Can better design save biopharma?

Transcription

1 White paper Clinical trial planning & design Can better design save biopharma? Frederic L. Sax, M.D., Senior Vice President, Quintiles Advisory Services Connecting insights Executive summary The current biopharma drug development paradigm, including an organizational structure centered around project teams, is not wellconstituted to embody design best practices even though design of clinical trials is at the heart of the drug development endeavor. Better outcomes Teams tend to be formed on the basis of technical or therapeutic expertise, not on the basis of wellhoned design skills. Furthermore, teams tend not to be incentivized to foster open approaches to design or to use structured design methodologies that lead to high quality and performance. As a result, there is great variability in the quality of the programs and trial designs produced by project teams. This results in less efficient clinical trial execution or failed outcomes. A more systematic approach to design grounded in best practices from other industries could result in improved success rates and more cost-effective, timely clinical trial execution. To achieve these results, biopharma first must recognize that design can be conducted as a structured process, and that design tools can be developed to facilitate such a process and ultimately, drive better outcomes.

2 Table of contents Multiple challenges, limited options 3 Better design, better medicine 4 Understanding failure 5 Using design to make better decisions 6 Bringing it all together 7 References 8 About the author 9

3 Multiple challenges, limited options For most biopharmaceutical companies today, the primary barrier to sustaining a successful business model is the fact that the cost of developing a new medical entity is too high relative to the probability of achieving a successful market entry for the product. Costs of development range from the oft-cited estimate of $803 million 1 to a more recent estimate of more than $1 billion 2. Perhaps more important, the success rates for investment are highly variable across different companies 3, highlighting both the risk and expense of iterative poor outcomes. Research spending per new drug 4 No. of drugs approved R&D spending per drug ($M) AstraZeneca 5 11, GlaxoSmithKline 10 8, Sanofi 8 7, Roche Holding AG 11 7, Pfizer Inc. 14 7, Johnson & Johnson 15 5, Eli Lilly & Co. 11 4, Abbott Laboratories 8 4, Merck & Co. Inc. 16 4, Bristol-Meyers Squibb Co. 11 4, Novartis AG 21 3, Amgen Inc. 9 3, This return on investment challenge is exacerbated by increasing evidentiary demands from payers and regulators further driving up expenses, and companies can no longer afford to pursue a large portfolio of products in an era in which there is a one-in-six chance of getting a new drug to market once it has entered human trials

4 Given these constraints, biopharmaceutical companies have two choices: either they reduce their development costs or they figure out more cost-effective ways to bring drugs to market. Because incremental cost-cutting can only go so far before R&D departments no longer have sufficient bandwidth to move compounds effectively through development, increasing the probability of success for molecules in the pipeline is critical. To increase this success rate, one of the few strategies biopharma can employ is to incorporate better planning and design principles into the various development stages. Better design, better medicines At the heart of a quality design process is the availability of good information. Without the ability to access multiple sources of data, as well as the proper tools for integrating, objectively analyzing and modeling such data, then applying it to design, biopharma companies will remain challenged to improve the success rate of drug candidates. Better use of information and information technology, however, is insufficient to change design outcomes; the design process itself needs to be re-examined. The traditional biopharma design process is largely structured around subject matter expertise brought together in project teams. Indeed, design activities are conducted as an expert teaming process, not as a true design process. Expertise is certainly a critical component. Yet, as seen in design best practice from other industries, design thinking can be performed in a structured way: first defining the problem well, then integrating all available information. Following this, the key element that distinguishes good design practice is prototyping, i.e., developing and testing different scenarios and trade-offs before crystallizing the final design decision. This step is particularly difficult for biopharma teams in the current paradigm, because constructing even a single program or trial design is complex and fraught with assumptions and contingencies. A more systematic, transparent approach could achieve far greater clarity of options and hence, a better probability or successful outcomes. Biopharma s historical approach of proceeding down the development path without better design approaches continues to result in an industry-wide failure rate of 50% for drug candidates entering Phase III. Further learning about design can be gleaned from a recent study of leading global companies across diverse industries to understand what constitutes best practice in design. 6 Conducted by Hay Group, an international management consultancy, and commissioned by a large pharma client, the study shows that companies most admired for their design practices share many of the same principles: 1. Mindset: Design is a disciplined, methodical approach embedded in the organization; there is a healthy tension between innovative and creative thinking and disciplined behavior and processes to design. 2. Open innovation: Models are vital to generate both collaboration and competition among internal and external partners. 3. Purpose: Design teams are created and exist to drive business outcomes. 4. Metrics: Companies use a few key metrics to assess design quality and performance, focused on activities that drive value. 5. Knowledge management: Ideas, technology, and projects are stored, re-cycled and re-used; companies excel in at least one of these dimensions and nothing is thrown away. 6. Execution: More so than strategy or structure, design processes and practices are viewed as having the greatest impact on execution. 7. Leadership: Leadership plays a central role in defining expected outcomes and conditions for success and takes a portfolio approach to design, while fostering bottom up generation of innovative approaches. 4

