Stability of mental motor-imagery classification in EEG depends on the choice of classifier model and experiment design, but not on signal preprocessing.

Introduction: Modern consciousness research has developed diagnostic tests to improve the diagnostic accuracy of different states of consciousness via electroencephalography (EEG)-based mental motor imagery (MI), which is still challenging and lacks a consensus on how to best analyse MI EEG-data. An optimally designed and analyzed paradigm must detect command-following in all healthy individuals, before it can be applied in patients, e.g., for the diagnosis of disorders of consciousness (DOC). Methods: We investigated the effects of two important steps in the raw signal preprocessing on predicting participant performance (F1) and machine-learning classifier performance (area-under-curve, AUC) in eight healthy individuals, that are based solely on MI using high-density EEG (HD-EEG): artifact correction (manual correction with vs. without Independent Component Analysis [ICA]), region of interest (ROI; motor area vs. whole brain), and machine-learning algorithm (support-vector machine [SVM] vs. k-nearest neighbor [KNN]). Results: Results revealed no significant effects of artifact correction and ROI on predicting participant performance (F1) and classifier performance (AUC) scores (all ps > 0.05) in the SVM classification model. In the KNN model, ROI had a significant influence on the classifier performance [F(1,8.939) = 7.585, p = 0.023]. There was no evidence for artifact correction and ROI selection changing the prediction of participants performance and classifier performance in EEG-based mental MI if using SVM-based classification (71-100% correct classifications across different signal preprocessing methods). The variance in the prediction of participant performance was significantly higher when the experiment started with a resting-state compared to a mental MI task block [X2(1) = 5.849, p = 0.016]. Discussion: Overall, we could show that classification is stable across different modes of EEG signal preprocessing when using SVM models. Exploratory analysis gave a hint toward potential effects of the sequence of task execution on the prediction of participant performance, which should be taken into account in future studies.

In silico prediction and biological assessment of novel angiogenesis modulators from traditional Chinese medicine.

Uncontrolled angiogenesis is a common denominator underlying many deadly and debilitating diseases such as myocardial infarction, chronic wounds, cancer, and age-related macular degeneration. As the current range of FDA-approved angiogenesis-based medicines are far from meeting clinical demands, the vast reserve of natural products from traditional Chinese medicine (TCM) offers an alternative source for developing pro-angiogenic or anti-angiogenic modulators. Here, we investigated 100 traditional Chinese medicine-derived individual metabolites which had reported gene expression in MCF7 cell lines in the Gene Expression Omnibus (GSE85871). We extracted literature angiogenic activities for 51 individual metabolites, and subsequently analysed their predicted targets and differentially expressed genes to understand their mechanisms of action. The angiogenesis phenotype was used to generate decision trees for rationalising the poly-pharmacology of known angiogenesis modulators such as ferulic acid and curculigoside and validated by an in vitro endothelial tube formation assay and a zebrafish model of angiogenesis. Moreover, using an in silico model we prospectively examined the angiogenesis-modulating activities of the remaining 49 individual metabolites. In vitro, tetrahydropalmatine and 1 beta-hydroxyalantolactone stimulated, while cinobufotalin and isoalantolactone inhibited endothelial tube formation. In vivo, ginsenosides Rb3 and Rc, 1 beta-hydroxyalantolactone and surprisingly cinobufotalin, restored angiogenesis against PTK787-induced impairment in zebrafish. In the absence of PTK787, deoxycholic acid and ursodeoxycholic acid did not affect angiogenesis. Despite some limitations, these results suggest further refinements of in silico prediction combined with biological assessment will be a valuable platform for accelerating the research and development of natural products from traditional Chinese medicine and understanding their mechanisms of action, and also for other traditional medicines for the prevention and treatment of angiogenic diseases.

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis.

We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.

Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models.

