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  • 1.
    Abrantes, João A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Solms, Alexander
    Bayer, Berlin, Germany.
    Garmann, Dirk
    Bayer, Wuppertal, Germany.
    Nielsen, Elisabet I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bayesian Forecasting Utilizing Bleeding Information to Support Dose Individualization of Factor VIII2019In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 8, no 12, p. 894-903Article in journal (Refereed)
    Abstract [en]

    Bayesian forecasting for dose individualization of prophylactic factor VIII replacement therapy using pharmacokinetic samples is challenged by large interindividual variability in the bleeding risk. A pharmacokinetic‐repeated time‐to‐event model‐based forecasting approach was developed to contrast the ability to predict the future occurrence of bleeds based on individual (i) pharmacokinetic, (ii) bleeding, and (iii) pharmacokinetic, bleeding and covariate information using observed data from the Long‐Term Efficacy Open‐Label Program in Severe Hemophilia A Disease (LEOPOLD) clinical trials (172 severe hemophilia A patients taking prophylactic treatment). The predictive performance assessed by the area under receiver operating characteristic (ROC) curves was 0.67 (95% confidence interval (CI), 0.65–0.69), 0.78 (95% CI, 0.76–0.80), and 0.79 (95% CI, 0.77–0.81) for patients ≥ 12 years when using pharmacokinetics, bleeds, and all data, respectively, suggesting that individual bleed information adds value to the optimization of prophylactic dosing regimens in severe hemophilia A. Further steps to optimize the proposed tool for factor VIII dose adaptation in the clinic are required.

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  • 2.
    Alskär, Oskar
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Model-Based Interspecies Scaling of Glucose Homeostasis2017In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 6, no 11, p. 778-786Article in journal (Refereed)
    Abstract [en]

    Being able to scale preclinical pharmacodynamic response to clinical would be beneficial in drug development. In this work, the integrated glucose insulin (IGI) model, developed on clinical intravenous glucose tolerance test (IVGTT) data, describing dynamic glucose and insulin concentrations during glucose tolerance tests, was scaled to describe data from similar tests performed in healthy rats, mice, dogs, pigs, and humans. Several approaches to scaling the dynamic glucose and insulin were investigated. The theoretical allometric exponents of 0.75 and 1, for clearances and volumes, respectively, could describe the data well with some species-specific adaptations: dogs and pigs showed slower first phase insulin secretion than expected from the scaling, pigs also showed more rapid insulin dependent glucose elimination, and rodents showed differences in glucose effectiveness. The resulting scaled IGI model was shown to accurately predict external preclinical IVGTT data and may be useful in facilitating translations of preclinical research into the clinic.

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  • 3.
    Bauer, Robert J.
    et al.
    ICON Clin Res LLC, Pharmacometr, R&D, Gaithersburg, MD USA..
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Mentre, France
    Univ Paris, INSERM, IAME, Paris, France..
    Tutorial for $DESIGN in NONMEM: Clinical trial evaluation and optimization2021In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 10, no 12, p. 1452-1465Article in journal (Refereed)
    Abstract [en]

    This NONMEM tutorial shows how to evaluate and optimize clinical trial designs, using algorithms developed in design software, such as PopED and PFIM 4.0. Parameter precision and model parameter estimability is obtained by assessing the Fisher Information Matrix (FIM), providing expected model parameter uncertainty. Model parameter identifiability may be uncovered by very large standard errors or inability to invert an FIM. Because evaluation of FIM is more efficient than clinical trial simulation, more designs can be investigated, and the design of a clinical trial can be optimized. This tutorial provides simple and complex pharmacokinetic/pharmacodynamic examples on obtaining optimal sample times, doses, or best division of subjects among design groups. Robust design techniques accounting for likely variability among subjects are also shown. A design evaluator and optimizer within NONMEM allows any control stream first developed for trial design exploration to be subsequently used for estimation of parameters of simulated or clinical data, without transferring the model to another software. Conversely, a model developed in NONMEM could be used for design optimization. In addition, the $DESIGN feature can be used on any model file and dataset combination to retrospectively evaluate the model parameter uncertainty one would expect given that the model generated the data, particularly if outliers of the actual data prevent a reasonable assessment of the variance-covariance. The NONMEM trial design feature is suitable for standard continuous data, whereas more elaborate trial designs or with noncontinuous data-types can still be accomplished in optimal design dedicated software like PopED and PFIM.

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  • 4.
    Boger, E.
    et al.
    AstraZeneca R&D, Dept Resp Inflammat & Autoimmun Innovat Med, Molndal, Sweden.;Univ Warwick, Sch Engn, Coventry, W Midlands, England..
    Evans, N.
    Univ Warwick, Sch Engn, Coventry, W Midlands, England..
    Chappell, M.
    Univ Warwick, Sch Engn, Coventry, W Midlands, England..
    Lundqvist, A.
    AstraZeneca R&D, Dept Resp Inflammat & Autoimmun Innovat Med, Molndal, Sweden..
    Ewing, P.
    AstraZeneca R&D, Dept Resp Inflammat & Autoimmun Innovat Med, Molndal, Sweden..
    Wigenborg, A.
    AstraZeneca R&D, Dept Resp Inflammat & Autoimmun Innovat Med, Molndal, Sweden..
    Fridén, Markus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. AstraZeneca R&D, Dept Resp Inflammat & Autoimmun Innovat Med, Molndal, Sweden..
    Systems Pharmacology Approach for Prediction of Pulmonary and Systemic Pharmacokinetics and Receptor Occupancy of Inhaled Drugs2016In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 5, no 4, p. 201-210Article in journal (Refereed)
    Abstract [en]

    Pulmonary drug disposition after inhalation is complex involving mechanisms, such as regional drug deposition, dissolution, and mucociliary clearance. This study aimed to develop a systems pharmacology approach to mechanistically describe lung disposition in rats and thereby provide an integrated understanding of the system. When drug-and formulation-specific properties for the poorly soluble drug fluticasone propionate were fed into the model, it proved predictive of the pharmacokinetics and receptor occupancy after intravenous administration and nose-only inhalation. As the model clearly distinguishes among drug-specific, formulation-specific, and system-specific properties, it was possible to identify key determinants of pulmonary selectivity of receptor occupancy of inhaled drugs: slow particle dissolution and slow drug-receptor dissociation. Hence, it enables assessment of factors for lung targeting, including molecular properties, formulation, as well as the physiology of the animal species, thereby providing a general framework for rational drug design and facilitated translation of lung targeting from animal to man.

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  • 5.
    Brussee, Janneke M.
    et al.
    Leiden Univ, LACDR, Div Syst Biomed & Pharmacol, Leiden, Netherlands.
    Yu, Huixin
    Leiden Univ, LACDR, Div Syst Biomed & Pharmacol, Leiden, Netherlands.
    Krekels, Elke H. J.
    Leiden Univ, LACDR, Div Syst Biomed & Pharmacol, Leiden, Netherlands.
    de Roos, Berend
    Leiden Univ, LACDR, Div Syst Biomed & Pharmacol, Leiden, Netherlands.
    Brill, Margreke JE
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    van den Anker, Johannes N.
    Univ Basel, Childrens Hosp, Div Paediat Pharmacol & Pharmacometr, Basel, Switzerland;Sophia Childrens Univ Hosp, Erasmus MC, Intens Care & Dept Pediat Surg, Rotterdam, Netherlands;Childrens Natl Hlth Syst, Div Clin Pharmacol, Washington, DC USA.
    Rostami-Hodjegan, Amin
    Simcyp Ltd, Sheffield, S Yorkshire, England;Univ Manchester, Ctr Appl Pharmacokinet Res, Manchester, Lancs, England.
    de Wildt, Saskia N.
    Radboud Univ Nijmegen, Dept Pharmacol & Toxicol, Med Ctr, Nijmegen, Netherlands;Sophia Childrens Univ Hosp, Erasmus MC, Intens Care & Dept Pediat Surg, Rotterdam, Netherlands.
    Knibbe, Catherijne A. J.
    Leiden Univ, LACDR, Div Syst Biomed & Pharmacol, Leiden, Netherlands;St Antonius Hosp, Dept Clin Pharm, Nieuwegein, Netherlands.
    First-Pass CYP3A-Mediated Metabolism of Midazolam in the Gut Wall and Liver in Preterm Neonates2018In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 7, no 6, p. 374-383Article in journal (Refereed)
    Abstract [en]

    To predict first-pass and systemic cytochrome P450 (CYP) 3A-mediated metabolism of midazolam in preterm neonates, a physiological population pharmacokinetic model was developed describing intestinal and hepatic midazolam clearance in preterm infants. On the basis of midazolam and 1-OH-midazolam concentrations from 37 preterm neonates (gestational age 26-34 weeks) receiving midazolam orally and/or via a 30-minute intravenous infusion, intrinsic clearance in the gut wall and liver were found to be very low, with lower values in the gut wall (0.0196 and 6.7 L/h, respectively). This results in a highly variable and high total oral bioavailability of 92.1% (range, 67-95%) in preterm neonates, whereas this is around 30% in adults. This approach in which intestinal and hepatic clearance were separately estimated shows that the high bioavailability in preterm neonates is explained by, likely age-related, low CYP3A activity in the liver and even lower CYP3A activity in the gut wall.

