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Ji Y, Sy SKB. Utility and impact of quantitative pharmacology on dose selection and clinical development of immuno-oncology therapy. Cancer Chemother Pharmacol 2024; 93:273-293. [PMID: 38430307 DOI: 10.1007/s00280-024-04643-x] [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: 09/25/2023] [Accepted: 01/23/2024] [Indexed: 03/03/2024]
Abstract
Immuno-oncology (IO) therapies have changed the cancer treatment landscape. Immune checkpoint inhibitors (ICIs) have improved overall survival in 20-40% of patients with malignancies that were previously refractory. Due to the uniqueness in biology, modalities and patient responses, drug development strategies for IO differed from that traditionally used for cytotoxic and target therapies in oncology, and quantitative pharmacology utilizing modeling approach can be applied in all phases of the development process. In this review, we used case studies to showcase how various modeling methodologies were applied from translational science and dose selection through to label change, using examples that included anti-programmed-death-1 (anti-PD-1), anti-programmed-death ligand-1 (anti-PD-L1), anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), and anti-glucocorticoid-induced tumor necrosis factor receptor-related protein (anti-GITR) antibodies. How these approaches were utilized to support phase I-III dose selection, the design of phase III trials, and regulatory decisions on label change are discussed to illustrate development strategies. Model-based quantitative approaches have positively impacted IO drug development, and a better understanding of the biology and exposure-response relationship may benefit the development and optimization of new IO therapies.
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Affiliation(s)
- Yan Ji
- Novartis Pharmaceuticals Corporation, 1 Health Plaza, East Hanover, NJ, 07936, USA.
| | - Sherwin K B Sy
- Novartis Pharmaceuticals Corporation, 1 Health Plaza, East Hanover, NJ, 07936, USA.
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2
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Abrams R, Kaddi CD, Tao M, Leiser RJ, Simoni G, Reali F, Tolsma J, Jasper P, van Rijn Z, Li J, Niesner B, Barrett JS, Marchetti L, Peterschmitt MJ, Azer K, Neves-Zaph S. A Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:374-383. [PMID: 32558397 PMCID: PMC7376290 DOI: 10.1002/psp4.12506] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 02/17/2020] [Indexed: 12/27/2022]
Abstract
Gaucher’s disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first‐line SRT approved for GD1, on treatment‐naïve or patients with ERT‐stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype‐phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population.
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Affiliation(s)
- Ruth Abrams
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Chanchala D Kaddi
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Mengdi Tao
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Randolph J Leiser
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Giulia Simoni
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Federico Reali
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | | | | | - Zachary van Rijn
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Jing Li
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Bradley Niesner
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Jeffrey S Barrett
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Luca Marchetti
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | | | - Karim Azer
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
| | - Susana Neves-Zaph
- Translational Disease Modelling, Digital Data Science, Sanofi, Bridgewater, New Jersey, USA
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3
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Coletti R, Leonardelli L, Parolo S, Marchetti L. A QSP model of prostate cancer immunotherapy to identify effective combination therapies. Sci Rep 2020; 10:9063. [PMID: 32493951 PMCID: PMC7270132 DOI: 10.1038/s41598-020-65590-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 05/06/2020] [Indexed: 12/19/2022] Open
Abstract
Immunotherapy, by enhancing the endogenous anti-tumor immune responses, is showing promising results for the treatment of numerous cancers refractory to conventional therapies. However, its effectiveness for advanced castration-resistant prostate cancer remains unsatisfactory and new therapeutic strategies need to be developed. To this end, systems pharmacology modeling provides a quantitative framework to test in silico the efficacy of new treatments and combination therapies. In this paper we present a new Quantitative Systems Pharmacology (QSP) model of prostate cancer immunotherapy, calibrated using data from pre-clinical experiments in prostate cancer mouse models. We developed the model by using Ordinary Differential Equations (ODEs) describing the tumor, key components of the immune system, and seven treatments. Numerous combination therapies were evaluated considering both the degree of tumor inhibition and the predicted synergistic effects, integrated into a decision tree. Our simulations predicted cancer vaccine combined with immune checkpoint blockade as the most effective dual-drug combination immunotherapy for subjects treated with androgen-deprivation therapy that developed resistance. Overall, the model presented here serves as a computational framework to support drug development, by generating hypotheses that can be tested experimentally in pre-clinical models.
