On the model-based optimization of secreting mammalian cell (GS-NS0) cultures.

The global bio-manufacturing industry requires improved process efficiency to satisfy the increasing demands for biochemicals, biofuels, and biologics. The use of model-based techniques can facilitate the reduction of unnecessary experimentation and reduce labor and operating costs by identifying the most informative experiments and providing strategies to optimize the bioprocess at hand. Herein, we investigate the potential of a research methodology that combines model development, parameter estimation, global sensitivity analysis, and selection of optimal feeding policies via dynamic optimization methods to improve the efficiency of an industrially relevant bioprocess. Data from a set of batch experiments was used to estimate values for the parameters of an unstructured model describing monoclonal antibody (mAb) production in GS-NS0 cell cultures. Global Sensitivity Analysis (GSA) highlighted parameters with a strong effect on the model output and data from a fed-batch experiment were used to refine their estimated values. Model-based optimization was used to identify a feeding regime that maximized final mAb titer. An independent fed-batch experiment was conducted to validate both the results of the optimization and the predictive capabilities of the developed model. The successful integration of wet-lab experimentation and mathematical model development, analysis, and optimization represents a unique, novel, and interdisciplinary approach that addresses the complicated research and industrial problem of model-based optimization of cell based processes.

Chemotherapy drug scheduling for the induction treatment of patients with acute myeloid leukemia.

Leukemia is an immediately life-threatening cancer wherein immature blood cells are overproduced, accumulate in the bone marrow (BM) and blood and causes immune and blood system failure. Treatment with chemotherapy can be intensive or nonintensive and can also be life-threatening since only relatively few patient-specific and leukemia-specific factors are considered in current protocols. We have already presented a mathematical model for one intensive chemotherapy cycle with intravenous (i.v.) daunorubicin (DNR), and cytarabine (Ara-C). This model is now extended to nonintensive subcutaneous (SC) Ara-C and for a standard intensive chemotherapy course (four cycles), consistent with clinical practice. Model parameters mainly consist of physiological patient data, indicators of tumor burden and characteristics of cell cycle kinetics. A sensitivity analysis problem is solved and cell cycle parameters are identified to control treatment outcome. Simulation results using published cell cycle data from two acute myeloid leukemia patients are presented for a course of standard treatment using intensive and nonintensive protocols. The aim of remission-induction therapy is to debulk the tumor and achieve normal BM function; by treatment completion, the total leukemic population should be reduced to at most 10(9) cells, at which point BM hypoplasia is achieved. The normal cell number should be higher than that of the leukemic, and a 3-log reduction is the maximum permissible level of population reduction. This optimization problem is formulated and solved for the two patient case studies. The results clearly present the benefits from the use of optimization as an advisory tool for treatment design.

Maintenance-based strategies for environmental risk minimization in the process industries.

Industry, environmental agencies and the scientific community have all emphasized the need to include environmental impact considerations next to profitability objectives in the design phase of modern chemical processes, responding to the increasing social concern over environmental degradation in the past years. Most environmental impact assessment and minimization approaches, however, are rather qualitative, providing general guidelines. In this work, to overcome their limitations and rigorously represent the defining elements of environmental risk, i.e. the mechanism of occurrence of unexpected events usually related to equipment failure and the severity of their consequences, detailed process, reliability and maintenance characteristics are incorporated within a process optimization framework. The objective concerns the optimization of overall process performance defined as a system effectiveness vector characterized by both the environmental and the profitability functions of the system. Implementation of the framework on a process example identifies the optimal combination of process design and operation as well as preventive maintenance strategies that accomplish the conflicting environmental and profitability targets and quantifies the existing trade-offs between them.

Prediction of optimal biofilm thickness for membrane-attached biofilms growing in an extractive membrane bioreactor.

This article presents a mathematical model of membrane-attached biofilm (MAB) behavior in a single-tube extractive membrane bioreactor (STEMB). MABs can be used for treatment of wastewaters containing VOCs, treatment of saline wastewaters, and nitrification processes. Extractive membrane bioreactors (EMBs) are employed to prevent the direct contact between a toxic volatile pollutant and the aerated gas by allowing counterdiffusion of substrates; i.e., pollutant diffuses from the tube side into the biofilm, whereas oxygen diffuses from the shell side into the biofilm. This reduces the air stripping problems usually found in conventional bioreactors. In this study, the biodegradation of a toxic VOC (1,2-dichloroethane, DCE) present in a synthetic wastewater has been investigated. An unstructured model is used to describe cell growth and cell decay in the MAB. The model has been verified by comparing model predicted trends with experimental data collected over 5 to 20-day periods, and has subsequently been used to model steady states in biofilm behavior over longer time scales. The model is capable of predicting the correct trends in system variables such as biofilm thickness, DCE flux across the membrane, carbon dioxide evolution, and suspended biomass. Steady states (constant biofilm thickness and DCE flux) are predicted, and factors that affect these steady states, i.e., cell endogeneous decay rate, and biofilm attrition, are investigated. Biofilm attrition does not have a great influence on biofilm behavior at low values of detachment coefficient close to those typically reported in the literature. Steady-state biofilm thickness is found to be an important variable; a thin biofilm results in a high DCE flux across the membrane, but with the penalty of a high loss of DCE via air stripping. The optimal biofilm thickness at steady state can be determined by trading off the decrease in air stripping (desirable) and the decrease in DCE flux (undesirable) which occur simultaneously as the thickness increases. (c) 1996 John Wiley & Sons, Inc.