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Teplytska O, Ernst M, Koltermann LM, Valderrama D, Trunz E, Vaisband M, Hasenauer J, Fröhlich H, Jaehde U. Machine Learning Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review. Clin Pharmacokinet 2024:10.1007/s40262-024-01409-9. [PMID: 39153056 DOI: 10.1007/s40262-024-01409-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/04/2024] [Indexed: 08/19/2024]
Abstract
INTRODUCTION In the last decade, various Machine Learning techniques have been proposed aiming to individualise the dose of anticancer drugs mostly based on a presumed drug effect or measured effect biomarkers. The aim of this scoping review was to comprehensively summarise the research status on the use of Machine Learning for precision dosing in anticancer drug therapy. METHODS This scoping review was conducted in accordance with the interim guidance by Cochrane and the Joanna Briggs Institute. We systematically searched the databases Medline (via PubMed), Embase and the Cochrane Library for research articles and reviews including results published after 2016. Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. RESULTS A total of 17 relevant studies was identified. In 12 of the included studies, Reinforcement Learning methods were used, including Classical, Deep, Double Deep and Conservative Q-Learning and Fuzzy Reinforcement Learning. Furthermore, classical Machine Learning methods were compared in terms of their performance and an artificial intelligence platform based on parabolic equations was used to guide dosing prospectively and retrospectively, albeit only in a limited number of patients. Due to the significantly different algorithm structures, a meaningful comparison between the various Machine Learning approaches was not possible. CONCLUSION Overall, this review emphasises the clinical relevance of Machine Learning methods for anticancer drug dose optimisation, as many algorithms have shown promising results enabling model-free predictions with the potential to maximise efficacy and minimise toxicity when compared to standard protocols.
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Affiliation(s)
- Olga Teplytska
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, An der Immenburg 4, 53121, Bonn, Germany
| | - Moritz Ernst
- Faculty of Medicine and University Hospital Cologne, Institute of Public Health, University of Cologne, Cologne, Germany
| | - Luca Marie Koltermann
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, An der Immenburg 4, 53121, Bonn, Germany
| | - Diego Valderrama
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Elena Trunz
- Institute of Computer Science II, Visual Computing, University of Bonn, Bonn, Germany
| | - Marc Vaisband
- Hausdorff Center for Mathematics, University of Bonn, Bonn, Germany
- Institute of Life & Medical Sciences (LIMES), University of Bonn, Bonn, Germany
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Salzburg Cancer Research Institute-Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR), Paracelsus Medical University, Cancer Cluster Salzburg, Salzburg, Austria
| | - Jan Hasenauer
- Hausdorff Center for Mathematics, University of Bonn, Bonn, Germany
- Institute of Life & Medical Sciences (LIMES), University of Bonn, Bonn, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | - Ulrich Jaehde
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, An der Immenburg 4, 53121, Bonn, Germany.
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Zhao Y, Chaw JK, Liu L, Chaw SH, Ang MC, Ting TT. Systematic literature review on reinforcement learning in non-communicable disease interventions. Artif Intell Med 2024; 154:102901. [PMID: 38838400 DOI: 10.1016/j.artmed.2024.102901] [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: 03/22/2023] [Revised: 02/21/2024] [Accepted: 06/02/2024] [Indexed: 06/07/2024]
Abstract
There is evidence that reducing modifiable risk factors and strengthening medical and health interventions can reduce early mortality and economic losses from non-communicable diseases (NCDs). Machine learning (ML) algorithms have been successfully applied to preventing and controlling NCDs. Reinforcement learning (RL) is the most promising of these approaches because of its ability to dynamically adapt interventions to NCD disease progression and its commitment to achieving long-term intervention goals. This paper reviews the preferred algorithms, data sources, design details, and obstacles to clinical application in existing studies to facilitate the early application of RL algorithms in clinical practice research for NCD interventions. We screened 40 relevant papers for quantitative and qualitative analysis using the PRISMA review flow diagram. The results show that researchers tend to use Deep Q-Network (DQN) and Actor-Critic as well as their improved or hybrid algorithms to train and validate RL models on retrospective datasets. Often, the patient's physical condition is the main defining parameter of the state space, while interventions are the main defining parameter of the action space. Mostly, changes in the patient's physical condition are used as a basis for immediate rewards to the agent. Various attempts have been made to address the challenges to clinical application, and several approaches have been proposed from existing research. However, as there is currently no universally accepted solution, the use of RL algorithms in clinical practice for NCD interventions necessitates more comprehensive responses to the issues addressed in this paper, which are safety, interpretability, training efficiency, and the technical aspect of exploitation and exploration in RL algorithms.
