Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification-Salvinorin A as a case study

Exploring natural products potential: A similarity-based target prediction tool for natural products

Natural products are invaluable resources in drug discovery due to their substantial structural diversity. However, predicting their interactions with druggable protein targets remains a challenge, primarily due to the limited availability of bioactivity data. This study introduces CTAPred (Compound-Target Activity Prediction), an open-source command-line tool designed to predict potential protein targets for natural products. CTAPred employs a two-stage approach, combining fingerprinting and similarity-based search techniques to identify likely drug targets for these bioactive compounds. Despite its simplicity, the tool's performance is comparable to that of more complex methods, demonstrating proficiency in target retrieval for natural product compounds. Furthermore, this study explores the optimal number of reference compounds most similar to the query compound, aiming to refine target prediction accuracy. The findings demonstrated the superior performance of considering only the most similar reference compounds for target prediction. CTAPred is freely available at https://github.com/Alhasbary/CTAPred, offering a valuable resource for deciphering natural product-target associations and advancing drug discovery.

Bedside to bench: the outlook for psychedelic research

There has recently been a resurgence of interest in psychedelic compounds based on studies demonstrating their potential therapeutic applications in treating post-traumatic stress disorder, substance abuse disorders, and treatment-resistant depression. Despite promising efficacy observed in some clinical trials, the full range of biological effects and mechanism(s) of action of these compounds have yet to be fully established. Indeed, most studies to date have focused on assessing the psychological mechanisms of psychedelics, often neglecting the non-psychological modes of action. However, it is important to understand that psychedelics may mediate their therapeutic effects through multi-faceted mechanisms, such as the modulation of brain network activity, neuronal plasticity, neuroendocrine function, glial cell regulation, epigenetic processes, and the gut-brain axis. This review provides a framework supporting the implementation of a multi-faceted approach, incorporating in silico, in vitro and in vivo modeling, to aid in the comprehensive understanding of the physiological effects of psychedelics and their potential for clinical application beyond the treatment of psychiatric disorders. We also provide an overview of the literature supporting the potential utility of psychedelics for the treatment of brain injury (e.g., stroke and traumatic brain injury), neurodegenerative diseases (e.g., Parkinson's and Alzheimer's diseases), and gut-brain axis dysfunction associated with psychiatric disorders (e.g., generalized anxiety disorder and major depressive disorder). To move the field forward, we outline advantageous experimental frameworks to explore these and other novel applications for psychedelics.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Integrated Whole-Transcriptome Profiling and Bioinformatics Analysis of the Polypharmacological Effects of Ganoderic Acid Me in Colorectal Cancer Treatment

Ganoderic acid Me (GA-Me) is a natural bioactive compound derived from Ganoderma lucidum. Our present results suggested that GA-Me inhibited proliferation, induced DNA fragmentation and significantly activated caspase-9 and caspase-3 in HCT116 cells. As shown in our previous studies, GA-Me targets several genes to prevent cancer, including colorectal cancer (CRC). Thus, we hypothesized that GA-Me might be a multitarget ligand against cancer. However, its exact mechanism in CRC remains unclear. Here, whole-transcriptome sequencing was employed to assess the long noncoding RNA (lncRNA), circular RNA (circRNA), microRNA (miRNA), and messenger RNA (mRNA) profiles of GA-Me-treated HCT116 cells. In total, 1572 differentially expressed (DE) lncRNAs, 123 DEcircRNAs, 87 DEmiRNAs, and 1508 DEmRNAs were identified. DCBLD2 and RAPGEF5 were validated as two core mRNAs in the DElncRNA, DEcircRNA, and DEmiRNA networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed the biological functions and potential mechanisms of TCONS-00008997, XR-925056.2, circRNA-07908, hsa-miR-100-3p, hsa-miR-1257, hsa-miR-3182, NAV3, ADAM20, and STARD4, which were altered after GA-Me treatment. The regulatory relationships of the XR-925056.2-hsa-miR-3182-NAV3/ADAM20/STARD4, circRNA-07908|Chr22:38986298-39025349-hsa-miR-3182-NAV3/ADAM20, ENST00000414039/ENST00000419190-novel874_mature-MMP9 and circRNA-00314|Chr1:35470863-35479212/circRNA-05460|Chr17:72592203-72649268-novel874_mature-MMP9 immune-regulatory networks involved both noncoding RNAs (ncRNAs) and mRNAs. Molecular docking studies showed that Zn2+ and the His201, His205, His211, Glu202, and Ala165 residues of MMP2 contributed to its high affinity for GA-Me. Zn2+ and the Glu402 and Gly186 residues of MMP9 are important for its interaction with GA-Me. Our results suggested and confirmed that GA-Me is a potential multitarget lead compound for CRC treatment with unique polypharmacological advantages.

