Biomedical subjects

Lemont B Kier

Publications and source records attributed to Lemont B Kier.

15 recordsLinked to original sources

PubMed2006

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds. Partitioning was accomplished by clustering the compounds in a structure descriptor space so that random sampling of 20% of the whole data set produced an external test set that is a good representative of the training set with respect to both structure and protein binding values. The four modeling techniques include multiple linear regression (MLR), artificial neural networks (ANN), k-nearest neighbors (kNN), and support vector machines (SVM). With the exception of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models. Training set correlation coefficients and mean absolute error ranged from r2=0.90 and MAE=7.6 for ANN to r2=0.61 and MAE=16.2 for MLR. Prediction results from the validation set yielded correlation coefficients and mean absolute errors which ranged from r2=0.70 and MAE=14.1 for ANN to a low of r2=0.59 and MAE=18.3 for the SVM model. Structure descriptors that contribute significantly to the models are discussed and compared with those found in other published models. For the ANN model, structure descriptor trends with respect to their affects on predicted protein binding can assist the chemist in structure modification during the drug design process.

Blood Proteins↗

PubMed2005

Modeling biochemical networks: a cellular-automata approach.

The potential of the cellular-automata (CA) method for modeling biological networks is demonstrated for the mitogen-activated protein kinase (MAPK) signaling cascade. The models derived reproduced the high signal amplification through the cascade and the deviation of the cascade enzymes from the Michaelis-Menten kinetics, evidencing cooperativity effects. The patterns of pathway change upon varying substrate concentrations and enzyme efficiencies were identified and used to show the ways for controlling pathway processes. Guidance in the selection of enzyme inhibition targets with minimum side effects is one outcome of the study.

Automation↗

PubMed2005

The prediction of ADMET properties using structure information representations.

The electrotopological state and molecular connectivity indices are defined as a system for molecular-structure description, using the term Structure-Information Representation. This system is built on the depiction of a molecule as a network composed of atoms of varying valence electron counts that constitute the valence state, bonded in discrete patterns constituting an electrotopological state. The system is employed in the structure-activity analysis of two sets of ADMET data. Models are created relating hepatotoxicity and human metabolic stability. The validity of these models makes them useful for activity prediction.

Humans↗

PubMed2005

QSAR modeling of carcinogenic risk using discriminant analysis and topological molecular descriptors.

A discriminant analysis model is presented for carcinogenic risk. The data set is obtained from the two-year rodent study FDA/CDER database and was divided into a training set of 1022 organic compounds and an external validation test set of 50 compounds. The model is designed to use as a decision support tool for a defined decision threshold, and is thus a binary discrimination into "high risk" and "low risk" categories. The carcinogenic risk classification is based on the method for estimating human risk from two-year rodent studies developed at the FDA/CDER/ICSAS. The paradigm chosen for this model allows a straightforward risk analysis based on historic information, as well as the computation of coverage, probability and confidence metrics that can further qualify the computed result. The molecular structures were represented as MDL mol files. The molecular structure information was obtained as topological structure descriptors, including atom-type and group-type E-State and hydrogen E-State indices, molecular connectivity chi indices, topological polarity, and counts of molecular features. The MDL QSAR software computed all these descriptors. Furthermore, the discriminant analyses were all performed with the MDL QSAR software. The reported model is based on fifty-three descriptors, using the nonparametric normal kernel method and the Mahalanobis distance to determine proximity. The model performed very well on the fifty compounds of the test set, yielding the following statistics: 76% correctly classified "high risk" (carcinogenic) and 84% correctly classified as "low risk" (non-carcinogenic).

Animals↗

PubMed2004

Bioisosterism: quantitation of structure and property effects.

The powerful concept of bioisosterism is presented as a method for selecting molecular groups for drug design and lead-compound development. Three group-structure characteristics are described for this purpose. The E-State value for an attached atom is used as a measure of electrotopological group impact. The volume of the group is estimated from counts of sigma, pi, and lone-pair n electrons, as embodied in the valence and simple connectivity delta values for atoms in the group. Polarity is described in terms of the polarity index Q(v). Specific examples are given for commonly used groups. Parameter spaces encoding these three attributes are presented as examples that may be used to guide bioisostere selection in late-stage drug-design procedures. The method presented here is of practical value in the decision processes of molecular modification.

