A model of fracture risk used to examine the link between bone mineral density and the impact of different therapeutic mechanisms on fracture outcomes in patients with osteoporosis

The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making

While some model-informed drug development frameworks are well recognized as enabling clinical trials, the value of disease progression modeling (DPM) in impacting medical product development has yet to be fully realized. The Clinical Trials Transformation Initiative assembled a diverse project team from across the patient, academic, regulatory, and industry sectors of practice to advance the use of DPM for decision making in clinical trials and medical product development. This team conducted a scoping review to explore current applications of DPM and convened a multi-stakeholder expert meeting to discuss its value in medical product development. In this article, we present the scoping review and expert meeting output and propose key questions that medical product developers and regulators may use to inform clinical development strategy, appreciate the therapeutic context and endpoint selection, and optimize trial design with disease progression models. By expanding awareness of the unique value of DPM, this article does not aim to be technical in nature but rather aims to highlight the potential of DPM to improve the quality and efficiency of medical product development.

Applications of Model-Based Meta-Analysis in Drug Development

Model-based meta-analysis (MBMA) is a quantitative approach that leverages published summary data along with internal data and can be applied to inform key drug development decisions, including the benefit-risk assessment of a treatment under investigation. These risk-benefit assessments may involve determining an optimal dose compared against historic external comparators of a particular disease indication. MBMA can provide a flexible framework for interpreting aggregated data from historic reference studies and therefore should be a standard tool for the model-informed drug development (MIDD) framework.In addition to pairwise and network meta-analyses, MBMA provides further contributions in the quantitative approaches with its ability to incorporate longitudinal data and the pharmacologic concept of dose-response relationship, as well as to combine individual- and summary-level data and routinely incorporate covariates in the analysis.A common application of MBMA is the selection of optimal dose and dosing regimen of the internal investigational molecule to evaluate external benchmarking and to support comparator selection. Two case studies provided examples in applications of MBMA in biologics (durvalumab + tremelimumab for safety) and small molecule (fenebrutinib for efficacy) to support drug development decision-making in two different but well-studied disease areas, i.e., oncology and rheumatoid arthritis, respectively.Important to the future directions of MBMA include additional recognition and engagement from drug development stakeholders for the MBMA approach, stronger collaboration between pharmacometrics and statistics, expanded data access, and the use of machine learning for database building. Timely, cost-effective, and successful application of MBMA should be part of providing an integrated view of MIDD.

Cigarette smoke-associated inflammation impairs bone remodeling through NFκB activation

BACKGROUND

Cigarette smoking constitutes a major lifestyle risk factor for osteoporosis and hip fracture. It is reported to impair the outcome of many clinical procedures, such as wound infection treatment and fracture healing. Importantly, although several studies have already demonstrated the negative correlation between cigarette consume and impaired bone homeostasis, there is still a poor understanding of how does smoking affect bone health, due to the lack of an adequately designed animal model. Our goal was to determine that cigarette smoke exposure impairs the dynamic bone remodeling process through induction of bone resorption and inhibition of bone formation.

METHODS

We developed cigarette smoke exposure protocols exposing mice to environmental smoking for 10 days or 3 months to determine acute and chronic smoke exposure effects. We used these models, to demonstrate the effect of smoking exposure on the cellular and molecular changes of bone remodeling and correlate these early alterations with subsequent bone structure changes measured by microCT and pQCT. We examined the bone phenotype alterations in vivo and ex vivo in the acute and chronic smoke exposure mice by measuring bone mineral density and bone histomorphometry. Further, we measured osteoclast and osteoblast differentiation gene expression levels in each group. The function changes of osteoclast or osteoblast were evaluated.

RESULTS

Smoke exposure caused a significant imbalance between bone resorption and bone formation. A 10-day exposure to cigarette smoke sufficiently and effectively induced osteoclast activity, leading to the inhibition of osteoblast differentiation, although it did not immediately alter bone structure as demonstrated in mice exposed to smoke for 3 months. Cigarette smoke exposure also induced DNA-binding activity of nuclear factor kappaB (NFκB) in osteoclasts, which subsequently gave rise to changes in bone remodeling-related gene expression.

CONCLUSIONS

Our findings suggest that smoke exposure induces RANKL activation-mediated by NFκB, which could be a "smoke sensor" for bone remodeling.