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  • 6.
    Bukkems, Laura
    et al.
    Amsterdam Univ Med Ctr, Hosp Pharm Clin Pharmacol, Amsterdam, Netherlands..
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Cnossen, Marjon O.
    Erasmus MC, Sophia Childrens Hosp Rotterdam, Dept Pediat Hematol, Rotterdam, Netherlands..
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Mathot, Ron A. A.
    Amsterdam Univ Med Ctr, Hosp Pharm Clin Pharmacol, Amsterdam, Netherlands.;Amsterdam Univ Med Ctr, POB 22660, NL-1100 DD Amsterdam, Netherlands..
    Relationship between factor VIII levels and bleeding for rFVIII-SingleChain in severe hemophilia A: A repeated time-to-event analysis2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 5, p. 706-718Article in journal (Refereed)
    Abstract [en]

    Publications on the exposure-effect relationships of factor concentrates for hemophilia treatment are limited, whereas such analyses give insight on treatment efficacy. Our objective was to examine the relationship between the dose, factor VIII (FVIII) levels and bleeding for rFVIII-SingleChain (lonoctocog alfa, Afstyla). Data from persons with severe hemophilia A on rFVIII-SingleChain prophylaxis from three clinical trials were combined. The published rFVIII-SingleChain population pharmacokinetic (PK) model was evaluated and expanded. The probability of bleeding was described with a parametric repeated time-to-event (RTTE) model. Data included 2080 bleeds, 2545 chromogenic stage assay, and 3052 one-stage assay FVIII levels from 241 persons (median age 19 years) followed for median 1090 days. The majority of the bleeds occurred in joints (65%) and the main bleeding reason was trauma (44%). The probability of bleeding decreased during follow-up and a FVIII level of 8.9 IU/dL (95% confidence interval: 6.9-10.9) decreased the bleeding hazard by 50% compared to a situation without FVIII in plasma. Variability in bleeding hazard between persons with similar FVIII levels was large, and the pre-study annual bleeding rate explained part of this variability. When a FVIII trough level of 1 or 3 IU/dL is targeted during prophylaxis, simulations predicted two (90% prediction interval [PI]: 0-17) or one (90% PI: 0-11) bleeds per year, respectively. In conclusion, the developed PK-RTTE model adequately described the relationship between dose, FVIII levels and bleeds for rFVIII-SingleChain. The obtained estimates were in agreement with those published for the FVIII concentrates BAY 81-8973 (octocog alfa) and BAY 94-9027 (damoctocog alfa pegol), indicating similar efficacy to reduce bleeding.

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  • 7.
    Bukkems, Vera E.
    et al.
    Radboud Univ Nijmegen, Med Ctr, Radboud Inst Hlth Sci RIHS, Dept Pharm, Nijmegen, Netherlands..
    Post, Teun M.
    Leiden Experts Adv Pharmacokinet & Pharmacodynam, Leiden, Netherlands..
    Colbers, Angela P.
    Radboud Univ Nijmegen, Med Ctr, Radboud Inst Hlth Sci RIHS, Dept Pharm, Nijmegen, Netherlands..
    Burger, David M.
    Radboud Univ Nijmegen, Med Ctr, Radboud Inst Hlth Sci RIHS, Dept Pharm, Nijmegen, Netherlands..
    Svensson, Elin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Radboud Univ Nijmegen, Med Ctr, Radboud Inst Hlth Sci RIHS, Dept Pharm, Nijmegen, Netherlands..
    A population pharmacokinetics analysis assessing the exposure of raltegravir once-daily 1200 mg in pregnant women living with HIV2021In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 10, no 2, p. 161-172Article in journal (Refereed)
    Abstract [en]

    Once-daily two 600 mg tablets (1200 mg q.d.) raltegravir offers an easier treatment option compared to the twice-daily regimen of one 400 mg tablet. No pharmacokinetic, efficacy, or safety data of the 1200 mg q.d. regimen have been reported in pregnant women to date as it is challenging to collect these clinical data. This study aimed to develop a population pharmacokinetic (PopPK) model to predict the pharmacokinetic profile of raltegravir 1200 mg q.d. in pregnant women and to discuss the expected pharmacodynamic properties of raltegravir 1200 mg q.d. during pregnancy based on previously reported concentration-effect relationships. Data from 11 pharmacokinetic studies were pooled (n = 221). A two-compartment model with first-order elimination and absorption through three sequential transit compartments best described the data. We assessed that the bio-availability of the 600 mg tablets was 21% higher as the 400 mg tablets, and the bio-availability in pregnant women was 49% lower. Monte-Carlo simulations were performed to predict the pharmacokinetic profile of 1200 mg q.d. in pregnant and nonpregnant women. The primary criteria for efficacy were that the lower bound of the 90% confidence interval (CI) of the concentration before next dose administration (C-trough) geometric mean ratio (GMR) of simulated pregnant/nonpregnant women had to be greater than 0.75. The simulated raltegravir C-trough GMR (90% CI) was 0.51 (0.41-0.63), hence not meeting the primary target for efficacy. Clinical data from two pregnant women using 1200 mg q.d. raltegravir showed a similar C-trough ratio pregnant/nonpregnant. Our pharmacokinetic results support the current recommendation of not using the raltegravir 1200 mg q.d. regimen during pregnancy until more data on the exposure-response relationship becomes available.

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  • 8.
    Chen, Chao
    et al.
    GlaxoSmithKline, Clin Pharmacol Modelling & Simulat, London, England..
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Yang, Shuying
    GlaxoSmithKline, Clin Pharmacol Modelling & Simulat, London, England..
    Plan, Elodie L.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item-score models2021In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 10, no 4, p. 309-317Article in journal (Refereed)
    Abstract [en]

    This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson's Disease Rating Scale could allow a smaller trial size and describe a drug's effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed: a cross-over formulation comparison trial in 161 patients with early-stage Parkinson's disease, and a 24-week, parallel-group, placebo-controlled efficacy trial in 393 patients with advanced-stage Parkinson's disease. We applied item response theory to estimate the patients' symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of -3.7 (-5.4 to -2.0) and -9.3 (-11 to -7.3) points by placebo and ropinirole. Comparable changes were estimated (with slightly higher precision) by item-score modeling as -2.0 (-4.0 to -1.0) and -9.0 (-11 to -8.0) points. The treatment duration was insufficient to estimate the symptom progression rate; hence the drug effect on the progression could not be assessed. The trial sizes to detect a drug effect with 80% power on total score and on symptom severity were estimated (at the type I error level of 0.05) as 88 and 58, respectively. Longitudinal item response analysis could markedly reduce sample size; it also has the potential for assessing drug effects on disease progression in longer trials.

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  • 9.
    Chen, Chunli
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Northeast Agr Univ, Coll Vet Med, 600 Changjiang Rd, Harbin 150030, Heilongjiang, Peoples R China. Heilongjiang Key Lab Anim Dis Control & Pharmaceu, 600 Changjiang Rd, Harbin 150030, Heilongjiang, Peoples R China..
    Wicha, Sebastian G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    de Knegt, Gerjo
    Erasmus MC, Department of Medical Microbiology and Infectious Disease, University Medical Centre Rotterdam, Rotterdam, the Netherlands.
    Ortega, Fatima
    Diseases of Developing World Medicines Development Campus, GlaxoSmithKline, Severo Ochoa 2, 28760, Tres Cantos, Madrid, Spain.
    Alameda, Laura
    Diseases of Developing World Medicines Development Campus, GlaxoSmithKline, Severo Ochoa 2, 28760, Tres Cantos, Madrid, Spain.
    Veronica, Sousa
    Diseases of Developing World Medicines Development Campus, GlaxoSmithKline, Severo Ochoa 2, 28760, Tres Cantos, Madrid, Spain.
    de Steenwinkel, Jurriaan
    Erasmus MC, Department of Medical Microbiology and Infectious Disease, University Medical Centre Rotterdam, Rotterdam, the Netherlands.
    Simonsson, Ulrika S H
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Assessing Pharmacodynamic Interactions in Mice using the Multistate Tuberculosis Pharmacometric and General Pharmacodynamic Interaction Models2017In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 6, no 11, p. 787-797Article in journal (Other academic)
    Abstract [en]

    The aim of this study was to investigate pharmacodynamic (PD) interactions in mice infected with Mycobacterium tuberculosis using population pharmacokinetics (PKs), the Multistate Tuberculosis Pharmacometric (MTP) model, and the General Pharmacodynamic Interaction (GPDI) model. Rifampicin, isoniazid, ethambutol, or pyrazinamide were administered in monotherapy for 4 weeks. Rifampicin and isoniazid showed effects in monotherapy, whereas the animals became moribund after 7 days with ethambutol or pyrazinamide alone. No PD interactions were observed against fast-multiplying bacteria. Interactions between rifampicin and isoniazid on killing slow and non-multiplying bacteria were identified, which led to an increase of 0.86 log(10) colony-forming unit (CFU)/lungs at 28 days after treatment compared to expected additivity (i.e., antagonism). An interaction between rifampicin and ethambutol on killing non-multiplying bacteria was quantified, which led to a decrease of 2.84 log(10) CFU/lungs at 28 days after treatment (i.e., synergism). These results show the value of pharmacometrics to quantitatively assess PD interactions in preclinical tuberculosis drug development.

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  • 10.
    Chen, Po-Wei
    et al.
    Amgen Inc, Clin Pharmacol Modeling & Simulat, Thousand Oaks, CA USA..
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Ueckert, Sebastian
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Pritchard-Bell, Ari
    Amgen Inc, Clin Pharmacol Modeling & Simulat, Thousand Oaks, CA USA..
    Hsu, Cheng-Pang
    AskGene Pharm Inc, Camarillo, CA USA..
    Dutta, Sandeep
    Amgen Inc, Clin Pharmacol Modeling & Simulat, Thousand Oaks, CA USA..
    Ahamadi, Malidi
    Amgen Inc, Clin Pharmacol Modeling & Simulat, Thousand Oaks, CA USA.;Amgen Inc, Clin Pharmacol Modeling & Simulat, 1 Amgen Ctr Dr, Thousand Oaks, CA 91320 USA..
    Evaluation of the effect of erenumab on migraine-specific questionnaire in patients with chronic and episodic migraine2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 12, p. 1988-2000Article in journal (Refereed)
    Abstract [en]

    Erenumab is a fully human anti-canonical calcitonin gene-related peptide receptor monoclonal antibody approved for migraine prevention. The Migraine-Specific Quality-of-Life Questionnaire (MSQ) is a 14-item patient-reported outcome instrument that measures the impact of migraine on health-related quality of life. Erenumab data from four phase II/III clinical trials were used to develop an item response theory (IRT) model within a nonlinear mixed effects framework, (i) evaluate the MSQ item information with respect to patient disability, (ii) characterize the longitudinal progression of the MSQ, and (iii) quantify the effect of erenumab on the MSQ in patients with migraine. The majority (80%) of information was found to be contained in 9 out of 14 items, extending the current knowledge on the reliability of the MSQ as a psychometric tool. Simulations across three MSQ domains show significant improvement from baseline, exceeding minimally important differences. Overall, the IRT model platform developed herein allows for systematic quantification of the effect of erenumab on the MSQ in patients with migraine.

