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Hughes MSA, Hughes JH, Endicott J, Langton M, Ahern JW, Keizer RJ. Developing Parametric and Nonparametric Models for Model-Informed Precision Dosing: A Quality Improvement Effort in Vancomycin for Patients With Obesity. Ther Drug Monit 2024:00007691-990000000-00223. [PMID: 38758633 DOI: 10.1097/ftd.0000000000001214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/02/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Both parametric and nonparametric methods have been proposed to support model-informed precision dosing (MIPD). However, which approach leads to better models remains uncertain. Using open-source software, these 2 statistical approaches for model development were compared using the pharmacokinetics of vancomycin in a challenging subpopulation of class 3 obesity. METHODS Patients on vancomycin at the University of Vermont Medical Center from November 1, 2021, to February 14, 2023, were entered into the MIPD software. The inclusion criteria were body mass index (BMI) of at least 40 kg/m2 and 1 or more vancomycin levels. A parametric model was created using nlmixr2/NONMEM, and a nonparametric model was created using metrics. Then, a priori and a posteriori predictions were evaluated using the normalized root mean squared error (nRMSE) for precision and the mean percentage error (MPE) for bias. The parametric model was evaluated in a simulated MIPD context using an external validation dataset. RESULTS In total, 83 patients were included in the model development, with a median age of 56.6 years (range: 24-89 years), and a median BMI of 46.3 kg/m2 (range: 40-70.3 kg/m2). Both parametric and nonparametric models were 2-compartmental, with creatinine clearance and fat-free mass as covariates to c clearance and volume parameters, respectively. The a priori MPE and nRMSE for the parametric versus nonparametric models were -6.3% versus 2.69% and 27.2% versus 30.7%, respectively. The a posteriori MPE and RMSE were 0.16% and 0.84%, and 13.8% and 13.1%. The parametric model matched or outperformed previously published models on an external validation dataset (n = 576 patients). CONCLUSIONS Minimal differences were found in the model structure and predictive error between the parametric and nonparametric approaches for modeling vancomycin class 3 obesity. However, the parametric model outperformed several other models, suggesting that institution-specific models may improve pharmacokinetics management.
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Affiliation(s)
| | | | | | - Meagan Langton
- University of Vermont Medical Center, Burlington, Vermont
| | - John W Ahern
- University of Vermont Medical Center, Burlington, Vermont
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Alsultan A, Dasuqi SA, Almohaizeie A, Aljutayli A, Aljamaan F, Omran RA, Alolayan A, Hamad MA, Alotaibi H, Altamimi S, Alghanem SS. External Validation of Obese/Critically Ill Vancomycin Population Pharmacokinetic Models in Critically Ill Patients Who Are Obese. J Clin Pharmacol 2024; 64:353-361. [PMID: 37862131 DOI: 10.1002/jcph.2375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 10/17/2023] [Indexed: 10/22/2023]
Abstract
Obesity combined with critical illness might increase the risk of acquiring infections and hence mortality. In this patient population the pharmacokinetics of antimicrobials vary significantly, making antimicrobial dosing challenging. The objective of this study was to assess the predictive performance of published population pharmacokinetic models of vancomycin in patients who are critically ill or obese for a cohort of critically ill patients who are obese. This was a multi-center retrospective study conducted at 2 hospitals. Adult patients with a body mass index of ≥30 kg/m2 were included. PubMed was searched for published population pharmacokinetic studies in patients who were critically ill or obese. External validation was performed using Monolix software. A total of 4 models were identified in patients who were obese and 5 models were identified in patients who were critically ill. In total, 138 patients who were critically ill and obese were included, and the most accurate models for these patients were the Goti and Roberts models. In our analysis, models in patients who were critically ill outperformed models in patients who were obese. When looking at the most accurate models, both the Goti and the Roberts models had patient characteristics similar to ours in terms of age and creatinine clearance. This indicates that when selecting the proper model to apply in practice, it is important to account for all relevant variables, besides obesity.
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Affiliation(s)
- Abdullah Alsultan
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Shereen A Dasuqi
- Department of Pharmacy, King Khalid University Hospital, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Abdullah Almohaizeie
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Abdullah Aljutayli
- Department of Pharmaceutics, Faculty of Pharmacy, Qassim University, Riyadh, Saudi Arabia
| | - Fadi Aljamaan
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Critical Care Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | - Rasha A Omran
- Department of Pharmaceutics and Pharmaceutical Technology, School of Pharmacy, University of Jordan, Amman, Jordan
| | - Abdulaziz Alolayan
- Pharmacy Department, Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia, Riyadh, Saudi Arabia
| | - Mohammed A Hamad
- Critical Care Department, King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
- Department of Acute Medicine, Wirral University Teaching Hospital NHS Foundation Trust, Arrowe Park Hospital, Wirral, UK
| | - Haifa Alotaibi
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Sarah Altamimi
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Sarah S Alghanem
- Department of Pharmacy Practice, College of Pharmacy at Kuwait University, Safat, Kuwait
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Oda K, Yamada T, Matsumoto K, Hanai Y, Ueda T, Samura M, Shigemi A, Jono H, Saito H, Kimura T. Model-informed precision dosing of vancomycin for rapid achievement of target area under the concentration-time curve: A simulation study. Clin Transl Sci 2023; 16:2265-2275. [PMID: 37718491 PMCID: PMC10651648 DOI: 10.1111/cts.13626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/19/2023] Open
Abstract
In this study, we aimed to evaluate limited sampling strategies for achieving the therapeutic ranges of the area under the concentration-time curve (AUC) of vancomycin on the first and second day (AUC0-24 , AUC24-48 , respectively) of therapy. A virtual population of 1000 individuals was created using a population pharmacokinetic (PopPK) model, which was validated and incorporated into our model-informed precision dosing tool. The results were evaluated using six additional PopPK models selected based on a study design of prospective or retrospective data collection with sufficient concentrations. Bayesian forecasting was performed to evaluate the probability of achieving the therapeutic range of AUC, defined as a ratio of estimated/reference AUC within 0.8-1.2. The Bayesian posterior probability of achieving the AUC24-48 range increased from 51.3% (a priori probability) to 77.5% after using two-point sampling at the trough and peak on the first day. Sampling on the first day also yielded a higher Bayesian posterior probability (86.1%) of achieving the AUC0-24 range compared to the a priori probability of 60.1%. The Bayesian posterior probability of achieving the AUC at steady-state (AUCSS ) range by sampling on the first or second day decreased with decreased kidney function. We demonstrated that second-day trough and peak sampling provided accurate AUC24-48 , and first-day sampling may assist in rapidly achieving therapeutic AUC24-48 , although the AUCSS should be re-estimated in patients with reduced kidney function owing to its unreliable predictive performance.