Biodiversity↗

PubMed2004

Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation.

Several QSPR models were developed for predicting intrinsic aqueous solubility, S(o). A data set of 5,964 neutral compounds was sub-divided into two classes, aromatic and non-aromatic compounds. Three models were created with different methods on both data sets: two regression models (multiple linear regression and partial least squares) and an artificial neural network model. These models were based on 3343 aromatic and 1674 non-aromatic compounds for training sets; 938 compounds were used in external validation testing. The range in -log S(o) is -1.6 to 10. Topological structure descriptors were used with all models. A genetic algorithm was used for descriptor selection for regression models. For the artificial neural network (ANN) model, descriptor selection was done with a backward elimination process. All models performed well with r2 values ranging 0.72 to 0.84 in external validation testing. The mean absolute errors in validation ranged from 0.44 to 0.80 for the classes of compounds for all the models. These statistical results indicate a sound ANN model. Furthermore, in a comparison with eight other available models, based on predictions using a validation test set (442 compounds), the artificial neural network model presented in this work (CSLogWS) was clearly superior based on both the mean absolute error and the percentage of residuals less than one log unit. In the ANN model both E-State and hydrogen E-State descriptors were found to be important.

Databases, Factual↗

PubMed2004

New predictors for several ADME/Tox properties: aqueous solubility, human oral absorption, and Ames genotoxicity using topological descriptors.

In silico predictive models for aqueous solubility, human intestinal absorption (HIA), and Ames genotoxicity were developed principally using artificial neural net (ANN) analysis and topological descriptors. Approximately 10,000 compounds spread across three data sets were used in the construction of these quantitative-structure-activity/property-relationship (QSAR/QSPR) models. For aqueous solubility, 5,037 chemically diverse compounds were used to construct ANN-QSPRs for intrinsic aqueous solubility. When these robust models were applied to 938 compounds in external validation, they gave an r2 = 0.78 with 84% predicted within 1 log unit for these new chemical entities (NCEs). 417 therapeutic drugs were used in the development of an ANN-QSPR to predict for percent oral absorption (%OA). For validation testing on 195 new drugs, 92% of the compounds were predicted to within 25% of their reported %OA values, which ranged from 0% to 100%. Polar surface area and logP, the octanol-water partition coefficient, were found to be important descriptors in our QSPR model. Development of an ANN-QSAR as a genotoxicity predictor for S. typhimurium employed 2963 compounds including 290 therapeutic drugs. Validation results on 400 NCEs with the ANN-QSAR gave a concordance of 83% which rose to 91% when a confidence indicator was applied. With new drugs a concordance of 92% was reached, which increased to 97% when the reliably indicator was invoked.

Administration, Oral↗

PubMed2004

Three new consensus QSAR models for the prediction of Ames genotoxicity.

Three QSAR methods, artificial neural net (ANN), k-nearest neighbors (kNN), and Decision Forest (DF), were applied to 3363 diverse compounds tested for their Ames genotoxicity. The ratio of mutagens to non-mutagens was 60/40 for this dataset. This group of compounds includes >300 therapeutic drugs. All models were developed using the same initial set of 148 topological indices: molecular connectivity chi indices and electrotopological state indices (atom-type, bond-type and group-type E-state), as well as binary indicators. While previous studies have found logP to be a determining factor in genotoxicity, it was not found to be important by any modeling method employed in this study. The three models yielded an average training/test concordance value of 88%, with a low percentage of false positives and false negatives. External validation testing on 400 compounds not used for QSAR model development gave an average concordance of 82%. This value increased to 92% upon removal of less reliable outcomes, as determined by a reliability criterion used within each model. The ANN model showed the best performance in predicting drug compounds, yielding 97% concordance (34/35 drugs) after the removal of less reliable predictions. The appreciable commonality found among the top 10 ranked descriptors from each model is of particular interest because of the diversity in the learning algorithms and descriptor selection techniques employed in this study. Forty percent of the most important descriptors in any one model are found in one or two other models. Fourteen of the most important descriptors relate directly to known toxicophores involved in potent genotoxic responses in Salmonella typhimurium. A comparison of the validation results with those of MULTICASE and DEREK indicated that the new models presented in this work perform substantially better than the former models in predicting genotoxicity of therapeutic drugs. Substantially higher specificity was achieved with these new models as compared with MULTICASE or DEREK with comparable sensitivities among all models.

