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Journal articles on the topic 'Model-informed precision dosing'

Author: Grafiati

Published: 30 June 2021

Last updated: 7 February 2022

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1

Ribba, Benjamin, Sherri Dudal, Thierry Lavé, and Richard W. Peck. "Model‐Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing." Clinical Pharmacology & Therapeutics 107, no. 4 (April 2020): 853–57. http://dx.doi.org/10.1002/cpt.1777.

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2

Polasek, Thomas M., Sepehr Shakib, and Amin Rostami-Hodjegan. "Precision medicine technology reality not hype - The example of model-informed precision dosing." F1000Research 8 (December 17, 2019): 1709. http://dx.doi.org/10.12688/f1000research.20489.2.

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Novel technologies labelled as ‘precision medicine’ are targeting all aspects of clinical care. Whilst some technological advances are undeniably exciting, many doctors at the frontline of healthcare view precision medicine as being out of reach for their patients. Model-informed precision dosing (MIPD) is a precision medicine technology that predicts drug concentrations and drug responses based on individual patient characteristics. In this opinion piece, the example of MIPD is used to illustrate eight features of a precision medicine technology less likely to be hyperbole and more likely to improve patient care. Positive features in this regard include: (1) fitting the definition of ‘precision medicine’; (2) addressing a major clinical problem that negatively impacts patient care; (3) a track record of high-quality medical science published via peer-reviewed literature; (4) well-defined clinical cases for application; (5) quality evidence of benefits measured by various clinical, patient and health economic endpoints; (6) strong economic drivers; (7) user friendliness, including easy integration into clinical workflow, and (8) recognition of importance by patients and their endorsement for broader clinical use. Barriers raised by critics of the approach are given to balance the view. The value of MIPD will be decided ultimately by the extent to which it can improve cost-effective patient care.

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Euteneuer, Joshua C., Suyog Kamatkar, Tsuyoshi Fukuda, Alexander A. Vinks, and Henry T. Akinbi. "Suggestions for Model-Informed Precision Dosing to Optimize Neonatal Drug Therapy." Journal of Clinical Pharmacology 59, no. 2 (September 11, 2018): 168–76. http://dx.doi.org/10.1002/jcph.1315.

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4

Keizer, Ron J., Rob ter Heine, Adam Frymoyer, Lawrence J. Lesko, Ranvir Mangat, and Srijib Goswami. "Model-Informed Precision Dosing at the Bedside: Scientific Challenges and Opportunities." CPT: Pharmacometrics & Systems Pharmacology 7, no. 12 (October 16, 2018): 785–87. http://dx.doi.org/10.1002/psp4.12353.

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5

Polasek, Thomas M., and Amin Rostami‐Hodjegan. "Virtual Twins: Understanding the Data Required for Model‐Informed Precision Dosing." Clinical Pharmacology & Therapeutics 107, no. 4 (April 2020): 742–45. http://dx.doi.org/10.1002/cpt.1778.

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6

Wright, Daniel F. B., Jennifer H. Martin, and Serge Cremers. "Spotlight Commentary: Model‐informed precision dosing must demonstrate improved patient outcomes." British Journal of Clinical Pharmacology 85, no. 10 (August 9, 2019): 2238–40. http://dx.doi.org/10.1111/bcp.14050.

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7

Lesko, Lawrence J. "K1 - Model-informed precision dosing at the bedside: Challenges and opportunities." Drug Metabolism and Pharmacokinetics 35, no. 1 (2020): S5. http://dx.doi.org/10.1016/j.dmpk.2020.04.286.

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Wicha, Sebastian G., Anne‐Grete Märtson, Elisabet I. Nielsen, Birgit C. P. Koch, Lena E. Friberg, Jan‐Willem Alffenaar, and Iris K. Minichmayr. "From Therapeutic Drug Monitoring to Model‐Informed Precision Dosing for Antibiotics." Clinical Pharmacology & Therapeutics 109, no. 4 (March 16, 2021): 928–41. http://dx.doi.org/10.1002/cpt.2202.

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9

Hughes, Jasmine H., Dominic M. H. Tong, Sarah Scarpace Lucas, Jonathan D. Faldasz, Srijib Goswami, and Ron J. Keizer. "Continuous Learning in Model‐Informed Precision Dosing: A Case Study in Pediatric Dosing of Vancomycin." Clinical Pharmacology & Therapeutics 109, no. 1 (November 21, 2020): 233–42. http://dx.doi.org/10.1002/cpt.2088.