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Affiliation(s)
- Kazutaka Oda
- Department of PharmacyKumamoto University HospitalKumamotoJapan
- Department of Infection ControlKumamoto University HospitalKumamotoJapan
| | - Tomoyuki Yamada
- Department of PharmacyOsaka Medical and Pharmaceutical University HospitalOsakaJapan
| | - Kazuaki Matsumoto
- Division of PharmacodynamicsKeio University Faculty of PharmacyTokyoJapan
| | - Yuki Hanai
- Department of Clinical Pharmacy, Faculty of Pharmaceutical SciencesToho UniversityChibaJapan
| | - Takashi Ueda
- Department of Infection Control and PreventionHyogo College of MedicineNishinomiyaHyogoJapan
| | - Masaru Samura
- Department of PharmacyYokohama General HospitalYokohamaKanagawaJapan
| | - Akari Shigemi
- Department of PharmacyKagoshima University HospitalKagoshima CityKagoshimaJapan
| | - Hirofumi Jono
- Department of PharmacyKumamoto University HospitalKumamotoJapan
| | - Hideyuki Saito
- Department of PharmacyKumamoto University HospitalKumamotoJapan
| | - Toshimi Kimura
- Department of PharmacyJuntendo University HospitalTokyoJapan
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Yoon S, Guk J, Lee SG, Chae D, Kim JH, Park K. Model-informed precision dosing in vancomycin treatment. Front Pharmacol 2023; 14:1252757. [PMID: 37876732 PMCID: PMC10593454 DOI: 10.3389/fphar.2023.1252757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/11/2023] [Indexed: 10/26/2023] Open
Abstract
Introduction: While vancomycin remains a widely prescribed antibiotic, it can cause ototoxicity and nephrotoxicity, both of which are concentration-associated. Overtreatment can occur when the treatment lasts for an unnecessarily long time. Using a model-informed precision dosing scheme, this study aims to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model for vancomycin to determine the optimal dosage regimen and treatment duration in order to avoid drug-induced toxicity. Methods: The data were obtained from electronic medical records of 542 patients, including 40 children, and were analyzed using NONMEM software. For PK, vancomycin concentrations were described with a two-compartment model incorporating allometry scaling. Results and discussion: This revealed that systemic clearance decreased with creatinine and blood urea nitrogen levels, history of diabetes and renal diseases, and further decreased in women. On the other hand, the central volume of distribution increased with age. For PD, C-reactive protein (CRP) plasma concentrations were described by transit compartments and were found to decrease with the presence of pneumonia. Simulations demonstrated that, given the model informed optimal doses, peak and trough concentrations as well as the area under the concentration-time curve remained within the therapeutic range, even at doses smaller than routine doses, for most patients. Additionally, CRP levels decreased more rapidly with the higher dose starting from 10 days after treatment initiation. The developed R Shiny application efficiently visualized the time courses of vancomycin and CRP concentrations, indicating its applicability in designing optimal treatment schemes simply based on visual inspection.
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Affiliation(s)
- Sukyong Yoon
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Brain Korea 21 Plus Project for Medical Science, Yonsei University, Seoul, Republic of Korea
| | - Jinju Guk
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Brain Korea 21 Plus Project for Medical Science, Yonsei University, Seoul, Republic of Korea
| | - Sang-Guk Lee
- Department of Laboratory Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dongwoo Chae
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jeong-Ho Kim
- Department of Laboratory Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyungsoo Park
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea
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de Klaver PAG, Keizer RJ, Ter Heine R, Smits L, Boekema PJ, Kuntzel I, Schaap T, de Vries A, Bloem K, Rispens T, Hoentjen F, Derijks LJJ. Early At-Home Measurement of Adalimumab Concentrations to Guide Anti-TNF Precision Dosing: A Pilot Study. Eur J Drug Metab Pharmacokinet 2023:10.1007/s13318-023-00835-7. [PMID: 37322238 DOI: 10.1007/s13318-023-00835-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/22/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Underdosing of adalimumab can result in non-response and poor disease control in patients with rheumatic disease or inflammatory bowel disease. In this pilot study we aimed to predict adalimumab concentrations with population pharmacokinetic model-based Bayesian forecasting early in therapy. METHODS Adalimumab pharmacokinetic models were identified with a literature search. A fit-for-purpose evaluation of the model was performed for rheumatologic and inflammatory bowel disease (IBD) patients with adalimumab peak (first dose) and trough samples (first and seventh dose) obtained by a volumetric absorptive microsampling technique. Steady state adalimumab concentrations were predicted after the first adalimumab administration. Predictive performance was calculated with mean prediction error (MPE) and normalised root mean square error (RMSE). RESULTS Thirty-six patients (22 rheumatologic and 14 IBD) were analysed in our study. After stratification for absence of anti-adalimumab antibodies, the calculated MPE was -2.6% and normalised RMSE 24.0%. Concordance between predicted and measured adalimumab serum concentrations falling within or outside the therapeutic window was 75%. Three patients (8.3%) developed detectable concentrations of anti-adalimumab antibodies. CONCLUSION This prospective study demonstrates that adalimumab concentrations at steady state can be predicted from early samples during the induction phase. CLINICAL TRIAL REGISTRATION The trial was registered in the Netherlands Trial Register with trial registry number NTR 7692 ( www.trialregister.nl ).
