Combined Application of CAR-T Cells and Chlorambucil for CLL Treatment: Insights from Nonlinear Dynamical Systems and Model-Based Design for Dose Finding

Abstract

Background: This work presents a mechanistic nonlinear model, formulated as a system of first-order Ordinary Differential Equations, to investigate the dynamics of Chronic Lymphocytic Leukemia (CLL) under combined chemoimmunotherapy using CAR-T cells and chlorambucil. Methods: Leveraging nonlinear dynamical systems theory and a model-based design approach, we derive optimal dosing strategies through extensive in silico simulations, providing hemato-oncologists with actionable tools to refine CLL treatment. The model captures the short- and long-term impacts of both therapies on leukemia cell populations, enabling precise predictions of therapeutic outcomes. Results: By analyzing local and global system dynamics, we establish sufficient conditions for dosing and protocol design, offering a framework to optimize therapies based on individual patient responses. Comparisons of dose-escalation and -de-escalation strategies further demonstrate potential trajectories, such as complete eradication, partial response, or relapse, providing data-driven guidance for improving patient care. Conclusions: This study highlights the power of mathematical modeling in blood cancer research, enhancing the development of personalized and effective CLL treatment regimens.

1. Introduction

When addressing the most difficult challenges faced by the scientific community, cancer stands at the forefront. Among its various manifestations, blood cancers include Chronic Lymphocytic Leukemia (CLL), which is one of the most prevalent subtypes in Western countries, accounting for $20\%$ to $30\%$ of all leukemia cases. In 2019 alone, more than 40,000 deaths were attributed to CLL worldwide. The incidence and mortality rates are notably higher in males and in regions such as North America, Europe, and Australia. Additionally, the global burden of CLL may be underestimated due to limited case reporting in underdeveloped countries [1].

As a blood cancer involving non-solid tumors, Chronic Lymphocytic Leukemia (CLL) is characterized by the accumulation of malignant B lymphocytes in the peripheral blood, bone marrow, lymph nodes, and spleen. Regarding risk factors, there is no evidence linking environmental causes; however, genetic and familial predispositions are well-documented. Patients undergoing therapies such as chemotherapy, immunotherapy, or their combination generally exhibit favorable survival rates. Nevertheless, cases of aggressive CLL necessitate referral to specialized oncology centers. Hematologists must adhere to standard guidelines to determine the optimal timing for initiating therapy and selecting treatment options [2]. Advances in CLL treatment have led to the development of specialized and targeted therapies [3], with chimeric antigen receptor cell therapy (CAR-T) emerging as a highly promising option for high-risk patients who develop resistance to novel agents.

Using CAR-T cells for cancer treatment involves the precise modification of a patient’s immune system to enable it to recognize and attack cancer cells. This therapy requires a comprehensive understanding of the patient’s immune health and includes the in vitro culture of the patient’s own T cells. Additionally, patients must undergo lymphodepletion to reduce cell population competition and enhance the therapy’s effectiveness. Preparing T cells to target specific tumor cells also necessitates specialized equipment [4]. CAR-T therapy has emerged as a chemo-free alternative; however, one of its primary challenges is resistance. As a result, physicians often adopt combined therapy approaches [5]. Among available therapies, chlorambucil remains a standard due to its low toxicity, affordability, and ease of oral administration. However, when used as a single agent, chlorambucil has shown limited efficacy [6].

Further investigations have been crucial in understanding the evolution of CAR-T cells therapy combined with chlorambucil. Despite the challenges, health specialists, in collaboration with mathematical biologists, have already developed strategies to enhance the effectiveness of therapies and prevention efforts [7]. These advancements have been made possible through the application of mathematical tools, such as modeling via Ordinary Differential Equations (ODEs), which captures the dynamics of this disease with remarkable detail.

With advanced tools in traditional mechanistic modeling, researchers are gaining deeper insights into tumor growth, innovative therapies, prevention strategies, early detection, and more [8]. Furthermore, mathematical biologists are integrating cutting-edge technologies to tackle the complex behavior of cancer. In this context, digital twins have emerged as a transformative approach. These high-precision models, implemented on high-performance computing platforms, enable in silico experimentation, drastically reducing reliance on expensive and time-consuming in vitro or in vivo experimental setups.

Digital twins not only enhance the precision of clinical trials but also allow for the exploration of alternative pathways that are challenging or even impossible to study in real-world scenarios with remarkable accuracy [9]. Additionally, the successful application of mathematical biology to the treatment and understanding of cancer has given rise to Quantitative Systems Pharmacology. This interdisciplinary approach provides specific tools to optimize clinical practice. By leveraging data collected from in vitro or in vivo experiments, in silico models can meet the stringent requirements of governmental health agencies, paving the way for highly personalized therapies with impressive outcomes [10]. Within this framework, model-based design emerges as a key methodology, offering a structured approach to therapy development by systematically integrating mathematical models with clinical and experimental data. By iteratively exploring different treatment scenarios, model-based design allows for the precise optimization of therapeutic protocols [11,12]. The synergy between model-based design, mechanistic models, and Systems Pharmacology enables the development of tailored strategies that address the unique complexities of each individual patient [13].

