Jon Roth | Gary Urban

Biorasi Making the Most of Trial Data

Biorasi Making the Most of Trial Data

Are you making the most of your clinical trial data? Why sponsors must think beyond the regulatory submissionBig data and real-time analytics have reinvented retail, financial services, and manufacturing, among other industries. Banks can detect fraud the instant it happens, saving them millions annually. Retail operations seem to recommend products as soon as we think of them. And manufacturers can perform maintenance on machines before they break down, preventing lost production time.

Even healthcare is tapping into analytics to support population health and precision medicine initiatives. Meanwhile, the pharmaceutical industry spends millions on drug development yet continues to suffer from declining success rates.

To control costs, improve efficiency, and successfully bring more drugs to market—life-changing, life-saving drugs—pharma and biopharma companies need to follow the lead of the financial industry and forward-thinking health systems. To prevent success rates from sliding further downward, they must tap into an underutilized resource held in abundance—data.

The average clinical trial generates up to three million data points.1 Why wait until regulatory submissions to put that data to use?

Sponsors and CROs can reduce preventable errors, costly delays, and data compromise by evaluating data throughout a clinical trial lifecycle rather than just at the end of it. Addressing potential issues as they occur—before they become major problems—saves time, reduces cost, and increases accuracy.

"Clinical data is the only tangible thing a pharmaceutical company gets from its entire research and development spend," says Jon Roth, Vice President of Data Sciences at Biorasi. " Initial, ongoing, and future leveraged use of the data is critical."

Here, we discuss how a data-driven approach to clinical trials—from patient recruitment to post-regulatory approval—can put an end to missed deadlines, failed studies, and wasted time and money.

Why is good data being ignored?As clinical trials become more complex, data collection and patient recruitment become more challenging. Most trials don't meet planned patient recruitment milestones, resulting in excess costs and needless setbacks. Some studies suggest each day of filing and approval delay can cost a sponsor $1 million in back-end sales.

Because patient recruitment and retention are so important, sponsors naturally focus heavily on enrollment, screening, and other patient-related issues. This "tunnel vision" often comes at the expense of initiatives such as ongoing data analytics to identify other areas of potential concern, or to ensure the quality and accuracy of the study results.

However, not reviewing data throughout the trial introduces a host of preventable errors. The FDA conducted a prospective review of drug application submissions between 2000 and 2012 for new molecular entities. Preventable deficiencies, including failure to select optimal drug doses and suitable study end points and inconsistent results between different trials or study sites, accounted for most of the unsuccessful applicants.2 By identifying and preventing the preventable, sponsors are more likely to have their application submissions accepted the first time, avoiding expensive amendments.

Some pharmaceutical companies also fail to see the value in investing in improved data analytics capabilities. As they resist change, they overlook opportunities to improve clinical trial efficiency at all levels, as well as develop safer and more effective drugs.

As therapies become more targeted, studies become more complicated, and finding patients becomes more laborious. Sponsors need more advanced tools to keep moving forward.

Types of dataSponsors have different types of data at their disposal, all of which serve different and important purposes in a data-driven approach.

Biorasi Making the Most of Trial Data

Clinical dataClinical data includes a wealth of patient information and assessments made about patients' conditions.

Raw data derived from participants includes the following:

• Demographic information such as gender and date of birth.

• Vital statistics such as blood pressure, weight, and heart rate.

• Medical history, including physical exam information, bloodwork, procedure, and imaging results.

• Self-reported data, such as quality of life measurements, pain levels, and symptoms.

• Consumer health data, such as readings from remote monitoring devices and health apps and consumer genomics data.

Depending on the study, investigators may interpret some of this raw data and enter it into report form. Examples of this abstracted data include reading a CT scan to evaluate tumor size or evaluating cholesterol levels from bloodwork. Raw data may also produce calculated data such as a change in weight from baseline.

Trial-specific dataIn addition to participant information, sponsors also have access to data about the clinical trial itself. This study-specific information, when analyzed correctly, helps sponsors understand whether a study is meeting its objectives. Questions this data can answer include:

• Are my sites meeting their enrollment goals?• Are the participants keeping their appointments? • How many queries is each site receiving?• Are they responding to queries in a timely manner?• Is a site prioritizing my study or do I have a "B" team?

"Sponsors or CROs should start looking at this data from the moment they get their first readings and continuously from that point forward," says Gary Urban, Senior Vice President of Process and Quality for Biorasi. "This saves time on the back end; there's less data cleaning and you get to lock more rapidly."

