Translational learning from clinical studies predicts drug pharmacokinetics across patient populations
Systems pharmacology: predicting population pharmacokinetics in silico Physiologically based modeling together with Bayesian statistics allows the prediction of drug pharmacokinetics in specific patient populations. An interdisciplinary group of clinicians and computational scientists led by Dr. Lar...
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Nature Portfolio
2017
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oai:doaj.org-article:ec1cdb4109a643779b3939519f2d88162021-12-02T11:42:12ZTranslational learning from clinical studies predicts drug pharmacokinetics across patient populations10.1038/s41540-017-0012-52056-7189https://doaj.org/article/ec1cdb4109a643779b3939519f2d88162017-03-01T00:00:00Zhttps://doi.org/10.1038/s41540-017-0012-5https://doaj.org/toc/2056-7189Systems pharmacology: predicting population pharmacokinetics in silico Physiologically based modeling together with Bayesian statistics allows the prediction of drug pharmacokinetics in specific patient populations. An interdisciplinary group of clinicians and computational scientists led by Dr. Lars Kuepfer from Bayer developed a generic workflow consisting of several consecutive learning steps where knowledge about both individual physiology as well as drug physicochemistry can be efficiently derived from plasma concentration profiles. The acquired information is then be used for the prediction of the pharmacokinetic behavior of a new drug candidate in a diseased population. This allows to simulate the variability in drug exposure virtually before starting clinical investigation in real patients in order to evaluate drug safety or efficacy through the simulation of virtual populations. Further development of this workflow could improve the safety of clinical development programs to assess the risk-benefit ratio of novel drug candidates in silico.Markus KraussUte HofmannClemens SchafmayerSvitlana IgelJan SchlenderChristian MuellerMario BroschWitigo von SchoenfelsWiebke ErhartAndreas SchuppertMichael BlockElke SchaeffelerGabriele BoehmerLinus GoerlitzJan HoeckerJoerg LippertReinhold KerbJochen HampeLars KuepferMatthias SchwabNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 3, Iss 1, Pp 1-11 (2017) |
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Biology (General) QH301-705.5 |
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Biology (General) QH301-705.5 Markus Krauss Ute Hofmann Clemens Schafmayer Svitlana Igel Jan Schlender Christian Mueller Mario Brosch Witigo von Schoenfels Wiebke Erhart Andreas Schuppert Michael Block Elke Schaeffeler Gabriele Boehmer Linus Goerlitz Jan Hoecker Joerg Lippert Reinhold Kerb Jochen Hampe Lars Kuepfer Matthias Schwab Translational learning from clinical studies predicts drug pharmacokinetics across patient populations |
| description |
Systems pharmacology: predicting population pharmacokinetics in silico Physiologically based modeling together with Bayesian statistics allows the prediction of drug pharmacokinetics in specific patient populations. An interdisciplinary group of clinicians and computational scientists led by Dr. Lars Kuepfer from Bayer developed a generic workflow consisting of several consecutive learning steps where knowledge about both individual physiology as well as drug physicochemistry can be efficiently derived from plasma concentration profiles. The acquired information is then be used for the prediction of the pharmacokinetic behavior of a new drug candidate in a diseased population. This allows to simulate the variability in drug exposure virtually before starting clinical investigation in real patients in order to evaluate drug safety or efficacy through the simulation of virtual populations. Further development of this workflow could improve the safety of clinical development programs to assess the risk-benefit ratio of novel drug candidates in silico. |
| format |
article |
| author |
Markus Krauss Ute Hofmann Clemens Schafmayer Svitlana Igel Jan Schlender Christian Mueller Mario Brosch Witigo von Schoenfels Wiebke Erhart Andreas Schuppert Michael Block Elke Schaeffeler Gabriele Boehmer Linus Goerlitz Jan Hoecker Joerg Lippert Reinhold Kerb Jochen Hampe Lars Kuepfer Matthias Schwab |
| author_facet |
Markus Krauss Ute Hofmann Clemens Schafmayer Svitlana Igel Jan Schlender Christian Mueller Mario Brosch Witigo von Schoenfels Wiebke Erhart Andreas Schuppert Michael Block Elke Schaeffeler Gabriele Boehmer Linus Goerlitz Jan Hoecker Joerg Lippert Reinhold Kerb Jochen Hampe Lars Kuepfer Matthias Schwab |
| author_sort |
Markus Krauss |
| title |
Translational learning from clinical studies predicts drug pharmacokinetics across patient populations |
| title_short |
Translational learning from clinical studies predicts drug pharmacokinetics across patient populations |
| title_full |
Translational learning from clinical studies predicts drug pharmacokinetics across patient populations |
| title_fullStr |
Translational learning from clinical studies predicts drug pharmacokinetics across patient populations |
| title_full_unstemmed |
Translational learning from clinical studies predicts drug pharmacokinetics across patient populations |
| title_sort |
translational learning from clinical studies predicts drug pharmacokinetics across patient populations |
| publisher |
Nature Portfolio |
| publishDate |
2017 |
| url |
https://doaj.org/article/ec1cdb4109a643779b3939519f2d8816 |
| work_keys_str_mv |
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