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Affiliation(s)
- Paul A G de Klaver
- Department of Clinical Pharmacy and Pharmacology, Máxima Medical Center, 5504 DB, Veldhoven, The Netherlands.
| | | | - Rob Ter Heine
- Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lisa Smits
- Department of Gastroenterology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Paul J Boekema
- Department of Gastroenterology, Máxima Medical Center, Veldhoven, The Netherlands
| | - Inge Kuntzel
- Department of Rheumatology, Máxima Medical Center, Eindhoven, The Netherlands
| | - Tiny Schaap
- Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The Netherlands
| | - Annick de Vries
- Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The Netherlands
| | - Karien Bloem
- Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The Netherlands
| | - Theo Rispens
- Biologics Laboratory, Sanquin Diagnostic Services, Amsterdam, The Netherlands
- Department of Immunopathology, Sanquin Research, Amsterdam, The Netherlands
- Landsteiner Laboratory, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
| | - Frank Hoentjen
- Department of Gastroenterology, Radboud University Medical Center, Nijmegen, The Netherlands
- Division of Gastroenterology, Department of Medicine, University of Alberta, Edmonton, Canada
| | - Luc J J Derijks
- Department of Clinical Pharmacy and Pharmacology, Máxima Medical Center, 5504 DB, Veldhoven, The Netherlands
- Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Center, Maastricht, The Netherlands
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Oda K, Saito H, Jono H. Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring. Pharmacol Ther 2023; 246:108433. [PMID: 37149156 DOI: 10.1016/j.pharmthera.2023.108433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/19/2023] [Accepted: 05/02/2023] [Indexed: 05/08/2023]
Abstract
As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.
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Affiliation(s)
- Kazutaka Oda
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto, Japan
| | - Hideyuki Saito
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto, Japan; Department of Clinical Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kumamoto University; 1-1-1, Honjo, Chuo-ku, Kumamoto, Japan
| | - Hirofumi Jono
- Department of Pharmacy, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-ku, Kumamoto, Japan; Department of Clinical Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kumamoto University; 1-1-1, Honjo, Chuo-ku, Kumamoto, Japan.
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Abouelkheir M, Almohaizeie A, Almutairi A, Almuhisen S, Alqahtani S, Alsultan A. Evaluation of vancomycin individualized model-based dosing approach in neonates. Pediatr Neonatol 2022; 64:327-334. [PMID: 36581523 DOI: 10.1016/j.pedneo.2022.10.006] [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: 08/05/2022] [Revised: 09/18/2022] [Accepted: 10/06/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Vancomycin is commonly used to treat methicillin-resistant staphylococcal infections in neonates. Consensus on its ideal dosing in neonates has not been achieved. Model-based dosing recently has evolved as an important tool to optimize vancomycin initial dosing. The aim of this is to evaluate a population pharmacokinetic model-based approach in achieving the vancomycin therapeutic target of an AUC0-24 400 as recommended by the recent IDSA treatment guidelines. This model was implemented as a simple Excel calculator to individualize and optimize vancomycin initial dosing in neonates. METHODS An Excel calculator was developed using a previously published population pharmacokinetic model in neonates. It was evaluated using retrospectively retrieved data. For each patient, the initial empiric dose was calculated using the proposed Excel model and the most widely used neonatal dosing references. The probability of achieving the target AUC0-24 of >400 mg h/L using the model-based method was calculated and compared with that of the empiric doses using other references. RESULTS This analysis included 225 neonates. The probability of achieving the target AUC0-24 >400 was 89% using our model-based approach compared with 11%-59% using tertiary neonatal dosing references (p < 0.01 for all comparisons). CONCLUSION These innovative personalized dosing calculators are promising to improve vancomycin initial dosing in neonates and are easily applicable in routine practices.
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Affiliation(s)
- Manal Abouelkheir
- Department of Clinical Pharmacy, College of Pharmacy, Misr International University, Cairo, Egypt
| | - Abdullah Almohaizeie
- Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Abdulrahman Almutairi
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia; Department of Pharmaceutical Care, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
| | - Sara Almuhisen
- Department of Clinical Pharmacy, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Saeed Alqahtani
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Abdullah Alsultan
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
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Chen A, Gupta A, Do DH, Nazer LH. Bayesian method application: Integrating mathematical modeling into clinical pharmacy through vancomycin therapeutic monitoring. Pharmacol Res Perspect 2022; 10:e01026. [PMID: 36398492 PMCID: PMC9672880 DOI: 10.1002/prp2.1026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 11/19/2022] Open
Abstract
The most recent consensus guidelines for dosing and monitoring vancomycin recommended the use of area-under-the-curve with Bayesian estimation for therapeutic monitoring. As this is a modern concept in the practice of clinical pharmacy, the main objective of this review is to introduce the fundamentals of Bayesian estimation and its mathematical application as it relates to vancomycin therapeutic drug monitoring. In addition, we aim to identify pharmacokinetic (PK) software programs that incorporate Bayesian estimation for vancomycin dosing and to describe the PK models utilized in those software programs for the adult population. Twelve software programs that utilize Bayesian estimation were identified, which included: Adult and Pediatric Kinetics, Best Dose, ClinCalc, DoseMeRx, ID-ODS, InsightRx, MwPharm++, NextDose, PrecisePK, TDMx, Tucuxi, and VancoCalc. The software programs varied in the population PK models used as the Bayesian a priori. With the presence of various vancomycin Bayesian software programs, it is important to choose those that utilize PK models reflective of the specific patient population.
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Affiliation(s)
- Ashley Chen
- University of CaliforniaSan DiegoCaliforniaUSA
| | - Anjum Gupta
- University of CaliforniaSan DiegoCaliforniaUSA,PreciseRx IncSan DiegoCaliforniaUSA
| | - Dylan Huy Do
- University of CaliforniaSan DiegoCaliforniaUSA,Canyon Crest AcademySan DiegoCaliforniaUSA
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Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill. Crit Care Res Pract 2022; 2022:7011376. [PMID: 36561549 PMCID: PMC9767744 DOI: 10.1155/2022/7011376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/02/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022] Open
Abstract
Purpose To assess the agreement in 24-hour area under the curve (AUC24) value estimates between commonly used vancomycin population pharmacokinetic models in the critically ill. Materials and Methods Adults admitted to intensive care who received intravenous vancomycin and had a serum vancomycin concentration available were included. AUC24 values were determined using Tucuxi (revision cd7bd7a8) for dosing intervals with a vancomycin concentration using three models (Goti 2018, Colin 2019, and Thomson 2009) previously evaluated in the critically ill. AUC24 values were categorized as subtherapeutic (<400 mg·h/L), therapeutic (400-600 mg·h/L), or toxic (>600 mg·h/L), assuming a minimum inhibitory concentration of 1 mg/L. AUC24 value categorization was compared across the three models and reported as percent agreement. Results Overall, 466 AUC24 values were estimated in 188 patients. Overall, 52%, 42%, and 47% of the AUC24 values were therapeutic for the Goti, Colin, and Thomson models, respectively. The agreement of AUC24 values between all three models was 48% (223/466), Goti-Colin 59% (193/466), Goti-Thomson 68% (318/466), and Colin-Thomson 67% (314/466). Conclusion In critically ill patients, vancomycin AUC24 values obtained from different pharmacokinetic models are often discordant, potentially contributing to differences in dosing decisions. This highlights the importance of selecting the optimal model.
