Drug repositioning for orphan diseases

Structural analysis of protein-ligand interactions: the binding of endogenous compounds and of synthetic drugs

The large number of macromolecular structures deposited with the Protein Data Bank (PDB) describing complexes between proteins and either physiological compounds or synthetic drugs made it possible a systematic analysis of the interactions occurring between proteins and their ligands. In this work, the binding pockets of about 4000 PDB protein-ligand complexes were investigated and amino acid and interaction types were analyzed. The residues observed with lowest frequency in protein sequences, Trp, His, Met, Tyr, and Phe, turned out to be the most abundant in binding pockets. Significant differences between drug-like and physiological compounds were found. On average, physiological compounds establish with respect to drugs about twice as many hydrogen bonds with protein atoms, whereas drugs rely more on hydrophobic interactions to establish target selectivity. The large number of PDB structures describing homologous proteins in complex with the same ligand made it possible to analyze the conservation of binding pocket residues among homologous protein structures bound to the same ligand, showing that Gly, Glu, Arg, Asp, His, and Thr are more conserved than other amino acids. Also in the cases in which the same ligand is bound to unrelated proteins, the binding pockets showed significant conservation in the residue types. In this case, the probability of co-occurrence of the same amino acid type in the binding pockets could be up to thirteen times higher than that expected on a random basis. The trends identified in this study may provide an useful guideline in the process of drug design and lead optimization.

A phenome-guided drug repositioning through a latent variable model

Background

The phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution.

Results

We developed our model on the phenome information in Side Effect Resource (SIDER). We first developed a LDA model optimized based on its recovery potential through perturbing the drug-phenotype matrix for each of the drug-indication pairs where each drug-indication relationship was switched to “unknown” one at the time and then recovered based on the remaining drug-phenotype pairs. Of the probabilistically significant pairs, 70% was successfully recovered. Next, we applied the model on the whole phenome to narrow down repositioning candidates and suggest alternative indications. We were able to retrieve approved indications of 6 drugs whose indications were not listed in SIDER. For 908 drugs that were present with their indication information, our model suggested alternative treatment options for further investigations. Several of the suggested new uses can be supported with information from the scientific literature.

Conclusions

The results demonstrated that the phenome can be further analyzed by a generative model, which can discover probabilistic associations between drugs and therapeutic uses. In this regard, LDA serves as an enrichment tool to explore new uses of existing drugs by narrowing down the search space.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-267) contains supplementary material, which is available to authorized users.

The influence of the European paediatric regulation on marketing authorisation of orphan drugs for children

Background

Drug development for rare diseases is challenging, especially when these orphan drugs (OD) are intended for children. In 2007 the EU Paediatric Drug Regulation was enacted to improve the development of high quality and ethically researched medicines for children through the establishment of Paediatric Investigation Plans (PIPs). The effect of the EU Paediatric Drug Regulation on the marketing authorisation (MA) of drugs for children with rare diseases was studied.

Methods

Data on all designated orphan drugs, their indication, MA, PIPs and indication group (adult or child) were obtained from the European Medicines Agency (EMA). The outcome and duration of the process from orphan drug designation (ODD) to MA, was compared, per indication, by age group. The effect of the Paediatric Drug Regulation, implemented in 2007, on the application process was assessed with survival analysis.

Results

Eighty-one orphan drugs obtained MA since 2000 and half are authorised for (a subgroup of) children; another 34 are currently undergoing further investigations in children through agreed PIPs. The Paediatric Drug Regulation did not significantly increase the number of ODDs with potential paediatric indications (58% before vs 64% after 2007 of ODDs, p = 0.1) and did not lead to more MAs for ODs with paediatric indications (60% vs 43%, p = 0.22). ODs authorised after 2007 had a longer time to MA than those authorised before 2007 (Hazard ratio (95% CI) 2.80 (1.84-4.28), p < 0.001); potential paediatric use did not influence the time to MA (Hazard ratio (95% CI) 1.14 (0.77-1.70), p = 0.52).

Conclusions

The EU Paediatric Drug Regulation had a minor impact on development and availability of ODs for children, was associated with a longer time to MA, but ensured the further paediatric development of drugs still off-label to children. The impact of the Paediatric Drug Regulation on research quantity and quality in children through PIPs is not yet clear.