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.

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.

Salvinorin A Does Not Affect Seizure Threshold in Mice

The κ-opioid receptor has recently gained attention as a new molecular target in the treatment of many psychiatric and neurological disorders including epilepsy. Salvinorin A is a potent plant-derived hallucinogen that acts as a highly selective κ-opioid receptor agonist. It has unique structure and pharmacological properties, but its influence on seizure susceptibility has not been studied so far. Therefore, the aim of the present study was to investigate the effect of salvinorin A on seizure thresholds in three acute seizure tests in mice. We also examined its effect on muscular strength and motor coordination. The obtained results showed that salvinorin A (0.1-10 mg/kg, i.p.) did not significantly affect the thresholds for the first myoclonic twitch, generalized clonic seizure, or forelimb tonus in the intravenous pentylenetetrazole seizure threshold test in mice. Likewise, it failed to affect the thresholds for tonic hindlimb extension and psychomotor seizures in the maximal electroshock- and 6 Hz-induced seizure threshold tests, respectively. Moreover, no changes in motor coordination (assessed in the chimney test) or muscular strength (assessed in the grip-strength test) were observed. This is a preliminary report only, and further studies are warranted to better characterize the effects of salvinorin A on seizure and epilepsy.

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.

Current computational methods for predicting protein interactions of natural products

Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.

Structural insight into the serotonin (5-HT) receptor family by molecular docking, molecular dynamics simulation and systems pharmacology analysis

Serotonin (5-HT) receptors are proteins involved in various neurological and biological processes, such as aggression, anxiety, appetite, cognition, learning, memory, mood, sleep, and thermoregulation. They are commonly associated with drug abuse and addiction due to their importance as targets for various pharmaceutical and recreational drugs. However, due to a high sequence similarity/identity among 5-HT receptors and the unavailability of the 3D structure of the different 5-HT receptor, no report was available so far regarding the systematical comparison of the key and selective residues involved in the binding pocket, making it difficult to design subtype-selective serotonergic drugs. In this work, we first built and validated three-dimensional models for all 5-HT receptors based on the existing crystal structures of 5-HT_1B, 5-HT_2B, and 5-HT_2C. Then, we performed molecular docking studies between 5-HT receptors agonists/inhibitors and our 3D models. The results from docking were consistent with the known binding affinities of each model. Sequentially, we compared the binding pose and selective residues among 5-HT receptors. Our results showed that the affinity variation could be potentially attributed to the selective residues located in the binding pockets. Moreover, we performed MD simulations for 12 5-HT receptors complexed with ligands; the results were consistent with our docking results and the reported data. Finally, we carried out off-target prediction and blood-brain barrier (BBB) prediction for Captagon using our established hallucinogen-related chemogenomics knowledgebase and in-house computational tools, with the hope to provide more information regarding the use of Captagon. We showed that 5-HT_2C, 5-HT_5A, and 5-HT₇ were the most promising targets for Captagon before metabolism. Overall, our findings can provide insights into future drug discovery and design of medications with high specificity to the individual 5-HT receptor to decrease the risk of addiction and prevent drug abuse.

