Sujay Jadhav

	Improving experimental drug success rate

 are top priorities for pharmaceutical companies. Careful decision making during drug development is essential to minimize development time, manage costs and improve the probability of commercial success. Recently, many of the 

 have begun to explore computer-based bio-simulation strategies to help generate the information necessary to make better decisions.

 These strategies go by many different names – clinical trial simulation (CTS), modeling and simulation (M&S), computer-assisted trial design (CATD), model-based drug development (MBDD), and model-informed drug discovery and development (MID3). The 

 regulatory agencies have also taken notice of M&S strategies in an effort to support improved drug development efficiencies.
	
	Computer-based modeling and simulation has already had a beneficial impact on many different fields and industries – 

. In finance, for example, professional investors traditionally work to find a handful of undervalued companies in which to invest, a process which typically involves interviewing management teams, researching corporate strategies, and analyzing demand for products and services. Artificial intelligence is changing all of that. Quantitative-investment, or "quant," funds rely on high-speed computers and trading models that evaluate publicly available data to make investment decisions – without ever talking to management teams. Today, one quant fund named Two Sigma is the fastest growing hedge fund on Wall Street, which manages over $35 billion in assets.
	
	Despite its promise, adoption of M&S in the pharmaceutical industry has lagged due to the complexity of modeling biological systems, insufficient scientific understanding of disease conditions, and lack of large amounts of real-world health outcome population data. Significant progress in these areas has occurred over the last decade, and M&S is now being promoted as having the potential to transform the drug development process from R&D all the way to commercialization and life-cycle management. Is this potential real or just hype?
	
	

What is Modeling and Simulation in Clinical Trials?


	
	With regards to the drug development process, M&S involves modeling compounds, mechanisms and disease level data based on historical observations. Computer simulations are run on these models to generate information that can be used to predict outcomes, thereby improving the quality, efficiency and cost-effectiveness of decision-making.
	
	For clinical trials specifically, a clinical trial simulation (CTS) would attempt to study the effects of a drug in a virtual patient population using mathematical models that incorporate information on physiological systems. Simulations can be used to test assumptions, improve predictability, better characterize risk and identify opportunities to optimize outcomes by observing the effects of different model inputs – an understanding of the full range of potential outcomes can be cultivated by observing the effects of more extreme model inputs than have been observed in real-world patients, for example. In this way, M&S can help investigators better plan and design clinical trials by exploring and quantifying risks prior to their start.
	
	

Examples of Modeling and Simulation in Clinical Trials


	
	Efficacy and safety issues are of the upmost importance in clinical trials – either the substance in question does not have sufficient biological activity, or it does not have manageable toxicity. But the level of efficacy or toxicity in a drug is very much related to the dose level and schedule used. Poor dose and scheduling choices can have serious consequences for drug safety and efficacy in a clinical trial, resulting late stage failures, or registration delays as problems must be investigated and corrected. As a result, determining the proper dose and scheduling of a drug prior to the start of a clinical trial is extremely important in order to avoid a preventable failure.

	The most mature application of M&S in clinical trials is 

pharmacokinetic modeling for dose and scheduling determination

. This M&S application has been successful in predicting optimal dosing regimens from preclinical to Phase III studies.

 By understanding patterns in the exposure-response relationship, population PK/PD analysis can also help to identify dose adjustments needed for special populations - children, the elderly, ethnic groups, patients with impaired renal/hepatic function, and patients likely to experience drug-drug interactions.

	
	Other uses for CTS may include answering those questions that can be difficult or impractical to answer using clinical trial methods. When the American Diabetes Association wanted to compare the effectiveness of current diabetes management approaches, for example, Archimedes Inc. simulated a 30-year clinical trial using a physiology-based model to predict outcomes in a patient population.

 Physiology-based models strive to model disease processes at a biological level using equations that are calibrated with data from empirical sources. When properly constructed, these models can be used to identify priorities in clinical trials, facilitate design of new trials, or conduct virtual comparative effectiveness trials.
	
	M&S also appears to have value in optimizing study design.

 The idea is to increase the efficiency of trials by establishing appropriate trial size and collecting relevant data at optimal times to generate knowledge. Simulations are used to explore different trial designs to select the best option.
	
