Durjoy Majumder

EXPLORING THE EFFECTIVENESS OF LOW CONTINUOUS CHEMO-BIO-THERAPEUTIC TREATMENT OF CANCER THROUGH ANALYTICAL SYSTEM MODELLING

Analytical models of tumor growth in the presence and absence of drug is important for studying the dynamics of treatment in oncology. In this paper, a time-variant state-space model is investigated. The model captures the inherent nonlinearity of the system. Physiological constraints of the system are considered in the model and controllability of the tumor load is examined. If it is possible to find a sequence of drug doses that can drive the tumor load to any arbitrary negligibly small final condition from any arbitrary initial condition in a given number of days without affecting the physiological state of the patient, the tumor is said to be controllable. Drug administration strategies like maximum tolerated dose, metronomic chemotherapy in the presence and absence of immunotherapy have been examined. It is found that low yet prolonged continuous dosing of drug along with tuning of biological factors may be a better workable strategy for positive treatment outcome.

PATHO-PHYSIOLOGICALLY BASED LOGISTICS FOR TREATMENT OF CANCER

Analytical modeling and computer simulation may provide an assessment for the success of different cancer therapeutic strategies (both maximum tolerable dosing and metronomic dosing), particularly for the presence of microscopically dormant cells. Different authors substantially showed that metronomic strategy is better than maximum tolerable dosing. In the present work, the different physiological constraints have been considered. Accumulation of drug, dead tumor cells as well as metabolites produced by living tumor cells produce toxicity that affect the physiological system. This affects the subsequent drug application and thereby the therapeutic procedure and its outcome. Translating these into logistics and incorporating them into analytical state-space models, the transformation of the overall system has been examined through computer simulations. The observation is that situations of frequent drug stoppages may occur due to patho-physiological constraints, which may in turn inactivate the cell-mediated immune response. Simulation results suggest that even the combination of different strategies does not improve the situation. Other ways of boosting the immunity and tuning of biological constraints would be required to get positive outcome.

Dynamical model for assessment of anti-angiogenic therapy of cancer

Different experimental models have substantially established that the anti-angiogenic (AAG)group of drugs are able to control the growth of tumor mass by cutting down the nutritive supply to the cancer cells. The mechanism of action of this group of drugs acts on the cells of the vascular endothelium. Recently, different AAG drugs have been in clinical trials. Initial clinical trials showed that application of AAG drugs produced different sorts of toxicity in patients,so calibration of the doses and drug application schedules are very important at present.Hence, development of analytical models would definitely help in this respect, particularly atthe individual level. The analytical model presented here may help to make a judicious choice of drug doses and drug schedule to control the growth of the tumor system under the condition of malignancy.

TUMOR ANGIOGENESIS BASED ANALYTICAL MODEL FOR THE ASSESSMENT OF MCT AND MTD CHEMOTHERAPEUTIC STRATEGIES IN CANCER

Solid tumor survives by the process of angiogenesis. In this process micro-vessels are generated around it. Two factors govern this process. One is Tumor Angiogenic Factor (TAF) secreted by the tumor cells and another is tissue Fibronectin (FNT) concentration in the extra-cellular space. These two factors help in mobilization of endothelial cells from nearby blood vessels, a process called angiogenesis. Metronomic chemotherapeutic (MCT) procedure is targeted at this angiogenic microvessels at the cancer milieu and thereby, limits the growth of cancer cells. Here, we have developed a fluid dynamical based analytical model. The model comprises tumor system and a microvasculature system around it. Another characteristic of the developed model is the incorporation of a tracking procedure of either the tumor or microvasculature system from the peripheral blood. Therefore, this analytical method makes a correlation between tumor system, its micro-vasculature system and the peripheral blood circulatory system. With this analytical armamentarium we have tested the effectiveness of MCT in comparison with the conventional maximum tolerable dosing (MTD) strategy. Our simulation result reveals that under the condition MCT is better compared to MTD in controlling tumor growth in a dynamical sense. The advantage of this analytical model is that the tumor system dynamics can be effectively traced through both invasive and non-invasive procedure as and when required.

Analytical Model for the Assessment of Efficiency of Stem Cell Transplantation with Suicidal Gene Construct for the Treatment of Leukemia

Hematopoietic stem cell transplantation is now being the emergent methodology for leukemia treatment. Due to HLA mis-match, there is chance of transplantation related mortality. However, with the increase in HLA mis-match between donor cells and recipient, there is more chance of complete removal of leukemic cells in host (patient). To tackle this, recently stem cells transplantation with suicidal tk-gene construct is being suggested. Due to unavailability of suitable analytical methods this option has limited applications in clinical cases. Present work provides an analytical platform to test the efficacy of this therapeutic procedure.

