Building an adaptive dose simulation framework to aid dose and schedule selection.

Establishing a dosing regimen that maximizes clinical benefit and minimizes adverse effects for novel therapeutics is a key objective for drug developers. Finding an optimal dose and schedule can be particularly challenging for compounds with a narrow therapeutic window such as in oncology. Modeling and simulation tools can be valuable to conduct in silico evaluations of various dosing scenarios with the goal to identify those that could minimize toxicities, avoid unscheduled dose interruptions, or minimize premature discontinuations, which all could limit the potential for therapeutic benefit. In this tutorial, we present a stepwise development of an adaptive dose simulation framework that can be used for dose optimization simulations. The tutorial first describes the general workflow, followed by a technical description with basic to advanced practical examples of its implementation in mrgsolve and is concluded with examples on how to use this in decision-making around dose and schedule optimization. The adaptive simulation framework is built with pharmacokinetic, pharmacodynamic (i.e., biomarkers, activity markers, target engagement markers, efficacy markers), and safety models that include evaluations of unexplained interindividual and intraindividual variability and covariate impact, which can be replaced and expanded (e.g., combination setting, comparator setting) with user-defined models. Subsequent adaptive simulations allow investigation of the impact of starting dose, dosing intervals, and event-driven (exposure or effect) dose modifications on any end point. The resulting simulation-derived insights can be used in quantitatively proposing dose and regimens that better balance benefit and adverse effects for further evaluation, aiding dose selection discussions, and designing dose modification recommendations, among others.

Longitudinal efficacy and safety modeling and simulation framework to aid dose selection of belantamab mafodotin for patients with multiple myeloma.

Belantamab mafodotin, a monomethyl auristatin F (MMAF)-containing monoclonal antibody-drug conjugate (ADC), demonstrated deep and durable responses in the DRiving Excellence in Approaches to Multiple Myeloma (DREAMM)-1 and pivotal DREAMM-2 studies in patients with relapsed/refractory multiple myeloma. As with other MMAF-containing ADCs, ocular adverse events were observed. To predict the effects of belantamab mafodotin dosing regimens and dose-modification strategies on efficacy and ocular safety end points, DREAMM-1 and DREAMM-2 data across a range of doses were used to develop an integrated simulation framework incorporating two separate longitudinal models and the published population pharmacokinetic model. A concentration-driven tumor growth inhibition model described the time course of serum M-protein concentration, a measure of treatment response, whereas a discrete time Markov model described the time course of ocular events graded with the GSK Keratopathy and Visual Acuity scale. Significant covariates included baseline ß2 -microglobulin on growth rate, baseline M-protein on kill rate, extramedullary disease on the effect compartment rate constant, and baseline soluble B cell maturation antigen on maximal effect. Efficacy and safety end points were simulated for various doses with dosing intervals of 1, 3, 6, and 9 weeks and various event-driven dose-modification strategies. Simulations predicted that lower doses and longer dosing intervals were associated with lower probability and lower overall time with Grade 3+ and Grade 2+ ocular events compared with the reference regimen (2.5 mg/kg every 3 weeks), with a less-than-proportional reduction in efficacy. The predicted improved benefit-risk profiles of certain dosing schedules and dose modifications from this integrated framework has informed trial designs for belantamab mafodotin, supporting dose-optimization strategies.

Extended use of levonorgestrel-releasing intrauterine system (LNG-IUS) 52 mg: A population pharmacokinetic approach to estimate in vivo levonorgestrel release rates and systemic exposure including comparison with two other LNG-IUSs.

