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Tosca EM, De Carlo A, Ronchi D, Magni P. Model-Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing. Clin Pharmacol Ther 2024. [PMID: 38989560 DOI: 10.1002/cpt.3356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 06/08/2024] [Indexed: 07/12/2024]
Abstract
Precision dosing, the tailoring of drug doses to optimize therapeutic benefits and minimize risks in each patient, is essential for drugs with a narrow therapeutic window and severe adverse effects. Adaptive dosing strategies extend the precision dosing concept to time-varying treatments which require sequential dose adjustments based on evolving patient conditions. Reinforcement learning (RL) naturally fits this paradigm: it perfectly mimics the sequential decision-making process where clinicians adapt dose administration based on patient response and evolution monitoring. This paper aims to investigate the potentiality of coupling RL with population PK/PD models to develop precision dosing algorithms, reviewing the most relevant works in the field. Case studies in which PK/PD models were integrated within RL algorithms as simulation engine to predict consequences of any dosing action have been considered and discussed. They mainly concern propofol-induced anesthesia, anticoagulant therapy with warfarin and a variety of anticancer treatments differing for administered agents and/or monitored biomarkers. The resulted picture highlights a certain heterogeneity in terms of precision dosing approaches, applied methodologies, and degree of adherence to the clinical domain. In addition, a tutorial on how a precision dosing problem should be formulated in terms of the key elements composing the RL framework (i.e., system state, agent actions and reward function), and on how PK/PD models could enhance RL approaches is proposed for readers interested in delving in this field. Overall, the integration of PK/PD models into a RL-framework holds great promise for precision dosing, but further investigations and advancements are still needed to address current limitations and extend the applicability of this methodology to drugs requiring adaptive dosing strategies.
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Affiliation(s)
- Elena Maria Tosca
- Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Alessandro De Carlo
- Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Davide Ronchi
- Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Paolo Magni
- Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
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2
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Moein A, Jin JY, Wright MR, Wong H. Quantitative characterization of the effects of fulvestrant alone or in combination with taselisib (PI3Kinase inhibitor) on longitudinal tumor growth in patients with estrogen receptor-positive, HER2-negative, PIK3CA-mutant, advanced or metastatic breast cancer. Cancer Chemother Pharmacol 2024:10.1007/s00280-024-04690-4. [PMID: 38937298 DOI: 10.1007/s00280-024-04690-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 06/14/2024] [Indexed: 06/29/2024]
Abstract
PURPOSE Among cases of breast cancer, estrogen receptor-positive (ER +), PIK3CA-mutant, HER2- advanced breast cancer stands as a particularly complex clinical indication where approximately 40% of ER + /HER2- breast carcinomas present mutations in the PIK3CA gene. A significant hurdle in treating ER + breast cancer lies in surmounting the challenges of endocrine resistance. In the clinical setting, a multifaceted approach is essential for this indication, one that not only explores the effectiveness of individual treatments but also delves into the potential gains in therapeutic outcome from combination therapies. METHODS In the current study, longitudinal tumor growth inhibition (TGI) models were developed to characterize tumor response over time in postmenopausal women with ER + /HER2- advanced or metastatic breast cancer undergoing treatment with fulvestrant alone or in combination with the PI3K inhibitor, taselisib. Impact of clinically relevant covariates on TGI metrics was assessed to identify patient subsets most likely to benefit from treatment with fulvestrant monotherapy or combination with taselisib. RESULTS Tumor growth rate constant (Kg) was found to increase with increasing baseline tumor size and in the absence of baseline endocrine sensitivity. Further, Kg decreased in the absence of baseline liver metastases both in fulvestrant monotherapy and combination therapy with taselisib. Overall, additive/potentially synergistic anti-tumor effects were observed in patients treated with the taselisib-fulvestrant combination. CONCLUSION These results have important implications for understanding the therapeutic impact of combination treatment approaches and individualized responses to these treatments. Finally, this work, emphasizes the importance of model informed drug development for targeted cancer therapy. CLINICAL TRIAL REGISTRATION NCT02340221 Registered January 16, 2015, NCT01296555 Registered February 14, 2011.
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Affiliation(s)
- Anita Moein
- Faculty of Pharmaceutical Sciences, The University of British Columbia, Office 5505, Pharmaceutical Sciences Building, Vancouver, BC, Canada
- Genentech, Inc., South San Francisco, CA, USA
| | - Jin Y Jin
- Genentech, Inc., South San Francisco, CA, USA
| | | | - Harvey Wong
- Faculty of Pharmaceutical Sciences, The University of British Columbia, Office 5505, Pharmaceutical Sciences Building, Vancouver, BC, Canada.
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3
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Kulesza A, Couty C, Lemarre P, Thalhauser CJ, Cao Y. Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice. J Pharmacokinet Pharmacodyn 2024:10.1007/s10928-024-09930-x. [PMID: 38904912 DOI: 10.1007/s10928-024-09930-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/07/2024] [Indexed: 06/22/2024]
Abstract
Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.
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Affiliation(s)
| | - Claire Couty
- Novadiscovery, 1 Place Giovanni Verrazzano, 69009, Lyon, France
| | - Paul Lemarre
- Novadiscovery, 1 Place Giovanni Verrazzano, 69009, Lyon, France
| | - Craig J Thalhauser
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
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4
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Cerou M, Thai H, Deyme L, Fliscounakis‐Huynh S, Comets E, Cohen P, Cartot‐Cotton S, Veyrat‐Follet C. Joint modeling of tumor dynamics and progression-free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I-II trials. CPT Pharmacometrics Syst Pharmacol 2024; 13:941-953. [PMID: 38558299 PMCID: PMC11179707 DOI: 10.1002/psp4.13128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 04/04/2024] Open
Abstract
A joint modeling framework was developed using data from 75 patients of early amcenestrant phase I-II AMEERA-1-2 dose escalation and expansion cohorts. A semi-mechanistic tumor growth inhibition (TGI) model was developed. It accounts for the dynamics of sensitive and resistant tumor cells, an exposure-driven effect on tumor proliferation of sensitive cells, and a delay in the initiation of treatment effect to describe the time course of target lesion tumor size (TS) data. Individual treatment exposure overtime was introduced in the model using concentrations predicted by a population pharmacokinetic model of amcenestrant. This joint modeling framework integrated complex RECISTv1.1 criteria information, linked TS metrics to progression-free survival (PFS), and was externally evaluated using the randomized phase II trial AMEERA-3. We demonstrated that the instantaneous rate of change in TS (TS slope) was an important predictor of PFS and the developed joint model was able to predict well the PFS of amcenestrant phase II monotherapy trial using only early phase I-II data. This provides a good modeling and simulation tool to inform early development decisions.
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Affiliation(s)
- Marc Cerou
- Data and Data Science, Translational Disease Modeling OncologySanofi R&DParisFrance
| | - Hoai‐Thu Thai
- Data and Data Science, Translational Disease Modeling OncologySanofi R&DParisFrance
| | - Laure Deyme
- Translational Medicine & Early Development, Modeling & SimulationSanofi R&DMontpellierFrance
| | | | - Emmanuelle Comets
- IAME, InsermUniversité Paris CitéParisFrance
- Irset (Institut de Recherche en Santé, Environnement et Travail) ‐ UMR_S 1085Univ Rennes, Inserm, EHESPRennesFrance
| | | | - Sylvaine Cartot‐Cotton
- Pharmacokinetics Dynamics and Metabolism, Translational Medicine & Early DevelopmentSanofi R&DChilly MazarinFrance
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5
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Metzcar J, Jutzeler CR, Macklin P, Köhn-Luque A, Brüningk SC. A review of mechanistic learning in mathematical oncology. Front Immunol 2024; 15:1363144. [PMID: 38533513 PMCID: PMC10963621 DOI: 10.3389/fimmu.2024.1363144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/20/2024] [Indexed: 03/28/2024] Open
Abstract
Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework.
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Affiliation(s)
- John Metzcar
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
- Informatics, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Catherine R. Jutzeler
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Paul Macklin
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Sarah C. Brüningk
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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6
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Lu Y, Chu Q, Li Z, Wang M, Gatenby R, Zhang Q. Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer. Brief Bioinform 2024; 25:bbae071. [PMID: 38493345 PMCID: PMC11174533 DOI: 10.1093/bib/bbae071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/11/2024] [Accepted: 02/03/2024] [Indexed: 03/18/2024] Open
Abstract
The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.
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Affiliation(s)
- Yitao Lu
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Qian Chu
- Department of Thoracic Oncology, Tongji Hospital, Huazhong University of Science and Technology, 430030, Wuhan, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, 430030, Wuhan, China
| | - Mengdi Wang
- Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning, Princeton University, 08544, NJ, U.S.A
| | - Robert Gatenby
- Department of Integrated Mathematical Oncology and the Cancer Biology and Evolution Program, H. Lee Moffitt Cancer Center and Research Institute, 33612, FL, USA
| | - Qingpeng Zhang
- Musketeers Foundation Institute of Data Science and the Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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7
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Laurie M, Lu J. Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE. NPJ Syst Biol Appl 2023; 9:58. [PMID: 37980358 PMCID: PMC10657412 DOI: 10.1038/s41540-023-00317-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/23/2023] [Indexed: 11/20/2023] Open
Abstract
While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law that possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients' overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.
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Affiliation(s)
- Mark Laurie
- Modeling & Simulation/Clinical Pharmacology, Genentech, South San Francisco, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - James Lu
- Modeling & Simulation/Clinical Pharmacology, Genentech, South San Francisco, CA, USA.
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8
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Hutson AD, Yu H, Attwood K. Leveraging homologous hypotheses for increased efficiency in tumor growth curve testing. Sci Rep 2023; 13:19890. [PMID: 37963974 PMCID: PMC10646053 DOI: 10.1038/s41598-023-47202-9] [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: 08/07/2023] [Accepted: 11/10/2023] [Indexed: 11/16/2023] Open
Abstract
In this note, we present an innovative approach called "homologous hypothesis tests" that focuses on cross-sectional comparisons of average tumor volumes at different time-points. By leveraging the correlation structure between time-points, our method enables highly efficient per time-point comparisons, providing inferences that are highly efficient as compared to those obtained from a standard two-sample t test. The key advantage of this approach lies in its user-friendliness and accessibility, as it can be easily employed by the broader scientific community through standard statistical software packages.
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Affiliation(s)
- Alan D Hutson
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY, 14623, USA.
| | - Han Yu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY, 14623, USA
| | - Kristopher Attwood
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY, 14623, USA
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9
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Lejeune J, Raoult V, Dubrasquet M, Chauvin R, Mallebranche C, Pellier I, Monceaux F, Bayart S, Grain A, Gyan E, Ravalet N, Herault O, Ternant D. Prediction of the Clinical Course of Immune Thrombocytopenia in Children by Platelet Kinetics. Hemasphere 2023; 7:e960. [PMID: 37908859 PMCID: PMC10615561 DOI: 10.1097/hs9.0000000000000960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 08/16/2023] [Indexed: 11/02/2023] Open
Abstract
Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. In this retrospective study, we used platelet counts from the 6 months following the diagnosis of ITP to model the kinetics of change in platelet count using a pharmacokinetic-pharmacodynamic model. In a learning set (n = 103), platelet counts were satisfactorily described by our kinetic model. The Kheal parameter, which describes spontaneous platelet recovery, allowed a distinction between acute and prolonged ITP with an area under the curve (AUC) of 0.74. In a validation set (n = 58), spontaneous platelet recovery was robustly predicted using platelet counts from 15 (AUC = 0.76) or 30 (AUC = 0.82) days after ITP diagnosis. In our model, platelet recovery quantified using the kheal parameter allowed prediction of the clinical course of ITP. Future prospective studies are needed to improve the predictivity of this model, in particular, by combining it with the predictive scores previously reported in the literature.