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10

Tang, Zhe, Jing Guan, Jingjing Li, Yanxia Yu, Miao Qian, Jing Cao, Weiwei Shuai, and Zheng Jiao. "Determination of vancomycin exposure target and individualised dosing recommendations for neonates: model-informed precision dosing." International Journal of Antimicrobial Agents 57, no. 3 (March 2021): 106300. http://dx.doi.org/10.1016/j.ijantimicag.2021.106300.

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11

Polasek, Thomas M., Carl M. J. Kirkpatrick, and Amin Rostami-Hodjegan. "Precision dosing to avoid adverse drug reactions." Therapeutic Advances in Drug Safety 10 (January 2019): 204209861989414. http://dx.doi.org/10.1177/2042098619894147.

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Adverse drug reactions (ADRs) have traditionally been managed by trial and error, adjusting drug and dose selection reactively following patient harm. With an improved understanding of ADRs, and the patient characteristics that increase susceptibility, precision medicine technologies enable a proactive approach to ADRs and support clinicians to change prescribing accordingly. This commentary revisits the famous pharmacology–toxicology continuum first postulated by Paracelsus 500 years ago and explains why precision dosing is needed to help avoid ADRs in modern clinical practice. Strategies on how to improve precision dosing are given, including more research to establish better precision dosing targets in the cases of greatest need, easier access to dosing instructions via e-prescribing, improved monitoring of patients with novel biomarkers of drug response, and further application of model-informed precision dosing.

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12

Maier, Corinna, Niklas Hartung, Charlotte Kloft, Wilhelm Huisinga, and Jana Wiljes. "Reinforcement learning and Bayesian data assimilation for model‐informed precision dosing in oncology." CPT: Pharmacometrics & Systems Pharmacology 10, no. 3 (March 2021): 241–54. http://dx.doi.org/10.1002/psp4.12588.

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13

Darwich, Adam S., Thomas M. Polasek, Jeffrey K. Aronson, Kayode Ogungbenro, Daniel F. B. Wright, Brahim Achour, Jean-Luc Reny, et al. "Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy." Annual Review of Pharmacology and Toxicology 61, no. 1 (January 6, 2021): 225–45. http://dx.doi.org/10.1146/annurev-pharmtox-033020-113257.

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Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

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14

Polasek, Thomas M., Craig R. Rayner, Richard W. Peck, Andrew Rowland, Holly Kimko, and Amin Rostami‐Hodjegan. "Toward Dynamic Prescribing Information: Codevelopment of Companion Model‐Informed Precision Dosing Tools in Drug Development." Clinical Pharmacology in Drug Development 8, no. 4 (November 30, 2018): 418–25. http://dx.doi.org/10.1002/cpdd.638.

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15

Kluwe, Franziska, Robin Michelet, Anna Mueller‐Schoell, Corinna Maier, Lena Klopp‐Schulze, Madelé Dyk, Gerd Mikus, Wilhelm Huisinga, and Charlotte Kloft. "Perspectives on Model‐Informed Precision Dosing in the Digital Health Era: Challenges, Opportunities, and Recommendations." Clinical Pharmacology & Therapeutics 109, no. 1 (October 17, 2020): 29–36. http://dx.doi.org/10.1002/cpt.2049.

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16

Heine, Rob, Ron J. Keizer, Krista Steeg, Elise J. Smolders, Matthijs Luin, Hieronymus J. Derijks, Cornelis P. C. Jager, Tim Frenzel, and Roger Brüggemann. "Prospective validation of a model‐informed precision dosing tool for vancomycin in intensive care patients." British Journal of Clinical Pharmacology 86, no. 12 (June 5, 2020): 2497–506. http://dx.doi.org/10.1111/bcp.14360.

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17

van Beek, Stijn W., Rob ter Heine, Jan-Willem C. Alffenaar, Cecile Magis-Escurra, Rob E. Aarnoutse, Elin M. Svensson, M. J. Boeree, et al. "A Model-Informed Method for the Purpose of Precision Dosing of Isoniazid in Pulmonary Tuberculosis." Clinical Pharmacokinetics 60, no. 7 (February 22, 2021): 943–53. http://dx.doi.org/10.1007/s40262-020-00971-2.

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18

Uster, David W., Sophie L. Stocker, Jane E. Carland, Jonathan Brett, Deborah J. E. Marriott, Richard O. Day, and Sebastian G. Wicha. "A Model Averaging/Selection Approach Improves the Predictive Performance of Model‐Informed Precision Dosing: Vancomycin as a Case Study." Clinical Pharmacology & Therapeutics 109, no. 1 (November 5, 2020): 175–83. http://dx.doi.org/10.1002/cpt.2065.