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

The United States of America is fighting against one of its worst-ever drug crises. Over 900 people a week die from opioid- or heroin-related overdoses, while millions more suffer from opioid prescription addiction. Recently, drug overdoses caused by fentanyl-laced cocaine specifically are on the rise. Due to drug synergy and an increase in side effects, polydrug addiction can cause more risk than addiction to a single drug. In the present work, we systematically analyzed the overdose and addiction mechanism of cocaine and fentanyl. First, we applied our established chemogenomics knowledgebase and machine-learning-based methods to map out the potential and known proteins, transporters, and metabolic enzymes and the potential therapeutic target(s) for cocaine and fentanyl. Sequentially, we looked into the detail of (1) the addiction to cocaine and fentanyl by binding to the dopamine transporter and the μ opioid receptor (DAT and μOR, respectively), (2) the potential drug-drug interaction of cocaine and fentanyl via p-glycoprotein (P-gp) efflux, (3) the metabolism of cocaine and fentanyl in CYP3A4, and (4) the physiologically based pharmacokinetic (PBPK) model for two drugs and their drug-drug interaction at the absorption, distribution, metabolism, and excretion (ADME) level. Finally, we looked into the detail of JWH133, an agonist of cannabinoid 2-receptor (CB2) with potential as a therapy for cocaine and fentanyl overdose. All these results provide a better understanding of fentanyl and cocaine polydrug addiction and future drug abuse prevention.

DAKB-GPCRs: An Integrated Computational Platform for Drug Abuse Related GPCRs

Drug abuse (DA) or drug addiction is a complicated brain disorder which is commonly considered as neurobiological impairments caused by both genetic factors and environmental effects. Among DA-related targets, G protein-coupled receptors (GPCRs) play an important role in DA therapy. However, only 52 GPCRs have been published with crystal structures in the recent two decades. In the effort to overcome the limitations of crystal structure and conformational diversity of GPCRs, we built homology models and performed conformational searches by molecular dynamics (MD) simulation. To accelerate and facilitate the drug abuse research, we construct a DA-related GPCR-specific chemogenomics knowledgebase (KB) (DAKB-GPCRs) for its research that can be implemented with our established and novel chemogenomics tools as well as algorithms for data analysis and visualization. Our established TargetHunter and HTDocking tools, as well as our novel tools that include target classification and Spider Plot, are compiled into the platform. Our DAKB-GPCRs provides the following results for a query compound: (1) blood-brain barrier (BBB) plot via our BBB predictor, (2) docking scores via HTDocking, (3) similarity score via TargetHunter, (4) target classification via machine learning methods that utilize both docking scores and similarity scores, and (5) a drug-target interaction network via Spider Plot.

Insight of Captagon Abuse by Chemogenomics Knowledgebase-guided Systems Pharmacology Target Mapping Analyses

Captagon, known by its genetic name Fenethylline, is an addictive drug that complicates the War on Drugs. Captagon has a strong CNS stimulating effect than its primary metabolite, Amphetamine. However, multi-targets issues associated with the drug and metabolites as well as its underlying mechanisms have not been fully defined. In the present work, we applied our established drug-abuse chemogenomics-knowledgebase systems pharmacology approach to conduct targets/off-targets mapping (SP-Targets) investigation of Captagon and its metabolites for hallucination addiction, and also analyzed the cell signaling pathways for both Amphetamine and Theophylline with data mining of available literature. Of note, Amphetamine, an agonist for trace amine-associated receptor 1 (TAAR1) with enhancing dopamine signaling (increase of irritability, aggression, etc.), is the main cause of Captagon addiction; Theophylline, an antagonist that blocks adenosine receptors (e.g. A2aR) in the brain responsible for restlessness and painlessness, may attenuate the behavioral sensitization caused by Amphetamine. We uncovered that Theophylline's metabolism and elimination could be retarded due to competition and/or blockage of the CYP2D6 enzyme by Amphetamine; We also found that the synergies between these two metabolites cause Captagon's psychoactive effects to act faster and far more potently than those of Amphetamine alone. We carried out further molecular docking modeling and molecular dynamics simulation to explore the molecular interactions between Amphetamine and Theophylline and their important GPCRs targets, including TAAR1 and adenosine receptors. All of the systems pharmacology analyses and results will shed light insight into a better understanding of Captagon addiction and future drug abuse prevention.