  • 11.
    Choy, Steve
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    de Winter, Willem
    Janssen Prevention Center, Janssen Pharmaceutical Companies of Johnson & Johnson, Leiden, The Netherlands.
    Weight-HbA1c-Insulin-Glucose Model for Describing Disease Progression of Type 2 Diabetes2016In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 5, no 1, p. 11-19Article in journal (Refereed)
    Abstract [en]

    A previous semi-mechanistic model described changes in fasting serum insulin (FSI), fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c) in patients with type 2 diabetic mellitus (T2DM) by modeling insulin sensitivity and β-cell function. It was later suggested that change in body weight could affect insulin sensitivity, which this study evaluated in a population model to describe the disease progression of T2DM. Nonlinear mixed effects modeling was performed on data from 181 obese patients with newly diagnosed T2DM managed with diet and exercise for 67 weeks. Baseline β-cell function and insulin sensitivity were 61% and 25% of normal, respectively. Management with diet and exercise (mean change in body weight = -4.1 kg) was associated with an increase of insulin sensitivity (30.1%) at the end of the study. Changes in insulin sensitivity were associated with a decrease of FPG (range, 7.8–7.3 mmol/L) and HbA1c (6.7–6.4%). Weight change as an effector on insulin sensitivity was successfully evaluated in a semi-mechanistic population model.

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    WHIG_model
  • 12.
    Damoiseaux, David
    et al.
    Department of Pharmacy and Pharmacology The Netherlands Cancer Institute Amsterdam The Netherlands.
    Amant, Frédéric
    Department of Gynecology The Netherlands Cancer Institute Amsterdam The Netherlands;Gynecologic Oncology UZ Leuven Leuven Belgium.
    Beijnen, Jos H.
    Department of Pharmacy and Pharmacology The Netherlands Cancer Institute Amsterdam The Netherlands;Utrecht Institute of Pharmaceutical Sciences Utrecht University Utrecht The Netherlands.
    Barnett, Shelby
    Newcastle University Centre for Cancer Newcastle University Newcastle upon Tyne UK.
    Veal, Gareth J.
    Newcastle University Centre for Cancer Newcastle University Newcastle upon Tyne UK.
    Huitema, Alwin D. R.
    Department of Pharmacy and Pharmacology The Netherlands Cancer Institute Amsterdam The Netherlands;Department of Pharmacology Princess Máxima Center for Pediatric Oncology Utrecht The Netherlands;Department of Clinical Pharmacy, University Medical Center Utrecht Utrecht University Utrecht The Netherlands.
    Dorlo, Thomas P. C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Department of Pharmacy and Pharmacology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
    Physiologically‐based pharmacokinetic model to predict doxorubicin and paclitaxel exposure in infants through breast milk2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 12, p. 1931-1944Article in journal (Refereed)
    Abstract [en]

    Limited information is available concerning infant exposure and safety when breastfed by mothers receiving chemotherapy. Whereas defining distribution to breast milk is important to infer drug exposure, infant pharmacokinetics also determine to what extent the infant will be exposed to potential toxic effects. We aimed to assess the impact of chemotherapy containing breast milk on infants by predicting systemic and local (intestinal) exposure of paclitaxel and doxorubicin in infants through breast milk using a physiologically-based pharmacokinetic (PBPK) approach. Whole-body PBPK models of i.v. paclitaxel and doxorubicin were extended from the literature, with an oral absorption component to enable predictions in infants receiving paclitaxel or doxorubicin-containing breast milk. For safety considerations, worst-case scenarios were explored. Finally, paclitaxel and doxorubicin exposures in plasma and intestinal tissue of infants following feeding of breast milk from paclitaxel- or doxorubicin-treated mothers were simulated and breast milk discarding strategies were evaluated. The upper 95th percentile of the predicted peak concentrations in peripheral venous blood were 3.48 and 0.74 nM (0.4%–1.7% and 0.1%–1.8% of on-treatment) for paclitaxel and doxorubicin, respectively. Intestinal exposure reached peak concentrations of 1.0 and 140 μM for paclitaxel and doxorubicin, respectively. Discarding breast milk for the first 3 days after maternal chemotherapy administration reduced systemic and tissue exposures even further, to over 90% and 80% for paclitaxel and doxorubicin, respectively. PBPK simulations of chemotherapy exposure in infants after breastfeeding with chemotherapy containing breast milk suggest that particularly local gastrointestinal adverse events should be monitored, whereas systemic adverse events are not expected.

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  • 13.
    Djebli, Nassim
    et al.
    Roche Innovat Ctr, Roche Pharmaceut Res & Early Dev, Basel, Switzerland.;Luzsana Biotechnol Clin Pharmacol & Early Dev, Basel, Switzerland..
    Parrott, Neil
    Roche Innovat Ctr, Roche Pharmaceut Res & Early Dev, Basel, Switzerland..
    Jaminion, Felix
    Roche Innovat Ctr, Roche Pharmaceut Res & Early Dev, Basel, Switzerland..
    O'Jeanson, Amaury
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Guerini, Elena
    Roche Innovat Ctr, Roche Pharmaceut Res & Early Dev, Basel, Switzerland..
    Carlile, David
    Roche Innovat Ctr, Roche Pharmaceut Res & Early Dev, Welwyn Garden City, England..
    Evaluation of the potential impact on pharmacokinetics of various cytochrome P450 substrates of increasing IL-6 levels following administration of the T-cell bispecific engager glofitamab2024In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 13, no 3, p. 396-409Article in journal (Refereed)
    Abstract [en]

    Glofitamab is a novel T cell bispecific antibody developed for treatment of relapsed-refractory diffuse large B cell lymphoma and other non-Hodgkin's lymphoma indications. By simultaneously binding human CD20-expressing tumor cells and CD3 on T cells, glofitamab induces tumor cell lysis, in addition to T-cell activation, proliferation, and cytokine release. Here, we describe physiologically-based pharmacokinetic (PBPK) modeling performed to assess the impact of glofitamab-associated transient increases in interleukin 6 (IL-6) on the pharmacokinetics of several cytochrome P450 (CYP) substrates. By refinement of a previously described IL-6 model and inclusion of in vitro CYP suppression data for CYP3A4, CYP1A2, and 2C9, a PBPK model was established in Simcyp to capture the induced IL-6 levels seen when glofitamab is administered at the intended dose and dosing regimen. Following model qualification, the PBPK model was used to predict the potential impact of CYP suppression on exposures of various CYP probe substrates. PBPK analysis predicted that, in the worst-case, the transient elevation of IL-6 would increase exposures of CYP3A4, CYP2C9, and CYP1A2 substrates by less than or equal to twofold. Increases for CYP3A4, CYP2C9, and CYP1A2 substrates were projected to be 1.75, 1.19, and 1.09-fold following the first administration and 2.08, 1.28, and 1.49-fold following repeated administrations. It is recommended that there are no restrictions on concomitant treatment with any other drugs. Consideration may be given for potential drug-drug interaction during the first cycle in patients who are receiving concomitant CYP substrates with a narrow therapeutic index via monitoring for toxicity or for drug concentrations.

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  • 14.
    Faraj, Alan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Knudsen, Tom
    Catalyst Biosci, San Francisco, CA USA..
    Desai, Shraddha
    Catalyst Biosci, San Francisco, CA USA..
    Neuman, Linda
    Catalyst Biosci, San Francisco, CA USA..
    Blouse, Grant E.
    Catalyst Biosci, San Francisco, CA USA..
    Simonsson, Ulrika S. H.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Phase III dose selection of marzeptacog alfa (activated) informed by population pharmacokinetic modeling: A novel hemostatic drug2022In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 11, no 12, p. 1628-1637Article in journal (Refereed)
    Abstract [en]

    Marzeptacog alfa (activated) (MarzAA) is an activated recombinant human FVII (rFVIIa) variant developed as subcutaneous (s.c.) administration for the treatment or prevention of bleeding episodes in patients with hemophilia A (HA) or hemophilia B (HB) with inhibitors and other rare bleeding disorders. Population pharmacokinetic (PK) modeling was applied for dose selection for a pivotal phase III clinical trial evaluating s.c. MarzAA for episodic treatment of spontaneous or traumatic bleeding episodes. The population PK model used MarzAA intravenous and s.c. data from previously completed clinical trials in patients with HA/HB with or without inhibitors. Based on the model, clinical trial simulations were performed to predict MarzAA exposure after different dosing regimens. The exposure target was identified using an exposure-matching strategy with a wild-type rFVIIa but adjusting for the difference in potency between the two compounds. Simulations demonstrated a sufficient absorption rate and prolonged exposure following a single 60 μg/kg dose leading to 51% and 70% of the population reaching levels above the target after 3 and 6 h, respectively. According to the phase III protocol, if a second dose was required after 3 h because of a lack of efficacy, 90% of the population was observed to be above target 6 h after the initial dose. The model-informed drug development approach integrated information from several trials and guided dose selection in the pivotal phase III clinical trial for episodic treatment of an acute bleeding event in individuals with HA or HB with inhibitors without the execution of a phase II trial for that indication.

