Applications of Artificial Intelligence in the Pharmaceutical Industry

Written by Dr. Shalini Tripathi

"AI is not a magic bullet and a work in progress, but it holds a lot of promise for the future of healthcare and drug development"— Stefan Harrer

Artificial intelligence in Pharma denotes the use of automated algorithms to perform tasks that generally depend on human intelligence. In the course of the most recent five years, the utilization of artificial intelligence in the pharmaceuticals has redefined how experts develop new drugs and challenge disease. The progress in Artificial intelligence (AI) has effectively spread into many areas such as computer vision, speech recognition, and natural language processing. AI is now quickly proliferating into the areas needing considerable field expertise such as biology and chemistry promising to speed up.

This article will cover the role of AI in developing new drugs and the way it helps in tackling diseases that were once supposed too difficult to take on. Moreover, the application of Ai in drug adherence and clinical trials has also briefly discussed.

In the pharmaceutical industry, the simplification of research and development (R&D) is possibly the most common use case for AI applications. There are various explanations for removing and establishing research statistics from clinical trial records and other medical documents. Moreover, there is software that can allegedly analyze data from images of drug compounds at the molecular level.

AI could also help pharmaceutical companies manufacture newly discovered drugs more efficiently. This is accomplished by making predictions about how the drug may react when manufacturers try to turn it into a pill, liquid medicine, or topical salve, for example. Data scientists can find out if a drug is likely to break down or become less useful when processed into these types of products before the pharmaceutical company actually takes that risk.

Research and development have always been a time taking and expensive process but pharmaceutical industries have recently recognize the significance and benefits of applying AI in their work. Pharmaceuticals with so much interest in AI are emerging quickly and new expansions have resulted in fine-tuning towards their specific goals. AI can do lots of clever, fast things with datasets, but it becomes more effective when it’s very focused, like imaging for oncology, accelerating molecule discoveries or identifying compounds.

Due to the high failure rate along with the sheer cost and time consumption for experimental drugs sometimes it needs to withdraw a drug candidate from further research and commercialization. The promise of AI particularly in data-heavy applications such as drug discovery is more inviting to drug developers.

AI brings with it the promise of using sophisticated machine learning algorithms to test targets for disease and drug candidates with speed and accuracy that would be difficult for human researchers, potentially saving pharmaceutical and biotech companies billions in drug development. Drug discovery can become extremely effective through the use of AI in a specific area as this is the current trend and is absolutely critical in rapid drug discovery processes with leading pharmaceutical companies also focusing on specific AI applications.

AI can be used for processing an estimated 30 million lab reports and data. More readily identify diseases.

In medical images such as MRIs, CT scans, ultrasounds, and x-rays, AI can spot disease signs faster and more accurately. Patients can be diagnosed more quickly and can start treatment sooner. All data were analyzed by experts at the very beginning of clinical research and such ways are currently referred to as the state of the art. In pathology, assay analysis and of course image analysis, which is perhaps the most visual of them all, the industry has moved to bring automation.

It's important to note that the same decision is most likely taken by an expert and the machine. This implies that we're talking about the actual expert and a robust algorithm that is well designed.

According to a study published in Trends in Pharmacological Sciences, artificial intelligence could enhance key parts of the clinical trial process including selection and recruit, and patient monitoring. It takes between 10 and 15 years and costs between $1.5 and $2.0 billion to bring a new drug onto the market, researchers noted, and about half of this time is spent on clinical trials and capital.

But regardless of significant investments, clinical trials still have high failure rates.

Clinical trial deficiencies are due primarily to ineffective recruiting and selection methods, as well as the inability to track patients effectively. Artificial intelligence technologies have emerged as a feasible way of improving these processes and rising success rates in clinical trials, researchers said.

Despite its potential to unlock new insights and streamline how providers and patients interact with health care data, AI may pose no significant threats to privacy issues, concerns about ethics, and medical errors.

Balancing the healthcare risks and rewards of AI may take a collaborative effort from technology creators, regulators, end-users, customers – and perhaps even major philosophers.