SNP-based analysis of neuroactive ligand-receptor interaction pathways implicates PGE2 as a novel mediator of antipsychotic treatment response: data from the CATIE study

Integrated network pharmacology analysis and experimental validation to investigate the mechanism of Flavan-3-ols and aromatic resins in depression

The present investigation delved into the pharmacological mechanisms underlying the management of depression through Flavan-3-ols and Aromatic Resins, employing in silico and in vivo methodologies. Network pharmacology was utilized to identify targets associated with the antidepressant activity of Flavan-3-ols and Aromatic Resins. Protein-protein interaction and KEGG analyses were conducted to enrich and explore key pathways. Molecular docking and simulation studies were executed to assess the targets. The antidepressant effects were studied using the Forced Swim Test and Tail Suspension Test on both unstressed mice and those subjected to the chronic unpredictable mild stress (CUMS) paradigm. The Compound-Target network analysis revealed a substantial impact of the components on numerous targets, with 332 nodes and 491 edges. Protein-protein interaction analysis indicated significant interactions with targets implicated in depression. KEGG analysis highlighted major pathways, including neuroactive ligand-receptor interaction, dopaminergic synapse, and long-term depression. Docking studies on EGCG demonstrated binding energies of -7.2 kcal/mol for serotonin 1 A (5-HT1A), -7.9 kcal/mol for D2, and - 9.6 kcal/mol for MOA-A. Molecular dynamics simulation indicated minute fluctuation, hence suggesting stable complexes formed between small molecules and proteins. The combination of Flavan-3-ols and Aromatic Resins significantly increased mobility time (p < 0.05) in the Forced Swim Test and Tail Suspension Test, while significantly decreasing immobility time and time freezing (p < 0.05) in both unstressed and CUMS mice. This study demonstrated the antidepressant characteristics of Flavan-3-ols and Aromatic Resins, underscoring the need for further research to develop a novel antidepressant medication.

Network pharmacology combined with molecular docking and experimental verification to elucidate the effect of flavan-3-ols and aromatic resin on anxiety

This study investigated the potential anxiolytic properties of flavan-3-ols and aromatic resins through a combined computational and experimental approach. Network pharmacology techniques were utilized to identify potential anxiolytic targets and compounds by analyzing protein-protein interactions and KEGG pathway data. Molecular docking and simulation studies were conducted to evaluate the binding interactions and stability of the identified targets. Behavioral tests, including the elevated plus maze test, open field test, light-dark test, actophotometer, and holeboard test, were used to assess anxiolytic activity. The compound-target network analysis revealed complex interactions involving 306 nodes and 526 edges, with significant interactions observed and an average node degree of 1.94. KEGG pathway analysis highlighted pathways such as neuroactive ligand-receptor interactions, dopaminergic synapses, and serotonergic synapses as being involved in anxiety modulation. Docking studies on EGCG (Epigallocatechin gallate) showed binding energies of -9.5 kcal/mol for MAOA, -9.2 kcal/mol for SLC6A4, and -7.4 kcal/mol for COMT. Molecular dynamic simulations indicated minimal fluctuations, suggesting the formation of stable complexes between small molecules and proteins. Behavioral tests demonstrated a significant reduction in anxiety-like behavior, as evidenced by an increased number of entries into and time spent in the open arm of the elevated plus maze test, light-dark test, open field center activity, hole board head dips, and actophotometer beam interruptions (p < 0.05 or p < 0.01). This research provides a comprehensive understanding of the multi-component, multi-target, and multi-pathway intervention mechanisms of flavan-3-ols and aromatic resins in anxiety treatment. Integrated network and behavioral analyses collectively support the anxiolytic potential of these compounds and offer valuable insights for future research in this area.

Network pharmacology -based study on the mechanism of traditional Chinese medicine in the treatment of glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is one of the most common primary malignant brain tumors. Yi Qi Qu Yu Jie Du Fang (YYQQJDF) is a traditional Chinese medicine (TCM) prescription for GBM. The present study aimed to use a network pharmacology method to analyze the underlying mechanism of YQQYJDF in treating GBM.

METHODS

GBM sample data, active ingredients and potential targets of YQQYJDF were obtained from databases. R language was used to screen differentially expressed genes (DEGs) between GBM tissues and normal tissues, and to perform enrichment analysis and weighted gene coexpression network analysis (WGCNA). The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to perform a protein‒protein interaction (PPI) analysis. A Venn diagram was used to obtain the core target genes of YQQYJDF for GBM treatment. Molecular docking was used to verify the binding between the active ingredient molecules and the proteins corresponding to the core target genes. Cell proliferation assays and invasion assays were used to verify the effect of active ingredients on the proliferation and invasion of glioma cells.

RESULTS

A total of 73 potential targets of YQQYJDF in the treatment of GBM were obtained. Enrichment analyses showed that the biological processes and molecular functions involved in these target genes were related to the activation of the G protein-coupled receptor (GPCR) signaling pathway and the regulation of hypoxia. The neuroactive ligand‒receptor pathway, the cellular senescence pathway, the calcium signaling pathway, the cell cycle pathway and the p53 signaling pathway might play important roles. Combining the results of WGCNA and PPI analysis, five core target genes and their corresponding four core active ingredients were screened. Molecular docking indicated that the core active ingredient molecules and the proteins corresponding to the core target genes had strong binding affinities. Cell proliferation and invasion assays showed that the core active ingredients of YQQYJDF significantly inhibited the proliferation and invasion of glioma cells (P < 0.01).

CONCLUSIONS

The present study predicted the possible active ingredients and targets of YQQYJDF in treating GBM, and analyzed its possible mechanism. These results may provide a basis and ideas for further research.

Combination of Transcriptomics and Proteomics Reveals Differentially Expressed Genes and Proteins in the Skin of EDAR Gene-Targeted and Wildtype Cashmere Goats

Cashmere goats play a pivotal role in the animal hair industry and are economically valuable. Cashmere is produced through the periodic growth of secondary hair follicles. To improve their yield of cashmere, the regulatory mechanisms of cashmere follicle growth and development need to be analysed. Therefore, in this study, EDAR gene-targeted cashmere goats were used as an animal model to observe the phenotypic characteristics of abnormal hair growth and development at the top of the head. Transcriptomic and proteomic techniques were used to screen for differentially expressed genes and proteins. In total, 732 differentially expressed genes were identified, including 395 upregulated and 337 downregulated genes. In addition, 140 differentially expressed proteins were identified, including 69 upregulated and 71 downregulated proteins. These results provide a research target for elucidating the mechanism through which EDAR regulates hair follicle growth in cashmere goats. It also enriches the available data on the regulatory network involved in hair follicle growth.

