Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development.

In drug development, preclinical safety and pharmacokinetics assessments of candidate drugs to ensure the safety profile are a must. While in vivo and in vitro tests are traditionally used, experimental determinations have disadvantages, as they are usually time-consuming and costly. In silico predictions of these preclinical endpoints have each been developed in the past decades. However, only a few web-based tools have integrated different models to provide a simple one-step platform to help researchers thoroughly evaluate potential drug candidates. To efficiently achieve this approach, a platform for preclinical evaluation must not only predict key ADMET (absorption, distribution, metabolism, excretion and toxicity) properties but also provide some guidance on structural modifications to improve the undesired properties. In this review, we organized and compared several existing integrated web servers that can be adopted in preclinical drug development projects to evaluate the subject of interest. We also introduced our new web server, Virtual Rat, as an alternative choice to profile the properties of drug candidates. In Virtual Rat, we provide not only predictions of important ADMET properties but also possible reasons as to why the model made those structural predictions. Multiple models were implemented into Virtual Rat, including models for predicting human ether-a-go-go-related gene (hERG) inhibition, cytochrome P450 (CYP) inhibition, mutagenicity (Ames test), blood-brain barrier penetration, cytotoxicity and Caco-2 permeability. Virtual Rat is free and has been made publicly available at https://virtualrat.cmdm.tw/.

PanGPCR: predictions for multiple targets, repurposing and side effects.

SUMMARY: Drug discovery targeting G protein-coupled receptors (GPCRs), the largest known class of therapeutic targets, is challenging. To facilitate the rapid discovery and development of GPCR drugs, we built a system, PanGPCR, to predict multiple potential GPCR targets and their expression locations in the tissues, side effects and possible repurposing of GPCR drugs. With PanGPCR, the compound of interest is docked to a library of 36 experimentally determined crystal structures comprising of 46 docking sites for human GPCRs, and a ranked list is generated from the docking studies to assess all GPCRs and their binding affinities. Users can determine a given compound's GPCR targets and its repurposing potential accordingly. Moreover, potential side effects collected from the SIDER (Side-Effect Resource) database and mapped to 45 tissues and organs are provided by linking predicted off-targets and their expressed sequence tag profiles. With PanGPCR, multiple targets, repurposing potential and side effects can be determined by simply uploading a small ligand. AVAILABILITY AND IMPLEMENTATION: PanGPCR is freely accessible at https://gpcrpanel.cmdm.tw/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Lipidomics of children and adolescents exposed to multiple industrial pollutants.

BACKGROUND: There are limited studies on the lipidomics of children and adolescents exposed to multiple industrial pollutants. OBJECTIVES: In this study, we aimed to investigate lipid profile perturbations in 99 children and adolescents (aged 9-15) who lived in a polluted area surrounding the largest petrochemical complex in Taiwan. Previous studies have reported increased risks of acute and chronic diseases including liver dysfunctions and chronic kidney disease (CKD) in residents living in this area. METHODS: We measured urinary concentrations of 11 metals and metalloids and polycyclic aromatic hydrocarbons (PAHs) metabolite 1-hydroxypyrene (1-OHP) as exposure biomarkers, and urinary oxidative stress biomarkers and serum acylcarnitines as early health effect biomarkers. The association between individual exposure biomarkers and early health effect biomarkers were analyzed using linear regression, while association of combined exposure biomarkers with four oxidative stress biomarkers and acylcarnitines were analyzed using weighted quantile sum (WQS) regression. Lipid profiles were analyzed using an untargeted liquid chromatography mass spectrometry-based technique. "Meet-in-the-middle" approach was applied to identify potential lipid features that linked multiple industrial pollutants exposure with early health effects. RESULTS: We identified 15 potential lipid features that linked elevated multiple industrial pollutants exposure with three increased oxidative stress biomarkers and eight deregulated serum acylcarnitines, including one lysophosphatidylcholines (LPCs), four phosphatidylcholines (PCs), and two sphingomyelins (SMs) that were up-regulated in high exposure group compared to low exposure group, and two LPCs, four PCs, and two phosphatidylinositols (PIs) down-regulated in high exposure group compared to low exposure group. CONCLUSION: Our findings could provide information for understanding the health effects, including early indicators and biological mechanism identification, of children and adolescents exposed to multiple industrial pollutants during critical stages of development.

PgpRules: a decision tree based prediction server for P-glycoprotein substrates and inhibitors.

