Pharmaceutical research has a wealth of available data sources to help elucidate the complex biological mechan- isms that lead to the development of diseases. However, the heterogeneous nature of these data and their wide- spread distribution over journal articles, patents and numerous databases makes searching and pattern discov- ery a tedious and manual task. From the perspective of a pharmaceutical research scientist, the ideal data infra- structure should make it easy to link and search across open data sources in order to identify novel and mean- ingful correlations and mechanisms. In this paper, we present work from the Linked Open DrugData (LODD) task force of the World Wide Web Consortium (W3C) Health Care and Life Science Interest Group (HCLS IG) that aims to address these issues by harnessing the power of new web technologies.
The third step is focused on transforming the source data into RDF, creating links and 5-star Linked Data datasets, and creating metadata descriptions of the dataset and its links to other datasets. All four methodologies define these tasks, with the LOD2 methodology being the most specific one. The LOD2 methodology, being the lat- est one, understandably contains activities which include classification, quality control, data evolution and version- ing. The use of the VoID vocabulary is explicitly stated in this phase in the methodologies of Hyland et al. and Hausenblas et al. The methodology of Villazón-Terrazas et al. defines this task in its next step, ‘4. Publish’, but as it contains other tasks which better fit in our next step, we left it out of this one. Here, we developed an OpenRefine transformation script which can be used with any source drugdata formatted with the CSV template from the previous step, in order to get high quality, 5-star Linked DrugData. For the purpose of generating addi- tional links between similar drugs in the dataset itself, we developed a SPARQL-based tool which can be used over any Linked Drug Dataset generated with the OpenRefine transformation script.
converting the z-scores of drug-treated expression data to a matrix of categorical data-type whereas rows repre- sent genes and drugs correspond to columns. In this matrix, genes are categorized as differentially expressed and non-differentially expressed genes. The differentially expressed genes are labelled by 1, for up-regulated, and − 1 for down-regulated. The non-differentially expressed genes are labelled by 0. In the second step, we measure the overlapping score between pairs of drugs by using a JI as described in Eqn. 1. The JI gives a ratio of differentially expressed genes which are common between a pair of drug-treated data w.r.t. all other genes which are differen- tially expressed in at least one drug-treated data. In the third step, we test the significance of the Jaccard Index. We perform the significance test with a non-parametric approach by randomizing gene labels of each drugdata vec- tor independently. This allows us to estimate the sampling distribution of the null hypothesis. A schematic over- view for the construction of a DAN is shown in Fig. 1D.
We utilized RxNorm and NDF-RT for normalizing and aggregating drug information in AERS in consider- ation of three reasons. First, these two ontologies are publicly available medication ontologies that have been intensively developed and used for drugdata integration [22,24,25]. Second, RxNorm aims to enable various sys- tems using different standardized drug nomenclatures to share and exchange data efficiently, which we believe meets the requirements for meaningful use of the ADE reporting data. In addition, since RxNorm only repre- sents a nomenclature of drugs and does not contain drug categorical information, we leveraged the categor- ical information extracted from NDF-RT for medication data aggregation. Third, as a part of the Unified Medical Language System (UMLS), RxNorm and NDF-RT can function as interoperable drug standards that can inte- grate with other health data, such as electronic health records (EHRs), so as to facilitate the semantic integra- tion of the data in the health domain.
The purpose of this paper is to study the G-CSF (Granulocyte-Colony Stimulating Factor) treatment of a simulations and data fit mathematical modeling of CN (Cyclical Neutropenia) with neutrophil count. This model is useful to account for the features of untreated G-CSF. It is also useful for treatment of dogs with CN. Therefore this model is considered as an accomplished one. There is fitting parameters for 3 days and not for 4 dogs for estimation or evaluation. It is also essential and necessary to model the more samples for increase in Neutrophil amplification. The proposed interventions are practical. It may reduce the amount of G-CSF. It required potential maintenance. Sometimes, it may even improve the treatment effects too. This model gives us good result in treatment. The changes would be practical and reduce the risk side as well as the cost of treatment in G-CSF. By using a four GC’s (grey collies) and ANC (Absolute Neutrophil Count), we establish some new sufficient parameters which ensure that every solution of this mathematical modeling for disease level decreases to maximum.
