PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (1080191)

Clipboard (0)
None

Related Articles

1.  Investigating drug repositioning opportunities in FDA drug labels through topic modeling 
BMC Bioinformatics  2012;13(Suppl 15):S6.
Background
Drug repositioning offers an opportunity to revitalize the slowing drug discovery pipeline by finding new uses for currently existing drugs. Our hypothesis is that drugs sharing similar side effect profiles are likely to be effective for the same disease, and thus repositioning opportunities can be identified by finding drug pairs with similar side effects documented in U.S. Food and Drug Administration (FDA) approved drug labels. The safety information in the drug labels is usually obtained in the clinical trial and augmented with the observations in the post-market use of the drug. Therefore, our drug repositioning approach can take the advantage of more comprehensive safety information comparing with conventional de novo approach.
Method
A probabilistic topic model was constructed based on the terms in the Medical Dictionary for Regulatory Activities (MedDRA) that appeared in the Boxed Warning, Warnings and Precautions, and Adverse Reactions sections of the labels of 870 drugs. Fifty-two unique topics, each containing a set of terms, were identified by using topic modeling. The resulting probabilistic topic associations were used to measure the distance (similarity) between drugs. The success of the proposed model was evaluated by comparing a drug and its nearest neighbor (i.e., a drug pair) for common indications found in the Indications and Usage Section of the drug labels.
Results
Given a drug with more than three indications, the model yielded a 75% recall, meaning 75% of drug pairs shared one or more common indications. This is significantly higher than the 22% recall rate achieved by random selection. Additionally, the recall rate grows rapidly as the number of drug indications increases and reaches 84% for drugs with 11 indications. The analysis also demonstrated that 65 drugs with a Boxed Warning, which indicates significant risk of serious and possibly life-threatening adverse effects, might be replaced with safer alternatives that do not have a Boxed Warning. In addition, we identified two therapeutic groups of drugs (Musculo-skeletal system and Anti-infective for systemic use) where over 80% of the drugs have a potential replacement with high significance.
Conclusion
Topic modeling can be a powerful tool for the identification of repositioning opportunities by examining the adverse event terms in FDA approved drug labels. The proposed framework not only suggests drugs that can be repurposed, but also provides insight into the safety of repositioned drugs.
doi:10.1186/1471-2105-13-S15-S6
PMCID: PMC3439728  PMID: 23046522
2.  The prince and the pauper. A tale of anticancer targeted agents 
Molecular Cancer  2008;7:82.
Cancer rates are set to increase at an alarming rate, from 10 million new cases globally in 2000 to 15 million in 2020. Regarding the pharmacological treatment of cancer, we currently are in the interphase of two treatment eras. The so-called pregenomic therapy which names the traditional cancer drugs, mainly cytotoxic drug types, and post-genomic era-type drugs referring to rationally-based designed. Although there are successful examples of this newer drug discovery approach, most target-specific agents only provide small gains in symptom control and/or survival, whereas others have consistently failed in the clinical testing. There is however, a characteristic shared by these agents: -their high cost-. This is expected as drug discovery and development is generally carried out within the commercial rather than the academic realm. Given the extraordinarily high therapeutic drug discovery-associated costs and risks, it is highly unlikely that any single public-sector research group will see a novel chemical "probe" become a "drug". An alternative drug development strategy is the exploitation of established drugs that have already been approved for treatment of non-cancerous diseases and whose cancer target has already been discovered. This strategy is also denominated drug repositioning, drug repurposing, or indication switch. Although traditionally development of these drugs was unlikely to be pursued by Big Pharma due to their limited commercial value, biopharmaceutical companies attempting to increase productivity at present are pursuing drug repositioning. More and more companies are scanning the existing pharmacopoeia for repositioning candidates, and the number of repositioning success stories is increasing. Here we provide noteworthy examples of known drugs whose potential anticancer activities have been highlighted, to encourage further research on these known drugs as a means to foster their translation into clinical trials utilizing the more limited public-sector resources. If these drug types eventually result in being effective, it follows that they could be much more affordable for patients with cancer; therefore, their contribution in terms of reducing cancer mortality at the global level would be greater.
doi:10.1186/1476-4598-7-82
PMCID: PMC2615789  PMID: 18947424
3.  Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity 
PLoS Computational Biology  2013;9(11):e1003315.
Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.
Author Summary
Small drug molecules usually bind to unintended off-targets, leading to unexpected drug responses such as side effects or drug repositioning opportunities. Thus, identifying unintended drug-target interactions (DTI) is particularly required for understanding complicated drug actions. It remains expensive nowadays to experimentally determine DTI, so various computational methods are developed. In this study, we initiatively demonstrated that target binding is directly correlated with drug induced genomic expression profiles in Connectivity Map (CMap). By improving data quality of CMap, we illustrated three important facts: (1) Drugs binding to common targets show higher gene-expression similarity than random compounds, indicating that upstream ligand binding could be characterized by downstream gene-expression change. (2) It is found that some targets are better characterized by CMap than others. To guarantee efficiency of DTI discovery, prediction models should be specifically built for those well characterized targets. (3) It is broadly observed in the predicted DTI that ligands for the same target may collectively interact with common off-target. This observation is consistent with published experimental evidence and can help illustrate the mechanisms of unexplained drug reactions. Based on CMap, our work established an efficient pipeline of identifying potential DTI. By extending the success in CMap to other genomic data sources, we believe more DTI would be discovered.
doi:10.1371/journal.pcbi.1003315
PMCID: PMC3820513  PMID: 24244130
4.  Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs) 
BMC Bioinformatics  2013;14:288.
Background
The development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Drug repositioning, i.e. finding new indications for approved drugs, could be one of the most cost- and time-effective strategies to cope with this problem, at least in a subset of cases. Therefore, many computational approaches based on the analysis of high throughput gene expression data have so far been proposed to reposition available drugs. However, most of these methods require gene expression profiles directly relevant to the pathologic conditions under study, such as those obtained from patient cells and/or from suitable experimental models. In this work we have developed a new approach for drug repositioning, based on identifying known drug targets showing conserved anti-correlated expression profiles with human disease genes, which is completely independent from the availability of ‘ad hoc’ gene expression data-sets.
