1
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Dutta R, Mohan A, Buros-Novik J, Goldmacher G, Akala OO, Topp B. A bootstrapping method to optimize go/no-go decisions from single-arm, signal-finding studies in oncology. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 38863167 DOI: 10.1002/psp4.13161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 04/30/2024] [Accepted: 05/07/2024] [Indexed: 06/13/2024] Open
Abstract
Phase Ib trials are common in oncology development but often are not powered for statistical significance. Go/no-go decisions are largely driven by observed trends in response data. We applied a bootstrapping method to systematically compare tumor dynamic end points to historical control data to identify drugs with clinically meaningful efficacy. A proprietary mathematical model calibrated to phase Ib anti-PD-1 therapy trial data (KEYNOTE-001) was used to simulate thousands of phase Ib trials (n = 30) with a combination of anti-PD-1 therapy and four novel agents with varying efficacy. A redacted bootstrapping method compared these results to a simulated phase III control arm (N = 511) while adjusting for differences in trial duration and cohort size to determine the probability that the novel agent provides clinically meaningful efficacy. Receiver operating characteristic (ROC) analysis showed strong ability to separate drugs with modest (area under ROC [AUROC] = 83%), moderate (AUROC = 96%), and considerable efficacy (AUROC = 99%) from placebo in early-phase trials (n = 30). The method was shown to effectively move drugs with a range of efficacy through an in silico pipeline with an overall success rate of 93% and false-positive rate of 7.5% from phase I to phase III. This model allows for effective comparisons of tumor dynamics from early clinical trials with more mature historical control data and provides a framework to predict drug efficacy in early-phase trials. We suggest this method should be employed to improve decision making in early oncology trials.
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Affiliation(s)
- Raunak Dutta
- Modeling and Simulation, Vantage Research, Chennai, India
| | - Aparna Mohan
- Modeling and Simulation, Vantage Research, Chennai, India
| | | | - Gregory Goldmacher
- Global Clinical Trial Operations, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Omobolaji O Akala
- Oncology Early Development, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Brian Topp
- Oncology Early Development, Merck & Co., Inc., Rahway, New Jersey, USA
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2
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Alvares D, Mercier F. Bridging the gap between two-stage and joint models: The case of tumor growth inhibition and overall survival models. Stat Med 2024. [PMID: 38831490 DOI: 10.1002/sim.10128] [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: 09/20/2023] [Revised: 04/03/2024] [Accepted: 05/20/2024] [Indexed: 06/05/2024]
Abstract
Many clinical trials generate both longitudinal biomarker and time-to-event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well-established methods exist for analyzing such data either sequentially (two-stage models) or simultaneously (joint models). Two-stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time-consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two-stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results.
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Affiliation(s)
- Danilo Alvares
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - François Mercier
- Modeling and Simulation, Roche Innovation Center, Basel, Switzerland
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3
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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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4
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Gonçalves A, Marchand M, Chan P, Jin JY, Guedj J, Bruno R. Comparison of two-stage and joint TGI-OS modeling using data from six atezolizumab clinical studies in patients with metastatic non-small cell lung cancer. CPT Pharmacometrics Syst Pharmacol 2024; 13:68-78. [PMID: 37877248 PMCID: PMC10787205 DOI: 10.1002/psp4.13057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/07/2023] [Accepted: 09/18/2023] [Indexed: 10/26/2023] Open
Abstract
Two-stage and joint modeling approaches are the two main approaches to investigate the link between longitudinal tumor size data and overall survival (OS) and anticipate clinical trial outcome. We here used a large database composed of one phase II and five phase III clinical trials evaluating atezolizumab (an immunotherapy) in monotherapy or in combination with chemotherapies in 3699 patients with non-small cell lung cancer to evaluate the differences between both approaches in terms of parameter estimates, magnitude of covariate effects, and ability to predict OS. Although the two-stage approach may underestimate the magnitude of the impact of tumor growth rate (KG ) on OS compared to joint modeling approach (hazard ratios [HRs] of 0.42-2.52 vs. 0.25-2.85, respectively, for individual KG varying from the 5th and 95th percentiles), this difference did not lead into poorer performance of the two-stage approach to describe the OS distribution in the six clinical studies. Overall, two-stage and joint modeling approaches accurately predicted OS HR with a median (range) difference with the observed OS HR of 0.02 (0.01-0.18) and 0.03 (0.00-0.19), in all cases considered, respectively (e.g., for IMpower150: 0.80 [0.66-0.95] vs. 0.82 [0.70-0.95], respectively, whereas the observed OS HR was 0.80). In our setting, the two-stage approach accurately predicted the benefit of atezolizumab on OS. Further work is needed to verify if similar results are achieved using phase Ib or phase II clinical trials where the number of patients and measurements is limited as well as in other cancer indications.
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Affiliation(s)
| | | | - Phyllis Chan
- Clinical Pharmacology, GenentechSouth San FranciscoCaliforniaUSA
| | - Jin Y. Jin
- Clinical Pharmacology, GenentechSouth San FranciscoCaliforniaUSA
| | | | - René Bruno
- Clinical Pharmacology, Genentech‐RocheMarseilleFrance
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5
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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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6
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Shemesh CS, Chan P, Marchand M, Gonçalves A, Vadhavkar S, Wu B, Li C, Jin JY, Hack SP, Bruno R. Early Decision Making in a Randomized Phase II Trial of Atezolizumab in Biliary Tract Cancer Using a Tumor Growth Inhibition-Survival Modeling Framework. Clin Pharmacol Ther 2023; 114:644-651. [PMID: 37212707 DOI: 10.1002/cpt.2953] [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: 02/22/2023] [Accepted: 05/09/2023] [Indexed: 05/23/2023]
Abstract
We assess the longitudinal tumor growth inhibition (TGI) metrics and overall survival (OS) predictions applied to patients with advanced biliary tract cancer (BTC) enrolled in IMbrave151 a multicenter randomized phase II, double-blind, placebo-controlled trial evaluating the efficacy and safety of atezolizumab with or without bevacizumab in combination with cisplatin plus gemcitabine. Tumor growth rate (KG) was estimated for patients in IMbrave151. A pre-existing TGI-OS model for patients with hepatocellular carcinoma in IMbrave150 was modified to include available IMbrave151 study covariates and KG estimates and used to simulate IMbrave151 study outcomes. At the interim progression-free survival (PFS) analysis (98 patients, 27 weeks follow-up), clear separation in tumor dynamic profiles with a faster shrinkage rate and slower KG (0.0103 vs. 0.0117 week-1 ; tumor doubling time 67 vs. 59 weeks; KG geometric mean ratio of 0.84) favoring the bevacizumab containing arm was observed. At the first interim analysis for PFS, the simulated OS hazard ratio (HR) 95% prediction interval (PI) of 0.74 (95% PI: 0.58-0.94) offered an early prediction of treatment benefit later confirmed at the final analysis, observed HR of 0.76 based on 159 treated patients and 34 weeks of follow-up. This is the first prospective application of a TGI-OS modeling framework supporting gating of a phase III trial. The findings demonstrate the utility for longitudinal TGI and KG geometric mean ratio as relevant end points in oncology studies to support go/no-go decision making and facilitate interpretation of the IMbrave151 results to support future development efforts for novel therapeutics for patients with advanced BTC.
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Affiliation(s)
- Colby S Shemesh
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | - Phyllis Chan
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | | | | | - Shweta Vadhavkar
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | - Benjamin Wu
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | - Chunze Li
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | - Jin Y Jin
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | - Stephen P Hack
- Product Development Oncology, Genentech Inc., South San Francisco, California, USA
| | - Rene Bruno
- Clinical Pharmacology, Genentech-Roche, Marseille, France
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7
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Baaz M, Cardilin T, Jirstrand M. Model-based prediction of progression-free survival for combination therapies in oncology. CPT Pharmacometrics Syst Pharmacol 2023; 12:1227-1237. [PMID: 37300376 PMCID: PMC10508530 DOI: 10.1002/psp4.13003] [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: 02/10/2023] [Revised: 05/12/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Progression-free survival (PFS) is an important clinical metric for comparing and evaluating similar treatments for the same disease within oncology. After the completion of a clinical trial, a descriptive analysis of the patients' PFS is often performed post hoc using the Kaplan-Meier estimator. However, to perform predictions, more sophisticated quantitative methods are needed. Tumor growth inhibition models are commonly used to describe and predict the dynamics of preclinical and clinical tumor size data. Moreover, frameworks also exist for describing the probability of different types of events, such as tumor metastasis or patient dropout. Combining these two types of models into a so-called joint model enables model-based prediction of PFS. In this paper, we have constructed a joint model from clinical data comparing the efficacy of FOLFOX against FOLFOX + panitumumab in patients with metastatic colorectal cancer. The nonlinear mixed effects framework was used to quantify interindividual variability (IIV). The model describes tumor size and PFS data well, and showed good predictive capabilities using truncated as well as external data. A machine-learning guided analysis was performed to reduce unexplained IIV by incorporating patient covariates. The model-based approach illustrated in this paper could be useful to help design clinical trials or to determine new promising drug candidates for combination therapy trials.
