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Why Most Published Research Findings Are False
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John P. A. Ioannidis
Abstract Top
Summary
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
Citation: Ioannidis JPA (2005 Why Most Published Research Findings Are False. PLoS Med 2(8 : e124. doi:10.1371/journal.pmed.0020124
Published: August 30, 2005
Copyright: © 2005 John P. A. Ioannidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Competing interests: The author has declared that no competing interests exist.
Abbreviation: PPV, positive predictive value
John P. A. Ioannidis is in the Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece, and Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts-New England Medical Center, Tufts University School of Medicine, Boston, Massachusetts, United States of America. E-mail: jioannid@cc.uoi.gr
Published research findings are sometimes refuted by subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [ 1–3] to the most modern molecular research [ 4, 5]. There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims [ 6–8]. However, this should not be surprising. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.
Modeling the Framework for False Positive Findings Top
Several methodologists have pointed out [ 9–11] that the high rate of nonreplication (lack of confirmation of research discoveries is a consequence of the convenient, yet ill-founded strategy of claiming conclusive research findings solely on the basis of a single study assessed by formal statistical significance, typically for a p -value less than 0.05. Research is not most appropriately represented and summarized by p -values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based only on p -values. Research findings are defined here as any relationship reaching formal statistical significance, e.g., effective interventions, informative predictors, risk factors, or associations. “Negative” research is also very useful. “Negative” is actually a misnomer, and the misinterpretation is widespread. However, here we will target relationships that investigators claim exist, rather than null findings.
It can be proven that most claimed research findings are false
As has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true (before doing the study , the statistical power of the study, and the level of statistical significance [ 10, 11]. Consider a 2 ? 2 table in which research findings are compared against the gold standard of true relationships in a scientific field. In a research field both true and false hypotheses can be made about the presence of relationships. 1 Let R be the ratio of the number of “true relationships” to “no relationships” among those tested in the field. R is characteristic of the field and can vary a lot depending on whether the field targets highly likely relationships or searches for only one or a few true relationships among thousands and millions of hypotheses that may be postulated. Let us also consider, for computational simplicity, circumscribed fields where either there is only one true relationship (among many that can be hypothesized or the power is similar to find any of the several existing true relationships. The pre-study probability of a relationship being true is R /( R + 1 . The probability of a study finding a true relationship reflects the power 1 - ? (one minus the Type II error rate . The probability of claiming a relationship when none truly exists reflects the Type I error rate, ?. Assuming that c relationships are being probed in the field, the expected values of the 2 ? 2 table are given in Table 1. After a research finding has been claimed based on achieving formal statistical significance, the post-study probability that it is true is the positive predictive value, PPV. The PPV is also the complementary probability of what Wacholder et al. have called the false positive report probability [ 10]. According to the 2 ? 2 table, one gets PPV = (1 - ? R /( R - ?R + ? . A research finding is thus more likely true than false if (1 - ? R > ?. Since usually the vast majority of investigators depend on a = 0.05, this means that a research finding is more likely true than false if (1 - ? R > 0.05.
Table 1. Research Findings and True Relationships
doi:10.1371/journal.pmed.0020124.t001
What is less well appreciated is that bias and the extent of repeated independent testing by different teams of investigators around the globe may further distort this picture and may lead to even smaller probabilities of the research findings being indeed true. We will try to model these two factors in the context of similar 2 ? 2 tables.
Bias Top
First, let us define bias as the combination of various design, data, analysis, and presentation factors that tend to produce research findings when they should not be produced. Let u be the proportion of probed analyses that would not have been “research findings,” but nevertheless end up presented and reported as such, because of bias. Bias should not be confused with chance variability that causes some findings to be false by chance even though the study design, data, analysis, and presentation are perfect. Bias can entail manipulation in the analysis or reporting of findings. Selective or distorted reporting is a typical form of such bias. We may assume that u does not depend on whether a true relationship exists or not. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed true. In the presence of bias ( Table 2 , one gets PPV = ([1 - ?] R + u ? R /( R + ? ? ? R + u ? u ? + u ? R , and PPV decreases with increasing u , unless 1 ? ? ? ?, i.e., 1 ? ? ? 0.05 for most situations. Thus, with increasing bias, the chances that a research finding is true diminish considerably. This is shown for different levels of power and for different pre-study odds in Figure 1. Conversely, true research findings may occasionally be annulled because of reverse bias. For example, with large measurement errors relationships are lost in noise [ 12], or investigators use data inefficiently or fail to notice statistically significant relationships, or there may be conflicts of interest that tend to “bury” significant findings [ 13]. There is no good large-scale empirical evidence on how frequently such reverse bias may occur across diverse research fields. However, it is probably fair to say that reverse bias is not as common. Moreover measurement errors and inefficient use of data are probably becoming less frequent problems, since measurement error has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated about their data. Regardless, reverse bias may be modeled in the same way as bias above. Also reverse bias should not be confused with chance variability that may lead to missing a true relationship because of chance.
Figure 1. PPV (Probability That a Research Finding Is True as a Function of the Pre-Study Odds for Various Levels of Bias, u
Panels correspond to power of 0.20, 0.50, and 0.80.
doi:10.1371/journal.pmed.0020124.g001
Table 2. Research Findings and True Relationships in the Presence of Bias
doi:10.1371/journal.pmed.0020124.t002
Testing by Several Independent Teams Top
Several independent teams may be addressing the same sets of research questions. As research efforts are globalized, it is practically the rule that several research teams, often dozens of them, may probe the same or similar questions. Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by single teams and interpret research experiments in isolation. An increasing number of questions have at least one study claiming a research finding, and this receives unilateral attention. The probability that at least one study, among several done on the same question, claims a statistically significant research finding is easy to estimate. For n independent studies of equal power, the 2 ? 2 table is shown in Table 3: PPV = R (1 ? ? n /( R + 1 ? [1 ? ?] n ? R ? n (not considering bias . With increasing number of independent studies, PPV tends to decrease, unless 1 - ?
< a, i.e., typically 1 ? ? < 0.05. This is shown for different levels of power and for different pre-study odds in Figure 2. For n studies of different power, the term ? n is replaced by the product of the terms ? i for i = 1 to n , but inferences are similar.
Figure 2. PPV (Probability That a Research Finding Is True as a Function of the Pre-Study Odds for Various Numbers of Conducted Studies, n
Panels correspond to power of 0.20, 0.50, and 0.80.
doi:10.1371/journal.pmed.0020124.g002
Table 3. Research Findings and True Relationships in the Presence of Multiple Studies
doi:10.1371/journal.pmed.0020124.t003
Corollaries Top
A practical example is shown in Box 1. Based on the above considerations, one may deduce several interesting corollaries about the probability that a research finding is indeed true.
Box 1. An Example: Science at Low Pre-Study Odds
Let us assume that a team of investigators performs a whole genome association study to test whether any of 100,000 gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the disease, it is reasonable to expect that probably around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.3 for the ten or so polymorphisms and with a fairly similar power to identify any of them. Then R = 10/100,000 = 10?4, and the pre-study probability for any polymorphism to be associated with schizophrenia is also R /( R + 1 = 10?4. 1 Let us also suppose that the study has 60% power to find an association with an odds ratio of 1.3 at ? = 0.05. Then it can be estimated that if a statistically significant association is found with the p -value barely crossing the 0.05 threshold, the post-study probability that this is true increases about 12-fold compared with the pre-study probability, but it is still only 12 ? 10 ?4 .
