Drug Industry

Over recent years, drug release / dissolution from solid pharmaceutical dosage forms has been the subject of intense and profitable scientific developments. Whenever a new solid dosage form is developed or produced, it is necessary to ensure that drug dissolution occurs in an appropriate manner. The pharmaceutical industry and the registration authorities do focus, nowadays, on drug dissolution studies. The quantitative analysis of the values obtained in dissolution / release tests is easier when mathematical formulas that express the dissolution results as a function of some of the dosage forms characteristics are used. In some cases, these mathematic models are derived from the theoretical analysis of the occurring process. In most of the cases the theoretical concept does not exist and some empirical equations have proved to be more appropriate. Drug dissolution from solid dosage forms has been described by kinetic models in which the dissolved amount of drug (Q) is a function of the test time, t or Q 5 f(t). Some analytical definitions of the Q(t) function are commonly used, such as zero order, first order, Hixson-Crowell, Weibull, Higuchi, Baker-Lonsdale, Korsmeyer-Peppas and Hopfenberg models. Other release parameters, such as dissolution time (t ), assay time (t ), dissolution efficacy (ED), difference factor ( f ),

The research and development costs of 68 randomly selected new drugs were obtained from a survey of 10 pharmaceutical firms. These data were used to estimate the average pre-tax cost of new drug development. The costs of compounds abandoned during testing were linked to the costs of compounds that obtained marketing approval. The estimated average out-of-pocket cost per new drug is US$ 403 million (2000 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a real discount rate of 11% yields a total pre-approval cost estimate of US$ 802 million (2000 dollars). When compared to the results of an earlier study with a similar methodology, total capitalized costs were shown to have increased at an annual rate of 7.4% above general price inflation.

To understand biology at the system level, we must examine the structure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism. Properties of systems, such as robustness, emerge as central issues, and understanding these properties may have an impact on the future of medicine. However, many breakthroughs in experimental devices, advanced software, and analytical methods are required before the achievements of systems biology can live up to their much-touted potential.

There are currently about 10 000 drug-like compounds. These are sparsely, rather than uniformly, distributed through chemistry space. True diversity does not exist in experimental combinatorial chemistry screening libraries. Absorption, distribution, metabolism, and excretion (ADME) and chemical reactivity-related toxicity is low, while biological receptor activity is higher dimensional in chemistry space, and this is partly explainable by evolutionary pressures on ADME to deal with endobiotics and exobiotics. ADME is hard to predict for large data sets because current ADME experimental screens are multi-mechanisms, and predictions get worse as more data accumulates. Currently, screening for biological receptor activity precedes or is concurrent with screening for properties related to``drugability.'' In the future,`d rugability'' screening may precede biological receptor activity screening. The level of permeability or solubility needed for oral absorption is related to potency. The relative importance of poor solubility and poor permeability towards the problem of poor oral absorption depends on the research approach used for lead generation. A``rational drug design'' approach as exemplified by Merck advanced clinical candidates leads to time-dependent higher molecular weight, higher H-bonding properties, unchanged lipophilicity, and, hence, poorer permeability. A high throughput screening (HTS)-based approach as exemplified by unpublished data on Pfizer (Groton, CT) early candidates leads to higher molecular weight, unchanged H-bonding properties, higher lipophilicity, and, hence, poorer aqueous solubility. D

Objective To investigate whether funding of drug studies by the pharmaceutical industry is associated with outcomes that are favourable to the funder and whether the methods of trials funded by pharmaceutical companies differ from the methods in trials with other sources of support. Methods Medline searches were supplemented with material identified in the references and in the authors' personal files. Data were independently abstracted by three of the authors and disagreements were resolved by consensus.

Developing a new drug from original idea to the launch of a finished product is a complex process which can take 12-15 years and cost in excess of $1 billion. The idea for a target can come from a variety of sources including academic and clinical research and from the commercial sector. It may take many years to build up a body of supporting evidence before selecting a target for a costly drug discovery programme. Once a target has been chosen, the pharmaceutical industry and more recently some academic centres have streamlined a number of early processes to identify molecules which possess suitable characteristics to make acceptable drugs. This review will look at key preclinical stages of the drug discovery process, from initial target identification and validation, through assay development, high throughput screening, hit identification, lead optimization and finally the selection of a candidate molecule for clinical development.

