Studies consistently show that only around 45% of cancer patients receive a drug dose that falls within the optimal therapeutic range. The rest are either under-dosed — with potential consequences for treatment efficacy — or over-dosed, increasing the risk of adverse side effects. For a field that has invested heavily in molecularly targeted therapies and personalised treatment protocols, it is a striking gap.
The cause is structural rather than scientific. Therapeutic drug monitoring — measuring whether a patient's actual drug exposure matches the intended therapeutic target — requires blood draws, laboratory analysis, and clinical follow-up. For patients already navigating intensive treatment schedules, the burden is often prohibitive. In practice, dosing decisions frequently default to population-based averages rather than individual pharmacokinetics.
True Dose, a company with roots in a 2012 Karolinska Institutet research programme, is developing an at-home capillary blood sampling system designed to lower that barrier. Patients collect a small blood sample via fingerprick. The sample is stable at room temperature, sent by post to a laboratory, and the resulting exposure data is returned to the treating physician for dose adjustment. The model is not novel in concept — dried blood spot sampling has been used in research settings for decades — but clinical-grade validation for oncology applications has been limited.
The Validation Question
Two peer-reviewed studies published in 2025, in Scientific Reports and BioMed Research International respectively, report comparable results between True Dose's capillary sampling method and conventional venous blood draws across several oncology drugs. Independent validation at scale, and data across broader patient populations, will be necessary before the method can be considered established clinical practice. Trials are ongoing at Karolinska University Hospital.
The Founding Context
The company was co-founded by Djavad Hedayati, a former technology consultant, and Elham Hedayati, a clinical oncologist with two decades of experience in breast cancer treatment. Elham Hedayati has cited the collapse of clinical trials due to sampling burden as a specific motivating problem — a practical friction point that the technology is designed to address. CE marking has been obtained for the initial product, providing a regulatory basis for commercial activity in European markets.
Which Drugs, And For Whom
The current assay portfolio covers Tamoxifen, Epirubicin, Taxanes, CDK4/6 inhibitors, and ALK inhibitors — agents used predominantly in breast and lung cancer. These are high-volume, widely prescribed treatments. The clinical rationale for exposure-guided dosing in these drug classes is reasonably well established in the literature; the practical implementation has lagged.
Precision Medicine's Missing Layer
The broader context is worth noting. Investment in oncology precision medicine — genomic profiling, targeted therapies, AI-assisted diagnostics — has been substantial. Less attention has been directed at the pharmacokinetic layer: whether patients are actually absorbing the drugs prescribed. Drug exposure varies considerably across individuals based on factors including age, body composition, co-medications, and metabolic enzyme activity. Without measuring exposure, personalised treatment protocols rest on incomplete information.
"The kit enables measurement. The dataset enables intelligence. Whether the platform value materialises depends on how far adoption scales."
The Data Layer
Beyond the sampling kit, each test generates pharmacokinetic exposure data under real-world conditions. If adoption scales, True Dose would accumulate a structured dataset spanning multiple oncology drugs, treatment cycles, and patient populations — something that does not currently exist in a standardised, accessible form.
The company has indicated plans to develop this into a product it calls True Dose Insights: a data layer analysing exposure variability patterns across age, sex, ethnicity, co-medications, and treatment regimens. The potential applications are identifiable — post-marketing pharmacokinetic studies for pharmaceutical companies, evidence-based protocol refinement for clinicians, health economic analysis for payers — though realising them depends on the volume and quality of data collected over time.
In oncology, dose optimisation is largely reactive: a patient presents with toxicity or treatment failure, and the dose is adjusted. Predictive dose modelling — anticipating how a specific patient is likely to metabolise a drug before problems emerge — remains an area of active research. Whether a real-world exposure dataset of this kind could meaningfully contribute to that goal is an open question, but not an unreasonable one to ask.
Where Things Stand
True Dose is at an early commercial stage. The scientific foundations are peer-reviewed and the regulatory pathway in Europe is established, but clinical adoption, reimbursement, and integration into oncology workflows remain to be demonstrated at scale. The problems the company is addressing — suboptimal dosing, sampling burden, fragmented pharmacokinetic data — are real and documented. Whether this particular approach proves to be the durable solution is what the coming years of clinical and commercial evidence will determine.