Results
By analyzing available data, we provide evidence that the genes displaying conserved anti-correlation with drug targets are antagonistically modulated in their expression by treatment with the relevant drugs. We then identified clusters of genes associated to similar phenotypes and showing conserved anticorrelation with drug targets. On this basis, we generated a list of potential candidate drug-disease associations. Importantly, we show that some of the proposed associations are already supported by independent experimental evidence.
Conclusions
Our results support the hypothesis that the identification of gene clusters showing conserved anticorrelation with drug targets can be an effective method for drug repositioning and provide a wide list of new potential drug-disease associations for experimental validation.
doi:10.1186/1471-2105-14-288
PMCID: PMC3851137  PMID: 24088245
5.  Computational drug repositioning through heterogeneous network clustering 
BMC Systems Biology  2013;7(Suppl 5):S6.
Background
Given the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms.
Results
Using known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials.
Conclusions
Previous computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery.
doi:10.1186/1752-0509-7-S5-S6
PMCID: PMC4029299  PMID: 24564976
6.  Discovery and preclinical validation of drug indications using compendia of public gene expression data 
Science translational medicine  2011;3(96):96ra77.
The application of established drug compounds to novel therapeutic indications, known as drug repositioning, offers several advantages over traditional drug development, including reduced development costs and shorter paths to approval. Recent approaches to drug repositioning employ high-throughput experimental approaches to assess a compound’s potential therapeutic qualities. Here we present a systematic computational approach to predict novel therapeutic indications based on comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds yielding predicted therapeutic potentials for these drugs. We demonstrate the ability to recover many known drug and disease relationships using computationally derived therapeutic potentials, and also predict many new indications for these drugs. We experimentally validated a prediction for the anti-ulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate both in vitro and in vivo using mouse xenograft models. This novel computational method provides a novel and systematic approach to reposition established drugs to treat a wide range of human diseases.
doi:10.1126/scitranslmed.3001318
PMCID: PMC3502016  PMID: 21849665
7.  DRUGSURV: a resource for repositioning of approved and experimental drugs in oncology based on patient survival information 
Cell Death & Disease  2014;5(2):e1051-.
The use of existing drugs for new therapeutic applications, commonly referred to as drug repositioning, is a way for fast and cost-efficient drug discovery. Drug repositioning in oncology is commonly initiated by in vitro experimental evidence that a drug exhibits anticancer cytotoxicity. Any independent verification that the observed effects in vitro may be valid in a clinical setting, and that the drug could potentially affect patient survival in vivo is of paramount importance. Despite considerable recent efforts in computational drug repositioning, none of the studies have considered patient survival information in modelling the potential of existing/new drugs in the management of cancer. Therefore, we have developed DRUGSURV; this is the first computational tool to estimate the potential effects of a drug using patient survival information derived from clinical cancer expression data sets. DRUGSURV provides statistical evidence that a drug can affect survival outcome in particular clinical conditions to justify further investigation of the drug anticancer potential and to guide clinical trial design. DRUGSURV covers both approved drugs (∼1700) as well as experimental drugs (∼5000) and is freely available at http://www.bioprofiling.de/drugsurv.
doi:10.1038/cddis.2014.9
PMCID: PMC3944280  PMID: 24503543
drug repositioning; clinical trial design; patient survival; thioridazine
8.  Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data 
PLoS ONE  2013;8(11):e78518.
Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems.
doi:10.1371/journal.pone.0078518
PMCID: PMC3823875  PMID: 24244318
9.  Novel Modeling of Cancer Cell Signaling Pathways Enables Systematic Drug Repositioning for Distinct Breast Cancer Metastases 
Cancer research  2013;73(20):6149-6163.
A new type of signaling network element, called cancer signaling bridges (CSB), has been shown to have the potential for systematic and fast-tracked drug repositioning. On the basis of CSBs, we developed a computational model to derive specific downstream signaling pathways that reveal previously unknown target–disease connections and new mechanisms for specific cancer subtypes. The model enables us to reposition drugs based on available patient gene expression data. We applied this model to repurpose known or shelved drugs for brain, lung, and bone metastases of breast cancer with the hypothesis that cancer subtypes have their own specific signaling mechanisms. To test the hypothesis, we addressed specific CSBs for each metastasis that satisfy (i) CSB proteins are activated by the maximal number of enriched signaling pathways specific to a given metastasis, and (ii) CSB proteins are involved in the most differential expressed coding genes specific to each breast cancer metastasis. The identified signaling networks for the three types of breast cancer metastases contain 31, 15, and 18 proteins and are used to reposition 15, 9, and 2 drug candidates for the brain, lung, and bone metastases. We conducted both in vitro and in vivo preclinical experiments as well as analysis on patient tumor specimens to evaluate the targets and repositioned drugs. Of special note, we found that the Food and Drug Administration-approved drugs, sunitinib and dasatinib, prohibit brain metastases derived from breast cancer, addressing one particularly challenging aspect of this disease.
doi:10.1158/0008-5472.CAN-12-4617
PMCID: PMC4005386  PMID: 24097821
10.  Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach 
PLoS ONE  2013;8(4):e60618.
The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.
doi:10.1371/journal.pone.0060618
PMCID: PMC3617101  PMID: 23593264
11.  Prediction of novel drug indications using network driven biological data prioritization and integration 
Background
With the rapid development of high-throughput genomic technologies and the accumulation of genome-wide datasets for gene expression profiling and biological networks, the impact of diseases and drugs on gene expression can be comprehensively characterized. Drug repositioning offers the possibility of reduced risks in the drug discovery process, thus it is an essential step in drug development.