An Overview of the Orphan Medicines Market in Turkey

BACKGROUND

The health policies of many countries and regions have already defined orphan drugs for rare diseases. Although there is no official definition of orphan drugs in Turkey, all orphan drugs are covered by reimbursement, regardless of their market authorization status. Thus, a pharmacoeconomic analysis does not have to be presented to the Social Security Institution (Sosyal Güvenlik Kurumu) for reimbursement decisions on orphan drugs.

OBJECTIVE

The aim of this study was to shed light on the use of orphan drugs to aid classifications of rare diseases and assessments of orphan drugs in Turkey.

METHODS

Data for sales of authorized orphan drugs and all other drugs were extracted from the IMS Turkey for 2008, 2009, and 2010. Nonauthorized orphan drug sales data were extracted from records of the Turkish Pharmacists' Association for the same years. Government prices were obtained from the Sosyal Güvenlik Kurumu.

RESULTS

The European Medicines Agency has classified more than 60 orphan drugs for rare diseases. Of these, 50 entered the Turkish market in recent years, half of which were authorized. The remaining drugs were imported through the early access procedure. Antineoplastic agents accounted for the largest percentage of orphan drugs, with 58% of the total market share. In 2010, there were 18 such agents in use, at a cost of €120 million.

CONCLUSIONS

Although legislation is not yet in place for orphan drugs in Turkey, recognized pricing and reimbursement policies are in operation. This situation facilitates an analysis of orphan drug prices and reimbursement policies in Turkey.

Topiramate use does not reduce flares of inflammatory bowel disease

BACKGROUND

Additional medications are needed for inflammatory bowel disease (IBD) as existing therapies are incompletely effective and can be costly and toxic. Preclinical studies suggest that topiramate (an anticonvulsant) may have disease-modifying properties in IBD, but its efficacy in humans is unknown.

AIM

To evaluate whether topiramate use is associated with clinical benefit in IBD patients.

METHODS

We conducted a retrospective cohort study using administrative claims data from the MarketScan databases. Persons with IBD were identified between 2000 and 2010. New users of topiramate were compared with users of other anticonvulsant and anti-migraine medications. The primary outcome was a new prescription for an oral steroid (≥14 days). Secondary outcomes included initiation of biologic agents, abdominal surgery, and hospitalization. Cox proportional hazard modeling was used to adjust for potential confounders.

RESULTS

We identified 773 new users of topiramate and 958 users of comparator drugs. After adjusting for potential confounders, topiramate use was not associated with the primary outcome of steroid prescriptions [hazard ratio (HR) 1.14, 95 % confidence interval (CI) 0.74, 1.73]. Results did not differ significantly by IBD subtype. There was no difference between topiramate users and users of comparator drugs with respect to post-exposure initiation of biologic agents (HR 0.93, 95 % CI 0.39, 2.19), abdominal surgery (HR 1.04, 95 % CI 0.17, 6.41), or hospitalization (HR 0.86, 95 % CI 0.62, 1.19).

CONCLUSION

In this large U.S. administrative claims study, topiramate use was not associated with markers of IBD flares. These results cast doubt on whether topiramate may be an effective adjunct to current IBD therapy.

PSEUDOXANTHOMA ELASTICUM: DIAGNOSTIC FEATURES, CLASSIFICATION, AND TREATMENT OPTIONS

INTRODUCTION

Pseudoxanthoma elasticum (PXE), a multisystem orphan disease, clinically affects the skin, the eyes, and the cardiovascular system with considerable morbidity and mortality. The clinical manifestations reflect the underlying pathology consisting of ectopic mineralization of peripheral connective tissues.

AREAS COVERED

The diagnostic criteria of PXE include characteristic clinical findings, together with histopathology of accumulation of pleiomorphic elastic structures in the dermis with progressive mineralization, and the presence of mutations in the ABCC6 gene. PXE-like cutaneous changes can also be encountered in other ectopic mineralization disorders, including generalized arterial calcification of infancy (GACI) caused by mutations in the ENPP1 gene. In some cases, overlapping clinical features of PXE/GACI, associated with mutations either in ABCC6 or ENPP1, have been noted. PXE demonstrates considerable inter- and intrafamilial heterogeneity, and consequently, accurate diagnosis is required for appropriate classification with prognostic implications. There is no effective and specific treatment for the systemic manifestations of PXE, but effective therapies to counteract the ocular complications are in current clinical use.