Systematic Selection Signature Analysis of Chinese Gamecocks Based on Genomic and Transcriptomic Data

Selection pressures driven by natural causes or human interference are key factors causing genome variants and signatures of selection in specific regions of the genome. Gamecocks were bred for cockfighting, presenting pea-combs, larger body sizes, stronger limbs, and higher levels of aggression than other chickens. In this study, we aimed to explore the genomic differences between Chinese gamecocks and commercial, indigenous, foreign, and cultivated breeds by detecting the regions or sites under natural or artificial selection using genome-wide association studies (GWAS), genome-wide selective sweeps based on the genetic differentiation index (F_ST), and transcriptome analyses. Ten genes were identified using GWAS and F_ST: gga-mir-6608-1, SOX5, DGKB, ISPD, IGF2BP1, AGMO, MEOX2, GIP, DLG5, and KCNMA1. The ten candidate genes were mainly associated with muscle and skeletal development, glucose metabolism, and the pea-comb phenotype. Enrichment analysis results showed that the differentially expressed genes between the Luxi (LX) gamecock and Rhode Island Red (RIR) chicken were mainly related to muscle development and neuroactive-related pathways. This study will help to understand the genetic basis and evolution of Chinese gamecocks and support the further use of gamecocks as an excellent breeding material from a genetic perspective.

Molecular Mechanism of the Saposhnikovia divaricata–Angelica dahurica Herb Pair in Migraine Therapy Based on Network Pharmacology and Molecular Docking

Objective. This work studied the molecular mechanism of the Saposhnikovia divaricata–Angelica dahurica herb pair (SAHP) in migraine treatment. Methods. The active ingredients of drugs were screened, and potential targets were predicted by the Traditional Chinese Medicine Systems Pharmacology (TCMSP), TCMID, ETCM, and other databases. Migraine-related targets were obtained by harnessing the GeneCards, DrugBank, OMIM, TTD, and other databases. The protein-protein interaction (PPI) network was constructed with STRING software by performing a Venn analysis with bioinformatics. Gene Ontology (GO) functional enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed with the Metascape platform. The component-target-pathway (C-T-P) network was constructed with Cytoscape 3.7.2 software, and molecular docking was assessed with AutoDockVina software. Results. A total of 183 relevant targets and 39 active ingredients in migraine therapy were obtained from SAHP. The active ingredients and targets were screened according to topological parameters: wogonin, anomalin, imperatorin, prangenin, 2-linoleoylglycerol, and methylenetanshinquinone were identified as key active ingredients. PTGS2, PIK3CA, PIK3CB, PIK3CD, F2, and AR were identified as key targets. The molecular docking results demonstrated high binding activity between the key active ingredients and key targets. A total of 20 important signaling pathways, including neural signaling pathways, calcium signaling pathways, pathways in cancer, cAMP signaling pathways, and PI3K-Akt signaling pathways, were obtained through enrichment analysis. Conclusion. Migraine with SAHP is mainly treated through anti-inflammatory and analgesic effects. The herb pair can be used for migraine using “multicomponent, multitarget, and multipathway” approaches.

Network Pharmacology and Experimental Validation to Investigate the Antidepressant Potential of Atractylodes lancea (Thunb.) DC

Atractylodes lancea (Thunb.) DC. (AL) has been indicated in traditional prescriptions for the treatment of depression. However, the mechanism of action of AL in the treatment of depression is still unclear. This study aimed to investigate the antidepressant potential of AL using network pharmacology, molecular docking, and animal experiments. The active components of AL were retrieved from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), and the depression-related targets were screened through the DisGeNET database. Overlapping targets of AL and depression were selected and analyzed. Ten active compounds of AL showed anti-depressant potential, including stigmasterol, 3β-acetoxyatractylone, wogonin, β-sitosterol, selina-4(14),7(11)-dien-8-one, atractylenolide I, atractylenolide II, atractylenolide III, patchoulene, and cyperene. These compounds target 28 potential antidepressant genes/proteins. Gene Ontology (GO) enrichment analysis revealed that the potential targets might directly influence neural cells and regulate neuroinflammation and neurotransmitter-related processes. The potential Kyoto Encyclopedia Genes and Genomes (KEGG) pathways for the antidepressant effects of AL include neuroactive ligand-receptor interactions, calcium signaling pathways, dopaminergic synapse, interleukin (IL)-17 signaling pathways, and the pathways of neurodegeneration. IL-6, nitric oxide synthase 3 (NOS), solute carrier family 6 member 4 (SLC6A4), estrogen receptor (ESR1), and tumor necrosis factor (TNF) were the most important proteins in the protein-protein interaction network and these proteins showed high binding affinities with the corresponding AL compounds. AL showed an antidepressant effect in mice by decreasing immobility time in the tail suspension test and increasing the total contact number in the social interaction test. This study demonstrated the antidepressant potential of AL, which provides evidence for pursuing further studies to develop a novel antidepressant.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data

Two common psychiatric disorders, schizophrenia (SCZ) and bipolar disorder (BP), confer lifelong disability and collectively affect 2% of the world population. Because the diagnosis of psychiatry is based only on symptoms, developing more effective methods for the diagnosis of psychiatric disorders is a major international public health priority. Furthermore, SCZ and BP overlap considerably in terms of symptoms and risk genes. Therefore, the clarity of the underlying etiology and pathology remains lacking for these two disorders. Although many studies have been conducted, a classification model with higher accuracy and consistency was found to still be necessary for accurate diagnoses of SCZ and BP. In this study, a comprehensive dataset was combined from five independent transcriptomic studies. This dataset comprised 120 patients with SCZ, 101 patients with BP, and 149 healthy subjects. The partial least squares discriminant analysis (PLS-DA) method was applied to identify the gene signature among multiple groups, and 341 differentially expressed genes (DEGs) were identified. Then, the disease relevance of these DEGs was systematically performed, including (α) the great disease relevance of the identified signature, (β) the hub genes of the protein-protein interaction network playing a key role in psychiatric disorders, and (γ) gene ontology terms and enriched pathways playing a key role in psychiatric disorders. Finally, a popular multi-class classifier, support vector machine (SVM), was applied to construct a novel multi-class classification model using the identified signature for SCZ and BP. Using the independent test sets, the classification capacity of this multi-class model was assessed, which showed this model had a strong classification ability.