SUMMARY: P-glycoprotein (P-gp) is a member of ABC transporter family that actively pumps xenobiotics out of cells to protect organisms from toxic compounds. P-gp substrates can be easily pumped out of the cells to reduce their absorption; conversely P-gp inhibitors can reduce such pumping activity. Hence, it is crucial to know if a drug is a P-gp substrate or inhibitor in view of pharmacokinetics. Here we present PgpRules, an online P-gp substrate and P-gp inhibitor prediction server with ruled-sets. The two models were built using classification and regression tree algorithm. For each compound uploaded, PgpRules not only predicts whether the compound is a P-gp substrate or a P-gp inhibitor, but also provides the rules containing chemical structural features for further structural optimization. AVAILABILITY AND IMPLEMENTATION: PgpRules is freely accessible at https://pgprules.cmdm.tw/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolomics of Children and Adolescents Exposed to Industrial Carcinogenic Pollutants.

Studies on metabolomes of carcinogenic pollutants among children and adolescents are limited. We aim to identify metabolic perturbations in 107 children and adolescents (aged 9-15) exposed to multiple carcinogens in a polluted area surrounding the largest petrochemical complex in Taiwan. We measured urinary concentrations of eight carcinogen exposure biomarkers (heavy metals and polycyclic aromatic hydrocarbons (PAHs) represented by 1-hydroxypyrene), and urinary oxidative stress biomarkers and serum acylcarnitines as biomarkers of early health effects. Serum metabolomics was analyzed using a liquid chromatography mass spectrometry-based method. Pathway analysis and "meet-in-the-middle" approach were applied to identify potential metabolites and biological mechanisms linking carcinogens exposure with early health effects. We found 10 potential metabolites possibly linking increased exposure to IARC group 1 carcinogens (As, Cd, Cr, Ni) and group 2 carcinogens (V, Hg, PAHs) with elevated oxidative stress and deregulated serum acylcarnitines, including inosine monophosphate and adenosine monophosphate (purine metabolism), malic acid and oxoglutaric acid (citrate cycle), carnitine (fatty acid metabolism), and pyroglutamic acid (glutathione metabolism). Purine metabolism was identified as the possible mechanism affected by children and adolescents' exposure to carcinogens. These findings contribute to understanding the health effects of childhood and adolescence exposure to multiple industrial carcinogens during critical periods of development.

Trowaglerix Venom Polypeptides As a Novel Antithrombotic Agent by Targeting Immunoglobulin-Like Domains of Glycoprotein VI in Platelet.

OBJECTIVE: Currently prescribed antiplatelet drugs have 1 common side effect-an increased risk of hemorrhage and thrombocytopenia. On the contrary, bleeding defects associated with glycoprotein VI (GPVI) expression deficiency are usually slightly prolonged bleeding times. However, GPVI antagonists are lacking in clinic. APPROACH AND RESULTS: Using reverse-phase high-performance liquid chromatography and sequencing, we revealed the partial sequence of trowaglerix α subunit, a potent specific GPVI-targeting snaclec (snake venom C-type lectin protein). Hexapeptide (Troα6 [trowaglerix a chain hexapeptide, CKWMNV]) and decapeptide (Troα10) derived from trowaglerix specifically inhibited collagen-induced platelet aggregation through blocking platelet GPVI receptor. Computational peptide design helped to design a series of Troα6/Troα10 peptides. Protein docking studies on these decapeptides and GPVI suggest that Troα10 was bound at the lower surface of D1 domain and outer surface of D2 domain, which was at the different place of the collagen-binding site and the scFv (single-chain variable fragment) D2-binding site. The newly discovered site was confirmed by inhibitory effects of polyclonal antibodies on collagen-induced platelet aggregation. This indicates that D2 domain of GPVI is a novel and important binding epitope on GPVI-mediated platelet aggregation. Troα6/Troα10 displayed prominent inhibitory effect of thrombus formation in fluorescein sodium-induced platelet thrombus formation of mesenteric venules and ferric chloride-induced carotid artery injury thrombosis model without prolonging the in vivo bleeding time. CONCLUSIONS: We develop a novel antithrombotic peptides derived from trowaglerix that acts through GPVI antagonism with greater safety-no severe bleeding. The binding epitope of polypeptides on GPVI is novel and important. These hexa/decapeptides have therapeutic potential for developing ideal small-mass GPVI antagonists for arterial thrombogenic diseases.

CypRules: a rule-based P450 inhibition prediction server.

