From what we inherit to what we can change — and how quickly knowledge becomes better lives
It is difficult to believe that twenty-five years have passed since 11 September 2001.
Time moves extraordinarily quickly when we look backwards. Yet for the families who lost someone that day, some moments will never feel distant. This Friday is first of all a moment to remember those who died, those who responded with extraordinary courage, and all those whose lives were changed.
Anniversaries also remind us of something easily taken for granted: time itself.
For patients, time has a different arithmetic.
For someone waiting for a diagnosis, it matters. For somebody carrying a genetic or other predisposition to disease, the years before symptoms appear may provide an invaluable window for screening, prevention and earlier intervention. For someone already diagnosed, receiving the right test or treatment today rather than months from now can alter the course of a life.
Time matters too for the person waiting for science to move: for research funding; for a clinical study to begin; to discover whether an appropriate trial exists; whether the eligibility criteria fit; whether there is a site within reach — and whether they will actually be invited to participate.
For a patient entering a randomised trial, there is another intensely human uncertainty: whether they will receive the investigational treatment or the comparator required to determine whether the intervention truly works.
For scientists and regulators, these are necessary stages in generating trustworthy evidence. For somebody living with progressing disease, they are also days and months that cannot be returned.
That is why rational allocation of health resources matters.
Resources are finite. Rational allocation is not simply about spending less. It is about funding the pathway in the right order: research before evidence can emerge; laboratories and training before testing can scale; diagnostics before targeted treatment can be selected; clinical-trial infrastructure before patients can participate; and treatment before the patient’s window of opportunity closes.
A policymaker personally confronted with serious illness may suddenly see very differently what previously appeared as separate budget lines: the research programme waiting to start; the laboratory requiring investment; the molecular test awaiting reimbursement; the clinician or pathologist needing training; the genomic platform requiring capacity; or the referral pathway that simply needs to move faster.
These are not disconnected costs.
They are parts of the same patient pathway.
And ultimately, the scarce resource being allocated is not only money.
It is time.
That is the common currency running through this week’s IPM Brief: science creates possibilities, evidence tells us whether they work, implementation determines whether patients can benefit, and rational allocation determines whether the pathway exists in time.
In this issue
- Deep Time: What Denisovans Still Tell Us About Ourselves: A 120,000-year cave record reminds genomic medicine that there has never been an average human genome.
- Nine Billion Possibilities: AI Tries to Read the Genome: AI can rank billions of genetic possibilities, but prediction still has to survive the journey to proof.
- From Reading DNA to Understanding What It Means: Base editing is turning uncertain variants into testable biology — and, in one case, a treatment built for one infant.
- Beyond the Genome: Editing the Memory of the Cell: Epigenome editing aims to change what a cell remembers without rewriting the DNA it inherited.
- Better Evidence, Not Just Bigger Data: A national genomics programme found plenty of molecular signals — and far fewer patients who reached matched treatment.
- When Precision Biology Meets Clinical Reality: Pelacarsen lowered the biomarker and missed the outcome — a costly reminder that the two are not the same thing.
- GLP-1s: Personalising Cardiometabolic Medicine: The GLP-1 story is moving beyond weight loss into cardiovascular, renal and heart-failure medicine — but patients still respond differently.
- Lower Prices Are Not Yet Personalised Access: Drug-price agreements can lower one barrier, but a cheaper treatment still cannot help a patient who never reaches the test.
- Beyond GDP: Measuring What Health Really Creates: The intervention appears on the budget; the cancer avoided and the healthy years gained often do not.
- Let Science Do Its Job: Hideki Shirakawa turned a laboratory mistake into a Nobel-winning field; policy should remember how discovery actually works.
| This is not another health news digest. It’s a twice-weekly readout of where evidence meets power and where power must turn into action. |
Deep Time: What Denisovans Still Tell Us About Ourselves
Personalised medicine begins with human variation — and some of that variation has a history far older than medicine itself. New discoveries from Bianfu Cave in southwestern China provide an unusually rich picture of the Denisovans: fossils, tools and evidence of intermittent occupation spanning roughly 190,000 to 70,000 years ago, alongside signs of hunting and technological adaptation. The work substantially expands what is known about an ancient human population originally identified through genetics. (Nature; Nature News)
But Denisovans are not simply part of the archaeological past. Their genetic inheritance persists in populations alive today, particularly in Oceania, while the Denisovan-related EPAS1 haplotype contributed to high-altitude adaptation among Tibetans. Modern populations carry biological histories shaped by migration, selection, admixture and adaptation. If genomic reference datasets fail to represent that diversity, personalised medicine risks becoming precise for some populations and uncertain for others. Personalisation begins by understanding variation — and variation has a very long history.