BACKGROUND: Proteins are an essential part of medical nutrition therapy in critically ill patients. Guidelines almost universally recommend a high protein intake without robust evidence supporting its use. METHODS: Using a large international database, we modelled associations between the hazard rate of in-hospital death and live hospital discharge (competing risks) and three categories of protein intake (low: < 0.8 g/kg per day, standard: 0.8-1.2 g/kg per day, high: > 1.2 g/kg per day) during the first 11 days after ICU admission (acute phase). Time-varying cause-specific hazard ratios (HR) were calculated from piece-wise exponential additive mixed models. We used the estimated model to compare five different hypothetical protein diets (an exclusively low protein diet, a standard protein diet administered early (day 1 to 4) or late (day 5 to 11) after ICU admission, and an early or late high protein diet). RESULTS: Of 21,100 critically ill patients in the database, 16,489 fulfilled inclusion criteria for the analysis. By day 60, 11,360 (68.9%) patients had been discharged from hospital, 4,192 patients (25.4%) had died in hospital, and 937 patients (5.7%) were still hospitalized. Median daily low protein intake was 0.49 g/kg [IQR 0.27-0.66], standard intake 0.99 g/kg [IQR 0.89- 1.09], and high intake 1.41 g/kg [IQR 1.29-1.60]. In comparison with an exclusively low protein diet, a late standard protein diet was associated with a lower hazard of in-hospital death: minimum 0.75 (95% CI 0.64, 0.87), and a higher hazard of live hospital discharge: maximum HR 1.98 (95% CI 1.72, 2.28). Results on hospital discharge, however, were qualitatively changed by a sensitivity analysis. There was no evidence that an early standard or a high protein intake during the acute phase was associated with a further improvement of outcome. CONCLUSIONS: Provision of a standard protein intake during the late acute phase may improve outcome compared to an exclusively low protein diet. In unselected critically ill patients, clinical outcome may not be improved by a high protein intake during the acute phase. Study registration ID number ISRCTN17829198.

Computational drug repositioning for ischemic stroke: neuroprotective drug discovery.

Background: A comprehensive approach to drug repositioning will be required to overcome translational hurdles and identify more neuroprotective drugs. Results & methods: Gene Set Enrichment Analysis was applied to identify related pathways and enriched genes. Candidate genes were optimized using ToppGene, ToppGenet and pBRIT. From the perspective of the local structures, gene-domain-substructure-drug relationships were constructed. Using the MCODE algorithm and K-means clustering, 31 functional subnetworks were obtained, and 252 drugs with proposed neuroprotective function were identified. Using computational analysis, 72 substructures with different scores were found to correspond to neuroprotective functions. The protective effects of benidipine and barnidipine were confirmed in vitro. Conclusion: The authors' research has great potential to discover more neuroprotective drugs and obtain more information regarding mechanisms of action and functional substructures.

Combination of Ginsenosides Rb2 and Rg3 Promotes Angiogenic Phenotype of Human Endothelial Cells via PI3K/Akt and MAPK/ERK Pathways.

Shexiang Baoxin Pill (SBP) is an oral formulation of Chinese materia medica for the treatment of angina pectoris. It displays pleiotropic roles in protecting the cardiovascular system. However, the mode of action of SBP in promoting angiogenesis, and in particular the synergy between its constituents is currently not fully understood. The combination of ginsenosides Rb2 and Rg3 were studied in human umbilical vein endothelial cells (HUVECs) for their proangiogenic effects. To understand the mode of action of the combination in more mechanistic detail, RNA-Seq analysis was conducted, and differentially expressed genes (DEGs), pathway analysis and Weighted Gene Correlation Network Analysis (WGCNA) were applied to further identify important genes that a play pivotal role in the combination treatment. The effects of pathway-specific inhibitors were observed to provide further support for the hypothesized mode of action of the combination. Ginsenosides Rb2 and Rg3 synergistically promoted HUVEC proliferation and tube formation under defined culture conditions. Also, the combination of Rb2/Rg3 rescued cells from homocysteine-induced damage. mRNA expression of CXCL8, CYR61, FGF16 and FGFRL1 was significantly elevated by the Rb2/Rg3 treatment, and representative signaling pathways induced by these genes were found. The increase of protein levels of phosphorylated-Akt and ERK42/44 by the Rb2/Rg3 combination supports the notion that it promotes endothelial cell proliferation via the PI3K/Akt and MAPK/ERK signaling pathways. The present study provides the hypothesis that SBP, via ginsenosides Rb2 and Rg3, involves the CXCR1/2 CXCL8 (IL8)-mediated PI3K/Akt and MAPK/ERK signaling pathways in achieving its proangiogenic effects.

