Chemotherapy drug regimen optimization using deterministic oscillatory search algorithm

Purpose: To schedule chemotherapy drug delivery using Deterministic Oscillatory Search algorithm, keeping the toxicity level within permissible limits and reducing the number of tumor cells within a predefined time period.Methods: A novel metaheuristic algorithm, deterministic oscillatory search, has been used to optimize the Gompertzian model of the drug regimen problem. The model is tested with fixed (fixed interval variable dose, FIVD) and variable (variable interval variable dose, VIVD) interval schemes and the dosage presented for 52 weeks. In the fixed interval, the treatment plan is fixed in such a way that doses are given on the first two days of every seven weeks such as day 7, day 14, etc.Results: On comparing the two schemes, FIVD provided a higher reduction in the number of tumor cells by 98 % compared to 87 % by VIVD after the treatment period. Also, a significant reduction in the number was obtained half way through the regimen. The dose level and toxicity are also reduced in the FIVD scheme. The value of drug concentration is more in FIVD scheme (50) compared to VIVD (41); however, it is well within the acceptable limits of concentration. The results proved the effectiveness of the proposed technique in terms of reduced drug concentration, toxicity, tumor size and drug level within a predetermined time period.Conclusion: Artificial intelligent techniques can be used as a tool to aid oncologists in the effective treatment of cancer through chemotherapy.Keywords: Deterministic Oscillatory Search, Chemotherapy scheduling, Drug schedule, Artificial intelligenc

Optimisation of cancer drug treatments using cell population dynamics

International audienceCancer is primarily a disease of the physiological control on cell population proliferation. Tissue proliferation relies on the cell division cycle: one cell becomes two after a sequence of molecular events that are physiologically controlled at each step of the cycle at so-called checkpoints, in particular at transitions between phases of the cycle [105]. Tissue proliferation is the main physiological process occurring in development and later in maintaining the permanence of the organism in adults, at that late stage mainly in fast renewing tissues such as bone marrow, gut and skin

Stochastic modelling of eukaryotic cell cycle

Stochastic models are developed to capture the inherent stochasticity of the biochemical networks associated to many biological processes. The objective of the present thesis is to present a detailed picture of stochastic approach for the mathematical modeling of eukaryotic cell cycle, to demonstrate an important application of such model in chemotherapy and to present a methodology for selecting the model parameters. The stochastic cell cycle model, developed using stochastic chemical kinetics approach, leads to the formation of an infinite dimensional differential equation in probabilities of system being in a specific state. Using Monte Carlo simulations of this model, dynamics of populations of eukaryotic cells such as yeasts or mammalian cells are obtained. Simulations are stochastic in nature and therefore exhibit variability among cells that is similar to the variability observed in natural populations. The model’s capability to predict heterogeneities in cell populations is used as a basis to implement it in a chemotherapic modeling framework to demonstrate how the model can be used to assist in the drug development stage by investigating drug administration strategies that can have different killing effect on cancer cells and healthy cells. Finally, basic cell cycle model is refined in a systematic way to make it more suitable for describing the population characteristics of budding yeast. Selection of model parameters using an evolutionary optimization strategy referred to as insilico evolution is described. The benefits of this approach lie in the fact that it generates an initial guess of reasonable set of parameters which in turn can be used in the least squares fitting of model to the steady state distributions obtained from flow cytometry measurements. The Insilco evolution algorithm serves as a tool for sensitivity analysis of the model parameters and leads to a synergistic approach of model and experiments guiding each other. To conclude, the stochastic model based on single cell kinetics will be useful for predicting the population distribution on whole organism level. Such models find applications in wide areas of biological and biomedical applications. Evolutionary optimization strategies can be used in parameter estimation methods based on steady state distributions

