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Biomedical subjects

Donald F Weaver

Publications and source records attributed to Donald F Weaver.

15 recordsLinked to original sources
PubMed2004

Benzhydryl as an efficient selective nitrogen protecting group for uracils.

Regioselective N-substitution of the less active nitrogen within uracil analogues has been achieved following preliminary N-protection at the more active N-position with a benzhydryl protecting group. This protecting group is stable to concentrated HCl (aqueous) at reflux temperature, TFA at room temperature, and Pd-C-catalyzed normal pressure hydrogenation at room temperature; the benzhydryl group can be removed quantitatively and selectively with a 10% triflic acid solution in TFA at 0 degree C.

Journal Article↗
PubMed2004

A comparison of methods for modeling quantitative structure-activity relationships.

A large number of methods are available for modeling quantitative structure-activity relationships (QSAR). We examine the predictive accuracy of several methods applied to data sets of inhibitors for angiotensin converting enzyme, acetylcholinesterase, benzodiazepine receptor, cyclooxygenase-2, dihydrofolate reductase, glycogen phosphorylase b, thermolysin, and thrombin. Descriptors calculated with CoMFA, CoMSIA, EVA, HQSAR, and traditional 2D and 2.5D descriptors were used for developing models with partial least squares (PLS). In addition, the genetic function approximation algorithm, genetic PLS, and back-propagation neural networks were used for deriving models from 2.5D descriptors (i.e., 2D descriptors and 3D descriptors calculated from CORINA structures and Gasteiger-Marsili charges). Predictive accuracy was assessed using designed test sets. It was found that HQSAR generally performs as well as CoMFA and CoMSIA; other descriptor sets performed less well. When 2.5D descriptors were used, only neural network ensembles were found to be similarly or more predictive than PLS models. In addition, we show that many cross-validation procedures yield similar estimates of the interpolative accuracy of methods. However, the lack of correspondence between cross-validated and test set predictive accuracy for four sets underscores the benefit of using designed test sets.

Algorithms↗
PubMed2004

Molecular modeling of the von Willebrand factor A2 Domain and the effects of associated type 2A von Willebrand disease mutations.

A homology model for the A2 domain of von Willebrand factor (VWF) is presented. A large number of target-template alignments were combined into a consensus alignment and used for constructing the model from the structures of six template proteins. Molecular dynamics simulation was used to study the structural and dynamic effects of eight mutations introduced into the model, all associated with type 2A von Willebrand disease. It was found that the group I mutations G1505R, L1540P and S1506L cause significant deviations over multiple regions of the protein, coupled to significant thermal fluctuations for G1505R and L1540P. This suggests that protein instability may be responsible for their intracellular retention. The group II mutations R1597W, E1638K and G1505E caused single loop displacements near the physiologic VWF proteolysis site between Y1605-M1606. These modest structural changes may affect interactions between VWF and the ADAMTS13 protease. The group II mutations I1628T and L1503Q caused no significant structural change in the protein, suggesting that inclusion of the protease in this model is necessary for understanding their effect. [Figure: see text]. Homology model of the von Willebrand factor A2 domain

Amino Acid Sequence↗
PubMed2004

Pruned receptor surface models and pharmacophores for three-dimensional database searching.

A pharmacophore represents the 3D arrangement of chemical features that are shared by molecules exhibiting activity at a protein receptor. Pharmacophores are routinely used in 3D database searching for identifying potential lead compounds. The lack of shape constraints causes the query to identify compounds that could not fit into the active site. In the absence of structural information, a receptor surface model (RSM) can be used to represent the active site. The RSM consists of a surface that envelops a set of known actives after these have been aligned using their common features. When used for database searching, a RSM is overconstraining as it restricts access to regions that could be occupied by ligands, such as the solvent-protein interface or unexplored pockets. We describe a protocol for developing pruned RSMs using information gleaned from 3D quantitative structure-activity relationship (QSAR) models. We examined the performance of queries that consist of pharmacophores used alone or with pruned or unpruned RSMs by performing searches on six databases containing known actives distributed among inactives. The pruned RSMs yield an average selectivity 1.8 times greater than that for pharmacophore queries, compared to 1.6 times for unpruned RSMs. However, the pruned RSMs retrieve on average 73% of the actives identified using the pharmacophores, compared to 40% for the unpruned RSMs. As such, pruned RSMs represent a useful compromise between the high sensitivity of pharmacophores and the high selectivity of unpruned RSMs.