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Affiliation(s)
- Julien Lejeune
- Pediatric Onco-Hematology Unit, CHU de Tours, France
- CNRS ERL7001, EA 7501 GICC, University of Tours, France
| | | | | | | | | | | | | | - Sophie Bayart
- Pediatric and Adolescent Unit, CHRU de Rennes, France
| | - Audrey Grain
- Pediatric Immuno-Hemato-Oncology Unit, CHU Nantes, France
| | - Emmanuel Gyan
- Pediatric Onco-Hematology Unit, CHU de Tours, France
- CNRS ERL7001, EA 7501 GICC, University of Tours, France
| | - Noémie Ravalet
- CNRS ERL7001, EA 7501 GICC, University of Tours, France
- Department of Biological Hematology, Tours University Hospital, Tours, France
| | - Olivier Herault
- CNRS ERL7001, EA 7501 GICC, University of Tours, France
- Department of Biological Hematology, Tours University Hospital, Tours, France
| | - David Ternant
- EA 7501 « Transplantation, Immunology, Inflammation », University of Tours, France
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10
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Ojara FW, Henrich A, Frances N, Nassar YM, Huisinga W, Hartung N, Geiger K, Holdenrieder S, Joerger M, Kloft C. A prognostic baseline blood biomarker and tumor growth kinetics integrated model in paclitaxel/platinum treated advanced non-small cell lung cancer patients. CPT Pharmacometrics Syst Pharmacol 2023; 12:1714-1725. [PMID: 36782356 PMCID: PMC10681433 DOI: 10.1002/psp4.12937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 01/11/2023] [Indexed: 02/15/2023] Open
Abstract
Paclitaxel/platinum chemotherapy, the backbone of standard first-line treatment of advanced non-small cell lung cancer (NSCLC), exhibits high interpatient variability in treatment response and high toxicity burden. Baseline blood biomarker concentrations and tumor size (sum of diameters) at week 8 relative to baseline (RS8) are widely investigated prognostic factors. However, joint analysis of data on demographic/clinical characteristics, blood biomarker levels, and chemotherapy exposure-driven early tumor response for improved prediction of overall survival (OS) is clinically not established. We developed a Weibull time-to-event model to predict OS, leveraging data from 365 patients receiving paclitaxel/platinum combination chemotherapy once every three weeks for ≤six cycles. A developed tumor growth inhibition model, combining linear tumor growth and first-order paclitaxel area under the concentration-time curve-induced tumor decay, was used to derive individual RS8. The median model-derived RS8 in all patients was a 20.0% tumor size reduction (range from -78% to +15%). Whereas baseline carcinoembryonic antigen, cytokeratin fragments, and thyroid stimulating hormone levels were not significantly associated with OS in a subset of 221 patients, and lactate dehydrogenase, interleukin-6 and neutrophil-to-lymphocyte ratio levels were significant only in univariate analyses (p value < 0.05); C-reactive protein (CRP) in combination with RS8 most significantly affected OS (p value < 0.01). Compared to the median population OS of 11.3 months, OS was 128% longer at the 5th percentile levels of both covariates and 60% shorter at their 95th percentiles levels. The combined paclitaxel exposure-driven RS8 and baseline blood CRP concentrations enables early individual prognostic predictions for different paclitaxel dosing regimens, forming the basis for treatment decision and optimizing paclitaxel/platinum-based advanced NSCLC chemotherapy.
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Affiliation(s)
- Francis Williams Ojara
- Department of Clinical Pharmacy and Biochemistry, Institute of PharmacyFreie Universitaet BerlinBerlinGermany
- Graduate Research Training Program PharMetrXBerlin/PotsdamGermany
| | - Andrea Henrich
- Department of Clinical Pharmacy and Biochemistry, Institute of PharmacyFreie Universitaet BerlinBerlinGermany
- Graduate Research Training Program PharMetrXBerlin/PotsdamGermany
| | - Nicolas Frances
- Department of Translational Modeling and Simulation, Roche Pharma Research and Early Development, Roche Innovation Center BaselF. Hoffmann‐La Roche LtdBaselSwitzerland
| | - Yomna M. Nassar
- Department of Clinical Pharmacy and Biochemistry, Institute of PharmacyFreie Universitaet BerlinBerlinGermany
- Graduate Research Training Program PharMetrXBerlin/PotsdamGermany
| | | | - Niklas Hartung
- Institute of MathematicsUniversity of PotsdamPotsdamGermany
| | - Kimberly Geiger
- Munich Biomarker Research Center, Institute of Laboratory Medicine, German Heart CenterTechnical University of MunichMunichGermany
| | - Stefan Holdenrieder
- Munich Biomarker Research Center, Institute of Laboratory Medicine, German Heart CenterTechnical University of MunichMunichGermany
| | - Markus Joerger
- Department of Oncology and HematologyCantonal Hospital St. GallenSt. GallenSwitzerland
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of PharmacyFreie Universitaet BerlinBerlinGermany
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11
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Melillo N, Dickinson J, Tan L, Mistry HB, Huber HJ. Radius additivity score: a novel combination index for tumour growth inhibition in fixed-dose xenograft studies. Front Pharmacol 2023; 14:1272058. [PMID: 37900154 PMCID: PMC10603293 DOI: 10.3389/fphar.2023.1272058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
The effect of combination therapies in many cancers has often been shown to be superior to that of monotherapies. This success is commonly attributed to drug synergies. Combinations of two (or more) drugs in xenograft tumor growth inhibition (TGI) studies are typically designed at fixed doses for each compound. The available methods for assessing synergy in such study designs are based on combination indices (CI) and model-based analyses. The former methods are suitable for screening exercises but are difficult to verify in in vivo studies, while the latter incorporate drug synergy in semi-mechanistic frameworks describing disease progression and drug action but are unsuitable for screening. In the current study, we proposed the empirical radius additivity (Rad-add) score, a novel CI for synergy detection in fixed-dose xenograft TGI combination studies. The Rad-add score approximates model-based analysis performed using the semi-mechanistic constant-radius growth TGI model. The Rad-add score was compared with response additivity, defined as the addition of the two response values, and the bliss independence model in combination studies derived from the Novartis PDX dataset. The results showed that the bliss independence and response additivity models predicted synergistic interactions with high and low probabilities, respectively. The Rad-add score predicted synergistic probabilities that appeared to be between those predicted with response additivity and the Bliss model. We believe that the Rad-add score is particularly suitable for assessing synergy in the context of xenograft combination TGI studies, as it combines the advantages of CI approaches suitable for screening exercises with those of semi-mechanistic TGI models based on a mechanistic understanding of tumor growth.
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Affiliation(s)
- Nicola Melillo
- Seda Pharmaceutical Developments Services Unit D Cheadle Royal Business Park, Stockport, United Kingdom
| | - Jake Dickinson
- Seda Pharmaceutical Developments Services Unit D Cheadle Royal Business Park, Stockport, United Kingdom
| | - Lu Tan
- Division Drug Discovery Sciences, Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria
| | - Hitesh B. Mistry
- Seda Pharmaceutical Developments Services Unit D Cheadle Royal Business Park, Stockport, United Kingdom
- Division of Pharmacy, University of Manchester, Manchester, United Kingdom
| | - Heinrich J. Huber
- Division Drug Discovery Sciences, Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria
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12
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Bruno R, Chanu P, Kågedal M, Mercier F, Yoshida K, Guedj J, Li C, Beyer U, Jin JY. Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models. Br J Cancer 2023; 129:1383-1388. [PMID: 36765177 PMCID: PMC10628227 DOI: 10.1038/s41416-023-02190-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 02/12/2023] Open
Abstract
Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.
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Affiliation(s)
- René Bruno
- Clinical Pharmacology, Genentech-Roche, Marseille, France.
| | - Pascal Chanu
- Clinical Pharmacology, Genentech-Roche, Lyon, France
| | - Matts Kågedal
- Clinical Pharmacology, Genentech-Roche, Solna, Sweden
| | | | - Kenta Yoshida
- Clinical Pharmacology, Genentech, South San Francisco, CA, USA
| | | | - Chunze Li
- Clinical Pharmacology, Genentech, South San Francisco, CA, USA
| | | | - Jin Y Jin
- Clinical Pharmacology, Genentech, South San Francisco, CA, USA
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13
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De Carlo A, Tosca EM, Melillo N, Magni P. A two-stages global sensitivity analysis by using the δ sensitivity index in presence of correlated inputs: application on a tumor growth inhibition model based on the dynamic energy budget theory. J Pharmacokinet Pharmacodyn 2023; 50:395-409. [PMID: 37422844 PMCID: PMC10460734 DOI: 10.1007/s10928-023-09872-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 06/16/2023] [Indexed: 07/11/2023]
Abstract
Global sensitivity analysis (GSA) evaluates the impact of variability and/or uncertainty of the model parameters on given model outputs. GSA is useful for assessing the quality of Pharmacometric model inference. Indeed, model parameters can be affected by high (estimation) uncertainty due to the sparsity of data. Independence between model parameters is a common assumption of GSA methods. However, ignoring (known) correlations between parameters may alter model predictions and, then, GSA results. To address this issue, a novel two-stages GSA technique based on the δ index, which is well-defined also in presence of correlated parameters, is here proposed. In the first step, statistical dependencies are neglected to identify parameters exerting causal effects. Correlations are introduced in the second step to consider the real distribution of the model output and investigate also the 'indirect' effects due to the correlation structure. The proposed two-stages GSA strategy was applied, as case study, to a preclinical tumor-in-host-growth inhibition model based on the Dynamic Energy Budget theory. The aim is to evaluate the impact of the model parameter estimate uncertainty (including correlations) on key model-derived metrics: the drug threshold concentration for tumor eradication, the tumor volume doubling time and a new index evaluating the drug efficacy-toxicity trade-off. This approach allowed to rank parameters according to their impact on the output, discerning whether a parameter mainly exerts a causal or 'indirect' effect. Thus, it was possible to identify uncertainties that should be necessarily reduced to obtain robust predictions for the outputs of interest.
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Affiliation(s)
- Alessandro De Carlo
- Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Elena Maria Tosca
- Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Nicola Melillo
- Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Systems Forecasting UK Ltd, Lancaster, UK
| | - Paolo Magni
- Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
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14
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Hutson AD, Yu H, Attwood K. Leveraging Homologous Hypotheses for Increased Efficiency in Tumor Growth Curve Testing. RESEARCH SQUARE 2023:rs.3.rs-3242375. [PMID: 37645958 PMCID: PMC10462185 DOI: 10.21203/rs.3.rs-3242375/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
In this note, we present an innovative approach called "homologous hypothesis tests" that focuses on cross-sectional comparisons of average tumor volumes at different time-points. By leveraging the correlation structure between time-points, our method enables highly efficient per time-point comparisons, providing inferences that are highly efficient as compared to those obtained from a standard two-sample t-test. The key advantage of this approach lies in its user-friendliness and accessibility, as it can be easily employed by the broader scientific community through standard statistical software packages.