Identification of hub genes and functional modules in colon adenocarcinoma based on public databases by bioinformatics analysis

Background

Colon adenocarcinoma (COAD) is one of the most common cancers in the world. Although an extensive effort has been made to elucidate its pathogenesis, the underlying molecular mechanisms and genetic characteristics remain elusive.

Methods

In this study, protein-coding transcript expression profiles of COAD were downloaded from the Cancer RNA-Seq Nexus (CRN) database. They were then integrated to identify the overlapping transcripts expressed in every COAD RNA sequencing (RNA-seq) subset. The functional annotation of these overlapping genes (OLGs) involved noting their biological process (BP), cellular components (CC), molecular function (MF) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway in the Database for Annotation, Visualization and Integrated Discovery (DAVID). Protein-protein interaction (PPI) networks were then constructed and analyzed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape 3.8.2.

Results

A total of 10 hub genes and 3 functional modules were screened by the plugin cytoHubba and MCODE, respectively. The plugin ClueGO and DAVID were used for the functional enrichment analyses of both hub genes and modules. The expression of hub genes was verified through the gene expression profiling interactive analysis (GEPIA) database. Survival analysis of the hub genes revealed that low expressions of ADCY5, GNG2, and PTPRC were significantly associated with an improved COAD prognosis. Furthermore, the expression level of ADCY5 in stages I/II was lower than that in stages III/IV, which seems to explain why the low expression of ADCY5 results in a better prognosis.

Conclusions

The identification of hub genes, functional modules, and pathways have the potential to improve our understanding of the causes and underlying molecular events of COAD. The hub gene ADCY5 could also be a prognostic monitoring indicator or therapeutic target in the treatment of COAD.

Toxic effects of Decabromodiphenyl ether (BDE-209) on thyroid of broiler chicks by transcriptome profile analysis

The wide usage of decabromodiphenyl ether (BDE-209) results in its increasing occurrence in the environment and increasing attention in regard to human and animal health. BDE-209 is an endocrine disruptor for hypothyroidism, but the toxicity mechanism is unclear. Here, the histopathology and transcriptome sequencing of thyroid tissue from broiler chicks were investigated by supplemental feeding with different concentrations of BDE-209 for 42 days (0-4 g/kg in basal diet), followed by determining the levels of thyroid hormones in serum. The results showed ruptured and even hyperplastic follicular epithelial cells in the thyroid, and a total of 501 differentially expressed genes were screened out: 222 upregulated and 279 downregulated. Based on the Kyoto Encyclopedia of Genes and Genomes database, neuroactive ligand-receptor interaction pathway was significantly enriched, and α1D-adrenergic receptor, follicle-stimulating hormone receptor, thyroid stimulating hormone receptor, and somatostatin receptor type 2 were shown to be candidate biomarkers. Thyroxine was a possible biomarker due to clear reduction in serum and significant correlation with exposure concentrations. These results suggested that oral intake of BDE-209 can cause structural injuries and even hyperplasia, and affect gene transcription involved in the neuroactive ligand-receptor interaction pathway of thyroid, as well as thyroid hormones in serum.

Study on the Sleep-Improvement Effects of Hemerocallis citrina Baroni in Drosophila melanogaster and Targeted Screening to Identify Its Active Components and Mechanism

Hemerocallis citrina Baroni (HC) is an edible plant in Asia, and it has been traditionally used for sleep-improvement. However, the bioactive components and mechanism of HC in sleep-improvement are still unclear. In this study, the sleep-improvement effect of HC hydroalcoholic extract was investigated based on a caffeine-induced insomnia model in Drosophila melanogaster (D. melanogaster), and the ultrahigh-performance liquid chromatography coupled with electrospray ionization quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-ESI-Orbitrap-MS) and network pharmacology strategy were further combined to screen systematically the active constituents and mechanism of HC in sleep-improvement. The results suggested HC effectively regulated the number of nighttime activities and total sleep time of D. melanogaster in a dose-dependent manner and positively regulated the sleep bouts and sleep duration of D. melanogaster. The target screening suggested that quercetin, luteolin, kaempferol, caffeic acid, and nicotinic acid were the main bioactive components of HC in sleep-improvements. Moreover, the core targets (Akt1, Cat, Ple, and Sod) affected by HC were verified by the expression of the mRNA of D. melanogaster. In summary, this study showed that HC could effectively regulate the sleep of D. melanogaster and further clarifies the multi-component and multi-target features of HC in sleep-improvement, which provides a new insight for the research and utilization of HC.

Characteristic Volatile Composition of Seven Seaweeds from the Yellow Sea of China

Plant volatile organic compounds (VOCs) represent a relatively wide class of secondary metabolites. The VOC profiles of seven seaweeds (Grateloupia filicina, Polysiphonia senticulosa, Callithamnion corymbosum, Sargassum thunbergii, Dictyota dichotoma, Enteromorpha prolifera and Ulva lactuca) from the Yellow Sea of China were investigated using multifiber headspace solid phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC–MS), among them, the VOCs of three red algae Grateloupia filicina, Polysiphonia senticulosa, and Callithamnion corymbosum were first reported. Principal component analysis (PCA) was used to disclose characteristic categories and molecules of VOCs and network pharmacology was performed to predict potential biomedical utilization of candidate seaweeds. Aldehyde was found to be the most abundant VOC category in the present study and (E)-β-ionone was the only compound found to exist in all seven seaweeds. The chemical diversity of aldehydes in E. prolifera suggest its potential application in chemotaxonomy and hinted that divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber is more suitable for aldehyde extraction. VOCs in D. dichotoma were characterized as sesquiterpenes and diterpenes and the most relevant pharmacological pathway was the neuroactive ligand–receptor interaction pathway, which suggests that D. dichotoma may have certain preventive and therapeutic values in cancer, especially in lung cancer, in addition to neuropsychiatric diseases.

Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses

Background

A typical task in bioinformatics consists of identifying which features are associated with a target outcome of interest and building a predictive model. Automated machine learning (AutoML) systems such as the Tree-based Pipeline Optimization Tool (TPOT) constitute an appealing approach to this end. However, in biomedical data, there are often baseline characteristics of the subjects in a study or batch effects that need to be adjusted for in order to better isolate the effects of the features of interest on the target. Thus, the ability to perform covariate adjustments becomes particularly important for applications of AutoML to biomedical big data analysis.

Results

We developed an approach to adjust for covariates affecting features and/or target in TPOT. Our approach is based on regressing out the covariates in a manner that avoids ‘leakage’ during the cross-validation training procedure. We describe applications of this approach to toxicogenomics and schizophrenia gene expression data sets. The TPOT extensions discussed in this work are available at https://github.com/EpistasisLab/tpot/tree/v0.11.1-resAdj.

Conclusions

In this work, we address an important need in the context of AutoML, which is particularly crucial for applications to bioinformatics and medical informatics, namely covariate adjustments. To this end we present a substantial extension of TPOT, a genetic programming based AutoML approach. We show the utility of this extension by applications to large toxicogenomics and differential gene expression data. The method is generally applicable in many other scenarios from the biomedical field.

Enhancing and Complementary Mechanisms of Synergistic Action of Acori Tatarinowii Rhizoma and Codonopsis Radix for Alzheimer's Disease Based on Systems Pharmacology

Materials and Methods

In this study, a systems pharmacology-based strategy was used to elucidate the synergistic mechanism of Acori Tatarinowii Rhizoma and Codonopsis Radix for the treatment of AD. This novel systems pharmacology model consisted of component information, pharmacokinetic analysis, and pharmacological data. Additionally, the related pathways were compressed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the organ distributions were determined in the BioGPS bank.

Results

Sixty-eight active ingredients with suitable pharmacokinetic profiles and biological activities were selected through ADME screening in silico. Based on 62 AD-related targets, such as APP, CHRM1, and PTGS1, systematic analysis showed that these two herbs were mainly involved in the PI3K-Akt signaling pathway, MAPK signaling pathway, neuroactive ligand-receptor interaction, and fluid shear stress and atherosclerosis, indicating that they had a synergistic effect on AD. However, ATR acted on the KDR gene, while CR acted on IGF1R, MET, IL1B, and CHUK, showing that they also had complementary effects on AD. The ingredient contribution score involved 29 ingredients contributing 90.14% of the total contribution score of this formula for AD treatment, which emphasized that the effective therapeutic effects of these herbs for AD were derived from both ATR and CR, not a single herb. Organ distribution showed that the targets of the active ingredients were mainly located in the whole blood, the brain, and the muscle, which are associated with AD.

Conclusions

In sum, our findings suggest that the systems pharmacology methods successfully revealed the synergistic and complementary mechanisms of ATR and CR for the treatment of AD.

An Analysis of the Anti-Neuropathic Effects of Qi She Pill Based on Network Pharmacology

Background

Qi She Pill (QSP) is a traditional prescription for the treatment of neuropathic pain (NP) that is widely used in China. However, no network pharmacology studies of QSP in the treatment of NP have been conducted to date.

Objective

To verify the potential pharmacological effects of QSP on NP, its components were analyzed via target docking and network analysis, and network pharmacology methods were used to study the interactions of its components.

Materials and Methods

Information on pharmaceutically active compounds in QSP and gene information related to NP were obtained from public databases, and a compound-target network and protein-protein interaction network were constructed to study the mechanism of action of QSP in the treatment of NP. The mechanism of action of QSP in the treatment of NP was analyzed via Gene Ontology (GO) biological process annotation and Kyoto Gene and Genomics Encyclopedia (KEGG) pathway enrichment, and the drug-like component-target-pathway network was constructed.

Results

The compound-target network contained 60 compounds and 444 corresponding targets. The key active compounds included quercetin and beta-sitosterol. Key targets included PTGS2 and PTGS1. The protein-protein interaction network of the active ingredients of QSP in the treatment of NP featured 48 proteins, including DRD2, CHRM, β2-adrenergic receptor, HTR2A, and calcitonin gene-related peptide. In total, 53 GO entries, including 35 biological process items, 7 molecular function items, and 11 cell related items, were identified. In addition, eight relevant (KEGG) pathways were identified, including calcium, neuroactive ligand-receptor interaction, and cAMP signaling pathways.

Conclusion

Network pharmacology can help clarify the role and mechanism of QSP in the treatment of NP and provide a foundation for further research.

A joint study of whole exome sequencing and structural MRI analysis in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide and influenced by both environmental and genetic factors. Genetic studies of MDD have focused on common variants and have been constrained by the heterogeneity of clinical symptoms.

METHODS

We sequenced the exome of 77 cases and 245 controls of Han Chinese ancestry and scanned their brain. Burden tests of rare variants were performed first to explore the association between genes/pathways and MDD. Secondly, parallel Independent Component Analysis was conducted to investigate genetic underpinnings of gray matter volume (GMV) changes of MDD.

RESULTS

Two genes (CSMD1, p = 5.32×10-6; CNTNAP5, p = 1.32×10-6) and one pathway (Neuroactive Ligand Receptor Interactive, p = 1.29×10-5) achieved significance in burden test. In addition, we identified one pair of imaging-genetic components of significant correlation (r = 0.38, p = 9.92×10-6). The imaging component reflected decreased GMV in cases and correlated with intelligence quotient (IQ). IQ mediated the effects of GMV on MDD. The genetic component enriched in two gene sets, namely Singling by G-protein coupled receptors [false discovery rate (FDR) q = 3.23×10-4) and Alzheimer Disease Up (FDR q = 6.12×10-4).

CONCLUSIONS

Both rare variants analysis and imaging-genetic analysis found evidence corresponding with the neuroinflammation and synaptic plasticity hypotheses of MDD. The mediation of IQ indicates that genetic component may act on MDD through GMV alteration and cognitive impairment.

