Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

Improved therapeutic approach for spinal muscular atrophy via ubiquitination-resistant survival motor neuron variant

BACKGROUND

Zolgensma is a gene-replacement therapy that has led to a promising treatment for spinal muscular atrophy (SMA). However, clinical trials of Zolgensma have raised two major concerns: insufficient therapeutic effects and adverse events. In a recent clinical trial, 30% of patients failed to achieve motor milestones despite pre-symptomatic treatment. In addition, more than 20% of patients showed hepatotoxicity due to excessive virus dosage, even after the administration of an immunosuppressant. Here, we aimed to test whether a ubiquitination-resistant variant of survival motor neuron (SMN), SMNK186R, has improved therapeutic effects for SMA compared with wild-type SMN (SMNWT).

METHODS

A severe SMA mouse model, SMA type 1.5 (Smn-/-; SMN2+/+; SMN∆7+/-) mice, was used to compare the differences in therapeutic efficacy between AAV9-SMNWT and AAV9-SMNK186R. All animals were injected within Postnatal Day (P) 1 through a facial vein or cerebral ventricle.

RESULTS

AAV9-SMNK186R-treated mice showed increased lifespan, body weight, motor neuron number, muscle weight and functional improvement in motor functions as compared with AAV9-SMNWT-treated mice. Lifespan increased by more than 10-fold in AAV9-SMNK186R-treated mice (144.8 ± 26.11 days) as compared with AAV9-SMNWT-treated mice (26.8 ± 1.41 days). AAV9-SMNK186R-treated mice showed an ascending weight pattern, unlike AAV9-SMNWT-treated mice, which only gained weight until P20 up to 5 g on average. Several motor function tests showed the improved therapeutic efficacy of SMNK186R. In the negative geotaxis test, AAV9-SMNK186R-treated mice turned their bodies in an upward direction successfully, unlike AAV9-SMNWT-treated mice, which failed to turn upwards from around P23. Hind limb clasping phenotype was rarely observed in AAV9-SMNK186R-treated mice, unlike AAV9-SMNWT-treated mice that showed clasping phenotype for more than 20 out of 30 s. At this point, the number of motor neurons (1.5-fold) and the size of myofibers (2.1-fold) were significantly increased in AAV9-SMNK186R-treated mice compared with AAV9-SMNWT-treated mice without prominent neurotoxicity. AAV9-SMNK186R had fewer liver defects compared with AAV9-SMNWT, as judged by increased proliferation of hepatocytes (P < 0.0001) and insulin-like growth factor-1 production (P < 0.0001). Especially, low-dose AAV9-SMNK186R (nine-fold) also reduced clasping time compared with SMNWT.

CONCLUSIONS

SMNK186R will provide improved therapeutic efficacy in patients with severe SMA with insufficient therapeutic efficacy. Low-dose treatment of SMA patients with AAV9-SMNK186R can reduce the adverse events of Zolgensma. Collectively, SMNK186R has value as a new treatment for SMA that improves treatment effectiveness and reduces adverse events simultaneously.

TTD: Therapeutic Target Database describing target druggability information

Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.

Revolutionizing Precision Medicine: Exploring Wearable Sensors for Therapeutic Drug Monitoring and Personalized Therapy

Precision medicine, particularly therapeutic drug monitoring (TDM), is essential for optimizing drug dosage and minimizing toxicity. However, current TDM methods have limitations, including the need for skilled operators, patient discomfort, and the inability to monitor dynamic drug level changes. In recent years, wearable sensors have emerged as a promising solution for drug monitoring. These sensors offer real-time and continuous measurement of drug concentrations in biofluids, enabling personalized medicine and reducing the risk of toxicity. This review provides an overview of drugs detectable by wearable sensors and explores biosensing technologies that can enable drug monitoring in the future. It presents a comparative analysis of multiple biosensing technologies and evaluates their strengths and limitations for integration into wearable detection systems. The promising capabilities of wearable sensors for real-time and continuous drug monitoring offer revolutionary advancements in diagnostic tools, supporting personalized medicine and optimal therapeutic effects. Wearable sensors are poised to become essential components of healthcare systems, catering to the diverse needs of patients and reducing healthcare costs.