EXPERT OPINION

A number of observations in the murine model, the Abcc6^-/- mouse, have indicated that the mineral composition of diet, particularly the magnesium content, can influence the severity of the mineralization phenotype. These observations suggest that appropriate dietary interventions, coupled with lifestyle modifications, including smoking cessation, might alleviate the symptoms and improve the quality of life of individuals affected with this, currently intractable, orphan disease.

Repositioning: the fast track to new anti-malarial medicines?

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); [(3)H] 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 Pf3D7(0087/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.

High-throughput screening for the identification of new therapeutic options for metastatic pheochromocytoma and paraganglioma

Drug repurposing or repositioning is an important part of drug discovery that has been growing in the last few years for the development of therapeutic options in oncology. We applied this paradigm in a screening of a library of about 3,800 compounds (including FDA-approved drugs and pharmacologically active compounds) employing a model of metastatic pheochromocytoma, the most common tumor of the adrenal medulla in children and adults. The collection of approved drugs was screened in quantitative mode, testing the compounds in compound-titration series (dose-response curves). Analysis of the dose-response screening data facilitated the selection of 50 molecules with potential bioactivity in pheochromocytoma cells. These drugs were classified based on molecular/cellular targets and signaling pathways affected, and selected drugs were further validated in a proliferation assay and by flow cytometric cell death analysis. Using meta-analysis information from molecular targets of the top drugs identified by our screening with gene expression data from human and murine microarrays, we identified potential drugs to be used as single drugs or in combination. An example of a combination with a synergistic effect is presented. Our study exemplifies a promising model to identify potential drugs from a group of clinically approved compounds that can more rapidly be implemented into clinical trials in patients with metastatic pheochromocytoma or paraganglioma.

Identification of levothyroxine antichagasic activity through computer-aided drug repurposing

Cruzipain (Cz) is the major cysteine protease of the protozoan Trypanosoma cruzi, etiological agent of Chagas disease. A conformation-independent classifier capable of identifying Cz inhibitors was derived from a 163-compound dataset and later applied in a virtual screening campaign on the DrugBank database, which compiles FDA-approved and investigational drugs. 54 approved drugs were selected as candidates, 3 of which were acquired and tested on Cz and T. cruzi epimastigotes proliferation. Among them, levothyroxine, traditionally used in hormone replacement therapy in patients with hypothyroidism, showed dose-dependent inhibition of Cz and antiproliferative activity on the parasite.

Computational drug repositioning through heterogeneous network clustering

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.

Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs)

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.

Computational methods for drug design and discovery: focus on China

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The leading computational techniques for drug design and discovery are reviewed.

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Successful applications of computational techniques are provided.

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A novel drug–target binding kinetics calculation approach is introduced.

In the past decades, China's computational drug design and discovery research has experienced fast development through various novel methodologies. Application of these methods spans a wide range, from drug target identification to hit discovery and lead optimization. In this review, we firstly provide an overview of China's status in this field and briefly analyze the possible reasons for this rapid advancement. The methodology development is then outlined. For each selected method, a short background precedes an assessment of the method with respect to the needs of drug discovery, and, in particular, work from China is highlighted. Furthermore, several successful applications of these methods are illustrated. Finally, we conclude with a discussion of current major challenges and future directions of the field.

Application of computer-aided drug repurposing in the search of new cruzipain inhibitors: discovery of amiodarone and bromocriptine inhibitory effects

Cruzipain (Cz) is the major cystein protease of the protozoan Trypanosoma cruzi , etiological agent of Chagas disease. From a 163 compound data set, a 2D-classifier capable of identifying Cz inhibitors was obtained and applied in a virtual screening campaign on the DrugBank database, which compiles FDA-approved and investigational drugs. Fifty-four approved drugs were selected as candidates, four of which were acquired and tested on Cz and T. cruzi epimastigotes. Among them, the antiparkinsonian and antidiabetic drug bromocriptine and the antiarrhythmic amiodarone showed dose-dependent inhibition of Cz and antiproliferative activity on the parasite.

Are GPCRs Still a Source of New Targets?