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19

van Beek, Stijn W., Rob ter Heine, Jan-Willem C. Alffenaar, Cecile Magis-Escurra, Rob E. Aarnoutse, Elin M. Svensson, M. J. Boeree, et al. "Correction to: A Model-Informed Method for the Purpose of Precision Dosing of Isoniazid in Pulmonary Tuberculosis." Clinical Pharmacokinetics 60, no. 7 (April 3, 2021): 955. http://dx.doi.org/10.1007/s40262-021-01018-w.

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20

Vinks, Alexander A., Richard W. Peck, Michael Neely, and Diane R. Mould. "Development and Implementation of Electronic Health Record–Integrated Model‐Informed Clinical Decision Support Tools for the Precision Dosing of Drugs." Clinical Pharmacology & Therapeutics 107, no. 1 (November 25, 2019): 129–35. http://dx.doi.org/10.1002/cpt.1679.

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21

Boland, Brigid S., Siddharth Singh, Parambir Dulai, Anjali Jain, John C. Panetta, and Thierry Dervieux. "Su444 MODEL INFORMED PRECISION DOSING OF ADALIMUMAB BY INDIVIDUALIZED PHARMACOKINETICS IN A LARGE COHORT OF PATIENTS WITH INFLAMMATORY BOWEL DISEASES." Gastroenterology 160, no. 6 (May 2021): S—693. http://dx.doi.org/10.1016/s0016-5085(21)02366-0.

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22

Schräpel, Christina, Lukas Kovar, Dominik Selzer, Ute Hofmann, Florian Tran, Walter Reinisch, Matthias Schwab, and Thorsten Lehr. "External Model Performance Evaluation of Twelve Infliximab Population Pharmacokinetic Models in Patients with Inflammatory Bowel Disease." Pharmaceutics 13, no. 9 (August 31, 2021): 1368. http://dx.doi.org/10.3390/pharmaceutics13091368.

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Infliximab is approved for treatment of various chronic inflammatory diseases including inflammatory bowel disease (IBD). However, high variability in infliximab trough levels has been associated with diverse response rates. Model-informed precision dosing (MIPD) with population pharmacokinetic models could help to individualize infliximab dosing regimens and improve therapy. The aim of this study was to evaluate the predictive performance of published infliximab population pharmacokinetic models for IBD patients with an external data set. The data set consisted of 105 IBD patients with 336 infliximab concentrations. Literature review identified 12 published models eligible for external evaluation. Model performance was evaluated with goodness-of-fit plots, prediction- and variability-corrected visual predictive checks (pvcVPCs) and quantitative measures. For anti-drug antibody (ADA)-negative patients, model accuracy decreased for predictions > 6 months, while bias did not increase. In general, predictions for patients developing ADA were less accurate for all models investigated. Two models with the highest classification accuracy identified necessary dose escalations (for trough concentrations < 5 µg/mL) in 88% of cases. In summary, population pharmacokinetic modeling can be used to individualize infliximab dosing and thereby help to prevent infliximab trough concentrations dropping below the target trough concentration. However, predictions of infliximab concentrations for patients developing ADA remain challenging.

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23

Darwich, A. S., K. Ogungbenro, A. A. Vinks, J. R. Powell, J.-L. Reny, N. Marsousi, Y. Daali, et al. "Why Has Model-Informed Precision Dosing Not Yet Become Common Clinical Reality? Lessons From the Past and a Roadmap for the Future." Clinical Pharmacology & Therapeutics 101, no. 5 (April 4, 2017): 646–56. http://dx.doi.org/10.1002/cpt.659.

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24

Mueller-Schoell, Anna, Lena Klopp-Schulze, Robin Michelet, Madelé van Dyk, Thomas E. Mürdter, Matthias Schwab, Markus Joerger, Wilhelm Huisinga, Gerd Mikus, and Charlotte Kloft. "Simulation-Based Assessment of the Impact of Non-Adherence on Endoxifen Target Attainment in Different Tamoxifen Dosing Strategies." Pharmaceuticals 14, no. 2 (February 3, 2021): 115. http://dx.doi.org/10.3390/ph14020115.

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Tamoxifen is widely used in breast cancer treatment and minimum steady-state concentrations of its active metabolite endoxifen (CSS,min ENDX) above 5.97 ng/mL have been associated with favourable disease outcome. Yet, about 20% of patients do not reach target CSS,min ENDX applying conventional tamoxifen dosing. Moreover, 4–75% of patients are non-adherent, resulting in worse disease outcomes. Assuming complete adherence, we previously showed model-informed precision dosing (MIPD) to be superior to conventional and CYP2D6-guided dosing in minimising the proportion of patients with subtarget CSS,min ENDX. Given the high non-adherence rate in long-term tamoxifen therapy, this study investigated the impact of non-adherence on CSS,min ENDX target attainment in different dosing strategies. We show that MIPD allows to account for the expected level of non-adherence (here: up to 2 missed doses/week): increasing the MIPD target threshold from 5.97 ng/mL to 9 ng/mL (the lowest reported CSS,min ENDX in CYP2D6 normal metabolisers) as a safeguard resulted in the lowest interindividual variability and proportion of patients with subtarget CSS,min ENDX even in non-adherent patients. This is a significant improvement to conventional and CYP2D6-guided dosing. Adding a fixed increment to the originally selected dose is not recommended, since it inflates interindividual variability.

