Retooling, not deals, is driving biopharma R&D efficiency

Print 13 July 2015
Marie Powers / BioWorld

Drugmakers are beginning to lower the cost and improve the efficiency of their R&D operations, and not just by slashing head count and shuttering facilities, according to a report from the Tufts Center for the Study of Drug Development (CSDD). The report, Profiles of New Approaches to Improving the Efficiency and Performance of Pharmaceutical Drug Development, concluded that biopharmas are launching successful initiatives in four principal areas: 1) new approaches to validate drug targets, 2) integration of real-world data into the R&D process, 3) adoption of adaptive clinical trial designs and 4) implementation of green manufacturing techniques.

There's little dispute that the mechanics of drug R&D need improving. Success rates in bringing a drug from discovery to commercialization are low and getting worse, according to previous Tufts CSDD research, which suggested that only 11.3 percent of drugs that enter clinical testing today will be approved in the U.S., down from a 16.4 percent success rate 10 years ago.

The degree of difficulty in conducting human trials also has escalated. On average, the number of procedures per protocol, number of eligibility criteria and number of investigative sites and countries where a given trial is conducted have increased dramatically during the past decade, according to the Tufts CSDD, creating more demanding protocols, both from a scientific and operational standpoint.

Volunteer recruitment and retention have become more difficult, as well. A previous CSDD study of several hundred global trials found that biopharmaceutical companies typically must double their planned enrollment periods to give sites sufficient time to recruit study volunteers and complete trials. Even with the extended time lines, 11 percent of investigative sites, on average, in any global, multicenter trial fail to enroll a single patient, and one in four sites under-enrolls.

The initiatives studied by Tufts offer the potential for cost savings, and all are in use by certain drugmakers, so they're worth considering. Some have gained more traction than others. For instance, efforts to improve target validation early in the discovery and development cycle are building momentum as companies seek to reduce the amount of capital they pour into pivotal trials.

"Target validation allows greater predictive insight for choosing candidates that will succeed in clinical testing," said Mary Jo Lamberti, senior research fellow at the Tufts CSDD and co-author of the report. That predictive insight can have an iterative effect. Higher success thresholds in preclinical testing can reduce the overall cost of animal models while helping to identify promising candidates for human studies with greater accuracy. Better validation in human trials, likewise, may lead to improved use of approved therapies in clinical practice.

Advances in target validation also are providing scientists better data faster, giving them greater confidence in nonclinical findings and, in turn, leading to more prudent R&D resource allocations, according to the report.

Drug R&D remains "a time-intensive process," Lamberti emphasized, so efficiencies that reduce the development cycle correspondingly lower the cost of drug development.

'PHARMA NEVER WANTED TO SHARE DATA'

Increased data sharing in a pre-competitive environment also offers the potential to improve the efficiency of drug R&D. In the past, "pharma never wanted to share data," Lamberti said. That's starting to change, in part through the emergence of working groups such as the Structural Genomics Consortium, cited in the report, which is seeking to improve the basic scientific understanding of the biology of diseases such as cancer, HIV, Alzheimer's disease and type 2 diabetes. If nothing else, these efforts are reducing the fruitless duplication of effort that has occurred when multiple biopharmas unwittingly chased the same failed mechanisms or targets.

It's certainly not news that efforts to enhance information technology, or corral "big data," also could enhance the drug development process. Tufts CSDD researchers suggested that combining clinical and health outcomes data, patient- and caregiver-reported outcomes and payer- and provider-reported experiences creates the type of real-world information that better informs drug development.

For example, the report identified the Life Science Project (LSP), built over a 30-year period by H. Lundbeck A/S, of Valby, Denmark. In the 1980s, the company started simply, combining compound and assay results with data from analytical procedures for screening drug candidates. Lundbeck gradually developed a data management system, expanding its database to provide data storage and retrieval and to incorporate decision and workflow support. The LSP now includes everything from data on genes, animals and compounds to late-stage exploratory toxicology studies.

The improved efficiency, according to the report, comes from having the logistics – registering and re-ordering reagents, plants and animals, for instance – handled in one system. Lab equipment also is connected to the database so that controlled file transfers to the equipment can be managed, progress can be monitored remotely and output data can be captured directly. The same system can be used by external chemistry partners, such as contract research organizations, to register new compounds into the database – all with the goal of speeding discovery and advancing promising compounds more quickly into development.

Big data also can be used to help enroll trials more quickly by identifying investigators, sites and participants.

"There's also the whole field of precision medicine – looking at improved effectiveness and speed in identifying new treatment pathways and mechanisms of action and targeting patient subpopulations that could, perhaps, respond to new treatments," Lamberti added.

Less popular, she said, is the adoption of adaptive trial designs, which have been used sparingly in phase II dose response assessments to improve phase III dose selection. Industrywide, adaptive designs have been used in only one of five trials, according to Lamberti.

The challenge with adaptive design is that "you have to gather the data throughout the study," requiring more interim looks and slowing the pace of the process. Although adaptive design offers the potential to enroll smaller patient populations into more tailored studies, balancing the longer timeline, it's still resource-intensive, she conceded.

GO GREEN

Finally, the Tufts CSDD researchers singled out the use of green manufacturing practices as contributors to improved drug R&D, mainly for the savings derived from improved operational efficiency.

Pressed on the nature of this relationship beyond simply aligning with a drugmaker's corporate values, Lamberti said, "We talked to a number of companies that were the early proponents of these practices. They instituted, for example, green chemistry and new manufacturing metrics – such as a solvent selection guide – to achieve better practices. These are actual practices that are being implemented to lower manufacturing and operating costs, reduce greenhouse gas emissions and reduce the carbon footprint."

Continuous batch processing, which consumes less energy and water, also contributes to lower drug development costs, she maintained, naming Johnson & Johnson, of New Brunswick, N.J., and London-based Glaxosmithkline plc as two proponents of sustainability.

The retooling approaches studied by Tufts shouldn't necessarily be used as a template to improve R&D efficiency, Lamberti cautioned. Each measure has advantages and disadvantages, adoption across the industry is uneven and it's not clear how much these efforts will ultimately reduce the average time or cost to bring a drug through FDA approval. Last year, CSDD researchers estimated that cost at 10 years and a stunning $2.558 billion of investment. (See BioWorld Today, Nov. 19, 2014.)

The mechanisms underlying some of these efforts, including increased collaboration and pre-competitive data sharing, could improve the overall drug development paradigm. But prioritizing the strategies isn't the same for every company.

"I wouldn't say that one is more effective than any of the others," Lamberti told BioWorld Today. "It all comes out of the challenges in R&D. They're inter-related, and it would be hard to say one or the other should come first. It depends on the strategic direction of the company, the leadership and the culture."

The Tufts CSDD report, which was funded by an unrestricted grant from the Pharmaceutical Research and Manufacturers of America, was developed from a review of published research as well as three dozen in-depth interviews with industry experts and company representatives, conducted in late 2014 and early 2015.

The Tufts CSDD researchers also convened a roundtable in 2015 to engage industry stakeholders in discussion and gather additional insights into the findings.

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