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  • 15.
    Faraj, Alan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Van Wijk, Rob C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Neuman, Linda
    Catalyst Biosci, South San Francisco, CA USA.
    Desai, Shraddha
    Catalyst Biosci, South San Francisco, CA USA.
    Blouse, Grant E.
    Catalyst Biosci, South San Francisco, CA USA.
    Knudsen, Tom
    Catalyst Biosci, South San Francisco, CA USA.
    Simonsson, Ulrika S. H.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Model-informed pediatric dose selection of marzeptacog alfa (activated): An exposure matching strategy2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 7, p. 977-987Article in journal (Refereed)
    Abstract [en]

    Marzeptacog alfa (activated) (MarzAA) is an activated recombinant human rFVII variant intended for subcutaneous (s.c.) administration to treat or prevent bleeding in individuals with hemophilia A (HA) or B (HB) with inhibitors, and other rare bleeding disorders. The s.c. administration provides benefits over i.v. injections. The objective of the study was to support the first-in-pediatric dose selection for s.c. MarzAA to treat episodic bleeding episodes in children up through 11 years in a registrational phase III trial. Assuming the same exposure-response relationship as in adults, an exposure matching strategy was used with a population pharmacokinetics model. A sensitivity analysis evaluating the impact of doubling in absorption rate and age-dependent allometric exponents on dose selection was performed. Subsequently, the probability of trial success, defined as the number of successful trials for a given pediatric dose divided by the number of simulated trials (n = 1000) was studied. A successful trial was defined as outcome where four, three, or two out of 24 pediatric subjects per trial were allowed to fall outside the adult exposures after s.c. administration of 60 mu g/kg. A dose of 60 mu g/ kg in children with HA/HB was supported by the clinical trial simulations to match exposures in adults. The sensitivity analyses further supported selection of the 60 mu g/kg dose level in all age groups. Moreover, the probability of trial success evaluations given a plausible design confirmed the potential of a 60 mu g/kg dose level. Taken together, this work demonstrates the utility of model-informed drug development and could be helpful for other pediatric development programs for rare diseases.

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  • 16.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Tutorials on the foundations of pharmacometrics and systems pharmacology2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e52-Article in journal (Refereed)
  • 17.
    Garcia, Luna Prieto
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. AstraZeneca, BioPharmaceut R&D, DMPK, Res & Early Dev Cardiovasc Renal & Metab, Gothenburg, Sweden.
    Lundahl, Anna
    AstraZeneca, BioPharmaceut R&D, Clin Pharmacol & Quantitat Pharmacol, Clin Pharmacol & Safety Sci, Gothenburg, Sweden..
    Ahlstrom, Christine
    AstraZeneca, BioPharmaceut R&D, DMPK, Res & Early Dev Cardiovasc Renal & Metab, Gothenburg, Sweden..
    Vildhede, Anna
    AstraZeneca, BioPharmaceut R&D, DMPK, Res & Early Dev Cardiovasc Renal & Metab, Gothenburg, Sweden..
    Lennernäs, Hans
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sjögren, Erik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Does the choice of applied physiologically-based pharmacokinetics platform matter?: A case study on simvastatin disposition and drug-drug interaction2022In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 11, no 9, p. 1194-1209Article in journal (Refereed)
    Abstract [en]

    Physiologically-based pharmacokinetic (PBPK) models have an important role in drug discovery/development and decision making in regulatory submissions. This is facilitated by predefined PBPK platforms with user-friendly graphical interface, such as Simcyp and PK-Sim. However, evaluations of platform differences and the potential implications for disposition-related applications are still lacking. The aim of this study was to assess how PBPK model development, input parameters, and model output are affected by the selection of PBPK platform. This is exemplified via the establishment of simvastatin PBPK models (workflow, final models, and output) in PK-Sim and Simcyp as representatives of established whole-body PBPK platforms. The major finding was that the choice of PBPK platform influenced the model development strategy and the final model input parameters, however, the predictive performance of the simvastatin models was still comparable between the platforms. The main differences between the structure and implementation of Simcyp and PK-Sim were found in the absorption and distribution models. Both platforms predicted equally well the observed simvastatin (lactone and acid) pharmacokinetics (20-80 mg), BCRP and OATP1B1 drug-gene interactions (DGIs), and drug-drug interactions (DDIs) when co-administered with CYP3A4 and OATP1B1 inhibitors/inducers. This study illustrates that in-depth knowledge of established PBPK platforms is needed to enable an assessment of the consequences of PBPK platform selection. Specifically, this work provides insights on software differences and potential implications when bridging PBPK knowledge between Simcyp and PK-Sim users. Finally, it provides a simvastatin model implemented in both platforms for risk assessment of metabolism- and transporter-mediated DGIs and DDIs.

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  • 18.
    Ghadzi, Siti Maisharah Sheikh
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Sains Malaysia, George Town, Malaysia.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Implications for Drug Characterization in Glucose Tolerance Tests Without Insulin: Simulation Study of Power and Predictions Using Model-Based Analysis2017In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 6, no 10, p. 686-694Article in journal (Refereed)
    Abstract [en]

    In antihyperglycemic drug development, drug effects are usually characterized using glucose provocations. Analyzing provocation data using pharmacometrics has shown powerful, enabling small studies. In preclinical drug development, high power is attractive due to the experiment sizes; however, insulin is not always available, which potentially impacts power and predictive performance. This simulation study was performed to investigate the implications of performing model-based drug characterization without insulin. The integrated glucose-insulin model was used to simulate and re-estimated oral glucose tolerance tests using a crossover design of placebo and study compound. Drug effects were implemented on seven different mechanisms of action (MOA); one by one or in two-drug combinations. This study showed that exclusion of insulin may severely reduce the power to distinguish the correct from competing drug effect, and to detect a primary or secondary drug effect, however, it did not affect the predictive performance of the model.

  • 19.
    Gong, Yuqing
    et al.
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD 20993 USA..
    Zhang, Peijue
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD 20993 USA..
    Yoon, Miyoung
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD 20993 USA..
    Zhu, Hao
    US FDA, Off Clin Pharmacol, Off Translat Sci, Ctr Drug Evaluat & Res, Silver Spring, MD USA..
    Kohojkar, Ameya
    Teva Pharmaceut, Regulatory Affairs, Fairfield, NJ USA..
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Ducharme, Murray P.
    Learn & Confirm Inc, St Laurent, PQ, Canada..
    Gobburu, Jogarao
    Univ Maryland, Ctr Translat Med, Sch Pharm, College Pk, MD USA..
    Celliere, Geraldine
    Simulat Plus, Lixoft Div, Paris, France..
    Gajjar, Parmesh
    Seda Pharmaceut Dev Serv, Cheadle, England..
    Li, Bing V.
    US FDA, Off Bioequivalence, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD USA..
    Velagapudi, Raja
    Sandoz Inc, Ctr Drug Evaluat & Res, Off Res & Stand, Off Gener Drugs, E Hanover, NJ USA..
    Tsang, Yu Chung
    Apotex Inc, Toronto, ON, Canada..
    Schwendeman, Anna
    Univ Michigan, Biointerfaces Inst, Dept Pharmaceut Sci, Ann Arbor, MI USA..
    Polli, James
    Univ Maryland, Sch Pharm, Dept Pharmaceut Sci, College Pk, MD USA..
    Fang, Lanyan
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD 20993 USA..
    Lionberger, Robert
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD 20993 USA..
    Zhao, Liang
    US FDA, Off Res & Stand, Off Gener Drugs, Ctr Drug Evaluat & Res, Silver Spring, MD 20993 USA..
    Establishing the suitability of model-integrated evidence to demonstrate bioequivalence for long-acting injectable and implantable drug products: Summary of workshop2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 5, p. 624-630Article, review/survey (Refereed)
    Abstract [en]

    On November 30, 2021, the US Food and Drug administration (FDA) and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop titled "Establishing the Suitability of Model-Integrated Evidence (MIE) to Demonstrate Bioequivalence for Long-Acting Injectable and Implantable (LAI) Drug Products. " This workshop brought relevant parties from the industry, academia, and the FDA in the field of modeling and simulation to explore, identify, and recommend best practices on utilizing MIE for bioequivalence (BE) assessment of LAI products. This report summerized presentations and panel discussions for topics including challenges and opportunities in development and assessment of generic LAI products, current status of utilizing MIE, recent research progress of utilizing MIE in generic LAI products, alternative designs for BE studies of LAI products, and model validation/verification strategies associated with different types of MIE approaches.

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  • 20.
    Guiastrennec, Benjamin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sonne, D. P.
    Univ Copenhagen, Gentofte Hosp, Dept Med, Ctr Diabet Res, Hellerup, Denmark..
    Hansen, M.
    Univ Copenhagen, Gentofte Hosp, Dept Med, Ctr Diabet Res, Hellerup, Denmark.;Novo Nordisk AS, Bagsvaerd, Denmark..
    Bagger, J. I.
    Univ Copenhagen, Gentofte Hosp, Dept Med, Ctr Diabet Res, Hellerup, Denmark..
    Lund, A.
    Univ Copenhagen, Gentofte Hosp, Dept Med, Ctr Diabet Res, Hellerup, Denmark..
    Rehfeld, J. F.
    Univ Copenhagen, Dept Clin Biochem, Rigshosp, Copenhagen, Denmark..
    Alskär, Oskar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Vilsboll, T.
    Univ Copenhagen, Gentofte Hosp, Dept Med, Ctr Diabet Res, Hellerup, Denmark..
    Knop, F. K.
    Univ Copenhagen, Gentofte Hosp, Dept Med, Ctr Diabet Res, Hellerup, Denmark..
    Bergstrand, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Mechanism-Based Modeling of Gastric Emptying Rate and Gallbladder Emptying in Response to Caloric Intake2016In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 5, no 12, p. 692-700Article in journal (Refereed)
    Abstract [en]

    Bile acids released postprandially modify the rate and extent of absorption of lipophilic compounds. The present study aimed to predict gastric emptying (GE) rate and gallbladder emptying (GBE) patterns in response to caloric intake. A mechanism-based model for GE, cholecystokinin plasma concentrations, and GBE was developed on data from 33 patients with type 2 diabetes and 33 matched nondiabetic individuals who were administered various test drinks. A feedback action of the caloric content entering the proximal small intestine was identified for the rate of GE. The cholecystokinin concentrations were not predictive of GBE, and an alternative model linking the nutrients amount in the upper intestine to GBE was preferred. Relative to fats, the potency on GBE was 68% for proteins and 2.3% for carbohydrates. The model predictions were robust across a broad range of nutritional content and may potentially be used to predict postprandial changes in drug absorption.

