Disease System Analysis: Basic Disease Progression Models in Degenerative Disease

Modeling of Parkinson's Disease Progression and Implications for Detection of Disease Modification in Treatment Trials

Background

Objectively measuring Parkinson's disease (PD) signs and symptoms over time is critical for the successful development of treatments aimed at halting the disease progression of people with PD.

Objective

To create a clinical trial simulation tool that characterizes the natural history of PD progression and enables a data-driven design of randomized controlled studies testing potential disease-modifying treatments (DMT) in early-stage PD.

Methods

Data from the Parkinson's Progression Markers Initiative (PPMI) were analyzed with nonlinear mixed-effect modeling techniques to characterize the progression of MDS-UPDRS part I (non-motor aspects of experiences of daily living), part II (motor aspects of experiences of daily living), and part III (motor signs). A clinical trial simulation tool was built from these disease models and used to predict probability of success as a function of trial design.

Results

MDS-UPDRS part III progresses approximately 3 times faster than MDS-UPDRS part II and I, with an increase of 3 versus 1 points/year. Higher amounts of symptomatic therapy is associated with slower progression of MDS-UPDRS part II and III. The modeling framework predicts that a DMT effect on MDS-UPDRS part III could precede effect on part II by approximately 2 to 3 years.

Conclusions

Our clinical trial simulation tool predicted that in a two-year randomized controlled trial, MDS-UPDRS part III could be used to evaluate a potential novel DMT, while part II would require longer trials of a minimum duration of 3 to 5 years underscoring the need for innovative trial design approaches including novel patient-centric measures.

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

Parameter reduction, sensitivity studies, and correlation analyses applied to a mechanobiologically regulated bone cell population model of the bone metabolism

When striving for reconstructing and predicting bone remodeling processes by means of mathematical models, cell population models have become a popular option. From a conceptual point of view, these models are able to take into account an arbitrary amount of regulatory mechanisms driving the development of bone cells and their activities. However, in most cases, the models include a large number of parameters; and most of those parameters cannot be measured, which certainly compromises the credibility of cell population models. Here, new insights are presented as to the potential improvement of this unsatisfactory situation. In particular, a previously published bone remodeling model was considered, and based on combination and merging of the original parameters, the total number of parameters could be reduced from 28 to 18, without impairing the model's versatility and significance. Furthermore, a comprehensive number of one- and two-variable sensitivity studies were performed, pointing out which parameters (alone and in combination with other parameters) influence the model predictions significantly - for that purpose, the mean squared relative error (MSRE) between simulations based on the original parameters and based on varied parameters was considered as failure measure. It has turned out that the model is significantly more sensitive to parameters which can be considered as phenomenological (such as differentiation, proliferation, and apoptosis rates) than to parameters which are directly related to specific processes (such as dissociation rate constants, and maximum concentrations of the involved factors). Using common correlation measures (such as Pearson, Spearman, and partial ranked correlation coefficients), correlation studies revealed that the correlations between most parameters and the MSRE are weak, while a few parameters exhibited moderate correlations. In conclusion, the results shown in this paper provide valuable insights concerning the design of new experiments allowing for measurement of the parameters which are most influential in the context of bone remodeling simulation.

Establishment of a Disease-Drug Trial Model for Postmenopausal Osteoporosis: A Zoledronic Acid Case Study

Costly and lengthy clinical trials hinder the development of safe and effective treatments for postmenopausal osteoporosis. To reduce the expense associated with these trials, we established a mechanistic disease-drug trial model for postmenopausal osteoporosis that can predict phase 3 trial outcome based on short-term bone turnover marker data. To this end, we applied a previously developed model for tibolone to bisphosphonates using zoledronic acid as paradigm compound by (1) linking the mechanistic bone cell interaction model to bone turnover markers as well as bone mineral density in lumbar spine and total hip, (2) employing a mechanistic disease progression function, and (3) accounting for zoledronic acid's mechanism of action. Once developed, we fitted the model to clinical trial data of 581 postmenopausal women receiving (1) 5-mg zoledronic acid in year 1 and saline in year 2, (2) 5-mg zoledronic acid in year 1 and year 2, or (3) placebo (saline), calcium (500 mg), and vitamin D (400 IU). All biomarker data was fitted reasonably well, with no apparent bias or model misspecification. Age, years since menopause, and body mass index at baseline were identified as significant covariates. In the future, the model can be modified to explore the link between short-term biomarkers and fracture risk.

Effects of long-term treatment of denosumab on bone mineral density: insights from an in-silico model of bone mineralization