UNLABELLED: Cytochrome P450 (CYPs) are the major enzymes involved in drug metabolism and bioactivation. Inhibition models were constructed for five of the most popular enzymes from the CYP superfamily in human liver. The five enzymes chosen for this study, namely CYP1A2, CYP2D6, CYP2C19, CYP2C9 and CYP3A4, account for 90% of the xenobiotic and drug metabolism in human body. CYP enzymes can be inhibited or induced by various drugs or chemical compounds. In this work, a rule-based CYP inhibition prediction online server, CypRules, was created based on predictive models generated by the rule-based C5.0 algorithm. CypRules can predict and provide structural rulesets for CYP inhibition for each compound uploaded to the server. Capable of fast execution performance, it can be used for virtual high-throughput screening (VHTS) of a large set of testing compounds. AVAILABILITY AND IMPLEMENTATION: CypRules is freely accessible at http://cyprules.cmdm.tw/ and models, descriptor and program files for all compounds are publically available at http://cyprules.cmdm.tw/sources/sources.rar.

Ion trace detection algorithm to extract pure ion chromatograms to improve untargeted peak detection quality for liquid chromatography/time-of-flight mass spectrometry-based metabolomics data.

Able to detect known and unknown metabolites, untargeted metabolomics has shown great potential in identifying novel biomarkers. However, elucidating all possible liquid chromatography/time-of-flight mass spectrometry (LC/TOF-MS) ion signals in a complex biological sample remains challenging since many ions are not the products of metabolites. Methods of reducing ions not related to metabolites or simply directly detecting metabolite related (pure) ions are important. In this work, we describe PITracer, a novel algorithm that accurately detects the pure ions of a LC/TOF-MS profile to extract pure ion chromatograms and detect chromatographic peaks. PITracer estimates the relative mass difference tolerance of ions and calibrates the mass over charge (m/z) values for peak detection algorithms with an additional option to further mass correction with respect to a user-specified metabolite. PITracer was evaluated using two data sets containing 373 human metabolite standards, including 5 saturated standards considered to be split peaks resultant from huge m/z fluctuation, and 12 urine samples spiked with 50 forensic drugs of varying concentrations. Analysis of these data sets show that PITracer correctly outperformed existing state-of-art algorithm and extracted the pure ion chromatograms of the 5 saturated standards without generating split peaks and detected the forensic drugs with high recall, precision, and F-score and small mass error.

Fasting but not changes of plasma metabolome during oral glucose tolerance tests improves the diagnosis of severe coronary arterial stenosis.

BACKGROUND: Noninvasive stress tests for the diagnosis of significant coronary arterial stenosis requiring intervention are not perfect. We investigated whether plasma metabolome during the oral glucose tolerance test (OGTT) can improve the diagnosis. METHODS: A total of 117 subjects with positive stress test results who received coronary angiography were recruited. After excluding subjects with a history of myocardial infarction and subjects who did not receive OGTT, the 18 subjects without significant stenosis were selected as controls. Another 18 age- and sex-matched subjects with significant stenosis were selected as cases. Plasma metabolome from samples obtained in fasting, 30 and 120 min after OGTT was measured using liquid chromatography combined with time-of-flight mass spectrometry. RESULTS: We found five metabolites which can identify patients with significant stenosis independent to clinical risk factors, including diabetes, hypertension, hypercholesterolaemia, smoking and history of percutaneous coronary intervention (all P < 0·05). The area under the receiver operating characteristic (AUROC) curve of these metabolites was 0·799-0·818 at fasting and 30 min after OGTT. The addition of metabolites to clinical factors increases the AUROC (0·616, 95%CI 0·429-0·803 for model with clinical factors only; 0·824, 95%CI 0·689-0·959 for model with four metabolites and clinical factors). The changes of plasma metabolite levels during OGTT did not significantly improve the diagnostic performance. CONCLUSIONS: Fasting plasma metabolome, but not change of plasma metabolome during OGTT, can improve the diagnosis of significant stenosis in patients with positive noninvasive stress test results.

Rule-based classification models of molecular autofluorescence.

