Background: T-cell engagers (TCEs) represent a transformative approach in cancer therapy, designed to redirect the immune system to selectively target cancer cells. Despite their promising efficacy, the activation of immune system poses the risk of rapid release of cytokines, leading to occurrence of Cytokine Release Syndrome (CRS), potentially leading to severe concerns around patient safety. Several semi-mechanistic modeling approaches have been employed to understand cytokines in context of the drug action [1,2], however extending these to have a predictive model of clinical CRS risk for individual patients remains elusive. Heterogeneity in patients responses and clinical read outs and incidence of CRS events for the same dosing remains the key challenge in linking such mechanism driven models to clinical observations. Data driven models can be useful to regress the observed CRS incidence as a function of the patient attributes and the observed dynamics of contributing factors, like elevation in pro- and anti-inflammatory cytokines. Nonetheless, the sparsity of the data on the different components of CRS and the large inter-individual variability can limit the utility of models in predicting CRS risk for future dosing regimens.The primary objective of this work is to link mechanism-driven models of cytokines to CRS events using a hybrid machine learning (ML)-based classifier approach that can overcome the aforementioned limitations to predict CRS risk for specific patient cohorts.Methods: In this work, we introduce a two-step predictive framework for predicting CRS in TCE therapy. The core of the framework is a mechanistic model capturing the homeostatic dynamics and the interplay between the immune system, TCE dosing, and cancer cells that can predict the dynamics of cytokine release. The output of this model is then used as predictor to a ML classifier that can take in several subject specific attributes, drug exposure metrics, baseline factors, and cytokine peaks for prediction of incidence of clinical CRS. We explore the utility of our approach using simulated data across various scenarios corresponding to different TCEs and cancer indications.Results: Our results indicate that using the mechanism-based approach can provide additional information to the traditional ML based classifier providing a more robust prediction of the clinical endpoint of interest, i.e. the CRS incidence. Using simulated examples corresponding to various tumor indications and targets, we show that using our hybrid approach can improve the accuracy of the classifier, potentially enabling optimization of drug dosing. We illustrate the utility of our approach by exploring the effectiveness of a step dosing/dose fractionating design to mitigate CRS risk.Conclusions: Recognizing CRS as a limiting factor for TCE development, this work highlights a practical tool for strategic model-based in silico optimization of TCE dosing and designing safer clinical trials.Citations: [1] Hosseini, I., Gadkar, K., Stefanich, E. et al. Mitigating the risk of cytokine release syndrome in a Phase I trial of CD20/CD3 bispecific antibody mosunetuzumab in NHL: impact of translational system modeling. npj Syst Biol Appl 6, 28 (2020). https://doi.org/10.1038/s41540-020-00145-7[2] Chen X, Kamperschroer C, Wong G, Xuan D. A Modeling Framework to Characterize Cytokine Release upon T-Cell-Engaging Bispecific Antibody Treatment: Methodology and Opportunities. Clin Transl Sci. 2019 Nov;12(6):600-608. doi: 10.1111/cts.12662. Epub 2019 Jul 26. PMID: 31268236; PMCID: PMC6853151.

Oncolytic viruses are an emerging class of immunotherapies for cancer treatment. Talimogene laherparepvec (T-VEC) is a first-in-class oncolytic virus approved globally for advanced melanoma. Herein, we describe the quantitative clinical pharmacology aspects of T-VEC that supported the development of this unique therapy. As a live therapy, the exposure characteristics of T-VEC are vastly different from the pharmacokinetics (PK) of traditional small molecules or therapeutic proteins and were characterized as tumor site oncolytic viral kinetics. Relatively flat relationships between T-VEC dose, lesion exposures, and efficacy were identified based on dose-exposure-response (D-E-R) analyses of 60 adult subjects, indicating that optimal drug effect was achieved over the studied dose range (106-108 plaque forming unit [PFU]/mL); hence, efficacy was not sensitive to dose variations within the range. The relatively flat D-E-R relationship for T-VEC was also beneficial for biopharmaceutic aspects unique for live viruses, including bridging small variations in viral infectivity observed from batch to batch during manufacturing. Additionally, the exposures in pediatric subjects (N = 15) were within the range, although generally lower in medians than adults (N = 60). The primary safety concern of T-VEC-related herpetic infection was evaluated using a mechanistic PK-PD model which indicated minimal infection risk over the up to 5 years follow-up duration. Overall, T-VEC demonstrated favorable PK-PD profiles and was well tolerated in adults and pediatric subjects at the approved dosing regimen. Quantitative clinical pharmacology analyses have supported the optimal development of T-VEC and are poised to accelerate the development of these promising therapeutic oncolytic viruses.

