Chemist and druggist.

"the newsweekly for pharmacy."

Establishments and products licensed under section 351 of the Public Health Service Act /

Description based on: June 1982.

Report on medical equipment and pharmaceuticals market-oriented, sector-selective (MOSS) discussions /

"January 1986."

Pharmacy benefit managers : early results on ventures with drug manufacturers : report to the Honorable Ron Wyden, House of Representatives /

"B-257388"--P. 1.

Prescription drug samples: Does this marketing strategy counteract policies for quality use of medicines?

Prescription drug samples, as used by the pharmaceutical industry to market their products, are of current interest because of their influence on prescribing, and their potential impact on consumer safety. Very little research has been conducted into the use and misuse of prescription drug samples, and the influence of samples on health policies designed to improve the rational use of medicines. This is a topical issue in the prescription drug debate, with increasing costs and increasing concerns about optimizing use of medicines. This manuscript critically evaluates the research that has been conducted to date about prescription drug samples, discusses the issues raised in the context of traditional marketing theory, and suggests possible alternatives for the future.

Acyl glucuronide reactivity in perspective: biological consequences

The metabolic conjugation of exogenous and endogenous carboxylic acid substrates with endogenous glucuronic acid, mediated by the uridine diphosphoglucuronosyl transferase (UGT) superfamily of enzymes, leads to the formation of acyl glucuronide metabolites. Since the late 1970s, acyl glucuronides have been increasingly identified as reactive electrophilic metabolites, capable of undergoing three reactions: intramolecular rearrangement, hydrolysis, and intermolecular reactions with proteins leading to covalent drug-protein adducts. This essential dogma has been accepted for over a decade. The key question proposed by researchers, and now the pharmaceutical industry, is: does or can the covalent modification of endogenous proteins, mediated by reactive acyl glucuronide metabolites, lead to adverse drug reactions, perhaps idiosyncratic in nature? This review evaluates the evidence for acyl glucuronide-derived perturbation of homeostasis, particularly that which might result from the covalent modification of endogenous proteins and other macromolecules. Because of the availability of acyl glucuronides for test tube/in vitro experiments, there is now a substantial literature documenting their rearrangement, hydrolysis and covalent modification of proteins in vitro. It is certain from in vitro experiments that serum albumin, dipeptidyl peptidase IV, tubulin and UGTs are covalently modified by acyl glucuronides. However, these in vitro experiments have been specifically designed to amplify any interference with a biological process in order to find biological effects. The in vivo situation is not at all clear. Certainly it must be concluded that all humans taking carboxylate drugs that form reactive acyl glucuronides will form covalent drug-protein adducts, and it must also be concluded that this in itself is normally benign. However, there is enough in vivo evidence implicating acyl glucuronides, which, when backed up by in vivo circumstantial and documented in vitro evidence, supports the view that reactive acyl glucuronides may initiate toxicity/immune responses. In summary, though acyl glucuronide-derived covalent modification of endogenous macromolecules is well-defined, the work ahead needs to provide detailed links between such modification and its possible biological consequences. (C) 2003 Elsevier Science Ireland Ltd. All rights reserved.

A perspective on Australia's national medicines policy

There is international interest in Australia's health care system for prescription medicines. The issue is particularly topical in Canada with the debate following publication of the Romanow Report into the future of health care in Canada. This Report recommended a new National Drug Agency. Australia has a National Medicines Policy with four arms-quality, safety and efficacy of medicines; equity of access; a viable and responsible pharmaceutical industry; quality use of medicines. The four arms of the Policy are interlinked and interdependent for optimal functioning. In this paper, an overview of how the prescription drug system in Australia works is presented. The manuscript focuses upon specific aspects of the Policy, describing how it functions and some of the processes integral to success, from the viewpoint of the author. The discussion includes some of the advantages of Australia's system for pharmaceuticals as well as some of the problems, as these present opportunities for development and change

Exploring the potential of xenobiotic-metabolising enzymes as biocatalysts: Evolving designer catalysts from polyfunctional cytochrome P450 enzymes

1. Biological catalysts have the advantage of being able to catalyse chemical reactions with an often exquisite degree of regio- and stereospecificity in contrast with traditional methods of organic synthesis. 2. The cytochrome P450 enzymes involved in human drug metabolism are ideal starting materials for the development of designer biocatalysts by virtue of their catalytic versatility and extreme substrate diversity. Applications can be envisaged in fine chemical synthesis, such as in the pharmaceutical industry and bioremediation. 3. A variety of techniques of enzyme engineering are currently being applied to P450 enzymes to explore their catalytic potential. Although most studies to date have been performed with bacterial P450s, reports are now emerging of work with mammalian forms of the enzymes. 4. The present minireview will explore the rationale and general techniques for redesigning P450s, review the results obtained to date with xenobiotic-metabolising forms and discuss strategies to overcome some of the logistic problems limiting the full exploitation of these enzymes as industrial-scale biocatalysts.