Results
Computational prediction of drug-disease interactions using gene expression profiling datasets and biological networks is a new direction in drug repositioning that has gained increasing interest. We developed a computational framework to build disease-drug networks using drug- and disease-specific subnetworks. The framework incorporates protein networks to refine drug and disease associated genes and prioritize genes in disease and drug specific networks. For each drug and disease we built multiple networks using gene expression profiling and text mining. Finally a logistic regression model was used to build functional associations between drugs and diseases.
Conclusions
We found that representing drugs and diseases by genes with high centrality degree in gene networks is the most promising representation of drug or disease subnetworks.
doi:10.1186/1758-2946-6-1
PMCID: PMC3896815  PMID: 24397863
Disease; Drug; Gene; Protein networks
12.  A novel method of transcriptional response analysis to facilitate drug repositioning for cancer therapy 
Cancer research  2011;72(1):33-44.
Little research has been done to address the huge opportunities that may exist to reposition existing approved or generic drugs for alternate uses in cancer therapy. Additionally, there has been little work on strategies to reposition experimental cancer agents for testing in alternate settings that could shorten their clinical development time. Progress in each area has lagged in part due to the lack of systematic methods to define drug off-target effects (OTEs) that might affect important cancer cell signaling pathways. In this study, we addressed this critical gap by developing an OTE-based method to repurpose drugs for cancer therapeutics, based on transcriptional responses made in cells before and after drug treatment. Specifically, we defined a new network component called cancer-signaling bridges (CSBs) and integrated it with Bayesian Factor Regression Model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof of concept studies were performed in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to >90% of FDA-approved drugs and >75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for several high-ranking drug-dose pairs suggested repositioning opportunities for cancer therapy, based on the ability to enforce Rb-dependent repression of important E2F-dependent cell cycle genes. Together, our findings establish new methods to identify opportunities for drug repositioning or to elucidate the mechanisms of action of repositioned drugs.
doi:10.1158/0008-5472.CAN-11-2333
PMCID: PMC3251651  PMID: 22108825
Off-target drug repositioning; cancer systems biology; cancer transcriptional response
13.  Drug repositioning for personalized medicine 
Genome Medicine  2012;4(3):27.
Human diseases can be caused by complex mechanisms involving aberrations in numerous proteins and pathways. With recent advances in genomics, elucidating the molecular basis of disease on a personalized level has become an attainable goal. In many cases, relevant molecular targets will be identified for which approved drugs already exist, and the potential repositioning of these drugs to a new indication can be investigated. Repositioning is an accelerated route for drug discovery because existing drugs have established clinical and pharmacokinetic data. Personalized medicine and repositioning both aim to improve the productivity of current drug discovery pipelines, which expend enormous time and cost to develop new drugs, only to have them fail in clinical trials because of lack of efficacy or toxicity. Here, we discuss the current state of research in these two fields, focusing on recent large-scale efforts to systematically find repositioning candidates and elucidate individual disease mechanisms in cancer. We also discuss scenarios in which personalized drug repositioning could be particularly rewarding, such as for diseases that are rare or have specific mutations, as well as current challenges in this field. With an increasing number of drugs being approved for rare cancer subtypes, personalized medicine and repositioning approaches are poised to significantly alter the way we diagnose diseases, infer treatments and develop new drugs.
doi:10.1186/gm326
PMCID: PMC3446277  PMID: 22494857
Personalized medicine; repositioning; repurposing; drug discovery; cancer; orphan diseases; high-throughput screening; computational drug design
14.  Reporting Bias in Drug Trials Submitted to the Food and Drug Administration: Review of Publication and Presentation 
PLoS Medicine  2008;5(11):e217.
Background
Previous studies of drug trials submitted to regulatory authorities have documented selective reporting of both entire trials and favorable results. The objective of this study is to determine the publication rate of efficacy trials submitted to the Food and Drug Administration (FDA) in approved New Drug Applications (NDAs) and to compare the trial characteristics as reported by the FDA with those reported in publications.
Methods and Findings
This is an observational study of all efficacy trials found in approved NDAs for New Molecular Entities (NMEs) from 2001 to 2002 inclusive and all published clinical trials corresponding to the trials within the NDAs. For each trial included in the NDA, we assessed its publication status, primary outcome(s) reported and their statistical significance, and conclusions. Seventy-eight percent (128/164) of efficacy trials contained in FDA reviews of NDAs were published. In a multivariate model, trials with favorable primary outcomes (OR = 4.7, 95% confidence interval [CI] 1.33–17.1, p = 0.018) and active controls (OR = 3.4, 95% CI 1.02–11.2, p = 0.047) were more likely to be published. Forty-one primary outcomes from the NDAs were omitted from the papers. Papers included 155 outcomes that were in the NDAs, 15 additional outcomes that favored the test drug, and two other neutral or unknown additional outcomes. Excluding outcomes with unknown significance, there were 43 outcomes in the NDAs that did not favor the NDA drug. Of these, 20 (47%) were not included in the papers. The statistical significance of five of the remaining 23 outcomes (22%) changed between the NDA and the paper, with four changing to favor the test drug in the paper (p = 0.38). Excluding unknowns, 99 conclusions were provided in both NDAs and papers, nine conclusions (9%) changed from the FDA review of the NDA to the paper, and all nine did so to favor the test drug (100%, 95% CI 72%–100%, p = 0.0039).
Conclusions
Many trials were still not published 5 y after FDA approval. Discrepancies between the trial information reviewed by the FDA and information found in published trials tended to lead to more favorable presentations of the NDA drugs in the publications. Thus, the information that is readily available in the scientific literature to health care professionals is incomplete and potentially biased.
Lisa Bero and colleagues review the publication status of all efficacy trials carried out in support of new drug approvals from 2001 and 2002, and find that a quarter of trials remain unpublished.
Editors' Summary
Background.
