Predicting overall survival (OS) and overall response (OR) following durvalumab treatment in patients with multiple cancer types using a hybrid modeling strategy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15161-e15161
Author(s):  
Ting Chen ◽  
Yanan Zheng ◽  
Lorin Roskos ◽  
Donald E Mager
Keyword(s):  
Kinetic Model ◽  
Kinetic Parameters ◽  
Rate Constant ◽  
Tumor Size ◽  
Hybrid Modeling ◽  
Tumor Growth Rate ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Cancer Types ◽  
Modeling Strategy

e15161 Background: This study aimed to predict OS/OR and identify key predictors in patients with diverse cancer types treated with durvalumab, a PD-L1 targeting monoclonal antibody, using a hybrid modeling strategy that combines population pharmacodynamic (PD) modeling and machine learning (ML) algorithms. Methods: Individual longitudinal tumor size measurements and OS/OR data were available for 855 patients who received durvalumab therapy (10 mg/kg Q2W or 20 mg/kg Q4W; NCT01693562). Nine cancer types included non-small cell lung cancer (NSCLC), bladder cancer (BC), microsatellite instability-high (MSI-H) cancer, hepatocellular carcinoma (HCC), squamous cell carcinoma of the head and neck (SCCHN), gastroesophageal cancer (GEC), ovarian cancer (OC), pancreatic adenocarcinoma (PDAC) and triple-negative breast cancer (TNBC). A tumor kinetic model was developed to characterize diverse temporal profiles using a population-based modeling approach. Individual estimated tumor kinetic model parameters and patient demographic/physiological factors were used as inputs for predicting OS/OR using several ML approaches. Results: The final tumor kinetic model with liver metastasis (LM), neutrophil/lymphocyte ratio (NLR), tumor size at baseline (TBSL) and cancer types as covariates characterized the temporal tumor size data well. HCC and MSI-H cancer have the slowest tumor growth rate constant (kg), while GEC, SCCHN and TNBC have the fastest kg. BC, NSCLC and OC have the highest tumor killing rate constant. The most important predictors of OS identified by ML approach were tumor kinetic parameters (kg, fraction of drug-sensitive cells, time-delay in immune response), along with baseline disease factors, including hemoglobin (HGBBL), albumin (ALB), and NLR. Decision tree-based algorithms showed the best performance in predicting OR with accuracy above 90%. In addition to tumor kinetic parameters, PD-L1 expression on tumor cells (TC) and ALB were the most important predictors of OR. Conclusions: A combined population PD/ML approach showed good predictions of OS/OR in patients with different cancer types treated with durvalumab. LM, NLR,TBSL and cancer types were found to be important factors for tumor kinetics. In addition to tumor kinetic parameters, HGBBL, ALB, and NLR were found to be important predictors of OS, and TC and ALB were found to be important predictors of OR. These findings could provide a guidance on patient selection in future clinical trials.

scholarly journals Estimation of the Kinetic Parameters of a Catalytic Cracking Model Case Study

2019 ◽  
Vol 70 (10) ◽  
pp. 3532-3537
Author(s):  
Cristina Popa ◽  
Nicoleta Nicolae ◽  
Cristian Patrascioiu
Keyword(s):  
Kinetic Model ◽  
Kinetic Parameters ◽  
Catalytic Cracking ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Outlet Temperature ◽  
Objective Functions ◽  
Model Case ◽  
Cracking Process ◽  
Matlab Programming

This paper presents the research regarding determination of kinetic model parameters from a catalytic cracking process. Starting from the Weekman kinetic model, the authors proposed a simplified version of this model and, based on experimental data form a catalytic cracking plant, they have numerical determined the coefficients of the new kinetic model. For this purpose, there were defined two objective functions; the first function is based on errors generated by estimation of the riser outlet temperature and the second function associated to the errors generated by the estimation of the gasoline yield. The minimization of the two objective functions has been solve by using Optimization Toolbox from MATLAB programming language. The results showed that the objective function that depends on gasoline yield allows more accurate estimation of the kinetic parameters from this model.

Estimation of Kinetic Parameters by a Hybrid Algorithm of Nonlinear Model Based Differential Evolution for Lactic Acid Production

2016 ◽  
Vol 11 (2) ◽  
pp. 141-148
Author(s):  
J. Satya Eswari
Keyword(s):  
Lactic Acid ◽  
Kinetic Model ◽  
Differential Evolution ◽  
Kinetic Parameters ◽  
Acid Production ◽  
Hybrid Algorithm ◽  
Lactic Acid Production ◽  
Model Parameters ◽  
Time Interval ◽  
Experimental Conditions

Abstract The kinetic model parameters are estimated for lactic acid production using mixed microbial consortium in batch fermentation by using different optimization methods. For every time interval concentrations are measured and formulated the parameter appraisal delinquent. Cellular progression kinetic model of exponential or logistic was verified for the effect of various substrates and lactic acid production of mixed culture. This paper proposed hybrid algorithm such as nonlinear models in conjunction with differential evolution and kinetic model. The nonlinear regression with graphical method and Nelder-Mead simplex linked kinetic model was compared with the differential evolution for parameter estimation. The optimized kinetic parameters are found to be within the range of experimental conditions for which the model is developed offers a significant enhancement of lactic acid production. From the computational results, the proposed kinetic model linked differential evolution strategy is thus found effective in exploring the input search space and optimizing the kinetic parameters.

