LongeCityNews Last Updated: 19 July 2025 - 04:44 PM

The dominant view of the regulation of medicine within academia and government is more or less that (a) people should not have the right to choose their own risks and make their own mistakes, (b) the role of regulators is to remove as much risk as possible, and © that the high cost of medicine and slow pace of introduction of new drugs is a better problem to have than greater freedom for patients. This is the background against which one can find papers such as today's open access discussion of rapamycin and the state of its use as a means to improve late life health and modestly slow degenerative aging.

Rapamycin has long been approved for use as an immunosuppressive drug, but of late has attracted far more attention for its ability to upregulate autophagy, slow aging, and extend life in animal studies. This has led to a significant degree of off-label prescription of rapamycin by physicians. Physicians have the discretion to prescribe any approved drug for any use that is defensible, but this only happens when there is a body of work to suggest that the novel use could be safe and useful.

Thus rapamycin is in the nebulous state occupied by many drugs that are prescribed off-label: animal studies indicate that it could be used in a novel way at a novel dose, in this case to slow aging at lower doses than its established immunosuppressive use, but little to no concrete human data exists to confirm that new use. That data is unlikely to emerge any time soon because clinical trials are expensive and genetic drugs cannot produce enough revenue to justify that cost. Meanwhile, a good fraction of academics and regulators are appalled by off-label use, as one might expect given their views on freedom, risk, and the purpose of regulators.

Rapamycin for longevity: the pros, the cons, and future perspectives

Rapamycin, an antibiotic discovered in the 1970s, has become a critical tool in biomedical research. Initially recognized for its potent antifungal and immunosuppressive properties, rapamycin has recently gained significant attention for anti-aging therapy and seizure treatment via mTOR pathway inhibition. The mechanistic target of the rapamycin (mTOR) pathway is an evolutionarily conserved metabolic signaling cascade that regulates cell division, growth, and survival. There is growing evidence that mTOR pathway activity accelerates aging and the development of age-related diseases including cancer, atherosclerosis, diabetes, and declining immune function. Therefore physicians and "biohackers" are using mTOR inhibition via rapamycin (and rapamycin analogs) off-label for prevention of age-related conditions despite not being widely recognized as a treatment by the broader clinical community.

As rapamycin gains popularity for its anti-aging potential, online longevity clinics have emerged offering access to the drug with minimal medical oversight. This semi-regulated availability raises ethical concerns regarding patient safety, misinformation, and the potential for serious harm. This is best illustrated by the widely publicized case of tech entrepreneur Bryan Johnson, who undertook an elaborate self-directed anti-aging regimen involving rapamycin, metformin, and over 100 daily supplements. Despite extensive physiological tracking, Johnson ultimately discontinued rapamycin and expressed regret over its use citing side effects such as elevated blood glucose, susceptibility to infection, and impaired healing. This case highlights the risks of bypassing peer-reviewed science in favor of anecdotal "biohacking" culture. Clinical literature has long documented rapamycin-associated toxicities that mirror the complaints reported by Johnson and others. The use of such a powerful immunosuppressant outside established indications, especially in otherwise healthy individuals, demands stronger ethical scrutiny and public education.

Lastly, while the FDA does not recognize aging as a disease, there is growing interest in approving therapeutics that enhance healthspan, or delay aging-related decline. However, FDA approvals are structured around specific, diagnosable indications, rather than generalized syndromes. Should rapamycin or related compounds demonstrate efficacy, they would be approved for specific indications (e.g., Alzheimer's) rather than aging per se under the current approval standards. Nonetheless, even within this evolving framework, it is important to note that most off-label prescribing-despite it being common clinical practice-rarely achieves FDA approval, as only about 30% of off-label prescribing is supported by adequate scientific evidence despite any clinically observed positive outcomes. These regulatory and evidentiary constraints must be considered when evaluating rapamycin's future clinical and research trajectory.

