1
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Yin A, Veerman GDM, van Hasselt JGC, Steendam CMJ, Dubbink HJ, Guchelaar H, Friberg LE, Dingemans AC, Mathijssen RHJ, Moes DJAR. Quantitative modeling of tumor dynamics and development of drug resistance in non-small cell lung cancer patients treated with erlotinib. CPT Pharmacometrics Syst Pharmacol 2024; 13:612-623. [PMID: 38375997 PMCID: PMC11015077 DOI: 10.1002/psp4.13105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 11/26/2023] [Accepted: 12/26/2023] [Indexed: 02/21/2024] Open
Abstract
Insight into the development of treatment resistance can support the optimization of anticancer treatments. This study aims to characterize the tumor dynamics and development of drug resistance in patients with non-small cell lung cancer treated with erlotinib, and investigate the relationship between baseline circulating tumor DNA (ctDNA) data and tumor dynamics. Data obtained for the analysis included (1) intensively sampled erlotinib concentrations from 29 patients from two previous pharmacokinetic (PK) studies, and (2) tumor sizes, ctDNA measurements, and sparsely sampled erlotinib concentrations from 18 patients from the START-TKI study. A two-compartment population PK model was first developed which well-described the PK data. The PK model was subsequently applied to investigate the exposure-tumor dynamics relationship. To characterize the tumor dynamics, models accounting for intra-tumor heterogeneity and acquired resistance with or without primary resistance were investigated. Eventually, the model assumed acquired resistance only resulted in an adequate fit. Additionally, models with or without exposure-dependent treatment effect were explored, and no significant exposure-response relationship for erlotinib was identified within the observed exposure range. Subsequently, the correlation of baseline ctDNA data on EGFR and TP53 variants with tumor dynamics' parameters was explored. The analysis indicated that higher baseline plasma EGFR mutation levels correlated with increased tumor growth rates, and the inclusion of ctDNA measurements improved model fit. This result suggests that quantitative ctDNA measurements at baseline have the potential to be a predictor of anticancer treatment response. The developed model can potentially be applied to design optimal treatment regimens that better overcome resistance.
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Affiliation(s)
- Anyue Yin
- Department of Clinical Pharmacy and ToxicologyLeiden University Medical CenterLeidenThe Netherlands
| | - G. D. Marijn Veerman
- Department of Medical OncologyErasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Johan G. C. van Hasselt
- Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research (LACDR)Leiden UniversityLeidenThe Netherlands
| | - Christi M. J. Steendam
- Department of Pulmonary DiseasesErasmus MC Cancer InstituteRotterdamThe Netherlands
- Department of Pulmonary DiseasesCatharina HospitalEindhovenThe Netherlands
| | | | - Henk‐Jan Guchelaar
- Department of Clinical Pharmacy and ToxicologyLeiden University Medical CenterLeidenThe Netherlands
| | | | | | - Ron H. J. Mathijssen
- Department of Medical OncologyErasmus MC Cancer InstituteRotterdamThe Netherlands
| | - Dirk Jan A. R. Moes
- Department of Clinical Pharmacy and ToxicologyLeiden University Medical CenterLeidenThe Netherlands
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2
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Bittner B, Schmidt J. Advancing Subcutaneous Dosing Regimens for Biotherapeutics: Clinical Strategies for Expedited Market Access. BioDrugs 2024; 38:23-46. [PMID: 37831325 PMCID: PMC10789662 DOI: 10.1007/s40259-023-00626-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 10/14/2023]
Abstract
In recent years, subcutaneous administration of biotherapeutics has made significant progress. The self-administration market for rheumatoid arthritis has witnessed the introduction of additional follow-on biologics, while the first subcutaneous dosing options for monoclonal antibodies have become available for multiple sclerosis. Oncology has also seen advancements with the authorization of high-volume subcutaneous formulations, facilitated by the development of high-concentration formulations and innovative delivery systems. Regulatory and Health Technology Assessment bodies increasingly consider preference data in filing dossiers, particularly in evaluating novel drug delivery methods. The adoption of a pharmacokinetic-based clinical bridging approach has become standard for transitioning from intravenous to subcutaneous administration. Non-inferiority studies with pharmacokinetics as the only primary endpoint have started deviating from traditional randomization schemes, favoring the subcutaneous route and comparing with historical intravenous data. While nonclinical and computational models made progress in predicting safety and immunogenicity for subcutaneously dosed antibodies, clinical trial evidence remains essential due to inter-individual variations and the impact of formulation parameters on anti-drug antibody formation. Ongoing technological advancements and the expanding knowledge base on pharmacokinetic-pharmacodynamic correlation across specialty areas are expected to further accelerate clinical development of subcutaneous biologics.
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Affiliation(s)
- Beate Bittner
- Global Product Strategy, Product Optimization, F. Hoffmann-La Roche, Grenzacher Strasse 124, 4070, Basel, Switzerland.
| | - Johannes Schmidt
- Global Product Strategy, Product Optimization, F. Hoffmann-La Roche, Grenzacher Strasse 124, 4070, Basel, Switzerland
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3
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Polasek TM, Schuck V. Improving the Efficiency of Clinical Pharmacology Studies. Clin Pharmacol Drug Dev 2023; 12:771-774. [PMID: 37350534 DOI: 10.1002/cpdd.1274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/02/2023] [Indexed: 06/24/2023]
Affiliation(s)
- Thomas M Polasek
- Certara, Princeton, New Jersey, USA
- Centre for Medicines Use and Safety, Monash University, Melbourne, Australia
| | - Virna Schuck
- Ribon Therapeutics Inc, Cambridge, Massachusetts, USA
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4
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Črček M, Grabnar I, Zdovc JA, Grosek Š, Kos MK. External validation of population pharmacokinetic models of gentamicin in paediatric population from preterm newborns to adolescents. ACTA PHARMACEUTICA (ZAGREB, CROATIA) 2023; 73:175-194. [PMID: 37307377 DOI: 10.2478/acph-2023-0027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/04/2023] [Indexed: 06/14/2023]
Abstract
The aim of this study was to externally validate the predictive performance of published population pharmacokinetic models of gentamicin in all paediatric age groups, from preterm newborns to adolescents. We first selected published population pharmacokinetic models of gentamicin developed in the paediatric population with a wide age range. The parameters of the literature models were then re-estimated using the PRIOR subroutine in NONMEM®. The predictive ability of the literature and the tweaked models was evaluated. Retrospectively collected data from a routine clinical practice (512 concentrations from 308 patients) were used for validation. The models with covariates characterising developmental changes in clearance and volume of distribution had better predictive performance, which improved further after re-estimation. The tweaked model by Wang 2019 performed best, with suitable accuracy and precision across the complete paediatric population. For patients treated in the intensive care unit, a lower proportion of patients would be expected to reach the target trough concentration at standard dosing. The selected model could be used for model-informed precision dosing in clinical settings where the entire paediatric population is treated. However, for use in clinical practice, the next step should include additional analysis of the impact of intensive care treatment on gentamicin pharmacokinetics, followed by prospective validation.