Model--Based Design of Cancer Chemotherapy Treatment Schedules

Cancer is the name given to a class of diseases characterized by an imbalance in cell proliferation and apoptosis, or programmed cell death. Once cancer has reached detectable sizes ($10^{6}$ cells or 1 mm$^3$), it is assumed to have spread throughout the body, and a systemic form of treatment is needed. Chemotherapy treatment is commonly used, and it effects both healthy and diseased tissue. This creates a dichotomy for clinicians who need develop treatment schedules which balance toxic side effects with treatment efficacy. Nominally, the optimal treatment schedule --- where schedule is defined as the amount and frequency of drug delivered --- is the one found to be the most efficacious from the set evaluated during clinical trials. In this work, a model based approach for developing drug treatment schedules was developed. Cancer chemotherapy modeling is typically segregated into drug pharmacokinetics (PK), describing drug distribution throughout an organism, and pharmacodynamics (PD), which delineates cellular proliferation, and drug effects on the organism. This work considers two case studies: (i) a preclinical study of the oral administration of the antitumor agent 9-nitrocamptothecin (9NC) to severe combined immunodeficient (SCID) mice bearing subcutaneously implanted HT29 human colon xenografts; and (ii) a theoretical study of intravenous chemotherapy from the engineering literature.Metabolism of 9NC yields the active metabolite 9-aminocamptothecin (9AC). Both 9NC and 9AC exist in active lactone and inactive carboxylate forms. Four different PK model structures are presented to describe the plasma disposition of 9NC and 9AC: three linear models at a single dose level (0.67 mg/kg 9NC); and a nonlinear model for the dosing range 0.44 -- 1.0 mg/kg 9NC. Untreated tumor growth was modeled using two approaches: (i) exponential growth; and (ii) a switched exponential model transitioning between two different rates of exponential growth at a critical size. All of the PK/PD models considered here have bilinear kill terms which decrease tumor sizes at rates proportional to the effective drug concentration and the current tumor size. The PK/PD model combining the best linear PK model with exponential tumor growth accurately characterized tumor responses in ten experimental mice administered 0.67 mg/kg of 9NC myschedule (Monday-Friday for two weeks repeated every four weeks). The nonlinear PK model of 9NC coupled to the switched exponential PD model accurately captured the tumor response data at multiple dose levels. Each dosing problem was formulated as a mixed--integer linear programming problem (MILP), which guarantees globally optimal solutions. When minimizing the tumor volume at a specified final time, the MILP algorithm delivered as much drug as possible at the end of the treatment window (up to the cumulative toxicity constraint). While numerically optimal, it was found that an exponentially growing tumor, with bilinear kill driven by linear PK would experience the same decrease in tumor volume at a final time regardless of when the drug was administered as long as the {it same amount} was administered. An alternate objective function was selected to minimize tumor volume along a trajectory. This is more clinically relevant in that it better represents the objective of the clinician (eliminate the diseased tissue as rapidly as possible). This resulted in a treatment schedule which eliminated the tumor burden more rapidly, and this schedule can be evaluated recursively at the end of each cycle for efficacy and toxicity, as per current clinical practice.The second case study consists of an intravenously administered drug with first order elimination treating a tumor under Gompertzian growth. This system was also formulated as a MILP, and the two different objectives above were considered. The first objective was minimizing the tumor volume at a final time --- the objective the original authors considered. The MILP solution was qualitatively similar to the solutions originally found using control vector parameterization techniques. This solution also attempted to administer as much drug as possible at the end of the treatment interval. The problem was then posed as a receding horizon trajectory tracking problem. Once again, a more clinically relevant objective returned promising results; the tumor burden was rapidly eliminated

Radiotherapy course optimization for patients with colorectal cancer

Работа направлена на оптимизацию проведения послеоперационного сочетанного курса лучевой терапии у пациентов с раком прямой кишки с целью снижения дозовой нагрузки на критические органы с учетом современных подходов к топометриии и дозиметрическому планированию.The work is aimed at optimizing the conduct of a postoperative combined course of radiation therapy in patients with colorectal cancer in order to reduce the dose load on critical organs, taking into account modern approaches to topometry and dosimetric planning

A Systems Biology Approach in Therapeutic Response Study for Different Dosing Regimens—a Modeling Study of Drug Effects on Tumor Growth using Hybrid Systems

Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drug’s pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study

Custom Made Versus Ready to Wear Treatments; Behavioral Propensities in Physician's Choices

To customize treatments to individual patients entails costs of coordination and cognition. Thus, providers sometimes choose treatments based on norms for broad classes of patients. We develop behavioral hypotheses explaining when and why doctors customize to the particular patient, and when instead they employ "ready-to-wear" treatments. Our empirical studies examining length of office visits and physician prescribing behavior find evidence of norm-following behavior. Some such behavior, from our studies and from the literature, proves sensible; but other behavior seems far from optimal.