].

Table 2:
Physicochemical properties of alloxan and phenytoin

Elevated plus maze

A commonly used rodent behavioral test is this in vivo CNS-related validated preclinical behavioral research task to evaluate the hypoarousal/hyperarousal and cognitive effects of toxins and therapeutic agents to characterize specific regions of the brain and mechanisms underlying anxiety-related behavior. Briefly, mice were placed at the junction of the four arms of the maze, facing an open arm, and the entries/duration was recorded. Additionally, ethological factors (head drop, rearing behavior, and stretched-attend postures) have been observed[12]. Elevated plus maze was an exteroceptive behavioral model to evaluate learning and memory. The elevated plus maze consisted of two open arms and two closed arms (50 × 10 × 40 cm) with an open roof arranged such that the two arms were opposite each other, and the maze was elevated to a height of 50 cm. On the 14th day, each mouse was placed at the end of the open arm facing away from the central platform, and the transfer latency was recorded. If an animal did not enter one of the covered arms within 90 seconds, it was gently pushed into one of the two covered arms, and the transfer latency was assigned as 90 seconds. The measurement of transfer latency on day one served as a parameter for acquisition, and those on the 14th day served as parameters for memory retention.

Y-maze spontaneous alternation

This behavioral assignment is generally used to evaluate spatial learning and memory. The Y-maze task in rodents commonly includes spontaneous alternation and recognition memory tests. Y-maze spontaneous alternation is a rodent behavioral assessment that quantifies the instinct and readiness (curiosity for exploration) to delve into a novel and new surroundings (unexplored arms). Normal rodents prefer to explore the new unexplored arm of the Y-maze rather than return to the previously acclimatized arm. Neuroanatomically, this behavioral study reflects the cognitive behavioral functions and neurophysiological changes associated with the hippocampus, septum, basal forebrain, and prefrontal cortex. The Y-maze task measures spatial working through the spontaneous alternation of behavior. The maze was made of black-painted wood (40 cm long, 13 cm high, 3 cm wide at the bottom, 10 cm wide at the top, and at an equal angle). Each mouse was placed at the end of one arm and allowed to move freely through the maze during an 8-minute session. The mice explored the maze systemically, entering each arm in turn. The ability to alternate requires that mice know which arm they have already visited. A series of arm entries were recorded visually, including possible returns in the same arm. The measurement of transfer latency on day one served as a parameter for acquisition, and those on the 14th day served as parameters for memory retention[13].

Hebb-Williams maze

This behavioral study can measure rodent visuospatial working memory. Interestingly, the Hebb-Williams maze behavioral study did not provide spatial cues to assist rodents. Instead, the maze required the subjects to rely on problem-solving skills. This test can compare learning and memory tasks between toxin/therapeutic drug-treated animals and appropriate controls. Hebb-Williams maze consists of an entirely enclosed rectangular box with an entry (A) and a reward chamber (B) appended at opposite ends. The box was partitioned with wooden slats into blind passages, leaving only a twisting corridor (C) leading from entry (A) to the reward chamber. The learning assessment for control and treated mice was conducted at the end of treatment (14th day) compared with the first-day acquisition score. The time taken by the mice to reach the award chamber was considered the learning of the trial. The average of the four trials was taken as the learning score of the day. Lower scores indicate efficient learning; in contrast, higher scores indicate poor learning in animals[14].

Passive avoidance paradigm

Passive avoidance signifies standard conditioning within the perspective of the step-down pattern when exposed to stress (foot shock). The rodent’s latency to react to stress is enhanced because of the recollection that foot shock was previously delivered after stepping onto the ground or floor. Long-term memory was examined using passive avoidance behavior based on negative reinforcement. The apparatus consisted of a box (27 × 27 × 27 cm) with three wood walls and one plexiglass wall featuring a grid floor with a wooden platform (10 × 7 × 1.7 cm) in the center of the grid floor. The box was illuminated with a 15 W bulb during the experimental period. Electric shock was delivered to the grid floor. The animals were initially trained and gently placed on a wooden platform at the center of the grid floor. When the mice stepped down and placed all of their paws on the grid floor, shocks (50 Hz: 1.5 mA; 1 second) were delivered for 15 seconds, and the step-down latency (SDL) was recorded. SDL was defined as the time taken by the animal to step down from the wooden platform to the grid floor, with all its paws on the grid floor[15].