Trial metadataClinical trial metadata, and groupings of metadata, can be governed, managed, and used for insight. Sponsors and CROs can also trace metadata elements through an entire clinical trial lifecycle. Metadata management helps studies maintain full regulatory compliance from capture through submission. Examples of questions answered through metadata include:

• How long is it taking to complete a step in my project plan?

• How quickly is data being processed?• How efficiently is the CRO staff working?

How sponsors can use clinical data before study completionReviewing clinical data throughout a study gives CROs and sponsors warning of problems at a time when they can correct them relatively easily. This clinical data review is most often in the form of a patient profile, which provides a detailed listing of all of the clinical data available on a patient. Addressing issues early helps keep a study moving forward, thus lowering risk of failure.

A few ways to use data before a study's completion include the following:

Improved site monitoringIs one site rating patients consistently higher or lower than others on a crucial scale? Early data analysis uncovers anomalies such as data bias or random error during study enrollment and implementation. After investigating the issue, the sponsor may learn the rater or investigator needs additional study-specific training.

Is one site reporting significantly more (or less) adverse events than others? The study team should conduct a formal investigation. Is there a concerning pattern developing? With ongoing data analysis, corrective actions can take place much earlier in the study and illuminate issues that simply cannot be detected via an on-site visit alone.

Biorasi Making the Most of Trial Data

Safety and Efficacy Information Using data analytics, sponsors can get an early indication of drug safety and efficacy. In a blinded study, usually you can't make any official determinations until the study is locked and the data is unblinded.

Sponsors can, however, look at the pooled variance and averages across patients or sites and determine if the drug is doing what's expected. In unblinded studies, sponsors can use data analytics in an ongoing manner to guide decision making rather than wait until the very end to “open the box.”

Fraud detection"Fraud is more common in the industry than people want to admit," says Roth. A deep dive using Big Data technology can pinpoint instances where patients seem too similar or unsimilar—a sign of potential fraud.

Investigators may fabricate study data or participants to boost enrollment numbers and receive higher compensation. Digit preference is one way data analysis detects this type of fraud. Digit preference means the tendency for humans to prefer certain numbers, such as 1s and 0s, to others. When people fabricate data, they tend to enter numbers they prefer. Data scientists can compare the prevalence of digits between sites to identify digit preference.

Other methods to detect fraud include using a big-data approach to identify unusual data patterns within sites and patients. In order to add depth, or “bigness” to the data, a CRO can use data from other sources, such as Real World Data or data from similar studies, as a bench mark.

Reviewing data throughout the trial can also uncover "professional patients": people who enroll for one study at multiple sites, which allows them to collect multiple payments. When the data reveals patients with identical birth dates, with similar initials, height, and weight, the sponsor or CRO can investigate the anomaly.

More efficient regulatory submissionData integrity is paramount for clinical research, for the participants, and for gaining regulatory approval. To ensure data integrity, a traditional risk-based monitoring plan often isn't enough. Closely monitoring data ensures you can address errors quickly and shows regulatory agencies you've fulfilled your obligations to protect data integrity.

How sponsors can use clinical data after study completionSponsors can use clinical trial data during the course of that study as well as after its completion. Data shows where a study succeeded and where it went off track. This information can inform future research in the following areas:

Efficacy and safetyConsistent data analytics helps a sponsor identify and correct efficacy and safety issues well before compiling information for FDA and other regulatory bodies. By integrating safety and efficacy data from multiple clinical studies, sponsors get a general understanding of safety and efficacy for a drug compound or device.

In a given submission for a new drug, a sponsor might submit 15 studies. By compiling a global database, the sponsor can draw conclusions that wouldn't be possible with one average-size study. For example, a sponsor may notice higher or lower efficacy in certain subpopulations or in people with certain genetic mutations.

Researchers out of Peking University People’s Hospital, in Beijing, China, analyzed 36 studies to determine the safety and efficacy of initial combination therapy compared with monotherapy in drug-naïve type 2 diabetes patients. Results found combination therapies resulted in significantly lowered HbA1c levels.3 AstraZeneca Ltd. (China), which funded the study, could not have achieved statistically significant results from one study alone.

"If you get a big enough pool, you can discern patterns across studies, even in different therapeutic areas," says Urban. "You're not just reading and reporting results; you're looking for relationships and patterns of relationships."

Improve patient recruitmentPatient recruitment and retention are two of the most challenging aspects of clinical trial management, and they're not getting easier. With drugs becoming more targeted, companies often compete for the same patients.

Biorasi Making the Most of Trial Data

Using data wisely, sponsors can use more than physicians' patient lists for recruitment. Powerful analytics tools can mine electronic medical records and even social media to find patients. These tools can also hone in on genetic information, disease status, and individual characteristics, allowing sponsors and CROs to find patients that meet inclusion and exclusion criteria quicker and easier.