OBJECTIVE: To characterize performance of levonorgestrel-releasing intrauterine system (LNG-IUS) 52mg (Mirena) over 8 years of use and facilitate comparisons with LNG-IUS 19.5mg and LNG-IUS 13.5mg. STUDY DESIGN: We estimated in vivo levonorgestrel (LNG) release rates and LNG plasma/serum concentrations for LNG-IUS 52mg up to 8 years of use with a population pharmacokinetic (popPK) approach using data from the Mirena Extension Trial (MET) and earlier clinical trials. We compared these with previously published release rates and exposure data for LNG-IUS 19.5mg and 13.5mg. Our 8-year popPK and release models were developed based on measured plasma/serum LNG and sex hormone-binding globulin concentrations and residual LNG content from removed LNG-IUS 52mg devices. RESULTS: Model-based estimated LNG release rates for LNG-IUS 52mg decreased from ∼21 µg/d after insertion to ∼7.0 µg/d after 8 years, similar to LNG-IUS 19.5mg after 5 years (7.6 µg/d) and higher than LNG-IUS 13.5mg after 3 years (5.5 µg/d). Model-based estimated and measured plasma/serum LNG concentrations showed satisfactory agreement. Average model-based estimated LNG concentrations after 8 years of LNG-IUS 52mg use (100 ng/L [coefficient of variance 39.9%]) were similar to LNG-IUS 19.5mg after 5 years (84.8 ng/L [39.9%]) and higher than LNG-IUS 13.5mg after 3 years (58.1 ng/L [40.8%]). CONCLUSIONS: The 8-year release and popPK models provide reliable in vivo LNG release rates and concentration estimates, respectively, facilitating direct comparisons between the 3 studied LNG-IUSs. LNG release rates from LNG-IUS 52mg at 8 years are similar to LNG-IUS 19.5mg at 5 years and higher than LNG-IUS 13.5mg at 3 years. IMPLICATIONS: Levonorgestrel release from intrauterine system reservoirs declines with duration of use in a predictable way, and in relation to the initial load. As release rates and plasma concentrations of levonorgestrel may influence endometrial and systemic side effects, these data may assist clinical decision-making.

Model-informed bridging of rivaroxaban doses for thromboprophylaxis in pediatric patients aged 9 years and older with congenital heart disease.

Rivaroxaban is approved in various regions for the treatment of acute venous thromboembolism (VTE) in children aged between 0 and 18 years and was recently investigated for thromboprophylaxis in children aged between 2 and 8 years (with body weights <30 kg) with congenital heart disease who had undergone the Fontan procedure. In the absence of clinical data, rivaroxaban doses for thromboprophylaxis in post-Fontan children aged 9 years and older or ≥30 kg were derived by a bridging approach that used physiologically-based pharmacokinetic (PBPK) and population pharmacokinetic (popPK) models based on pharmacokinetic (PK) data from 588 pediatric patients and from adult patients who received 10 mg once daily for thromboprophylaxis after major orthopedic surgeries as a reference. Both models showed a tendency toward underestimating rivaroxaban exposure in post-Fontan patients aged between 2 and 5 years but accurately described rivaroxaban PK in post-Fontan patients aged between 5 and 8 years. Under the assumption that hepatic function is not impaired in post-Fontan patients, PBPK and popPK simulations indicated that half of the rivaroxaban doses for the same body weight given to pediatric patients treated for acute VTE would yield in pediatric post-Fontan patients exposures similar to the exposure observed in adult patients receiving 10 mg rivaroxaban once daily for thromboprophylaxis. Simulation-derived doses (7.5 mg rivaroxaban once daily for body weights 30-<50 kg and 10 mg once daily for body weights ≥50 kg) were therefore included in the recent US label of rivaroxaban for thromboprophylaxis in children aged 2 years and older with congenital heart disease who have undergone the Fontan procedure.

Understanding effect site pharmacology of uprifosbuvir, a hepatitis C virus nucleoside inhibitor: Case study of a multidisciplinary modeling approach in drug development.

Uprifosbuvir is a uridine nucleoside monophosphate prodrug inhibitor of the hepatitis C virus NS5B RNA polymerase. To quantitatively elucidate key metabolic pathways, assess the link between unmeasurable effect site concentrations and viral load reduction, and evaluate the influence of intrinsic and extrinsic factors on pharmacokinetics and pharmacodynamics, a model-informed drug development (MIDD) framework was initiated at an early stage. Originally scoped as a modeling effort focused on minimal physiologically based pharmacokinetic and covariate analyses, this project turned into a collaborative effort focused on gaining a deeper understanding of the data from drug metabolism, biopharmaceutics, pharmacometrics, and clinical pharmacology perspectives. This article presents an example of the practical execution of a MIDD-based, cooperative multidisciplinary modeling approach, creating a model that grows along with the team's integrated knowledge. Insights gained from this process could be used in forming optimal collaborations between disciplines in drug development for other investigative compounds.