	Use of M&S in clinical trials seems to have a dramatic impact on FDA approval and labeling decisions. A 2011 review conducted by the FDA found a dramatic increase in both the number of reviews with pharmacometric analysis and the impact of those analyses on drug approval and labeling decisions. Pharmacometric analysis was found to have made an important contribution to 126 drug approval decisions (64%) between the years 2000 and 2008. Additionally, pharmacometric analysis was found to impact labeling decisions in 133 applications (67%) during this time period.

 Finally, in the midst of an influenza epidemic in 2009, the FDA used M&S to identify and 

 of an experimental drug that had never been studied in children – peramivir.
	
	

Modeling and Simulation Throughout the Drug Development Process


	
	The pharmaceutical industry is slowly beginning to adopt M&S across many different aspects of the drug development process. One example is the use of M&S to assess structure-affinity relationships of experimental drug compounds to predict toxicity and safety.

 Results of these kind of simulations are increasingly being utilized by regulatory agencies. Another example is the use of M&S to predict the overall cost-effectiveness of new medicines in the health technology assessment process.

 M&S is also being used to provide for more effectively management of a biopharma company’s R&D development portfolio.

	The opportunities for cost and time savings in the drug development process by utilizing M&S are enormous. This economic incentive is being supported by the rapid growth of computational power and patient health data, along with advances in scientific understanding. Because of this “perfect storm” combination of factors, scientists are starting to utilize M&S to tackle some of medicine’s toughest what-if questions, and new applications for M&S throughout the drug development process are being discovered rapidly.


	
	While adoption of M&S by the pharmaceutical industry has been slower than in other industries, recent years have seen M&S utilized in all phases of the drug development process. M&S practices support knowledge-based approaches that can make drug development processes more efficient and informative, thereby enhancing return on investment for drug developers in today’s challenging business environment. M&S offers drug developers in pharmaceutical companies the opportunity to quantify problems, test assumptions, increase predictability, improve decision-making, and ultimately lower costs.
	
	As advances in computational power, patient health data, and scientific understanding continue to grow, M&S will likely play a larger role in the development of lifesaving medicines – in terms of supporting both pharmaceutical company internal decision-making, and applications for regulatory approval. Increased reliance on M&S will lead to new, more collaborative ways of working, as experts from diverse fields will be required to come together to frame the questions and quantify assumptions for simulations. Ultimately, these collaborative efforts will serve to improve the drug development process, leading to better medicines delivered to patients in a timelier fashion.

		“Clinical development success rates for investigational drugs,” Nature Biotechnology 32, 40-51 (2014). Michael Hay et al. Available at: 

http://www.nature.com/nbt/journal/v32/n1/full/nbt.2786.html#access

		Drug Approvals - From Invention to Market...A 12-Year Trip. MedicineNet.com 

http://www.medicinenet.com/script/main/art.asp?articlekey=9877

		“Building a Modeling Culture in Manufacturing at Pfizer,” Pharmaceutical Manufacturing, 2011. Joe Cancelarich. Available at: 

http://www.pharmamanufacturing.com/articles/2011/023/

		“Novartis Savors Early Modeling Success,”Bio-IT World, March 2009. John Russell. Available at: 

http://www.bio-itworld.com/pb/2009/03/19/novartis-modeling-stanski.html

		“Pharmacokinetic and Pharmacodynamic Modeling of the Relationship Between TAK-442 Exposure and Clinical Outcome In Patients Undergoing Elective Knee Arthroplasty,” Blood 2010 116:3326; Stringer et. al. Available at: 

http://www.bloodjournal.org/content/116/21/3326?sso-checked=true

		“Use of Modeling and Simulation in the Design and Conduct of Pediatric Clinical Trials and the Optimization of Individualized Dosing Regimens,” CPT Pharmacometrics Syst Pharmacol. 2015 Nov; 4(11): 630–640; Stockmann et. al. Available at: 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4716585/

		“The pharmacokinetics and pharmacodynamics of cisatracurium in critically ill patients with severe sepsis,” Br J Clin Pharmacol. 2012 May; 73(5): 741–749; Liu et. al. Available at: 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403201/

		“Life Cycle Modeling and Simulation: Current Practice and Future Impact on Health Care Innovation and Delivery,” Therapeutic Innovation & Regulatory Science 47(2) 175-182; O’Kelly et. al. Available at: 

http://journals.sagepub.com/doi/abs/10.1177/2168479012471831

		“Model-Based Optimal Design and Execution of the First-Inpatient Trial of the Anti-IL-6, Olokizumab,” CPT Pharmacometrics Syst Pharmacol. 2014 Jun; 3(6): e119; Kretsos et. al. Available at: 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4076804/