Fluorescence-based detection has been commonly used in high-throughput screening (HTS) assays. Autofluorescent compounds, which can emit light in the absence of artificial fluorescent markers, often interfere with the detection of fluorophores and result in false positive signals in these assays. This interference presents a major issue in fluorescence-based screening techniques. In an effort to reduce the time and cost that will be spent on prescreening of autofluorescent compounds, in silico autofluorescence prediction models were developed for selected fluorescence-based assays in this study. Five prediction models were developed based on the respective fluorophores used in these HTS assays, which absorb and emit light at specific wavelengths (excitation/emission): Alexa Fluor 350 (A350) (340 nm/450 nm), 7-amino-4-trifluoromethyl-coumarin (AFC) (405 nm/520 nm), Alexa Fluor 488 (A488) (480 nm/540 nm), Rhodamine (547 nm/598 nm), and Texas Red (547 nm/618 nm). The C5.0 rule-based classification algorithm and PubChem 2D chemical structure fingerprints were used to develop prediction models. To optimize the accuracies of these prediction models despite the highly imbalanced ratio of fluorescent versus nonfluorescent compounds presented in the collected data sets, oversampling and undersampling strategies were applied. The average final accuracy achieved for the training set was 97%, and that for the testing set was 92%. In addition, five external data sets were used to further validate the models. Ultimately, 14 representative structural features (or rules) were determined to efficiently predict autofluorescence in data sets containing both fluorescent and nonfluorescent compounds. Several cases were illustrated in this study to demonstrate the applicability of these rules.

Rule-Based Prediction Models of Cytochrome P450 Inhibition.

Hepatotoxicity, drug-induced liver injury, and competitive Cytochrome P-450 (CYP) isozyme binding are serious problems associated with drug use. It would be favorable to avoid or to understand potential CYP inhibition at the developmental stages. However, current in silico CYP prediction models or available public prediction servers can provide only yes/no classification results for just one or a few CYP enzymes. In this study, we utilized a rule-based C5.0 algorithm with different descriptors, including PaDEL, Mold(2), and PubChem fingerprints, to construct rule-based inhibition prediction models for five major CYP enzymes-CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4-that account for 90% of drug oxidation or hydrolysis. We also developed a rational sampling algorithm for the selection of compounds in the training data set, to enhance the performance of these CYP prediction models. The optimized models include several improved features. First, the final models significantly outperformed all of the currently available models. Second, the final models can also be used for rapid virtual screening of a large set of compounds due to their ruleset-based nature. Moreover, such rule-based prediction models can provide rulesets for structural features related to the five major CYP enzymes. The five most significant rules for CYP inhibition were identified for each CYP enzymes and discussed. An example was chosen for each of the five CYP enzymes to demonstrate how rule-based models can be used to gain insights into structural features that correspond with CYP inhibitions. A newer version of the freely accessible CYP prediction server, CypRules, is presented here as a result of the aforementioned improvements.

Batch Normalizer: a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography/time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods.

Metabolomics is a powerful tool for understanding phenotypes and discovering biomarkers. Combinations of multiple batches or data sets in large cross-sectional epidemiology studies are frequently utilized in metabolomics, but various systematic biases can introduce both batch and injection order effects and often require proper calibrations prior to chemometric analyses. We present a novel algorithm, Batch Normalizer, to calibrate large scale metabolomic data. Batch Normalizer utilizes a regression model with consideration of the total abundance of each sample to improve its calibration performance, and it is able to remove both batch effect and injection order effects. This calibration method was tested using liquid chromatography/time-of-flight mass spectrometry (LC/TOF-MS) chromatograms of 228 plasma samples and 23 pooled quality control (QC) samples. We evaluated the performance of Batch Normalizer by examining the distribution of relative standard deviation (RSD) for all peaks detected in the pooled QC samples, the average Pearson correlation coefficients for all peaks between any two of QC samples, and the distribution of QC samples in the scores plot of a principal component analysis (PCA). After calibration by Batch Normalizer, the number of peaks in QC samples with RSD less than 15% increased from 11 to 914, all of the QC samples were closely clustered in PCA scores plot, and the average Pearson correlation coefficients for all peaks of QC samples increased from 0.938 to 0.976. This method was compared to 7 commonly used calibration methods. We discovered that using Batch Normalizer to calibrate LC/TOF-MS data produces the best calibration results.

Distribution-based classification method for baseline correction of metabolomic 1D proton nuclear magnetic resonance spectra.

Baseline distortion in 1D (1)H NMR data complicates the quantification of individual components of biofluids in metabolomic experiments. Current 1D (1)H NMR baseline correction methods usually require manual parameter and filter tuning by experienced users to obtain desirable results from complex metabolomic spectra, thus becoming prone to correction variation and biased quantification. We present a novel alternative method, BaselineCorrector, for automatically estimating the baselines of 1D (1)H NMR metabolomic data. By collecting the standard deviations of spectral intensities, using a moving window to slide through a spectrum, BaselineCorrector can model the distribution of noise standard deviation as a derived chi-squared distribution in each window and then determine optimal parameters for least-error classification of signal and noise. Due to the universal property of noise distributions, BaselineCorrector can robustly recognize the baseline segments in various spectra. In addition to the commonly used 1D NOESY and CPMG pulse sequences, BaselineCorrector also provides an algorithm for correcting diffusion-edited NMR spectra. Using its classification model, BaselineCorrector is able to preserve low signal peaks and correctly handle wide, overlapping peaks in complex metabolomic spectra.