Prediction of Fracture Risk From Early-Stage Bone Markers in Patients With Osteoporosis Treated With Once-Yearly Administered Zoledronic Acid

The prevention of fractures is the ultimate goal of osteoporosis treatments. To achieve this objective, developing a method to predict fracture risk in the early stage of osteoporosis treatment would be clinically useful. This study aimed to develop a mathematical model quantifying the long‐term fracture risk after 2 annual doses of 5 mg of once‐yearly administered zoledronic acid or placebo based on the short‐term measurement of bone turnover markers or bone mineral density (BMD). The data used in this analysis were obtained from a randomized, placebo‐controlled, double‐blind, 2‐year study of zoledronic acid that included 656 patients with primary osteoporosis. Two‐year individual bone resorption marker (tartrate‐resistant acid phosphatase 5b [TRACP‐5b]) and lumbar spine (L2‐L4) BMD profiles were simulated using baseline values and short‐term measurements (at 3 months for TRACP‐5b and 6 months for BMD) according to the pharmacodynamic model. A new parametric time‐to‐event model was developed to describe the risk of clinical fractures. Fracture risk was estimated using TRACP‐5b or BMD and the number of baseline vertebral fractures. As a result, the fracture risk during the 2 years was successfully predicted using TRACP‐5b or BMD. The 90% prediction intervals well covered the observed fracture profiles in both models. Therefore, TRACP‐5b or BMD is useful to predict the fracture risk of patients with osteoporosis, and TRACP‐5b would be more useful because it is an earlier marker. Importantly, the developed model allows clinicians to inform patients of their predicted response at the initial stage of zoledronic acid treatment.

Quantitative prediction of bone mineral density by using bone turnover markers in response to antiresorptive agents in postmenopausal osteoporosis: A model-based meta-analysis

AIMS

This study aimed to predict time course of bone mineral density (BMD) by using corresponding response of bone turnover markers (BTMs) in women with postmenopausal osteoporosis 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 were described by complex exponential onset models. The relationship between BTM changes and BMD changes at lumbar spine and total hip 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 28 800 women with postmenopausal osteoporosis. The time courses of BTM changes for different drugs were differentiated by maximal effect and onset rate 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 remodelling-based semimechanistic model, including zero-order production and first-order elimination induced by N-telopeptide of type 1 collagen 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 linking BTMs and BMD among antiresorptive agents, as well as a modelling 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.

Pharmacology of bisphosphonates

The biological effects of the bisphosphonates (BPs) as inhibitors of calcification and bone resorption were first described in the late 1960s. In the 50 years that have elapsed since then, the BPs have become the leading drugs for the treatment of skeletal disorders characterized by increased bone resorption, including Paget's disease of bone, bone metastases, multiple myeloma, osteoporosis and several childhood inherited disorders. The discovery and development of the BPs as a major class of drugs for the treatment of bone diseases is a paradigm for the successful journey from "bench to bedside and back again". Several of the leading BPs achieved "blockbuster" status as branded drugs. However, these BPs have now come to the end of their patent life, making them highly affordable. The opportunity for new clinical applications for BPs also exists in other areas of medicine such as ageing, cardiovascular disease and radiation protection. Their use as inexpensive generic medicines is therefore likely to continue for many years to come. Fifty years of research into the pharmacology of bisphosphonates have led to a fairly good understanding about how these drugs work and how they can be used safely in patients with metabolic bone diseases. However, while we seemingly know much about these drugs, a number of key aspects related to BP distribution and action remain incompletely understood. This review summarizes the existing knowledge of the (pre)clinical and translational pharmacology of BPs, and highlights areas in which understanding is lacking.

Pharmacometrics and systems pharmacology for metabolic bone diseases

Mathematical modelling and simulation (M&S) of drug concentrations, pharmacologic effects and the (patho)physiologic systems within which they interact can be powerful tools for the preclinical, translational and clinical development of drugs. Indeed, the Prescription Drug User Fee Act (PDUFA VI), incorporated as part of the FDA Reauthorization Act of 2017 (FDARA), highlights the goal of advancing model-informed drug development (MIDD). MIDD can benefit development across many drug classes, including for metabolic bone diseases such as osteoporosis, cancer-related and numerous rare metabolic bone diseases; conditions characterized by significant morbidity and mortality. A drought looms in terms of the availability of new drugs to better treat these devastating diseases. This review provides an overview of several M&S approaches ranging from simple pharmacokinetic to integrated pharmacometric and systems pharmacology modelling. Examples are included to illustrate the use of these approaches during the development of several drugs for metabolic bone diseases such as bisphosphonates, denosumab, teriparatide and sclerostin inhibitors (romosozumab and blosozumab).