Reducing Barriers to Efficient Capital Allocation
While many recent medical breakthroughs such as immuno-therapies, gene therapies, and gene-editing techniques offer new hope for patients, they have also made biomedical innovation riskier, and more complex and expensive. In the face of multiple and growing uncertainties, the need for greater accuracy in predicting clinical trial outcomes has also grown. More accurate forecasts means fewer drug failures, faster approval times, a lower cost of capital, and more funding available for bringing new therapies to patients.
In managing their portfolios of investigational drugs, biopharma companies typically use simple averages of regulatory approval rates based on historically observed relative frequencies.
Traditional estimates based on therapeutic averages do not take into account important predictive features related to therapeutic modalities, trial design, sponsor track record, or indication-specific characteristics.
Drug and device developers must be able to accurately assess probability of success and the impact of key drivers to efficiently allocate capital to opportunities with the highest potential
With Modern AI-Based Tools We Can Do Better
QLS applies machine-learning techniques to predict multiple features of randomized clinical trials including outcomes, duration, and other key drivers of commercial decisions. These AI-based forecasts employ over 200 predictive factors including drug and device characteristics, clinical trial design, prior trial outcomes, and sponsor track record.
More accurate forecasts of the probability of approval will reduce the uncertainty surrounding drug development, leading to more efficient allocation of capital and greater amounts of funding.
Unlock Your Portfolio’s Potential
At QLS, we take a fully integrated data-driven view of biomedical portfolio and risk management. Our Real-Time Portfolio Monitor™ subscription service includes daily updates and user-defined alerts of all probability of success estimates, correlations, trial durations, related therapeutic approvals/failures, and other key factors driving the risk and returns of the pipeline products in your portfolio.
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