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Affiliation(s)
- Mateja Črček
- 1University of Ljubljana, Faculty of Pharmacy, Department of Biopharmacy and Pharmacokinetics, 1000 Ljubljana Slovenia
| | - Iztok Grabnar
- 1University of Ljubljana, Faculty of Pharmacy, Department of Biopharmacy and Pharmacokinetics, 1000 Ljubljana Slovenia
| | - Jurij Aguiar Zdovc
- 1University of Ljubljana, Faculty of Pharmacy, Department of Biopharmacy and Pharmacokinetics, 1000 Ljubljana Slovenia
| | - Štefan Grosek
- 2University of Ljubljana, Faculty of Medicine, Department of Pediatrics 1000 Ljubljana, Slovenia
- 3University Medical Centre Ljubljana Division of Obstetrics and Gynecology, Department of Perinatology Neonatology Section, 1000 Ljubljana Slovenia
- 4University Medical Centre Ljubljana Division of Paediatrics, Department of Paediatric Intensive Therapy, 1000 Ljubljana, Slovenia
| | - Mojca Kerec Kos
- 1University of Ljubljana, Faculty of Pharmacy, Department of Biopharmacy and Pharmacokinetics, 1000 Ljubljana Slovenia
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5
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Wilbaux M, Yang S, Jullion A, Demanse D, Porta DG, Myers A, Meille C, Gu Y. Integration of Pharmacokinetics, Pharmacodynamics, Safety, and Efficacy into Model-Informed Dose Selection in Oncology First-in-Human Study: A Case of Roblitinib (FGF401). Clin Pharmacol Ther 2022; 112:1329-1339. [PMID: 36131557 DOI: 10.1002/cpt.2752] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 09/09/2022] [Indexed: 01/31/2023]
Abstract
Model-informed dose selection has been drawing increasing interest in oncology early clinical development. The current paper describes the example of FGF401, a selective fibroblast growth factor receptor 4 (FGFR4) inhibitor, in which a comprehensive modeling and simulation (M&S) framework, using both pharmacometrics and statistical methods, was established during its first-in-human clinical development using the totality of pharmacokinetics (PK), pharmacodynamic (PD) biomarkers, and safety and efficacy data in patients with cancer. These M&S results were used to inform FGF401 dose selection for future development. A two-compartment population PK (PopPK) model with a delayed 0-order absorption and linear elimination adequately described FGF401 PK. Indirect PopPK/PD models including a precursor compartment were independently established for two biomarkers: circulating FGF19 and 7α-hydroxy-4-cholesten-3-one (C4). Model simulations indicated a close-to-maximal PD effect achieved at the clinical exposure range. Time-to-progression was analyzed by Kaplan-Meier method which favored a trough concentration (Ctrough )-driven efficacy requiring Ctrough above a threshold close to the drug concentration producing 90% inhibition of phospho-FGFR4. Clinical tumor growth inhibition was described by a PopPK/PD model that reproduced the dose-dependent effect on tumor growth. Exposure-safety analyses on the expected on-target adverse events, including elevation of aspartate aminotransferase and diarrhea, indicated a lack of clinically relevant relationship with FGF401 exposure. Simulations from an indirect PopPK/PD model established for alanine aminotransferase, including a chain of three precursor compartments, further supported that maximal target inhibition was achieved and there was a lack of safety-exposure relationship. This M&S framework supported a dose selection of 120 mg once daily fasted or with a low-fat meal and provides a practical example that might be applied broadly in oncology early clinical development.
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Affiliation(s)
| | - Shu Yang
- Pharmacometrics, Novartis, East Hanover, New Jersey, USA
| | - Astrid Jullion
- Early Development Analytics, Novartis, Basel, Switzerland
| | - David Demanse
- Early Development Analytics, Novartis, Basel, Switzerland
| | - Diana Graus Porta
- Oncology, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Andrea Myers
- Global Drug Development, Novartis, East Hanover, New Jersey, USA
| | | | - Yi Gu
- Pharmacokinetic Sciences, Translational Medicine, Novartis, Cambridge, Massachusetts, USA
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6
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Rhodes S, Smith N, Evans T, White R. Identifying COVID-19 optimal vaccine dose using mathematical immunostimulation/immunodynamic modelling. Vaccine 2022; 40:7032-7041. [PMID: 36272876 PMCID: PMC9574467 DOI: 10.1016/j.vaccine.2022.10.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/03/2022] [Accepted: 10/07/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Identifying optimal COVID-19 vaccine dose is essential for maximizing their impact. However, COVID-19 vaccine dose-finding has been an empirical process, limited by short development timeframes, and therefore potentially not thoroughly investigated. Mathematical IS/ID modelling is a novel method for predicting optimal vaccine dose which could inform future COVID-19 vaccine dose decision making. METHODS Published clinical data on COVID-19 vaccine dose-response was identified and extracted. Mathematical models were calibrated to the dose-response data stratified by subpopulation, where possible to predict optimal dose. Predicted optimal doses were summarised across vaccine type and compared to chosen dose for the primary series of COVID-19 vaccines to identify vaccine doses that may benefit from re-evaluation. RESULTS 30 clinical dose-response datasets in adults and elderly population were extracted for four vaccine types and optimal doses predicted using the models. Results suggest that, if re-assessed for dose, COVID-19 vaccines Ad26.cov, ChadOx1 n-Cov19, BNT162b2, Coronavac, and NVX-CoV2373 could benefit from increased dose in adults and mRNA-1273 and Coronavac, could benefit from increased and decreased dose for the elderly population, respectively. DISCUSSION Future iterations of COVID-19 vaccines could benefit from re-evaluating dose to ensure most effective use of the vaccine and mathematical modelling can support this.
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Affiliation(s)
- Sophie Rhodes
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK,Corresponding author
| | - Neal Smith
- Defence and Science Technology Laboratory, UK
| | | | - Richard White
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK
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7
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Yin A, van Hasselt JGC, Guchelaar HJ, Friberg LE, Moes DJAR. Anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance. Sci Rep 2022; 12:4206. [PMID: 35273301 PMCID: PMC8913638 DOI: 10.1038/s41598-022-08012-7] [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: 08/16/2021] [Accepted: 02/17/2022] [Indexed: 12/18/2022] Open
Abstract
Quantitative characterization of evolving tumor resistance under targeted treatment could help identify novel treatment schedules, which may improve the outcome of anti-cancer treatment. In this study, a mathematical model which considers various clonal populations and evolving treatment resistance was developed. With parameter values fitted to the data or informed by literature data, the model could capture previously reported tumor burden dynamics and mutant KRAS levels in circulating tumor DNA (ctDNA) of patients with metastatic colorectal cancer treated with panitumumab. Treatment schedules, including a continuous schedule, intermittent schedules incorporating treatment holidays, and adaptive schedules guided by ctDNA measurements were evaluated using simulations. Compared with the continuous regimen, the simulated intermittent regimen which consisted of 8-week treatment and 4-week suspension prolonged median progression-free survival (PFS) of the simulated population from 36 to 44 weeks. The median time period in which the tumor size stayed below the baseline level (TTS<TS0) was prolonged from 52 to 60 weeks. Extending the treatment holiday resulted in inferior outcomes. The simulated adaptive regimens showed to further prolong median PFS to 56–64 weeks and TTS<TS0 to 114–132 weeks under different treatment designs. A prospective clinical study is required to validate the results and to confirm the added value of the suggested schedules.
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Affiliation(s)
- Anyue Yin
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan G C van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Lena E Friberg
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Dirk Jan A R Moes
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. .,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands.
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8
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A Computational Platform Integrating a Mechanistic Model of Crohn's Disease for Predicting Temporal Progression of Mucosal Damage and Healing. Adv Ther 2022; 39:3225-3247. [PMID: 35581423 PMCID: PMC9239932 DOI: 10.1007/s12325-022-02144-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/24/2022] [Indexed: 12/16/2022]
Abstract
INTRODUCTION Physicians are often required to make treatment decisions for patients with Crohn's disease on the basis of limited objective information about the state of the patient's gastrointestinal tissue while aiming to achieve mucosal healing. Tools to predict changes in mucosal health with treatment are needed. We evaluated a computational approach integrating a mechanistic model of Crohn's disease with a responder classifier to predict temporal changes in mucosal health. METHODS A hybrid mechanistic-statistical platform was developed to predict biomarker and tissue health time courses in patients with Crohn's disease. Eligible patients from the VERSIFY study (n = 69) were classified into archetypical response cohorts using a decision tree based on early treatment data and baseline characteristics. A virtual patient matching algorithm assigned a digital twin to each patient from their corresponding response cohort. The digital twin was used to forecast response to treatment using the mechanistic model. RESULTS The responder classifier predicted endoscopic remission and mucosal healing for treatment with vedolizumab over 26 weeks, with overall sensitivities of 80% and 75% and overall specificities of 69% and 70%, respectively. Predictions for changes in tissue damage over time in the validation set (n = 31), a measure of the overall performance of the platform, were considered good (at least 70% of data points matched), fair (at least 50%), and poor (less than 50%) for 71%, 23%, and 6% of patients, respectively. CONCLUSION Hybrid computational tools including mechanistic components represent a promising form of decision support that can predict outcomes and patient progress in Crohn's disease.