Now let us suppose that the investigators manipulate their design, analyses, and reporting so as to make more relationships cross the p = 0.05 threshold even though this would not have been crossed with a perfectly adhered to design and analysis and with perfect comprehensive reporting of the results, strictly according to the original study plan. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results. Commercially available “data mining” packages actually are proud of their ability to yield statistically significant results through data dredging. In the presence of bias with u = 0.10, the post-study probability that a research finding is true is only 4.4 ? 10?4. Furthermore, even in the absence of any bias, when ten independent research teams perform similar experiments around the world, if one of them finds a formally statistically significant association, the probability that the research finding is true is only 1.5 ? 10?4, hardly any higher than the probability we had before any of this extensive research was undertaken!
Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. Small sample size means smaller power and, for all functions above, the PPV for a true research finding decreases as power decreases towards 1 ? ? = 0.05. Thus, other factors being equal, research findings are more likely true in scientific fields that undertake large studies, such as randomized controlled trials in cardiology (several thousand subjects randomized [ 14] than in scientific fields with small studies, such as most research of molecular predictors (sample sizes 100-fold smaller [ 15].
Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true. Power is also related to the effect size. Thus research findings are more likely true in scientific fields with large effects, such as the impact of smoking on cancer or cardiovascular disease (relative risks 3–20 , than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases (relative risks 1.1–1.5 [ 7]. Modern epidemiology is increasingly obliged to target smaller effect sizes [ 16]. Consequently, the proportion of true research findings is expected to decrease. In the same line of thinking, if the true effect sizes are very small in a scientific field, this field is likely to be plagued by almost ubiquitous false positive claims. For example, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than 1.05, genetic or nutritional epidemiology would be largely utopian endeavors.
Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. As shown above, the post-study probability that a finding is true (PPV depends a lot on the pre-study odds (R . Thus, research findings are more likely true in confirmatory designs, such as large phase III randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and creative given the wealth of the assembled and tested information, such as microarrays and other high-throughput discovery-oriented research [ 4, 8, 17], should have extremely low PPV.
Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true. Flexibility increases the potential for transforming what would be “negative” results into “positive” results, i.e., bias, u . For several research designs, e.g., randomized controlled trials [ 18–20] or meta-analyses [ 21, 22], there have been efforts to standardize their conduct and reporting. Adherence to common standards is likely to increase the proportion of true findings. The same applies to outcomes. True findings may be more common when outcomes are unequivocal and universally agreed (e.g., death rather than when multifarious outcomes are devised (e.g., scales for schizophrenia outcomes [ 23]. Similarly, fields that use commonly agreed, stereotyped analytical methods (e.g., Kaplan-Meier plots and the log-rank test [ 24] may yield a larger proportion of true findings than fields where analytical methods are still under experimentation (e.g., artificial intelligence methods and only “best” results are reported. Regardless, even in the most stringent research designs, bias seems to be a major problem. For example, there is strong evidence that selective outcome reporting, with manipulation of the outcomes and analyses reported, is a common problem even for randomized trails [ 25]. Simply abolishing selective publication would not make this problem go away.
Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. Conflicts of interest and prejudice may increase bias, u . Conflicts of interest are very common in biomedical research [ 26], and typically they are inadequately and sparsely reported [ 26, 27]. Prejudice may not necessarily have financial roots. Scientists in a given field may be prejudiced purely because of their belief in a scientific theory or commitment to their own findings. Many otherwise seemingly independent, university-based studies may be conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. Such nonfinancial conflicts may also lead to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review process the appearance and dissemination of findings that refute their findings, thus condemning their field to perpetuate false dogma. Empirical evidence on expert opinion shows that it is extremely unreliable [ 28].
Corollary 6: The hotter a scientific field (with more scientific teams involved , the less likely the research findings are to be true. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally see major excitement followed rapidly by severe disappointments in fields that draw wide attention. With many teams working on the same field and with massive experimental data being produced, timing is of the essence in beating competition. Thus, each team may prioritize on pursuing and disseminating its most impressive “positive” results. “Negative” results may become attractive for dissemination only if some other team has found a “positive” association on the same question. In that case, it may be attractive to refute a claim made in some prestigious journal. The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations [ 29]. Empirical evidence suggests that this sequence of extreme opposites is very common in molecular genetics [ 29].
These corollaries consider each factor separately, but these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be small may be more likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings. Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings. Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.
Most Research Findings Are False for Most Research Designs and for Most Fields Top
In the described framework, a PPV exceeding 50% is quite difficult to get. Table 4 provides the results of simulations using the formulas developed for the influence of power, ratio of true to non-true relationships, and bias, for various types of situations that may be characteristic of specific study designs and settings. A finding from a well-conducted, adequately powered randomized controlled trial starting with a 50% pre-study chance that the intervention is effective is eventually true about 85% of the time. A fairly similar performance is expected of a confirmatory meta-analysis of good-quality randomized trials: potential bias probably increases, but power and pre-test chances are higher compared to a single randomized trial. Conversely, a meta-analytic finding from inconclusive studies where pooling is used to “correct” the low power of single studies, is probably false if R ? 1:3. Research findings from underpowered, early-phase clinical trials would be true about one in four times, or even less frequently if bias is present. Epidemiological studies of an exploratory nature perform even worse, especially when underpowered, but even well-powered epidemiological studies may have only a one in five chance being true, if R = 1:10. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones 1,000-fold (e.g., 30,000 genes tested, of which 30 may be the true culprits [ 30, 31], PPV for each claimed relationship is extremely low, even with considerable standardization of laboratory and statistical methods, outcomes, and reporting thereof to minimize bias.
Table 4. PPV of Research Findings for Various Combinations of Power (1 - ? , Ratio of True to Not-True Relationships (R , and Bias (u
doi:10.1371/journal.pmed.0020124.t004
Claimed Research Findings May Often Be Simply Accurate Measures of the Prevailing Bias Top
As shown, the majority of modern biomedical research is operating in areas with very low pre- and post-study probability for true findings. Let us suppose that in a research field there are no true findings at all to be discovered. History of science teaches us that scientific endeavor has often in the past wasted effort in fields with absolutely no yield of true scientific information, at least based on our current understanding. In such a “null field,” one would ideally expect all observed effect sizes to vary by chance around the null in the absence of bias. The extent that observed findings deviate from what is expected by chance alone would be simply a pure measure of the prevailing bias.
For example, let us suppose that no nutrients or dietary patterns are actually important determinants for the risk of developing a specific tumor. Let us also suppose that the scientific literature has examined 60 nutrients and claims all of them to be related to the risk of developing this tumor with relative risks in the range of 1.2 to 1.4 for the comparison of the upper to lower intake tertiles. Then the claimed effect sizes are simply measuring nothing else but the net bias that has been involved in the generation of this scientific literature. Claimed effect sizes are in fact the most accurate estimates of the net bias. It even follows that between “null fields,” the fields that claim stronger effects (often with accompanying claims of medical or public health importance are simply those that have sustained the worst biases.
