| 1. |
- Baaz, Marcus, 1993, et al.
(författare)
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A Model Based Approach for Translation in Oncology - From Xenografts to RECIST
- 2022
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- A major problem in drug development is translating results from preclinical studies to the clinical setting. Therefore, we ev alu ate the translational potential of semi mechanistic tumor models (based on xenograft data) to predict clinical oncology results (RECIST data). Two commonly used translational methods are evaluated: (1) replacement with human PK, and (2) allometric scaling of PD pa rameters. We then compute optimal scaling coefficients given the observed clinical data and relate them to the standard allom etr ic exponents in method (2). The analysis is performed for three drug combinations: binimetinib/encorafenib (shown below), binime tin ib/ribociclib, and cetuximab/encorafenib.
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| 2. |
- Baaz, Marcus, 1993, et al.
(författare)
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Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology
- 2023
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Ingår i: Bmc Cancer. - 1471-2407. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Background To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data. Methods We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves. Results The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 mu g/mL of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication. Conclusions A simulation- based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE- curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.
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| 3. |
- Baaz, Marcus, 1993, et al.
(författare)
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Model-based prediction of progression-free survival for combination therapies in oncology
- 2023
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Ingår i: Cpt-Pharmacometrics & Systems Pharmacology. - 2163-8306. ; 12:9, s. 1227-37
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Tidskriftsartikel (refereegranskat)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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| 4. |
- Baaz, Marcus, 1993, et al.
(författare)
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Model-based Prediction of Progression-Free Survival for Combination Therapies in Oncology
- 2023
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: Extend a joint modeling approach for predicting progression-free survival (PFS) for monotherapies [1] to combination therapies. Test the model’s predictive capabilities by performing different cross-validations. Methods: PFS is an important clinical metric for comparing and evaluating similar treatments for the same disease within oncology. Using the RECIST (version 1.1) guidelines all tumor lesions have to be accounted for by a combination of target and non-target lesions. A patient’s PFS time is set by target progression (TP) if there is at least a 20%- and 5-mm increase of the sum of the largest target diameters (SLD) compared to the nadir [2]. If the patient dies, a new lesion has appeared, or the non-target lesions are deemed unequivocal progressing the PFS time is instead set by non-target progression (NTP). If a patient leaves the trial before this occurs the patient is censored at that time point. We present a joint modeling approach for predicting PFS for combination therapies where we link the risk of adverse events such as e.g., tumor metastasis or death with the derivative of SLD. Thus, the joint model consists of both a tumor growth inhibition (TGI) model, for the SLD time series, and a time-to-event (TTE) model to model the risk of adverse events. In addition, a Weibull TTE model is used to account for dropout. Monolix [3] was used to calibrate the models with data coming from a clinical study comparing the efficacy of FOLFOX versus FOLFOX + panitumumab in metastatic colorectal cancer patients. The data were provided to us by ProjectDataSphere [4]. We did not have data for panitumumab given as a monotherapy and therefore assume that there were no interaction effects between the drugs. To adequately quantify the variability in the data the nonlinear mixed effects framework was used. After the models were calibrated they were combined to make predictions of PFS. The algorithm below summarizes how the predictions are performed, Generate artificial patients and simulate time series of SLD using the TGI model. Estimate time when SLD has increased by 20% and at least 5 mm for each patient. Construct individual survival curves and sample time of non-target progression event. Sample dropout times using the estimated Weibull distribution. Pick the time that occurs first for each patient, record the PFS trigger, and repeat it 1000 times. If dropout occurs first, the patient is censored that that time. From this procedure, we obtain both a median prediction along with a 95% confidence interval for the prediction. To both test the model’s predictive capabilities and validate the assumption of no interaction between the drugs we predicted the median PFS time for panitumumab given as a monotherapy and compared it with results from the ASPECCT study [5]. We also recalibrated the model with truncated data at 3,7, and 27 months and then made forward predictions of the remaining study. Results: All models were successfully calibrated to the data and validated based on, e.g., the precision of parameter estimates, individual fits, distribution of Empirical Bayes Estimates (EBEs), and analysis of residuals. Furthermore, the combined (PFS) model was able to describe the PFS for both treatment arms of the study. When we recalibrated the model with truncated data, the forward predictions were very good for both the 7 and 27 months truncation points. The prediction for