Statistical analysis

Data are reported as the mean ± standard error of the mean (SEM). Statistical analysis was performed using a t test (P < 0.05, considered statistically significant). Statistical analyses were performed using Prism V software (Dr. Harvey Motulsky, La Jolla, CA, USA).


The in silico studies provided predicted pharmacokinetic and pharmacodynamic profiles for the drugs (alloxan and phenytoin) used in the current study. The docking score and binding affinity illustrate a linear and geometrical method to evaluate a “biological target (example-protein, macromolecule) and endogenous/exogenous ligand interaction.” This in silico method is currently used in academia and industry as a rapid and efficient scientific tool to assess the binding characteristic features of ligand docking (usually a small-molecular-weight molecule) on a specific target and to predict binding affinity to envisage pharmacological effects. The current study established the docking score and binding affinity of alloxan and phenytoin on GABA-A and insulin kinase receptors. Furthermore, it compared them with ligands with comparable pharmacodynamic actions on the two targets (Table 1).

The next step was to assess the ADME features of alloxan and compare it with the well-established hyperglycemic agent, STZ. In terms of absorption, alloxan has a lower SASA value than STZ, which indicates higher absorption and bioavailability. Additionally, alloxan and STZ are less active in the CNS, making them pharmacodynamically valuable drugs in the peripheral system. Alloxan is a preferred hyperglycemic drug because it shows a potential profile as an oral drug compared to STZ based on the predicted physiochemical properties, such as fewer hydrogen bond acceptor and donor interactions, making alloxan likely to have higher oral absorption and better distribution (Table 2). However, phenytoin has high oral bioavailability and significantly affects the CNS (Table 2).

Regarding the general behavioral assessment (Table 3), the current dose and route of administration of alloxan and phenytoin did not induce any abnormal posture, aggressive behavior (agitation, fighting), hypersensitivity reactions, GI (Gastrointestinal) problems (diarrhea/constipation), ophthalmic issues (redness or swelling in the eye), movement disorders (tremor, rigidity), sleep alterations, respiratory illness, tumor, or mortality.

Table 3 - General behavioral parameters
Behavioral parameters Control Alloxan + phenytoin
Abnormal posture (head press) No No
Aggressive behavior (fight) No No
Agitation No No
Allergic reaction (redness of the skin/eye) No No
Anaphylactic shock/death No No
Bowel movement No No
Diarrhea No No
Drooling No No
Eye bulging No No
Fighting (aggressive behavior) No No
Grinding teeth/chattering No No
Grooming No Yes
Hair coat erection No No
Head twitching No No
Hematuria No No
Hind limb abduction No No
Hyperactivity (excessive jumping) No No
Licking body No No
Licking genitals No No
Mortality observed No No
Locomotion (increase/decrease) Normal Normal
Narcolepsy No No
Open mouth breathing No No
Penile erection (stimulatory behavior) No No
Rapid breathing No Yes
Salivation/drool No No
Seizure No No
Sniffing No No
Stool color No Brown
Straub tail No No
Sunken eyes/lack of blinking No No
Tremor No No
Tumor No No
Wiggling whiskers No No

The effect of a hyperglycemic/hypoarousal animal model on elevated plus maze

Cognitive behavioral testing is a behavioral study that has established face, construct, and predictive validity regarding cognition. In the elevated plus test, alloxan + phenytoin treatment had a significantly higher transfer latency than the control on the 14th day (n = 6, *P < 0.001; Figure 1).