Denosumab is one of the most commonly prescribed anti-resorptive drugs for the treatment of postmenopausal osteoporosis. The therapeutic effect of denosumab is to inhibit osteoclast differentiation and consequently bone resorption. Gains in bone mineral density (BMD) are achieved based on the ability of the bone matrix to undergo secondary mineralization. Experimental data show that the increase of BMD after commencing denosumab treatment are bone site specific. In this paper, we developed a comprehensive mechanistic pharmacokinetic-pharmacodymamic (PK-PD) model of the effect of denosumab on bone remodeling in postmenopausal osteoporosis (PMO). The PD model is based on a bone cell population model describing the bone remodeling process at the tissue scale. The conceptual model of the bone mineralization process, originally proposed by Boivin and Meunier, is quantitatively incorporated using a FIFO (First-In-First-Out) queue algorithm. The latter takes into account the balance of mineral within bone tissue due to the mineralization process, distinguishing the primary and secondary phases and removal of bone matrix due to bone resorption. The numerical simulations show that the model is able to predict the bone-site specific increase in BMD as was observed in the experimental data of Bone et al. 2008 for a typical denosumab administration pattern of 60 mg every 6 months. At the hip a 5 % increase in BMD was observed, while at the lumbar spine a 7.5 % increase of BMD was achieved after a 2 year treatment period. The difference in BMD is due to the fact that bone turnover at the hip is lower compared to lumbar spine and consequently has less potential for secondary mineralization. Parametric studies revealed that the rate of bone mineralization is an essential parameter regulating BMD gains. If mineralization is neglected only minimal increases in BMD are observed.

Modeling Sex Differences in Anti-inflammatory Effects of Dexamethasone in Arthritic Rats

PURPOSE

Collagen-induced arthritic (CIA) rats are used commonly for preclinical pharmacologic research into rheumatoid arthritis (RA). Dexamethasone (DEX), a potent corticosteroid (CS), remains an important component in combination therapy for RA. Although sex differences in RA and CS pharmacokinetics/pharmacodynamics (PK/PD) have been documented in humans, there has been no such comprehensive evaluation of sex differences in CIA rats.

METHODS

Paw size measurements were obtained for males and females from four groups of animals: healthy controls, non-drug treated arthritic animals, and both 0.225 and 2.25 mg/kg DEX-treated arthritic animals. A turnover model for disease progression, minimal PBPK model for drug concentrations, and inhibitory indirect response model were applied using population PK/PD modeling.

RESULTS

The clearances of DEX were 43% greater in males, but other PK parameters were similar. The temporal profiles of paw swelling exhibited earlier progression, peak edema times, and disease remission in females. DEX suppressed paw edema well in both males and females with similar capacity (I_max) values (=1.0), but DEX potency was less in females with higher IC₅₀ values (0.101 versus 0.015 ng/mL).

CONCLUSIONS

The pharmacology of DEX was well characterized in CIA rats. This study addresses knowledge gaps about sex differences and can be a guide for more mechanistic assessment of sex, drug, and disease differences in RA.

Regulatory Perspectives in Pharmacometric Models of Osteoporosis

Osteoporosis is a disorder of the bones in which they are weakened to the extent that they become more prone to fracture. There are various forms of osteoporosis: some of them are induced by drugs, and others occur as a chronic progressive disorder as an individual gets older. As the median age of the population rises across the world, the chronic form of the bone disease is drawing attention as an important worldwide health issue. Developing new treatments for osteoporosis and comparing them with existing treatments are complicated processes due to current acceptance by regulatory authorities of bone mineral density (BMD) and fracture risk as clinical end points, which require clinical trials to be large, prolonged, and expensive to determine clinically significant impacts in BMD and fracture risk. Moreover, changes in BMD and fracture risk are not always correlated, with some clinical trials showing BMD improvement without a reduction in fractures. More recently, bone turnover markers specific to bone formation and resorption have been recognized that reflect bone physiology at a cellular level. These bone turnover markers change faster than BMD and fracture risk, and mathematically linking the biomarkers via a computational model to BMD and/or fracture risk may help in predicting BMD and fracture risk changes over time during the progression of a disease or when under treatment. Here, we discuss important concepts of bone physiology, osteoporosis, treatment options, mathematical modeling of osteoporosis, and the use of these models by the pharmaceutical industry and the Food and Drug Administration.

Modeling Sex Differences in Pharmacokinetics, Pharmacodynamics, and Disease Progression Effects of Naproxen in Rats with Collagen-Induced Arthritis

Naproxen (NPX) is a frequently used nonsteroidal anti-inflammatory drug for rheumatoid arthritis (RA). Lack of quantitative information about the drug exposure-response relationship has resulted in empirical dosage regimens for use of NPX in RA. Few studies to date have included sex as a factor, although RA predominates in women. A pharmacokinetic, pharmacodynamic, and disease progression model described the anti-inflammatory effects of NPX in collagen-induced arthritic (CIA) male and female rats. Three groups of rats were included for each sex: healthy animals, CIA controls, and CIA rats given a single 50-mg/kg dose of NPX intraperitoneally. Paw volumes of healthy rats indicated natural growth, and disease status was measured by paw edema. An innovative minimal physiologically based pharmacokinetic (mPBPK) model incorporating nonlinear albumin binding of NPX in both plasma and interstitial fluid (ISF) was applied. Arthritic rats exhibited lower plasma and ISF albumin concentrations and reduced clearances of unbound drug to explain pharmacokinetic profiles. The unbound ISF NPX concentrations predicted by the mPBPK model were used as the driving force for pharmacological effects of NPX. A logistic growth function accounting for natural paw growth and an indirect response model for paw edema and drug effects (inhibition of k_in) was applied. Female rats showed a higher incidence of CIA, earlier disease onset, and more severe symptoms. NPX had stronger effects in males, owing to higher unbound ISF NPX concentrations and lower IC₅₀ values. The model described the pharmacokinetics, unbound NPX in ISF, time course of anti-inflammatory effects, and sex differences in CIA rats.

