Five Things You’re Getting Wrong in Your Pipeline Planning

R&D pipelines are the lifeblood of the pharma industry – so why are we still getting it wrong?

More so than perhaps any other industry, the product pipeline is critical to pharma and biotech businesses. Long R&D timelines, regulatory hurdles, patent expirations and an ever-changing competitive landscape means that no company can afford to take its eye off the ball.

And yet there are a lot of businesses still getting it – if not wrong, then definitely not right. There are no easy answers and no shortcuts, but here are five things that you might need to consider if you’re currently thinking your pipeline is in top shape.

1. 1. Making investment and prioritisation decisions without really understanding product differentiation
      Do you really know how an R&D candidate stacks up against standard of care? What about how it will be differentiated in the market? If you’re launching into a market with established products, why would a doctor prescribe your new drug and why would a patient want it? There is always risk in new drugs, so be clear about your value story, whether it’s around better efficacy, better safety profile, or ease of use.
      
      How to get it right: Even when you’re dealing with a hypothetical situation, work to develop scenarios for your product go-to-market strategy. Partial or early data is a good start, and make sure you look at worst case and best case scenarios so you can plan effectively.
   2. Failing to track new entrants to the market or planning for market disruptors
      Competition is constantly evolving, as are standards of care across every sector of the market. The constant treadmill of press releases, company announcements and industry conferences provide a steady drumbeat of updates on your competitors’ developments but it doesn’t tell you about their strategies. While “known unknowns” are challenging to plan for in commercial assessments, that’s no excuse for not trying.
      
      How to get it right: Applying pipeline data and predictive metrics to examine market evolution is a big step forward in planning for the unexpected. How might treatment paradigms change? And what does that mean for your asset strategies or investments.
   3. Misjudging the addressable patient populations for your products
      While the pharma press tends to focus on deal-making, share prices and regulatory decisions, the bottom line is that healthcare starts and ends with patients. All analysis of asset and market potential has to focus on patients. What are the unmet needs? What do you know about biomarkers, treatment histories or disease severities? And how does your product fit into that landscape? Getting this wrong in either direction can be hugely damaging. Too high and you risk disappointing the market. Too low and you won’t get the investment or deal-making opportunities that your product deserves.
      
      How to get it right: Effective modelling is critical in understanding your market size and potential. Make sure that you have granular epidemiology data with detailed patient segmentation data so you can forecast effectively.
   4. Ignoring payers and access in early portfolio decisions
      Your new drug may be the clinical equivalent of the best thing since sliced bread, with ground-breaking patient outcomes, but if it comes with eye-watering costs, you may be out of the game before you’ve even warmed up. Every healthcare system in the world uses some form of cost-benefit analysis and you need to make sure that your value story will pass muster, particularly if you’re entering a market where existing treatments are available at what looks like a more reasonable price.
      
      How to get it right: Make sure you’re factoring in payer perspectives as early in the pipeline as possible. You’ll need to keep coming back to your assumptions, but leaving it until you’re ready to build your go-to-market strategy is never wise. Understanding what payers are looking for in a new drug will give you a much better chance of commercial success.
   5. Basing portfolio prioritisation decisions on partial or subjective assumptions
      Of course you love your new drug, it’s your baby and you’ve nurtured it since it was a glint in the clinician’s eye. But clear eyes are necessary to make sure you’re taking the right drugs in your pipeline forward at the right time. Failure to make evidence-based assumptions backed by unbiased data could lead to a potential blockbuster languishing in the pipeline while you push on with a product that’s going to be niche, at best.
      
      How to get it right: Include data-driven evidence in your planning process and source external data. It may sound obvious but incorporating benchmarks and other contextual data, along with views of experts without skin in the game is critical. You might want someone to tell you that your baby is beautiful, but if that’s not the case, you need to know.