5 Many, if not most, of these practices are not evident in the design practices of most biopharma companies, and the consequences are clear: biopharma s historical approach of proceeding down the development path without better design approaches continues to result in an industry-wide failure rate of 50% for drug candidates entering Phase III 7. And this doesn t include resources expended in Phase II with compounds that are moved along in the development cycle that should have been killed early. Up to 51% of compounds entering later stages of development are thought to fail for efficacy reasons 8 ; and an additional 29% are not developed for strategic reasons ; both of these outcomes have potential roots in the design process. The difference in organizational/cultural mindset around design is perhaps best illustrated by an example at the other end of the design spectrum: BMW. The German automaker, recognized by both customers and peers for its outstanding designs, is known to have created a culture of design thinking, often including multiple design teams that compete. The teams are incentivized to produce the best possible designs within the specifications they are given (design remit), but nobody loses because the best elements from competing designs are brought together in the end. This gives the product teams multiple options to play with, which in turn leads to a more robust design. And the internal competitive edge, so often missing in biopharma, leads to higher quality, more focused results. One of the underlying tenets of design thinking is to integrate information to determine what is known, and more importantly, to determine the unknown. A design thinking approach highlights one of the most glaring dissimilarities between biopharma and other industries recognized for design excellence: biopharma is poor at modeling or prototyping potential research designs before reaching design/investment decisions that have far-reaching consequences (i.e., the end result achieved is usually pre-ordained during the design phase). To a large degree, most project teams don t develop different options, don t play with different scenarios, don t properly investigate various tradeoffs, and don t adequately model different options against potential outcomes. Doing such activities would make the underlying assumptions and risks therein far more transparent, and thus more addressable. One of the underlying tenets of design thinking is to integrate information to determine what is known, and more importantly, to determine the unknown (e.g., hypotheses, assumptions, risks). This allows the design effort to focus directly on addressing these unknowns, while minimizing the introduction of inadvertent bias. Whether due to a lack of access to information, time, resource or good process, biopharma s difficulty with integration and prototyping makes it difficult to test designs and thus influence outcomes. The availability a proper design tool could markedly facilitate a company s ability to visualize and explore data-driven options, increasing the probability of better design decisions. Understanding failure Failed clinical trials can broadly be attributed to one (or more) of the following areas: 1. Bad compound. Although advances in discovery technology have reduced the number of bad compounds brought into development, there are still cases in which the investigational drug does not have the proper pharmaceutical properties for human use, has predictable side effects that limit its therapeutic window, or simply fails to hit its biologic target. 2. Translational failure. Translational research, though much improved over the past two decades, is still an inexact science with many unknowns, and researchers often struggle to make the appropriate linkage between animal biology, human biology and hard clinical outcomes. 3. Unexpected safety findings. Clinical research will always contain the unknown and often produces safety signals that simply could not have been anticipated until large populations are studied. 5

6 4. Poor design. Poor design can be attributed to a number of reasons, from trying to save costs and time, to designing to achieve marketing aspirations rather than using hard scientific data. However, the three most common root causes of poor design are a lack of clear definition (design remit), a lack of full information integration and a lack of prototyping. Human bias (especially in a team process) factors into each of these steps, as well as overall design decisions (see Daniel Kahneman s excellent book on biases in decision-making) 9 and can be a major factor in design outcomes unless addressed objectively and transparently. What can be learned from this type of failure analysis? For the most part, there are only two levers a company can pull to drive better outcomes: (1) selection of the compounds they choose to take into human development, and (2) better design processes and decisions. The other two factors translational failure and unexpected safety findings are far more capricious. Generally, however, they are relatively uniform for all companies, and thus they don t account for the differences in expenditure vs. probability of success highlighted earlier. As design is one of the two major outcomes factors that can be influenced, not focusing attention on this has consequences. Poor design can result in research that isn t focused on answering the right questions for a particular upcoming investment decision. Studies may be underpowered (or over-powered relative to the real risk, resulting in great excesses in cost and time), lack proper dosing information, or not identify the optimal target population appropriately. So when results are obtained, they can be ambiguous and, in the worst cases, not provide the information necessary to inform the company whether it was the drug that didn t work or the design that didn t work. When this happens, drug candidates might be killed or progressed inappropriately (at great expense vs. the probable outcome); companies might also need to repeat studies in order to get clarity as to whether to continue development. At the extreme, a Phase III trial with a marginal outcome (e.g., a p-value of 0.07) could mean that the drug is actually working, but the design chosen was insufficient to enable registration. A failure of this type is painful for companies of any size, and could be especially perilous for smaller companies with more at stake on a single molecule. Using design to make better decisions Good planning and design should achieve two broad outcomes: a higher probability of obtaining a clear scientific outcome and better operational outcomes to drive down costs and shorten development time. Ultimately, the goal is to allow companies to make better, quicker and more accurate investment decisions and only progress the candidates with the most potential for success. This, in turn, reduces investment costs and improves return on investment. This is critical in an industry so dependent on progressively increasing large capital investment with each major milestones; design must therefore be centered on providing sufficient accurate data to make the next investment decision. 6

