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Garcia G, van Dijkman SC, Pavord I, Singh D, Oosterholt S, Fulmali S, Majumdar A, Della Pasqua O. A Simulation Study of the Effect of Clinical Characteristics and Treatment Choice on Reliever Medication Use, Symptom Control and Exacerbation Risk in Moderate-Severe Asthma. Adv Ther 2024; 41:3196-3216. [PMID: 38916810 PMCID: PMC11263416 DOI: 10.1007/s12325-024-02914-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 05/29/2024] [Indexed: 06/26/2024]
Abstract
INTRODUCTION The relationship between immediate symptom control, reliever medication use and exacerbation risk on treatment response and factors that modify it have not been assessed in an integrated manner. Here we apply simulation scenarios to evaluate the effect of individual baseline characteristics on treatment response in patients with moderate-severe asthma on regular maintenance dosing monotherapy with fluticasone propionate (FP) or combination therapy with fluticasone propionate/salmeterol (FP/SAL) or budesonide/formoterol (BUD/FOR). METHODS Reduction in reliever medication use (puffs/24 h), change in symptom control scores (ACQ-5), and annualised exacerbation rate over 12 months were simulated in a cohort of patients with different baseline characteristics (e.g. time since diagnosis, asthma control questionnaire (ACQ-5) symptom score, smoking status, body mass index (BMI) and sex) using drug-disease models derived from large phase III/IV clinical studies. RESULTS Simulation scenarios show that being a smoker, having higher baseline ACQ-5 and BMI, and long asthma history is associated with increased reliever medication use (p < 0.01). This increase correlates with a higher exacerbation risk and higher ACQ-5 scores over the course of treatment, irrespective of the underlying maintenance therapy. Switching non-responders to ICS monotherapy to combination therapy after 3 months resulted in immediate reduction in reliever medication use (i.e. 1.3 vs. 1.0 puffs/24 h for FP/SAL and BUD/FOR, respectively). In addition, switching patients with ACQ-5 > 1.5 at baseline to FP/SAL resulted in 34% less exacerbations than those receiving regular dosing BUD/FOR (p < 0.01). CONCLUSIONS We have identified baseline characteristics of patients with moderate to severe asthma that are associated with greater reliever medication use, poor symptom control and higher exacerbation risk. Moreover, the effects of different inhaled corticosteroid (ICS)/long-acting beta agonist (LABA) combinations vary significantly when considering long-term treatment performance. These factors should be considered in clinical practice as a basis for personalised management of patients with moderate-severe asthma symptoms.
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Affiliation(s)
| | - Sven C van Dijkman
- Clinical Pharmacology Modelling and Simulation, GSK, GSK House, 980 Great West Rd, London, TW8 9GS, UK
| | - Ian Pavord
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Dave Singh
- University of Manchester, Manchester University NHS Foundations Trust, Manchester, UK
| | - Sean Oosterholt
- Clinical Pharmacology Modelling and Simulation, GSK, GSK House, 980 Great West Rd, London, TW8 9GS, UK
| | - Sourabh Fulmali
- GSK, Global Classic and Established Medicines, Singapore, Singapore
| | - Anurita Majumdar
- GSK, Global Classic and Established Medicines, Singapore, Singapore
| | - Oscar Della Pasqua
- Clinical Pharmacology Modelling and Simulation, GSK, GSK House, 980 Great West Rd, London, TW8 9GS, UK.
- Clinical Pharmacology & Therapeutics Group, University College London, London, UK.
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Knights J, Bangieva V, Passoni M, Donegan ML, Shen J, Klein A, Baker J, DuBois H. A framework for precision "dosing" of mental healthcare services: algorithm development and clinical pilot. Int J Ment Health Syst 2023; 17:21. [PMID: 37408006 DOI: 10.1186/s13033-023-00581-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/18/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients. METHODS Historical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as "session dosing": 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients. RESULTS The developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified. CONCLUSIONS It is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.