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9
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PBPK Modeling and Simulation and Therapeutic Drug Monitoring: Possible Ways for Antibiotic Dose Adjustment. Processes (Basel) 2021. [DOI: 10.3390/pr9112087] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Pharmacokinetics (PK) is a branch of pharmacology present and of vital importance for the research and development (R&D) of new drugs, post-market monitoring, and continued optimizations in clinical contexts. Ultimately, pharmacokinetics can contribute to improving patients’ clinical outcomes, helping enhance the efficacy of treatments, and reducing possible adverse side effects while also contributing to precision medicine. This article discusses the methods used to predict and study human pharmacokinetics and their evolution to the current physiologically based pharmacokinetic (PBPK) modeling and simulation methods. The importance of therapeutic drug monitoring (TDM) and PBPK as valuable tools for Model-Informed Precision Dosing (MIPD) are highlighted, with particular emphasis on antibiotic therapy since dosage adjustment of antibiotics can be vital to ensure successful clinical outcomes and to prevent the spread of resistant bacterial strains.
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10
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The role of DMPK science in improving pharmaceutical research and development efficiency. Drug Discov Today 2021; 27:705-729. [PMID: 34774767 DOI: 10.1016/j.drudis.2021.11.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/09/2021] [Accepted: 11/03/2021] [Indexed: 12/14/2022]
Abstract
The successful regulatory authority approval rate of drug candidates in the drug development pipeline is crucial for determining pharmaceutical research and development (R&D) efficiency. Regulatory authorities include the US Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceutical and Food Safety Bureau Japan (PFSB), among others. Optimal drug metabolism and pharmacokinetics (DMPK) properties influence the progression of a drug candidate from the preclinical to the clinical phase. In this review, we provide a comprehensive assessment of essential concepts, methods, improvements, and challenges in DMPK science and its significance in drug development. This information provides insights into the association of DMPK science with pharmaceutical R&D efficiency.
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11
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Moradi Kashkooli F, Soltani M. Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach. Sci Rep 2021; 11:21475. [PMID: 34728726 PMCID: PMC8563754 DOI: 10.1038/s41598-021-00989-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 10/21/2021] [Indexed: 12/22/2022] Open
Abstract
The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches-maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)-as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy.
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Affiliation(s)
| | - M Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
- Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran.
- Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran.
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12
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Lommerse J, Plock N, Cheung SYA, Sachs JR. V 2 ACHER: Visualization of complex trial data in pharmacometric analyses with covariates. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1092-1106. [PMID: 34242494 PMCID: PMC8452296 DOI: 10.1002/psp4.12679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 05/17/2021] [Accepted: 05/28/2021] [Indexed: 11/06/2022]
Abstract
Pharmacometric models can enhance clinical decision making, with covariates exposing potential contributions to variability of subpopulation characteristics, for example, demographics or disease status. Intuitive visualization of models with multiple covariates is needed because sparsity of data in visualizations trellised by covariate values can raise concerns about the credibility of the underlying model. V2 ACHER, introduced here, is a stepwise transformation of data that can be applied to a variety of static (non-ordinary-differential-equation-based) pharmacometric analyses. This work uses four examples of increasing complexity to show how the transformation elucidates the relationship between observations and model results and how it can also be used in visual predictive checks to confirm the quality of a model. V2 ACHER facilitates consistent, intuitive, single-plot visualization of a multicovariate model with a complex data set, thereby enabling easier model communication for modelers and for cross-functional development teams and facilitating confident use in support of decisions.
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Affiliation(s)
- Jos Lommerse
- Certara Strategic Consulting, Princeton, NJ, USA
| | - Nele Plock
- Certara Strategic Consulting, Princeton, NJ, USA
| | | | - Jeffrey R Sachs
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism-Quantitative Pharmacology and Pharmacometrics, Research Laboratories of Merck & Co., Inc., Kenilworth, NJ, USA
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13
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Dangi M, Khichi A, Jakhar R, Chhillar AK. Growing Preferences towards Analog-based Drug Discovery. Curr Pharm Biotechnol 2021; 22:1030-1045. [PMID: 32900347 DOI: 10.2174/1389201021666200908121409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/29/2020] [Accepted: 08/21/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND The major concern of today's time is the developing resistance in most of the clinically derived pathogenic micro-organisms for available drugs through several mechanisms. Therefore, there is a dire need to develop novel molecules with drug-like properties that can be effective against the otherwise resistant micro-organisms. METHODS New drugs can be developed using several methods like structure-based drug design, ligandbased drug design, or by developing analogs of the available drugs to further improve their effects. However, the smartness is to opt for the techniques that have comparatively less expenditure, lower failure rates, and faster discovery rates. RESULTS Analog-Based Drug Design (ABDD) is one such technique that researchers worldwide are opting to develop new drug-like molecules with comparatively lower market values. They start by first designing the analogs sharing structural and pharmacological similarities to the existing drugs. This method embarks on scaffold structures of available drugs already approved by the clinical trials, but are left ineffective because of resistance developed by the pathogens. CONCLUSION In this review, we have discussed some recent examples of anti-fungal and anti-bacterial (antimicrobial) drugs that were designed based on the ABDD technique. Also, we have tried to focus on the in silico tools and techniques that can contribute to the designing and computational screening of the analogs, so that these can be further considered for in vitro screening to validate their better biological activities against the pathogens with comparatively reduced rates of failure.
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Affiliation(s)
- Mehak Dangi
- Centre for Bioinformatics, M.D. University, Rohtak-124001, Haryana, India
| | - Alka Khichi
- Centre for Bioinformatics, M.D. University, Rohtak-124001, Haryana, India
| | - Ritu Jakhar
- Centre for Bioinformatics, M.D. University, Rohtak-124001, Haryana, India
| | - Anil K Chhillar
- Centre for Bioinformatics, M.D. University, Rohtak-124001, Haryana, India
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14
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Walker M, Hamley JID, Milton P, Monnot F, Kinrade S, Specht S, Pedrique B, Basáñez MG. Supporting drug development for neglected tropical diseases using mathematical modelling. Clin Infect Dis 2021; 73:e1391-e1396. [PMID: 33893482 PMCID: PMC8442785 DOI: 10.1093/cid/ciab350] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Indexed: 11/14/2022] Open
Abstract
Drug-based interventions are at the heart of global efforts to reach elimination as a public health problem (trachoma, soil-transmitted helminthiases, schistosomiasis, lymphatic filariasis) or elimination of transmission (onchocerciasis) for 5 of the most prevalent neglected tropical diseases tackled via the World Health Organization preventive chemotherapy strategy. While for some of these diseases there is optimism that currently available drugs will be sufficient to achieve the proposed elimination goals, for others—particularly onchocerciasis—there is a growing consensus that novel therapeutic options will be needed. Since in this area no high return of investment is possible, minimizing wasted money and resources is essential. Here, we use illustrative results to show how mathematical modeling can guide the drug development pathway, yielding resource-saving and efficiency payoffs, from the refinement of target product profiles and intended context of use to the design of clinical trials.
