Using mechanistic models to simulate comparative effectiveness trials of therapy and to estimate long-term outcomes in HIV care

Model-Based Approach for Optimizing Study Design and Clinical Drug Performances of Extended-Release Formulations of Methylphenidate for the Treatment of ADHD

Methylphenidate (MPH) is currently used to treat children with attention deficit hyperactivity disorder (ADHD). Several extended-release (ER) formulations characterized by a dual release process were developed to improve efficacy over an extended duration. In this study, a model-based approach using literature data was developed to: 1) evaluate the most efficient pharmacokinetic (PK) model to characterize the complex PK profile of MPH ER formulations; 2) provide PK endpoint metrics for comparing ER formulations; 3) define criteria for optimizing development of ER formulations using a convolution-based model linking in vitro release, in vivo release, and hour-by-hour behavioral ratings of ADHD symptoms; and 4) define an optimized trial design for assessing the activity of MPH in pediatric populations. The convolution-based model accurately described the complex PK profiles of a variety of ER MPH products, providing a natural framework for establishing an in vitro/in vivo correlation and for defining criteria for assessing comparative bioequivalence of MPH ER products.

Modeling and simulation of the exposure-response and dropout pattern of guanfacine extended-release in pediatric patients with ADHD

Guanfacine extended-release (GXR) is a selective α2A-adrenergic receptor agonist approved in the United States for once-daily administration for the treatment of attention-deficit hyperactivity disorder (ADHD) in children and adolescents ages 6-17 years old either as monotherapy or adjunctive to stimulant medications. This analysis integrates exposure-response, placebo, and dropout data from 10 clinical trials that used GXR in adolescents and children with ADHD. In these trials, the ADHD Rating Scale-IV (ADHD RS-IV) score was collected longitudinally within patients over the course of 6-13 weeks. Non-linear mixed effects models were developed and used to describe the exposure-response of the GXR and placebo time course. The OpenBUGS program was utilized to describe the dropout time course across the trials. Placebo time course was best described by an inverse Bateman function with a 3-group mixture model that allowed for the onset and offset of the placebo response. Dropout time modeling indicated a missing at random mechanism for dropouts which was best described by a Weibull distribution with an estimated percentage of non-dropout patients. A linear exposure-response model with an adolescent effect on maximum slope (SLPmax), and a time delay for reaching SLPmax, provided the best description of the GXR exposure-response time course. The GXR exposure-response model indicated that the typical (95 % confidence interval) decrease in ADHD RS-IV score from the placebo-response trajectory would be 37.1 % (32.2, 42.0 %) per 0.1 mg/kg of GXR exposure. There was little noticeable difference between the exposure-response in adolescents and children or across ADHD subtypes.

A simulation-based approach for improving utilization of thrombolysis in acute brain infarction

BACKGROUND

Treatment with tissue plasminogen activator (tPA) is the most effective treatment in acute brain infarction. However, estimated worldwide treatment rates are <10%, with many barriers hampering broad implementation. Organization and resource-intense randomized controlled trials cannot address all potential barriers simultaneously. Simulation, however, may provide an efficient research means for testing interventions aimed at resolving barriers along the care pathway.

RESEARCH DESIGN

A simulation-based approach reflecting the setup of a regional Dutch acute stroke pathway was used. First, barriers along the overall pathway were identified. Next, solutions to barriers were configured, and subsequently tested using simulation.

RESULTS

Barriers along the stroke pathway and possible solutions were identified from the literature and expert consultation. The simulation model closely reproduced actually observed tPA treatment rate and overall process time (21.8% and 129 min for model outcomes vs. 22.1% and 127 min, P=0.89 and 0.64, respectively). Two barriers were overcome: (1) time spent by ambulance personnel on scene by a scoop-and-run protocol (1.4% increase in tPA rate, 7 min decrease in overall process time), and (2) time to laboratory results by introducing a point-of-care diagnostic device (3.2% increase in tPA rate, 20 min decrease in overall process time).