For fields with very low PPV, the few true relationships would not distort this overall picture much. Even if a few relationships are true, the shape of the distribution of the observed effects would still yield a clear measure of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators have viewed large and highly significant effects with excitement, as signs of important discoveries. Too large and too highly significant effects may actually be more likely to be signs of large bias in most fields of modern research. They should lead investigators to careful critical thinking about what might have gone wrong with their data, analyses, and results.
Of course, investigators working in any field are likely to resist accepting that the whole field in which they have spent their careers is a “null field.” However, other lines of evidence, or advances in technology and experimentation, may lead eventually to the dismantling of a scientific field. Obtaining measures of the net bias in one field may also be useful for obtaining insight into what might be the range of bias operating in other fields where similar analytical methods, technologies, and conflicts may be operating.
How Can We Improve the Situation? Top
Is it unavoidable that most research findings are false, or can we improve the situation? A major problem is that it is impossible to know with 100% certainty what the truth is in any research question. In this regard, the pure “gold” standard is unattainable. However, there are several approaches to improve the post-study probability.
Better powered evidence, e.g., large studies or low-bias meta-analyses, may help, as it comes closer to the unknown “gold” standard. However, large studies may still have biases and these should be acknowledged and avoided. Moreover, large-scale evidence is impossible to obtain for all of the millions and trillions of research questions posed in current research. Large-scale evidence should be targeted for research questions where the pre-study probability is already considerably high, so that a significant research finding will lead to a post-test probability that would be considered quite definitive. Large-scale evidence is also particularly indicated when it can test major concepts rather than narrow, specific questions. A negative finding can then refute not only a specific proposed claim, but a whole field or considerable portion thereof. Selecting the performance of large-scale studies based on narrow-minded criteria, such as the marketing promotion of a specific drug, is largely wasted research. 1 Moreover, one should be cautious that extremely large studies may be more likely to find a formally statistical significant difference for a trivial effect that is not really meaningfully different from the null [ 32–34].
Second, most research questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of any single team. What matters is the totality of the evidence. Diminishing bias through enhanced research standards and curtailing of prejudices may also help. However, this may require a change in scientific mentality that might be difficult to achieve. In some research designs, efforts may also be more successful with upfront registration of studies, e.g., randomized trials [ 35]. Registration would pose a challenge for hypothesis-generating research. Some kind of registration or networking of data collections or investigators within fields may be more feasible than registration of each and every hypothesis-generating experiment. Regardless, even if we do not see a great deal of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more widely borrowed from randomized controlled trials.
Finally, instead of chasing statistical significance, we should improve our understanding of the range of R values—the pre-study odds—where research efforts operate [ 10]. Before running an experiment, investigators should consider what they believe the chances are that they are testing a true rather than a non-true relationship. Speculated high R values may sometimes then be ascertained. As described above, whenever ethically acceptable, large studies with minimal bias should be performed on research findings that are considered relatively established, to see how often they are indeed confirmed. I suspect several established “classics” will fail the test [ 36].
Nevertheless, most new discoveries will continue to stem from hypothesis-generating research with low or very low pre-study odds. We should then acknowledge that statistical significance testing in the report of a single study gives only a partial picture, without knowing how much testing has been done outside the report and in the relevant field at large. Despite a large statistical literature for multiple testing corrections [ 37], usually it is impossible to decipher how much data dredging by the reporting authors or other research teams has preceded a reported research finding. Even if determining this were feasible, this would not inform us about the pre-study odds. Thus, it is unavoidable that one should make approximate assumptions on how many relationships are expected to be true among those probed across the relevant research fields and research designs. The wider field may yield some guidance for estimating this probability for the isolated research project. Experiences from biases detected in other neighboring fields would also be useful to draw upon. Even though these assumptions would be considerably subjective, they would still be very useful in interpreting research claims and putting them in context.
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DOMINIC COYLE
Blockbuster drugs coming off patent will knock a major hole in our export figures and tax revenues
PHARMACEUTICALS HAVE been a driving force for Ireland’s export success in recent years. Even through the darkest days of our financial collapse and recession, the sector, dominated by the large multinational players, continued to deliver export growth and a glimmer of hope of economic recovery.
However, the most recent trade figures point to a looming problem for the Government. Reporting a 9 per cent fall in exports in December, the Central Statistics Office was unusually frank and detailed in stating that “a substantial part of the decline in the value of exports was due to a high value product in the chemicals and related products sector coming off patent”.
The drug is Lipitor, Pfizer’s blockbuster cholesterol lowering therapy and the world’s best-selling drug in recent years, accounting for revenues of $10.7 billion in 2010. Pfizer’s Cork plant produces 100 per cent of the company’s global requirements for the active pharmaceutical ingredient in the drug and a significant portion of the finished tablets.
Coming off patent will knock a major hole in the future revenues Pfizer can expect to get from the drug as generic competition kicks in. As a rule, loss of patent protection can hit the value of sales by anything between 40 and 70 per cent over time – and not much time at that.
For Ireland, the concern in the December figures was that, for now, generic competition to Lipitor is limited. If that was enough to skew the export figures so dramatically, the worry is what damage future, more intense competition will do to our trade balance.
And Lipitor is just one of a number of key drugs in which Ireland has a commercial interest and which are coming off patent. Chris van Egeraat, a lecturer in economic geography at NUI Maynooth, says seven of the 10 largest-selling drugs worldwide which are losing patent protection are currently produced in Ireland. They include the best-selling drug worldwide in 2010 after Lipitor; Sanofi/Bristol Myers Squibb’s blood clotting treatment Plavix, with sales of $9.43 billion. It comes off patent in May.
Globally, it is estimated that as much as $100 billion in sales will be lost to drug companies between 2009 and 2014 as a result of drugs coming off patent. Expected pipeline delivery in terms of market revenue over the same time amounts to about $30 billion, Dr van Egeraat says.
While he doesn’t expect the loss of patents to lead to huge imminent job losses, it does highlight the ambition of the Government’s new Action Plan for Jobs, which has targeted the health and life sciences sector for significant growth in the coming years to help reach the Government’s 100,000 job target.
However, loss of market sales will clearly impact on trade figures and tax revenues. The Irish Pharmaceutical Healthcare Association (IPHA notes that the pharmaceuticals sector accounts for roughly half of all exports and is the largest contributor to corporation tax, accounting for roughly 50 per cent of the ˆ3.5 billion collected last year.
In employment terms, IPHA president and Pfizer country manager David Gallagher says that about 25,000 people are employed directly in the industry, with a roughly similar number working in related sectors. He notes that pharmaceuticals has been more resilient than other sectors of the economy during recent “economically challenging times”.
The message is pointed, especially at a time when the sector is locked in a dispute with the Government over access to market for its new drugs and the contribution it can make to savings in the health budget sought by the State.
