Phase II trial of lapatinib for brain metastases in patients with HER2+ breast cancer

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Background: One-third of women with HER2+ metastatic breast cancer develop central nervous system (CNS) metastases. This study evaluated the safety and efficacy of lapatinib, an oral inhibitor of EGFR and HER2, in patients with HER2+ brain metastases. Methods: Eligible patients (pts) had HER2+ breast cancer, new or progressive brain metastases, and at least one measurable (LD ≥1.0cm) lesion. Pts received lapatinib 750 mg PO BID. Tumor response was assessed by MRI every 8 wks. FDG-PET scans were performed at baseline, and repeated at wks 1 and 8. The primary endpoint was objective response (CR+PR) in the brain by RECIST. Secondary endpoints included safety, quality-of-life (QOL), and PET changes. Sample size was calculated using a 2-stage design to distinguish objective response of 5% (H0) vs 20% (HA); ≥ 4 objective responses were required to reject the null hypothesis. Results: 39 pts were enrolled, mean age 52 yrs (range 31–76). All pts developed CNS disease on trastuzumab; 38 progressed after prior radiation. Toxicity data are available for 38 pts; the most common AEs were diarrhea (grade 3, 21%), fatigue (grade 3, 16%), and rash (grade 3, 5%). Three pts remain on active treatment. Two pts achieved a PR by RECIST, and remained on study for 158 and 347 days. An additional pt achieved >30% decrease in LD of her CNS lesion but, upon central radiology review, did not meet RECIST criteria for measurable disease and was excluded from analysis of the primary endpoint. Five additional pts achieved SD≥16 wks. Median time to treatment failure was 3.2 mo (95% CI 2.3 to 3.8). Preliminary volumetric analysis of 20/39 pts demonstrates 5 pts with ≥30% volumetric decline in CNS lesions, and an additional 3 pts with 15–30% volumetric decline. Analyses of QOL and correlation of PET with clinical outcomes will be presented. Conclusion: Lapatinib is well-tolerated in this population. Although the study failed to demonstrate the hypothesized level of activity as assessed by RECIST, there is sufficient evidence of clinical effect, albeit preliminary, to suggest that lapatinib can penetrate the CNS. Further investigation of lapatinib in HER2+ CNS disease is warranted and ongoing. Acknowledgements: AVON PFP award; NCI-SPORE in Breast Cancer at DF/HCC(CA89393), UNC(CA58223), Georgetown; ASCO YIA.

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Spinal cord compression and dorsal root injury cause up-regulation of activating transcription factor-3 in large-diameter dorsal root ganglion neurons

Spinal cord injury causes damage to ascending and descending tracts, as well as to local circuits, but relatively little is known about the effect of such injury on sensory neurons located within adjoining ganglia. We have therefore used immunocytochemistry for activating transcription factor-3 (ATF3), a sensitive marker of axonal damage, in order to examine the effects of spinal cord injury in rats on dorsal root ganglion (DRG) neurons. A 50-g static compression injury applied to the dorsal surface of the T12 thoracic spinal cord led to an up-regulation of ATF3 that was maximal at 1 day and affected 12-14% of DRG neurons in ganglia caudal to the injury (T13-L3). A similar response was seen after a T12 hemisection that transected the dorsal columns except that compression injury, but not hemisection, also evoked ATF3 expression in ganglia just rostral to the injury (T10, T11). ATF3 was up-regulated exclusively in DRG neurons that were of large diameter and immunoreactive for heavy neurofilament. Small-diameter cells, including the population that binds the lectin Grifffonia simplicifolia IB4, did not express ATF3 immunoreactivity. A similar pattern of ATF3 expression was induced by dorsal rhizotomy. The data show for the first time that ATF3 is up-regulated after spinal cord and dorsal root injury, but that this up-regulation is confined to the large-diameter cell population.