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Affiliation(s)
- Yanfeng Zhao
- Institute of Visual Informatics, National University of Malaysia, Bangi, Selangor, Malaysia
| | - Jun Kit Chaw
- Institute of Visual Informatics, National University of Malaysia, Bangi, Selangor, Malaysia.
| | - Lin Liu
- Henan Vocational University of Science and Technology, Zhoukou, Henan, China
| | - Sook Hui Chaw
- Department of Anaesthesiology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Mei Choo Ang
- Institute of Visual Informatics, National University of Malaysia, Bangi, Selangor, Malaysia
| | - Tin Tin Ting
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Negeri Sembilan, Malaysia
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3
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Meesters K, Balbas-Martinez V, Allegaert K, Downes KJ, Michelet R. Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians. Paediatr Drugs 2024; 26:365-379. [PMID: 38755515 DOI: 10.1007/s40272-024-00633-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/25/2024] [Indexed: 05/18/2024]
Abstract
The widespread use of drugs for unapproved purposes remains common in children, primarily attributable to practical, ethical, and financial constraints associated with pediatric drug research. Pharmacometrics, the scientific discipline that involves the application of mathematical models to understand and quantify drug effects, holds promise in advancing pediatric pharmacotherapy by expediting drug development, extending applications, and personalizing dosing. In this review, we delineate the principles of pharmacometrics, and explore its clinical applications and prospects. The fundamental aspect of any pharmacometric analysis lies in the selection of appropriate methods for quantifying pharmacokinetics and pharmacodynamics. Population pharmacokinetic modeling is a data-driven method ('top-down' approach) to approximate population-level pharmacokinetic parameters, while identifying factors contributing to inter-individual variability. Model-informed precision dosing is increasingly used to leverage population pharmacokinetic models and patient data, to formulate individualized dosing recommendations. Physiologically based pharmacokinetic models integrate physicochemical drug properties with biological parameters ('bottom-up approach'), and is particularly valuable in situations with limited clinical data, such as early drug development, assessing drug-drug interactions, or adapting dosing for patients with specific comorbidities. The effective implementation of these complex models hinges on strong collaboration between clinicians and pharmacometricians, given the pivotal role of data availability. Promising advancements aimed at improving data availability encompass innovative techniques such as opportunistic sampling, minimally invasive sampling approaches, microdialysis, and in vitro investigations. Additionally, ongoing research efforts to enhance measurement instruments for evaluating pharmacodynamics responses, including biomarkers and clinical scoring systems, are expected to significantly bolster our capacity to understand drug effects in children.
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Affiliation(s)
- Kevin Meesters
- Department of Pediatrics, University of British Columbia, 4480 Oak Street, Vancouver, BC, V6H 3V4, Canada.
- Vaccine Evaluation Center, BC Children's Hospital Research Institute, Vancouver, BC, Canada.
| | | | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
- Department of Hospital Pharmacy, Erasmus MC, Rotterdam, The Netherlands
| | - Kevin J Downes
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Infectious Diseases, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Robin Michelet
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Berlin, Germany
- qPharmetra LLC, Berlin, Germany
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De Carlo A, Tosca EM, Fantozzi M, Magni P. Reinforcement Learning and PK-PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma. Clin Pharmacol Ther 2024; 115:825-838. [PMID: 38339803 DOI: 10.1002/cpt.3176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/15/2023] [Indexed: 02/12/2024]
Abstract
The integration of pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulations with artificial intelligence/machine learning algorithms is one of the most attractive areas of the pharmacometric research. These hybrid techniques are currently under investigation to perform several tasks, among which precision dosing. In this scenario, this paper presents and evaluates a new framework embedding PK-PD models into a reinforcement learning (RL) algorithm, Q-learning (QL), to personalize pharmacological treatment. Each patient is represented with a set of PK-PD parameters and has a personal QL agent which optimizes the individual treatment. In the training phase, leveraging PK-PD simulations, the QL agent assesses different actions, defined consistently with the clinical knowledge to consider only plausible dose-adjustments, in order to find the optimal rules. The proposed framework was evaluated to optimize the erdafitinib treatment in patients with metastatic urothelial carcinoma. This drug was approved by the US Food and Drug Administration (FDA) with a dose-adaptive protocol based on monitoring the levels of serum phosphate, which represent a biomarker of both treatment efficacy and toxicity. To evaluate the flexibility of the methodology, a heterogeneous virtual population of 141 patients was generated using an erdafitinib population PK (PopPK)-PD literature model. For each patient, treatment response was simulated by using both QL-optimized protocol and the clinical one. QL agents outperform the approved dose-adaptive rules, increasing more than 10% the efficacy and the safety of treatment at each end point. Results confirm the great potentialities of the integration of PopPK-PD models and RL algorithms to optimize precision dosing tasks.