Objectives: The longitudinal changes in tumor size along with the RECIST guidelines are the basis to determine efficacy endpoints like progression free survival (PFS) and objective response rate (ORR) for solid tumor indications. Tumor growth inhibition (TGI) models aim to capture the observed dynamics of tumor size in response to therapy and are recently being used to make predictions for PFS and ORR to obtain early read on the drug efficacy. The variability associated with patient heterogeneity, measurement noise, and the potential uncertainty in model parameters can limit the accuracy of prediction of the TGI models. The objective of this work is to investigate the propagation of the model uncertainty and the variability in the TGI models w.r.t the prediction of drug efficacy metrics of ORR and PFS. Using a systematic analysis, we explore the potential biases in ORR/PFS predictions from TGI models due to parameter uncertainty, inter-individual variability (IIV) in parameters, and residual unexplained variability (RUV) in tumor size. Methods: We explore the ability of the modeling framework to recapitulate the ground truth for several simulated scenarios for multiple TGI model structures. Time of progression and response is determined using sum of longest diameters (SLD) of target lesions, progression of nontarget and new lesions, according to RECIST guidelines [1]. We initiate our analyses of a mono-exponential tumor growth/inhibition with a analytical approach to understand the impact of the variability and uncertainty on extending the TGI models to predict ORR and PFS, and then extend the analysis for more complex TGI models that include treatment resistance and tumor heterogeneity using numerical simulations. To illustrate the implication on real world example we utilize the example simulation of a Doxorubicin TGI model with treatment resistance [1] driven by a population PK model [2]. Results: Our analysis of the mono-exponential TGI model show that while PFS predictions are sensitive to the typical value of effective tumor growth rate, ORR prediction is sensitive to both its typical value and IIV. Including uncertainty in the population parameters increases prediction intervals of PFS and ORR but does not affect median PFS. The simulation-based analysis confirms these insights. Conclusions: Our analysis illustrates how sources of variability like IIV, RUV and parameter uncertainty may affect the prediction intervals of PFS and ORR and may bias median estimates of PFS and ORR, and can be further used for optimizing the modeling framework and/or trial design for early prediction of survival endpoints.Citations: [1] Jiajie Yu et al., A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics, CPT Pharmacometrics Syst. Pharmacol. (2020) 9[2] Vijay S. Kumar et al., Population pharmacokinetics of doxorubicin in Indian cancer patients using NONMEM, Clinical Research and Regulatory Affairs, (2009) 26(4)

Bispecific T‐cell engagers have revolutionized the treatment and management of hematological malignancies and more recently have started making similar strides for solid tumor indications, with opportunities to become best‐in‐ class therapeutics for cancer. Xaluritamig is a novel bivalent XmAb® 2+1 T cell engager with two STEAP1 binding sites and one CD3 binding site being developed for solid tumors with the primary indication of metastatic castrate resistant prostate cancer (mCRPC). The First‐In‐Human (FIH) study showed promising anti‐tumor activity in mCRPC patients, and the program is currently in late phase clinical development. Xaluritamig was administered as an intravenous infusion once weekly (QW) or once every other week (Q2W) in the dose escalation of the FIH study at dose levels ranging from 0.001 to 2 mg. Initial pharmacokinetic (PK) characterization of xaluritamig exhibited approximately dose‐proportional increase in exposures over the dose levels explored, with an estimated terminal half‐life of ≈9 days, assuming subjects had no anti‐drug antibodies, calculated via the population PK model. The time at which maximum concentration (C max ) occurred was typically at the end of infusion (median ≈ 1 h), as expected with IV administration. Additionally, thorough dose‐exposure‐response analyses integrated observed data and model‐based simulations of PK, key efficacy endpoints, and safety events to support the evaluation of the target doses 0.75 mg QW, 1.5 mg QW, and 1.5 mg Q2W in dose expansion. This work provides the framework for which modeling and simulations can be used to guide dose selection for dose expansion at an early stage of development adhering to the recent principles of Project Optimus.

