Artificial Intelligence (AI) and Machine Learning (ML) have begun to transform healthcare. AI & ML can be used to enhance certain aspects in a clinical trial,...
biospectrum

Oct 08, 2019:

Artificial Intelligence (AI) and Machine Learning (ML) have begun to transform healthcare. Major technology giants like Google, Microsoft and IBM have developed intelligent platforms to aid in better healthcare. Recently, Google’s CEO, Sundar Pichai discussed the benefits of their carefully designed AI technology that can potentially be used to identify the risk of a heart attack, 48 hours before it could occur or even diagnose the presence of diabetic retinopathy from a picture of an eye! IBM Watson Care Manager, has been built for UK healthcare services to better allocate resources by matching individuals to specific plans and provide key insights into prior healthcare issues or data available.

Artificial intelligence could transform drug development and improve patient safety

The pharmaceutical industry too is not far behind in adopting this technology to fuel their goals to bring life-saving medicines to the public. There are billions of dollars spent every year on clinical trials that are vital for developing new treatment strategies for people in need. AI & ML can be used to circumvent some of the limitations in the effective execution of a clinical trial, especially in the process of patient recruitment and data analysis. Patient recruitment is a difficult procedure and a bottle neck for any clinical trial, which affect timelines and increase costs.

AI & ML in patient recruitment

In order to overcome this, researchers at Cincinnati Children's Hospital Medical Center developed an AI system that could be used to identify patients who are eligible for a study from the available electronic health records (EHRs). The researchers found that there was a

  • 34% reduction in patient screening time
  • 11% improvement in patient enrollment
  • Nearly 15% increase in patients screened
  • 11% increase in patients approached

Not only does this streamline and improve patient recruitment but it also allows the busy hospital staff to focus their time on assessing the highest quality of patients.

The US Food and Drug Administration (FDA) has also identified some of the ways AI could help in improving patient selection.

  • Lowering heterogeneity among the population
  • Identifying patients who may have a higher likelihood of having a quantifiable endpoint
  • Identifying patient who have a higher likelihood of responding to the treatment

AI & ML in disease modelling

Other areas of clinical trials also benefit from the use of AI & ML. Prototypes are being tested for neurological diseases, in which the biomarkers are too invasive or are expensive to measure. A clinical trial simulation tool for Alzheimer’s disease was recently successfully advanced through the review process of European Medicines Agency (EMA) and the US FDA. This tool includes drug modelling, highly nuanced characterization of neurological diseases, and progression of mild Alzheimer’s disease which will help in developing a model-based trial design. Such accurate modeling will provide precise detailing of complexity, especially for neurological diseases like Alzheimer’s, where disease altering drugs are not available yet.

AI & ML in selecting lead component for clinical trials

Predictive AI & ML can be used for preclinical compound discovery as well as for identifying the lead compounds for a study. The use of this technology has been shown to provide competent and efficient associations between implications and biomarkers. AI &ML would help in identifying promising lead candidates that have a higher likelihood of success during clinical trials, helping in eliminating candidates which may fail.

Connecting the dots

Thus, AI & ML can be used to enhance certain aspects in a clinical trial, or could also be used to create a seamless structure across the clinical trial, which would provide real time and high-quality data, allowing better optimization. With clear systems in place, it becomes easier to recognize bottle necks or fault areas which could affect timelines and cost. OneClinical is a platform that monitors the progress of a trial, providing near-real-time data and insights to sponsors, enabling proactive decision making to improve outcomes and enhance efficiencies. Such platforms give sponsors better control over the clinical trials process and impact the time and cost of running a trial significantly.

AI could significantly improve multiple facets of clinical trials, right from design, to setup, to conduct, to closure, improving quality and efficiency, and lowering failure rates and cost. Thus AI & ML could help in bringing lifesaving medicines faster to the market, with a greater onus on patient safety.

Author:

  Dr Ayaaz Hussain Khan
Global Head, Generics,
Navitas Life Sciences

 

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