The Drug of the Future Will Be Designed by an Algorithm
In the United States, getting a new drug approved can take up to 10 years and cost billions of dollars. AI promises to reduce timeframes, risks, and costs. But under what conditions?
It can take as long as a decade for a new drug to reach the market in the U.S., starting from the early research and testing phases. That’s an extraordinarily long time—especially when it comes to human health. “So why don’t we try to change the system?” asked Martin Makary, a commissioner at the FDA (Food and Drug Administration), the U.S. federal agency responsible for regulating food and pharmaceutical products.
The idea is to integrate artificial intelligence into the approval process to streamline procedures and shorten timelines. This is especially relevant given that the future of research may lead to increasingly personalized medicines. But to get there, we must ensure greater efficiency without compromising patient safety.
According to Wired, which cites sources close to the matter, there has already been a meeting between the FDA and OpenAI (the company behind ChatGPT) to explore this possibility. The discussion is still in its early stages, but it doesn’t come as a surprise to industry insiders, given the ongoing debate about how tech can reshape the pharmaceutical sector. However, there are two major obstacles that cannot be overlooked.
The first is precisely what we've already hinted at: a mistake in such a delicate field can have serious consequences. Then there’s the matter of data availability and security, including the data needed to train AI models.
As is often the case, the direction is clear, but the path forward is complex, involving pharmaceutical companies, regulators, policymakers—and, indirectly, patients.
Lowering Prices
That access to medicines remains a highly pressing issue is also evident from another recent development. U.S. President Donald Trump signed an executive order aimed at significantly lowering prescription drug prices.
Pharmaceutical companies will be required to align drug prices with those in other developed countries, following the "most-favored nation" principle. Otherwise, the administration may resort to alternative measures, such as importing drugs from abroad or imposing tariffs. The goal, according to Trump, is to achieve price cuts of up to 90%.
Naturally, the pharmaceutical industry has pushed back against the measure, which could limit investment in research and development. According to John Crowley, CEO of BIO (the main biotech industry group in the U.S.), this move could particularly hurt small and mid-sized biotech firms.
On a practical level, the policy is also difficult to implement: many drugs sold in the U.S. simply aren’t available in other countries, making price comparisons challenging.
A Timely Debate
In short, Trump’s proposal faces several practical hurdles and political resistance, but it taps into a widespread concern: access to treatment. Even the Biden administration had tried to address the issue, albeit less aggressively.
The Inflation Reduction Act, for instance, included provisions allowing Medicare—the U.S. public health insurance program—to negotiate the price of certain drugs. It also capped the price of insulin and made mandatory vaccines free of charge.
That’s why the question of how innovation can improve this landscape is more relevant than ever. From making processes more efficient to the exciting prospect of using AI to design new molecules, the potential is huge—but so are the challenges.
Challenges and Opportunities
Silvia Ondategui-Parra, Managing Partner and Global Life Sciences Leader at BIP, outlined four key challenges that AI must overcome to be fully integrated into the pharmaceutical sector:
Lack of trust: There’s a reluctance to delegate critical decisions to algorithms in clinical, medical, and regulatory settings—especially when AI is seen as a "black box" (which is also a cultural issue).
Skills gap: Many professionals fear that relying on AI could make them obsolete, when in fact, better understanding of algorithms could enhance their value and competitiveness.
Data chaos: There's a shortage of structured, harmonized, and secure data, which are essential to training and implementing AI models.
Stricter regulations: The FDA has already issued preliminary guidelines on AI governance, while the EU’s AI regulation is set to take effect in August.
Still, AI is already playing a crucial role in the pharmaceutical world. To ignore it would be like walking down a brightly lit street with your eyes closed. In fact, the 2024 Nobel Prize in Chemistry was awarded to Demis Hassabis, co-founder of Google DeepMind, for his groundbreaking use of AI in medicine.
New Frontiers
Many pharmaceutical companies are already using AI to optimize internal processes, such as rapid access to scientific literature, document management, and decision support systems based on dedicated models. In many cases, these companies are developing their own custom versions of ChatGPT.
AI is also capable of generating “virtual patients” to enhance models and simulate trials before real clinical phases begin. These are synthetic representations based on anonymized and aggregated real data, which allow researchers to test a drug’s efficacy and safety across different patient profiles. This approach offers a double benefit: shorter timelines and lower risks.
Finally, technology will help shape the next generation of medicines, opening the door to hyper-personalized healthcare. While we may never be able to tailor drugs for every single individual, we can certainly create therapies suited for homogeneous groups of patients.
The medicine of the future will not only be faster, but also closer to the patient. Artificial intelligence will not replace science—it will enhance it, paving the way for more targeted, accessible, and safer treatments.
This content was contributed by Gabriele Oliva, Director at BIPxTech.
1,200 Driverless Cars Pulled After Series of Minor Accidents
Waymo, the robotaxi company operating in several major U.S. cities, has temporarily withdrawn 1,200 vehicles from its fleet following a string of minor incidents involving doors, chains, and other inanimate objects on the road. While no injuries were reported, the decision to pull the cars comes amid growing concerns. No official statements have been released so far.
Artificial Intelligence to Help Eliminate Malaria
The Indonesian government, through its National Research and Innovation Agency (BRIN), has announced the launch of an AI-powered solution to improve the diagnostic phase in the fight against malaria — a disease still prevalent in parts of the country. According to BRIN, this new tool boosts diagnostic accuracy by 80.6%, outperforming traditional microscope-only methods.
Jaguar to Stay in the UK, Denies U.S. Move
Amid rising tariffs, many brands are reassessing production locations to avoid significant new costs. Jaguar has publicly confirmed it will remain in the UK, as reported by the BBC, pushing back against speculation of a move to the United States. Like Stellantis and Mercedes-Benz, the company is currently withholding profit forecasts due to the volatile U.S. trade stance.
The documentary produced by the U.S. magazine American Pharmaceutical Review showcases the latest processes behind drug research and design, following the integration of advanced technologies throughout the entire supply chain.
Our word for this post is Virtual Patients.
Virtual patients are digital models of human beings, created from real but anonymized clinical data, used to simulate reactions to drugs and therapies during the preclinical phase. These simulations represent a significant advancement in medical research, as they allow for testing the efficacy and safety of treatments without directly involving real subjects in the early stages of experimentation.
Powered by artificial intelligence and advanced predictive models, virtual patients can replicate complex biological conditions and adapt to various clinical profiles, such as age, comorbidities, or genetic predispositions. Their use helps reduce drug development timelines, cut initial costs, and minimize risks, while also improving the selection of the most promising therapies.
This technology aligns with the vision of increasingly personalized medicine, capable of anticipating clinical outcomes and refining therapeutic protocols. While it cannot replace trials on real patients, the virtual patient is set to become a strategic asset for accelerating pharmaceutical innovation and making care more accessible, faster, and more targeted.
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