By observing recruitment rates at sites early and often, sponsors and CROs can quickly identify lagging sites. Based on the data, project managers can determine whether to add sites or increase enrollment at high-performing sites.

Gain insight for future studiesThe average pharmaceutical company has volumes of data from past studies to use to benefit further research. With data analytics and text mining, pharmaceutical companies can mow through peer-reviewed medical journals, regulatory information, reports of side effects, and other information to discover and develop new drugs and find new indications for existing drugs. With the right tools, a sponsor can combine real world data and past-study results to discover a new drug that targets a rare disease, or find an alternative use for a drug already on the market.

Mining data may also help pharmaceutical companies have more successes than failures. By looking at data from failed studies, they could gain insights that help ensure they don't repeat the same mistakes.

Ensure and protect patient privacyMost clinical trials have airtight processes in place to protect patient privacy. Patient-identifying information rarely makes it into the datasets. However, mistakes do happen. Watching data closely throughout the trial ensures any identifying information is quickly spotted and removed.

Observing data often also ensures sites inform all patients of their rights as a participant. You don't want to get to the end of a study, only to discover a site didn't follow proper protocol.

The Value of Data ScienceUnlike most CROs, Biorasi has a Data Sciences group and traditional data management and biostats units. The data science team converts sponsor data into CDISC format from the beginning of clinical data collection, so analysts can review that data early in a project.

This ensures that we assess all of the patient’s data, not just a limited subset. It also means that our On-Study Analytics engine is able to assess data from different studies without extensive reprogramming and customization.

Using TALOS™, Biorasi's clinical trial platform, we build our workflow around CDISC's SDTM and ADaM standards. This allows us to achieve consistent data between studies and further reduce the chance of errors prior to and during regulatory submissions. We identify anomalies early and pass along this information to site monitors so they can conduct investigations or follow up as needed.

TALOS also helps us determine whether an investigator or site staff needs additional training or whether there's an issue with data bias. We use study data as it develops and grows, rather than waiting until the last phase of the process to evaluate that data.

ConclusionThe traditional approach—focusing solely on operational issues during a clinical trial—is due for reinvention. site monitoring approach is due for reinvention. The pharmaceutical industry has the resources to integrate, manage, and analyze data at all stages of the clinical trial lifecycle. Those that use those resources to their full advantage to inform decisionmaking will gradually profit from more innovation, more breakthroughs, and no more stagnation.

"The data doesn't lie," says Urban. "It doesn't have feelings, a point of view, or a position. It just is. Our job is to tease out the story it can tell."

Biorasi Making the Most of Trial Data

1. Institute of Medicine (US) Roundtable on Research and Development of Drugs, Biologics, and Medical Devices; Davis JR, Nolan VP, Woodcock J, et al., editors. Assuring Data Quality and Validity in Clinical Trials for Regulatory Decision Making: Workshop Report. Washington (DC): National Academies Press (US); 1999. Final Comments. Available from: https://www.ncbi.nlm.nih.gov/books/NBK224576

2. Sacks LV, Shamsuddin HH, Yasinskaya YI, Bouri K, Lanthier ML, Sherman RE. Scientific and Regulatory Reasons for Delay and Denial of FDA Approval of Initial Applications for New Drugs, 2000-2012. JAMA.2014;311(4):378–384. doi:10.1001/jama.2013.282542

3. Cai X, Gao X, Yang W, Han X, Ji L. Efficacy and Safety of Initial Combination Therapy in Treatment-Naïve Type 2 Diabetes Patients: A Systematic Review and Meta-analysis. Diabetes Ther. 2018;9(5):1995-2014.

Resources

About the Authors

Jon Roth is an early pioneer in electronic data processing for clinical trials. Roth's experience is a crucial component of Biorasi's success as a data and bioinformatics leader.

Jon Roth | VP, Data Sciences & Biometrics, Biorasi

Gary Urban comes to Biorasi after decades of experience in management and technology consulting. His insights and experience guide Biorasi's continuous improvement. Urban is also the lead architect of the TALOS platform.

Gary Urban | Senior VP, Process & Quality, Biorasi

19495 Biscayne Blvd. Suite 900 | Miami, Florida 33180786.388.0700 | www.biorasi.com

Biorasi, founded in 2002, has received the coveted CRO Leadership Award from Life Science Leader magazine and has placed on the Inc. 500 list of America’s fastest growing companies. Biorasi has collaborated with sponsors to enable FDA, EMA, and multi-venue approvals for numerous small molecules and biologics. Biorasi, headquartered in Miami, Florida,

maintains office-based teams around the globe.