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Affiliation(s)
- Martin Walker
- Department of Pathobiology and Population Sciences and London Centre for Neglected Tropical Disease Research, Royal Veterinary College, UK.,MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research, Imperial College London, UK
| | - Jonathan I D Hamley
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research, Imperial College London, UK
| | - Philip Milton
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research, Imperial College London, UK
| | - Frédéric Monnot
- Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland
| | - Sally Kinrade
- Medicines Development for Global Health, Southbank VIC, Australia
| | - Sabine Specht
- Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland
| | - Bélen Pedrique
- Drugs for Neglected Diseases initiative (DNDi), Geneva, Switzerland
| | - Maria-Gloria Basáñez
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology and London Centre for Neglected Tropical Disease Research, Imperial College London, UK
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15
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Roganović M, Homšek A, Jovanović M, Topić-Vučenović V, Ćulafić M, Miljković B, Vučićević K. Concept and utility of population pharmacokinetic and pharmacokinetic/pharmacodynamic models in drug development and clinical practice. ARHIV ZA FARMACIJU 2021. [DOI: 10.5937/arhfarm71-32901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
Due to frequent clinical trial failures and consequently fewer new drug approvals, the need for improvement in drug development has, to a certain extent, been met using model-based drug development. Pharmacometrics is a part of pharmacology that quantifies drug behaviour, treatment response and disease progression based on different models (pharmacokinetic - PK, pharmacodynamic - PD, PK/PD models, etc.) and simulations. Regulatory bodies (European Medicines Agency, Food and Drug Administration) encourage the use of modelling and simulations to facilitate decision-making throughout all drug development phases. Moreover, the identification of factors that contribute to variability provides a basis for dose individualisation in routine clinical practice. This review summarises current knowledge regarding the application of pharmacometrics in drug development and clinical practice with emphasis on the population modelling approach.
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16
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Seurat J, Girard P, Goteti K, Mentré F. Comparison of Various Phase I Combination Therapy Designs in Oncology for Evaluation of Early Tumor Shrinkage Using Simulations. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:686-694. [PMID: 33080100 PMCID: PMC7762808 DOI: 10.1002/psp4.12564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/21/2020] [Indexed: 12/11/2022]
Abstract
There is still a lack of efficient designs for identifying the dose response in oncology combination therapies in early clinical trials. The concentration response relationship can be identified using the early tumor shrinkage time course, which has been shown to be a good early response marker of clinical efficacy. The performance of various designs using an exposure–tumor growth inhibition model was explored using simulations. Different combination effects of new drug M and cetuximab (reference therapy) were explored first assuming no effect of M on cetuximab (to investigate the type I error (α)), and subsequently assuming additivity or synergy between cetuximab and M. One‐arm, two‐arm, and four‐arm designs were evaluated. In the one‐arm design, 60 patients received cetuximab + M. In the two‐arm design, 30 patients received cetuximab and 30 received cetuximab + M. In the four‐arm design, in addition to cetuximab and cetuximab + M as standard doses, combination arms with lower doses of cetuximab were evaluated (15 patients/arm). Model‐based predictions or “simulated observations” of early tumor shrinkage at week 8 (ETS8) were compared between the different arms. With the same number of individuals, the one‐arm design showed better statistical power than other designs but led to strong inflation of α in case of misestimated reference for ETS8 value. The two‐arm design protected against this misestimation and, with the same total number of subjects, would provide higher statistical power than a four‐arm design. However, a four‐arm design would be helpful for exploring more doses of cetuximab in combination with M to better understand the interaction.
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Affiliation(s)
- Jérémy Seurat
- Université de Paris, INSERM, IAME, F-75006 Paris, France
| | - Pascal Girard
- Merck Institute for Pharmacometrics, Merck Serono S.A, Lausanne, Switzerland
| | | | - France Mentré
- Université de Paris, INSERM, IAME, F-75006 Paris, France
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17
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Ayyar VS, Jusko WJ. Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids. Pharmacol Rev 2020; 72:414-438. [PMID: 32123034 DOI: 10.1124/pr.119.018101] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.
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Affiliation(s)
- Vivaswath S Ayyar
- Department of Pharmaceutical Sciences University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
| | - William J Jusko
- Department of Pharmaceutical Sciences University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
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18
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Selen A, Müllertz A, Kesisoglou F, Ho RJY, Cook JA, Dickinson PA, Flanagan T. Integrated Multi-stakeholder Systems Thinking Strategy: Decision-making with Biopharmaceutics Risk Assessment Roadmap (BioRAM) to Optimize Clinical Performance of Drug Products. AAPS JOURNAL 2020; 22:97. [PMID: 32719954 DOI: 10.1208/s12248-020-00470-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 06/04/2020] [Indexed: 12/20/2022]
Abstract
Decision-making in drug development benefits from an integrated systems approach, where the stakeholders identify and address the critical questions for the system through carefully designed and performed studies. Biopharmaceutics Risk Assessment Roadmap (BioRAM) is such a systems approach for application of systems thinking to patient focused and timely decision-making, suitable for all stages of drug discovery and development. We described the BioRAM therapy-driven drug delivery framework, strategic roadmap, and integrated risk assessment instrument (BioRAM Scoring Grid) in previous publications (J Pharm Sci 103:3377-97, 2014; J Pharm Sci 105:3243-55, 2016). Integration of systems thinking with pharmaceutical development, manufacturing, and clinical sciences and health care is unique to BioRAM where the developed strategy identifies the system and enables risk characterization and balancing for the entire system. Successful decision-making process in BioRAM starts with the Blueprint (BP) meetings. Through shared understanding of the system, the program strategy is developed and captured in the program BP. Here, we provide three semi-hypothetical examples for illustrating risk-based decision-making in high and moderate risk settings. In the high-risk setting, which is a rare disease area, two completely alternate development approaches are considered (gene therapy and small molecule). The two moderate-risk examples represent varied knowledge levels and drivers for the programs. In one moderate-risk example, knowledge leveraging opportunities are drawn from the manufacturing knowledge and clinical performance of a similar drug substance. In the other example, knowledge on acute tolerance patterns for a similar mechanistic pathway is utilized for identifying markers to inform the drug release profile from the dosage form with the necessary "flexibility" for dosing. All examples illustrate implementation of the BioRAM strategy for leveraging knowledge and decision-making to optimize the clinical performance of drug products for patient benefit.
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Affiliation(s)
- Arzu Selen
- US Food and Drug Administration, Center for Drug Evaluation and Research, Office of Pharmaceutical Quality, Office of Testing and Research, 10903 New Hampshire Ave., Silver Spring, Maryland, 20993, USA.
| | - Anette Müllertz
- Bioneer: FARMA, Department of Pharmacy, University of Copenhagen, 2100, Copenhagen, Denmark
| | - Filippos Kesisoglou
- Biopharmaceutics, Pharmaceutical Sciences and Clinical Supply, Merck & Co, Inc., West Point, Pennsylvania, 19486, USA
| | - Rodney J Y Ho
- University of Washington, Seattle, Washington, 98195, USA
| | - Jack A Cook
- Clinical Pharmacology Department, Global Product Development, Pfizer, Inc., Groton, Connecticut, 06340, USA
| | - Paul A Dickinson
- Seda Pharmaceutical Development Services, Alderley Park, Alderley Edge, Cheshire, SK10 4TG, UK
| | - Talia Flanagan
- UCB Pharma S.A., Avenue de l'Industrie, 1420, Braine - l'Alleud, Belgium
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19
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Designing antifilarial drug trials using clinical trial simulators. Nat Commun 2020; 11:2685. [PMID: 32483209 PMCID: PMC7264235 DOI: 10.1038/s41467-020-16442-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 05/03/2020] [Indexed: 12/01/2022] Open
Abstract
Lymphatic filariasis and onchocerciasis are neglected tropical diseases (NTDs) targeted for elimination by mass (antifilarial) drug administration. These drugs are predominantly active against the microfilarial progeny of adult worms. New drugs or combinations are needed to improve patient therapy and to enhance the effectiveness of interventions in persistent hotspots of transmission. Several therapies and regimens are currently in (pre-)clinical testing. Clinical trial simulators (CTSs) project patient outcomes to inform the design of clinical trials but have not been widely applied to NTDs, where their resource-saving payoffs could be highly beneficial. We demonstrate the utility of CTSs using our individual-based onchocerciasis transmission model (EPIONCHO-IBM) that projects trial outcomes of a hypothetical macrofilaricidal drug. We identify key design decisions that influence the power of clinical trials, including participant eligibility criteria and post-treatment follow-up times for measuring infection indicators. We discuss how CTSs help to inform target product profiles. Drugs for filariases are under development and clinical trial simulators could help to inform the design of clinical trials. Here, Walker et al. use an individual-based onchocerciasis transmission model to project trial outcomes of a hypothetical macrofilaricidal drug, resolving key design choices.