David Gallagher noted recently that an increasing number of innovative medicines are currently not being reimbursed by the Department of Health, despite being approved by regulators and meeting health technology assessments.
He recently accused the department of acting in bad faith by refusing to approve drugs for reimbursement as provided for under the industry’s current pricing agreement with the Department, even though the industry had delivered savings of about ˆ540 million over the past five years, a figure he says equates to a 20 per cent cut.
Even before the latest row, the IPHA said the delay between approval and market access had jumped by over 50 per cent to 157 days in recent years, and only 64 per cent of drugs that received market authorisation in the EU between 2007 and 2009 were made available to patients here.
Matt Moran, director of Ibec group PharmaChem Ireland, said Government policy “needs to urgently recognise the very serious challenges facing the industry”.
“A number of blockbuster drugs are coming off patent and healthcare spending in Ireland has been cut by ˆ600 million in the last five years,” he said. “The future success of the sector must not be taken for granted.”
In a speech last year, Gallagher said further price concessions were “simply untenable”, citing preliminary 2011 figures pointing to a 5.2 per cent decline in the value of the Irish market. “There is a limit to the amount which can be taken out of a market without its effective operation and employment being jeopardised,” he said.
Ireland is not alone. The commercial prospects for big pharma were also thrown into sharp focus with a report on the UK’s pharmaceuticals price regulation scheme, which reported collective industry losses of ?142 million in 2009 despite rising sales.
For its part, the Department of Health needs to find cuts in its budget. In a recent report on pharma pricing, the Economic and Social Research Institute (ESRI said that drug costs account for about 17.5 per cent of public health expenditure in Ireland, up from 14 per cent in 2000.
In 2009, the ESRI says, spending per head of population in Ireland on pharmaceuticals was “amongst the highest in the OECD”.
It is understood the Department of Health is targeting a saving of about ˆ112 million from the drugs bill – either in terms of pricing and access for new medicines or pricing of generics.
The ESRI report recommended a number of approaches. These included pricing drugs on the basis of the lowest cost in a basket of European markets rather than the average, and more regular price updates to capture the impact of falling prices earlier.
The industry says that, despite the small physical size of the Irish market, such a move would be negative in two ways. First, Ireland is itself a component of pricing baskets in eight other larger EU markets. A “match the lowest” price here will inevitably further eat into prices in other more important markets.
Secondly, the industry points to Ireland’s importance as a base of operations for most of the main players in the sector. An increasingly adversarial approach with the State will only damage the prospects for future investment, they say, with one industry source saying the recent approach of the department to market access for new drugs was creating a very poor impression in a number of important boardrooms State-side.
The seriousness with which the pharmaceutical sector views the current price negotiations in Ireland – where eight of the top 10 global players have operations – is highlighted by the engagement of some of the industry’s leading figures with the Government.
The chief executive of one major global player has made a point of briefly visiting Ireland next week. The message in his first visit to the State will not be lost on ministers. The following week, leading executives from another top 10 drug manufacturers gather in Dublin for a meeting at which the attitude of the State to the sector is certain to figure.
On the Government’s side, there is concern too at any adverse impact on such a major employer and contributor to the exchequer. Taoiseach Enda Kenny has recently engaged directly in private meetings with top industry figures here to assure them of the Government’s support despite the ongoing budgetary squeeze.
For their part, the drug companies say that current pricing pressures are restricting innovations. Without adequate compensation, they say, companies simply will not be able to invest in new products given the costs involved and the risk of failure.
This isn’t unique to Ireland. Reporting annual results earlier this month, Bayer chief executive Marijn Dekkers expressed concern “about the side-effects” of health service reforms taking place around the world “because the money we earn from today’s medicines pays for the development of tomorrow’s medicines”.
Pointing to the ˆ2 billion research and development cost of Xarelto, a new drug developed with Johnson Johnson to prevent blood clotting, he said: “We need innovative pharmaceuticals more than ever, because so many known diseases still cannot be treated adequately, or at all, with medicines.”
But that’s part of the problem for the major pharmaceutical companies. Many of the easy treatment areas are now well catered for. A good portion of the drugs that do so are shortly coming off patent and are easily accessible to generic competition.
The opportunities of the future lie in increasingly niche conditions or very high risk areas such as oncology and, especially, neurology. Added to this is the move to biologics and the trend towards more personalised medicines.
The challenge is evident in the fact that, last year, the US drug regulator, the Food and Drug Administration, licensed just a handful of new pharmaceutical therapies. Getting this more select group of drugs to as many markets as possible is increasingly critical for big pharma.
The age of the blockbuster is fading, along with the fat profit margins it offered. That presents major issues for the sector. Over time, through consolidation and acquisition, they have grown into massive unwieldy entities with poorly directed research failing to deliver sufficient pipeline.
In recent years, much effort has been devoted to streamlining operations and increasing productivity, especially on the research side. Thousands of jobs have been shed worldwide, and greater emphasis placed on outsourcing much of the early-stage RD work.
A case in point is Elan’s prospective Alzheimer’s treatment bapineuzumab. Originally developed by the company in association with Wyeth, it is now controlled by Pfizer (which acquired Wyeth to fill a perceived weakness in its biopharmaceuticals operations and Johnson Johnson, which bought an 18.4 per cent stake in Elan in 2009 in a deal valued at $1 billion. Its interest was driven largely by the Irish company’s pipeline – particularly bapineuzumab which is seen as one of the more promising candidates to address a disease with limited treatment options at present and which reports critical Phase III trial data later this year.
The second focus is on developing new markets. But that presents its own problems, not least with business practices that have reflected poorly on the industry.
Several of the largest drug companies have been implicated in an ongoing legal action in Serbia in which a group of 10 doctors and drug company officials were charged with taking, or offering, more than ˆ500,000 in bribes to use specific products. While all deny guilt in this case, an examination of US Securities and Exchange Commission (SEC filings by the world’s top 10 drug companies has found that eight of them recently warned of potential costs related to charges of corruption in overseas markets.
Life is unlikely to get any easier for the sector over the coming two or three years. That raises the stakes in the ongoing price negotiations. The new accord was due to come into force yesterday and, as the leaked Commission assessment this week illustrated, pressure on the Government to deliver the necessary savings to ensure their budgetary projections is only likely to intensify.
“Babies born just a few weeks early have a higher risk of poor health,” The Guardian reported today. According to the newspaper, new research has found that being born just a few weeks early can raise their risk of conditions such as asthma.
It is already known that babies born prematurely (before 37 weeks of pregnancy may have a higher risk of immediate or longer-term health problems, and the earlier a baby is born, the higher the risk. To examine the issue researchers followed the health of over 14,000 children born between 2000 and 2002, and examined their health at the ages of three and five years old. Outcomes including growth, hospital admissions, use of medication, asthma and long-standing illnesses were looked at particularly in relation to whether the children were moderately premature (32-36 weeks of pregnancy or born at what the researchers called “early” full term (37-38 weeks . Babies born moderately prematurely or at early term were more likely to have been re-admitted to hospital in the first few months of life than babies born at 39-41 weeks. Babies born moderately prematurely also had a higher risk of asthma symptoms than full-term babies.