Systems pharmacology approach to investigate the molecular mechanisms of herb Rhodiola rosea L. radix

Rhodiola rosea L. radix (RRL) is one of the most popular medical herb which has been widely used for the treatment of different diseases effectively, including cardiovascular diseases and nerve system diseases. However, due to the multiple compounds in RRL, the underlying molecular mechanisms of RRL are remained unclear. To decipher the action mechanisms of RRL from a systematic perspective, a systems pharmacology approach integrated absorption, distribution, metabolism, and excretion (ADME) system, drug targeting, and network analysis was introduced. First, by the ADME screening system and the target fishing process, 56 potential active compounds and 62 targets were obtained, respectively. In addition, compound-target network demonstrated that most compounds interacted with multiple targets, indicating that RRL may enhance its therapeutic effects probably through hitting on multiple targets in a holistic level. Moreover, target-pathway network and gene ontology analysis showed that multiple targets of RRL were involved in several biological pathways, i.e. Neuroactive ligand-receptor interaction, calcium signaling pathway, adrenergic signaling in cardiomyocytes, and VEGF signaling pathway, which dissecting the therapeutic effects of RRL on various diseases, such as cardiovascular diseases, depression, adaptation diseases, etc. In summary, this work successfully explains the potential active compounds and the multi-scale curative action mechanisms of RRL for treating various diseases; meanwhile, it implies that RRL could be applied as a novel therapeutic agent in arthritic diseases. Most importantly, this work provides an in silico strategy to understand the action mechanisms of herbal medicines from molecular/system levels, which will promote the new drug development of traditional Chinese medicine.

Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk

Genome-wide association studies (GWAS) identified the chromosome 15q25.1 locus as a leading susceptibility region for lung cancer. However, the pathogenic pathways, through which susceptibility SNPs within chromosome 15q25.1 affects lung cancer risk, have not been explored. We analyzed three cohorts with GWAS data consisting 42,901 individuals and lung expression quantitative trait loci (eQTL) data on 409 individuals to identify and validate the underlying pathways and to investigate the combined effect of genes from the identified susceptibility pathways. The KEGG neuroactive ligand receptor interaction pathway, two Reactome pathways, and 22 Gene Ontology terms were identified and replicated to be significantly associated with lung cancer risk, with P values less than 0.05 and FDR less than 0.1. Functional annotation of eQTL analysis results showed that the neuroactive ligand receptor interaction pathway and gated channel activity were involved in lung cancer risk. These pathways provide important insights for the etiology of lung cancer.

System Pharmacology-Based Strategy to Decode the Synergistic Mechanism of Zhi-zhu Wan for Functional Dyspepsia

Functional dyspepsia (FD) is a widely prevalent gastrointestinal disorder throughout the world, whereas the efficacy of current treatment in the Western countries is limited. As the symptom is equivalent to the traditional Chinese medicine (TCM) term "stuffiness and fullness," FD can be treated with Zhi-zhu Wan (ZZW) which is a kind of Chinese patent medicine. However, the "multi-component" and "multi-target" feature of Chinese patent medicine makes it challenge to elucidate the potential therapeutic mechanisms of ZZW on FD. Presently, a novel system pharmacology model including pharmacokinetic parameters, pharmacological data, and component contribution score (CS) is constructed to decipher the potential therapeutic mechanism of ZZW on FD. Finally, 61 components with favorable pharmacokinetic profiles and biological activities were obtained through ADME (absorption, distribution, metabolism, and excretion) screening in silico. The related targets of these components are identified by component targeting process followed by GO analysis and pathway enrichment analysis. And systematic analysis found that through acting on the target related to inflammation, gastrointestinal peristalsis, and mental disorder, ZZW plays a synergistic and complementary effect on FD at the pathway level. Furthermore, the component CS showed that 29 components contributed 90.18% of the total CS values of ZZW for the FD treatment, which suggested that the effective therapeutic effects of ZZW for FD are derived from all active components, not a few components. This study proposes the system pharmacology method and discovers the potent combination therapeutic mechanisms of ZZW for FD. This strategy will provide a reference method for other TCM mechanism research.

Incorporating epigenetic mechanisms to advance fetal programming theories

Decades of fetal programming research indicates that we may be able to map the origins of many physical, psychological, and medical variations and morbidities before the birth of the child. While great strides have been made in identifying associations between prenatal insults, such as undernutrition or psychosocial stress, and negative developmental outcomes, far less is known about how adaptive responses to adversity regulate the developing phenotype to match stressful conditions. As the application of epigenetic methods to human behavior has exploded in the last decade, research has begun to shed light on the role of epigenetic mechanisms in explaining how prenatal conditions shape later susceptibilities to mental and physical health problems. In this review, we describe and attempt to integrate two dominant fetal programming models: the cumulative stress model (a disease-focused approach) and the match-mismatch model (an evolutionary-developmental approach). In conjunction with biological sensitivity to context theory, we employ these two models to generate new hypotheses regarding epigenetic mechanisms through which prenatal and postnatal experiences program child stress reactivity and, in turn, promote development of adaptive versus maladaptive phenotypic outcomes. We conclude by outlining priority questions and future directions for the fetal programming field.

The genetic architecture of schizophrenia, bipolar disorder, obsessive-compulsive disorder and autism spectrum disorder

Considerable evidence suggests that autism spectrum disorders (ASD), schizophrenia (SCZ), bipolar disorder (BD) and obsessive-compulsive disorder (OCD) share a common molecular aetiology, despite their unique clinical diagnostic criteria. The aim of this study was therefore to determine and characterise the common and unique molecular architecture of ASD, SCZ, BD and OCD. Gene lists were obtained from previously published studies for ASD, BD, SCZ and for OCD. Genes identified to be common to all disorders, or unique to one specific disorder, were included for enrichment analyses using the web-server tool Enrichr. Ten genes were identified to be commonly associated with the aetiology of ASD, SCZ, BD and OCD. Enrichment analyses determined that these genes are predominantly involved in the dopaminergic and serotonergic pathways, the voltage-gated calcium ion channel gene network, folate metabolism, regulation of the hippo signaling pathway, and the regulation of gene silencing and expression. In addition to well-characterised and previously described pathways, regulation of the hippo signaling pathway was commonly associated with ASD, SCZ, BD and OCD, implicating neural development and neuronal maintenance as key in neuropsychiatric disorders. In contrast, a large number of previously associated genes were shown to be disorder-specific. And unique disorder-specific pathways and biological processes were presented for ASD, BD, SCZ and OCD aetiology. Considering the current global incidence and prevalence rates of mental health disorders, focus should be placed on cross-disorder commonalities in order to realise actionable and translatable results to combat mental health disorders.