In the GRADE approach, randomized trials start as high-quality evidence and observational studies as low-quality evidence, but both can be rated down if a body of evidence is associated with a high risk of publication bias. Even when individual studies included in bestevidence summaries have a low risk of bias, publication bias can result in substantial overestimates of effect. Authors should suspect publication bias when available evidence comes from a number of small studies, most of which have been commercially funded. A number of approaches based on examination of the pattern of data are available to help assess publication bias. The most popular of these is the funnel plot; all, however, have substantial limitations. Publication bias is likely frequent, and caution in the face of early results, particularly with small sample size and number of events, is warranted. Ó

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

| Maintaining research and development (R&D) productivity at a sustainable level is one of the main challenges currently facing the pharmaceutical industry. In this article, we discuss the results of a comprehensive longitudinal review of AstraZeneca's small-molecule drug projects from 2005 to 2010. The analysis allowed us to establish a framework based on the five most important technical determinants of project success and pipeline quality, which we describe as the five 'R's: the right target, the right patient, the right tissue, the right safety and the right commercial potential. A sixth factor -the right culture -is also crucial in encouraging effective decision-making based on these technical determinants. AstraZeneca is currently applying this framework to guide its R&D teams, and although it is too early to demonstrate whether this has improved the company's R&D productivity, we present our data and analysis here in the hope that it may assist the industry overall in addressing this key challenge.

In this paper, analytical methods for the trace-level determination of 60 pharmaceuticals in aqueous samples are presented. The list of compounds amenable to the methods comprises analgesics, antiphlogistics, antirheumatics, b-blockers, broncholytics, lipid-lowering agents (or their metabolites), antiepileptics, vasodilators, tranquillizers, antineoplastic drugs, iodinated X-ray contrast media, and antibiotics of different kind, mainly sulfonamides, macrolides, and penicillins. All methods are based on automated solid-phase extraction followed by GC-MS (after derivatization of the acid compounds) or HPLC-electrospray ionization MS-MS. After an intense validation, which included the determination of performance data according to the German standard method DIN 32645 (limit of detection, limit of identification, limit of determination), the determination of linearity, recovery, and repeatability and the study of matrix effects, the analytical methods were applied within a monitoring program on the occurrence of pharmaceuticals in groundwaters of Baden-Wurttemberg. During this monitoring program, it was found that several of the compounds under investigation could be detected in groundwaters and their occurrence could be traced back to an impact of municipal or industrial waste water.

In order to survive in a highly competitive environment, freshwater or marine algae have to develop defense strategies that result in a tremendous diversity of compounds from different metabolic pathways. Recent trends in drug research from natural sources have shown that algae are promising organisms to furnish novel biochemically active compounds. The current review describes the main substances biosynthesized by algae with potential economic impact in food science, pharmaceutical industry and public health. Emphasis is given to fatty acids, steroids, carotenoids, polysaccharides, lectins, mycosporine-like amino acids, halogenated compounds, polyketides and toxins.

The need for new antimicrobial agents is greater than ever because of the emergence of multidrug resistance in common pathogens, the rapid emergence of new infections, and the potential for use of multidrug-resistant agents in bioweapons. Paradoxically, some pharmaceutical companies have indicated that they are curtailing anti-infective research programs. We evaluated the United States Food and Drug Administration (FDA) databases of approved drugs and the research and development programs of the world's largest pharmaceutical and biotechnology companies to document trends in the development of new antimicrobial agents. FDA approval of new antibacterial agents decreased by 56% over the past 20 years (1998-2002 vs. 1983-1987).

To evaluate the link between antidepressants and suicidal behavior and ideation (suicidality) in youth, adverse events from pediatric clinical trials were classified in order to identify suicidal events. The authors describe the Columbia Classification Algorithm for Suicide Assessment (C-CASA), a standardized suicidal rating system that provided data for the pediatric suicidal risk analysis of antidepressants conducted by the Food and Drug Administration (FDA). Adverse events (N=427) from 25 pediatric antidepressant clinical trials were systematically identified by pharmaceutical companies. Randomly assigned adverse events were evaluated by three of nine independent expert suicidologists using the Columbia classification algorithm. Reliability of the C-CASA ratings and agreement with pharmaceutical company classification were estimated. Twenty-six new, possibly suicidal events (behavior and ideation) that were not originally identified by pharmaceutical companies were identified in the C-CASA, and 12 events originally labeled as suicidal by pharmaceutical companies were eliminated, which resulted in a total of 38 discrepant ratings. For the specific label of "suicide attempt," a relatively low level of agreement was observed between the C-CASA and pharmaceutical company ratings, with the C-CASA reporting a 50% reduction in ratings. Thus, although the C-CASA resulted in the identification of more suicidal events overall, fewer events were classified as suicide attempts. Additionally, the C-CASA ratings were highly reliable (intraclass correlation coefficient [ICC]=0.89). Utilizing a methodical, anchored approach to categorizing suicidality provides an accurate and comprehensive identification of suicidal events. The FDA's audit of the C-CASA demonstrated excellent transportability of this approach. The Columbia algorithm was used to classify suicidal adverse events in the recent FDA adult antidepressant safety analyses and has also been mandated to be applied to all anticonvulsant trials and other centrally acting agents and nonpsychotropic drugs.

