Quantitative Prediction of Bone Mineral Density by Using Bone Turnover Markers in Response to Antiresorptive Agents in Postmenopausal Osteoporosis: A Model-based Meta-analysis.
Br J Clin Pharmacol. 2020 Jul 21;: Authors: Wu J, Wang C, Li GF, Tang A, Zheng Q
AIM: This study aims to predict time course of bone mineral density (BMD) by using corresponding response of bone turnover markers (BTMs) in postmenopausal osteoporosis women under antiresorptive treatments. METHODS: Data were extracted from literature searches in accessible public database. Time courses of percent change from baseline in serum C-telopeptide of type 1 collagen (sCTX) and N-telopeptide of type 1 collagen (P1NP) were described by complex exponential onset models. Then the relationship between BTMs changes and BMD changes at lumbar spine (LS) and total hip (TH) was described using a multiscale indirect response model. RESULTS: The dataset included 41 eligible published trials of 5 US-approved antiresorptive agents (alendronate, ibandronate, risedronate, zoledronic acid and denosumab), containing over 28800 postmenopausal osteoporosis women. The time courses of BTMs changes for different drugs were differentiated by maximal effect (Emax ) and onset rate (kon ) in developed model, while sCTX responses to zoledronic acid and denosumab were captured by another model formation. Furthermore, asynchronous relationship between BTMs and BMD was described by a bone-remodeling based semi-mechanism model, including zero-order production and first-order elimination induced by P1NP and sCTX, separately. After external and informative validations, the developed models were able to predict BMD increase using 1-year data. CONCLUSION: This exploratory analysis built a quantitative framework linked BTMs and BMD among antiresorptive agents, as well as a modeling approach to enhance comprehension of dynamic relationship between early and later endpoints among agents in a certain mechanism of action. Moreover, the developed models can offer predictions of BMD from BTMs supporting early drug development. PMID: 32692857 [PubMed - as supplied by publisher]