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20
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Germovsek E, Barker CIS, Sharland M, Standing JF. Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance. Clin Pharmacokinet 2020; 58:39-52. [PMID: 29675639 PMCID: PMC6325987 DOI: 10.1007/s40262-018-0659-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies.
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Affiliation(s)
- Eva Germovsek
- Infection, Inflammation and Rheumatology Section, UCL Great Ormond Street Institute of Child Heath, University College London, London, UK. .,Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, PO Box 591, 751 24, Uppsala, Sweden.
| | - Charlotte I S Barker
- Infection, Inflammation and Rheumatology Section, UCL Great Ormond Street Institute of Child Heath, University College London, London, UK.,Paediatric Infectious Diseases Research Group, Institute for Infection and Immunity, St George's, University of London, London, UK.,St George's University Hospitals NHS Foundation Trust, London, UK
| | - Mike Sharland
- Paediatric Infectious Diseases Research Group, Institute for Infection and Immunity, St George's, University of London, London, UK.,St George's University Hospitals NHS Foundation Trust, London, UK
| | - Joseph F Standing
- Infection, Inflammation and Rheumatology Section, UCL Great Ormond Street Institute of Child Heath, University College London, London, UK.,Paediatric Infectious Diseases Research Group, Institute for Infection and Immunity, St George's, University of London, London, UK
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21
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Yin A, Moes DJAR, van Hasselt JGC, Swen JJ, Guchelaar HJ. A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:720-737. [PMID: 31250989 PMCID: PMC6813171 DOI: 10.1002/psp4.12450] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 05/17/2019] [Indexed: 12/19/2022]
Abstract
Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.
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Affiliation(s)
- Anyue Yin
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Dirk Jan A R Moes
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan G C van Hasselt
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands
| | - Jesse J Swen
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands.,Leiden Network for Personalized Therapeutics, Leiden University Medical Center, Leiden, The Netherlands
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22
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Abstract
The pharmaceutical industry is one of the most research and development (R&D)-intensive industries. This industry has tried many strategies to overcome the limitations of a business model that had a high return and high risk. In recent years, the fourth industrial revolution has affected many industries, causing them to update their traditional production and business strategies to a “data science-based” approach. This data science methodology, based on the largely increased size of the data environment, has actively changed the pharmaceutical industry. Therefore, this study aimed to identify specific characteristics of data science innovation in the pharmaceutical industry through the analysis of patent data from the triadic patent databases from the United States, Japan, and Europe.
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23
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Nøhr‐Nielsen A, De Bruin ML, Thomsen M, Pipper CB, Lange T, Bjerrum OJ, Lund TM. Body of evidence and approaches applied in the clinical development programme of fixed-dose combinations in the European Union from 2010 to 2016. Br J Clin Pharmacol 2019; 85:1829-1840. [PMID: 31077427 PMCID: PMC6624404 DOI: 10.1111/bcp.13986] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 04/16/2019] [Accepted: 04/29/2019] [Indexed: 12/21/2022] Open
Abstract
AIMS To provide insights into the clinical development pathway for fixed-dose combinations (FDCs), to consider strategies, and to elucidate the path to approval by assessing the body of evidence, as summarized in the European Public Assessment Reports. METHODS The main resource was the European Public Assessment Reports for 36 FDCs, which included 239 clinical trials with 157 514 patients. The analyses focused on how prior knowledge of the active substances or combination, use of pharmacokinetic-pharmacodynamic modelling, and clinical trial design choice impact the size and strategy of the clinical development programme. RESULTS FDC products primarily comprised 2 previously approved components (21/36, 71%) and had only 1 approved combination (21/36, 71%). Utilizing previously approved active substances resulted in fewer clinical trials, arms and patients, but FDC doses studied in the clinical development programme. Furthermore, dose-finding trials were performed for less than half of FDCs consisting of 2 previously approved active substances. The standard approach to demonstrate contribution of active substances was through a factorial or single combination study. Finally, the use of pharmacokinetic modelling showed a significant decrease in the number of FDC doses studied. CONCLUSIONS The field of FDCs seems to be on the rise, utilizing new molecular entities, prior knowledge and re-profiling drugs. However, a way to move FDC development forward might be through new regulatory and scientific paradigms, in which it is encouraged to utilize model-based approaches to develop FDCs with multiple dose levels and dose ratios for exposure-based treatment that will enable personalization.
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Affiliation(s)
- Asbjørn Nøhr‐Nielsen
- Department of Drug Design and Pharmacology, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
- Copenhagen Centre for Regulatory ScienceUniversity of CopenhagenCopenhagenDenmark
| | | | | | | | - Theis Lange
- Department of Public HealthUniversity of CopenhagenCopenhagenDenmark
| | - Ole Jannik Bjerrum
- Department of Drug Design and Pharmacology, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Trine Meldgaard Lund
- Department of Drug Design and Pharmacology, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
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24
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Papathanasiou T, Strathe A, Overgaard RV, Lund TM, Hooker AC. Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response Models. AAPS JOURNAL 2019; 21:95. [DOI: 10.1208/s12248-019-0365-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 07/09/2019] [Indexed: 12/30/2022]
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25
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Gal J, Milano G, Ferrero JM, Saâda-Bouzid E, Viotti J, Chabaud S, Gougis P, Le Tourneau C, Schiappa R, Paquet A, Chamorey E. Optimizing drug development in oncology by clinical trial simulation: Why and how? Brief Bioinform 2019; 19:1203-1217. [PMID: 28575140 DOI: 10.1093/bib/bbx055] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Indexed: 12/11/2022] Open
Abstract
In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after an extensive and expensive preclinical development period. Methodologies such as computer modeling and clinical trial simulation (CTS) might represent a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the preclinical phase to postmarketing. However, they are barely used and are poorly regarded for drug approval, despite Food and Drug Administration and European Medicines Agency recommendations. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by clinical trial studies and hospital databases. Data sharing and data merging raise legal, policy and technical issues that will need to be addressed. Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increased computing speed should allow this leap forward. The realization of CTS requires not only bioinformatics tools to allow interconnection and global integration of all clinical data but also a universal legal framework to protect the privacy of every patient. While recognizing that CTS can never replace 'real-life' trials, they should be implemented in future drug development schemes to provide quantitative support for decision-making. This in silico medicine opens the way to the P4 medicine: predictive, preventive, personalized and participatory.
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Affiliation(s)
- Jocelyn Gal
- Epidemiology and Biostatistics Unit at the Antoine Lacassagne Center, Nice, France
| | | | | | | | | | | | - Paul Gougis
- Pitie´-Salp^etrie`re Hospital in Paris, France
| | | | | | - Agnes Paquet
- Molecular and Cellular Pharmacology Institute of Sophia Antipolis, Valbonne, France
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26
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Rhodes SJ, Knight GM, Kirschner DE, White RG, Evans TG. Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling. J Theor Biol 2019; 465:51-55. [PMID: 30639297 PMCID: PMC6860008 DOI: 10.1016/j.jtbi.2019.01.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 12/12/2018] [Accepted: 01/09/2019] [Indexed: 12/17/2022]
Abstract
Current methods to optimize vaccine dose are purely empirically based, whereas in the drug development field, dosing determinations use far more advanced quantitative methodology to accelerate decision-making. Applying these established methods in the field of vaccine development may reduce the currently large clinical trial sample sizes, long time frames, high costs, and ultimately have a better potential to save lives. We propose the field of immunostimulation/immunodynamic (IS/ID) modelling, which aims to translate mathematical frameworks used for drug dosing towards optimizing vaccine dose decision-making. Analogous to Pharmacokinetic/Pharmacodynamic (PK/PD) modelling, the mathematical description of drug distribution (PK) and effect (PD) in host, IS/ID modelling approaches apply mathematical models to describe the underlying mechanisms by which the immune response is stimulated by vaccination (IS) and the resulting measured immune response dynamics (ID). To move IS/ID modelling forward, existing datasets and further data on vaccine allometry and dose-dependent dynamics need to be generated and collate, requiring a collaborative environment with input from academia, industry, regulators, governmental and non-governmental agencies to share modelling expertise, and connect modellers to vaccine data.
