Rothman AMK

Toshner M

Villar SS

Wason J

Wilkins MR

Newcastle University E-Prints

Pulmonary CirculationEDITORIALDrug Development for Pulmonary Arterial Hypertension:Unleashing the Potential of Single‐Patient Studies UsingContinuous MonitoringMartin R. Wilkins1 | Sofia S. Villar2,3 | James Wason4 | Mark Toshner5 | Alexander M. K. Rothman6,71National Heart and Lung Institute, Imperial College London, London, UK | 2MRC Biostatistics Unit, University of Cambridge, Cambridge, UK | 3PapworthTrials Unit Collaboration, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK | 4Population Health Sciences Institute, Newcastle University,UK | 5Department of Medicine, University of Cambridge, Cambridge, UK | 6Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital,Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK | 7Department of Clinical Medicine, The University of Sheffield, Sheffield, UKCorrespondence: Martin R. Wilkins (m.wilkins@imperial.ac.uk)Funding: This study was supported by Medical Research Council ‐ National Institute for Health and Care Research Efficacy and Mechanism Evaluationprogramme (Project NIHR128465).In the late 1940s, the US Air Force recorded an unacceptablenumber of crashes and emergency landings, prompting a formalenquiry. It was resolved that neither the engine nor the pilotwas at fault. Attention turned instead to the design of thecockpit, which was based on the ‘average’ airman from 1926.Could it be that the body proportions of airmen 20 years on hadchanged? A subsequent survey of over 4000 airmen collected132 detailed measurements on each pilot [1]. From this list, 10were chosen that seemed most relevant to cockpit design andthe number of airmen who fell into the middle 30% range for all10 was calculated. The answer was 0. Even with three measures,under 3.5% of airmen were ‘average′. The survey report con-cluded that “The tendency to think in terms of the ‘averageman’ is a pitfall… it is virtually impossible to find an ‘averageman’ in the Air Force population” [2]. A contemporaneouscompetition to find ‘Norma′, the average American woman,based on average body proportions came to the same conclusionfor women; the ‘average woman’ is rare [3].These stories are well known [4]. Yet we continue to rely onaverage measurements in a variety of situations, including whendesigning and interpreting the results from clinical trials. In drugdevelopment, this may cause us to miss an efficacy signal thatexists in a subset of subjects in a study that fails to meet a pre‐defined average endpoint: perhaps stopping a promising candidatedrug from progressing further. Or it might lead to pursuing ahigher dose than is necessary for ‘responders′, to achieve a pre‐defined averaged measurement for the whole study group. More-over, translation of the mean result from a randomized clinical trial(RCT) to the clinic is frequently disappointing. Valid estimates aredifficult to come by and numbers will depend upon the indicationbut less than a half and perhaps lower than a third of patients likelyrespond to the licensed dose [5, 6]. Relying solely on averagepatient data to inform a dose and dose regimen to treat an indi-vidual patient is a pitfall analogous to that of designing a cockpit forthe average US Air Force pilot.A common approach to addressing variation in drug response isto conduct a subgroup analysis of a RCT and identify ‘responders’(i.e. a subgroup that appears to derive benefit from the drug)based on dichotomizing the outcome; using a threshold mea-surement that is viewed as clinically meaningful. But this ispredicated on the assumption that the recorded response for eachpatient is reliable and reproducible; if the response cannot bereplicated, then our identification of ‘responders’ is insecure [7].N‐of‐1 studies are a subset of cross‐over trials designed to mea-sure the variation in response of a patient to a certain treatmentgiven more than once and on separate occasions. The patient isthe unit of observation; they act as their own control andreproduction of the response on reintroduction of the study drugprovides confidence that the patient is a responder to the drug.This type of study has a long history, but it is under‐employed inmedical research [5, 8]. One reason is that certain criteria need tobe satisfied to allow a N‐of‐1 study design. These include a stablebackground to provide a data set for meaningful comparison,easily measurable endpoints that can be objectively repeated,interventions with a relatively short half‐life to allow for washoutThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properlycited.