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Affiliation(s)
- Jonathan Knights
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA.
| | - Victoria Bangieva
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Michela Passoni
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Macayla L Donegan
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Jacob Shen
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Audrey Klein
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Justin Baker
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Holly DuBois
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
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Kareva I, Zutshi A, Gupta P, Kabilan S. Bispecific antibodies: A guide to model informed drug discovery and development. Heliyon 2021; 7:e07649. [PMID: 34381902 PMCID: PMC8334385 DOI: 10.1016/j.heliyon.2021.e07649] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 07/02/2021] [Accepted: 07/20/2021] [Indexed: 11/27/2022] Open
Abstract
Affinity (KD) optimization of monoclonal antibodies is one of the factors that impacts the stoichiometric binding and the corresponding efficacy of a drug. This impacts the dose and the dosing regimen, making the optimum KD a critical component of drug discovery and development. Its importance is further enhanced for bispecific antibodies, where affinity of the drug needs to be optimized with respect to two targets. Mathematical modeling can have critical impact on lead compound optimization. Here we build on previous work of using mathematical models to facilitate lead compound selection, expanding analysis from two membrane bound targets to soluble targets as well. Our analysis reveals the importance of three factors for lead compound optimization: drug affinity to both targets, target turnover rates, and target distribution throughout the body. We describe a method that leverages this information to help make early stage decisions on whether to optimize affinity, and if so, which arm of the bispecific should be optimized. We apply the proposed approach to a variety of scenarios and illustrate the ability to make improved decisions in each case. We integrate results to develop a bispecific antibody KD optimization guide that can be used to improve resource allocation for lead compound selection, accelerating advancement of better compounds. We conclude with a discussion of possible ways to assess the necessary levels of target engagement for affecting disease as part of an integrative approach for model-informed drug discovery and development.
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Joseph JF, Gronbach L, GarcĂa-Miller J, Cruz LM, Wuest B, Keilholz U, Zoschke C, Parr MK. Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine. Pharmaceutics 2020; 12:E413. [PMID: 32366029 PMCID: PMC7284432 DOI: 10.3390/pharmaceutics12050413] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/21/2020] [Accepted: 04/29/2020] [Indexed: 12/13/2022] Open
Abstract
Cancer treatment often lacks individual dose adaptation, contributing to insufficient efficacy and severe side effects. Thus, personalized approaches are highly desired. Although various analytical techniques are established to determine drug levels in preclinical models, they are limited in the automated real-time acquisition of pharmacokinetic profiles. Therefore, an online UHPLC-MS/MS system for quantitation of drug concentrations within 3D tumor oral mucosa models was generated. The integration of sampling ports into the 3D tumor models and their culture inside the autosampler allowed for real-time pharmacokinetic profiling without additional sample preparation. Docetaxel quantitation was validated according to EMA guidelines. The tumor models recapitulated the morphology of head-and-neck cancer and the dose-dependent tumor reduction following docetaxel treatment. The administration of four different docetaxel concentrations resulted in comparable courses of concentration versus time curves for 96 h. In conclusion, this proof-of-concept study demonstrated the feasibility of real-time monitoring of drug levels in 3D tumor models without any sample preparation. The inclusion of patient-derived tumor cells into our models may further optimize the pharmacotherapy of cancer patients by efficiently delivering personalized data of the target tissue.
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Affiliation(s)
- Jan F. Joseph
- Core Facility BioSupraMol, Freie Universität Berlin, 14195 Berlin, Germany;
| | - Leonie Gronbach
- Institute of Pharmacy (Pharmacology & Toxicology), Freie Universität Berlin, 14195 Berlin, Germany; (L.G.); (J.G.-M.); (L.M.C.); (C.Z.)
| | - Jill GarcĂa-Miller
- Institute of Pharmacy (Pharmacology & Toxicology), Freie Universität Berlin, 14195 Berlin, Germany; (L.G.); (J.G.-M.); (L.M.C.); (C.Z.)
| | - Leticia M. Cruz
- Institute of Pharmacy (Pharmacology & Toxicology), Freie Universität Berlin, 14195 Berlin, Germany; (L.G.); (J.G.-M.); (L.M.C.); (C.Z.)
| | | | - Ulrich Keilholz
- Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Comprehensive Cancer Center, 10117 Berlin, Germany;
| | - Christian Zoschke
- Institute of Pharmacy (Pharmacology & Toxicology), Freie Universität Berlin, 14195 Berlin, Germany; (L.G.); (J.G.-M.); (L.M.C.); (C.Z.)
