Effects of Large Blood Vessel Locations during High Intensity Focused Ultrasound Therapy for Hepatic Tumors: a finite element study

High-Intensity Focused Ultrasound (HIFU) has become a viable alternative for treatment of primary and metastatic liver tumors. We evaluated the effects of presence of a large blood vessel and its distance to the tumor on lesion size during HIFU heating. A finite element method (FEM) was used to obtain the temperature distribution for a transfer equation based on large blood vessels convection effect. In 3-D FEM simulation, a 4-mm diameter, 10-mm height cylindrical tumor tissue was heated by different heating schemes with a large blood vessel (10-mm diameter) located at different positions nearby. The distance between the vessel and the tumor tissue varied from 1 mm to 3 mm. For HIFU therapy, the large blood vessel of different locations do not have significant effect on temperature distribution and thermal dose profile, when the heating duration is short (~2s) or the distance of the large blood vessel from the tumor is larger than 2 mm. The domain of thermal lesion can effectively cover the desired therapeutic region with short ultrasound exposure duration (~2s).

Severe chronic allergic (and related) diseases: a uniform approach--a MeDALL--GA2LEN--ARIA position paper

Concepts of disease severity, activity, control and responsiveness to treatment are linked but different. Severity refers to the loss of function of the organs induced by the disease process or to the occurrence of severe acute exacerbations. Severity may vary over time and needs regular follow-up. Control is the degree to which therapy goals are currently met. These concepts have evolved over time for asthma in guidelines, task forces or consensus meetings. The aim of this paper is to generalize the approach of the uniform definition of severe asthma presented to WHO for chronic allergic and associated diseases (rhinitis, chronic rhinosinusitis, chronic urticaria and atopic dermatitis) in order to have a uniform definition of severity, control and risk, usable in most situations. It is based on the appropriate diagnosis, availability and accessibility of treatments, treatment responsiveness and associated factors such as comorbidities and risk factors. This uniform definition will allow a better definition of the phenotypes of severe allergic (and related) diseases for clinical practice, research (including epidemiology), public health purposes, education and the discovery of novel therapies.

International consensus on (ICON) pediatric asthma

Asthma is the most common chronic lower respiratory disease in childhood throughout the world. Several guidelines and/or consensus documents are available to support medical decisions on pediatric asthma. Although there is no doubt that the use of common systematic approaches for management can considerably improve outcomes, dissemination and implementation of these are still major challenges. Consequently, the International Collaboration in Asthma, Allergy and Immunology (iCAALL), recently formed by the EAACI, AAAAI, ACAAI, and WAO, has decided to propose an International Consensus on (ICON) Pediatric Asthma. The purpose of this document is to highlight the key messages that are common to many of the existing guidelines, while critically reviewing and commenting on any differences, thus providing a concise reference. The principles of pediatric asthma management are generally accepted. Overall, the treatment goal is disease control. To achieve this, patients and their parents should be educated to optimally manage the disease, in collaboration with healthcare professionals. Identification and avoidance of triggers is also of significant importance. Assessment and monitoring should be performed regularly to re-evaluate and fine-tune treatment. Pharmacotherapy is the cornerstone of treatment. The optimal use of medication can, in most cases, help patients control symptoms and reduce the risk for future morbidity. The management of exacerbations is a major consideration, independent of chronic treatment. There is a trend toward considering phenotype-specific treatment choices; however, this goal has not yet been achieved.

[ARIA (Allergic Rhinitis and its Impact on Asthma) achievements in 10 years and future needs]

Allergic rhinitis and asthma represent global health problems for all age groups. Asthma and rhinitis frequently co-exist in the same subjects. Allergic Rhinitis and its Impact on Asthma (ARIA) was initiated during a World Health Organization (WHO) workshop in 1999 and was published in 2001. ARIA has reclassified allergic rhinitis as mild/moderate-severe and intermittent/persistent. This classification schema closely reflects the impact of allergic rhinitis on patients. In its 2010 Revision, ARIA developed clinical practice guidelines for the management of allergic rhinitis and asthma co-morbidities based on GRADE (Grading of Recommendation, Assessment, Development and Evaluation). ARIA has been disseminated and implemented in over 50 countries of the world. In Turkey, it is important to make a record of ARIA achievements and to identify the still unmet clinical, research and implementation needs in order to strengthen the 2011 EU Priority on allergy and asthma in children.

A Computer Method for Validating Traditional Chinese Medicine Herbal Prescriptions

Traditional Chinese medicine (TCM) has been widely practiced and is considered as an alternative to conventional medicine. TCM herbal prescriptions contain a mixture of herbs that collectively exert therapeutic actions and modulating effects. Traditionally defined herbal properties, related to the pharmacodynamic, pharmacokinetic and toxicological, as well as physicochemical properties of their principal ingredients, have been used as the basis for formulating TCM multi-herb prescriptions. These properties are used in this work to develop a computer program for predicting whether a multi-herb recipe is a valid TCM prescription. This program is based on a statistical learning method, support vector machine (SVM), and it is trained by using 575 well-known TCM prescriptions and 1961 non-TCM recipes generated by random combination of TCM herbs. Testing results by using 72 well-known TCM prescriptions and 5039 non-TCM recipes showed that 73.6% of the TCM prescriptions and 99.9% of non-TCM recipes are correctly classified by this system. A further test by using 48 TCM prescriptions published in recent years found that 68.7% of these are correctly classified. These accuracies are comparable to those of SVM classification of other biological systems. Our study indicates the potential of SVM for facilitating the analysis of TCM prescriptions.