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Affiliation(s)
- Marcus Baaz
- Fraunhofer‐Chalmers Research Centre for Industrial MathematicsGothenburgSweden
- Department of Mathematical SciencesChalmers University of Technology and University of GothenburgGothenburgSweden
| | - Tim Cardilin
- Fraunhofer‐Chalmers Research Centre for Industrial MathematicsGothenburgSweden
| | - Mats Jirstrand
- Fraunhofer‐Chalmers Research Centre for Industrial MathematicsGothenburgSweden
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8
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Chen T, Zheng Y, Roskos L, Mager DE. Comparison of sequential and joint nonlinear mixed effects modeling of tumor kinetics and survival following Durvalumab treatment in patients with metastatic urothelial carcinoma. J Pharmacokinet Pharmacodyn 2023:10.1007/s10928-023-09848-w. [PMID: 36906878 DOI: 10.1007/s10928-023-09848-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 02/09/2023] [Indexed: 03/13/2023]
Abstract
Standard endpoints such as objective response rate are usually poorly correlated with overall survival (OS) for treatment with immune checkpoint inhibitors. Longitudinal tumor size may serve as a more useful predictor of OS, and establishing a quantitative relationship between tumor kinetics (TK) and OS is a crucial step for successfully predicting OS based on limited tumor size measurements. This study aims to develop a population TK model in combination with a parametric survival model by sequential and joint modeling approaches to characterize durvalumab phase I/II data from patients with metastatic urothelial cancer, and to evaluate and compare the performance of the two modeling approaches in terms of parameter estimates, TK and survival predictions, and covariate identification. The tumor growth rate constant was estimated to be greater for patients with OS ≤ 16 weeks as compared to that for patients with OS > 16 weeks with the joint modeling approach (kg= 0.130 vs. 0.0551 week-1, p-value < 0.0001), but similar for both groups (kg = 0.0624 vs.0.0563 week-1, p-value = 0.37) with the sequential modeling approach. The predicted TK profiles by joint modeling appeared better aligned with clinical observations. Joint modeling also predicted OS more accurately than the sequential approach according to concordance index and Brier score. The sequential and joint modeling approaches were also compared using additional simulated datasets, and survival was predicted better by joint modeling in the case of a strong association between TK and OS. In conclusion, joint modeling enabled the establishment of a robust association between TK and OS and may represent a better choice for parametric survival analyses over the sequential approach.
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Affiliation(s)
- Ting Chen
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA
| | - Yanan Zheng
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, South San Francisco, CA, USA.,Gilead Sciences, Foster City, CA, USA
| | - Lorin Roskos
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, South San Francisco, CA, USA.,Exelixis, Alameda, CA, USA
| | - Donald E Mager
- Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, Buffalo, NY, 14214, USA. .,Enhanced Pharmacodynamics, LLC, Buffalo, NY, USA.
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9
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Krishnan SM, Friberg LE. Bayesian forecasting of tumor size metrics and overall survival. CPT Pharmacometrics Syst Pharmacol 2022; 11:1604-1613. [PMID: 36194478 PMCID: PMC9755925 DOI: 10.1002/psp4.12869] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 12/23/2022] Open
Abstract
The tumor size ratio (TSR), time-to-tumor growth (TTG), and tumor growth rate (kG) are frequently suggested as model-based predictors of overall survival (OS) for different types of tumors. When the tumor metrics are applied in forecasting of the outcome for individual patients at an early stage, the tumor data might be sparse resulting in imprecise prediction. This simulation study aimed to investigate how the tumor follow-up data and estimation approaches influence the accuracy in the tumor size metrics and the predicted hazard of death for individual patients. Longitudinal tumor size and OS data were simulated using tumor growth inhibition and Weibull distribution models, respectively. Based on the model and increasing measurement durations, the accuracy (defined as 80-125% of the simulated "true" value) in individual metrics and hazard was computed. TSR week 6 (TSRw6) accuracy was adequate for 91% of the patients when tumor size was measured up to 12 weeks. For TTG and kG metrics, the highest accuracy observed was lower (43 and 77%, respectively) and occurred later (42 and 60 weeks, respectively). The simultaneous (joint) and sequential estimation approaches resulted in similar accuracies, however, in general, the sequential approach where individual tumor size parameters are fixed, demonstrated inferior estimation properties. The TSRw6 and the model-predicted tumor time course (absolute or relative change) had better forecasting properties than TTG or kG. The population pharmacokinetic (PK) parameters and data approach performed similarly or better than the simultaneous approach and had a better accuracy in estimating individuals' hazard of death than the individual PK parameters method.
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10
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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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11
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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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12
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Early response dynamics predict treatment failure in patients with recurrent and/or metastatic head and neck squamous cell carcinoma treated with cetuximab and nivolumab. Oral Oncol 2022; 127:105787. [DOI: 10.1016/j.oraloncology.2022.105787] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 02/09/2022] [Accepted: 02/20/2022] [Indexed: 12/18/2022]
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13
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Lu L, Dercle L, Zhao B, Schwartz LH. Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging. Nat Commun 2021; 12:6654. [PMID: 34789774 PMCID: PMC8599694 DOI: 10.1038/s41467-021-26990-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 10/21/2021] [Indexed: 12/23/2022] Open
Abstract
In current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. However, evaluation of tumor response to anti-vascular endothelial growth factor therapies in metastatic colorectal cancer (mCRC) is limited because morphological change in tumor may occur earlier than tumor size change. Here we present an analysis utilizing a deep learning (DL) network to characterize tumor morphological change for response assessment in mCRC patients. We retrospectively analyzed 1,028 mCRC patients who were prospectively included in the VELOUR trial (NCT00561470). We found that DL network was able to predict early on-treatment response in mCRC and showed better performance than its size-based counterpart with C-Index: 0.649 (95% CI: 0.619,0.679) vs. 0.627 (95% CI: 0.567,0.638), p = 0.009, z-test. The integration of DL network with size-based methodology could further improve the prediction performance to C-Index: 0.694 (95% CI: 0.661,0.720), which was superior to size/DL-based-only models (all p < 0.001, z-test). Our study suggests that DL network could provide a noninvasive mean for quantitative and comprehensive characterization of tumor morphological change, which may potentially benefit personalized early on-treatment decision making.
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Affiliation(s)
- Lin Lu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Laurent Dercle
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, 10032, USA.
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14
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Krishnan SM, Friberg LE, Bruno R, Beyer U, Jin JY, Karlsson MO. Multistate model for pharmacometric analyses of overall survival in HER2-negative breast cancer patients treated with docetaxel. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1255-1266. [PMID: 34313026 PMCID: PMC8520749 DOI: 10.1002/psp4.12693] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/09/2021] [Accepted: 06/24/2021] [Indexed: 11/16/2022]
Abstract
The aim of this study was to develop a multistate model for overall survival (OS) analysis, based on parametric hazard functions and combined with an investigation of predictors derived from a longitudinal tumor size model on the transition hazards. Different states – stable disease, tumor response, progression, second‐line treatment, and death following docetaxel treatment initiation (stable state) in patients with HER2‐negative breast cancer (n = 183) were used in model building. Past changes in tumor size prospectively predicts the probability of state changes. The hazard of death after progression was lower for subjects who had longer treatment response (i.e., longer time‐to‐progression). Young age increased the probability of receiving second‐line treatment. The developed multistate model adequately described the transitions between different states and jointly the overall event and survival data. The multistate model allows for simultaneous estimation of transition rates along with their tumor model derived metrics. The metrics were evaluated in a prospective manner so not to cause immortal time bias. Investigation of predictors and characterization of the time to develop response, the duration of response, the progression‐free survival, and the OS can be performed in a single multistate modeling exercise. This modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, thereby facilitating early clinical interventions to improve anticancer therapy.
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Affiliation(s)
| | - Lena E Friberg
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - René Bruno
- Clinical Pharmacology, Roche/Genentech, Marseille, France
| | - Ulrich Beyer
- Biostatistics, F. Hoffmann-La-Roche Ltd, Basel, Switzerland
| | - Jin Y Jin
- Clinical Pharmacology Roche/Genentech, South San Francisco, California, USA
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15
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Chan P, Marchand M, Yoshida K, Vadhavkar S, Wang N, Lin A, Wu B, Ballinger M, Sternheim N, Jin JY, Bruno R. Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1171-1182. [PMID: 34270868 PMCID: PMC8520743 DOI: 10.1002/psp4.12686] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/18/2021] [Accepted: 07/02/2021] [Indexed: 12/23/2022]
Abstract
The objectives of the study were to use tumor size data from 10 phase II/III atezolizumab studies across five solid tumor types to estimate tumor growth inhibition (TGI) metrics and assess the impact of TGI metrics and baseline prognostic factors on overall survival (OS) for each tumor type. TGI metrics were estimated from biexponential models and posttreatment longitudinal data of 6699 patients. TGI‐OS full models were built using parametric survival regression by including all significant baseline covariates from the Cox univariate analysis followed by a backward elimination step. The model performance was evaluated for each trial by 1000 simulations of the OS distributions and hazard ratios (HR) of the atezolizumab‐containing arms versus the respective controls. The tumor growth rate estimate was the most significant predictor of OS across all tumor types. Several baseline prognostic factors, such as inflammatory status (C‐reactive protein, albumin, and/or neutrophil‐to‐lymphocyte ratio), tumor burden (sum of longest diameters, number of metastatic sites, and/or presence of liver metastases), Eastern Cooperative Oncology Group performance status, and lactate dehydrogenase were also highly significant across multiple studies in the final multivariate models. TGI‐OS models adequately described the OS distribution. The model‐predicted HRs indicated good model performance across the 10 studies, with observed HRs within the 95% prediction intervals for all study arms versus controls. Multivariate TGI‐OS models developed for different solid tumor types were able to predict treatment effect with various atezolizumab monotherapy or combination regimens and could be used to support design and analysis of future studies.
