Despite the extensive research and development process for new medications, along with costly and lengthy clinical trials, it is crucial that prescription drugs continue to be monitored after they’ve been put on the market in order to study the effects of the drug and identify areas of improvement. 
 
Once pharmacovigilance data is collected, the real challenge is bringing it all together to make sense of it. Liaison Technologies’ vice president of Healthcare and Life Sciences Solutions, Gary Palgon, discusses how drug data can be used to reveal meaningful insights to better develop new drugs. –KB
 
 
Contract Pharma: In what new ways is data being collected in drug development?
 
Gary Palgon: Data is currency in the Information Age, and that model holds true in all industries, including pharma. Pharmaceutical companies need data to discover new products, enhance the patient experience and increase profits, so they’re incorporating new data sources throughout the pharmaceutical lifecycle, development through post-marketing surveillance in order to drive additional insights. New data sources for drug development include digital data from external clinical organizations (such as patient data from EMRs), wearables, retail websites, and sentiment information from social media, for example.
 
CP: How is data being used to gain meaningful insights during clinical development?
 
GP: If they’re able to integrate and manage their data with an advanced platform, pharma companies can create a big data repository of information from a myriad of existing and new sources, harmonize it and mine it for actionable development insights. For example, social media data can provide valuable clues about positive or negative sentiment to enable product improvement. Disease-specific records from patient registries can enable pharma companies to identify patterns within populations, breaking down regional barriers for a global view or drilling down to look at regional data as appropriate.
 
CP: How can data help streamline the drug development process?
 
GP: Today, Contract Research Organizations (CROs) are handling clinical trials for an increasing number of pharma and biopharma firms. It’s imperative that CROs and pharma sponsors are able to share data before and during the trial. Most organizations don’t standardize the data in CDISC until it needs to be submitted to the FDA for approval, while the best-in-class pharma companies utilize integration and data management capabilities to do this before a trial starts, which helps to streamline the development process, allowing CROs and sponsors to effectively exchange information throughout the trial. Pharma sponsors also need actionable visibility into their data for ongoing development efforts. When data flows freely between the organizations, it can shorten the development cycle significantly.
 
CP: What data is needed/used to meet payer demands?
 
GP: There’s been a shift in healthcare to a value-based model, and that has payers analyzing the price of pharma products in relation to the benefit they provide even more closely. Payers are also looking to generic formulas to reduce costs, so pharma companies that offer new, more expensive drugs must make a strong business case in terms of efficacy and safety. Demonstrating improved efficacy and safety requires extensive data, so pharma companies with a big data platform that can ingest information from multiple sources (CROs, registries, medical records, pharmacovigilance data, etc.) will be better positioned to conduct analysis and present a compelling business case to payers to include products on a formulary. 
 
CP: How does standardization impact data collection?
 
GP: Standardization is crucial because pharma companies are receiving unstructured data in a variety of formats, and the information they receive in non-standard formats may contain critical insights. For example, EDC applications typically contain standard fields that streamline data collection, but frequently, clinicians add notes in a comments field or unstructured clinical notes in the patient record. A platform capable of using technologies like Natural Language Processing (NLP) to analyze clinical notes can enable pharma companies to identify relationships between terms and map them out visually. It’s critically important to be able to integrate data from disparate sources and bring all information into a standardized format.