This work is outlined as follows. Section 2 presents a detailed description of our mechanistic model formulated to explore both short- and long-term effects of a combined chemoimmunotherapy strategy involving CAR-T cells and chlorambucil for the treatment of CLL. Additionally, parameter values and assumptions are provided, along with corresponding references and the mathematical oncology background. In Section 3, nonlinear system theories are applied to analyze the local and global dynamics of the system. Results allow us to establish sufficient conditions for therapy dosing and to design appropriate treatment protocols. Section 4 focuses on in silico experimentation, where we illustrate and compare the results of dose-escalation and dose-de-escalation protocols, as well as scenarios in which therapies may fail to completely eradicate the cancer, eventually leading to relapse of the disease. Conclusions of this work are presented in Section 5. Finally, an Appendix is included with notations and mathematical foundations related to nonlinear system theories.

2. Materials and Methods

In this section, we formulate our mechanistic model to explore the effects of both single and combined applications of chemotherapy and immunotherapy on CLL cancer cells within the bloodstream compartment. The mathematical foundations of the model are presented along with the biological implications of each term. Additionally, the numerical values of all parameters are derived, and the corresponding constraints are established to illustrate different clinically expected behavior of tumor-immune dynamics.

2.1. The CLL Mathematical Model

The mathematical model proposed in this work aims to explore the response of CLL cells $\left[x\left(t\right)\right]$ to the combined application of CAR-T cells $\left[y\left(t\right)\right]$ and chlorambucil $\left[z\left(t\right)\right]$ as a chemoimmunotherapy treatment strategy. The system is formulated by the following three nonlinear ODEs:

$$\begin{array}{ccc}\hfill \dot{x}& =& {\kappa }_{1}x\left(1-{\displaystyle \frac{x}{{\kappa }_2}}\right)-{\kappa }_{3}xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}},\hfill \end{array}$$

(1)

$$\begin{array}{ccc}\hfill \dot{y}& =& \psi -{\psi }_{1}y+{\psi }_{2}xy-{\displaystyle \frac{{\psi }_{3}yz}{{\psi }_4+z}},\hfill \end{array}$$

(2)

$$\begin{array}{ccc}\hfill \dot{z}& =& ϵ-{ϵ}_{1}z,\hfill \end{array}$$

(3)

where the time-evolution $\left(t\right)$ is measured in days and interactions among variables are modeled within the bloodstream compartment. Hence, to estimate the total concentration of cells and drugs, we assume a blood volume of 5 L in the human body (see Chapter 6 in [14]). Descriptions and units of all parameters are given in

Table 1. Parameter information for the CLL mathematical model in (1)–(3).

Parameter	Description	Units
${\kappa }_1$	CLL cancer cell growth rate	${\mathrm{days}}^{-1}$
${\kappa }_2$	Maximum leukemia cell carrying capacity	cells
${\kappa }_3$	Eradication rate of CLL cancer cells by CAR-T cells	(cells $\times$ days)−1
${\kappa }_4$	Chlorambucil cytotoxicity rate on CLL cancer cells	${\mathrm{days}}^{-1}$
${\kappa }_5$	Chlorambucil concentration producing $50\%$ of the maximum cytotoxicity on CLL cancer cells	mg
${\psi }_1$	Death rate of CAR-T cells	${\mathrm{days}}^{-1}$
${\psi }_2$	Activation rate of CAR-T cells due to encounters with CLL cancer cells	(cells $\times$ days)−1
${\psi }_3$	Chlorambucil cytotoxicity rate on CAR-T cells	${\mathrm{days}}^{-1}$
${\psi }_4$	Chlorambucil concentration producing $50\%$ of the maximum cytotoxicity on CAR-T cells	mg
${ϵ}_1$	Decay rate of the chlorambucil drug	${\mathrm{days}}^{-1}$
$\psi$	External CAR-T cell therapy administration	cells
$ϵ$	External chlorambucil drug administration	mg

, while their values and references are discussed in the next section.