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Affiliation(s)
- Alessandro De Carlo
- Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Elena Maria Tosca
- Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Martina Fantozzi
- Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Paolo Magni
- Electrical, Computer, and Biomedical Engineering, University of Pavia, Pavia, Italy
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5
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Stankevičiūtė K, Woillard JB, Peck RW, Marquet P, van der Schaar M. Bridging the Worlds of Pharmacometrics and Machine Learning. Clin Pharmacokinet 2023; 62:1551-1565. [PMID: 37803104 DOI: 10.1007/s40262-023-01310-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/19/2023] [Indexed: 10/08/2023]
Abstract
Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaque to machine learning practitioners, and state-of-the-art machine learning methods inaccessible to pharmacometricians. To help bridge the two worlds, we provide an introduction to current problems and techniques in pharmacometrics that ranges from pharmacokinetic and pharmacodynamic modeling to pharmacometric simulations, model-informed precision dosing, and systems pharmacology, and review some of the machine learning approaches to address them. We hope this would facilitate collaboration between experts, with complementary strengths of principled pharmacometric modeling and flexibility of machine learning leading to synergistic effects in pharmacological applications.
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Affiliation(s)
- Kamilė Stankevičiūtė
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK
| | - Jean-Baptiste Woillard
- INSERM U1248 P&T, University of Limoges, 2 rue du Pr Descottes, 87000, Limoges, France.
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France.
| | - Richard W Peck
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- Pharma Research and Development, Roche Innovation Center, Basel, Switzerland
| | - Pierre Marquet
- INSERM U1248 P&T, University of Limoges, 2 rue du Pr Descottes, 87000, Limoges, France
- Department of Pharmacology and Toxicology, CHU Limoges, Limoges, France
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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6
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Augustin D, Lambert B, Robinson M, Wang K, Gavaghan D. Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case. Front Pharmacol 2023; 14:1270443. [PMID: 37927586 PMCID: PMC10621790 DOI: 10.3389/fphar.2023.1270443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. Neural network regression; 2. Deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74%. In comparison, the regression model and the deep RL model have success rates of 47.0% and 65.8%, and median TTRs of 45% and 68%. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.
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Affiliation(s)
- David Augustin
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ben Lambert
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Ken Wang
- Research and Early Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - David Gavaghan
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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7
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Kluwe F, Michelet R, Huisinga W, Zeitlinger M, Mikus G, Kloft C. Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data. Clin Pharmacokinet 2023; 62:1461-1477. [PMID: 37603216 PMCID: PMC10520167 DOI: 10.1007/s40262-023-01274-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND AND OBJECTIVES Model-informed precision dosing (MIPD) frequently uses nonlinear mixed-effects (NLME) models to predict and optimize therapy outcomes based on patient characteristics and therapeutic drug monitoring data. MIPD is indicated for compounds with narrow therapeutic range and complex pharmacokinetics (PK), such as voriconazole, a broad-spectrum antifungal drug for prevention and treatment of invasive fungal infections. To provide guidance and recommendations for evidence-based application of MIPD for voriconazole, this work aimed to (i) externally evaluate and compare the predictive performance of a published so-called 'hybrid' model for MIPD (an aggregate model comprising features and prior information from six previously published NLME models) versus two 'standard' NLME models of voriconazole, and (ii) investigate strategies and illustrate the clinical impact of Bayesian forecasting for voriconazole. METHODS A workflow for external evaluation and application of MIPD for voriconazole was implemented. Published voriconazole NLME models were externally evaluated using a comprehensive in-house clinical database comprising nine voriconazole studies and prediction-/simulation-based diagnostics. The NLME models were applied using different Bayesian forecasting strategies to assess the influence of prior observations on model predictivity. RESULTS The overall best predictive performance was obtained using the aggregate model. However, all NLME models showed only modest predictive performance, suggesting that (i) important PK processes were not sufficiently implemented in the structural submodels, (ii) sources of interindividual variability were not entirely captured, and (iii) interoccasion variability was not adequately accounted for. Predictive performance substantially improved by including the most recent voriconazole observations in MIPD. CONCLUSION Our results highlight the potential clinical impact of MIPD for voriconazole and indicate the need for a comprehensive (pre-)clinical database as basis for model development and careful external model evaluation for compounds with complex PK before their successful use in MIPD.
