Big data and analytics are a major catalyst in the pharmaceutical R&D revolution. The explosion of data created across the health care sectors provides a great opportunity for accelerating innovation. A compelling question currently facing the pharmaceutical industry is how to effectively utilize this data to create value. On May 12th, CURE Bioscience ClubhouseCT showcased SystaMedic’s novel cause-effect analysis platform that can efficiently predict the probability of R&D success.

SystaMedic Inc., founded in 2009, consists of twelve former Pfizer leaders and scientists with an average of 20+ years pharmaceutical R&D experience with multi-disciplinary expertise including medicinal chemistry, prodrug design, synthesis and development, indications discovery, identification of new drug combinations and targets, safety pharmacology, drug metabolism, clinical statistics, cardiovascular and metabolic diseases, neuropharmacology, due diligence and patent law.

At this ClubhouseCT event, Dr. Bob Volkmann, Chief Scientific Officer of SystaMedic, shared details on the company’s prediction platform and how it mitigates risks and identifies opportunities in drug discovery.

Over the past sixty years of drug discovery, the number of new drugs approved per billion US dollars spent on R&D has halved roughly every 9 years (Scannell, Blanckley et al. 2012). One main factor that accentuates the decline in R&D efficiency is the incomplete translation of drug-induced effects on proteins into medically useful effects on patients. Because the whole organism is comprised of complex protein networks, the conventional structure-function analysis methods that focus on single protein components are insufficient. In addition, there is a lack of methods for comparing drug-induced effects in clinical trials.

To reduce the drug R&D expenses and to identify drugs for orphan diseases, SystaMedic developed a systems-based approach for complex biological analysis that enables the cross correlation of drug, disease, proteomic, genomic and clinical effect information. By deploying the proprietary data mining algorithm and data visualization tools, SystaMedic’s technology platform first generates target, pathway, network and effect fingerprints (biospectra) associated with medicines and diseases, then sorts, visualizes and compares these fingerprints. The platform has been proven effective in predicting side effects, molecular structure and primary pharmacology of medicines.

The future capability of SystaMedic’s analytic methodology is limited in theory only by the extent, integrity and availability of clinical and preclinical data. The wealth of new reliable data and the cutting edge analytical tools like this platform built by SystaMedic will enhance future innovation and strengthen drug discovery and development.

By Shannon Rao,
Business Development Assistant, CURE

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“There is no doubt that valuable interactions can be generated in the Clubhouse. This is a great way for scientists to learn about companies and benefit from the one on one interactions.”

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“Using the big data mining approaches in Pharma drug discovery is an exciting field. I learned a lot about this field today at this event.”


Scannell, J. W., A. Blanckley, H. Boldon and B. Warrington (2012). “Diagnosing the decline in pharmaceutical R&D efficiency.” Nat Rev Drug Discov 11(3): 191-200.