Tumor area and microscopic extent of invasion to determine circulating tumor DNA fraction in plasma and detectability of colorectal cancer (CRC).

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 243-243
Author(s):  
Joerg Bredno ◽  
Jafi Lipson ◽  
Oliver Venn ◽  
Samuel Gross ◽  
Alexander P. Fields ◽  
...  
Keyword(s):  
Surface Area ◽  
Tumor Size ◽  
Clinical Stage ◽  
Circulating Tumor Dna ◽  
Linear Increase ◽  
Model Parameters ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Genome Bisulfite Sequencing ◽  
Cancer Types

243 Background: Circulating Cell-free Genome Atlas (CCGA; NCT02889978) is a multi-center, case-control, observational study with longitudinal follow-up to develop a cfDNA assay in which classifiers were trained on whole-genome bisulfite sequencing (WGBS) and targeted methylation (TM) sequencing data for detection of multiple cancer types. Previously, we showed that the fraction of ctDNA fragments (TF) was a stronger predictor of cancer detection than clinical stage and an equivalent predictor for survival. Given that CRC tumors can be described via surface area (TSA) and microscopic tumor extent (microinvasion), CRC was used as a model to examine the biophysical determinants of TF. Methods: Detection of multiple cancers with WGBS at 98% and TM at > 99% specificity, and methods for determining TF, were previously reported. A model to predict the presence of detectable cfDNA fragments for CRC adenocarcinomas of stages I, II, and III included TSA and microinvasion beyond the subserosa. Predictors were combined assuming a linear increase of cfDNA shedding with tumor size, with scaling factors depending on microinvasion. Model parameters were determined for 27 participants (7, 11, 9 for stages I, II, III, resp.) with WGBS and applied to 40 participants (12, 15, 13 for I, II, III, resp.) with TM assay and information on tumor size and microinvasion. Results: CRC detection at stages I/II/III was 33/46, 61/73, 57/74% for WGBS/TM. TF predicted detection with AUC = 97.6. The model predicted TF as TSA multiplied by 3.81*10−6 / mm2 for tumors that invaded beyond the subserosa (p < 0.001). This was 4.4x higher than estimates for tumors below the subserosa. The model trained on the WGBS assay predicted CRC detection in the TM assay with an AUC of 0.844. Conclusions: This model used TSA (number of tumor cells) and microinvasion (bloodstream access) to predict the fraction of CRC ctDNA fragments in blood without needing to account for stage. Tumors not penetrating the subserosa had low ctDNA shedding that likely limited detection. These findings may generalize to other cancer types, providing principles to predict ctDNA shedding and thus cancer detectability based on microinvasion and surface area. Clinical trial information: NCT02889978.

scholarly journals Estimation of the Kinetic Parameters of a Catalytic Cracking Model Case Study

2019 ◽  
Vol 70 (10) ◽  
pp. 3532-3537
Keyword(s):  
Kinetic Model ◽  
Kinetic Parameters ◽  
Catalytic Cracking ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Outlet Temperature ◽  
Objective Functions ◽  
Model Case ◽  
Cracking Process ◽  
Matlab Programming

This paper presents the research regarding determination of kinetic model parameters from a catalytic cracking process. Starting from the Weekman kinetic model, the authors proposed a simplified version of this model and, based on experimental data form a catalytic cracking plant, they have numerical determined the coefficients of the new kinetic model. For this purpose, there were defined two objective functions; the first function is based on errors generated by estimation of the riser outlet temperature and the second function associated to the errors generated by the estimation of the gasoline yield. The minimization of the two objective functions has been solve by using Optimization Toolbox from MATLAB programming language. The results showed that the objective function that depends on gasoline yield allows more accurate estimation of the kinetic parameters from this model. Keywords: kinetic model, optimization, catalytic cracking

Kinetics of Benzene Alkylation with Long Chain Olefin over Solid Acid Catalyst

2011 ◽  
Vol 233-235 ◽  
pp. 1455-1459
Author(s):  
Hai Kuan Yuan ◽  
Zheng Yi Cao ◽  
Jie Ren
Keyword(s):  
Kinetic Model ◽  
Rate Constant ◽  
Solid Acid ◽  
Solid Acid Catalyst ◽  
Acid Catalyst ◽  
Fixed Bed Reactor ◽  
Model Parameters ◽  
Benzene Alkylation ◽  
External Diffusion ◽  
Long Chain

The benzene alkylation with long chain olefin over solid acid catalyst in the fixed-bed reactor was carried out under near critical conditions. The kinetic model of alkylation correlated with external diffusion and yield model of products were determined, and the model parameters, such as, the external diffusion factor, olefin conversion rate constant and the rate constant forming three products, were estimated. The statistical analysis showed the kinetic model had the higher simulation precision. Compared with the catalyst with 12~16 mesh (Cat-2), the activity and selectivity of linear alkyl benzene (LAB) of catalyst with 20~40 mesh (Cat-1) was higher.

