Dynamical Behaviour of a Tumor-Immune System with Chemotherapy and Optimal Control

We have considered a tumor growth model with the effect of tumor-immune interaction and chemotherapeutic drug. We have considered two immune components—helper (resting) T-cells which stimulate CTLs and convert them into active (hunting) CTL cells and active (hunting) CTL cells which attack, destroy, or ingest the tumor cells. In our model there are four compartments, namely, tumor cells, active CTL cells, helper T-cells, and chemotherapeutic drug. We have discussed the behaviour of the solutions of our system.The dynamical behaviour of our system by analyzing the existence and stability of the system at various equilibrium points is discussed elaborately. We have set up an optimal control problem relative to the model so as to minimize the number of tumor cells and the chemotherapeutic drug administration. Here we used a quadratic control to quantify this goal and have considered the administration of chemotherapy drug as control to reduce the spread of the disease. The important mathematical findings for the dynamical behaviour of the tumor-immune model with control are also numerically verified using MATLAB. Finally, epidemiological implications of our analytical findings are addressed critically.


Introduction
It is well known that cancer is one of the greatest killer diseases in the world.Cancer, known medically as a malignant neoplasm, is characterized by an abnormal growth of cells.In cancer, cells divide and grow uncontrollably forming malignant tumors and invade nearby parts of the human body.The cancer may also spread to more distant parts of the body through the lymphatic system or bloodstream.Not all tumors are cancerous.The tumors which do not grow uncontrollably, do not invade neighbouring tissues, and do not spread throughout the body are not cancerous.There are over 200 different cancers including breast cancer, skin cancer, lung cancer, ovarian cancer, brain cancer, colon cancer, prostate cancer, and lymphoma and cancer.that afflict humans.Cancer symptoms vary widely based on the type of cancer.
Cancer immunology is the study of interactions between the immune system and cancer cells.It is also a growing field of research that aims to discover innovative cancer immunotherapies to treat and retard progression of this disease.In the last few decades, immunotherapy has become a significant part of treating several types of cancer.Human immune system is a collection of organs, special cells, and substances that help to protect from infections and some other diseases.Immune system cells and the substances they make travel through the body to protect it from pathogens (germs) causing infections.They also help to protect from cancer in some ways.
Pathogens (viruses, bacteria, and parasites) are foreign armies as they are not normally found in the body.They try to invade human body to use its resources to serve their own purposes and so they can hurt the body in the process.In fact, people often use the word foreign to describe invading germs or other substances not normally found in the body.The immune system is acting as body's defense force.It helps keep invading germs out or helps kill them if they do get into the body.The immune system basically works by keeping track of all of the substances normally found in the body, and any new substance in the body that the immune system does not recognize raises an alarm to attack it.Substances that cause an immune system response are known as antigens.The immune response can lead to destruction of anything containing the antigen, such as pathogens or cancer cells.
Pathogens (viruses, bacteria, and parasites) have substances on their outer surfaces, such as certain proteins, that are not normally found in the human body.The immune system identifies these foreign substances as antigens.Cancer cells are also different from normal cells in the body, and they often have unusual substances on their outer surfaces that can act as antigens.But the immune system is much better at recognizing and attacking pathogens than cancer cells.Pathogens are very different from normal human cells and are often easily identified as foreign whereas cancer cells and normal cells have fewer clear differences.Due to this reason the immune system may not always recognize cancer cells as foreign.Cancer cells are less like soldiers of an invading army and more like betrayers within the ranks of the human cell population.So, the immune system's normal ability to attack cancer is limited, and this is the reason that many people with healthy immune systems still develop cancer.Sometimes the immune system recognizes the cancer cells, but the response may not be strong enough to kill them.Also, cancer cells themselves may give off substances that keep the immune system in check.
To overcome this, scientists have designed ways to help the immune system to recognize cancer cells and strengthen its response so that it will destroy them.The immune response including the recognition of cancer specific antigens is of particular interest in this field as knowledge gained drives the development of new vaccines and antibody therapies.Cellmediated immunity involves the production of cytotoxic Tlymphocytes (CTLs), activated macrophages, and release of various cytokines in response to an antigen.
A cytotoxic T-cell (CTL) is a T-lymphocyte (a type of white blood cell) that kills cancer cells, cells that are infected or cells that are damaged in other ways.Recently, increasing importance is being given to the stimulation of a CD4+ T helper cell response in cancer immunotherapy.T helper cells are a subgroup of lymphocytes, a type of white blood cell, that play an important role in the immune system, particularly in the adaptive immune system.They help in the activity of other immune cells by releasing T-cell cytokines.They assist other white blood cells in immunologic processes, including maturation of B-cells into plasma cells and memory B-cells and activation of cytotoxic T-cells (CTLs) and macrophages.