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25

Marquez-Megias, Silvia, Amelia Ramon-Lopez, Patricio Más-Serrano, Marcos Diaz-Gonzalez, Maria Remedios Candela-Boix, and Ricardo Nalda-Molina. "Evaluation of the Predictive Performance of Population Pharmacokinetic Models of Adalimumab in Patients with Inflammatory Bowel Disease." Pharmaceutics 13, no. 8 (August 12, 2021): 1244. http://dx.doi.org/10.3390/pharmaceutics13081244.

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Adalimumab is a monoclonal antibody used for inflammatory bowel disease. Due to its considerably variable pharmacokinetics, the loss of response and the development of anti-antibodies, it is highly recommended to use a model-informed precision dosing approach. The aim of this study is to evaluate the predictive performance of different population-pharmacokinetic models of adalimumab for inflammatory bowel disease to determine the pharmacokinetic model(s) that best suit our population to use in the clinical routine. A retrospective observational study with 134 patients was conducted at the General University Hospital of Alicante between 2014 and 2019. Model adequacy of each model was evaluated by the distribution of the individual pharmacokinetic parameters and the NPDE plots whereas predictive performance was assessed by calculating bias and precision. Moreover, stochastic simulations were performed to optimize the maintenance doses in the clinical protocols, to reach the target of 8 mg/L in at least 75% of the population. Two population-pharmacokinetic models were selected out of the six found in the literature which performed better in terms of adequacy and predictive performance. The stochastic simulations suggested the benefits of increasing the maintenance dose in protocol to reach the 8 mg/L target.

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26

Mueller-Schoell, Anna, Robin Michelet, Lena Klopp-Schulze, Madelé van Dyk, Thomas E. Mürdter, Matthias Schwab, Markus Joerger, Wilhelm Huisinga, Gerd Mikus, and Charlotte Kloft. "Computational Treatment Simulations to Assess the Need for Personalized Tamoxifen Dosing in Breast Cancer Patients of Different Biogeographical Groups." Cancers 13, no. 10 (May 18, 2021): 2432. http://dx.doi.org/10.3390/cancers13102432.

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Tamoxifen is used worldwide to treat estrogen receptor-positive breast cancer. It is extensively metabolized, and minimum steady-state concentrations of its metabolite endoxifen (CSS,min ENDX) >5.97 ng/mL have been associated with favorable outcome. Endoxifen formation is mediated by the enzyme CYP2D6, and impaired CYP2D6 function has been associated with lower CSS,min ENDX. In the Women’s Healthy Eating and Living (WHEL) study proposing the target concentration, 20% of patients showed subtarget CSS,min ENDX at tamoxifen standard dosing. CYP2D6 allele frequencies vary largely between populations, and as 87% of the patients in the WHEL study were White, little is known about the risk for subtarget CSS,min ENDX in other populations. Applying pharmacokinetic simulations, this study investigated the risk for subtarget CSS,min ENDX at tamoxifen standard dosing and the need for dose individualization in nine different biogeographical groups with distinct CYP2D6 allele frequencies. The high variability in CYP2D6 allele frequencies amongst the biogeographical groups resulted in an up to three-fold difference in the percentages of patients with subtarget CSS,min ENDX. Based on their CYP2D6 allele frequencies, East Asian breast cancer patients were identified as the population for which personalized, model-informed precision dosing would be most beneficial (28% of patients with subtarget CSS,min ENDX).

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27

Keutzer, Lina, Sebastian G. Wicha, and Ulrika SH Simonsson. "Mobile Health Apps for Improvement of Tuberculosis Treatment: Descriptive Review." JMIR mHealth and uHealth 8, no. 4 (April 21, 2020): e17246. http://dx.doi.org/10.2196/17246.