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Evaluating and Improving Neonatal Gentamicin Pharmacokinetic Models Using Aggregated Routine Clinical Care Data. Pharmaceutics 2022; 14:pharmaceutics14102089. [PMID: 36297524 PMCID: PMC9609639 DOI: 10.3390/pharmaceutics14102089] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 09/03/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
Model-informed precision dosing (MIPD) can aid dose decision-making for drugs such as gentamicin that have high inter-individual variability, a narrow therapeutic window, and a high risk of exposure-related adverse events. However, MIPD in neonates is challenging due to their dynamic development and maturation and by the need to minimize blood sampling due to low blood volume. Here, we investigate the ability of six published neonatal gentamicin population pharmacokinetic models to predict gentamicin concentrations in routine therapeutic drug monitoring from nine sites in the United State (n = 475 patients). We find that four out of six models predicted with acceptable levels of error and bias for clinical use. These models included known important covariates for gentamicin PK, showed little bias in prediction residuals over covariate ranges, and were developed on patient populations with similar covariate distributions as the one assessed here. These four models were refit using the published parameters as informative Bayesian priors or without priors in a continuous learning process. We find that refit models generally reduce error and bias on a held-out validation data set, but that informative prior use is not uniformly advantageous. Our work informs clinicians implementing MIPD of gentamicin in neonates, as well as pharmacometricians developing or improving PK models for use in MIPD.
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11
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Aguiar Zdovc J, Vaupotič M, Marolt G, Knez L, Režonja Kukec R, Čufer T, Vovk T, Grabnar I. Population pharmacokinetics of cisplatin in small cell lung cancer patients guided with informative priors. Cancer Chemother Pharmacol 2022; 90:301-313. [DOI: 10.1007/s00280-022-04465-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/08/2022] [Indexed: 11/02/2022]
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Goyal RK, Moffett BS, Gobburu JVS, Al Mohajer M. Population Pharmacokinetics of Vancomycin in Pregnant Women. Front Pharmacol 2022; 13:873439. [PMID: 35734401 PMCID: PMC9207242 DOI: 10.3389/fphar.2022.873439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 05/23/2022] [Indexed: 11/22/2022] Open
Abstract
Objective: Vancomycin is a glycopeptide antibacterial indicated for serious gram-positive infections. Pharmacokinetics (PK) of vancomycin have not been described in pregnant women. This study aims to characterize the PK disposition of vancomycin in pregnant women based on data acquired from a database of routine hospital care for therapeutic drug monitoring to better inform dosing decisions. Methods: In this study, plasma drug concentration data from 34 pregnant hospitalized women who were administered intravenous vancomycin was analyzed. A population pharmacokinetic (PPK) model was developed using non-linear mixed effects modeling. Model selection was based on statistical criterion, graphical analysis, and physiologic relevance. Using the final model AUC0-24 (PK efficacy index of vancomycin) was compared with non-pregnant population. Results: Vancomycin PK in pregnant women were best described by a two-compartment model with first-order elimination and the following parameters: clearance (inter individual variability) of 7.64 L/hr (32%), central volume of 67.35 L, inter-compartmental clearance of 9.06 L/h, and peripheral volume of 37.5 L in a typical patient with 175 ml/min creatinine clearance (CRCL) and 45 kg fat-free mass (FFM). The calculated geometric mean of AUC0-24 for the pregnant population was 223 ug.h/ ml and 226 ug.h/ ml for the non-pregnant population. Conclusion: Our analysis suggests that vancomycin PK in pregnant women is consistent with non-pregnant adults and the dosing regimens used for non-pregnant patients may also be applicable to pregnant patients.
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Affiliation(s)
| | - Brady S. Moffett
- Texas Children’s Hospital, Houston, TX, United States
- Baylor College of Medicine, Houston, TX, United States
| | - Jogarao V. S. Gobburu
- University of Maryland, Baltimore, MD, United States
- *Correspondence: Jogarao V. S. Gobburu,
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Uster DW, Wicha SG. Optimized sampling to estimate vancomycin drug exposure: Comparison of pharmacometric and equation-based approaches in a simulation-estimation study. CPT Pharmacometrics Syst Pharmacol 2022; 11:711-720. [PMID: 35259285 PMCID: PMC9197536 DOI: 10.1002/psp4.12782] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 02/24/2022] [Accepted: 02/25/2022] [Indexed: 12/31/2022] Open
Abstract
Vancomycin dosing should be accompanied by area under the concentration‐time curve (AUC)–guided dosing using model‐informed precision dosing software according to the latest guidelines. Although a peak plus a trough sample is considered the gold standard to determine the AUC, single‐sample strategies might be more economic. Yet, optimal sampling times for AUC determination of vancomycin have not been systematically evaluated. In the present study, automated one‐ or two‐sample strategies were systematically explored to estimate the AUC with a model averaging and a model selection algorithm. Both were compared with a conventional equation‐based approach in a simulation‐estimation study mimicking a heterogenous patient population (n = 6000). The optimal single‐sample timepoints were identified between 2–6.5 h post dose, with varying bias values between −2.9% and 1.0% and an imprecision of 23.3%–24.0% across the population pharmacokinetic approaches. Adding a second sample between 4.5–6.0 h improved the predictive performance (−1.7% to 0.0% bias, 17.6%–18.6% imprecision), although the difference in the two‐sampling strategies were minor. The equation‐based approach was always positively biased and hence inferior to the population pharmacokinetic approaches. In conclusion, the approaches always preferred samples to be drawn early in the profile (<6.5 h), whereas sampling of trough concentrations resulted in a higher imprecision. Furthermore, optimal sampling during the early treatment phase could already give sufficient time to individualize the second dose, which is likely unfeasible using trough sampling.