These findings are broadly in line with what is already known about the effects of prematurity, and do not change the UK’s current definition of full-term pregnancy as 37 weeks and over. However, the study does show how different degrees of prematurely may affect health. Further study of the issue would be valuable, to explore longer-term health outcomes that may be caused by prematurity and the factors that may influence the likelihood of these poor health outcomes.
Where did the story come from?
The study was carried out by researchers from the University of Leicester and other UK institutions. It was funded by the Bupa Foundation and published in the peer-reviewed British Medical Journal.
The media generally covered this research in a balanced way.
What kind of research was this?
In the UK, the normal length of a pregnancy is classed as 37 weeks or above. It is already known that babies born prematurely (before 37 weeks may be at increased risk of immediate and longer-term health problems, and that the risks are higher the earlier a baby is born. However, the authors say that there has been minimal research into the longer-term health outcomes of infants specifically born moderately preterm (which this study defines as 32-36 weeks and at what the researchers termed as ‘early full term’ (37-38 weeks .
To investigate this, the researchers used a cohort study. This is a good way to follow up and compare health outcomes in groups of people that have been exposed to different factors. In this case, the exposure was the number of weeks of pregnancy at which the babies were born. However, a cohort study that looks at a group’s health relies on the accuracy of reported health outcomes and diagnoses. For example, one condition this study looked at was asthma, and the researchers asked parents about whether their child had wheezing symptom or asthma. However, this does not necessarily equate to a medical diagnosis of asthma.
This type of study also needs to take into account potential factors that could be related to both risk of prematurity and risk of the health outcome. For example, parental smoking is linked to an increase risk of prematurity, and also to an increased risk of asthma in the child.
What did the research involve?
This study involved participants of the Millennium Cohort Study (MCS , a piece of research in which the subjects were gathered by random sampling of child benefit registers. It featured 18,818 infants born in the UK between 2000 and 2002. The number of weeks of pregnancy at birth was calculated from the mother’s report of her expected due date. Births were grouped into:
- very preterm (defined by the authors as 23-31 weeks
- moderate preterm (32-33 weeks
- late preterm (34-36 weeks
- early term (37-38 weeks
- full term (39-41 weeks
These are not the standard accepted definitions. For example, the charity BLISS, for “babies born too soon”, defines full-term pregnancy as 37 weeks or more, moderately premature as 35-37 weeks, very premature as 29-34 weeks, and extremely premature as birth before 29 weeks.
Child health outcomes were monitored over five years of follow-up. Outcomes assessed included:
- child height, weight and body mass index at three and five years
- parental reports of the number of hospital admissions (not related to accidents since birth or the previous interview, collected at nine months and at three and five years.
- parental reports of any longstanding illness or disability of more than three months’ duration and diagnosed by a health professional, collected at three and five years (a limiting longstanding illness was defined as one which limited activities that are normal for the child’s age group
- parental reports of wheezing within the previous 12 months, and parental reports of asthma collected at three and five years
- parental reports of the use of prescribed drugs, collected at five years
- parents’ ratings of child health, defined as excellent, very good, good, fair or poor, collected at five years
The researchers used statistical methods to look at the outcomes in groups groups born at different stages of pregnancy and compared them to (their definition of full-term babies. Analyses were adjusted to account for various potential confounding factors, principally numerous social and demographic factors. The researchers also estimated “population attributable fractions” (PAFs associated with preterm and early term birth, which is an estimate of the contribution that a particular risk factor has to a health outcome. PAF represents the reduction in the proportion of people in the population with a particular health problem that could be expected if the exposure to a risk factor were reduced to the ideal exposure. In this case, it would represent the proportion of children that would no longer have a particular health problem if all babies were born at full term rather than preterm.
What were the basic results?
After the researchers excluded participants in the MCS study with incomplete data on time in the womb at birth, they interviewed the parents of 14,273 children at 3 years of age and 14,056 at 5 years. They found certain sociodemographic factors, such as lower maternal educational status and maternal smoking, to be associated with prematurity, as is already known.
The researchers generally found a “dose response” effect of prematurity, meaning that the more premature a baby was, the higher the likelihood of general health problems, hospital admissions and longstanding illnesses. They calculated the odds of each outcome compared to children born at 39-41 weeks. The full details of these outcomes are as follows:
The odds for three or more hospital admissions by five years of age were:
- 6.0 times higher for children born at 23-31 weeks
- 3.0 times higher for children born at 32-33 weeks
- 1.9 times higher for children born at 34-36 weeks
- 1.4 times higher for children born at 37-38 weeks
The odds for any longstanding illness at five years of age were:
- 2.4 times higher for children born at 23-31 weeks
- 2.0 times higher for children born at 32-33 weeks
- 1.5 times higher for children born at 34-36 weeks
- 1.1 times higher for children born at 37-38 weeks
The odds for the child’s health being rated as only fair or poor by parents at five years of age were:
- 2.3 times higher for children born at 23-31 weeks
- 2.8 times higher for children born at 32-33 weeks
- 1.5 times higher for children born at 34-36 weeks
- 1.3 times higher for children born at 37-38 weeks
The odds for asthma and wheezing at five years of age were:
- 2.9 times higher for children born at 23-31 weeks
- 1.7 times higher for children born at 32-33 weeks
- 1.5 times higher for children born at 34-36 weeks
- 1.2 times higher for children born at 37-38 weeks
The greatest contribution to the burden of disease at three and five years was among children born at late/moderate preterm or early term. The calculated PAFs for being admitted to hospital at least three times between the ages of 9 months and 5 years were:
- 5.7% for children born at 32-36 weeks (i.e. you would expect a 5.7% reduction in the number of young children admitted three or more times if babies were born at full term rather than moderate preterm
- 7.2% for children born at 37-38 weeks (you would expect a 7.2% reduction in the number of young children being admitted if babies were born at full term rather than early term
- 3.8% for children born before 37 weeks (you would expect a 3.8% reduction in the number of young children being admitted if babies were born at full term rather than very preterm
Similarly, PAFs for longstanding illnesses were:
- 5.4% for early term births
- 5.4% for moderate or late preterm births
- 2.7% for very preterm births
How did the researchers interpret the results?
The researchers concluded that “the health outcomes of moderate/late preterm and early term babies are worse than those of full term babies.” They say that it would be useful for further research to look into how much of the effect is due to prematurity itself, and how much is due to other factors such as maternal or foetal complications.
Conclusion
This valuable research examined childhood health outcomes in a large group of children born at different stages of pregnancy.
Important points to consider when interpreting this research include:
- The authors generally found that the likelihood of poorer health outcomes was higher with increasing prematurity (a dose response effect . This is in line with what is already known about the generally poor immediate and longer-term health outcomes among babies born increasingly prematurely.
- The greatest contribution to overall burden of disease at ages three and five years was calculated to be among children born at 32-36 weeks or at 37-38 weeks. Though a gestation of less than 32 weeks might be expected to have a greater influence on the burden of disease, it must be remembered that many more babies are born above 32 weeks of gestation than below it. Therefore, in the population as a whole, the greater number of babies born within the 32-38 week range would have a greater effect than the small number of babies born extremely early.