Bispecific T-cell engagers (Bi-TCEs) have revolutionized the treatment and management of both hematological and solid tumor indications with opportunities to become best-in-class therapeutics for cancer. However, defining the dose and dosing regimen for the first-in-human (FIH) studies of Bi-TCEs can be challenging, as a high starting dose can expose subjects to serious toxicity while a low starting dose based on traditional minimal anticipated biological effect level (MABEL) approach could lead to lengthy dose escalations that exposes seriously ill patients to sub-therapeutic dosing. Leveraging our in-depth and broad clinical development experience across three generations of Bi-TCEs across both liquid and solid tumor indications, we developed an innovative modified MABEL approach for starting dose selection that integrates knowledge based on the target biology, indication, toxicology, in vitro, in vivo pharmacological evaluations, and translational pharmacokinetic/pharmacodynamic (PK/PD) modeling, together with anticipated safety profile. Compared to the traditional MABEL approach in which high effector to target (E:T) cell ratios are typically used, our innovative approach utilized an optimized E:T cell ratio that better reflects the tumor microenvironment. This modified MABEL approach was successfully applied to FIH dose selection for a half-life extended (HLE) Bi-TCE for gastric cancer. This modified MABEL approach enabled a 10-fold higher starting dose that was deemed safe and well tolerated and saved at least two dose-escalation cohorts before reaching the projected efficacious dose. This approach was successfully accepted by global regulatory agencies and can be applied for Bi-TCEs across both hematological and solid tumor indications for accelerating the clinical development for Bi-TCEs.

Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure-response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases. On the contrary, common end points such as progression-free survival (PFS) and objective response rate (ORR) are directly derived from longitudinal tumor measurements and are routinely published. To this end, a Bayesian generative model relating underlying tumor dynamics to summary PFS and ORR data is introduced to learn TGI model parameters using only published summary data. The parameterized model can describe the tumor dynamics, quantify treatment effect, and account for differences in the study population. The utility of this model is shown by applying it to several published studies, and learned parameters are combined to simulate an in silico trial of a novel combination therapy in a novel setting.

BACKGROUND:Carfilzomib is an irreversible second-generation proteasome inhibitor that has a short elimination half-life but much longer pharmacodynamic (PD) effect based on its irreversible mechanism of action, making it amenable to longer dosing intervals. A mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model was built using a bottom-up approach, based on the mechanism of action of carfilzomib and the biology of the proteasome, to provide further evidence of the comparability of once-weekly and twice-weekly dosing.METHODS:The model was qualified using clinical data from the phase III ENDEAVOR study, where the safety and efficacy of bortezomib (a reversible proteasome inhibitor) and carfilzomib were compared. Simulations were performed to compare the average proteasome inhibition across five cycles of treatment for the 20/70 mg/m2 once-weekly (70 QW) and 20/56 mg/m2 twice-weekly (56 BIW) regimens.RESULTS:Results indicated that while 70 QW had a higher maximum concentration (Cmax) and lower steady-state area under the concentration-time curve (AUC) than 56 BIW, the average proteasome inhibition after five cycles of treatment between the regimens was comparable. Presumably, the higher Cmax of carfilzomib from 70 QW compensates for the lower overall AUC compared with 56 BIW, and hence 70 QW is expected to have comparable proteasome inhibition, and therefore comparable efficacy, to 56 BIW. The comparable model-predicted proteasome inhibition between 70 QW and 56 BIW also translated to comparable clinical response, in terms of overall response rate and progression-free survival.CONCLUSION:This work provides a framework for which mechanistic PK/PD modeling can be used to guide optimization of dosing intervals for therapeutics with significantly longer PD effects than PK, and help further justify patient-convenient, longer dosing intervals.