A population pharmacokinetics analysis assessing the exposure of raltegravir once-daily 1200 mg in pregnant women living with HIV.

Once-daily two 600 mg tablets (1200 mg q.d.) raltegravir offers an easier treatment option compared to the twice-daily regimen of one 400 mg tablet. No pharmacokinetic, efficacy, or safety data of the 1200 mg q.d. regimen have been reported in pregnant women to date as it is challenging to collect these clinical data. This study aimed to develop a population pharmacokinetic (PopPK) model to predict the pharmacokinetic profile of raltegravir 1200 mg q.d. in pregnant women and to discuss the expected pharmacodynamic properties of raltegravir 1200 mg q.d. during pregnancy based on previously reported concentration-effect relationships. Data from 11 pharmacokinetic studies were pooled (n = 221). A two-compartment model with first-order elimination and absorption through three sequential transit compartments best described the data. We assessed that the bio-availability of the 600 mg tablets was 21% higher as the 400 mg tablets, and the bio-availability in pregnant women was 49% lower. Monte-Carlo simulations were performed to predict the pharmacokinetic profile of 1200 mg q.d. in pregnant and nonpregnant women. The primary criteria for efficacy were that the lower bound of the 90% confidence interval (CI) of the concentration before next dose administration (Ctrough ) geometric mean ratio (GMR) of simulated pregnant/nonpregnant women had to be greater than 0.75. The simulated raltegravir Ctrough GMR (90% CI) was 0.51 (0.41-0.63), hence not meeting the primary target for efficacy. Clinical data from two pregnant women using 1200 mg q.d. raltegravir showed a similar Ctrough ratio pregnant/nonpregnant. Our pharmacokinetic results support the current recommendation of not using the raltegravir 1200 mg q.d. regimen during pregnancy until more data on the exposure-response relationship becomes available.

Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.

The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.

Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages.

nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.

Perspective on the State of Pharmacometrics and Systems Pharmacology Integration.

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.

Frequency-Domain Response Analysis for Quantitative Systems Pharmacology Models.

Drug dosing regimen can significantly impact drug effect and, thus, the success of treatments. Nevertheless, trial and error is still the most commonly used method by conventional pharmacometric approaches to optimize dosing regimen. In this tutorial, we utilize four distinct classes of quantitative systems pharmacology models to introduce frequency-domain response analysis, a method widely used in electrical and control engineering that allows the analytical optimization of drug treatment regimen from the dynamics of the model.

Prediction of nomegestrol acetate pharmacokinetics in healthy female adolescents and adults by whole-body physiology-based pharmacokinetic modelling and clinical validation.

OBJECTIVE: Nomegestrol acetate (NOMAC), a selective progestogen, and 17ß-estradiol (E2), which is identical to endogenous oestrogen, are components of a new monophasic combined oral contraceptive--NOMAC/E2. This study aimed to compare pharmacokinetics (PK) of NOMAC in adolescent and adult women following a single dose of NOMAC/E2. STUDY DESIGN: Healthy postmenarcheal adolescent (14-17years) and adult (18-50years) women received a single dose of NOMAC/E2 (2.5mg/1.5mg) in this single-centre, open-label, parallel-group Phase 1 study (EudraCT# 2008-002142-38). Blood samples were obtained for PK analysis, and concentrations of NOMAC, E2 and its metabolite estrone (E1) were determined for up to 129h following dosing to obtain PK data. An independent whole-body physiology-based pharmacokinetic (WB-PBPK) simulation model of NOMAC based on an independent Phase 3 dataset was used to scale NOMAC concentration-time plots to adolescents. RESULTS: Overall, 52 women were screened, of whom 30 (15 adolescents and 15 adults) were enrolled. No statistically significant differences were observed between the adolescent and adult groups for the clinically evaluated NOMAC PK parameters [maximum concentration (Cmax), area under the curve (AUC) and half-life (t1/2)]. The PK of E2 and E1 showed extensive overlap between both age groups. The WB-PBPK model accurately predicted NOMAC AUC and Cmax values in both groups. CONCLUSIONS: No differences were observed in the clinically evaluated PK parameters for NOMAC between adolescent and adult women after a single dose of NOMAC/E2. The WB-PBPK model accurately predicted NOMAC PK data (EudraCT# 2008-002142-38). IMPLICATIONS: PK studies in adolescents are challenging because of ethical considerations. The whole-body physiology-based model described here complements classic noncompartmental and population PK approaches. The utility of this method is its ability to expand to adolescent postmenarcheal girls by using virtual postmenarcheal adolescent population data and applying physiological scaling.