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Affiliation(s)
- Phyllis Chan
- Department of Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA
| | | | - Kenta Yoshida
- Department of Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA
| | - Shweta Vadhavkar
- Department of Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA
| | - Nina Wang
- Department of Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA
| | - Alyse Lin
- Department of Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA
| | - Benjamin Wu
- Department of Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA
| | - Marcus Ballinger
- Department of Clinical Science, Genentech, Inc., South San Francisco, California, USA
| | - Nitzan Sternheim
- Department of Product Development, Genentech, Inc., South San Francisco, California, USA
| | - Jin Y Jin
- Department of Clinical Pharmacology, Genentech, Inc., South San Francisco, California, USA
| | - René Bruno
- Department of Clinical Pharmacology, Genentech/Roche, Marseille, France
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16
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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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17
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Chan P, Zhou X, Wang N, Liu Q, Bruno R, Jin JY. Application of Machine Learning for Tumor Growth Inhibition - Overall Survival Modeling Platform. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 10:59-66. [PMID: 33280255 PMCID: PMC7825187 DOI: 10.1002/psp4.12576] [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: 06/23/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022]
Abstract
Machine learning (ML) was used to leverage tumor growth inhibition (TGI) metrics to characterize the relationship with overall survival (OS) as a novel approach and to compare with traditional TGI‐OS modeling methods. Historical dataset from a phase III non‐small cell lung cancer study (OAK, atezolizumab vs. docetaxel, N = 668) was used. ML methods support the validity of TGI metrics in predicting OS. With lasso, the best model with TGI metrics outperforms the best model without TGI metrics. Boosting was the best linear ML method for this dataset with reduced estimation bias and lowest Brier score, suggesting better prediction accuracy. Random forest did not outperform linear ML methods despite hyperparameter optimization. Kernel machine was marginally the best nonlinear ML method for this dataset and uncovered nonlinear and interaction effects. Nonlinear ML may improve prediction by capturing nonlinear effects and covariate interactions, but its predictive performance and value need further evaluation with larger datasets.
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Affiliation(s)
- Phyllis Chan
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA
| | - Xiaofei Zhou
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA.,Formerly of Department of Statistics, The Ohio State University, Columbus, Ohio, USA
| | - Nina Wang
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA
| | - Qi Liu
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA
| | - René Bruno
- Clinical Pharmacology, Roche/Genentech, Marseille, France
| | - Jin Y Jin
- Clinical Pharmacology, Roche/Genentech, South San Francisco, California, USA
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18
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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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19
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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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20
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Dai HI, Vugmeyster Y, Mangal N. Characterizing Exposure-Response Relationship for Therapeutic Monoclonal Antibodies in Immuno-Oncology and Beyond: Challenges, Perspectives, and Prospects. Clin Pharmacol Ther 2020; 108:1156-1170. [PMID: 32557643 PMCID: PMC7689749 DOI: 10.1002/cpt.1953] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 05/27/2020] [Indexed: 12/20/2022]
Abstract
Recent data from immuno-oncology clinical studies have shown the exposure-response (E-R) relationship for therapeutic monoclonal antibodies (mAbs) was often confounded by various factors due to the complex interplay of patient characteristics, disease, drug exposure, clearance, and treatment response and presented challenges in characterization and interpretation of E-R analysis. To tackle the challenges, exposure relationships for therapeutic mAbs in immuno-oncology and oncology are reviewed, and a general framework for an integrative understanding of E-R relationship is proposed. In this framework, baseline factors, drug exposure, and treatment response are envisioned to form an interconnected triangle, driving the E-R relationship and underlying three components that compose the apparent relationship: exposure-driven E-R, baseline-driven E-R, and response-driven E-R. Various strategies in data analysis and study design to decouple those components and mitigate the confounding effect are reviewed for their merits and limitations, and a potential roadmap for selection of these strategies is proposed. Specifically, exposure metrics based on a single-dose pharmacokinetic model can be used to mitigate response-driven E-R, while multivariable analysis and/or case control analysis of data obtained from multiple dose levels in a randomized study may be used to account for the baseline-driven E-R. In this context, the importance of collecting data from multiple dose levels, the role of prognostic factors and predictive factors, the potential utility of clearance at baseline and its change over time, and future directions are discussed.
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Affiliation(s)
- Haiqing Isaac Dai
- Clinical Pharmacology/Quantitative Pharmacology/Translational Medicine, EMD Serono (a business of Merck KGaA, Darmstadt, Germany), Billerica, MA, USA
| | - Yulia Vugmeyster
- Clinical Pharmacology/Quantitative Pharmacology/Translational Medicine, EMD Serono (a business of Merck KGaA, Darmstadt, Germany), Billerica, MA, USA
| | - Naveen Mangal
- Clinical Pharmacology/Quantitative Pharmacology/Translational Medicine, EMD Serono (a business of Merck KGaA, Darmstadt, Germany), Billerica, MA, USA
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21
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Yu J, Wang N, Kågedal M. A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:177-184. [PMID: 32036626 PMCID: PMC7080535 DOI: 10.1002/psp4.12499] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 01/12/2020] [Indexed: 12/19/2022]
Abstract
Progression-free survival (PFS) has been increasingly used as a primary endpoint for early clinical development. The aim of the present work was to develop a model where target lesion dynamics and risk for nontarget progression are jointly modeled for predicting PFS. The model was developed based on a pooled platinum-resistant ovarian cancer dataset comprising four different treatments and a wide range of dose levels. The target lesion progression was derived from tumor growth dynamics based on the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. The nontarget progression hazard was correlated to the first derivative of target lesion tumor size with respect to time. The PFS time was determined by the first occurring event, target lesion progression, or nontarget progression. The final joint model not only captured target lesion tumor growth dynamics but also predicted PFS well. A similar approach can potentially be used to predict PFS in future oncology studies.
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Affiliation(s)
- Jiajie Yu
- Department of Clinical PharmacologyGenentech Research and Early DevelopmentSouth San FranciscoCaliforniaUSA
| | - Nina Wang
- Department of Clinical PharmacologyGenentech Research and Early DevelopmentSouth San FranciscoCaliforniaUSA
| | - Matts Kågedal
- Department of Clinical PharmacologyGenentech Research and Early DevelopmentSouth San FranciscoCaliforniaUSA
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22
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Bruno R, Bottino D, de Alwis DP, Fojo AT, Guedj J, Liu C, Swanson KR, Zheng J, Zheng Y, Jin JY. Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models. Clin Cancer Res 2019; 26:1787-1795. [PMID: 31871299 DOI: 10.1158/1078-0432.ccr-19-0287] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 10/31/2019] [Accepted: 12/19/2019] [Indexed: 12/17/2022]
Abstract
There is a need for new approaches and endpoints in oncology drug development, particularly with the advent of immunotherapies and the multiple drug combinations under investigation. Tumor dynamics modeling, a key component to oncology "model-informed drug development," has shown a growing number of applications and a broader adoption by drug developers and regulatory agencies in the past years to support drug development and approval in a variety of ways. Tumor dynamics modeling is also being investigated in personalized cancer therapy approaches. These models and applications are reviewed and discussed, as well as the limitations and issues open for further investigations. A close collaboration between stakeholders like clinical investigators, statisticians, and pharmacometricians is warranted to advance clinical cancer therapeutics.
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Affiliation(s)
| | - Dean Bottino
- Millennium Pharmaceuticals, a wholly owned subsidiary of Takeda Pharmaceuticals, Inc. Cambridge, Massachusetts
| | | | | | - Jérémie Guedj
- IAME, UMR 1137, INSERM, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Chao Liu
- U.S. Food and Drug Administration, Silver Spring, Maryland
| | | | | | | | - Jin Y Jin
- Genentech-Roche, South San Francisco, California
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23
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Alternative dosing regimens for atezolizumab: an example of model-informed drug development in the postmarketing setting. Cancer Chemother Pharmacol 2019; 84:1257-1267. [PMID: 31542806 PMCID: PMC6820606 DOI: 10.1007/s00280-019-03954-8] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 09/03/2019] [Indexed: 01/24/2023]
Abstract
PURPOSE To determine the exposure-response (ER) relationships between atezolizumab exposure and efficacy or safety in patients with advanced non-small cell lung cancer (NSCLC) or urothelial carcinoma (UC) and to identify alternative dosing regimens. METHODS ER analyses were conducted using pooled NSCLC and UC data from phase 1 and 3 studies (PCD4989g, OAK, IMvigor211; ClinicalTrials.gov IDs, NCT01375842, NCT02008227, and NCT02302807, respectively). Objective response rate, overall survival, and adverse events were evaluated vs pharmacokinetic (PK) metrics. Population PK-simulated exposures for regimens of 840 mg every 2 weeks (q2w) and 1680 mg every 4 weeks (q4w) were compared with the approved regimen of 1200 mg every 3 weeks (q3w) and the maximum assessed dose (MAD; 20 mg/kg q3w). Phase 3 IMpassion130 (NCT02425891) data were used to validate the PK simulations for 840 mg q2w. Observed safety data were evaluated by exposure and body weight subgroups. RESULTS No significant ER relationships were observed for safety or efficacy. Predicted exposures for 840 mg q2w and 1680 mg q4w were comparable to 1200 mg q3w and the MAD and consistent with observed PK data from IMpassion130. Observed safety was similar between patients with a Cmax above and below the predicted Cmax for 1680 mg q4w and between patients in the lowest and upper 3 body weight quartiles. CONCLUSION Atezolizumab regimens of 840 mg q2w and 1680 mg q4w are expected to have comparable efficacy and safety as the approved regimen of 1200 mg q3w, supporting their interchangeable use and offering patients greater flexibility.