Bispecific T-cell engaging therapies harness the immune system to elicit an effective anticancer response. Modulating the immune activation avoiding potential adverse effects such as cytokine release syndrome (CRS) is a critical aspect to realizing the full potential of this therapy. The use of suitable exogenous intervention strategies to mitigate the CRS risk without compromising the antitumoral capability of bispecific antibody treatment is crucial. To this end, computational approaches can be instrumental to systematically exploring the effects of combining bispecific antibodies with CRS intervention strategies. Here, we employ a logical model to describe the action of bispecific antibodies and the complex interplay of various immune system components and use it to perform simulation experiments to improve the understanding of the factors affecting CRS. We performed a sensitivity analysis to identify the comedications that could ameliorate CRS without impairing tumor clearance. Our results agree with publicly available experimental data suggesting anti-TNF and anti-IL6 as possible co-treatments. Furthermore, we suggest anti-IFNγ as a suitable candidate for clinical studies.

7536 Background: AMG 330 binds both CD33 and CD3 and redirects T cells toward CD33+ cells leading to T-cell‒mediated cytotoxicity against AML blasts. An ongoing open label phase I dose-escalation study (NCT02520427) has shown preliminary activity and acceptable safety in relapsed or refractory (R/R) acute myeloid leukemia (AML) patients (pts) (Ravandi et al. ASH 2018). Pharmacokinetics and exposure-response (E-R) relationships of AMG 330 were characterized in this trial. Methods: A continuous IV infusion of AMG 330 was evaluated at escalating target doses (range from 0.5 to 720 μg/day) using a 3+3 design with pts receiving step dose/s prior to reaching target doses of ≥ 30 μg/day. Population pharmacokinetics (popPK) using non-linear mixed effects modeling and E-R analyses were conducted to characterize relationships between AMG 330 exposure (steady-state concentration [Css]) at target dose, the baseline tumor burden, clinical response per revised IWG criteria and incidence of cytokine release syndrome (CRS). Results: As of Dec 10, 2019, 55 patients (males, 56.4%; median age, 58.0 [18.0–80.0] years) were enrolled in 16 cohorts. AMG 330 PK was best described by a one-compartment linear PK model. Dose dependent increases were observed in AMG 330 Css exposures. Responders typically showed higher AMG 330 Css than non-responders. Preliminary exploratory analysis indicated that higher AMG 330 exposures, lower baseline leukemic burden in bone marrow and CD33+ AML cells in peripheral blood, and higher baseline Effector:Target cell ratio may be associated with clinical response. Additionally, a positive relationship was observed for AMG 330 exposures and baseline leukemic burden (p < 0.05) with probability of CRS occurrence and severity. Based on the model, at a baseline leukemic burden of 20%, a 240 µg/day target dose is predicted to result in a 28% and 4% probability of developing CRS of grade ≥ 2 and ≥ 3, respectively. Conclusions: Clinical pharmacokinetic profile and E-R relationships of AMG 330 were characterized to identify optimal AMG 330 dosing regimens that minimize the risk for CRS in ongoing and planned clinical investigations. Clinical trial information: NCT02520427 .

We leveraged a clinical pharmacokinetic (PK)/pharmacodynamics (PD)/efficacy relationship established with an oral phosphatidylinositol 3-kinase (PI3K)δ inhibitor (Idelalisib) in a nasal allergen challenge study to determine whether a comparable PK/PD/efficacy relationship with PI3Kδ inhibitors was observed in preclinical respiratory models of type 2 T helper cell (TH2) and type 1 T helper cell (TH1) inflammation. Results from an in vitro rat blood basophil (CD63) activation assay were used as a PD biomarker. IC50 values for PI3Kδ inhibitors, MSD-496486311, MSD-126796721, Idelalisib, and Duvelisib, were 1.2, 4.8, 0.8, and 0.5 μM. In the ovalbumin Brown Norway TH2 pulmonary inflammation model, all PI3Kδ inhibitors produced a dose-dependent inhibition of bronchoalveolar lavage eosinophils (maximum effect between 80% and 99%). In a follow-up experiment designed to investigate PK attributes [maximum (or peak) plasma concentration (Cmax), area under the curve (AUC), time on target (ToT)] that govern PI3Kδ efficacy, MSD-496486311 [3 mg/kg every day (QD) and 100 mg/kg QD] produced 16% and 93% inhibition of eosinophils, whereas doses (20 mg/kg QD, 10 mg/kg twice per day, and 3 mg/kg three times per day) produced 54% to 66% inhibition. Our profiling suggests that impact of PI3Kδ inhibitors on eosinophils is supported by a PK target with a ToT over the course of treatment close to the PD IC50 rather than strictly driven by AUC, Cmax, or Cmin (minimum blood plasma concentration) coverage. Additional studies in an Altenaria alternata rat model, a sheep Ascaris-sensitive sheep model, and a TH1-driven rat ozone exposure model did not challenge our hypothesis, suggesting that an IC50 level of TE (target engagement) sustained for 24 hours is required to produce efficacy in these traditional models. We conclude that the PK/PD observations in our animal models appear to align with clinical results associated with a TH2 airway disease.

