Disease Progression and Pharmacodynamics in Parkinson Disease – Evidence for Functional Protection with Levodopa and Other Treatments

Basic PK/PD principles of drug effects in circular/proliferative systems for disease modelling

Disease progression modelling can provide information about the time course and outcome of pharmacological intervention on the disease. The basic PK/PD principles of proliferative and circular systems within the context of modelling disease progression and the effect of treatment thereupon are illustrated with the goal to better understand/predict eventual clinical outcome. Circular/proliferative systems can be very complex. To facilitate the understanding of how a dosing regimen can be defined in such systems we have shown the derivation of a system parameter named the Reproduction Minimum Inhibitory Concentration (RMIC) which represents the critical concentration at which the system switches from growth to extinction. The RMIC depends on two parameters (RMIC = (R₀ − 1) × IC₅₀): the basic reproductive ratio (R₀) a fundamental parameter of the circular/proliferative system that represents the number of offspring produced by one replicating species during its lifespan, and the IC₅₀, the potency of the drug to inhibit the proliferation of the system. The RMIC is constant for a given system and a given drug and represents the lowest concentration that needs to be achieved for eradication of the system. When exposure is higher than the RMIC, success can be expected in the long term. Time varying inhibition of replicating species proliferation is a natural consequence of the time varying inhibitor drug concentrations and when combined with the dynamics of the circular/proliferative system makes it difficult to predict the eventual outcome. Time varying inhibition of proliferative/circular systems can be handled by calculating the equivalent effective constant concentration (ECC), the constant plasma concentration that would give rise to the average inhibition at steady state. When ECC is higher than the RMIC, eradication of the system can be expected. In addition, it is shown that scenarios that have the same steady state ECC whatever the dose, dosage schedule or PK parameters have also the same average R₀ in the presence of the inhibitor (i.e. R_0-INH) and therefore lead to the same outcome. This allows predicting equivalent active doses and dosing schedules in circular and proliferative systems when the IC₅₀ and pharmacokinetic characteristics of the drugs are known. The results from the simulations performed demonstrate that, for a given system (defined by its RMIC), treatment success depends mainly on the pharmacokinetic characteristics of the drug and the dosing schedule.

Review: Efficient Rehabilitation Trial Designs Using Disease Progress Modeling: A Pediatric Traumatic Brain Injury Example

Background. The identification of possible treatment effects against a background of spontaneous recovery is a major challenge to the successful completion of randomized clinical trials (RCTs) in rehabilitation research. Conventional trial outcomes such as the differences between group means of an outcome measure at a fixed time point are inefficient to an extent that is a major problem, particularly for exploratory studies seeking preliminary evidence of efficacy. Objective . To quantitate gains in study power over conventional fixed-end-point designs by using parametric end points derived from the modeling of the time course of recovery after brain injury. Methods. Nonlinear mixed effects (NLME) modeling of the recovery trajectories of 103 children rehabilitating after traumatic brain injury (TBI) as reflected in serial WeeFIM scores was performed. Pseudoreplicate data sets were generated replicating the statistical characteristics of the original data set, and these formed the basis of clinical trial simulations to derive robust estimates of study power. Results. Parametric end points derived from modeling of recovery improve study power (and reduce necessary sample size) by up to 5 times in this example. Conclusions. Parametric end points derived from models of recovery trajectories offer an efficient alternative design for exploratory clinical studies of rehabilitation interventions.

Endpoints and analyses to discern disease-modifying drug effects in early Parkinson's disease

Parkinson's disease is an age-related degenerative disorder of the central nervous system that often impairs the sufferer's motor skills and speech, as well as other functions. Symptoms can include tremor, stiffness, slowness of movement, and impaired balance. An estimated four million people worldwide suffer from the disease, which usually affects people over the age of 60. Presently, there is no precedent for approving any drug as having a modifying effect (i.e., slowing or delaying) for disease progression of Parkinson's disease. Clinical trial designs such as delayed start and withdrawal are being proposed to discern symptomatic and protective effects. The current work focused on understanding the features of delayed start design using prior knowledge from published and data submitted to US Food and Drug Administration (US FDA) as part of drug approval or protocol evaluation. Clinical trial simulations were conducted to evaluate the false-positive rate, power under a new statistical analysis methodology, and various scenarios leading to patient discontinuations from clinical trials. The outcome of this work is part of the ongoing discussion between the US FDA and the pharmaceutical industry on the standards required for demonstrating disease-modifying effect using delayed start design.

