Cargando…
A pharmacodynamic model of clinical synergy in multiple myeloma
BACKGROUND: Multiagent therapies, due to their ability to delay or overcome resistance, are a hallmark of treatment in multiple myeloma (MM). The growing number of therapeutic options in MM requires high-throughput combination screening tools to better allocate treatment, and facilitate personalized...
Autores principales: | , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7136599/ https://www.ncbi.nlm.nih.gov/pubmed/32268267 http://dx.doi.org/10.1016/j.ebiom.2020.102716 |
_version_ | 1783518285672742912 |
---|---|
author | Sudalagunta, Praneeth Silva, Maria C. Canevarolo, Rafael R. Alugubelli, Raghunandan Reddy DeAvila, Gabriel Tungesvik, Alexandre Perez, Lia Gatenby, Robert Gillies, Robert Baz, Rachid Meads, Mark B. Shain, Kenneth H. Silva, Ariosto S. |
author_facet | Sudalagunta, Praneeth Silva, Maria C. Canevarolo, Rafael R. Alugubelli, Raghunandan Reddy DeAvila, Gabriel Tungesvik, Alexandre Perez, Lia Gatenby, Robert Gillies, Robert Baz, Rachid Meads, Mark B. Shain, Kenneth H. Silva, Ariosto S. |
author_sort | Sudalagunta, Praneeth |
collection | PubMed |
description | BACKGROUND: Multiagent therapies, due to their ability to delay or overcome resistance, are a hallmark of treatment in multiple myeloma (MM). The growing number of therapeutic options in MM requires high-throughput combination screening tools to better allocate treatment, and facilitate personalized therapy. METHODS: A second-order drug response model was employed to fit patient-specific ex vivo responses of 203 MM patients to single-agent models. A novel pharmacodynamic model, developed to account for two-way combination effects, was tested with 130 two-drug combinations. We have demonstrated that this model is sufficiently parameterized by single-agent and fixed-ratio combination responses, by validating model estimates with ex vivo combination responses for different concentration ratios, using a checkerboard assay. This new model reconciles ex vivo observations from both Loewe and BLISS synergy models, by accounting for the dimension of time, as opposed to focusing on arbitrary time-points or drug effect. Clinical outcomes of patients were simulated by coupling patient-specific drug combination models with pharmacokinetic data. FINDINGS: Combination screening showed 1 in 5 combinations (21.43% by LD50, 18.42% by AUC) were synergistic ex vivo with statistical significance (P < 0.05), but clinical synergy was predicted for only 1 in 10 combinations (8.69%), which was attributed to the role of pharmacokinetics and dosing schedules. INTERPRETATION: The proposed framework can inform clinical decisions from ex vivo observations, thus providing a path toward personalized therapy using combination regimens. FUNDING: This research was funded by the H. Lee Moffitt Cancer Center Physical Sciences in Oncology (PSOC) Grant (1U54CA193489-01A1) and by H. Lee Moffitt Cancer Center's Team Science Grant. This work has been supported in part by the PSOC Pilot Project Award (5U54CA193489-04), the Translational Research Core Facility at the H. Lee Moffitt Cancer Center & Research Institute, an NCI-designated Comprehensive Cancer Center (P30-CA076292), the Pentecost Family Foundation, and Miles for Moffitt Foundation. |
format | Online Article Text |
id | pubmed-7136599 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-71365992020-04-10 A pharmacodynamic model of clinical synergy in multiple myeloma Sudalagunta, Praneeth Silva, Maria C. Canevarolo, Rafael R. Alugubelli, Raghunandan Reddy DeAvila, Gabriel Tungesvik, Alexandre Perez, Lia Gatenby, Robert Gillies, Robert Baz, Rachid Meads, Mark B. Shain, Kenneth H. Silva, Ariosto S. EBioMedicine Research paper BACKGROUND: Multiagent therapies, due to their ability to delay or overcome resistance, are a hallmark of treatment in multiple myeloma (MM). The growing number of therapeutic options in MM requires high-throughput combination screening tools to better allocate treatment, and facilitate personalized therapy. METHODS: A second-order drug response model was employed to fit patient-specific ex vivo responses of 203 MM patients to single-agent models. A novel pharmacodynamic model, developed to account for two-way combination effects, was tested with 130 two-drug combinations. We have demonstrated that this model is sufficiently parameterized by single-agent and fixed-ratio combination responses, by