1
|
Mahmoud I, Battini V, Carnovale C, Clementi E, Kragholm K, Sessa M. New data-driven method to predict the therapeutic indication of redeemed prescriptions in secondary data sources: a case study on antiseizure medications users aged ≥65 identified in Danish registries. BMJ Open 2024; 14:e080126. [PMID: 38844392 PMCID: PMC11163620 DOI: 10.1136/bmjopen-2023-080126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 05/09/2024] [Indexed: 06/12/2024] Open
Abstract
OBJECTIVES We aimed to develop a new data-driven method to predict the therapeutic indication of redeemed prescriptions in secondary data sources using antiepileptic drugs among individuals aged ≥65 identified in Danish registries. DESIGN This was an incident new-user register-based cohort study using Danish registers. SETTING The study setting was Denmark and the study period was 2005-2017. PARTICIPANTS Participants included antiepileptic drug users in Denmark aged ≥65 with a confirmed diagnosis of epilepsy. PRIMARY AND SECONDARY OUTCOME MEASURES Sensitivity served as the performance measure of the algorithm. RESULTS The study population comprised 8609 incident new users of antiepileptic drugs. The sensitivity of the algorithm in correctly predicting the therapeutic indication of antiepileptic drugs in the study population was 65.3% (95% CI 64.4 to 66.2). CONCLUSIONS The algorithm demonstrated promising properties in terms of overall sensitivity for predicting the therapeutic indication of redeemed antiepileptic drugs by older individuals with epilepsy, correctly identifying the therapeutic indication for 6 out of 10 individuals using antiepileptic drugs for epilepsy.
Collapse
Affiliation(s)
- Israa Mahmoud
- Department of Drug Design and Pharmacology, University of Copenhagen, Kobenhavn, Denmark
| | - Vera Battini
- Department of Drug Design and Pharmacology, University of Copenhagen, Kobenhavn, Denmark
- Università degli Studi di Milano, Milano, Italy
| | | | | | - Kristian Kragholm
- Unit of Epidemiology and Biostatistics, Aalborg Universitetshospital, Aalborg, Denmark
| | - Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Kobenhavn, Denmark
| |
Collapse
|
2
|
Taylor ZL, Poweleit EA, Paice K, Somers KM, Pavia K, Vinks AA, Punt N, Mizuno T, Girdwood ST. Tutorial on model selection and validation of model input into precision dosing software for model-informed precision dosing. CPT Pharmacometrics Syst Pharmacol 2023; 12:1827-1845. [PMID: 37771190 PMCID: PMC10725261 DOI: 10.1002/psp4.13056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 09/30/2023] Open
Abstract
There has been rising interest in using model-informed precision dosing to provide personalized medicine to patients at the bedside. This methodology utilizes population pharmacokinetic models, measured drug concentrations from individual patients, pharmacodynamic biomarkers, and Bayesian estimation to estimate pharmacokinetic parameters and predict concentration-time profiles in individual patients. Using these individualized parameter estimates and simulated drug exposure, dosing recommendations can be generated to maximize target attainment to improve beneficial effect and minimize toxicity. However, the accuracy of the output from this evaluation is highly dependent on the population pharmacokinetic model selected. This tutorial provides a comprehensive approach to evaluating, selecting, and validating a model for input and implementation into a model-informed precision dosing program. A step-by-step outline to validate successful implementation into a precision dosing tool is described using the clinical software platforms Edsim++ and MwPharm++ as examples.
Collapse
Affiliation(s)
- Zachary L. Taylor
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Ethan A. Poweleit
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of Biomedical InformaticsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
- Division of Biomedical InformaticsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Research in Patient ServicesCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Kelli Paice
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Critical Care Medicine, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Katherine M. Somers
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Critical Care Medicine, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Hematology and Oncology, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Kathryn Pavia
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Division of Critical Care Medicine, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Alexander A. Vinks
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
- Division of Research in Patient ServicesCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| | - Nieko Punt
- Department of Clinical Pharmacy and Pharmacology, University of GroningenUniversity Medical Center GroningenGroningenThe Netherlands
- MedimaticsMaastrichtThe Netherlands
| | - Tomoyuki Mizuno
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Sonya Tang Girdwood
- Division of Clinical PharmacologyCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
- Division of Hospital Medicine, Department of PediatricsCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
| |
Collapse
|
3
|
Wang W, Battini V, Carnovale C, Noordam R, van Dijk KW, Kragholm KH, van Heemst D, Soeorg H, Sessa M. A novel approach for pharmacological substantiation of safety signals using plasma concentrations of medication and administrative/healthcare databases: a case study using Danish registries for an FDA warning on lamotrigine. Pharmacol Res 2023:106811. [PMID: 37268178 DOI: 10.1016/j.phrs.2023.106811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 05/29/2023] [Accepted: 05/29/2023] [Indexed: 06/04/2023]
Abstract
PHARMACOM-EPI is a novel framework to predict plasma concentrations of drugs at the time of occurrence of clinical outcomes. In early 2021, the U.S. Food and Drug Administration (FDA) issued a warning on the antiseizure drug lamotrigine claiming that it has the potential to increase the risk of arrhythmias and related sudden cardiac death due to a pharmacological sodium channel-blocking effect. We hypothesized that the risk of arrhythmias and related death is due to toxicity. We used the PHARMACOM-EPI framework to investigate the relationship between lamotrigine's plasma concentrations and the risk of death in older patients using real-world data. Danish nationwide administrative and healthcare registers were used as data sources and individuals aged 65 years or older during the period 1996 - 2018 were included in the study. According to the PHARMACOM-EPI framework, plasma concentrations of lamotrigine were predicted at the time of death and patients were categorized into non-toxic and toxic groups based on the therapeutic range of lamotrigine (3-15mg/L). Over 1 year of treatment, the incidence rate ratio (IRR) of all-cause mortality was calculated between the propensities score matched toxic and non-toxic groups. In total, 7286 individuals were diagnosed with epilepsy and were exposed to lamotrigine, 432 of which had at least one plasma concentration measurement The pharmacometric model by Chavez et al. was used to predict lamotrigine's plasma concentrations considering the lowest absolute percentage error among identified models (14.25%, 95% CI: 11.68-16.23). The majority of lamotrigine associated deaths were cardiovascular-related and occurred among individuals with plasma concentrations in the toxic range. The IRR of mortality between the toxic group and non-toxic group was 3.37 [95% CI: 1.44-8.32] and the cumulative incidence of all-cause mortality exponentially increased in the toxic range. Application of our novel framework PHARMACOM-EPI provided strong evidence to support our hypothesis that the increased risk of all-cause and cardiovascular death was associated with a toxic plasma concentration level of lamotrigine among older lamotrigine users.
