Predicting nonlinear changes in bone mineral density over time using a multiscale systems pharmacology model.
ABSTRACT: A mathematical model component that extends an existing physiologically based multiscale systems pharmacology model (MSPM) of calcium and bone homeostasis was developed, enabling prediction of nonlinear changes in lumbar spine bone mineral density (LSBMD). Data for denosumab, a monoclonal antibody osteoporosis treatment, dosed at several levels and regimens, was used for fitting the BMD component. Bone marker and LSBMD data extracted from the literature described on/off-treatment effects of denosumab over 48 months [Miller, P.D. et al. Effect of denosumab on bone density and turnover in postmenopausal women with low bone mass after long-term continued, discontinued, and restarting of therapy: a randomized blinded phase 2 clinical trial. Bone 43, 222-229 (2008)]. An indirect model linking bone markers to LSBMD was embedded in the existing MSPM, reasonably predicting nonlinear increases in LSBMD during treatment (24 months); LSBMD declines following discontinuation and increases upon treatment reinstitution. This study demonstrates the utility of MSPM extension to describe a phenomena of interest not originally in a model, and the ability of this updated MSPM to predict nonlinear longitudinal changes in the clinically relevant endpoint, LSBMD, with denosumab treatment.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e14; doi:10.1038/psp.2012.15; advance online publication 14 November 2012.
Project description:Integrating quantitative systems pharmacology (QSP) into pharmacokinetics/pharmacodynamics (PKPD) has resulted in models that are highly complex and often not amenable to further exploration via estimation or design. Because QSP models are usually depicted using nonlinear differential equations it is not straightforward to apply some model reduction techniques, such as proper lumping. In this study, we explore the combined use of linearization and proper lumping as a general method to simplification of a nonlinear QSP model. We illustrate this with a bone biology model and the reduced model was then applied to describe bone mineral density (BMD) changes due to denosumab dosing. The methodologies used in this study can be applied to other multiscale models for developing a mechanism-based structural model for future analyses.
Project description:Welcome to the first issue of CPT: Pharmacometrics and Systems Pharmacology (CPT:PSP), a new journal from the American Society for Clinical Pharmacology and Therapeutics. CPT:PSP is a cross-disciplinary journal devoted to publishing advances in quantitative, model-based approaches as applied in pharmacology, (patho)physiology, and disease to aid the discovery, development, and utilization of human therapeutics. The emphasis of CPT:PSP will be on the application of modeling and simulation and the impact of Pharmacometrics and Systems Pharmacology on the discovery and development of innovative therapies.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e8; doi:10.1038/psp.2012.8; advance online publication 26 September 2012.
Project description:A long-term sodium nitrite infusion is intended for the treatment of vascular disorders. Phase I data demonstrated a significant nonlinear dose-exposure-toxicity relationship within the therapeutic dosage range. This study aims to develop a quantitative systems pharmacology model characterizing nitric oxide (NO) metabolome and methemoglobin after sodium nitrite infusion. Nitrite, nitrate, and methemoglobin concentration-time profiles in plasma and RBC were used for model development. Following intravenous sodium nitrite administration, nitrite undergoes conversion in RBC and tissue. Nitrite sequestered by RBC interacts more extensively with deoxyhemoglobin, which contributes greatly to methemoglobin formation. Methemoglobin is formed less-than-proportionally at higher nitrite doses as characterized with facilitated methemoglobin removal. Nitrate-to-nitrite reduction occurs in tissue and via entero-salivary recirculation. The less-than-proportional increase in nitrite and nitrate exposure at higher nitrite doses is modeled with a dose-dependent increase in clearance. The model provides direct insight into NO metabolome disposition and is valuable for nitrite dosing selection in clinical trials.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e60; doi:10.1038/psp.2013.35; published online 31 July 2013.
Project description:Sirolimus, the prototypical inhibitor of the mammalian target of rapamycin, has substantial antitumor activity. In this study, sirolimus showed nonlinear pharmacokinetic characteristics over a wide dose range (from 1 to 60?mg/week). The objective of this study was to develop a population pharmacokinetic (PopPK) model to describe the nonlinearity of sirolimus. Whole blood concentration data, obtained from four phase I clinical trials, were analyzed using a nonlinear mixed-effects modeling (NONMEM) approach. The influence of potential covariates was evaluated. Model robustness was assessed using nonparametric bootstrap and visual predictive check approaches. The data were well described by a two-compartment model incorporating a saturable Michaelis-Menten kinetic absorption process. A covariate analysis identified hematocrit as influencing the oral clearance of sirolimus. The visual predictive check indicated that the final pharmacokinetic model adequately predicted observed concentrations. The pharmacokinetics of sirolimus, based on whole blood concentrations, appears to be nonlinear due to saturable absorption.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e17; doi:10.1038/psp.2012.18; advance online publication 5 December 2012.