Cancer treatment includes chemotherapy, radiation therapy, immunotherapy, surgery, and monoclonal antibody therapy.The choice of therapy depends upon the location and grade of the tumor and the stage of the disease, as well as the general state of the patient.A number of experimental cancer treatments are also under development.Complete removal of the cancer without damage to the rest of the body is the goal of treatment.Sometimes this can be accomplished by surgery, but the propensity of cancers to invade adjacent tissue or to spread to distant sites by microscopic metastasis often limits its effectiveness.Chemotherapy and radiotherapy unfortunately have a negative effect on normal cells.
Chemotherapy is the treatment of cancer with one or more cytotoxic antineoplastic drugs ("chemotherapeutic agents") as part of a standardized regimen.Traditional chemotherapy drugs act by killing cells that divide rapidly, one of the main properties of the most cancer cells.This means that chemotherapy also harms cells that divide rapidly under normal circumstances: cells in the bone marrow, digestive tract, and hair follicles.This results in the most common side effects of chemotherapy: myelosuppression (decreased production of blood cells), hence, also immunosuppression, mucositis (inflammation of the lining of the digestive tract), alopecia (hair loss), and so forth.Some newer anticancer drugs (e.g., various monoclonal antibodies) are not indiscriminately cytotoxic, but rather target proteins that are abnormally expressed in cancer cells and that are essential for their growth.Such treatments are often referred to as targeted therapy (as distinct from classic chemotherapy) and are often used alongside traditional chemotherapeutic agents in antineoplastic treatment regimens.
Theoretical study of tumor-immune dynamics is very useful.Mathematical modelling in tumor growth has helped to shape our understanding of tumor-immune dynamics.Kuznetsov and Knott [1] have developed a deterministic model that describes the interplay of the cancer cells and the cytotoxic killer cells.Though they have considered only one immune cell population, they have discussed effectively the mechanisms of tumor growth, suppression, and regrowth.Kuznetsov et al. [2] presented a mathematical model of the cytotoxic T-lymphocyte response to the growth of an immunogenic tumor.Through mathematical modelling Kirschner and Panetta [3] have illustrated the dynamics between tumor cells, immune cells, and interleukin-2.Kolev [4] presented a mathematical model, showing competition between tumor cells and immune cells considering the role of antibodies.De Pillis et al. [5] presented a mathematical model on tumor growth using mixed immunotherapy and chemotherapy.De Pillis and Radunskaya [6] presented a mathematical model, showing competition between normal cells and tumor cells considering the role of chemotherapeutic drug.There are some other research works on tumorimmune dynamics [7][8][9][10][11][12][13][14][15][16][17][18].A more common problem is found in the literature which minimizes the tumor volume at a final time subject to toxicity constraints [19,20].There are some researchers who have worked on the tumor growth models with optimal control strategies [6,[20][21][22][23][24][25][26].These are very helpful to predict the most effective therapy and strategy to control the spread of diseases minimizing total drug administered.Ledzewicz and Schättler [27] presented a complete solution for a mathematical model for tumor antiangiogenesis for the problem of optimally scheduling a given amount of inhibitors in order to minimize the primary tumor volume.
In this paper, we have considered a tumor growth model together with the effect of tumor-immune interaction and chemotherapeutic drug.We have considered two immune components: (i) helper (resting) T-cells which are not able to attack and destroy tumor cells directly but release interleukin-2 which stimulates CTLs and convert them into active (hunting) CTL cells and (ii) active (hunting) CTL cells which attack, destroy, or ingest the tumor cells.In our model there are four compartments, namely, tumor cells, active CTL cells, helper T-cells, and chemotherapeutic drug.The model construction and assumptions are described in Section 2. It should be mentioned here that the recent trend is to incorporate a more complex biochemistry [28].Besides tumor cells and the drug, two species of T-lymphocytes (TLs) enter the model: helper (resting) TLs and active (hunting) cytotoxic TLs, which actually destroy the tumor cells.This is an oversimplification, since in the model the two types of Tcells are supposed to interact by direct contact, ignoring the role of mediators (interleukin-2 and interferon alpha).However, dealing with a simplified model allows to give us clear insights and to get some results that may hopefully have some applicability.In Section 3, we have discussed the behaviour of the solutions of our system.The dynamical behaviour of our system by analyzing the existence and stability of the system at various equilibrium points is discussed in Section 4. In the next section, we have set up an optimal control problem relative to the model so as to minimize the number of tumor cells and the chemotherapeutic drug administration.
Here we have used a quadratic control to quantify this goal and considered the administration of chemotherapeutic drug as control to reduce the spread of the disease.The quadratic control reflects the severity of the side effects of the drug imposed [29,30].When chemotherapeutic drugs are administered in high dose, they are toxic to the human body, which justifies the use of quadratic control.The important mathematical findings for the dynamical behaviour of the tumor-immune model with control are also numerically verified using MATLAB in Section 6.Finally, Section 7 contains the general discussions and conclusions of the paper and epidemiological implications of our mathematical findings.

