‘Eroom’s law’ is the observation that drug discovery becomes slower and more expensive every day. This reality is thwarting the pharma industry’s ability to rapidly bring new drugs to market, and it’s time for that to change. The cost of developing a new drug has doubled every nine years since 1950, which means the money we spend to develop just one successful drug today would have covered the cost to develop 90 drugs 70 years ago – and that takes into account inflation adjusted terms.

It’s an untenable model that stifles potential for innovation and hinders financial gains.

Most biopharmaceutical companies have more promising molecules in their portfolio than they have the budget to develop. With massive development costs, choosing a select few products for development – based on their strategic goals, risk tolerance, market demands and development resources – is one of the most important decisions a company can make.

One of the critical decision points driving Eroom’s law is execution of the ‘go/no-go’ decision for assets in the portfolio. This decision sits with portfolio managers and committees that determine the value and risks of each asset in relation to every other asset. Those teams collect data and perspectives from a diverse group of internal stakeholders, including clinical strategy, clinical development planners, statistics and commercial teams as well as those in R&D. But while each group shares data with the portfolio committee making decisions, they rarely work together. And that is a problem.

Today’s decision-making processes fail to provide a framework for experts to engage in meaningful technical dialog about each asset in the portfolio. Instead, the portfolio team makes decisions based on static and isolated data sets, interpreted through their own perspective.

They may still choose a promising candidate, but it might not be the best option. And without the ability to model different development scenarios or a technical framework to discuss options across teams with different backgrounds, it is harder to find the best path forward for maximum value and minimal risk.

What if portfolio decision-making wasn’t a data collection problem where teams submitted their data into the ‘black box’ of a decision committee - but instead a ‘glass box’ that makes the entire decision-making process transparent and dynamic for all parties?

4 Steps to Achieving a ‘Glass Box’ Approach In Portfolio Decision-Making

The drug development landscape is extremely risky due to long drug development cycles and high rates of failure. In addition, in many cases, leaders heavily weigh decisions based on inherent biases, without deep and holistic understanding of the candidate risk profile, realistic development costs vs. return, or the opportunity risk that results from not pursuing other options.

Pharma companies can reduce these risks and biases by adopting a more open portfolio management process and using digital platforms that allow for more streamlined data sharing and analysis. Below are four key mindset changes that are required to realize this:

1. Choose value measures that make sense. This transformation starts with creating an open, valuation-centered culture around portfolio management. Clinical development plans should be built with a set of specific value measures – expected net present value, expected number of patients treated, likelihood of being first to market – that are measurable and allow a fact-centered discussion around value drivers for each asset. Those value measures must explicitly consider risk, as well as allow an ‘apples-to-apples’ comparison between assets, their potential value and their alignment with the company’s overall mission and goals – all in a tangible, measurable way.
2. Develop the right clinical development plan. Portfolio decision teams don’t always ‘deep dive’ into a clinical development plan (CDP) – often due to lack of time or sufficient benchmark data. However, leading portfolio teams recognize that small changes to CDP assumptions make big differences in time to market or overall cost. Today’s best portfolio teams build custom CDPs for each candidate and evaluate the key drivers of cost and time in those plans.
3. Seek iterative cross-functional feedback. Critical to a successful decision around investment is adjusting the discussion based on the feedback from the cross-functional stakeholders involved. In many successful biopharma companies, portfolio decision-makers allow multiple rounds of feedback in each round of planning, rather than just accepting one static ‘best’ submission from each functional group. That approach allows the model to be ‘pressure-tested’ with holistic and updated information, and it encourages a two-way communication where groups can see the current evaluation of any asset in development.
4. Make hypothesis testing a habit. Most companies today develop only one-to-three scenarios for each asset in development because of the time and effort involved. Tools such as IQVIA’s Pipeline Architect, make this process fast and easy, giving companies the freedom to generate as many scenarios as they need. Whether testing the development hypotheses for each candidate and developing alternative clinical development plans to identify optimal trial design, or looking at other indications, it’s critical to balance risk, time and cost and arrive at the optimal plan.

Beating Eroom’s Law

These four approaches allow portfolio groups more robust tools to compare opportunities across the portfolio, to find synergies and to align investments to strategic goals for maximum results. By building better data into their portfolio decision-making, opening the channels for two-way communication, and reducing the time it takes to collect and synthesize data, pharmaceutical companies are making better decisions, faster. And that agility allows teams to continuously verify whether the candidate they chose continues to represent the best path forward – or if new information indicates that it is time to cut their losses and move on.

When companies combine a better investment review process with advanced data analytics tools, they can beat Eroom’s law, giving them the confidence that they are getting the most return for their investments.

To learn more about IQVIA’s Pipeline Architect platform click here or contact us at PipelineArchitect@iqvia.com