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24
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Gal J, Milano G, Ferrero JM, Saâda-Bouzid E, Viotti J, Chabaud S, Gougis P, Le Tourneau C, Schiappa R, Paquet A, Chamorey E. Optimizing drug development in oncology by clinical trial simulation: Why and how? Brief Bioinform 2019; 19:1203-1217. [PMID: 28575140 DOI: 10.1093/bib/bbx055] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Indexed: 12/11/2022] Open
Abstract
In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after an extensive and expensive preclinical development period. Methodologies such as computer modeling and clinical trial simulation (CTS) might represent a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the preclinical phase to postmarketing. However, they are barely used and are poorly regarded for drug approval, despite Food and Drug Administration and European Medicines Agency recommendations. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by clinical trial studies and hospital databases. Data sharing and data merging raise legal, policy and technical issues that will need to be addressed. Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increased computing speed should allow this leap forward. The realization of CTS requires not only bioinformatics tools to allow interconnection and global integration of all clinical data but also a universal legal framework to protect the privacy of every patient. While recognizing that CTS can never replace 'real-life' trials, they should be implemented in future drug development schemes to provide quantitative support for decision-making. This in silico medicine opens the way to the P4 medicine: predictive, preventive, personalized and participatory.
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Affiliation(s)
- Jocelyn Gal
- Epidemiology and Biostatistics Unit at the Antoine Lacassagne Center, Nice, France
| | | | | | | | | | | | - Paul Gougis
- Pitie´-Salp^etrie`re Hospital in Paris, France
| | | | | | - Agnes Paquet
- Molecular and Cellular Pharmacology Institute of Sophia Antipolis, Valbonne, France
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25
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Tardivon C, Desmée S, Kerioui M, Bruno R, Wu B, Mentré F, Mercier F, Guedj J. Association Between Tumor Size Kinetics and Survival in Patients With Urothelial Carcinoma Treated With Atezolizumab: Implication for Patient Follow-Up. Clin Pharmacol Ther 2019; 106:810-820. [PMID: 30985002 DOI: 10.1002/cpt.1450] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 03/21/2019] [Indexed: 12/14/2022]
Abstract
We characterized the association between tumor size kinetics and survival in patients with advanced urothelial carcinoma treated with atezolizumab (anti-programmed death-ligand 1, Tecentriq) using a joint model. The model, developed on data from 309 patients of a phase II clinical trial, identified the time-to-tumor growth and the instantaneous changes in tumor size as the best on-treatment predictors of survival. On the validation dataset containing data from 457 patients from a phase III study, the model predicted individual survival probability using 3-month or 6-month tumor size follow-up data with an area under the receptor-occupancy curve between 0.75 and 0.84, as compared with values comprised between 0.62 and 0.75 when the model included only information available at treatment initiation. Including tumor size kinetics in a relevant statistical framework improves the prediction of survival probability during immunotherapy treatment and may be useful to identify most-at-risk patients in "real-time."
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Affiliation(s)
| | - Solène Desmée
- UMR 1246, Université de Tours, Université de Nantes, Inserm SPHERE, Tours, France
| | - Marion Kerioui
- Université de Paris, IAME, INSERM, F-75018 Paris, France.,UMR 1246, Université de Tours, Université de Nantes, Inserm SPHERE, Tours, France
| | - René Bruno
- Clinical Pharmacology, Roche/Genentech, Marseille, France
| | - Benjamin Wu
- Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | - France Mentré
- Université de Paris, IAME, INSERM, F-75018 Paris, France
| | - François Mercier
- Clinical Pharmacology, Roche Innovation Center, Basel, Switzerland
| | - Jérémie Guedj
- Université de Paris, IAME, INSERM, F-75018 Paris, France
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26
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Sostelly A, Mercier F. Tumor Size and Overall Survival in Patients With Platinum-Resistant Ovarian Cancer Treated With Chemotherapy and Bevacizumab. CLINICAL MEDICINE INSIGHTS-ONCOLOGY 2019; 13:1179554919852071. [PMID: 31191068 PMCID: PMC6540487 DOI: 10.1177/1179554919852071] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Accepted: 04/23/2019] [Indexed: 01/28/2023]
Abstract
Introduction: Ovarian cancer is now recognized as a constellation of distinct subtypes of neoplasia involving the ovary and related structures. As a consequence of this heterogeneity, the analysis of covariates influencing the overall survival is crucial in this disease segment. In this work, an overall survival model incorporating tumor kinetics metrics in patients with platinum-resistant ovarian cancer was developed from the randomized, open label, phase 3 AURELIA trial. Methods: Tumor size data from 361 patients randomly allocated to the bevacizumab + chemotherapy or chemotherapy study arm were collected at baseline and every 8 to 9 weeks until disease progression. Patients continued to be followed for survival after treatment discontinuation. A landmarked Cox proportional hazard survival model was developed to characterize the overall survival distribution. Results: Two sets of factors were found to be influential on survival time: those describing the type and severity of disease (Eastern Cooperative Oncology Group [ECOG], Féderation Internationale de Gynécologie et d’Obstétrique [FIGO] stages, presence of ascites) and those summarizing the key features of the tumor kinetic model (tumor shrinkage at week 8 and tumor size at treatment onset). The treatment group was not required in the final model as the drug effect was accounted for in the tumor kinetics model. Conclusions: This work has identified both ascites and tumor kinetics metrics as being the 2 most influential factors to explain variability in overall survival in patients with platinum-resistant ovarian cancer.
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Affiliation(s)
- Alexandre Sostelly
- Clinical Pharmacology and Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - François Mercier
- Clinical Pharmacology and Pharmaceutical Sciences, Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
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27
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Hobbs BP, Barata PC, Kanjanapan Y, Paller CJ, Perlmutter J, Pond GR, Prowell TM, Rubin EH, Seymour LK, Wages NA, Yap TA, Feltquate D, Garrett-Mayer E, Grossman W, Hong DS, Ivy SP, Siu LL, Reeves SA, Rosner GL. Seamless Designs: Current Practice and Considerations for Early-Phase Drug Development in Oncology. J Natl Cancer Inst 2019; 111:118-128. [PMID: 30561713 PMCID: PMC6376915 DOI: 10.1093/jnci/djy196] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 08/30/2018] [Accepted: 10/03/2018] [Indexed: 02/06/2023] Open
Abstract
Traditionally, drug development has evaluated dose, safety, activity, and comparative benefit in a sequence of phases using trial designs and endpoints specifically devised for each phase. Innovations in drug development seek to consolidate the phases and rapidly expand accrual with "seamless" trial designs. Although consolidation and rapid accrual may yield efficiencies, widespread use of seamless first-in-human (FiH) trials without careful consideration of objectives, statistical analysis plans, or trial oversight raises concerns. A working group formed by the National Cancer Institute convened to consider and discuss opportunities and challenges for such trials as well as encourage responsible use of these designs. We reviewed all abstracts presented at American Society of Clinical Oncology annual meetings from 2010 to 2017 for FiH trials enrolling at least 100 patients. We identified 1786 early-phase trials enrolling 57 559 adult patients. Fifty-one of the trials (2.9%) investigated 50 investigational new drugs, were seamless, and accounted for 14.6% of the total patients. The seamless trials included a median of 3 (range = 1-13) expansion cohorts. The overall risk of clinically significant treatment-related adverse events (grade 3-4) was 49.1% (range = 0.0-100%), and seven studies reported at least one toxic death. Rapid expansion of FiH trials may lead to earlier drug approval and corresponding widespread patient access to active therapeutics. Nevertheless, seamless designs must adhere to established ethical, scientific, and statistical standards. Protocols should include prospectively planned analyses of efficacy in disease- or biomarker-defined cohorts of sufficient rigor to support accelerated approval.