Now, let us describe the system as follows. Equation (1) models the dynamics of CLL cells $\left[x\left(t\right)\right]$ under the combined treatment of chemoimmunotherapy. The first term describes the development of leukemia cells using a logistic growth law (see Section 4.3 in [15]), which implies a growth rate and a maximum carrying capacity. The second term represents the overall immune response of CAR-T cells against the leukemia cells based on the law of mass action (see Section 2.3 in [16]). The third term accounts for the chemotherapy effect through a pharmacodynamics of maximum response [17]. In the scope of our model, CAR-T cell immunotherapy is considered as the primary treatment strategy; hence, we assume an eradication of cancer cells that follows the log-kill hypothesis [18], i.e., each consecutive application of the treatment should eradicate a fraction of leukemia cells regardless of its total concentration. Chlorambucil drug is used as a secondary treatment; therefore, its cytotoxicity on the leukemia cells is modeled by the Michaelis–Menten kinetics [19]. Equation (2) models the overall behavior of CAR-T cells $\left[y\left(t\right)\right]$. The first term accounts for the external application of the effector cells, while the second term captures their natural death. The third term describes the activation and proliferation of CAR-T cells upon interaction with leukemia cells in the bloodstream. The fourth term reflects the cytotoxic effect of the chlorambucil drug on CAR-T cells when both therapies are administered into the system at simultaneous periods of time. Equation (3) models the pharmacokinetics of chlorambucil $\left[z\left(t\right)\right]$ as a first-order process, measured in milligrams. The first term represents the external administration of the chemotherapy, while the second term accounts for its natural decay over time [20]. The schematic representation of the system is illustrated in Figure 1.

Concerning the overall dynamics, one may apply the positivity property for nonlinear systems established by De Leenheer and Aeyels in [21] and conclude the following.

Remark 1.

Positive system. All forward solutions $\varphi \left(t,{\tau }_0\right)$ of the system are nonnegative, and all semi-trajectories $\varphi \left(·,\tau \right)$ of Equations (1)–(3) will be positively forward-invariant for nonnegative initial conditions $\left({\tau }_0\ge 0\right)$ inside the following orthant $\tau ={\left[x,y,z\right]}^T$:

$${\mathbf{R}}_{+,0}^3=\left\{x\left(t\right),y\left(t\right),z\left(t\right)\ge 0\right\}.$$

Now, let us consider the cell eradication threshold assumption proposed in [22], which states, “If a solution describing the growth of a cell population goes below the value of 1 cell, then it is possible to assume the complete eradication of such population”. This leads to the following remark.

Remark 2.

Clinically significant biological behavior. All solutions for the CLL cells, $x\left(t\right)$, and CAR-T cells, $y\left(t\right)$, will exhibit biologically meaningful dynamics if their populations persist above the value of one cell. Otherwise, one can assume complete eradication. Hence,

$$x\left(t\right)=0\forall x\left(t\right)<1,$$

and

$$y\left(t\right)=0\forall y\left(t\right)<1.$$

These remarks are helpful in drawing conclusions from the in silico experimentation results. First, it is essential to recognize that, with positive initial conditions, representing biologically feasible cell and drug concentrations, all solutions will remain within the nonnegative orthant ${\mathbf{R}}_{+,0}^3$. Additionally, Remark 2 offers a practical threshold to assess cell eradication, emphasizing the interplay between nonlinear systems theory and oncology in the context of a mathematical model describing the combined application of chemotherapy and immunotherapy treatments.

2.2. Parameter Estimation

Although the system in (1)–(3) was formulated based on a set of well-established assumptions from the mathematical oncology literature, the values for each parameter were carefully selected or estimated to accurately represent tumor dynamics within the bloodstream of the human body.

First, let us discuss the parameters from Equation (1) that model the dynamics of CLL cells $\left[x\left(t\right)\right]$. The leukemia cell growth rate, ${\kappa }_1$, was derived from the experimental validation performed by Guzev et al. in [23]. The authors determined the growth rate of A20 murine leukemic cells to be $0.07{\mathrm{h}}^{-1}$, which, when converted to days, yields a value of $1.680{\mathrm{days}}^{-1}$. It should be noted that this rate was estimated from cells isolated in vitro, i.e., without the influence of the immune response or therapeutic interventions. Additionally, in vivo studies conducted by the authors estimated a lower growth rate of $0.01{\mathrm{h}}^{-1}$$\left(0.240{\mathrm{days}}^{-1}\right)$ [24]. While A20 cells are not identical to human CLL cells, they share key biological, pharmacological, and immunological features that make them a practical and scalable model. Their immune response makes them a relevant tool for investigating human CLL dynamics, particularly when paired with in silico experiments. Studies by Guzev et al. [23,24,25] have explored the applicability of A20 leukemic cells in deriving conclusions about the cytotoxic effects of chemotherapy drugs on CLL cells, as well as estimating growth, death, and inhibition rates under varying drug dosages. Furthermore, A20 cells have been widely used to study the tumor microenvironment in different tissues such as the spleen, brain, and eyes [26], as well as their ability to infiltrate other organs like the liver, lungs, spleen, and bone marrow [27]. Hence, the growth rate of CLL cancer cells can be defined within the following interval:

