To remain competitive, Pharmaceutical companies need to be more information-driven and are turning to enterprise search technologies to break down data silos. Such technologies enable companies to make faster, more informed decisions by identifying relevant information from the wealth of new and historical data, both from within their organisation and from public and subscription sources.
Enterprise search platforms provide the scalable, high performance infrastructure to enable secure access to millions of documents from across the whole organisation and deliver content analytics from a single portal. However, users can typically only search for exactly what was written by the author of a document. The inconsistent use of synonyms during data entry makes it difficult to identify and collate all relevant data for a disease or target of interest.
At SciBite we understand the complexity of science. We bring scientific understanding to any enterprise search technology by utilising world class ontologies to semantically enrich and contextualise content, opening up new possibilities to mine the data more effectively and derive valuable insights.
Pharmaceutical companies have a wealth of data at their disposal. However, the volume of data combined with limited availability of experts and data scientists constrains their ability to use it effectively.
Artificial Intelligence (AI) has been touted as a way to revolutionise the entire Pharmaceutical value chain, to the extent that it will deliver a ‘cure for cancer’.
Despite such promises, tangible evidence of how AI is actually helping research remains elusive. Coupled with some high profile failures, there is growing scepticism of what AI can realistically achieve.
One of the more promising applications of AI is Machine Learning: the development of a computational model followed by ‘training’ using sample datasets resulting in decisions or predictions and the inclusion of a feedback loop to refine the approach based on whether a given decision is right or wrong.
However, industry optimism is still tempered with caution – Gartner’s Hype Cycle positions Machine Learning at the ‘peak of inflated expectations’, about to enter the ‘Trough of Disillusionment’.
Looking past the hyperbole and future promises of AI, here we provide a range of real-world examples that illustrate how SciBite is pioneering the use of Machine Learning and Semantic Analytics to transform common scientific and business processes and delivering real business value.
Pharmacovigilance can take up an enormous amount of resource.
Incorporating semantic analytics into a pharmacovigilance strategy allows teams the flexibility to include wider sources of reporting systems, contextualise unstructured text and make connections across all of this, saving time and gaining total awareness.
This whitepaper describes how Semantic Analytics enables an integrated approach to Pharmacovigilance, unlocks the potential of biomedical content and will enable pharmaceutical companies to:
Regulatory bodies expect Pharmaceutical companies to maintain an up-to-date awareness of the safety implications of not only their own drugs but also those from the same drug class and with the same target that are marketed by competitors.
This places significant demands on Pharmacovigilance teams, who are challenged to maintain safety and compliance with the same, or fewer, resources amid increasingly stringent, globally diverse regulations.
The exponentially growing volumes and diversity of data make it almost impossible to maintain a comprehensive and up-to-date understanding. The result is that the legacy approach, involving manually scanning biomedical sources, is prohibitively time consuming, has a high risk of missing safety signals and is no longer a viable option.
SciBite provides a resource-effective solution to the challenges faced by Pharmacovigilance teams, enabling them to efficiently and comprehensively monitor a wide range of heterogeneous and cross-disciplinary sources and be fully aware of all safety signals directly and indirectly associated with one or more drugs of interest.
One of the most valuable assets for any organisation is its data. However, most pharmaceutical companies are unable to realise its true value as a result of either i) deploying a data management system that is geared towards entering rather than mining data and/or ii) replacing such systems over time, resulting in silos of legacy data.
The way in which an organisation captures and manages its data is fundamental to addressing this problem. A wider scientific community initiative has resulted in the establishment of the FAIR principles1 to ensure that data is Findable, Accessible, Interoperable and Reusable. Although initially focused on the accessibility of public domain data, the FAIR principles are rapidly gaining interest from the pharmaceutical industry2.
The benefits of FAIR can be illustrated using the example of bioassay data management. A significant proportion of the pre-clinical data that has been accumulated by every pharmaceutical company is a result of conducting a range of biological assays to characterise drug targets and evaluate potential therapeutic molecules. Databases dedicated to managing bioassay data contain an amazing wealth of R&D knowledge and, as such, provide a rich resource for mining with both scientific and operational questions.
The identification and application of biomarkers in basic and clinical research is almost a mandatory process in any productive pipeline of a biopharmaceutical organisation.
Validated biomarkers play a crucial role in the prediction of clinical outcome, support the translation from candidate discovery to successful clinical treatment.
The process to discover and validate new biomarkers depends on effective methodologies often calling on text mining approaches to extract insight from biomedical literature.
The following white paper evaluates SciBite’s capabilities in identifying new gene biomarkers in Breast Cancer against a published methodology.
Given the wealth of information available in biomedical literature, an important thing is to be knowledgeable of all the existing biomarkers and also other biomolecules that may be suitable as new biomarkers.
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