A better understanding of the molecular pathways regulating the bone remodeling process should help in the development of new antiresorptive regulators and anabolic regulators, that is, regulators of bone resorption and of bone formation. Understanding the mechanisms by which parathyroid hormone (PTH) influences bone formation and how it switches from anabolic to catabolic action is important for treating osteoporosis (Poole and Reeve in Curr Opin Pharmacol 5:612–617, 2005). In this paper we describe a mathematical model of bone remodeling that incorporates, extends, and integrates several models of particular aspects of this biochemical system (Cabal et al. in J Bone Miner Res 28(8):1830–1836, 2013; Lemaire et al. in J Theor Biol 229:293–309, 2004; Peterson and Riggs in Bone 46:49–63, 2010; Raposo et al. in J Clin Endocrinol Metab 87(9):4330–4340, 2002; Ross et al. in J Disc Cont Dyn Sys Series B 17(6):2185–2200, 2012). We plan to use this model as a bone homeostasis platform to develop anabolic and antiresorptive compounds. The model will allow us to test hypotheses about the dynamics of compounds and to test the potential benefits of combination therapies. At the core of the model is the idealized account of osteoclast and osteoblast signaling given by Lemaire et al. (J Theor Biol 229:293–309, 2004). We have relaxed some of their assumptions about the roles of osteoprotegerin, transforming growth factor \(\upbeta \), and receptor activator of nuclear factor \(\upkappa \)B ligand; we have devised more detailed models of the interactions of these species. We have incorporated a model of the effect of calcium sensing receptor antagonists on remodeling (Cabal et al. in J Bone Miner Res 28(8):1830–1836, 2013). We have also incorporated a basic model of the effects of vitamin D on calcium homeostasis. We have included a simple model of the mechanism proposed by Bellido et al. (2003), Ross et al. (J Disc Cont Dyn Sys Series B 17(6):2185–2200, 2012), of the influence of PTH on osteoblast apoptosis, a mechanism that accounts for the anabolic response to pulsatile PTH administration. Finally, we have devised a simple model of the administration and effects of bisphosphonates. The biomarkers in the model are procollagen type 1 amino-terminal propeptide and C-terminal telopeptide. Bone mineral density is the model’s principal endpoint.

Methods: The deposition module is based on the typical path lung model [2] and considers three deposition mechanisms: diffusion, sedimentation, and inertial impaction based on drug particle properties such as density and diameter. Parameters associated with dissolution, absorption, transport, distribution, and partition [1] were calibrated to describe the observed plasma and lung exposure profiles of IT-delivered mometasone in rats exposed to allergen lipopolysaccharide (data not shown). Given these PK parameters, PD parameters were calibrated with the neutrophil response after same allergen and inhaled mometasone for 7 doses, assuming a particle diameter of 3 um as calculated using gravimetric and analytical data [1]. The baseline error was defined as the mean square error of the fit w.r.t. the PD response for all doses. Fixing all other parameters, the increase in the error of the fit was computed w.r.t. the baseline error when varying the particle diameter.

BACKGROUNDUnderstanding the relationship between dose, lung exposure, and drug efficacy continues to be a challenging aspect of inhaled drug development. An experimental inhalation platform was developed using mometasone furoate to link rodent lung exposure to its in vivo pharmacodynamic (PD) effects.METHODSWe assessed the effect of mometasone delivered directly to the lung in two different rodent PD models of lung inflammation. The data obtained were used to develop and evaluate a mathematical model to estimate drug dissolution, transport, distribution, and efficacy, following inhaled delivery in rodents and humans.RESULTSMometasone directly delivered to the lung, in both LPS and Alternaria alternata rat models, resulted in dose dependent inhibition of BALf cellular inflammation. The parameters for our mathematical model were calibrated to describe the observed lung and systemic exposure profiles of mometasone in humans and in animal models. We found that physicochemical properties, such as lung fluid solubility and lipophilicity, strongly influenced compound distribution and lung retention.CONCLUSIONSPresently, we report on a novel and sophisticated mathematical model leading to improvements in a current inhaled drug development practices by providing a quantitative understanding of the relationship between PD effects and drug concentration in lungs.