$${\kappa }_1\in \left[0.240,1.680\right]{\mathrm{days}}^{-1}.$$

The maximum leukemia cell carrying capacity, ${\kappa }_2$, is derived as follows. Previous studies have estimated around 1.8–2$\times {10}^{12}$ immune cells in the human body [28,29]. Among these, four main lymphocyte types can be considered: T cells, B cells, NK cells, and plasma cells. According to Sender et al. [28], the total number of B cells is estimated to be $3\times {10}^{11}$, with a $95\%$ confidence interval of $\left[2\times {10}^{11},4\times {10}^{11}\right]$. Therefore, we set the value of ${\kappa }_2$ as follows:

$${\kappa }_2=3\times {10}^{11}\mathrm{cells}.$$

Porter et al. published a case report in [30] for a clinical trial involving a patient diagnosed with stage I CLL. In this trial, they designed a lentiviral vector expressing a CAR specific to the B cell antigen CD19. Some key results include the following: CAR-T cells expanded to greater than 1000 times the initial level in vivo; complete remission was achieved and sustained for at least 10 months post-treatment; CAR-T cells persisted at high levels in both the blood and bone marrow for 6 months; a more than 3-log expansion of CAR-T cells was observed by day 21 post-infusion; the doubling time of CAR-T cells in the blood was approximately $1.2$ days, with an elimination half-life $\left(t_{1/2}\right)$ of 31 days. Assuming an exponential decay, the death rate of CAR-T cells, ${\psi }_1$, is estimated as follows:

$${\psi }_1={\displaystyle \frac{ln2}{t_{1/2}}}={\displaystyle \frac{ln2}{31}}=22.360\times {10}^{-3}{\mathrm{days}}^{-1}.$$

Similarly, the decay rate of the chlorambucil drug, ${ϵ}_1$, which has a half-life of $1.5$ h [31], is calculated as

$${ϵ}_1={\displaystyle \frac{ln2}{t_{1/2}}}={\displaystyle \frac{ln2}{1.5/24}}=11.090{\mathrm{days}}^{-1}.$$

Regarding the activation rate of CAR-T cells due to encounters with CLL cells, ${\psi }_2$, our mathematical analysis (discussed further in the next section) leads to the following conclusion.

Remark 3.

Persistence of CAR-T cells. The long-term persistence of CAR-T cells in the system in (1)–(3) is ensured if and only if the following condition is satisfied:

$${\psi }_2>{\displaystyle \frac{{\psi }_1}{{\kappa }_2}},$$

(4)

otherwise, in the absence of continuous immunotherapy administration, these cells will eventually be depleted.

Therefore, to fulfill Condition (4), the value for the activation rate of CAR-T cells is set to at least ${\psi }_2=1.1\times {\psi }_1/{\kappa }_2$ in order to illustrate CAR-T cells’ persistence in the in silico experimentation. Now, concerning the cytotoxicity effect of both CAR-T cells $\left({\kappa }_3\right)$ and chlorambucil $\left({\kappa }_4\right)$ on leukemia cells, we have formulated the following two equations, recently published in [32]:

$$\begin{array}{ccc}\hfill {\kappa }_3& =& {\gamma }_1+{\displaystyle \frac{{\psi }_1{\psi }_2}{{\gamma }_2+{\psi }_1{\psi }_2}}+{\displaystyle \frac{{\kappa }_1{\psi }_2}{{\gamma }_3+{\kappa }_1{\psi }_2}},\hfill \end{array}$$

(5)

$$\begin{array}{ccc}\hfill {\kappa }_4& =& {log}_{10}{\left(1+{\epsilon }_1\varkappa \right)}^{1/{\epsilon }_2},\hfill \end{array}$$

(6)

with

$$\varkappa ={\displaystyle \frac{ϵ}{5L}},$$

where $\varkappa$ represents the chlorambucil concentration in milligrams per liter, and the values of the parameters for Equations (5) and (6) are provided below in

Table 2. Parameter values for the cytotoxicity equations, i.e., Equations (5) and (6).

Parameter	Value	Units
${\gamma }_1$	$1.256\times {10}^{-7}$	Dimensionless
${\gamma }_2$	$1.221\times {10}^{-1}$	${\left({\mathrm{days}}^2\times \mathrm{cells}\right)}^{-1}$
${\gamma }_3$	$6.777\times {10}^{-2}$	${\left({\mathrm{days}}^2\times \mathrm{cells}\right)}^{-1}$
${\epsilon }_1$	$4.799\times {10}^{-2}$	L/mg
${\epsilon }_2$	$1.693$	Dimensionless

.