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Affiliation(s)
- Franziska Kluwe
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Kelchstraße 31, 12169 Berlin, Germany
- Graduate Research Training Program PharMetrX, Berlin/Potsdam, Germany
| | - Robin Michelet
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Kelchstraße 31, 12169 Berlin, Germany
| | - Wilhelm Huisinga
- Institute of Mathematics, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476 Potsdam, Germany
| | - Markus Zeitlinger
- Department of Clinical Pharmacology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Gerd Mikus
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Kelchstraße 31, 12169 Berlin, Germany
- Department of Clinical Pharmacology and Pharmacoepidemiology, University Hospital Heidelberg, Im Neuenheimer Feld 419, 69120 Heidelberg, Germany
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Kelchstraße 31, 12169 Berlin, Germany
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Briki M, André P, Thoma Y, Widmer N, Wagner AD, Decosterd LA, Buclin T, Guidi M, Carrara S. Precision Oncology by Point-of-Care Therapeutic Drug Monitoring and Dosage Adjustment of Conventional Cytotoxic Chemotherapies: A Perspective. Pharmaceutics 2023; 15:pharmaceutics15041283. [PMID: 37111768 PMCID: PMC10147065 DOI: 10.3390/pharmaceutics15041283] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 04/29/2023] Open
Abstract
Therapeutic drug monitoring (TDM) of conventional cytotoxic chemotherapies is strongly supported yet poorly implemented in daily practice in hospitals. Analytical methods for the quantification of cytotoxic drugs are instead widely presented in the scientific literature, while the use of these therapeutics is expected to keep going for longer. There are two main issues hindering the implementation of TDM: turnaround time, which is incompatible with the dosage profiles of these drugs, and exposure surrogate marker, namely total area under the curve (AUC). Therefore, this perspective article aims to define the adjustment needed from current to efficient TDM practice for cytotoxics, namely point-of-care (POC) TDM. For real-time dose adjustment, which is required for chemotherapies, such POC TDM is only achievable with analytical methods that match the sensitivity and selectivity of current methods, such as chromatography, as well as model-informed precision dosing platforms to assist the oncologist with dose fine-tuning based on quantification results and targeted intervals.
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Affiliation(s)
- Myriam Briki
- Service and Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
- Bio/CMOS Interfaces Laboratory, École Polytechnique Fédérale de Lausanne-EPFL, 2002 Neuchâtel, Switzerland
| | - Pascal André
- Service and Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Yann Thoma
- School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland, 1401 Yverdon-les-Bains, Switzerland
| | - Nicolas Widmer
- Service and Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
- Pharmacy of the Eastern Vaud Hospitals, 1847 Rennaz, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland
| | - Anna D Wagner
- Service of Medical Oncology, Department of Oncology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Laurent A Decosterd
- Service and Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Thierry Buclin
- Service and Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Monia Guidi
- Service and Laboratory of Clinical Pharmacology, Department of Laboratory Medicine and Pathology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland
- Centre for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Sandro Carrara
- Bio/CMOS Interfaces Laboratory, École Polytechnique Fédérale de Lausanne-EPFL, 2002 Neuchâtel, Switzerland
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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 teicoplanin for the rapid achievement of the target area under the concentration-time curve: A simulation study. Clin Transl Sci 2023; 16:704-713. [PMID: 36748688 PMCID: PMC10087075 DOI: 10.1111/cts.13484] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 01/09/2023] [Accepted: 01/13/2023] [Indexed: 02/08/2023] Open
Abstract
Teicoplanin, a glycopeptide antimicrobial, is recommended for therapeutic drug monitoring, but it remains unclear how to target the area under the concentration-time curve (AUC). This simulation study purposed to demonstrate the potential of the Bayesian forecasting approach for the rapid achievement of the target AUC for teicoplanin. We generated concordant and discordant virtual populations against a Japanese population pharmacokinetic model. The predictive performance of the Bayesian posterior AUC in limited sampling on the first day against the reference AUC was evaluated as an acceptable target AUC ratio within the range of 0.8-1.2. In the concordant population, the probability for the maximum a priori or Bayesian posterior AUC on the first day (AUC0-24 ) was 61.3% or more than 77.0%, respectively. The Bayesian posterior AUC on the second day (AUC24-48 ) was more than 75.1%. In the discordant population, the probability for the maximum a priori or Bayesian posterior AUC0-24 was 15.5% or 11.7-80.7%, respectively. The probability for the maximum a priori or Bayesian posterior AUC24-48 was 23.4%, 30.2-82.1%. The AUC at steady-state (AUCSS ) was correlated with trough concentration at steady-state, with a coefficient of determination of 0.930; the coefficients on days 7 and 4 were 0.442 and 0.125, respectively. In conclusion, this study demonstrated that early sampling could improve the probability of AUC0-24 and AUC24-48 but did not adequately predict AUCSS . Further studies are necessary to apply early sampling-based model-informed precision dosing in the clinical settings.