CONCLUSIONS

A simulation-based approach is well suited to efficiently assess solutions to barriers along the overall stroke pathway. Substantial improvements in treatment rates and efficacy of thrombolysis may be achieved by implementing a scoop-and-run protocol and point-of-care device.

Life expectancy living with HIV: recent estimates and future implications

PURPOSE OF REVIEW

The life expectancy of people living with HIV has dramatically increased since effective antiretroviral therapy has been available, and still continues to improve. Here, we review the latest literature on estimates of life expectancy and consider the implications for future research.

RECENT FINDINGS

With timely diagnosis, access to a variety of current drugs and good lifelong adherence, people with recently acquired infections can expect to have a life expectancy which is nearly the same as that of HIV-negative individuals. Modelling studies suggest that life expectancy could improve further if there were increased uptake of HIV testing, better antiretroviral regimens and treatment strategies, and the adoption of healthier lifestyles by those living with HIV. In particular, earlier diagnosis is one of the most important factors associated with better life expectancy. A consequence of improved survival is the increasing number of people with HIV who are aged over 50 years old, and further research into the impact of ageing on HIV-positive people will therefore become crucial. The development of age-specific HIV treatment and management guidelines is now called for.

SUMMARY

Analyses on cohort studies and mathematical modelling studies have been used to estimate life expectancy of those with HIV, providing useful insights of importance to individuals and healthcare planning.

Bridging paradigms: hybrid mechanistic-discriminative predictive models

Many disease processes are extremely complex and characterized by multiple stochastic processes interacting simultaneously. Current analytical approaches have included mechanistic models and machine learning (ML), which are often treated as orthogonal viewpoints. However, to facilitate truly personalized medicine, new perspectives may be required. This paper reviews the use of both mechanistic models and ML in healthcare as well as emerging hybrid methods, which are an exciting and promising approach for biologically based, yet data-driven advanced intelligent systems.

Resistance evolution in HIV - modeling when to intervene

The treatment of HIV is complicated by the evolution of antiviral drug resistant virus and the limited availability of antigenically independent antiviral regimens. The consequences to the patient of successive virological failures is such that many strategies to minimize the occurrence of such failures are being investigated. In this paper, a Markov chain-based model of virological failure is introduced. This model considers sequential failure events, and differentiates between several modes of virological failure. This model is then used to evaluate the resistance- targeted interventions by means of testing the impact of a viral load preconditioning strategy on total treatment regimen longevity in HIV patients. It is shown that a proposed intervention targeting pre-existing resistance has the potential to increase the expected time to three sequential virological failures by an average of 3.3 years per patient. When combined with an intervention targeting patient compliance, the total potential increase in the time to three sequential virological failures is as high as 11.2 years. The impact on patient and public health is discussed.

Modelling the impact of treatment with individual antiretrovirals

PURPOSE OF REVIEW

The aim of this paper is to review the recent literature on HIV mathematical models that evaluate the effect of antiretroviral treatment on mortality, morbidity, HIV and other key outcomes. The focus of our attention is models which explicitly model specific effects of individual antiretroviral drugs.

RECENT FINDINGS

The number of studies that use mathematical models to evaluate the impact of antiretroviral drugs as a treatment or prevention strategy is increasing.Many mathematical models are deterministic compartmental models, and do not have the level of detail of specific effects of individual drugs. However, models that include specific antiretroviral drugs have been increasingly employed to assess the cost-effectiveness of prevention interventions, to evaluate benefits and harms (toxicities, side-effects, resistance development) of different regimens and different intervention timing and to predict long-term outcomes of randomized controlled trials (RCTs) that are not usually measured in the time frame of a trial.

SUMMARY

The number of models that consider specific antiretroviral drugs, with their own peculiarities, is limited. This factor is particularly the case for dynamic individual-based stochastic models. In order to address some research questions it is necessary to accurately take into consideration implications of toxicities, side-effects, and resistance acquisition, and hence to model specific drugs or at least specific drug classes.