Hence, by substituting the corresponding values for ${\psi }_1$ and ${\psi }_2$, the value of ${\kappa }_3$ may be approximated as follows for both the minimum and maximum growth rate of CLL cells:

$${\kappa }_3=1.256\times {10}^{-7}{\left(\mathrm{cells}\times \mathrm{days}\right)}^{-1}.$$

The values for the cytotoxicity rate of the chemotherapy on CLL cells, ${\kappa }_4$, are further discussed in the in silico experimentation section, as different concentrations of the chlorambucil drug $\left(\varkappa \right)$ are used in the protocol administration design and results from Guzev et al. [23] indicate that cytotoxicity increases when higher doses of the drug are applied, i.e., greater cell growth inhibition. However, as indicated in off-label dosing guidelines [33,34], total concentration values should be set within the following range:

$$ϵ\in \left[0.4,0.8\right]{\displaystyle \frac{\mathrm{mg}}{\mathrm{day}}}\mathrm{per}1\mathrm{kg}\mathrm{of}\mathrm{the}\mathrm{patient},$$

for up to 24 cycles. Furthermore, as has been established in the vast mathematical oncology literature [19,35], the chemotherapy drug should exhibit a greater capacity for killing cancer cells than effector cells for the treatment to be successful. The latter is to be expected, as cancer cells proliferate at a much faster rate than effector cells; hence, the drug must be more effective in killing these malignant cells than healthy or immune cells. Therefore, we set the following constraint for the chlorambucil cytotoxicity rate on CAR-T cells, ${\psi }_3$:

$${\psi }_3<{\kappa }_4,$$

(7)

and, by following results from de Pillis et al. [19], we set ${\psi }_3=0.054\times {\kappa }_4$.

Now, concerning the chlorambucil concentration that produces $50\%$ of the maximum response, ${\kappa }_5$, we found an in vivo study conducted by Silber et al. [36], in which the chemosensitivity of lymphocytes from patients with B-cell CLL to chlorambucil was determined. The study involved 65 patients diagnosed with CLL, each with at least $5\times {10}^3/$µL of monoclonal B lymphocytes positive for CD5, CD19, and CD20 markers. The overall results are summarized as follows: the median $I{C}_{50}$ for the total patient group was 61 µmol/L. This value was used as a threshold to categorize the B lymphocytes as sensitive ($I{C}_{50}<61$ µmol/L) or resistant $(I{C}_{50}\ge 61$ µmol/L). Therefore, the value of ${\kappa }_5$ can be calculated in milligrams as $I{C}_{50}\times C_{mol}\times 5$ L, where the molecular weight of chlorambucil is $C_{mol}=304.212\times {10}^3$ mg/mol [37]:

$$\begin{array}{ccc}\hfill {\kappa }_5& <& 92.785\mathrm{mg}\mathrm{for}\mathrm{the}\mathrm{sensitive}\mathrm{CLL}\mathrm{cancer}\mathrm{cells},\hfill \\ \hfill {\kappa }_5& \ge & 92.785\mathrm{mg}\mathrm{for}\mathrm{the}\mathrm{resistant}\mathrm{CLL}\mathrm{cancer}\mathrm{cells}.\hfill \end{array}$$

For the concentration of chlorambucil that produces $50\%$ of the maximum cytotoxicity on CAR-T cells, ${\psi }_4$, we follow the same line of thought as in Condition (7) and establish the next condition:

$${\psi }_4>{\kappa }_5,$$

(8)

based on the assumption that a higher dose of the chemotherapy drug is required to achieve greater cytotoxicity in CAR-T cells. Similar results were reported by Guzev et al. [24].

3. Results: Nonlinear Dynamical Properties of the System

In this section, we investigate local and global dynamics of Equations (1)–(3) by applying nonlinear system theories such as the Localization of Compact Invariant Sets (LCIS) method to determine lower and upper bounds on all forward solutions of the CLL mechanistic model; Lyapunov’s direct and indirect methods to establish sufficient conditions for eradication of leukemia cells and persistence in both CAR-T cells and cancer cells; and Lipschitz conditions for the existence and uniqueness of solutions. Fundamentals and mathematical preliminaries are shown in Appendix A.

3.1. Localizing Domain

The localizing domain containing all compact invariant sets of the system characterizes all biologically meaningful behaviors that may emerge during tumor evolution, such as various concentrations of long-persisting tumor burdens, tumor-free states, periods of cancer cell dormancy followed by heightened effector cells activity, and active immune responses exhibiting abrupt changes in effector and cancer cell concentrations, among other dynamics. Now, by following the LCIS methodology one can establish the next result.

Result 1.