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Background Mobile health (mHealth) is a rapidly emerging market, which has been implemented in a variety of different disease areas. Tuberculosis remains one of the most common causes of death from an infectious disease worldwide, and mHealth apps offer an important contribution to the improvement of tuberculosis treatment. In particular, apps facilitating dose individualization, adherence monitoring, or provision of information and education about the disease can be powerful tools to prevent the development of drug-resistant tuberculosis or disease relapse. Objective The aim of this review was to identify, describe, and categorize mobile and Web-based apps related to tuberculosis that are currently available. Methods PubMed, Google Play Store, Apple Store, Amazon, and Google were searched between February and July 2019 using a combination of 20 keywords. Apps were included in the analysis if they focused on tuberculosis, and were excluded if they were related to other disease areas or if they were games unrelated to tuberculosis. All apps matching the inclusion criteria were classified into the following five categories: adherence monitoring, individualized dosing, eLearning/information, diagnosis, and others. The included apps were then summarized and described based on publicly available information using 12 characteristics. Results Fifty-five mHealth apps met the inclusion criteria and were included in this analysis. Of the 55 apps, 8 (15%) were intended to monitor patients’ adherence, 6 (11%) were designed for dosage adjustment, 29 (53%) were designed for eLearning/information, 3 (6%) were focused on tuberculosis diagnosis, and 9 (16%) were related to other purposes. Conclusions The number of mHealth apps related to tuberculosis has increased during the past 3 years. Although some of the discovered apps seem promising, many were found to contain errors or provided harmful or wrong information. Moreover, the majority of mHealth apps currently on the market are focused on making information about tuberculosis available (29/55, 53%). Thus, this review highlights a need for new, high-quality mHealth apps supporting tuberculosis treatment, especially those supporting individualized optimized treatment through model-informed precision dosing and video observed treatment.

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28

van der Veen, A., RJ Keizer, W. de Boode, A. Somers, R. Brüggemann, R. ter Heine, and P. De Cock. "P99 Clinical validation of published vancomycin population PK models in critically ill neonates." Archives of Disease in Childhood 104, no. 6 (May 17, 2019): e58.2-e59. http://dx.doi.org/10.1136/archdischild-2019-esdppp.137.

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BackgroundVancomycin is commonly used for treatment of severe Gram+ neonatal infections. Currently, even with the use of optimized dosing regimens and therapeutic drug monitoring (TDM), target attainment rates are abominable, leaving patients at risk for therapeutic failure and toxicity. Model-informed precision dosing (MIPD) offers a large potential to improve therapy in the individual patient.The aim of this study was to identify a suitable model for bedside MIPD by assessing the predictive performance of published population pharmacokinetic (popPK) models.MethodsA literature search was conducted to identify parametric popPK models. PK vancomycin data were retrospectively collected from NICU patients at the Radboud University Hospital, Nijmegen, The Netherlands. The model predictive performance was assessed by comparison of predictions to observations, calculation of bias (Mean Percentage Errors, MPE) and imprecision (Normalized Root Mean Squared Errors, NRMSE). Evaluations included both a priori (model covariate input) and a posteriori (model covariate and TDM concentration input) scenarios.Results265 TDM measurements from 65 neonates (median postmenstrual age:32 weeks [range:25–45 weeks]; median weight:1281g [range:597–5360g]; median serum creatinine:0,48 mg/dL [range:0,15–1,28 mg/dL]) were used for model evaluation. Six popPK models were evaluated1–6. A posteriori predictions of all models were consistently more accurate and precise compared to the a priori (starting dose) predictions. PopPK models of Frymoyer et al. and Capparelli et al. consistently performed best through all evaluations in both the a priori and a posteriori scenario (MPE ranging from -18 to 6,4% in a priori scenario and -6,5 to -3,8% in a posteriori scenario; NRMSE ranging from 34 to 40% in a priori scenario and 23 to 24% in a posteriori scenario).ConclusionLarge differences in predictive performance of popPK models were observed. Repeated therapeutic drug monitoring remains necessary to increase target attainment rate. Best performing models for bedside MIPD were identified in our patient population.ReferencesZhao W, Lopez E, Biran V, et al. ( 2013). Vancomycin continuous infusion in neonates: Dosing optimisation and therapeutic drug monitoring. Arch Dis Child;98(6):449–453.Capparelli EV, Lane JR, Romanowski GL, et al. ( 2001). The influences of renal function and maturation on vancomycin elimination in newborns and infants. J Clin Pharmacol, 41:927–934.De Cock RFW, Allegaert K, Brussee JM, et al. ( 2014). Simultaneous pharmacokinetic modeling of gentamicin, tobramycin and vancomycin clearance from neonates to adults: towards a semi-physiological function for maturation in glomerular filtration. Pharm Res;31(10):2642–2654.Frymoyer A, Hersh AL, El-Komy MH, et al. ( 2014). Association between vancomycin trough concentration and area under the concentration-time curve in neonates. Antimicrob Agents Chemother, 58(11):6454–6461.Anderson BJ, Allegaert K, Van Den Anker JN, Cossey V, Holford NHG. ( 2006). Vancomycin pharmacokinetics in preterm neonates and the prediction of adult clearance. Br J Clin Pharmacol;63(1):75–84.Germovsek E, Osborne L, Gunaratnam F, Lounis SA, Busquets FB, Sinha AK. ( 2019). Development and external evaluation of a population pharmacokinetic model for continuous and intermittent administration of vancomycin in neonates and infants using prospectively collected data. J Antimicrob Chemother, 1–9.Disclosure(s)R. Keizer is an employee and stockholder of InsightRX.

