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What happens when the data you need is hidden in silos, or when billions of dollars are riding on drug testing data you can’t access? How do you see a long-term view of 10 billion records to understand biological response to drugs? Researchers in the pharmaceutical industry turn to Hortonworks for advanced big data analytics on integrated translational data and to gain a holistic view of their pharmaceutical data.
Merck ha ottimizzato il rendimento dei suoi vaccini, analizzando i dati di produzione per isolare le variabili più utili nell'elaborazione di previsioni, alla ricerca di un "lotto d'oro". I leader di Merck si erano basati a lungo sui principi di lean manufacturing per aumentare i volumi e ridurre i costi, ma scoprire modi per migliorare il rendimento in maniera incrementale era diventato sempre più difficile. Per un quadro più dettagliato su come ridurre i costi e aumentare il rendimento ulteriormente, l'azienda ha preso in considerazione Open Enterprise di Hadoop. Merck si è rivolta a Hortonworks per estrarre dati dai record di oltre 255 lotti di un vaccino, risalendo fino a 10 anni prima. I dati, distribuiti in 16 sistemi preposti alla manutenzione e gestione degli stabilimenti, comprendevano i dati di precisione di sensori per la calibrazione, pressione, temperatura e umidità dell'aria. Dopo avere riunito i dati in Hortonworks Data Platform ed elaborato 15 miliardi di calcoli, Merck ha finalmente ottenuto risposta alle domande che si era posta per dieci anni. Fra centinaia di variabili, il team di Merck è riuscito a individuare quelle in grado di ottimizzare il rendimento. L'azienda ha quindi applicato lo stesso procedimento agli altri vaccini di sua produzione, concentrandosi sulla realizzazione di farmaci di qualità al minor prezzo possibile. Guarda l'intervista di Doug Henschen per InformationWeek a George Llado di Merck.
One Hortonworks pharmaceutical customer uses HDP for a single view of its supply chain and their self-declared “War on Waste”. The operations team added up the ingredients going into making their drugs, and compared that with the physical product they shipped. They found a big gap between the two and launched their War on Waste, using HDP big data analytics to identify where those valuable resources were going. Once it identifies those root causes of waste, real-time alerts in HDP notify the team when they are at risk of exceeding pre-determined thresholds.
The goal of Translational Research is to apply the results of laboratory research towards improving human health. Hadoop empowers researchers, clinicians, and analysts to unlock insights from translational data to drive evidence-based medicine programs. The data sources for translational research are complex and typically locked in data siloes, making it difficult for scientists to obtain an integrated, holistic view of their data. Other challenges revolve around data latency (the delay in getting data loaded into traditional data stores), handling unstructured and semi-structured types of data, and bridging lack of collaborative analysis between translation and clinical development groups. Researchers are turning to Open Enterprise Hadoop as a cost-effective, reliable platform for managing big data in clinical trials and performing advanced analytics on integrated translational data. HDP allows translational and clinical groups to combine key data from sources such as: Omics (genomics, proteomics, transcription profiling, etc) Preclinical data Electronic lab notebooks Clinical data warehouses Tissue imaging data Medical devices and sensors File sources (such as Excel and SAS) Medical literature Through Hadoop, analysts can build a holistic view that helps them understand biological response and molecular mechanisms for compounds or drugs. They’re also able to uncover biomarkers for use in R&D and clinical trials. Finally, they can be assured that all data will be stored forever, in its native format, for analysis with multiple future applications.
IT systems cannot economically store and process next generation sequencing (NGS) data. For example, primary sequencing results are in large image format and are too costly to store over the long term. Point solutions have lacked the flexibility to keep up with changing analytical methodologies, and are often expensive to customize and maintain. Open Enterprise Hadoop overcomes those challenges by helping data scientists and researchers unlock insights from NGS data while preserving the raw results on a reliable, cost-effective platform. NGS scientists are discovering the benefits of large-scale processing and analysis delivered by HDP components such as Apache Spark. Pharmaceutical researchers are using Hadoop to easily ingest diverse data types from external sources of genetic data, such as TCGA , GENBank , and EMBL. Another clear advantage of HDP for NGS is that researchers have access to cutting-edge bioinformatics tools built specifically for Hadoop. These enable analysis of various NGS data formats, sorting of reads, and merging of results. This takes NGS to the next level through: Batch processing of large NGS data sets Integration of internal with publically available external sequence data Permanent data storage for large image files, in their native format Substantial cost savings on data processing and storage.
Real-World Evidence (RWE) promises to quantify improvements to health outcomes and treatments, but this data must be available at scale. High data storage and processing costs, challenges with merging structured and unstructured data, and an over-reliance on informatics resources for analysis-ready data have all slowed the evolution of RWE. With Hadoop, RWE groups are combining key data sources, including claims, prescriptions, electronic medical records, HIE, and social media, to obtain a full view of RWE. With big data analytics in the pharmaceutical industry, analysts are unlocking real insights and delivering advanced insights via cost-effective and familiar tools such as SAS® ,R®, TIBCO™ Spotfire®, or Tableau®. RWE through Hadoop delivers value with optimal health resource utilization across different patient cohorts, a holistic view of cost/quality tradeoffs, analysis of treatment pathways, competitive pricing studies, concomitant medication analysis, clinical trial targeting based on geographic & demographic prevalence of disease, prioritization of pipelined drug candidates, metrics for performance-based pricing contracts, drug adherence studies, and permanent data storage for compliance audits.
HDP Uses Real-World Data to Deliver Real-World Evidence
Real-World Evidence (RWE) promises to quantify improvements to health outcomes and treatments, but this data must be available at scale. High data storage and processing costs, challenges with merging structured and unstructured data, and an over-reliance on informatics resources for analysis-ready data have all slowed the evolution of RWE. With Hadoop, RWE groups are combining key data sources, including claims, prescriptions, electronic medical records, HIE, and social media, to obtain a full view of RWE. Analysts are unlocking real insights and delivering advanced analytic insights via cost-effective and familiar tools such as SAS:registered: ,R:registered:, TIBCO:tm: Spotfire:registered:, or Tableau:registered:. RWE through Hadoop delivers value with optimal health resource utilization across different patient cohorts, a holistic view of cost/quality tradeoffs, analysis of treatment pathways, competitive pricing studies, concomitant medication analysis, clinical trial targeting based on geographic & demographic prevalence of disease, prioritization of pipelined drug candidates, metrics for performance-based pricing contracts, drug adherence studies, and permanent data storage for compliance audits.
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