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Affiliation(s)
- Brian P Hobbs
- Quantitative Health Sciences and Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH
| | - Pedro C Barata
- Division of Hematology and Medical Oncology, Taussig Cancer Institute Cleveland Clinic, Cleveland, OH
- Department of Internal Medicine, Division of Hematology and Medical Oncology, Tulane University Medical School, New Orleans, LA
| | - Yada Kanjanapan
- Division of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
- Department of Medical Oncology, Prince of Wales Hospital, Sydney, Australia
| | - Channing J Paller
- Department of Oncology, Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD
| | | | - Gregory R Pond
- Department of Oncology, McMaster University, Hamilton, ON, Canada
| | - Tatiana M Prowell
- Office of Hematology & Oncology Products, Food and Drug Administration, Silver Spring, MD
- Breast Cancer Program, Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD
| | - Eric H Rubin
- Global Clinical Oncology, Merck Research Laboratories, Kenilworth, NJ
| | - Lesley K Seymour
- Canadian Cancer Trials Group, Queen's University, Kingston, ON, Canada
| | - Nolan A Wages
- Division of Translational Research & Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Timothy A Yap
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - David Feltquate
- Early Clinical Development, Bristol-Myers Squibb, Princeton, NJ
| | | | - William Grossman
- Cancer Immunotherapy- Global Product Development Oncology, Genentech, Inc., San Francisco, CA
- Bellicum Inc., Brisbane, CA
| | - David S Hong
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - S Percy Ivy
- National Cancer Institute, Cancer Therapy Evaluation Program, Rockville, MD
| | - Lillian L Siu
- Division of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
| | - Steven A Reeves
- National Cancer Institute, Coordinating Center for Clinical Trials, Rockville, MD
| | - Gary L Rosner
- Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Johns Hopkins, Baltimore, MD
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28
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Ji Y, Jin JY, Hyman DM, Kim G, Suri A. Challenges and Opportunities in Dose Finding in Oncology and Immuno-oncology. Clin Transl Sci 2018; 11:345-351. [PMID: 29392871 PMCID: PMC6039198 DOI: 10.1111/cts.12540] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 01/04/2018] [Indexed: 12/27/2022] Open
Affiliation(s)
- Yan Ji
- PK SciencesNovartis Institutes for BioMedical ResearchEast HanoverNew JerseyUSA
| | - Jin Y. Jin
- Clinical PharmacologyGenentech Inc.South San FranciscoCaliforniaUSA
| | - David M. Hyman
- Early Drug Development ServiceMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Geoffrey Kim
- Office of Hematology and Oncology Products (OHOP)U.S. Food and Drug AdministrationSilver SpringMarylandUSA
| | - Ajit Suri
- Quantitative Clinical PharmacologyTakeda International Inc.CambridgeMassachusettsUSA
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Comparison of tumor size assessments in tumor growth inhibition-overall survival models with second-line colorectal cancer data from the VELOUR study. Cancer Chemother Pharmacol 2018; 82:49-54. [PMID: 29700575 DOI: 10.1007/s00280-018-3587-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 04/20/2018] [Indexed: 12/15/2022]
Abstract
PURPOSE To compare lesion-level and volumetric measures of tumor burden with sum of the longest dimensions (SLD) of target lesions on overall survival (OS) predictions using time-to-growth (TTG) as predictor. METHODS Tumor burden and OS data from a phase 3 randomized study of second-line FOLFIRI ± aflibercept in metastatic colorectal cancer were available for 918 patients out of 1216 treated (75%). A TGI model that estimates TTG was fit to the longitudinal tumor size data (nonlinear mixed effect modeling) to estimate TTG with: SLD, sum of the measured lesion volumes (SV), individual lesion diameters (ILD), or individual lesion volumes (ILV). A parametric OS model was built with TTG estimates and assessed for prediction of the hazard ratio (HR) for survival. RESULTS Individual lesions had consistent dynamics within individuals. Between-lesion variability in rate constants was lower (typically < 27% CV) than inter-patient variability (typically > 50% CV). Estimates of TTG were consistent (around 12 weeks) across tumor size assessments. TTG was highly significant in a log-logistic parametric model of OS (median over 12 months). When individual lesions were considered, TTG of the fastest progressing lesions best predicted OS. TTG obtained from the lesion-level analyses were slightly better predictors of OS than estimates from the sums, with ILV marginally better than ILD. All models predicted VELOUR HR equally well and all predicted study success. CONCLUSION This analysis revealed consistent TGI profiles across all tumor size assessments considered. TTG predicted VELOUR HR when based on any of the tumor size measures.
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Claret L, Jin JY, Ferté C, Winter H, Girish S, Stroh M, He P, Ballinger M, Sandler A, Joshi A, Rittmeyer A, Gandara D, Soria JC, Bruno R. A Model of Overall Survival Predicts Treatment Outcomes with Atezolizumab versus Chemotherapy in Non–Small Cell Lung Cancer Based on Early Tumor Kinetics. Clin Cancer Res 2018; 24:3292-3298. [DOI: 10.1158/1078-0432.ccr-17-3662] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 02/22/2018] [Accepted: 04/17/2018] [Indexed: 11/16/2022]
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Nie L, Rubin EH, Mehrotra N, Pinheiro J, Fernandes LL, Roy A, Bailey S, de Alwis DP. Rendering the 3 + 3 Design to Rest: More Efficient Approaches to Oncology Dose-Finding Trials in the Era of Targeted Therapy. Clin Cancer Res 2018; 22:2623-9. [PMID: 27250933 DOI: 10.1158/1078-0432.ccr-15-2644] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 04/11/2016] [Indexed: 11/16/2022]
Abstract
Selection of the maximum tolerated dose (MTD) as the recommended dose for registration trials based on a dose-escalation trial using variations of an MTD/3 + 3 design often occurs in the development of oncology products. The MTD/3 + 3 approach is not optimal and may result in recommended doses that are unacceptably toxic for many patients and in dose reduction/interruptions that might have an impact on effectiveness. Instead of the MTD/3 + 3 approach, the authors recommend an integrated approach. In this approach, typically an adaptive/Bayesian model provides a general framework to incorporate and make decisions for dose escalation based on nonclinical data, such as animal efficacy and toxicity data; clinical data, including pharmacokinetics/pharmacodynamics data; and dose/exposure-response data for efficacy and safety. To improve dose-ranging trials, model-based estimation, rather than hypothesis testing, should be used to maximize and integrate the information gathered across trials and doses. This approach may improve identification of optimal recommended doses, which can then be confirmed in registration trials. Clin Cancer Res; 22(11); 2623-9. ©2016 AACR SEE ALL ARTICLES IN THIS CCR FOCUS SECTION, "NEW APPROACHES FOR OPTIMIZING DOSING OF ANTICANCER AGENTS".
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Affiliation(s)
- Lei Nie
- Division of Biometrics V, OB/OTS/CDER/FDA, Silver Spring, Maryland
| | - Eric H Rubin
- Merck Research Laboratories, North Wales, Pennsylvania
| | - Nitin Mehrotra
- Division of Pharmacometrics, OCP/OTS/CDER/FDA, Silver Spring, Maryland
| | | | | | - Amit Roy
- Bristol-Myers Squibb, Princeton, New Jersey
| | - Stuart Bailey
- Novartis Institutes for Biomedical Research, Cambridge, Massachusetts
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Zheng Y, Narwal R, Jin C, Baverel PG, Jin X, Gupta A, Ben Y, Wang B, Mukhopadhyay P, Higgs BW, Roskos L. Population Modeling of Tumor Kinetics and Overall Survival to Identify Prognostic and Predictive Biomarkers of Efficacy for Durvalumab in Patients With Urothelial Carcinoma. Clin Pharmacol Ther 2018; 103:643-652. [PMID: 29243222 PMCID: PMC5873369 DOI: 10.1002/cpt.986] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 12/11/2017] [Accepted: 12/12/2017] [Indexed: 12/13/2022]
Abstract
Durvalumab is an anti‐PD‐L1 monoclonal antibody approved for patients with locally advanced or metastatic urothelial carcinoma (UC) that has progressed after platinum‐containing chemotherapy. A population tumor kinetic model, coupled with dropout and survival models, was developed to describe longitudinal tumor size data and predict overall survival in UC patients treated with durvalumab (NCT01693562) and to identify prognostic and predictive biomarkers of clinical outcomes. Model‐based covariate analysis identified liver metastasis as the most influential factor for tumor growth and immune‐cell PD‐L1 expression and baseline tumor burden as predictive factors for tumor killing. Tumor or immune‐cell PD‐L1 expression, liver metastasis, baseline hemoglobin, and albumin levels were identified as significant covariates for overall survival. These model simulations provided further insights into the impact of PD‐L1 cutoff values on treatment outcomes. The modeling framework can be a useful tool to guide patient selection and enrichment strategies for immunotherapies across various cancer indications.
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Affiliation(s)
| | | | - ChaoYu Jin
- MedImmune, Mountain View, California, USA
| | | | | | | | | | - Bing Wang
- MedImmune, Mountain View, California, USA
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Emens LA, Bruno R, Rubin EH, Jaffee EM, McKee AE. Report on the Third FDA–AACR Oncology Dose-Finding Workshop. Cancer Immunol Res 2017; 5:1058-1061. [DOI: 10.1158/2326-6066.cir-17-0590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Mistry HB. On the relationship between tumour growth rate and survival in non-small cell lung cancer. PeerJ 2017; 5:e4111. [PMID: 29201573 PMCID: PMC5712205 DOI: 10.7717/peerj.4111] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 11/09/2017] [Indexed: 01/20/2023] Open
Abstract
A recurrent question within oncology drug development is predicting phase III outcome for a new treatment using early clinical data. One approach to tackle this problem has been to derive metrics from mathematical models that describe tumour size dynamics termed re-growth rate and time to tumour re-growth. They have shown to be strong predictors of overall survival in numerous studies but there is debate about how these metrics are derived and if they are more predictive than empirical end-points. This work explores the issues raised in using model-derived metric as predictors for survival analyses. Re-growth rate and time to tumour re-growth were calculated for three large clinical studies by forward and reverse alignment. The latter involves re-aligning patients to their time of progression. Hence, it accounts for the time taken to estimate re-growth rate and time to tumour re-growth but also assesses if these predictors correlate to survival from the time of progression. I found that neither re-growth rate nor time to tumour re-growth correlated to survival using reverse alignment. This suggests that the dynamics of tumours up until disease progression has no relationship to survival post progression. For prediction of a phase III trial I found the metrics performed no better than empirical end-points. These results highlight that care must be taken when relating dynamics of tumour imaging to survival and that bench-marking new approaches to existing ones is essential.