Disease Systems Analysis of Bone Mineral Density and Bone Turnover Markers in Response to Alendronate, Placebo, and Washout in Postmenopausal Women

A previously established mechanism-based disease systems model for osteoporosis that is based on a mathematically reduced version of a model describing the interactions between osteoclast (bone removing) and osteoblast (bone forming) cells in bone remodeling has been applied to clinical data from women (n = 1,379) receiving different doses and treatment regimens of alendronate, placebo, and washout. The changes in the biomarkers, plasma bone-specific alkaline phosphatase activity (BSAP), urinary N-telopeptide (NTX), lumbar spine bone mineral density (BMD), and total hip BMD, were linked to the underlying mechanistic core of the model. The final model gave an accurate description of all four biomarkers for the different treatments. Simulations were used to visualize the dynamics of the underlying network and the natural disease progression upon alendronate treatment and discontinuation. These results complement the previous applications of this mechanism-based disease systems model to data from various treatments for osteoporosis.

Systems pharmacology - Towards the modeling of network interactions

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior.

Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors

The objective of this work was to establish the quantitative relationship between Lanreotide Autogel® (LAN) on serum chromogranin A (CgA) and progression-free survival (PFS) in patients with nonfunctioning gastroenteropancreatic neuroendocrine tumors (GEP-NETs) through an integrated pharmacokinetic/pharmacodynamic (PK/PD) model. In CLARINET, a phase III, randomized, double-blind, placebo-controlled study, 204 patients received deep subcutaneous injections of LAN 120 mg (n = 101) or placebo (n = 103) every 4 weeks for 96 weeks. Data for 810 LAN and 1298 CgA serum samples (n = 632 placebo and n = 666 LAN) were used to develop a parametric time-to-event model to relate CgA levels and PFS (76 patients experienced disease progression: n = 49 placebo and n = 27 LAN). LAN serum profiles were described by a one-compartment disposition model. Absorption was characterized by two parallel pathways following first- and zero-order kinetics. As PFS data were considered informative dropouts, CgA and PFS responses were modeled jointly. The LAN-induced decrease in CgA levels was described by an inhibitory E MAX model. Patient age and target lesions at baseline were associated with an increment in baseline CgA. Weibull model distribution showed that decreases in CgA from baseline reduced the hazard of disease progression significantly (P < 0.001). Covariates of tumor location in the pancreas and tumor hepatic tumor load were associated with worse prognosis (P < 0.001). We established a semimechanistic PK/PD model to better understand the effect of LAN on a surrogate endpoint (serum CgA) and ultimately the clinical endpoint (PFS) in treatment-naive patients with nonfunctioning GEP-NETs.

A population model of early rheumatoid arthritis disease activity during treatment with methotrexate, sulfasalazine and hydroxychloroquine

AIMS

To develop a population model describing the disease activity (DAS28) time course in patients with early rheumatoid arthritis (RA) treated with triple disease-modifying anti-rheumatic drug (DMARD) therapy (methotrexate, sulfasalazine and hydroxychloroquine).

METHODS

DAS28 was obtained in 263 patients with early RA from initiation of therapy until 60 weeks. Using NONMEM(®), base models (DAS28 vs. time) and covariate influences were investigated for the population.

RESULTS

The best model was an exponential model of DAS28 vs. time that was additive to baseline DAS28, with covariance between parameters, and a combined residual error model. Age and patient smoking history were covariates significantly affecting response to therapy. Population estimates were baseline DAS28 (5.7), extent of change in DAS28 (-2.8) and the half-life of disease activity (6.2 weeks; time to steady disease state achieved within approximately 30 weeks). Older individuals exhibited more severe baseline DAS28, described by a power function centred around 57 years (baseline DAS28 for 40- and 70-year-old patients were 5.4 vs. 5.8, respectively) and current smokers took longer to achieve a steady disease state (approximately 50 weeks). There was considerable within-patient random variability in DAS28 over time (empirical 90% CI for DAS28 in a population typical patient at 60 weeks: 1.8, 4.2 with median value of 2.8).

CONCLUSIONS

This is the first report of a disease activity model for early RA treated with triple DMARD therapy. Smoking and age were identified as covariates.

Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response

In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic–pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed.

Application of a Systems Pharmacology-Based Placebo Population Model to Analyze Long-Term Data of Postmenopausal Osteoporosis

Osteoporosis is a progressive bone disease characterized by decreased bone mass resulting in increased fracture risk. The objective of this investigation was to test whether a recently developed disease systems analysis model for osteoporosis could describe disease progression in a placebo-treated population from the Early Postmenopausal Intervention Cohort (EPIC) study. First, we qualified the model using a subset from the placebo arm of the EPIC study of 222 women who had similar demographic characteristics as the 149 women from the placebo arm of the original population. Second, we applied the model to all 470 women. Bone mineral density (BMD) dynamics were changed to an indirect response model to describe lumbar spine and total hip BMD in this second population. This updated disease systems analysis placebo model describes the dynamics of all biomarkers in the corresponding datasets to a very good approximation; a good description of an individual placebo response will be valuable for evaluating treatments for osteoporosis.

Evaluation of the long-term durability and glycemic control of fasting plasma glucose and glycosylated hemoglobin for pioglitazone in Japanese patients with type 2 diabetes

BACKGROUND

This study applied a pharmacodynamic model-based approach to evaluate the long-term durability and glycemic control of pioglitazone in comparison with other oral glucose-lowering drugs in Japanese type 2 diabetes mellitus (T2DM) patients.

