Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose

Melatonin attenuates fentanyl - induced behavioral sensitization and circadian rhythm disorders in mice

Melatonin is a neurohormone synthesized by the pineal gland to regulate the circadian rhythms and has proven to be effective in treating drug addiction and dependence. However, the effects of melatonin to modulate the drug-seeking behavior of fentanyl and its underlying molecular mechanism is elusive. This study was designed to investigate the effects of melatonin on fentanyl - induced behavioral sensitization and circadian rhythm disorders in mice. The accompanying changes in the expression of Brain and Muscle Arnt-Like (BMAL1), tyrosine hydroxylase (TH), and monoamine oxidase A (MAO-A) in relevant brain regions including the suprachiasmatic nucleus (SCN), nucleus accumbens (NAc), prefrontal cortex (PFC), and hippocampus (Hip) were investigated by western blot assays to dissect the mechanism by which melatonin modulates fentanyl - induced behavioral sensitization and circadian rhythm disorders. The present study suggest that fentanyl (0.05, 0.1 and 0.2 mg/kg) could induce behavioral sensitization and melatonin (30.0 mg/kg) could attenuate the behavioral sensitization and circadian rhythm disorders in mice. Fentanyl treatment reduced the expression of BMAL1 and MAO-A and increased that of TH in relevant brain regions. Furthermore, melatonin treatment could reverse the expression levels of BMAL1, MAO-A, and TH. In conclusion, our study demonstrate for the first time that melatonin has therapeutic potential for fentanyl addiction.

Explaining the high mortality among opioid-cocaine co-users compared to opioid-only users. A systematic review

RATIONALE

The opioid crisis in North America has recently seen a fourth wave, which is dominated by drug-related deaths due to the combined use of illicitly manufactured fentanyl [IMF] and stimulants such as cocaine and methamphetamine.

OBJECTIVES

A systematic review addressing the question why drug users combine opioids and stimulants and why the combination results in such a high overdose mortality: from specific and dangerous pharmacokinetic or pharmacodynamic interactions or from accidental poisoning?

RESULTS

Motives for the combined use include a more intensive high or rush when used at the same time, and some users have the unfounded and dangerous belief that co-use of stimulants will counteract opioid-induced respiratory depression. Overdose deaths due to combined (intravenous) use of opioids and stimulants are not likely to be caused by specific pharmacokinetic or pharmacodynamic interactions between the two drugs and it is unlikely that the main cause of overdose deaths is due to accidental poisoning.

CONCLUSION

The unexpectedly high overdose rates in this population could not be attributed to accidental overdosing or pharmacokinetic/pharmacodynamic interactions. The most likely explanation for the high rate of drug-related deaths in opioid-cocaine co-users is careless overdosing with either cocaine, opioid(s) or both, probably facilitated by the high level of preexisting impulsivity in these co-users and a further acute increase in impulsivity following cocaine use. The primary corollary is that cocaine users should avoid IMF use in the same time window. In addition, IMF users should refrain from cocaine use to avoid impulsive IMF overdosing.

Artificial intelligence and machine learning in clinical pharmacological research

BACKGROUND

Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research.

METHODS

Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized.

RESULTS

ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers.

CONCLUSIONS

ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

Virus-CKB 2.0: Viral-Associated Disease-Specific Chemogenomics Knowledgebase

Transmissible and infectious viruses can cause large-scale epidemics around the world. This is because the virus can constantly mutate and produce different variants and subvariants to counter existing treatments. Therefore, a variety of treatments are urgently needed to keep up with the mutation of the viruses. To facilitate the research of such treatment, we updated our Virus-CKB 1.0 to Virus-CKB 2.0, which contains 10 kinds of viruses, including enterovirus, dengue virus, hepatitis C virus, Zika virus, herpes simplex virus, Andes orthohantavirus, human immunodeficiency virus, Ebola virus, Lassa virus, influenza virus, coronavirus, and norovirus. To date, Virus-CKB 2.0 archived at least 65 antiviral drugs (such as remdesivir, telaprevir, acyclovir, boceprevir, and nelfinavir) in the market, 178 viral-related targets with 292 available 3D crystal or cryo-EM structures, and 3766 chemical agents reported for these target proteins. Virus-CKB 2.0 is integrated with established tools for target prediction and result visualization; these include HTDocking, TargetHunter, blood-brain barrier (BBB) predictor, Spider Plot, etc. The Virus-CKB 2.0 server is accessible at https://www.cbligand.org/g/virus-ckb. By using the established chemogenomic tools and algorithms and newly developed tools, we can screen FDA-approved drugs and chemical compounds that may bind to these proteins involved in viral-associated disease regulation. If the virus strain mutates and the vaccine loses its effect, we can still screen drugs that can be used to treat the mutated virus in a fleeting time. In some cases, we can even repurpose FDA-approved drugs through Virus-CKB 2.0.