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Affiliation(s)
- Hitesh B Mistry
- Division of Pharmacy, University of Manchester, Manchester, United Kingdom
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Teng Z, Gupta N, Hua Z, Liu G, Samnotra V, Venkatakrishnan K, Labotka R. Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development. Clin Transl Sci 2017; 11:218-225. [PMID: 29168990 PMCID: PMC5867027 DOI: 10.1111/cts.12524] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 10/23/2017] [Indexed: 12/24/2022] Open
Abstract
The failure rate for phase III trials in oncology is high; quantitative predictive approaches are needed. We developed a model‐based meta‐analysis (MBMA) framework to predict progression‐free survival (PFS) from overall response rates (ORR) in relapsed/refractory multiple myeloma (RRMM), using data from seven phase III trials. A Bayesian analysis was used to predict the probability of technical success (PTS) for achieving desired phase III PFS targets based on phase II ORR data. The model demonstrated a strongly correlated (R2 = 0.84) linear relationship between ORR and median PFS. As a representative application of the framework, MBMA predicted that an ORR of ∼66% would be needed in a phase II study of 50 patients to achieve a target median PFS of 13.5 months in a phase III study. This model can be used to help estimate PTS to achieve gold‐standard targets in a target product profile, thereby enabling objectively informed decision‐making.
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Affiliation(s)
- Zhaoyang Teng
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Neeraj Gupta
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Zhaowei Hua
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Guohui Liu
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Vivek Samnotra
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Karthik Venkatakrishnan
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
| | - Richard Labotka
- Millennium Pharmaceuticals, Inc., a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, Cambridge, Massachusetts, USA
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Han K, Chanu P, Jonsson F, Winter H, Bruno R, Jin J, Stroh M. Exposure–Response and Tumor Growth Inhibition Analyses of the Monovalent Anti-c-MET Antibody Onartuzumab (MetMAb) in the Second- and Third-Line Non-Small Cell Lung Cancer. AAPS JOURNAL 2016; 19:527-533. [DOI: 10.1208/s12248-016-0029-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2016] [Accepted: 12/12/2016] [Indexed: 12/12/2022]
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Claret L, Zheng J, Mercier F, Chanu P, Chen Y, Rosbrook B, Yazdi P, Milligan PA, Bruno R. Model-based prediction of progression-free survival in patients with first-line renal cell carcinoma using week 8 tumor size change from baseline. Cancer Chemother Pharmacol 2016; 78:605-10. [DOI: 10.1007/s00280-016-3116-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 07/22/2016] [Indexed: 11/28/2022]
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Han K, Claret L, Sandler A, Das A, Jin J, Bruno R. Modeling and simulation of maintenance treatment in first-line non-small cell lung cancer with external validation. BMC Cancer 2016; 16:473. [PMID: 27412292 PMCID: PMC4944249 DOI: 10.1186/s12885-016-2455-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Accepted: 06/20/2016] [Indexed: 12/03/2022] Open
Abstract
Background Maintenance treatment (MTx) in responders following first-line treatment has been investigated and practiced for many cancers. Modeling and simulation may support interpretation of interim data and development decisions. We aimed to develop a modeling framework to simulate overall survival (OS) for MTx in NSCLC using tumor growth inhibition (TGI) data. Methods TGI metrics were estimated using longitudinal tumor size data from two Phase III first-line NSCLC studies evaluating bevacizumab and erlotinib as MTx in 1632 patients. Baseline prognostic factors and TGI metric estimates were assessed in multivariate parametric models to predict OS. The OS model was externally validated by simulating a third independent NSCLC study (n = 253) based on interim TGI data (up to progression-free survival database lock). The third study evaluated pemetrexed + bevacizumab vs. bevacizumab alone as MTx. Results Time-to-tumor-growth (TTG) was the best TGI metric to predict OS. TTG, baseline tumor size, ECOG score, Asian ethnicity, age, and gender were significant covariates in the final OS model. The OS model was qualified by simulating OS distributions and hazard ratios (HR) in the two studies used for model-building. Simulations of the third independent study based on interim TGI data showed that pemetrexed + bevacizumab MTx was unlikely to significantly prolong OS vs. bevacizumab alone given the current sample size (predicted HR: 0.81; 95 % prediction interval: 0.59–1.09). Predicted median OS was 17.3 months and 14.7 months in both arms, respectively. These simulations are consistent with the results of the final OS analysis published 2 years later (observed HR: 0.87; 95 % confidence interval: 0.63–1.21). Final observed median OS was 17.1 months and 13.2 months in both arms, respectively, consistent with our predictions. Conclusions A robust TGI-OS model was developed for MTx in NSCLC. TTG captures treatment effect. The model successfully predicted the OS outcomes of an independent study based on interim TGI data and thus may facilitate trial simulation and interpretation of interim data. The model was built based on erlotinib data and externally validated using pemetrexed data, suggesting that TGI-OS models may be treatment-independent. The results supported the use of longitudinal tumor size and TTG as endpoints in early clinical oncology studies. Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2455-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kelong Han
- GlaxoSmithKline, Clinical Pharmacology Modeling & Simulations, 709 Swedeland Rd, King of Prussia, PA, 19406, USA.
| | - Laurent Claret
- Genentech/Roche, 84 Chemin des Grives, 13013, Marseille, France
| | - Alan Sandler
- Genentech Inc, Product Development Oncology, South San Francisco, CA, USA
| | - Asha Das
- Tocagen Inc, Clinical Development and Medical Affairs, San Diego, CA, USA
| | - Jin Jin
- Genentech Inc, Clinical Pharmacology, South San Francisco, CA, USA
| | - Rene Bruno
- Genentech/Roche, 84 Chemin des Grives, 13013, Marseille, France.
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Han K, Claret L, Piao Y, Hegde P, Joshi A, Powell JR, Jin J, Bruno R. Simulations to Predict Clinical Trial Outcome of Bevacizumab Plus Chemotherapy vs. Chemotherapy Alone in Patients With First-Line Gastric Cancer and Elevated Plasma VEGF-A. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2016; 5:352-8. [PMID: 27404946 PMCID: PMC4961078 DOI: 10.1002/psp4.12064] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Accepted: 01/26/2016] [Indexed: 12/31/2022]
Abstract
To simulate clinical trials to assess overall survival (OS) benefit of bevacizumab in combination with chemotherapy in selected patients with gastric cancer (GC), a modeling framework linking OS with tumor growth inhibition (TGI) metrics and baseline patient characteristics was developed. Various TGI metrics were estimated using TGI models and data from two phase III studies comparing bevacizumab plus chemotherapy vs. chemotherapy as first‐line therapy in 976 GC patients. Time‐to‐tumor‐growth (TTG) was the best TGI metric to predict OS. TTG, Eastern Cooperative Oncology Group (ECOG) score, albumin level, and Asian ethnicity were significant covariates in the final OS model. The model correctly predicted a decreased hazard ratio favorable to bevacizumab in patients with high baseline plasma VEGF‐A above the median of 113.4 ng/L. Based on trial simulations, in trials enrolling patients with elevated baseline plasma VEGF‐A (500 patients per arm), the expected hazard ratio was 0.82 (95% prediction interval: 0.70–0.95), independent of ethnicity.
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Affiliation(s)
- K Han
- Genentech Inc, Clinical Pharmacology, South San Francisco, California, USA
| | - L Claret
- Pharsight Consulting Services, Pharsight, a Certara Company, Marseille, France
| | - Y Piao
- Roche Product Development in Asia Pacific, Shanghai, China
| | - P Hegde
- Genentech Inc, Biomarker, South San Francisco, California, USA
| | - A Joshi
- Genentech Inc, Clinical Pharmacology, South San Francisco, California, USA
| | - J R Powell
- Pharmaceutical Research and Early Development (pRED), Roche, Beijing, China
| | - J Jin
- Genentech Inc, Clinical Pharmacology, South San Francisco, California, USA
| | - R Bruno
- Pharsight Consulting Services, Pharsight, a Certara Company, Marseille, France
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Miyake H, Harada KI, Ozono S, Fujisawa M. Prognostic Significance of Early Tumor Shrinkage Under Second-Line Targeted Therapy for Metastatic Renal Cell Carcinoma: A Retrospective Multi-Institutional Study in Japan. Mol Diagn Ther 2016; 20:385-92. [DOI: 10.1007/s40291-016-0206-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data. Cancer Chemother Pharmacol 2016; 77:927-38. [PMID: 26940939 PMCID: PMC4844653 DOI: 10.1007/s00280-016-2994-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 02/16/2016] [Indexed: 12/25/2022]
Abstract
Purpose Measures derived from longitudinal tumour size data have been increasingly utilised to predict survival of patients with solid tumours. The aim of this study was to examine the prognostic value of such measures for patients with metastatic pancreatic cancer undergoing gemcitabine therapy. Methods The control data from two Phase III studies were retrospectively used to develop (271 patients) and validate (398 patients) survival models. Firstly, 31 baseline variables were screened from the training set using penalised Cox regression. Secondly, tumour shrinkage metrics were interpolated for each patient by hierarchical modelling of the tumour size time-series. Subsequently, survival models were built by applying two approaches: the first aimed at incorporating model-derived tumour size metrics in a parametric model, and the second simply aimed at identifying empirical factors using Cox regression. Finally, the performance of the models in predicting patient survival was evaluated on the validation set. Results Depending on the modelling approach applied, albumin, body surface area, neutrophil, baseline tumour size and tumour shrinkage measures were identified as potential prognostic factors. The distributional assumption on survival times appeared to affect the identification of risk factors but not the ability to describe the training data. The two survival modelling approaches performed similarly in predicting the validation data. Conclusions A parametric model that incorporates model-derived tumour shrinkage metrics in addition to other baseline variables could predict reasonably well survival of patients with metastatic pancreatic cancer. However, the predictive performance was not significantly better than a simple Cox model that incorporates only baseline characteristics. Electronic supplementary material The online version of this article (doi:10.1007/s00280-016-2994-x) contains supplementary material, which is available to authorized users.