© 2025 The Author(s). Pulmonary Circulation published by Wiley Periodicals LLC on behalf of the Pulmonary Vascular Research Institute.1 of 4Pulmonary Circulation, 2025; 15:e70177https://doi.org/10.1002/pul2.70177and cross‐over of treatment and a protocol that provides carefulmanagement of the changeover treatment periods.The opportunity to evaluate a new treatment in a small numberof patients makes the N‐of‐1 study of particular interest to rareconditions, such as pulmonary arterial hypertension (PAH).PAH meets many of the criteria. It is a chronic condition wherenew treatments are introduced to affected patients who arestable on background licensed treatments. Established clinicalmeasures of response can be repeated [9]. But there is an ethicalconcern around the safety of drug withdrawal in PAH. Whiledesirable in drug assessment from a regulatory standpoint, as areturn of clinical measures towards baseline provides addedinformation with respect to drug effect, clinicians are sensitisedto relatively rapid changes in cardiopulmonary haemodynamicsthat follow reduced or missed doses of some short‐acting va-sodilators (e.g. prostacyclins) [10].Here the introduction of wireless devices to medicine is apotential ‘game changer′. In addition to wearables, implanteddevices, such as pulmonary artery pressure monitors and heart‐rate activity recorders, can be used to capture daily haemody-namic information during changes in therapy [11, 12]. Im-planted devices, unlike wearables, cannot be forgotten ordiscarded, although patient adherence to data uploads is nec-essary. Regular remote data acquisition is not subject to ter-minal digit preference or seasonal variability [13, 14] and offersa dual benefit: it allows for the establishment of a stable base-line before a new drug is introduced, and it enables close,continuous monitoring of patients as their therapy changes.While the current number of PAH patients with implantedsensors is small compared to patients with left heart failure,patient acceptance has been high [15], there have been no safetyconcerns around the devices and the emerging results showcasetheir value [11, 12]. The attributes of digital technology per-mitting the collection of precise high‐frequency longitudinaldata at the individual patient level augments what can beachieved with traditional clinical outcomes, where multiplerepeated endpoints are expensive, time consuming and resourceintensive, and allows the implementation of high‐quality single‐patient designs in PAH [16].A recent study re‐evaluating imatinib as a treatment for PAHexemplifies the detail that can be obtained from the use of im-planted devices in a single‐patient study setting [17]. This studysubstantiated an exposure‐dependent improvement in cardio‐pulmonary haemodynamics, namely total pulmonary resistance,over a tolerated dose range, based not on a single reading aver-age, as is the case in RCTs, but the 3‐day rolling average. Byelucidating the temporal relationship of response to exposure, thestudy suggested an earlier haemodynamic effect than commonlythought possible, an insight helpful in planning the timing ofendpoints in future studies, and one that could enable the designof a shorter, more efficient future RCT. The study also providedevidence of a gradual return of haemodynamic measurements tobaseline after drug withdrawal, an observation that assuagesconcerns about the impact of occasional missed doses and couldinform dosing frequency in clinical practice.The reproducibility of the hemodynamic response was shownby repeated exposure in the same patient (Figure 1). Theoptimum number of cycles of treatment required to reliablyassess within patient variation in response to a treatmentdepends upon a number of factors, including the effect size, theprecision of the measurement, and the stability of the disease.Another consideration is the length of time needed to producean effect and the length of the washout period. This timecommitment can place a burden on the patient as well ashaving cost implications for funders. Of course, the N‐of‐1cross‐over design is only feasible if the treatment effect wearsoff within a reasonable timeframe after dosing is stopped; butthat is itself instructive, as if no return to baseline is observed ondrug withdrawal, this may go a long way towards answeringquestions around whether a drug is disease modifying. Wherethere is a reversal of response, a second challenge may be suf-ficient to provide confidence in a patient′s responder status (i.e.responder or nonresponder) in the context of daily recordingsfrom implanted devices.Data from a series of N‐of‐1 studies of the same intervention