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Affiliation(s)
- Alan D Hutson
- Roswell Park Comprehensive Cancer Center, Department of Biostatistics and Bioinformatics, Elm and Carlton Streets, Buffalo, NY 14623
| | - Han Yu
- Roswell Park Comprehensive Cancer Center, Department of Biostatistics and Bioinformatics, Elm and Carlton Streets, Buffalo, NY 14623
| | - Kristopher Attwood
- Roswell Park Comprehensive Cancer Center, Department of Biostatistics and Bioinformatics, Elm and Carlton Streets, Buffalo, NY 14623
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15
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Engelhardt J, Montalibet V, Saut O, Loiseau H, Collin A. Evaluation of four tumour growth models to describe the natural history of meningiomas. EBioMedicine 2023; 94:104697. [PMID: 37413890 PMCID: PMC10345245 DOI: 10.1016/j.ebiom.2023.104697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND The incidence of newly diagnosed meningiomas, particularly those diagnosed incidentally, is continually increasing. The indication for treatment is empirical because, despite numerous studies, the natural history of these tumours remains difficult to describe and predict. METHODS This retrospective single-centre study included 294 consecutive patients with 333 meningiomas who underwent three or more brain imaging scans. Linear, exponential, power, and Gompertz models were constructed to derive volume-time curves, by using a mixed-effect approach. The most accurate model was used to analyse tumour growth and predictors of rapid growth. FINDINGS The Gompertz model provided the best results. Hierarchical clustering at the time of diagnosis and at the end of follow-up revealed at least three distinct groups, which can be described as pseudoexponential, linear, and slowing growth with respect to their parameters. Younger patients and smaller tumours were more frequent in the pseudo-exponential clusters. We found that the more "aggressive" the cluster, the higher the proportion of patients with grade II meningiomas and who have had a cranial radiotherapy. Over a mean observation period of 56.5 months, 21% of the tumours moved to a cluster with a lower growth rate, consistent with the Gompertz's law. INTERPRETATION Meningiomas exhibit multiple growth phases, as described by the Gompertz model. The management of meningiomas should be discussed according to the growth phase, comorbidities, tumour location, size, and growth rate. Further research is needed to evaluate the associations between radiomics features and the growth phases of meningiomas. FUNDING No funding.
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Affiliation(s)
- Julien Engelhardt
- Service de Neurochirurgie B, CHU de Bordeaux, Place Amélie Raba-Léon, Bordeaux Cédex 33076, France; Univ. Bordeaux, Inria Bordeaux-Sud-Ouest, Bordeaux INP, CNRS, IMB, UMR 5251, Talence F-33400, France.
| | - Virginie Montalibet
- Univ. Bordeaux, Inria Bordeaux-Sud-Ouest, Bordeaux INP, CNRS, IMB, UMR 5251, Talence F-33400, France
| | - Olivier Saut
- Univ. Bordeaux, Inria Bordeaux-Sud-Ouest, Bordeaux INP, CNRS, IMB, UMR 5251, Talence F-33400, France
| | - Hugues Loiseau
- Service de Neurochirurgie B, CHU de Bordeaux, Place Amélie Raba-Léon, Bordeaux Cédex 33076, France
| | - Annabelle Collin
- Univ. Bordeaux, Inria Bordeaux-Sud-Ouest, Bordeaux INP, CNRS, IMB, UMR 5251, Talence F-33400, France
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16
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Radulski DR, Stipp MC, Galindo CM, Acco A. Features and applications of Ehrlich tumor model in cancer studies: a literature review. TRANSLATIONAL BREAST CANCER RESEARCH : A JOURNAL FOCUSING ON TRANSLATIONAL RESEARCH IN BREAST CANCER 2023; 4:22. [PMID: 38751464 PMCID: PMC11093101 DOI: 10.21037/tbcr-23-32] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 07/25/2023] [Indexed: 05/18/2024]
Abstract
Background and Objective Breast cancer is the most prevalent cancer worldwide, responsible for a large number of deaths, especially among women. Therapeutic options for breast cancer include surgery, radiotherapy, chemotherapy, hormone therapy, and immunotherapy, but further studies of the pathogenesis of this disease and new treatments are still needed. In vitro and in vivo cancer models are important research tools. Murine Ehrlich tumors are one of these models, especially for hormone-positive breast cancer. The present narrative review discusses characteristics of the Ehrlich tumor model, laboratory manipulations of Ehrlich cells (ECs), and applications in pharmacological, pathological, and translational studies. Methods This review was based on scientific articles, books, and theses on Ehrlich tumors. We searched the PubMed, SciELO, Google Scholar, Google, and Clarivate databases. Key Content and Findings Hormone-positive ECs produce solid Ehrlich carcinoma (SEC) and ascitic Ehrlich carcinoma (AEC), with different features and applications. The presence of SEC or AEC induces systemic and immunological alterations that are similar to cancer in humans, what makes this model applicable to different studies in the cancer field. Conclusions Ehrlich tumors are a relevant tool for improving our understanding of the pathogenesis of breast cancer and investigating the tumor microenvironment, side effects of therapies, and new treatment options. Despite some limitations, such as the absence of an invasive phenotype to produce metastasis, both SEC and AEC are relevant in preclinical and translational studies of breast cancer.
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Affiliation(s)
| | | | | | - Alexandra Acco
- Department of Pharmacology, Federal University of Paraná, Curitiba, PR, Brazil
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17
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Del Core L, Pellin D, Wit EC, Grzegorczyk MA. A mixed-effects stochastic model reveals clonal dominance in gene therapy safety studies. BMC Bioinformatics 2023; 24:228. [PMID: 37268887 DOI: 10.1186/s12859-023-05269-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 04/04/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND Mathematical models of haematopoiesis can provide insights on abnormal cell expansions (clonal dominance), and in turn can guide safety monitoring in gene therapy clinical applications. Clonal tracking is a recent high-throughput technology that can be used to quantify cells arising from a single haematopoietic stem cell ancestor after a gene therapy treatment. Thus, clonal tracking data can be used to calibrate the stochastic differential equations describing clonal population dynamics and hierarchical relationships in vivo. RESULTS In this work we propose a random-effects stochastic framework that allows to investigate the presence of events of clonal dominance from high-dimensional clonal tracking data. Our framework is based on the combination between stochastic reaction networks and mixed-effects generalized linear models. Starting from the Kramers-Moyal approximated Master equation, the dynamics of cells duplication, death and differentiation at clonal level, can be described by a local linear approximation. The parameters of this formulation, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones and are not sufficient to describe situation in which clones exhibit heterogeneity in their fitness that can lead to clonal dominance. In order to overcome this limitation, we extend the base model by introducing random-effects for the clonal parameters. This extended formulation is calibrated to the clonal data using a tailor-made expectation-maximization algorithm. We also provide the companion package RestoreNet, publicly available for download at https://cran.r-project.org/package=RestoreNet . CONCLUSIONS Simulation studies show that our proposed method outperforms the state-of-the-art. The application of our method in two in-vivo studies unveils the dynamics of clonal dominance. Our tool can provide statistical support to biologists in gene therapy safety analyses.
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Affiliation(s)
- Luca Del Core
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands.
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK.
| | - Danilo Pellin
- Harvard Medical School, Harvard University, Boston, MA, USA.
| | - Ernst C Wit
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands.
- Institute of Computing, Università della Svizzera italiana, Lugano, Switzerland.
| | - Marco A Grzegorczyk
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands.
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18
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A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development. J Pharmacokinet Pharmacodyn 2023; 50:147-172. [PMID: 36870005 PMCID: PMC10169901 DOI: 10.1007/s10928-023-09850-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023]
Abstract
Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.
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19
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Model selection for assessing the effects of doxorubicin on triple-negative breast cancer cell lines. J Math Biol 2022; 85:65. [PMID: 36352309 DOI: 10.1007/s00285-022-01828-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 07/15/2022] [Accepted: 10/03/2022] [Indexed: 11/11/2022]
Abstract
Doxorubicin is a chemotherapy widely used to treat several types of cancer, including triple-negative breast cancer. In this work, we use a Bayesian framework to rigorously assess the ability of ten different mathematical models to describe the dynamics of four TNBC cell lines (SUM-149PT, MDA-MB-231, MDA-MB-453, and MDA-MB-468) in response to treatment with doxorubicin at concentrations ranging from 10 to 2500 nM. Each cell line was plated and serially imaged via fluorescence microscopy for 30 days following 6, 12, or 24 h of in vitro drug exposure. We use the resulting data sets to estimate the parameters of the ten pharmacodynamic models using a Bayesian approach, which accounts for uncertainties in the models, parameters, and observational data. The ten candidate models describe the growth patterns and degree of response to doxorubicin for each cell line by incorporating exponential or logistic tumor growth, and distinct forms of cell death. Cell line and treatment specific model parameters are then estimated from the experimental data for each model. We analyze all competing models using the Bayesian Information Criterion (BIC), and the selection of the best model is made according to the model probabilities (BIC weights). We show that the best model among the candidate set of models depends on the TNBC cell line and the treatment scenario, though, in most cases, there is great uncertainty in choosing the best model. However, we show that the probability of being the best model can be increased by combining treatment data with the same total drug exposure. Our analysis points to the importance of considering multiple models, built on different biological assumptions, to capture the observed variations in tumor growth and treatment response.
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20
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Fostvedt LK, Nickens DJ, Tan W, Parivar K. Tumor growth inhibition modeling to support the starting dose for dacomitinib. CPT Pharmacometrics Syst Pharmacol 2022; 11:1256-1267. [PMID: 35818811 PMCID: PMC9893889 DOI: 10.1002/psp4.12841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 06/16/2022] [Accepted: 06/19/2022] [Indexed: 11/10/2022] Open
Abstract
Dacomitinib is a second-generation, irreversible EGFR tyrosine kinase inhibitor for first-line treatment of patients with metastatic non-small cell lung cancer and EGFR-activating mutations. A high rate of dose reductions in the pivotal trial led to an observed inverse exposure-response (ER) relationship with the primary end points. Three ER models were developed to determine if the starting dose from the pivotal trial, 45 mg once daily (q.d.) dose, is appropriate: a longitudinal logistic regression model for adverse event-related dose changes, a Claret tumor growth inhibition (TGI) model, and a Cox model for progression-free survival (PFS) based on the TGI model predictions. This analysis included 266 patients taking dacomitinib with a starting dose of 45 mg (N = 250) or 30 mg (N = 16) q.d. The ER relationships with the time-varying exposure metrics, most recent maximum plasma concentration (Cmax ) and average concentration (Cavg ) from the first dose, were established for the dose reduction and TGI models, respectively. The TGI model characterized the tumor inhibition over time with constant growth rate (kL = 0.0012 years-1 ) and highly variable kill rate (kD = 1.002 years-1 /[μg/L]θcavg , coefficient of variation [CV] = 89%) and drug resistance (λ = 14.47 years-1 , CV = 96%) leading to prolonged tumor shrinkage. The ER relationship was characterized using an exposure parameter with a power parameterization (θcavg = 0.454, p < 0.0001). The Cox model found that baseline tumor size (p = 0.0166) and week 8 tumor shrinkage rate (p = 0.0726) were the best predictors of PFS. Simulations of dose reductions and drug interruptions on tumor shrinkage over time showed greater and more prolonged tumor shrinkage with a starting dose of 45 mg q.d.