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van Hoeve, K., E. Dreesen, I. Hoffman, M. Ferrante, and S. Vermeire. "P389 Post induction infliximab trough levels predict long-term endoscopic remission in paediatric patients with inflammatory bowel disease." Journal of Crohn's and Colitis 14, Supplement_1 (January 2020): S362—S363. http://dx.doi.org/10.1093/ecco-jcc/jjz203.518.

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Abstract Background Although higher infliximab (IFX) trough levels (TL) have been associated with better outcomes, the ideal predictive sampling time and cut-points to achieve endoscopic remission remain unclear in children with inflammatory bowel disease (IBD). Therefore, we evaluated the pharmacokinetics of IFX during induction to predict long-term outcome of IFX. Methods All children with Crohn’s disease (CD) or ulcerative colitis (UC) starting IFX therapy (5 mg/kg at weeks 0–2–6–12) for active luminal disease from May 2017 till May 2019 were followed prospectively. IFX levels were measured by Ridascreen IFX Monitoring ELISA (TL at weeks 2–6–12, peak at weeks 0–2–6 and intermediate at weeks 1–4). IFX levels and cumulative drug exposure (area under the curve (AUC) till week 12) were correlated with the outcome at month 6. Clinical remission was defined as PUCAI/PCDAI <10, biochemical remission as CRP ≤5 mg/l + ESR ≤20 mm/h, endoscopic remission as SES-CD <3 or Mayo endoscopic sub-score = 0 and deep remission if both clinical + endoscopic remission. Results were analysed using Mann–Whitney U-test (presented as median [IQR]). Results A total of 252 serum induction levels were included from 32 patients (20 CD and 12 UC; 38% male; median age at start of IFX 13.8 years [11.3–14.9]; 84% on concomitant thiopurines). Clinical remission was achieved in 24 (75%) patients and 18 (56%) were in endoscopic remission (all in deep remission) at month 6. Endoscopic remission at month 6 was associated with significantly higher median IFX TL at week 4 (38.8 µg/ml [24.3–46.0] vs. 23.5 µg/ml [10.5–36.6], p = 0.017), at week 6 (19.9 µg/ml [10.1–26.3] vs. 11.1 µg/ml [3.7–19.9], p = 0.031), at week 12 (9.6 µg/ml [5.5–11.9] vs. 3.5 µg/ml [2.7–7.2], p = 0.004; fig1.) and higher AUC week 0–12 (4574.7 µg*day/ml [3783.0–5160.8] vs. 3722.9 µg*day/ml [3102.2–3991.9], p = 0.008). Median IFX TL at week 12 were significantly higher in children with clinical remission (8.6 µg/ml [5.1–12.0] vs. 4.3 µg/ml [3.1–5.9], p = 0.033), but not for biological remission (6.7 µg/ml [4.0–12.0] vs. 4.3 µg/ml [1.2–7.2], p = 0.250; Figure 2) at month 6. ROC analysis identified an IFX TL at week 12 ≥ 5.0 µg/ml and an AUC weeks 0–12 ≥ 4056.0 µg*day/ml as minimal target to achieve endoscopic remission at mo. 6 (AUROC: 0.796 [95% CI: 0.62–0.97] and AUROC: 0.778 [95% CI: 0.61–0.94], respectively; Figure 3.). Height, haemoglobin and PCDAI score at start of IFX therapy, significantly correlated with week 12 IFX TL. Conclusion Adequate IFX exposure during induction in paediatric IBD patients is associated with significantly better clinical, endoscopic and deep remission rates at month 6. Model-informed precision dosing can assist physicians to achieve optimal exposure during induction more precisely (and rapidly) what is essential for an optimal outcome.

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Zwart, Tom C., Henk-Jan Guchelaar, Paul J. M. van der Boog, Jesse J. Swen, Teun van Gelder, Johan W. de Fijter, and Dirk Jan A. R. Moes. "Model-informed precision dosing to optimise immunosuppressive therapy in renal transplantation." Drug Discovery Today, June 2021. http://dx.doi.org/10.1016/j.drudis.2021.06.001.