APRENDIZAGEM ORGANIZACIONAL EM SUAS DIFERENTES DIMENSÕES: A VISÃO DOS GERENTES DE UMA INDÚSTRIA FARMACÊUTICA

Esse estudo tem como propósito investigar como se desenvolve a aprendizagem organizacional (AO) e identificar como a aprendizagem individual e grupal contribui para este processo. A aprendizagem pode-se tornar uma vantagem competitiva, uma vez que a organização aprende mais rápido e busca aumentar a capacidade da empresa em ações de melhoria de desempenho. Enquanto parâmetros metodológicos adotou-se uma abordagem qualitativa de pesquisa, da qual participaram seis gestores de nível intermediário pertencentes a uma organização do ramo farmacêutico. O instrumento para coleta de dados configurou-se na entrevista baseada em roteiro semiestruturado. Os resultados evidenciaram que a aprendizagem organizacional se manifesta enquanto processo e como resultado, bem como sua importância para o desenvolvimento da vantagem competitiva na empresa, além de apontar também a relevância e a contribuição da aprendizagem individual e grupal para a AO.

A principled approach to interactive hierarchical non-linear visualization of high-dimensional data

Hierarchical visualization systems are desirable because a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex high-dimensional data sets. We extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: bf(1) we allow for em non-linear projection manifolds (the basic building block is the Generative Topographic Mapping -- GTM), bf(2) we introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree, bf(3) we describe folding patterns of low-dimensional projection manifold in high-dimensional data space by computing and visualizing the manifold's local directional curvatures. Quantities such as magnification factors [3] and directional curvatures are helpful for understanding the layout of the nonlinear projection manifold in the data space and for further refinement of the hierarchical visualization plot. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. We demonstrate the visualization system principle of the approach on a complex 12-dimensional data set and mention possible applications in the pharmaceutical industry.

Strategies and therapeutic opportunities for the delivery of drugs to the esophagus

Targeting of drugs and therapies locally to the esophagus is an important objective in the development of new and more effective dosage forms. Therapies that are retained within the oral cavity for both local and systemic action have been utilized for many years, although delivery to the esophagus has been far less reported. Esophageal disease states, including infections, motility disorders, gastric reflux, and cancers, would all benefit from localized drug delivery. Therefore, research in this area provides significant opportunities. The key limitation to effective drug delivery within the esophagus is sufficient retention at this site coupled with activity profiles to correspond with these retention times; therefore, a suitable formulation needs to provide the drug in a ready-to-work form at the site of action during the rapid transit through this organ. A successfully designed esophageal-targeted system can overcome these obstacles. This review presents a range of dosage form approaches for targeting the esophagus, including bioadhesive liquids and orally retained lozenges, chewing gums, gels, and films, as well as endoscopically delivered therapeutics. The techniques used to measure efficacy both in vitro and in vivo are also discussed. Drug delivery is a growing driver within the pharmaceutical industry and offers benefits both in terms of clinical efficacy, as well as in market positioning, as a means of extending a drug's exclusivity and profitability. Emerging systems that can be used to target the esophagus are reported within this review, as well as the potential of alternative formulations that offer benefits in this exciting area.

Understanding the yeast host cell response to recombinant membrane protein production

Membrane proteins are drug targets for a wide range of diseases. Having access to appropriate samples for further research underpins the pharmaceutical industry's strategy for developing new drugs. This is typically achieved by synthesizing a protein of interest in host cells that can be cultured on a large scale, allowing the isolation of the pure protein in quantities much higher than those found in the protein's native source. Yeast is a popular host as it is a eukaryote with similar synthetic machinery to that of the native human source cells of many proteins of interest, while also being quick, easy and cheap to grow and process. Even in these cells, the production of human membrane proteins can be plagued by low functional yields; we wish to understand why. We have identified molecular mechanisms and culture parameters underpinning high yields and have consolidated our findings to engineer improved yeast host strains. By relieving the bottlenecks to recombinant membrane protein production in yeast, we aim to contribute to the drug discovery pipeline, while providing insight into translational processes.