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Affiliation(s)
| | | | - Weiwei Tan
- Global Product DevelopmentPfizer Inc.La JollaCaliforniaUSA
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21
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Cheng Y, Hong K, Chen N, Yu X, Peluso T, Zhou S, Li Y. Aiding early clinical drug development by elucidation of the relationship between tumor growth inhibition and survival in relapsed/refractory multiple myeloma patients. EJHAEM 2022; 3:815-827. [PMID: 36051011 PMCID: PMC9422038 DOI: 10.1002/jha2.494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/16/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Early prognosis of clinical efficacy is an urgent need for oncology drug development. Herein, we systemically examined the quantitative approach of tumor growth inhibition (TGI) and survival modeling in the space of relapsed and refractory multiple myeloma (MM), aiming to provide insights into clinical drug development. Longitudinal serum M-protein and progression-free survival (PFS) data from three phase III studies (N = 1367) across six treatment regimens and different patient populations were leveraged. The TGI model successfully described the longitudinal M-protein data in patients with MM. The tumor inhibition and growth parameters were found to vary as per each study, likely due to the patient population and treatment regimen difference. Based on a parametric time-to-event model for PFS, M-protein reduction at week 4 was identified as a significant prognostic factor for PFS across the three studies. Other factors, including Eastern Cooperative Oncology Group performance status, prior anti-myeloma therapeutics, and baseline serum ß2-microglobulin level, were correlated with PFS as well. In conclusion, patient disease characteristics (i.e., baseline tumor burden and treatment lines) were important determinants of tumor inhibition and PFS in MM patients. M-protein change at week 4 was an early prognostic biomarker for PFS.
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Affiliation(s)
- Yiming Cheng
- Clinical Pharmacology & PharmacometricsBristol Myers SquibbNew JerseyUSA
| | - Kevin Hong
- Global Drug DevelopmentBristol Myers SquibbNew JerseyUSA
| | - Nianhang Chen
- Clinical Pharmacology & PharmacometricsBristol Myers SquibbNew JerseyUSA
| | - Xin Yu
- Global Biometric SciencesBristol Myers SquibbNew JerseyUSA
| | - Teresa Peluso
- Global Drug Development Bristol Myers SquibbBoudrySwitzerland
| | - Simon Zhou
- Clinical Pharmacology & PharmacometricsBristol Myers SquibbNew JerseyUSA
| | - Yan Li
- Clinical Pharmacology & PharmacometricsBristol Myers SquibbNew JerseyUSA
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22
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Nasim A, Yates J, Derks G, Dunlop C. A Spatially Resolved Mechanistic Growth Law for Cancer Drug Development Predicting Tumor Growing Fractions. CANCER RESEARCH COMMUNICATIONS 2022; 2:754-761. [PMID: 36923310 PMCID: PMC10010375 DOI: 10.1158/2767-9764.crc-22-0032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/25/2022] [Accepted: 07/08/2022] [Indexed: 11/16/2022]
Abstract
Mathematical models used in preclinical drug discovery tend to be empirical growth laws. Such models are well suited to fitting the data available, mostly longitudinal studies of tumor volume; however, they typically have little connection with the underlying physiologic processes. This lack of a mechanistic underpinning restricts their flexibility and potentially inhibits their translation across studies including from animal to human. Here we present a mathematical model describing tumor growth for the evaluation of single-agent cytotoxic compounds that is based on mechanistic principles. The model can predict spatial distributions of cell subpopulations and account for spatial drug distribution effects within tumors. Importantly, we demonstrate that the model can be reduced to a growth law similar in form to the ones currently implemented in pharmaceutical drug development for preclinical trials so that it can integrated into the current workflow. We validate this approach for both cell-derived xenograft and patient-derived xenograft (PDX) data. This shows that our theoretical model fits as well as the best performing and most widely used models. However, in addition, the model is also able to accurately predict the observed growing fraction of tumours. Our work opens up current preclinical modeling studies to also incorporating spatially resolved and multimodal data without significant added complexity and creates the opportunity to improve translation and tumor response predictions. Significance This theoretical model has the same mathematical structure as that currently used for drug development. However, its mechanistic basis enables prediction of growing fraction and spatial variations in drug distribution.
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Affiliation(s)
- Adam Nasim
- Department of Mathematics, University of Surrey, Guildford, United Kingdom
| | - James Yates
- Oncology R&D, AstraZeneca, Cambridge, United Kingdom
| | - Gianne Derks
- Department of Mathematics, University of Surrey, Guildford, United Kingdom
| | - Carina Dunlop
- Department of Mathematics, University of Surrey, Guildford, United Kingdom
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23
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Wilbaux M, Demanse D, Gu Y, Jullion A, Myers A, Katsanou V, Meille C. Contribution of machine learning to tumor growth inhibition modeling for hepatocellular carcinoma patients under Roblitinib (FGF401) drug treatment. CPT Pharmacometrics Syst Pharmacol 2022; 11:1122-1134. [PMID: 35728123 PMCID: PMC9381917 DOI: 10.1002/psp4.12831] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 12/19/2022] Open
Abstract
Machine learning (ML) opens new perspectives in identifying predictive factors of efficacy among a large number of patients’ characteristics in oncology studies. The objective of this work was to combine ML with population pharmacokinetic/pharmacodynamic (PK/PD) modeling of tumor growth inhibition to understand the sources of variability between patients and therefore improve model predictions to support drug development decisions. Data from 127 patients with hepatocellular carcinoma enrolled in a phase I/II study evaluating once‐daily oral doses of the fibroblast growth factor receptor FGFR4 kinase inhibitor, Roblitinib (FGF401), were used. Roblitinib PKs was best described by a two‐compartment model with a delayed zero‐order absorption and linear elimination. Clinical efficacy using the longitudinal sum of the longest lesion diameter data was described with a population PK/PD model of tumor growth inhibition including resistance to treatment. ML, applying elastic net modeling of time to progression data, was associated with cross‐validation, and allowed to derive a composite predictive risk score from a set of 75 patients’ baseline characteristics. The two approaches were combined by testing the inclusion of the continuous risk score as a covariate on PD model parameters. The score was found as a significant covariate on the resistance parameter and resulted in 19% reduction of its variability, and 32% variability reduction on the average dose for stasis. The final PK/PD model was used to simulate effect of patients’ characteristics on tumor growth inhibition profiles. The proposed methodology can be used to support drug development decisions, especially when large interpatient variability is observed.
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Affiliation(s)
| | - David Demanse
- Early Development Analytics, Novartis, Basel, Switzerland
| | - Yi Gu
- Pharmacokinetic Sciences, Novartis Institutes for Biomedical Research, Cambridge, USA
| | - Astrid Jullion
- Early Development Analytics, Novartis, Basel, Switzerland
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24
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Ellingson BM, Gerstner ER, Lassman AB, Chung C, Colman H, Cole PE, Leung D, Allen JE, Ahluwalia MS, Boxerman J, Brown M, Goldin J, Nduom E, Hassan I, Gilbert MR, Mellinghoff IK, Weller M, Chang S, Arons D, Meehan C, Selig W, Tanner K, Alfred Yung WK, van den Bent M, Wen PY, Cloughesy TF. Hypothetical generalized framework for a new imaging endpoint of therapeutic activity in early phase clinical trials in brain tumors. Neuro Oncol 2022; 24:1219-1229. [PMID: 35380705 PMCID: PMC9340639 DOI: 10.1093/neuonc/noac086] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Imaging response assessment is a cornerstone of patient care and drug development in oncology. Clinicians/clinical researchers rely on tumor imaging to estimate the impact of new treatments and guide decision making for patients and candidate therapies. This is important in brain cancer, where associations between tumor size/growth and emerging neurological deficits are strong. Accurately measuring the impact of a new therapy on tumor growth early in clinical development, where patient numbers are small, would be valuable for decision making regarding late-stage development activation. Current attempts to measure the impact of a new therapy have limited influence on clinical development, as determination of progression, stability or response does not currently account for individual tumor growth kinetics prior to the initiation of experimental therapies. Therefore, we posit that imaging-based response assessment, often used as a tool for estimating clinical effect, is incomplete as it does not adequately account for growth trajectories or biological characteristics of tumors prior to the introduction of an investigational agent. Here, we propose modifications to the existing framework for evaluating imaging assessment in primary brain tumors that will provide a more reliable understanding of treatment effects. Measuring tumor growth trajectories prior to a given intervention may allow us to more confidently conclude whether there is an anti-tumor effect. This updated approach to imaging-based tumor response assessment is intended to improve our ability to select candidate therapies for later-stage development, including those that may not meet currently sought thresholds for "response" and ultimately lead to identification of effective treatments.
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Affiliation(s)
- Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Elizabeth R Gerstner
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew B Lassman
- Division of Neuro-Oncology, Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, Herbert Irving Comprehensive Cancer Center, NewYork-Presbyterian Hospital, New York, New York, USA
| | - Caroline Chung
- University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Howard Colman
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | | | - David Leung
- Bristol Myers Squibb, Princeton, New Jersey, USA
| | | | | | - Jerrold Boxerman
- Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Matthew Brown
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Jonathan Goldin
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Edjah Nduom
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Islam Hassan
- Servier Pharmaceuticals, Boston, Massachusetts, USA
| | - Mark R Gilbert
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Ingo K Mellinghoff
- Department of Neurology and Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Switzerland
| | - Susan Chang
- Division of Neuro-Oncology, University of California San Francisco, San Francisco, California, USA
| | - David Arons
- National Brain Tumor Society, Newton, Massachusetts, USA
| | - Clair Meehan
- National Brain Tumor Society, Newton, Massachusetts, USA
| | | | - Kirk Tanner
- National Brain Tumor Society, Newton, Massachusetts, USA
| | - W K Alfred Yung
- University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Martin van den Bent
- Brain Tumor Center at Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Patrick Y Wen
- Dana Farber Cancer Institute, Harvard University, Boston, Massachusetts, USA
| | - Timothy F Cloughesy
- UCLA Neuro Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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25
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Huang L, Wang J, Fang B, Meric-Bernstam F, Roth JA, Ha MJ. CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts. Sci Rep 2022; 12:12984. [PMID: 35906256 PMCID: PMC9338066 DOI: 10.1038/s41598-022-16933-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 07/18/2022] [Indexed: 12/14/2022] Open
Abstract
Anticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, most PDX combination study designs focus on single dose levels, and dose-response surface models are not appropriate for testing synergism. We propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. We provide an interactive web server, combPDX ( https://licaih.shinyapps.io/CombPDX/ ), to analyze PDX tumor growth curve data and perform power analyses.
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Affiliation(s)
- Licai Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Quantitative Sciences Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA
| | - Jing Wang
- Departments of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Bingliang Fang
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Funda Meric-Bernstam
- Department of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Jack A Roth
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Min Jin Ha
- Department of Biostatistics, Graduate School of Public Health, Yonsei University, Seoul, South Korea.
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26
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Janssen JM, Verheijen RB, van Duijl TT, Lin L, van den Heuvel MM, Beijnen JH, Steeghs N, van den Broek D, Huitema ADR, Dorlo TPC. Longitudinal nonlinear mixed effects modeling of EGFR mutations in ctDNA as predictor of disease progression in treatment of EGFR-mutant non-small cell lung cancer. Clin Transl Sci 2022; 15:1916-1925. [PMID: 35775126 PMCID: PMC9372429 DOI: 10.1111/cts.13300] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 11/28/2022] Open
Abstract
Correlations between increasing concentrations of circulating tumor DNA (ctDNA) in plasma and disease progression have been shown. A nonlinear mixed effects model to describe the dynamics of epidermal growth factor receptor (EGFR) ctDNA data from patients with non-small cell lung cancer (NSCLC) combined with a parametric survival model were developed to evaluate the ability of these modeling techniques to describe ctDNA data. Repeated ctDNA measurements on L858R, exon19del, and T790M mutants were available from 54 patients with EGFR mutated NSCLC treated with erlotinib or gefitinib. Different dynamic models were tested to describe the longitudinal ctDNA concentrations of the driver and resistance mutations. Subsequently, a parametric time-to-event model for progression-free survival (PFS) was developed. Predicted L858R, exon19del, and T790M concentrations were used to evaluate their value as predictor for disease progression. The ctDNA dynamics were best described by a model consisting of a zero-order increase and first-order elimination (19.7/day, 95% confidence interval [CI] 14.9-23.6/day) of ctDNA concentrations. In addition, time-dependent development of resistance (5.0 × 10-4 , 95% CI 2.0 × 10-4 -7.0 × 10-4 /day) was included in the final model. Relative change in L858R and exon19del concentrations from baseline was identified as most significant predictor of disease progression (p = 0.001). The dynamic model for L858R, exon19del, and T790M concentrations in ctDNA and time-to-event model adequately described the observed concentrations and PFS data in our clinical cohort. In addition, it was shown that nonlinear mixed effects modeling is a valuable method for the analysis of longitudinal and heterogeneous biomarker datasets obtained from clinical practice.
