Recent Research Shows That Artificial Intelligence Has the Potential to Make the Pharma Sector More Effective 

According to recent research, AI can play a role in drug production, making it more economical, efficient, and sustainable. Scientists from the UCC, alongside industry leaders from the US and Ireland, have joined forces for a new initiative called InTeleCat, aiming to speed up the pharmaceuticals’ development. The team consists of experts in computational chemistry, catalysis, and data science. The InTeleCat’s primary goal is to take advantage of Machine Learning and Artificial Intelligence to improve and predict catalytic processes that are imperative in the pharma industry, aiming to lower the time and cost necessary to recognize the best route to a desired API (Active Pharmaceutical Ingredient) and to identify which APIs can be used for specific reaction conditions.  

It is no secret that the drug discovery and development process is complex, but AI is notably transforming the industry.  In fact, statistics show that AI applications can create a value between $350 billion and $410 billion annually for pharmaceutical companies by next year, with the market projected to increase by 42.68%, equaling a growth of $15 billion between 2024-2029. As the latest surveys show, 80% of life sciences and pharma professionals rely on AI for the drug discovery process, which has been proven to reduce the drug discovery time significantly – from 5-6 years to only one year.  

The application of AI in the pharma industry covers many imperative processes

Artificial intelligence algorithms are a game-changer for the pharmaceutical market, impacting everything from the discovery and development of life-saving drugs to the production of clinical trials and drug target identification. In 2021, the influence of AI and machine learning on this sector generated a staggering $100B across the US healthcare system. Given this aspect, it’s worth diving deeper into how AI influences each area of the pharmaceutical industry. 

Manufacturing process

The first thing that comes to mind when it comes to the impact of AI is manufacturing, which can boost processes and optimize production while also ensuring that the tasks are conducted with precision. Besides providing high-quality work, AI also analyzes the processes carefully to identify weak spots and enhance the decision-making process. 

Drug discovery

AI significantly impacts drug discovery, accelerating the identification of drug candidates while enhancing molecular design. By analyzing biological data, AI makes predicting drug efficacy much more efficient, reducing the time from laboratory to market.  The first drug discovery stage often takes as much as six years; on average, the next stage of clinical trials requires at least five more years to complete. Only an insignificant number of initially tested candidates for new drugs go to clinical trials (namely, five out of ten thousand!). Also, during the drug design process, just one medical product out of ten makes it to clinical trials and gets the approval of the FDA for patients’ use. AI is impacting this area through molecular modeling, virtual screening, and predictive analysis – these models can examine massive chemical and biological data to recognize potential drug candidates and enhance lead components while also predicting their properties. Ultimately, the outcome of AI-driven approaches is more efficient medications. 

Clinical trials 

AI applications boost trial processes like patient recruitment, enhancing trial design and real-time monitoring through vast dataset analysis. To this end, AI algorithms are efficient as they help identify suitable candidates and optimize predictive modeling, accelerating the trial process and reducing the costs involved. The predictive modeling method is utilized to make outcome predictions for clinical trials, and it’s a supervised learning method that predicts aspects like treatment efficacy, patient response, and safety outcomes by using historical clinical trial data to train the model, including treatment interventions, patient characteristics, and trial outcomes. AI can impact many areas of clinical trials, such as cohort composition, recruitment, patient retention, monitoring, and study closeout. 

AI is transforming not only the results of pharma market research but is also making the process less time-consuming

Throughout time, market research methods in the pharma sector involved analyzing historical datasets and searching for information from disparate sources – a process that was done manually and was time-consuming and tedious. In an era of information overload, separating relevant insights from the market’s noise can be a herculean task, and the good news is that AI is here to change that, providing an approach rooted in efficiency, speed, and thoroughness, which are integral qualities in the pharmaceutical landscape. 

Thanks to AI, market research can now offer high-quality results at a fast speed, extracting relevant data and patterns from the market that humans would have a hard time detecting by relying solely on manual processes. Unlike the time and expenses involved in manual research, platforms using AI have an exponentially higher ROI. 

 In fact, AI advancements have gone as far as understanding the purpose and context behind search terms, delivering the most relevant results on a specific topic. Whether a company is looking for information about a competitor’s new products, pricing benchmarks, or customer insights, a market research platform can provide all the relevant data by leveraging AI. However, it’s recommended to hire a market research company to ensure the process will be efficient, as market research isn’t an easy job. Savanta USA,for example, uses efficient market research instruments to offer valuable insights in the pharma industry.

Despite the opportunities, the use of AI in pharma also poses challenges that can bring specific limitations

Artificial intelligence is undoubtedly a powerful force in the pharma sector, but it equally poses challenges that can include a lack of transparency, biases in data, lack of data availability, consideration of ethics, a lack of clinical expertise, and a complex biological system, to name a few. 

Hence, AI-based models should be combined with traditional experimental methods to guarantee the efficacy and safety of drugs. These challenges have been tackled thanks to the improvements in data availability, computational power, and deep learning algorithms effectively. Still, misreported data remains a critical issue, as it introduces bias and distorts AI models’ accuracy. As a mitigation measure, FAIR data principles should be adopted to improve data quality and boost the reliability of AI-driven analyses. 

The bottom line

Artificial intelligence is transforming the pharmaceutical industry in unimaginable ways through breakthrough efficiencies. While the journey ahead could present challenges due to ethical concerns and data privacy, the benefits cannot be overlooked. Harnessing the full potential of AI could revolutionize drug development significantly, leading to notable improvements in patient outcomes and ultimately making the healthcare system more efficient.