Oversampling to overcome overfitting: exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods.

The traditional biological assay is very time-consuming, and thus the ability to quickly screen large numbers of compounds against a specific biological target is appealing. To speed up the biological evaluation of compounds, high-throughput screening is widely used in the fields of biomedical, biological information, and drug discovery. The research presented in this study focuses on the use of support vector machines, a machine learning method, various classes of molecular descriptors, and different sampling techniques to overcome overfitting to classify compounds for cytotoxicity with respect to the Jurkat cell line. The cell cytotoxicity data set is imbalanced (a few active compounds and very many inactive compounds), and the ability of the predictive modeling methods is adversely affected in these situations. Commonly imbalanced data sets are overfit with respect to the dominant classified end point; in this study the models routinely overfit toward inactive (noncytotoxic) compounds when the imbalance was substantial. Support vector machine (SVM) models were used to probe the proficiency of different classes of molecular descriptors and oversampling ratios. The SVM models were constructed from 4D-FPs, MOE (1D, 2D, and 21/2D), noNP+MOE, and CATS2D trial descriptors pools and compared to the predictive abilities of CATS2D-based random forest models. Compared to previous results in the literature, the SVM models built from oversampled data sets exhibited better predictive abilities for the training and external test sets.

Dependence of QSAR models on the selection of trial descriptor sets: a demonstration using nanotoxicity endpoints of decorated nanotubes.

Little attention has been given to the selection of trial descriptor sets when designing a QSAR analysis even though a great number of descriptor classes, and often a greater number of descriptors within a given class, are now available. This paper reports an effort to explore interrelationships between QSAR models and descriptor sets. Zhou and co-workers (Zhou et al., Nano Lett. 2008, 8 (3), 859-865) designed, synthesized, and tested a combinatorial library of 80 surface modified, that is decorated, multi-walled carbon nanotubes for their composite nanotoxicity using six endpoints all based on a common 0 to 100 activity scale. Each of the six endpoints for the 29 most nanotoxic decorated nanotubes were incorporated as the training set for this study. The study reported here includes trial descriptor sets for all possible combinations of MOE, VolSurf, and 4D-fingerprints (FP) descriptor classes, as well as including and excluding explicit spatial contributions from the nanotube. Optimized QSAR models were constructed from these multiple trial descriptor sets. It was found that (a) both the form and quality of the best QSAR models for each of the endpoints are distinct and (b) some endpoints are quite dependent upon 4D-FP descriptors of the entire nanotube-decorator complex. However, other endpoints yielded equally good models only using decorator descriptors with and without the decorator-only 4D-FP descriptors. Lastly, and most importantly, the quality, significance, and interpretation of a QSAR model were found to be critically dependent on the trial descriptor sets used within a given QSAR endpoint study.

Template-based de novo design for type II kinase inhibitors and its extented application to acetylcholinesterase inhibitors.

There is a compelling need to discover type II inhibitors targeting the unique DFG-out inactive kinase conformation since they are likely to possess greater potency and selectivity relative to traditional type I inhibitors. Using a known inhibitor, such as a currently available and approved drug or inhibitor, as a template to design new drugs via computational de novo design is helpful when working with known ligand-receptor interactions. This study proposes a new template-based de novo design protocol to discover new inhibitors that preserve and also optimize the binding interactions of the type II kinase template. First, sorafenib (Nexavar) and nilotinib (Tasigna), two type II inhibitors with different ligand-receptor interactions, were selected as the template compounds. The five-step protocol can reassemble each drug from a large fragment library. Our procedure demonstrates that the selected template compounds can be successfully reassembled while the key ligand-receptor interactions are preserved. Furthermore, to demonstrate that the algorithm is able to construct more potent compounds, we considered kinase inhibitors and other protein dataset, acetylcholinesterase (AChE) inhibitors. The de novo optimization was initiated using a template compound possessing a less than optimal activity from a series of aminoisoquinoline and TAK-285 inhibiting type II kinases, and E2020 derivatives inhibiting AChE respectively. Three compounds with greater potency than the template compound were discovered that were also included in the original congeneric series. This template-based lead optimization protocol with the fragment library can help to design compounds with preferred binding interactions of known inhibitors automatically and further optimize the compounds in the binding pockets.