Clinically significant bounds. Bounds regarding all compact invariant sets of the CLL System (1)–(3) describing clinically significant behavior when applying either single or combined therapy are given within the next domain:

$$K_{xyz}=K_x\cap K_y\cap K_z,$$

where each set is defined as follows:

$$\begin{array}{ccc}\hfill K_x& =& \left\{0\le x\left(t\right)\le x_{sup}\right\},\hfill \\ \hfill K_y& =& \left\{y_{inf}\le y\left(t\right)\le y_{sup}\right\},\hfill \\ \hfill K_z& =& \left\{z_{inf}\le z\left(t\right)\le z_{sup}\right\},\hfill \end{array}$$

and expressions for the lower and upper bounds of CAR-T cell and CLL cell populations are defined with respect to therapy administration cases, as shown below.

Chemotherapy off	Chemotherapy on
$y_{inf}={\displaystyle \frac{\psi }{{\psi }_1}}$	$y_{inf}={\displaystyle \frac{\psi }{{\psi }_1+{\psi }_3}}$
$y_{sup}={\displaystyle \frac{\psi }{{\psi }_1}}+{\displaystyle \frac{{\kappa }_2{\left({\kappa }_1+{\psi }_1\right)}^2}{4{\psi }_1{\kappa }_1}}$	$y_{sup}={\displaystyle \frac{\psi }{\alpha }}+{\displaystyle \frac{{\kappa }_2{\left({\kappa }_1+{\psi }_1\right)}^2}{4\alpha {\kappa }_1}},$ where $\alpha ={\psi }_1+{\displaystyle \frac{{\psi }_3{z}_{inf}}{{\psi }_4+z_{inf}}}$

Immunotherapy	Chemotherapy	Chemoimmunotherapy
$x_{sup}={\kappa }_2\left(1-{\displaystyle \frac{{\kappa }_3\psi }{{\kappa }_1{\psi }_1}}\right)$	$x_{sup}={\kappa }_2\left(1-{\displaystyle \frac{{\kappa }_4{z}_{inf}}{{\kappa }_1\left({\kappa }_5+z_{inf}\right)}}\right)$	$x_{sup}={\kappa }_2\left(1-{\displaystyle \frac{{\kappa }_3\psi }{{\kappa }_1\left({\psi }_1+{\psi }_3\right)}}-{\displaystyle \frac{{\kappa }_4{z}_{inf}}{{\kappa }_1\left({\kappa }_5+z_{inf}\right)}}\right)$

Proof. 

Now, in order to demonstrate Result 1, we first compute the bounds concerning all forward solutions $z\left(t\right)$ by integrating Equation (3) with the initial condition $z\left(0\right)=z_0\in {\mathbf{R}}_{+,0}$, as follows:

$$z\left(t\right)={\displaystyle \frac{ϵ}{{ϵ}_1}}-\left({\displaystyle \frac{ϵ}{{ϵ}_1}}-z_0\right)e^{-{ϵ}_{1}t},$$

where it is evident that

$$\underset{t\to \infty }{lim}z\left(t\right)={\displaystyle \frac{ϵ}{{ϵ}_1}},$$

which also represents the equilibrium point for the constant infusion of the drug into the system. Now, bounds for $z\left(t\right)$ may be defined as shown below:

$$K_z=\left\{z_{inf}\le z\left(t\right)\le z_{sup}\right\},$$

from which different cases should be considered regarding the initial condition.

Case 1:

$z_0=0$. In this case, chemotherapy is not considered at the beginning of the treatment; therefore,

$$z_{inf}=0\mathrm{and}{z}_{sup}={\displaystyle \frac{ϵ}{{ϵ}_1}}.$$

Case 2:

$z_0<ϵ/{ϵ}_1$. In this case, bounds are given as follows:

$$z_{inf}=z_0\mathrm{and}{z}_{sup}={\displaystyle \frac{ϵ}{{ϵ}_1}}.$$

Case 3:

$z_0>ϵ/{ϵ}_1$. In this case, bounds are defined as follows:

$$z_{inf}={\displaystyle \frac{ϵ}{{ϵ}_1}}\mathrm{and}{z}_{sup}=z_0.$$

Case 4:

$z_0=ϵ$. In this case, the initial condition is given by the chemotherapy dose. Thus, when considering parameter values, bounds are given by

$$z_{inf}={\displaystyle \frac{ϵ}{{ϵ}_1}}\mathrm{and}{z}_{sup}=ϵ.$$

Here, it is important to note that, in all numerical simulations, only Case 1 arises, as chemotherapy is not administered as the initial treatment in the designed protocols. These bounds will be necessary to compute lower and upper bounds for both cells’ populations when applying the Iterative Theorem from the LCIS method. First, let us explore the next localizing function:

$$h_1=y,$$

by computing its Lie derivative

$$L_f{h}_1=\psi -{\psi }_{1}y+{\psi }_{2}xy-{\displaystyle \frac{{\psi }_{3}yz}{{\psi }_4+z}},$$