All health-care professionals want their patients to have the best available clinical care—but how can they identify the optimum drug or intervention? In the past, clinicians used their own experience or advice from colleagues to make treatment decisions. Nowadays, they rely on evidence-based medicine—the systematic review and appraisal of clinical research findings. So, for example, before a new drug is approved for the treatment of a specific disease in the United States and becomes available for doctors to prescribe, the drug's sponsors (usually a pharmaceutical company) must submit a “New Drug Application” (NDA) to the US Food and Drug Administration (FDA). The NDA tells the story of the drug's development from laboratory and animal studies through to clinical trials, including “efficacy” trials in which the efficacy and safety of the new drug and of a standard drug for the disease are compared by giving groups of patients the different drugs and measuring several key (primary) “outcomes.” FDA reviewers use this evidence to decide whether to approve a drug.
Why Was This Study Done?
Although the information in NDAs is publicly available, clinicians and patients usually learn about new drugs from articles published in medical journals after drug approval. Unfortunately, drug sponsors sometimes publish the results only of the trials in which their drug performed well and in which statistical analyses indicate that the drug's improved performance was a real effect rather than a lucky coincidence. Trials in which a drug did not show a “statistically significant benefit” or where the drug was found to have unwanted side effects often remain unpublished. This “publication bias” means that the scientific literature can contain an inaccurate picture of a drug's efficacy and safety relative to other therapies. This may lead to clinicians preferentially prescribing newer, more expensive drugs that are not necessarily better than older drugs. In this study, the researchers test the hypothesis that not all the trial results in NDAs are published in medical journals. They also investigate whether there are any discrepancies between the trial data included in NDAs and in published articles.
What Did the Researchers Do and Find?
The researchers identified all the efficacy trials included in NDAs for totally new drugs that were approved by the FDA in 2001 and 2002 and searched the scientific literature for publications between July 2006 and June 2007 relating to these trials. Only three-quarters of the efficacy trials in the NDAs were published; trials with favorable outcomes were nearly five times as likely to be published as those without favorable outcomes. Although 155 primary outcomes were in both the papers and the NDAs, 41 outcomes were only in the NDAs. Conversely, 17 outcomes were only in the papers; 15 of these favored the test drug. Of the 43 primary outcomes reported in the NDAs that showed no statistically significant benefit for the test drug, only half were included in the papers; for five of the reported primary outcomes, the statistical significance differed between the NDA and the paper and generally favored the test drug in the papers. Finally, nine out of 99 conclusions differed between the NDAs and the papers; each time, the published conclusion favored the test drug.
What Do These Findings Mean?
These findings indicate that the results of many trials of new drugs are not published 5 years after FDA approval of the drug. Furthermore, unexplained discrepancies between the data and conclusions in NDAs and in medical journals are common and tend to paint a more favorable picture of the new drug in the scientific literature than in the NDAs. Overall, these findings suggest that the information on the efficacy of new drugs that is readily available to clinicians and patients through the published scientific literature is incomplete and potentially biased. The recent introduction in the US and elsewhere of mandatory registration of all clinical trials before they start and of mandatory publication in trial registers of the full results of all the predefined primary outcomes should reduce publication bias over the next few years and should allow clinicians and patients to make fully informed treatment decisions.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050217.
This study is further discussed in a PLoS Medicine Perspective by An-Wen Chan
PLoS Medicine recently published a related article by Ida Sim and colleagues: Lee K, Bacchetti P, Sim I (2008) Publication of clinical trials supporting successful new drug applications: A literature analysis. PLoS Med 5: e191. doi:10.1371/journal.pmed.0050191
The Food and Drug Administration provides information about drug approval in the US for consumers and for health-care professionals; detailed information about the process by which drugs are approved is on the Web site of the FDA Center for Drug Evaluation and Research (in English and Spanish)
NDAs for approved drugs can also be found on this Web site
The ClinicalTrials.gov Web site provides information about the US National Institutes of Health clinical trial registry, background information about clinical trials, and a fact sheet detailing the requirements of the FDA Amendments Act 2007 for trial registration
The World Health Organization's International Clinical Trials Registry Platform is working toward setting international norms and standards for the reporting of clinical trials (in several languages)
doi:10.1371/journal.pmed.0050217
PMCID: PMC2586350  PMID: 19067477
15.  The functional therapeutic chemical classification system 
Bioinformatics  2013;30(6):876-883.
Motivation: Drug repositioning is the discovery of new indications for compounds that have already been approved and used in a clinical setting. Recently, some computational approaches have been suggested to unveil new opportunities in a systematic fashion, by taking into consideration gene expression signatures or chemical features for instance. We present here a novel method based on knowledge integration using semantic technologies, to capture the functional role of approved chemical compounds.
Results: In order to computationally generate repositioning hypotheses, we used the Web Ontology Language to formally define the semantics of over 20 000 terms with axioms to correctly denote various modes of action (MoA). Based on an integration of public data, we have automatically assigned over a thousand of approved drugs into these MoA categories. The resulting new resource is called the Functional Therapeutic Chemical Classification System and was further evaluated against the content of the traditional Anatomical Therapeutic Chemical Classification System. We illustrate how the new classification can be used to generate drug repurposing hypotheses, using Alzheimers disease as a use-case.
Availability: https://www.ebi.ac.uk/chembl/ftc; https://github.com/loopasam/ftc.
Contact: croset@ebi.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btt628
PMCID: PMC3957075  PMID: 24177719
16.  Pathway-based drug repositioning using causal inference 
BMC Bioinformatics  2013;14(Suppl 16):S3.
Background
Recent in vivo studies showed new hopes of drug repositioning through causality inference from drugs to disease. Inspired by their success, here we present an in silico method for building a causal network (CauseNet) between drugs and diseases, in an attempt to systematically identify new therapeutic uses of existing drugs.
Methods
Unlike the traditional 'one drug-one target-one disease' causal model, we simultaneously consider all possible causal chains connecting drugs to diseases via target- and gene-involved pathways based on rich information in several expert-curated knowledge-bases. With statistical learning, our method estimates transition likelihood of each causal chain in the network based on known drug-disease treatment associations (e.g. bexarotene treats skin cancer).