Nine Billion Possibilities: AI Tries to Read the Genome
The bottleneck in genomic medicine is increasingly not sequencing. It is interpretation. Google DeepMind’s AlphaGenome Atlas attempts something extraordinary: predicting the molecular consequences of approximately nine billion possible single-letter changes across the human genome. Its potential value is greatest in regulatory regions outside protein-coding genes, where interpretation remains difficult and patients with rare disease can spend years moving through candidate variants without a diagnosis. (Nature; The Guardian)
But prediction is not proof. An AI system can identify where scientists should look; it cannot alone establish that a variant caused disease in a particular patient or that changing it will improve an outcome. The same discipline applies to OpenAI’s claimed breakthrough on the Navier–Stokes existence and smoothness problem, which prompted excitement alongside demands for conventional scrutiny, independent validation and clear attribution. AI can accelerate the journey from question to hypothesis. It cannot abolish the journey from hypothesis to evidence.
From Reading DNA to Understanding What It Means
Personalised medicine has spent much of the genomic era learning how to read DNA. The harder challenge is determining which differences actually matter. Base editing allows researchers to alter individual DNA letters without creating the double-strand breaks associated with conventional CRISPR–Cas9 editing. Its importance goes beyond therapy: by introducing specific changes and observing their consequences, researchers can move from statistical association towards functional understanding. That matters because sequencing is producing enormous numbers of variants whose clinical significance remains uncertain. (Nature; The New England Journal of Medicine)
The therapeutic transition is already visible. Base-edited stem cells have produced encouraging results in haemoglobin disorders; in-vivo editing of targets such as PCSK9 is being explored in cardiovascular disease; and in 2025 clinicians developed a bespoke base-editing treatment for an infant with a severe metabolic disorder — perhaps the clearest expression yet of treatment designed around one individual’s mutation. The distinction between somatic treatment and heritable genome editing remains fundamental. Technical capability does not itself establish clinical or ethical acceptability. Personalised medicine will be defined not by how many genomes we can sequence, but by whether we understand them well enough to act safely, effectively and in time.
Beyond the Genome: Editing the Memory of the Cell
Epigenome editing introduces another possibility: changing how genes behave without changing the underlying DNA sequence. Rather than rewriting genetic code, epigenetic editors modify the molecular controls that determine whether genes are activated, reduced or silenced. The therapeutic ambition is not to ‘reset’ the epigenome — normal epigenetic regulation is essential to life — but to correct specific regulatory states that contribute to disease. Systems such as CRISPRoff have demonstrated durable gene silencing, while PCSK9-targeting work in non-human primates has produced substantial and persistent reductions in PCSK9 and LDL cholesterol without changing the underlying DNA. (Cell; Nature Biotechnology; Nature Communications)
In human cellular models of Prader–Willi syndrome, epigenome editing has even been used to reactivate functioning genes that are present in the genome but normally silenced. Early clinical development is also testing targeted epigenetic silencing against persistent hepatitis B viral DNA. Important questions remain around delivery, specificity, durability and causality: an epigenetic abnormality associated with disease may be driving that disease — or merely reflecting it. For personalised medicine, the ambition is therefore exceptionally precise: the right gene, in the right cell, in the right direction, to the right degree, for the right duration.
Better Evidence, Not Just Bigger Data
Medicine has never possessed so much data. Genomics, electronic records, registries, imaging and AI can identify associations across increasingly narrow groups of patients. But precision of measurement is not necessarily precision of evidence. In a nationwide Japanese study of 54,185 patients undergoing comprehensive genomic profiling, 16.6% had alterations linked to regulator-approved drugs and a further 8.1% had alterations supported by strong clinical evidence or expert consensus. Yet only around 8% of the full cohort ultimately received an approved or experimental treatment matched to a genomic biomarker. (Nature Medicine)
That gap captures the last-mile problem. Finding an alteration is not the same as establishing that it is actionable. Establishing actionability is not the same as having an effective treatment. And having an effective treatment does not mean that the patient will reach it. Randomised evidence remains essential where feasible. Real-world evidence is equally important — particularly for rare molecular subgroups, treatment sequencing and populations poorly represented in conventional trials — but methodology matters. The answer is not less real-world evidence. It is better real-world evidence.
