7 Horrible Mistakes You’re Making with Your Drug Development Strategy

7 Horrible Mistakes You’re Making with Your Drug Development Strategy

In thinking about the complex nature of drug development, I’m often reminded of a Dwight Eisenhower quote:

“In preparing for battle, I’ve always found that plans are useless, but planning is essential,”

Drug development is a risky business. According to a 2014 study from the Tufts Center for the Study of Drug Development, “the estimated average pre-tax industry cost per new prescription drug approval (inclusive of failures and capital costs) is $2.5 billion. When so much is at stake—time, money, and the needs of our patients—having a solid drug development strategy is critical. Yet, I frequently see sponsors make the same mistakes over and over again. In this blog post, I’ll discuss these common strategic blunders and provide suggestions of how to address them.

Mistake #1: Your development strategy isn’t aligned with your drug’s market niche

There is no dominant drug development strategy. Thus, there is no single answer to how to create a strategy with the greatest likelihood of regulatory and commercial success. Rather, each strategy must be crafted with a profound understanding of how this drug will uniquely benefit patients. As I discuss our business with my colleagues, I am amazed at the diversity of clinical and regulatory strategies1 our clients are pursuing. Here are some examples:

  • Drug is a first-in-class breakthrough treating a large unmet medical need.
  • Drug is “me-too-but-better,” with minimal incremental effectiveness, but with reduced side effects, a different formulation, and/or a more convenient dosing regimen.
  • Drug is a highly efficacious “me-too” entering a large and competitive market.
  • Drug offers an alternate (but not necessarily superior) mechanism of action for a widespread ailment.
  • Drug offers significant benefits, but only to certain refractory patients.
  • Drug treats only pediatric patients.
  • Drug is intended for use only in specific countries.
  • Drug is meant to be used primarily as adjunctive therapy.
  • Drug treats a rare or orphan condition.
  • Drug is highly expensive, but prevents even more costly and problematic complications.
  • Drug treats a non-fatal lifestyle condition.

[1] List adapted from Michael Martorelli of Fairmount Partners, Pharmaceutical Outsourcing Monitor, October 21, 2015

In our business, there just simply isn’t one “go to market” strategy. Have you aligned your strategy with the value proposition for your drug and how it will be received by patients, clinicians, and payers?

Mistake #2: You don’t know how to maximize the value of your drug program

If your drug is a first-in-class breakthrough that treats a large unmet medical need, you might be well served investing in technology that helps you save time. For example, you could use modeling and simulation (M&S) technology (also known as model-based drug development) such as physiologically-based pharmacokinetic (PBPK) models to quantify the risk and magnitude of potential drug-drug interactions (DDIs). In addition to gaining valuable insights into drug mechanisms, these data could potentially be used to attain a waiver for a DDI trial from a regulatory agency, thus saving months of time.

Or, you might find it useful to leverage a global regulatory writing team to compress the time it takes to prepare your regulatory documents for submission. In any case, you will win big in this scenario if you can get to market faster.

But what if your drug is best served by a different strategy? If your drug is “me-too-but-better,” you might benefit most from a dosing regimen that maximizes the risk/benefit profile for your patient population. Maybe your drug’s target population includes children? Or patients with organ impairment? Or treats patients in ethnic groups different from those evaluated in actual clinical trials? In these cases, you might find getting the absolute best regimen through intensive pharmacometrics and/or systems pharmacology approaches will add the most value. This may be true even if adopting M&S approaches means that your time-to-market may be longer.

Mistake #3: You’re stymied by challenging clinical trials

Certain patient populations are difficult to study in clinical trials due to either ethical constraints or logistical challenges. For example, patients suffering from rare diseases treated by “orphan drugs.” Major challenges in orphan drug development include sparse existing data available from limited populations and few patients on which to run new studies. To get the most information from each precious trial patient, you might consider combining sparse sampling strategies with population pharmacokinetic/pharmacodynamic (PK/PD) models to inform dosing and trial designs.