Quantitative structure-toxicity relationships (QSTRs): a comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis

Prediction of toxicity of 203 nitro- and cyano-aromatic chemicals to Tetrahymena pyriformis was carried out by radial basis function neural network, general regression neural network and support vector machine, in non-linear response surface methodology. Toxicity was predicted from hydrophobicity parameter (log Kow) and maximum superdelocalizability (Amax). Special attention was drawn to prediction ability and robustness of the models, investigated both in a leave-one-out and 10-fold cross validation (CV) processes. The influence that the corresponding changes in the learning sets during these CV processes could have on a common external test set including 41 compounds was also examined. This allowed us to establish the stability of the models. The non linear results slightly outperform (as expected) multilinear relationships (MLR) and also favourably compete with various other non linear approaches recently proposed by Ren (J. Chem. Inf. Comput. Sci., 43 1679 (2003)).

Support vector machines-based quantitative structure-property relationship for the prediction of heat capacity

The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function networks (RBFNNs) were also utilized to construct quantitative linear and nonlinear models to compare with the results obtained by SVM. The root-mean-square (rms) errors in heat capacity predictions for the whole data set given by MLR, RBFNNs, and SVM were 4.648, 4.337, and 2.931 heat capacity units, respectively. The prediction results are in good agreement with the experimental value of heat capacity; also, the results reveal the superiority of the SVM over MLR and RBFNNs models.

Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression

Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data sets were evaluated. The first one involves an application of SVM to the development of a QSAR model for the prediction of toxicities of 153 phenols, and the second investigation deals with the QSAR model between the structures and the activities of a set of 85 cyclooxygenase 2 (COX-2) inhibitors. For each application, the molecular structures were described using either the physicochemical parameters or molecular descriptors. In both studied cases, the predictive ability of the SVM model is comparable or superior to those obtained by MLR and RBFNN. The results indicate that SVM can be used as an alternative powerful modeling tool for QSAR studies.

Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines

The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds' molecular descriptors calculated from the structure alone and the composition features of tissues. The heuristic method (HM) was used to select the appropriate molecular descriptors and build the linear model. The prediction result of the LS-SVM model is much better than that obtained by HM method and the prediction values of tissue/blood partition coefficients based on the LS-SVM model are in good agreement with the experimental values, which proved that nonlinear model can simulate the relationship between the structural descriptors, the tissue composition and the tissue/blood partition coefficients more accurately as well as LS-SVM was a powerful and promising tool in the prediction of the tissue/blood partition behaviour of compounds. Furthermore, this paper provided a new and effective method for predicting the tissue/blood partition behaviour of the compounds in the different tissues from their structures and gave some insight into structural features related to the partition process of the organic compounds in different tissues.

Diagnosing anorexia based on partial least squares, back propagation neural network, and support vector machines

Support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a predictive model for early diagnosis of anorexia. It was based on the concentration of six elements (Zn, Fe, Mg, Cu, Ca, and Mn) and the age extracted from 90 cases. Compared with the results obtained from two other classifiers, partial least squares (PLS) and back-propagation neural network (BPNN), the SVM method exhibited the best whole performance. The accuracies for the test set by PLS, BPNN, and SVM methods were 52%, 65%, and 87%, respectively. Moreover, the models we proposed could also provide some insight into what factors were related to anorexia.

Application of support vector machine (SVM) for prediction toxic activity of different data sets

As a new method, support vector machine (SVM) were applied for prediction of toxicity of different data sets compared with other two common methods, multiple linear regression (MLR) and RBFNN. Quantitative structure-activity relationships (QSAR) models based on calculated molecular descriptors have been clearly established. Among them, SVM model gave the highest q(2) and correlation coefficient R. It indicates that the SVM performed better generalization ability than the MLR and RBFNN methods, especially in the test set and the whole data set. This eventually leads to better generalization than neural networks, which implement the empirical risk minimization principle and may not converge to global solutions. We would expect SVM method as a powerful tool for the prediction of molecular properties.

QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor

A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as log (RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure-activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 log (RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.

Study of probabilistic neural networks to classify the active compounds in medicinal plants

Probabilistic neural networks (PNNs) were utilized for the classifications of 102 active compounds from diverse medicinal plants with anticancer activity against human rhinopharyngocele cell line KB. Molecular descriptors calculated from structure alone were used to represent molecular structures. A subset of the calculated descriptors selected using factor correlation analysis and forward stepwise regression was used to construct the prediction models. Linear discriminant analysis (LDA) was also utilized to construct the classification model to compare the results with those obtained by PNNs. The accuracy of the training set, the cross-validation set, and the test set given by PNNs and LDA were 100, 92.3, 90.9% and 71.8, 92.3, 54.5%, respectively, which indicated that the results obtained by PNNs agree well with the experimental values of these compounds and also revealed the superiority of PNNs over LDA approach for the classification of anticancer activities of compounds. The models built in this work would be of potential help in the design of novel and more potent anticancer agents.

QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines

The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modelling results in a nonlinear, seven-descriptor model based on SVMs with root mean-square errors of 0.107 and 0.136 for training and prediction sets, respectively. The best classification results are found using SVMs: the accuracy for training and test sets is 91.2% and 88.2%, respectively. This paper proposes a new and effective method for drug design and screening.

3D QSAR studies on antimalarial alkoxylated and hydroxylated chalcones by CoMFA and CoMSIA

The 3D QSAR analyses of antimalarial alkoxylated and hydroxylated chalcones were first conducted by Comparative molecular field analysis (CoMFA) and Comparative similarity indices analysis (CoMSIA) to determine the factors required for the activity of these compounds. Satisfactory results were obtained after performing a leave-one-out (LOO) cross-validation study with cross-validation q(2) and conventional r(2) values of 0.740 and 0.972 by the CoMFA model, 0.714 and 0.976 by the CoMSIA model, respectively. The results provided the tools for predicting the affinity of related compounds, and for guiding the design and synthesis of novel and more potent antimalarial agents.

The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine

Support vector machine (SVM), as a novel machine learning technique, was used for the prediction of the human oral absorption for a large and diverse data set using the five descriptors calculated from the molecular structure alone. The molecular descriptors were selected by heuristic method (HM) implemented in CODESSA. At the same time, in order to show the influence of different molecular descriptors on absorption and to well understand the absorption mechanism, HM was used to build several multivariable linear models using different numbers of molecular descriptors. Both the linear and non-linear model can give satisfactory prediction results: the square of correlation coefficient R(2) was 0.78 and 0.86 for the training set, and 0.70 and 0.73 for the test set respectively. In addition, this paper provides a new and effective method for predicting the absorption of the drugs from their structures and gives some insight into structural features related to the absorption of the drugs.

Quantitative Prediction of logk of Peptides in High-Performance Liquid Chromatography Based on Molecular Descriptors by Using the Heuristic Method and Support Vector Machine

A new method support vector machine (SVM) and the heuristic method (HM) were used to develop the nonlinear and linear models between the capacity factor (logk) and seven molecular descriptors of 75 peptides for the first time. The molecular descriptors representing the structural features of the compounds only included the constitutional and topological descriptors, which can be obtained easily without optimizing the structure of the molecule. The seven molecular descriptors selected by the heuristic method in CODESSA were used as inputs for SVM. The results obtained by SVM were compared with those obtained by the heuristic method. The prediction result of the SVM model is better than that of heuristic method. For the test set, a predictive correlation coefficient R = 0.9801 and root-mean-square error of 0.1523 were obtained. The prediction results are in very good agreement with the experimental values. But the linear model of the heuristic method is easier to understand and ready to use for a chemist. This paper provided a new and effective method for predicting the chromatography retention of peptides and some insight into the structural features which are related to the capacity factor of peptides.

QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine

The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear prediction models, leading to a good correlation coefficient (R2) of 0.86 and 0.94 and root-mean-square errors (rms) of 0.212 and 0.134 albumin drug binding affinity units, respectively. Furthermore, the models were evaluated by a 10 compound external test set, yielding R2 of 0.71 and 0.89 and rms error of 0.430 and 0.222. The specific information described by the heuristic linear model could give some insights into the factors that are likely to govern the binding affinity of the compounds and be used as an aid to the drug design process; however, the prediction results of the nonlinear SVM model seem to be better than that of the HM.