The effect of therapeutic and supratherapeutic oral doses of nomegestrol acetate (NOMAC)/17ß-estradiol (E2) on QTcF intervals in healthy women: results from a randomized, double-blind, placebo- and positive-controlled trial.

BACKGROUND AND OBJECTIVE: Nomegestrol acetate (NOMAC)/17ß-estradiol (E2) is a monophasic oral contraceptive that contains a progesterone-derived progestogen (NOMAC), and E2, a bio-identical estrogen. The primary objective of this thorough QT/QTc study was to investigate whether once-daily administration of therapeutic (2.5/1.5 mg) and supratherapeutic (12.5/7.5 mg) doses of NOMAC/E2 were associated with prolongation of the mean Fridericia-corrected QT (QTcF) interval in electrocardiograms at steady-state concentrations of NOMAC/E2 versus placebo. The co-primary objective was to establish assay sensitivity after a single dose of moxifloxacin (positive control). METHODS: This was a randomized, double-blind, parallel-group trial comparing 2.5/1.5 mg of NOMAC/E2 (therapeutic dose), 12.5/7.5 mg of NOMAC/E2 (supratherapeutic dose), placebo, and moxifloxacin 400 mg. Double-blind study medication was administered from day -1 to 14. Healthy women aged 18-50 years were randomized. RESULTS: The largest time-matched mean QTcF difference compared with placebo for the therapeutic dose of NOMAC/E2 was 1.6 ms, with an upper limit (UL) of a one-sided 95% confidence interval (CI) of 5.2 ms, and 3.1 ms with an UL 95% CI of 7.0 ms for the supratherapeutic dose. The UL for the time-matched QTcF differences compared with placebo were below the 10 ms threshold defined in the ICH E14 guideline for all time points, both for the therapeutic and the supratherapeutic dose. For moxifloxacin, assay sensitivity was demonstrated. CONCLUSIONS: This thorough QT/QTc study showed that therapeutic and supratherapeutic doses of NOMAC/E2 were not associated with clinically relevant QTc interval prolongation in healthy women after a 2-week period of dosing.

Application of a mechanism-based disease systems model for osteoporosis to clinical data.

A recently proposed mechanism-based disease systems model for osteoporosis (Schmidt et al., J Pharmacokinet Pharmacodyn 38:873-900, 2011) was applied to clinical data from post-menopausal women (n = 767) receiving various doses of the selective estrogen receptor modulator tibolone. Plasma bone-specific alkaline phosphatase activity and urinary N-telopeptide were used as biomarkers reflecting the activity of osteoblasts (bone forming cells) and osteoclasts (bone removing cells), respectively. These data were analyzed in conjunction with data on osteocalcin and on bone mineral density (BMD) (both lumbar spine and total hip), which reflect the activity of both cell types. While the dynamics of bone turnover markers changes rapidly, closely following changes in the activity of bone cells, changes in BMD are slower and have their own dynamics. Application of the mechanism-based disease systems model to the clinical data allowed for an adequate description of the data and yielded parameter estimates that are consistent with physiological values reported in the literature (Lemaire et al., J Theor Biol 229:293-309, 2004). The fitted model enabled characterization of (i) the critical time scales involved in disease progression, (ii) the dynamics of the system during onset and offset of the therapeutic intervention, and (iii) the distinction between responders and low-responders to tibolone treatment.