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Affiliation(s)
- Julie M Janssen
- Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Remy B Verheijen
- Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Tirsa T van Duijl
- Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Lishi Lin
- Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Michel M van den Heuvel
- Department of Respiratory Disease, Radboud University Medical Centre, Nijmegen, The Netherlands.,Department of Thoracic Oncology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Jos H Beijnen
- Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands.,Department of Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Neeltje Steeghs
- Department of Medical Oncology and Clinical Pharmacology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Daan van den Broek
- Department of Laboratory Medicine, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands
| | - Alwin D R Huitema
- Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands.,Department of Clinical Pharmacy, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Thomas P C Dorlo
- Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, The Netherlands
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27
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Yates JWT, Fairman DA. How translational modeling in oncology needs to get the mechanism just right. Clin Transl Sci 2021; 15:588-600. [PMID: 34716976 PMCID: PMC8932697 DOI: 10.1111/cts.13183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 11/28/2022] Open
Abstract
Translational model‐based approaches have played a role in increasing success in the development of novel anticancer treatments. However, despite this, significant translational uncertainty remains from animal models to patients. Optimization of dose and scheduling (regimen) of drugs to maximize the therapeutic utility (maximize efficacy while avoiding limiting toxicities) is still predominately driven by clinical investigations. Here, we argue that utilizing pragmatic mechanism‐based translational modeling of nonclinical data can further inform this optimization. Consequently, a prototype model is demonstrated that addresses the required fundamental mechanisms.
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Affiliation(s)
| | - David A Fairman
- Clinical Pharmacology, Modelling and Simulation, GSK, Stevenage, UK
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28
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Thai HT, Gaudel N, Cerou M, Ayral G, Fau JB, Sebastien B, van de Velde H, Semiond D, Veyrat-Follet C. Joint modelling and simulation of M-protein dynamics and progression-free survival for alternative isatuximab dosing with pomalidomide/dexamethasone. Br J Clin Pharmacol 2021; 88:2052-2064. [PMID: 34705283 PMCID: PMC9298821 DOI: 10.1111/bcp.15123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/29/2021] [Accepted: 10/21/2021] [Indexed: 11/29/2022] Open
Abstract
AIMS Addition of isatuximab (Isa) to pomalidomide/dexamethasone (Pd) significantly improved progression-free survival (PFS) in patients with relapsed/refractory multiple myeloma (RRMM). We aimed to characterize the relationship between serum M-protein kinetics and PFS in the phase 3 ICARIA-MM trial (NCT02990338), and to evaluate an alternative dosing regimen of Isa by simulation. METHODS Data from the ICARIA-MM trial comparing Isa 10 mg/kg weekly for 4 weeks then every 2 weeks (QW-Q2W) in combination with Pd versus Pd in 256 evaluable RRMM patients were used. A joint model of serum M-protein dynamics and PFS was developed. Trial simulations were then performed to evaluate whether efficacy is maintained after switching to a monthly dosing regimen. RESULTS The model identified instantaneous changes (slope) in serum M-protein as the best on-treatment predictor for PFS and baseline patient characteristics impacting serum M-protein kinetics (albumin and β2-microglobulin on baseline levels, non-IgG type on growth rate) and PFS (presence of plasmacytomas). Trial simulations demonstrated that switching to a monthly Isa regimen at 6 months would shorten median PFS by 2.3 weeks and induce 42.3% patients to progress earlier. CONCLUSIONS Trial simulations supported selection of the approved Isa 10 mg/kg QW-Q2W regimen and showed that switching to a monthly regimen after 6 months may reduce clinical benefit in the overall population. However, patients with good prognostic characteristics and with a stable, very good partial response may switch to a monthly regimen after 6 months without compromising the risk of disease progression. This hypothesis will be tested in a prospective clinical trial.
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Affiliation(s)
- Hoai-Thu Thai
- Translational Disease Modeling, Data and Data Science, Sanofi, France
| | - Nadia Gaudel
- Clinical Modeling and Evidence Integration, Data and Data Science, Sanofi, France
| | - Marc Cerou
- Translational Disease Modeling, Data and Data Science, Sanofi, France
| | | | | | - Bernard Sebastien
- Clinical Modeling and Evidence Integration, Data and Data Science, Sanofi, France
| | | | - Dorothée Semiond
- Translational Medicine and Early Development, Cambridge, MA, USA
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29
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Yin O, Zahir H, French J, Polhamus D, Wang X, van de Sande M, Tap WD, Gelderblom H, Wagner AJ, Healey JH, Greenberg J, Shuster D, Stacchiotti S. Exposure-response analysis of efficacy and safety for pexidartinib in patients with tenosynovial giant cell tumor. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1422-1432. [PMID: 34585528 PMCID: PMC8592513 DOI: 10.1002/psp4.12712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/30/2021] [Accepted: 09/01/2021] [Indexed: 11/08/2022]
Abstract
This analysis was conducted to assess exposure–response relationships for efficacy and safety of pexidartinib in patients with tenosynovial giant cell tumor. Efficacy was assessed categorically by overall response rate (ORR) with Response Evaluation Criteria in Solid Tumors version 1.1 and longitudinally (changes in tumor size and volume). Safety included hepatic parameters (i.e., alanine aminotransferase [ALT], aspartate aminotransferase [AST], and total bilirubin). Average pexidartinib concentration (Cavg) was identified as the primary exposure parameter correlated with response. In categorical and longitudinal analyses, higher Cavg coincided with greater ORR and tumor size reduction, respectively, with smaller joint size having a greater impact. For safety, a significant relationship was observed between Cavg and incidence of ALT‐related and AST‐related adverse events (AEs). With increased exposure, an increase in efficacy was predicted with near maximum effect at 800 mg/day. Higher initial dose (1000 mg/day) during the first 2 weeks did not improve efficacy. Higher doses were associated with an increased risk of ALT‐related and AST‐related AEs. These results support the US Food and Drug Administration–approved dose (400 mg two times/day without initial loading dose).
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Affiliation(s)
- Ophelia Yin
- Quantitative Clinical Pharmacology and Translational Sciences, Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA
| | - Hamim Zahir
- Quantitative Clinical Pharmacology and Translational Sciences, Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA
| | | | | | - Xiaoning Wang
- Metrum Research Group, Tariffville, Connecticut, USA
| | - Michiel van de Sande
- Department of Orthopedics, Leiden University Medical Center, Leiden, Netherlands
| | - William D Tap
- Sarcoma Medical Oncology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Hans Gelderblom
- Medical Oncology, Leiden University Medical Center, Leiden, Netherlands
| | - Andrew J Wagner
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - John H Healey
- Orthopaedic Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jonathan Greenberg
- Global Oncology R&D, Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA
| | - Dale Shuster
- Global Oncology R&D, Daiichi Sankyo, Inc., Basking Ridge, New Jersey, USA
| | - Silvia Stacchiotti
- Department of Medical Oncology, Fondazione IRCCS Instituto Nazionale dei Tumori, Milan, Italy
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30
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Yan X, Bauer R, Koch G, Schropp J, Perez Ruixo JJ, Krzyzanski W. Delay differential equations based models in NONMEM. J Pharmacokinet Pharmacodyn 2021; 48:763-802. [PMID: 34302262 DOI: 10.1007/s10928-021-09770-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/12/2021] [Indexed: 10/20/2022]
Abstract
Delay differential equations (DDEs) are commonly used in pharmacometric models to describe delays present in pharmacokinetic and pharmacodynamic data analysis. Several DDE solvers have been implemented in NONMEM 7.5 for the first time. Two of them are based on algorithms already applied elsewhere, while others are extensions of existing ordinary differential equations (ODEs) solvers. The purpose of this tutorial is to introduce basic concepts underlying DDE based models and to show how they can be developed using NONMEM. The examples include previously published DDE models such as logistic growth, tumor growth inhibition, indirect response with precursor pool, rheumatoid arthritis, and erythropoiesis-stimulating agents. We evaluated the accuracy of NONMEM DDE solvers, their ability to handle stiff problems, and their performance in parameter estimation using both first-order conditional estimation (FOCE) and the expectation-maximization (EM) method. NONMEM control streams and excerpts from datasets are provided for all discussed examples. All DDE solvers provide accurate and precise solutions with the number of significant digits controlled by the error tolerance parameters. For estimation of population parameters, the EM method is more stable than FOCE regardless of the DDE solver.
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Affiliation(s)
- Xiaoyu Yan
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Robert Bauer
- Pharmacometrics R&D, ICON Clinical Research LLC, Gaithersburg, MD, USA
| | - Gilbert Koch
- Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel UKBB, University of Basel, Basel, Switzerland
| | - Johannes Schropp
- Department of Mathematics and Statistics, University of Konstanz, Konstanz, Germany
| | | | - Wojciech Krzyzanski
- Department of Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, NY, USA.
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31
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Javadi A, Keighobadi F, Nekoukar V, Ebrahimi M. Finite-Set Model Predictive Control of Melanoma Cancer Treatment Using Signaling Pathway Inhibitor of Cancer Stem Cell. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1504-1511. [PMID: 31514151 DOI: 10.1109/tcbb.2019.2940658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Drug delivery is one of the most important issues in the treatment of cancer and surviving the patient. Recently, with a combination of mathematical models of the tumor growth and control theory, optimal drug delivery can be planned, individually. The goal is reducing the tumor volume with minimum side effects on the patient. One of the most important challenges of the modeling is considering the drug resistance, which may lead to failure of the treatment. In this paper, a mathematical model is proposed for describing the growth dynamics of the melanoma tumor cells. It is assumed that the melanoma cancer is treated with Notch signaling pathway inhibitors of the cancer stem cells. The model parameters are identified based on experimental data obtained from 13 male nude mice with an induced melanoma cancer involved in a dual antiplatelet therapy (DAPT) program. The mathematical model is used to determine if DAPT can reduce the growth rate of the tumor. Then an optimal drug delivery plan for the treatment of every animal model is presented, individually using finite-set model predictive control method. The results show that the proposed model can estimate the drug's effect on the treatment of melanoma cancer.