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Affiliation(s)
- Kazutaka Oda
- Department of Pharmacy, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan.,Department of Infection Control, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
| | - Tomoyuki Yamada
- Department of Pharmacy, Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Osaka, Japan
| | - Kazuaki Matsumoto
- Division of Pharmacodynamics, Keio University Faculty of Pharmacy, Minato, Tokyo, Japan
| | - Yuki Hanai
- Department of Clinical Pharmacy, Faculty of Pharmaceutical Sciences, Toho University, Funabashi, Chiba, Japan
| | - Takashi Ueda
- Department of Infection Control and Prevention, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan
| | - Masaru Samura
- Department of Pharmacy, Yokohama General Hospital, Yokohama, Kanagawa, Japan
| | - Akari Shigemi
- Department of Pharmacy, Kagoshima University Hospital, Kagoshima, Kagoshima, Japan
| | - Hirofumi Jono
- Department of Pharmacy, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
| | - Hideyuki Saito
- Department of Pharmacy, Kumamoto University Hospital, Chuo-ku, Kumamoto, Japan
| | - Toshimi Kimura
- Department of Pharmacy, Juntendo University Hospital, Bunkyo-ku, Tokyo, Japan
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10
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Capobianco E, Dominietto M. Translating Data Science Results into Precision Oncology Decisions: A Mini Review. J Clin Med 2023; 12:438. [PMID: 36675367 PMCID: PMC9862106 DOI: 10.3390/jcm12020438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.
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Affiliation(s)
- Enrico Capobianco
- The Jackson Laboratory, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Marco Dominietto
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen, Switzerland
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11
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Ribba B, Bräm DS, Baverel PG, Peck RW. Model enhanced reinforcement learning to enable precision dosing: A theoretical case study with dosing of propofol. CPT Pharmacometrics Syst Pharmacol 2022; 11:1497-1510. [PMID: 36177959 PMCID: PMC9662205 DOI: 10.1002/psp4.12858] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/09/2022] [Indexed: 11/24/2022] Open
Abstract
Extending the potential of precision dosing requires evaluating methodologies offering more flexibility and higher degree of personalization. Reinforcement learning (RL) holds promise in its ability to integrate multidimensional data in an adaptive process built toward efficient decision making centered on sustainable value creation. For general anesthesia in intensive care units, RL is applied and automatically adjusts dosing through monitoring of patient's consciousness. We further explore the problem of optimal control of anesthesia with propofol by combining RL with state-of-the-art tools used to inform dosing in drug development. In particular, we used pharmacokinetic-pharmacodynamic (PK-PD) modeling as a simulation engine to generate experience from dosing scenarios, which cannot be tested experimentally. Through simulations, we show that, when learning from retrospective trial data, more than 100 patients are needed to reach an accuracy within the range of what is achieved with a standard dosing solution. However, embedding a model of drug effect within the RL algorithm improves accuracy by reducing errors to target by 90% through learning to take dosing actions maximizing long-term benefit. Data residual variability impacts accuracy while the algorithm efficiently coped with up to 50% interindividual variability in the PK and 25% in the PD model's parameters. We illustrate how extending the state definition of the RL agent with meaningful variables is key to achieve high accuracy of optimal dosing policy. These results suggest that RL constitutes an attractive approach for precision dosing when rich data are available or when complemented with synthetic data from model-based tools used in model-informed drug development.