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Increasing the Time Interval between PCV Chemotherapy Cycles as a Strategy to Improve Duration of Response in Low-Grade Gliomas: Results from a Model-Based Clinical Trial Simulation. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:297903. [PMID: 26788118 PMCID: PMC4693002 DOI: 10.1155/2015/297903] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 11/20/2015] [Accepted: 11/29/2015] [Indexed: 12/04/2022]
Abstract
Background. We previously developed a mathematical model capturing tumor size dynamics of adult low-grade gliomas (LGGs) before and after treatment either with PCV (Procarbazine, CCNU, and Vincristine) chemotherapy alone or with radiotherapy (RT) alone. Objective. The aim of the present study was to present how the model could be used as a simulation tool to suggest more effective therapeutic strategies in LGGs. Simulations were performed to identify schedule modifications that might improve PCV chemotherapy efficacy. Methods. Virtual populations of LGG patients were generated on the basis of previously evaluated parameter distributions. Monte Carlo simulations were performed to compare treatment efficacy across in silico clinical trials. Results. Simulations predicted that RT plus PCV would be more effective in terms of duration of response than RT alone. Additional simulations suggested that, in patients treated with PCV chemotherapy, increasing the interval between treatment cycles up to 6 months from the standard 6 weeks can increase treatment efficacy. The predicted median duration of response was 4.3 years in LGGs treated with PCV cycles given every 6 months versus 3.1 years in patients treated with the classical regimen. Conclusion. The present study suggests that, in LGGs, mathematical modeling could facilitate clinical research by helping to identify, in silico, potentially more effective therapeutic strategies.
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Mazzocco P, Barthélémy C, Kaloshi G, Lavielle M, Ricard D, Idbaih A, Psimaras D, Renard M, Alentorn A, Honnorat J, Delattre J, Ducray F, Ribba B. Prediction of Response to Temozolomide in Low-Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics. CPT Pharmacometrics Syst Pharmacol 2015; 4:728-37. [PMID: 26904387 PMCID: PMC4759703 DOI: 10.1002/psp4.54] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 04/23/2015] [Accepted: 05/04/2015] [Indexed: 01/27/2023] Open
Abstract
Both molecular profiling of tumors and longitudinal tumor size data modeling are relevant strategies to predict cancer patients' response to treatment. Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy. The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide. Using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, the model was able to predict the duration and magnitude of response, especially in those patients in whom repeated assessment of tumor response was obtained during the first 3 months of treatment. Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas.
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Affiliation(s)
- P Mazzocco
- Inria, project‐team Numed, Ecole Normale Supérieure de LyonLyonFrance
| | - C Barthélémy
- Inria, project‐team Popix, Université Paris‐SudOrsayFrance
| | - G Kaloshi
- AP‐HP, Groupe Hospitalier Pitié‐Salpêtrière, Service de Neurologie Mazarin; INSERM, U975, Centre de Recherche de l'Institut du Cerveau et de la Moelle, Université Pierre & Marie Curie Paris VI, Faculté de Médecine Pitié‐Salpêtrière, CNRS UMR 7225 and UMR‐S975ParisFrance
| | - M Lavielle
- Inria, project‐team Popix, Université Paris‐SudOrsayFrance
| | - D Ricard
- Hôpital d'instruction des Armées du Val‐de‐GrâceParisFrance
| | - A Idbaih
- AP‐HP, Groupe Hospitalier Pitié‐Salpêtrière, Service de Neurologie Mazarin; INSERM, U975, Centre de Recherche de l'Institut du Cerveau et de la Moelle, Université Pierre & Marie Curie Paris VI, Faculté de Médecine Pitié‐Salpêtrière, CNRS UMR 7225 and UMR‐S975ParisFrance
| | - D Psimaras
- AP‐HP, Groupe Hospitalier Pitié‐Salpêtrière, Service de Neurologie Mazarin; INSERM, U975, Centre de Recherche de l'Institut du Cerveau et de la Moelle, Université Pierre & Marie Curie Paris VI, Faculté de Médecine Pitié‐Salpêtrière, CNRS UMR 7225 and UMR‐S975ParisFrance
| | - M‐A Renard
- AP‐HP, Groupe Hospitalier Pitié‐Salpêtrière, Service de Neurologie Mazarin; INSERM, U975, Centre de Recherche de l'Institut du Cerveau et de la Moelle, Université Pierre & Marie Curie Paris VI, Faculté de Médecine Pitié‐Salpêtrière, CNRS UMR 7225 and UMR‐S975ParisFrance
| | - A Alentorn
- AP‐HP, Groupe Hospitalier Pitié‐Salpêtrière, Service de Neurologie Mazarin; INSERM, U975, Centre de Recherche de l'Institut du Cerveau et de la Moelle, Université Pierre & Marie Curie Paris VI, Faculté de Médecine Pitié‐Salpêtrière, CNRS UMR 7225 and UMR‐S975ParisFrance
| | - J Honnorat
- Hospices Civils de Lyon, Hôpital Neurologique, Neuro‐oncologie; Université de Lyon, Claude Bernard Lyon 1, Lyon Neuroscience Research Center INSERM U1028/CNRS UMRLyonFrance
| | - J‐Y Delattre
- AP‐HP, Groupe Hospitalier Pitié‐Salpêtrière, Service de Neurologie Mazarin; INSERM, U975, Centre de Recherche de l'Institut du Cerveau et de la Moelle, Université Pierre & Marie Curie Paris VI, Faculté de Médecine Pitié‐Salpêtrière, CNRS UMR 7225 and UMR‐S975ParisFrance
| | - F Ducray
- Hospices Civils de Lyon, Hôpital Neurologique, Neuro‐oncologie; Université de Lyon, Claude Bernard Lyon 1, Lyon Neuroscience Research Center INSERM U1028/CNRS UMRLyonFrance
| | - B Ribba
- Inria, project‐team Numed, Ecole Normale Supérieure de LyonLyonFrance
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Sachs JR, Mayawala K, Gadamsetty S, Kang SP, de Alwis DP. Optimal Dosing for Targeted Therapies in Oncology: Drug Development Cases Leading by Example. Clin Cancer Res 2015; 22:1318-24. [DOI: 10.1158/1078-0432.ccr-15-1295] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 10/05/2015] [Indexed: 11/16/2022]
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Jonsson F, Ou Y, Claret L, Siegel D, Jagannath S, Vij R, Badros A, Aggarwal S, Bruno R. A Tumor Growth Inhibition Model Based on M-Protein Levels in Subjects With Relapsed/Refractory Multiple Myeloma Following Single-Agent Carfilzomib Use. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:711-9. [PMID: 26904385 PMCID: PMC4759707 DOI: 10.1002/psp4.12044] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 09/27/2015] [Indexed: 11/10/2022]
Abstract
Change in tumor size estimated using longitudinal tumor growth inhibition (TGI) modeling is an early predictive biomarker of clinical outcomes for multiple cancer types. We present the application of TGI modeling for subjects with multiple myeloma (MM). Longitudinal time course changes in M‐protein data from relapsed and/or refractory MM subjects who received single‐agent carfilzomib in phase II studies (n = 456) were fit to a TGI model. The tumor growth rate estimate was similar to that of other anti‐myeloma agents, indicating that the model is robust and treatment‐independent. An overall survival model was subsequently developed, which showed that early change in tumor size (ECTS) at week 4, Eastern Cooperative Oncology Group performance status (ECOG PS), hemoglobin, sex, percent bone marrow cell involvement, and number of prior regimens were significant independent predictors for overall survival (P < 0.001). ECTS based on M‐protein modeling could be an early biomarker for survival in MM following exposure to single‐agent carfilzomib.