Amidines↗
PubMed2004

Density functional theory investigations on the chemical basis of the selectivity filter in the K+ channel protein.

The chemical-physical basis of loading and release of K(+) and Na(+) ions in and out of the selectivity filter of the K(+) channel has been investigated using the B3LYP method of density functional theory. We have shown that the difference between binding free energies of K(+) and Na(+) to the cavity end of the filter is smaller than the difference between the K(+) and Na(+) solvation free energies. Thus, the loading of K(+) ions into the cavity end of the selectivity filter from the solution phase is suggested to be selective prior to the subsequent conduction process. It is shown that the extracellular end of the filter is only optimal for K(+) ions, because K(+) ions prefer the coordination environment of eight carbonyl oxygens. Na(+) ions do not fit into the extracellular end of the filter, since they prefer the coordination environment of six carbonyl oxygens. Overall, the results suggest that the rigid C(4) symmetric selectivity filter is specifically designed for conduction of K(+) ions.

Cations, Monovalent↗
PubMed2004

"Organic" pseudoseizures as an unrecognized side-effect of anticonvulsant therapy.

Although pseudoseizures are a cause of drug toxicity (as escalating doses are used in an attempt to suppress seemingly intractable spells), in this brief report the exact converse is argued: namely, that a sub-group of pseudoseizures may arise as a reversible idiosyncratic toxic side-effect of GABAergic anticonvulsants. The term "organic pseudoseizures" is used to denote this group of medication related behavioural alterations.

Anticonvulsants↗
PubMed2004

Implementing a bioassay to screen molecules for antiepileptogenic activity: chronic pilocarpine versus subdudral haematoma models.

BACKGROUND: There is a need to discover novel chemical compounds that will inhibit the pathological process of epileptogenesis (i.e. agents that will prevent the long-term formation of an active seizure focus following a brain insult). The goal of this paper is to identify a bioassay of value in drug design when screening new chemical entities as putative antiepileptogenic agents. METHODS: We focused on two models: the pilocarpine chronic seizure model of spontaneous recurrent seizures (SRSs) and a chronic subdural haematoma model of SRSs. Both models were evaluated using more than 20 Sprague-Dawley rats for each model. RESULTS: In the pilocarpine-induced model of SRSs, 80% of animals went on to develop SRSs when the dose of pilocarpine was 380 mg/kg i.p. In 50 animals that developed SRSs, the average number of seizures per 15 days of observation was 3.8 seizures with a range of 2-23 seizures per 15-day period. The chronic subdural model was inefficient in producing SRSs. CONCLUSIONS: A pilocarpine-induced SRS model of epilepsy affords a reliable model of epileptogenesis suitable for evaluating new chemical entities as putative antiepileptogenics.

Animals↗
PubMed2004

A mathematical model for prediction of drug molecule diffusion across the blood-brain barrier.

BACKGROUND: Predicting the ability of drugs to enter the brain is a longstanding problem in neuropharmacology. The first step in creating a much-needed computational algorithm for predicting whether a drug will enter brain is to devise a rigorous mathematical model. METHODS: Employing two experimental measures of blood-brain barrier (BBB) penetrability (brain/plasma ratio and the brain-uptake index) and 14 theoretically derived biophysical predictors, a mathematical model was developed to quantitatively correlate molecular structure with ability to traverse the BBB. RESULTS: This mathematical model employs Stein's hydrogen bonding number and Randic's topological descriptors to correlate structure with ability to cross the BBB. The final model accurately predicts the ability of test molecules to cross the BBB. CONCLUSIONS: A mathematical method to predict blood-brain barrier penetrability of drug molecules has been successfully devised. As a result of bioinformatics, chemoinformatics and other informatics-based technologies, the number of small molecules being developed as potential therapeutics is increasing exponentially. A biophysically rigorous method to predict BBB penetrability will be a much-needed tool for the evaluation of these molecules.

