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Siebinga H, de Wit-van der Veen BJ, de Vries-Huizing DMV, Vogel WV, Hendrikx JJMA, Huitema ADR. Quantification of biochemical PSA dynamics after radioligand therapy with [ 177Lu]Lu-PSMA-I&T using a population pharmacokinetic/pharmacodynamic model. EJNMMI Phys 2024; 11:39. [PMID: 38656678 PMCID: PMC11043318 DOI: 10.1186/s40658-024-00642-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/12/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND There is an unmet need for prediction of treatment outcome or patient selection for [177Lu]Lu-PSMA therapy in patients with metastatic castration-resistant prostate cancer (mCRPC). Quantification of the tumor exposure-response relationship is pivotal for further treatment optimization. Therefore, a population pharmacokinetic (PK) model was developed for [177Lu]Lu-PSMA-I&T using SPECT/CT data and, subsequently, related to prostate-specific antigen (PSA) dynamics after therapy in patients with mCRPC using a pharmacokinetic/pharmacodynamic (PKPD) modelling approach. METHODS A population PK model was developed using quantitative SPECT/CT data (406 scans) of 76 patients who received multiple cycles [177Lu]Lu-PSMA-I&T (± 7.4 GBq with either two- or six-week interval). The PK model consisted of five compartments; central, salivary glands, kidneys, tumors and combined remaining tissues. Covariates (tumor volume, renal function and cycle number) were tested to explain inter-individual variability on uptake into organs and tumors. The final PK model was expanded with a PD compartment (sequential fitting approach) representing PSA dynamics during and after treatment. To explore the presence of a exposure-response relationship, individually estimated [177Lu]Lu-PSMA-I&T tumor concentrations were related to PSA changes over time. RESULTS The population PK model adequately described observed data in all compartments (based on visual inspection of goodness-of-fit plots) with adequate precision of parameters estimates (< 36.1% relative standard error (RSE)). A significant declining uptake in tumors (k14) during later cycles was identified (uptake decreased to 73%, 50% and 44% in cycle 2, 3 and 4-7, respectively, compared to cycle 1). Tumor growth (defined by PSA increase) was described with an exponential growth rate (0.000408 h-1 (14.2% RSE)). Therapy-induced PSA decrease was related to estimated tumor concentrations (MBq/L) using both a direct and delayed drug effect. The final model adequately captured individual PSA concentrations after treatment (based on goodness-of-fit plots). Simulation based on the final PKPD model showed no evident differences in response for the two different dosing regimens currently used. CONCLUSIONS Our population PK model accurately described observed [177Lu]Lu-PSMA-I&T uptake in salivary glands, kidneys and tumors and revealed a clear declining tumor uptake over treatment cycles. The PKPD model adequately captured individual PSA observations and identified population response rates for the two dosing regimens. Hence, a PKPD modelling approach can guide prediction of treatment response and thus identify patients in whom radioligand therapy is likely to fail.
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Affiliation(s)
- Hinke Siebinga
- Department of Pharmacy and Pharmacology, The Netherlands Cancer Institute: Antoni Van Leeuwenhoek, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Department of Nuclear Medicine, The Netherlands Cancer Institute: Antoni Van Leeuwenhoek, Amsterdam, The Netherlands.