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Affiliation(s)
- David W Uster
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
| | - Sebastian G Wicha
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
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14
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Aljutayli A, Thirion DJG, Bonnefois G, Nekka F. Pharmacokinetic equations versus Bayesian guided vancomycin monitoring: Pharmacokinetic model and model-informed precision dosing trial simulations. Clin Transl Sci 2022; 15:942-953. [PMID: 35170243 PMCID: PMC9010252 DOI: 10.1111/cts.13210] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/04/2021] [Accepted: 11/20/2021] [Indexed: 02/01/2023] Open
Abstract
The recently released revised vancomycin consensus guideline endorsed area under the concentration‐time curve (AUC) guided monitoring. Means to AUC‐guided monitoring include pharmacokinetic (PK) equations and Bayesian software programs, with the latter approach being preferable. We aimed to evaluate the predictive performance of these two methods when monitoring using troughs or peaks and troughs at varying single or mixed dosing intervals (DIs), and evaluate the significance of satisfying underlying assumptions of steady‐state and model transferability. Methods included developing a vancomycin population PK model and conducting model‐informed precision dosing clinical trial simulations. A one‐compartment PK model with linear elimination, exponential between‐subject variability, and mixed (additive and proportional) residual error model resulted in the best model fit. Conducted simulations demonstrated that Bayesian‐guided AUC can, potentially, outperform that of equation‐based AUC predictions depending on the quality of model diagnostics and met assumptions. Ideally, Bayesian‐guided AUC predictive performance using a trough from the first DI was equivalent to that of PK equations using two measurements (peak and trough) from the fifth DI. Model transferability diagnostics can guide the selection of Bayesian priors but are not strong indicators of predictive performance. Mixed versus single fourth and/or fifth DI sampling seems indifferent. This study illustrated cases associated with the most reliable AUC predictions and showed that only proper Bayesian‐guided monitoring is always faster and more reliable than equations‐guided monitoring in pre‐steady‐state DIs in the absence of a loading dose. This supports rapid Bayesian monitoring using data as sparse and early as a trough at the first DI.
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Affiliation(s)
- Abdullah Aljutayli
- Faculty of Pharmacy, Université de Montréal, Montréal, Quebec, Canada.,Department of Pharmaceutics, Faculty of Pharmacy, Qassim University, Buraydah, Saudi Arabia
| | - Daniel J G Thirion
- Faculty of Pharmacy, Université de Montréal, Montréal, Quebec, Canada.,Department of Pharmacy, McGill University Health Center, Montréal, Quebec, Canada
| | | | - Fahima Nekka
- Department of Pharmacy, McGill University Health Center, Montréal, Quebec, Canada.,Laboratoire de Pharmacométrie, Faculté de Pharmacie, Université de Montréal, Montréal, Quebec, Canada.,Centre de recherches mathématiques, Université de Montréal, Montréal, Quebec, Canada
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15
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Hanai Y, Takahashi Y, Niwa T, Mayumi T, Hamada Y, Kimura T, Matsumoto K, Fujii S, Takesue Y. Clinical practice guidelines for therapeutic drug monitoring of teicoplanin: a consensus review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring. J Antimicrob Chemother 2022; 77:869-879. [PMID: 35022752 PMCID: PMC8969460 DOI: 10.1093/jac/dkab499] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Background Owing to its low risk of adverse effects, teicoplanin has been extensively used in patients with infections caused by MRSA. To promote the better management of patients receiving teicoplanin, we have updated the guidelines for therapeutic drug monitoring (TDM). Methods The guidelines were developed by a committee following the methodology handbook published by the Japanese Medical Information Distribution Service. Nine clinical questions were selected. The committee conducted a systematic review and meta-analysis to establish evidence-based recommendations for the target trough concentration (Cmin). An initial electronic database search returned 515 articles, and 97 articles qualified for a full review. Four and five studies were included for the efficacy evaluation of cut-off Cmin values of 15 and 20 mg/L, respectively. Results Compared with Cmin < 15 mg/L, a target Cmin value of 15–30 mg/L resulted in increased clinical efficacy in patients with non-complicated MRSA infections (OR = 2.68; 95% CI = 1.14–6.32) without an increase in adverse effects. Although there was insufficient evidence, target Cmin values of 20–40 mg/L were suggested in patients with complicated or serious MRSA infections. A 3 day loading regimen followed by maintenance treatment according to renal function was recommended to achieve the target trough concentrations. Because of the prolonged half-life of teicoplanin, measurement of the Cmin value on Day 4 before reaching steady state was recommended. Conclusions The new guideline recommendations indicate the target Cmin value for TDM and the dosage regimen to achieve this concentration and suggest practices for specific subpopulations.
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Affiliation(s)
- Yuki Hanai
- Department of Pharmacy, Toho University Omori Medical Center, Tokyo, Japan
| | - Yoshiko Takahashi
- Department of Pharmacy, Hyogo College of Medicine, Nishinomiya, Japan
| | - Takashi Niwa
- Department of Pharmacy, Gifu University Hospital, Gifu, Japan
| | - Toshihiko Mayumi
- Department of Emergency Medicine, School of Medicine, University of Occupational and Environmental Health, Fukuoka, Japan
| | - Yukihiro Hamada
- Department of Pharmacy, Tokyo Women's Medical University Hospital, Tokyo, Japan
| | - Toshimi Kimura
- Department of Pharmacy, Tokyo Women's Medical University Hospital, Tokyo, Japan
| | - Kazuaki Matsumoto
- Division of Pharmacodynamics, Keio University Faculty of Pharmacy, Tokyo, Japan
| | - Satoshi Fujii
- Department of Hospital Pharmacy, Sapporo Medical University Hospital, Hokkaido, Japan
| | - Yoshio Takesue
- Department of Infection Control and Prevention, Hyogo College of Medicine, Nishinomiya, Japan
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16
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Maier C, de Wiljes J, Hartung N, Kloft C, Huisinga W. A continued learning approach for model-informed precision dosing: updating models in clinical practice. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 11:185-198. [PMID: 34779144 PMCID: PMC8846635 DOI: 10.1002/psp4.12745] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/28/2021] [Accepted: 10/28/2021] [Indexed: 11/12/2022]
Abstract
Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to include also altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, since only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step towards building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.