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  • 21.
    Guiastrennec, Benjamin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sonne, David P.
    University of Copenhagen, Copenhagen, Denmark.
    Bergstrand, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Pharmetheus AB, Uppsala, Sweden.
    Vilsbøll, Tina
    University of Copenhagen, Copenhagen, Denmark.
    Knop, Filip K.
    University of Copenhagen, Copenhagen, Denmark.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Model-based prediction of plasma concentration and enterohepatic circulation of total bile acids in humans2018In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 7, no 9, p. 603-612Article in journal (Refereed)
    Abstract [en]

    Bile acids released postprandially can modify the rate and extent of lipophilic compounds’ absorption. This study aimed to predict the enterohepatic circulation (EHC) of total bile acids (TBAs) in response to caloric intake from their spillover in plasma. A model for TBA EHC was combined with a previously developed gastric emptying (GE) model. Longitudinal gallbladder volumes and TBA plasma concentration data from 30 subjects studied after ingestion of four different test drinks were supplemented with literature data. Postprandial gallbladder refilling periods were implemented to improve model predictions. The TBA hepatic extraction was reduced with the high‐fat drink. Basal and nutrient‐induced gallbladder emptying rates were altered by type 2 diabetes (T2D). The model was predictive of the central trend and the variability of gallbladder volume and TBA plasma concentration for all test drinks. Integration of this model within physiological pharmacokinetic modeling frameworks could improve the predictions for lipophilic compounds’ absorption considerably.

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  • 22.
    Hansson, E K
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Amantea, M A
    Westwood, P
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Milligan, P A
    Houk, B E
    French, J
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    PKPD Modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as Predictors of Tumor Dynamics and Overall Survival Following Sunitinib Treatment in GIST2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e84-Article in journal (Refereed)
    Abstract [en]

    The predictive value of longitudinal biomarker data (vascular endothelial growth factor (VEGF), soluble VEGF receptor (sVEGFR)-2, sVEGFR-3, and soluble stem cell factor receptor (sKIT)) for tumor response and survival was assessed based on data from 303 patients with imatinib-resistant gastrointestinal stromal tumors (GIST) receiving sunitinib and/or placebo treatment. The longitudinal tumor size data were well characterized by a tumor growth inhibition model, which included, as significant descriptors of tumor size change, the model-predicted relative changes from baseline over time for sKIT (most significant) and sVEGFR-3, in addition to sunitinib exposure. Survival time was best described by a parametric time-to-event model with baseline tumor size and relative change in sVEGFR-3 over time as predictive factors. Based on the proposed modeling framework to link longitudinal biomarker data with overall survival using pharmacokinetic-pharmacodynamic models, sVEGFR-3 demonstrated the greatest predictive potential for overall survival following sunitinib treatment in GIST.

  • 23.
    Hansson, E K
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ma, G
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Amantea, M A
    French, J
    Milligan, P A
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    PKPD Modeling of Predictors for Adverse Effects and Overall Survival in Sunitinib-Treated Patients With GIST2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e85-Article in journal (Refereed)
    Abstract [en]

    A modeling framework relating exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)-2, -3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was extended to include adverse effects (myelosuppression, hypertension, fatigue, and hand-foot syndrome (HFS)). Longitudinal pharmacokinetic-pharmacodynamic models of sunitinib were developed based on data from 303 patients with gastrointestinal stromal tumor. Myelosuppression was characterized by a semiphysiological model and hypertension with an indirect response model. Proportional odds models with a first-order Markov model described the incidence and severity of fatigue and HFS. Relative change in sVEGFR-3 was the most effective predictor of the occurrence and severity of myelosuppression, fatigue, and HFS. Hypertension was correlated best with sunitinib exposure. Baseline tumor size, time courses of neutropenia, and relative increase of diastolic blood pressure were identified as predictors of OS. The framework has potential to be used for early monitoring of adverse effects and clinical response, thereby facilitating dose individualization to maximize OS.

  • 24. Harnisch, L
    et al.
    Matthews, I
    Chard, J
    Karlsson, M O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Drug and disease model resources: a consortium to create standards and tools to enhance model-based drug development2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e34-Article in journal (Refereed)
    Abstract [en]

    Model-based drug development (MBDD) is accepted as a vital approach in understanding patients' drug-related benefit and risk by integrating quantitative information integration from diverse sources collected throughout drug development.(1) This perspective introduces the activities of the Drug and Disease Model Resources (DDMoRe) consortium, founded in 2011 through the Innovative Medicines Initiative Joint Undertaking (IMI-JU)(2) as a European public-private partnership to address a lack of common tools, languages, and standards for modeling and simulation (M&S) to improve model-based knowledge integration.

  • 25.
    Ibrahim, Eman
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Assessment of ibrutinib scheduling on leukocyte, lymph node size and blood pressure dynamics in chronic lymphocytic leukemia through pharmacokinetic-pharmacodynamic models2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 9, p. 1305-1318Article in journal (Refereed)
    Abstract [en]

    Ibrutinib is a Bruton tyrosine kinase (Btk) inhibitor for treating chronic lymphocytic leukemia (CLL). It has also been associated with hypertension. The optimal dosing schedule for mitigating this adverse effect is currently under discussion. A quantification of relationships between systemic ibrutinib exposure and efficacy (i.e., leukocyte count and sum of the product of perpendicular diameters [SPD] of lymph nodes) and hypertension toxicity (i.e., blood pressure), and their association with overall survival is needed. Here, we present a semi-mechanistic pharmacokinetic-pharmacodynamic modeling framework to characterize such relationships and facilitate dose optimization. Data from a phase Ib/II study were used, including ibrutinib plasma concentrations to derive daily 0-24-h area under the concentration-time curve, leukocyte count, SPD, survival, and blood pressure measurements. A nonlinear mixed effects modeling approach was applied, considering ibrutinib's pharmacological action and CLL cell dynamics. The final framework included (i) an integrated model for SPD and leukocytes consisting of four CLL cell subpopulations with ibrutinib inhibiting phosphorylated Btk production, ( ii) a turnover model in which ibrutinib stimulates an increase in blood pressure, and (iii) a competing risk model for dropout and death. Simulations predicted that the approved dosing schedule had a slightly higher efficacy (24-month, progression- free survival [PFS] 98%) than de-escalation schedules (24-month, average PFS approximate to 97%); the latter had, on average, approximate to 20% lower proportions of patients with hypertension. The developed modeling framework offers an improved understanding of the relationships among ibrutinib exposure, efficacy and toxicity biomarkers. This framework can serve as a platform to assess dosing schedules in a biologically plausible manner.

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  • 26.
    Ibrahim, Moustafa M. A.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Helwan Univ, Dept Pharm Practice, Cairo, Egypt.
    Ghadzi, Siti Maisharah Sheikh
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Sains Malaysia, Sch Pharmaceut Sci, Gelugor, Malaysia.
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Study Design Selection in Early Clinical Anti-Hyperglycemic Drug Development: A Simulation Study of Glucose Tolerance Tests2018In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 7, no 7, p. 432-441Article in journal (Refereed)
    Abstract [en]

    In antidiabetic drug development, phase I studies usually involve short-term glucose provocations. Multiple designs are available for these provocations (e.g., meal tolerance tests (MTTs) and graded glucose infusions (GGIs)). With a highly nonlinear, complex system as the glucose homeostasis, the various provocations will contribute with different information offering a rich choice. Here, we investigate the most appropriate study design in phase I for several hypothetical mechanisms of action of a study drug. Five drug effects in diabetes therapeutic areas were investigated using six study designs. Power to detect drug effect was assessed using the likelihood ratio test, whereas precision and accuracy of the quantification of drug effect was assessed using stochastic simulation and estimations. An overall summary was developed to aid designing the studies of antihyperglycemic drug development using model-based analysis. This guidance is to be used when the integrated glucose insulin model is used, involving the investigated drug mechanisms of action.

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  • 27.
    Karlsson, Kristin E
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Vong, Camille
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bergstrand, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jonsson, E Niclas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Comparisons of Analysis Methods for Proof-of-Concept Trials2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e23-Article in journal (Refereed)
    Abstract [en]

    Drug development struggles with high costs and time consuming processes. Hence, a need for new strategies has been accentuated by many stakeholders in drug development. This study proposes the use of pharmacometric models to rationalize drug development. Two simulated examples, within the therapeutic areas of acute stroke and type 2 diabetes, are utilized to compare a pharmacometric model–based analysis to a t-test with respect to study power of proof-of-concept (POC) trials. In all investigated examples and scenarios, the conventional statistical analysis resulted in several fold larger study sizes to achieve 80% power. For a scenario with a parallel design of one placebo group and one active dose arm, the difference between the conventional and pharmacometric approach was 4.3- and 8.4-fold, for the stroke and diabetes example, respectively. Although the model-based power depend on the model assumptions, in these scenarios, the pharmacometric model–based approach was demonstrated to permit drastic streamlining of POC trials.

  • 28.
    Karlsson, M O
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Mentré, F
    Best practices in population modeling should always be evolving2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e52-Article in journal (Refereed)
  • 29.
    Keizer, Ron J
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, M O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e50-Article in journal (Refereed)
    Abstract [en]

    Several software tools are available that facilitate the use of the NONMEM software and extend its functionality. This tutorial shows how three commonly used and freely available tools, Pirana, PsN, and Xpose, form a tightly integrated workbench for modeling and simulation with NONMEM. During the tutorial, we provide some guidance on what diagnostics we consider most useful in pharmacokinetic model development and how to construct them using these tools.

  • 30.
    Keutzer, Lina
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Akhondipour, Yasamin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Forsman, Lina Davies
    Karolinska Inst, Dept Med Solna, Div Infect Dis, Stockholm, Sweden.;Karolinska Univ Hosp, Dept Infect Dis, Stockholm, Sweden..
    Simonsson, Ulrika S. H.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    A modeling-based proposal for safe and efficacious reintroduction of bedaquiline after dose interruption: A population pharmacokinetics study2022In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 11, no 5, p. 628-639, article id 12768Article in journal (Refereed)
    Abstract [en]

    Bedaquiline (BDQ) is recommended for treatment of multidrug-resistant tuberculosis (MDR-TB) for the majority of patients. Given its long terminal half-life and safety concerns, such as QTc-prolongation, re-introducing BDQ after multiple dose interruption is not intuitive and there are currently no existing guidelines. In this simulation-based study, we investigated different loading dose strategies for BDQ re-introduction, taking safety and efficacy into account. Multiple scenarios of time and length of interruption as well as BDQ re-introduction, including no loading dose, 1- and 2-week loading doses (200 mg and 400 mg once daily), were simulated from a previously published population pharmacokinetic (PK) model describing BDQ and its main metabolite M2 PK in patients with MDR-TB. The efficacy target was defined as 95.0% of the average BDQ concentration without dose interruption during standard treatment. Because M2 is the main driver for QTc-prolongation, the safety limit was set to be below the maximal average M2 metabolite concentration in a standard treatment. Simulations suggest that dose interruptions between treatment weeks 3 and 72 (interruption length: 1 to 6 weeks) require a 2-week loading dose of 200 mg once daily in the typical patient. If treatment was interrupted for longer than 8 weeks, a 2-week loading dose (400 mg once daily) was needed to reach the proposed efficacy target, slightly exceeding the safety limit. In conclusion, we here propose a strategy for BDQ re-introduction providing guidance to clinicians for safe and efficacious BDQ dosing.

