{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T03:49:15Z","timestamp":1793159355947,"version":"4.5.0"},"reference-count":31,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Pharma and Therapeutics"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:p>In rare diseases, such as hemophilia A, the development of accurate population pharmacokinetic (PK) models is often hindered by the limited availability of data. Most PK models are specific to a single recombinant factor VIII (rFVIII) concentrate or measurement assay, and are generally unsuited for answering counterfactual (\u201cwhat\u2010if\u201d) queries. Ideally, data from multiple hemophilia treatment centers are combined but this is generally difficult as patient data are kept private. In this work, we utilize causal inference techniques to produce a hybrid machine learning (ML) PK model that corrects for differences between rFVIII concentrates and measurement assays. Next, we augment this model with a generative model that can simulate realistic virtual patients as well as impute missing data. This model can be shared instead of actual patient data, resolving privacy issues. The hybrid ML\u2010PK model was trained on chromogenic assay data of lonoctocog alfa and predictive performance was then evaluated on an external data set of patients who received octocog alfa with FVIII levels measured using the one\u2010stage assay. The model presented higher accuracy compared with three previous PK models developed on data similar to the external data set (root mean squared error = 14.6 IU\/dL vs. mean of 17.7 IU\/dL). Finally, we show that the generative model can be used to accurately impute missing data (&lt;\u200918% error). In conclusion, the proposed approach introduces interesting new possibilities for model development. In the context of rare disease, the introduction of generative models facilitates sharing of synthetic data, enabling the iterative improvement of population PK models.<\/jats:p>","DOI":"10.1002\/cpt.3203","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T06:10:38Z","timestamp":1708323038000},"page":"881-889","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Generative and Causal Pharmacokinetic Model for Factor <scp>VIII<\/scp> in Hemophilia A: A Machine Learning Framework for Continuous Model Refinement"],"prefix":"10.1002","volume":"115","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0719-0831","authenticated-orcid":false,"given":"Alexander","family":"Janssen","sequence":"first","affiliation":[{"name":"Department of Clinical Pharmacology Hospital Pharmacy, Amsterdam UMC, University of Amsterdam  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louk","family":"Smalbil","sequence":"additional","affiliation":[{"name":"Quantitative Data Analytics Group, Department of Computer Science VU Amsterdam  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6233-9101","authenticated-orcid":false,"given":"Frank C.","family":"Bennis","sequence":"additional","affiliation":[{"name":"Follow Me &amp; Emma Neuroscience Group Emma Children's Hospital, Amsterdam UMC, University of Amsterdam  Amsterdam The Netherlands"},{"name":"Amsterdam Reproduction and Development  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1557-2995","authenticated-orcid":false,"given":"Marjon H.","family":"Cnossen","sequence":"additional","affiliation":[{"name":"Department of Pediatric Hematology Erasmus MC Sophia Children's Hospital, Erasmus University Medical Center Rotterdam  Rotterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8401-0879","authenticated-orcid":false,"given":"Ron A. A.","family":"Math\u00f4t","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacology Hospital Pharmacy, Amsterdam UMC, University of Amsterdam  Amsterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the OPTI\u2010CLOT study group and SYMPHONY consortium","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,2,19]]},"reference":[{"key":"e_1_2_9_1_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2011-07-360594"},{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/hae.14078"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2021005096"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/hae.14393"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.14864"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0040-1709522"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12894"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.thromres.2020.11.016"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood.2020005843"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/hae.12185"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0040-1710591"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40262-015-0277-z"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/jth.13932"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12808"},{"key":"e_1_2_9_15_1","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"Lakshminarayanan B.","year":"2017","journal-title":"Adv. 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