Improving Pharma Productivity One Step at a Time

May 17, 2012

I had a rare and refreshing experience yesterday afternoon. In meeting with a startup team from a big pharma company, the subject of source data verification came up. We like to use a digital pen CRF as source data, obviating the need for source data verification, one of the most inefficient steps in the research process. Why waste a third of a study budget comparing transcribed values to source data when you can eliminate transcription?

The reason for such large-scale waste on SDV is that change is difficult for everybody—CROs, sponsors and sites. In the case of the study under discussion, our understanding was that the site’s standard practice was to record data first in their EMR system. We checked and the EMR system couldn’t grant selective access to study-specific data, therefore we couldn’t use CRFs as source data. We also couldn’t access data to do remote monitoring. We were stuck with verifying transcriptions from the EMR system and doing tedious, costly onsite data verification. We couldn’t be efficient because it was a given that we couldn’t ask the site to change its workflow and the site wouldn’t change if asked.

When we meet to plan clinical studies, the givens always have a seat at the table. In this case, it was a given that we couldn’t interfere with how a site works. I recall a similar issue coming up more than two decades ago when we started using EDC (RDE at that time for those of you old enough): most site staff barely knew how to use computers so it was a stretch to ask them to enter data at the computer keyboard. We chose sites then based on criteria such as therapeutic expertise and patient access and we still do. The difference is that back then, we couldn’t do EDC because it was a given that site staff couldn’t deal with PCs and enter data. Now everybody can do EDC, but it’s often a given that we can’t use CRFs as source data because of site workflow.

What made yesterday’s meeting so refreshing was that things were different. This time, the sponsor said, let’s have the sites change their workflow a bit. We hadn’t even considered that possibility. It was the sponsor who was willing to break with routine and ask a site to make changes. I can’t tell you how different this is from dealing with big pharma companies that specify performing 100% SDV onsite for all of their studies and aren’t interested in hearing about alternative data-capture and monitoring methods.

Result: the digital pen will be capturing data as source (our database receives an electronic copy immediately and the paper CRF is left behind in the patient’s file). A report will be on the sponsor’s desk within minutes. The sponsor will spend a lot less on monitoring because of reduced SDV. No transcription errors will get past a bleary-eyed monitor and into the study database. Perhaps best of all, rapid access to actionable data will enable timely decision making that keeps the study on track. A bit of change at the site will provide an enormous benefit to the sponsor who was bold enough to ask.

We all acknowledge a crisis in pharma productivity that demands bold thinking and big changes. We need fresh thinking about our whole approach to clinical development with three phases and white space and p-values and delays that have been givens for decades. We need to think big. At the same time, we shouldn’t forget to think small. This sponsor wasn’t willing to accept familiar routines as an excuse for wasting resources and asked for a small change at the site. The lesson for us all is to look for potential improvements in the areas where we don’t even think about change because well, it’s a given that that’s how things are done.

The next post will tackle one of the big questions: In the age of real-time data, Bayesian statistics and instant informatics, why can’t development programs be continuous?

Why is Enrollment So Hard?

May 10, 2012

Although enrollment is one of the most pivotal determinants of study success, most researchers acknowledge doing poorly yet seem unable to improve their methods. As an industry, our performance is shockingly feeble: on average, a paltry 15% of studies enroll on time. In most businesses, that kind of performance would be a clear definition of failure. Combine this with other surprising metrics such as spending 1/3 of study budgets on rework and you begin to wonder how long you would survive if you were in any other industry.

Extended timelines directly hurt study costs – take the painful change order that is a staple of the CRO. Most study budgets end up being at least 20-30% over original pricing, often 100%, and this is driven primarily by slow enrollment. A far bigger price is the indirect cost of delayed time to market, where opportunity costs range between 4 and 80 million dollars a month and can transform a potential market leader into an also-ran.