Pharmacokinetic profile of nomegestrol acetate and 17ß-estradiol after multiple and single dosing in healthy women.

BACKGROUND: The pharmacokinetics of the monophasic oral contraceptive nomegestrol acetate (NOMAC) plus 17ß-estradiol (E(2)) were investigated after a single dose and multiple dosing. STUDY DESIGN: NOMAC/E2 (2.5 mg/1.5 mg) was administered daily to healthy women (18-50 years, n=23) for 24 days; blood samples for pharmacokinetic analysis were obtained on Day 24 and again, after a 10-day pill-free interval, on Day 35 after a single dose. RESULTS: NOMAC reached steady state after 5 days with mean ±standard deviation (SD) trough NOMAC concentration (C(av)) of 4.4±1.4 ng/mL. On Day 24, mean±SD peak NOMAC concentration (Cmax, 12.3±3.5 ng/mL) was reached in mean 1.5 h (t(max)); the mean±SD elimination half-life (t(½)) was 45.9±15.3 h. After a single dose, NOMAC mean±SD C(max) was 7.2±2.0 ng/mL and mean±SD t(½) was 41.9±16.2 h. On Day 24, E2 mean±SD C(av) was 50.3±25.7 pg/mL; mean±SD Cmax was 86.0±51.3 pg/mL. After a single dose, mean±SD E2 Cmax was 253±179 pg/mL. CONCLUSIONS: These data demonstrate that NOMAC/E2 has a pharmacokinetic profile consistent with once-daily dosing.

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.

Bone physiology, disease and treatment: towards disease system analysis in osteoporosis.

Osteoporosis is a chronic progressive disorder and is regarded as an important worldwide health issue. The development of novel treatments and the comparison of the effects of novel and existing treatments in osteoporosis are complicated by the difficulties of establishing drug effects on disease progression, as reflected in the slowly changing primary biomarker, bone mineral density. In recent years, research has considerably improved our understanding of the pathophysiology of osteoporosis. Specifically, various biomarkers have been identified that reflect bone physiology at the cellular level. These biomarkers mirror the dynamics of bone formation and degradation on a shorter timescale than bone mineral density as a composite measure. These markers can therefore, in principle, be used to characterize the underlying regulatory system and to quantify drug effects in osteoporosis. Recently, the concept of disease system analysis has been proposed as a novel approach to characterize, in a strictly quantitative manner, drug effects on disease progression. This approach integrates physiology, disease progression and drug treatment in a comprehensive mechanism-based model, using dynamic information on a network of biomarkers. This review focuses on the use of disease system analysis for the characterization of drug effects on osteoporosis. It is concluded that, although the development of fully mechanistic disease system models may be practically impossible, parsimonious--but mechanism-based--disease system models may ultimately be used to adequately predict the long-term effects of drug treatment on clinical outcomes.

Extensions to the visual predictive check to facilitate model performance evaluation.

The Visual Predictive Check (VPC) is a valuable and supportive instrument for evaluating model performance. However in its most commonly applied form, the method largely depends on a subjective comparison of the distribution of the simulated data with the observed data, without explicitly quantifying and relating the information in both. In recent adaptations to the VPC this drawback is taken into consideration by presenting the observed and predicted data as percentiles. In addition, in some of these adaptations the uncertainty in the predictions is represented visually. However, it is not assessed whether the expected random distribution of the observations around the predicted median trend is realised in relation to the number of observations. Moreover the influence of and the information residing in missing data at each time point is not taken into consideration. Therefore, in this investigation the VPC is extended with two methods to support a less subjective and thereby more adequate evaluation of model performance: (i) the Quantified Visual Predictive Check (QVPC) and (ii) the Bootstrap Visual Predictive Check (BVPC). The QVPC presents the distribution of the observations as a percentage, thus regardless the density of the data, above and below the predicted median at each time point, while also visualising the percentage of unavailable data. The BVPC weighs the predicted median against the 5th, 50th and 95th percentiles resulting from a bootstrap of the observed data median at each time point, while accounting for the number and the theoretical position of unavailable data. The proposed extensions to the VPC are illustrated by a pharmacokinetic simulation example and applied to a pharmacodynamic disease progression example.