Algorithms↗

PubMed2003

A theory of inhaled anesthetic action by disruption of ligand diffusion chreodes.

A theory is proposed for the clinical actions and behavior of volatile anesthetic agents. Evidence supports the nonspecific and ubiquitous effects of these drugs in which many ligand-receptor systems may be involved. The volatile anesthetic drugs interfere with the diffusion of ligands to receptors across the hydrodynamic landscape on the protein surface. The hydropathic states of the amino acid side chains on the landscape of an active site influence the water to form evanescent paths, called chreodes as defined by Waddington and offered as an appropriate term by Kier. These chreodes are altered by the presence of volatile anesthetic drugs, leading to a reduction in the diffusion of many ligands to their active sites. This effect is sufficient to decrease the responses of these receptors, leading to an anesthetic state.

Anesthetics, Inhalation↗

PubMed2002

A cellular automata model of ligand passage over a protein hydrodynamic landscape.

The subject of ligand passage to an active site on a protein is addressed. Current views on the mechanism and the possible role of surface water are discussed. A theory is presented in which the pattern of hydropathic states of protein surface amino acid side chains is invoked as the influence on the relative hydrophobic effects of nearby water. The theory describes a ligand passage through the hydrodynamic near-surface water which exhibits temporary organized cavities resembling the chreodes introduced by Waddington. The passage of the ligand to the active site is facilitated by this dynamic mechanism. Cellular automata models of preferential directional diffusion through these chreodes support the theory. The theory may be invoked to explain a number of ligand-active site observations and serves as an idea for further studies.

Animals↗

PubMedDate not supplied

Modeling blood-brain barrier partitioning using the electrotopological state.

The challenging problem of modeling blood-brain barrier partitioning is approached through topological representation of molecular structure. A QSAR model is developed for in vivo blood-brain partitioning data treated as the logarithm of the blood-brain concentration ratio. The model consists of three structure descriptors: the hydrogen E-State index for hydrogen bond donors, HS(T)(HBd); the hydrogen E-State index for aromatic CHs, HS(T)(arom); and the second order difference valence molecular connectivity index, d(2)chi(v) (q(2) = 0.62.) The model for the set of 106 compounds is validated through use of an external validation test set (20 compounds of the 106, MAE = 0.33, rms = 0.38), 5-fold cross-validation (MAE = 0.38, rms = 0.47), prediction of +/- values for an external test set (27/28 correct), and estimation of logBB values for a large data set of 20 039 drugs and drug-like compounds. Because no 3D structure information is used, computation of logBB by the model is very fast. The quality of the validation statistics supports the claim that the model may be used for estimation of logBB values for drug and drug-like molecules. Detailed structure interpretation is given for the structure indices in the model. The model indicates that molecules that penetrate the blood-brain barrier have large HS(T)(arom) values (presence of aromatic groups) but small values of HS(T)(HBd) (fewer or weaker H-Bond donors) and smaller d(2)chi(v) values (less branched molecules with fewer electronegative atoms). These three structure descriptors encode influence of molecular context of groups as well as counts of those groups.

Blood-Brain Barrier↗

PubMedDate not supplied

A cellular automata model of water structuring by a chiral solute.