Melt Crystallization of Celecoxib-Carbamazepine Cocrystals with the Synchronized Release of Drugs

PURPOSE

The fixed-dose combination drug products have been increasingly used to treat some complex diseases. A cocrystal containing two therapeutic components, named as a drug-drug cocrystal, is an ideal solid form to formulate as a fixed-dose combination product. The aim of the study is to prepare celecoxib-carbamazepine (CEL-CBZ) cocrystals by melt crystallization to achieve the synchronized release of drugs.

METHOD

The crystal structure of the CEL-CBZ cocrystal was determined from the cocrystals harvested from melt by single crystal X-ray diffraction. The binary phase diagram and crystal growth kinetics of the CEL-CBZ cocrystal from melt were studied to optimize the process parameters of hot-melt extrusion for manufacturing large-scale cocrystals. The intrinsic dissolution rate studies were conducted to compare the dissolution profiles of drugs in the cocrystal and their individual forms.

RESULT

The CEL-CBZ cocrystal crystallized in the triclinic space group with one CEL and one CBZ molecule in the asymmetric unit. The crystallization of CEL-CBZ cocrystals were observed both in the supercooled liquid and glassy state. The formation of drug-drug cocrystals significantly alter the intrinsic dissolution rates of the parent drugs to favor the synchronized release.

CONCLUSION

Melt crystallization is an alternative, efficient and eco-friendly approach for preparing drug-drug cocrystals on a large scale. The synchronized drug release by drug-drug cocrystals can be used to modulate the release profiles of parent drugs in the fixed-dose combination products.

A systematic review on the state-of-the-art strategies for protein representation

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

Bacteria-mediated cancer therapy: A versatile bio-sapper with translational potential

Bacteria are important symbionts for humans, which sustain substantial influences on our health. Interestingly, some bastrains have been identified to have therapeutic applications, notably for antitumor activity. Thereby, oncologists have developed various therapeutic models and investigated the potential antitumor mechanisms for bacteria-mediated cancer therapy (BCT). Even though BCT has a long history and exhibits remarkable therapeutic efficacy in pre-clinical animal models, its clinical translation still lags and requires further breakthroughs. This review aims to focus on the established strains of therapeutic bacteria and their antitumor mechanisms, including the stimulation of host immune responses, direct cytotoxicity, the interference on cellular signal transduction, extracellular matrix remodeling, neoangiogenesis, and metabolism, as well as vehicles for drug delivery and gene therapy. Moreover, a brief discussion is proposed regarding the important future directions for this fantastic research field of BCT at the end of this review.

A Network-Biology led Computational Drug repurposing Strategy to prioritize therapeutic options for COVID-19

The alarming pandemic situation of novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection, high drug development cost and slow process of drug discovery have made repositioning of existing drugs for therapeutics a popular alternative. It involves the repurposing of existing safe compounds which results in low overall development costs and shorter development timeline. In the present study, a computational network-biology approach has been used for comparing three candidate drugs i.e. quercetin, N-acetyl cysteine (NAC), and 2-deoxy-glucose (2-DG) to be effectively repurposed against COVID-19. For this, the associations between these drugs and genes of Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) diseases were retrieved and a directed drug-gene-gene-disease interaction network was constructed. Further, to quantify the associations between a target gene and a disease gene, the shortest paths from the target gene to the disease genes were identified. A vector DV was calculated to represent the extent to which a disease gene was influenced by these drugs. Quercetin was quantified as the best among the three drugs, suited for repurposing with DV of -70.19, followed by NAC with DV of -39.99 and 2-DG with DV of -13.71. The drugs were also assessed for their safety and efficacy balance (in terms of therapeutic index) using network properties. It was found that quercetin was a forerunner than other two drugs.

Evaluation of Acute and Sub-Acute Toxicity Profile of 5-Methylcoumarin-4β-Glucoside in Mice