- The definitions that the authors used for the purposes of this study are not standard definitions. For example, the standard definition of full-term pregnancy is birth at 37 weeks or more, and it is not split into “early term” at 37-38 weeks and “full term” only at 39-41 weeks. Similarly, definitions of prematurity differ from those used by other UK health organisations.
- There is a possibility of inaccuracy as both age at birth and health outcomes were reported by parents, rather than assessed through medical records. For example, a parental report of wheezing or asthma does not necessarily constitute a confirmed medical diagnosis of asthma.
Overall, the study found that the more premature a baby is, the greater the likelihood of health problems in childhood, and that some effect of prematurity may even be seen in pregnancies approaching full term. Further study in this area would be valuable, both to explore the wider range of longer-term health outcomes that may be caused by prematurity, and to look into associated factors (medical or sociodemographic, for example that may influence the likelihood of these outcomes.
Analysis by Bazian
Links To The Headlines
Infancy health risk linked to early birth by research. BBC News, March 2 2012
Babies born a few weeks early 'suffer health risks'. The Guardian, March 2 2012
Links To Science
Boyle EM, Poulsen G, Field DJ et al. Effects of gestational age at birth on health outcomes at 3 and 5 years of age: population based cohort study. BMJ 2012; 344
Press release: Population-based cohort study of the effects of gestational age at birth on health outcomes at three and five years of age. BMJ, March 1 2012
Patients with a common type of metal hip implant should have annual health checks for as long as they have the implant, according to the UK body for regulating medical devices. The all-metal devices have been found to wear down at an accelerated rate in some patients, potentially causing damage and deterioration in the bone and tissue around the hip. There are also concerns that they could leak traces of metal into the bloodstream, which the annual medical checks will monitor.
Hours before critical coverage from the British Medical Journal and the BBC, the Medicines and Healthcare products Regulatory Agency (MHRA issued new guidelines on larger forms of ‘metal-on-metal’ (MoM hip implants. Advice on smaller metal devices or those featuring a plastic or ceramic head has not changed. Previously, guidelines suggested larger MoM implants should only be checked annually for five years after surgery. The agency now says the annual check-ups should be continued for the life of the implant. Check-ups, they say, are a precautionary measure to reduce the “small risk” of complications and the need for further surgery.
Together with the recent controversy over PIP breast implants, the news has caused the media and patient groups to call for tighter regulation of medical devices, perhaps bringing the approval process into line with that of medicines. Before they can be approved for wider use drugs must undergo several years of laboratory, animal and human testing .
What types of implants are involved?
There are numerous designs and materials used to make hip implants. In recent days the MHRA has issued major updates to its advice on a type of metal-on-metal (MoM hip replacement. As the name implies, MoM implants feature a joint made of two metal surfaces – a metal ‘ball’ that replaces the ball found at the top of the thigh bone (femur and a metal ‘cup’ that acts like the socket found in the pelvis.
The MHRA’s updated advice concerns the type of MoM implant in which the head of the femur is 36mm or greater. This is often referred to as a ‘large head’ implant. The agency now says that patients fitted with this type of implant should be monitored annually for the life of the implant, and that they should also have tests to measure levels of metal particles (ions in their blood. Patients with these implants who have symptoms should also have MRI or ultrasound scans, and patients without symptoms should have a scan if their blood levels of metal ions are rising. The previous guidance on this type of hip implant, issued in April 2010, advised that patients should be monitored annually for no fewer than five years.
What about other types of hip implants?
Advice on monitoring patients with other types of hip implants remains the same, and guidance has not changed on:
- MoM hip resurfacing implants – where the socket and ball of the hip bone has a metal surface applied to it rather than being totally replaced.
- Total MoM implants where the replacement ball is less than 36mm wide.
- A particular range of hip replacements called DePuy ASR – these hip replacements were recalled by their manufacturer, DePuy, in 2010 because of high failure rates. The company made three types of ASR implant.
- Implants featuring plastic or ceramic heads.
How many people are affected?
It is estimated that, in total, 49,000 people in the UK have been given metal-on-metal implants with a width of 36mm or above. This represents a minority of the patients given hip replacements, who mostly have devices featuring plastic, ceramics or smaller metal heads.
In 2010 there were 68,907 new hip replacements fitted, and approximately 1,300 of these surgeries used an MoM implant sized 36mm or above – a rate of around 2%.
What exactly is the problem with MoM implants?
All hip implants will wear down over time as the ball and cup slide against each other during walking and running. Although many people live the rest of their lives without needing their implant to be replaced, any implant may eventually need surgery to remove or replace its components. Surgery to remove or replace part of the implant is known as ‘revision’ and, of the 76,759 procedures performed in 2010, some 7,852 were revision surgeries.
However, data now suggest that large head MoM hip implants (those with a width of 36mm or greater wear down at a faster rate than other types of implants. As friction acts upon their surfaces it can cause tiny metal particles (medically referred to as ‘debris’ to break off and enter the space around the implant. Individuals are thought to react differently to the presence of these metal particles, but, in some people, they can trigger inflammation and discomfort in the area around the implant. Over time this can cause damage and deterioration in the bone and tissue surrounding the implant and joint. This, in turn, may cause the implant to become loose and cause painful symptoms, meaning that further surgery is required.
News coverage has also focused on the MHRA’s recommendation to check for the presence of metal ions in the bloodstream, potentially released either from debris or the implant itself. Ions are electrically charged molecules. Levels of ions in the bloodstream, particularly of the cobalt and chromium used in the surface of the implants, may, therefore, indicate how much wear there is to the artificial hip.
There has been no definitive link between ions from MoM implants and illness, although there has been a small number of cases in which high levels of metal ions in the bloodstream have been associated with symptoms or illnesses elsewhere in the body, including effects on the heart, nervous system and thyroid gland.
The MHRA points out that most patients with MoM implants have well functioning hips and are thought to be at low risk of developing serious problems. However, a small number of patients with these hip implants develop soft tissue reactions to the debris associated with some MoM implants.
How are medical devices regulated?
In the UK, the MHRA is the government agency responsible for ensuring that medical devices work and are safe. The MHRA audits the performance of private sector organisations (called notified bodies that assess and approve medical devices. Once a product is on the market and in use, the MHRA has a system for receiving reports of problems with these products, and will issue warnings if these problems are confirmed through their investigations. It also inspects companies that manufacture products to ensure they comply with regulations.
This system differs greatly from that for testing and approving drugs. Drugs require several years of research testing and trials before they can be approved for clinical use.
What action have regulators taken?
The MHRA has convened an expert advisory group to look at the problems associated with MoM implants. This meets regularly to assess new scientific evidence and reports from doctors and medical staff treating patients. The agency says it is continuing to monitor closely all the latest evidence about these devices and may issue further advice in the future.
In the US, the Food and Drug Administration (FDA says it is gathering additional information about adverse events in patients with MoM implants. In the meantime, it advises patients with MoM hip implants who have no symptoms to attend follow-up appointments as normal with their surgeon. Patients who develop symptoms should see their surgeon promptly for further evaluation.
What actions have critics called for?