Mathematical Model
In this section we have constructed a mathematical model of tumor growth with an immune response and chemotherapy.
The model can be presented by the following set of ordinary differential equations: with initial conditions where (),  This model involves certain assumptions which consist of the followings.
(i) The tumor cell population is assumed to grow logistically in the absence of active (hunting) CTL cells and chemotherapeutic drug.
(ii) The tumor cells are being destroyed at a rate proportional to the product of densities of tumor cells and active (hunting) CTL cells.
(iii) There is a loss in the active CTL cells due to encounters of tumor cells which is assumed to be proportional to the product of the densities of tumor cells and active CTL cells.
(iv) Helper T-cells are converted into active CTL cells either by direct contact with them or by contact with cytokines produced by the helper T-cells according to the law of mass action.
(v) The helper T-cell population is also assumed to grow logistically in the absence of active CTL cells and chemotherapeutic drug.
(vi) Chemotherapeutic drug destroys tumor cells as well as helper T-cells and active CTL cells; that is, chemotherapeutic drug has a negative effect on both tumor cells and immune cells.
Proof.From the first equation of system (1) it follows that From the standard Kamke comparison theory [15], we get lim Similarly, from the third equation of system (1) it follows that From the standard Kamke comparison theory, we get lim From the fourth equation of system (1), we get Therefore, lim Now, from the second equation of system (1) it follows that Let us assume that sup() =   , inf () =   , sup   () =    , inf   () =    , sup () =   , inf () =   as (),   (), and () are bounded.Now, Therefore, So, lim Therefore, we can conclude that   () may be bounded under some conditions among the parameters and the bounds of (),   (), () for  > 0.
Hence, the theorem.
Theorem 4. The tumor free equilibrium  3 of the system (1) exists and is locally asymptotically stable if and another tumor free equilibrium  4 of the system (1) exists and is locally asymptotically stable if Coexisting Equilibrium. * ( * ,  *  ,  *  ,  * ) exists when Now, the variational matrix of system (1) at  * ( * ,  *  ,  *  ,  * ) is given by Therefore, eigenvalues of the characteristic equation of ( * ) are − and the solution of the following equation, where By the Routh-Hurwitz criterion [32], it follows that the roots of ( 27) have negative real part if and only if Now, it is easy to be noted that then  * will be locally asymptotically stable.So, we came to the following result.