Novel Strategies in Cancer Therapeutics: Targeting Enzymes Involved in Cell Cycle Regulation and Cellular Proliferation

Tumor development, growth, and progression depend on some combination of altered cell cycle regulation, excessive growth factor pathway activation, and decreased apoptosis. Understanding the complex molecular mechanisms that underlie these processes should therefore lead to the identification of potential targets for therapeutic intervention. The estrogen receptor and HER-2/neu were among the earliest targets investigated, ultimately leading to the widespread use of tamoxifen and trastuzumab, respectively, in the treatment of breast cancer. Major research advances have since led to other classes of targeted therapies, including cyclin-dependent kinase inhibitors, histone deactylase inhibitors, and receptor tyrosine kinase inhibitors. The following review provides a discussion of the molecular biology associated with each of these types of therapies as well as a detailed summary of the preclinical and clinical data published on selected compounds from each of these subgroups.

Study of the Quantitative Structure-Mobility Relationship of Carboxylic Acids in Capillary Electrophoresis Based on Support Vector Machines

The support vector machines (SVM), as a novel type of learning machine, were used to develop a quantitative structure-mobility relationship (QSMR) model of 58 aliphatic and aromatic carboxylic acids based on molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) were also utilized to construct the linear and the nonlinear model to compare with the results obtained by SVM. The root-mean-square errors in absolute mobility predictions for the whole data set given by MLR, RBFNNs, and SVM were 1.530, 1.373, and 0.888 mobility units (10(-5) cm(2) S(-1) V(-1)), respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and RBFNNs models for the prediction of the absolute mobility of carboxylic acids. Moreover, the models we proposed could also provide some insight into what structural features are related to the absolute mobility of aliphatic and aromatic carboxylic acids.

An Accurate QSPR Study of O−H Bond Dissociation Energy in Substituted Phenols Based on Support Vector Machines

The support vector machine (SVM), as a novel type of learning machine, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the O-H bond dissociation energy (BDE) of 78 substituted phenols. The six descriptors calculated solely from the molecular structures of compounds selected by forward stepwise regression were used as inputs for the SVM model. The root-mean-square (rms) errors in BDE predictions for the training, test, and overall data sets were 3.808, 3.320, and 3.713 BDE units (kJ mol(-1)), respectively. The results obtained by Gaussian-kernel SVM were much better than those obtained by multiple linear regression, radial basis function neural networks, linear-kernel SVM, and other QSPR approaches.

Prediction of the Isoelectric Point of an Amino Acid Based on GA-PLS and SVMs

The support vector machine (SVM), as a novel type of a learning machine, for the first time, was used to develop a QSPR model that relates the structures of 35 amino acids to their isoelectric point. Molecular descriptors calculated from the structure alone were used to represent molecular structures. The seven descriptors selected using GA-PLS, which is a sophisticated hybrid approach that combines GA as a powerful optimization method with PLS as a robust statistical method for variable selection, were used as inputs of RBFNNs and SVM to predict the isoelectric point of an amino acid. The optimal QSPR model developed was based on support vector machines, which showed the following results: the root-mean-square error of 0.2383 and the prediction correlation coefficient R=0.9702 were obtained for the whole data set. Satisfactory results indicated that the GA-PLS approach is a very effective method for variable selection, and the support vector machine is a very promising tool for the nonlinear approximation.

In vitro antiviral activities of Caesalpinia pulcherrima and its related flavonoids

The aim of this study was to search for new antiviral agents from Chinese herbal medicine. Pure flavonoids and aqueous extracts of Caesalpinia pulcherrima Swartz were used in experiments to test their influence on a series of viruses, namely herpesviruses (HSV-1, HSV-2) and adenoviruses (ADV-3, ADV-8, ADV-11). The EC50 was defined as the concentration required to achieve 50% protection against virus-induced cytopathic effects, and the selectivity index (SI) was determined as the ratio of CC50 (concentration of 50% cellular cytotoxicity) to EC50. Results showed that aqueous extracts of C. pulcherrima and its related quercetin possessed a broad-spectrum antiviral activity. Among them, the strongest activities against ADV-8 were fruit and seed (EC50 = 41.2 mg/l, SI = 83.2), stem and leaf (EC50 = 61.8 mg/l, SI = 52.1) and flower (EC50 = 177.9 mg/l, SI = 15.5), whereas quercetin possessed the strongest anti-ADV-3 activity (EC50 = 24.3 mg/l, SI = 20.4). In conclusion, some compounds of C. pulcherrima which possess antiviral activities may be derived from the flavonoid of quercetin. The mode of action of quercetin against HSV-1 and ADV-3 was found to be at the early stage of multiplication and with SI values greater than 20, suggesting the potential use of this compound for treatment of the infection caused by these two viruses.

QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: an inhibitor of AP-1 and NF-kappa B mediated gene expression based on support vector machines

The support vector machine, as a novel type of learning machine, for the first time, was used to develop a QSAR model of 57 analogues of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl)pyrimidine-5-carboxylate (EPC), an inhibitor of AP-1 and NF-kappa B mediated gene expression, based on calculated quantum chemical parameters. The quantum chemical parameters involved in the model are Kier and Hall index (order3) (KHI3), Information content (order 0) (IC0), YZ Shadow (YZS) and Max partial charge for an N atom (MaxPCN), Min partial charge for an N atom (MinPCN). The mean relative error of the training set, the validation set, and the testing set is 1.35%, 1.52%, and 2.23%, respectively, and the maximum relative error is less than 5.00%.

Diagnosing breast cancer based on support vector machines

The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI machine learning repository. SVM outperformed k-means cluster and two artificial neural networks on the whole. It can be concluded that nine samples could be mislabeled from the comparison of several machine learning techniques.

Solid-phase microextraction from small volumes of sample in a glass capillary

A new sampling method is proposed for solid-phase microextraction (SPME), in which the extraction is carried out in a glass capillary containing a few microliters of sample. When an adsorption-type fiber is used for SPME, the equilibrium between aqueous sample and coating can be described by a Langmuir isotherm. Since the total amount of analytes and coexisting substances stays at a low level in a small volume of sample, the linear concentration range of analytes will be extended for SPME to be applied in quantification and the interference caused by sample matrix will be reduced. In addition, sampling in a capillary has a short diffusion distance and extraction equilibrium is established in 5-10 min. It is important in clinical analysis and therapeutic drug monitoring to be able to analyse sample volumes of samples. The feasibility of the new sampling method is demonstrated by the extractions of p-hydroxybenzaldehyde and a synthetic solution containing 1-naphthol, paeonol and 1-naphthylamine.

Quantitative prediction of liquid chromatography retention of N-benzylideneanilines based on quantum chemical parameters and radial basis function neural network

Based on quantum chemical parameters and a simple numerical coding, the liquid chromatography retention of bifunctionally substituted N-benzylideneaniles (NBA) has been predicted using a radial basis function neural network (RBFNN) model. The quantum chemical parameters involved in the model are dipole moment (m), energies of the highest occupied and lowest unoccupied molecular orbitals (E(homo,) E(lumo)), net charge of the most negative atom (Q(min)), sum of absolute values of the charges of all atoms in two given functional groups (Delta), total energy of the molecule (E(T)), weight of the molecule (W), and numerical coding (N). N was used to indicate the different positions of two substituents. The predictive values are consistent with the experimental results. The mean relative error of the testing set is 1.6%, and the maximum relative error is less than 5.0%. In this work the success of the whole modeling process only depends on the optimization of the spread parameter in network.

Toxicity of different industrial effluents in Taiwan: a comparison of the sensitivity of Daphnia similis and Microtox

Industrial effluents are known to exhibit toxicity toward different aquatic organisms. In Taiwan management of these discharges still relies on chemical and physical and physical characteristics of water, although various standard method for assessing aquatic toxicity have been proposed by the Taiwan Environmental Protection Administration. In this study we examined the toxicity and compared the sensitivity of different types of industrial effluents using two proposed toxicity tests: the Daphnia similis acute toxicity test and the Microtox acute assay (Vibrio fischeri). Results showed that electroplating effluents were the most toxic of all the effluents tested, followed by acrylonitrile manufacturing, pulp/paper, and tannery effluents. The EC50 of an electroplating effluent for D. similis and V. fischeri (15 min) was as low as, respectively, 2.9% and 3.9% of the whole effluent. The other effluents were not acutely toxic to either organism tested. However, the tests exhibited different sensitivity toward various discharges. Only the electroplating and acrylonitrile manufacturing effluents had effects on both organisms. These results indicate the importance of the incorporation of aquatic toxicity tests into the management scheme for treated wastewaters.