Mistake #4: You forgot to create a regulatory writing plan

You put blood, sweat, and tears into developing a new therapy. But, regulatory agencies won’t see your years of hard work. They see only the documents you submit. These documents must guide the reviewer through the research demonstrating the drug’s safety/efficacy profile.

Companies often assemble teams lacking experience in crafting drug submissions. Engaging a partner experienced with the entire regulatory process can reduce time and costs. An experienced partner can help you succeed by:

  • Planning the submission strategy early in development
  • Organizing the team’s collective knowledge into briefing documents and submissions
  • Allocating submission tasks to appropriate resources to optimize time-to-filing
  • Managing questions from health authorities

Mistake #5: You aren’t considering the competition

It’s a dog-eat-eat world out there. Are you considering how your drug will stack up against the competition in safety and efficacy? Are you leveraging publicly available information on approved and developing drugs to support your market positioning? Model-based meta-analysis (MBMA) is an emerging pharmacometric methodology that quantifies available efficacy, tolerability, and safety information to enable better-informed drug development decisions.

Performing MBMA requires a systematic search and tabulation of summary efficacy and safety results from public sources which may be combined with proprietary clinical trial data. These data are then analyzed using nonlinear regression models which quantify the impacts of drug class, drug, dose, and time on the response(s) of interest. In addition, the potential influence of study population characteristics or the trial conduct may be explored.

There are very few active comparison trials in drug development. However, it is often important to assess a compound’s safety and efficacy profile in comparison to the standard of care and/or competitor drugs in development. MBMA enables indirect comparison, taking into account the impact of treatment, patient population, and trial characteristics. This type of analysis can help estimate the probability that a drug is superior to its competitors in the same drug class or across drug classes, leading to stronger market positioning and improved commercial uptake of your new therapy.

Mistake #6: You missed the boat on label optimization

Your drug label is the culmination of years of work and millions, if not billions of dollars. The inclusions and exclusions on the label will determine the size of the market that the drug can be promoted to, and ultimately the drug’s profitability.

While M&S has been an important element in drug development for some time, its impact on labels over the past 18 months has been profound. Specifically, FDA’s acceptance of PBPK modeling and simulation has impacted labels in more than fifteen cases, driving down R&D costs and time lines, and allowing for greater label precision.

From guiding clinical study designs with smaller and more precise trials to the waiving of studies, including DDI and special population studies, label optimization is an essential part of a successful drug strategy. Your drug’s label can be informed by both mechanistic modeling (bottom up) and pharmacokinetic-pharmacodynamic PK/PD modeling (top down).

Mistake #7: Your drug is “one-size-fits-all,” but your patients aren’t

Precision dosing provides the right drug at the right dose for the right patient at the right time.M&S now plays a pivotal role in achieving that goal and moving the industry away from the “one-size-fits-all” approach to drug development. Accounting for individual variability is central to the use of M&S technology for drug development. By modeling how a drug will interact with the body, and how the body will interact with a drug, we can now inform dose regimens accounting for DDIs, food effects and other ‘personalized’ impacts.

I’ve included this short video where you can learn how incorporating M&S technology can support your program’s overall strategy.

Insights yielded from M&S technology inform drug development strategy

My colleagues, Drs. Karen Rowland Yeo and Suzanne Minton, recently wrote an article for Clinical Leader that illustrates how M&S technology is revolutionizing the way in which the pharmaceutical industry does business. I hope that you’ll read this article and let me know what you think in the comments section below!

Mark Hovde

About the Author

Mark Hovde is the Senior Vice President of Strategy and Corporate Development at Certara. He brings more than 20 years of product development, marketing, sales, and general management leadership to his work with Certara’s customers and partners. Prior to his current role, Hovde held a number of leadership positions in several innovative technology companies, including Entelos and Fast Track Systems (now Medidata Solutions). Prior to entering the R&D technology and services industry, he held leadership positions in commercial banking and management consulting. Hovde is an author and speaker on issues facing R&D management. He serves on the finance committee of the Association of Clinical Pharmacology and Therapeutics. He holds a B.S. in economics from the Wharton School of the University of Pennsylvania and an M.B.A. from Harvard Business School.