canbe combined to understand the potential average treatment dif-ference. The primary methods for accomplishing this are a Meta‐Analysis of Individual Treatment Effects (where each N‐of‐1 trialprovides an individual‐level treatment effect estimate to yieldan average treatment effect for the studied population) and anIndividual Participant Data (IPD) Meta‐Analysis using Hierar-chical or Mixed Models (where hierarchical models are then usedto simultaneously model within‐patient responses and between‐patient variability). In the presence of large heterogeneous treat-ment effects, a meta‐analysis of N‐of‐1 trials may offer efficiencygains over a traditional RCT approach to learn about effects in thepopulation as opposed to the individual. By providing granulardata and leveraging within‐patient comparisons to preciselyquantify individual variability and overall effects, an N‐of‐1 seriescan identify and support clustering patients into distinct responsesubgroups.But in considering N‐of‐1 studies with continuous monitor-ing for PAH, the idea is to complement rather than replaceRCTs. Their optimal deployment is in early phase develop-ment, recognising that animal models are poor predictors ofclinical efficacy and the best model organism is the human[18]. The N‐of‐1 study provides an early opportunity to con-duct a proof‐of‐concept study in a small number of patientswith a ‘fail early, fail fast’ strategy, the philosophy of re-cognising early when a drug is not effective or may even beharmful [19]; the drug can then be dropped before too manypatients are exposed and resources can be re‐directed towardsthe next promising candidate, a priority for a rare condition.It can address efficiently the need to be inclusive of diversepopulations; a positive response in just one patient from anunderrepresented population is very powerful. As illustratedby the imatinib study, the N‐of‐1 design can accommodatenovel trial designs [20] as well as instruct key design ele-ments for larger trials, such as dose and dosing interval, trulymeaningful effect sizes and optimal timeframes for treatmentonset. This approach aligns with Project Optimus, a FDAinitiative to reform and modernise dose selection and opti-misation in oncology drug development by advocating for theidentification of an optimal biologic dose [21]. The insightsgained are invaluable for informing and refining the imple-mentation of adaptive designs, allowing for more efficient2 of 4 Pulmonary Circulation, 2025 20458940, 2025, 4, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/pul2.70177 by Newcastle University, Wiley Online Library on [27/10/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons Licensemid‐trial adjustments and better resource allocation. Later onin the drug life cycle, N‐of‐1 studies have a place post‐marketing in that they provide the opportunity to monitorpatients closely and evaluate different treatment combina-tions; rather than simply add new therapies to an ever‐increasing number of background drugs, once patients arestable, one or more drugs might be withdrawn and the impactcarefully measured.The answer to the ‘cockpit problem’ of the 1940s was anadjustable design that could be adapted to each individual air-man. Ultimately, if wearables and/or implanted devices becomemore commonplace for patients with PAH, more patients maytake part in their own N‐of‐1 study, allowing personalizedtreatment to target decisions [22]. This would align the man-agement of PAH with the direction of travel for other chronicconditions, such as heart failure and diabetes. It parallels thebroader necessity in modern drug development to employ ex-perimental designs that can efficiently and robustly learn aboutpopulation effects, while simultaneously facilitate the person-alization of treatment.Author ContributionsThe manuscript was drafted by Martin R. Wilkins and all authorscontributed to the content and editing. It is not being considered else-where for publication.AcknowledgmentsMedical Research Council ‐ National Institute for Health and Care ResearchEfficacy and Mechanism Evaluation programme (Project NIHR128465).Ethics StatementThe authors have nothing to report.References1. H. T. E. Hertzberg, G. S. Daniels, and E. Churchill “Anthropometry ofFlying Personnel – 1950,” accessed 21 June 2025, https://apps.dtic.mil/sti/tr/pdf/AD0047953.pdf.2. G. S. Daniels “The ‘Average Man’,” accessed 21 June 2025, https://apps.dtic.mil/sti/tr/pdf/AD0010203.pdf.FIGURE 1 | Rechallenging a patient with imatinib added to background therapy. Risk estimated according to ERS/ESC guidelines (modifiedfrom Ref [17]). Adapted with permission