A census of P. longum's phytochemicals and their network pharmacological evaluation for identifying novel drug-like molecules against various diseases, with a special focus on neurological disorders

Piper longum (P. longum, also called as long pepper) is one of the common culinary herbs that has been extensively used as a crucial constituent in various indigenous medicines, specifically in traditional Indian medicinal system known as Ayurveda. For exploring the comprehensive effect of its constituents in humans at proteomic and metabolic levels, we have reviewed all of its known phytochemicals and enquired about their regulatory potential against various protein targets by developing high-confidence tripartite networks consisting of phytochemical—protein target—disease association. We have also (i) studied immunomodulatory potency of this herb; (ii) developed subnetwork of human PPI regulated by its phytochemicals and could successfully associate its specific modules playing important role in diseases, and (iii) reported several novel drug targets. P10636 (microtubule-associated protein tau, that is involved in diseases like dementia etc.) was found to be the commonly screened target by about seventy percent of these phytochemicals. We report 20 drug-like phytochemicals in this herb, out of which 7 are found to be the potential regulators of 5 FDA approved drug targets. Multi-targeting capacity of 3 phytochemicals involved in neuroactive ligand receptor interaction pathway was further explored via molecular docking experiments. To investigate the molecular mechanism of P. longum’s action against neurological disorders, we have developed a computational framework that can be easily extended to explore its healing potential against other diseases and can also be applied to scrutinize other indigenous herbs for drug-design studies.

Accumulation of minor alleles and risk prediction in schizophrenia

Schizophrenia is a common neuropsychiatric disorder with a lifetime risk of 1%. Accumulation of common polygenic variations has been found to be an important risk factor. Recent studies showed a role for the enrichment of minor alleles (MAs) of SNPs in complex diseases such as Parkinson’s disease. Here we similarly studied the role of genome wide MAs in schizophrenia using public datasets. Relative to matched controls, schizophrenia cases showed higher average values in minor allele content (MAC) or the average amount of MAs per subject. By risk prediction analysis based on weighted genetic risk score (wGRS) of MAs, we identified an optimal MA set consisting of 23 238 variants that could be used to predict 3.14% of schizophrenia cases, which is comparable to using 22q11 deletion to detect schizophrenia cases. Pathway enrichment analysis of these SNPs identified 30 pathways with false discovery rate (FDR) <0.02 and of significant P-value, most of which are known to be linked with schizophrenia and other neurological disorders. These results suggest that MAs accumulation may be a risk factor to schizophrenia and provide a method to genetically screen for this disease.

A new bioinformatic insight into the associated proteins in psychiatric disorders

BACKGROUND

Psychiatric diseases severely affect the quality of patients' lives and bring huge economic pressure to their families. Also, the great phenotypic variability among these patients makes it difficult to investigate the pathogenesis. Nowadays, bioinformatics is hopeful to be used as an effective tool for the diagnosis of psychiatric disorders, which can identify sensitive biomarkers and explore associated signaling pathways.

METHODS

In this study, we performed an integrated bioinformatic analysis on 1945 mental-associated proteins including 91 secreted proteins and 593 membrane proteins, which were screened from the Universal Protein Resource (Uniport) database. Then the function and pathway enrichment analyses, ontological classification, and constructed PPI network were executed.

RESULTS

Our present study revealed that the majority of mental proteins were closely related to metabolic processes and cellular processes. We also identified some significant molecular biomarkers in the progression of mental disorders, such as HRAS, ALS2, SLC6A1, SLC39A12, SIL1, IDUA, NEPH2 and XPO1. Furthermore, it was found that hub proteins, such as COMT, POMC, NPS and BDNF, might be the potential targets for mental disorders therapy. Finally, we demonstrated that psychiatric disorders may share the same signaling pathways with cancers, involving ESR1, BCL2 and MAPK3.

CONCLUSION

Our data are expected to contribute to explaining the possible mechanisms of psychiatric diseases and providing a useful reference for the diagnosis and therapy of them.

Evaluation of genetic association of neurodevelopment and neuroimmunological genes with antipsychotic treatment response in schizophrenia in Indian populations

Neurodevelopmental and neuroimmunological genes critically regulate antipsychotic treatment outcome. We report genetic associations of antipsychotic response in 742 schizophrenia patients from Indian populations of Indo‐European and Dravidian ancestry, segregated by disease severity. Meta‐analysis comparing the two populations identified CCL2 [rs4795893: OR (95% CI) = 1.79 (1.27–2.52), P = 7.62 × 10⁻⁴; rs4586: OR (95% CI) = 1.74 (1.24–2.43), P = 1.13 × 10⁻³] and GRIA4 [rs2513265: OR (95% CI) = 0.53 (0.36–0.78), P = 1.44 × 10⁻³] in low severity group; and, ADCY2 [rs1544938: OR (95% CI) = 0.36 (0.19–0.65), P = 7.68 × 10⁻⁴] and NRG1 [rs13250975, OR (95% CI) = 0.42 (0.23–0.79), P = 6.81 × 10⁻³; rs17716295, OR (95% CI) = 1.78 (1.15–2.75), P = 8.71 × 10⁻³] in high severity group, with incomplete response toward antipsychotics. To our knowledge, this is the first study to identify genetic polymorphisms associated with the efficacy of antipsychotic treatment of schizophrenia patients from two major India populations.

Inflammation in schizophrenia: A question of balance

In the past decade, there has been renewed interest in immune/inflammatory changes and their associated oxidative/nitrosative consequences as key pathophysiological mechanisms in schizophrenia and related disorders. Both brain cell components (microglia, astrocytes, and neurons) and peripheral immune cells have been implicated in inflammation and the resulting oxidative/nitrosative stress (O&NS) in schizophrenia. Furthermore, down-regulation of endogenous antioxidant and anti-inflammatory mechanisms has been identified in biological samples from patients, although the degree and progression of the inflammatory process and the nature of its self-regulatory mechanisms vary from early onset to full-blown disease. This review focuses on the interactions between inflammation and O&NS, their damaging consequences for brain cells in schizophrenia, the possible origins of inflammation and increased O&NS in the disorder, and current pharmacological strategies to deal with these processes (mainly treatments with anti-inflammatory or antioxidant drugs as add-ons to antipsychotics).