		“Impact of Pharmacometric Analyses on New Drug Approval and Labelling Decisions A Review of 198 Submissions Between 2000 and 2008,” 

 50(10):627-635 · January 2011; Lee et.al.  Available at: 

https://www.researchgate.net/publication/296935744_Impact		_of_Pharmacometric_Analyses_on_New_Drug_Approval_and_Labelling		_Decisions_A_Review_of_198_Submissions_Between_2000_and_2008

		“(Q)SAR modeling and safety assessment in regulatory review,” Clin Pharmacol Ther. 2012 Mar;91(3):529-34; Kruhlak NL et. al. Available at: 

https://www.ncbi.nlm.nih.gov/pubmed/22258468

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		“Lifecycle Modeling and Simulation: Applications in Clinical Development,” Applied Clinical Trials, Apr 15, 2014; Garrett et. al. Available at: 

http://www.appliedclinicaltrialsonline.com/lifecycle-modeling-and-simulation-applications-clinical-development?pageID=2

Articles

The adoption of M&S to the pharma industry has been slow, but more noticeable in recent years as these practices have improved the efficiency of drug development processes.

Modeling and Simulation in Clinical Trials: Real Potential or Hype?

	If you have ever tried to find the right clinical trial for yourself or a loved one, you know the process can be confusing and challenging. For example, in the age of precision medicine, oncologists often offer multiple treatment options, from FDA approved indications to experimental therapeutics that are not yet on the market. Meanwhile, for sponsors, effective patient recruitment for clinical trials is critical to meeting timelines and budget goals.
	
	Recognizing the need for alignment, several so-called clinical trial to patient matching services have emerged. Though well-intentioned, some of the current formats adopted by these service providers may inadvertently be introducing additional complexity into the lives of patients looking for life saving therapies. In order to properly utilize these services, patients obviously must first be aware of the availability of the service. Additionally, it is important for patients to understand if these services have a non-biased perspective, as there is obvious conflict of interest potential with matching services being provided by sponsors. Ultimately, selecting the right service (or even being aware of its existence) can be more formidable than working with your physician to find a clinical trial in the first place. How can that be the case? To understand this assertion, first we must explore the plethora of options available and how they operate.
	
	

Clinical Trial to Patient Matching Options


	
	There are many different types of clinical trial matching services, but they all aim to connect the right patients with the right studies, aiding sites and promoting clinical research awareness.
	
	For sake of clarity, these clinical trial matching services can be categorized as: services that require registration and auto-match patients to clinical trials, services that do not require registration and simply allow patients to browse online databases of clinical trials, and disease-specific services.
	
	

 Similar to a dating site, patients seeking a clinical trial enter key demographic and health information into an interface. Based on this information, the service matches each patient with the most suitable studies currently enrolling, providing basic study information, as well as, typically allowing communication with other patients.
	
	An example of this kind of service is 

, which allows users to create an account and enter basic demographic information to get ongoing notifications about clinical trials they may qualify for. Once registered, patients can also connect with other patients who are working to manage the same condition, share health experiences and track treatment history. In the process, users generate data about the real-world nature of disease that help researchers, pharmaceutical companies, regulators, providers, and nonprofits develop more effective products, services and care. With more than 325,000 members, PatientsLikeMe has become a trusted source for real-world disease information, as well as, a clinically robust resource that has published more than 60 peer-reviewed research studies.
	
	

Services that do not require registration:

 These services typically offer an easily searchable online database, allowing patients to search clinical trials that are recruiting patients and contact those that they are potentially interested in. Some of these kinds of “no registration required” services will allow you to contact them by phone, where a representative will speak with you about your condition and let you know about clinical trials that you may qualify for.
	
	An example of this kind of service is 

. First launched in 1994, this service provides the largest online database of industry-sponsored global clinical trials actively seeking patient volunteers. CenterWatch provides patients with unbiased information on thousands of currently enrolling clinical trials. Patients can search the CenterWatch database by medical condition, geographic location or research center to find the trial that best meets their needs. Contact information is shown along with each trial listed, allowing patients to contact trial researchers directly once they have identified a trial they are interested in. Patients can also sign up for a free e-mail newsletter that will send them a message whenever new trials are added in their area of interest.
	