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Affiliation(s)
- Sophie J Rhodes
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK.
| | - Gwenan M Knight
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK
| | | | - Richard G White
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK
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27
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Goto A, Abe S, Koshiba S, Yamaguchi K, Sato N, Kurahashi Y. Current status and future perspective on preclinical pharmacokinetic and pharmacodynamic (PK/PD) analysis: Survey in Japan pharmaceutical manufacturers association (JPMA). Drug Metab Pharmacokinet 2019; 34:148-154. [PMID: 30827921 DOI: 10.1016/j.dmpk.2019.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 12/24/2018] [Accepted: 01/15/2019] [Indexed: 11/29/2022]
Abstract
Preclinical pharmacokinetic/pharmacodynamic (PK/PD) analysis is an efficient tool for the translational research and proof of mechanism/concept in animals. The questionnaire survey on the practice of preclinical PK/PD analysis was conducted in the member companies of the Japan Pharmaceutical Manufacturers Association (JPMA). According to the survey, 60% of companies conducted preclinical PK/PD analysis and its impact for drug development was different between each of the companies. The frequently analyzed therapeutic areas of preclinical PK/PD analysis were neurology, inflammation and metabolic disease, and those are different from the therapeutic area (infectious disease and oncology) in which PK/PD analysis was considered as effective by the present survey. Many companies which have used preclinical PK/PD analysis for the translation to human PK/PD and for the prediction of dose/regimen had good communication with other research & development (R&D) departments (e.g. pharmacology/clinical pharmacology). The increase in resources for preclinical PK/PD analysis including education was highly demanded. As a future perspective, the closer collaboration between pharmacokinetics scientists, pharmacologists, toxicologists and clinical pharmacologists and the increase in resources including upskilling and the comprehension of preclinical PK/PD analysis by the project team are considered to lead to efficient contributions to improve the success ratio of drug discovery and development.
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Affiliation(s)
- Akihiko Goto
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Drug Metabolism and Pharmacokinetics Research Laboratories, Takeda Pharmaceutical Company Limited, 2-26-1, Muraoka-Higashi, Fujisawa, Kanagawa, 251-8555, Japan.
| | - Sadahiro Abe
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Clinical Pharmacology, Clinical Research, Pfizer R&D Japan, Inc., Shinjuku Bunka Quint Building, 3-22-7, Yoyogi, Shibuya-ku, Tokyo, 151-8589, Japan
| | - Shoko Koshiba
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Pharmacokinetic Research Laboratories, Translational Research Unit, R&D Division, Kyowa Hakko Kirin Co., Ltd., 1188 Shimotogari, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8731, Japan
| | - Koji Yamaguchi
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan
| | - Nobuo Sato
- Non-Clinical Evaluation Expert Committee, Drug Evaluation Committee, Japan Pharmaceutical Manufacturers Association (JPMA), 2-3-11, Nihombashi-honcho, Chuo-ku, Tokyo, 103-0023, Japan; Pharmacokinetics and Analysis Laboratory, Pharmaceutical Research Center, Meiji Seika Pharma Co., Ltd., 760 Morooka-cho, Kohoku-ku, Yokohama, 222-8567, Japan
| | - Yoshikazu Kurahashi
- Drug Metabolism & Pharmacokinetics Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho Takatsuki, Osaka, 569-1125, Japan
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28
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Polasek TM, Rostami-Hodjegan A, Yim DS, Jamei M, Lee H, Kimko H, Kim JK, Nguyen PTT, Darwich AS, Shin JG. What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing. AAPS JOURNAL 2019; 21:17. [PMID: 30627939 DOI: 10.1208/s12248-018-0286-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 12/10/2018] [Indexed: 12/11/2022]
Abstract
Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, "What does it take to make MIPD common practice?" Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care.
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Affiliation(s)
- Thomas M Polasek
- Certara, 100 Overlook Center, Suite 101, Princeton, New Jersey, 08540, USA. .,Centre for Medicines Use and Safety, Monash University, Melbourne, Australia.
| | - Amin Rostami-Hodjegan
- Certara, 100 Overlook Center, Suite 101, Princeton, New Jersey, 08540, USA.,Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Dong-Seok Yim
- Department of Pharmacology, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Masoud Jamei
- Certara, 100 Overlook Center, Suite 101, Princeton, New Jersey, 08540, USA
| | - Howard Lee
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, South Korea.,Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Holly Kimko
- Janssen Research and Development, Lower Gwynedd Township, Pennsylvania, USA
| | - Jae Kyoung Kim
- Korea Advanced Institute of Advanced Technology, Daedoek Innopolis, Daejeon, South Korea
| | - Phuong Thi Thu Nguyen
- Department of Pharmacology and Clinical Pharmacology, Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.,Faculty of Pharmacy, Haiphong University of Medicine and Pharmacy, Haiphong, Vietnam
| | - Adam S Darwich
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Jae-Gook Shin
- Department of Pharmacology and Clinical Pharmacology, Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
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29
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Calin A, Martin M, Lopez-Tarruella S. Simulation modeling approaches to answer clinically relevant questions in breast cancer low-risk populations. ANNALS OF TRANSLATIONAL MEDICINE 2019; 6:S80. [PMID: 30613655 DOI: 10.21037/atm.2018.10.68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Ana Calin
- Radiation Oncology Service, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Miguel Martin
- Medical Oncology Service, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Universidad Complutense, CiberOnc, GEICAM, Madrid, Spain
| | - Sara Lopez-Tarruella
- Medical Oncology Service, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Universidad Complutense, CiberOnc, GEICAM, Madrid, Spain
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30
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Polasek TM, Rayner CR, Peck RW, Rowland A, Kimko H, Rostami‐Hodjegan A. Toward Dynamic Prescribing Information: Codevelopment of Companion Model‐Informed Precision Dosing Tools in Drug Development. Clin Pharmacol Drug Dev 2018; 8:418-425. [DOI: 10.1002/cpdd.638] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2018] [Accepted: 11/05/2018] [Indexed: 12/18/2022]
Affiliation(s)
- Thomas M. Polasek
- Certara Princeton NJ USA
- Centre for Medicines Use and SafetyMonash University Melbourne Australia
| | - Craig R. Rayner
- Certara Princeton NJ USA
- Centre for Medicines Use and SafetyMonash University Melbourne Australia
| | - Richard W. Peck
- Pharma Research and Exploratory DevelopmentRoche Innovation Centre Basel Basel Switzerland
| | - Andrew Rowland
- College of Medicine and Public HealthFlinders University Adelaide Australia
| | - Holly Kimko
- Janssen Research and Development Exton PA USA
| | - Amin Rostami‐Hodjegan
- Certara Princeton NJ USA
- Centre for Applied Pharmacokinetic ResearchUniversity of Manchester Manchester UK
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31
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Robertson S. Clinical Studies for the Sake of Negative Data: The Proof Is in the Pudding. Clin Transl Sci 2018; 11:535-536. [PMID: 29992722 PMCID: PMC6226110 DOI: 10.1111/cts.12568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 05/11/2018] [Indexed: 11/25/2022] Open
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32
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Rhodes SJ, Guedj J, Fletcher HA, Lindenstrøm T, Scriba TJ, Evans TG, Knight GM, White RG. Using vaccine Immunostimulation/Immunodynamic modelling methods to inform vaccine dose decision-making. NPJ Vaccines 2018; 3:36. [PMID: 30245860 PMCID: PMC6141590 DOI: 10.1038/s41541-018-0075-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 06/30/2018] [Accepted: 07/12/2018] [Indexed: 12/14/2022] Open
Abstract
Unlike drug dose optimisation, mathematical modelling has not been applied to vaccine dose finding. We applied a novel Immunostimulation/Immunodynamic mathematical modelling framework to translate multi-dose TB vaccine immune responses from mice, to predict most immunogenic dose in humans. Data were previously collected on IFN-γ secreting CD4+ T cells over time for novel TB vaccines H56 and H1 adjuvanted with IC31 in mice (1 dose groups (0.1-1.5 and 15 μg H56 + IC31), 45 mice) and humans (1 dose (50 μg H56/H1 + IC31), 18 humans). A two-compartment mathematical model, describing the dynamics of the post-vaccination IFN-γ T cell response, was fitted to mouse and human data, separately, using nonlinear mixed effects methods. We used these fitted models and a vaccine dose allometric scaling assumption, to predict the most immunogenic human dose. Based on the changes in model parameters by mouse H56 + IC31 dose and by varying the H56 dose allometric scaling factor between mouse and humans, we established that, at a late time point (224 days) doses of 0.8-8 μg H56 + IC31 in humans may be the most immunogenic. A 0.8-8 μg of H-series TB vaccines in humans, may be as, or more, immunogenic, as larger doses. The Immunostimulation/Immunodynamic mathematical modelling framework is a novel, and potentially revolutionary tool, to predict most immunogenic vaccine doses, and accelerate vaccine development.