The organization of water around a solute molecule with surface features of varying hydropathic states is studied. A stationary solute molecule and mobile water solvent molecules are modeled using cellular automata dynamics. It is shown that varying hydropathic states of solute molecule surface features influence the relative affinities of water for these features. In the case of a simulated chiral solute, a chiral pattern of associated water molecules binding to the surface is produced. This finding is in agreement with published simulations and circular dichroism measurements. A pattern of water molecules at locations beyond the surface of the solute molecules is detected, evidence of an emergent property in this solvent-solute system.

Journal Article↗

PubMedDate not supplied

Studies of ligand diffusion pathways over a protein surface.

Studies were conducted on the behavior of simulated molecules diffusing within organized water, postulated to form from the hydropathic states of protein surface amino acid side chains. This organization is postulated to facilitate the diffusion of ligands across the protein surface to their effector. These studies reveal that the organized water can be disrupted in their diffusion facilitating function by the presence of some other solute in high concentration. It was also found from cellular automata simulations that chiral isomers behaved in a slightly different manner when in an asymmetric enclosure simulating a fragment of the organized water pathway. These findings have relevance to observations about the mechanism of action of nonspecific anesthetic agents.

Amino Acids↗

PubMedDate not supplied

Modeling drug albumin binding affinity with e-state topological structure representation.

The binding affinity to human serum albumin for 94 drugs was modeled with topological descriptors of molecular structure, using as experimental data the HPLC chromatographic retention index [logk(HSA)] on immobilized albumin. The electrotopological state (E-State) along with the molecular connectivity chi indices provided the basis for a satisfactory model: r(2) = 0.77, s = 0.29, q(2) = 0.70, s(press) = 0.33. The 10% leave-group-out (LGO) cross-validation method yielded q(2) (= r(2)(press)) = 0.69. Further, the model was tested on a 10 compound external validation set, yielding a mean absolute error, MAE = 0.31; q(2) (= r(2)(press)) = 0.74. MDL QSAR software was used for setting up the data set, creation of combination descriptors, modeling, and database management. All the statistical tests indicate that the topological model is useful for property estimation. Internal and external validation methods were used, and the results indicate that the model is useful for prediction. Randomizations of the activity values also indicate statistically sound models are very different from random statistics. The model indicates that positive factors for binding affinity include electron accessibility and the number of aromatic rings, aliphatic CH groups (-CH(3), -CH(2)-, >CH-), halogens (fluorine and chlorine), and -OH groups. Five-membered heteroatomic rings present a negative factor, whereas six-membered heteroatomic rings present a positive factor. The specific information described can be used as an aid to the drug design process.

Albumins↗

PubMedDate not supplied

QSAR modeling of beta-lactam binding to human serum proteins.

The binding of beta-lactams to human serum proteins was modeled with topological descriptors of molecular structure. Experimental data was the concentration of protein-bound drug expressed as a percent of the total plasma concentration (percent fraction bound, PFB) for 87 penicillins and for 115 beta-lactams. The electrotopological state indices (E-State) and the molecular connectivity chi indices were found to be the basis of two satisfactory models. A data set of 74 penicillins from a drug design series was successfully modeled with statistics: r2 = 0.80, s = 12.1, q2 = 0.76, spress = 13.4. This model was then used to predict protein binding (PFB) for 13 commercial penicillins, resulting in a very good mean absolute error, MAE = 12.7 and correlation coefficient, q2 = 0.84. A group of 28 cephalosporins were combined with the penicillin data to create a dataset of 115 beta-lactams that was successfully modeled: r2 = 0.82, s = 12.7, q2 = 0.78, spress = 13.7. A ten-fold 10% leave-group-out (LGO) cross-validation procedure was implemented, leading to very good statistics: MAE = 10.9, spress = 14.0, q2 (or r2press) = 0.78. The models indicate a combination of general and specific structure features that are important for estimating protein binding in this class of antibiotics. For the beta-lactams, significant factors that increase binding are presence and electron accessibility of aromatic rings, halogens, methylene groups, and =N- atoms. Significant negative influence on binding comes from amine groups and carbonyl oxygen atoms.

Blood Proteins↗