Towards understanding antimicrobial activity, cytotoxicity and the mode of action of dichapetalins A and M using in silico and in vitro studies.

Dichapetalum madagascariense Poir (Dichapetalaceae) is traditionally used to treat bacterial infections, jaundice, urethritis and viral hepatitis in Africa. Its root contains a broad spectrum of biologically active dichapetalins. To evaluate the plant's effect on human MCF-7 cells and its' antibacterial and antiparasitic potentials, we isolated and identified the known dichapetalins A and M from the roots. Both dichapetalins were tested on six bacterial strains (Shigella flexneri, Bacillus cereus, Salmonella paratyphi B, Listeria monocytogenes, Escherichia coli, Staphylococcus aureus) and two parasite strains; Trypanosoma brucei brucei, and Leishmania donovani using the Alamar Blue assay system. Dichapetalins A and M were more potent against B. cereus with IC50 values of 11.15 and 3.15 µg/ml, respectively, compared to the positive control ampicillin (IC50 = 19.50 µg/ml). Dichapetalins A (IC50 = 74.22 µg/ml) and M (IC50 = 72.34 µg/ml) were less active against T. b. brucei, compared to the standard Suramin (IC50 = 4.96 µg/ml). Dichapetalin M showed moderate activity against L. donovani (Amphotericin B: IC50 = 0.21 µg/ml) with an IC50 of 16.80 µg/ml. In human MCF-7 cells expressing the NR1I2 receptor, the activity of dichapetalin M was higher (IC50 = 4.71 µM and 3.95 µM) for 48 and 72 h of treatment, respectively compared to Curcumin with IC50 of 17.49 µM and 12.53 µM for 48 and 72 h of treatment, respectively. Results from in vitro expression studies with qPCR confirmed an antagonistic effect of dichapetalin M on PXR (NR1I2) signaling; supporting the PXR signaling pathway as a possible mode of action of dichapetalin M as predicted by in silico results. These findings confirm previous studies that D. madagascariense can be a source of potential lead compounds for development of novel antibiotic, antiparasitic and anticancer medicines, and provide further insights into the mechanism of action of the dichapetalins.

Transcriptomics predicts compound synergy in drug and natural product treated glioblastoma cells.

Pathway analysis is an informative method for comparing and contrasting drug-induced gene expression in cellular systems. Here, we define the effects of the marine natural product fucoxanthin, separately and in combination with the prototypic phosphatidylinositol 3-kinase (PI3K) inhibitor LY-294002, on gene expression in a well-established human glioblastoma cell system, U87MG. Under conditions which inhibit cell proliferation, LY-294002 and fucoxanthin modulate many pathways in common, including the retinoblastoma, DNA damage, DNA replication and cell cycle pathways. In sharp contrast, we see profound differences in the expression of genes characteristic of pathways such as apoptosis and lipid metabolism, contributing to the development of a differentiated and distinctive drug-induced gene expression signature for each compound. Furthermore, in combination, fucoxanthin synergizes with LY-294002 in inhibiting the growth of U87MG cells, suggesting complementarity in their molecular modes of action and pointing to further treatment combinations. The synergy we observe between the dietary nutraceutical fucoxanthin and the synthetic chemical LY-294002 in producing growth arrest in glioblastoma, illustrates the potential of nutri-pharmaceutical combinations in targeting this challenging disease.

Understanding Conditional Associations between ToxCast in Vitro Readouts and the Hepatotoxicity of Compounds Using Rule-Based Methods.