Epidemic Model with Control
In the context of mathematical modelling in cancer growth with chemotherapy, it is essential to frame an optimal control problem so that the total amount of drug used is minimized.This is done because of the implicit understanding that chemotherapy has damaging side effects.We have considered the tumor growth model (1).Now let us assume that the dose of chemotherapeutic drug is given as a function of time denoted by ().We will use () as a control to decrease the tumor burden minimizing total drug administered.Here we consider a quadratic control to quantify this goal.Therefore, our tumor growth model with control becomes satisfying The objective functional [16,29,30,[37][38][39][40] is defined as where  1 ,  2 are positive constants representing the weights of the terms.The first term represents number of cancer cells and the second term represents harmful effects of drug on body.The square of the control variable ( 2 ()) reflects the severity of the side effects of the drug imposed [29,30].When chemotherapeutic drugs are administered in high dose, they are toxic to the human body, which justifies the quadratic terms in the functional.Here the functional given in (36) should be minimized.So, we seek an optimal control  * such that where  = { :  is measurable, 0 ≤ () ≤ 1,  ∈ [0,   ]} is the admissible control set.
(1) The class of all initial conditions with a control () in the admissible control set along with each state equation being satisfied is not empty.(2) The admissible control set  is closed and convex.
(3) Each right-hand side of the state system (34)  We let ⃗ (, ⃗ ) be the right-hand side of the system (34) without () and let with Using the boundedness of the solutions, we see that where  1 depends on the coefficients of the system.

Characterization of the Optimal Control.
In order to derive the necessary conditions for the optimal control, Pontryagin's Maximum Principle [43] is invoked.The Hamiltonian is defined as follows: where   (),  = 1, 2, 3, 4, are the adjoint functions to be determined suitably.
The form of the adjoint equations and transversality conditions are standard results from Pontryagin's Maximum Principle [43].The adjoint system can be obtained as follows: The transversality conditions (or boundary conditions) are By the optimality condition, we have (50) By using the bounds for the control  1 (), we get In compact notation, Using ( 52) we obtain the following optimal system: subject to the following conditions: The previous analysis can be summarized in the following theorem.

Theorem 7.
There exist an optimal control  * and corresponding solutions  * ,  *  ,  *  ,  * that minimize (()) over U.The explicit optimal controls are connected to the existence of continuous specific functions   (),  = 1, 2, 3, 4, the solutions of the following adjoint system: subject to the transversality conditions: Furthermore, the following property holds:

Numerical Simulations
Analytical studies can never be completed without numerical verification of the derived results.In this section we present computer simulation of some important analytic results of our system discussed earlier.Beside verification of our analytical findings, these numerical simulations are very important from practical point of view.
The optimal system has been solved numerically and the results have been presented graphically.This optimal system is a two-point boundary value problem with separated boundary conditions at times  = 0 and  =   .Here, we have solved this two-point boundary value optimality problem for   = 20.The value is chosen to represent the time in days at which treatment is stopped.An efficient method to solve two-point BVPs numerically is collocation.A convenient collocation code is the solver BVP4c implemented under MATLAB, which can be used to solve nonlinear two-point BVPs.To solve our BVP we have used collocation method with collocation code solver BVP4c.It is a powerful method to solve the two-point BVP resulting from the optimality conditions.
The different variables (populations and control functions) in the objective functional given in (36) have different scales.Hence, they are balanced by choosing weight constants  1 = 5,  2 = 10 in the objective functional given in (36).The numerical results for the optimal problem are obtained by using the parameter values given in Table 2 [2,[33][34][35][36].At first we search for the optimal control function ().This optimal control function () is designed in such a way that it minimizes the objective functional given by (36), that is, minimizes the number of tumor cells and the chemotherapeutic drug administration.In Figure 1 we have presented the time series diagrams of tumor cells, active CTL cells, and helper T-cells without any control ( = 0).In   2 with  1 = 5,  2 = 10.