	

 Some clinical trial matching services specialize in certain diseases and conditions. Most of these are hosted by patient-advocacy groups that play matchmaker - linking patients to researchers who need them for clinical trials. These services usually allow patients to connect with and be a part of a larger community of people who are all focused and motivated to find effective treatments for the same condition. HIV/AIDS groups in the 1980’s started the trend of patient communities focusing on trial recruitment and retention, and dozens of disease-specific groups have followed suit - breast cancer, autism, diabetes, asthma, etc.  
	
	An example of this kind of service is 

The Michael J. Fox Foundation for Parkinson's Research

 has helped to recruit more than 60,000 volunteers for Parkinson's disease trials. Potential volunteers register on the website and create a profile with their location, gender, date of birth and brief medical history. The system then generates a listing of possible trial matches from the database of recruiting Parkinson's disease trials, and offers a secure messaging service to contact investigators. Researchers can also review anonymous profiles of eligible volunteers and contact them through the messaging system to further explore participation.


	
	The sheer number and diversity of clinical trial matching services has expanded rapidly in recent years, creating its own set of problems.

	It used to be that the education and motivation required for patients to find the right clinical trial to participate in left many potential volunteers feeling overwhelmed. Organizations like the 

 (NIH) tried to help by providing a set of questions that patients should ask before enrolling in a clinical trial:

		Who is sponsoring the study, and who has reviewed and approved it?

		What kinds of tests, medicines, surgery, or devices are involved? Are any procedures painful?

		What are the possible risks, side effects, and benefits of taking part in the study?

		How might this trial affect my daily life? Will I have to be in the hospital?

		Who will pay for the tests and treatments I receive?

		Will I be reimbursed for other expenses (for example, travel and child care)?

 publish lists of questions for patients to ask themselves when vetting clinical trial matching services:

		Does the service keep my information confidential?

		Where does the service get its list of clinical trials?

		Does the service rank the studies in any particular order, and is the rank based on fees they receive?

	The rise of various clinical trial matching services is certainly a step in the right direction towards making clinical trial participation an easier process for potential volunteers, but there is still work to be done to streamline the process for patients. The bottom line is that busy lifestyles and the frenetic pace of life effectively assures that clinical trial recruitment will only succeed when the process is made easy and hassle-free for the patient.  
	
	

Key to Halting Declining Trial Enrollment?


	
	Patient recruitment in clinical trials is an area of growing challenge and concern for the pharmaceutical industry. Several factors have contributed to declining enrollment rates - increasing volume of clinical trials conducted worldwide, increasing number of inclusion/exclusion criteria, the nature of modern medical practice (doctors lacking time, resources and staff to enroll patients), etc.
	
	While these match-making solutions hold much promise for rectifying the problem of declining patient enrollment in clinical trials worldwide, the reality is that further centralization of the patient matching process needs to happen in order to facilitate an easy, smooth and efficient experience for potential trial volunteers. In other words, if clinical trial to patient matching services are going to play a role in reversing recruitment shortfalls, they need to be more patient-centric.
	
	

Taking a Lesson from Consumer Centric Services


	
	Clearinghouses exist everywhere for aggregating data sources and serving the data up to the consumer at the right time in the right context. When you shop for rental cars, hotels, or flights, it is a one-stop shop experience across multiple vendors through platforms like Expedia, Travelocity, and others. Guidance is provided as to when to purchase and how to get the best deal, while ancillary services and offers are provided to simplify planning.
	
	Could this consumer centric approach be applied to clinical trials? The notion of patient centricity is becoming a significant strategic focus across all sectors of healthcare. Defined as the process of designing a service or solution around the patient, patient centricity can also be thought of as meeting the patient needs at the right time in the right context. When you need a clinical trial, the relevant context typically involves your physician. Perhaps matching services could involve the physician, offering a view for both the provider and the patient? Such a design would ensure that the patient gets the information they need while the provider plays the role of examining protocols and medical terminology.
	
	We need a clearinghouse of matching services, a Travelocity for clinical trials. Should the patient be on the front lines when choosing a clinical trial? Is that truly patient centric? The solution must involve technologies and data sources that are aligned to the patient and physician, offering tailored information and services that guide each party through the process and onto the best result for the patient. Now that would be 

How Do Clinical Trials Protect Participants?

 US Dept. of Health and Human Services, NIH Website. Available at: 

http://www.nhlbi.nih.gov/studies/clinicaltrials/protect

How Do I Find a Clinical Trial That’s Right for Me?