Current in vitro models for hepatotoxicity commonly suffer from low detection rates due to incomplete coverage of bioactivity space. Additionally, in vivo exposure measures such as Cmax are used for hepatotoxicity screening and are unavailable early on. Here we propose a novel rule-based framework to extract interpretable and biologically meaningful multiconditional associations to prioritize in vitro end points for hepatotoxicity and understand the associated physicochemical conditions. The data used in this study were derived for 673 compounds from 361 ToxCast bioactivity measurements and 29 calculated physicochemical properties against two lowest effective levels (LEL) of rodent hepatotoxicity from ToxRefDB, namely 15 mg/kg/day and 500 mg/kg/day. To achieve 80% coverage of toxic compounds, 35 rules with accuracies ranging from 96% to 73% using 39 unique ToxCast assays are needed at a threshold level of 500 mg/kg/day, whereas to describe the same coverage at a threshold of 15 mg/kg/day, 20 rules with accuracies of between 98% and 81% were needed, comprising 24 unique assays. Despite the 33-fold difference in dose levels, we found relative consistency in the key mechanistic groups in rule clusters, namely (i) activities against Cytochrome P, (ii) immunological responses, and (iii) nuclear receptor activities. Less specific effects, such as oxidative stress and cell cycle arrest, were used more by rules to describe toxicity at the level of 500 mg/kg/day. Although the endocrine disruption through nuclear receptor activity formulated an essential cluster of rules, this bioactivity was not covered in four commercial assay setups for hepatotoxicity. Using an external set of 29 drugs with drug-induced liver injury (DILI) labels, we found that promiscuity over important assays discriminates between compounds with different levels of liver injury. In vitro-in vivo associations were also improved by incorporating physicochemical properties especially for the potent, 15 mg/kg/day toxicity level as well for assays describing nuclear receptor activity and phenotypic changes. The most frequently used physicochemical properties, predictive for hepatotoxicity in combination with assay activities, are linked to bioavailability, which were the number of rotatable bonds (less than 7) at a of level of 15 mg/kg/day and the number of rings (of less than 3) at level of 500 mg/kg/day. In summary, hepatotoxicity cannot very well be captured by single assay end points, but better by a combination of bioactivities in relevant assays, with the likelihood of hepatotoxicity increasing with assay promiscuity. Together, these findings can be used to prioritize assay combinations that are appropriate to assess potential hepatotoxicity.

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening.

The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the common scenario in early stage drug discovery where lots of inactive data but no active data points are available (i.e., extrapolation from the low-activity to the high-activity range) has not been thoroughly examined yet. To this aim, we have designed an iterative virtual screening strategy which was evaluated on 25 diverse bioactivity data sets from ChEMBL. We benchmark the efficiency of random forest (RF), multiple linear regression, ridge regression, similarity searching, and random selection of compounds to identify a highly active molecule in the test set among a large number of low-potency compounds. We use the number of iterations required to find this active molecule to evaluate the performance of each experimental setup. We show that linear and ridge regression often outperform RF and similarity searching, reducing the number of iterations to find an active compound by a factor of 2 or more. Even simple regression methods seem better able to extrapolate to high-bioactivity ranges than RF, which only provides output values in the range covered by the training set. In addition, examination of the scaffold diversity in the data sets used shows that in some cases similarity searching and RF require two times as many iterations as random selection depending on the chemical space covered in the initial training data. Lastly, we show using bioactivity data for COX-1 and COX-2 that our framework can be extended to multitarget drug discovery, where compounds are selected by concomitantly considering their activity against multiple targets. Overall, this study provides an approach for iterative screening where only inactive data are present in early stages of drug discovery in order to discover highly potent compounds and the best experimental set up in which to do so.

Common structural and pharmacophoric features of mPGES-1 and LTC4S.

Prostaglandins and leukotrienes are produced in the COX and 5-LOX pathways of the inflammatory process. The current drugs target the upstream enzymes of either of the two pathways, leading to side effects. We have attempted to target the downstream enzymes simultaneously. Two compounds 2 and 3 (10 µM), identified by virtual screening, inhibited mPGES-1 activity by 53.4 ± 4.0 and 53.9 ± 8.1%, respectively. Structural and pharmacophore studies revealed a set of common residues between LTC4S and mPGES-1 as well as four-point pharmacophore mapping onto the inhibitors of both these enzymes as well as 2 and 3. These structural and pharmacophoric features may be exploited for ligand- and structure-based screening of inhibitors and designing of dual inhibitors.