of the American Thoracic Society. Copyright © 2025 American Thoracic Society. All rights reserved.3 of 4 20458940, 2025, 4, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/pul2.70177 by Newcastle University, Wiley Online Library on [27/10/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License3. D. S. Cambers “The Law of Averages 1: Normman and Norma,” accessed21 June 2025, https://www.cabinetmagazine.org/issues/15/cambers.php.4. T. Rose, The End of Average (Penguin Books, 2017).5. S. Senn, “Individual Response to Treatment: Is it a Valid Assump-tion?,” BMJ 329, no. 7472 (2004): 966–968.6. N. J. Schork, “Personalized Medicine: Time for One‐Person Trials,”Nature 520 (2015): 609–611.7. S. Senn, “Statistical Pitfalls of Personalized Medicine,” Nature 563,no. 7733 (2018): 619–621.8. H. P. Selker, D. Dulko, D. J. Greenblatt, M. Palm, and L. Trinquart,“The Use of N‐of‐1 Trials to Generate Real‐World Evidence for OptimalTreatment of Individuals and Populations,” Journal of Clinical andTranslational Science 7, no. 1 (2023): e203.9. M. Humbert, G. Kovacs, M. M. Hoeper, et al., “2022 ESC/ERSGuidelines for the Diagnosis and Treatment of Pulmonary Hyper-tension,” European Respiratory Journal 61, no. 1 (2023): 2200879.10. L. J. Rubin, “Primary Pulmonary Hypertension,” New EnglandJournal of Medicine 336, no. 2 (1997): 111–117.11. R. L. Benza, M. Doyle, D. Lasorda, et al., “Monitoring PulmonaryArterial Hypertension Using an Implantable Hemodynamic Sensor,”Chest 156, no. 6 (2019): 1176–1186.12. J. T. Middleton, S. Binmahfooz, H. Zafar, et al. “Remote Eva-luation of Risk and Physiological Response to Therapeutic Escalationand Clinical Worsening in Patients With Pulmonary Hypertension[preprint],” medRxiv; 2024, accessed November 26, 2024, https://www.medrxiv.org/content/10.1101/2023.04.27.23289153v1.13. A. M. K. Rothman, J. Wason, and M. Toshner, “Digital DataCollection Reduces Terminal Digit Bias in Haemodynamic andPhysical Activity Measures in Pulmonary Arterial Hypertension,”medRxiv 1 (2025): 25328105, https://doi.org/10.1101/2025.05.21.2532810514. A. M. K. Rothman, J. Middleton, H. Zafar, et al., “Remote Acti-graphy Is Seasonally Variable With Implications for Clinical Mon-itoring,” In medRxiv (2025) https://doi.org/10.1101/2025.05.22.25328110.15. PHA UK, “Helping People Get the Most From Their PH Treatments:Phoenix Study Proposal Findings,” (2021), https://www.phauk.org/app/uploads/2021/04/Phoenix-study-research-report.pdf.16. M. S. Tackney, J. R. Carpenter, and S. S. Villar, “Unleashing the FullPotential of Digital Outcome Measures in Clinical Trials: Eight Ques-tions That Need Attention,” BMC Medicine 22 (2024): 413. https://doi.org/10.1186/s12916-024-03590-x17. A. M. K. Rothman, S. S. Villar, J. Middleton, et al., “PositioningImatinib for Pulmonary Arterial Hypertension: A Dose‐Finding Phase 2Study,” American Journal of Respiratory and Critical Care Medicine 211,no. 6 (2025): 1018–1027.18. G. FitzGerald, D. Botstein, R. Califf, et al., “The Future of Humansas Model Organisms,” Science 361, no. 6402 (2018): 552–553.19. O. Sitbon, A. Skride, J. Feldman, et al., “Safety and Efficacy ofRodatristat Ethyl for the Treatment of Pulmonary Arterial Hyper-tension (ELEVATE‐2): A Dose‐Ranging, Randomised, Multicentre,Phase 2b Trial,” Lancet Respiratory Medicine 12, no. 11 (2024):865–876.20. M. R. Wilkins, M. A. Mckie, M. Law, et al., “Positioning Imatinib forPulmonary Arterial Hypertension: A Phase I/II Design ComprisingDose Finding and Single‐Arm Efficacy,” Pulmonary Circulation 11(2021): 20458940211052823.21. US Food and Drug Administration, “Project Optimus,” accessed July 1,2025, https://www.fda.gov/about-fda/oncology-center-excellence/project-optimus.22. F. Varian, J. Dick, C. Battersby, et al., “Pulmonary Hypertension:Intensification and Personalization of Combination Rx (PHoenix): APhase IV Randomized Trial for the Evaluation of Dose‐Response andClinical Efficacy of Riociguat and Selexipag Using Implanted Technol-ogies,” Pulmonary Circulation 14, no. 1 (2024): e12337.4 of 4 Pulmonary Circulation, 2025 20458940, 2025, 4, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/pul2.70177 by Newcastle University, Wiley Online Library on [27/10/2025]. 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Drug Development for Pulmonary Arterial Hypertension: Unleashing the Potential of Single-Patient Studies Using Continuous Monitoring

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Drug Development for Pulmonary Arterial Hypertension: Unleashing the Potential of Single-Patient Studies Using Continuous Monitoring

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