When Precision Biology Meets Clinical Reality
Few cardiovascular targets appeared more convincing than lipoprotein(a). Lp(a) is predominantly genetically determined, remains relatively stable throughout life and is strongly associated with cardiovascular risk. Then came Lp(a)HORIZON. Novartis announced that pelacarsen failed to meet the primary cardiovascular endpoint in its Phase III trial involving 8,323 patients, despite lowering Lp(a). The result surprised cardiology because the biological rationale had appeared unusually persuasive (Reuters; ClinicalTrials.gov)
But the biomarker is not the outcome. A biomarker may accurately identify risk and a medicine may successfully move it; neither guarantees that the patient’s clinical outcome will improve. The complete HORIZON dataset will matter: was Lp(a) reduced sufficiently, was treatment started too late after decades of vascular exposure, does the therapeutic mechanism matter, and could defined subgroups still benefit? Outcomes trials of Amgen’s olpasiran and Lilly’s lepodisiran now become even more informative. The entire chain must hold: risk marker → causal biology → effective intervention → meaningful patient outcome. Negative evidence is not wasted time if it prevents years of pursuing the wrong assumption.
GLP-1s: Personalising Cardiometabolic Medicine
GLP-1-based treatments are moving beyond a simple weight-loss story towards personalised cardiometabolic medicine — integrating obesity, cardiovascular risk, kidney disease, heart failure and individual treatment response. SELECT demonstrated cardiovascular benefit with semaglutide, FLOW demonstrated kidney-related benefit, and SUMMIT showed benefits from tirzepatide in obesity-related heart failure with preserved ejection fraction. Together, the trials have widened the clinical argument for these medicines far beyond a number on a scale. (SELECT — NEJM; FLOW — NEJM; SUMMIT — NEJM; Reuters)
But patients do not respond identically. A 2026 study involving 27,885 GLP-1 users identified genetic variation associated with modest differences in weight-loss response and adverse effects — an early glimpse of how pharmacogenomics might eventually refine treatment selection, though clinical response remains far more useful than routine genetic testing today. Persistence matters too: substantial weight can return when treatment stops. The personalised question is no longer merely ‘Who should start?’ It is who benefits most, who should continue, for how long, with what monitoring and at what sustainable cost. The right medicine for the right patient is insufficient if the pathway cannot sustain treatment.
Lower Prices Are Not Yet Personalised Access
The Trump administration has announced further most-favoured-nation pricing agreements with pharmaceutical manufacturers as part of its effort to lower US drug costs. The latest agreements bring another group of manufacturers into the scheme, linking Medicaid pricing to lower international benchmarks while tying participation to commitments around US investment and tariff treatment. If the arrangements produce substantial and sustainable savings, that matters. But for personalised medicine, affordability is only one gate in a much longer patient pathway. (Barron’s)
A targeted cancer medicine can become cheaper, but it provides no benefit to somebody who is never molecularly tested. A rare-disease therapy achieves little for someone whose diagnosis takes years. And an innovative treatment cannot reach the patient if there is no specialist, reimbursed diagnostic, appropriate infrastructure or functioning referral pathway. The patient pathway is the real unit of access: research → diagnosis → testing → interpretation → referral → reimbursement → treatment → follow-up. Funding cannot be concentrated only at the end of that chain. Otherwise we create extraordinary innovation at the end of pathways that too few patients can navigate.
Beyond GDP: Measuring What Health Really Creates
Healthcare is still too often described principally as a cost. But preventing cancer, avoiding a cardiovascular event or adding years of healthy life creates value that traditional economic measures can struggle to capture. Healthy Lifetime Income offers one way to connect material prosperity with the years people can expect to live in good health, rather than treating GDP as a sufficient account of human progress. This matters for personalised medicine because screening, diagnostics and prevention often require expenditure today while producing benefits years later. (Social Science & Medicine; BMJ)
Health systems record the cost of an intervention more easily than the value of the event that never happened: the late-stage cancer avoided; the hospitalisation prevented; the ineffective treatment never given; the disability postponed. Those absences may represent some of the greatest returns a health system can produce. Rational allocation therefore requires looking beyond the immediate price of a test, laboratory or medicine and asking what the investment creates across the complete patient pathway. Sometimes it buys time before disease develops. Sometimes it preserves independence. The real return on medical innovation is not how much healthcare activity it generates, but how much healthy life it creates.