Results
To demonstrate its validity, our method showed high performance (AUC = 0.859) in cross validation. Moreover, our top scored prediction results are highly enriched in literature and clinical trials. As a showcase of its utility, we show several drugs for potential re-use in Crohn's Disease.
Conclusions
We successfully developed a computational method for discovering new uses of existing drugs based on casual inference in a layered drug-target-pathway-gene- disease network. The results showed that our proposed method enables hypothesis generation from public accessible biological data for drug repositioning.
doi:10.1186/1471-2105-14-S16-S3
PMCID: PMC3853312  PMID: 24564553
17.  Repositioning: the fast track to new anti-malarial medicines? 
Malaria Journal  2014;13:143.
Background
Repositioning of existing drugs has been suggested as a fast track for developing new anti-malarial agents. The compound libraries of GlaxoSmithKline (GSK), Pfizer and AstraZeneca (AZ) comprising drugs that have undergone clinical studies in other therapeutic areas, but not achieved approval, and a set of US Food and Drug Administration (FDA)-approved drugs and other bio-actives were tested against Plasmodium falciparum blood stages.
Methods
Molecules were tested initially against erythrocytic co-cultures of P. falciparum to measure proliferation inhibition using one of the following methods: SYBR®I dye DNA staining assay (3D7, K1 or NF54 strains); [3H] hypoxanthine radioisotope incorporation assay (3D7 and 3D7A strain); or 4’,6-diamidino-2-phenylindole (DAPI) DNA imaging assay (3D7 and Dd2 strains). After review of the available clinical pharmacokinetic and safety data, selected compounds with low μM activity and a suitable clinical profile were tested in vivo either in a Plasmodium berghei four-day test or in the P. falciparum Pf3D70087/N9 huSCID ‘humanized’ mouse model.
Results
Of the compounds included in the GSK and Pfizer sets, 3.8% (9/238) had relevant in vitro anti-malarial activity while 6/100 compounds from the AZ candidate drug library were active. In comparison, around 0.6% (24/3,800) of the FDA-approved drugs and other bio-actives were active. After evaluation of available clinical data, four investigational drugs, active in vitro were tested in the P. falciparum humanized mouse model: UK-112,214 (PAF-H1 inhibitor), CEP-701 (protein kinase inhibitor), CEP-1347 (protein kinase inhibitor), and PSC-833 (p-glycoprotein inhibitor). Only UK-112,214 showed significant efficacy against P. falciparum in vivo, although at high doses (ED90 131.3 mg/kg [95% CI 112.3, 156.7]), and parasitaemia was still present 96 hours after treatment commencement. Of the six actives from the AZ library, two compounds (AZ-1 and AZ-3) were marginally efficacious in vivo in a P. berghei model.
Conclusions
Repositioning of existing therapeutics in malaria is an attractive proposal. Compounds active in vitro at μM concentrations were identified. However, therapeutic concentrations may not be effectively achieved in mice or humans because of poor bio-availability and/or safety concerns. Stringent safety requirements for anti-malarial drugs, given their widespread use in children, make this a challenging area in which to reposition therapy.
doi:10.1186/1475-2875-13-143
PMCID: PMC4021201  PMID: 24731288
Malaria; Anti-malarial drugs; Drug repositioning; in vitro; in vivo; Plasmodium falciparum; Plasmodium berghei; Candidate drug re-profiling
18.  Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity 
BMC Medical Genomics  2013;6(Suppl 2):S3.
Background
Using annotations to the articles in MEDLINE®/PubMed®, over six thousand chemical compounds with pharmacological actions have been tracked since 1996. Medical Subject Heading Over-representation Profiles (MeSHOPs) quantitatively leverage the literature associated with biological entities such as diseases or drugs, providing the opportunity to reposition known compounds towards novel disease applications.
Methods
A MeSHOP is constructed by counting the number of times each medical subject term is assigned to an entity-related research publication in the MEDLINE database and calculating the significance of the count by comparing against the count of the term in a background set of publications. Based on the expectation that drugs suitable for treatment of a disease (or disease symptom) will have similar annotation properties to the disease, we successfully predict drug-disease associations by comparing MeSHOPs of diseases and drugs.
Results
The MeSHOP comparison approach delivers an 11% improvement over bibliometric baselines. However, novel drug-disease associations are observed to be biased towards drugs and diseases with more publications. To account for the annotation biases, a correction procedure is introduced and evaluated.
Conclusions
By explicitly accounting for the annotation bias, unexpectedly similar drug-disease pairs are highlighted as candidates for drug repositioning research. MeSHOPs are shown to provide a literature-supported perspective for discovery of new links between drugs and diseases based on pre-existing knowledge.
doi:10.1186/1755-8794-6-S2-S3
PMCID: PMC3654871  PMID: 23819887
19.  Cost-Effectiveness Study of Three Antimalarial Drug Combinations in Tanzania 
PLoS Medicine  2006;3(10):e373.
Background
As a result of rising levels of drug resistance to conventional monotherapy, the World Health Organization (WHO) and other international organisations have recommended that malaria endemic countries move to combination therapy, ideally with artemisinin-based combinations (ACTs). Cost is a major barrier to deployment. There is little evidence from field trials on the cost-effectiveness of these new combinations.
Methods and Findings
An economic evaluation of drug combinations was designed around a randomised effectiveness trial of combinations recommended by the WHO, used to treat Tanzanian children with non-severe slide-proven malaria. Drug combinations were: amodiaquine (AQ), AQ with sulfadoxine-pyrimethamine (AQ+SP), AQ with artesunate (AQ+AS), and artemether-lumefantrine (AL) in a six-dose regimen. Effectiveness was measured in terms of resource savings and cases of malaria averted (based on parasitological failure rates at days 14 and 28). All costs to providers and to patients and their families were estimated and uncertain variables were subjected to univariate sensitivity analysis. Incremental analysis comparing each combination to monotherapy (AQ) revealed that from a societal perspective AL was most cost-effective at day 14. At day 28 the difference between AL and AQ+AS was negligible; both resulted in a gross savings of approximately US$1.70 or a net saving of US$22.40 per case averted. Varying the accuracy of diagnosis and the subsistence wage rate used to value unpaid work had a significant effect on the number of cases averted and on programme costs, respectively, but this did not change the finding that AL and AQ+AS dominate monotherapy.
