Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis
A specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses following extravascular dosing. Unfortunately, an inappropriate absorption model may force other model parameters to be poorly estimated. An added complexity arises in population PK analyses when different i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Pharmaceutics |
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| Online Access: | https://www.mdpi.com/1999-4923/13/6/797 |
| _version_ | 1797532523098537984 |
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| author | Mutaz M. Jaber Burhaneddin Yaman Kyriakie Sarafoglou Richard C. Brundage |
| author_facet | Mutaz M. Jaber Burhaneddin Yaman Kyriakie Sarafoglou Richard C. Brundage |
| author_sort | Mutaz M. Jaber |
| collection | DOAJ |
| description | A specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses following extravascular dosing. Unfortunately, an inappropriate absorption model may force other model parameters to be poorly estimated. An added complexity arises in population PK analyses when different individuals appear to have different absorption patterns. The aim of this study is to demonstrate that a deep neural network (DNN) can be used to prescreen data and assign an individualized absorption model consistent with either a first-order, Erlang, or split-peak process. Ten thousand profiles were simulated for each of the three aforementioned shapes and used for training the DNN algorithm with a 30% hold-out validation set. During the training phase, a 99.7% accuracy was attained, with 99.4% accuracy during in the validation process. In testing the algorithm classification performance with external patient data, a 93.7% accuracy was reached. This algorithm was developed to prescreen individual data and assign a particular absorption model prior to a population PK analysis. We envision it being used as an efficient prescreening tool in other situations that involve a model component that appears to be variable across subjects. It has the potential to reduce the time needed to perform a manual visual assignment and eliminate inter-assessor variability and bias in assigning a sub-model. |
| first_indexed | 2024-03-10T11:00:24Z |
| format | Article |
| id | doaj.art-589d5de84ac14df7b8292fc13b258a48 |
| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-10T11:00:24Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj.art-589d5de84ac14df7b8292fc13b258a482023-11-21T21:31:54ZengMDPI AGPharmaceutics1999-49232021-05-0113679710.3390/pharmaceutics13060797Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic AnalysisMutaz M. Jaber0Burhaneddin Yaman1Kyriakie Sarafoglou2Richard C. Brundage3Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USADepartment of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USADepartment of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USADepartment of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USAA specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses following extravascular dosing. Unfortunately, an inappropriate absorption model may force other model parameters to be poorly estimated. An added complexity arises in population PK analyses when different individuals appear to have different absorption patterns. The aim of this study is to demonstrate that a deep neural network (DNN) can be used to prescreen data and assign an individualized absorption model consistent with either a first-order, Erlang, or split-peak process. Ten thousand profiles were simulated for each of the three aforementioned shapes and used for training the DNN algorithm with a 30% hold-out validation set. During the training phase, a 99.7% accuracy was attained, with 99.4% accuracy during in the validation process. In testing the algorithm classification performance with external patient data, a 93.7% accuracy was reached. This algorithm was developed to prescreen individual data and assign a particular absorption model prior to a population PK analysis. We envision it being used as an efficient prescreening tool in other situations that involve a model component that appears to be variable across subjects. It has the potential to reduce the time needed to perform a manual visual assignment and eliminate inter-assessor variability and bias in assigning a sub-model.https://www.mdpi.com/1999-4923/13/6/797pharmacokineticsdeep learningmachine learningabsorption modelsvisual inspectionindividualized models |
| spellingShingle | Mutaz M. Jaber Burhaneddin Yaman Kyriakie Sarafoglou Richard C. Brundage Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis Pharmaceutics pharmacokinetics deep learning machine learning absorption models visual inspection individualized models |
| title | Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis |
| title_full | Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis |
| title_fullStr | Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis |
| title_full_unstemmed | Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis |
| title_short | Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis |
| title_sort | application of deep neural networks as a prescreening tool to assign individualized absorption models in pharmacokinetic analysis |
| topic | pharmacokinetics deep learning machine learning absorption models visual inspection individualized models |
| url | https://www.mdpi.com/1999-4923/13/6/797 |
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