Application of Information Theory for Understanding of HLA Gene Regulation in Leukemia

The classical concept of information entropy can be useful in analyzing data pertaining to bioinformatics. In the present work, this has been utilized in understanding of the regulation of HLA gene expression by the inducible promoter region binding transcription factors (TFs). Human HLA surface expression data acquired through flow cytometry and corresponding different TFs expression data acquired through semi-quantitative PCR have been used in this work. The gene regulation phenomenon is considered as an information propagation channel with an amount of distortion. Information entropies computed for the source, receiver and computation of channel equivocation and mutual information are used to characterize the phenomenon of HLA gene regulation. The results obtained in the current exercise reveals that the state of leukemia alters the role of each TF, which tally with the current hypotheses about HLA gene regulation in different leukemias. Hence, this work shows the applicability of information theory in understanding of HLA gene regulation derived from human data.

Efficacy and Toxicity Assessment of Different Antibody Based Antiangiogenic Drugs by Computational Docking Method

Bevacizumab and trastuzumab are two antibody based antiangiogenic drugs that are in clinical practice for the treatment of different cancers. Presently applications of these drugs are based on the empirical choice of clinical experts that follow towards population based clinical trials and, hence, their molecular efficacies in terms of quantitative estimates are not being explored. Moreover, different clinical trials with these drugs showed different toxicity symptoms in patients. Here, using molecular docking study, we made an attempt to reveal the molecular rationale regarding their efficacy and off-target toxicity. Though our study reinforces their antiangiogenic potentiality and, among the two, trastuzumab has much higher efficacy; however, this study also reveals that compared to bevacizumab, trastuzumab has higher toxicity effect, specially on the cardiovascular system. This study also reveals the molecular rationale of ocular dysfunction by antiangiogenic drugs. The molecular rationale of toxicity as revealed in this study may help in the judicious choice as well as therapeutic scheduling of these drugs in different cancers.

Systems biology markup language for cancer system

In literature it has been mentioned that several factors are responsible for the successful accomplishment of cancer therapy. Moreover, during the course of treatment patients has undergone different physiological states. In recent time several analytical models are developed to address these issue and further progresses are in the process. This actually prompted us to develop a multi-modal reasoning decision-support system based on some rationality that has already been addressed in those models and in cancer literature. This would help in the development of a coherent system model for cancer therapy.

Optimal Choice Between Chemotherapy and Immunotherapy for Leukemia Treatment Depends on Individual Patients’ Patho-physiological State

Chemotherapy is the firsthand choice of any cancer therapy including leukemia. However, immunosuppression is commonly seen in leukemic patients. So for the management of leukemia, cytokine-based immunotherapy is also suggested as either a combination therapy along with the conventional chemotherapy or alone. However, therapy is applied on individual patients on the basis of evidence-based medicine, i.e., population-based statistical analysis and/or on the basis of clinicians’ personal experience. Here, we propose an analytical rationality for therapeutic selection among these two options. Our simulation runs suggest that choice would be based on individual patients’ patho-physiological state like immunity profile or another hematological status. Simulation runs also suggest that in some cases chemotherapy may bring detrimental effect and direct immunotherapy would be beneficial for long-term successful therapeutic outcome. Further, this model helps in the optimization of cytokine-based immunotherapy protocol.

An Analytical Approach for the Determination of Chemotherapeutic Drug Application Trade-Offs in Leukemia

For the treatment of different leukemia different chemotherapies are available. However the success rate of any particular drug scheduling may vary with leukemic condition. In general, low dose of chemotherapy is suggested for chronic leukemia, whereas application of high dose (myeloablative) chemotherapy is applied for acute and vigorous type of leukemia. In present work we have shown that chronic type of leukemia is controlled; however, for controlling vigorously growing leukemia is a challenge due to chemotherapeutic toxicity to the normal cells of the hematopoietic system. Hence for its management, we developed a control analysis model. This model may help to design an optimal chemotherapeutic schedule so that the controlling of the vigorously growing leukemic growth can be possible in one hand with the sustenance of the normal non-leukemic cell population on the other hand. This work shows that for long-term chemotherapeutic success in individual leukemic patients demands a judicious choice of drug dosing strategy that may determine the trade-off between leukemic growth and restoration time of normal cell population of the hematopoietic system.