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  • 31.
    Kim, Seongho
    et al.
    Wayne State Univ, Dept Oncol, Detroit, MI 48201 USA..
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Shi, Yu
    Univ Calif Los Angeles, Dept Biostat, Los Angeles, CA 90024 USA..
    Kim, Grace Hyun J.
    Univ Calif Los Angeles, Dept Biostat, Los Angeles, CA 90024 USA..
    Wong, Weng Kee
    Univ Calif Los Angeles, Dept Biostat, Los Angeles, CA 90024 USA..
    Metaheuristics for pharmacometrics2021In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 10, no 11, p. 1297-1309Article, review/survey (Refereed)
    Abstract [en]

    Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature-inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed-effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often-used technique to evaluate high dimensional integrals, to search for D-efficient designs for estimating parameters in nonlinear mixed-effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature-inspired metaheuristic algorithms.

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  • 32.
    Knöchel, Jane
    et al.
    AstraZeneca, Clin Pharmacol & Quantitat Pharmacol, Clin Pharmacol & Safety Sci, R&D, Gothenburg, Sweden..
    Bergenholm, Linnéa
    AstraZeneca, BioPharmaceut R&D, DMPK, Res & Early Dev,Cardiovasc Renal & Metab CVRM, Gothenburg, Sweden..
    Ibrahim, Eman
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Kechagias, Stergios
    Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden..
    Hansson, Sara
    AstraZeneca, BioPharmaceut R&D, Translat Sci & Expt Med, Res & Early Dev,Cardiovasc Renal & Metab CVRM, Gothenburg, Sweden..
    Liljeblad, Mathias
    AstraZeneca, BioPharmaceut R&D, Translat Sci & Expt Med, Res & Early Dev,Cardiovasc Renal & Metab CVRM, Gothenburg, Sweden..
    Nasr, Patrik
    Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden..
    Carlsson, Björn
    AstraZeneca, BioPharmaceut R&D, Translat Sci & Expt Med, Res & Early Dev,Cardiovasc Renal & Metab CVRM, Gothenburg, Sweden..
    Ekstedt, Mattias
    Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden..
    Ueckert, Sebastian
    AstraZeneca, Clin Pharmacol & Quantitat Pharmacol, Clin Pharmacol & Safety Sci, R&D, Gothenburg, Sweden..
    A Markov model of fibrosis development in nonalcoholic fatty liver disease predicts fibrosis progression in clinical cohorts2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 12, p. 2038-2049Article in journal (Refereed)
    Abstract [en]

    Disease progression in nonalcoholic steatohepatitis (NASH) is highly heterogenous and remains poorly understood. Fibrosis stage is currently the best predictor for development of end-stage liver disease and mortality. Better understanding and quantifying the impact of factors affecting NASH and fibrosis is essential to inform a clinical study design. We developed a population Markov model to describe the transition probability between fibrosis stages and mortality using a unique clinical nonalcoholic fatty liver disease cohort with serial biopsies over 3 decades. We evaluated covariate effects on all model parameters and performed clinical trial simulations to predict the fibrosis progression rate for external clinical cohorts. All parameters were estimated with good precision. Age and diagnosis of type 2 diabetes (T2D) were found to be significant predictors in the model. Increase in hepatic steatosis between visits was the most important predictor for progression of fibrosis. Fibrosis progression rate (FPR) was twofold higher for fibrosis stages 0 and 1 (F0-1) compared to fibrosis stage 2 and 3 (F2-3). A twofold increase in FPR was observed for T2D. A two-point steatosis worsening increased the FPR 11-fold. Predicted fibrosis progression was in good agreement with data from external clinical cohorts. Our fibrosis progression model shows that patient selection, particularly initial fibrosis stage distribution, can significantly impact fibrosis progression and as such the window for assessing drug efficacy in clinical trials. Our work highlights the increase in hepatic steatosis as the most important factor in increasing FPR, emphasizing the importance of well-defined lifestyle advise for reducing variability in NASH progression during clinical trials.

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  • 33. Koele, Simon E
    et al.
    Dorlo, Thomas P. C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Upton, Caryn M
    Aarnoutse, Rob E
    Svensson, Elin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Power to identify exposure-response relationships in phase IIa pulmonary tuberculosis trials with multi-dimensional bacterial load modeling.2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306Article in journal (Refereed)
    Abstract [en]

    Adequate power to identify an exposure-response relationship in a phase IIa clinical trial for pulmonary tuberculosis (TB) is important for dose selection and design of follow-up studies. Currently, it is not known what response marker provides the pharmacokinetic-pharmacodynamic (PK-PD) model more power to identify an exposure-response relationship. We simulated colony-forming units (CFU) and time-to-positivity (TTP) measurements for four hypothetical drugs with different activity profiles for 14 days. The power to identify exposure-response relationships when analyzing CFU, TTP, or combined CFU + TTP data was determined at 60 total participants, or with 25 out of 60 participants in the lowest and highest dosing groups (unbalanced design). For drugs with moderate bactericidal activity, power was low (<59%), irrespective of the data analyzed. Power was 1.9% to 29.4% higher when analyzing TTP data compared to CFU data. Combined analysis of CFU and TTP further improved the power, on average by 4.2%. For a drug with a medium-high activity, the total sample size needed to achieve 80% power was 136 for CFU, 72 for TTP, and 68 for combined CFU + TTP data. The unbalanced design improved the power by 16% over the balanced design. In conclusion, the power to identify an exposure-response relationship is low for TB drugs with moderate bactericidal activity or with a slow onset of activity. TTP provides the PK-PD model with more power to identify exposure-response relationships compared to CFU, and combined analysis or an unbalanced dosing group study design offers modest further improvement.

  • 34.
    Krekels, E. H. J.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Novakovic, Ana M.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Vermeulen, A. M.
    Janssen Res & Dev, Beerse, Belgium.
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Item Response Theory to Quantify Longitudinal Placebo and Paliperidone Effects on PANSS Scores in Schizophrenia2017In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 6, no 8, p. 543-551Article in journal (Refereed)
    Abstract [en]

    As biomarkers are lacking, multi-item questionnaire-based tools like the Positive and Negative Syndrome Scale (PANSS) are used to quantify disease severity in schizophrenia. Analyzing composite PANSS scores as continuous data discards information and violates the numerical nature of the scale. Here a longitudinal analysis based on Item Response Theory is presented using PANSS data from phase III clinical trials. Latent disease severity variables were derived from item-level data on the positive, negative, and general PANSS subscales each. On all subscales, the time course of placebo responses were best described with Weibull models, and dose-independent functions with exponential models to describe the onset of the full effect were used to describe paliperidone's effect. Placebo and drug effect were most pronounced on the positive subscale. The final model successfully describes the time course of treatment effects on the individual PANSS item-levels, on all PANSS subscale levels, and on the total score level.

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  • 35.
    Krishnan, Sreenath M.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Bayesian forecasting of tumor size metrics and overall survival2022In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 11, no 12, p. 1604-1613Article in journal (Refereed)
    Abstract [en]

    The tumor size ratio (TSR), time-to-tumor growth (TTG), and tumor growth rate (kG) are frequently suggested as model-based predictors of overall survival (OS) for different types of tumors. When the tumor metrics are applied in forecasting of the outcome for individual patients at an early stage, the tumor data might be sparse resulting in imprecise prediction. This simulation study aimed to investigate how the tumor follow-up data and estimation approaches influence the accuracy in the tumor size metrics and the predicted hazard of death for individual patients. Longitudinal tumor size and OS data were simulated using tumor growth inhibition and Weibull distribution models, respectively. Based on the model and increasing measurement durations, the accuracy (defined as 80–125% of the simulated “true” value) in individual metrics and hazard was computed. TSR week 6 (TSRw6) accuracy was adequate for 91% of the patients when tumor size was measured up to 12 weeks. For TTG and kG metrics, the highest accuracy observed was lower (43 and 77%, respectively) and occurred later (42 and 60 weeks, respectively). The simultaneous (joint) and sequential estimation approaches resulted in similar accuracies, however, in general, the sequential approach where individual tumor size parameters are fixed, demonstrated inferior estimation properties. The TSRw6 and the model-predicted tumor time course (absolute or relative change) had better forecasting properties than TTG or kG. The population pharmacokinetic (PK) parameters and data approach performed similarly or better than the simultaneous approach and had a better accuracy in estimating individuals' hazard of death than the individual PK parameters method.

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  • 36.
    Krishnan, Sreenath M.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Bruno, René
    Clin Pharmacol, Roche Genentech, Marseille, France..
    Beyer, Ulrich
    F Hoffmann La Roche Ltd, Biostat, Basel, Switzerland..
    Jin, Jin Y.
    Clin Pharmacol Roche Genentech, San Francisco, CA USA..
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Multistate model for pharmacometric analyses of overall survival in HER2-negative breast cancer patients treated with docetaxel2021In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 10, no 10, p. 1255-1266Article in journal (Refereed)
    Abstract [en]

    The aim of this study was to develop a multistate model for overall survival (OS) analysis, based on parametric hazard functions and combined with an investigation of predictors derived from a longitudinal tumor size model on the transition hazards. Different states - stable disease, tumor response, progression, second-line treatment, and death following docetaxel treatment initiation (stable state) in patients with HER2-negative breast cancer (n = 183) were used in model building. Past changes in tumor size prospectively predicts the probability of state changes. The hazard of death after progression was lower for subjects who had longer treatment response (i.e., longer time-to-progression). Young age increased the probability of receiving second-line treatment. The developed multistate model adequately described the transitions between different states and jointly the overall event and survival data. The multistate model allows for simultaneous estimation of transition rates along with their tumor model derived metrics. The metrics were evaluated in a prospective manner so not to cause immortal time bias. Investigation of predictors and characterization of the time to develop response, the duration of response, the progression-free survival, and the OS can be performed in a single multistate modeling exercise. This modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, thereby facilitating early clinical interventions to improve anticancer therapy.

