Dear clients and friends: Given your interest in health and medicine, we would like to share with you our collection of the most interesting perspectives on our industry's trends and developments. We are happy to share them with you — and hope you share your thoughts with us.

1. Blame Canada

In the U.S., there are multiple policies proposed to lower costs of pharmaceuticals. Would they? Two economists offer an answer that will delight some and enrage others:  

The U.S. subsidizes the worldwide pharmaceutical market. One reason is U.S. prices are higher than elsewhere. If each drug had a single international price across the highest-income OECD countries, and total pharmaceutical firm profits were held fixed, then U.S. prices would fall by half and every other country’s prices would increase (by 28 to 300%). International prices would maintain firms’ R&D incentives and more equitably share the costs of pharmaceutical research.

2. Good Question 

Alex Telford, a biotech analyst and blogger, asks 27 questions about biotech that he “finds interesting.” But he doesn’t venture any answers. Instead, he invites readers to collaborate on blog posts that respond to the questions. A few examples: 

How much better can we get at pharmaceutical revenue forecasts? 

Was externalization good for innovation? 

What’s the best way to incentivize new models or surrogate endpoints? 

What is the false negative rate of preclinical testing?

Does investment in basic research in a given therapeutic area correlate with therapeutic progress in that area?

3. AI Warfare: Payers vs. Doctors

A new AI battle is being waged between physicians and insurance companies. According to the Times, “some experts fear that the prior-authorization process will devolve into an A.I. ‘arms race.’” Insurers are allegedly using technology to assess – and deny – claims. Physicians are using generative AI to fight back:

For a growing number of doctors, A.I. chatbots — which can draft letters to insurers in seconds — are opening up a new front in the battle to approve costly claims, accomplishing in minutes what years of advocacy and attempts at health care reform have not.

4. AI Warfare: PPM vs. MCI 

Alzheimer’s researchers have built a “predictive prognostic model” (PPM). It uses AI to predict future brain health. The consequences could be profound: 

Our clinical AI-guided marker has strong potential to help clinicians assign patients to the clinical management pathway that best meets their needs (i.e. reducing invasive diagnostic testing and hospitalization rates). For example, mild MCI patients stratified by the PPM as rapidly progressive may need to proceed with invasive diagnostic testing (e.g. PET scans) and pharmacological treatments, while patients stratified as stable and at lower risk of conversion to AD may be recommended life-style interventions and follow up at a later time. Thus, the PPM-derived marker has strong potential to aid clinicians standardize diagnosis and interventions across healthcare systems and allocate resources to those that need them the most, reducing costs and inequalities in dementia care.

5. Biology for Dummies

Why is drug development slow – and why haven’t we cured cancer? According to Peter Thiel, part of the reason is that our best and brightest don’t study biology. They pick physics or math. “You can sort of think of it in Darwinian terms,” Thiel says. Ruxandra Teslo, writing on her substack, disagrees. The reason isn’t talent, but process:

One of the reasons innovation in areas like machine learning seems to happen so much faster is that one can iterate and carry little “digital experiments” at a much faster pace than one can do in biology, where one has to wait for results from laborious and slow experiments. Looking at feedback loops in the area of medicine, they are even slower. To figure out if a drug works or not, one has to wait for what is approximately a decade of pre-clinical validation followed by clinical trials.

Teslo also sees a bright future: 

If feedback loops are so important, what does the future of the field look like? Overall, I am bullish on biology and more bearish on medicine. The reason is simple: we are getting better and better at doing large-scale, so-called multiplexed experiments, where many outcomes are measured in the same experiment. Some believe that the future of biology lies entirely in iterations of these large scale experiments, followed by feeding the results into AI models and refining predictions. This is what an increasing number of biologists across academia and the private sector are attempting to do.