SUBJECTS AND METHODS

Japanese T2DM patients were enrolled in a prospective, randomized, open-label, blinded-end point study and received pioglitazone with or without other oral glucose-lowering drugs (excluding another thiazolidinedione [TZD]) (n=293) or oral glucose-lowering drugs excluding TZD (n=294). Treatment was adjusted to achieve glycosylated hemoglobin (HbA1c) <6.9%, and samples for fasting plasma glucose (FPG) and HbA1c were collected over 2.5-4 years. A simultaneous cascading indirect response model structure was applied to describe the time course of FPG and HbA1c. HbA1c levels were described using both an FPG-dependent and an FPG-independent function. To account for titration, drug effects for both treatment groups were implemented using a time-dependent Emax model.

RESULTS

Pioglitazone was superior in both time to maximum effect and the magnitude of reduction achieved in FPG and HbA1c. A greater reduction in median FPG (-21 mg/dL vs. -9 mg/dL) was observed with pioglitazone (P<0.05). Maximum drug effect for FPG was predicted to occur earlier (11 months) for pioglitazone than for the control group (14 months). The simulated additional reduction in FPG and HbA1c achieved with pioglitazone was predicted to be maintained beyond the currently observed study duration.

CONCLUSIONS

Pioglitazone was found to result in improved glycemic control and durability compared with control treatment. This model-based approach enabled the quantification of differences in FPG and HbA1c for both treatment groups and simulation to evaluate longer-term durability on FPG and HbA1c.

Clinical pharmacology = disease progression + drug action

Clinical pharmacology is concerned with understanding how to use medicines to treat disease. Pharmacokinetics and pharmacodynamics have provided powerful methodologies for describing the time course of concentration and effect in individuals and in populations. This population approach may also be applied to describing the progression of disease and the action of drugs to change disease progress. Quantitative models for symptomatic and disease-modifying effects of drugs are valuable not only for describing drugs and diseases but also for identifying criteria to distinguish between types of drug actions, with implications for regulatory decisions and long-term patient care.

Modeling and design of challenge tests: Inflammatory and metabolic biomarker study examples

Given the complexity of pharmacological challenge experiments, it is perhaps not surprising that design and analysis, and in turn interpretation and communication of results from a quantitative point of view, is often suboptimal. Here we report an inventory of common designs sampled from anti-inflammatory, respiratory and metabolic disease drug discovery studies, all of which are based on animal models of disease involving pharmacological and/or patho/physiological interaction challenges. The corresponding data are modeled and analyzed quantitatively, the merits of the respective approach discussed and inferences made with respect to future design improvements. Although our analysis is limited to these disease model examples, the challenge approach is generally applicable to the vast majority of pharmacological intervention studies. In the present five Case Studies results from pharmacodynamic effect models from different therapeutic areas were explored and analyzed according to five typical designs. Plasma exposures of test compounds were assayed by either liquid chromatography/mass spectrometry or ligand binding assays. To describe how drug intervention can regulate diverse processes, turnover models of test compound-challenger interaction, transduction processes, and biophase time courses were applied for biomarker response in eosinophil count, IL6 response, paw-swelling, TNFα response and glucose turnover in vivo. Case Study 1 shows results from intratracheal administration of Sephadex, which is a glucocorticoid-sensitive model of airway inflammation in rats. Eosinophils in bronchoalveolar fluid were obtained at different time points via destructive sampling and then regressed by the mixed-effects modeling. A biophase function of the Sephadex time course was inferred from the modeled eosinophil time courses. In Case Study 2, a mouse model showed that the time course of cytokine-induced IL1β challenge was altered with or without drug intervention. Anakinra reversed the IL1β induced cytokine IL6 response in a dose-dependent manner. This Case Study contained time courses of test compound (drug), challenger (IL1β) and cytokine response (IL6), which resulted in high parameter precision. Case Study 3 illustrates collagen-induced arthritis progression in the rat. Swelling scores (based on severity of hind paw swelling) were used to describe arthritis progression after the challenge and the inhibitory effect of two doses of an orally administered test compound. In Case Study 4, a cynomolgus monkey model for lipopolysaccharide LPS-induced TNFα synthesis and/or release was investigated. This model provides integrated information on pharmacokinetics and in vivo potency of the test compounds. Case Study 5 contains data from an oral glucose tolerance test in rats, where the challenger is the same as the pharmacodynamic response biomarker (glucose). It is therefore convenient to model the extra input of glucose simultaneously with baseline data and during intervention of a glucose-lowering compound at different dose levels. Typically time-series analyses of challenger- and biomarker-time data are necessary if an accurate and precise estimate of the pharmacodynamic properties of a test compound is sought. Erosion of data, resulting in the single-point assessment of drug action after a challenge test, should generally be avoided. This is particularly relevant for situations where one expects time-curve shifts, tolerance/rebound, impact of disease, or hormetic concentration-response relationships to occur.