Taking a lesson from other industries, particularly manufacturing, the answer is remarkably simple – a matter of having certain fundamental tools and access to timely information that enables competent decision making. This basic principle, timely and actionable information, is what is largely (but not entirely) lacking in pharma. This issue is: what information do I need to access to be able to know how to improve enrollment? Three important questions affect enrollment:

• What is getting people in the door?
• What then prevents people from getting into the study?
• Why do we lose people during a study?

With these questions answered, the majority of the issues faced in the enrollment process are no longer troublesome. A project manager who is aware of all these aspects of her study can uncover and share best practices among sites in order to solve these common enrollment issues. Below I’ll provide some examples of how information access and empowered decision making by study project managers made a substantial difference in the success of enrollment. These examples are taken from actual studies that utilize sophisticated technology.

First let’s look at an example of getting people in the door. In this instance, the study was for a new STD treatment. The project manager had systems that provided immediate field data of enrollment rates by site. When she noticed that one site was enrolling much faster than the rest, she quickly determined from the site coordinator why: recruitment flyers in the restrooms of local nightclubs and bars. The project manager swiftly notified the other sites of this technique and the three sites which chose to implement this method subsequently showed a substantial increase in enrollment rate, allowing the study to enroll several months ahead of schedule. Timely knowledge of the success of an unusual idea at one site made all the difference.

Next, let’s look at a case of enrolling people into a study once we’ve gotten them into the door. Oftentimes this can be a tricky piece of the enrollment puzzle. In the case of a pediatric growth hormone study, the project manager noticed a sudden surge in screen fails at a key site in the study. He immediately contacted the site coordinator to start investigating the cause of the spike. It was soon discovered that potential patients were screen failing due to an issue with the QC of the instrument being used to test samples. Once they fixed that issue, the kits from those subjects were once again usable. It was an analysis issue, and not that the subjects themselves were out of range. In a study that was otherwise difficult to enroll, many viable patients would have been excluded if the project manager had not been able to quickly spot the issue and work to solve it.

The moral of the story is to ensure access to actionable, role-specific information in near real-time. This is the rare exception in pharma, but this approach is being implemented by some CROs right now. It’s time for the rest of pharma to catch up.

The Importance of Information-Flow: The Case of the Thwarted Startup

May 3, 2012

In my last post, I discussed the cultural problem of excessive risk-aversion preventing pharma and CROs from using methods that could improve R&D productivity. This issue is never more evident than when applied to the critical area of information flow and decision making. Here’s an example of how a potentially important drug failed in a manner that might have been prevented if the company had good information flow, based on capabilities that existed at the time.

The overall study results produced a p-value of slightly greater than 0.05, which unambiguously spelled disaster. But a closer look revealed a single site in Europe with results that differed from most of the other sites. The company’s former CEO claimed that these deviations were due to failure to radiate patients at the time required by the protocol, and that exclusion of the results from this single site dropped the overall study results below the magic 0.05 level. It was unclear whether the outliers were actual protocol deviations or just extremes, and the FDA rejected the NDA. The CEO insisted the drug was successful and important for patients, and filed its application “over protest” from the FDA, submitting an analysis that excluded the sites felt to have performed poorly.

Without first-hand knowledge of the study, it is impossible to say whether the CEO’s interpretation of events was correct. However, one thing is clear: the sponsor and the team managing the study did not find out about serious problems in the field until it was too late to intervene and save the study. The outcome might have been dramatically different if problems had immediately been detected and corrected.

This case is a perfect example of how lack of access to timely performance metrics unnecessarily adds risk. Typical site monitoring schedules leave studies open to potentially devastating failures, and most EDC systems focus on collecting data to the exclusion of indices of how a site is performing, including key measures that affect study cost and timelines such as enrollment. Such indices are far more important for ensuring effective study management than subject data yet our industry generally regards these as an afterthought at best. The most regrettable part of this approach is that the means to avoid such problems exist and have been long used, albeit not widely.