An integrated mathematical model of thrombin-, histamine-and VEGF-mediated signalling in endothelial permeability

BACKGROUND

Endothelial permeability is involved in injury, inflammation, diabetes and cancer. It is partly regulated by the thrombin-, histamine-, and VEGF-mediated myosin-light-chain (MLC) activation pathways. While these pathways have been investigated, questions such as temporal effects and the dynamics of multi-mediator regulation remain to be fully studied. Mathematical modeling of these pathways facilitates such studies. Based on the published ordinary differential equation models of the pathway components, we developed an integrated model of thrombin-, histamine-, and VEGF-mediated MLC activation pathways.

RESULTS

Our model was validated against experimental data for calcium release and thrombin-, histamine-, and VEGF-mediated MLC activation. The simulated effects of PAR-1, Rho GTPase, ROCK, VEGF and VEGFR2 over-expression on MLC activation, and the collective modulation by thrombin and histamine are consistent with experimental findings. Our model was used to predict enhanced MLC activation by CPI-17 over-expression and by synergistic action of thrombin and VEGF at low mediator levels. These may have impact in endothelial permeability and metastasis in cancer patients with blood coagulation.

CONCLUSION

Our model was validated against a number of experimental findings and the observed synergistic effects of low concentrations of thrombin and histamine in mediating the activation of MLC. It can be used to predict the effects of altered pathway components, collective actions of multiple mediators and the potential impact to various diseases. Similar to the published models of other pathways, our model can potentially be used to identify important disease genes through sensitivity analysis of signalling components.

Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

Sequence-derived structural and physicochemical features have been extensively used for analyzing and predicting structural, functional, expression and interaction profiles of proteins and peptides. PROFEAT has been developed as a web server for computing commonly used features of proteins and peptides from amino acid sequence. To facilitate more extensive studies of protein and peptides, numerous improvements and updates have been made to PROFEAT. We added new functions for computing descriptors of protein–protein and protein–small molecule interactions, segment descriptors for local properties of protein sequences, topological descriptors for peptide sequences and small molecule structures. We also added new feature groups for proteins and peptides (pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, total amino acid properties and atomic-level topological descriptors) as well as for small molecules (atomic-level topological descriptors). Overall, PROFEAT computes 11 feature groups of descriptors for proteins and peptides, and a feature group of more than 400 descriptors for small molecules plus the derived features for protein–protein and protein–small molecule interactions. Our computational algorithms have been extensively tested and used in a number of published works for predicting proteins of specific structural or functional classes, protein–protein interactions, peptides of specific functions and quantitative structure activity relationships of small molecules. PROFEAT is accessible free of charge at http://bidd.cz3.nus.edu.sg/cgi-bin/prof/protein/profnew.cgi.

HIT: linking herbal active ingredients to targets

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients' Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.

Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines

Multitarget agents have been increasingly explored for enhancing efficacy and reducing countertarget activities and toxicities. Efficient virtual screening (VS) tools for searching selective multitarget agents are desired. Combinatorial support vector machines (C-SVM) were tested as VS tools for searching dual-inhibitors of 11 combinations of 9 anticancer kinase targets (EGFR, VEGFR, PDGFR, Src, FGFR, Lck, CDK1, CDK2, GSK3). C-SVM trained on 233-1,316 non-dual-inhibitors correctly identified 26.8%-57.3% (majority >36%) of the 56-230 intra-kinase-group dual-inhibitors (equivalent to the 50-70% yields of two independent individual target VS tools), and 12.2% of the 41 inter-kinase-group dual-inhibitors. C-SVM were fairly selective in misidentifying as dual-inhibitors 3.7%-48.1% (majority <20%) of the 233-1,316 non-dual-inhibitors of the same kinase pairs and 0.98%-4.77% of the 3,971-5,180 inhibitors of other kinases. C-SVM produced low false-hit rates in misidentifying as dual-inhibitors 1,746-4,817 (0.013%-0.036%) of the 13.56 M PubChem compounds, 12-175 (0.007%-0.104%) of the 168 K MDDR compounds, and 0-84 (0.0%-2.9%) of the 19,495-38,483 MDDR compounds similar to the known dual-inhibitors. C-SVM was compared to other VS methods Surflex-Dock, DOCK Blaster, kNN and PNN against the same sets of kinase inhibitors and the full set or subset of the 1.02 M Zinc clean-leads data set. C-SVM produced comparable dual-inhibitor yields, slightly better false-hit rates for kinase inhibitors, and significantly lower false-hit rates for the Zinc clean-leads data set. Combinatorial SVM showed promising potential for searching selective multitarget agents against intra-kinase-group kinases without explicit knowledge of multitarget agents.

Salt dependent premelting base pair opening probabilities of B and Z DNA Poly [d(G-C)] and significance for the B-Z transition

We calculate room temperature thermal fluctuational base pair opening probabilities of B and Z DNA Poly[d(G-C)] at various salt concentrations and discuss the significance of thermal fluctuation in facilitating base pair disruption during B to Z transition. Our calculated base pair opening probability of the B DNA at lower salt concentrations and the probability of the Z DNA at high salt concentrations are in agreement with observations. The salt dependence of the probabilities indicates a B to Z transition at a salt concentration close to the observed concentration.

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design.

Trends in the exploration of anticancer targets and strategies in enhancing the efficacy of drug targeting

A number of therapeutic targets have been explored for developing anticancer drugs. Continuous efforts have been directed at the discovery of new targets as well as the improvement of therapeutic efficacy of agents directed at explored targets. There are 84 and 488 targets of marketed and investigational drugs for the treatment of cancer or cancer related illness. Analysis of these targets, particularly those of drugs in clinical trials and US patents, provides useful information and perspectives about the trends, strategies and progresses in targeting key cancer-related processes and in overcoming the difficulties in developing efficacious drugs against these targets. The efficacy of anticancer drugs directed at these targets is frequently compromised by counteractive molecular interactions and network crosstalk, negative and adverse secondary effects of drugs, and undesired ADMET profiles. Multi-component therapies directed at multiple targets and improved drug targeting methods are being explored for alleviating these efficacy-reducing processes. Investigation of the modes of actions of these combinations and targeting methods offers clues to aid the development of more effective anticancer therapies.