Fragment-Based Drug Discovery of Phosphodiesterase Inhibitors.

Phosphodiesterases are proving to be fruitful targets for drug discovery. At the same time fragment-based drug discovery has matured into a powerful and widely applied technique. In this communication we review the application of fragment-based drug discovery for the successful identification of novel 3',5'-cyclic nucleotide phosphodiesterase (PDE) inhibitors, concentrating on both experimental and computational strategies for fragment screening and hit-to-lead development. To this end, we also mine the open access databases ChEMBL and PDB for fragments showing PDE inhibitory activity, as well as SureChEMBL for recent PDE related patents, to provide a wider context for exploring fragment diversity. Together these approaches form an integrated experimental and computational platform to exploit fragment-based drug discovery for this important gene superfamily.

Toward Understanding the Cold, Hot, and Neutral Nature of Chinese Medicines Using in Silico Mode-of-Action Analysis.

One important, however, poorly understood, concept of Traditional Chinese Medicine (TCM) is that of hot, cold, and neutral nature of its bioactive principles. To advance the field, in this study, we analyzed compound-nature pairs from TCM on a large scale (>23 000 structures) via chemical space visualizations to understand its physicochemical domain and in silico target prediction to understand differences related to their modes-of-action (MoA) against proteins. We found that overall TCM natures spread into different subclusters with specific molecular patterns, as opposed to forming coherent global groups. Compounds associated with cold nature had a lower clogP and contain more aliphatic rings than the other groups and were found to control detoxification, heat-clearing, heart development processes, and have sedative function, associated with "Mental and behavioural disorders" diseases. While compounds associated with hot nature were on average of lower molecular weight, have more aromatic ring systems than other groups, frequently seemed to control body temperature, have cardio-protection function, improve fertility and sexual function, and represent excitatory or activating effects, associated with "endocrine, nutritional and metabolic diseases" and "diseases of the circulatory system". Compounds associated with neutral nature had a higher polar surface area and contain more cyclohexene moieties than other groups and seem to be related to memory function, suggesting that their nature may be a useful guide for their utility in neural degenerative diseases. We were hence able to elucidate the difference between different nature classes in TCM on the molecular level, and on a large data set, for the first time, thereby helping a better understanding of TCM nature theory and bridging the gap between traditional medicine and our current understanding of the human body.

Improving Screening Efficiency through Iterative Screening Using Docking and Conformal Prediction.

High-throughput screening, where thousands of molecules rapidly can be assessed for activity against a protein, has been the dominating approach in drug discovery for many years. However, these methods are costly and require much time and effort. In order to suggest an improvement to this situation, in this study, we apply an iterative screening process, where an initial set of compounds are selected for screening based on molecular docking. The outcome of the initial screen is then used to classify the remaining compounds through a conformal predictor. The approach was retrospectively validated using 41 targets from the Directory of Useful Decoys, Enhanced (DUD-E), ensuring scaffold diversity among the active compounds. The results show that 57% of the remaining active compounds could be identified while only screening 9.4% of the database. The overall hit rate (7.6%) was also higher than when using docking alone (5.2%). When limiting the search to the top scored compounds from docking, 39.6% of the active compounds could be identified, compared to 13.5% when screening the same number of compounds solely based on docking. The use of conformal predictors also gives a clear indication of the number of compounds to screen in the next iteration. These results indicate that iterative screening based on molecular docking and conformal prediction can be an efficient way to find active compounds while screening only a small part of the compound collection.

Diversity selection, screening and quantitative structure-activity relationships of osmolyte-like additive effects on the thermal stability of a monoclonal antibody.

