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

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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.

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Acknowledgment

We would like to acknowledge Parkinson’s Study Group and NIH Exploratory Trials in Parkinson’s Disease (NET-PD) Group for providing access to clinical trial data. We also thank the Division of Pharmacometrics, Office of Clinical Pharmacology for helpful discussions over the years. The views expressed in this article are those of the authors and do not necessarily reflect the official views of the Food and Drug Administration.

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Authors and Affiliations

  1. Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Ave., Room 3198, Building 51, Silver Spring, Maryland, 20993-0002, USA

    Joo Yeon Lee & Jogarao V. S. Gobburu

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  1. Joo Yeon Lee
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  2. Jogarao V. S. Gobburu
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Correspondence to Joo Yeon Lee.

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Lee, J.Y., Gobburu, J.V.S. Bayesian Quantitative Disease–Drug–Trial Models for Parkinson’s Disease to Guide Early Drug Development. AAPS J 13, 508–518 (2011). https://doi.org/10.1208/s12248-011-9293-6

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