The Need for New Models in Identifying New Molecules

Probabilistic rather than deterministic modeling is essential, says Quintiles’ Davis Walp.

By Paul Thomas, Senior Editor

It’s no secret that that the drug industry is inefficient in getting new molecular entities approved and to market, and that companies are “re-rationalizing” their drug development strategies and approaches. Main reasons for past failures include poor portfolio modeling (and an overdependence upon static, deterministic models), as well as an overdependence on net present value (NPV) in conducting portfolio analyses, says Davis Walp, head of Value-based Solutions at Quintiles. Walp spoke recently at DIA 2011 in Chicago about “The Role of Simulation in Representing Real World Risk” in Biopharma and Pharma Drug Portfolios.

Walp has seen a wide range of methods applied to portfolio prioritization across pharma and biopharma, but he singles out deterministic modeling as to blame for poor portfolio decisions. “You’re assuming away a lot of real-world risk,” he said. “We need to find new methods and practices for incorporating real-world risk into the way we make decisions.”

The traditional approach based primarily upon net present value (NPV) assessments, Walp continued, just doesn’t adequately address risk. “We’ve attempted to get around this by doing scenario analyses, but this is not good enough,” he said. Assessing risk using “base”, “low” or “high” designations for given molecules is not sophisticated enough, he noted.

There are often times, he continued, that many other metrics, such as number of launches in a certain timeframe, number of POCs, or even a revenue objective in a certain year, are not considered within current portfolio prioritization schemas. Using simulation approaches, manufacturers can construct portfolios of assets that will enhance chances of hitting these objectives, and help decisionmakers understand how changes to portfolio composition impact the probability that the portfolio will get them there.

A few things that portfolio managers must keep in mind include:

  1. The likelihood that the current portfolio composition will drive business objectives (using probabilistic frameworks, not deterministic).
  2. The change in probability that the manufacturer will achieve business objectives resulting from the addition and subtraction of projects to the portfolio 
  3. The impact that cross-project dependencies have upon the riskiness of the overall portfolio

A Solution
“I propose a simulation-based approach that will take into account the multiple future states of a portfolio, and factor in what happens in each of those states,” Walp said. “What we really need is better identification and quantification of the levers that drive value and risk in a portfolio, and what will happen to our portfolio when we make changes to it.”

One option is Monte Carlo simulation, allowing the drug company to:

  1. Better define uncertain variables, such as clinical trial outcomes, time in phase, development costs, and commercial performance.
  2. Take random draws of all uncertain variables and observe the model outcome (iteration) 
  3. Repeat numerous times (more than 1,000, he suggested) to assure a sufficiently large number of observations. 
  4. Characterize the range and distribution of outcomes observed.

“In this way, we create a framework for real option analysis,” Walp said.

Simulation of this type should facilitate the ability to predict a drug’s success. “It’s really hard today to understand what the world that you’re launching that drug into will look like,” he continued. “These approaches can help manufacturers construct better portfolios using similar tools that capital market portfolio managers use to construct portfolios of stocks and bonds.”

Concluding, Walp noted that there is no perfect solution for improving the success of NME’s. “No matter how complex the model, simulation is really no better than the quality of the inputs,” he said. Nevertheless, “ultimately, simulation-based approaches will offset the inefficiency of deterministic, static approaches.”

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