Integrating real‐world data and modeling to project changes in femoral neck bone mineral density and fracture risk in premenopausal women

Beck, Denise; Winzenborg, Insa; Gao, Wei; Mostafa, Nael M.; Noertersheuser, Peter; Chiuve, Stephanie E; Owens, Charlotte D.; Shebley, Mohamad

doi:10.1111/cts.13006

Cited by 5 publications

(18 citation statements)

References 12 publications

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“…Once the model that best described observed FN‐BMD changes in the placebo arm was selected, 25 the FN‐BMD response to elagolix treatment was added via an indirect response model (step 3 in Figure 1). Priors for model development were generated by re‐estimating a previously published exposure‐LS‐BMD model 23 with FN‐BMD data from the Elaris UF studies (UF‐1, UF‐2 and UF‐Extend) 29,30 .…”

Section: Methodsmentioning

confidence: 99%

“…The final exposure‐response model conceptualized as an indirect response model described the change from placebo response (PLAC) and assumed a baseline steady state between bone formation and resorption described by the following equations:

\frac{normald R ((), t)}{normald t} goodbreak= k_{in} goodbreak- k_{out} \cdot normalR ((), t)

BMD ((), t) goodbreak= PLAC ((), t) \cdot normalR ((), t)

and at baseline:

R ((), 0) goodbreak= 1 and k_{out} goodbreak= k_{in} / 1

where d R ( t )/d t is the change in BMD over time, k in is a zero‐order rate constant reflecting bone formation, k out is a first‐order rate constant reflecting bone resorption, BMD( t ) is the BMD at time t and R ( t ) is the change in BMD from placebo response (PLAC) at time t . The bi‐exponential model for characterizing the placebo response has been published previously 25 and was parameterized in terms of maximum FN‐BMD (PLAC max ) and parameters describing the formation and resorption rate constants in FN‐BMD over age ( k 1 and k 2 ), respectively, as follows:

\begin{matrix} true \\ PLAC (t) = {PLAC}_{\max} \times \frac{k_{1}}{k_{1} - k_{2}} \times ({normale}^{- k_{2} \times (AGE + \frac{t}{365})} - {normale}^{- k_{1} \times (AGE + \frac{t}{365})}) \times \\ (1 + {fac}_{Lunar}) \end{matrix}

where PLAC( t ) is the FN‐BMD at time after baseline t in days, t /365 is the time since baseline observation time in years and fac Lunar is the factor to account for differences in BMD measured with the Hologic and Lunar machine types.…”

Section: Methodsmentioning

confidence: 99%

“…The 10‐year risk of hip fracture and MOF were estimated for each scenario based on the simulated BMD for Hologic or Lunar machine types at multiple timepoints (ie, various ages) and patient characteristics via the FRAX tool. Based on the simulated patient‐level longitudinal data, the age when a particular patient reached the risk‐based threshold for anti‐osteoporosis treatment was defined as the earliest age when the patient reached a 10‐year risk of hip fractures ≥3% or a 10‐year risk of MOF ≥20% 25,34 . The median difference in FN‐BMD, 10‐year risk of hip fractures and MOF, and the proportion of patients reaching the risk‐based threshold for anti‐osteoporosis treatment between the elagolix‐treated populations (with and without recovery) and the untreated population was calculated for each replicate, and the median, 95% CI and 95% PI were calculated across the 100 replicates.…”

Section: Methodsmentioning

confidence: 99%

“…25 The natural changes in FN-BMD were best described by a bi-exponential model with first-order BMD formation (k 1 ) and resorption (k 2 ) rate constants. 25 The output of the FN-BMD model was then translated into the long-term postmenopausal fracture risk of premenopausal women with an epidemiological model, the FRAX tool (https://www.sheffield.ac.uk/FRAX/tool.aspx). 15,[26][27][28] Here, we extend the FN-BMD model in an untreated population to an exposure-response analysis by addition of the response to elagolix treatment via an indirect response model.…”

Section: Introductionmentioning

confidence: 99%

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Interdisciplinary model‐informed drug development for extending duration of elagolix treatment in patients with uterine fibroids