Mathematical modeling of postmenopausal osteoporosis and its treatment by the anti-catabolic drug denosumab

Denosumab, a fully human monoclonal antibody, has been approved for the treatment of postmenopausal osteoporosis. The therapeutic effect of denosumab rests on its ability to inhibit osteoclast differentiation. Here, we present a computational approach on the basis of coupling a pharmacokinetics model of denosumab with a pharmacodynamics model for quantifying the effect of denosumab on bone remodeling. The pharmacodynamics model comprises an integrated systems biology-continuum micromechanics approach, including a bone cell population model, considering the governing biochemical factors of bone remodeling (including the action of denosumab), and a multiscale micromechanics-based bone mechanics model, for implementing the mechanobiology of bone remodeling in our model. Numerical studies of postmenopausal osteoporosis show that denosumab suppresses osteoclast differentiation, thus strongly curtailing bone resorption. Simulation results also suggest that denosumab may trigger a short-term bone volume gain, which is, however, followed by constant or decreasing bone volume. This evolution is accompanied by a dramatic decrease of the bone turnover rate by more than one order of magnitude. The latter proposes dominant occurrence of secondary mineralization (which is not anymore impeded through cellular activity), leading to higher mineral concentration per bone volume. This explains the overall higher bone mineral density observed in denosumab-related clinical studies.

A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

Optimizing disease progression study designs for drug effect discrimination

Investigate the possibility to directly optimize a clinical trial design for statistical power to detect a drug effect and compare to optimal designs that focus on parameter precision. An improved statistic derived from the general formulation of the Wald approximation was used to predict the statistical power for given trial designs of a disease progression study. The predicted value was compared, together with the classical Wald statistic, to a type I error-corrected model-based power determined via clinical trial simulations. In a second step, a study design for maximal power was determined by directly maximizing the new statistic. The resulting power-optimal designs and their corresponding performance based on empirical power calculations were compared to designs focusing on parameter precision. Comparisons of empirically determined power and the newly developed statistic, showed excellent agreement across all scenarios investigated. This was in contrast to the classical Wald statistic, which consistently over-predicted the reference power with deviations of up to 90 %. Designs maximized using the proposed metric differed from traditional optimal designs and showed equal or up to 20 % higher power in the subsequent clinical trial simulations. Furthermore, the proposed method was used to minimize the number of individuals required to achieve 80 % power through a simultaneous optimization of study size and study design. The targeted power of 80 % was confirmed in subsequent simulation study. A new statistic was developed, allowing for the explicit optimization of a clinical trial design with respect to statistical power.

Population pharmacodynamic modeling of exenatide after 2-week treatment in STZ/NA diabetic rats

The purpose of this study is to investigate the effect of exenatide on glycemic control following two administration routes in a streptozotocin/nicotinamide (STZ/NA)-induced diabetic rat model, and to develop a pharmacodynamic model to better understand the disease progression and the action of exenatide in this experimental system. Two groups of STZ/NA-induced diabetic rats were treated for 2 weeks with 20 (μg/kg/day) of exenatide, either by continuous subcutaneous (SC) infusion or two SC injections daily. Disease progression was associated with slower glucose utilization. Fasting blood glucose was significantly reduced by 30 mg/dL in both treatment groups at the end of 2 weeks. A subsequent intravenous glucose tolerance test (IVGTT) confirmed an improved glucose tolerance in both treatment groups; however, overall glycemic control was similar between groups, likely due to the relatively low and short-term drug exposure. A population indirect response model was successfully developed to simultaneously describe the STZ/NA-induced disease progression, responses to an IVGTT, and exenatide effects on these systemic challenges. The unified model includes a single set of parameters, and the cumulative area under the drug-receptor concentration curve was used as a unique driving force to account for systemic effects long after drug elimination.

Translation of drug effects from experimental models of neuropathic pain and analgesia to humans

Neuropathic pain research remains a challenging undertaking owing to: (i) the lack of understanding about the underlying disease processes; and (ii) poor predictive validity of the current models of evoked pain used for the screening of novel compounds. Common consensus is that experimental models replicate symptoms (i.e. have face validity but no construct validity). Another issue that requires attention is the sensitivity of endpoints to discriminate drug effects that are relevant to the disease in humans. In this paper we provide an overview of the pre-clinical models that can be used in conjunction with a model-based approach to facilitate the prediction of drug effects in humans. Our review strongly suggests that evidence of the concentration-effect relationship is necessary for translational purposes.

Structural models describing placebo treatment effects in schizophrenia and other neuropsychiatric disorders

Large variation in placebo response within and among clinical trials can substantially affect conclusions about the efficacy of new medications in psychiatry. Developing a robust placebo model to describe the placebo response is important to facilitate quantification of drug effects, and eventually to guide the design of clinical trials for psychiatric treatment via a model-based simulation approach. In addition, high dropout rates are very common in the placebo arm of psychiatric clinical trials. While developing models to evaluate the effect of placebo response, the data from patients who drop out of the trial should be considered for accurate interpretation of the results. The objective of this paper is to review the various empirical and semi-mechanistic models that have been used to quantify the placebo response in schizophrenia trials. Pros and cons of each placebo model are discussed. Additionally, placebo models used in other neuropsychiatric disorders like depression, Alzheimer's disease and Parkinson's disease are also reviewed with the objective of finding those placebo models that could be useful for clinical studies of both acute and chronic schizophrenic disease conditions. Better understanding of the patterns of dropout and the factors leading to dropouts are crucial in identifying the true placebo response. We therefore also review dropout models that are used in the development of models for treatment effects and in the optimization of clinical trials by simulation approaches. The use of an appropriate modelling strategy that is capable of identifying the potential sources of variable placebo responses and dropout rates is recommended for improving the sensitivity in discriminating between the effects of active treatment and placebo.