Project description:Low trauma fractures due to osteoporosis are a major health concern worldwide. Despite the availability of many therapeutic compounds to reduce fracture risk, osteoporosis remains undertreated and the burden of osteoporotic fractures remains high. Denosumab is a novel agent that acts to reduce bone turnover, improve bone mineral density, and reduce fracture risk, offering a favorable efficacy and safety profile.This review covers the pharmacology and major clinical trials with extension/post-marketing follow-up, including trials for all FDA-approved indications of denosumab to date.Denosumab is an efficacious and safe osteoporosis treatment option, with current data from up to 8 years of continued use showing continued improvement in bone density with sustained fracture risk reduction. Safety profiles overall are similar to placebo, with no new safety concerns in extension trials, though a theoretical increased risk of infection exists with RANKL inhibition. Future considerations include safety of prolonged treatment beyond 8 years, and efficacy/fracture risk after discontinuation or with non-adherence, given the characteristic pharmacodynamic profile of denosumab.
Project description:Potent antiresorptive drugs (bisphosphonate and denosumab) are often used to protect bone health in postmenopausal breast cancer patients. In addition, clinical trials have shown that these drugs increase disease-free survival, though the mechanism of adjuvant benefit is largely unknown. Here we review the bone health and adjuvant data for both classes of antiresorptive drugs and highlight differences in their pharmacology. Inhibition of bone resorption is vitally important to protect against osteoporotic fractures, and may also contribute to adjuvant survival benefits by making the bone microenvironment less amenable to breast cancer metastasis. After a course of therapy, stoppage of bisphosphonates yields a persistent antiresorptive effect, whereas discontinuation of denosumab causes a rebound increase in bone resorption markers and a loss of bone mineral density to baseline levels. Whether the potential adjuvant benefits of denosumab are also rapidly lost after drug discontinuation deserves further investigation.
Project description:Generalized low bone mass and osteopenia have been reported in the axial and peripheral skeleton of adolescent idiopathic scoliosis (AIS) patients. Recently, many studies have shown that gene polymorphisms are related to osteoporosis. However, no studies have linked the association between gene polymorphisms and bone mass of AIS. Therefore, this study examined the association between the bone mass and RANKL, RANK, and OPG gene polymorphisms in 198 girls diagnosed with AIS. OPG 163 A --> G, 209 G --> A, 245 T --> G, and 1181 G --> C polymorphisms; RANK 421 C --> T and 575 C --> T polymorphisms; and RANKL rs12721445 and rs2277438 polymorphisms, as well as the bone mineral density at the lumbar spine (LSBMD) and femoral neck (FNBMD) were analyzed. The 163 A --> G, 209 G --> A, and 245 T --> G polymorphisms in the OPG gene were in complete linkage. No RANK 421 C --> T and 575 C --> T polymorphisms or RANKL rs12711445 polymorphism were observed. There was a significant association between the OPG gene 1181 G --> C polymorphism and LSBMD. LSBMD in AIS with the CC genotype was found to be significantly higher than in AIS with the GC (P < 0.05) or GG (P < 0.01) genotype. However, there was no significant association between LSBMD or FNBMD and the OPG gene 245 T --> G polymorphism or the RANKL rs2277438 polymorphism. These results suggest that the OPG gene 1181 G --> C polymorphism is associated with LSBMD in girls with AIS.
Project description:Reliance on modeling and simulation in drug discovery and development has dramatically increased over the past decade. Two disciplines at the forefront of this activity, pharmacometrics and systems pharmacology (SP), emerged independently from different fields; consequently, a perception exists that only few examples integrate these approaches. Herein, we review the state of pharmacometrics and SP integration and describe benefits of combining these approaches in a model-informed drug discovery and development framework.
Project description:There is considerable interindividual variability in the growth of abdominal aortic aneurysms (AAAs), but an individual's growth observations, risk factors, and biomarkers could potentially be used to tailor surveillance. To assess the potential for tailoring surveillance, this study determined the accuracy of individualized predictions of AAA size at the next surveillance observation. A hierarchical Bayesian model was fitted to a total of 1,732 serial ultrasound measurements from 299 men in whom ultrasound screening identified an AAA. The data were best described by a nonlinear model with a constant first derivative of the AAA growth rate with size. The area under the receiver operating characteristic (ROC) curves for predicting whether an AAA was ?40 or ?50?mm at the next observation were 0.922 and 0.979, respectively, and the median root mean squared error was 2.52?mm. These values were nearly identical for models with or without plasma D-dimer effects.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e12; doi:10.1038/psp.2012.13; advance online publication 24 October 2012.
Project description:Hepatitis C viral kinetic analysis based on nonlinear mixed effect models can be used to individualize treatment. For that purpose, it is necessary to obtain precise estimation of individual parameters. Here, we evaluated by simulation the influence on Bayesian individual parameter estimation and outcome prediction of a priori information on population parameters, viral load sampling designs, and methods for handling data below detection limit (BDL). We found that a precise estimation of both individual parameters and treatment outcome could be obtained using as few as six measurements in the first month of therapy. This result remained valid even when incorrect a priori information on population parameters was set as long as the parameters were identifiable and BDL data were properly handled. However, setting wrong values for a priori population parameters could lead to severe estimation/prediction errors if BDL data were ignored and not properly accounted in the likelihood function.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e56; doi:10.1038/psp.2013.31; published online 17 July 2013.