MCCS, a novel characterization method for protein-ligand complex

Delineating the fingerprint or feature vector of a receptor/protein will facilitate the structural and biological studies, as well as the rational design and development of drugs with high affinities and selectivity. However, protein is complicated by its different functional regions that can bind to some of its protein partner(s), substrate(s), orthosteric ligand(s) or allosteric modulator(s) where cogent methods like molecular fingerprints do not work well. We here elaborate a scoring-function-based computing protocol Molecular Complex Characterizing System to help characterize the binding feature of protein-ligand complexes. Based on the reported receptor-ligand interactions, we first quantitate the energy contribution of each individual residue which may be an alternative of MD-based energy decomposition. We then construct a vector for the energy contribution to represent the pattern of the ligand recognition at a receptor and qualitatively analyze the matching level with other receptors. Finally, the energy contribution vector is explored for extensive use in similarity and clustering. The present work provides a new approach to cluster proteins, a perspective counterpart for determining the protein characteristics in the binding, and an advanced screening technique where molecular docking is applicable.

Levo-tetrahydropalmatine attenuates the acquisition of fentanyl-induced conditioned place preference and the changes in ERK and CREB phosphorylation expression in mice

Levo-tetrahydropalmatine (L-THP) is the main active ingredient of Corydalis and Stephania and is widely used for its sedative, analgesic, and neuroleptic effects. Though L-THP is an antagonist of dopamine receptors and has been proven to be effective in treating drug addiction, its effect on fentanyl-induced reward learning still remains unclear. This experiment was designed to investigate the effects of L-THP on fentanyl-induced rewarding behavior through conditioned place preference (CPP) in mice. Western blot assays were used to dissect the accompanying changes in the phosphorylation of extracellular signal-regulated kinase (ERK) and cAMP response element binding protein (CREB) in related brain regions, including the hippocampus (Hip), caudate putamen (CPu), prefrontal cortex (PFC), and nucleus accumbens (NAc), which may mediate the effects of L-THP on fentanyl-induced CPP. The results revealed that fentanyl could induce CPP in mice at doses of 0.025 mg/kg, 0.05 mg/kg, 0.1 mg/kg, and 0.2 mg/kg, and L-THP could attenuate the acquisition of fentany-induced CPP at a dose of 10.0 mg/kg. The levels of p-ERK and p-CREB of the saline+fentanyl group (0.05 mg/kg) increased significantly in the Hip, NAc, and PFC compared to the saline+saline group. Furthermore, L-THP (10.0 mg/kg) co-administered with fentanyl during conditioning prevented the enhanced phosphorylation of ERK and CREB in the Hip, NAc, and PFC. Our research revealed that L-THP could suppress the rewarding properties of fentanyl-induced CPP, the inhibitory effect may be related to the suppression of ERK and CREB phosphorylation in the Hip, NAc, and PFC of mice. Thus, L-THP may have therapeutic potential for fentanyl addiction.

