Can AI help address drug shortages in the United States?

Can AI help address drug shortages in the United States?

It’s been more than a year since the start of some of the worst drug shortages in U.S. history. You may think I’m referring to the shortage of Adderall — the most common drug for treating attention-deficit/hyperactivity disorder (ADHD), which was confirmed by the FDA on October 12, 2022. Although these shortages persist (and the multiple failed recovery efforts) are painful in Best case scenario, and I’m actually referring to the thousands of other drugs – including life-saving and life-improving treatments for cancer and hundreds of diseases – that should be undergoing research trials right now. Surprisingly, this problem has not received the same level of attention in the media.

Consider the following: In October 2023, 16 FDA-approved oncology drugs were in shortage (up from 11 in July 2023). The situation represents a major problem for patients and the doctors who treat them. In every drug shortage, doctors are forced to choose another method of treatment (a different drug or another type of treatment) or abandon treatment until options improve. We’ve seen how well this works with drugs like Adderall (but it doesn’t). In fact, Adderall shortages have had a cascading effect; When doctors began prescribing other medications for ADHD, it led to widespread shortages of multiple medications. But when it comes to more specialized drugs, such as cisplatin, the outcome can be even bleaker. Cisplatin — a powerful chemotherapy drug frequently used to treat multiple cancers including ovarian, bladder, brain, larynx, cervical and lung — was in short supply in December 2022, when manufacturer Entas Pharmaceuticals halted production in the face of FDA concerns about it. Quality. The move immediately suspended US access to 50% of its cisplatin supply, leaving cancer patients with suboptimal treatment options, and no other manufacturer stepped in.

Quality concerns, failure to build capacity, and ripple effects are not the only reasons medicine shortages are leading to or worsening. In fact, until recently, regulatory restrictions were the most cited reason for drug shortages.

The biggest problem, and the reason we struggle with the same drug shortages year after year, is that we simply rinse and repeat the same solutions that never stick. The only way out of this cycle is to solve the real problem: not having enough medications to choose from. This problem begins long before the drugs reach the market. In fact, only one in ten potential drugs makes it through pre-clinical or clinical trials. So far this year, the US Food and Drug Administration (FDA) has approved only 43 new drugs to treat human medical conditions. We may never know how many drug candidates failed to pass clinical trials or how many lives they could have saved. Perhaps most troubling is the fact that most drugs fail for entirely preventable reasons, including human error, flawed study design, or weak clinical trials with too few participants.

There are ways to fix the problem and give every useful drug candidate a chance. Translational medicine concepts are already being applied to drug trials to help reduce human error. Artificial intelligence is being deployed to make the drug development process more efficient than ever before. My own company, VeriSIM Life, deploys hybrid AI methodologies to identify the most effective compounds and combinations with the fewest side effects. Importantly, we achieve this before it comes to a human patient – ​​a step that reduces the risks of the trial process. When applied correctly, our approach can significantly reduce time to market (a process that currently takes 10-12 years on average to complete).

But even a short drug development timeline will take a long time to wait when current treatments are not already available. That’s why applying AI to reformulation – capsule to patch or pill to injection – could be useful now. AI methodologies can accelerate the development of new applications for existing drugs by running simulations that provide optimal dosing across multiple application types.

Although the focus now is on moments of hope, like the recent approval of several generics for Vyvanse (an alternative to Adderall), shortages of hundreds of other drugs will not only continue this year but will return unless we do something about it. He. She. In my view, this means thinking outside the box to change the pharmaceutical industry from the bottom up.

AI can really help. So the big question is whether the industry will move fast enough to change the paradigm that is in desperate need of technology-led evolution.

    (Tags for translation) Drug Shortages 

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