Figure 2 we have presented the time series diagrams of tumor cells, active CTL cells, and helper T-cells with control (𝑢 ̸
= 0).As it is depicted in Figures 1 and 2, the tumor cell population () level obtained using chemotherapeutic drug control is lower than its counterpart which results from practicing without control.From this observation we can conclude that the optimal control is much more effective for reducing the number of tumor cells to near zero.In perspective, one could conclude from the optimal control diagram (Figure 3) that we should give full effort in chemotherapeutic drug control in the beginning of the disease to reduce the spread of tumor cells.This means that chemotherapeutic drug is very much important in the beginning of the disease than when the disease prevails.From Figure 4 we observe that the chemotherapeutic drug control function () minimizes the objective functional  given in (36).Overall the numerical analysis demonstrates that the control () decreases the tumor burden minimizing total drug administered.Numerical simulations also agreed with the theoretical characterization of the optimal control.

Discussions and Conclusions
In this paper, we have considered a malignant tumor growth model together with the effect of tumor-immune interaction and chemotherapeutic drug.Here we have explored the effects and interactions of tumor cells and CTL immune   2 with  1 = 5,  2 = 10.cells through a system of nonlinear differential equations.We have considered the effects of chemotherapeutic drug on the tumor cells as well as immune cells.Two types of CTL immune cells enter the model: (i) helper (resting) T-cells which are not able to attack and destroy tumor cells directly but release interleukin-2 which simulates CTLs and convert them into active (hunting) CTL cells and (ii) active (hunting) CTL cells which attack, destroy, or ingest the tumor cells.Next we have discussed dynamical behaviour of our system by analyzing the existence and stability of our system at various equilibrium points.For example, we have taken a set of estimated parameter values.Using them we have found that only one tumor free equilibrium becomes stable whereas the same equilibrium point becomes unstable without any chemotherapeutic drug.This sufficiently implies the necessity of using chemotherapeutic drug into the system.
The main focus of this paper is to set up an optimal control problem related to the model so as to minimize the number of tumor cells.We have considered the administration of chemotherapeutic drug as control to reduce the spread of the disease.Here we have used a quadratic control to quantify this goal.The quadratic control reflects the severity of the side effects of the drug imposed [29,30].When chemotherapeutic drugs are administered in high dose, they are toxic to the human body, which justifies the use of quadratic control.The control function () is designed in such a way that minimizes the objective functional or cost function as given in (36).
The important mathematical findings for the dynamical behaviour of the tumor-immune model with control are also numerically verified using MATLAB.The graphical representations of the model with control as well as without control are presented for tumor cells and two types of immune cells so that we can compare them and can understand the effectiveness of using the control.It is observed that the optimal control is much more effective for reducing the number of tumor cells to near zero.Overall the numerical analysis demonstrates that a burst of treatment at the beginning is the best way to fight against the tumor cells.Numerical simulations agreed with the theoretical characterization of the optimal control.
The mathematical models on diseases are rather simple, but, nevertheless, they give insight into some of the consequences of public health policies.Our model formulation is based on the effects and interactions of tumor cells and immune cells and also the effects of chemotherapeutic drug on both tumor cells and CTL immune cells.We have also considered a model with control where the administration of chemotherapeutic drug is treated as control.Our model can provide an approximate estimation of timing and dosage of therapy that would be the best complement of the patient's own defense mechanism versus the tumor cells.As with many models, the mathematical model presented in this paper should be treated with circumspection due to the assumptions made and the difficulties in the estimation of the model parameters.Most of the parameters are dependent on many factors, so they are rarely constants.But for the simplification of the system, these parameters are taken as constants.The activation of CTL cells, attacking and destroying tumor cells, is not instantaneous; rather there may be some time lags.Therefore as a part of the future work the model considered here can be refined to incorporate time delays in the system to make it more realistic.Also, there are many components in this model that may be regarded as stochastic rather than deterministic, and these variations may significantly alter the dynamics of the system.Therefore, as our future work we can incorporate stochastic differential equations in modelling and study its dynamics.Controlling the spread of tumor cells is now a challenging and important issue to study.Chemotherapy, immunotherapy, radiation therapy, surgery, and so forth are most useful therapies to control and reduce the spread of tumor cells.So, the development of these therapies and identification of the most effective therapy against the spread of tumor cells are the primary goal of health administrators, policy-makers, and researchers.Our model study is a small step towards the goal by which we want to identify the parameters of interest for further study.