Coping with time scales in disease systems analysis: application to bone remodeling

In this study we demonstrate the added value of mathematical model reduction for characterizing complex dynamic systems using bone remodeling as an example. We show that for the given parameter values, the mechanistic RANK-RANKL-OPG pathway model proposed by Lemaire et al. (J Theor Biol 229:293-309, 2004) can be reduced to a simpler model, which can describe the dynamics of the full Lemaire model to very good approximation. The response of both models to changes in the underlying physiology and therapeutic interventions was evaluated in four physiologically meaningful scenarios: (i) estrogen deficiency/estrogen replacement therapy, (ii) Vitamin D deficiency, (iii) ageing, and (iv) chronic glucocorticoid treatment and its cessation. It was found that on the time scale of disease progression and therapeutic intervention, the models showed negligible differences in their dynamic properties and were both suitable for characterizing the impact of estrogen deficiency and estrogen replacement therapy, Vitamin D deficiency, ageing, and chronic glucocorticoid treatment and its cessation on bone forming (osteoblasts) and bone resorbing (osteoclasts) cells. It was also demonstrated how the simpler model could help in elucidating qualitative properties of the observed dynamics, such as the absence of overshoot and rebound, and the different dynamics of onset and washout.

The use of decision-analytic models in Parkinson's disease: a systematic review and critical appraisal

The aims of this review were to review decision-analytic models used to evaluate interventions in idiopathic Parkinson's disease (PD), and to consider the future directions for development of methods to model the progression of PD over time. A systematic search of the healthcare literature up to June 2010 identified model-based economic evaluations in PD. The modelling methods used in the identified studies were appraised using good practice guidelines for decision-analytic modelling. The review identified 18 model-based evaluations of interventions in PD. All models evaluated treatments targeted towards the motor symptoms of PD or the motor complications of PD treatment. There were no models identified that evaluated interventions targeted towards the non-motor symptoms of PD, such as neuropsychiatric problems or autonomic dysfunction. Consequently, models characterized disease progression in PD using clinical measures of motor functioning. Most studies (n = 13) evaluated medications, three evaluated diagnostic technologies and two examined surgical procedures. Overall, the models reported structural components and data inputs appropriately and clearly, although limited evidence was provided to support choices made on the structures used in the models or the data synthesis reported. Models did not adequately consider structural uncertainty or internal/external consistency. Modelling methods used to date do not capture the full impact of PD. The emphasis in the current literature is on the motor symptoms of PD, characterizing the clinical nature of disease progression, largely neglecting the important impacts of non-motor symptoms. Modelling methods reported for the motor symptoms of PD may not be suitable for future interventions targeted towards modifying disease progression in PD across the entire spectrum of PD. More comprehensive models of disease progression, including both motor and non-motor symptoms will be needed where it is important to capture the effects of interventions more broadly.

Bone events and evolution of biologic markers in Gaucher disease before and during treatment

INTRODUCTION

Known biomarkers of Gaucher-disease activity are platelets, chitotriosidase, angiotensin-converting enzyme (ACE), tartrate-resistant acid phosphatase (TRAP) and ferritin. The aim of this study was to retrospectively evaluate the frequency of bone events (BE) and biomarker changes during two periods: diagnosis to first enzyme-replacement therapy (ERT) and the latter to the closing date.

METHODS

BE of 62 treated patients, among the 73-patient cohort followed at Beaujon Hospital, Clichy, France, were described with Kaplan-Meier curves, and linear-mixed models were used to analyze their biomarker changes and the influence of several covariates (splenectomy, diagnosis year, genotype, age at diagnosis and sex).

RESULTS

BE occurred before (54 events in 21 patients), but also during, ERT (12 events in 10 patients), with respective frequencies (95% confidence interval) at 10 years of 22.4% (13.3 to 36.3) and 20.0% (10.2 to 36.9). Biomarker slope changes before and during ERT differed significantly for platelets (+190/mm3/year and 7,035/mm3/year, respectively; P < 0.0001) and ferritin (+4% and -14%; P < 0.0001). High ferritin levels and low platelet counts at ERT onset were significantly associated with BE during ERT (P = 0.019 and 0.039, respectively). Covariates significantly influenced biomarker changes (baseline and/or slope): splenectomy affected platelets (baseline and changes), TRAP changes and chitotriosidase changes; diagnosis date influenced ACE and TRAP baseline values; and genotype influenced chitotriosidase baseline and changes.

CONCLUSIONS

Platelet counts and ferritin levels and their slope changes at ERT onset seem to predict BE during treatment. Biomarker baseline values and changes are dependent on several covariables.