Scientists have created an AI model that stratifies Alzheimer’s patients into subgroups that progress slowly or rapidly. When applied to a real-world failed trial, it revealed a robust effect in the former subgroup [1].

Stratify and conquer

Drugs don’t work for everyone equally. Unfortunately, clinical trials are not always able to account for that, which creates several potential problems: what if a drug shown to be ineffective in a trial actually works for a subset of patients? How do we identify this subset and make sure that a useful treatment does not get discarded?

Scientists have suspected for quite a while that this is the case for some experimental treatments for Alzheimer’s disease (AD). The success rate in AD trials is abysmal. Might this be due to the heterogeneity of the patient population?

In a new study from the University of Cambridge, published in Nature Communications, scientists trained their Predictive Prognostic Model (PPM) on data from the ADNI (Alzheimer’s Disease Neuroimaging Initiative) study in order to differentiate their subgroups. The sample size (256 patients) and the number of parameters (just three: β-amyloid, APOE4, and medial temporal lobe grey matter density), were rather small, but according to the authors, it was enough to achieve 91.1% classification accuracy.

The hidden effect

The team then applied their tool to AMARANT, a real-world clinical trial that had failed to show efficacy of lanabecestat, a BACE1 inhibitor designed to reduce the production of β-amyloid plaques in the brain. The AI model analyzed each patient’s baseline data and assigned a prognostic score with which to judge the progression of Alzheimer’s.

“Our AI model gives us a score to show how quickly each patient will progress towards Alzheimer’s disease,” said Professor Zoe Kourtzi in the University of Cambridge’s Department of Psychology, senior author of the study. “This allowed us to precisely split the patients in the clinical trial into two groups: slow- and rapid-progressing, so we could look at the effects of the drug on each group.”

When the researchers analyzed the drug’s efficacy for the slow-progressing subgroup, they found a 46% slowdown in the disease’s progression in patients that received the higher 50mg dose. This is significantly more than what the best treatments that passed their trials achieved for the entire patient population. When the researchers lumped the subgroups back together, the cognitive benefit disappeared, confirming the original outcome and proving that the effect was masked by the heterogeneity of the initial trial population.

Alzheimer’s escape velocity

The team then asked another question: did the treatment prevent patients from transitioning from slow progress to more rapid progress? The answer was positive, as high-dose lanabecestat kept patients in the slow-progressing, hence probably more treatable, subgroup for longer. For the placebo group, 60% of “slow progressors” transitioned to “rapid,” while for the 50mg lanabecestat group, only 33.3% did.

This outcome might be highly relevant considering that new treatments for dementia are nearing approval and will likely be more effective in slowly progressing patients. Combined with better early-stage diagnostics, this might create a sort of “Alzheimer’s escape velocity,” when one treatment slows the disease’s progression enough for upcoming treatments to take over.

“AI can guide us to the patients who will benefit from dementia medicines, by treating them at the stage when the drugs will make a difference, so we can finally start fighting back against these cruel diseases,” said Kourtzi. “Making clinical trials faster, cheaper and better, guided by AI, has strong potential to accelerate the discovery of new precise treatments for individual patients, reducing side effects and costs for health care services.”

Cheaper trials

Stratifying patients with this new AI tool from the start might help make Alzheimer’s clinical trials cheaper and likelier to succeed. The researchers calculated that to detect the drug’s effect in an AI-selected slowly progressing group, a future trial would only need 82 patients per group (treatment vs. placebo). In contrast, to find an effect in a mixed group, a trial would require 762 patients per group. This amounts to a 90% reduction in the required sample size, which could save hundreds of millions of dollars and years of time in drug development. This might seem like abandoning the rapid-progressing patient population, but as this study shows, many of them are former “slow progressors.”

“Promising new drugs fail when given to people too late, when they have no chance of benefiting from them,” Kourtzi said. “With our AI model, we can finally identify patients precisely and match the right patients to the right drugs. This makes trials more precise, so they can progress faster and cost less, turbocharging the search for a desperately needed precision medicine approach for dementia treatment.”