Conclusions
In an area of high drug resistance, there is evidence that AL and AQ+AS are the most cost-effective drugs despite being the most expensive, because they are significantly more effective than other options and therefore reduce the need for further treatment. This is not necessarily the case in parts of Africa where recrudescence following SP and AQ treatment (and their combination) is lower so that the relative advantage of ACTs is smaller, or where diagnostic services are not accurate and as a result much of the drug goes to those who do not have malaria.
A randomised effectiveness trial of antimalarial drug combinations used to treat Tanzanian children found artemether-lumefantrine to be the most cost-effective.
Editors' Summary
Background.
For many years, malaria was treated with a course of a single drug. This type of treatment made it easy for malaria parasites to become resistant to antimalarial drugs. This is a major factor contributing to the continuing high death rate from the disease. However, although parasites can easily adapt to resist one drug, adapting to combinations of two or three drugs is much harder. Scientists have therefore developed combinations of antimalarial drugs. One component of these combinations is artemisinin—derived from a Chinese shrub. However, these combination therapies are much more expensive than the older treatments.
The regions worst affected by malaria—Africa and Asia—are also the poorest. And, in these areas, where both individual and government resources are scarce, antimalarial treatments must be cost-effective as well as clinically effective.
Why Was This Study Done?
Most of the estimated 1 million to 3 million people worldwide killed by malaria every year are young children in sub-Saharan Africa. Growing drug resistance, poor prevention programs, and a frequent inability of patients to pay for treatment mean that effective therapy is desperately needed in this part of the world. However, because of differences in drug resistance between regions, a drug combination will not work everywhere. In addition, because of low annual incomes (the average in Tanzania is US$120), heavy subsidies will probably be required to ensure that combination treatments are widely used. With several healthcare problems competing for resources, policymakers are likely to subsidize only the most cost-effective treatments. The researchers wanted to provide policymakers with information on how different combinations of malaria drugs compare in terms of costs, health effects, and cost-effectiveness, so that they can decide which treatment is best for their region.
What Did the Researchers Do and Find?
They compared three combinations that the World Health Organization recommends for countries when making the transition from single-drug therapy. The three combinations—amodiaquine (AQ) and sulfadoxine-pyrimethamine (SP); AQ and artesunate (AS); and artemether-lumefantrine (AL)—were used to treat Tanzanian children. The researchers wanted to find out how many cases of malaria each combination averted (which is also an indication of how much money it saved) and how much the treatment cost. They looked at costs and savings from the perspectives of both healthcare providers and patients.
Compared with no treatment, AL proved to be the most cost-effective; although it cost more for the provider (US$3.01 at day 28 of treatment) than the others, its effectiveness in getting rid of the parasite meant it would save the cost of future treatment. By day 28, AL had averted 382 cases of malaria compared with 279 for AQ+AS, 181 for AQ+SP, and 57 for AQ alone. Also, higher proportions of inaccurate diagnoses of malaria led to lower cost-effectiveness of treatments.
What Do These Findings Mean?
Despite being more expensive, newer drugs can be cost-effective where alternatives fail. Although AL was the most cost-effective in places (such as Tanzania) where the malaria parasites are highly resistant to SP and AQ, the picture is likely to change for other areas. In West Africa, for example, AQ resistance is lower, and AQ+SP and AQ+AS would probably be more cost-effective. And in areas where both these combinations are just as good as AL in preventing recurring disease, they would be more cost-effective than AL. However, since AQ and SP have been used singly for many years, the likelihood is that resistance to these drugs will continue to increase. Accurate diagnosis turns out to be very important for maintaining the cost-effectiveness of combination antimalarial therapies. This will be essential if they are to be incorporated as a sustainable part of local health policies. The researchers also point out that, depending on which perspective is taken (provider or patient), the cost-effectiveness of treatments differs, making it important to compare like with like.
Although investing in costly AL treatments and improving diagnostic capabilities will be a challenge for African governments that currently spend less than US$5 per person per year on healthcare, it will be necessary if they are to seriously tackle the malaria epidemic.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030373
The US enters for Disease Control and Prevention provides malaria information aimed at the general public, physicians, and health workers
The Wellcome Trust; also has malaria information for the general public and covers the science of malaria research, including a downloadable animation of the parasite's life cycle
Medicines for Malaria Venture (MMV) is a charity created to develop new antimalarial drugs through public-private partnerships
doi:10.1371/journal.pmed.0030373
PMCID: PMC1592341  PMID: 17032059
20.  Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation 
BMC Medical Genomics  2013;6(Suppl 3):S4.
Background
During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.
Methods
We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.
Results
We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.
Conclusions
We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.
doi:10.1186/1755-8794-6-S3-S4
PMCID: PMC3980383  PMID: 24565337
21.  Using Functional Signatures to Identify Repositioned Drugs for Breast, Myelogenous Leukemia and Prostate Cancer 
PLoS Computational Biology  2012;8(2):e1002347.