The inability to measure local lung concentrations responsible for lung efficacy is the main challenge common to any inhalation drug delivery program targeting the lungs. The model described in this work is a multiscale mechanism-based integrated computational platform (lung platform) to provide mechanistic insights into key physiological elements associated with pulmonary drug delivery: deposition, mucociliary clearance, dissolution, absorption, transport, distribution, partition, and action. Two versions of the lung platform (LP) were developed for translational purposes, one for rats (RLP) and one for humans (HLP) to account for the species specific physiological based differences in airway morphology and drug distribution throughout the body. All these components facilitate a prediction of regional distribution of drug within the lungs. Published data for two inhaled corticosteroids (mometasone furoate, budesonide), a short-acting beta-agonist (salbutamol), and a long-acting beta-agonist (formoterol) in both rats and humans were used to qualify the model and illustrate its prediction ability. The translational pharmacokinetic benefits of the lung platform are illustrated using the selected compounds, where the physicochemical properties and the drug delivery details constitute the main input to the model. Clearance was the only parameter adjusted using weight based allometric scaling to translate from rats to humans. The results show the feasibility of the proposed modeling approach, once it has been properly qualified using existing data, toward bridging the gap between inhaled data in preclinical species and the prediction of human lung and systemic exposure.

Over the past decade, different types of mechanism-based models have had an increasing impact on drug development. Published pharmacokinetic-pharmacodynamic (PK-PD)-disease models of osteoporosis have varying degrees of biological complexity ranging from purely descriptive of disease to detailed system models spanning various spatial scales, as well as mechanistic models of bone strength. A more integrative approach allows for a mechanism-based description of osteoporosis by explicitly including bone physiology as the underlying mechanism. Various short- and long-term markers at various levels and timescales of the disease and drug action can then be combined and evaluated. In this chapter, specific applied examples of pharmacometrics in osteoporosis are discussed, which include two mechanism-based bone cell interaction models and an example discussing finite element analysis. The ultimate goal is to integrate all sources of information to comprehensively describe the pathophysiology of osteoporosis while including treatment and disease. In the future, further integration of these approaches and end-points will provide higher and earlier predictiveness from biomarkers to fractures and deliver on the promise of model-based drug development in osteoporosis, where the model is continuously developed in parallel with the drug.

JTT-305/MK-5442 is a calcium-sensing receptor (CaSR) allosteric antagonist being investigated for the treatment of osteoporosis. JTT-305/MK-5442 binds to CaSRs, thus preventing receptor activation by Ca2+. In the parathyroid gland, this results in the release of parathyroid hormone (PTH). Sharp spikes in PTH secretion followed by rapid returns to baseline are associated with bone formation, whereas sustained elevation in PTH is associated with bone resorption. We have developed a semimechanistic, nonpopulation model of the time-course relationship between JTT-305/MK-5442 and whole plasma PTH concentrations to describe both the secretion of PTH and the kinetics of its return to baseline levels. We obtained mean concentration data for JTT-305/MK-5442 and whole PTH from a multiple dose study in U. S. postmenopausal women at doses of 5, 10, 15, and 20 mg. We hypothesized that PTH is released from two separate sources: a reservoir that is released rapidly (within minutes) in response to reduction in Ca2+ binding, and a second source released more slowly following hours of reduced Ca2+ binding. We modeled the release rates of these reservoirs as maximum pharmacologic effect (E-max) functions of JTT-305/MK-5442 concentration. Our model describes both the dose-dependence of PTH time of occurrence for maximum drug concentration (T-max) and maximum concentration of drug (C-max), and the extent and duration of the observed nonmonotonic return of PTH to baseline levels following JTT-305/MK-5442 administration. (C) 2013 American Society for Bone and Mineral Research.