Algorithms↗
PubMed2004

Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) modelling using comparative molecular similarity indices analysis (CoMSIA) was applied to a series of 406 structurally diverse dihydrofolate reductase (DHFR) inhibitors from Pneumocystis carinii (pc) and rat liver (rl). X-ray crystal structures of three inhibitors bound to pcDHFR were used for defining the alignment rule. For pcDHFR, a QSAR model containing 6 components was selected using leave-10%-out cross-validation (n= 240, q2 = 0.65), while a 4-component model was selected for rlDHFR (n= 237, q2 = 0.63); both include steric, electrostatic and hydrophobic contributions. The models were validated using a large test set, designed to maximise its diversity and to verify the predictive accuracy of models for extrapolation. The pcDHFR model has r2 = 0.60 and mean absolute error (MAE) = 0.57 for the test set after removing 4 outliers, and the rlDHFR model has r2 = 0.60 and MAE = 0.69 after removing 4 test set outliers. In addition, classification models predicting selectivity for pcDHFR over rlDHFR were developed using soft independent modelling by class analogy (SIMCA), with a selectivity ratio of 2 (IC50,rlDHFR/ IC50,pcDHFR) used for delimiting classes. A 5-component model including steric and electrostatic contributions has cross-validated and test set classification rates of 0.67 and 0.68 for selective inhibitors, and 0.85 and 0.72 for unselective inhibitors. The predictive accuracy of models, together with the identification of important contributions in QSAR and classification models, offer the possibility of designing potent selective inhibitors and estimating their activity prior to synthesis.

Animals↗
PubMed2003

Functionalized amido ketones: new anticonvulsant agents.

We have reported that functionalized amino acids (FAA) are potent anticonvulsants. Replacing the N-terminal amide group in FAA with phenethyl, styryl, and phenylethynyl units provided a series of functionalized amido ketones (FAK). We show that select FAK exhibit significant anticonvulsant activities thereby providing information about the structural requirements for FAA and FAK bioactivity.

Amides↗
PubMed2003

Epileptogenesis, ictogenesis and the design of future antiepileptic drugs.

There is still no medical cure for epilepsy. Clinical epileptology is in need of a "paradigm shift" when it comes to the continuing development of therapeutics. An important first step in this conceptual evolution is differentiating between the notions of ictogenesis and epileptogenesis. All traditional therapeutics are anti-ictogenic, not antiepileptogenic. The future of antiepileptic drug development lies in the discovery of antiepileptogenics. Just as aspirin is not the drug of choice for meningitis, an anticonvulsant is not the drug of choice for epilepsy. Drug design for epilepsy needs to discover a penicillin, not more aspirins.

Anticonvulsants↗
PubMed2002

Patients' attitudes and prior treatments in neuropathic pain: a pilot study.