Drug similarity based on 3D structure was integrated into the target ChEMBL data through a model that generates all possible drug-target combinations with an associated scoring (3D score). The model compares for each drug the similarity against the set of drugs known to bind each target. If the same drug-target combination is generated in repeated occasions with different scores, i.e., from the comparison of different drug pairs, only the maximum score is retained and the “origin” (drug known to interact with the target and data about potency and assay type) is associated as additional information to the drug-target candidate. In this way each drug-target candidate has associated the maximum similarity score against drugs interacting with the same target in ChEMBL. Out of all the possible drug-target combinations that the predic- tor generates, some combinations are already found in ChEMBL (positive cases) whereas the other combina- tions are new associations. ROC curves, precision and enrichment factor (EF) against random results were pro- vided to assess the quality of the predictor:
In 2002, international  and US guidelines  pro- posed the combination of a non-tricyclic antidepressant (SSRI or bupropion) with mood stabilizer (lithium or lamotrigine) as a treatment option. In accordance with these recommendations and with modern guidelines, that in bipolar depression antidepressants should be pre- scribed in combination with mood stabilizing and anti- manic drugs only we observed that antidepressants are combined mainly with lithium, valproic acid, quetiapine and lamotrigine. However, the evidence based combin- ation recommended by the guidelines, olanzapine plus fluoxetine (OFC), was found to be prescribed only in very few patients (cf. ). Bupropion, listed in inter- national guidelines as an antidepressant specifically recommended for bipolar depression, is administered very rarely. Moreover, antidepressants with a high poten- tial for pharmacokinetic interactions, i.e. paroxetine, flu- oxetine and fluvoxamine, are not used within the usual combinations. Hence, critical drug-drug interactions are avoided despite increasing polypharmacy (cf. ). A trend to polypharmacy has already been described in the treatment of bipolar disorder generally [17-19], a sys- tematic description of this trend for the treatment of bi- polar depression is - to our knowledge - given for the first time in our previous  and present analysis.
In this paper, we described a brand new graph similarity measure, community primarily based graph similarity, and proposed an statistics propagation version to transform a big network into a hard and fast of multidimensional vectors, where state-of-the-art indexing and similarity seek algorithms are available. We proved, beneath this degree, that subgraph similarity seek is NP tough, whilst graph similarity fit is polynomial. We delivered a criterion to choose the best propagation price with respect to extraordinary node labels in a graph. We in addition investigated the strategies to index the community vectors and to compress them with the aid of deleting non- discriminative labels, as a consequence optimizing the query processing time. The proposed method, called Ness, isn't simplest green, but additionally robust in opposition to shape adjustments and data loss. Empirical effects display that it can quickly and as it should be discover incredible fits in massive networks, with negligible time value. In destiny paintings, it is going to be thrilling to remember the graph alignment trouble, whilst the node labels in graphs are not exactly same, i.E the same person may have slightly extraordinary usernames in Facebook and Twitter
Factors such as limited availability of free and second-line drugs in developing countries such as Yemen, a prolonged duration of treatment, high prices, treatment-associated toxicity, the lack of spe- cialists and laboratory facilities, and the practice of selling second-line TB drugs in private sectors hinder the effective characterisation of treatment outcomes of drug resistance therapy [7–11]. Because of the civil war in Yemen, conditions have recently worsened, and information regarding MDR-TB is scarce. More- over, treatment outcomes and disease management have not been explored in this country. In this study, we aimed to evaluate the risk factors associated with
drug disposition parameters, or integrating in-life study parameters with bioanalytical profiles of new drug candidates.