Solvents used for therapeutic proteins in downstream processing and in formulations often contain stabilizing additives that inhibit denaturation and aggregation. Such additives are mostly selected based on their positive effect on thermal stability of the protein, and are often derived from naturally occuring osmolytes. To better understand the structural basis underlying the effect of additives, we selected a diverse library of compounds comprising 79 compounds of the polyol, amino acid and methylamine chemical classes and determined the effect of each compound on thermal stability of a monoclonal antibody as a function of compound concentration. Thermal stabilization of the antibody was influenced by solution pH. Quantitative structure-activity relationships (QSAR) were derived by partial least squares regression for individual compound classes and globally. The global model suggests that ligands with a phenyl ring will decrease the Tm, while highly soluble, polar compounds with at least two hydrogen bond donors will increase the Tm. This approach may be beneficial for further studies on the influence of other solution conditions like ionic strength and buffer species on additive-mediated protein stabilization.

Understanding the mode-of-action of Cassia auriculata via in silico and in vivo studies towards validating it as a long term therapy for type II diabetes.

ETHNOPHARMACOLOGICAL RELEVANCE: Cassia auriculata (CA) is used as an antidiabetic therapy in Ayurvedic and Siddha practice. This study aimed to understand the mode-of-action of CA via combined cheminformatics and in vivo biological analysis. In particular, the effect of 10 polyphenolic constituents of CA in modulating insulin and immunoprotective pathways were studied. MATERIALS AND METHODS: In silico target prediction was first employed to predict the probability of the polyphenols interacting with key protein targets related to insulin signalling, based on a model trained on known bioactivity data and chemical similarity considerations. Next, CA was investigated in in vivo studies where induced type 2 diabetic rats were treated with CA for 28 days and the expression levels of genes regulating insulin signalling pathway, glucose transporters of hepatic (GLUT2) and muscular (GLUT4) tissue, insulin receptor substrate (IRS), phosphorylated insulin receptor (AKT), gluconeogenesis (G6PC and PCK-1), along with inflammatory mediators genes (NF-κB, IL-6, IFN-γ and TNF-α) and peroxisome proliferators-activated receptor gamma (PPAR-γ) were determined by qPCR. RESULTS: In silico analysis shows that several of the top 20 enriched targets predicted for the constituents of CA are involved in insulin signalling pathways e.g. PTPN1, PCK-α, AKT2, PI3K-γ. Some of the predictions were supported by scientific literature such as the prediction of PI3K for epigallocatechin gallate. Based on the in silico and in vivo findings, we hypothesized that CA may enhance glucose uptake and glucose transporter expressions via the IRS signalling pathway. This is based on AKT2 and PI3K-γ being listed in the top 20 enriched targets. In vivo analysis shows significant increase in the expression of IRS, AKT, GLUT2 and GLUT4. CA may also affect the PPAR-γ signalling pathway. This is based on the CA-treated groups showing significant activation of PPAR-γ in the liver compared to control. PPAR-γ was predicted by the in silico target prediction with high normalisation rate although it was not in the top 20 most enriched targets. CA may also be involved in the gluconeogenesis and glycogenolysis in the liver based on the downregulation of G6PC and PCK-1 genes seen in CA-treated groups. In addition, CA-treated groups also showed decreased cholesterol, triglyceride, glucose, CRP and Hb1Ac levels, and increased insulin and C-peptide levels. These findings demonstrate the insulin secretagogue and sensitizer effect of CA. CONCLUSION: Based on both an in silico and in vivo analysis, we propose here that CA mediates glucose/lipid metabolism via the PI3K signalling pathway, and influence AKT thereby causing insulin secretion and insulin sensitivity in peripheral tissues. CA enhances glucose uptake and expression of glucose transporters in particular via the upregulation of GLUT2 and GLUT4. Thus, based on its ability to modulate immunometabolic pathways, CA appears as an attractive long term therapy for T2DM even at relatively low doses.

Data-Driven Derivation of an "Informer Compound Set" for Improved Selection of Active Compounds in High-Throughput Screening.