| | | | - Daphne M V de Vries-Huizing
- Department of Nuclear Medicine, The Netherlands Cancer Institute: Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
| | - Wouter V Vogel
- Department of Nuclear Medicine, The Netherlands Cancer Institute: Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
- Department of Radiation Oncology, The Netherlands Cancer Institute: Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
| | - Jeroen J M A Hendrikx
- Department of Pharmacy and Pharmacology, The Netherlands Cancer Institute: Antoni Van Leeuwenhoek, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Nuclear Medicine, The Netherlands Cancer Institute: Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
| | - Alwin D R Huitema
- Department of Pharmacy and Pharmacology, The Netherlands Cancer Institute: Antoni Van Leeuwenhoek, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Clinical Pharmacy, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Pharmacology, Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
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2
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Chalova P, Tazky A, Skultety L, Minichova L, Chovanec M, Ciernikova S, Mikus P, Piestansky J. Determination of short-chain fatty acids as putative biomarkers of cancer diseases by modern analytical strategies and tools: a review. Front Oncol 2023; 13:1110235. [PMID: 37441422 PMCID: PMC10334191 DOI: 10.3389/fonc.2023.1110235] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of non-digestible carbohydrates in the gastrointestinal tract. They can be seen as the major flow of carbon from the diet, through the microbiome to the host. SCFAs have been reported as important molecules responsible for the regulation of intestinal homeostasis. Moreover, these molecules have a significant impact on the immune system and are able to affect inflammation, cardiovascular diseases, diabetes type II, or oncological diseases. For this purpose, SCFAs could be used as putative biomarkers of various diseases, including cancer. A potential diagnostic value may be offered by analyzing SCFAs with the use of advanced analytical approaches such as gas chromatography (GC), liquid chromatography (LC), or capillary electrophoresis (CE) coupled with mass spectrometry (MS). The presented review summarizes the importance of analyzing SCFAs from clinical and analytical perspective. Current advances in the analysis of SCFAs focused on sample pretreatment, separation strategy, and detection methods are highlighted. Additionally, it also shows potential areas for the development of future diagnostic tools in oncology and other varieties of diseases based on targeted metabolite profiling.
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Affiliation(s)
- Petra Chalova
- Department of Pharmaceutical Analysis and Nuclear Pharmacy, Faculty of Pharmacy, Comenius University, Bratislava, Slovakia
- Biomedical Research Center of the Slovak Academy of Sciences, Institute of Virology, Bratislava, Slovakia
| | - Anton Tazky
- Department of Pharmaceutical Analysis and Nuclear Pharmacy, Faculty of Pharmacy, Comenius University, Bratislava, Slovakia
- Toxicological and Antidoping Center, Faculty of Pharmacy, Comenius University, Bratislava, Slovakia
| | - Ludovit Skultety
- Biomedical Research Center of the Slovak Academy of Sciences, Institute of Virology, Bratislava, Slovakia
- Institute of Microbiology, Academy of Sciences of the Czech Republic, Prague, Czechia
| | - Lenka Minichova
- Biomedical Research Center of the Slovak Academy of Sciences, Institute of Virology, Bratislava, Slovakia
| | - Michal Chovanec
- 2nd Department of Oncology, Faculty of Medicine, Comenius University and National Cancer Institute, Bratislava, Slovakia
| | - Sona Ciernikova
- Biomedical Research Center of the Slovak Academy of Sciences, Cancer Research Institute, Bratislava, Slovakia
| | - Peter Mikus
- Department of Pharmaceutical Analysis and Nuclear Pharmacy, Faculty of Pharmacy, Comenius University, Bratislava, Slovakia
- Toxicological and Antidoping Center, Faculty of Pharmacy, Comenius University, Bratislava, Slovakia
| | - Juraj Piestansky
- Toxicological and Antidoping Center, Faculty of Pharmacy, Comenius University, Bratislava, Slovakia
- Department of Galenic Pharmacy, Faculty of Pharmacy, Comenius University, Bratislava, Slovakia
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Aulin LB, de Lange DW, Saleh MA, van der Graaf PH, Völler S, van Hasselt JC. Biomarker-Guided Individualization of Antibiotic Therapy. Clin Pharmacol Ther 2021; 110:346-360. [PMID: 33559152 PMCID: PMC8359228 DOI: 10.1002/cpt.2194] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/02/2021] [Indexed: 12/11/2022]
Abstract
Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker-based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL-6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker-based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient-based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis.