G-protein–coupled receptors (GPCRs) still offer enormous scope for new therapeutic targets. Currently marketed agents are dominated by those with activity at aminergic receptors and yet they account for only ~10% of the family. Progress up until now with other subfamilies, notably orphans, Family A/peptide, Family A/lipid, Family B, Family C, and Family F, has been, at best, patchy. This may be attributable to the heterogeneous nature of GPCRs, their endogenous ligands, and consequently their binding sites. Our appreciation of receptor similarity has arguably been too simplistic, and screening collections have not necessarily been well suited to identifying leads in new areas. Despite the relative shortage of high-quality tool molecules in a number of cases, there is an emerging, and increasingly substantial, body of evidence associating many as yet “undrugged” receptors with a very wide range of diseases. Significant advances in our understanding of receptor pharmacology and technical advances in screening, protein X-ray crystallography, and ligand design methods are paving the way for new successes in the area. Exploitation of allosteric mechanisms; alternative signaling pathways such as G12/13, Gβγ, and β-arrestin; the discovery of “biased” ligands; and the emergence of GPCR-protein complexes as potential drug targets offer scope for new and much improved drugs.

Open-source approaches for the repurposing of existing or failed candidate drugs: learning from and applying the lessons across diseases

Repurposing has the objective of targeting existing drugs and failed, abandoned, or yet-to-be-pursued clinical candidates to new disease areas. The open-source model permits for the sharing of data, resources, compounds, clinical molecules, small libraries, and screening platforms to cost-effectively advance old drugs and/or candidates into clinical re-development. Clearly, at the core of drug-repurposing activities is collaboration, in many cases progressing beyond the open sharing of resources, technology, and intellectual property, to the sharing of facilities and joint program development to foster drug-repurposing human-capacity development. A variety of initiatives under way for drug repurposing, including those targeting rare and neglected diseases, are discussed in this review and provide insight into the stakeholders engaged in drug-repurposing discovery, the models of collaboration used, the intellectual property-management policies crafted, and human capacity developed. In the case of neglected tropical diseases, it is suggested that the development of human capital be a central aspect of drug-repurposing programs. Open-source models can support human-capital development through collaborative data generation, open compound access, open and collaborative screening, preclinical and possibly clinical studies. Given the urgency of drug development for neglected tropical diseases, the review suggests elements from current repurposing programs be extended to the neglected tropical diseases arena.

Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds

OBJECTIVE

To identify potential opportunities for drug repurposing by developing an automated approach to pre-screen the predicted proteomes of any organism against databases of known drug targets using only freely available resources.

MATERIALS AND METHODS

We employed a combination of Ruby scripts that leverage data from the DrugBank and ChEMBL databases, MySQL, and BLAST to predict potential drugs and their targets from 13 published genomes. Results from a previous cell-based screen to identify inhibitors of Cryptosporidium parvum growth were used to validate our in-silico prediction method.

RESULTS

In-vitro validation of these results, using a cell-based C parvum growth assay, showed that the predicted inhibitors were significantly more likely than expected by chance to have confirmed activity, with 8.9-15.6% of predicted inhibitors confirmed depending on the drug target database used. This method was then used to predict inhibitors for the following 13 disease-causing protozoan parasites, including: C parvum, Entamoeba histolytica, Giardia intestinalis, Leishmania braziliensis, Leishmania donovani, Leishmania major, Naegleria gruberi (in proxy of Naegleria fowleri), Plasmodium falciparum, Plasmodium vivax, Toxoplasma gondii, Trichomonas vaginalis, Trypanosoma brucei and Trypanosoma cruzi.

CONCLUSIONS

Although proteome-wide screens for drug targets have disadvantages, in-silico methods can be developed that are fast, broad, inexpensive, and effective. In-vitro validation of our results for C parvum indicate that the method presented here can be used to construct a library for more directed small molecule screening, or pipelined into structural modeling and docking programs to facilitate target-based drug development.

Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity

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.