Trends in the exploration of anticancer targets and strategies in enhancing the efficacy of drug targeting

A number of therapeutic targets have been explored for developing anticancer drugs. Continuous efforts have been directed at the discovery of new targets as well as the improvement of therapeutic efficacy of agents directed at explored targets. There are 84 and 488 targets of marketed and investigational drugs for the treatment of cancer or cancer related illness. Analysis of these targets, particularly those of drugs in clinical trials and US patents, provides useful information and perspectives about the trends, strategies and progresses in targeting key cancer-related processes and in overcoming the difficulties in developing efficacious drugs against these targets. The efficacy of anticancer drugs directed at these targets is frequently compromised by counteractive molecular interactions and network crosstalk, negative and adverse secondary effects of drugs, and undesired ADMET profiles. Multi-component therapies directed at multiple targets and improved drug targeting methods are being explored for alleviating these efficacy-reducing processes. Investigation of the modes of actions of these combinations and targeting methods offers clues to aid the development of more effective anticancer therapies.

A laser-Compton scattering prototype experiment at 100 MeV linac of Shanghai Institute of Applied Physics

As a prototype of the Shanghai Laser Electron Gamma Source in the Shanghai Synchrotron Radiation Facility, an x-ray source based on laser-Compton scattering (LCS) has been installed at the terminal of the 100 MeV linac of the Shanghai Institute of Applied Physics. LCS x-rays are generated by interactions between Q-switched Nd:yttrium aluminum garnet laser pulses [with wavelength of 1064 nm and pulse width of 21 ns (full width at half maximum)] and electron bunches [with energy of 108 MeV and pulse width of 0.95 ns (rms)] at an angle of 42 degrees between laser and electron beam. In order to measure the energy spectrum of LCS x-rays, a Si(Li) detector along the electron beam line axis is positioned at 9.8 m away from a LCS chamber. After background subtraction, the LCS x-ray spectrum with the peak energy of 29.1+/-4.4|(stat)+/-2.1|(syst) keV and the peak width (rms) of 7.8+/-2.8|(stat)+/-0.4|(syst) keV is observed. Normally the 100 MeV linac operates with the electron macropulse charge of 1.0 nC/pulse, and the electron and laser collision repetition rate of 20 Hz. Therefore, the total LCS x-ray flux of (5.2+/-2.0) x 10(2) Hz can be achieved.

Virtual screening of Abl inhibitors from large compound libraries by support vector machines

Abl promotes cancers by regulating cell morphogenesis, motility, growth, and survival. Successes of several marketed and clinical trial Abl inhibitors against leukemia and other cancers and appearances of reduced efficacies and drug resistances have led to significant interest in and efforts for developing new Abl inhibitors. In silico methods of pharmacophore, fragment, and molecular docking have been used in some of these efforts. It is desirable to explore other in silico methods capable of searching large compound libraries at high yields and reduced false-hit rates. We evaluated support vector machines (SVM) as a virtual screening tool for searching Abl inhibitors from large compound libraries. SVM trained and tested by 708 inhibitors and 65,494 putative noninhibitors correctly identified 84.4 to 92.3% inhibitors and 99.96 to 99.99% noninhibitors in 5-fold cross validation studies. SVM trained by 708 pre-2008 inhibitors and 65 494 putative noninhibitors correctly identified 50.5% of the 91 inhibitors reported since 2008 and predicted as inhibitors 29,072 (0.21%) of 13.56M PubChem, 659 (0.39%) of 168K MDDR, and 330 (5.0%) of 6638 MDDR compounds similar to the known inhibitors. SVM showed comparable yields and substantially reduced false-hit rates against two similarity based and another machine learning VS methods based on the same training and testing data sets and molecular descriptors. These suggest that SVM is capable of searching Abl inhibitors from large compound libraries at low false-hit rates.

Large anisotropy in colossal magnetoresistance of charge orbital ordered epitaxial Sm(0.5)Ca(0.5)MnO(3) films

We investigated the structure and magnetotransport properties of Sm(0.5)Ca(0.5)MnO(3) (SCMO) films epitaxially grown on (011)-oriented SrTiO(3) substrates, which exhibited clear charge/orbital ordering transition. A significant anisotropy of ∼1000 in the colossal magnetoresistance (CMR) effect was observed in the films with a thickness between 50 and 80 nm, which was distinctly different from the basically isotropic CMR effect in bulk SCMO. The large anisotropy in the CMR can be ascribed to the intrinsic asymmetric strain in the film, which plays an important role in tuning the spin-orbit coupling in manganite films. The origin of the peculiar CMR effect is discussed.

Though with constraints imposed by endosymbiosis, preferential attachment is still a plausible mechanism responsible for the evolution of the chloroplast metabolic network

Chloroplasts evolved as a result of endosymbiosis, during which sophisticated mechanisms evolved to translocate nucleus-encoded plastid-targeted enzymes into the chloroplast to form the chloroplast metabolic network. Given the constraints and complexity of endosymbiosis, will preferential attachment still be a plausible mechanism for chloroplast metabolic network evolution? We answer this question by analysing the metabolic network properties of the chloroplast and a cyanobacterium, Synechococcus sp. WH8102 (syw). First, we found that enzymes related to more ancient pathways are more connected, and synthetases have the highest connectivity. Most of the enzymes shared by the two densest cores between the chloroplast and syw are synthetases. Second, the highly conserved functional modules mainly consist of highly connected enzymes. Finally, isozymes and enzymes from endosymbiotic gene transfer (EGT) were distributed mainly in conserved modules and showed higher connectivity than nonisozymes or non-EGT enzymes. These results suggest that even with severe evolutionary constraints imposed by endosymbiosis, preferential attachment is still a plausible mechanism responsible for the evolution of the chloroplast metabolic network. However, the current analysis may not completely differentiate whether the chloroplast network properties reflect the evolution of the chloroplast network through preferential attachment or has been inherited from its cyanobacterial ancestor. To fully differentiate these two possibilities, further analyses of the metabolic network structure properties of organisms at various intermediate evolutionary stages between cyanobacteria and the chloroplast are needed.