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  • 37.
    Krishnan, Sreenath Madathil
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Laarif, Sofiene S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy.
    Bender, Brendan C.
    Genentech Inc, 460 Point San Bruno Blvd, San Francisco, CA 94080 USA..
    Quartino, Angelica L.
    Genentech Inc, 460 Point San Bruno Blvd, San Francisco, CA 94080 USA..
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Tumor growth inhibition modeling of individual lesion dynamics and interorgan variability in HER2-negative breast cancer patients treated with docetaxel2021In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 10, no 5, p. 511-521Article in journal (Refereed)
    Abstract [en]

    Information on individual lesion dynamics and organ location are often ignored in pharmacometric modeling analyses of tumor response. Typically, the sum of their longest diameters is utilized. Herein, a tumor growth inhibition model was developed for describing the individual lesion time-course data from 183 patients with metastatic HER2-negative breast cancer receiving docetaxel. The interindividual variability (IIV), interlesion variability (ILV), and interorgan variability of parameters describing the lesion time-courses were evaluated. Additionally, a model describing the probability of new lesion appearance and a time-to-event model for overall survival (OS), were developed. Before treatment initiation, the lesions were largest in the soft tissues and smallest in the lungs, and associated with a significant IIV and ILV. The tumor growth rate was 2.6 times higher in the breasts and liver, compared with other metastatic sites. The docetaxel drug effect in the liver, breasts, and soft tissues was greater than or equal to 1.2 times higher compared with other organs. The time-course of the largest lesion, the presence of at least 3 liver lesions, and the time since study enrollment, increased the probability of new lesion appearance. New lesion appearance, along with the time to growth and time-course of the largest lesion at baseline, were identified as the best predictors of OS. This tumor modeling approach, incorporating individual lesion dynamics, provided a more complete understanding of heterogeneity in tumor growth and drug effect in different organs. Thus, there may be potential to tailor treatments based on lesion location, lesion size, and early lesion response to provide better clinical outcomes.

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  • 38.
    Kunina, Hanna
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Al-Mashat, Alex
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Chien, Jenny Y.
    Lilly Corp Ctr, Lilly Res Labs, Global Pharmacokinet Pharmacodynam & Pharmacometr, Indianapolis, IN 46285 USA..
    Garhyan, Parag
    Lilly Corp Ctr, Lilly Res Labs, Global Pharmacokinet Pharmacodynam & Pharmacometr, Indianapolis, IN 46285 USA..
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Optimization of trial duration to predict long-term HbA1c change with therapy: A pharmacometrics simulation-based evaluation2022In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 11, no 11, p. 1443-1457Article in journal (Refereed)
    Abstract [en]

    Glycated hemoglobin (HbA1c) is the main biomarker of diabetes drug development. However, because of its delayed turnover, trial duration is rarely shorter than 12 weeks, and being able to predict long-term HbA1c with precision using data from shorter studies would be beneficial. The feasibility of reducing study duration was therefore investigated in this study, assuming a model-based analysis. The aim was to investigate the predictive performance of 24- and 52-week extrapolations using data from up to 4, 6, 8 or 12 weeks, with six previously published pharmacometric models of HbA1c. Predictive performance was assessed through simulation-based dose-response predictions and model averaging (MA) with two hypothetical drugs. Results were consistent across the methods of assessment, with MA supporting the results derived from the model-based framework. The models using mean plasma glucose (MPG) or nonlinear fasting plasma glucose (FPG) effect, driving the HbA1c formation, showed good predictive performance despite a reduced study duration. The models, using the linear effect of FPG to drive the HbA1c formation, were sensitive to the limited amount of data in the shorter studies. The MA with bootstrap demonstrated strongly that a 4-week study duration is insufficient for precise predictions of all models. Our findings suggest that if data are analyzed with a pharmacometric model with MPG or FPG with a nonlinear effect to drive HbA1c formation, a study duration of 8 weeks is sufficient with maintained accuracy and precision of dose-response predictions.

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  • 39.
    Lacroix, Brigitte
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Friberg, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Simultaneous Exposure-Response Modeling of ACR20, ACR50, and ACR70 Improvement Scores in Rheumatoid Arthritis Patients With Certolizumab Pegol2014In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 3, no 10, p. 1-11Article in journal (Refereed)
    Abstract [en]

    The Markovian approach has been proposed to model ACR response (ACR20, ACR50 or ACR70) reported in rheumatoid arthritis clinical trials to account for the dependency of the scores over time. However, dichotomizing the composite ACR assessment discards much information. Here we propose a new approach for modeling together the 3 thresholds: a continuous-time Markov exposure-response model was developed, based on data from 5 placebo-controlled certolizumab pegol clinical trials. This approach allows adequate prediction of individual ACR20/50/70 time-response, even for non-periodic observations. An exposure-response was established over a large range of licensed and unlicensed doses including phase II dose-ranging data. Simulations from the model (50 to 400 mg every other week) illustrated the range and sustainability of response (ACR20: 56 to 68%, ACR50: 27 to 42%, ACR70: 11 to 22% at week 24) with maximum clinical effect achieved at the recommended maintenance dose of 200 mg every other week.

  • 40. Langenhorst, Jurgen B
    et al.
    Dorlo, Thomas P C
    Netherlands Cancer Institute.
    van Kesteren, Charlotte
    van Maarseveen, Erik M
    Nierkens, Stefan
    de Witte, Moniek A
    Boelens, Jaap Jan
    Huitema, Alwin D R
    Clinical Trial Simulation To Optimize Trial Design for Fludarabine Dosing Strategies in Allogeneic Hematopoietic Cell Transplantation.2020In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 9, no 5, p. 272-281Article in journal (Refereed)
    Abstract [en]

    Optimal fludarabine exposure has been associated with improved treatment outcome in allogeneic hematopoietic cell transplantation, suggesting potential benefit of individualized dosing. A randomized controlled trial (RCT) comparing alternative fludarabine dosing strategies to current practice may be warranted, but should be sufficiently powered for a relevant end point, while still feasible to enroll. To find the optimal design, we simulated RCTs comparing current practice (160 mg/m2 ) to either covariate-based or therapeutic drug monitoring (TDM)-guided dosing with potential outcomes being nonrelapse mortality (NRM), graft failure, or relapse, and ultimately overall survival (covering all three aforementioned outcomes). The inclusion in each treatment arm (n) required to achieve 80% power was calculated for all combinations of end points and dosing comparisons. The trial requiring the lowest n for sufficient power compared TDM-guided dosing to current practice with NRM as primary outcome (n = 70, NRM decreasing from 21% to 5.7%). We conclude that a superiority trial is feasible.

  • 41.
    Leohr, Jennifer
    et al.
    Lilly Corp Ctr, Lilly Res Labs, Dept Pharmacokinet Pharmacodynam, Indianapolis, IN USA..
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Evaluation of postprandial total triglycerides within the TIGG model for characterizing postprandial response of glucose, insulin, and GLP-12023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 10, p. 1529-1540Article in journal (Refereed)
    Abstract [en]

    The TIGG model is the first model to integrate glucose and insulin regulation, incretin effect, and triglyceride (TG) response in the lipoprotein subclasses of chylomicrons and VLDL-V6. This model described the response following a high-fat meal in individuals who are lean, obese, and very obese and provided insights into the possible regulation of glucose homeostasis in the extended period following a meal. Often, total TGs are analyzed within clinical studies, instead of lipoprotein subclasses. We extended the existing TIGG model to capture the observed total TGs and determined if this model could be used to predict the postprandial TG response of chylomicron and VLDL-V6 when only total TGs are available. To assess if the lipoprotein distinction was important for the model, a second model (tTIGG) was developed using only the postprandial response in total TGs, instead of postprandial TG response in chylomicrons and VLDL-V6. The two models were compared on their predictability to characterize the postprandial response of glucose, insulin, and active GLP-1. Both models were able to characterize the postprandial TG response in individuals who are lean, obese, or very obese following a high-fat meal. The extended TIGG model resulted in a better model fit of the glucose data compared to the tTIGG model, indicating that chylomicron and VLDL-V6 provided additional information compared to total TGs. Furthermore, the expanded TIGG model was able to predict the postprandial TG response of chylomicrons and VLDL-V6 using the total TGs and could therefore be used in studies where only total TGs were collected.

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  • 42.
    Lippert, Joerg
    et al.
    Bayer AG, Clin Pharmacometr, Wuppertal, Germany.
    Burghaus, Rolf
    Bayer AG, Clin Pharmacometr, Wuppertal, Germany.
    Edginton, Andrea
    Univ Waterloo, Sch Pharm, Waterloo, ON, Canada.
    Frechen, Sebastian
    Bayer AG, Clin Pharmacometr, Wuppertal, Germany.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kovar, Andreas
    Sanofi Aventis, Pharmacokinet Dynam & Metab, Frankfurt, Germany.
    Lehr, Thorsten
    Saarland Univ, Clin Pharm, Saarbrucken, Germany.
    Milligan, Peter
    Pharmetheus, Uppsala, Sweden.
    Nock, Valerie
    Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany.
    Ramusovic, Sergej
    Sanofi Aventis, Pharmacokinet Dynam & Metab, Frankfurt, Germany.
    Riggs, Matthew
    Metrum Res Grp, Tariffville, CT USA.
    Schaller, Stephan
    EsqLABS GmbH, Saterland, Germany.
    Schlender, Jan
    Bayer AG, Clin Pharmacometr, Wuppertal, Germany.
    Schmidt, Stephan
    Univ Florida, Coll Pharm, Orlando, FL USA.
    Sevestre, Michael
    Design2Code Inc, Waterloo, ON, Canada.
    Sjögren, Erik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Pharmetheus, Uppsala, Sweden.
    Solodenko, Juri
    Bayer AG, Clin Pharmacometr, Wuppertal, Germany.
    Staab, Alexander
    Boehringer Ingelheim Pharma GmbH & Co KG, Biberach, Germany.
    Teutonico, Donato
    Sanofi Aventis R&D, Pharmacokinet Dynam & Metab, Alfortville, France.
    Open Systems Pharmacology Community-An Open Access, Open Source, Open Science Approach to Modeling and Simulation in Pharmaceutical Sciences2019In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 8, no 12, p. 878-882Article in journal (Refereed)
    Abstract [en]

    Systems pharmacology integrates structural biological and pharmacological knowledge and experimental data, enabling dissection of organism and drug properties and providing excellent predictivity. The development of systems pharmacology models is a significant task requiring massive amounts of background information beyond individual trial data. The qualification of models needs repetitive demonstration of successful predictions. Open Systems Pharmacology is a community that develops, qualifies, and shares professional open source software tools and models in a collaborative open-science way.