Despite the usefulness of high-throughput screening (HTS) in drug discovery, for some systems, low assay throughput or high screening cost can prohibit the screening of large numbers of compounds. In such cases, iterative cycles of screening involving active learning (AL) are employed, creating the need for smaller "informer sets" that can be routinely screened to build predictive models for selecting compounds from the screening collection for follow-up screens. Here, we present a data-driven derivation of an informer compound set with improved predictivity of active compounds in HTS, and we validate its benefit over randomly selected training sets on 46 PubChem assays comprising at least 300,000 compounds and covering a wide range of assay biology. The informer compound set showed improvement in BEDROC(α = 100), PRAUC, and ROCAUC values averaged over all assays of 0.024, 0.014, and 0.016, respectively, compared to randomly selected training sets, all with paired t-test p-values <10(-15). A per-assay assessment showed that the BEDROC(α = 100), which is of particular relevance for early retrieval of actives, improved for 38 out of 46 assays, increasing the success rate of smaller follow-up screens. Overall, we showed that an informer set derived from historical HTS activity data can be employed for routine small-scale exploratory screening in an assay-agnostic fashion. This approach led to a consistent improvement in hit rates in follow-up screens without compromising scaffold retrieval. The informer set is adjustable in size depending on the number of compounds one intends to screen, as performance gains are realized for sets with more than 3,000 compounds, and this set is therefore applicable to a variety of situations. Finally, our results indicate that random sampling may not adequately cover descriptor space, drawing attention to the importance of the composition of the training set for predicting actives.

Immediate responses to individual dialogic music therapy in patients in low awareness states.

OBJECTIVE: The aim of this study was to analyse immediate responses to individual dialogic music therapy (IDMT) of patients with unresponsive wakefulness syndrome (UWS) and individuals in a minimally conscious state (MCS) and to develop an assessment tool for IDMT. METHODS: Seven patients were subjected to three conditions: (1) sounds and stimuli of the daily environment immediately before IDMT, (2) specific improvisational music therapy intended to establish a dialogue with the patient (IDMT) and (3) sounds and stimuli of the daily environment immediately after IDMT. Video recordings were analysed by six independent assessors using 'Music Therapy in a Vegetative or Minimally Conscious State (MUVES)', an assessment tool developed in this study. Diagnosis of UWS or MCS was established using the coma recovery scale-revised (CRS-R). RESULTS: During IDMT, MUVES total score was higher than during the other conditions (mean difference = 3.36; p = 0.02). During IDMT, there was no significant difference in MUVES total score between the UWS and MCS sub-groups (p = 0.29). Mean inter-rater-reliability of MUVES total score was 0.76. CONCLUSIONS: IDMT may induce immediate responses in patients in low awareness states, particularly also in patients with UWS. MUVES appears to be an acceptably reliable assessment tool for IDMT.

Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes.

Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes from in silico target prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, "the foundation of acquired constitution" that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications.

Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis In-house HTS Data.

With increased automation and larger compound collections, the development of high-throughput screening (HTS) started replacing previous approaches in drug discovery from around the 1980s onward. However, even today it is not always appropriate, or even feasible, to screen large collections of compounds in a particular assay. Here, we present an efficient method for iterative screening of small subsets of compound libraries. With this method, the retrieval of active compounds is optimized using their structural information and biological activity fingerprints. We validated this approach retrospectively on 34 Novartis in-house HTS assays covering a wide range of assay biology, including cell proliferation, antibacterial activity, gene expression, and phosphorylation. This method was employed to retrieve subsets of compounds for screening, where selected hits from any given round of screening were used as starting points to select chemically and biologically similar compounds for the next iteration. By only screening ∼1% of the full screening collection (∼15 000 compounds), the method consistently retrieves diverse compounds belonging to the top 0.5% of the most active compounds for the HTS campaign. For most of the assays, over half of the compounds selected by the method were found to be among the 5% most active compounds of the corresponding full-deck HTS. In addition, the stringency of the iterative method can be modified depending on the number of compounds one can afford to screen, making it a flexible tool to discover active compounds efficiently.