Characterization and Optical Properties of the Single Crystalline SnS Nanowire Arrays

The SnS nanowire arrays have been successfully synthesized by the template-assisted pulsed electrochemical deposition in the porous anodized aluminum oxide template. The investigation results showed that the as-synthesized nanowires are single crystalline structures and they have a highly preferential orientation. The ordered SnS nanowire arrays are uniform with a diameter of 50 nm and a length up to several tens of micrometers. The synthesized SnS nanowires exhibit strong absorption in visible and near-infrared spectral region and the direct energy gap E(g) of SnS nanowires is 1.59 eV.

Prevalence of asthma among Chinese adolescents living in Canada and in China

BACKGROUND

Studies of the prevalence of asthma among migrating populations may help in identifying environmental risk factors.

METHODS

We analyzed data from Vancouver, Canada, and from Guangzhou, Beijing and Hong Kong, China, collected during phase 3 of the International Study of Asthma and Allergies in Childhood. We subdivided the Vancouver adolescents according to whether they were Chinese immigrants to Canada, Canadian-born Chinese or Canadian-born non-Chinese. We compared the prevalence of asthma and wheezing among Chinese adolescents born in Canada, Chinese adolescents who had immigrated to Canada and Chinese adolescents living in China.

RESULTS

Of 7794 Chinese adolescents who met the inclusion criteria, 3058 were from Guangzhou, 2824 were from Beijing, and 1912 were from Hong Kong. Of 2235 adolescents in Vancouver, Canada, 475 were Chinese immigrants, 617 were Canadian-born Chinese, and 1143 were Canadian-born non-Chinese. The prevalence of current wheezing among boys ranged from 5.9% in Guangzhou to 11.2% in Canadian-born Chinese adolescents. For girls, the range was 4.3% in Guangzhou to 9.8% in Canadian-born Chinese adolescents. The prevalence of ever having had asthma ranged from 6.6% to 16.6% for boys and from 2.9% to 15.0% for girls. Prevalence gradients persisted after adjustment for other environmental variables (odds ratios for ever having had asthma among Canadian-born Chinese compared with native Chinese in Guangzhou: 2.72 [95% confidence interval 1.75-4.23] for boys and 5.50 [95% confidence interval 3.21-9.44] for girls; p < 0.001 for both). Among Chinese adolescents living in Vancouver, the prevalence of ever wheezing increased with duration of residence, from 14.5% among those living in Canada for less than 7 years to 20.9% among those living their entire life in Canada. The same pattern was observed for the prevalence of ever having had asthma, from 7.7% to 15.9%.

INTERPRETATION

Asthma symptoms in Chinese adolescents were lowest among residents of mainland China, were greater for those in Hong Kong and those who had immigrated to Canada, and were highest among those born in Canada. These findings suggest that environmental factors and duration of exposure influence asthma prevalence.

Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds

Virtual screening performance of support vector machines (SVM) depends on the diversity of training active and inactive compounds. While diverse inactive compounds can be routinely generated, the number and diversity of known actives are typically low. We evaluated the performance of SVM trained by sparsely distributed actives in six MDDR biological target classes composed of a high number of known actives (983-1645) of high, intermediate, and low structural diversity (muscarinic M1 receptor agonists, NMDA receptor antagonists, thrombin inhibitors, HIV protease inhibitors, cephalosporins, and renin inhibitors). SVM trained by regularly sparse data sets of 100 actives show improved yields at substantially reduced false-hit rates compared to those of published studies and those of Tanimoto-based similarity searching method based on the same data sets and molecular descriptors. SVM trained by very sparse data sets of 40 actives (2.4%-4.1% of the known actives) predicted 17.5-39.5%, 23.0-48.1%, and 70.2-92.4% of the remaining 943-1605 actives in the high, intermediate, and low diversity classes, respectively, 13.8-68.7% of which are outside the training compound families. SVM predicted 99.97% and 97.1% of the 9.997 M PUBCHEM and 167K remaining MDDR compounds as inactive and 2.6%-8.3% of the 19,495-38,483 MDDR compounds similar to the known actives as active. These suggest that SVM has substantial capability in identifying novel active compounds from sparse active data sets at low false-hit rates.

Finite-size effect on magnetic properties in iron sulfide nanowire arrays

We report the size effect on the magnetic properties in Fe(7)S(8) nanowire arrays. Samples with diameters in the range of 50-200 nm have been prepared by electrodeposition with AAO films. The Mössbauer measurement results show that four parameters (hyperfine fields, isomer shift, quadrupole splitting, full width at half-maximum) increased with decreasing the diameter of the nanowires. The magnetic properties were investigated. The hysteresis loop shape and the magnetization are dependent on the diameter of the nanowires. The thermomagnetic measurements on the as-synthesized nanowire samples and the corresponding bulk display a mixed-type curve and a Weiss-type curve, respectively.

Homology-free prediction of functional class of proteins and peptides by support vector machines

Protein and peptide sequences contain clues for functional prediction. A challenge is to predict sequences that show low or no homology to proteins or peptides of known function. A machine learning method, support vector machines (SVM), has recently been explored for predicting functional class of proteins and peptides from sequence-derived properties irrespective of sequence similarity, which has shown impressive performance for predicting a wide range of protein and peptide classes including certain low- and non- homologous sequences. This method serves as a new and valuable addition to complement the extensively-used alignment-based, clustering-based, and structure-based functional prediction methods. This article evaluates the strategies, current progresses, reported prediction performances, available software tools, and underlying difficulties in using SVM for predicting the functional class of proteins and peptides.