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Affiliation(s)
- Corinna Maier
- Institute of Mathematics, University of Potsdam, Germany.,Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling, Freie Universität Berlin and University of Potsdam, Germany
| | - Jana de Wiljes
- Institute of Mathematics, University of Potsdam, Germany
| | - Niklas Hartung
- Institute of Mathematics, University of Potsdam, Germany
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Germany
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17
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Smith NM, Chan A, Wilkinson LA, Chua HC, Nguyen TD, de Souza H, Shah AP, D'Argenio DZ, Mergenhagen KA. Open-source maximum a posteriori-bayesian dosing AdDS to current therapeutic drug monitoring: Adapting to the era of individualized therapy. Pharmacotherapy 2021; 41:953-963. [PMID: 34618919 DOI: 10.1002/phar.2631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 09/15/2021] [Accepted: 09/20/2021] [Indexed: 11/07/2022]
Abstract
Recent updates in the therapeutic drug monitoring (TDM) guidelines for vancomycin have rekindled interest in maximum a posteriori-Bayesian (MAP-Bayesian) estimation of patient-specific pharmacokinetic parameters. To create a versatile infrastructure for MAP-Bayesian dosing of vancomycin or other drugs, a freely available, R-based software package, Advanced Dosing Solutions (AdDS), was created to facilitate clinical implementation of these improved TDM methods. The objective of this study was to utilize AdDS for pre- and post-processing of data in order to streamline the therapeutic management of vancomycin in healthy and obese veterans. Patients from a local Veteran Affairs hospital were utilized to compare the process of full re-estimation versus Bayesian updating of priors on healthy adult and obese patient populations for use with AdDS. Twenty-four healthy veterans were utilized to train (14/24) and test (10/24) the base pharmacokinetic model of vancomycin while comparing the effects of updated and fully re-estimated priors. This process was repeated with a total of 18 obese veterans for both training (11/18) and testing (7/18). Comparison of MAP objective function between the original and re-estimated models for healthy adults indicated that 78.6% of the subjects in the training and 70.0% of the subjects in the testing datasets had similar or improved predictions by the re-estimated model. For obese veterans, 81.8% of subjects in the training dataset and 85.7% of subjects in the testing dataset had similar or improved predictions. Re-estimation of model parameters provided more significant improvements in objective function compared with Bayesian updating, which may be a useful strategy in cases where sufficient samples and subjects are available. The generation of bespoke regimens based on patient-specific clearance and minimal sampling may improve patient care by addressing fundamental pharmacokinetic differences in healthy and obese veteran populations.
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Affiliation(s)
- Nicholas M Smith
- School of Pharmacy & Pharmaceutical Sciences, University at Buffalo, Buffalo, New York, USA
- New York State Center for Excellence in Bioinformatics and Life Sciences, Buffalo, New York, USA
| | - Arthur Chan
- Veterans Affair Hospital of Western New York, New York, New York, USA
| | - Laura A Wilkinson
- Veterans Affair Hospital of Western New York, New York, New York, USA
| | - Hubert C Chua
- CHI Baylor St. Luke's Medical Center, Houston, Texas, USA
| | - Thomas D Nguyen
- School of Pharmacy & Pharmaceutical Sciences, University at Buffalo, Buffalo, New York, USA
- New York State Center for Excellence in Bioinformatics and Life Sciences, Buffalo, New York, USA
| | - Harriet de Souza
- School of Pharmacy & Pharmaceutical Sciences, University at Buffalo, Buffalo, New York, USA
- New York State Center for Excellence in Bioinformatics and Life Sciences, Buffalo, New York, USA
| | - Anant P Shah
- School of Pharmacy & Pharmaceutical Sciences, University at Buffalo, Buffalo, New York, USA
- New York State Center for Excellence in Bioinformatics and Life Sciences, Buffalo, New York, USA
| | - David Z D'Argenio
- Biomedical Simulations Resource, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
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18
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New Ways to Skin a Cat or Still a Cat Chasing Its Tail? Bayesian Vancomycin Monitoring in the ICU. Crit Care Med 2021; 49:1844-1847. [PMID: 34529619 DOI: 10.1097/ccm.0000000000005121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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19
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Hughes JH, Keizer RJ. A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1150-1160. [PMID: 34270885 PMCID: PMC8520755 DOI: 10.1002/psp4.12684] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/18/2021] [Accepted: 07/02/2021] [Indexed: 12/19/2022]
Abstract
Model‐informed precision dosing (MIPD) approaches typically apply maximum a posteriori (MAP) Bayesian estimation to determine individual pharmacokinetic (PK) parameters with the goal of optimizing future dosing regimens. This process combines knowledge about the individual, in the form of drug levels or pharmacodynamic biomarkers, with prior knowledge of the drug PK in the general population. Use of “flattened priors” (FPs), in which the weight of the model priors is reduced relative to observations about the patient, has been previously proposed to estimate individual PK parameters in instances where the patient is poorly described by the PK model. However, little is known about the predictive performance of FPs and when to apply FPs in MIPD. Here, FP is evaluated in a data set of 4679 adult patients treated with vancomycin. Depending on the PK model, prediction error could be reduced by applying FPs in 42–55% of PK parameter estimations. Machine learning (ML) models could identify instances where FPs would outperform MAPs with a specificity of 81–86%, reducing overall root mean squared error (RMSE) of PK model predictions by 12–22% (0.5–1.2 mg/L) relative to MAP alone. The factors most indicative of the use of FPs were past prediction residuals and bias in past PK predictions. A more clinically practical minimal model was developed using only these two features, reducing RMSE by 5–18% (0.20–0.93 mg/L) relative to MAP. This hybrid ML/PK approach advances the precision dosing toolkit by leveraging the power of ML while maintaining the mechanistic insight and interpretability of PK models.
