Biomedical subjects

Donald F Weaver

Publications and source records attributed to Donald F Weaver.

23 records · Page 2Linked to original sources

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↗

PubMedDate not supplied

Challenges in the clinical diagnosis of Alzheimer's disease: influence of "family coaching" on the mini-mental state examination.

The Mini-Mental State Examination (MMSE) is a commonly used clinical tool for evaluating the cognitive aspects of mental function. In this study, 40 consecutive patients presenting to a memory disorder clinic and their caregivers were evaluated for coaching of the patient with respect to MMSE content over the 24 hours prior to clinical evaluation. Caregivers completed a questionnaire concerning MMSE practice sessions with the patient prior to the physician encounter; then, the patients were asked to spell the word "WORM" backwards instead of "WORLD" during the MMSE test. Some or all of the MMSE content was reviewed with the patient prior to the interview by 42.5 percent of the caregivers; 17.5 percent of patients spelled or attempted to spell "WORLD" backwards instead of "WORM". These results demonstrate that coaching of patients prior to administration of the MMSE is not uncommon, and that this needs to be taken into consideration when forming therapeutic decisions based on MMSE results.

Aged↗