The Inhaled Steroid Treatment As Regular Therapy in Early Asthma (START) study 5-year follow-up: effectiveness of early intervention with budesonide in mild persistent asthma

BACKGROUND

The Inhaled Steroid Treatment as Regular Therapy in Early Asthma (START) study enrolled 7241 patients aged 5 to 66 years with recent-onset, mild persistent asthma to assess early intervention with the inhaled corticosteroid budesonide on long-term asthma control.

OBJECTIVE

The open-label phase of the START study was included to determine the effect on lung function and asthma control of adding budesonide to the reference group patients who had not initially received inhaled corticosteroids.

METHODS

Patients were randomized to double-blind treatment with budesonide, 200 mug (those aged < 11 years) or 400 mug once daily, or placebo plus the usual asthma therapy for 3 years, after which all patients received 2 years of open-label treatment with budesonide once daily.

RESULTS

During the full 5-year study period, postbronchodilator FEV(1) percent predicted decreased, irrespective of randomized treatment during the double-blind phase, by an average of 2.22% (SE, 0.15%). However, patients with inhaled budesonide in the double-blind phase had a significantly lower risk (odds ratio, 0.61; P < .001) of a severe asthma-related event during the full 5-year study period than those in the reference group. Moreover, patients in the reference group used more additional asthma medications during both the open-label and double-blind phases.

CONCLUSIONS

In mild persistent asthma early intervention with inhaled budesonide was associated with improved asthma control and less additional asthma medication use.

Regression methods for developing QSAR and QSPR models to predict compounds of specific pharmacodynamic, pharmacokinetic and toxicological properties

Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models have been extensively used for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property from structure-derived physicochemical and structural features. These models can be developed by using various regression methods including conventional approaches (multiple linear regression and partial least squares) and more recently explored genetic (genetic function approximation) and machine learning (k-nearest neighbour, neural networks, and support vector regression) approaches. This article describes the algorithms of these methods, evaluates their advantages and disadvantages, and discusses the application potential of the recently explored methods. Freely available online and commercial software for these regression methods and the areas of their applications are also presented.

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >or=1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.

Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

Computational methods for predicting compounds of specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) property are useful for facilitating drug discovery and evaluation. Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property. These methods are particularly useful for compounds of diverse structures to complement QSAR methods, and for cases of unavailable receptor 3D structure to complement structure-based methods. A number of studies have demonstrated the potential of these methods for predicting such compounds as substrates of P-glycoprotein and cytochrome P450 CYP isoenzymes, inhibitors of protein kinases and CYP isoenzymes, and agonists of serotonin receptor and estrogen receptor. This article is intended to review the strategies, current progresses and underlying difficulties in using machine learning methods for predicting these protein binders and as potential virtual screening tools. Algorithms for proper representation of the structural and physicochemical properties of compounds are also evaluated.

Trends in the exploration of therapeutic targets for the treatment of endocrine, metabolic and immune disorders

A number of therapeutic targets have been explored for developing drugs in the treatment of endocrine, metabolic and immune disorders. Continuous efforts and increasing interest have been directed at the search of new targets. Data from the therapeutic target database at http://bidd.nus.edu.sg/group/cjttd/ttd.asp, shows that there are 26, 24, and 22 targets of marketed drugs for the treatment of these three classes of diseases, respectively. The number of targets of investigational agents has reached 98, 124, and 72, respectively. An analysis of these targets, particularly those of recently approved drugs and patented investigational agents, provides useful hint about the general trends of target exploration, with current focus on drug discovery and the difficulties encountered in developing drugs against these targets. Multiple profiles of these targets have been analyzed to probe the sequence, structural, physicochemical and systems-related features contributing to the successful exploration of a target against these diseases.

Prediction of factor Xa inhibitors by machine learning methods

Factor Xa (FXa) inhibitors have been explored as anticoagulants for treatment and prevention of thrombotic diseases. Molecular docking, pharmacophore, quantitative structure-activity relationships, and support vector machines (SVM) have been used for computer prediction of FXa inhibitors. These methods achieve promising prediction accuracies of 69-80% for FXa inhibitors and 85-99% for non-inhibitors. Prediction performance, particularly for inhibitors, may be further improved by exploring methods applicable to more diverse range of compounds and by using more appropriate set of molecular descriptors. We tested the capability of several machine learning methods (C4.5 decision tree, k-nearest neighbor, probabilistic neural network, and support vector machine) by using a much more diverse set of 1098 compounds (360 inhibitors and 738 non-inhibitors) than those in other studies. A feature selection method was used for selecting molecular descriptors appropriate for distinguishing FXa inhibitors and non-inhibitors. The prediction accuracies of these methods are 89.1-97.5% for FXa inhibitors and 92.3-98.1% for non-inhibitors. In particular, compared to other studies, support vector machine gives a substantially improved accuracy of 94.6% for FXa non-inhibitors and maintains a comparable accuracy of 98.1% for inhibitors, based-on a more rigorous test with more diverse range of compounds. Our study suggests that machine learning methods such as SVM are useful for facilitating the prediction of FXa inhibitors.

Formulation development of transdermal dosage forms: Quantitative structure-activity relationship model for predicting activities of terpenes that enhance drug penetration through human skin

Terpenes and terpenoids have been used as enhancers in transdermal formulations for facilitating penetration of drugs into human skin. Knowledge of the correlation between the human skin penetration effect (HSPE) and the physicochemical properties of these enhancers is important for facilitating the discovery and development of more enhancers. In this work, the HSPE of 49 terpenes and terpenoids were compared by the in vitro permeability coefficients of haloperidol (HP) through excised human skin. A first-order multiple linear regression (MLR) model was constructed to link the permeability coefficient of the drug to the lipophilicity, molecular weight, boiling point, the terpene type and the functional group of each enhancer. The Quantitative Structure-Activity Relationship (QSAR) model was derived from our data generated by using standardized experimental protocols, which include: HP in propylene glycol (PG) of 3 mg/ml as the donor solution containing 5% (w/v) of the respective terpene, the same composition and volume of receptor solution, similar human skin samples, in the same set of automated flow-through diffusion cells. The model provided a simple method to predict the enhancing effects of terpenes for drugs with physicochemical properties similar to HP. Our study suggested that an ideal terpene enhancer should possess at least one or combinations of the following properties: hydrophobic, in liquid form at room temperature, with an ester or aldehyde but not acid functional group, and is neither a triterpene nor tetraterpene. Possible mechanisms revealed by the QSAR model were discussed.