In light of the PIP breast implant controversy and this new information on hip implants, there is currently intense scrutiny on the way medical devices are regulated in the UK and Europe, with patient groups and the media arguing that medical devices should be regulated in a similar way to medicines.
Clearing a medicine for use in the UK is a lengthy process involving several stages of laboratory and animal testing, and then carefully controlled and monitored tests in humans. Only once there is enough evidence to suggest that a medicine is reasonably safe can it enter clinical use, and even then patients will be monitored to look at the longer-term effects of the drug.
However, medical devices are not required to go through human trials before entering use, and can currently be approved on the basis of mechanical tests and animal research. While certain devices, such as hip implants, have been monitored through systems such as the National Joint Registry, in light of the recent health concerns over PIP breast implants, patient groups are calling for more testing before devices are allowed into clinical use, and closer mandatory monitoring schemes to ensure their safety once they enter the market.
Links To The Headlines
Annual blood tests for hip patients over poison fears. The Daily Telegraph, February 29 2012
Hip replacement toxic risk could affect 50,000. The Independent, February 29 2012
MHRA: Metal hip implant patients need life-long checks. BBC News, February 29 2012
Metal scare over hip replacement joints. The Guardian, February 29 2012
Toxic metal hip implants 'could affect thousands more people than PIP breast scandal. Daily Mail, February 29 2012
NATIONAL CONFERENCE ON
Current Trends in Medicinal, Aromatic plants and Plant Products
17th & 18th March 2012
Organized by
Osmania University, Hyderabad
OSMANIA UNIVERSITY- A BRIEF PROFILE :
Osmania university established in the year 1918, is the seventh oldest university in the country, third oldest in the South India and oldest in the sate of Andhra Pradesh. It was founded by His exalted Highness Mir Osman Ali Khan, the Seventh Nizam of Hyderabad State. It was the first University to impart higher education through Urdu as the medium of instruction. It is the largest affiliating university in Asia with close to 800 affiliated colleges spread over 3 districts of Telangana (Hyderabad , Ranga Reddy and Medak providing academic and research facilities for nearly five lakhs students. It was accredited with a ‘FIVE STAR’ rating by the NAAC in the year 2001 and reaccredited with the highest grade ‘A’ in 2008. It has been ranked 7th among
Botany
DEPARTMENT, OSMANIA UNIVERSITY :
The department of
Botany
, Osmania University is one of the oldest and pioneering department in the country with decades of academic contributions of excellence. The department has excellent infrastructural facilities and is carrying out research in cutting edge areas of pant sciences. The department has received grants from several national funding agencies like UGC in the form of SAP & COSIST,
CSIR
,
DBT
, DST, MoENF and APNL. DST identified the department under FIST programme.
The department is offering following specializations of contemporary relevance at
M.Sc
., level:
1. Applied Mycology and Molecular Plant pathology
2. Applied Physiology and
molecular biology
3. Biodiversity of Angiosperms, Phytochemistry and Biodiversity of Medicinal Plants.
4. Cytogenetics, Genetics and Molecular Genetics.
5. Applied Palynology and Palaeophytology.
ABOUT THE CONFERENCE :
Medicinal and Aromatic Plants have been used in different ways and methods in overcoming various health problems. Phytochemicals derived from medicinal Plants are been used in different systems of Medicine like Aurveda, Siddha, Unani, Homeopathy, Folklore and Allopathy. Majority of the Drugs used in Allopathy are derived from Medicinal Plants. Many of the Pharmaceutical and Agrochemical Industries are currently engaged actively in Research and Development of Eco-Friendly,organisms, Phytochemicals, and plant products like biofertilizers,biopesticides nutraceuticals,and dietary suppliments. Nearly 70-80 % of Population of Developing Countries are relining on Products which are of plant origin. This trend indicates the demanding position of Medicinal and Aromatic plants in the present market. Therefore this Conference reviews, discuseses,deliberate and share their Research experience for the benefit of students, teachers and researchers and comman man.
TECHNICAL PROGRAMME :
Theme of the Seminar will Review, Discuss and Deliberate on the following areas.
1. BIODERVISITY AND CONSERVATION OF MEDICINAL PLANTS
2. PHYTOCHEMISTRY,SECONDARY METABOLITES AND NATURAL PRODUCTS
3. CULTIVATION, MICROPROPAGATION AND
Biotechnology
OF MEDICINAL PLANTS
4. PHARMACOLOGY, PHARMOCOGNOSY AND ETHANOBOTANY
5. HERBAL PRODUCTS, EFFECT OF BOTANICALS ON MICROBES AND INSECTS
6. MEDICINAL PLANTS AND MICROBIAL INTERACTIONS
SUBMISSION OF ABSTRACTS :
The official language of the conference will be English. There will be Oral and Poster presentations. Oral presentation will be by invitations through respective technical session, Conveners. Contributed papers will be presented as posters. Abstracts of both invited and contributed papers will be printed and distributed to registered participants. Abstracts should be electronically typed on A4 size papers with 3cm margins on all sides, should not exceeded 300 words. Those interested to participate in the conference are requested to fill in the enclosed registration form and send it on or before 7th March, 2012
REGISTRATION FEES :
For participants : Rs 600. For students:300
PosterSize : 4ft×3ft
Please send with registration fees and abstract on or before 7th March2012 Positively to following address
Prof. G. Bagyanarayana
Convener
Department of
Botany
, Department of
Botany
Osmania university , Hyderabad-500007
Email: gbagyan@gmail.com
Prof. S. Gangadhar Rao
Organizing secretary
Osmania university, Hyderabad 500007
Email:
gangadharrao53@gmail.com
Deadline : 07.03.12
Technology is on the boom and medicine has arrive way because the turn of the century. Growing up nowadays, teenagers as well as adults are only taught to utilize over-the-counter medication and prescription power acne medicine. If that doesn't work, the only other option is to go see your family dermatologist. Through the years people have lost sight of acne home cures or all-natural cures to acne. Acne home remedies are undoubtedly less expensive than over-the-counter acne medication or seeing your loved ones dermatologist.
Doctors can still get free samples of medicines, though not football tickets or lunch for their spouses, under a revised code of conduct drafted by a global drug industry trade group.
The new rules clarify the differences between gifts, promotional aids and items such as anatomical models that can be used in medical practice, the
International Federation of Pharmaceutical Manufacturers & Associations said today in a statement. The federation’s member companies will be required to adopt the new guidelines and provide related training to their 1.3 million employees, the group said.
The code may help curb legal expenses related to marketing, particularly in the U.S., where
GlaxoSmithKline Plc (GSK last year agreed to pay $3 billion to resolve criminal and civil investigations and other matters.
Pfizer Inc. (PFE and
Eli Lilly & Co. (LLY each paid more than $1 billion to settle marketing allegations in 2009.
Novartis AG (NOVN and
AstraZeneca Plc (AZN are among drugmakers that in the past year have disclosed U.S. subpoenas seeking information on the selling of certain products.
“The new code provides a framework for the industry to act with integrity and build trust,” AstraZeneca Chief Executive Officer David Brennan, the federation’s president, said in the statement. “This is not about doing the easy thing, but the right thing.”