the median PFS time for patients given only panitumumab was similar to what was found in the ASPECCT study. Conclusions: We successfully calibrated a joint model using clinical SLD and TTE data for a combination therapy. Using the model, we were able to first describe the PFS time of the same study well and then make model predictions. Predictions were performed on both truncated data sets and for data coming from a different study. In both cases, the model was shown to have good predictive capabilities. References: [1] Yu J, Wang N, Kågedal M. A New Method to Model and Predict Progression Free Survival Based on Tumor Growth Dynamics. CPT Pharmacomet Syst Pharmacol 2020;9:177–84. https://doi.org/10.1002/psp4.12499. [2] Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New Response Evaluation Criteria in Solid Tumours: Revised RECIST Guideline (Version 1.1). Eur J Cancer 2009;45:228–47. https://doi.org/10.1016/j.ejca.2008.10.026. [3] Monolix 2021R2 Lixoft SAS, a Simulations Plus company. [4] Project Data Sphere 2022. https://www.projectdatasphere.org/. [5] Kim TW, Peeters M, Thomas A, Gibbs P, Hool K, Zhang J, et al. Impact of Emergent Circulating Tumor DNA RAS Mutation in Panitumumab-Treated Chemoresistant Metastatic Colorectal Cancer. Clin Cancer Res 2018;24:5602–9. https://doi.org/10.1158/1078-0432.CCR-17-3377.
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| 5. |
- Baaz, Marcus, 1993, et al.
(författare)
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Optimized scaling of translational factors in oncology: from xenografts to RECIST
- 2022
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Ingår i: Cancer Chemotherapy and Pharmacology. - : Springer Science and Business Media LLC. - 0344-5704 .- 1432-0843. ; 90:3, s. 239-250
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Tidskriftsartikel (refereegranskat)abstract
- Purpose Tumor growth inhibition (TGI) models are regularly used to quantify the PK-PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response. Method To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data. Results The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of - 0.25. Conclusions We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.
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| 6. |
- Baaz, Marcus, 1993, et al.
(författare)
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Population Modeling of Toxicological Combination Effects
- 2022
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Aim: Nonlinear mixed effects (NLME) modeling is currently the state-of-the-art mathematical framework for analyzing population data in medicine. We aim to illustrate how NLME modeling and the Tumor Static Exposure (TSE) concept can be beneficial for analyzing the effects of combined pollutants in marine life. Results: TSE is defined as all drug exposure that results in tumor stasis and therefore separates the space of all exposures into a region of tumor growth and a region of tumor shrinkage. TSE is derived from the equations of the NLME model and when two drugs are investigated the TSE for the median individual can be illustrated in a diagram with each axis representing the exposure of one of the drugs. We apply a similar approach to a toxicological model that describes the combined toxicological effects of two pollutants on marine animals. The model is based on a set of ordinary differential equations and from these, we derive a curve, similar to TSE, which describes all exposure combinations that result in a critical toxicological event. We use simulated data to calibrate the model and illustrate how predictions of toxicity can be made on a population level. Discussion/Conclusions: Since all possible combinations of pollutants cannot be tested experimentally the modified version of the TSE-curve can be useful to explore how different combinations affect marine life populations. Thus, it could be used to rank which pollutants are most important to reduce in the oceans. The NLME framework provides a powerful method for analyzing time-series data and could increase the statistical power when analyzing data from animal studies. In addition, it allows for simulation-based analysis, which could help reduce the number of animal experiments.
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| 7. |
- Cardilin, Tim, 1989
(författare)
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Data-driven modeling of combination therapy in oncology
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis contains two manuscripts: Tumor Static Concentration Curves in Combination Therapy and Extending the Tumor Static Concentration Curve to Exposure - A Combination Therapy Example with Radiation Therapy. There is also an introductory chapter presenting some basic facts necessary to understand the appended manuscripts. The manuscripts share the common goal of developing model-based data-driven tools and techniques to quantitatively assess the effectiveness of anticancer combinations. The first paper presents a dynamical systems model for combination therapy with the anticancer drugs cetuximab and cisplatin. Using a mixed-effects approach the model is shown to adequately describe a preclinical dataset. The model is then analyzed by introducing the Tumor Static Concentration (TSC) curve, a curve of cetuximab-cisplatin concentration pairs all of which, if maintained, result in tumor stasis. The TSC analysis reveals a modest gain from combining the compounds. The variability of the TSC curve across the population is also explored. In the second paper we develop a dynamical systems model for combination therapy with ionizing radiation and a test compound. For this combination we introduce an extension of the TSC curve called the (average) Tumor Static Exposure (TSE) curve based on average, as opposed to pointwise, tumor stasis. The TSE analysis for combinations of radiation and the test compound demonstrates a large synergistic effect.
