Patent information professionals gathered in sunny San Francisco for the 2015 PIUG Biotechnology Conference on February 16–18th.

The conference, hosted at Genentech, offered a mix of workshops, presentations, vendor exhibitions and networking opportunities that brought together patent searchers from diverse biotechnology organizations.

The central theme of this year’s conference was “Maximizing Value in Biotechnology Searching with New Technologies and Trends”. Delegates were eager to enhance existing search strategies which included a mix of content provider search tools, keyword search, in-house developed programming/machine learning, manual curation outsourcing and, for many, Linguamatics I2E.

They all had one thing in common, everyone was interested in finding new trends, techniques and technologies that would help them return more relevant patent information more efficiently.

The conference started on the first day with a series of workshops. David Milward, our CTO, delivered a workshop on new developments in text mining patents.

The workshop included an overview of updates to our text mining platform I2E, to allow easier embedding and automation, multilingual processing, improved visualization and simpler extraction of information from tables – all of which resonated well with this year’s theme.

I read with interest a recent publication which sheds light on the complex interactions of synapse protein complexes with human disease.

The study (run by the Genes to Cognition neuroscience research programme) combined wet-lab research with bioinformatics and text analytics to uncover genetic associations with these protein complexes in over seventy human brain diseases, including Alzheimer’s Disease, Schizophrenia and Autism spectrum disorders.

The idea was to identify and develop suitable screening assays for synapse proteomes from post-mortem and neurosurgical brain samples, focusing specifically on Membrane-associated guanylate kinase (MAGUK) associated signalling complexes (MASC).

Our CTO, David Milward was involved in the text analytics work. He used the natural language processing capabilities of Linguamatics I2E platform to extract gene-mutation-disease associations from PubMed abstracts. The flexibility of I2E enabled an appropriate balance of recall and precision, thus providing comprehensive results while not overloading curators with noise. Queries were built using linguistic patterns to allow associations to be discovered between a list of several thousand relevant gene identifiers, and appropriate MedDRA disease terms.

The key aim was to provide comprehensive results with suitable accuracy to allow fast curation. These text-mined results were combined with data from Online Mendelian Inheritance in Man (OMIM) on human MASC genes and genetic disease associations.

What challenges were seen in competitive R&D and clinical stages? What outcomes were measured in related trials? Does the drug I am creating have potential efficacy or safety challenges? What does the patient population look like?

These are the sort of critical business questions that many life science researchers need to answer. And now, there’s a solution that can help you.

We all know the importance of high quality content you can depend on when it comes to making key business decisions across the pharma life cycle. We also know that the best way to get from textual data to new insights is using natural language processing-based text analytics. And that’s where our partnership with Thomson Reuters comes in. We’ve worked together on a solution to bring Linguamatics market-leading text mining platform, I2E, together with Thomson Reuters Cortellis high-quality clinical and epidemiology content: Cortellis Informatics Clinical Text Analytics for I2E.

Cortellis Informatics Clinical Text Analytics for I2E applies the power of natural language processing-based text mining from Linguamatics I2E to Cortellis clinical and epidemiology content sets. Taking this approach allows users to rapidly extract relevant information using the advanced search capabilities of I2E. The solution also allows users to identify concepts using a rich set of combined vocabularies from Thomson Reuters and Linguamatics.

The 2014 Ebola outbreak is officially the deadliest in history. Governments and organizations are searching for ways to halt the spread – both responding with humanitarian help, and looking for treatments to prevent or cure the viral infection. 

Ebola virus disease (or Ebola haemorrhagic fever) is caused by the Ebola filovirus 

A couple of weeks ago we received a tweet from Chris Southan, who has been looking at crowdsourcing anti-Ebola medicinal chemistry. He asked us to mine Ebola C07D patents (i.e. those for heterocyclic small molecules, the standard chemistry for most drugs) using our text analytics tool I2E, and provide him with the resulting chemical structures.

We wanted to help. What anti-Ebola research has been patented, that might provide value to the scientific community? Searching patents for chemistry using an automated approach is notoriously tricky; patent documents are long, and often purposefully obfuscated with chemicals frequently being obscured by the complex language used to described them or corrupted by OCR errors and destroyed by the overall poor formatting of the patents.

Since the human genome was published in 2001, we have been talking about the potential application of this knowledge to personalized medicine, and in the last couple of years, we seem at last to be approaching this goal.

A better understanding of the molecular basis of diseases is key to development of personalized medicine across pharmaceutical R&D, as was discussed last year by Janet Woodcock, Director of the FDA’s Center for Drug Evaluation and Research (CDER).

FDA CDER has been urging adoption of pharmacogenomics strategies and pursuit of targeted therapies for a variety of reasons. These include the potential for decreasing the variability of response, improving safety, and increasing the size of treatment effect, by stratifying patient populations.

Pharmacogenomics is the study of the role an individual’s genome plays in drug response, which can vary from  adverse drug reactions to lack of therapeutic efficacy. With the recent explosion in sequence data from next generation sequencing (NGS) technologies, one of the bottlenecks in application of genomic variation data to understanding disease is access to annotation.

From NGS workflows, scientists can quickly identify long lists of candidate genes that differ between two conditions (case-control, or family hierarchies, for example). Gene annotations are essential to interpret these gene lists and to discover fundamental properties like gene function and disease relevance.