 American Cancer Society, September 2016. Available at: 

http://www.cancer.org/treatment/treatmentsandsideeffects/clinicaltrials/whatyouneedtoknowaboutclinicaltrials/clinical-trials-what-you-need-to-know-picking-a-clin-trial

Finding the right clinical trial for yourself or others can be confusing and challenging. Matching services could provide potential relief, but only if aligned with both the physician and the patient.

Are Clinical Trial Matching Services Truly Patient-Centric?

	Imagine it’s 2025. Participating in a clinical trial does not require travel to a clinical research facility or doctor’s office. Your mobile device (maybe a phone, watch, or even your glasses) is your link to the clinical research study and how you report general information and adverse events. Wearable sensors record data such as body temperature and blood glucose levels, which are sent automatically to the study electronic data capture (EDC) record. The study personnel visit you at home for drug administration and follow-up. When a visit is approaching, your mobile device provides automated reminders, allowing you to reschedule the appointment within a time frame permitted by the study protocol. And that’s just the beginning of what the future is likely to hold for so-called virtual clinical trials.

	Virtual clinical trials represent a relatively new method of collecting safety and efficacy data from clinical trial participants, from study start-up through execution to follow-up. These trials take full advantage of technologies (apps, monitoring devices, etc.) and online social engagement platforms to conduct each stage of the clinical trial from the comfort of the patients home--including recruitment, informed consent, patient counseling, through to measuring clinical endpoints and adverse reactions. By relying on electronic processes, many argue that virtually conducted clinical trials offer opportunities for a more patient-centered approach.

	There are a number of advantages that virtual trials have over the traditional model, which uses multiple study sites and requires multiple patient visits to the site in order to conduct the study protocol. The most obvious advantage is that the virtual trial design maximizes patient availability and enrollment in the study. Patient recruitment and enrollment is often the longest stage of a clinical trial with almost 80% of trials failing to meet initial targets.

 Unlike site-based clinical trials, which require frequent visits to a designated research facility, remote clinical trials are based from the patient's home so those with mobility issues--such as the elderly or patients who live in rural areas--are also able to participate in the trial. The convenience of a virtual methodology alone will increase numbers of patients willing and able to enroll. Also, electronic health records can help identify increasingly targeted trial subjects and online patient support networks which could be used more to raise awareness of trials and directly recruit subjects. While virtual trials still require the study site to house support staff and invest in data collection and analysis platforms, they are potentially significantly more cost effective because they don't require the traditional brick-and-mortar set-up of multiple study sites.

	Another advantage to virtual trials is their potential to keep subjects engaged with the study. As many as 40% of Phase III trial subjects become disengaged and drop out of the study.

 Some of the causes of this attrition are related to convenience--due to issues like the inconvenience of traveling to study sites, or the complexity of the trial design and data collection. Virtual clinical trials could remove the need for frequent travel to study sites and automate data collection, increasing patient engagement and retention.

	Virtual trials also offer the ability to reduce risk in the drug development process. Data from remote monitoring devices could be accessed by trial investigators in real time, opening up possible efficiencies in data cleaning, which could move to an on-going process rather than cyclical. Remote monitoring capabilities could thus facilitate an adaptive clinical trial approach, allowing improvements in trial design based on the accumulating data. Decisions to terminate a drug’s development could also be made faster, improving patient safety and reducing expenditure on failed trials that have unfortunately become the norm in the drug discovery process.

	Finally, the virtual trial design may allow groups who have a vested interest in the success of the trial (including investors, physicians, government agencies, patient advocacy groups and even the patients themselves) to have more opportunities to play an active role in the study, potentially leading to better data quality and shorter timelines.

	In 2011, Pfizer pioneered the virtual clinical trial model with its Research On Electronic Monitoring of Overactive Bladder Treatment Experience (REMOTE) trial.

 The REMOTE trial was the first randomized clinical trial using web- and smartphone-based patient recruitment, enrollment and collection of study data without requiring patients to visit a physical study site.  One of the main goals was to compare the virtual approach to a conventional Phase IV clinical study in order to determine if the virtual trial design would be a feasible way to conduct future trials. Unfortunately, Pfizer’s REMOTE trial faced a host of challenges, not least of which was the issue of patient recruitment (most members of the target patient group were older, so the use of a technology-based trial was an unknown.)

	Early this year, Sanofi announced its intention to support a virtual diabetes trail (VERKKO) to be conducted remotely in Europe.

