Identifying Areas of Competitive Advantage: Developing a Predictive Tool to Determine New Drug Approval Success Open Access
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Healthcare costs are at an all-time high and there is no sign of ever relinquishing its upward trend or being controlled at the current level. The high cost of healthcare is partly attributed to the high cost of medicine. Research and development (R&D;) costs have been identified as a contributor to the high cost of medicine. Prior to the enactment of the Hatch-Waxman Act in 1984, brand drugs, which are the first innovative drugs on the market, was dominating the market and was difficult for generic drugs to compete with. The Hatch-Waxman Act was passed to ensure a flat level playground for both brand drugs and generics drugs. Currently, generic drugs have outperformed brand drugs in areas including high approval, and low prices. Given that the generic drug business is more lucrative now than brand drugs, many of the drug manufacturing companies who concentrated on drug discovery of innovative drugs are now producing generic drugs. This has resulted in low drug discovery and low return on investment for brand drugs. In recent years, research and development costs have skyrocketed, and it takes many years (up to 15) for new drugs to reach the approval stage which, in turn, results in a high delay time for other unmet medical needs to be on the radar for research activities to begin. This praxis aims to identify areas of competitive advantages for brand drugs and develop a predictive model to determine drug approval success during the clinical trial phase. First, areas of competitive advantages are identified through literature review and validated through drug utilization data. The prediction method was developed through statistical analysis tools including sample t-test, Wilcoxon test, and logistic regression analysis. Independent variables found to be statistically significant was used to develop a probability predictive tool for individual drugs to predict drug approval success. The model was validated using clinical trial data to demonstrate the application of results obtained. The proposed methods can provide information for the pharmaceutical industry to predict drug approval status early in the development process in order to make a risk based decision on a new drug candidate’s path forward.