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Affiliation(s)
- Benjamin Ribba
- Roche Pharma Research and Early Development (pRED)F. Hoffmann La Roche Ltd.BaselSwitzerland
| | - Dominic Stefan Bräm
- Roche Pharma Research and Early Development (pRED)F. Hoffmann La Roche Ltd.BaselSwitzerland,Present address:
University Children’s Hospital BaselSpitalstrasse 33, 4056BaselSwitzerland.
| | - Paul Gabriel Baverel
- Roche Pharma Research and Early Development (pRED)F. Hoffmann La Roche Ltd.BaselSwitzerland,Present address:
Molecular Partners AGWagistrasse 14, 8952SchlierenSwitzerland.
| | - Richard Wilson Peck
- Roche Pharma Research and Early Development (pRED)F. Hoffmann La Roche Ltd.BaselSwitzerland,Present address:
Department of Pharmacology & TherapeuticsUniversity of LiverpoolUK.
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12
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Karas S, Mathijssen RH, van Schaik RH, Forrest A, Wiltshire T, Innocenti F, Bies RR. Model-Based Prediction of Irinotecan-Induced Grade 4 Neutropenia in Advanced Cancer Patients: Influence of Demographic and Clinical Factors. Clin Pharmacol Ther 2022; 112:316-326. [PMID: 35467016 PMCID: PMC9843820 DOI: 10.1002/cpt.2621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/15/2022] [Indexed: 01/19/2023]
Abstract
Severe neutropenia is the major dose-liming toxicity of irinotecan-based chemotherapy. The objective was to assess to what extent a population pharmacokinetic/pharmacodynamic model including patient-specific demographic/clinical characteristics, individual pharmacokinetics, and absolute neutrophil counts (ANCs) can predict irinotecan-induced grade 4 neutropenia. A semimechanistic population pharmacokinetic/pharmacodynamic model was developed to describe neutrophil response over time in 197 patients with cancer receiving irinotecan. For covariate analysis, sex, race, age, pretreatment total bilirubin, and body surface area were evaluated to identify significant covariates on system-related parameters (mean transit time (MTT) and ɣ) and sensitivity to neutropenia effects of irinotecan and SN-38 (SLOPE). The model-based simulation was performed to assess the contribution of the identified covariates, individual pharmacokinetics, and baseline ANC alone or with incremental addition of weekly ANC up to 3 weeks on predicting irinotecan-induced grade 4 neutropenia. The time course of neutrophil response was described using the model assuming that irinotecan and SN-38 have toxic effects on bone marrow proliferating cells. Sex and pretreatment total bilirubin explained 10.5% of interindividual variability in MTT. No covariates were identified for SLOPE and γ. Incorporating sex and pretreatment total bilirubin (area under the receiver operating characteristic curve (AUC-ROC): 50%, 95% CI 50-50%) or with the addition of individual pharmacokinetics (AUC-ROC: 62%, 95% CI 53-71%) in the model did not result in accurate prediction of grade 4 neutropenia. However, incorporating ANC only at baseline and week 1 in the model achieved a good prediction (AUC-ROC: 78%, 95% CI 69-88%). These results demonstrate the potential applicability of a model-based approach to predict irinotecan-induced neutropenia, which ultimately allows for personalized intervention to maximize treatment outcomes.
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Affiliation(s)
- Spinel Karas
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Ron H.J. Mathijssen
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | | | - Alan Forrest
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Tim Wiltshire
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Federico Innocenti
- Oncology Early Development, AbbVie, Inc., South San Francisco, California, United States,Corresponding Author: Federico Innocenti, M.D., Ph.D., AbbVie, Inc., Oncology Early Development, South San Francisco, California 94080,
| | - Robert R. Bies
- Department of Pharmaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, United States,Institute for Computational and Data Sciences, University at Buffalo, State University of New York at Buffalo, Buffalo, New York, United States,Corresponding Author: Robert R. Bies, Pharm.D., Ph.D., 118 Pharmacy Building, The University at Buffalo School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York 14214-8033, Phone: (716) 645-7315,
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13
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Kantasiripitak W, Outtier A, Wicha SG, Kensert A, Wang Z, Sabino J, Vermeire S, Thomas D, Ferrante M, Dreesen E. Multi‐model averaging improves the performance of model‐guided infliximab dosing in patients with inflammatory bowel diseases. CPT Pharmacometrics Syst Pharmacol 2022; 11:1045-1059. [PMID: 35706358 PMCID: PMC9381887 DOI: 10.1002/psp4.12813] [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: 04/08/2022] [Revised: 04/08/2022] [Accepted: 05/05/2022] [Indexed: 11/11/2022] Open
Abstract