Let Science Do Its Job
It is easy to be wise in hindsight. Science rarely has that luxury. The life of Japanese chemist Hideki Shirakawa, who died recently aged 90, offers an elegant reminder. During experiments on polyacetylene in 1967, an accidental excess of catalyst produced something unexpected: a silvery film rather than the anticipated material. The important part was not the mistake. It was what happened next. Shirakawa investigated the anomaly, and his subsequent work with Alan MacDiarmid and Alan Heeger helped establish the field of conductive polymers, earning the three scientists the 2000 Nobel Prize in Chemistry. (Associated Press; Reuters)
That principle is worth remembering as the United States continues examining the conduct of scientists involved in COVID-19 and the still-unresolved question of SARS-CoV-2’s origins. Accountability matters: preserve records, disclose conflicts and investigate genuine wrongdoing. But whether a scientific interpretation was correct, whether administrative rules were followed and whether a crime was committed are different questions. Changing an interpretation because the evidence changes is not a weakness of science; it is one of its safeguards. Personalised medicine needs an environment in which researchers can question assumptions, investigate anomalies, publish negative results and say, when necessary, ‘We do not yet know.’ Demand transparency. Investigate misconduct when the evidence warrants it. But allow science to question, revise and discover.
The IPM Take
The Common Currency
Twenty-five years can disappear remarkably quickly when viewed from a distance. For a patient, however, a month can change everything: a month waiting for a diagnosis; a month waiting for a clinical trial to open; a month before receiving the molecular test that identifies the treatment; a month during which disease continues to progress.
Perhaps that makes time one of the most useful measures of personalised medicine: time before disease becomes advanced; time saved through prevention and earlier diagnosis; time gained through effective treatment; and ultimately, time lived in better health. That is why rational allocation of resources is not simply an economic exercise.
A laboratory, a molecular diagnostic, a trained pathologist, a clinical-trial network, a genomic platform and an innovative medicine may appear on different budget lines.
They are links in the same pathway. Break one link, and the patient may never reach the innovation at the end. Science needs time to discover. Evidence needs time to mature. But health systems must become much better at eliminating the time that serves no scientific, clinical or patient purpose at all.
Personalised medicine is ultimately about using science, evidence and resources intelligently enough that avoidable months — and avoidable years — are not lost. The right prevention. The right research. The right test. The right treatment. The right patient. The right time. That is rational allocation measured not simply in money — but in healthy life.
September Watch
Registrations also remain open for upcoming IPM Alliance events in New York on 24 September and Dublin on 2 October. Visit the IPM Alliance website for programme updates.
| 14–17 September — Europe’s medicines committee meets: |
| EMA’s CHMP holds its September plenary in Amsterdam, where recommendations on new medicines, extensions and post-authorisation questions can move towards EU-wide decisions. (EMA) |
| 14–18 September — Drug-safety reporting moves deeper into E2B(R3): |
| EMA runs a hands-on EudraVigilance course as the EU’s mandatory ISO/ICH E2B(R3) safety-reporting requirements become operational reality rather than a technical footnote. (EMA) |
| 15 September — Healthcare goes in front of the capital markets: |
| Baird’s Global Healthcare Conference brings senior executives and investors together in New York; watch for financing signals, pipeline priorities and the stories companies choose to sell. (Baird) |
| 16 September — Male contraception asks the adoption question: |
| A WHO session examines what drives willingness to use novel male contraceptive methods — a reminder that scientific feasibility and social uptake are separate endpoints. (WHO) |
| 17 September — World Patient Safety Day puts NCD care under the light: |
| The 2026 theme is ‘Safe care for noncommunicable diseases’, with the slogan ‘Safe care for life!’ — bringing medication safety, continuity and long-term care into the centre of the campaign. (WHO) |
Follow the last-mile conversation!
IPM Alliance tracks the decisions, bottlenecks and implementation gaps shaping personalised medicine access. Follow us for briefings, events, policy signals and practical insights from across regions and disease areas.
We want to hear from you
When a patient becomes eligible for personalised care, where does the system most often lose them: identification, testing, referral, reimbursement, delivery capacity or equity?
Know someone who should be reading this?
IPM Brief is built for people working where science, policy and patient access collide. If one colleague would find this useful, forward it to them and help bring them into the conversation.
International Alliance for Personalised Medicine
Avenue de l’Armée / Legerlaan 10, 1041 Brussels, Belgium
