In the field of drug discovery and monitoring, Artificial intelligence (AI) can transform post-market surveillance completely.
AI-driven surveillance systems can swiftly and accurately identify and react to possible dangers by processing massive volumes of pharmacological data in real time.
In the context of pharmaceuticals and medical devices, “post-market surveillance” means the continuous monitoring that occurs after these products have been approved for sale to the public. It examines medications people consume in different situations over a long period.
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How AI contributes
Artificial intelligence (AI) could significantly contribute to this process in various ways:
Continuous Tracking
In order to track the safety of drugs in real time, AI-driven drug discovery uses data from social media and electronic health records. The system quickly identifies and flags any possible safety concerns, enabling prompt interventions to guarantee the well-being and safety of patients.
Detecting Signals
Analysing massive patient data archives is an important task for AI algorithms. With the help of these algorithms, we can find signals that might point to drug-related side effects. For rare or unexpected adverse effects that weren’t picked up on in the early stages of clinical trials, this capability is crucial.
Assessment of Potential Dangers
To estimate the likelihood of side effects associated with a particular drug, artificial intelligence algorithms are employed. These algorithms take into account relevant factors in addition to unique patient characteristics. Improved patient safety and treatment outcomes are possible thanks to AI’s ability to identify individuals at higher risk of adverse effects, which allows for targeted interventions and personalised medical care.
Interactions Between Drugs
Artificial intelligence (AI) can study complex patterns and interactions between drugs to predict which ones might have harmful interactions. The ability to make informed decisions about drug combinations is crucial for healthcare providers. It reduces the risk of harmful interactions and improves patient safety.
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Examples of AI Drug Discovery in Practice
There have been several high-profile case studies that highlight the effective use of AI methodologies in the drug discovery process.
Discovering Inhibitors of the MEK Protein
Recently, it was documented that Machine Learning (ML) was used to identify inhibitors for the MEK protein successfully. This protein is an important target in cancer therapy. Discovering effective inhibitors for MEK has been a difficult task. Nevertheless, by utilising ML algorithms, scientists have been able to identify new inhibitors, demonstrating the effectiveness of AI-driven approaches in tackling intricate biomedical challenges.
The Dementia Stroke Therapeutic Aims
A crucial protein implicated in the progression of the disease, beta-secretase (BACE1), has been identified through the use of machine learning (ML) algorithms, which have simplified the discovery of new inhibitors. Highlighting the role of AI in advancing therapeutic solutions for intricate health challenges, this successful incorporation of AI methods has unlocked fresh opportunities for addressing complex neurodegenerative disorders.
New Antibiotics Found
The capacity to identify novel antibiotics has been enhanced by AI-driven drug discovery. A database of more than 100 million molecules has been combed through using state-of-the-art machine learning algorithms to find promising antibiotic candidates. The outcome is a potent antibiotic that is effective against tuberculosis and other drug-resistant bacteria. The crucial role of AI in fighting important global health threats is highlighted by this remarkable achievement.
Research on COVID-19 Treatment
With the help of ML algorithms, the continuing research into preventing the spread of COVID-19 has achieved remarkable strides. Artificial intelligence (AI) has been instrumental in the discovery of effective anti-viral drugs by analysing massive data sets. This specific application showcases how AI can quickly and easily respond to new worldwide health emergencies, solidifying its indispensable role in current drug development initiatives.
Both the pharmaceutical business and healthcare could undergo radical changes by using artificial intelligence (AI) in medication discovery. Although AI has the potential to improve post-market surveillance greatly, it should only supplement human healthcare providers and regulatory agencies in their roles.