Inferring drug-disease associations based on known protein complexes

Inferring drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their functions through interacting others. To overcome this issue, we propose a novel methodology that discover the drug-disease association based on protein complexes. Firstly, the integrated heterogeneous network consisting of drugs, protein complexes, and disease are constructed, where we assign weights to the drug-disease association by using probability. Then, from the tripartite network, we get the indirect weighted relationships between drugs and diseases. The larger the weight, the higher the reliability of the correlation. We apply our method to mental disorders and hypertension, and validate the result by using comparative toxicogenomics database. Our ranked results can be directly reinforced by existing biomedical literature, suggesting that our proposed method obtains higher specificity and sensitivity. The proposed method offers new insight into drug-disease discovery. Our method is publicly available at http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download.html.

EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles

We previously developed dmGWAS to search for dense modules in a human protein-protein interaction (PPI) network; it has since become a popular tool for network-assisted analysis of genome-wide association studies (GWAS). dmGWAS weights nodes by using GWAS signals. Here, we introduce an upgraded algorithm, EW_dmGWAS, to boost GWAS signals in a node- and edge-weighted PPI network. In EW_dmGWAS, we utilize condition-specific gene expression profiles for edge weights. Specifically, differential gene co-expression is used to infer the edge weights. We applied EW_dmGWAS to two diseases and compared it with other relevant methods. The results suggest that EW_dmGWAS is more powerful in detecting disease-associated signals.

Evidence for schizophrenia susceptibility alleles in the Indian population: An association of neurodevelopmental genes in case-control and familial samples

Schizophrenia is a severe psychiatric disorder with lifetime prevalence of ~1% worldwide. A genotyping study was conducted using a custom panel of Illumina 1536 SNPs in 840 schizophrenia cases and 876 controls (351 patients and 385 controls from North India; and 436 patients, 401 controls and 143 familial samples with 53 probands containing 37 complete and 16 incomplete trios from South India). Meta-analysis of this population of Indo-European and Dravidian ancestry identified three strongly associated variants with schizophrenia: STT3A (rs548181, p=1.47×10(-5)), NRG1 (rs17603876, p=8.66×10(-5)) and GRM7 (rs3864075, p=4.06×10(-3)). Finally, a meta-analysis was conducted comparing our data with data from the Schizophrenia Psychiatric Genome-Wide Association Study Consortium (PGC-SCZ) that supported rs548181 (p=1.39×10(-7)). In addition, combined analysis of sporadic case-control association and a transmission disequilibrium test in familial samples from South Indian population identified three associations: rs1062613 (p=3.12×10(-3)), a functional promoter variant of HTR3A; rs6710782 (p=3.50×10(-3)), an intronic variant of ERBB4; and rs891903 (p=1.05×10(-2)), an intronic variant of EBF1. The results support the risk variants observed in the earlier published work and suggest a potential role of neurodevelopmental genes in the schizophrenia pathogenesis.

Paliperidone prevents brain toll-like receptor 4 pathway activation and neuroinflammation in rat models of acute and chronic restraint stress

BACKGROUND

Alterations in the innate immune/inflammatory system have been proposed to underlie the pathophysiology of psychotic disease, but the mechanisms implicated remain elusive. The main agents of the innate immunity are the family of toll-like receptors (TLRs), which detect circulating pathogen-associated molecular patterns and endogenous damage-associated molecular patterns (DAMPS). Current antipsychotics are able to modulate pro- and anti-inflammatory pathways, but their actions on TLRs remain unexplored.

METHODS

This study was conducted to elucidate the effects of paliperidone (1mg/Kg i.p.) on acute (6 hours) and chronic (6 hours/day during 21 consecutive days) restraint stress-induced TLR-4 pathway activation and neuroinflammation, and the possible mechanism(s) related (bacterial translocation and/or DAMPs activation). The expression of the elements of a TLR-4-dependent proinflammatory pathway was analyzed at the mRNA and protein levels in prefrontal cortex samples.

RESULTS

Paliperidone pre-treatment prevented TLR-4 activation and neuroinflammation in the prefrontal cortices of stressed rats. Regarding the possible mechanisms implicated, paliperidone regulated stress-induced increased intestinal inflammation and plasma lipopolysaccharide levels. In addition, paliperidone also prevented the activation of the endogenous activators of TLR-4 HSP70 and HGMB-1.

CONCLUSIONS

Our results showed a regulatory role of paliperidone on brain TLR-4, which could explain the therapeutic benefits of its use for the treatment of psychotic diseases beyond its effects on dopamine and serotonin neurotransmission. The study of the mechanisms implicated suggests that gut-increased permeability, inflammation, and bacterial translocation of Gram-negative microflora and HSP70 and HGMB1 expression could be potential adjuvant therapeutic targets for the treatment of psychotic and other stress-related psychiatric pathologies.

Pro-/antiinflammatory dysregulation in early psychosis: results from a 1-year follow-up study

BACKGROUND

Previous studies indicated a systemic deregulation of the pro-/antiinflammatory balance in subjects after 6 months of a first psychotic episode. This disruption was reexamined 12 months after diagnosis to identify potential risk/protective factors and associations with symptom severity.

METHODS

Eighty-five subjects were followed during 12 months and the determination of the same pro-/antiinflammatory mediators was carried out in plasma and peripheral blood mononuclear cells. Multivariate logistic regression analyses were used to identify risk/protective factors. Multiple linear regression models were performed to detect the change of each biological marker during follow-up in relation to clinical characteristics and confounding factors.

RESULTS

This study suggests a more severe systemic pro-/antiinflammatory deregulation than in earlier pathological stages in first psychotic episode, because not only were intracellular components of the inflammatory response increased but also the majority of soluble elements. Nitrite plasma levels and cyclooxygenase-2 expression in peripheral blood mononuclear cells are reliable potential risk factors and 15d-prostaglandin-J2 plasma levels a protection biomarker. An interesting relationship exists between antipsychotic dose and the levels of prostaglandin-E2 (inverse) and 15d-prostaglandin-J2 (direct). An inverse relationship between the Global Assessment of Functioning scale and lipid peroxidation is also present.

