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Physicians' Desk Reference (physician + desk_reference)
Selected AbstractsPostmarketing drug dosage changes of 499 FDA-approved new molecular entities, 1980,1999,PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 6 2002James Cross MS Abstract Purpose Risks and benefits of marketed drugs can be improved by changing their labels to optimize dosage regimens for indicated populations. Such postmarketing label changes may reflect the quality of pre-marketing development, regulatory review, and postmarketing surveillance. We documented dosage changes of FDA-approved new molecular entities (NMEs), and investigated trends over time and across therapeutic groups, on the premise that improved drug development methods have yielded fewer postmarketing label changes over time. Methods We compiled a list of NMEs approved by FDA from 1 January 1980 to 31 December 1999 using FDA's website, Freedom of Information Act request, and PhRMA (Pharmaceutical Research and Manufacturers of America) database. Original labeled dosages and indicated patient populations were tracked in labels in the Physician's Desk Reference®. Time and covariate-adjusted risks for dosage changes by 5-year epoch and therapeutic groups were estimated by survival analysis. Results Of 499 NMEs, 354 (71%) were evaluable. Dosage changes in indicated populations occurred in 73 NMEs (21%). A total of 58 (79%) were safety-motivated, net dosage decreases. Percentage of NMEs with changes by therapeutic group ranged from 27.3% for neuropharmacologic drugs to 13.6% for miscellaneous drugs. Median time to change following approval fell from 6.5 years (1980,1984) to 2.0 years (1995,1999). Contrary to our premise, 1995,1999 NMEs were 3.15 times more likely to change in comparison to 1980,1984 NMEs (p,=,0.008, Cox analysis). Conclusions Dosages of one in five NMEs changed, four in five changes were safety reductions. Increasing frequency of changes, independent of therapeutic group, may reflect intensified postmarketing surveillance and underscores the need to improve pre-marketing optimization of dosage and indicated population. Copyright © 2002 John Wiley & Sons, Ltd. [source] Assessment of the sensitivity of the computational programs DEREK, TOPKAT, and MCASE in the prediction of the genotoxicity of pharmaceutical moleculesENVIRONMENTAL AND MOLECULAR MUTAGENESIS, Issue 3 2004Ronald D. Snyder Abstract Computational models are currently being used by regulatory agencies and within the pharmaceutical industry to predict the mutagenic potential of new chemical entities. These models rely heavily, although not exclusively, on bacterial mutagenicity data of nonpharmaceutical-type molecules as the primary knowledge base. To what extent, if any, this has limited the ability of these programs to predict genotoxicity of pharmaceuticals is not clear. In order to address this question, a panel of 394 marketed pharmaceuticals with Ames Salmonella reversion assay and other genetic toxicology findings was extracted from the 2000,2002 Physicians' Desk Reference and evaluated using MCASE, TOPKAT, and DEREK, the three most commonly used computational databases. These evaluations indicate a generally poor sensitivity of all systems for predicting Ames positivity (43.4,51.9% sensitivity) and even poorer sensitivity in prediction of other genotoxicities (e.g., in vitro cytogenetics positive; 21.3,31.9%). As might be expected, all three programs were more highly predictive for molecules containing carcinogenicity structural alerts (i.e., the so-called Ashby alerts; 61% ± 14% sensitivity) than for those without such alerts (12% ± 6% sensitivity). Taking all genotoxicity assay findings into consideration, there were 84 instances in which positive genotoxicity results could not be explained in terms of structural alerts, suggesting the possibility of alternative mechanisms of genotoxicity not relating to covalent drug-DNA interaction. These observations suggest that the current computational systems when applied in a traditional global sense do not provide sufficient predictivity of bacterial mutagenicity (and are even less accurate at predicting genotoxicity in tests other than the Salmonella reversion assay) to be of significant value in routine drug safety applications. This relative inability of all three programs to predict the genotoxicity of drugs not carrying obvious DNA-reactive moieties is discussed with respect to the nature of the drugs whose positive responses were not predicted and to expectations of improving the predictivity of these programs. Limitations are primarily a consequence of incomplete understanding of the fundamental genotoxic mechanisms of nonstructurally alerting drugs rather than inherent deficiencies in the computational programs. Irrespective of their predictive power, however, these programs are valuable repositories of structure-activity relationship mutagenicity data that can be useful in directing chemical synthesis in early drug discovery. Environ. Mol. Mutagen. 