Vernonia glaberrima leaves are traditionally used to alleviate bodily pain, skin cancer, and other skin-related disorders. The purpose of the study was to investigate the acute and sub-acute toxicity of 5-methylcoumarin-4β-glucoside, a promising chemotherapeutic agent against colon cancer isolated from the leaves of Vernonia glaberrima. 5-methylcoumarin-4β-glucoside was isolated from the methanol leaf extract of Vernonia glaberrima following a previously described method. The acute toxicity study involved a two-phase 24 h observation for signs of mortality and toxicity following single oral dose administration of the isolated compound. For the sub-acute study, four groups of mice, averagely aged eight weeks, were administered graded doses of the compound (250, 500 and 1000 mg/kg) or vehicle for 28 days. On the 29th day, the mice were fasted, anesthetized, euthanized, then their blood and tissues were harvested for hematological, biochemical and histopathological evaluations. There were no signs of mortality or moribund status with an increasing dose of up to 5000 mg/kg over a 24 h period in the acute study. Also, there was no evidence of toxicity on the biochemical or hematopoietic systems in the sub-acute study (p < 0.05). At the dose of 1000 mg/kg, the mice showed some distorted histology with no corresponding alterations in serum biochemicals. Overall, the results showed that 5-methylcoumarin-4β-glucoside at dosages up to 500 mg/kg is tolerable in mice.

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5-methylcoumarin-4β-glucoside is a promising therapeutic agent against colon cancer.

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Acute toxicity study of the coumarine derivative gave an LD₅₀ above 5000 mg/kg.

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The compound demonstrated minimal histopathological toxicity at the highest dose.

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It also revealed no toxicity in sero-biochemical terms after a 28-day toxicity study.

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5-methylcoumarin-4β-glucoside is not toxic in mice at doses below 1000 mg/kg.

Feature, Function, and Information of Drug Transporter Related Databases

With the rapid progress in pharmaceutical experiments and clinical investigations, extensive knowledge of drug transporters (DTs) has accumulated, which is valuable data for the understanding of drug metabolism and disposition. However, such data is largely dispersed in the literature, which hampers its utility and significantly limits its possibility for comprehensive analysis. A variety of databases have, therefore, been constructed to provide DT-related data, and they were reviewed in this study. First, several knowledge bases providing data regarding clinically important drugs and their corresponding transporters were discussed, which constituted the most important resources of DT-centered data. Second, some databases describing the general transporters and their functional families were reviewed. Third, various databases offering transporter information as part of their entire data collection were described. Finally, customized database functions that are available to facilitate DT-related research were discussed. This review provided an overview of the whole collection of DT-related databases, which might facilitate research on precision medicine and rational drug use. Significance Statement A collection of well-established databases related to DTs were comprehensively reviewed, which were organized according to their importance in drug ADME research. These databases could collectively contribute to the research on rational drug use.

Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs

Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004–17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Our analysis confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and further revealed a simple rule for identifying the speedy human targets through clinical trials (from the earliest phase I to the 1st drug approval within 8 years). This simple rule correctly identified 75.0% of the 28 speedy human targets and only unexpectedly misclassified 13.2% of 53 non-speedy human targets. Certain extraordinary circumstances were also discovered to likely contribute to the misclassification of some human targets by this simple rule. Investigation and knowledge of trial-speed differentiating features enable prioritized drug discovery and development.

Qing-Yi Decoction in the Treatment of Acute Pancreatitis: An Integrated Approach Based on Chemical Profile, Network Pharmacology, Molecular Docking and Experimental Evaluation

Background: Qing-Yi Decoction (QYD) is a classic precompounded prescription with satisfactory clinical efficacy on acute pancreatitis (AP). However, the chemical profile and overall molecular mechanism of QYD in treating AP have not been clarified.

Methods: In the present study, a rapid, simple, sensitive and reliable ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS)-based chemical profile was first established. An integration strategy of network pharmacology analysis and molecular docking based identified ingredients was further performed to screen out the potential targets and pathways involved in the treatment of QYD on AP. Finally, SD rats with acute pancreatitis were constructed to verify the predicted results through a western blot experiment.

Results: A total of 110 compounds, including flavonoids, phenolic acids, alkaloids, monoterpenes, iridoids, triterpenes, phenylethanoid glycosides, anthraquinones and other miscellaneous compounds were identified, respectively. Eleven important components, 47 key targets and 15 related pathways based on network pharmacology analysis were obtained. Molecular docking simulation indicated that ERK1/2, c-Fos and p65 might play an essential role in QYD against AP. Finally, the western blot experiments showed that QYD could up-regulate the expression level of ERK1/2 and c-Fos, while down-regulate the expression level of p65.

Conclusion: This study predicted and validated that QYD may treat AP by inhibiting inflammation and promoting apoptosis, which provides directions for further experimental studies.

Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease

An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein–protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?

