Beyond the single average tumor: Understanding IO combinations using a clinical QSP model that incorporates heterogeneity in patient response

Abstract A quantitative systems pharmacology model for metastatic melanoma was developed for immuno‐oncology with the goal of predicting efficacy of combination checkpoint therapy with pembrolizumab and ipilimumab. This literature‐based model is developed at multiple scales: (i) tumor and immune cel...

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Main Authors: Rukmini Kumar, Kannan Thiagarajan, Lakshmanan Jagannathan, Liming Liu, Kapil Mayawala, Dinesh deAlwis, Brian Topp
Format: Article
Language:English
Published: Wiley 2021-07-01
Series:CPT: Pharmacometrics & Systems Pharmacology
Online Access:https://doi.org/10.1002/psp4.12637
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spelling doaj-8335f32eb6fb40229c929cb7fa393af02021-07-23T22:08:25ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062021-07-0110768469510.1002/psp4.12637Beyond the single average tumor: Understanding IO combinations using a clinical QSP model that incorporates heterogeneity in patient responseRukmini Kumar0Kannan Thiagarajan1Lakshmanan Jagannathan2Liming Liu3Kapil Mayawala4Dinesh deAlwis5Brian Topp6Vantage Research LLC Delaware City Delaware USAVantage Research Pvt. Ltd Chennai IndiaVantage Research Pvt. Ltd Chennai IndiaMerck & Co., Inc. Kenilworth New Jersey USAMerck & Co., Inc. Kenilworth New Jersey USAMerck & Co., Inc. Kenilworth New Jersey USAMerck & Co., Inc. Kenilworth New Jersey USAAbstract A quantitative systems pharmacology model for metastatic melanoma was developed for immuno‐oncology with the goal of predicting efficacy of combination checkpoint therapy with pembrolizumab and ipilimumab. This literature‐based model is developed at multiple scales: (i) tumor and immune cell interactions at a lesion level; (ii) multiple heterogeneous target lesions, nontarget lesion growth, and appearance of new metastatic lesion at a patient level; and (iii) interpatient differences at a population level. The model was calibrated to pembrolizumab and ipilimumab monotherapy in patients with melanoma from Robert et al., specifically, waterfall plot showing target lesion response and overall response rate (Response Evaluation Criteria in Solid Tumors [RECIST] version 1.1), which additionally considers nontarget lesion growth and appearance of new metastatic lesions. We then used the model to predict waterfall and RECIST version 1.1 for combination treatment reported in Long et al. A key insight from this work was that nontarget lesions growth and appearance of new metastatic lesion contributed significantly to disease progression, despite reduction in target lesions. Further, the lesion level simulations of combination therapy show substantial efficacy in warm lesions (intermediary immunogenicity) but limited advantage of combination in both cold and hot lesions (low and high immunogenicity). Because many patients with metastatic disease are expected to have a mixture of these lesions, disease progression in such patients may be driven by a subset of cold lesions that are unresponsive to checkpoint inhibitors. These patients may benefit more from the combinations which include therapies to target cold lesions than double checkpoint inhibitors.https://doi.org/10.1002/psp4.12637
collection DOAJ
language English
format Article
sources DOAJ
author Rukmini Kumar
Kannan Thiagarajan
Lakshmanan Jagannathan
Liming Liu
Kapil Mayawala
Dinesh deAlwis
Brian Topp
spellingShingle Rukmini Kumar
Kannan Thiagarajan
Lakshmanan Jagannathan
Liming Liu
Kapil Mayawala
Dinesh deAlwis
Brian Topp
Beyond the single average tumor: Understanding IO combinations using a clinical QSP model that incorporates heterogeneity in patient response
CPT: Pharmacometrics & Systems Pharmacology
author_facet Rukmini Kumar
Kannan Thiagarajan
Lakshmanan Jagannathan
Liming Liu
Kapil Mayawala
Dinesh deAlwis
Brian Topp
author_sort Rukmini Kumar
title Beyond the single average tumor: Understanding IO combinations using a clinical QSP model that incorporates heterogeneity in patient response
title_short Beyond the single average tumor: Understanding IO combinations using a clinical QSP model that incorporates heterogeneity in patient response
title_full Beyond the single average tumor: Understanding IO combinations using a clinical QSP model that incorporates heterogeneity in patient response
title_fullStr Beyond the single average tumor: Understanding IO combinations using a clinical QSP model that incorporates heterogeneity in patient response
title_full_unstemmed Beyond the single average tumor: Understanding IO combinations using a clinical QSP model that incorporates heterogeneity in patient response
title_sort beyond the single average tumor: understanding io combinations using a clinical qsp model that incorporates heterogeneity in patient response
publisher Wiley
series CPT: Pharmacometrics & Systems Pharmacology
issn 2163-8306
publishDate 2021-07-01
description Abstract A quantitative systems pharmacology model for metastatic melanoma was developed for immuno‐oncology with the goal of predicting efficacy of combination checkpoint therapy with pembrolizumab and ipilimumab. This literature‐based model is developed at multiple scales: (i) tumor and immune cell interactions at a lesion level; (ii) multiple heterogeneous target lesions, nontarget lesion growth, and appearance of new metastatic lesion at a patient level; and (iii) interpatient differences at a population level. The model was calibrated to pembrolizumab and ipilimumab monotherapy in patients with melanoma from Robert et al., specifically, waterfall plot showing target lesion response and overall response rate (Response Evaluation Criteria in Solid Tumors [RECIST] version 1.1), which additionally considers nontarget lesion growth and appearance of new metastatic lesions. We then used the model to predict waterfall and RECIST version 1.1 for combination treatment reported in Long et al. A key insight from this work was that nontarget lesions growth and appearance of new metastatic lesion contributed significantly to disease progression, despite reduction in target lesions. Further, the lesion level simulations of combination therapy show substantial efficacy in warm lesions (intermediary immunogenicity) but limited advantage of combination in both cold and hot lesions (low and high immunogenicity). Because many patients with metastatic disease are expected to have a mixture of these lesions, disease progression in such patients may be driven by a subset of cold lesions that are unresponsive to checkpoint inhibitors. These patients may benefit more from the combinations which include therapies to target cold lesions than double checkpoint inhibitors.
url https://doi.org/10.1002/psp4.12637
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