and constructing the set $S\left(h_1\right)=\left\{L_f{h}_1=0\right\}:$

$$S\left(h_1\right)=\left\{\psi -\left({\psi }_1+{\psi }_3\right)y+{\psi }_{2}xy+{\displaystyle \frac{{\psi }_3{\psi }_{4}y}{{\psi }_4+z}}=0\right\},$$

which is rewritten as follows:

$$S\left(h_1\right)=\left\{y={\displaystyle \frac{\psi }{{\psi }_1+{\psi }_3}}+{\displaystyle \frac{{\psi }_{2}xy}{{\psi }_1+{\psi }_3}}+{\displaystyle \frac{{\psi }_3{\psi }_{4}y}{\left({\psi }_4+z\right)\left({\psi }_1+{\psi }_3\right)}}\right\},$$

From the latter, the lower bound for the CAR-T cells’ forward solution $\left[y\left(t\right)\right]$ is given by

$$K\left(h_1\right)=\left\{y\left(t\right)\ge y_{inf}\right\},$$

where the next two cases should be differentiated:

$$\begin{array}{ccc}\hfill Case1\left[\mathrm{Chemotherapy}\mathrm{off}\right]& :& \mathrm{If}z\left(t\right)=0\Rightarrow y_{inf}={\displaystyle \frac{\psi }{{\psi }_1}},\hfill \\ \hfill Case2\left[\mathrm{Chemotherapy}\mathrm{on}\right]& :& \mathrm{If}z\left(t\right)>0\Rightarrow y_{inf}={\displaystyle \frac{\psi }{{\psi }_1+{\psi }_3}}.\hfill \end{array}$$

Hence, it is evident that

$${\displaystyle \frac{\psi }{{\psi }_1+{\psi }_3}}<{\displaystyle \frac{\psi }{{\psi }_1}},$$

as is to be expected because the chemotherapy drug is cytotoxic to both cancer cells and CAR-T cells. Now, an upper bound for the CAR-T cells population may be estimated from the next localizing function:

$$h_2=x+y,$$

whose Lie derivative is calculated as

$$L_f{h}_2={\kappa }_{1}x\left(1-{\displaystyle \frac{x}{{\kappa }_2}}\right)-{\kappa }_{3}xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}}+\psi -{\psi }_{1}y+{\psi }_{2}xy-{\displaystyle \frac{{\psi }_{3}yz}{{\psi }_4+z}},$$

then, set $S\left(h_2\right)=\left\{L_f{h}_2=0\right\}$ is given below:

$$S\left(h_2\right)=\left\{\psi +{\kappa }_{1}x-{\displaystyle \frac{{\kappa }_1}{{\kappa }_2}}{x}^2-\left({\psi }_1+{\displaystyle \frac{{\psi }_{3}z}{{\psi }_4+z}}\right)y-\left({\kappa }_3-{\psi }_2\right)xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}}=0\right\},$$

From the latter, the following condition must be satisfied, implying that the cytotoxic rate of CAR-T cells against cancer cells exceeds the inactivation rate of cancer cells on CAR-T cells:

$${\kappa }_3>{\psi }_2,$$

(9)

and set $S\left(h_2\right)$ is rewritten as follows:

$$S\left(h_2\right)\cap \left\{z=z_{inf}\right\}\subset \left\{{\alpha }_1{h}_2\le \psi +{\displaystyle \frac{{\kappa }_2{\left({\kappa }_1+{\psi }_1\right)}^2}{4{\kappa }_1}}-f_1\left(x,y,z\right)\right\},$$

with

$$\alpha ={\psi }_1+{\displaystyle \frac{{\psi }_3{z}_{inf}}{{\psi }_4+z_{inf}}}>0,$$

(10)

and

$$f_1\left(x,y,z\right)={\displaystyle \frac{{\kappa }_1}{{\kappa }_2}}{\left(x-{\displaystyle \frac{{\kappa }_2\left({\kappa }_1+{\psi }_1\right)}{2{\kappa }_1}}\right)}^2+\left({\kappa }_3-{\psi }_2\right)xy+{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}}.$$