Chemogenomics Systems Pharmacology Mapping of Potential Drug Targets for Treatment of Traumatic Brain Injury

Traumatic brain injury (TBI) is associated with high mortality and morbidity. Though the death rate of initial trauma has dramatically decreased, no drug has been developed to effectively limit the progression of the secondary injury caused by TBI. TBI appears to be a predisposing risk factor for Alzheimer's disease (AD), whereas the molecular mechanisms remain unknown. In this study, we have conducted a research investigation of computational chemogenomics systems pharmacology (CSP) to identify potential drug targets for TBI treatment. TBI-induced transcriptional profiles were compared with those induced by genetic or chemical perturbations, including drugs in clinical trials for TBI treatment. The protein-protein interaction network of these predicted targets were then generated for further analyses. Some protein targets when perturbed, exhibit inverse transcriptional profiles in comparison with the profiles induced by TBI, and they were recognized as potential therapeutic targets for TBI. Drugs acting on these targets are predicted to have the potential for TBI treatment if they can reverse the TBI-induced transcriptional profiles that lead to secondary injury. In particular, our results indicated that TRPV4, NEUROD1, and HPRT1 were among the top therapeutic target candidates for TBI, which are congruent with literature reports. Our analyses also suggested the strong associations between TBI and AD, as perturbations on AD-related genes, such as APOE, APP, PSEN1, and MAPT, can induce similar gene expression patterns as those of TBI. To the best of our knowledge, this is the first CSP-based gene expression profile analyses for predicting TBI-related drug targets, and the findings could be used to guide the design of new drugs targeting the secondary injury caused by TBI.

Cutting-Edge Search for Safer Opioid Pain Relief: Retrospective Review of Salvinorin A and Its Analogs

Over the years, pain has contributed to low life quality, poor health, and economic loss. Opioids are very effective analgesic drugs for treating mild, moderate, or severe pain. Therapeutic application of opioids has been limited by short and long-term side effects. These side effects and opioid-overuse crisis has intensified interest in the search for new molecular targets and drugs. The present review focuses on salvinorin A and its analogs with the aim of exploring their structural and pharmacological profiles as clues for the development of safer analgesics. Ethnopharmacological reports and growing preclinical data have demonstrated the antinociceptive effect of salvinorin A and some of its analogs. The pharmacology of analogs modified at C-2 dominates the literature when compared to the ones from other positions. The distinctive binding affinity of these analogs seems to correlate with their chemical structure and in vivo antinociceptive effects. The high susceptibility of salvinorin A to chemical modification makes it an important pharmacological tool for cellular probing and developing analogs with promising analgesic effects. Additional research is still needed to draw reliable conclusions on the therapeutic potential of salvinorin A and its analogs.

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

The efficacy and safety of cilostazol as an alternative to aspirin in Chinese patients with aspirin intolerance after coronary stent implantation: a combined clinical study and computational system pharmacology analysis

Dual antiplatelet therapy (DAT) with aspirin and clopidogrel is the standard regimen to achieve rapid platelet inhibition and prevent thrombotic events. Currently, little information is available regarding alternative antiplatelet therapy in patients with an allergy or intolerance to aspirin. Although cilostazol is already a common alternative to aspirin in clinical practice in China, its efficacy and safety remain to be determined. We retrospectively analyzed 613 Chinese patients who had undergone primary percutaneous coronary intervention (PCI). Among them, 405 patients received standard DAT (aspirin plus clopidogrel) and 205 patients were identified with intolerance to aspirin and received alternative DAT (cilostazol plus clopidogrel). There were no significant differences between the two groups in their baseline clinical characteristics. The main outcomes of the study included major adverse cardiac events (MACEs) and bleeding events during 12 months of follow-up. The MACEs endpoint was reached in 10 of 205 patients treated with cilostazol (4.9%) and in 34 of 408 patients treated with aspirin (8.3%). No statistically significant difference was observed in MACEs between the two groups. However, patients in the cilostazol group had less restenosis than did patients in the aspirin group (1.5% vs 4.9%, P=0.035). The occurrence of bleeding events tended to be lower in the cilostazol group (0.49% vs 2.7%, P=0.063). These clinical observations were further analyzed using network system pharmacology analysis, and the outcomes were consistent with clinical observations and preclinical data reports. We conclude that in Chinese patients with aspirin intolerance undergoing coronary stent implantation, the combination of clopidogrel with cilostazol may be an efficacious and safe alternative to the standard DAT regimen.