 This virtual clinical trial has one key difference compared to Pfizer’s REMOTE study in that no drug is being tested.  Instead, Sanofi has teamed up with three other organizations to test a 3G-capable, wireless glucose meter. This trial represents significant advancement in the clinical trial community, as it is the first clinical trial using an electronic informed consent approved by European regulatory agencies.

	Though the FDA has stated that they see benefits in the appropriate use of technology in clinical trials, they are still in the process of learning about virtual clinical trials, the bring-your-own-device (BYOD) model of provisioning and other aspects of today's tech-enabled research environment.

 A docket has been established to gather feedback on how researchers are using technology and what barriers are stopping more widespread adoption.

 The agency is seeking input on four specific issues:

		How the FDA could encourage adoption of such tools.

		What barriers are seen as blocking uptake today.

		How new models of research will affect patients.

		Whether the need to comply with regulatory requirements is seen as an impediment to the application of virtual technology in trials, as well as whether gaining clearance from institutional review boards is an issue.

	The fact that the FDA is seeking input is no surprise, as it is part of the FDA’s role to support, and even encourage, innovation. In fact, the FDA’s recently published new draft guidance document  “Use of Electronic Informed Consent in Clinical Investigations”

 explains how federal regulators will permit companies to use electronic media (like interactive websites) to help facilitate the informed consent process. This will certainly serve to help companies conduct virtual clinical trials. Most companies are finding the FDA to be very supportive of virtual trials. Transparency Life Sciences, a Boston-based biotechnology startup, for example, recently secured FDA approval for an entirely tele-monitored trial protocol in a mere 30 days, and was encouraged to do more innovation in the direction of virtual trials.

	The Pfizer case study and FDA docket suggest a multitude of challenges to come in the journey to make virtual clinical trials a viable choice for study sponsors. The new reality will require thinking differently about patient privacy and protection, as potential concerns about the inadvertent disclosure of patient data are likely to increase with virtual data collection practices. As the Pfizer study suggests, recruitment and retention of patients with low computer literacy is a major concern that can greatly impact the trial and data that it generates. In some study populations, this will likely present significant risks that call for careful monitoring and thoughtful mitigation strategies.

	Looking to the future, several scenarios seem plausible. Perhaps virtual studies will augment rather than replace traditional study practices and workflows. Virtualizing aspects of the study may be leveraged when the circumstances call for it--similar to how remote monitoring workflows are being adopted by study oversight teams today. On-site monitoring is still a mainstay of the study, for example, but much of the data is monitored remotely, as appropriate.  Or perhaps virtual clinical trials are used in rescue studies, where traditional models have failed (e.g.. for geographically dispersed groups or rare disease populations). Interestingly, the Sanofi VERKKO study does not test a drug but instead focuses on a wireless glucose meter. Perhaps virtual studies will lend themselves well to sensors and diagnostics, which will continue to increase in importance as the technology evolves. And hybrid models are likely to emerge as sponsors increasingly step forward to test the new model. As Pfizer’s experience shows us, it is not an easy road, but it is one likely to offer significant benefits for certain studies and select patient populations.

		Clinical trial delays: America’s patient recruitment dilemma” July 2012. Available at: 

http://www.drugdevelopment-technology.com/features/featureclinical-trial-patient-recruitment/

		Pfizer Conducts First “Virtual” Clinical Trial Allowing Patients to Participate Regardless Of Geography.” 7 June 2011.  Available at: 

http://press.pfizer.com/press-release/pfizer-conducts-first-virtual-clinical-trial-allowing-patients-participate-regardless-

		Europe's First Fully Remote Diabetes Trial Approved.” 10 February 2015. Available at:  

http://www.prnewswire.com/news-releases/europes-first-fully-remote-diabetes-trial-approved-291395161.html

		Gaffney, Alexander.  Regulatory Affairs Professionals Society.  28 October 2014. “FDA Wants Input on How to Improve Clinical Trials Process for Industry.” Available at: 

http://www.raps.org/Regulatory-Focus/News/2014/10/28/20636/FDA-Wants-Input-on-How-to-Improve-Clinical-Trials-Process-for-Industry/

		“Using Technologies and Innovative Methods To Conduct Food and Drug Administration-Regulated Clinical Investigations of Investigational Drugs; Establishment of a Public Docket.” A Notice by the Food and Drug Administration (29 October 2015). Available at: 

https://www.federalregister.gov/articles/2015/10/29/2015-27581/using-technologies-and-innovative-methods-to-conduct-food-and-drug-administration-regulated-clinical

		“Use of Electronic Informed Consent in Clinical Investigations.” FDA Guidance for Industry, March 2015. Available at: 

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM436811.pdf

		PharmaTimes article published in 2014 entitled, “Disrupting Clinical Trials”. 

