{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T13:20:16Z","timestamp":1787664016848,"version":"build-2736575974"},"reference-count":80,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100004312","name":"Eli Lilly and Company","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004312","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["accp1.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Clinical Pharma"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Type 2 diabetes (T2D) is a chronic metabolic disorder associated with high morbidity and mortality, necessitating the urgent need for the research of novel antidiabetic compounds. Population pharmacokinetic\/pharmacodynamic (PK\/PD) models have been used in T2D drug development to inform dose selection for antidiabetic compounds. However, most population PK\/PD models are empirical, and physiological processes (e.g., glucose\u2010mediated hemoglobin glycation) are not routinely incorporated. Conversely, semi\u2010mechanistic models in people with T2D incorporate some relevant physiological processes, enabling prediction of long\u2010term glycated hemoglobin (HbA1c) using only short\u2010term glucose and HbA1c data. Semi\u2010mechanistic T2D models may also have improved ability for extrapolation to multiple antidiabetic drug classes. This narrative review summarizes six published semi\u2010mechanistic models commonly used or discussed for HbA1c prediction in people with T2D: A Dynamic HbA1c EndpOint Prediction Tool (ADOPT), FPG\u2010FSI\u2010HbA1c (FFH), FPG\u2010Hb\u2010HbA1c (FHH), Integrated Glucose\u2010RBC\u2010HbA1c (IGRH), Weight\u2010HbA1c\u2010Insulin\u2010Glucose (WHIG), and Body Weight\u2010Directed Disease Trial (BWDDT) models. An overview of the HbA1c predictive performance is presented for four of these models (ADOPT, FFH, FHH, and IGRH models). Strengths and limitations of the six semi\u2010mechanistic T2D models are evaluated based on model assumptions, HbA1c model predictive performance, predictive accuracy for collected biomarker data, model complexity, and computational considerations. A decision tree framework is provided for model selection. Practical strategies for model development are discussed, including approaches for incorporating different drug effects into the models. Future directions include model validation with early\u2010phase data from various antidiabetic drug classes, and exploration of alternative implementation of disease progression models.<\/jats:p>","DOI":"10.1002\/jcph.70274","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T19:04:31Z","timestamp":1787339071000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Review of Semi\u2010Mechanistic HbA1c Models in People with Type 2 Diabetes: Strengths, Limitations, and Applications"],"prefix":"10.1002","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8801-4187","authenticated-orcid":false,"given":"Austin Yue Feng","family":"Tan","sequence":"first","affiliation":[{"name":"Department of Pharmacy and Pharmaceutical Sciences National University of Singapore  Singapore Singapore"},{"name":"Lilly Centre for Clinical Pharmacology Pte Ltd. 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