Similarly, you might remember a high-profile story from a couple of years ago where a large biopharm company received a 483 (FDA notice of deficiency) for incorrectly dosing pediatric patients. This was another protocol failure that could have been corrected by getting timely, accurate data in readily interpreted reports that alerted the sponsor when the study started down a path that was the last thing the sponsor wanted or expected. Processes that could have allowed prompt intervention and prevented recurrences of such unfortunate events do exist. The question is why pharma companies that have invested heavily in basic science, preclinical and first-in-man studies allow costly later phase studies to proceed without ensuring access to accurate information about what is happening in the field. Fortunately, proven technology and processes exist that can powerfully reduce the risk of such errors.

In my next post, I’ll talk about other operational issues that remain the most immediate and powerful means of enabling faster, less risky, and more efficient study conduction.

Breaking with Bad Culture: Trading Risk-Aversion for Efficiency

April 27, 2012

A recent article in Nature Reviews Drug Discovery by Jack Scannell and his colleagues at Sanford Bernstein identifies four primary causes of declining pharma R&D productivity, including the “throw money at it” tendency. Scannell and co-authors list the increasing number and scale of clinical trials as secondary symptoms. David Shaywitz provides an excellent recap, and Bruce Booth enlivens the debate by blaming “the bulk of the last decade’s productivity decline” on a “culture problem” in the drug industry.

Slashing R&D budgets (see Sanofi, Merck and others) may solve the tendency to throw money at problems, but it won’t create new drugs. Eli Lilly CEO John Lechleiter recently articulated a similar sentiment, saying, “I don’t think we can save our way out of the enormous challenge we face…the best course is to maintain our focus on advancing our pipeline.” Like Lechleiter, I believe the answer lies in improving the efficiency of clinical development.

However, I also agree with Booth’s cultural diagnosis because it is consistent with what I see in clinical trials. The number and size of trials are issues, but the biggest problem is the typical inefficiency with which the pharma industry runs each trial. As a micro example, take the burgeoning use of outsourcing. The point of outsourcing clinical trials is to increase efficiency, but pharma often requires CROs to bid based on using inefficient methods dictated in the RFP. Pharma companies seldom ask CROs to achieve a goal, such as ensuring data quality. Instead, they require a certain number and timing of monitoring visits as well as 100% SDV. This raises costs far above what is actually required to ensure high-quality data. It also excludes bids based on more efficient methods. The only explanation I can think of for mandating inefficiency in the depths of a productivity crisis is cultural—an excessively risk-averse outlook based on ingrained fear of regulatory scrutiny of even the smallest decisions.

If we can find a way to advance good drug candidates through clinical development faster and at a lower cost, we will go a long way towards solving the pharma industry’s productivity crisis. In my next post, I’ll talk about a few positive steps we can take in that direction.

Welcome to Trials Without Tribulations

April 19, 2012

Greetings!  As the CEO of Health Decisions, a CRO based in Durham, NC, I’m on the front lines of clinical research day in and day out. I’d like to welcome you to my new blog, Trials Without Tribulations.  

TWT will express my viewpoint on the challenges facing our industry and the coming era of exponential change.  You can expect to find things like explorations of techniques in study design and operations and commentary on industry news and regulatory guidances.  I’ll ask provocative questions, such as:  Why can’t clinical development be continuous?  What can we do to reduce the number of costly late-stage failures?  How can we make clinical development of personalized medicine affordable?  I hope you’ll join me in coming up with solutions to these difficult but surmountable challenges. I encourage readers to comment on anything and everything to help make TWT a productive and meaningful forum. 

I am convinced of this: If the product works, the trial should succeed—without wasting a single day or dollar. Right now, we are falling far short of that goal. I believe we can do much better. By examining pharma’s uncertainties and embracing change, together we can find the way to move forward with greater efficiency.  I invite you to become active readers and look forward to a lively discourse on issues in clinical development.  Welcome to Trials Without Tribulations!  Come back often.

Michael Rosenberg, MD, MPH