Network-based drug repositioning

Network-based computational biology, with the emphasis on biomolecular interactions and omics-data integration, has had success in drug development and created new directions such as drug repositioning and drug combination. Drug repositioning, i.e., revealing a drug's new roles, is increasingly attracting much attention from the pharmaceutical community to tackle the problems of high failure rate and long-term development in drug discovery. While drug combination or drug cocktails, i.e., combining multiple drugs against diseases, mainly aims to alleviate the problems of the recurrent emergence of drug resistance and also reveal their synergistic effects. In this paper, we unify the two topics to reveal new roles of drug interactions from a network perspective by treating drug combination as another form of drug repositioning. In particular, first, we emphasize that rationally repositioning drugs in the large scale is driven by the accumulation of various high-throughput genome-wide data. These data can be utilized to capture the interplay among targets and biological molecules, uncover the resulting network structures, and further bridge molecular profiles and phenotypes. This motivates many network-based computational methods on these topics. Second, we organize these existing methods into two categories, i.e., single drug repositioning and drug combination, and further depict their main features by three data sources. Finally, we discuss the merits and shortcomings of these methods and pinpoint some future topics in this promising field.

Investigating drug repositioning opportunities in FDA drug labels through topic modeling

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.

In silico drug repositioning: what we need to know

Drug repositioning, exemplified by sildenafil and thalidomide, is a promising way to explore alternative indications for existing drugs. Recent research has shown that bioinformatics-based approaches have the potential to offer systematic insights into the complex relationships among drugs, targets and diseases necessary for successful repositioning. In this article, we propose the key bioinformatics steps essential for discovering valuable repositioning methods. The proposed steps (repurposing with a purpose, repurposing with a strategy and repurposing with confidence) are aimed at providing a repurposing pipeline, with particular focus on the proposed Drugs of New Indications (DNI) database, which can be used alongside currently available resources to improve in silico drug repositioning.

Drug repurposing in pediatrics and pediatric hematology oncology

Drug 'repurposing', that is, using old drugs for new indications, has been proposed as a more efficient strategy for drug development than the current standard of beginning with novel agents. In this review, we explore the scope of drug repurposing in pediatric hematology oncology and in pediatrics in general. Drugs commonly used in children were identified using the Harriet Lane Handbook (HLH) and searched in PubMed for different uses. Additional drugs were identified by searching PubMed and Google.com for 'drug repurposing' or 'drug repositioning'. Almost 10% of drugs with primary uses in pediatrics have been repurposed in pediatric hematology oncology or pediatrics. The observant clinician, pharmacologist and translational bioinformatician, as well as structural targeting, will have a role in discovering new repurposing opportunities.

A synopsis on the role of tyrosine hydroxylase in Parkinson's disease

Parkinson's disease (PD) is a common chronic progressive neurodegenerative disorder in elderly people. A consistent neurochemical abnormality in PD is degeneration of dopaminergic neurons in substantia nigra pars compacta, leading to a reduction of striatal dopamine (DA) levels. As tyrosine hydroxylase (TH) catalyses the formation of L-dihydroxyphenylalanine (L-DOPA), the rate-limiting step in the biosynthesis of DA, the disease can be considered as a TH-deficiency syndrome of the striatum. Problems related to PD usually build up when vesicular storage of DA is altered by the presence of either α-synuclein protofibrils or oxidative stress. Phosphorylation of three physiologically-regulated specific sites of N-terminal domain of TH is vital in regulating its kinetic and protein interaction. The concept of physiological significance of TH isoforms is another interesting aspect to be explored further for a comprehensive understanding of its role in PD. Thus, a logical and efficient strategy for PD treatment is based on correcting or bypassing the enzyme deficiency by the treatment with L-DOPA, DA agonists, inhibitors of DA metabolism or brain grafts with cells expressing a high level of TH. Neurotrophic factors are also attracting the attention of neuroscientists because they provide the essential neuroprotective and neurorestorative properties to the nigrostriatal DA system. PPAR-γ, a key regulator of immune responses, is likewise a promising target for the treatment of PD, which can be achieved by the use of agonists with the potential to impact the expression of pro- and anti-inflammatory cytokines at the transcriptional level in immune cells via expression of TH. Herein, we review the primary biochemical and pathological features of PD, and describe both classical and developing approaches aimed to ameliorate disease symptoms and its progression.

Drug repositioning for personalized medicine

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.

A novel method of transcriptional response analysis to facilitate drug repositioning for cancer therapy

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. In addition, 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, because of the lack of systematic methods to define drug off-target effects (OTE) 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 (CSB) and integrated it with a Bayesian factor regression model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof-of-concept studies were conducted in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to more than 90% of drugs approved by the U.S. Food and Drug Administration and more than 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 retinoblastoma-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.

Systematic drug repositioning based on clinical side-effects

Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this “clinical phenotypic assay”.