validating model estimates with ex vivo combination responses for different concentration ratios, using a checkerboard assay. This new model reconciles ex vivo observations from both Loewe and BLISS synergy models, by accounting for the dimension of time, as opposed to focusing on arbitrary time-points or drug effect. Clinical outcomes of patients were simulated by coupling patient-specific drug combination models with pharmacokinetic data. FINDINGS: Combination screening showed 1 in 5 combinations (21.43% by LD50, 18.42% by AUC) were synergistic ex vivo with statistical significance (P < 0.05), but clinical synergy was predicted for only 1 in 10 combinations (8.69%), which was attributed to the role of pharmacokinetics and dosing schedules. INTERPRETATION: The proposed framework can inform clinical decisions from ex vivo observations, thus providing a path toward personalized therapy using combination regimens. FUNDING: This research was funded by the H. Lee Moffitt Cancer Center Physical Sciences in Oncology (PSOC) Grant (1U54CA193489-01A1) and by H. Lee Moffitt Cancer Center's Team Science Grant. This work has been supported in part by the PSOC Pilot Project Award (5U54CA193489-04), the Translational Research Core Facility at the H. Lee Moffitt Cancer Center & Research Institute, an NCI-designated Comprehensive Cancer Center (P30-CA076292), the Pentecost Family Foundation, and Miles for Moffitt Foundation. Elsevier 2020-04-05 /pmc/articles/PMC7136599/ /pubmed/32268267 http://dx.doi.org/10.1016/j.ebiom.2020.102716 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research paper Sudalagunta, Praneeth Silva, Maria C. Canevarolo, Rafael R. Alugubelli, Raghunandan Reddy DeAvila, Gabriel Tungesvik, Alexandre Perez, Lia Gatenby, Robert Gillies, Robert Baz, Rachid Meads, Mark B. Shain, Kenneth H. Silva, Ariosto S. A pharmacodynamic model of clinical synergy in multiple myeloma |
title | A pharmacodynamic model of clinical synergy in multiple myeloma |
title_full | A pharmacodynamic model of clinical synergy in multiple myeloma |
title_fullStr | A pharmacodynamic model of clinical synergy in multiple myeloma |
title_full_unstemmed | A pharmacodynamic model of clinical synergy in multiple myeloma |
title_short | A pharmacodynamic model of clinical synergy in multiple myeloma |
title_sort | pharmacodynamic model of clinical synergy in multiple myeloma |
topic | Research paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7136599/ https://www.ncbi.nlm.nih.gov/pubmed/32268267 http://dx.doi.org/10.1016/j.ebiom.2020.102716 |
work_keys_str_mv | AT sudalaguntapraneeth apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT silvamariac apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT canevarolorafaelr apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT alugubelliraghunandanreddy apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT deavilagabriel apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT tungesvikalexandre apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT perezlia apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT gatenbyrobert apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT gilliesrobert apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT bazrachid apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT meadsmarkb apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT shainkennethh apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT silvaariostos apharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT sudalaguntapraneeth pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT silvamariac pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT canevarolorafaelr pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT alugubelliraghunandanreddy pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT deavilagabriel pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT tungesvikalexandre pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT perezlia pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT gatenbyrobert pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT gilliesrobert pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT bazrachid pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT meadsmarkb pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT shainkennethh pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma AT silvaariostos pharmacodynamicmodelofclinicalsynergyinmultiplemyeloma |