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Affiliation(s)
- Linda B.S. Aulin
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Dylan W. de Lange
- Department of Intensive Care MedicineUniversity Medical CenterUniversity UtrechtUtrechtThe Netherlands
| | - Mohammed A.A. Saleh
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Piet H. van der Graaf
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- CertaraCanterburyUK
| | - Swantje Völler
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Pharmacy, Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - J.G. Coen van Hasselt
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
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Zwep LB, Duisters KLW, Jansen M, Guo T, Meulman JJ, Upadhyay PJ, van Hasselt JGC. Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling. CPT Pharmacometrics Syst Pharmacol 2021; 10:350-361. [PMID: 33792207 PMCID: PMC8099445 DOI: 10.1002/psp4.12603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 01/07/2021] [Accepted: 02/01/2021] [Indexed: 12/26/2022] Open
Abstract
Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics-based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two-step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient-derived xenograft (PDX) models treated with a variety of mono- and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates-derived TGI parameter values were regressed using the LASSO on high-dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.
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Affiliation(s)
- Laura B. Zwep
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Mathematical InstituteLeiden UniversityLeidenThe Netherlands
| | | | - Martijn Jansen
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Tingjie Guo
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Department of Intensive Care MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | | | - Parth J. Upadhyay
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
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Centanni M, Moes DJAR, Trocóniz IF, Ciccolini J, van Hasselt JGC. Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors. Clin Pharmacokinet 2020; 58:835-857. [PMID: 30815848 PMCID: PMC6584248 DOI: 10.1007/s40262-019-00748-2] [Citation(s) in RCA: 199] [Impact Index Per Article: 49.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical impact in improving overall survival of several malignancies associated with poor outcomes; however, only 20–40% of patients will show long-lasting survival. Further clarification of factors related to treatment response can support improvements in clinical outcome and guide the development of novel immune checkpoint therapies. In this article, we have provided an overview of the pharmacokinetic (PK) aspects related to current ICIs, which include target-mediated drug disposition and time-varying drug clearance. In response to the variation in treatment exposure of ICIs and the significant healthcare costs associated with these agents, arguments for both dose individualization and generalization are provided. We address important issues related to the efficacy and safety, the pharmacodynamics (PD), of ICIs, including exposure–response relationships related to clinical outcome. The unique PK and PD aspects of ICIs give rise to issues of confounding and suboptimal surrogate endpoints that complicate interpretation of exposure–response analysis. Biomarkers to identify patients benefiting from treatment with ICIs have been brought forward. However, validated biomarkers to monitor treatment response are currently lacking.
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Affiliation(s)
- Maddalena Centanni
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Dirk Jan A R Moes
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
| | - Joseph Ciccolini
- SMARTc, CRCM Inserm U1068 Aix Marseille Univ and La Timone University Hospital of Marseille, Marseille, France
| | - J G Coen van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.
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6
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Zhang W, Segers K, Mangelings D, Van Eeckhaut A, Hankemeier T, Vander Heyden Y, Ramautar R. Assessing the suitability of capillary electrophoresis-mass spectrometry for biomarker discovery in plasma-based metabolomics. Electrophoresis 2019; 40:2309-2320. [PMID: 31025710 PMCID: PMC6767474 DOI: 10.1002/elps.201900126] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 01/20/2023]
Abstract
The actual utility of capillary electrophoresis-mass spectrometry (CE-MS) for biomarker discovery using metabolomics still needs to be assessed. Therefore, a simulated comparative metabolic profiling study for biomarker discovery by CE-MS was performed, using pooled human plasma samples with spiked biomarkers. Two studies have been carried out in this work. Focus of study I was on comparing two sets of plasma samples, in which one set (class I) was spiked with five isotope-labeled compounds, whereas another set (class II) was spiked with six different isotope-labeled compounds. In study II, focus was also on comparing two sets of plasma samples, however, the isotope-labeled compounds were spiked to both class I and class II samples but with concentrations which differ by a factor two between both classes (with one compound absent in each class). The aim was to determine whether CEMS-based metabolomics could reveal the spiked biomarkers as the main classifiers, applying two different data analysis software tools (MetaboAnalyst and Matlab). Unsupervised analysis of the recorded metabolic profiles revealed a clear distinction between class I and class II plasma samples in both studies. This classification was mainly attributed to the spiked isotope-labeled compounds, thereby emphasizing the utility of CE-MS for biomarker discovery.
