New Pharma Regs Have Created a Perfect Storm for the Industry – AI Is the Fix

Following a recent slew of regulations, pharma companies are increasingly struggling to roll out their products, falling at the commercialisation hurdle. US and EU policies have created a perfect storm for the industry, raising costs and reducing successful launches of new drugs.

While many firms are turning to AI for research support, I predict that the technology’s greatest value will lie in this area, and in fixing this challenge. By identifying the patient subgroups who would benefit the most from the drug, and be the most commercially viable, in the right markets, and with the right evidence, firms can make better investment decisions and accelerate their launches.

This technology couldn’t come at a better time. And while it’s true that every developed region is, to some extent, affected by recent policy shifts, it will be most welcome in Europe, which has been hit hardest of all.

MFN Is Putting Pressure on EU Revenues

This is largely down to two sets of regulations that have simultaneously hit the EU, impacting pharma’s profitability. The first of these comes from the US, with the Trump administration’s Most-Favoured-Nation (MFN) pricing scheme. Launched as an executive order in May last year, this policy ties domestic drug prices to those of other developed nations. It was implemented to address the fact that US citizens pay three to four times as much for prescription drugs as their foreign peers.

As a result of this policy, the pharmaceutical industry has been delaying launches outside of America to avoid lowering the price of drugs at home. EU markets saw drug launches fall by 35% in the 10 months since MFN’s launch, as firms attempted to keep US prices high. The US alone accounts for nearly half of the global pharma market, meaning drug companies may prioritise American profits at the expense of other regions.

JCA Is Raising Pharma’s Costs

The second shock to the EU is its own Joint Clinical Assessment (JCA) process, which introduces a single, standardised evidence base for all member states. Also launched last year, but gradually increasing in scope, JCA sets a new standard for drug approval across the Union. It requires firms to submit a huge, single evidence dossier to the centralised Health Technology Assessment (HTA) Coordination Group. National regulators then draw on that centralised assessment for their own pricing and reimbursement decisions.

While the long-term impacts remain to be seen, it’s anticipated that JCA will increase the upfront cost of EU launches – adding to the pressure that MFN is already exerting on the market. It raises the bar for evidence generation, requiring firms to address the concerns of all member states at once, taking into account the unique relevant populations of many different countries in one go, and extending launch timelines. The result is a potent cocktail of rising costs and increasing commercialisation pressures, felt on both sides of the Atlantic.

AI Needs to Inform Commercialisation Earlier

Pharma companies must now adapt to deal with these shifts, and AI presents an effective path forward. It’s already being used within drug discovery and development efforts – but it needs to be brought into commercial assessments much more. And sooner, rather than later.

Even after a drug has been proven safe and effective, developers need to pull together evidence that demonstrates genuine clinical value and cost-effectiveness in order to receive reimbursement from national health bodies – and the recent regulation changes have added more steps to this process. But it hasn’t always been possible, or economically feasible, for pharma firms to perform evidence-generation and carry out these assessments with the necessary rigour, speed, and scale. It could often take years, slowing the roll-out of a drug and increasing the amount spent on development.

AI has changed that. It can carry out analytical work faster than any human: automating systematic literature reviews, building economic models, and supporting evidence synthesis. While not replacing human expertise, it can act as a research partner of sorts – conducting complex analytical work while helping researchers identify blind spots, strategically focus their analysis, and determine the best patient subgroups and markets to aim for. It makes commercialisation simpler and quicker, reducing costs overall.

Pharma Must Have Broader Ambitions for AI Healthtech

My own ambition for the industry, then, is that firms will be able to simulate ‘HTA in a day’ – modelling the entire approval process in less than 24 hours. By using AI, firms could rapidly assess the efficacy and cost-effectiveness of their own drugs compared to competitors. Placing this information in the context of local markets, they could build far more detailed commercialisation pathways, increasing their chances of success.

This is entirely possible, but will require a slight mindset shift across the industry. The current scope of AI healthtech shows promise, but there is not nearly enough focus on how the technology can be used in this area.

Most LLM initiatives are using the technology to support other development processes – typically to accelerate scientific research. For example, the UK government is developing a “sandbox” to run controlled tests on AI healthtech products, but has no current commercialisation application. Elsewhere, companies are creating “digital twins” of human patients to simulate medical processes, or employing LLMs to recruit patients for clinical trials.

In other words, while healthtech investment is booming, it’s so far concentrated in a specific area. And not the one with the most immediate potential.

The healthtech industry is doing fantastic things. But we need to realise that the greatest gains for pharma will come from evolving commercial strategies. Accelerating scientific development will only increase drugs’ success if it is matched with smart commercial pathways.

MFN and JCA are not going away any time soon. Bringing AI into the commercial evidence process is a vital step – one that must be taken now.