Conflicts of interest between physicians' commitment to patient care and the desire of pharmaceutical companies and their representatives to sell their products pose challenges to the principles of medical professionalism. These conflicts occur when physicians have motives or are in situations for which reasonable observers could conclude that the moral requirements of the physician's roles are or will be compromised. Although physician groups, the manufacturers, and the federal government have instituted self-regulation of marketing, research in the psychology and social science of gift receipt and giving indicates that current controls will not satisfactorily protect the interests of patients. More stringent regulation is necessary, including the elimination or modification of common practices related to small gifts, pharmaceutical samples, continuing medical education, funds for physician travel, speakers bureaus, ghostwriting, and consulting and research contracts. We propose a policy under which academic medical centers would take the lead in eliminating the conflicts of interest that still characterize the relationship between physicians and the health care industry.

The advent of high through-put screening in the drug discovery process has resulted in compounds with high lipophilicity and poor solubility. Increasing the solubility of such compounds poses a major challenge to formulation scientists. Various approaches have been adopted to address this including preparation of solid dispersions and solid solutions. Hot-melt extrusion is an efficient technology for producing solid molecular dispersions with considerable advantages over solvent-based processes such as spray drying and co-precipitation. Hot-melt extrusion has been demonstrated to provide sustained, modified, and targeted drug delivery. Improvements in bioavailability utilizing the hot-melt extrusion technique demonstrate the value of the technology as a potential drug delivery processing tool. The interest in hot-melt extrusion technology for pharmaceutical applications is evident from the increasing number of patents and publications in the scientific literature. Part II of this article reviews the myriad of hot-melt extrusion applications for pharmaceutical dosage forms including granules, pellets, tablets, implants, transmucosal, and transdermal systems.

In the absence of chemical-specific data, the threshold of toxicological concern (TTC) provides a method to determine a conservative estimate of a chronic oral exposure below which there is a very low probability of risk. The TTC approach was originally developed to support exposures to indirect food additives and was based on linear low-dose risk estimates to assure protection in the event that the chemical was later determined to be a carcinogen. Subsequently, TTC values based on noncancer endpoints were proposed for chemicals without structural alerts for genotoxicity. The original database supporting the TTC values for noncancer endpoints includes >600 structurally diverse chemicals. The objectives of this work were to evaluate the applicability of the TTC database to ingredients used in consumer products based on a comparison of the diversity of chemical structures with those in the original TTC database and to confirm that the range of NOELs for these ingredients is consistent with the range of NOELs in the original database. The results show good coverage of the product ingredient structures and confirm that the NOELs for the ingredient chemicals are similar in range to the original dataset, supporting the use of the TTC for ingredients in consumer products.

Low levels of pharmaceuticals are detected in surface, ground, and drinking water worldwide. Usage and incorrect disposal have been considered the major environmental sources of these microcontaminants. Recent publications, however, suggest that wastewater from drug production can potentially be a source of much higher concentrations in certain locations. The present study investigated the environmental fate of active pharmaceutical ingredients in a major production area for the global bulk drug market. Water samples were taken from a common effluent treatment plant near Hyderabad, India, which receives process water from approximately 90 bulk drug manufacturers. Surface water was analyzed from the recipient stream and from two lakes that are not contaminated by the treatment plant. Water samples were also taken from wells in six nearby villages. The samples were analyzed for the presence of 12 pharmaceuticals with liquid chromatography-mass spectrometry. All wells were determined to be contaminated with drugs. Ciprofloxacin, enoxacin, cetirizine, terbinafine, and citalopram were detected at more than 1 mg/L in several wells. Very high concentrations of ciprofloxacin (14 mg/L) and cetirizine (2.1 mg/L) were found in the effluent of the treatment plant, together with high concentrations of seven additional pharmaceuticals. Very high concentrations of ciprofloxacin (up to 6.5 mg/L), cetirizine (up to 1.2 mg/L), norfloxacin (up to 0.52 mg/L), and enoxacin (up to 0.16 mg/L) were also detected in the two lakes, which clearly shows that the investigated area has additional environmental sources of insufficiently treated industrial waste. Thus, insufficient wastewater management in one of the world's largest centers for bulk drug production leads to unprecedented drug contamination of surface, ground, and drinking water. This raises serious concerns regarding the development of antibiotic resistance, and it creates a major challenge for producers and regulatory agencies to improve the situation.