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Affiliation(s)
- Wei Zhang
- Biomedical Microscale AnalyticsDivision of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityThe Netherlands
| | - Karen Segers
- Biomedical Microscale AnalyticsDivision of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityThe Netherlands
- Department of Analytical ChemistryApplied Chemometrics and Molecular ModellingVrije Universiteit BrusselBrusselBelgium
- Department of Pharmaceutical ChemistryDrug Analysis and Drug InformationCenter for NeurosciencesVrije Universiteit BrusselBrusselBelgium
| | - Debby Mangelings
- Department of Analytical ChemistryApplied Chemometrics and Molecular ModellingVrije Universiteit BrusselBrusselBelgium
| | - Ann Van Eeckhaut
- Department of Pharmaceutical ChemistryDrug Analysis and Drug InformationCenter for NeurosciencesVrije Universiteit BrusselBrusselBelgium
| | - Thomas Hankemeier
- Biomedical Microscale AnalyticsDivision of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityThe Netherlands
| | - Yvan Vander Heyden
- Department of Analytical ChemistryApplied Chemometrics and Molecular ModellingVrije Universiteit BrusselBrusselBelgium
| | - Rawi Ramautar
- Biomedical Microscale AnalyticsDivision of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityThe Netherlands
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7
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Pinart M, Kunath F, Lieb V, Tsaur I, Wullich B, Schmidt S. Prognostic models for predicting overall survival in metastatic castration-resistant prostate cancer: a systematic review. World J Urol 2018; 38:613-635. [PMID: 30554274 DOI: 10.1007/s00345-018-2574-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Accepted: 11/20/2018] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Prognostic models are developed to estimate the probability of the occurrence of future outcomes incorporating multiple variables. We aimed to identify and summarize existing multivariable prognostic models developed for predicting overall survival in patients with metastatic castration-resistant prostate cancer (mCRPC). METHODS The protocol was prospectively registered (CRD42017064448). We systematically searched Medline and reference lists up to May 2018 and included experimental and observational studies, which developed and/or internally validated prognostic models for mCRPC patients and were further externally validated or updated. The outcome of interest was overall survival. Two authors independently performed literature screening and quality assessment. RESULTS We included 12 studies that developed models including 8750 patients aged 42-95 years. Models included 4-11 predictor variables, mostly hemoglobin, baseline PSA, alkaline phosphatase, performance status, and lactate dehydrogenase. Very few incorporated Gleason score. Two models included predictors related to docetaxel and mitoxantrone treatments. Model performance after internal validation showed similar discrimination power ranging from 0.62 to 0.73. Overall survival models were mainly constructed as nomograms or risk groups/score. Two models obtained an overall judgment of low risk of bias. CONCLUSIONS Most models were not suitable for clinical use due to methodological shortcomings and lack of external validation. Further external validation and/or model updating is required to increase prognostic accuracy and clinical applicability prior to their incorporation in clinical practice as a useful tool in patient management.
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Affiliation(s)
- M Pinart
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Erlangen, Germany
- UroEvidence@Deutsche Gesellschaft für Urologie, Berlin, Germany
| | - F Kunath
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Erlangen, Germany
- UroEvidence@Deutsche Gesellschaft für Urologie, Berlin, Germany
| | - V Lieb
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Erlangen, Germany
| | - I Tsaur
- Department of Urology, University Medicine Mainz, Mainz, Germany
| | - B Wullich
- Department of Urology and Pediatric Urology, University Hospital Erlangen, Erlangen, Germany
| | - Stefanie Schmidt
- UroEvidence@Deutsche Gesellschaft für Urologie, Berlin, Germany.