| | - Maria K. Parr
- Freie Universität Berlin, Institute of Pharmacy (Pharmaceutical and Medicinal Chemistry), 14195 Berlin, Germany
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van Dijkman SC, Alvarez-Jimenez R, Danhof M, Della Pasqua O. Pharmacotherapy in pediatric epilepsy: from trial and error to rational drug and dose selection - a long way to go. Expert Opin Drug Metab Toxicol 2016; 12:1143-56. [PMID: 27434782 DOI: 10.1080/17425255.2016.1203900] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Whereas ongoing efforts in epilepsy research focus on the underlying disease processes, the lack of a physiologically based rationale for drug and dose selection contributes to inadequate treatment response in children. In fact, limited information on the interindividual variation in pharmacokinetics and pharmacodynamics of anti-epileptic drugs (AEDs) in children drive prescription practice, which relies primarily on dose regimens according to a mg/kg basis. Such practice has evolved despite advancements in pediatric pharmacology showing that growth and maturation processes do not correlate linearly with changes in body size. AREAS COVERED In this review we aim to provide 1) a comprehensive overview of the sources of variability in the response to AEDs, 2) insight into novel methodologies to characterise such variation and 3) recommendations for treatment personalisation. EXPERT OPINION The use of pharmacokinetic-pharmacodynamic principles in clinical practice is hindered by the lack of biomarkers and by practical constraints in the evaluation of polytherapy. The identification of biomarkers and their validation as tools for drug development and therapeutics will require some time. Meanwhile, one should not miss the opportunity to integrate the available pharmacokinetic data with modeling and simulation concepts to prevent further delays in the development of personalised treatments for pediatric patients.
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Affiliation(s)
- Sven C van Dijkman
- a Division of Pharmacology , Leiden Academic Centre for Drug Research , Leiden , The Netherlands
| | - Ricardo Alvarez-Jimenez
- a Division of Pharmacology , Leiden Academic Centre for Drug Research , Leiden , The Netherlands
| | - Meindert Danhof
- a Division of Pharmacology , Leiden Academic Centre for Drug Research , Leiden , The Netherlands
| | - Oscar Della Pasqua
- b Clinical Pharmacology and Discovery Medicine , GlaxoSmithKline , Stockley Park , UK.,c Clinical Pharmacology and Therapeutics , University College London , London , UK
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Bender BC, Schindler E, Friberg LE. Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response. Br J Clin Pharmacol 2015; 79:56-71. [PMID: 24134068 PMCID: PMC4294077 DOI: 10.1111/bcp.12258] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 09/30/2013] [Indexed: 12/26/2022] Open
Abstract
In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic–pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed.
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Affiliation(s)
- Brendan C Bender
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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Bernard A, Kimko H, Mital D, Poggesi I. Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development. Expert Opin Drug Metab Toxicol 2012; 8:1057-69. [PMID: 22632710 DOI: 10.1517/17425255.2012.693480] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Approaches aiming to model the time course of tumor growth and tumor growth inhibition following a therapeutic intervention have recently been proposed for supporting decision making in oncology drug development. When considered in a comprehensive model-based approach, tumor growth can be included in the cascade of quantitative and causally related markers that lead to the prediction of survival, the final clinical response. AREAS COVERED The authors examine articles dealing with the modeling of tumor growth and tumor growth inhibition in both preclinical and clinical settings. In addition, the authors review models describing how pharmacological markers can be used to predict tumor growth and models describing how tumor growth can be linked to survival endpoints. EXPERT OPINION Approaches and success stories of application of model-based drug development centered on tumor growth modeling are growing. It is also apparent that these approaches can answer practical questions on drug development more effectively than that in the past. For modeling purposes, some improvements are still needed related to study design and data quality. Further efforts are needed to encourage the mind shift from a simple description of data to the prediction of untested conditions that modeling approaches allow.
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Affiliation(s)
- Apexa Bernard
- Clinical Pharmacology, Janssen Research and Development, LLC, Raritan, NJ, USA.
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