Virtual clinical trials use tech devices and social engagement platforms to conduct trials from a patient’s home. These electronic processes offer new opportunities for a patient-centric approach to clinical research.

Virtual Clinical Trials: The Future of Patient Engagement?

	Many factors contribute to the increasing challenges and costs of developing medicines, including more complex disease targets, increasing regulatory requirements, larger clinical trial sizes, and greater focus on targeting chronic and degenerative diseases. Companies are exploring ways to reduce development times and increase the odds of success using new research models, adaptive clinical trial designs and methodologies, eClinical tools, innovative approaches to patient recruitment, and automated techniques for analyzing data. Still, challenges remain. Despite increasing expenditure in R&D, output has declined, at least in terms of the number of compounds. Also known as the “Innovation Gap,” the graph below (Figure 1) shows how the number of new molecular entities (NMEs) has generally declined in the last 30 years, despite a three-fold increase in R&D spending. While critics argue that not all NMEs are equally innovative (for example, one NME may offer an important new way to treat a disease, while another may work in a way that is similar to drugs already on the market), these numbers do tell a story.

As we look to the future of drug development, it is worth considering the relentless influx of new technologies unfolding on many fronts.

	Continued adoption of eClinical cloud-based solutions such as clinical trial management (CTMS), electronic data capture (EDC), electronic trial master file (eTMF), and study startup (SSU) has resulted in a quantum leap in terms of automating manual processes and addressing inefficiencies at different stages of clinical trial conduct. But what emergent technologies have the potential to further disrupt the clinical trial process, as it exists today?

	Some interesting potential disruptions on the horizon:

	There has been an explosion of new devices and applications that collect, report, and respond to information from a patient’s body, a trend that is sometimes referred to as “the quantified self.” The earliest devices took advantage of the sensors already available on most smart phones, such as accelerometers, magnetometers and gyroscopes – to track movement, count steps, and recognize sleep patterns. Because most tracking devices use phones and tablets for the user interface and rely on variants of the same sensor technology, the applications are almost unlimited. Most of the innovation occurs in the sensors used in the continuous data collection process. As such, new and custom sensors are beginning to emerge, designed for specific purposes. 

, for example, is a hearing aid that also features over fifteen sensors, tracking things like heart rate, respiration, oxygen saturation, and more. Alcon (Novartis) recently 

 with Google to in-license their “smart-lens” technology, which may lead to a number of innovations. Plans include the development of contact lenses with sensors capable of measuring biometric data such as glucose levels. 

	As sensors evolve, become cheaper and more readily combinable, the range and sophistication of applications is sure to expand, allowing us to combine them in ways we never thought possible. There are literally hundreds of devices and/or applications available now that can track vitals like heart rate, blood pressure, temperature, sleep patterns and even snoring during sleep. With this kind of data available from potentially millions of patients, what impact will this have on how data is collected and analyzed? Will we have remote clinical trials relying on both electronic patient reported information, as well as, sensor-derived data that is collected automatically 24x7, leveraging the power of a mobile platform without the need for costly infrastructure? Can we identify adverse events remotely and in real-time by using sensors? How will the advent of data sensors impact patient recruitment in clinical trials, including concerns about data privacy? The possibilities, while exciting, carry a mind boggling number of implications for the regulatory authorities and companies conducting clinical research, such as (but not limited to EDC, eTMF, study participation and follow up.

	Moving beyond sensors, wearable devices go beyond passive data collection and use the information about the individual to help them make decisions or change behavior to produce a desired result. 

 is working on making inserts for shoes that can help stroke victims improve their walking. 

 has developed a wearable sensor device that is used to measure and report on impacts to a player’s head – providing instant alerts about the possibility of concussions. 

 offers an FDA-approved device that stimulates the brain to block pain receptors for patients with chronic conditions. The device is worn around the calf in a neoprene wrap. In the future will we consider running clinical trials for pain with and without such devices? Will we recruit new patient populations into clinical trials by targeting users of certain wearable devices and their apps?