The ability of a room-temperature air-atmosphere (RTAA) co-precipitation method to tune the magnetic properties of iron oxide nanoparticles was investigated. It was demonstrated that superparamagnetic nanoparticles with different particle sizes ranging from 7 to 25 nm and magnetic properties with saturation magnetization between 2 to 75 emu g −1 can be synthesized by simply controlling the molar ratio of ferrous to ferric ions and the concentration of ammonium solution, without heat treatment or oxygen-level control. It was revealed that the tuning of the magnetic properties was associated with the compositional control between magnetite and maghemite. Ammonium concentration was also an important factor to obtain dispersed superparamagnetic (SPM) or ferrimagnetic (FM) nanoparticles.

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Experts in Chronic Myeloid Leukemia large group of CML experts the unsustainable prices of cancer drugs: from the perspective of a The price of drugs for chronic myeloid leukemia (CML) is a reflection of http://bloodjournal.hematologylibrary.org/content/121/22/4439.full.html Updated information and services can be found at: (1782 articles) Free Research Articles (2 articles) Blood Forum

While humans have taken limited advantage of natural populations of microalgae for centuries (Nostoc in Asia and Spirulina in Africa and North America for sustenance), it is only recently that we have come to realize the potential of microalgal biotechnology. Microalgal biotechnology has the potential to produce a vast array of products including foodstuffs, industrial chemicals, compounds with therapeutic applications and bioremediation solutions from a virtually untapped source. From an industrial (i.e. commercial) perspective, the goal of microalgal biotechnology is to make money by developing marketable products. For such a business to succeed the following steps must be taken: identify a desirable metabolite and a microalga that produces and accumulates the desired metabolite, establish a large-scale production process for the desired metabolite, and market the desired metabolite. So far, the commercial achievements of microalgal biotechnology have been modest. Microalgae that produce dozens of desirable metabolites have been identified. Aided by high throughput screening technology even more leads will become available. However, the successes in large-scale production and product marketing have been few. We will discuss those achievements and difficulties from the industrial point of view by considering examples from industry, specially our own experience at Mera Pharmaceuticals. #

Rarely a new research area has gotten such an overwhelming amount of attention as have microRNAs. Although several basic questions regarding their biological principles still remain to be answered, many specific characteristics of microRNAs in combination with compelling therapeutic efficacy data and a clear involvement in human disease have triggered the biotechnology community to start exploring the possibilities of viewing microRNAs as therapeutic entities. This review serves to provide some general insight into some of the current microRNAs targets, how one goes from the initial bench discovery to actually developing a therapeutically useful modality, and will briefly summarize the current patent landscape and the companies that have started to explore microRNAs as the next drug target.

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers' practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques.

Background: Pharmaceutical companies spent $57.5 billion on pharmaceutical promotion in the United States in 2004. The industry claims that promotion provides scientific and educational information to physicians. While some evidence indicates that promotion may adversely influence prescribing, physicians hold a wide range of views about pharmaceutical promotion. The objective of this review is to examine the relationship between exposure to information from pharmaceutical companies and the quality, quantity, and cost of physicians' prescribing.

Prediction of chemical-induced hepatotoxicity in humans from in vitro data continues to be a significant challenge for the pharmaceutical and chemical industries. Generally, conventional in vitro hepatic model systems (i.e. 2-D static monocultures of primary or immortalized hepatocytes) are limited by their inability to maintain histotypic and phenotypic characteristics over time in culture, including stable expression of clearance and bioactivation pathways, as well as complex adaptive responses to chemical exposure. These systems are less than ideal for longerterm toxicity evaluations and elucidation of key cellular and molecular events involved in primary and secondary adaptation to chemical exposure, or for identification of important mediators of inflammation, proliferation and apoptosis. Progress in implementing a more effective strategy for in vitro-in vivo extrapolation and human risk assessment depends on significant advances in tissue culture technology and increasing their level of biological complexity. This article describes the current and ongoing need for more relevant, organotypic in vitro surrogate systems of human liver and recent efforts to recreate the multicellular architecture and hemodynamic properties of the liver using novel culture platforms. As these systems become more widely used for chemical and drug toxicity testing, there will be a corresponding need to establish standardized testing conditions, endpoint analyses and acceptance criteria. In the future, a balanced approach between sample throughput and biological relevance should provide better in vitro tools that are complementary with animal testing and assist in conducting more predictive human risk assessment.