CONCLUSIONS

Summing up, pro-/antiinflammatory mediators can be used as risk/protection biomarkers. The inverse association between oxidative/nitrosative damage and the Global Assessment of Functioning scale, and the possibility that one of the targets of antipsychotics could be the restoration of the pro-/antiinflammatory balance support the use of antiinflammatory drugs as coadjuvant to antipsychotics.

Pro-/anti-inflammatory dysregulation in patients with first episode of psychosis: toward an integrative inflammatory hypothesis of schizophrenia

BACKGROUND

Schizophrenia is a chronic syndrome of unknown etiology, predominantly defined by signs of psychosis. The onset of the disorder occurs typically in late adolescence or early adulthood. Efforts to study pathophysiological mechanisms in early stages of the disease are crucial in order to prompt intervention.

METHODS

Case-control study of first-episode psychotic (FEP) patients and matched controls. We recruited 117 patients during the first year after their FEP according to the DSM-IV criteria and recruited 106 gender-, race-, and age-matched controls between September 2010 and June 2011.

RESULTS

Biochemical studies carried out in peripheral mononuclear blood cells (PMBC) and plasma evidence a significant increase in intracellular components of a main proinflammatory pathway, along with a significant decrease in the anti-inflammatory ones. Multivariate logistic regression analyses identified the expression of inducible isoforms of nitric oxide synthase and cyclooxygenase in PMBC and homocysteine plasma levels as the most reliable potential risk factors and the inhibitor of the inflammatory transcription factor NFκB, IκBα, and the anti-inflammatory prostaglandin 15d-PGJ2 as potential protection factors.

DISCUSSION

Taken as a whole, the results of this study indicate robust phenotypical differences at the cellular machinery level in PMBC of patients with FEP. Although more scientific evidence is needed, the determination of multiple components of pro- and anti-inflammatory cellular pathways including the activity of nuclear receptors has interesting potential as biological markers and potential risk/protective factors for FEP. Due to its soluble nature, a notable finding in this study is that the anti-inflammatory mediator 15d-PGJ2 might be used as plasmatic biomarker for first episodes of psychosis.

Genotype-based ancestral background consistently predicts efficacy and side effects across treatments in CATIE and STAR*D

Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient’s unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient’s unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions.

Ziprasidone versus other atypical antipsychotics for schizophrenia

BACKGROUND

In many countries of the industrialised world second generation ('atypical') antipsychotics have become the first line drug treatment for people with schizophrenia. The question as to whether, and if so how much, the effects of the various new generation antipsychotics differ is a matter of debate. In this review we examined how the efficacy and tolerability of ziprasidone differs from that of other second generation antipsychotics.

OBJECTIVES

To evaluate the effects of ziprasidone compared with other atypical antipsychotics for people with schizophrenia and schizophrenia-like psychoses.

SEARCH STRATEGY

We searched the Cochrane Schizophrenia Group Specialised Register (April 2007) and references of all identified studies for further trial citations. We contacted pharmaceutical companies and authors of trials for additional information.

SELECTION CRITERIA

We included all randomised, at least single-blind, controlled trials comparing oral ziprasidone with oral forms of amisulpride, aripiprazole, clozapine, olanzapine, quetiapine, risperidone or zotepine in people with schizophrenia or schizophrenia-like psychoses.

DATA COLLECTION AND ANALYSIS

We extracted data independently. For continuous data, we calculated weighted mean differences (MD) for dichotomous data we calculated relative risks (RR) and their 95% confidence intervals (CI) on an intention-to-treat basis based on a random-effects model. We calculated numbers needed to treat/harm (NNT/NNH) where appropriate.

MAIN RESULTS

The review currently includes nine randomised controlled trials (RCTs) with 3361 participants. The overall rate of premature study discontinuation was very high (59.1%). Data for the comparisons of ziprasidone with amisulpride, clozapine, olanzapine, quetiapine and risperidone were available. Ziprasidone was a less acceptable treatment than olanzapine (leaving the studies early for any reason: 5 RCTs, n=1937, RR 1.26 CI 1.18 to 1.35, NNH 7 CI 5 to 10) and risperidone (3 RCTs, n=1029, RR 1.11 CI 1.02 to 1.20, NNH 14 CI 8 to 50), but not than the other second generation antipsychotic drugs. Ziprasidone was less efficacious than amisulpride (leaving the study early due to inefficacy: 1 RCT, n=123, RR 4.72 CI 1.06 to 20.98, NNH 8 CI 5 to 50) olanzapine (PANSS total score: 4 RCTs, n=1291, MD 8.32 CI 5.64 to 10.99) and risperidone (PANSS total score: 3 RCTs, n=1016, MD 3.91 CI 0.27 to 7.55). Based on limited data there were no significant differences in tolerability between ziprasidone and amisulpride or clozapine. Ziprasidone produced less weight gain than olanzapine (5 RCTs, n=1659, MD -3.82 CI -4.69 to -2.96), quetiapine (2 RCTs, n=754, RR 0.45 CI 0.28 to 0.74) or risperidone (3 RCTs, n=1063, RR 0.49 CI 0.33 to 0.74). It was associated with less cholesterol increase than olanzapine, quetiapine and risperidone. Conversely ziprasidone produced slightly more extrapyramidal side-effects than olanzapine (4 RCTs, n=1732, RR 1.43 CI 1.03 to 1.99, NNH not estimable) and more prolactin increase than quetiapine (2 RCTs, n=754, MD 4.77 CI 1.37 to 8.16), but less movement disorders (2 RCTs, n=822, RR 0.70 CI 0.51 to 0.97, NNT not estimable) and less prolactin increase (2 RCTs, n=767, MD -21.97 CI -27.34 to -16.60) than risperidone.

AUTHORS' CONCLUSIONS

Ziprasidone may be a slightly less efficacious antipsychotic drug than amisulpride, olanzapine and risperidone. Its main advantage is the low propensity to induce weight gain and associated adverse effects. However, the high overall rate of participants leaving the studies early limits the validity of any findings.