The cost and time to develop a drug continues to be a major barrier to widespread distribution of medication. Although the genomic revolution appears to have had little impact on this problem, and might even have exacerbated it because of the flood of additional and usually ineffective leads, the emergence of high throughput resources promises the possibility of rapid, reliable and systematic identification of approved drugs for originally unintended uses. In this paper we develop and apply a method for identifying such repositioned drug candidates against breast cancer, myelogenous leukemia and prostate cancer by looking for inverse correlations between the most perturbed gene expression levels in human cancer tissue and the most perturbed expression levels induced by bioactive compounds. The method uses variable gene signatures to identify bioactive compounds that modulate a given disease. This is in contrast to previous methods that use small and fixed signatures. This strategy is based on the observation that diseases stem from failed/modified cellular functions, irrespective of the particular genes that contribute to the function, i.e., this strategy targets the functional signatures for a given cancer. This function-based strategy broadens the search space for the effective drugs with an impressive hit rate. Among the 79, 94 and 88 candidate drugs for breast cancer, myelogenous leukemia and prostate cancer, 32%, 13% and 17% respectively are either FDA-approved/in-clinical-trial drugs, or drugs with suggestive literature evidences, with an FDR of 0.01. These findings indicate that the method presented here could lead to a substantial increase in efficiency in drug discovery and development, and has potential application for the personalized medicine.
Author Summary
The effective drug of a given disease is aimed to bring abnormal functions associated with disease back to the normal state. Using expression profile as the surrogate marker of the cellular function, we introduce a novel procedure to identify candidate therapeutics by searching for those bioactive compounds that either down-regulate abnormally over-expressed genes, or up-regulate those that are abnormally under-expressed. We show that the approach detects a pool of plausible candidates as repositioning/new drugs. In contrast to previous studies, our approach uses a variable big number of genes and/or gene combinations as a representation of functional signatures to identify bioactive compounds that modulate a given disease, irrespective of the particular genes that contribute to the cellular functions; therefore it covers potential drugs with heterogeneous properties. The method may also have potential application for the personalized medicine.
doi:10.1371/journal.pcbi.1002347
PMCID: PMC3276504  PMID: 22346740
22.  A cross-species analysis method to analyze animal models' similarity to human's disease state 
BMC Systems Biology  2012;6(Suppl 3):S18.
Background
Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism.
Results
We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery.
We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning.
Conclusions
We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology.
doi:10.1186/1752-0509-6-S3-S18
PMCID: PMC3524072  PMID: 23282076
23.  Predicting New Indications for Approved Drugs Using a Proteo-Chemometric Method 
Journal of medicinal chemistry  2012;55(15):6832-6848.
The most effective way to move from target identification to the clinic is to identify already approved drugs with the potential for activating or inhibiting unintended targets (repurposing or repositioning). This is usually achieved by high throughput chemical screening, transcriptome matching or simple in silico ligand docking. We now describe a novel rapid computational proteo-chemometric method called “Train, Match, Fit, Streamline” (TMFS) to map new drug-target interaction space and predict new uses. The TMFS method combines shape, topology and chemical signatures, including docking score and functional contact points of the ligand, to predict potential drug-target interactions with remarkable accuracy. Using the TMFS method, we performed extensive molecular fit computations on 3,671 FDA approved drugs across 2,335 human protein crystal structures. The TMFS method predicts drug-target associations with 91% accuracy for the majority of drugs. Over 58% of the known best ligands for each target were correctly predicted as top ranked, followed by 66%, 76%, 84% and 91% for agents ranked in the top 10, 20, 30 and 40, respectively, out of all 3,671 drugs. Drugs ranked in the top 1–40, that have not been experimentally validated for a particular target now become candidates for repositioning. Furthermore, we used the TMFS method to discover that mebendazole, an anti-parasitic with recently discovered and unexpected anti-cancer properties, has the structural potential to inhibit VEGFR2. We confirmed experimentally that mebendazole inhibits VEGFR2 kinase activity as well as angiogenesis at doses comparable with its known effects on hookworm. TMFS also predicted, and was confirmed with surface plasmon resonance, that dimethyl celecoxib and the anti-inflammatory agent celecoxib can bind cadherin-11, an adhesion molecule important in rheumatoid arthritis and poor prognosis malignancies for which no targeted therapies exist. We anticipate that expanding our TMFS method to the >27,000 clinically active agents available worldwide across all targets will be most useful in the repositioning of existing drugs for new therapeutic targets.
doi:10.1021/jm300576q
PMCID: PMC3419493  PMID: 22780961
24.  Publication of Clinical Trials Supporting Successful New Drug Applications: A Literature Analysis 
PLoS Medicine  2008;5(9):e191.
Background
The United States (US) Food and Drug Administration (FDA) approves new drugs based on sponsor-submitted clinical trials. The publication status of these trials in the medical literature and factors associated with publication have not been evaluated. We sought to determine the proportion of trials submitted to the FDA in support of newly approved drugs that are published in biomedical journals that a typical clinician, consumer, or policy maker living in the US would reasonably search.
Methods and Findings
We conducted a cohort study of trials supporting new drugs approved between 1998 and 2000, as described in FDA medical and statistical review documents and the FDA approved drug label. We determined publication status and time from approval to full publication in the medical literature at 2 and 5 y by searching PubMed and other databases through 01 August 2006. We then evaluated trial characteristics associated with publication. We identified 909 trials supporting 90 approved drugs in the FDA reviews, of which 43% (394/909) were published. Among the subset of trials described in the FDA-approved drug label and classified as “pivotal trials” for our analysis, 76% (257/340) were published. In multivariable logistic regression for all trials 5 y postapproval, likelihood of publication correlated with statistically significant results (odds ratio [OR] 3.03, 95% confidence interval [CI] 1.78–5.17); larger sample sizes (OR 1.33 per 2-fold increase in sample size, 95% CI 1.17–1.52); and pivotal status (OR 5.31, 95% CI 3.30–8.55). In multivariable logistic regression for only the pivotal trials 5 y postapproval, likelihood of publication correlated with statistically significant results (OR 2.96, 95% CI 1.24–7.06) and larger sample sizes (OR 1.47 per 2-fold increase in sample size, 95% CI 1.15–1.88). Statistically significant results and larger sample sizes were also predictive of publication at 2 y postapproval and in multivariable Cox proportional models for all trials and the subset of pivotal trials.