Beck

Winzenborg

Gao

et al. 2022

Brit J Clinical Pharma

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Aim Elagolix, a gonadotropin‐releasing hormone receptor antagonist, was recently approved for heavy menstrual bleeding associated with uterine fibroids (UF, Oriahnn) at a dose of 300 mg twice daily (BID) in combination with add‐back therapy (oestradiol 1 mg/norethindrone acetate 0.5 mg [E2/NETA] once daily) for 24 months use. The limited duration of treatment is related to elagolix dose‐ and duration‐dependent decrease in oestrogen that is mechanistically linked to changes in bone mineral density (BMD). The work herein supported the extended treatment duration of 24 months. Methods An integrated exposure‐response and epidemiological modelling framework of elagolix effects on femoral neck BMD (FN‐BMD), informed by real‐world data and phase 3 clinical trials data, was developed to predict the time course and magnitude of changes in BMD and its relation to risk of bone fracture in women with UF. Results Model results indicated that women treated with elagolix 300 mg BID + E2/NETA in the long term (ie, >24 months) may experience less than 1% loss in FN‐BMD per year, relative to placebo. The exposure‐response model simulations and clinical risk factors were used to estimate 10‐year risk of fractures using the clinically validated Fracture Risk Assessment Tool (FRAX). The impact of elagolix 300 mg BID + E2/NETA treatment on the 10‐year risk of hip or major osteoporotic fractures estimated from the FRAX model was minimal compared to that of placebo. Conclusion The elagolix integrated exposure‐BMD analysis and translation to fracture risk provided an interdisciplinary model‐informed drug development framework for clinical benefit‐risk evaluation and enabled approval of longer treatment duration to benefit the patient.

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Section: Methodsmentioning

confidence: 99%

\frac{normald R ((), t)}{normald t} goodbreak= k_{in} goodbreak- k_{out} \cdot normalR ((), t)

BMD ((), t) goodbreak= PLAC ((), t) \cdot normalR ((), t)

and at baseline:

R ((), 0) goodbreak= 1 and k_{out} goodbreak= k_{in} / 1

\begin{matrix} true \\ PLAC (t) = {PLAC}_{\max} \times \frac{k_{1}}{k_{1} - k_{2}} \times ({normale}^{- k_{2} \times (AGE + \frac{t}{365})} - {normale}^{- k_{1} \times (AGE + \frac{t}{365})}) \times \\ (1 + {fac}_{Lunar}) \end{matrix}

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interdisciplinary model‐informed drug development for extending duration of elagolix treatment in patients with uterine fibroids

Beck

Winzenborg

Gao

et al. 2022

Brit J Clinical Pharma

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“…

The article by Beck et al, 1 was published with an error in Figure 4b. The order and color assignments of labels in the published Figure 4b were inadvertently swapped in the original figure and do not correctly describe the data in Figure 4b.

…”

mentioning

confidence: 99%

Corrigendum to: Integrating real‐world data and modeling to project changes in femoral neck bone mineral density and fracture risk in premenopausal women

2021

Clinical Translational Sci

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Modeling tumor size dynamics based on real‐world electronic health records and image data in advanced melanoma patients receiving immunotherapy

Courlet

Abler

Guidi

et al. 2023

CPT Pharmacom & Syst Pharma

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The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model‐informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real‐world setting. We developed a tumor growth inhibition model based on real‐world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image‐based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high‐dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

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Integrating real‐world data and modeling to project changes in femoral neck bone mineral density and fracture risk in premenopausal women

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 5 publications

References 12 publications

Interdisciplinary model‐informed drug development for extending duration of elagolix treatment in patients with uterine fibroids

Interdisciplinary model‐informed drug development for extending duration of elagolix treatment in patients with uterine fibroids

Corrigendum to: Integrating real‐world data and modeling to project changes in femoral neck bone mineral density and fracture risk in premenopausal women

Modeling tumor size dynamics based on real‐world electronic health records and image data in advanced melanoma patients receiving immunotherapy

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