| 1. |
- Bonate, Peter L., et al.
(författare)
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Training the next generation of pharmacometric modelers : a multisector perspective
- 2023
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Nature. - 1567-567X .- 1573-8744. ; 51:1, s. 5-31
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Tidskriftsartikel (refereegranskat)abstract
- The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970’s and early 1980’s and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.
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| 2. |
- Kjellsson, Maria C., et al.
(författare)
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Modeling sleep data for a new drug in development using Markov mixed-effects models
- 2011
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Ingår i: Pharmaceutical research. - : Springer Science and Business Media LLC. - 0724-8741 .- 1573-904X. ; 28:10, s. 2610-2627
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Tidskriftsartikel (refereegranskat)abstract
- To characterize the time-course of sleep in insomnia patients as well as placebo and concentration-effect relationships of two hypnotic compounds, PD 0200390 and zolpidem, using an accelerated model-building strategy based on mixed-effects Markov models. Data were obtained in a phase II study with the drugs. Sleep stages were recorded during eight hours of sleep for two nights per treatment for the five treatments. First-order Markov models were developed for one transition at a time in a sequential manner; first a baseline model, followed by placebo and lastly the drug models. To accelerate the process, predefined models were selected based on a priori knowledge of sleep, including inter-subject and inter-occasion variability. Baseline sleep was described using piece-wise linear models, depending on time of night and duration of sleep stage. Placebo affected light sleep stages; drugs also affected slow-wave sleep. Administering PD 0200390 30 min earlier than standard dosing was shown through simulations to reduce latency to persistent sleep by 40%. The proposed accelerated model-building strategy resulted in a model well describing sleep patterns of insomnia patients with and without treatments.
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