BACKGROUND: Ongoing research continues to expand the knowledge of neuropathic pain. It is vital that established treatments and valuable discoveries ultimately improve patient care. OBJECTIVES: Attitudes and prior treatments of patients being screened for neuropathic pain trials were evaluated to provide further understanding of the barriers to the management of neuropathic pain. METHODS: A questionnaire was completed by patients with neuropathic pain who were either referred by local physicians or self referred in response to clinical trial advertisements from the authors' facility. RESULTS: In total, 151 patients completed the questionnaire. Diagnoses included diabetic neuropathy (55.6%), postherpetic neuralgia (29.8%), idiopathic peripheral neuropathy (9.3%) and others (5.3%). The mean pain duration was 4.7 years, and the mean daily pain (on a score of 0 to 10) was 7.6. During questioning, 72.8% complained of inadequate pain control and 25.2% had never tried any antineuropathic analgesics (tricyclic antidepressants, opioids or anticonvulsants). New antineuropathic analgesics (eg, gabapentin) were being used by only 16.6%. Opioids, tricyclic antidepressants and anticonvulsants had never been tried by 41.1%, 59.6% and 72.2%, respectively. Fears of addiction and adverse effects were expressed by 31.8% and 48.3%, respectively. CONCLUSIONS: New, and even conventional, therapies are often not pursued, despite inadequate pain control. Several issues are discussed, including patient barriers to seeking pain management, patient and physician barriers to analgesic drug therapy, and appropriate use of and access to multidisciplinary pain centres. Failure to implement therapeutic advances in pain management not only hinders improvement in patient care, but also may render futile decades of research. Widespread professional, patient and public education, as well as continued interdisciplinary research on treatment barriers, is essential.

Aged↗
PubMedDate not supplied

Development of quantitative structure-activity relationships and classification models for anticonvulsant activity of hydantoin analogues.

Classification and QSAR analysis was performed on a large set of hydantoin derivatives with measured anticonvulsant activity in mice and rats. The classification set comprised 287 hydantoins having maximal electroshock (MES) activity expressed in qualitative form. A subset of 94 hydantoins with MES ED(50) values was used for QSAR analysis. Numerical descriptors were generated to encode topological, geometric/structural, electronic, and thermodynamic properties of molecules. Analyses were performed with training and test sets of diverse compounds selected using their representation in a principal component space. Cell- and distance metric-based selection methods were employed in this process. For QSAR, a genetic algorithm (GA) was used for selecting subsets of 5-9 descriptors that minimize the rms error on the training sets. The most predictive models have rms errors of 0.86 (r(2) = 0.64) and 0.73 (r(2) = 0.75) ln(1/ED(50)) units on the cell- and distance metric-derived test sets, respectively, and showed convergence in the selected descriptors. Classification models were developed using recursive partitioning (RP) and spline-fitting with a GA (SFGA), a novel method we have implemented. The most predictive RP and SFGA models have classification rates of 75% and 80% on the test sets; both methods produced models with similar discriminating features. For QSAR and classification, consensus schemes gave improved predictive accuracy.

Algorithms↗
PubMedDate not supplied

Spline-fitting with a genetic algorithm: a method for developing classification structure-activity relationships.

Classification methods allow for the development of structure-activity relationship models when the target property is categorical rather than continuous. We describe a classification method which fits descriptor splines to activities, with descriptors selected using a genetic algorithm. This method, which we identify as SFGA, is compared to the well-established techniques of recursive partitioning (RP) and soft independent modeling by class analogy (SIMCA) using five series of compounds: cyclooxygenase-2 (COX-2) inhibitors, benzodiazepine receptor (BZR) ligands, estrogen receptor (ER) ligands, dihydrofolate reductase (DHFR) inhibitors, and monoamine oxidase (MAO) inhibitors. Only 1-D and 2-D descriptors were used. Approximately 40% of compounds in each series were assigned to a test set, "cherry-picked" from the complete set such that they lie outside the training set as much as possible. SFGA produced models that were more predictive for all but the DHFR set, for which SIMCA was most predictive. RP gave the least predictive models for all but the MAO set. A similar trend was observed when using training and test sets to which compounds were randomly assigned and when gradually eliminating compounds from the (designed) training set. The stability of models was examined for the random and reduced sets, where stability means that classification statistics and the selected descriptors are similar for models derived from different sets. Here, SIMCA produced the most stable models, followed by SFGA and RP. We show that a consensus approach that combines all three methods outperforms the single best model for all data sets.

Algorithms↗