Cerebral response to patient's own name in the vegetative and minimally conscious states

BACKGROUND

A challenge in the management of severely brain-damaged patients with altered states of consciousness is the differential diagnosis between the vegetative state (VS) and the minimally conscious state (MCS), especially for the gray zone separating these clinical entities.

OBJECTIVE

To evaluate the differences in brain activation in response to presentation of the patient's own name spoken by a familiar voice (SON-FV) in patients with VS and MCS.

METHODS

By using fMRI, we prospectively studied residual cerebral activation to SON-FV in seven patients with VS and four with MCS. Behavioral evaluation was performed by means of standardized testing up to 3 months post-fMRI.

RESULTS

Two patients with VS failed to show any significant cerebral activation. Three patients with VS showed SON-FV induced activation within the primary auditory cortex. Finally, two patients with VS and all four patients with MCS not only showed activation in primary auditory cortex but also in hierarchically higher order associative temporal areas. These two patients with VS showing the most widespread activation subsequently showed clinical improvement to MCS observed 3 months after their fMRI scan.

CONCLUSION

The cerebral responses to patient's own name spoken by a familiar voice as measured by fMRI might be a useful tool to preclinically distinguish minimally conscious state-like cognitive processing in some patients behaviorally classified as vegetative.

Application of support vector machines to in silico prediction of cytochrome p450 enzyme substrates and inhibitors

Cytochrome P450 enzymes are responsible for phase I metabolism of the majority of drugs and xenobiotics. Identification of the substrates and inhibitors of these enzymes is important for the analysis of drug metabolism, prediction of drug-drug interactions and drug toxicity, and the design of drugs that modulate cytochrome P450 mediated metabolism. The substrates and inhibitors of these enzymes are structurally diverse. It is thus desirable to explore methods capable of predicting compounds of diverse structures without over-fitting. Support vector machine is an attractive method with these qualities, which has been employed for predicting the substrates and inhibitors of several cytochrome P450 isoenzymes as well as compounds of various other pharmacodynamic, pharmacokinetic, and toxicological properties. This article introduces the methodology, evaluates the performance, and discusses the underlying difficulties and future prospects of the application of support vector machines to in silico prediction of cytochrome P450 substrates and inhibitors.

[Effects of 2-n-nonyl-1,3-dioxolane as an enhanceron transdermal absorption of Salvia miltiorrhiza gel]

OBJECTIVE

To choose the most suitable concentration of 2-n-nonyl-1,3-dioxolane as a penetration enhancer in tanshinone gel preparation.

METHOD

In vitro, transdermal absorption was studied using improved Frans equipment and rats skin. Tanshinone II A was tested by HPLC.

RESULT

The 4.0% concentration of 2-n-nonyl-1,3-dioxolane enhanced the transdermal absorption significantly in the preparation.

CONCLUSION

2-n-nonyl-1,3-dioxolane was a new effective permeaton enhancer.

Effectiveness of early budesonide intervention in Caucasian versus Asian patients with asthma: 3-year results of the START study

OBJECTIVE AND BACKGROUND

Few studies have assessed the effectiveness of inhaled corticosteroid therapy exclusively in Asian patients with asthma. The present analysis compared the efficacy of early intervention with inhaled budesonide in Caucasian and Asian patients over the first 3 years of the inhaled Steroid Treatment As Regular Therapy in early asthma study.

METHODS

Patients aged 5-66 years with mild persistent asthma of <or=2 years' duration were randomized to 3 years of double-blind treatment with once-daily budesonide 200 microg (for patients aged<11 years) or 400 microg administered via Turbuhaler or placebo, plus usual asthma therapy.

RESULTS

Budesonide significantly improved asthma outcomes in both Caucasian (n=4661) and Asian (n=1995) patients compared with reference therapy (placebo plus usual asthma therapy). Budesonide reduced the risk of a first severe asthma-related event by 42% and 49% in Caucasian and Asian patients, respectively, over the 3-year treatment period (P<0.001 for both). Moreover, budesonide significantly increased symptom-free days, decreased nights with sleeping problems, improved pre- and postbronchodilator FEV1 and reduced the need for additional asthma medications of particular drug classes compared with reference therapy. Except for differences in the patterns of use of additional asthma medications, outcomes with budesonide and overall adverse events were similar in the Caucasian and Asian patient populations.

CONCLUSION

Inhaled budesonide administered once daily in Asian patients with recent-onset, mild persistent asthma significantly improved asthma control and pulmonary function compared with reference therapy. Moreover, this effectiveness paralleled that observed in Caucasian patients.

Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes

Traditional Chinese medicine (TCM) has been widely practiced and is considered as an attractive to conventional medicine. Multi-herb recipes have been routinely used in TCM. These have been formulated by using TCM-defined herbal properties (TCM-HPs), the scientific basis of which is unclear. The usefulness of TCM-HPs was evaluated by analyzing the distribution pattern of TCM-HPs of the constituent herbs in 1161 classical TCM prescriptions, which shows patterns of multi-herb correlation. Two artificial intelligence (AI) methods were used to examine whether TCM-HPs are capable of distinguishing TCM prescriptions from non-TCM recipes. Two AI systems were trained and tested by using 1161 TCM prescriptions, 11,202 non-TCM recipes, and two separate evaluation methods. These systems correctly classified 83.1-97.3% of the TCM prescriptions, 90.8-92.3% of the non-TCM recipes. These results suggest that TCM-HPs are capable of separating TCM prescriptions from non-TCM recipes, which are useful for formulating TCM prescriptions and consistent with the expected correlation between TCM-HPs and the physicochemical properties of herbal ingredients responsible for producing the collective pharmacological and other effects of specific TCM prescriptions.