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20
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Narayan SW, Thoma Y, Drennan PG, Yejin Kim H, Alffenaar JW, Van Hal S, Patanwala AE. Predictive Performance of Bayesian Vancomycin Monitoring in the Critically Ill. Crit Care Med 2021; 49:e952-e960. [PMID: 33938713 DOI: 10.1097/ccm.0000000000005062] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES It is recommended that therapeutic monitoring of vancomycin should be guided by 24-hour area under the curve concentration. This can be done via Bayesian models in dose-optimization software. However, before these models can be incorporated into clinical practice in the critically ill, their predictive performance needs to be evaluated. This study assesses the predictive performance of Bayesian models for vancomycin in the critically ill. DESIGN Retrospective cohort study. SETTING Single-center ICU. PATIENTS Data were obtained for all patients in the ICU between 1 January, and 31 May 2020, who received IV vancomycin. The predictive performance of three Bayesian models were evaluated based on their availability in commercially available software. Predictive performance was assessed via bias and precision. Bias was measured as the mean difference between observed and predicted vancomycin concentrations. Precision was measured as the SD of bias, root mean square error, and 95% limits of agreement based on Bland-Altman plots. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS A total of 466 concentrations from 188 patients were used to evaluate the three models. All models showed low bias (-1.7 to 1.8 mg/L), which was lower with a posteriori estimate (-0.7 to 1.8 mg/L). However, all three models showed low precision in terms of SD (4.7-8.8 mg/L) and root mean square error (4.8-8.9 mg/L). The models underpredicted at higher observed vancomycin concentrations (bias 0.7-3.2 mg/L for < 20 mg/L; -5.1 to -2.3 for ≥ 20 mg/L) and the Bland-Altman plots showed a great deviation between observed and predicted concentrations. CONCLUSIONS Bayesian models of vancomycin show not only low bias, but also low precision in the critically ill. Thus, Bayesian-guided dosing of vancomycin in this population should be used cautiously.
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Affiliation(s)
- Sujita W Narayan
- 1 The University of Sydney, Faculty of Medicine and Health, School of Pharmacy, Sydney, NSW, Australia. 2 Reconfigurable and Embedded Digital Systems Institute, School of Business and Engineering Vaud, University of Applied Sciences Western Switzerland, Yverdon-les-Bains, Switzerland. 3 Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom. 4 Westmead Hospital, Westmead, NSW, Australia. 5 Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, NSW, Australia. 6 New South Wales Health Pathology, Department of Infectious Diseases and Microbiology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia. 7 Royal Prince Alfred Hospital, Sydney, NSW, Australia
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21
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Evaluation of the MeroRisk Calculator, A User-Friendly Tool to Predict the Risk of Meropenem Target Non-Attainment in Critically Ill Patients. Antibiotics (Basel) 2021; 10:antibiotics10040468. [PMID: 33924047 PMCID: PMC8074046 DOI: 10.3390/antibiotics10040468] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The MeroRisk-calculator, an easy-to-use tool to determine the risk of meropenem target non-attainment after standard dosing (1000 mg; q8h), uses a patient's creatinine clearance and the minimum inhibitory concentration (MIC) of the pathogen. In clinical practice, however, the MIC is rarely available. The objectives were to evaluate the MeroRisk-calculator and to extend risk assessment by including general pathogen sensitivity data. METHODS Using a clinical routine dataset (155 patients, 891 samples), a direct data-based evaluation was not feasible. Thus, in step 1, the performance of a pharmacokinetic model was determined for predicting the measured concentrations. In step 2, the PK model was used for a model-based evaluation of the MeroRisk-calculator: risk of target non-attainment was calculated using the PK model and agreement with the MeroRisk-calculator was determined by a visual and statistical (Lin's concordance correlation coefficient (CCC)) analysis for MIC values 0.125-16 mg/L. The MeroRisk-calculator was extended to include risk assessment based on EUCAST-MIC distributions and cumulative-fraction-of-response analysis. RESULTS Step 1 showed a negligible bias of the PK model to underpredict concentrations (-0.84 mg/L). Step 2 revealed a high level of agreement between risk of target non-attainment predictions for creatinine clearances >50 mL/min (CCC = 0.990), but considerable deviations for patients <50 mL/min. For 27% of EUCAST-listed pathogens the median cumulative-fraction-of-response for the observed patients receiving standard dosing was < 90%. CONCLUSIONS The MeroRisk-calculator was successfully evaluated: For patients with maintained renal function it allows a reliable and user-friendly risk assessment. The integration of pathogen-based risk assessment substantially increases the applicability of the tool.
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22
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Hughes JH, Tong DMH, Lucas SS, Faldasz JD, Goswami S, Keizer RJ. Continuous Learning in Model-Informed Precision Dosing: A Case Study in Pediatric Dosing of Vancomycin. Clin Pharmacol Ther 2020; 109:233-242. [PMID: 33068298 PMCID: PMC7839485 DOI: 10.1002/cpt.2088] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/05/2020] [Indexed: 12/21/2022]
Abstract
Model‐informed precision dosing (MIPD) leverages pharmacokinetic (PK) models to tailor dosing to an individual patient’s needs, improving attainment of therapeutic drug exposure targets and thus potentially improving drug efficacy or reducing adverse events. However, selection of an appropriate model for supporting clinical decision making is not trivial. Error or bias in dose selection may arise if the selected model was developed in a population not fully representative of the intended MIPD population. One previously proposed approach is continuous learning, in which an initial model is used in MIPD and then updated as additional data becomes available. In this case study of pediatric vancomycin MIPD, the potential benefits of the continuous learning approach are investigated. Five previously published models were evaluated and found to perform adequately in a data set of 273 pediatric patients in the intensive care unit. Additionally, two predefined simple PK models were fitted on separate populations of 50–350 patients in an approach mimicking clinical implementation of automated continuous learning. With these continuous learning models, prediction error using population PK parameters could be reduced by 2–13% compared with previously published models. Sample sizes of at least 200 patients were found suitable for capturing the interindividual variability in vancomycin at this institution, with limited benefits of larger data sets. Although comprised mostly of trough samples, these sparsely sampled routine clinical data allowed for reasonable estimation of simulated area under the curve (AUC). Together, these findings lay the foundations for a continuous learning MIPD approach.
