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How Artificial Intelligence and Advanced Analytics Are Delivering Value for Clinical Trials

An MIT study on clinical trial success rates found that the overall probability of success for all drugs and vaccines is 13.8 percent. Two-thirds of trial failures occur due to inadequate efficiency, flawed design, inappropriate endpoints, or under-enrolment.

While many professionals are aware of the use of Artificial  Intelligence  (AI)  to support clinical data analytics,  the full range of its potential is less well known. AI has the potential to transform many of the key steps in clinical trials,  from protocol design to study execution, reduce the cost of conducting clinical trials and increase the probability of studies’ success.  AI can also help enterprise and mid-market sponsors, as well as contract research organizations (CROs), analyse and develop strategies from vast amounts of historical data, which  can  also  be  a  good  database  for  future trials but an impossible manual task.

Improving clinical operations in trial design

When we think of AI in clinical trials, we usually imagine using data analytics after data  is  already  collected.  However, AI can be equally if not more important in the pre-trial phase.  Trials leveraging AI technology and  big  data  in  the  planning  phases  can  optimize  trial  design.  Through measuring and benchmarking site burden, real world evidence can help researchers identify target patient populations, allowing for better matching of trials to sites with required site characteristics. In a recent survey of clinical trial sites, respondents identified patient recruitment and enrolment as their top two concerns.  Patient recruitment has also been called the “biggest challenge” in clinical trials.

Imagine if you had started  a  clinical trial  for  a  rare  disease  treatment  but  realize  midway  through  the  patient  recruitment  process  that  incidence  rates  are  lower  than  expected in the country. We saw this last April, when several COVID-19 vaccine clinical trials in China had to be cancelled due to shortage of patients.  This was due to a combination of factors, including that not many of the existing patients in China met study requirements (age, gender, and prior  health conditions) and a lack of total patient population for large-scale advanced drug studies. Restarting and relocating clinical  trials  requires  time,  costs, staffing, and more. Identifying optimal countries and sites with the help of AI and big data can help in balancing speed, cost, and quality.  It can also predict enrolment at  site, country, and trial levels.

However, it is not industry standard for researchers to consider implementing AI at the pre-trial stage. Especially among enterprise mid-market sponsors,  traditional operating processes and lack of resources may limit access to clinical trial databases and intelligent trial solutions.

Single arm trials as a practical solution to patient recruitment

Technologies such  as  single  arm  trials  can  leverage  available standardized data from historical trials to increase sample size and offer a practical solution to studies where randomization  is  not  feasible.  This can  be  especially  beneficial  for  researchers  targeting  patients  with  life-threatening diseases who are dissuaded from participating in a trial due to the possibility of landing in a control arm, such as placebo or ineffective standard-of-care treatment.  Additionally, research studies  targeting  small  or  hard-to-reach patient populations such as rare diseases may find it  even  more  challenging  to  recruit  a  substantial  pool  of  participants.  Compounded with  the  inevitability  of  some  patient  incompletions,  data  collection  from  these  patient  populations are further limited.

While randomized  controlled  trials  are  the  gold  standard  for  evaluating  the  safety  and  efficacy  of  new  medical treatments, maintaining a concurrent control arm is sometimes not feasible and can lead to increased patient burden  and  threaten  the  completion  of  a  trial.  Instead of  collecting  data  from  patients  recruited  for  a  trial  who  have  been  assigned  to  the  control  or  standard-of-care  arm,  synthetic  control  arms  use  real-world  data  that  has  previously been collected from sources such as health data generated  during  routine  care,  including  electronic  health  records, claims data, patient-generated data, and historical clinical trials data.

With AI technology simulating trial data, more patients can be treated with the experimental therapy. A traditional, randomized controlled  trial  needing  1,000  patients  to  demonstrate  the  effectiveness  of  a  new  therapy  —  500  for  the  active  arm,  500  for  the  control  arm  —  need  now  only recruit 500 participants when a synthetic control arm is employed. By reducing or eliminating the need to enroll control  participants,  a  synthetic  control  arm  can  increase  efficiency,  reduce  delays,  lower  trial  costs,  and  speed  lifesaving therapies to market.

According  to  a  Deloitte  survey,  top  biopharma  companies ranked synthetic control arms as one of the most impactful future applications of real-world evidence. Despite this recognition, 70 percent of companies in the survey noted that the lack of research-grade data is hindering efforts to leverage real world evidence in R&D.   Greater partnerships with  health  systems  and  other  stakeholders  is  critical  in  curating  purpose-built  data  sets,  for  SCA  and  related  technology to ultimately better inform clinical development.

In  addition,  scepticism  from  regulators  and  lack  of  methodology guidelines and standards on such innovative technologies trickle down to reluctance from researchers. Real world evidence and synthetic arms could also play a role in evaluating existing and repurposed drugs that could be effective against Covid-19 and similar cases in the future. Fortunately, attitudes are changing along with the pandemic, as shown  through  its  approvals  to  be  used  in  Covid-19  vaccine trials.

Even when you work tirelessly to get the best data for your clinical  development  program,  you  can  still  end  up  with data gaps and unconnected data that can jeopardize the  success  of  clinical  trials.  Real  world  evidence  and  AI  technology can help deliver insights to increase efficiencies and  probabilities  for  success.  Yet, apart  from  a  small  segment of large companies, most biopharma companies lack  the  internal  knowledge,  expertise, and  resources  to  effectively leverage real world evidence and AI technologies to improve business and research outcomes. Nonetheless, the notion that AI and real-world evidence are trends for the near future is outdated; AI in clinical trials is what we need now – AI enabled technologies can be a necessary tool for clinical trials during and beyond this pandemic. Transforming the industry requires partnerships and collaborations across the ecosystem  to  ensure  sustainable  AI  adoption and application. [APBN]


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About the Author

Edwin Ng, Senior Vice President, General Manager, APAC, Medidata Solutions Edwin joined Medidata in 2014 and heads the Life Science industry of Dassault Systemes across the Asia Pacific region. Under Edwin’s leadership, the company’s business in the region has recorded significant year on year growth and achieved multiple leadership positions and awards in different countries. With the push for transformation in the healthcare sector through digitalization, Medidata is committed to serve our sponsors and partners by continued investments in the APAC sales, marketing, solution consultant, professional services and customer support teams. The Medidata, BIOVIA and other Dassault Systemes combined applications across research, development, quality, manufacturing, and clinical trials create a broader range of end-to-end solutions for our customers, and we are excited to extend the reach of our platform beyond clinical trials to research and development, commercialization, and manufacturing. Edwin has spent the last 26 years of his career managing enterprise solutions businesses, including at companies such as Dell, APJ Hewlett-Packard, StorageTek and Sun Microsystems.Edwin is a graduate of Nanyang Technological University in Singapore with a degree in Bachelor of Applied Science (Computer Engineering).