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Affiliation(s)
- Roberta Coletti
- University of Trento, Department of mathematics, Trento, 38123, Italy
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy
| | - Lorena Leonardelli
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy
| | - Silvia Parolo
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy
| | - Luca Marchetti
- Fondazione The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, 38068, Italy.
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4
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Makaryan SZ, Cess CG, Finley SD. Modeling immune cell behavior across scales in cancer. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1484. [PMID: 32129950 PMCID: PMC7317398 DOI: 10.1002/wsbm.1484] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 01/07/2020] [Accepted: 02/04/2020] [Indexed: 12/17/2022]
Abstract
Detailed, mechanistic models of immune cell behavior across multiple scales in the context of cancer provide clinically relevant insights needed to understand existing immunotherapies and develop more optimal treatment strategies. We highlight mechanistic models of immune cells and their ability to become activated and promote tumor cell killing. These models capture various aspects of immune cells: (a) single‐cell behavior by predicting the dynamics of intracellular signaling networks in individual immune cells, (b) multicellular interactions between tumor and immune cells, and (c) multiscale dynamics across space and different levels of biological organization. Computational modeling is shown to provide detailed quantitative insight into immune cell behavior and immunotherapeutic strategies. However, there are gaps in the literature, and we suggest areas where additional modeling efforts should be focused to more prominently impact our understanding of the complexities of the immune system in the context of cancer. This article is categorized under:Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Cellular Models
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Affiliation(s)
- Sahak Z Makaryan
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA
| | - Colin G Cess
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA
| | - Stacey D Finley
- Department of Biomedical Engineering, Mork Family Department of Chemical Engineering and Materials Science, Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
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5
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HPO-Shuffle: an associated gene prioritization strategy and its application in drug repurposing for the treatment of canine epilepsy. Biosci Rep 2019; 39:BSR20191247. [PMID: 31427480 PMCID: PMC6732366 DOI: 10.1042/bsr20191247] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 08/03/2019] [Accepted: 08/12/2019] [Indexed: 12/16/2022] Open
Abstract
Epilepsy is a common neurological disorder that affects mammalian species including human beings and dogs. In order to discover novel drugs for the treatment of canine epilepsy, multiomics data were analyzed to identify epilepsy associated genes. In this research, the original ranking of associated genes was obtained by two high-throughput bioinformatics experiments: Genome Wide Association Study (GWAS) and microarray analysis. The association ranking of genes was enhanced by a re-ranking system, HPO-Shuffle, which integrated information from GWAS, microarray analysis and Human Phenotype Ontology database for further downstream analysis. After applying HPO-Shuffle, the association ranking of epilepsy genes were improved. Afterward, a weighted gene coexpression network analysis (WGCNA) led to a set of gene modules, which hinted a clear relevance between the high ranked genes and the target disease. Finally, WGCNA and connectivity map (CMap) analysis were performed on the integrated dataset to discover a potential drug list, in which a well-known anticonvulsant phensuximide was included.