7 Investment decision Candidate drug nomination Proof-ofconcept Development for launch Launch Discovery TPP Design with the end in mind Rx Lifecycle B-R-V B-R-V Benefit- Risk-Value claims B-R-V Iterative assessment of Benefit-Risk-Value proposition (B-R-V) for an assest a critical investment decision points Proper clinical trial design should be implemented and executed to enable decisions at each major milestone in the drug candidate s lifecycle. At every milestone, something more is learned about the benefit-risk-value proposition of an asset, and the unknowns with regard to biology, safety and efficacy are reduced. Every element in the process is designed against the next milestone in an effort to get better information for the next investment decision. Bringing it all together Good design practice should sit at the heart of biopharma s efforts to transform itself and break the nonsustainable cycle of product development failure and poor return on investment. Biopharma needs to embrace and move toward a culture of design thinking by incorporating best design practices across the drug development lifecycle, from discovery to commercialization. Design skill needs to be embedded in the organization, design teams geared to drive outcomes, design activities assessed on whether they create value, and each step of the process geared toward providing more information to enable knowledge-based decision making. Indeed, re-ordering design priorities may be critical to biopharma s decision-making model. Instead of a model emphasizing compound survival and market size, biopharma must establish a culture of design that emphasizes compound probability of success, which will likely to lead to better R&D investment decision-making. By placing information at the heart of the design process, biopharma can utilize the power of information available to them and their design process to create solutions that drive better outcomes. In doing so, biopharmaceutical companies will increase their traditionally poor success rate, which would in turn save the industry and allow it to flourish. Good design practice should sit at the heart of biopharma s efforts to transform itself and break the non-sustainable cycle of product development failure and poor return on investment. 7

8 References 1. DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: New estimates of drug development costs. Journal of Health Economics. 2003;22; Tufts Center for the Study of Drug Development. Outlook Available from: edu/_documents/www/outlook2010.pdf 3. Herper M. The Truly Staggering Cost of Inventing New Drugs, Forbes, 10 Feb Available from: inventing-new-drugs/ 4. Ibid. 5. Tufts Center for the Study of Drug Development. Outlook Hay Group, Best Practices in Design and Sustainable Innovation, (For further information contact: 7. Arrowsmith J. Trial watch: Phase III and submission failures: Nature Reviews Drug Discovery Feb;10(2): Gitig D. Increasing Percentages of New Drugs are Failing Phase II and III Trials, Highlight HEALTH. 1 January Kahneman D.; Thinking, Fast and Slow; Farrar, Straus and Giroux; New York,

9 About the author Frederic L. Sax, M.D. Senior Vice President, Quintiles Advisory Services Frederic Rick Sax leads Quintiles dedicated planning and design unit, the Center for Integrated Drug Development, supporting customer need for clinical program designs. The unit provides design expertise at every step of the drug development spectrum to help partners make knowledge-based decisions about the benefitrisk-value trade-offs of their compounds. Sax also leads Quintiles Integrated Clinical Services division, comprised of Cardiac Safety Services, Biostatistics, Medical Writing, Regulatory Affairs and Lifecycle Safety. Sax joined Quintiles from AstraZeneca s clinical development leadership team where he served as VP, Clinical Design Strategies. For more than a decade, Sax has led the design of solutions to enhance the quality of program and trial design while driving efficiencies in cost, time and process. Sax s career spans nearly 20 years in the biopharmaceutical industry. Prior to joining the industry, he served as an academic cardiologist. Sax graduated with a B.A. in Biology and Philosophy from Yale University, and an M.D. from Columbia University. 9

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