Quantitative disease, drug, and trial models

Quantitative disease-drug-trial models allow learning from prior experience and summarize the knowledge in a ready to apply format. Employing these models to plan future development is proposed as a powerful solution to improve pharmaceutical R&D productivity. The disease and trial models are, to a large extent, independent of the product, but the drug model is not. The goals are to apply the disease and trial models to future development and regulatory decisions, and publicly share them. We propose working definitions of these models, describe the various subcomponents, provide examples, and discuss the challenges and potential solutions for developing such models. Building useful disease-drug-trial models is a challenging task and cannot be achieved by any single organization. It requires a consorted effort by industry, academic, and regulatory scientists. We also describe the strategic goals of the FDA Pharmacometrics group.

L-dopa therapy for Parkinson's disease: past, present, and future

Dopamine (DA) supplementation therapy by l-dopa for Parkinson's disease (PD) was established around 1970. The dose of l-dopa can be reduced by the combined administration of inhibitors of peripheral l-amino acid decarboxylase (AADC), catechol O-methyltransferase (COMT), or monoamine oxidase B (MAO B). DA in the striatum may be produced from exogenously administered l-dopa by various AADC-containing cells, such as serotonin neurons. The long-term administration of l-dopa in PD patients may produce l-dopa-induced dyskinesia (LID), which may be due to chronic overstimulation of supersensitive DA D1 receptors. l-dopa may be used in combination with various new strategies such as gene therapy or transplantation in the future.

Pharmacokinetics and pharmacodynamics of levodopa

The pharmacokinetics and pharmacodynamics of levodopa are dominated by two features: the short plasma half-life of the drug and the portion of the antiparkinsonian response that parallels the plasma levodopa levels, the so-called short-duration response. These features are the basis of motor fluctuations that complicate long-term therapy with levodopa. Motor fluctuations will predictably improve with measures that prolong the elevations of plasma levodopa or prolong the efficacy of dopamine synthesized from exogenous levodopa. Because dyskinesia is closely linked to the short-duration response and conceivably part of the short-duration response, it is less clear that dyskinesia will be improved by therapeutic strategies that reduce motor fluctuations.

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.

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.

Levodopa Slows Progression of Parkinson’s Disease. External Validation by Clinical Trial Simulation

PURPOSE

To externally validate the model predictions of a DATATOP cohort analysis through application of clinical trial simulation with the study design of the ELLDOPA trial.

METHODS

The stochastic pharmacokinetic-pharmacodynamic and disease progress model was developed from the large DATATOP cohort of patients followed for 8 years. ELLDOPA was designed to detect a difference between placebo and levodopa treated arms in the total Unified Parkinson's Disease Rating Scale (UPDRS) taken at baseline and following 2 weeks levodopa washout after 40 weeks of treatment. The total UPDRS response was simulated with different assumptions on levodopa effect (symptomatic with/without disease modifying capability) and washout speed of symptomatic effect.

RESULTS

The observed results of ELLDOPA were similar to the model predictions assuming levodopa slows disease progression and has a slow washout of symptomatic effect.

CONCLUSIONS

This simulation work confirmed the conclusion of the DATATOP analysis finding that levodopa slows disease progression. The simulation results also showed that a dose-related increased rate of progression in Parkinson's disease, obscured by symptomatic benefit, is very unlikely. Finally, the simulation results also shown that 2 weeks washout period was not adequate to completely eliminate the symptomatic benefits of levodopa.