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32
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Vera-Yunca D, Parra-Guillen ZP, Girard P, Trocóniz IF, Terranova N. Relevance of primary lesion location, tumour heterogeneity and genetic mutation demonstrated through tumour growth inhibition and overall survival modelling in metastatic colorectal cancer. Br J Clin Pharmacol 2021; 88:166-177. [PMID: 34087010 DOI: 10.1111/bcp.14937] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/21/2021] [Accepted: 05/30/2021] [Indexed: 12/20/2022] Open
Abstract
AIMS The aims of this work were to build a semi-mechanistic tumour growth inhibition (TGI) model for metastatic colorectal cancer (mCRC) patients receiving either cetuximab + chemotherapy or chemotherapy alone and to identify early predictors of overall survival (OS). METHODS A total of 1716 patients from 4 mCRC clinical studies were included in the analysis. The TGI model was built with 8973 tumour size measurements where the probability of drop-out was also included and modelled as a time-to-event variable using parametric survival models, as it was the case in the OS analysis. The effects of patient- and tumour-related covariates on model parameters were explored. RESULTS Chemotherapy and cetuximab effects were included in an additive form in the TGI model. Development of resistance was found to be faster for chemotherapy (drug effect halved at wk 8) compared to cetuximab (drug effect halved at wk 12). KRAS wild-type status and presenting a right-sided primary lesion were related to a 3.5-fold increase in cetuximab drug effect and a 4.7× larger cetuximab resistance, respectively. The early appearance of a new lesion (HR = 4.14), a large tumour size at baseline (HR = 1.62) and tumour heterogeneity (HR = 1.36) were the main predictors of OS. CONCLUSIONS Semi-mechanistic TGI and OS models have been developed in a large population of mCRC patients receiving chemotherapy in combination or not with cetuximab. Tumour-related predictors, including a machine learning derived-index of tumour heterogeneity, were linked to changes in drug effect, resistance to treatment or OS, contributing to the understanding of the variability in clinical response.
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Affiliation(s)
- Diego Vera-Yunca
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Zinnia P Parra-Guillen
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Pascal Girard
- Merck Serono S.A., Switzerland, an affiliate of Merck KGaA, Merck Institute for Pharmacometrics, Darmstadt, Germany
| | - Iñaki F Trocóniz
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Nadia Terranova
- Merck Serono S.A., Switzerland, an affiliate of Merck KGaA, Merck Institute for Pharmacometrics, Darmstadt, Germany
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33
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Husband HR, Campagne O, He C, Zhu X, Bianski BM, Baker SJ, Shelat AA, Tinkle CL, Stewart CF. Model-based evaluation of image-guided fractionated whole-brain radiation therapy in pediatric diffuse intrinsic pontine glioma xenografts. CPT Pharmacometrics Syst Pharmacol 2021; 10:599-610. [PMID: 33939327 PMCID: PMC8213420 DOI: 10.1002/psp4.12627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 11/09/2022] Open
Abstract
Radiation therapy (RT) is currently the standard treatment for diffuse intrinsic pontine glioma (DIPG), the most common cause of death in children with brain cancer. A pharmacodynamic model was developed to describe the radiation-induced tumor shrinkage and overall survival in mice bearing DIPG. CD1-nude mice were implanted in the brain cortex with luciferase-labeled patient-derived orthotopic xenografts of DIPG (SJDIPGx7 H3F3AWT / K27 M and SJDIPGx37 H3F3AK27M / K27M ). Mice were treated with image-guided whole-brain RT at 1 or 2 Gy/fraction 5-days-on 2-days-off for a cumulative dose of 20 or 54 Gy. Tumor progression was monitored with bioluminescent imaging (BLI). A mathematical model describing BLI and overall survival was developed with data from mice receiving 2 Gy/fraction and validated using data from mice receiving 1 Gy/fraction. BLI data were adequately fitted with a logistic tumor growth function and a signal distribution model with linear radiation-induced killing effect. A higher tumor growth rate in SJDIPGx37 versus SJDIPGx7 xenografts and a killing effect decreasing with higher tumor baseline (p < 0.0001) were identified. Cumulative radiation dose was suggested to inhibit the tumor growth rate according to a Hill function. Survival distribution was best described with a Weibull hazard function in which the hazard baseline was a continuous function of tumor BLI. Significant differences were further identified between DIPG cell lines and untreated versus treated mice. The model was adequately validated with mice receiving 1 Gy/fraction and will be useful in guiding future preclinical trials incorporating radiation and to support systemic combination therapies with RT.
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Affiliation(s)
- Hillary R. Husband
- Department of Pharmaceutical SciencesSt. Jude Children’s Research HospitalMemphisTNUSA
- College of Engineering and ScienceLouisiana Tech UniversityRustonLAUSA
| | - Olivia Campagne
- Department of Pharmaceutical SciencesSt. Jude Children’s Research HospitalMemphisTNUSA
| | - Chen He
- Department of Developmental NeurobiologySt. Jude Children’s Research HospitalMemphisTNUSA
| | - Xiaoyan Zhu
- Department of Developmental NeurobiologySt. Jude Children’s Research HospitalMemphisTNUSA
| | - Brandon M. Bianski
- Department of Radiation OncologySt. Jude Children’s Research HospitalMemphisTNUSA
| | - Suzanne J. Baker
- Department of Developmental NeurobiologySt. Jude Children’s Research HospitalMemphisTNUSA
| | - Anang A. Shelat
- Department of Chemical Biology and TherapeuticsSt. Jude Children’s Research HospitalMemphisTNUSA
| | | | - Clinton F. Stewart
- Department of Pharmaceutical SciencesSt. Jude Children’s Research HospitalMemphisTNUSA
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34
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Yates JWT, Cheung SYA. A meta-analysis of tumour response and relapse kinetics based on 34,881 patients: A question of cancer type, treatment and line of treatment. Eur J Cancer 2021; 150:42-52. [PMID: 33892406 DOI: 10.1016/j.ejca.2021.03.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 03/05/2021] [Accepted: 03/13/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design. EXPERIMENTAL DESIGN Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA. RESULTS Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported. CONCLUSIONS Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used.
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35
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McQueen A, Escuer J, Aggarwal A, Kennedy S, McCormick C, Oldroyd K, McGinty S. Do we really understand how drug eluted from stents modulates arterial healing? Int J Pharm 2021; 601:120575. [PMID: 33845150 DOI: 10.1016/j.ijpharm.2021.120575] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 01/04/2023]
Abstract
The advent of drug-eluting stents (DES) has revolutionised the treatment of coronary artery disease. These devices, coated with anti-proliferative drugs, are deployed into stenosed or occluded vessels, compressing the plaque to restore natural blood flow, whilst simultaneously combating the evolution of restenotic tissue. Since the development of the first stent, extensive research has investigated how further advancements in stent technology can improve patient outcome. Mathematical and computational modelling has featured heavily, with models focussing on structural mechanics, computational fluid dynamics, drug elution kinetics and subsequent binding within the arterial wall; often considered separately. Smooth Muscle Cell (SMC) proliferation and neointimal growth are key features of the healing process following stent deployment. However, models which depict the action of drug on these processes are lacking. In this article, we start by reviewing current models of cell growth, which predominantly emanate from cancer research, and available published data on SMC proliferation, before presenting a series of mathematical models of varying complexity to detail the action of drug on SMC growth in vitro. Our results highlight that, at least for Sodium Salicylate and Paclitaxel, the current state-of-the-art nonlinear saturable binding model is incapable of capturing the proliferative response of SMCs across a range of drug doses and exposure times. Our findings potentially have important implications on the interpretation of current computational models and their future use to optimise and control drug release from DES and drug-coated balloons.
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Affiliation(s)
- Alistair McQueen
- Division of Biomedical Engineering, University of Glasgow, Glasgow, UK
| | - Javier Escuer
- Aragón Institute for Engineering Research (I3A), University of Zaragoza, Spain
| | - Ankush Aggarwal
- Glasgow Computational Engineering Centre, Division of Infrastructure and Environment, University of Glasgow, Glasgow, UK
| | - Simon Kennedy
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | | | - Keith Oldroyd
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Sean McGinty
- Division of Biomedical Engineering, University of Glasgow, Glasgow, UK; Glasgow Computational Engineering Centre, Division of Infrastructure and Environment, University of Glasgow, Glasgow, UK.
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36
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Mueller-Schoell A, Groenland SL, Scherf-Clavel O, van Dyk M, Huisinga W, Michelet R, Jaehde U, Steeghs N, Huitema ADR, Kloft C. Therapeutic drug monitoring of oral targeted antineoplastic drugs. Eur J Clin Pharmacol 2021; 77:441-464. [PMID: 33165648 PMCID: PMC7935845 DOI: 10.1007/s00228-020-03014-8] [Citation(s) in RCA: 108] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/01/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE This review provides an overview of the current challenges in oral targeted antineoplastic drug (OAD) dosing and outlines the unexploited value of therapeutic drug monitoring (TDM). Factors influencing the pharmacokinetic exposure in OAD therapy are depicted together with an overview of different TDM approaches. Finally, current evidence for TDM for all approved OADs is reviewed. METHODS A comprehensive literature search (covering literature published until April 2020), including primary and secondary scientific literature on pharmacokinetics and dose individualisation strategies for OADs, together with US FDA Clinical Pharmacology and Biopharmaceutics Reviews and the Committee for Medicinal Products for Human Use European Public Assessment Reports was conducted. RESULTS OADs are highly potent drugs, which have substantially changed treatment options for cancer patients. Nevertheless, high pharmacokinetic variability and low treatment adherence are risk factors for treatment failure. TDM is a powerful tool to individualise drug dosing, ensure drug concentrations within the therapeutic window and increase treatment success rates. After reviewing the literature for 71 approved OADs, we show that exposure-response and/or exposure-toxicity relationships have been established for the majority. Moreover, TDM has been proven to be feasible for individualised dosing of abiraterone, everolimus, imatinib, pazopanib, sunitinib and tamoxifen in prospective studies. There is a lack of experience in how to best implement TDM as part of clinical routine in OAD cancer therapy. CONCLUSION Sub-therapeutic concentrations and severe adverse events are current challenges in OAD treatment, which can both be addressed by the application of TDM-guided dosing, ensuring concentrations within the therapeutic window.
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Affiliation(s)
- Anna Mueller-Schoell
- Dept. of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Berlin, Germany
- Graduate Research Training Program, PharMetrX, Berlin/Potsdam, Germany
| | - Stefanie L Groenland
- Department of Clinical Pharmacology, Division of Medical Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Oliver Scherf-Clavel
- Institute of Pharmacy and Food Chemistry, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Madelé van Dyk
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Wilhelm Huisinga
- Institute of Mathematics, University of Potsdam, Potsdam, Germany
| | - Robin Michelet
- Dept. of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Berlin, Germany
| | - Ulrich Jaehde
- Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany
| | - Neeltje Steeghs
- Department of Clinical Pharmacology, Division of Medical Oncology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Alwin D R Huitema
- Department of Pharmacy & Pharmacology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, The Netherlands
- Department of Clinical Pharmacy, University Medical Center, Utrecht University, Utrecht, The Netherlands
| | - Charlotte Kloft
- Dept. of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Berlin, Germany.
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Zwep LB, Duisters KLW, Jansen M, Guo T, Meulman JJ, Upadhyay PJ, van Hasselt JGC. Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling. CPT Pharmacometrics Syst Pharmacol 2021; 10:350-361. [PMID: 33792207 PMCID: PMC8099445 DOI: 10.1002/psp4.12603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 01/07/2021] [Accepted: 02/01/2021] [Indexed: 12/26/2022] Open
Abstract
Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics-based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two-step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient-derived xenograft (PDX) models treated with a variety of mono- and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates-derived TGI parameter values were regressed using the LASSO on high-dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.