Modeling the effectiveness of paliperidone ER and olanzapine in schizophrenia: meta-analysis of 3 randomized, controlled clinical trials

The time course of Positive and Negative Syndrome Scale (PANSS) scores in adult schizophrenia patients was modeled, and the effectiveness of paliperidone extended-release tablets (paliperidone ER) and olanzapine was quantified. Data from 3 randomized, double-blind phase III studies were used. Patients received paliperidone ER (3, 6, 9, 12, or 15 mg), olanzapine 10 mg, or matched placebo once daily for 6 consecutive weeks. An indirect response model implemented using a nonlinear mixed effects approach described the time course of the PANSS. Deterioration rate was modeled as a function of baseline PANSS score, placebo, and drug effects, and the dropout effect. An exponential decrease of the placebo response was also implemented. Paliperidone ER and olanzapine treatment were characterized by a long-lasting drug effect (13%), with a larger but short-lasting placebo effect (40%) and a notable dropout rate. The covariate exploration failed to identify any clinically relevant factors. The nonparametric bootstrap analysis confirmed the acceptable precision of parameter estimates. The visual predictive check supported the model's adequacy to reproduce observed PANSS time courses. The population model describes the time course of PANSS scores in schizophrenia patients and is appropriate for use in clinical trial simulation activities.

Therapeutic options for rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic, systemic inflammatory disorder primarily targeting the synovium. Unchecked disease activity is associated with significant morbidity and an increased mortality. Recent advances in the understanding of the pathogenesis of RA and the capability of biologically engineered treatments for RA have expanded the armamentarium of antirheumatic agents.

METHODS

A systematic literature review was conducted through PubMed.

RESULTS/CONCLUSIONS

At present, a common strategy for the treatment of RA uses methotrexate either as monotherapy or in combination with a variety of conventional and/or biologic disease-modifying antirheumatic drugs (DMARDs), with the goal of inducing remission of active disease. The choice of which agent(s) to use is based upon patient-specific criteria (activity of disease, comorbidities, patient preferences, costs etc.). Emerging therapies that target specific cytokines and growth factors in the inflammatory cascade of RA offer a potent new means of modifying disease activity, but many questions regarding their use remain unanswered.

Methods of utilizing baseline values for indirect response models

This study derives and assesses modified equations for Indirect Response Models (IDR) for normalizing data for baseline values (R (0)) and evaluates different methods of utilizing baseline information. Pharmacodynamic response equations for the four basic IDR models were adjusted to reflect a ratio to, a change from (e.g., subtraction), or percent change relative to baseline. The original and modified IDR equations were fitted individually to simulated data sets and compared for recovery of true parameter values. Handling of baseline values was investigated using: estimation (E), fixing at the starting value (F1), and fixing at an average of starting and returning values of response profiles (F2). The performance of each method was evaluated using simulated data with variability under various scenarios of different doses, numbers of data points, type of IDR model, and degree of residual errors. The median error and inter-quartile range relative to true values were used as indicators of bias and precision for each method. Applying IDR models to normalized data required modifications in writing differential equations and initial conditions. Use of an observed/baseline ratio led to parameter estimates of k (in) = k (out) and inability to detect differences in k (in) values for groups with different R (0), whereas the modified equations recovered the true values. An increase in variability increased the %Bias and %Imprecision for each R (0) fitting method and was more pronounced for 'F1'. The overall performance of 'F2' was as good as that of 'E' and better than 'F1'. The %Bias in estimation of parameters SC(50) (IC(50)) and k (out) followed the same trend, whereas use of 'F1' or 'F2' resulted in the least bias for S (max) (I (max)). The IDR equations need modifications to directly assess baseline-normalized data. In general, Method 'E' resulted in lesser bias and better precision compared to 'F1'. With rich datasets including sufficient information on the return to baseline, Method 'F2' is reasonable. Method 'E' offers no significant advantage over 'F1' with datasets lacking information on the return to baseline phase. Handling baseline responses properly is an essential aspect of applying pharmacodynamic models.

From trial and error to trial simulation. Part 1: the importance of model-based drug development for antidepressant drugs

Clinical trial simulation (CTS) allows the investigation of the influence of design characteristics on important aspects of clinical trials such as power and type I error. Simulation scenarios may be critical to decision making and prevention of study failure. The analysis and simulation of clinical trials in depression have, however, suffered from a lack of disease/dropout models that describe the individual time course of the clinical scale of interest. We propose a new model with dual random effects (DREM) derived from functional data analysis, which provides unbiased estimates of parameters and is suitable for the purposes of clinical trial simulation. A comparison of model performance is presented, along with standard statistical methods using various goodness-of-fit criteria. Our results show that data simulated using the DREM closely match individual patient data, including real-life dropout scenarios. In addition, parameterization in terms of interindividual variability ensures easier explanation of findings to clinical scientists, who ultimately make the relevant decisions.

Scaling pharmacodynamics from in vitro and preclinical animal studies to humans

An important feature of mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) models is the identification of drug- and system-specific factors that determine the intensity and time-course of pharmacological effects. This provides an opportunity to integrate information obtained from in vitro bioassays and preclinical pharmacological studies in animals to anticipate the clinical and adverse responses to drugs in humans. The fact that contemporary PK/PD modeling continues to evolve and seeks to emulate systems level properties should provide enhanced capabilities to scale-up pharmacodynamic data. Critical steps in drug discovery and development, such as lead compound and first in human dose selection, may become more efficient with the implementation and further refinement of translational PK/PD modeling. In this review, we highlight fundamental principles in pharmacodynamics and the basic expectations for in vitro bioassays and traditional allometric scaling in PK/PD modeling. Discussion of PK/PD modeling efforts for recombinant human erythropoietin is also included as a case study showing the potential for advanced systems analysis to facilitate extrapolations and improve understanding of inter-species differences in drug responses.