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Abdulla, Alan, Angela E. Edwina, Robert B. Flint, Karel Allegaert, Enno D. Wildschut, Birgit C. P. Koch, and Matthijs de Hoog. "Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review." Frontiers in Pediatrics 9 (February 23, 2021). http://dx.doi.org/10.3389/fped.2021.624639.

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Optimal pharmacotherapy in pediatric patients with suspected infections requires understanding and integration of relevant data on the antibiotic, bacterial pathogen, and patient characteristics. Because of age-related physiological maturation and non-maturational covariates (e.g., disease state, inflammation, organ failure, co-morbidity, co-medication and extracorporeal systems), antibiotic pharmacokinetics is highly variable in pediatric patients and difficult to predict without using population pharmacokinetics models. The intra- and inter-individual variability can result in under- or overexposure in a significant proportion of patients. Therapeutic drug monitoring typically covers assessment of pharmacokinetics and pharmacodynamics, and concurrent dose adaptation after initial standard dosing and drug concentration analysis. Model-informed precision dosing (MIPD) captures drug, disease, and patient characteristics in modeling approaches and can be used to perform Bayesian forecasting and dose optimization. Incorporating MIPD in the electronic patient record system brings pharmacometrics to the bedside of the patient, with the aim of a consisted and optimal drug exposure. In this narrative review, we evaluated studies assessing optimization of antibiotic pharmacotherapy using MIPD in pediatric populations. Four eligible studies involving amikacin and vancomycin were identified from 418 records. Key articles, independent of year of publication, were also selected to highlight important attributes of MIPD. Although very little research has been conducted until this moment, the available data on vancomycin indicate that MIPD is superior compared to conventional dosing strategies with respect to target attainment. The utility of MIPD in pediatrics needs to be further confirmed in frequently used antibiotic classes, particularly aminoglycosides and beta-lactams.

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Polasek, Thomas M., Amin Rostami-Hodjegan, Dong-Seok Yim, Masoud Jamei, Howard Lee, Holly Kimko, Jae Kyoung Kim, Phuong Thi Thu Nguyen, Adam S. Darwich, and Jae-Gook Shin. "What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing." AAPS Journal 21, no. 2 (January 9, 2019). http://dx.doi.org/10.1208/s12248-018-0286-6.

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Mizuno, Tomoyuki, Min Dong, Zachary L. Taylor, Laura B. Ramsey, and Alexander A. Vinks. "Clinical implementation of pharmacogenetics and model‐informed precision dosing to improve patient care." British Journal of Clinical Pharmacology, July 8, 2020. http://dx.doi.org/10.1111/bcp.14426.

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34

Zwart, Tom C., Dirk Jan A. R. Moes, Paul J. M. van der Boog, Nielka P. van Erp, Johan W. de Fijter, Henk-Jan Guchelaar, Ron J. Keizer, and Rob ter Heine. "Model-Informed Precision Dosing of Everolimus: External Validation in Adult Renal Transplant Recipients." Clinical Pharmacokinetics, July 27, 2020. http://dx.doi.org/10.1007/s40262-020-00925-8.

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Alihodzic, Dzenefa, Astrid Broeker, Michael Baehr, Stefan Kluge, Claudia Langebrake, and Sebastian Georg Wicha. "Impact of Inaccurate Documentation of Sampling and Infusion Time in Model-Informed Precision Dosing." Frontiers in Pharmacology 11 (March 3, 2020). http://dx.doi.org/10.3389/fphar.2020.00172.

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Kantasiripitak, Wannee, Ruth Van Daele, Matthias Gijsen, Marc Ferrante, Isabel Spriet, and Erwin Dreesen. "Software Tools for Model-Informed Precision Dosing: How Well Do They Satisfy the Needs?" Frontiers in Pharmacology 11 (May 7, 2020). http://dx.doi.org/10.3389/fphar.2020.00620.

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37

Klopp-Schulze, Lena, Anna Mueller-Schoell, Patrick Neven, Stijn L. W. Koolen, Ron H. J. Mathijssen, Markus Joerger, and Charlotte Kloft. "Integrated Data Analysis of Six Clinical Studies Points Toward Model-Informed Precision Dosing of Tamoxifen." Frontiers in Pharmacology 11 (March 31, 2020). http://dx.doi.org/10.3389/fphar.2020.00283.

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Keutzer, Lina, and Ulrika S. H. Simonsson. "Individualized Dosing With High Inter-Occasion Variability Is Correctly Handled With Model-Informed Precision Dosing—Using Rifampicin as an Example." Frontiers in Pharmacology 11 (May 27, 2020). http://dx.doi.org/10.3389/fphar.2020.00794.

