Bayesian Quantitative Disease–Drug–Trial Models for Parkinson’s Disease to Guide Early Drug Development

Abstract

The problem we have faced in drug development is in its efficiency. Almost a half of registration trials are reported to fail mainly because pharmaceutical companies employ one-size-fits-all development strategies. Our own experience at the regulatory agency suggests that failure to utilize prior experience or knowledge from previous trials also accounts for trial failure. Prior knowledge refers to both drug-specific and nonspecific information such as placebo effect and the disease course. The information generated across drug development can be systematically compiled to guide future drug development. Quantitative disease-drug-trial models are mathematical representations of the time course of biomarker and clinical outcomes, placebo effects, a drug's pharmacologic effects, and trial execution characteristics for both the desired and undesired responses. Applying disease-drug-trial model paradigms to design a future trial has been proposed to overcome current problems in drug development. Parkinson's disease is a progressive neurodegenerative disorder characterized by bradykinesia, rigidity, tremor, and postural instability. A symptomatic effect of drug treatments as well as natural rate of disease progression determines the rate of disease deterioration. Currently, there is no approved drug which claims disease modification. Regulatory agency has been asked to comment on the trial design and statistical analysis methodology. In this work, we aim to show how disease-drug-trial model paradigm can help in drug development and how prior knowledge from previous studies can be incorporated into a current trial using Parkinson's disease model as an example. We took full Bayesian methodology which can allow one to translate prior information into probability distribution.

A review of disease progression models of Parkinson's disease and applications in clinical trials

“…Lee and Gobburu modeled UPDRS as change from baseline at each visit, rather than raw scores, using linear and monomolecular functions analogous to the Guimaraes et al and Bhattaram et al models (Table ) . The focus of their article was to use Bayesian inference to quantify disease progression parameters, that is, to use prior estimates of UPDRS changes from one clinical trial to quantify UPDRS changes from another trial to limit the uncertainty in parameter estimates.…”

“…In each PD model article, the authors demonstrated how disease progression models could be incorporated into designing or analyzing clinical trials. For example, some of the authors used the models to generate data to simulate a clinical trial . Because the models reflect data and there is some knowledge of their error, one can simulate many trials to calculate power and sample sizes and to estimate the treatment effects of pharmacologic agents on disease progression.…”

“…There have been several publications from individuals at the Division of Pharmacometrics at the FDA advocating the use of disease modeling and simulation for drug development and regulatory decisions in PD. 38,39 Bhattaram and colleagues developed models of PD progression using placebo data from 4 unspecified clinical trials with durations of 3 to 18 months. 38 Participants from these studies did not require symptomatic treatment and were early in the course of disease.…”

An Innovative Disease‐Drug‐Trial Framework to Guide Binge Eating Disorder Drug Development: A Case Study for Topiramate

“…One common theme that consistently has shown to “de‐risk” drug development is the application of prior knowledge regarding drug‐specific properties and disease‐specific phenomena using quantitative translational methods. Quantitative disease‐drug‐trial models have been recently used in neuropsychiatry to mathematically represent the time course of a disease, placebo effect, a drug's pharmacologic effect, and trial characteristics to guide drug development and regulatory decision making . Examples of the use of disease‐drug‐trials in psychiatry include the longitudinal evaluation of changes in Positive and Negative Syndrome Scale scores for proper comparison of clinically relevant treatment effects, the development of a placebo‐dropout model using Young Mania Rating Scale scores in patients with bipolar disorder to inform future trial design, and the use of the Hamilton Rating Scale for Depression scores to forecast the probability of being a placebo responder …”

“…Quantitative disease-drug-trial models have been recently used in neuropsychiatry to mathematically represent the time course of a disease, placebo effect, a drug's pharmacologic effect, and trial characteristics to guide drug development and regulatory decision making. 4,5 Examples of the use of disease-drug-trials in psychiatry include the longitudinal evaluation of changes in Positive and Negative Syndrome Scale scores for proper comparison of clinically relevant treatment effects, the development of a placebo-dropout model using Young Mania Rating Scale scores in patients with bipolar disorder to inform future trial design, and the use of the Hamilton Rating Scale for Depression scores to forecast the probability of being a placebo responder. [6][7][8] Binge eating disorder (BED) is a psychiatric disorder characterized by recurrent and distressing binge-eating episodes, defined as eating unusually large quantities of food with a sense of lack of control over eating, without inappropriate compensatory weight loss behaviors.…”