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Affiliation(s)
- F Jonsson
- Pharsight, a Certara company St. Louis Missouri USA
| | - Y Ou
- Onyx Pharmaceuticals, Inc., an Amgen subsidiary South San Francisco California USA
| | - L Claret
- Pharsight, a Certara company St. Louis Missouri USA
| | - D Siegel
- John Theurer Cancer Center Hackensack New Jersey USA
| | - S Jagannath
- Mount Sinai Medical Center New York New York USA
| | - R Vij
- Washington University School of Medicine St. Louis Missouri USA
| | - A Badros
- Greenebaum Cancer Center University of Maryland Baltimore Maryland USA
| | - S Aggarwal
- Onyx Pharmaceuticals, Inc., an Amgen subsidiary South San Francisco California USA
| | - R Bruno
- Pharsight, a Certara company St. Louis Missouri USA
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Claret L, Mercier F, Houk BE, Milligan PA, Bruno R. Modeling and simulations relating overall survival to tumor growth inhibition in renal cell carcinoma patients. Cancer Chemother Pharmacol 2015. [DOI: 10.1007/s00280-015-2820-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Ulmschneider MB, Searson PC. Mathematical models of the steps involved in the systemic delivery of a chemotherapeutic to a solid tumor: From circulation to survival. J Control Release 2015; 212:78-84. [PMID: 26103439 DOI: 10.1016/j.jconrel.2015.06.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 06/18/2015] [Accepted: 06/19/2015] [Indexed: 11/29/2022]
Abstract
The efficacy of an intravenously administered chemotherapeutic for treatment of a solid tumor is dependent on a sequence of steps, including circulation, extravasation by the enhanced permeability and retention effect, transport in the tumor microenvironment, the mechanism of cellular uptake and trafficking, and the mechanism of drug action. These steps are coupled since the time dependent concentration in circulation determines the concentration and distribution in the tumor microenvironment, and hence the amount taken up by individual cells within the tumor. Models have been developed for each of the steps in the delivery process although their predictive power remains limited. Advances in our understanding of the steps in the delivery process will result in refined models with improvements in predictive power and ultimately allow the development of integrated models that link systemic administration of a drug to the probability of survival. Integrated models that predict outcomes based on patient specific data could be used to select the optimum therapeutic regimens. Here we present an overview of current models for the steps in the delivery process and highlight knowledge gaps that are key to developing integrated models.
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Affiliation(s)
- Martin B Ulmschneider
- Department of Materials Science and Engineering, Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD 21218, United States; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21231
| | - Peter C Searson
- Department of Materials Science and Engineering, Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD 21218, United States; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21231.
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Petrelli F, Pietrantonio F, Cremolini C, Di Bartolomeo M, Coinu A, Lonati V, de Braud F, Barni S. Early tumour shrinkage as a prognostic factor and surrogate end-point in colorectal cancer: a systematic review and pooled-analysis. Eur J Cancer 2015; 51:800-7. [PMID: 25794604 DOI: 10.1016/j.ejca.2015.02.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Revised: 02/11/2015] [Accepted: 02/19/2015] [Indexed: 12/16/2022]
Abstract
PURPOSE Early tumour shrinkage (ETS), defined as a reduction of at least 20% in tumour size at first reassessment, has been recently investigated retrospectively in first-line trials of metastatic colorectal cancer (CRC), and appears to be associated with better outcomes. We have performed a systematic review and meta-analysis of published trials to evaluate the prognostic value of ETS in CRC in terms of overall survival (OS) and progression-free survival (PFS). MATERIAL AND METHODS An electronic search of the PubMed, SCOPUS, EMBASE, the Web of Science, and the Cochrane Central Register of Controlled Trial databases identified trials that compared outcomes of patients with or without ETS during first-line chemotherapy for metastatic CRC. The OS, reported as a hazard ratio (HR) with a 95% confidence interval (CI), was the primary outcome measure; the correlation coefficient (R) between ETS with median OS was also estimated. RESULTS Twenty-one trials from 10 publications were analysed. Overall, patients with ETS were associated with a better OS (HR, 0.58; 95% CI, 0.53 to 0.64; P<0.00001) and PFS (HR, 0.57; 95% CI, 0.47-0.69; P<0.00001) compared with patients who were early non-responders. However, ETS was poorly correlated with OS in terms of surrogacy (R=0.37; 95% CI - 0.31-0.78; P=0.28). CONCLUSIONS ETS is a good prognostic factor but an inappropriate surrogate for predicting outcome in CRC patients. These findings support ETS as prognostic tool in ascertaining earlier non-responders; however, its role as a surrogate end-point deserves further evaluation.
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Affiliation(s)
- Fausto Petrelli
- Department of Oncology, Medical Oncology Unit, Azienda Ospedaliera Treviglio, Piazzale Ospedale 1, 24047 Treviglio (BG), Italy.
| | - Filippo Pietrantonio
- Department of Medical Oncology, Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Fondazione IRCCS Istituto Nazionale dei Tumori, via Venezian 1, 20133 Milan, Italy.
| | - Chiara Cremolini
- UO Oncologia Medica, Azienda Ospedaliero-Universitaria Pisana and Università di Pisa, via Roma 67, 56126 Pisa, Italy.
| | - Maria Di Bartolomeo
- Department of Medical Oncology, Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Fondazione IRCCS Istituto Nazionale dei Tumori, via Venezian 1, 20133 Milan, Italy.
| | - Andrea Coinu
- Department of Oncology, Medical Oncology Unit, Azienda Ospedaliera Treviglio, Piazzale Ospedale 1, 24047 Treviglio (BG), Italy.
| | - Veronica Lonati
- Department of Oncology, Medical Oncology Unit, Azienda Ospedaliera Treviglio, Piazzale Ospedale 1, 24047 Treviglio (BG), Italy.
| | - Filippo de Braud
- Department of Medical Oncology, Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Fondazione IRCCS Istituto Nazionale dei Tumori, via Venezian 1, 20133 Milan, Italy.
| | - Sandro Barni
- Department of Oncology, Medical Oncology Unit, Azienda Ospedaliera Treviglio, Piazzale Ospedale 1, 24047 Treviglio (BG), Italy.
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Kirouac DC, Lahdenranta J, Du J, Yarar D, Onsum MD, Nielsen UB, McDonagh CF. Model-Based Design of a Decision Tree for Treating HER2+ Cancers Based on Genetic and Protein Biomarkers. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015. [PMID: 26225238 PMCID: PMC4394616 DOI: 10.1002/psp4.19] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Human cancers are incredibly diverse with regard to molecular aberrations, dependence on oncogenic signaling pathways, and responses to pharmacological intervention. We wished to assess how cellular dependence on the canonical PI3K vs. MAPK pathways within HER2+ cancers affects responses to combinations of targeted therapies, and biomarkers predictive of their activity. Through an integrative analysis of mechanistic model simulations and in vitro cell line profiling, we designed a six-arm decision tree to stratify treatment of HER2+ cancers using combinations of targeted agents. Activating mutations in the PI3K and MAPK pathways (PIK3CA and KRAS), and expression of the HER3 ligand heregulin determined sensitivity to combinations of inhibitors against HER2 (lapatinib), HER3 (MM-111), AKT (MK-2206), and MEK (GSK-1120212; trametinib), in addition to the standard of care trastuzumab (Herceptin). The strategy used to identify effective combinations and predictive biomarkers in HER2-expressing tumors may be more broadly extendable to other human cancers.
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Affiliation(s)
- D C Kirouac
- Merrimack Pharmaceuticals Cambridge, Massachusetts, USA
| | - J Lahdenranta
- Merrimack Pharmaceuticals Cambridge, Massachusetts, USA
| | - J Du
- Merrimack Pharmaceuticals Cambridge, Massachusetts, USA
| | - D Yarar
- Merrimack Pharmaceuticals Cambridge, Massachusetts, USA
| | - M D Onsum
- Merrimack Pharmaceuticals Cambridge, Massachusetts, USA
| | - U B Nielsen
- Merrimack Pharmaceuticals Cambridge, Massachusetts, USA
| | - C F McDonagh
- Merrimack Pharmaceuticals Cambridge, Massachusetts, USA
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50
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Mould DR, Walz AC, Lave T, Gibbs JP, Frame B. Developing Exposure/Response Models for Anticancer Drug Treatment: Special Considerations. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015. [PMID: 26225225 PMCID: PMC4369756 DOI: 10.1002/psp4.16] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Anticancer agents often have a narrow therapeutic index (TI), requiring precise dosing to ensure sufficient exposure for clinical activity while minimizing toxicity. These agents frequently have complex pharmacology, and combination therapy may cause schedule-specific effects and interactions. We review anticancer drug development, showing how integration of modeling and simulation throughout development can inform anticancer dose selection, potentially improving the late-phase success rate. This article has a companion article in Clinical Pharmacology & Therapeutics with practical examples.
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Affiliation(s)
- D R Mould
- Projections Research Phoenixville, Pennsylvania, USA
| | - A-C Walz
- Roche Pharma Research and Early Development, Modeling & Simulation, Pharmaceutical Sciences, Roche Innovation Center Basel F. Hoffmann-La Roche, Basel, Switzerland
| | - T Lave
- Roche Pharma Research and Early Development, Modeling & Simulation, Pharmaceutical Sciences, Roche Innovation Center Basel F. Hoffmann-La Roche, Basel, Switzerland
| | - J P Gibbs
- Amgen Thousand Oaks, California, USA
| | - B Frame
- Projections Research Phoenixville, Pennsylvania, USA
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