Our focus has progressed during the past few in three stages: from a data storage and access para- digm centred on protein targets to the development of a compound-centric view integrating early dis- covery data to the state we have entered, today, where we are focusing on the complete project and the recognition that the most valuable data for this project tends to be the late-stage study data that deal with the compilation of numerous animal tests. Additionally we need to be able to apply traceability to the data, themselves, as they are analysed, processed, translated and transcribed into study reports. Integrating across this diverse platform has definitely presented numerous chal- lenges. One basic strategy that we are taking is to spend considerable time analysing work processes and use cases to before we incorporate any addi- tional changes into our Informatics environment. We have found it imperative that the user be a key participant in the design of any solution and a sig- nificant investment in time is taken to work through an iterative process of system deployment. Since we have already built out a number of sub- stantial platforms, the most cost-effective approach has been to leverage against the present state as we decommission old, rebuild active appli- cations where new features are truly needed, and deploy selected new systems (from completely home-grown to completely commercial ‘out-of- the-box’ solutions) where completely new solu- tions are required.
these four species, 7043 orthologous genes are ~28%, which means the other ~72% expressed non- orthologous proteins in these four species are very different in their protein sequences. Even if humans and chimpanzees are considered as the closest primate relatives in the animal kingdom, only 13 454 pairs of orthologue genes are iden- tified consisting ~50% of their own expressed genes, which means the other ~50% expressed non- orthologous proteins are very different in their amino acid sequences. Taken together this above genetic evidence, it is very clear that if the drug target of the animal model is structur- ally different from the one of human, drugs targeting the animal protein will perform a significantly different effect between animal experiment and clinical experiment. The interaction between drug and its target is caused by hydrogen bonds, Van der Waals force and π-π interac- tion, which are exerting their interactive forces within less than 4 Angstrom. One or two amino acid mutations within the binding pocket of the drug target can make a big difference.
This pack sets out the investment in drug treatment in your area. It also gives key performance information about your treatment system and national data for comparison. It presents data from the National Drug Treatment Monitoring System (NDTMS), the Treatment Outcomes Profile (TOP), the Drug Interventions Programme (DIP) and estimates of the prevalence of opiate and/or crack cocaine use. Although drug treatment services treat dependence for all drugs, heroin users remain the group with most complex problems, so separate data is provided for them.
be true and can be considered as drug repositioning candidates in the real-world drug discovery.
Of 4066 found drug-disease associations in ClinicalTrials.gov (not
included in the training set), our FP associations cover 21%. Therefore, our predictions statistically overlap drug-disease
First, we compared combined data for all compounds for the purpose of identifying the sensitivity metric that provides the best agreement between databases and, therefore, is the most reproducible quantitative assessment of the drug sensitivity for the pooled pharmacogenomic analysis. IC 50 , EC 50 and unadjusted AUC drug sensitivity metrics produced mild to moderate agreement between the pharmacogenomic databases (Figure 1C, Supplementary Table S5, Supplementary Figures S2-S4, QAPC portal). When adjusted for the range of drug concentrations tested, CCLE drug sensitivity data agreed very well with that of CTRP (Pearson correlation, r, for adjusted AUC IC50 = 0.82), and moderately well with the pharmacologic data from GDSC (r for adjusted AUC IC50 = 0.69). An improvement in the agreement between drug sensitivities measured with adjusted AUC is particularly noticeable when CTRP data is included, which is expected, because the CTRP tested highest maximal drug concentration among 3 studies (Supplementary Table S1) that skews the CTRP unadjusted AUC data towards the higher values. Both CCLE and CTRP projects were performed by the Broad Institute and used the same proliferation assay (CellTiterGlo), which likely contributed to the good reproducibility between these two studies. The correlation between CTRP and GDSC data was moderate (r for adjusted AUC IC50 = 0.65). Flexible curve modeling (EC 50 , AUC EC50 ) did not provide an additional advantage (Figure 1C).
