The QLS Vantage platform enables flexible plug-and-play deal modeling, robust asset forecasting, and AI-powered scenario optimization to systematically derisk drug development using the latest ML techniques and a wide array of public and proprietary data.
Analytics
In managing their portfolios of investigational drugs, biopharma companies typically use simple averages of regulatory approval rates based on historically observed relative frequencies.
The QLS Vantage Platform leverages huge quantities of public and proprietary data to estimate the most important model parameters and enable data-driven scenario planning and decision-making. Precisely tailored features enable users to interrogate key drivers behind outcomes and explore the landscape of strategic options.
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 reduce the uncertainty surrounding drug development, leading to more efficient allocation of capital and greater amounts of funding. Our ML-based probability forecasts for drug development programs are designed to be transparent, enabling productive conversations for all stakeholders in the development process.
Valuation
Financial models are often bespoke, limited, and inflexible leaving insights on the table.
The QLS Vantage Platform enables best-practice model building customized for deal terms and delivers fully realized value metrics “out of the box”. Our statistical approach enables objective investment decisions not possible with traditional point-estimate valuations.
The full-depth, bottom-up financial diligence begins with a standardized fundamental valuation process. Market models determine riskless value for each opportunity, and risk-adjusted sales of pipeline assets and marketed products are combined with qualitative datapoints gathered from experts to determine DCF valuations.
At the portfolio level, programs are aggregated with information “readthroughs” that translate a drug pipeline into an investment portfolio of embedded options. Viewing development decisions as a series of “real options” is a powerful strategy used throughout finance to identify and quantify complex risks.
These initial valuations are subsequently enhanced through a statistical lens. Monte Carlo simulations provide statistical overlays on DCF models that enable probabilistic valuations based on distributions of risk and return. The predictive power of these simulations has been validated extensively across therapeutic areas and phases of development.
Ultimately, the QLS Vantage Platform enables guided, best-in-class deal modeling with real-time AI-powered insights all available in a structured, intuitive platform.