Binding Characterization of Agonists and Antagonists by MCCS: A Case Study from Adenosine A2A Receptor

Characterizing the structural basis of ligand recognition of adenosine A_2A receptor (AA_2AR) will facilitate its rational design and development of small molecules with high affinity and selectivity, as well as optimal therapeutic effects for pain, cancers, drug abuse disorders, etc. In the present work, we applied our reported algorithm, molecular complex characterizing system (MCCS), to characterize the binding features of AA_2AR based on its reported 3D structures of protein-ligand complexes. First, we compared the binding score to the reported experimental binding affinities of each compound. Then, we calculated an output example of residue energy contribution using MCCS and compared the results with data obtained from MM/GBSA. The consistency in results indicated that MCCS is a powerful, fast, and accurate method. Sequentially, using a receptor-ligand data set of 57 crystallized structures of AA_2ARs, we characterized the binding features of the binding pockets in AA_2AR, summarized the key residues that distinguish antagonist from agonist, produced heatmaps of residue energy contribution for clustering various statuses of AA_2ARs, explored the selectivity between AA_2AR and AA_1AR, etc. All the information provided new insights into the protein features of AA_2AR and will facilitate its rational drug design.

IsAb: a computational protocol for antibody design

The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.

Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need for medicines that can help before vaccines are available. In this study, we present a viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) and apply our computational systems pharmacology-target mapping to rapidly predict the FDA-approved drugs which can quickly progress into clinical trials to meet the urgent demand of the COVID-19 outbreak. Virus-CKB reuses the underlying platform of our DAKB-GPCRs but adds new features like multiple-compound support, multi-cavity protein support and customizable symbol display. Our one-stop computing platform describes the chemical molecules, genes and proteins involved in viral-associated diseases regulation. To date, Virus-CKB archived 65 antiviral drugs in the market, 107 viral-related targets with 189 available 3D crystal or cryo-EM structures and 2698 chemical agents reported for these target proteins. Moreover, Virus-CKB is implemented with web applications for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc. The Virus-CKB server is accessible at https://www.cbligand.org/g/virus-ckb.

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

Polysubstance use in the U.S. opioid crisis

Interventions to address the U.S. opioid crisis primarily target opioid use, misuse, and addiction, but because the opioid crisis includes multiple substances, the opioid specificity of interventions may limit their ability to address the broader problem of polysubstance use. Overlap of opioids with other substances ranges from shifts among the substances used across the lifespan to simultaneous co-use of substances that span similar and disparate pharmacological categories. Evidence suggests that nonmedical opioid users quite commonly use other drugs, and this polysubstance use contributes to increasing morbidity and mortality. Reasons for adding other substances to opioids include enhancement of the high (additive or synergistic reward), compensation for undesired effects of one drug by taking another, compensation for negative internal states, or a common predisposition that is related to all substance consumption. But consumption of multiple substances may itself have unique effects. To achieve the maximum benefit, addressing the overlap of opioids with multiple other substances is needed across the spectrum of prevention and treatment interventions, overdose reversal, public health surveillance, and research. By addressing the multiple patterns of consumption and the reasons that people mix opioids with other substances, interventions and research may be enhanced.

Pain-CKB, A Pain-Domain-Specific Chemogenomics Knowledgebase for Target Identification and Systems Pharmacology Research

A traditional single-target analgesic, though it may be highly selective and potent, may not be sufficient to mitigate pain. An alternative strategy for alleviation of pain is to seek simultaneous modulation at multiple nodes in the network of pain-signaling pathways through a multitarget analgesic or drug combinations. Here we present a comprehensive pain-domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated computing tools for target identification and systems pharmacology research. Pain-CKB is constructed on the basis of our established chemogenomics technology with new features, including multiple compound support, multicavity protein support, and customizable symbol display. The determination of bioactivity is also revised to avoid the use of complex machine learning models. Our one-stop computing platform describes the chemical molecules, genes, and proteins involved in pain regulation. To date, Pain-CKB has archived 272 analgesics in the market, 84 pain-related targets with 207 available 3D crystal or cryo-EM structures, and 234 662 chemical agents reported for these target proteins. Moreover, Pain-CKB implements user-friendly web-interfaced computing tools and applications for the prediction and analysis of the relevant protein targets and visualization of the outputs, including HTDocking, TargetHunter, BBB permeation predictor, NGL viewer, Spider Plot, etc. The Pain-CKB server is accessible at https://www.cbligand.org/g/pain-ckb.