Infliximab dosage de‐escalation without prior knowledge of drug concentrations may put patients at risk for underexposure and trigger the loss of response. A single‐model approach for model‐informed precision dosing during infliximab maintenance therapy has proven its clinical benefit in patients with inflammatory bowel diseases. We evaluated the predictive performances of two multi‐model approaches, a model selection algorithm and a model averaging algorithm, using 18 published population pharmacokinetic models of infliximab for guiding dosage de‐escalation. Data of 54 patients with Crohn’s disease and ulcerative colitis who underwent infliximab dosage de‐escalation after an earlier escalation were used. A priori prediction (based solely on covariate data) and maximum a posteriori prediction (based on covariate data and trough concentrations) were compared using accuracy and precision metrics and the classification accuracy at the trough concentration target of 5.0 mg/L. A priori prediction was inaccurate and imprecise, with the lowest classification accuracies irrespective of the approach (median 59%, interquartile range 59%–63%). Using the maximum a posteriori prediction, the model averaging algorithm had systematically better predictive performance than the model selection algorithm or the single‐model approach with any model, regardless of the number of concentration data. Only a single trough concentration (preferably at the point of care) sufficed for accurate and precise prediction. Predictive performance of both single‐ and multi‐model approaches was robust to the lack of covariate data. Model averaging using four models demonstrated similar predictive performance with a five‐fold shorter computation time. This model averaging algorithm was implemented in the TDMx software tool to guide infliximab dosage de‐escalation in the forthcoming prospective MODIFI study (NCT04982172).
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Affiliation(s)
- Wannee Kantasiripitak
- Department of Pharmaceutical and Pharmacological Sciences University of Leuven Leuven Belgium
| | - An Outtier
- Department of Gastroenterology and Hepatology University Hospitals Leuven Leuven Belgium
- Department of Chronic Diseases and Metabolism University of Leuven Leuven Belgium
| | - Sebastian G. Wicha
- Department of Clinical Pharmacy, Institute of Pharmacy University of Hamburg Hamburg Germany
| | - Alexander Kensert
- Department of Pharmaceutical and Pharmacological Sciences University of Leuven Leuven Belgium
- Department of Chemical Engineering Vrije Universiteit Brussels Brussels Belgium
| | - Zhigang Wang
- Department of Pharmaceutical and Pharmacological Sciences University of Leuven Leuven Belgium
| | - João Sabino
- Department of Gastroenterology and Hepatology University Hospitals Leuven Leuven Belgium
- Department of Chronic Diseases and Metabolism University of Leuven Leuven Belgium
| | - Séverine Vermeire
- Department of Gastroenterology and Hepatology University Hospitals Leuven Leuven Belgium
- Department of Chronic Diseases and Metabolism University of Leuven Leuven Belgium
| | - Debby Thomas
- Department of Pharmaceutical and Pharmacological Sciences University of Leuven Leuven Belgium
| | - Marc Ferrante
- Department of Gastroenterology and Hepatology University Hospitals Leuven Leuven Belgium
- Department of Chronic Diseases and Metabolism University of Leuven Leuven Belgium
| | - Erwin Dreesen
- Department of Pharmaceutical and Pharmacological Sciences University of Leuven Leuven Belgium
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14
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Primas C, Reinisch W, Panetta JC, Eser A, Mould DR, Dervieux T. Model Informed Precision Dosing Tool Forecasts Trough Infliximab and Associates with Disease Status and Tumor Necrosis Factor-Alpha Levels of Inflammatory Bowel Diseases. J Clin Med 2022; 11:jcm11123316. [PMID: 35743387 PMCID: PMC9225059 DOI: 10.3390/jcm11123316] [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: 05/02/2022] [Revised: 05/31/2022] [Accepted: 06/08/2022] [Indexed: 02/05/2023] Open
Abstract
Background: Substantial inter-and intra-individual variability of Infliximab (IFX) pharmacokinetics necessitates tailored dosing approaches. Here, we evaluated the performances of a Model Informed Precision Dosing (MIPD) Tool in forecasting trough Infliximab (IFX) levels in association with disease status and circulating TNF-α in patients with Inflammatory Bowel Diseases (IBD). Methods: Consented patients undergoing every 8-week maintenance therapy with IFX were enrolled. Midcycle specimens were collected, IFX, antibodies to IFX, albumin were determined and analyzed with weight using nonlinear mixed effect models coupled with Bayesian data assimilation to forecast trough levels. Accuracy of forecasted as compared to observed trough IFX levels were evaluated using Demings’s regression. Association between IFX levels, CRP-based clinical remission and TNF-α levels were analyzed using logistic regression and linear mixed effect models. Results: In 41 patients receiving IFX (median dose = 5.3 mg/Kg), median IFX levels decreased from 13.0 to 3.9 µg/mL from mid to end of cycle time points, respectively. Midcycle IFX levels forecasted trough with Deming’s slope = 0.90 and R2 = 0.87. Observed end cycle and forecasted trough levels above 5 µg/mL associated with CRP-based clinical remission (OR = 7.2 CI95%: 1.7−30.2; OR = 21.0 CI95%: 3.4−127.9, respectively) (p < 0.01). Median TNF-α levels increased from 4.6 to 8.0 pg/mL from mid to end of cycle time points, respectively (p < 0.01). CRP and TNF-α levels associated independently and additively to decreased IFX levels (p < 0.01). Conclusions: These data establish the value of our MIPD tool in forecasting trough IFX levels in patients with IBD. Serum TNF-α and CRP are reflective of inflammatory burden which impacts exposure.