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Kohler I, Hankemeier T, van der Graaf PH, Knibbe CA, van Hasselt JC. Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine. Eur J Pharm Sci 2017; 109S:S15-S21. [DOI: 10.1016/j.ejps.2017.05.018] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 05/10/2017] [Indexed: 12/21/2022]
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Lavezzi SM, Borella E, Carrara L, De Nicolao G, Magni P, Poggesi I. Mathematical modeling of efficacy and safety for anticancer drugs clinical development. Expert Opin Drug Discov 2017; 13:5-21. [DOI: 10.1080/17460441.2018.1388369] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Silvia Maria Lavezzi
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Elisa Borella
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Letizia Carrara
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Giuseppe De Nicolao
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Paolo Magni
- Dipartimento di Ingegneria Industriale e dell’Informazione, Università degli Studi di Pavia, Pavia, Italy
| | - Italo Poggesi
- Global Clinical Pharmacology, Janssen Research and Development, Cologno Monzese, Italy
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Systems pharmacology-based identification of pharmacogenomic determinants of adverse drug reactions using human iPSC-derived cell lines. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Abstract
Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic-pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer's disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.
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12
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Modeling the Relationship Between Exposure to Abiraterone and Prostate-Specific Antigen Dynamics in Patients with Metastatic Castration-Resistant Prostate Cancer. Clin Pharmacokinet 2016; 56:55-63. [DOI: 10.1007/s40262-016-0425-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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13
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van Hasselt JGC, Gupta A, Hussein Z, Beijnen JH, Schellens JHM, Huitema ADR. Integrated Simulation Framework for Toxicity, Dose Intensity, Disease Progression, and Cost Effectiveness for Castration-Resistant Prostate Cancer Treatment With Eribulin. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:374-85. [PMID: 26312161 PMCID: PMC4544051 DOI: 10.1002/psp4.48] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Accepted: 04/22/2015] [Indexed: 01/08/2023]
Abstract
Quantitative model-based analyses are helpful to support decision-making in drug development. In oncology, disease progression/clinical outcome (DPCO) models have been used for early predictions of clinical outcome, but most of such approaches did not include adverse events or dose intensity. In addition, cost-effectiveness evaluations of investigational compounds are becoming increasingly important. Here, we developed an integrated model-based framework including relevant treatment effects for patients with castration-resistant prostate cancer treated with the anticancer agent eribulin. The framework included (i) a DPCO model relating prostate-specific antigen (PSA) dynamics to survival; (ii) models for adverse events including dose-limiting neutropenia and other graded toxicities; (iii) a model for Eastern Cooperative Oncology Group (ECOG) performance score; (iv) a model for dropout; (v) the consideration of cost effectiveness. The model allowed simulation of realistic treatment courses. Subsequently, simulations evaluating alternative treatment protocols or patient characteristics were performed in order to derive inferences on expected efficacy and cost effectiveness.
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Affiliation(s)
- J G C van Hasselt
- Department of Clinical Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands ; Department of Pharmacy & Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands ; Division of Pharmacology, Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University Leiden, The Netherlands
| | | | | | - J H Beijnen
- Department of Clinical Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands ; Division of Pharmacoepidemiology & Clinical Pharmacology, Department of Pharmaceutical Sciences, Utrecht University Utrecht, The Netherlands
| | - J H M Schellens
- Department of Pharmacy & Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands ; Division of Pharmacoepidemiology & Clinical Pharmacology, Department of Pharmaceutical Sciences, Utrecht University Utrecht, The Netherlands
| | - A D R Huitema
- Department of Clinical Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands ; Department of Pharmacy & Pharmacology, Netherlands Cancer Institute Amsterdam, The Netherlands
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