Collapse
Affiliation(s)
- Wenyi Wang
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Vera Battini
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Italy; Department of Drug Design and Pharmacology, University of Copenhagen, Denmark
| | - Carla Carnovale
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Italy
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics; Leiden University Medical Center, Leiden, Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands; Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, Netherlands; Leiden Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, Netherlands
| | | | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics; Leiden University Medical Center, Leiden, Netherlands
| | - Hiie Soeorg
- Department of Microbiology, Institute of Biomedicine and Translational Medicine, Faculty of Medicine, University of Tartu, Estonia.
| | - Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Denmark.
| |
Collapse
|
4
|
Soeorg H, Sverrisdóttir E, Andersen M, Lund TM, Sessa M. Artificial Neural Network vs. Pharmacometric Model for Population Prediction of Plasma Concentration in Real-World Data: A Case Study on Valproic Acid. Clin Pharmacol Ther 2022; 111:1278-1285. [PMID: 35263452 DOI: 10.1002/cpt.2577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 03/01/2022] [Indexed: 11/08/2022]
Abstract
We compared the predictive performance of an artificial neural network to traditional pharmacometric modeling for population prediction of plasma concentrations of valproate in real-world data. We included individuals aged 65 years or older with epilepsy who redeemed their first prescription of valproate after the diagnosis of epilepsy and had at least one valproate plasma concentration measured. A long short-term memory neural network (LSTM) was developed using the training data set to fit the LSTM and the test data set to validate the model. Predictions from the LSTM were compared with those obtained from the population predictions from a pharmacometric model by Birnbaum et al. which had the best predictive performance for population predictions of valproate concentrations in Danish databases. We used the cutoff of ± 20 mg/L of prediction error to define good predictions. A total of 1,252 individuals were included in the study. The LSTM fitted using the training data set had poor predictive performance in the test data set, but better than that of the pharmacometric model. The proportion of individuals with at least one predicted concentration within ± 20 mg/L of observed concentration was largest in case of the LSTM (64.4%, 95% confidence interval (CI): 58.4-70.2%) compared with the pharmacometric model by Birnbaum et al. (49.8%, 95% CI: 47.0-52.6%). LSTM shows better predictive performance to predict valproate plasma concentrations compared with a traditional pharmacometric model in the investigated setting with real-world data in older patients with epilepsy where information on exact timepoints for both dosing and plasma concentration measurement are missing.
Collapse
Affiliation(s)
- Hiie Soeorg
- Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.,Pharmacometrics Research Group, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Eva Sverrisdóttir
- Pharmacometrics Research Group, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Morten Andersen
- Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Trine Meldgaard Lund
- Pharmacometrics Research Group, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Maurizio Sessa
- Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
5
|
Pazzagli L, Liang D, Andersen M, Linder M, Khan AR, Sessa M. Rationale and performances of a data-driven method for computing the duration of pharmacological prescriptions using secondary data sources. Sci Rep 2022; 12:6245. [PMID: 35428827 PMCID: PMC9012860 DOI: 10.1038/s41598-022-10144-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 03/25/2022] [Indexed: 11/15/2022] Open
Abstract
The assessment of the duration of pharmacological prescriptions is an important phase in pharmacoepidemiologic studies aiming to investigate persistence, effectiveness or safety of treatments. The Sessa Empirical Estimator (SEE) is a new data-driven method which uses k-means algorithm for computing the duration of pharmacological prescriptions in secondary data sources when this information is missing or incomplete. The SEE was used to compute durations of exposure to pharmacological treatments where simulated and real-world data were used to assess its properties comparing the exposure status extrapolated with the method with the “true” exposure status available in the simulated and real-world data. Finally, the SEE was also compared to a Researcher-Defined Duration (RDD) method. When using simulated data, the SEE showed accuracy of 96% and sensitivity of 96%, while when using real-world data, the method showed sensitivity ranging from 78.0 (nortriptyline) to 95.1% (propafenone). When compared to the RDD, the method had a lower median sensitivity of 2.29% (interquartile range 1.21–4.11%). The SEE showed good properties and may represent a promising tool to assess exposure status when information on treatment duration is not available.
Collapse
Affiliation(s)
- Laura Pazzagli
- Department of Medicine Solna, Centre for Pharmacoepidemiology, Karolinska Institutet, Stockholm, Sweden.
| | - David Liang
- Ferring Pharmaceuticals, Copenhagen, Denmark
| | - Morten Andersen
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Marie Linder
- Department of Medicine Solna, Centre for Pharmacoepidemiology, Karolinska Institutet, Stockholm, Sweden
| | - Abdul Rauf Khan
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.,Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Maurizio Sessa
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|