Computer prediction of cardiovascular and hematological agents by statistical learning methods

Computational methods have been explored for predicting agents that produce therapeutic or adverse effects in cardiovascular and hematological systems. The quantitative structure-activity relationship (QSAR) method is the first statistical learning methods successfully used for predicting various classes of cardiovascular and hematological agents. In recent years, more sophisticated statistical learning methods have been explored for predicting cardiovascular and hematological agents particularly those of diverse structures that might not be straightforwardly modelled by single QSAR models. These methods include partial least squares, multiple linear regressions, linear discriminant analysis, k-nearest neighbour, artificial neural networks and support vector machines. Their application potential has been exhibited in the prediction of various classes of cardiovascular and hematological agents including 1, 4-dihydropyridine calcium channel antagonists, angiotensin converting enzyme inhibitors, thrombin inhibitors, AchE inhibitors, HERG potassium channel inhibitors and blockers, potassium channel openers, platelet aggregation inhibitors, protein kinase inhibitors, dopamine antagonists and torsade de pointes causing agents. This article reviews the strategies, current progresses and problems in using statistical learning methods for predicting cardiovascular and hematological agents. It also evaluates algorithms for properly representing and extracting the structural and physicochemical properties of compounds relevant to the prediction of cardiovascular and hematological agents.

MODEL—molecular descriptor lab: A web-based server for computing structural and physicochemical features of compounds

Molecular descriptors represent structural and physicochemical features of compounds. They have been extensively used for developing statistical models, such as quantitative structure activity relationship (QSAR) and artificial neural networks (NN), for computer prediction of the pharmacodynamic, pharmacokinetic, or toxicological properties of compounds from their structure. While computer programs have been developed for computing molecular descriptors, there is a lack of a freely accessible one. We have developed a web-based server, MODEL (Molecular Descriptor Lab), for computing a comprehensive set of 3,778 molecular descriptors, which is significantly more than the approximately 1,600 molecular descriptors computed by other software. Our computational algorithms have been extensively tested and the computed molecular descriptors have been used in a number of published works of statistical models for predicting variety of pharmacodynamic, pharmacokinetic, and toxicological properties of compounds. Several testing studies on the computed molecular descriptors are discussed. MODEL is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/model/model.cgi free of charge for academic use.

Therapeutic targets: progress of their exploration and investigation of their characteristics

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with approximately 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach

Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Internet resources related to drug action and human response: a review

It has been demonstrated that numerous proteins interact with drugs or their metabolites. Knowledge of these proteins is necessary to understand the mechanisms of drug action and human response. Progress in modern genetics, molecular biology, biochemistry and pharmacology is generating a comprehensive mechanistic understanding of drug-target interaction on the molecular level. This is valuable for researchers and pharmaceutical companies in their efforts to improve the efficacy of existing drugs and to discover new ones. Most recently, the integration of a systems biology approach into drug discovery processes calls for more holistic knowledge and easily accessible resources of the proteins that are important in drug action and human response. We have reviewed many publicly accessible internet resources of these proteins, according to their roles in drug action and human response, such as therapeutic effect, adverse reaction, absorption, distribution, metabolism and excretion.

In cis autosomal dominant mutation of Senataxin associated with tremor/ataxia syndrome

Senataxin mutations are the molecular basis of two distinct syndromes: (1) ataxia oculomotor apraxia type 2 (AOA2) and (2) juvenile amyotrophic lateral sclerosis 4 (ALS4). The authors describe clinical and molecular genetic studies of mother and daughter who display symptoms of cerebellar ataxia/atrophy, oculomotor defects, and tremor. Both patients share Senataxin mutations N603D and Q653K in cis (N603D-Q653K), adjacent to an N-terminal domain thought to function in protein-protein interaction. The N-terminal and helicase domains appear to harbor missense mutation clusters associated with AOA2 and ALS4. Working synergistically, the N603D-Q653K mutations may confer a partial dominant negative effect, acting on the senataxin N-terminal, further expanding the phenotypic spectrum associated with Senataxin mutations.

Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation

BACKGROUND AND PURPOSE

Traditional Chinese Medicine (TCM) is widely practised and is viewed as an attractive alternative to conventional medicine. Quantitative information about TCM prescriptions, constituent herbs and herbal ingredients is necessary for studying and exploring TCM.

EXPERIMENTAL APPROACH

We manually collected information on TCM in books and other printed sources in Medline. The Traditional Chinese Medicine Information Database TCM-ID, at http://tcm.cz3.nus.edu.sg/group/tcm-id/tcmid.asp, was introduced for providing comprehensive information about all aspects of TCM including prescriptions, constituent herbs, herbal ingredients, molecular structure and functional properties of active ingredients, therapeutic and side effects, clinical indication and application and related matters.

RESULTS

TCM-ID currently contains information for 1,588 prescriptions, 1,313 herbs, 5,669 herbal ingredients, and the 3D structure of 3,725 herbal ingredients. The value of the data in TCM-ID was illustrated by using some of the data for an in-silico study of molecular mechanism of the therapeutic effects of herbal ingredients and for developing a computer program to validate TCM multi-herb preparations.

CONCLUSIONS AND IMPLICATIONS

The development of systems biology has led to a new design principle for therapeutic intervention strategy, the concept of 'magic shrapnel' (rather than the 'magic bullet'), involving many drugs against multiple targets, administered in a single treatment. TCM offers an extensive source of examples of this concept in which several active ingredients in one prescription are aimed at numerous targets and work together to provide therapeutic benefit. The database and its mining applications described here represent early efforts toward exploring TCM for new theories in drug discovery.