Figure 1.:
Effect of alloxan and phenytoin treatment on an elevated plus maze. Alloxan + phenytoin treatment exhibited significantly more transfer latency compared to the control (n = 6, *P < 0.001).
Figure 2.:
Effect of alloxan and phenytoin treatment on the Y-maze. There was a significant increase in the number of entries because of the administration of cognitive impairment induced by alloxan + phenytoin (n = 6, *P < 0.001).
Figure 3.:
Effect of alloxan and phenytoin treatment on Hebb-Williams maze. Alloxan + phenytoin induced an increase in latency learning time because of the memory deficit induced by alloxan + phenytoin (n = 6, *P < 0.001).
Figure 4.:
Effect of alloxan and phenytoin treatment on passive avoidance. The alteration of the step-down latencies significantly decreased because of alloxan + phenytoin treatment (n = 6, *P < 0.001).

The effect of hyperglycemic/hypoarousal animal model on Y-maze

Normally, a mouse in the Y-maze test takes 20–25 entries in a 5-minute trial. However, there was a significant increase in the number of entries owing to the administration of alloxan + phenytoin on the 14th day (n = 6, *P < 0.001; Figure 2).

The effect of hyperglycemic/hypoarousal animal model on Hebb-Williams maze

In the Hebb-Williams maze, alloxan + phenytoin induced a significant increase in latency learning time because of the memory deficit induced by alloxan + phenytoin on the 14th day (n = 6, *P < 0.001; Figure 3).

Effect of hyperglycemic/hypoarousal animal model on passive avoidance paradigm

Alloxan + phenytoin significantly decreased the alteration of the SDL because of the reversal of memory deficit after applied shock on the 14th day (n = 6, *P < 0.001; Figure 4).


Alloxan and STZ are experimental chemicals that are extensively used to induce hyperglycemic states in animal models. Alloxan has also been reported to induce cardiotoxicity associated with hyperglycemia[16]. Alloxan is chemically stable at acidic pH, supporting its viability as an oral dosage; however, rapid degradation occurs near a physiological pH of 7.4. Fortunately, one mechanism by which alloxan induces insulin-dependent diabetes mellitus is glucokinase inhibition, achieved within 1 minute of exposure to alloxan. However, this indicates that an oral dose of alloxan could be effective, provided that degradation does not occur before intestinal absorption. STZ is stable at a pH of 4.5 but rapidly degrades at higher pH, making oral dosage much more complicated as the pH rises throughout the gastrointestinal tract. Given that both chemical structures rely on glucose transporter 2 (GLUT 2) transport to induce their diabetogenic (hyperglycemic) effect, our in silico predictions, in conjunction with published pharmacokinetics of both drugs, indicate specific key differences in predicting oral dosage efficacy.

Phenytoin decreases abnormal electrical activity in the brain, thereby decreasing the spread of seizure activity. Phenytoin is used to treat different types of seizures, such as grand mal and complex partial seizures, and to prevent and treat seizures during or after neurosurgery. Some serious adverse effects of phenytoin include cardiac arrhythmias, hypotension, respiratory arrest, and related deaths, particularly in cardiology[17–20]. Phenytoin is believed to protect against seizures by blocking the voltage-gated sodium channels. This block sustains high-frequency repetitive firing of action potentials, which is accomplished by reducing the amplitude of the sodium-dependent action potentials by enhancing steady-state inactivation. A literature review revealed multiple reports of phenytoin-induced hyperglycemia, usually from insulin resistance and decreased insulin secretion. The secretion of insulin is inhibited when intracellular sodium is depleted, and phenytoin prolongs the effective refractory period by decreasing the influx of sodium ions across the cell membranes. Therefore, this may inhibit insulin release and increase insulin resistance[21]. The effects of hyperglycemia on neuronal and blood-brain barrier functions are becoming more evident.

