In an era when speed, accuracy, and efficiency are critical, the biopharma industry stands on the verge of a huge transformation. Artificial intelligence (AI) is moving beyond just a mere buzzword; it is fast becoming an important factor in how drugs are discovered, developed, and marketed.
The question is not whether AI will transform drug development, but how far-reaching the impact will be.
The ability of this technology to analyze massive data sets, speed up clinical trials, and foster collaboration is setting a new benchmark. But will AI-driven collaboration reshape the very structure of drug development? The short answer is yes—and here’s why.
The need for speed
AI’s role in drug discovery
Drug development is, in fact, a long and laborious process due to extremely large monetary investment. On average, it takes over a decade and billions of dollars for a new medicine to be transformed from an idea into something that is commercially viable.
The promise of AI is to cut this cycle down significantly—very, very significantly.
The algorithms of machine learning can handle huge amounts of data, such as genetic information, chemical structure, and disease profiles, at astounding speeds, well beyond the scope of human capability. Take, for instance, in early 2020, Exscientia, a UK-based AI company, discovered a potential drug molecule for the treatment of Obsessive-Compulsive Disorder (OCD) in just 12 months, a process that traditionally would have taken four to five years.
The assumption-based drug discovery is becoming data-driven.
By allowing the automation of processes such as target identification and molecular screening, AI has actually managed to save time and reduce human error. This same data-driven efficiency is propelling big pharma toward quickly investing in AI platforms to enhance their R&D pipelines.
Collaborative AI
A New Era for Partnerships
As you would know, biopharma development is inherently collaborative, with pharmaceutical companies, biotech firms, universities, and research institutions like yours working together. AI is now the enabling factor, facilitating collaboration between these different entities like never before. This tech combines data from across the drug development pipeline, from drug target discovery to molecule/drug design to clinical trials and post-marketing surveillance. By doing so, AI equally aligns the incentives and goals of the multiple parties involved, making collaboration much more profitable and sustainable.
Not only that…
AI platforms can integrate real-time data from different collaborators, ultimately leading to faster decisions and better coordination. This way, it eliminates the common bottleneck involved in a partnership. For example, Pfizer partnered with IBM Watson to leverage the AI system for immuno-oncology research. The tech sifted through unending scientific literature and clinical trial data to develop new drug candidates.
Furthermore, AI ensures that data is not only available but also interpretable. This is crucial when different teams, perhaps located across the globe, are drawing upon the same strain.
AI in clinical trials: Streamlining processes and reducing costs
One of the most challenging aspects of drug development is conducting clinical trials. They are laborious, and typically costly.
AI-powered collaboration is succeeding well in resolving these barricades.
Take patient recruitment, for example; AI can search a database of patient records to pick the perfect person for that trial—not just based on genetics but other medical information and even social determinants of health.
In doing so, AI increases the number of patients who can be connected to trials and reduces recruitment time and cost by providing a more personalized match between patients and treatment combinations.
Once the trial is underway, it could be used to spot trends, predict trial outcomes, and catch unsafe interventions. This capability can lead to faster trial adjustments, improved patient monitoring, and more robust data collection, all while ensuring the integrity of the trial.
A study by MIT found that only 14% of drugs that enter clinical trials eventually gain approval. AI, however, is helping to shift these odds by cutting time and increasing the probability of success through trial design optimization and data analysis.
AI and precision medicine
The future of treatment
One of the most exciting AI applications in biopharma is precision medicine. Rather than developing one-size-fits-all therapies, precision medicine is the practice of customising treatments to individual patients based on their genes, the lifestyles they lead, and environmental and other factors.
AI is key to enabling this kind of tailoring of medicines by using data to look for patterns and models. For example, within biopharma, through the analysis of genetic data and patient-specific biomarkers with AI, firms like yours are able to identify which patients are most likely to respond positively to a particular treatment and those who won’t.
Doing so can improve patient outcomes, reduce potential harmful side effects, and reduce wasted time on unsuccessful treatments. For instance, AI is being used to match cancer patients with the most effective immunotherapies based on their genetic profiles, a breakthrough that could significantly improve survival rates in the future.
Regulatory hurdles
As promising as AI-driven collaboration is, it also comes with its fair share of regulatory hurdles.
The biopharma industry is highly regulated, so any AI tool must adhere to stringent regulations that define its safety, effectiveness, and transparency. Regulators like the FDA are now finding their feet when it comes to assessing AI-powered drugs and medical devices.
A major challenge in the development of AI-powered drugs is ensuring the transparency and explainability of the AI algorithms. In other words, the regulator must be able to understand how an AI-led recommendation or conclusion was reached, especially in safety-critical areas like drug development.
The FDA has taken the first steps in this direction by examining how AI models should be assessed in drug clearance. Numerous companies have adopted a “human-in-the-loop” approach, where AI recommendations are initially screened by a human, making it easier for regulators.
The good news is that regulators are starting to realize the potential of AI in drug development. FDA’s Center for Drug Evaluation and Research (CDER) is looking to derive value from AI-driven models to shorten clinical trial durations and aid in the quick release of drugs to the market.
Calm down for AI, as it states it’s got no boundaries
The biopharma industry is just scratching the surface when it comes to AI-driven collaboration. As technologies evolve, the drug development process will increasingly change, being pushed towards consolidation of innovation, efficiency, and collaboration throughout and across the whole pharma industry.
Envision a future where disease prediction becomes possible before its symptoms manifest itself, where therapies would be individualized, and the period it would take for the whole process of drug development would become months, not years.
This is the promise of AI in biopharma—a future that’s already beginning to take shape.
To biopharma leaders like you, it is evidently clear: Those who embrace AI-driven collaboration will not only survive but soar, being the real leaders of the coming healthcare revolution. There isn’t a debate about whether or not AI will reshape drug development, but on how fast you can get on board.
