{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T03:42:20Z","timestamp":1792208540539,"version":"4.4.0"},"reference-count":34,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Pharma and Therapeutics"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:p>\n                    Digital patients and\n                    <jats:italic>in silico<\/jats:italic>\n                    clinical trials offer new opportunities to advance pharmacological research, clinical development, and regulatory decision\u2010making by enabling the simulation of treatment effects prior to testing in patients. Among the most promising of these opportunities is the combined use of quantitative systems pharmacology (QSP) and causal inference (CI), two complementary paradigms that, together, address different but interlocking aspects of causal reasoning in drug development: one grounded in mechanistic simulation, the other in data\u2010driven estimation. Mechanistic quantitative systems pharmacology (QSP) models generate physiologically interpretable predictions by encoding biological knowledge\u2014such as compartmental flows, receptor kinetics, and feedback loops\u2014typically in systems of differential equations. As such, QSP addresses the question: given specified mechanistic assumptions, how does the system behave under intervention? Complementing this mechanistic lens, causal inference approaches start from observed data and estimate the effects of interventions using formal causal frameworks and comparative outcome analysis. These methods explicitly address confounding, multicausality, and clinical heterogeneity, asking instead: given the data, what are the causal effects of intervening in the real world? These paradigms reflect fundamentally different representations of causality\u2014mechanism\u2010driven versus data\u2010driven\u2014two complementary lenses on the same underlying goal of understanding and predicting intervention effects. This distinction raises a central question: how can causality be consistently represented, inferred, and validated across modeling approaches, and what is required to integrate them to support robust and credible decision\u2010making?\n                  <\/jats:p>","DOI":"10.1002\/cpt.70486","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:30:54Z","timestamp":1790163054000},"page":"1255-1265","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Biological Mechanisms to Causal Inference: Quantitative Systems Pharmacology and Causal Frameworks in Drug Development"],"prefix":"10.1002","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0745-4940","authenticated-orcid":false,"given":"Thomas","family":"Klabunde","sequence":"first","affiliation":[{"name":"Translational Medicine Unit, Disease Modeling Sanofi R&amp;D  Frankfurt am Main Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramon","family":"Hernandez","sequence":"additional","affiliation":[{"name":"Translational Medicine Unit, Real World Evidence Sanofi R&amp;D  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Piet H.","family":"van der Graaf","sequence":"additional","affiliation":[{"name":"Certara Applied BioSimulation  Sheffield UK"},{"name":"Leiden Academic Centre for Drug Research  Leiden The Netherlands"},{"name":"Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0734-0043","authenticated-orcid":false,"given":"Tommaso","family":"Andreani","sequence":"additional","affiliation":[{"name":"Translational Medicine Unit, Disease Modeling Sanofi R&amp;D  Frankfurt am Main Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"e_1_2_8_1_1","unstructured":"U.S. Food and Drug Administration (FDA)&U.S. Department of Health and Human Services CDER & CBER.Population Pharmacokinetics: Guidance for Industry(2022)."},{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.13128"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-022-09811-1"},{"key":"e_1_2_8_4_1","unstructured":"Klabunde T.&Sanofi.R&D Day 2023: Play to Win\u2014Next Chapter of Growth (Investor Presentation)(2023) New York NY; Slide 121. <https:\/\/www.sanofi.com\/assets\/dotcom\/content\u2010app\/events\/investor\u2010presentation\/2023\/r\u2010and\u2010d\u2010day\u20102023\/Presentation.pdf>."},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.3451"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurjpc\/zwae254"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43441-025-00849-0"},{"key":"e_1_2_8_8_1","doi-asserted-by":"crossref","DOI":"10.3390\/mca31050179","article-title":"Causal inference and pathway embeddings with real\u2010world data for enhanced trial design across diseases","volume":"31","author":"Blanchon M.","year":"2026","journal-title":"Math. Comput. Appl."},{"key":"e_1_2_8_9_1","unstructured":"Sorger P.K. Allerheiligen S.R.B. Abernethy D.R.et al.Quantitative and systems pharmacology in the postgenomic era: New approaches to discovering drugs and understanding therapeutic mechanismsNIH QSP Workshop Group White Paper; (2011)."},{"key":"e_1_2_8_10_1","first-page":"307","volume-title":"Handbook of Experimental Pharmacology","author":"Vicini P.","year":"2025"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01188-4"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-0716-2265-0_8"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/cts.13501"},{"key":"e_1_2_8_14_1","unstructured":"Bertolini D. Fuller F. Smith A.M. Walsh J.R.&Zhuang R.Digital twins as synthetic controls in single\u2010arm trials(2026) arXiv preprint arXiv:2605.12832. <https:\/\/doi.org\/10.48550\/arXiv.2605.12832>."},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/trc2.70181"},{"key":"e_1_2_8_16_1","unstructured":"Sanofi&Klabunde T.Digital \u201cTwinning\u201d: Clinical Trials Powered by AISanofi Magazine (2024)."},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12282"},{"key":"e_1_2_8_18_1","article-title":"A first\u2010in\u2010human, single and multiple dose study of lunsekimig, a novel anti\u2010TSLP\/anti\u2010IL\u201013 NANOBODY\u00ae compound, in healthy volunteers","volume":"18","author":"Deiteren A.","year":"2025","journal-title":"Clin. Transl. 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