Objective To compare the methodological quality and conclusions in Cochrane reviews with those in industry supported meta-analyses and other meta-analyses of the same drugs. Design Systematic review comparing pairs of meta-analyses that studied the same two drugs in the same disease and were published within two years of each other. Data sources Cochrane Database of Systematic Reviews (2003, issue 1), PubMed, and Embase. Data extraction Two observers independently extracted data and used a validated scale to judge the methodological quality of the reviews. Results 175 of 1596 Cochrane reviews had a meta-analysis that compared two drugs. Twenty four meta-analyses that matched the Cochrane reviews were found: eight were industry supported, nine had undeclared support, and seven had no support or were supported by non-industry sources. On a 0-7 scale, the median quality score was 7 for Cochrane reviews and 3 for other reviews (P < 0.01). Compared with industry supported reviews and reviews with undeclared support, Cochrane reviews had more often considered the potential for bias in the review-for example, by describing the method of concealment of allocation and describing excluded patients or studies. The seven industry supported reviews that had conclusions recommended the experimental drug without reservations, compared with none of the Cochrane reviews (P = 0.02), although the estimated treatment effect was similar on average (z = 0.46, P = 0.64). Reviews with undeclared support and reviews with not for profit support or no support had conclusions that were similar in cautiousness to the Cochrane reviews. Conclusions Industry supported reviews of drugs should be read with caution as they were less transparent, had few reservations about methodological limitations of the included trials, and had more favourable conclusions than the corresponding Cochrane reviews.

Strains belonging to the yeast species Kluyveromyces marxianus have been isolated from a great variety of habitats, which results in a high metabolic diversity and a substantial degree of intraspecific polymorphism. As a consequence, several different biotechnological applications have been investigated with this yeast: production of enzymes (βgalactosidase, β-glucosidase, inulinase, and polygalacturonases, among others), of single-cell protein, of aroma compounds, and of ethanol (including high-temperature and simultaneous saccharification-fermentation processes); reduction of lactose content in food products; production of bioingredients from cheese-whey; bioremediation; as an anticholesterolemic agent; and as a host for heterologous protein production. Compared to its congener and model organism, Kluyveromyces lactis, the accumulated knowledge on K. marxianus is much smaller and spread over a number of different strains. Although there is no publicly available genome sequence for this species, 20% of the CBS 712 strain genome was randomly sequenced (Llorente et al. in FEBS Lett 487:71-75, 2000). In spite of these facts, K. marxianus can envisage a great biotechnological future because of some of its qualities, such as a broad substrate spectrum, thermotolerance, high growth rates, and less tendency to ferment when exposed to sugar excess, when compared to K. lactis. To increase our knowledge on the biology of this species and to enable the potential applications to be converted into industrial practice, a more systematic approach, including the careful choice of (a) reference strain(s) by the scientific community, would certainly be of great value.

The available mathematical models describing tumor growth and the effect of anticancer treatments on tumors in animals are of limited use within the drug industry. A simple and effective model would allow applying quantitative thinking to the preclinical development of oncology drugs. In this article, a minimal pharmacokinetic-pharmacodynamic model is presented, based on a system of ordinary differential equations that link the dosing regimen of a compound to the tumor growth in animal models. The growth of tumors in nontreated animals is described by an exponential growth followed by a linear growth. In treated animals, the tumor growth rate is decreased by a factor proportional to both drug concentration and number of proliferating tumor cells. A transit compartmental system is used to model the process of cell death, which occurs at later times. The parameters of the pharmacodynamic model are related to the growth characteristics of the tumor, to the drug potency, and to the kinetics of the tumor cell death. Therefore, such parameters can be used for ranking compounds based on their potency and for evaluating potential differences in the tumor cell death process. The model was extensively tested on discovery candidates and known anticancer drugs. It fitted well the experimental data, providing reliable parameter estimates. On the basis of the parameters estimated in a first experiment, the model successfully predicted the response of tumors exposed to drugs given at different dose levels and/or schedules. It is, thus, possible to use the model prospectively, optimizing the design of new experiments.