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  • 43.
    Liu, Han
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Milenković‐Grišić, Ana‐Marija
    Merck Healthcare KGaA Darmstadt Germany.
    Krishnan, Sreenath M.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Jönsson, Siv
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Friberg, Lena E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Girard, Pascal
    Merck Institute of Pharmacometrics, an affiliate of Merck KGaA Lausanne Switzerland.
    Venkatakrishnan, Karthik
    EMD Serono Research &amp; Development Institute, Inc., an affiliate of Merck KGaA Billerica Massachusetts USA.
    Vugmeyster, Yulia
    EMD Serono Research &amp; Development Institute, Inc., an affiliate of Merck KGaA Billerica Massachusetts USA.
    Khandelwal, Akash
    Merck Healthcare KGaA Darmstadt Germany.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    A multistate modeling and simulation framework to learn dose-response of oncology drugs: Application to bintrafusp alfa in non‐small cell lung cancer2023In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 12, no 11, p. 1738-1750Article in journal (Refereed)
    Abstract [en]

    The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-β and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.

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  • 44. Marshall, S F
    et al.
    Hemmings, R
    Josephson, F
    Karlsson, M O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Posch, M
    Steimer, J-L
    Modeling and simulation to optimize the design and analysis of confirmatory trials, characterize risk-benefit, and support label claims2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e27-Article in journal (Refereed)
    Abstract [en]

    The role of modeling and simulation (M&S) in the design and interpretation of phase III studies, from break out session 4 of the European Medicines Agency (EMA)/European Federation of Pharmaceutical Industries and Associations (EFPIA) M&S workshop, was divided into themes illustrated with case studies (Table 1): (1) M&S being conducted to support the design of confirmatory trials; (2) longitudinal model-based test as primary inferential analysis (biosimilarity and disease progression trials); (3) assessment of benefit–risk ratio, approval and labeling of an unstudied dose or dosing regimen, and development of future regulatory guidance.

  • 45. Mentré, F
    et al.
    Chenel, M
    Comets, E
    Grevel, J
    Hooker, Andrew C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, M O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lavielle, M
    Gueorguieva, I
    Current Use and Developments Needed for Optimal Design in Pharmacometrics: A Study Performed Among DDMoRe's European Federation of Pharmaceutical Industries and Associations Members2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e46-Article in journal (Refereed)
    Abstract [en]

    Methods and software tools for optimal design in nonlinear mixed effect models, based on the Fisher information matrix, have been developed for a decade.1,2 Academic groups regularly proposed new versions.3–5 Present tools do not incorporate adaptive designs for these models although prior information is needed and adaptive designs are increasingly used in drug development.6 We conducted a study among drug companies of the Drug and Disease Model Resources consortium7 to identify current practices and expectations.

  • 46.
    Montepiedra, Grace
    et al.
    Harvard T.H. Chan School of Public Health Boston Massachusetts USA.
    Svensson, Elin M.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Department of Pharmacy Radboud University Medical Center Nijmegen The Netherlands;Department of Pharmacy Uppsala University Uppsala Sweden.
    Wong, Weng Kee
    University of California Los Angeles Los Angeles California USA.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Department of Pharmacy Uppsala University Uppsala Sweden.
    Optimizing the design of a pharmacokinetic trial to evaluate the dosing scheme of a novel tuberculosis drug in children living with or without HIV2024In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 13, no 2, p. 270-280Article in journal (Refereed)
  • 47.
    Musuamba, F. T.
    et al.
    EMA Modelling & Simulat Working Grp, London, England.;Fed Agcy Med & Hlth Prod, Brussels, Belgium.;Univ Limoges, INSERM, UMR850, Limoges, France..
    Manolis, E.
    EMA Modelling & Simulat Working Grp, London, England.;European Med Agcy, London, England..
    Holford, N.
    Univ Auckland, Dept Pharmacol & Clin Pharmacol, Auckland, New Zealand..
    Cheung, S. Y. A.
    AstraZeneca UK Ltd, London, England..
    Friberg, Lena E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ogungbenro, K.
    Univ Manchester, Manchester, Lancs, England..
    Posch, M.
    Med Univ Vienna, Ctr Med Stat Informat & Intelligent Syst, Vienna, Austria..
    Yates, J. W. T.
    AstraZeneca UK Ltd, London, England..
    Berry, S.
    Berry Consultants, Austin, TX USA..
    Thomas, N.
    Pfizer, London, England..
    Corriol-Rohou, S.
    AstraZeneca UK Ltd, London, England..
    Bornkamp, B.
    Novartis, London, England..
    Bretz, F.
    Med Univ Vienna, Ctr Med Stat Informat & Intelligent Syst, Vienna, Austria.;Novartis, London, England..
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Van der Graaf, P. H.
    Leiden Acad Ctr Drug Res, Leiden, Netherlands.;Certara QSP, Canterbury, Kent, England..
    Standing, J. F.
    EMA Modelling & Simulat Working Grp, London, England.;UCL, London, England..
    Hay, J.
    EMA Modelling & Simulat Working Grp, London, England.;Med & Healthcare Prod Regulatory Agcy, London, England..
    Cole, S.
    EMA Modelling & Simulat Working Grp, London, England.;Med & Healthcare Prod Regulatory Agcy, London, England..
    Gigante, V.
    EMA Modelling & Simulat Working Grp, London, England.;Agenzia Italiana Farmaco, Rome, Italy..
    Karlsson, K.
    EMA Modelling & Simulat Working Grp, London, England.;Med Prod Agcy, Uppsala, Sweden..
    Dumortier, T.
    Novartis, London, England..
    Benda, N.
    EMA Modelling & Simulat Working Grp, London, England.;Bundesinst Arzneimittel & Med Prod, Bonn, Germany..
    Serone, F.
    EMA Modelling & Simulat Working Grp, London, England.;Agenzia Italiana Farmaco, Rome, Italy..
    Das, S.
    AstraZeneca UK Ltd, London, England..
    Brochot, A.
    ABLYNX, Ghent, Belgium..
    Ehmann, F.
    European Med Agcy, London, England..
    Hemmings, R.
    Med & Healthcare Prod Regulatory Agcy, London, England..
    Rusten, I. Skottheim
    EMA Modelling & Simulat Working Grp, London, England.;Norvegian Med Agcy, Oslo, Norway..
    Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014)2017In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 6, no 7, p. 418-429Article in journal (Refereed)
    Abstract [en]

    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 dosefinding 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.

  • 48. Møller, J B
    et al.
    Kristensen, N R
    Klim, S
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ingwersen, S H
    Kjellsson, M C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches2014In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 3, no 7, p. e122-Article in journal (Refereed)
    Abstract [en]

    The link between glucose and HbA1c at steady state has previously been described using steady-state or longitudinal relationships. We evaluated five published methods for prediction of HbA1c after 26/28 weeks using data from four clinical trials. Methods (1) and (2): steady-state regression of HbA1c on fasting plasma glucose and mean plasma glucose, respectively, (3) an indirect response model of fasting plasma glucose effects on HbA1c, (4) model of glycosylation of red blood cells, and (5) coupled indirect response model for mean plasma glucose and HbA1c. Absolute mean prediction errors were 0.61, 0.38, 0.55, 0.37, and 0.15% points, respectively, for Methods 1 through 5. This indicates that predictions improved by using mean plasma glucose instead of fasting plasma glucose, by inclusion of longitudinal glucose data and further by inclusion of longitudinal HbA1c data until 12 weeks. For prediction of trial outcome, the longitudinal models based on mean plasma glucose (Methods 4 and 5) had substantially better performance compared with the other methods.

  • 49. Møller, J B
    et al.
    Kristensen, N R
    Klim, S
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ingwersen, S H
    Kjellsson, Maria C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches.2016In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 5, no 7, p. 388-9Article in journal (Refereed)
  • 50. Møller, J B
    et al.
    Overgaard, R V
    Kjellsson, Maria C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kristensen, N R
    Klim, S
    Ingwersen, S H
    Karlsson, Mats O
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Longitudinal Modeling of the Relationship Between Mean Plasma Glucose and HbA1c Following Antidiabetic Treatments2013In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 2, p. e82-Article in journal (Refereed)
    Abstract [en]

    Late-phase clinical trials within diabetes generally have a duration of 12-24 weeks, where 12 weeks may be too short to reach steady-state glycated hemoglobin (HbA1c). The main determinant for HbA1c is blood glucose, which reaches steady state much sooner. In spite of this, few publications have used individual data to assess the time course of both glucose and HbA1c, for predicting HbA1c. In this paper, we present an approach for predicting HbA1c at end-of-trial (24-28 weeks) using glucose and HbA1c measurements up to 12 weeks. The approach was evaluated using data from 4 trials covering 12 treatment arms (oral antidiabetic drug, glucagon-like peptide-1, and insulin treatment) with measurements at 24-28 weeks to evaluate predictions vs. observations. HbA1c percentage was predicted for each arm at end-of-trial with a mean prediction error of 0.14% [0.01;0.24]. Furthermore, end points in terms of HbA1c reductions relative to comparator were accurately predicted. The proposed model provides a good basis to optimize late-stage clinical development within diabetes.

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