Innovative Tool Applies Machine Learning to Improve Drug Pipeline Forecasting by 25%
When Compared to Traditional Benchmarking Models
Clarivate Analytics has launched an advanced predictive analytics tool, Cortellis Analytics – Drug Timeline and Success Rates (DTSR), part of the Cortellis suite of intelligence solutions for drug development and commercialization. Accurate drug pipeline forecasting is imperative for companies to effectively bring novel therapies to market, especially in today’s hyper-competitive landscape. Cortellis Analytics – DTSR applies machine learning to forecast the timeline and probability of success for a drug, enabling continuous and dramatic improvements in pipeline forecasting and R&D investment decisions.
Pharmaceutical R&D spending outpaces that of nearly every other industry, yet returns have continually fallen over the past few years. Recent analysis from The Centre for Medicines Research (CMR) concluded that the probability of successfully moving a new drug candidate from Phase I to market was less than 10% across all therapy areas, with nearly one-third of R&D costs spent in Phase 3 alone. Additional analysis from CMR shows that the cost to bring a drug to market is approximately $3.2 billion, an all-time high. With the vast majority of these costs spent on drug targets that are never launched, it is more imperative than ever that ineffective drug targets are identified as failures faster so that investment may be shifted to more promising therapies.
Unlike traditional forecasting models that rely on standard benchmarks, fixed template-based algorithms and insufficient data inputs, the patent-pending Cortellis model takes a completely different approach to predicting success. In addition to using historic data from Cortellis, including 15 years of pharmaceutical intelligence spanning 70,000 drug programs and drug development trends analyses, the model looks at the target’s upcoming trial milestones as well as the unique key traits that may impact success or failure to generate probabilities of success at each stage of development. When tested against standard industry benchmarking approaches, Cortellis Analytics – DTSR outperformed those by 25%, providing greater confidence to decision-makers.
“Current approaches to pipeline forecasting fall well short of meeting the industry’s needs,” says Mukhtar Ahmed, President, Life Sciences, Clarivate Analytics. “The Cortellis Analytics tool applies an advanced statistical algorithm based on data science and machine learning to generate forecasts that are continuously updated based on the latest available data. Through this dynamic approach, researchers can more confidently – and efficiently – make critical decisions related to pipeline forecasting and portfolio planning throughout the development lifecycle.”
Analysis is generated in a fraction of the time compared to manual forecasting methods. The tool’s algorithm learns and refines drug pipeline forecasts on a daily basis as new data is made available. This allows companies to modify clinical research plans, refine portfolio strategies and better set R&D spend expectations. Users can also leverage outputs to track competitive assets against their own targets, model net present value (NPV), or create multi-target forecasting models that examine overall portfolio timelines.