What edge does generative AI have over traditional methods?
Before the pharmaceutical industry started warming up to generative AI, most operations and processes were conducted with minimal AI involvement. Even in cases where AI was incorporated, the algorithms were slow and inefficient, leading to increased (1)costs and operational complexities. In addition, the industry was also faced with a workforce shortage and inflation, creating the need for innovative solutions.
With accelerated drug discovery, efficient clinical trials, accurate pattern recognition, quicker regulatory approvals, cost reduction, and more, gen AI is transforming nearly all aspects of the pharmaceutical industry, revamping the way companies operate and potentially unlocking billions of dollars in value. It has been estimated that the technology could generate (2)$60 billion to $110 billion a year in economic value for the pharma and medical-product industries.
How can AI tools aid in complex scientific challenges and accelerate drug discovery approaches?
A recent report found that data analysis is the key reason why (3)42 per cent of companies use AI, and the pharma industry is no exception. AI-based solutions can simplify data assessment and analysis, helping experts in the industry sort through varied and extensive datasets, recognise position and correlation patterns, and accelerate drug discovery. For instance, AI algorithms can analyse (4) millions of chemical compounds in one hour, a process that otherwise would take years to complete.
To add to this, AI-driven models have the ability to mimic organic systems and predict drug reactions, which, in turn, helps pharmaceutical companies find the most plausible drug candidates. AI can also tailor the drug discovery process by incorporating personalised details like genes and environmental inputs, easing the predictions for how the same drug could produce different results based on the hosts’ genetic makeup. This level of insight can assist pharmaceutical experts in conducting their trials and tests more efficiently, measuring the predictions against real-time results.
In fact, during the COVID-19 vaccine discovery process, scientists across the globe (5) leveraged AI to run vaccine trials, expedite distribution and meet the needs of individuals. Without the incorporation of AI, the process could have taken longer to complete, leaving the global population at risk.
Can AI simulate diverse patient responses and predict adverse effects? How does this speed up the rate of clinical trials?
Presently, (6)only 14 per cent of drugs that enter phase-1 clinical trials gain approval from the FDA. Now, as AI has intervened to simulate diverse patient responses and predict adverse effects, the pace of clinical trials could be improved in the coming years.
With AI, pharmaceutical professionals can make use of sophisticated simulation models and techniques for the development of biological replicas, which can show the disparities in patient responses based on age, gender, genes, and previous medical history.
Also, by utilising big data, AI can perform virtual clinical trials. These virtual simulations can help eliminate a large number of potential challenges and roadblocks in the preliminary phase, resulting in an overall shorter time to market.
Lastly, scientists can leverage AI to create projections of potential complications and introduce subsequent modifications to the trial protocols. This preemptive risk management practice lessens the potential for unforeseen adverse effects, which can cause the trials to be postponed or unsuccessful.
Is it possible for AI to cut the costs associated with traditional clinical trials?
Clinical trials can get expensive with multiple phases, candidates, tests, and the time span between testing and the time to market.
During the lengthy phases of clinical trials, R&D costs can escalate, potentially leading to a no-win situation where resources are immensely consumed but revenue is not generated. And as the time to market extends, patent exclusivity becomes a regular concern. Competitors would have launched similar drugs sooner, putting all the efforts into vain and yielding no results.
This prolonged timeline affects not only pharmaceutical companies but also patients. As drugs undergo extensive clinical trials, precious time is lost in delivering potentially life-saving or disease-preventing treatments to those in need.
Hence, it is no wonder that AI is projected to save pharmaceutical companies almost 7 $54 billion in R&D costs each year.
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