Predictive toxicology modeling: protocols for exploring hERG classification and Tetrahymena pyriformis end point predictions.

The inclusion and accessibility of different methodologies to explore chemical data sets has been beneficial to the field of predictive modeling, specifically in the chemical sciences in the field of Quantitative Structure-Activity Relationship (QSAR) modeling. This study discusses using contemporary protocols and QSAR modeling methods to properly model two biomolecular systems that have historically not performed well using traditional and three-dimensional QSAR methodologies. Herein, we explore, analyze, and discuss the creation of a classification human Ether-a-go-go Related Gene (hERG) potassium channel model and a continuous Tetrahymena pyriformis (T. pyriformis) model using Support Vector Machine (SVM) and Support Vector Regression (SVR), respectively. The models are constructed with three types of molecular descriptors that capture the gross physicochemical features of the compounds: (i) 2D, 2 1/2D, and 3D physical features, (ii) VolSurf-like molecular interaction fields, and (iii) 4D-Fingerprints. The best hERG SVM model achieved 89% accuracy and the three-best SVM models were able to screen a Pubchem data set with an accuracy of 97%. The best T. pyriformis model had an R(2) value of 0.924 for the training set and was able to predict the continuous end points for two test sets with R(2) values of 0.832 and 0.620, respectively. The studies presented within demonstrate the predictive ability (classification and continuous end points) of QSAR models constructed from curated data sets, biologically relevant molecular descriptors, and Support Vector Machines and Support Vector Regression. The ability of these protocols and methodologies to accommodate large data sets (several thousands compounds) that are chemically diverse - and in the case of classification modeling unbalanced (one experimental outcome dominates the data set) - allows scientists to further explore a remarkable amount of biological and chemical information.

The great descriptor melting pot: mixing descriptors for the common good of QSAR models.

The usefulness and utility of QSAR modeling depends heavily on the ability to estimate the values of molecular descriptors relevant to the endpoints of interest followed by an optimized selection of descriptors to form the best QSAR models from a representative set of the endpoints of interest. The performance of a QSAR model is directly related to its molecular descriptors. QSAR modeling, specifically model construction and optimization, has benefited from its ability to borrow from other unrelated fields, yet the molecular descriptors that form QSAR models have remained basically unchanged in both form and preferred usage. There are many types of endpoints that require multiple classes of descriptors (descriptors that encode 1D through multi-dimensional, 4D and above, content) needed to most fully capture the molecular features and interactions that contribute to the endpoint. The advantages of QSAR models constructed from multiple, and different, descriptor classes have been demonstrated in the exploration of markedly different, and principally biological systems and endpoints. Multiple examples of such QSAR applications using different descriptor sets are described and that examined. The take-home-message is that a major part of the future of QSAR analysis, and its application to modeling biological potency, ADME-Tox properties, general use in virtual screening applications, as well as its expanding use into new fields for building QSPR models, lies in developing strategies that combine and use 1D through nD molecular descriptors.

A pilot study of metabolomic pathways associated with fatigue in patients with colorectal cancer receiving chemotherapy.

PURPOSE: The aim of this pilot study was to evaluate for differences in metabolomic profiles between fatigued and non-fatigued patients with colorectal cancer (CRC) during chemotherapy (CTX). METHOD: Patients were recruited from the department of surgery in a large medical center in Taiwan. In this longitudinal pilot study, the Fatigue Symptom Inventory and fasting blood samples were collected at three assessments (i.e., prior to surgery (T0), three months (T1) and six months (T2) after surgery). Metabolomic profile analysis was used. Multilevel regression and pathway analyses were performed to identify differences in metabolomic profiles between the fatigued and non-fatigued groups. RESULTS: Of the 49 patients, 55.1% (n = 27) were in the fatigue group. All of the 15 metabolites that had statistically significant group × time interactions in the differential metabolite analysis were entered into the pathway analysis. Two pathways were enriched for these metabolites, namely galactose metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis. CONCLUSIONS: The results from this pilot study suggest that pathways involved in galactose metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis are associated with cancer-related fatigue (CRF) in patients with CRC during CTX. These findings are consistent with the hypotheses that alterations in energy metabolism and increases in inflammation are associated with the development and maintenance of CRF.