Conclusions
Over half of all supporting trials for FDA-approved drugs remained unpublished ≥ 5 y after approval. Pivotal trials and trials with statistically significant results and larger sample sizes are more likely to be published. Selective reporting of trial results exists for commonly marketed drugs. Our data provide a baseline for evaluating publication bias as the new FDA Amendments Act comes into force mandating basic results reporting of clinical trials.
Ida Sim and colleagues investigate the publication status and publication bias of trials submitted to the US Food and Drug Administration (FDA) for a wide variety of approved drugs.
Editors' Summary
Background.
Before a new drug becomes available for the treatment of a specific human disease, its benefits and harms are carefully studied, first in the laboratory and in animals, and then in several types of clinical trials. In the most important of these trials—so-called “pivotal” clinical trials—the efficacy and safety of the new drug and of a standard treatment are compared by giving groups of patients the different treatments and measuring several predefined “outcomes.” These outcomes indicate whether the new drug is more effective than the standard treatment and whether it has any other effects on the patients' health and daily life. All this information is then submitted by the sponsor of the new drug (usually a pharmaceutical company) to the government body responsible for drug approval—in the US, this is the Food and Drug Administration (FDA).
Why Was This Study Done?
After a drug receives FDA approval, information about the clinical trials supporting the FDA's decision are included in the FDA “Summary Basis of Approval” and/or on the drug label. In addition, some clinical trials are described in medical journals. Ideally, all the clinical information that leads to a drug's approval should be publicly available to help clinicians make informed decisions about how to treat their patients. A full-length publication in a medical journal is the primary way that clinical trial results are communicated to the scientific community and the public. Unfortunately, drug sponsors sometimes publish the results only of trials where their drug performed well; as a consequence, trials where the drug did no better than the standard treatment or where it had unwanted side effects remain unpublished. Publication bias like this provides an inaccurate picture of a drug's efficacy and safety relative to other therapies and may lead to excessive prescribing of newer, more expensive (but not necessarily more effective) treatments. In this study, the researchers investigate whether selective trial reporting is common by evaluating the publication status of trials submitted to the FDA for a wide variety of approved drugs. They also ask which factors affect a trial's chances of publication.
What Did the Researchers Do and Find?
The researchers identified 90 drugs approved by the FDA between 1998 and 2000 by searching the FDA's Center for Drug Evaluation and Research Web site. From the Summary Basis of Approval for each drug, they identified 909 clinical trials undertaken to support these approvals. They then searched the published medical literature up to mid-2006 to determine if and when the results of each trial were published. Although 76% of the pivotal trials had appeared in medical journals, usually within 3 years of FDA approval, only 43% of all of the submitted trials had been published. Among all the trials, those with statistically significant results were nearly twice as likely to have been published as those without statistically significant results, and pivotal trials were three times more likely to have been published as nonpivotal trials, 5 years postapproval. In addition, a larger sample size increased the likelihood of publication. Having statistically significant results and larger sample sizes also increased the likelihood of publication of the pivotal trials.
What Do These Findings Mean?
Although the search methods used in this study may have missed some publications, these findings suggest that more than half the clinical trials undertaken to support drug approval remain unpublished 5 years or more after FDA approval. They also reveal selective reporting of results. For example, they show that a pivotal trial in which the new drug does no better than an old drug is less likely to be published than one where the new drug is more effective, a publication bias that could establish an inappropriately favorable record for the new drug in the medical literature. Importantly, these findings provide a baseline for monitoring the effects of the FDA Amendments Act 2007, which was introduced to improve the accuracy and completeness of drug trial reporting. Under this Act, all trials supporting FDA-approved drugs must be registered when they start, and the summary results of all the outcomes declared at trial registration as well as specific details about the trial protocol must be publicly posted within a year of drug approval on the US National Institutes of Health clinical trials site.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050191.
PLoS Medicine recently published an editorial discussing the FDA Amendment Act and what it means for medical journals: The PLoS Medicine Editors (2008) Next Stop, Don't Block the Doors: Opening Up Access to Clinical Trials Results. PLoS Med 5(7): e160
The US Food and Drug Administration provides information about drug approval in the US for consumers and for health care professionals; detailed information about the process by which drugs are approved is on the Web site of the FDA Center for Drug Evaluation and Research (in English and Spanish)
ClinicalTrials.gov provides information about the US National Institutes of Health clinical trial registry, background information about clinical trials, and a fact sheet detailing the requirements of the FDA Amendments Act 2007 for trial registration
The World Health Organization's International Clinical Trials Registry Platform is working toward international norms and standards for reporting the findings of clinical trials
doi:10.1371/journal.pmed.0050191
PMCID: PMC2553819  PMID: 18816163
25.  Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts 
PLoS Computational Biology  2009;5(7):e1000450.
The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.
Author Summary
Molecular connectivity maps between drugs and a wide range of bio-molecular entities can help researchers to study and compare the molecular therapeutic/toxicological profiles of many candidate drugs. Recent studies in this area have focused on linking drug molecules and genes in specific disease contexts using drug-perturbed gene expression experiments, which can be costly and time-consuming to derive. In this paper, we developed a computational framework to build disease-specific drug-protein connectivity maps, by mining molecular interaction networks and PubMed abstracts. Using Alzheimer's Disease (AD) as a case study, we described how drug-protein molecular connectivity maps can be constructed to overcome data coverage and noise issues inherent in automatically extracted results. We showed that this new approach outperformed both curated drug target databases and conventional text mining systems in retrieving disease-related drugs, with an overall balanced performance of sensitivity, specificity, and positive predictive values. The AD molecular connectivity map contained novel information on AD-related genes/proteins, AD candidate drugs, and protein therapeutic/toxicological profiles of all the AD candidate drugs. Bi-clustering of the molecular connectivity map revealed interesting patterns of functionally similar proteins and drugs, therefore creating new opportunities for future drug development applications.
doi:10.1371/journal.pcbi.1000450
PMCID: PMC2709445  PMID: 19649302

Results 1-25 (1080191)