43:143,158, 2004. © 2004 Wiley-Liss, Inc. [source] What is prescription labeling communicating to doctors about hepatotoxic drugs?PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 4 2004A study of FDA approved product labeling Abstract Purpose The objective of this study was to evaluate the informativeness and consistency of product labeling of hepatotoxic drugs marketed in the United States. Methods We searched the Physicians' Desk Reference,2000 for prescription drugs with hepatic failure and/or hepatic necrosis listed in the labeling. We used a six-item checklist to evaluate the ,informativeness' and consistency of the labeling content. An informativeness score equaled the proportion of checklist items present in each drug's labeling. Results Ninety-five prescription drugs were included in the study. Eleven (12%) of the drugs had information related to hepatic failure in a Black Boxed Warning, 52 (54%) in the Warnings section and 32 (34%) in the Adverse Reactions section of the label. The mean informativeness score was 35%; the score was significantly higher, 61%, when the risk was perceived to be high. The informativeness of labeling was not affected by the time of the labeling, but differed across the Center for Drug Evaluation and Research (CDER) Review Division responsible for the labeling. Conclusions The information provided in labeling is variable and affected by many factors, including the perceived level of risk and review division strategy. Product labeling may benefit from current FDA initiatives to improve the consistency of risk-related labeling. Published in 2003 by John Wiley & Sons, Ltd. [source] G-protein coupled receptors: SAR analyses of neurotransmitters and antagonistsJOURNAL OF CLINICAL PHARMACY & THERAPEUTICS, Issue 3 2004C. L. Kuo MS Summary Background:, From the deductive point of view, neurotransmitter receptors can be divided into categories such as cholinergic (muscarinic, nicotinic), adrenergic (, - and , -), dopaminergic, serotoninergic (5-HT1,5-HT5), and histaminergic (H1 and H2). Selective agonists and antagonists of each receptor subtype can have specific useful therapeutic applications. For understanding the molecular mechanisms of action, an inductive method of analysis is useful. Objective:, The aim of the present study is to examine the structure,activity relationships of agents acting on G-protein coupled receptors. Method:, Representative sets of G-PCR agonists and antagonists were identified from the literature and Medline [P.M. Walsh (2003) Physicians' desk reference; M.J. O'Neil (2001) The Merck index]. The molecular weight (MW), calculated logarithm of octanol/water partition coefficient (C log P) and molar refraction (CMR), dipole moment (DM), Elumo (the energy of the lowest unoccupied molecular orbital, a measure of the electron affinity of a molecule and its reactivity as an electrophile), Ehomo (the energy of the highest occupied molecular orbital, related to the ionization potential of a molecule, and its reactivity as a nucleophile), and the total number of hydrogen bonds (Hb) (donors and receptors), were chosen as molecular descriptors for SAR analyses. Results:, The data suggest that not only do neurotransmitters share common structural features but their receptors belong to the same ensemble of G-protein coupled receptor with seven to eight transmembrane domains with their resultant dipoles in an antiparallel configuration. Moreover, the analysis indicates that the receptor exists in a dynamic equilibrium between the closed state and the open state. The energy needed to open the closed state is provided by the hydrolysis of GTP. A composite 3-D parameter frame setting of all the neurotransmitter agonists and antagonists are presented using MW, Hb and , as independent variables. Conclusion:, It appears that all neurotransmitters examined in this study operate by a similar mechanism with the G-protein coupled receptors. [source] | ||||||||||