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| 8. |
- Cardilin, Tim, 1989, et al.
(författare)
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Evaluation and translation of combination therapies in oncology – A quantitative approach
- 2018
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Ingår i: European Journal of Pharmacology. - : Elsevier BV. - 0014-2999 .- 1879-0712. ; 834, s. 327-336
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Tidskriftsartikel (refereegranskat)abstract
- Quantitative techniques improve our understanding of tumor volume data for combination treatments and its translation across in vivo models and species. The focus of this paper is therefore on understanding in vivo data, highlighting key structural elements of pharmacodynamic tumor models, and challenging these methods from a translational point of view. We introduce the concept of Tumor Static Exposure (TSE) both for single and multiple combined anticancer agents. The TSE curve separates all possible exposure combinations into regions of tumor growth and tumor shrinkage. Moreover, the degree of curvature of the TSE curve indicates the degree of synergy or antagonism. We demonstrate the TSE approach by two case studies. The first examines a combination of the drugs cetuximab and cisplatin. The TSE curve associated with this combination reveals a weak synergistic effect, suggesting only modest gains from combination therapy. The second case study examines combinations of ionizing radiation and a radiosensitizing agent. In this case, the TSE curve exhibits a pronounced curvature, indicating a strong synergistic effect; tumor regression can be achieved at significantly lower exposure levels and/or radiation doses. Finally, an allometric approach to human dose prediction demonstrates the translational power of the model and the TSE concept. We conclude that the TSE approach, which embodies model-based measures of both drug (potency) and target properties (tumor growth rate), has a strong potential for ranking of compounds, supporting compound selection, and translating preclinical findings to humans.
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| 9. |
- Cardilin, Tim, 1989, et al.
(författare)
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Exposure-response modeling improves selection of radiation and radiosensitizer combinations
- 2022
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 49:2, s. 167-178
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Tidskriftsartikel (refereegranskat)abstract
- A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.
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| 10. |
- Cardilin, Tim, 1989, et al.
(författare)
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Extending the Tumor Static Concentration curve to average doses - a combination therapy example using radiation therapy
- 2016
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Ingår i: Proceedings of the 25th Annual meeting of the Population Approach Group in Europe, PAGE2016. Lisboa, Portugal, 7-10 Juni 2016.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: The recently developed concept of Tumor Static Concentration (TSC) is a valuable modeling tool for the quantitative analysis of combination therapies [2]. Here, we set out to extend TSC to situations where (average) doses are known but drug exposure data is not available. Methods: Data consisted of Patient-Derived xenografts from combination therapy studies using ionizing radiation and a probe compound. Modelling was based on a Tumor Growth Inhibition (TGI) model [3] modified for radiation treatment. Model parameters were estimated using a mixed-effects approach implemented in Mathematica 10 [1]. A TSC-like curve was derived from tumor stasis assumptions where one of the plasma concentrations was replaced with average radiation dose over time. Results: Drug exposure of the probe compound was successfully modeled using a one compartment exposure model. Initial attempts to model the combination efficacy data were not able to explain the effect from the combination arm. The TGI model was subsequently modified to account for potential interaction effects between the probe compound and radiation treatments. The radiation treatment-modified TGI model was then used to derive a TSC-like curve that determines all pairs of radiation doses and drug concentrations for which the tumor is kept in stasis. This curve exhibits significant curvature, reflecting the synergistic effects of administering the radiation therapy and drug together. The TSC-like curve can be used to improve the administration schedule of the treatment. Conclusions: A model-based method for evaluation of anticancer combination therapy was extended from the use of tumor static plasma concentrations to also include average drug doses. Although used for radiation therapy in this example, the method can also be applied for regular compounds when drug exposure data is not available.
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