Binding Characterization of GPCRs-Modulator by Molecular Complex Characterizing System (MCCS)

Increasing attention has been devoted to allosteric modulators as the preferred therapeutic agents for their colossal advantages such as higher selectivity, fewer side effects, and lower toxicity since they bind at allosteric sites that are topographically distinct from the classic orthosteric sites. However, the allosteric binding pockets are not conserved and there are no cogent methods to comprehensively characterize the features of allosteric sites with the binding of modulators. To overcome this limitation, our lab has developed a novel algorithm that can quantitatively characterize the receptor-ligand binding feature named Molecular Complex Characterizing System (MCCS). To illustrate the methodology and application of MCCS, we take G protein coupled receptors (GPCRs) as an example. First, we summarized and analyzed the reported allosteric binding pockets of class A GPCRs using MCCS. Sequentially, a systematic study was conducted between cannabinoid receptor type 1 (CB1) and its allosteric modulators, where we used MCCS to analyze the residue energy contribution and the interaction pattern. Finally, we validated the predicted allosteric binding site in CB2 via MCCS in combination with molecular dynamics (MD) simulation. Our results demonstrate that the MCCS program is advantageous in recapitulating the allosteric regulation pattern of class A GPCRs of the reported pockets as well as in predicting potential allosteric binding pockets. This MCCS program can serve as a valuable tool for the discovery of small-molecule allosteric modulators for class A GPCRs.

Pain Chemogenomics Knowledgebase (Pain-CKB) for Systems Pharmacology Target Mapping and Physiologically Based Pharmacokinetic Modeling Investigation of Opioid Drug-Drug Interactions

More than 50 million adults in America suffer from chronic pain. Opioids are commonly prescribed for their effectiveness in relieving many types of pain. However, excessive prescribing of opioids can lead to abuse, addiction, and death. Non-steroidal anti-inflammatory drugs (NSAIDs), another major class of analgesic, also have many problematic side effects including headache, dizziness, vomiting, diarrhea, nausea, constipation, reduced appetite, and drowsiness. There is an urgent need for the understanding of molecular mechanisms that underlie drug abuse and addiction to aid in the design of new preventive or therapeutic agents for pain management. To facilitate pain related small-molecule signaling pathway studies and the prediction of potential therapeutic target(s) for the treatment of pain, we have constructed a comprehensive platform of a pain domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated data mining computing tools. Our new computing platform describes the chemical molecules, genes, proteins, and signaling pathways involved in pain regulation. Pain-CKB is implemented with a friendly user interface for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, and Spider Plot. Combining these with other novel tools, we performed three case studies to systematically demonstrate how further studies can be conducted based on the data generated from Pain-CKB and its algorithms and tools. First, systems pharmacology target mapping was carried out for four FDA approved analgesics in order to identify the known target and predict off-target interactions. Subsequently, the target mapping outcomes were applied to build physiologically based pharmacokinetic (PBPK) models for acetaminophen and fentanyl to explore the drug-drug interaction (DDI) between this pair of drugs. Finally, pharmaco-analytics was conducted to explore the detailed interaction pattern of acetaminophen reactive metabolite and its hepatotoxicity target, thioredoxin reductase.

Brief Report: Rates of Fentanyl Use Among Psychiatric Emergency Room Patients

BACKGROUND AND OBJECTIVES

Opioid overdose-related deaths increased from approximately 18 000 deaths in 2007 to 46 802 deaths in 2018. Fentanyl is primarily responsible for the increase in opioid overdose deaths from 2011 through 2017. The primary aim of this study is to determine the rates of fentanyl in the urine drug screens of all patients who presented to the psychiatric emergency room at VA Connecticut, over 7 months in 2018.

METHODS

Data were collected for all patient presentations between June 2018 and December 2018. There were 746 total patient presentations, with 497 being unique. Collected data included basic demographic information, psychiatric diagnosis, and urine drug screen for various illicit substances, including fentanyl.

RESULTS

Over 15% of patients screened positive for fentanyl. Patients who tested positive for fentanyl were further classified based on positive urine drug screening results for other opioids, cocaine, or both. Twenty percent of patients who screened positive for fentanyl and cocaine tested negative for other opioids. This category suggests that some veterans might be consuming fentanyl with cocaine.