In order to understand and represent the importance of nodes within networks better, most of the studies that investigate graphs compute the nodes' centrality within their network(s) of interest. In the literature, the most frequent measures used are degree, closeness and/or betweenness centrality, even if other measures might be valid candidates for representing the importance of nodes within networks. The main contribution of this paper is the development of a methodology that allows one to understand, compare and validate centrality indices when studying a particular network of interest. The proposed methodology integrates the following steps: choosing the centrality measures for the network of interest; developing a theoretical taxonomy of these measures; identifying, by means of Principal Component Analysis (PCA), latent dimensions of centrality within the network of interest; verifying the proposed taxonomy of centrality measures; and identifying the centrality measures that best represent the network of interest. Also, we applied the proposed methodology to an existing graph of interest, in our case a real friendship student network. We chose eighteen centrality measures that were developed in SNA and are available and computed in a specific library (CINNA), defined them thoroughly, and proposed a theoretical taxonomy of these eighteen measures. PCA showed the emergence of six latent dimensions of centrality within the student network and saturation of most of the centrality indices on the same categories as those proposed by the theoretical taxonomy. Additionally, the results suggest that indices other than the ones most frequently applied might be more relevant for research on friendship student networks. Finally, the integrated methodology that we propose can be applied to other centrality indices and/or other network types than student graphs.

Precision Dosing Priority Criteria: Drug, Disease, and Patient Population Variables

The administered dose of a drug modulates whether patients will experience optimal effectiveness, toxicity including death, or no effect at all. Dosing is particularly important for diseases and/or drugs where the drug can decrease severe morbidity or prolong life. Likewise, dosing is important where the drug can cause death or severe morbidity. Since we believe there are many examples where more precise dosing could benefit patients, it is worthwhile to consider how to prioritize drug-disease targets. One key consideration is the quality of information available from which more precise dosing recommendations can be constructed. When a new more precise dosing scheme is created and differs significantly from the approved label, it is important to consider the level of proof necessary to either change the label and/or change clinical practice. The cost and effort needed to provide this proof should also be considered in prioritizing drug-disease precision dosing targets. Although precision dosing is being promoted and has great promise, it is underutilized in many drugs and disease states. Therefore, we believe it is important to consider how more precise dosing is going to be delivered to high priority patients in a timely manner. If better dosing schemes do not change clinical practice resulting in better patient outcomes, then what is the use? This review paper discusses variables to consider when prioritizing precision dosing candidates while highlighting key examples of precision dosing that have been successfully used to improve patient care.

Electrochemical and in silico approaches for liver metabolic oxidation of antitumor-active triazoloacridinone C-1305

5-Dimethylaminopropylamino-8-hydroxytriazoloacridinone (C-1305) is a promising antitumor compound developed in our laboratory. A better understanding of its metabolic transformations is still needed to explain the multidirectional mechanism of pharmacological action of triazoloacridinone derivatives at all. Thus, the aim of the current work was to predict oxidative pathways of C-1305 that would reflect its phase I metabolism. The multi-tool analysis of C-1305 metabolism included electrochemical conversion and in silico sites of metabolism predictions in relation to liver microsomal model. In the framework of the first approach, an electrochemical cell was coupled on-line to an electrospray ionization mass spectrometer. The effluent of the electrochemical cell was also injected onto a liquid chromatography column for the separation of different products formed prior to mass spectrometry analysis. In silico studies were performed using MetaSite software. Standard microsomal incubation was employed as a reference procedure. We found that C-1305 underwent electrochemical oxidation primarily on the dialkylaminoalkylamino moiety. An unknown N-dealkylated and hydroxylated C-1305 products have been identified. The electrochemical system was also able to simulate oxygenation reactions. Similar pattern of C-1305 metabolism has been predicted using in silico approach. Both proposed strategies showed high agreement in relation to the generated metabolic products of C-1305. Thus, we conclude that they can be considered as simple alternatives to enzymatic assays, affording time and cost efficiency.

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Three different strategies for the investigation of C-1305 oxidative metabolism were presented.

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Phase I products of the antitumor agent were easily generated within a matrix-free environment.

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Products of C-1305 electrochemical oxidation were typical for P450-catalyzed reactions.

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We observed a good accordance between electrochemical, in silico, and enzymatic results.

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Electrochemical and in silico methods are fast alternatives for enzymatic assays.

Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics

Knowledge of therapeutic targets and early drug candidates is useful for improved drug discovery. In particular, information about target regulators and the patented therapeutic agents facilitates research regarding druggability, systems pharmacology, new trends, molecular landscapes, and the development of drug discovery tools. To complement other databases, we constructed the Therapeutic Target Database (TTD) with expanded information about (i) target-regulating microRNAs and transcription factors, (ii) target-interacting proteins, and (iii) patented agents and their targets (structures and experimental activity values if available), which can be conveniently retrieved and is further enriched with regulatory mechanisms or biochemical classes. We also updated the TTD with the recently released International Classification of Diseases ICD-11 codes and additional sets of successful, clinical trial, and literature-reported targets that emerged since the last update. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. In case of possible web connectivity issues, two mirror sites of TTD are also constructed (http://db.idrblab.org/ttd/ and http://db.idrblab.net/ttd/).

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility

Aims

As one of the most fundamental questions in modern science, “what causes schizophrenia (SZ)” remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures’ robustness. These irreproducible results have significantly limited our understanding of the etiology of SZ.

Methods

In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted.

Results

Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ.

Conclusion

A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics

Because of the extended period of clinic data collection and huge size of analyzed samples, the long-term and large-scale pharmacometabonomics profiling is frequently encountered in the discovery of drug/target and the guidance of personalized medicine. So far, integration of the results (ReIn) from multiple experiments in a large-scale metabolomic profiling has become a widely used strategy for enhancing the reliability and robustness of analytical results, and the strategy of direct data merging (DiMe) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the ReIn, the DiMe has not yet been widely adopted in current metabolomics studies, due to the difficulty in removing unwanted variations and the inexistence of prior knowledges on the performance of the available merging methods. It is therefore urgently needed to clarify whether DiMe can enhance the performance of metabolic profiling or not. Herein, the performance of DiMe on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria (classification capacity, robustness and false discovery rate). As a result, integration/merging-based strategies (ReIn and DiMe) were found to perform better under all criteria than those strategies based on single experiment. Moreover, DiMe was discovered to outperform ReIn in classification capacity and robustness, while the ReIn showed superior capacity in controlling false discovery rate. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.

k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification

In this paper, a computational method based on machine learning technique for identifying Alzheimer's disease genes is proposed. Compared with most existing machine learning based methods, existing methods predict Alzheimer's disease genes by using structural magnetic resonance imaging (MRI) technique. Most methods have attained acceptable results, but the cost is expensive and time consuming. Thus, we proposed a computational method for identifying Alzheimer disease genes by use of the sequence information of proteins, and classify the feature vectors by random forest. In the proposed method, the gene protein information is extracted by adaptive k-skip-n-gram features. The proposed method can attain the accuracy to 85.5% on the selected UniProt dataset, which has been demonstrated by the experimental results.

Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein⁻protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.

Systematically Characterizing Chemical Profile and Potential Mechanisms of Qingre Lidan Decoction Acting on Cholelithiasis by Integrating UHPLC-QTOF-MS and Network Target Analysis

Qingre Lidan Decoction (QRLDD), a classic precompounded prescription, is widely used as an effective treatment for cholelithiasis clinically. However, its chemical profile and mechanism have not been characterized and elucidated. In the present study, a rapid, sensitive, and reliable ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry method was established for comprehensively identifying the major constituents in QRLDD. Furthermore, a network pharmacology strategy based on the chemical profile was applied to clarify the synergetic mechanism. A total of 72 compounds containing flavonoids, terpenes, phenolic acid, anthraquinones, phenethylalchohol glycosides, and other miscellaneous compounds were identified, respectively. 410 disease genes, 432 compound targets, and 71 related pathways based on cholelithiasis-related and compound-related targets databases as well as related pathways predicted by the Kyoto Encyclopedia of Genes and Genomes database were achieved. Among these pathways and genes, pathway in cancer and MAPK signaling pathway may play an important role in the development of cholelithiasis. EGFR may be a crucial target in the conversion of gallstones to gallbladder carcinoma. Regulation of PRKCB/RAF1/MAP2K1/MAPK1 is associated with cell proliferation and differentiation. Thus, the fingerprint coupled with network pharmacology analysis could contribute to simplifying the complex system and providing directions for further research of QRLDD.