Incorporating receptor theory in mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) modeling

Pharmacokinetic-Pharmacodynamic (PK-PD) modeling helps to better understand drug efficacy and safety and has, therefore, become a powerful tool in the learning-confirming cycles of drug-development. In translational drug research, mechanism-based PK-PD modeling has been recognized as a tool for bringing forward early insights in drug efficacy and safety into the clinical development. These models differ from descriptive PK-PD models in that they quantitatively characterize specific processes in the causal chain between drug administration and effect. This includes target site distribution, binding and activation, pharmacodynamic interactions, transduction and homeostatic feedback mechanisms. Compared to descriptive models mechanism-based PK-PD models that utilize receptor theory concepts for characterization of target binding and target activation processes have improved properties for extrapolation and prediction. In this respect, receptor theory constitutes the basis for 1) prediction of in vivo drug concentration-effect relationships and 2) characterization of target association-dissociation kinetics as determinants of hysteresis in the time course of the drug effect. This approach intrinsically distinguishes drug- and system specific parameters explicitly, allowing accurate extrapolation from in vitro to in vivo and across species. This review provides an overview of recent developments in incorporating receptor theory in PK-PD modeling with a specific focus on the identifiability of these models.

In silico simulation of corticosteroids effect on an NFkB- dependent physicochemical model of systemic inflammation

BACKGROUND

During the onset of an inflammatory response signaling pathways are activated for "translating" extracellular signals into intracellular responses converging to the activation of nuclear factor (NF)-kB, a central transcription factor in driving the inflammatory response. An inadequate control of its transcriptional activity is associated with the culmination of a hyper-inflammatory response making it a desired therapeutic target. Predicated upon the nature of the response, a systems level analysis might provide rational leads for the development of strategies that promote the resolution of the response.

METHODOLOGY AND FINDINGS

A physicochemical host response model is proposed to integrate biological information in the form of kinetic rules and signaling cascades with pharmacokinetic models of drug action for the modulation of the response. The unifying hypothesis is that the response is triggered by the activation of the NFkB signaling module and corticosteroids serve as a template for assessing anti-inflammatory strategies. The proposed in silico model is evaluated through its ability to predict and modulate uncontrolled responses. The pre-exposure of the system to hypercortisolemia, i.e. 6 hr before or simultaneously with the infectious challenge "reprograms" the dynamics of the host towards a balanced inflammatory response. However, if such an intervention occurs long before the inflammatory insult a symptomatic effect is observed instead of a protective relief while a steroid infusion after inducing inflammation requires much higher drug doses.

CONCLUSIONS AND SIGNIFICANCE

We propose a reversed engineered inflammation model that seeks to describe how the system responds to a multitude of external signals. Timing of intervention and dosage regimes appears to be key determinants for the protective or symptomatic effect of exogenous corticosteroids. Such results lie in qualitative agreement with in vivo human studies exposed both to LPS and corticosteroids under various time intervals thus improving our understanding of how interacting modules generate a behavior.

Translational potential of systems-based models of inflammation

A critical goal of translational research is to convert basic science to clinically relevant actions related to disease prevention, diagnosis, and eventually enable physicians to identify and evaluate treatment strategies. Integrated initiatives are identified as valuable in uncovering the mechanism underpinning the progression of human diseases. Tremendous opportunities have emerged in the context of systems biology that aims at the deconvolution of complex phenomena to their constituent elements and the quantification of the dynamic interactions between these components through the development of appropriate computational and mathematical models. In this review, we discuss the potential role systems-based translation research can have in the quest to better understand and modulate the inflammatory response.

Modeling corticosteroid effects in a rat model of rheumatoid arthritis I: mechanistic disease progression model for the time course of collagen-induced arthritis in Lewis rats

A mechanism-based model was developed to describe the time course of arthritis progression in the rat. Arthritis was induced in male Lewis rats with type II porcine collagen into the base of the tail. Disease progression was monitored by paw swelling, bone mineral density (BMD), body weights, plasma corticosterone (CST) concentrations, and tumor necrosis factor (TNF)-alpha, interleukin (IL)-1beta, IL-6, and glucocorticoid receptor (GR) mRNA expression in paw tissue. Bone mineral density was determined by PIXImus II dual energy X-ray densitometry. Plasma CST was assayed by high-performance liquid chromatography. Cytokine and GR mRNA were determined by quantitative real-time polymerase chain reaction. Disease progression models were constructed from transduction and indirect response models and applied using S-ADAPT software. A delay in the onset of increased paw TNF-alpha and IL-6 mRNA concentrations was successfully characterized by simple transduction. This rise was closely followed by an up-regulation of GR mRNA and CST concentrations. Paw swelling and body weight responses peaked approximately 21 days after induction, whereas bone mineral density changes were greatest at 23 days after induction. After peak response, the time course in IL-1beta, IL-6 mRNA, and paw edema slowly declined toward a disease steady state. Model parameters indicate TNF-alpha and IL-1beta mRNA most significantly induce paw edema, whereas IL-6 mRNA exerted the most influence on BMD. The model for bone mineral density captures rates of turnover of cancellous and cortical bone and the fraction of each in the different regions analyzed. This small systems model integrates and quantitates multiple factors contributing to arthritis in rats.