DISCUSSION AND CONCLUSIONS

Fentanyl was found at a high rate, even in the absence of other opioids, which suggests that some veterans might be consuming fentanyl with cocaine. Consequently, harm reduction strategies should be broadened to include all patients at risk of fentanyl overdose, including patients who use substances (eg, cocaine) that are potentially adulterated with fentanyl.

SCIENTIFIC SIGNIFICANCE

This study is the first one of its kind that looked at rates of fentanyl use in all presentations to a psychiatric emergency room. While it is well-known that fentanyl is highly prevalent, these findings extend the current state of knowledge by replication in a psychiatric emergency population. (Am J Addict 2021;30:92-95).

A cost-effective fluorescence biosensor for cocaine based on a "mix-and-detect" strategy

The efficient detection of illicit drugs such as cocaine continues to be important for the fight against drug trafficking. Herein, we report a one-step method for rapid and specific cocaine detection. The method is based on our finding that small-molecule Thioflavin T (ThT) can act as a fluorescence indicator, which can be bonded with the anti-cocaine aptamer (MNS-4.1) to generate an enhanced fluorescence signal. More interestingly, upon cocaine binding, the intercalated ThT can be replaced, causing a drastic fluorescence reduction. We further optimized the sequence of MNS-4.1 and a new anti-cocaine aptamer (coc.ap2-GC) was obtained. This aptamer showed a higher affinity to both ligands, which increased the ThT binding fluorescence intensity and showed the highest quenching efficiency. Based on the fluorescence change induced by competitive binding, cocaine detection could be accomplished by a "mix-and-detect" strategy within seconds. Such a label-free method exhibits high sensitivity to cocaine with a low detection limit of 250 nM. Moreover, the practical sample analysis (2.5% human urine and saliva) also exhibits good precision and high sensitivity.

Insight into Ginkgo biloba L. Extract on the Improved Spatial Learning and Memory by Chemogenomics Knowledgebase, Molecular Docking, Molecular Dynamics Simulation, and Bioassay Validations

Epilepsy is a common cause of serious cognitive disorders and is known to have impact on patients' memory and executive functions. Therefore, the development of antiepileptic drugs for the improvement of spatial learning and memory in patients with epileptic cognitive dysfunction is important. In the present work, we systematically predicted and analyzed the potential effects of Ginkgo terpene trilactones (GTTL) on cognition and pathologic changes utilizing in silico and in vivo approaches. Based on our established chemogenomics knowledgebase, we first conducted the network systems pharmacology analysis to predict that ginkgolide A/B/C may target 5-HT 1A, 5-HT 1B, and 5-HT 2B. The detailed interactions were then further validated by molecular docking and molecular dynamics (MD) simulations. In addition, status epilepticus (SE) was induced by lithium-pilocarpine injection in adult Wistar male rats, and the results of enzyme-linked immunosorbent assay (ELISA) demonstrated that administration with GTTL can increase the expression of brain-derived neurotrophic factor (BDNF) when compared to the model group. Interestingly, recent studies suggest that the occurrence of a reciprocal involvement of 5-HT receptor activation along with the hippocampal BDNF-increased expression can significantly ameliorate neurologic changes and reverse behavioral deficits in status epilepticus rats while improving cognitive function and alleviating neuronal injury. Therefore, we evaluated the effects of GTTL (bilobalide, ginkgolide A, ginkgolide B, and ginkgolide C) on synergistic antiepileptic effect. Our experimental data showed that the spatial learning and memory abilities (e.g., electroencephalography analysis and Morris water maze test for behavioral assessment) of rats administrated with GTTL were significantly improved under the middle dose (80 mg/kg, GTTL) and high dose (160 mg/kg, GTTL). Moreover, the number of neurons in the hippocampus of the GTTL group increased when compared to the model group. Our studies showed that GTTL not only protected rat cerebral hippocampal neurons against epilepsy but also improved the learning and memory ability. Therefore, GTTL may be a potential drug candidate for the prevention and/or treatment of epilepsy.