To evaluate our method, we trained DDI-induced ADR models by using different input sets to examine the im- pact of varying the training set composition. This is im- portant because DDI-induced ADR information is noisy, given that it is generated from patient and physician re- ports instead of being derived from carefully designed clinical trials. False positives are unavoidable and true negatives of high confidence are rare. A report of an ADR induced by DDIs between two drugs indicates that the drugs were co-administered and likely induced the ADR via a DDI. In contrast, the absence of a report of an ADR attributed to a drug pair may mean that no DDI exists for the ADR (a true negative); the ADR requires some time to develop or be recognized under co- administration conditions (a false negative); or the two drugs have yet to be co-administered (an unknown). Thus, strictly speaking, there are few if any true negative samples for training and evaluating the models. To overcome this issue, some studies have developed and evaluated DDI prediction models by using reported DDI-inducing drug pairs as positives and an equal num- ber of randomly made-up drug pairs as negatives [8, 9, 11]. In this study, we trained each ADR model by using drug pairs positive for the ADR as positive samples and all other drug pairs in the TWOSIDES database as puta- tive negatives. We designated the putative negatives as “ baseline samples ” to recognize that they may contain heretofore unknown positives. Thus, the resulting models were aimed at discriminating positive samples from the background baseline samples. The premise underlying this approach is that the number of drug
The massive growth of high-throughput data demands new models to better understand and interpret high-dimensional data and to extract the key information of most clinical and biological relevance. To meet some of these challenges, I have developed three novel models: (i) DSS, to quantify drug response, (ii) TAS, to capture the addiction signature of the cells, and (iii) the ZIP model, for scoring drug combination effects. The above models have been systematically evaluated, and demonstrated that these models were robust and perform better than existing models. All these models, i.e. DSS, TAS, and delta scoring using the ZIP model, are implemented in the R programing language and are freely distributed as open source R packages. The clinical and pharmacological relevance of the results are shown in both cancer cell lines and patients samples. The methods will significantly contribute to the effective analysis of high-throughput data from cancer cell lines and patient-derived samples, promote better understanding of cancer progression and develop individualized treatment options.
The incomplete or total lack of reporting of drug trials is so common that our perceptions of the true bene- fits and harms of drugs are generally much too positive [5,6,9,12,13]. There have been prominent cases of well known drugs, such as gabapentin, oseltamivir, and rofe- coxib where the analysis of unpublished data revealed important insights about the benefits and harms of those drugs not previously identified in their initial publica- tions .Therefore, it is critical that systematic reviews of drugs, which are often used as the basis for clinical practice guidelines, identify and include unpublished data from drug trials. The Cochrane Collaboration is a major producer of rigorous systematic reviews of health care in- terventions, but only 12% of Cochrane reviews from 2000 to 2006 included unpublished trials . The Cochrane Handbook for Systematic Reviews of Interventions suggests identifying unpublished data by contacting experts, pharmaceutical companies, and national and international trial registers . No specific guidance for searching for drug trials is provided and specific sources of drug trial data such as regulatory agencies or drug com- pany archives resulting from legal settlements are not mentioned. In addition, little advice is provided in The Cochrane Handbook or elsewhere about strategies for obtaining the data from different sources.
Critical process parameter (CPP) affects CQA and these parameters or variables should be studied based on risk assessment and statistically designed experiment. Types of risk assessment tool are described in ICH Q9. Initial risk assessment can be performed to study the impact of unit operation to CQAs. Initial list of potential critical parameters can be quite extensive, but this can be refined through experimentation. Conventional approach to study effect of process parameter one-factor-at-a-time should not be considered in QbD approach. Instead, DoE should be performed to screen potential critical parameters with reduced number of experimentation. Once CPPs are identified, more detailed DoE study usually at pilot scale can be performed to gain higher level of process understanding and to establish control strategy. A range of process scales building towards commercial scale can be proposed based on prior knowledge or empirical experiment data. Thereafter, the effect of scale up for each of the unit operation should also be studied or discussed. Scale-up factor can be used for some equipment if properly justified. Once adequate product and process understanding are established at lab and pilot scale, the next step is to transfer this knowledge to the actual manufacturing site. Manufacturing at commercial scale may be significantly different from small scale processing. In fact, some aspects of manufacturing process can only be studied at commercial scale. Effective technology transfer to commercial scale is a critical step to the future process validation and routine manufacturing. Manufacturer may conduct partial scale process to provide more assurance of capabilities at full scale. Thereafter, validation of conformance lots can then commence to confirm the success of QbD development and scale-up.