Free Samples
Free samples of medicines may be given to doctors who are authorized to prescribe them to patients, the federation said in the code. Companies should avoid extravagant venues for meetings, be transparent in promotional materials, and shouldn’t offer items for the personal benefit of a doctor or nurse, such as gift certificates or concert tickets, according to the guidelines.
“As a general rule, the hospitality provided must not exceed what participants would normally be prepared to pay for themselves,” the federation said in the code. “Companies should not pay any costs associated with individuals accompanying invited health-care professionals.”
The rules don’t address direct-to-consumer advertising, pricing or other trade terms for supplying wholesalers and other commercial customers, or promotion of medical devices, the federation said. Drugmakers that fund patient advocacy groups shouldn’t insist on being the sole sponsor of such an organization, and should outline in writing the nature of the financial support, according to the code.
To contact the reporter on this story: Kristen Hallam in London at
khallam@bloomberg.net
To contact the editor responsible for this story: Phil Serafino at
pserafino@bloomberg.net
LONDON, March 1 |
(Reuters - The global pharmaceutical industry is tightening its code of practice in a bid to stamp out bribery and corruption, particularly in emerging markets.LONDON, March 1
The International Federation of Pharmaceutical Manufacturers and Associations said on Thursday it had expanded and strengthened the code to ensure "the highest ethical and professional standards".
FREE GUIDES AND REPORTS FROM DIANOMIADVERTISEMENTBribes paid to foreign doctors and other state employees are shaping up as a major legal liability threat for Big Pharma, which has already forked out billions of dollars to settle mis-selling scandals in the United States.
Johnson & Johnson settled for $78 million with British and U.S. authorities last April, after disclosing payments to doctors in
Greece, Poland and Romania, while Pfizer reached an outline deal in a separate case late last year.
The new IFPMA code extends the rules covering drug company behaviour to also include interactions with medical institutions and patient organisations, as well as healthcare professionals, such as prescribing doctors.
It also makes clearer the dividing line between promotional aid and items of medical utility - which are allowed, and personal and cash gifts - which are not.
Permitted payments for entertainment are being curtailed, although they will still be allowed when interactions with drug firms are of a scientific or educational nature, including events at large medical meetings.
"The new code provides a framework for the industry to act with integrity and build trust," said IFPMA President and AstraZeneca CEO David Brennan. "This is not about doing the easy thing, but the right thing."
The Geneva-based organisation sees a particular role for its expanded code of practice in thinly regulated and smaller emerging markets, where national pharmaceutical organisations may have no presence.
But Tim Reed, director of Health Action International, an Amsterdam-based group that is critical of many industry practices, is not convinced the IFPMA has the teeth to make sure its edicts are implemented on the ground.
In the five years since the publication of the last code, the IFPMA has examined only four complaints against member companies - although more cases have been taken up by national organisations.
"There is a difference between intent and action," Reed said. "When you drill down to what is happening in developing countries, it is clear that it is just not applied. There is a real problem with enforcement because there is no punitive action as a result of transgression." (Editing by
Kate Kelland and
Jodie Ginsberg
 |
Nassau, Bahamas - It has come to the attention of the Ministry of Health that some counterfeit drugs are being sold in The Bahamas.
As a result, the Ministry together with The Bahamas Pharmacy Council has appointed a Committee chaired by the Chief Medical Officer, Dr. Merceline Dahl-Regis, to investigate allegations of the importation and distribution of counterfeit drugs by at least one pharmacy in The Bahamas.
The Committee will be assisted in its investigation by the Pan American Health Organisation (PAHO ...
 |
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Hawaii 5-0 actor Alex O'Loughlin is on the mend.
The Australian actor who currently appears on CBS's primetime hit as Lt. Commander Steve McGarrett has decided to seek treatment to help manage his recovery from a recent shoulder injury.
PHOTOS: Celebrity health scares
"Alex is taking a short break from Hawaii Five-0 to receive supervised treatment for prescription pain medication due to [his injury]," O'Loughlin's rep confirmed to
Us Weekly Friday.
Though the series has currently shot 19 episodes to date, O'Loughlin will miss work on at least one episode during his recovery.
VIDEO: The week's top celeb news headlines
"We respect and support Alex's decision," O'Loughlin's production colleagues tell Us in a statement. "Everyone at CBS Television Studios and Hawaii Five-0 wishes him well and we look forward to his return."
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"Hawaii 5-0" actor Alex O'Loughlin is on the mend.
The Australian actor, who currently appears on CBS's primetime hit, has decided to seek treatment to help manage his recovery from a recent shoulder injury.
PHOTOS
Celebrity health scares
"Alex is taking a short break from 'Hawaii Five-0' to receive supervised treatment for prescription pain medication due to [his injury]," O'Loughlin's rep confirmed to Us Weekly Friday.
PHOTOS:
Winter TV preview
Though the series has currently shot 19 episodes to date, O'Loughlin will miss work on at least one episode during his recovery.
VIDEO:
The week's top celeb news headlines
"We respect and support Alex's decision," O'Loughlin's production colleagues tell Us in a statement. "Everyone at CBS Television Studios and 'Hawaii Five-0' wishes him well and we look forward to his return."
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At the risk of being labeled obsessed myself, I’m still on the Gibbons et al article [ Suicidal Thoughts and Behavior With Antidepressant Treatment] published on-line this month in the Archives of General Psychiatry about treatment emergent suicidality with the SSRIs. They said of their data sources for this meta-analysis, " we obtained complete longitudinal data for RCTs of fluoxetine hydrochloride conducted by Eli Lilly and Co, the Treatment for Adolescents With Depression Study of fluoxetine in children by the National Institute of Mental Health, and adult studies for venlafaxine hydrochloride conducted by Wyeth. " In the abstract, they say, " Data Sources: All intent-to-treat person-level longitudinal data of major depressive disorder from 12 adult, 4 geriatric, and 4 youth randomized controlled trials of fluoxetine hydrochloride and 21 adult trials of venlafaxine hydrochloride. " I wasn’t interested in the adult data, but went looking for the mentioned studies for children and adolescents other than TADS. There are four studies listed and reviewed in the FDA Medical Review for Prozac’s approval for MDD and OCD for children and adolescents in January 2003 and also in the FDA Hammonds Review in August 2004 prior to the black box warning [published full text on-line]:
STUDY DX SPONSOR YEAR N PBO FLX DURATION HCCJ MDD Lilly 1984 40 19 21 6 weeks X065 MDD NIMH? 1991 96 48 48 8 weeks HCJE MDD Lilly 1998 219 110 109 13 weeks HCJW OCD Lilly 1999 103 71 32 9 weeks subtotal 458 248 210 TADS MDD NIMH 2000 433 206 227 36 weeks total 891 454 437
… subtotal 458 248 210 TADS MDD NIMH 2000 439 216 223 12 weeks total 897 464 433
Feels like an orchestrated campaign to me. A biostatistics driven article with no data? full text on-line? data coming soon? a Medscape piece titled No Link Between Antidepressant and Suicide in Kids? with commentary and glossy photos?…
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