Classification of a diverse set of Tetrahymena pyriformis toxicity chemical compounds from molecular descriptors by statistical learning methods

Toxicity of various compounds has been measured in many studies by their toxic effects against Tetrahymena pyriformis. Efforts have also been made to use computational quantitative structure-activity relationship (QSAR) and statistical learning methods (SLMs) for predicting Tetrahymena pyriformis toxicity (TPT) at impressive accuracies. Because of the diversity of compounds and toxicity mechanisms, it is desirable to explore additional methods and to examine if these methods are applicable to more diverse sets of compounds. We tested several SLMs (logistic regression, C4.5 decision tree, k-nearest neighbor, probabilistic neural network, support vector machines) for their capability in predicting TPT by using 1129 compounds (841 TPT and 288 non-TPT agents) which are more diverse than those in other studies. A feature selection method was used for improving prediction performance and selecting molecular descriptors responsible for distinguishing TPT and non-TPT agents. The prediction accuracies are 86.9% approximately 94.2% for TPT and 71.2% approximately 87.5% for non-TPT agents based on 5-fold cross-validation studies, which are comparable to some of earlier studies despite the use of more diverse sets of compounds. The selected molecular descriptors are consistent with those used in other studies and experimental findings. These suggest that SLMs are useful for predicting TPT potential of diverse sets of compounds and for characterizing the molecular descriptors associated with TPT.

Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods

Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure-activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1-97.6% for ER binders and 80.2-96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.

In Silico Prediction of Pregnane X Receptor Activators by Machine Learning Approache

Pregnane X receptor (PXR) regulates drug metabolism and is involved in drug-drug interactions. Prediction of PXR activators is important for evaluating drug metabolism and toxicity. Computational pharmacophore and quantitative structure-activity relationship models have been developed for predicting PXR activators. Because of the structural diversity of PXR activators, more efforts are needed for exploring methods applicable to a broader spectrum of compounds. We explored three machine learning methods (MLMs) for predicting PXR activators, which were trained and tested by using significantly higher number of compounds, 128 PXR activators (98 human) and 77 PXR non-activators, than those of previous studies. The recursive feature-selection method was used to select molecular descriptors relevant to PXR activator prediction, which are consistent with conclusions from other computational and structural studies. In a 10-fold cross-validation test, our MLM systems correctly predicted 81.2 to 84.0% of PXR activators, 80.8 to 85.0% of hPXR activators, 61.2 to 70.3% of PXR nonactivators, and 67.7 to 73.6% of hPXR nonactivators. Our systems also correctly predicted 73.3 to 86.7% of 15 newly published hPXR activators. MLMs seem to be useful for predicting PXR activators and for providing clues to physicochemical features of PXR activation.

Effects of early intervention with inhaled budesonide on lung function in newly diagnosed asthma

STUDY OBJECTIVES

Asthmatic patients lose lung function faster than normal subjects. The effectiveness of early intervention with inhaled corticosteroids on this decline in lung function is not established in recent-onset disease.

DESIGN

The Inhaled Steroid Treatment as Regular Therapy in Early Asthma study was a randomized, double-blind study in 7,165 patients (5 to 66 years old), with persistent asthma for < 2 years to determine whether early intervention with low-dose inhaled budesonide prevents severe asthma-related events and the decline in lung function. Patients received budesonide (200 mug qd for children < 11 years old and 400 mug qd for others) or placebo for 3 years in addition to usual asthma medications.

RESULTS

Treatment with budesonide significantly improved prebronchodilator and postbronchodilator FEV(1) percentage of predicted and reduced the mean declines from baseline for postbronchodilator FEV(1) at 1 year and 3 years: - 0.62% and - 1.79% for budesonide and - 2.11% and - 2.68% for placebo, respectively (p < 0.001). The decline was more marked for male patients, active smokers, and patients > 18 years old, and the smallest treatment effects were in adolescents.

CONCLUSIONS

Long-term, once-daily treatment with low-dose budesonide improved both prebronchodilator and postbronchodilator FEV(1) in patients with recent-onset, persistent asthma, and reduced the loss of lung function over time.

Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties

Peptide binding to MHC is critical for antigen recognition by T-cells. To facilitate vaccine design, computational methods have been developed for predicting MHC-binding peptides, which achieve impressive prediction accuracies of 70-90% for binders and 40-80% for non-binders. These methods have been developed for peptides of fixed lengths, for a limited number of alleles, trained from small number of non-binders, and in some cases based straightforwardly on sequence. These limit prediction coverage and accuracy particularly for non-binders. It is desirable to explore methods that predict binders of flexible lengths from sequence-derived physicochemical properties and trained from diverse sets of non-binders. This work explores support vector machines (SVM) as such a method for developing prediction systems of 18 MHC class I and 12 class II alleles by using 4208-3252 binders and 234,333-168,793 non-binders, and evaluated by an independent set of 545-476 binders and 110,564-84,430 non-binders. Binder accuracies are 86-99% for 25 and 70-80% for 5 alleles, non-binder accuracies are 96-99% for 30 alleles. Binder accuracies are comparable and non-binder accuracies substantially improved against other results. Our method correctly predicts 73.3% of the 15 newly-published epitopes in the last 4 months of 2005. Of the 251 recently-published HLA-A*0201 non-epitopes predicted as binders by other methods, 63 are predicted as binders by our method. Screening of HIV-1 genome shows that, compared to other methods, a comparable percentage (75-100%) of its known epitopes is correctly predicted, while a lower percentage (0.01-5% for 24 and 5-8% for 6 alleles) of its constituent peptides are predicted as binders. Our software can be accessed at .

Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods

Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.