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Affiliation(s)
- Christian Primas
- Division of Gastroenterology & Hepatology, Medical University of Vienna, A-1090 Vienna, Austria; (C.P.); (A.E.)
| | - Walter Reinisch
- Division of Gastroenterology & Hepatology, Medical University of Vienna, A-1090 Vienna, Austria; (C.P.); (A.E.)
- Correspondence: (W.R.); (T.D.)
| | - John C. Panetta
- St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Alexander Eser
- Division of Gastroenterology & Hepatology, Medical University of Vienna, A-1090 Vienna, Austria; (C.P.); (A.E.)
| | | | - Thierry Dervieux
- Prometheus Laboratories, San Diego, CA 92121, USA
- Correspondence: (W.R.); (T.D.)
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15
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Ribba B. Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry. Front Pharmacol 2022; 13:1094281. [PMID: 36873047 PMCID: PMC9981647 DOI: 10.3389/fphar.2022.1094281] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/12/2022] [Indexed: 02/19/2023] Open
Abstract
Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)-a set of computational methods addressing optimization problems as a continuous learning process-shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry-a way to characterize mental dysfunctions in terms of aberrant brain computations-and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities.
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Affiliation(s)
- Benjamin Ribba
- Roche Pharma Research and Early Development (pRED), F. Hoffmann-La Roche Ltd, Basel, Switzerland
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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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Eckardt JN, Wendt K, Bornhäuser M, Middeke JM. Reinforcement Learning for Precision Oncology. Cancers (Basel) 2021; 13:4624. [PMID: 34572853 PMCID: PMC8472712 DOI: 10.3390/cancers13184624] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/13/2021] [Accepted: 09/13/2021] [Indexed: 01/19/2023] Open
Abstract
Precision oncology is grounded in the increasing understanding of genetic and molecular mechanisms that underly malignant disease and offer different treatment pathways for the individual patient. The growing complexity of medical data has led to the implementation of machine learning techniques that are vastly applied for risk assessment and outcome prediction using either supervised or unsupervised learning. Still largely overlooked is reinforcement learning (RL) that addresses sequential tasks by exploring the underlying dynamics of an environment and shaping it by taking actions in order to maximize cumulative rewards over time, thereby achieving optimal long-term outcomes. Recent breakthroughs in RL demonstrated remarkable results in gameplay and autonomous driving, often achieving human-like or even superhuman performance. While this type of machine learning holds the potential to become a helpful decision support tool, it comes with a set of distinctive challenges that need to be addressed to ensure applicability, validity and safety. In this review, we highlight recent advances of RL focusing on studies in oncology and point out current challenges and pitfalls that need to be accounted for in future studies in order to successfully develop RL-based decision support systems for precision oncology.
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Affiliation(s)
- Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, 01307 Dresden, Germany; (M.B.); (J.M.M.)
| | - Karsten Wendt
- Institute of Software and Multimedia Technology, Technical University Dresden, 01069 Dresden, Germany;
| | - Martin Bornhäuser
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, 01307 Dresden, Germany; (M.B.); (J.M.M.)
- German Consortium for Translational Cancer Research, 69120 Heidelberg, Germany
- National Center for Tumor Diseases, 01307 Dresden, Germany
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, 01307 Dresden, Germany; (M.B.); (J.M.M.)
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18
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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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