Jiangsu Hengrui Pharmaceuticals

 

Based on numerous publications and analysis, high readiness to innovation with utilising modern technologies extensively helps companies to develop services and products in a more efficient way and within shorten timelines. 

Despite several pharmaceutical giants are listed among the readiest innovators, overall use of innovative approaches by pharmaceutical industry. remains on a relatively low level. And this needs to and will be improved in the nearest future.

What do we know about the options to consider? There are dozens of them, and one of the most interesting is artificial intelligence (AI) and machine learning (ML). AI and ML are more and more actively coming to the market, providing novel services and solutions for different industries in fulfilling different routine needs and bringing results with fewer resources spent. 

AI/ML has a great potential to transform pharmaceutical industry, for example, with drug discovery, with accelerating timelines of research and development, etc. Thus, helping new drugs to get quicker approval and by this getting to the market faster with a higher chance to become more affordable for payers. 

Let's briefly highlight several applications of AI/ML in pharmaceutical industry:

1. Drug discovery process and design.

Design of new molecules, identifying of the drug targets with its validation plays one of the key roles in the success of the whole development program. And AI/ML can step here in, by so leading to the reduction of time required for the entire development process. 

2. Research and Development

Having targets identified, and novel molecules designed, AI/ML can exceptionally help with identification of diseases patterns, suitable formulations, or specific symptoms of diseases that can benefit most and lead to successful outcomes from novel assets in development.  

“There are no doubts that soon we will see a boom in the implementation of AI/ML in the pharmaceutical industry”

3. Diagnosis

With evolving of IT security, patients’ electronic medical records (EMR) are more often stored in a centralized storage or in a cloud. Analyzing this data with the help of AI/ML physicians have option to assess effects of different traits and treatments like lifestyle, genetic results, medicines, etc., on a patient's health and to help physician with a proper treatment selection for the patient. 

4. Epidemic prediction.

AI/ML can help to monitor and assess how infections are spreading worldwide by gathering and analyzing information collected from open sources like web.There are already predicting models available. Malaria outbreak prediction model be one of examples that helps healthcare providers to take the best actions in fighting against this disease.  

5. Identifying clinical trials candidates.

Healthcare providers, including physicians participating in the studies, can identify eligible patients, i.e., using specific inclusion/exclusion criteria while searching through the EMR databases. Utilizing AI/ML can help to work with EMR databases and collect, process and analyze giant volumes of patients' data.  As this technology can process and analyze massive amount of data quickly, this leads to the faster identification of patients eligible to participate in the study, quicker enrollment and faster completion of the study.

6. Drug adherence and dosage. 

Patients' compliance with the study protocol remains an actual problem for the pharmaceutical industry. To avoid distortion in the results, patients not following the rules of the study should be excluded. AI/ML can help with remote monitoring and algorithms that can predict test results by this helping in identifying non-followers among patients. 

Above mentioned examples is the tip of the iceberg, among other points of AI/ML application in pharmaceutical industry can be mentioned manufacturing, quality control, marketing, etc.

As AI/ML can be adjusted to almost any needs and requests, there is growing demand for the sources of data, joint approved rules for safe use of personal and commercial information, relevant transparency and active interactions between stakeholders. There are no doubts that soon we will see a boom in the implementation of AI/ML in the pharmaceutical industry.