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Affiliation(s)
- Sophie J. Rhodes
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Jeremie Guedj
- IAME, UMR 1137, INSERM, Université Paris Diderot, Sorbonne Paris Cité Paris, France
- Univ Paris Diderot, Sorbonne Paris Cité, F-75018 Paris, France
| | - Helen A. Fletcher
- Immunology and Infection Department, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Thomas J. Scriba
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | | | - Gwenan M. Knight
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Richard G. White
- TB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, London, UK
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33
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Swift B, Jain L, White C, Chandrasekaran V, Bhandari A, Hughes DA, Jadhav PR. Innovation at the Intersection of Clinical Trials and Real-World Data Science to Advance Patient Care. Clin Transl Sci 2018; 11:450-460. [PMID: 29768712 PMCID: PMC6132367 DOI: 10.1111/cts.12559] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 03/29/2018] [Indexed: 02/01/2023] Open
Abstract
While efficacy and safety data collected from randomized clinical trials are the evidentiary standard for determining market authorization, this alone may no longer be sufficient to address the needs of key stakeholders (regulators, providers, and payers) and guarantee long-term success of pharmaceutical products. There is a heightened interest from stakeholders on understanding the use of real-world evidence (RWE) to substantiate benefit-risk assessment and support the value of a new drug. This review provides an overview of real-world data (RWD) and related advances in the regulatory framework, and discusses their impact on clinical research and development. A framework for linking drug development decisions with the value proposition of the drug, utilizing pharmacokinetic-pharmacodynamic-pharmacoeconomic models, is introduced. The summary presented here is based on the presentations and discussion at the symposium entitled Innovation at the Intersection of Clinical Trials and Real-World Data to Advance Patient Care at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2017 Annual Meeting.
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Affiliation(s)
| | - Lokesh Jain
- Quantitative Pharmacology and PharmacometricsMerck & Co., Inc.RahwayNew JerseyUSA
| | - Craig White
- Harvard PhD program in Health PolicyCambridgeMassachusettsUSA
| | - Vasu Chandrasekaran
- Center for Observational and Real World EvidenceMerck & Co., Inc.BostonMassachusettsUSA
| | - Aman Bhandari
- Center for Observational and Real World EvidenceMerck & Co., Inc.BostonMassachusettsUSA
| | - Dyfrig A. Hughes
- Centre for Health Economics and Medicines EvaluationBangor UniversityBangorGwyneddUK
| | - Pravin R. Jadhav
- Corporate ProjectsResearch & Development (R&D) InnovationOtsuka Pharmaceutical Development and Commercialization (OPDC)PrincetonNew JerseyUSA
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34
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Geerts H, Gieschke R, Peck R. Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases. Expert Rev Clin Pharmacol 2018; 11:789-795. [PMID: 30019953 DOI: 10.1080/17512433.2018.1501555] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
INTRODUCTION The success rate of pharmaceutical Research & Development (R&D) is much lower compared to other industries such as micro-electronics or aeronautics with the probability of a successful clinical development to approval in central nervous system (CNS) disorders hovering in the single digits (7%). Areas covered: Inspired by adjacent engineering-based industries, we argue that quantitative modeling in CNS R&D might improve success rates. We will focus on quantitative techniques in early clinical development, such as PharmacoKinetic-PharmacoDynamic modeling, clinical trial simulation, model-based meta-analysis and the mechanism-based physiology-based pharmacokinetic modeling, and quantitative systems pharmacology. Expert commentary: Mechanism-based computer modeling rely less on existing clinical datasets, therefore can better generalize than Big Data analytics, including prospectively and quantitatively predicting the clinical outcome of new drugs. More specifically, exhaustive post-hoc analysis of failed trials using individual virtual human trial simulation could illuminate underlying causes such as lack of sufficient functional target engagement, negative pharmacodynamic interactions with comedications and genotypes, and mismatched patient population. These insights are beyond the capacity of artificial intelligence (AI) methods as they are many more possible combinations than subjects. Unlike 'black box' approaches in AI, mechanism-based platforms are transparent and based on biologically sound assumptions that can be interrogated.
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Affiliation(s)
- Hugo Geerts
- a In Silico Biosciences, Computational Neuropharmacology , Berwyn , PA , USA
| | - Ronald Gieschke
- b Early Development , Clinical Pharmacology, Roche Innovation Center , Basel , Switzerland
| | - Richard Peck
- b Early Development , Clinical Pharmacology, Roche Innovation Center , Basel , Switzerland
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35
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Hirai T, Itoh T, Kimura T, Echizen H. Evaluation of a pharmacokinetic-pharmacodynamic model for hypouricaemic effects of febuxostat using datasets obtained from real-world patients. Br J Clin Pharmacol 2018; 84:2260-2269. [PMID: 29876951 DOI: 10.1111/bcp.13666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 05/16/2018] [Accepted: 05/27/2018] [Indexed: 01/24/2023] Open
Abstract
AIM Febuxostat is an active xanthine oxidase (XO) inhibitor which is widely used in the treatment of hyperuricaemia. We aimed to evaluate the predictive performance of a pharmacokinetic-pharmacodynamic (PK-PD) model of the hypouricaemic effects of febuxostat. METHODS Previously, we formulated a PK-PD model for predicting the hypouricaemic effects of febuxostat as a function of baseline serum urate levels, body weight, renal function and drug dose, using datasets reported in preapproval studies. Using an updated model with a sensitivity analysis, we examined the predictive performance of the PK-PD model, using datasets obtained from the medical records of patients who received febuxostat from March 2011 to December 2015 at Tokyo Women's Medical University Hospital. Multivariate regression analysis was performed to explore clinical variables to improve the predictive performance of the model. RESULTS A total of 1199 serum urate values were retrieved from 168 patients (age: 60.5 ± 17.7 years; 71.4% male) who were receiving febuxostat as a treatment for hyperuricaemia. There was a significant correlation (r = 0.68; P < 0.01) between the serum urate levels observed and those predicted by the modified PK-PD model. A multivariate regression analysis revealed that the predictive performance of the model could be improved further by considering comorbidities (such as diabetes mellitus), estimated glomerular filtration rate (eGFR) and the coadministration of loop diuretics (r = 0.77, P < 0.01). CONCLUSIONS The PK-PD model may be useful for predicting individualized maintenance doses of febuxostat in real-world patients.