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Affiliation(s)
- Laura B. Zwep
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Mathematical InstituteLeiden UniversityLeidenThe Netherlands
| | | | - Martijn Jansen
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Tingjie Guo
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Department of Intensive Care MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | | | - Parth J. Upadhyay
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
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Dickinson J, de Matas M, Dickinson PA, Mistry HB. Exploring a model-based analysis of patient derived xenograft studies in oncology drug development. PeerJ 2021; 9:e10681. [PMID: 33569251 PMCID: PMC7847196 DOI: 10.7717/peerj.10681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 12/09/2020] [Indexed: 11/21/2022] Open
Abstract
Purpose To assess whether a model-based analysis increased statistical power over an analysis of final day volumes and provide insights into more efficient patient derived xenograft (PDX) study designs. Methods Tumour xenograft time-series data was extracted from a public PDX drug treatment database. For all 2-arm studies the percent tumour growth inhibition (TGI) at day 14, 21 and 28 was calculated. Treatment effect was analysed using an un-paired, two-tailed t-test (empirical) and a model-based analysis, likelihood ratio-test (LRT). In addition, a simulation study was performed to assess the difference in power between the two data-analysis approaches for PDX or standard cell-line derived xenografts (CDX). Results The model-based analysis had greater statistical power than the empirical approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25% whereas the empirical approach required at least 50% TGI. The simulation study confirmed the findings and highlighted that CDX studies require fewer animals than PDX studies which show the equivalent level of TGI. Conclusions The study conducted adds to the growing literature which has shown that a model-based analysis of xenograft data improves statistical power over the common empirical approach. The analysis conducted showed that a model-based approach, based on the first mathematical model of tumour growth, was able to detect smaller size of effect compared to the empirical approach which is common of such studies. A model-based analysis should allow studies to reduce animal use and experiment length providing effective insights into compound anti-tumour activity.
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Affiliation(s)
- Jake Dickinson
- Seda Pharma Development Services Ltd., Alderley Edge, United Kingdom
| | - Marcel de Matas
- Seda Pharma Development Services Ltd., Alderley Edge, United Kingdom
| | - Paul A Dickinson
- Seda Pharma Development Services Ltd., Alderley Edge, United Kingdom
| | - Hitesh B Mistry
- Seda Pharma Development Services Ltd., Alderley Edge, United Kingdom.,Division of Pharmacy, University of Manchester, Manchester, United Kingdom
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Guerreiro N, Jullion A, Ferretti S, Fabre C, Meille C. Translational Modeling of Anticancer Efficacy to Predict Clinical Outcomes in a First-in-Human Phase 1 Study of MDM2 Inhibitor HDM201. AAPS JOURNAL 2021; 23:28. [DOI: 10.1208/s12248-020-00551-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/15/2020] [Indexed: 01/03/2023]
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40
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González-García I, Pierre V, Dubois VFS, Morsli N, Spencer S, Baverel PG, Moore H. Early predictions of response and survival from a tumor dynamics model in patients with recurrent, metastatic head and neck squamous cell carcinoma treated with immunotherapy. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:230-240. [PMID: 33465293 PMCID: PMC7965835 DOI: 10.1002/psp4.12594] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 10/28/2020] [Accepted: 12/07/2020] [Indexed: 01/05/2023]
Abstract
We developed and evaluated a method for making early predictions of best overall response (BOR) and overall survival at 6 months (OS6) in patients with cancer treated with immunotherapy. This method combines machine learning with modeling of longitudinal tumor size data. We applied our method to data from durvalumab‐exposed patients with recurrent/metastatic head and neck cancer. A fivefold cross‐validation was used for model selection. Independent trial data, with various degrees of data truncation, were used for model validation. Mean classification error rates (90% confidence intervals [CIs]) from cross‐validation were 5.99% (90% CI 2.98%–7.50%) for BOR and 19.8% (90% CI 15.8%–39.3%) for OS6. During model validation, the area under the receiver operating characteristic curves was preserved for BOR (0.97, 0.97, and 0.94) and OS6 (0.85, 0.84, and 0.82) at 24, 18, and 12 weeks, respectively. These results suggest our method predicts trial outcomes accurately from early data and could be used to aid drug development.
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Affiliation(s)
| | - Vadryn Pierre
- Clinical Pharmacology & Safety Sciences, AstraZeneca, Gaithersburg, Maryland, USA.,Clinical Pharmacology, EMD Serono, Billerica, Massachusetts, USA
| | | | | | | | - Paul G Baverel
- Clinical Pharmacology & Safety Sciences, AstraZeneca, Cambridge, UK.,Clinical Pharmacology, Hoffmann-La Roche Research and Early Development, Roche Innovation Center, Basel, Switzerland
| | - Helen Moore
- Applied Mathematics, Applied BioMath, Concord, Massachusetts, USA
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Dromain C, Loaiza-Bonilla A, Mirakhur B, Beveridge TJR, Fojo AT. Novel Tumor Growth Rate Analysis in the Randomized CLARINET Study Establishes the Efficacy of Lanreotide Depot/Autogel 120 mg with Prolonged Administration in Indolent Neuroendocrine Tumors. Oncologist 2021; 26:e632-e638. [PMID: 33393112 PMCID: PMC8018300 DOI: 10.1002/onco.13669] [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: 04/21/2020] [Accepted: 12/21/2020] [Indexed: 11/10/2022] Open
Abstract
Introduction Tumor quantity while receiving cancer therapy is the sum of simultaneous regression of treatment‐sensitive and growth of treatment‐resistant fractions at constant rates. Exponential rate constants for tumor regression/decay (d) and growth (g) can be estimated. Previous studies established g as a biomarker for overall survival; g increases after treatment cessation, can estimate doubling times, and can assess treatment effectiveness in small cohorts by benchmarking to large reference data sets. Using this approach, we analyzed data from the clinical trial CLARINET, evaluating lanreotide depot/autogel 120 mg/4 weeks (LAN) for treatment of neuroendocrine tumors (NETs). Methods and Materials Computed tomography imaging data from 97 LAN‐ and 101 placebo‐treated patients from CLARINET were analyzed to estimate g and d. Results Data from 92% of LAN‐ and 94% of placebo‐treated patients could be fit to one of the equations to derive g and d (p < .001 in most data sets). LAN‐treated patients demonstrated significantly slower g than placebo recipients (p = .00315), a difference of 389 days in doubling times. No significant difference was observed in d. Over periods of LAN administration up to 700 days, g did not change appreciably. Simulated analysis with g as the endpoint showed a sample size of 48 sufficient to detect a difference in median g with 80% power. Conclusion Although treatment of NETs with LAN can affect tumor shrinkage, LAN primarily slows tumor growth rather than accelerates tumor regression. Evidence of LAN efficacy across tumors was identified. The growth‐retarding effect achieved with LAN was sustained for a prolonged period of time. Implications for Practice The only curative treatment for neuroendocrine tumors (NETs) is surgical resection; however, because of frequent late diagnosis, this is often impossible. Because of this, treatment of NETs is challenging and often aims to reduce tumor burden and delay progression. A novel method of analysis was used to examine data from the CLARINET trial, confirming lanreotide depot/autogel is effective at slowing tumor growth and extending progression‐free survival. By providing the expected rate and doubling time of tumor growth early in the course of treatment, this method of analysis has the potential to guide physicians in their management of patients with NETs. Treatment of neuroendocrine tumors is challenging, mainly aiming to reduce tumor burden and delay disease progression. This article reports on the kinetics of tumor growth using a novel method of analysis and data from the CLARINET study.
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Affiliation(s)
| | | | - Beloo Mirakhur
- Ipsen Biopharmaceuticals, Inc., Cambridge, Massachusetts, USA
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42
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Gavrilov S, Zhudenkov K, Helmlinger G, Dunyak J, Peskov K, Aksenov S. Longitudinal Tumor Size and Neutrophil-to-Lymphocyte Ratio Are Prognostic Biomarkers for Overall Survival in Patients With Advanced Non-Small Cell Lung Cancer Treated With Durvalumab. CPT Pharmacometrics Syst Pharmacol 2021; 10:67-74. [PMID: 33319498 PMCID: PMC7825193 DOI: 10.1002/psp4.12578] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/10/2020] [Indexed: 12/11/2022] Open
Abstract
Therapy optimization remains an important challenge in the treatment of advanced non-small cell lung cancer (NSCLC). We investigated tumor size (sum of the longest diameters (SLD) of target lesions) and neutrophil-to-lymphocyte ratio (NLR) as longitudinal biomarkers for survival prediction. Data sets from 335 patients with NSCLC from study NCT02087423 and 202 patients with NSCLC from study NCT01693562 of durvalumab were used for model qualification and validation, respectively. Nonlinear Bayesian joint models were designed to assess the impact of longitudinal measurements of SLD and NLR on patient subgrouping (by Response Evaluation Criteria in Solid Tumors 1.1 criteria at 3 months after therapy start), long-term survival, and precision of survival predictions. Various validation scenarios were investigated. We determined a more distinct patient subgrouping and a substantial increase in the precision of survival estimates after the incorporation of longitudinal measurements. The highest performance was achieved using a multivariate SLD and NLR model, which enabled predictions of NSCLC clinical outcomes.
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Affiliation(s)
- Sergey Gavrilov
- M&S Decisions LLCMoscowRussia
- Faculty CMC of Lomonosov MSUMoscowRussia
| | | | - Gabriel Helmlinger
- M&S Decisions LLCMoscowRussia
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology & Safety SciencesBioPharmaceuticals R&DAstraZenecaBostonMassachusettsUSA
- Present address:
Clinical Pharmacology & Toxicology, Obsidian TherapeuticsCambridgeMassachusettsUSA
| | - James Dunyak
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology & Safety SciencesBioPharmaceuticals R&DAstraZenecaBostonMassachusettsUSA
| | - Kirill Peskov
- M&S Decisions LLCMoscowRussia
- Computational Oncology GroupI.M. Sechenov First Moscow State Medical UniversityMoscowRussia
| | - Sergey Aksenov
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology & Safety SciencesBioPharmaceuticals R&DAstraZenecaBostonMassachusettsUSA
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Irurzun-Arana I, Rackauckas C, McDonald TO, Trocóniz IF. Beyond Deterministic Models in Drug Discovery and Development. Trends Pharmacol Sci 2020; 41:882-895. [PMID: 33032836 PMCID: PMC7534664 DOI: 10.1016/j.tips.2020.09.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 07/28/2020] [Accepted: 09/10/2020] [Indexed: 02/06/2023]
Abstract
The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug-disease models more versatile and realistic.
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Affiliation(s)
- Itziar Irurzun-Arana
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain.
| | - Christopher Rackauckas
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Thomas O McDonald
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, 31008, Spain; Navarra Institute for Health Research (IdisNA), University of Navarra, 31080, Pamplona, Spain; Institute of Data Science and Artificial Intelligence, DATAI, University of Navarra, Pamplona, 31080, Spain.
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44
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Yates JWT, Mistry H. Clone Wars: Quantitatively Understanding Cancer Drug Resistance. JCO Clin Cancer Inform 2020; 4:938-946. [PMID: 33112660 DOI: 10.1200/cci.20.00089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
A key aim of early clinical development for new cancer treatments is to detect the potential for efficacy early and to identify a safe therapeutic dose to take forward to phase II. Because of this need, researchers have sought to build mathematical models linking initial radiologic tumor response, often assessed after 6 to 8 weeks of treatment, with overall survival. However, there has been mixed success of this approach in the literature. We argue that evolutionary selection pressure should be considered to interpret these early efficacy signals and so optimize cancer therapy.