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Milberg O, Gong C, Jafarnejad M, Bartelink IH, Wang B, Vicini P, Narwal R, Roskos L, Popel AS. A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade. Sci Rep 2019; 9:11286. [PMID: 31375756 PMCID: PMC6677731 DOI: 10.1038/s41598-019-47802-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 07/24/2019] [Indexed: 01/12/2023] Open
Abstract
Over the past decade, several immunotherapies have been approved for the treatment of melanoma. The most prominent of these are the immune checkpoint inhibitors, which are antibodies that block the inhibitory effects on the immune system by checkpoint receptors, such as CTLA-4, PD-1 and PD-L1. Preclinically, blocking these receptors has led to increased activation and proliferation of effector cells following stimulation and antigen recognition, and subsequently, more effective elimination of cancer cells. Translation from preclinical to clinical outcomes in solid tumors has shown the existence of a wide diversity of individual patient responses, linked to several patient-specific parameters. We developed a quantitative systems pharmacology (QSP) model that looks at the mentioned checkpoint blockade therapies administered as mono-, combo- and sequential therapies, to show how different combinations of specific patient parameters defined within physiological ranges distinguish different types of virtual patient responders to these therapies for melanoma. Further validation by fitting and subsequent simulations of virtual clinical trials mimicking actual patient trials demonstrated that the model can capture a wide variety of tumor dynamics that are observed in the clinic and can predict median clinical responses. Our aim here is to present a QSP model for combination immunotherapy specific to melanoma.
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Affiliation(s)
- Oleg Milberg
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
| | - Chang Gong
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mohammad Jafarnejad
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Imke H Bartelink
- Clinical Pharmacology, Pharmacometrics and DMPK (CPD), MedImmune, South San Francisco, California, USA.,Department of Clinical Pharmacology and Pharmacy, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bing Wang
- Clinical Pharmacology, Pharmacometrics and DMPK (CPD), MedImmune, South San Francisco, California, USA
| | - Paolo Vicini
- Clinical Pharmacology, Pharmacometrics and DMPK, MedImmune, Cambridge, United Kingdom
| | | | | | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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7
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Kaddi CD, Niesner B, Baek R, Jasper P, Pappas J, Tolsma J, Li J, van Rijn Z, Tao M, Ortemann‐Renon C, Easton R, Tan S, Puga AC, Schuchman EH, Barrett JS, Azer K. Quantitative Systems Pharmacology Modeling of Acid Sphingomyelinase Deficiency and the Enzyme Replacement Therapy Olipudase Alfa Is an Innovative Tool for Linking Pathophysiology and Pharmacology. CPT Pharmacometrics Syst Pharmacol 2018; 7:442-452. [PMID: 29920993 PMCID: PMC6063739 DOI: 10.1002/psp4.12304] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 03/27/2018] [Accepted: 04/10/2018] [Indexed: 12/12/2022] Open
Abstract
Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disorder with heterogeneous clinical manifestations, including hepatosplenomegaly and infiltrative pulmonary disease, and is associated with significant morbidity and mortality. Olipudase alfa (recombinant human acid sphingomyelinase) is an enzyme replacement therapy under development for the non-neurological manifestations of ASMD. We present a quantitative systems pharmacology (QSP) model supporting the clinical development of olipudase alfa. The model is multiscale and mechanistic, linking the enzymatic deficiency driving the disease to molecular-level, cellular-level, and organ-level effects. Model development was informed by natural history, and preclinical and clinical studies. By considering patient-specific pharmacokinetic (PK) profiles and indicators of disease severity, the model describes pharmacodynamic (PD) and clinical end points for individual patients. The ASMD QSP model provides a platform for quantitatively assessing systemic pharmacological effects in adult and pediatric patients, and explaining variability within and across these patient populations, thereby supporting the extrapolation of treatment response from adults to pediatrics.
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Affiliation(s)
| | - Bradley Niesner
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | - Rena Baek
- Sanofi Genzyme, CambridgeMassachusettsUSA
| | | | | | | | - Jing Li
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | - Zachary van Rijn
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | - Mengdi Tao
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | | | - Rachael Easton
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
| | - Sharon Tan
- Sanofi Genzyme, CambridgeMassachusettsUSA
| | | | - Edward H. Schuchman
- Genetics & Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
| | | | - Karim Azer
- Translational Informatics, TMED, Sanofi, BridgewaterNew JerseyUSA
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