Phenytoin has been implicated in causing a decline in cognitive function. The molecular mechanisms underlying the changes in insulin secretion/action by phenytoin resulting in cognitive deficiencies have not been fully elucidated. It has been reported that patients receiving phenytoin consistently performed poorly on memory tasks compared with untreated patients. However, the side effects of phenytoin are reversible after complete therapy withdrawal[22,23]. Patients receiving phenytoin displayed a difference in intelligence and visuomotor performance, and these effects appeared dose-dependent[23]. Another study reported slower performance in information-processing tasks between patients treated with phenytoin than with carbamazepine but no differences in memory or selective attention[24]. It has been reported that chronic phenytoin use induces impairment of learning and memory, with associated changes in brain acetylcholine esterase activity and monoamine levels[25]. According to another study, phenytoin treatment may be associated with changes in the brain structure[22]. Phenytoin may accumulate in the cerebral cortex over long periods and can cause atrophy of the cerebellum. Numerous hypotheses have been identified as the potentiation of neurological impairments by phenytoin, including modulation of the serotonergic pathway, decreased acetylcholinesterase activity in the hippocampus, abnormal changes in neuropeptides, and hippocampal injuries. Furthermore, it has been shown that antiepileptic drugs like valproic acid, phenytoin, and carbamazepine induce abnormal production of reactive oxygen species[26].

Oxidative injury plays a vital role in cognitive deficit[26]. Along with the “one drug-one target” model of drug discovery, the “multiple drugs-multiple targets” notion (also identified as polypharmacology) is widely spreading in the field of drug discovery and development[27]. Network pharmacology is a rapidly growing concept comprising integrated knowledge of polypharmacology and system biology, favorably elaborating this model[28]. Network pharmacology plays two crucial roles in drug development. First, it contributes to establishing a logical network model and predicting drug targets based on the existing literature in databases. Second, the mode of drug action can be explored based on the network equilibrium principle[28]. In network pharmacology, molecular docking plays a crucial role in investigating the mode of action of therapeutic moieties, particularly in a multi-ingredient regimen[29]. This study describes network pharmacology use, including molecular docking, to predict the mode underlying the chemically induced hyperglycemic and hypoarousal environment. Drug-related protein targets were identified in this approach, and core functions were identified through the protein-protein interaction network and molecular docking. To our knowledge, a network pharmacology approach has not yet been used to study the mode of action of these drugs. Therefore, our approach offers a potential strategy for exploring how diabetes mellitus (hyperglycemia) affects cognition. Overall, this study demonstrates that alloxan is preferred to STZ because it shows a potential profile as an oral drug based on in silico predictions. The results of this study demonstrate the harmful effect of cardiotoxic drugs, such as alloxan and phenytoin, on cognition, including memory. Further investigations using a combination of alloxan and other antiepileptic drugs are warranted to explore the full potential of alloxan and phenytoin in drug-induced cognitive impairment resulting from hyperglycemia and hypoarousal.


SKKS, MD, KRVR, and IS participated in research design. SKKS, MR, and KASMS participated in the performance of the research. MA, IS, JD, SR, MYG, MA, RMN, RJB, JR, FS, TM, and MD contributed new reagents or analytic tools. MD, JD, SKKS, MR, KASMS, and KRVR participated in data analysis. SKKS, MA, IS, JD, SR, MYG, MA, SP, AMR, KASMS, PL, RMN, RJB, JR, KRVR, FS, TM, and MD participated in the drafting, writing, and revising of the paper. The manuscript was reviewed and approved by all authors. The requirements for authorship have been met. Each author attests to the integrity of the work.


The authors declare that they have no conflict of interest with regard to the content of this manuscript.


We would like to thank the Harrison College of Pharmacy at Auburn University.


All data generated or analyzed during this study are included in this published article.


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Animal model; Cognitive impairment; Hyperglycemia; Long-term memory; Short-term memory

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