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Affiliation(s)
| | | | - Sarah Scarpace Lucas
- Department of Clinical Pharmacy, UCSF Medical Center, University of California, San Francisco, San Francisco, California, USA
| | | | | | - Ron J Keizer
- Department of Clinical Pharmacy, UCSF Medical Center, University of California, San Francisco, San Francisco, California, USA
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23
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Uster DW, Stocker SL, Carland JE, Brett J, Marriott DJE, Day RO, Wicha SG. A Model Averaging/Selection Approach Improves the Predictive Performance of Model-Informed Precision Dosing: Vancomycin as a Case Study. Clin Pharmacol Ther 2020; 109:175-183. [PMID: 32996120 DOI: 10.1002/cpt.2065] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 09/12/2020] [Indexed: 11/10/2022]
Abstract
Many important drugs exhibit substantial variability in pharmacokinetics and pharmacodynamics leading to a loss of the desired clinical outcomes or significant adverse effects. Forecasting drug exposures using pharmacometric models can improve individual target attainment when compared with conventional therapeutic drug monitoring (TDM). However, selecting the "correct" model for this model-informed precision dosing (MIPD) is challenging. We derived and evaluated a model selection algorithm (MSA) and a model averaging algorithm (MAA), which automates model selection and finds the best model or combination of models for each patient using vancomycin as a case study, and implemented both algorithms in the MIPD software "TDMx." The predictive performance (based on accuracy and precision) of the two algorithms was assessed in (i) a simulation study of six distinct populations and (ii) a clinical dataset of 180 patients undergoing TDM during vancomycin treatment and compared with the performance obtained using a single model. Throughout the six virtual populations the MSA and MAA (imprecision: 9.9-24.2%, inaccuracy: less than ± 8.2%) displayed more accurate predictions than the single models (imprecision: 8.9-51.1%; inaccuracy: up to 28.9%). In the clinical dataset, the predictive performance of the single models applying at least one plasma concentration varied substantially (imprecision: 28-62%, inaccuracy: -16 to 25%), whereas the MSA or MAA utilizing these models simultaneously resulted in unbiased and precise predictions (imprecision: 29% and 30%, inaccuracy: -5% and 0%, respectively). MSA and MAA approaches implemented in TDMx might thereby lower the burden of fit-for-purpose validation of individual models and streamline MIPD.
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Affiliation(s)
- David W Uster
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
| | - Sophie L Stocker
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, New South Wales, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Jane E Carland
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, New South Wales, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Jonathan Brett
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, New South Wales, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Deborah J E Marriott
- St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,Department of Clinical Microbiology and Infectious Diseases, St. Vincent's Hospital, Sydney, New South Wales, Australia
| | - Richard O Day
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, New South Wales, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Sebastian G Wicha
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Hamburg, Hamburg, Germany
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24
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Ter Heine R, Keizer RJ, van Steeg K, Smolders EJ, van Luin M, Derijks HJ, de Jager CPC, Frenzel T, Brüggemann R. Prospective validation of a model-informed precision dosing tool for vancomycin in intensive care patients. Br J Clin Pharmacol 2020; 86:2497-2506. [PMID: 32415710 PMCID: PMC7688533 DOI: 10.1111/bcp.14360] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 04/24/2020] [Accepted: 05/01/2020] [Indexed: 02/04/2023] Open
Abstract
AIMS Vancomycin is an important antibiotic for critically ill patients with Gram-positive bacterial infections. Critically ill patients typically have severely altered pathophysiology, which leads to inefficacy or toxicity. Model-informed precision dosing may aid in optimizing the dose, but prospectively validated tools are not available for this drug in these patients. We aimed to prospectively validate a population pharmacokinetic model for purpose model-informed precision dosing of vancomycin in critically ill patients. METHODS We first performed a systematic evaluation of various models on retrospectively collected pharmacokinetic data in critically ill patients and then selected the best performing model. This model was implemented in the Insight Rx clinical decision support tool and prospectively validated in a multicentre study in critically ill patients. The predictive performance was obtained as mean prediction error and relative root mean squared error. RESULTS We identified 5 suitable population pharmacokinetic models. The most suitable model was carried forward to a prospective validation. We found in a prospective multicentre study that the selected model could accurately and precisely predict the vancomycin pharmacokinetics based on a previous measurement, with a mean prediction error and relative root mean squared error of respectively 8.84% (95% confidence interval 5.72-11.96%) and 19.8% (95% confidence interval 17.47-22.13%). CONCLUSION Using a systematic approach, with a retrospective evaluation and prospective verification we showed the suitability of a model to predict vancomycin pharmacokinetics for purposes of model-informed precision dosing in clinical practice. The presented methodology may serve a generic approach for evaluation of pharmacometric models for the use of model-informed precision dosing in the clinic.
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Affiliation(s)
- Rob Ter Heine
- Radboud Institute for Health Sciences, Department of Pharmacy, Radboud university medical center, Nijmegen, The Netherlands
| | | | - Krista van Steeg
- Department of Clinical Pharmacy, Ziekenhuisgroep Twente, Almelo, The Netherlands
| | - Elise J Smolders
- Department of Pharmacy, Isala Hospital, Zwolle, The Netherlands & Department of Pharmacy, Radboud University Medical Center, Radboud Institute of Health Sciences, Nijmegen, The Netherlands
| | - Matthijs van Luin
- Department of Clinical Pharmacy, Rijnstate Hospital, Arnhem, The Netherlands
| | - Hieronymus J Derijks
- Department of Pharmacy, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands/Department of Pharmacy, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Cornelis P C de Jager
- Department of Intensive Care Medicine, Jeroen Bosch Hospital, 's-Hertogenbosch, the Netherlands
| | - Tim Frenzel
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Roger Brüggemann
- Radboud Institute for Health Sciences, Department of Pharmacy, Radboud university medical center, Nijmegen, The Netherlands
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