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Affiliation(s)
- Toshinori Hirai
- Department of Pharmacy, Tokyo Women's Medical University Medical Center East, 2-1-10, Nishiogu, Arakawa-ku, Tokyo, 116-8567, Japan
| | - Toshimasa Itoh
- Department of Pharmacy, Tokyo Women's Medical University Medical Center East, 2-1-10, Nishiogu, Arakawa-ku, Tokyo, 116-8567, Japan
| | - Toshimi Kimura
- Department of Pharmacy, Tokyo Women's Medical University Hospital, 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666, Japan
| | - Hirotoshi Echizen
- Department of Pharmacotherapy, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo, 204-8588, Japan
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36
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Jamkhande PG, Ghante MH, Ajgunde BR. Software based approaches for drug designing and development: A systematic review on commonly used software and its applications. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.bfopcu.2017.10.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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37
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Musuamba FT, Manolis E, Holford N, Cheung S, Friberg LE, Ogungbenro K, Posch M, Yates J, Berry S, Thomas N, Corriol-Rohou S, Bornkamp B, Bretz F, Hooker AC, Van der Graaf PH, Standing JF, Hay J, Cole S, Gigante V, Karlsson K, Dumortier T, Benda N, Serone F, Das S, Brochot A, Ehmann F, Hemmings R, Rusten IS. Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014). CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:418-429. [PMID: 28722322 PMCID: PMC5529745 DOI: 10.1002/psp4.12196] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 03/27/2017] [Accepted: 03/27/2017] [Indexed: 02/05/2023]
Abstract
Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late‐stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well‐established and regulatory‐acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4–5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP‐Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)‐based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well‐designed dose‐finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.
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Affiliation(s)
- F T Musuamba
- EMA Modelling and Simulation Working Group, London, UK.,Federal Agency for Medicines and Health Products, Brussels, Belgium.,UMR850 INSERM, Université de Limoges, Limoges, France
| | - E Manolis
- EMA Modelling and Simulation Working Group, London, UK.,European Medicines Agency, London, UK
| | - N Holford
- Department of Pharmacology & Clinical Pharmacology, University of Auckland, Auckland, New Zealand
| | | | | | | | - M Posch
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | | | - S Berry
- Berry consultants, Austin, Texas, USA
| | | | | | | | - F Bretz
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.,Novartis, London, UK
| | | | - P H Van der Graaf
- Leiden Academic Centre for Drug Research, Leiden, The Netherlands.,Certara QSP, Canterbury, UK
| | - J F Standing
- EMA Modelling and Simulation Working Group, London, UK.,University College London, London, UK
| | - J Hay
- EMA Modelling and Simulation Working Group, London, UK.,Medicines and Healthcare Products Regulatory Agency, London, UK
| | - S Cole
- EMA Modelling and Simulation Working Group, London, UK.,Medicines and Healthcare Products Regulatory Agency, London, UK
| | - V Gigante
- EMA Modelling and Simulation Working Group, London, UK.,Agenzia Italiana del Farmaco, Roma, Italy
| | - K Karlsson
- EMA Modelling and Simulation Working Group, London, UK.,Medical Products Agency, Uppsala, Sweden
| | | | - N Benda
- EMA Modelling and Simulation Working Group, London, UK.,Bundesinstitut für Arzneimittel und Medizinprodukte, Bonn, Germany
| | - F Serone
- EMA Modelling and Simulation Working Group, London, UK.,Agenzia Italiana del Farmaco, Roma, Italy
| | - S Das
- AstraZeneca UK Limited, London, UK
| | | | - F Ehmann
- European Medicines Agency, London, UK
| | - R Hemmings
- Medicines and Healthcare Products Regulatory Agency, London, UK
| | - I Skottheim Rusten
- EMA Modelling and Simulation Working Group, London, UK.,Norvegian Medicines Agency, Oslo, Norway
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38
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Helmlinger G, Al-Huniti N, Aksenov S, Peskov K, Hallow KM, Chu L, Boulton D, Eriksson U, Hamrén B, Lambert C, Masson E, Tomkinson H, Stanski D. Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet. Eur J Pharm Sci 2017; 109S:S39-S46. [PMID: 28506868 DOI: 10.1016/j.ejps.2017.05.028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 05/12/2017] [Indexed: 10/19/2022]
Abstract
Modeling & simulation (M&S) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (R&D), regulatory approval, and marketing of novel therapeutics. Applications of M&S help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical R&D.
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Affiliation(s)
- Gabriel Helmlinger
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Waltham, MA, USA.
| | - Nidal Al-Huniti
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Waltham, MA, USA
| | - Sergey Aksenov
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Waltham, MA, USA
| | | | - Karen M Hallow
- College of Public Health, University of Georgia, Athens, GA, USA; College of Engineering, University of Georgia, Athens, GA, USA
| | - Lulu Chu
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Waltham, MA, USA
| | - David Boulton
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Gaithersburg, MD, USA
| | - Ulf Eriksson
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden
| | - Bengt Hamrén
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Mölndal, Sweden
| | - Craig Lambert
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | - Eric Masson
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Waltham, MA, USA
| | - Helen Tomkinson
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Cambridge, UK
| | - Donald Stanski
- Early Clinical Development, IMED Biotech Unit, AstraZeneca, Gaithersburg, MD, USA
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Yin Q, Li J, Zheng Q, Yang X, Lv R, Ma L, Liu J, Zhu T, Zhang W. The quaternary lidocaine derivative QX-314 in combination with bupivacaine for long-lasting nerve block: Efficacy, toxicity, and the optimal formulation in rats. PLoS One 2017; 12:e0174421. [PMID: 28334014 PMCID: PMC5363931 DOI: 10.1371/journal.pone.0174421] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2016] [Accepted: 03/08/2017] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE The quaternary lidocaine derivative (QX-314) in combination with bupivacaine can produce long-lasting nerve blocks in vivo, indicating potential clinical application. The aim of the study was to investigate the efficacy, safety, and the optimal formulation of this combination. METHODS QX-314 and bupivacaine at different concentration ratios were injected in the vicinity of the sciatic nerve in rats; bupivacaine and saline served as controls (n = 6~10). Rats were inspected for durations of effective sensory and motor nerve blocks, systemic adverse effects, and histological changes of local tissues. Mathematical models were established to reveal drug-interaction, concentration-effect relationships, and the optimal ratio of QX-314 to bupivacaine. RESULTS 0.2~1.5% QX-314 with 0.03~0.5% bupivacaine produced 5.8~23.8 h of effective nerve block; while 0.5% bupivacaine alone was effective for 4 h. No systemic side effects were observed; local tissue reactions were similar to those caused by 0.5% bupivacaine if QX-314 were used < 1.2%. The weighted modification model was successfully established, which revealed that QX-314 was the main active ingredient while bupivacaine was the synergist. The formulation, 0.9% QX-314 plus 0.5% bupivacaine, resulted in 10.1 ± 0.8 h of effective sensory and motor nerve blocks. CONCLUSION The combination of QX-314 and bupivacaine facilitated prolonged sciatic nerve block in rats with a satisfactory safety profile, maximizing the duration of nerve block without clinically important systemic and local tissue toxicity. It may emerge as an alternative approach to post-operative pain treatment.
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Affiliation(s)
- Qinqin Yin
- Laboratory of Anesthesia and Critical Care Medicine, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Jun Li
- North Sichuan Medical College, Nanchong, Sichuan, P. R. China
| | - Qingshan Zheng
- Center for Drug Clinical Research, Shanghai University of Chinese Medicine, Shanghai, P. R. China
| | - Xiaolin Yang
- North Sichuan Medical College, Nanchong, Sichuan, P. R. China
| | - Rong Lv
- Laboratory of Anesthesia and Critical Care Medicine, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Longxiang Ma
- Kunming Medical University, Kunming, Yunnan, P. R. China
| | - Jin Liu
- Laboratory of Anesthesia and Critical Care Medicine, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Tao Zhu
- Laboratory of Anesthesia and Critical Care Medicine, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Wensheng Zhang
- Laboratory of Anesthesia and Critical Care Medicine, Translational Neuroscience Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
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40
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Isbister GK, Bies R. Pharmacometrics: so much mathematics and why planes achieve their destinations with almost perfect results …. Br J Clin Pharmacol 2014; 79:1-3. [PMID: 25223922 DOI: 10.1111/bcp.12514] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Accepted: 09/10/2014] [Indexed: 01/25/2023] Open
Affiliation(s)
- Geoffrey K Isbister
- Discipline of Clinical Pharmacology, University of Newcastle, Newcastle, New South Wales, Australia; Department of Clinical Toxicology and Pharmacology, Calvary Mater Newcastle, Newcastle, New South Wales, Australia
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