Present perspectives on the automated classification of the G-protein coupled receptors (GPCRs) at the protein sequence level

The G-protein coupled receptors--or GPCRs--comprise simultaneously one of the largest and one of the most multi-functional protein families known to modern-day molecular bioscience. From a drug discovery and pharmaceutical industry perspective, the GPCRs constitute one of the most commercially and economically important groups of proteins known. The GPCRs undertake numerous vital metabolic functions and interact with a hugely diverse range of small and large ligands. Many different methodologies have been developed to efficiently and accurately classify the GPCRs. These range from motif-based techniques to machine learning as well as a variety of alignment-free techniques based on the physiochemical properties of sequences. We review here the available methodologies for the classification of GPCRs. Part of this work focuses on how we have tried to build the intrinsically hierarchical nature of sequence relations, implicit within the family, into an adaptive approach to classification. Importantly, we also allude to some of the key innate problems in developing an effective approach to classifying the GPCRs: the lack of sequence similarity between the six classes that comprise the GPCR family and the low sequence similarity to other family members evinced by many newly revealed members of the family.

Non-linear hierarchical visualisation

This thesis applies a hierarchical latent trait model system to a large quantity of data. The motivation for it was lack of viable approaches to analyse High Throughput Screening datasets which maybe include thousands of data points with high dimensions. High Throughput Screening (HTS) is an important tool in the pharmaceutical industry for discovering leads which can be optimised and further developed into candidate drugs. Since the development of new robotic technologies, the ability to test the activities of compounds has considerably increased in recent years. Traditional methods, looking at tables and graphical plots for analysing relationships between measured activities and the structure of compounds, have not been feasible when facing a large HTS dataset. Instead, data visualisation provides a method for analysing such large datasets, especially with high dimensions. So far, a few visualisation techniques for drug design have been developed, but most of them just cope with several properties of compounds at one time. We believe that a latent variable model (LTM) with a non-linear mapping from the latent space to the data space is a preferred choice for visualising a complex high-dimensional data set. As a type of latent variable model, the latent trait model can deal with either continuous data or discrete data, which makes it particularly useful in this domain. In addition, with the aid of differential geometry, we can imagine the distribution of data from magnification factor and curvature plots. Rather than obtaining the useful information just from a single plot, a hierarchical LTM arranges a set of LTMs and their corresponding plots in a tree structure. We model the whole data set with a LTM at the top level, which is broken down into clusters at deeper levels of t.he hierarchy. In this manner, the refined visualisation plots can be displayed in deeper levels and sub-clusters may be found. Hierarchy of LTMs is trained using expectation-maximisation (EM) algorithm to maximise its likelihood with respect to the data sample. Training proceeds interactively in a recursive fashion (top-down). The user subjectively identifies interesting regions on the visualisation plot that they would like to model in a greater detail. At each stage of hierarchical LTM construction, the EM algorithm alternates between the E- and M-step. Another problem that can occur when visualising a large data set is that there may be significant overlaps of data clusters. It is very difficult for the user to judge where centres of regions of interest should be put. We address this problem by employing the minimum message length technique, which can help the user to decide the optimal structure of the model. In this thesis we also demonstrate the applicability of the hierarchy of latent trait models in the field of document data mining.

Advances in predicting and manipulating the immunogenicity of biotherapeutics and vaccines

Therapeutic proteins are vital to the future of human health provision and the survival and profitability of the global pharmaceutical industry. Returns from protein therapeutics are experiencing unprecedented growth: both their number and their economic dividend have increased by an order of magnitude in the last 10 years. The potential immunogenicity of protein therapeutics raises many clinical and safety concerns. Many poorly understood factors relating to both product and host affect immune responses. Available laboratory measurement of immunogenicity is of little utility for predicting the clinical properties of biotherapeutics. Coupled with assay variability and standardization issues, this precludes adequate prediction of the biological or clinical responses of therapeutic proteins, arguing for the utilization of informatic strategies in the analysis and prediction of protein immunogenicity. Currently, many unresolved issues must be addressed and thus circumvented before effective prediction can become routine.