Literature

[1] Vaghari, D., Mohankumar, G., Tan, K., Lowe, A., Shering, C., Tino, P., & Kourtzi, Z. (2025). AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial. Nature Communications, 16(1), 1-12.

After a protein is created in the cell, it must be folded into the right conformation in order to function correctly. A complex set of mechanisms is focused on (a) achieving correct folding and (b) removing misfolded proteins when the process fails. Research into protein misfolding is weighted heavily to the consideration of the comparatively few proteins that form solid aggregates when misfolded, largely because this is an evident and measurable form of pathology that is demonstrably a cause of pathology in conditions such as Alzheimer's disease and the varied forms of amyloidosis. What about all of the other misfolded proteins, however, those that remain soluble? Researchers here point out that hundreds of different misfolded proteins can be found in the aged rat brain, and we might reasonably think that their collective role in neurodegeneration is significant.

Many studies have found that the proteostasis network, which functions to keep proteins properly folded, is impaired with age, suggesting that there may be many proteins that incur structural alterations with age. Here, we have used limited proteolysis mass spectrometry (LiP-MS) to identify proteins that vary in structure in the hippocampus of aged rats with or without cognitive impairment, which we have defined as CASC proteins.

We identified 215 CASC proteins in the CA1 hippocampal region. Research in aging, dementia, and neurodegenerative disease has long made a connection between these disease processes and protein misfolding; however, emphasis has historically been paid to proteins that form amyloids or other insoluble aggregates. We have focused on the soluble fraction of the hippocampal proteome and used a methodology that can sensitively detect subtle changes in protein structure. The results enable us to conclude that protein misfolding is perhaps a more pervasive feature in cognitive decline than previously appreciated and that many of these misfolded forms persist as soluble species.

This finding suggests that there may be previously unidentified avenues for potential therapeutic targets and diagnostic biomarkers for cognitive decline than the small subset of amyloid-forming proteins frequently studied. Of course, these interventions would need to be conformation specific, creating additional opportunities and challenges.

Link: https://doi.org/10.1126/sciadv.adt3778

Researchers here demonstrate that the GrimAge and GrimAge2 epigenetic clocks beat out other clocks while performing more or less equivalently to one another when it comes to predicting mortality in a novel study population. The higher a patient's epigenetic age relative to their chronological age, the higher the risk of future mortality. Epigenetic clock results are not actionable, however. Since researchers do not yet understand how the specific epigenetic marks incorporated into the clock algorithm correlate with mechanisms of age-related dysfunction and disease, they cannot describe why a result is bad or good, nor inform any action taken in response. So at the present time it doesn't matter what an epigentic clock result looks like - one should seek to improve one's health in the same ways regardless.

Epigenetic clocks have been widely applied to assess biological ageing, with Age Acceleration (AA) serving as a key metric linked to adverse health outcomes, including mortality. However, the comparative predictive value of AAs derived from different epigenetic clocks for mortality risk has not been systematically evaluated. In this retrospective cohort study based on 1,942 NHANES participants (median age 65 years; 944 women), we examined the associations between AAs from multiple epigenetic clocks and the risks of all-cause, cancer-specific, and cardiac mortality.

Restricted cubic spline models were used to assess the shape of these associations, and Cox proportional hazards regression was employed to quantify risk estimates. Model performance was compared using the Akaike Information Criterion (AIC) and concordance index (C-index).

Our findings revealed that only GrimAge AA and GrimAge2 AA demonstrated approximately linear and positive associations with all three mortality outcomes. Both were significantly associated with increased risks of death, and these associations were consistent across most subgroups. GrimAge and GrimAge2 AAs showed very similar performance in predicting all-cause, cancer, and cardiac mortality, with only small differences in AIC values and C-index scores. These findings suggest that both GrimAge and GrimAge2 are effective epigenetic biomarkers for mortality risk prediction and may be valuable tools in future ageing-related research.

Link: https://doi.org/10.1080/15592294.2025.2530618