Application of the convection-dispersion equation to modelling oral drug absorption.

Models of systemic drug absorption after oral administration are frequently based on a direct or a delayed first-order rate process. In practice, the use of the first-order approach to predict drug concentrations in blood plasma frequently yields a considerable mismatch between predicted and measured concentration profiles. This is particularly true for the upswing of the plasma concentration after oral administration. The current investigation explores an alternative model to describe the absorption rate based on the convection-dispersion equation describing the transport of chemicals through the GI tract. This equation is governed by two parameters, transport velocity and dispersion coefficient. One solution of this equation for a specific set of initial and boundary conditions was used to model absorption of paracetamol in a 22-year-old man after oral administration. The GI-tract passage rate in this subject was influenced by co-administration of drugs that stimulate or delay gastric emptying. The transport-limited absorption function is more accurate in describing the plasma concentration versus time curve after oral administration than the first-order model. Additionally, it provides a mechanistic explanation for the observed curve through the differences in GI-tract passage rate.

A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying Type 2 Diabetes Mellitus.

Effective long-term treatment of Type 2 Diabetes Mellitus (T2DM) implies modification of the disease processes that cause this progressive disorder. This paper proposes a mechanism-based approach to disease progression modeling of T2DM that aims to provide the ability to describe and quantify the effects of treatment on the time-course of the progressive loss of beta-cell function and insulin-sensitivity underlying T2DM. It develops a population pharmacodynamic model that incorporates mechanism-based representations of the homeostatic feedback relationships between fasting levels of plasma glucose (FPG) and fasting serum insulin (FSI), and the physiological feed-forward relationship between FPG and glycosylated hemoglobin A1c (HbA1c). This model was developed on data from two parallel one-year studies comparing the effects of pioglitazone relative to metformin or sulfonylurea treatment in 2,408 treatment-naïve T2DM patients. It was found that the model provided accurate descriptions of the time-courses of FPG and HbA1c for different treatment arms. It allowed the identification of the long-term effects of different treatments on loss of beta-cell function and insulin-sensitivity, independently from their immediate anti-hyperglycemic effects modeled at their specific sites of action. Hence it avoided the confounding of these effects that is inherent in point estimates of beta-cell function and insulin-sensitivity such as the widely used HOMA-%B and HOMA-%S. It was also found that metformin therapy did not result in a reduction in FSI levels in conjunction with reduced FPG levels, as expected for an insulin-sensitizer, whereas pioglitazone therapy did. It is concluded that, although its current implementation leaves room for further improvement, the mechanism-based approach presented here constitutes a promising conceptual advance in the study of T2DM disease progression and disease modification.

Disease system analysis: basic disease progression models in degenerative disease.

PURPOSE: To describe the disease status of degenerative diseases (i.e., type 2 diabetes mellitus, Parkinson's disease) as function of disease process and treatment effects, a family of disease progression models is introduced. METHODS: Disease progression is described using a progression rate (Rdp) acting on the synthesis or elimination parameters of the indirect response model. Symptomatic effects act as disease-dependent or -independent effects on the synthesis or elimination parameters. Protective drug effects act as disease dependent or -independent effects on Rdp. RESULTS: Simulations with the ten disease models show distinctly different signature profiles of treatment effects on disease status. Symptomatic effects result in improvement of disease status with a subsequent deterioration. Treatment cessation results in a disease status equal to the situation where treatment had not been applied. Protective effects result in a lasting reduction, or even reversal, of the disease progression rate and the resulting disease status during the treatment period. After cessation of treatment the natural disease course will continue from the disease status at that point. CONCLUSION: Disease system analysis constitutes a scientific basis for the distinction between symptomatic versus protective drug effects in relation to specific disease processes as well as the identification of the exposure-response relationship during the time-course of disease.