	As the proliferation of big data continues, from DNA sequencing to EHRs and imaging data, multi-faceted data transformation and analysis has become the next big challenge in research and clinical development. Bioinformatics continues to face challenges in integrating molecular biological data with patient information in the quest to accelerate the discovery of useful new therapies. Turning big data into “smart data” can lead to real-time solutions in disease prevention, prognosis, diagnostics, and therapeutics. Though researchers are struggling to analyze the enormous troves of 'omic' data in the quest for patient-centered health care, progress is beginning to emerge. 

 in Iceland has demonstrated the power of population-scale genomics, by combining extensive genealogy and medical-history records with genome data from 150,000 people. These findings have allowed deCODE to determine the population-wide distribution of known genetic risk factors, including gene variants linked to breast cancer, diabetes and Alzheimer's disease. This has enabled studies in humans that normally require the use of genetically modified animals. “We have established that there are about 10,000 Icelanders who have loss-of-function mutations in both copies of about 1,500 different genes,” said Kári Stefánsson, the company's chief executive. “We're putting significant effort into figuring out what impact the knockout of these genes has on individuals.”

	These efforts do not come without significant hurdles and challenges. A recent Nature article warns that the computing resources needed to handle genome data will soon exceed those of Twitter and YouTube.

 This team of biologists and computer scientists are worried that their discipline is not geared up to cope with the pending tsunami of genomics data.

 Indeed, the realization of the landmark $1,000 genome (which brought great opportunity for Big Data and clinical research) also created profound challenges for storage, transfer and security of information. In fact, it may now be less expensive to generate the data than it is to store it.

	Some see the ‘big data’ or ‘analytics’ future involving analysis of bioinformatics and patient data as only part of the picture. Is there an untapped source of data in social media that is not being leveraged? The crowd is currently unwittingly providing us with valuable data (which we are not capturing and saving) that can be readily extracted from the web and social networks. How can we leverage this vast trove of information to improve clinical research? Using technology that has enabled the collection of over a billion social media conversations from thousands of different websites, for example, a company named 

 is collecting real-time conversations that involve patient experiences using medications for various conditions. These conversations are interpreted and organized on Treato’s website for easy access by patients looking for relevant information on the medication they are using to treat their condition. Treato is also a valuable resource for pharmaceutical companies looking gain insights into patient experience on various medications and/or discover health trends that would normally take many years of research to discern. Some may argue that we are moving into the era of drug safety analysis, drug repurposing and marketing by sentiment analysis using social media stream mining tools such as 

	Some social media companies are focusing on health and treatment-related discussions that allow users to give feedback on the effectiveness of a given treatment. 

, for example, has collected data on treatment effectiveness for over 600 disease conditions. Clinical trials could potentially leverage these condition-focused patients in ways that improve the efficiency of the drug discovery process. One potential problem created by the proliferation of treatment effectiveness data being readily available online is patients involved in clinical trial using this information to ascertain whether they are receiving the placebo of the medication. This could certainly provide an interesting challenge for CROs managing clinical trials. 

	Finally, swarm intelligence is an emerging field of biological artificial intelligence that offers solutions to complex problems and has already been used for disease surveillance and predicting health interventions. Where this can lead us, has yet to been seen.

	If at some point all of these technologies converge and rationalize, we may have nothing short of a revolution in clinical research on the horizon – as we shift from relying on patient reported outcomes to sensors and wearable devices that live and breathe with the patient. In this new order, each patient becomes a big-data producer. Imagine a world where patient genotype and medical histories are mashed up with social media data sources and sensor-derived patient surveillance information. What happens when the data collected by the sensor(s) conflict with the experience reported by the patient?  This is not something we have had to consider before and probably represents the first of many new issues we’ll need to face from the resulting paradigm shift.

		Eisenstein, Michael. Nature. “Big data: The power of petabytes.” 527, S2–S4 (05 November 2015) doi:10.1038/527S2a. Available at 

http://www.nature.com/nature/journal/v527/n7576_supp/full/527S2a.html

		Check Hayden, Erika. Nature. “Genome researchers raise alarm over big data.” (07 July 2015) Available at:  

http://www.nature.com/news/genome-researchers-raise-alarm-over-big-data-1.17912

Despite challenges in the pharma sector, we are experiencing a technological shift in the way clinical research data is obtained from patients. New devices have allowed for the collection, reporting and response to data from a patient’s body using sensor technology.

Sujay Jadhav

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