Overview. ScopiaRx DB is a pharmacy information database designed to address the fastest growing expense, and a dangerous aspect of modern health care: polypharmacy – the prescribing of multiple medications to a patient.

Unique Features of ScopiaRx DB

  • Indexing of medication safety information by outcome: Individual drugs are known to cause a specific set of adverse events with a particular frequency. The ScopiaRx DB normalizes this information from the FDA and categorizes adverse events as affecting 1-10% of patients or more than 10% of patients. Rare side effect frequencies are estimated from aftermarket reporting statistics as defined below. A unique feature of ScopiaRx DB is the indexing of drug-drug and drug-disease interactions by outcome. A severe interaction may exist between two drugs, but historically it has been up to the clinician to assess what the likely outcomes would be. In the ScopiaRx DB, the most likely side effects of all drug-drug and drug-clinical interactions are indexed. The end result of this method is that all of the risks for a specific negative outcome can be quickly identified. By assigning each risk type, a composite score for each negative outcome can be calculated based on the unique mixture of medications and clinical characteristics. ScopiaRx polypharmacy risk scores can be easily incorporated into population health algorithms that consider other variables such as demographics, social considerations, or past behavior when predicting specific risks.
  • After market case reports: The ScopiaRx DB includes reporting frequencies for thousands of adverse effects based on aftermarket drug safety data from millions of reports to the FDA. Importantly, the side effect terminology of ScopiaRx DB allows the safety information from product labels to be merged with aftermarket reporting frequencies to get a comprehensive view of both common and rare drug related adverse effects.
  • Comprehensive database of clinical risks: A wealth of information regarding patient specific drug safety is described on FDA approved product labels. However, this information is in free text format. Using a proprietary data indexing method that was refined over several years, ScopiaRx DB contains 75,000 drug-clinical interactions using a concise clinical indexing system that links to ICD-10 codes. Pharmacogenetic traits and reduced kidney function play very important roles. With ScopiaRx DB, these specific risks can be easily simulated and combined with other clinical characteristics and multiple medications to predict adverse outcomes in high risk groups of patients.

Patent Pending Technology: ScopiaRx DB simplifies a complex problem. For a patient on 10 medications, there are hundreds of interactions and side effects to consider. Currently, most of this risk information is ignored, but with ScopiaRx DB, the population health consequences of polypharmacy can be rapidly quantified.

The drug information in the ScopiaRx database comes from FDA approved drug labels and summaries of aftermarket case reports that are derived from the FDA’s FAERS system.  For herbals and supplements, safety information is obtained from the National Institutes of Health Office of Dietary Supplements and the National Institutes of Health Center for Complementary and Integrative Health.

Patent pending data indexing and prioritization routines sorts through this data and normalizes side effect frequency information as well as comprehensive set of drug-drug, drug-clinical, drug-herbal, drug-demographic, and drug-pharmacogenetic interactions. All interactions are linked to clinical outcomes. The result is a set of concise pharmacy information tables that can be quickly imported into relational databases for use with existing population health algorithms.


Applications for the ScopiaRx DB

  1. Population Health
    • Augment current risk prediction algorithms with polypharmacy risks
    • Assess the humanistic and financial costs of high risk polypharmacy prescribing
    • Identify groups at high risk of harm from over the counter medications
    • Identify groups at high risk of hospitalization from drug related adverse events from sudden changes in kidney function
    • Enhance the risk predictions from pharmacogenetic testing by including polypharmacy interaction assessments
    • Optimize tolerability and adherence to high cost specialty medications by identifying and eliminating bothersome polypharmacy interactions
    • Read more…
  2. Artificial Intelligence for Drug Discovery
    • Augment in silico lead identification methods with adverse event reporting patterns from millions of case reports collected during 15 years of aftermarket surveillance
    • Improve lead optimization strategies by mining the structure activity relationships of over 2,000 approved drugs and their indexed safety profiles
    • Simulate the polypharmacy risks of late stage clinical candidates based on real world clinical situations where multiple comorbid conditions exist
    • Compare the polypharmacy profile of clinical candidates to existing products on the market
    • Read more…