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Affiliation(s)
| | - Hitesh Mistry
- Division of Pharmacy and Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
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45
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Therapeutic Apheresis, Circulating PLD, and Mucocutaneous Toxicity: Our Clinical Experience through Four Years. Pharmaceutics 2020; 12:pharmaceutics12100940. [PMID: 33008072 PMCID: PMC7600532 DOI: 10.3390/pharmaceutics12100940] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/17/2020] [Accepted: 09/28/2020] [Indexed: 12/12/2022] Open
Abstract
Cancer treatment has been greatly improved by the combined use of targeted therapies and novel biotechnological methods. Regarding the former, pegylated liposomal doxorubicin (PLD) has a preferential accumulation within cancer tumors, thus having lower toxicity on healthy cells. PLD has been implemented in the targeted treatment of sarcoma, ovarian, breast, and lung cancer. In comparison with conventional doxorubicin, PLD has lower cardiotoxicity and hematotoxicity; however, PLD can induce mucositis and palmo-plantar erythrodysesthesia (PPE, hand-foot syndrome), which limits its use. Therapeutical apheresis is a clinically proven solution against early PLD toxicity without hindering the efficacy of the treatment. The present review summarizes the pharmacokinetics and pharmacodynamics of PLD and the beneficial effects of extracorporeal apheresis on the incidence of PPE during chemoradiotherapy in cancer patients.
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46
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Yates JWT, Byrne H, Chapman SC, Chen T, Cucurull-Sanchez L, Delgado-SanMartin J, Di Veroli G, Dovedi SJ, Dunlop C, Jena R, Jodrell D, Martin E, Mercier F, Ramos-Montoya A, Struemper H, Vicini P. Opportunities for Quantitative Translational Modeling in Oncology. Clin Pharmacol Ther 2020; 108:447-457. [PMID: 32569424 DOI: 10.1002/cpt.1963] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 06/04/2020] [Indexed: 12/16/2022]
Abstract
A 2-day meeting was held by members of the UK Quantitative Systems Pharmacology Network () in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.
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Affiliation(s)
| | | | | | - Tao Chen
- University of Surrey, Surrey, UK
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47
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Longitudinal analysis of organ-specific tumor lesion sizes in metastatic colorectal cancer patients receiving first line standard chemotherapy in combination with anti-angiogenic treatment. J Pharmacokinet Pharmacodyn 2020; 47:613-625. [DOI: 10.1007/s10928-020-09714-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 08/19/2020] [Indexed: 12/20/2022]
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48
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Yan X, Xu XS, Weisel KC, Mateos MV, Sonneveld P, Dimopoulos MA, Usmani SZ, Bahlis NJ, Puchalski T, Ukropec J, Bellew K, Ming Q, Sun S, Zhou H. Early M-Protein Dynamics Predicts Progression-Free Survival in Patients With Relapsed/Refractory Multiple Myeloma. Clin Transl Sci 2020; 13:1345-1354. [PMID: 32583948 PMCID: PMC7719372 DOI: 10.1111/cts.12836] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 05/27/2020] [Indexed: 11/28/2022] Open
Abstract
This study aimed to predict long‐term progression‐free survival (PFS) using early M‐protein dynamic measurements in patients with relapsed/refractory multiple myeloma (MM). The PFS was modeled based on dynamic M‐protein data from two phase III studies, POLLUX and CASTOR, which included 569 and 498 patients with relapsed/refractory MM, respectively. Both studies compared active controls (lenalidomide and dexamethasone, and bortezomib and dexamethasone, respectively) alone vs. in combination with daratumumab. Three M‐protein dynamic features from the longitudinal M‐protein data were evaluated up to different time cutoffs (1, 2, 3, and 6 months). The abilities of early M‐protein dynamic measurements to predict the PFS were evaluated using Cox proportional hazards survival models. Both univariate and multivariable analyses suggest that maximum reduction of M‐protein (i.e., depth of response) was the most predictive of PFS. Despite the statistical significance, the baseline covariates provided very limited predictive value regarding the treatment effect of daratumumab. However, M‐protein dynamic features obtained within the first 2 months reasonably predicted PFS and the associated treatment effect of daratumumab. Specifically, the areas under the time‐varying receiver operating characteristic curves for the model with the first 2 months of M‐protein dynamic data were ~ 0.8 and 0.85 for POLLUX and CASTOR, respectively. Early M‐protein data within the first 2 months can provide a prospective and reasonable prediction of future long‐term clinical benefit for patients with MM.
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Affiliation(s)
- Xiaoyu Yan
- Faculty of Medicine, School of Pharmacy, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, Hong Kong
| | - Xu Steven Xu
- Janssen Research and Development, Raritan, Princeton, New Jersey, USA
| | - Katja C Weisel
- Universitätsklinikum Hamburg - Eppendorf II. Medizinische Klinik und Poliklinik, Hamburg, Germany.,University of Tuebingen, Tuebingen, Germany
| | - Maria-Victoria Mateos
- University Hospital of Salamanca-Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain
| | - Pieter Sonneveld
- Department of Hematology, Erasmus Medical Center, Rotterdam, Netherlands
| | | | - Saad Zafar Usmani
- Levine Cancer Institute, Carolinas HealthCare System, Charlotte, North Carolina, USA
| | - Nizar J Bahlis
- Arnie Charbonneau Cancer Institute, University of Calgary Tom Baker Cancer Centre, Calgary, Alberta, Canada
| | - Thomas Puchalski
- Janssen Research and Development, Spring House, Pennsylvania, USA
| | - Jon Ukropec
- Janssen Research and Development, Spring House, Pennsylvania, USA
| | - Kevin Bellew
- Janssen Research and Development, Spring House, Pennsylvania, USA
| | - Qi Ming
- Janssen Research and Development, Spring House, Pennsylvania, USA
| | - Steven Sun
- Janssen Research and Development, Raritan, Princeton, New Jersey, USA
| | - Honghui Zhou
- Janssen Research and Development, Spring House, Pennsylvania, USA
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Kogame A, Ishikawa K, DeJongh J, Tagawa Y, Matsui H, Moriya Y, Kondo T, Asahi S. Pharmacokinetic and pharmacodynamic modeling of the metastin/kisspeptin analog, TAK-448, for its anti-tumor efficacy in a rat xenograft model. Biopharm Drug Dispos 2020; 41:283-294. [PMID: 32562504 DOI: 10.1002/bdd.2245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 04/28/2020] [Accepted: 06/03/2020] [Indexed: 11/10/2022]
Abstract
TAK-448 is the investigational metastin/kisspeptin analog, which is known to have an anti-tumor effect through suppression of androgen hormones (luteinizing hormone and testosterone) levels. This study developed pharmacokinetic-pharmacodynamic (PK/PD) models of TAK-448 and leuprorelin acetate (TAP-144) in a rat vertebral-cancer of the prostate (VCaP) androgen-sensitive prostate cancer xenograft model to quantitatively assess and compare the anti-tumor effects of both drugs. A potential contribution of the hormone-independent direct effects of TAK-448 to the tumor growth inhibition was also investigated in the in vivo rat xenograft model, because our in vitro experiments revealed that TAK-448 may also directly suppress VCaP cellular proliferation. The PK/PD model successfully described the time course of tumor growth inhibition after drug treatment as well as the development of resistance to the inhibition of androgen hormones, following drug treatment or castration. The EC50 of the hormone-dependent inhibitory effect of TAK-448 was much lower than that of TAP-144, and TAK-448 also has a faster onset of anti-tumor effect than TAP-144, demonstrating that TAK-448 has a stronger overall anti-tumor effect than TAP-144. In addition, model inference, by incorporating a hormone-independent inhibition pathway of TAK-448 into the PK-PD model, suggested that such a direct inhibition pathway for TAK-448 cannot be excluded, as also indicated by in vitro studies, but its EC50 would be approximately three orders of magnitude higher than that of the hormone-dependent pathway. This study helps to understand the potential and mechanism of TAK-448 as a prostate cancer treatment.
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Affiliation(s)
- Akifumi Kogame
- Drug Metabolism and Pharmacokinetics Research Laboratories, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | - Kaori Ishikawa
- Oncology Drug Discovery Unit, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | - Joost DeJongh
- Leiden Experts on Advanced Pharmacokinetics & Pharmacodynamics, Leiden, the Netherlands
| | - Yoshihiko Tagawa
- Drug Metabolism and Pharmacokinetics Research Laboratories, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | - Hisanori Matsui
- XVGen Drug Discovery Unit, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | - Yuu Moriya
- Drug Metabolism and Pharmacokinetics Research Laboratories, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | - Takahiro Kondo
- Drug Metabolism and Pharmacokinetics Research Laboratories, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | - Satoru Asahi
- Drug Metabolism and Pharmacokinetics Research Laboratories, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
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Klopp-Schulze L, Mueller-Schoell A, Neven P, Koolen SLW, Mathijssen RHJ, Joerger M, Kloft C. Integrated Data Analysis of Six Clinical Studies Points Toward Model-Informed Precision Dosing of Tamoxifen. Front Pharmacol 2020; 11:283. [PMID: 32296331 PMCID: PMC7136483 DOI: 10.3389/fphar.2020.00283] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 02/27/2020] [Indexed: 12/16/2022] Open
Abstract
Introduction At tamoxifen standard dosing, ∼20% of breast cancer patients do not reach proposed target endoxifen concentrations >5.97 ng/mL. Thus, better understanding the large interindividual variability in tamoxifen pharmacokinetics (PK) is crucial. By applying non-linear mixed-effects (NLME) modeling to a pooled ‘real-world’ clinical PK database, we aimed to (i) dissect several levels of variability and identify factors predictive for endoxifen exposure and (ii) assess different tamoxifen dosing strategies for their potential to increase the number of patients reaching target endoxifen concentrations. Methods Tamoxifen and endoxifen concentrations with genetic and demographic data of 468 breast cancer patients from six reported studies were used to develop a NLME parent-metabolite PK model. Different levels of variability on model parameters or measurements were investigated and the impact of covariates thereupon explored. The model was subsequently applied in a simulation-based comparison of three dosing strategies with increasing degree of dose individualization for a large virtual breast cancer population. Interindividual variability of endoxifen concentrations and the fraction of patients at risk for not reaching target concentrations were assessed for each dosing strategy. Results and Conclusions The integrated NLME model enabled to differentiate and quantify four levels of variability (interstudy, interindividual, interoccasion, and intraindividual). Strong influential factors, i.e., CYP2D6 activity score, drug–drug interactions with CYP3A and CYP2D6 inducers/inhibitors and age, were reliably identified, reducing interoccasion variability to <20% CV. Yet, unexplained interindividual variability in endoxifen formation remained large (47.2% CV). Hence, therapeutic drug monitoring seems promising for achieving endoxifen target concentrations. Three tamoxifen dosing strategies [standard dosing (20 mg QD), CYP2D6-guided dosing (20, 40, and 60 mg QD) and individual model-informed precision dosing (MIPD)] using three therapeutic drug monitoring samples (5–120 mg QD) were compared, leveraging the model. The proportion of patients at risk for not reaching target concentrations was 22.2% in standard dosing, 16.0% in CYP2D6-guided dosing and 7.19% in MIPD. While in CYP2D6-guided- and standard dosing interindividual variability in endoxifen concentrations was high (64.0% CV and 68.1% CV, respectively), it was considerably reduced in MIPD (24.0% CV). Hence, MIPD demonstrated to be the most promising strategy for achieving target endoxifen concentrations.
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Affiliation(s)
- Lena Klopp-Schulze
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Free University of Berlin, Berlin, Germany
| | - Anna Mueller-Schoell
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Free University of Berlin, Berlin, Germany.,Graduate Research Training Program PharMetrX, Berlin, Germany
| | - Patrick Neven
- Vesalius Research Center, University Hospitals Leuven, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Stijn L W Koolen
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Ron H J Mathijssen
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Markus Joerger
- Department of Medical Oncology and Hematology, Cantonal Hospital, St., Gallen, Switzerland
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Free University of Berlin, Berlin, Germany
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