"The research-based drug industry is racing to boost its research pipelines, as existing patents expire and development times continue to lengthen," said Tufts CSDD director Kenneth I Kaitin. "Drug companies are exploring new approaches to product development that focus on increasing the probability of clinical success and speeding time to market."
One approach, summarized in the report, focuses on statistical models that help predict clinical success. For example, a R&D team created a simple algorithmic model called the Approved New Drug Index (ANDI) that predicts which oncology products emerging from Phase II testing are likely to receive marketing approval. The team concluded that, compared to the prevailing industry metric, the data support assigning a much higher probability of success to oncology drugs with top ANDI scores of 7 and 8, and much lower probabilities of success to those with scores of 0 to 4.
Other points summarized in the report include: a shift in approach to decision making that favors data-driven models; more rigorous use of risk-adjusted value calculations earlier in clinical development to improve decision making on how to structure clinical trials; as well as advances in development of personal genomic information that offers significant potential to transform clinical trials.