scholarly journals A Methodological Investigation of Healthy Tissue, Hepatocellular Carcinoma and other Lesions with Dynamic 68Ga-FAPI-04 PET/CT Imaging

2020 ◽  
Author(s):  
Barbara Katharina Geist ◽  
Haiqun Xing ◽  
Jingnan Wang ◽  
Ximin Shi ◽  
Haitao Zhao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Kinetic Model ◽  
Kinetic Parameters ◽  
Input Function ◽  
Information Criteria ◽  
Healthy Tissue ◽  
Model Parameters ◽  
Time Activity ◽  
Tissue Compartment ◽  
Pet Scans

Abstract Background: The study aimed to establish a 68Ga-FAPI-04 kinetic model in hepatic lesions, to determine the potential role of kinetic parameters in the differentiation of hepatocellular carcinoma (HCC) from non-HCC lesions.Material and Methods: Time activity curves (TACs) were extracted from seven HCC lesions and five non-HCC lesions obtained from 68Ga-FAPI-04 dynamic positron emission tomography (PET) scans of eight patients. Three kinetic models were applied to the TACs, using image derived hepatic artery and/or portal vein as input functions. For input functions and the lesions, the according voxel with the maximum standardized uptake value (SUVmax) was taken, for the healthy tissue mean SUV values. The optimum model was chosen after applying the Schwartz information criteria to the TACs, differences in model parameters between HCC, non-HCC lesions, and healthy tissue were evaluated with the ANOVA test. Results: A reversible two-tissue compartment model using both the arterial as well as venous input function was most preferred and showed significant differences in the kinetic parameters VND, VT and BPND between HCC, non-HCC lesions and healthy regions (p < 0.01). Conclusion: Several Model parameters derived from a two-tissue compartment kinetic model with two image-derived input function from vein and aorta and using SUVmax allow a differentiation between HCC and non-HCC lesions, obtained from dynamically performed PET scans using FAPI.

scholarly journals A methodological investigation of healthy tissue, hepatocellular carcinoma, and other lesions with dynamic 68Ga-FAPI-04 PET/CT imaging

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Barbara Katharina Geist ◽  
Haiqun Xing ◽  
Jingnan Wang ◽  
Ximin Shi ◽  
Haitao Zhao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Kinetic Model ◽  
Kinetic Parameters ◽  
Hepatic Artery ◽  
Input Function ◽  
Healthy Tissue ◽  
Model Parameters ◽  
Time Activity ◽  
Tissue Compartment ◽  
Pet Scans

Abstract Background The study aimed to establish a 68Ga-FAPI-04 kinetic model in hepatic lesions, to determine the potential role of kinetic parameters in the differentiation of hepatocellular carcinoma (HCC) from non-HCC lesions. Material and methods Time activity curves (TACs) were extracted from seven HCC lesions and five non-HCC lesions obtained from 68Ga-FAPI-04 dynamic positron emission tomography (PET) scans of eight patients. Three kinetic models were applied to the TACs, using image-derived hepatic artery and/or portal vein as input functions. The maximum standardized uptake value (SUVmax) was taken for the lesions, the hepatic artery, and for the portal veins—the mean SUV for all healthy regions. The optimum model was chosen after applying the Schwartz information criteria to the TACs, differences in model parameters between HCC, non-HCC lesions, and healthy tissue were evaluated with the ANOVA test. Results A reversible two-tissue compartment model using both the arterial as well as venous input function was most preferred and showed significant differences in the kinetic parameters VND, VT, and BPND between HCC, non-HCC lesions, and healthy regions (p < 0.01). Conclusion Several model parameters derived from a two-tissue compartment kinetic model with two image-derived input function from vein and aorta and using SUVmax allow a differentiation between HCC and non-HCC lesions, obtained from dynamically performed PET scans using FAPI.

Regeneration Mechanism Studied by Potential-Time Response to Sinusoidal Current-Time Function

1997 ◽  
Vol 62 (10) ◽  
pp. 1511-1526
Author(s):  
María-Luisa Alcaraz ◽  
Ángela Molina
Keyword(s):  
Kinetic Parameters ◽  
Rate Constant ◽  
Theoretical Study ◽  
Time Function ◽  
Time Response ◽  
Current Time ◽  
Regeneration Mechanism ◽  
Sinusoidal Current ◽  
Regeneration Processes

A theoretical study of the potential-time response to sinusoidal current applied to static and dynamic electrodes for regeneration processes is presented. Methods for determination of the regeneration fraction, rate constant of the chemical reaction and heterogeneous kinetic parameters are proposed.

scholarly journals Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 463
Author(s):  
Gopinathan R. Abhijith ◽  
Leonid Kadinski ◽  
Avi Ostfeld
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Mechanistic Model ◽  
Water Distribution Systems ◽  
Sensitivity Study ◽  
Operating Conditions ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Bacterial Regrowth ◽  
The Impact

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

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