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Frymoyer, Adam, Hayden T. Schwenk, Yvonne Zorn, Laura Bio, Jeffrey D. Moss, Bhavin Chasmawala, Joshua Faulkenberry, Srijib Goswami, Ron J. Keizer, and Shabnam Ghaskari. "Model-Informed Precision Dosing of Vancomycin in Hospitalized Children: Implementation and Adoption at an Academic Children’s Hospital." Frontiers in Pharmacology 11 (April 29, 2020). http://dx.doi.org/10.3389/fphar.2020.00551.

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Dong, Min, Chie Emoto, Tsuyoshi Fukuda, Danielle E. Arnold, Parinda A. Mehta, Rebecca A. Marsh, and Alexander A. Vinks. "Model‐informed precision dosing for alemtuzumab in paediatric and young adult patients undergoing allogeneic haematopoietic cell transplantation." British Journal of Clinical Pharmacology, July 18, 2021. http://dx.doi.org/10.1111/bcp.14955.

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"Erratum: A Model Averaging/Selection Approach Improves the Predictive Performance of Model‐Informed Precision Dosing: Vancomycin as a Case Study." Clinical Pharmacology & Therapeutics, July 27, 2021. http://dx.doi.org/10.1002/cpt.2353.

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Badaoui, Sarah, Ashley M. Hopkins, A. David Rodrigues, John O. Miners, Michael J. Sorich, and Andrew Rowland. "Application of Model Informed Precision Dosing to Address the Impact of Pregnancy Stage and CYP2D6 Phenotype on Foetal Morphine Exposure." AAPS Journal 23, no. 1 (January 2021). http://dx.doi.org/10.1208/s12248-020-00541-1.

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Roggeveen, Luca F., Tingjie Guo, Ronald H. Driessen, Lucas M. Fleuren, Patrick Thoral, Peter H. J. van der Voort, Armand R. J. Girbes, Rob J. Bosman, and Paul Elbers. "Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients." Frontiers in Pharmacology 11 (May 15, 2020). http://dx.doi.org/10.3389/fphar.2020.00646.

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Shukla, Praveen, Srijib Goswami, Ron J. Keizer, Beth Apsel Winger, Sandhya Kharbanda, Christopher C. Dvorak, and Janel Long-Boyle. "Assessment of a Model-Informed Precision Dosing Platform Use in Routine Clinical Care for Personalized Busulfan Therapy in the Pediatric Hematopoietic Cell Transplantation (HCT) Population." Frontiers in Pharmacology 11 (July 2, 2020). http://dx.doi.org/10.3389/fphar.2020.00888.

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45

Zhu, Xiuqing, Tao Xiao, Shanqing Huang, Shujing Liu, Xiaolin Li, Dewei Shang, and Yuguan Wen. "Case Report: Predicting the Range of Lamotrigine Concentration Using Pharmacokinetic Models Based on Monte Carlo Simulation: A Case Study of Antiepileptic Drug-Related Leukopenia." Frontiers in Pharmacology 12 (July 20, 2021). http://dx.doi.org/10.3389/fphar.2021.706329.

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Lamotrigine (LTG), a wide-spectrum antiepileptic drug, is frequently associated with cutaneous side-effects, whereas hematological side-effects such as leukopenia have rarely been reported for it. We report the case of a 15-year-old Chinese female epileptic patient weighing 60 kg who developed combined asymptomatic leukopenia after receiving concomitant therapy with LTG and valproate acid (VPA). In this case report, antiepileptic drug-related leukopenia may have occurred in definite relation to an increase in LTG concentration and reversed with the discontinuation of VPA. Monte Carlo (MC) simulations were performed to estimate the steady-state serum concentrations (Css) of LTG for different dosing regimens in adolescent Chinese epileptic patients weighing the same as the patient considered in the case study, based on pharmacokinetic (PK) models published in past research. Adjustments to the dosage of LTG for the patient were analyzed to illustrate the application of MC simulations and verify the results. The predicted LTG concentrations within a prediction interval between the 10th and 90th percentiles that represented 80% of the simulated populations, could adequately capture the measured LTG concentrations of the patient, indicating that MC simulations are a useful tool for estimating drug concentrations. Clinicians may benefit from the timely probabilistic predictions of the range of drug concentration based on an MC simulation that considers a large sample of virtual patients. The case considered here highlights the importance of therapeutic drug monitoring (TDM) and implementing model-informed precision dosing in the course of a patient’s individualized treatment to minimize adverse reactions.

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