Therefore, by neglecting the negative function on the right-hand side of the inequality, the next result is obtained:

$$K\left(h_2\right)=\left\{x\left(t\right)+y\left(t\right)\le {\displaystyle \frac{\psi }{\alpha }}+{\displaystyle \frac{{\kappa }_2{\left({\kappa }_1+{\psi }_1\right)}^2}{4\alpha {\kappa }_1}}\right\},$$

hence, the following upper bound for the CAR-T cells can be extracted:

$$K_y\left(h_2\right)=\left\{y\left(t\right)\le y_{sup}={\displaystyle \frac{\psi }{\alpha }}+{\displaystyle \frac{{\kappa }_2{\left({\kappa }_1+{\psi }_1\right)}^2}{4\alpha {\kappa }_1}}\right\},$$

where parameter ${\alpha }_1$ can have one of the following two values:

$$\begin{array}{ccc}\hfill Case1\left[\mathrm{Chemotherapy}\mathrm{off}\right]& :& \mathrm{If}z\left(t\right)=0\Rightarrow \alpha ={\psi }_1,\hfill \\ \hfill Case2\left[\mathrm{Chemotherapy}\mathrm{on}\right]& :& \mathrm{If}z\left(t\right)>0\Rightarrow \alpha ={\psi }_1+{\displaystyle \frac{{\psi }_3{z}_{inf}}{{\psi }_4+z_{inf}}}.\hfill \end{array}$$

Now, let us exploit the localizing function:

$$h_3=x,$$

to derive results concerning the ultimate upper bound for the CAR-T cells in the case of single therapy and combined therapy. Thus, the Lie derivative is computed as follows:

$$L_f{h}_3={\kappa }_{1}x\left(1-{\displaystyle \frac{x}{{\kappa }_2}}\right)-{\kappa }_{3}xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}},$$

then, set $S\left(h_3\right)=\left\{L_f{h}_3=0\right\}$ is written as given below:

$$S\left(h_3\right)=\left\{{\displaystyle \frac{{\kappa }_1}{{\kappa }_2}}x={\kappa }_1-{\kappa }_{3}y-{\displaystyle \frac{{\kappa }_{4}z}{{\kappa }_5+z}}\right\}\cup \left\{x=0\right\}.$$

Thus, the Iterative Theorem can be applied to determine the following cases concerning single or combined treatment strategies.

Case 1:

Immunotherapy. The Iterative Theorem of the LCIS method is applied as follows:

$$S\left(h_3\right)\cap \left\{y=y_{inf}\right\}\subset \left\{x\le {\kappa }_2-{\displaystyle \frac{{\kappa }_2}{{\kappa }_1}}{\kappa }_3{y}_{inf}\right\},$$

and, by substituting the corresponding value of Case 1 $\left[\mathrm{Chemotherapy}\mathrm{off}\right]$ for the CAR-T cells’ lower bound, one can obtain the next result:

$$K_1\left(h_3\right)=\left\{x\left(t\right)\le x_{sup}={\kappa }_2\left(1-{\displaystyle \frac{{\kappa }_3\psi }{{\kappa }_1{\psi }_1}}\right)\right\}.$$

Case 2:

Chemotherapy. When only the chlorambucil drug is being considered, the Iterative Theorem is applied, as shown below:

$$S\left(h_3\right)\cap \left\{z=z_{inf}\right\}\subset \left\{x\le {\kappa }_2-{\displaystyle \frac{{\kappa }_2}{{\kappa }_1}}{\displaystyle \frac{{\kappa }_4{z}_{inf}}{{\kappa }_5+z_{inf}}}\right\},$$

and the lower bound is defined as follows:

$$K_2\left(h_3\right)=\left\{x\left(t\right)\le x_{sup}={\kappa }_2\left(1-{\displaystyle \frac{{\kappa }_4{z}_{inf}}{{\kappa }_1\left({\kappa }_5+z_{inf}\right)}}\right)\right\}.$$

Case 3:

Chemoimmunotherapy. Now, let us explore the case when the combined therapy is applied: thus, the Iterative Theorem yields the next result:

$$S\left(h_3\right)\cap \left\{y=y_{inf}\right\}\cap \left\{z=z_{inf}\right\}\subset \left\{x\le {\kappa }_2-{\displaystyle \frac{{\kappa }_2}{{\kappa }_1}}{\kappa }_3{y}_{inf}-{\displaystyle \frac{{\kappa }_2}{{\kappa }_1}}{\displaystyle \frac{{\kappa }_4{z}_{inf}}{{\kappa }_5+z_{inf}}}\right\},$$

then, when substituting the corresponding value of Case 2 $\left[\mathrm{Chemotherapy}\mathrm{on}\right]$ for the CAR-T cells’ lower bound, the next result can be established:

$$K_3\left(h_3\right)=\left\{x\left(t\right)\le x_{sup}={\kappa }_2\left(1-{\displaystyle \frac{{\kappa }_3\psi }{{\kappa }_1\left({\psi }_1+{\psi }_3\right)}}-{\displaystyle \frac{{\kappa }_4{z}_{inf}}{{\kappa }_1\left({\kappa }_5+z_{inf}\right)}}\right)\right\}.$$

Therefore, the proof for Result 1 is now complete. □

3.2. Eradication Conditions

In this section, we establish sufficient conditions by means of Lyapunov’s Direct Method to ensure the complete eradication of the CLL cell population described by System (1)–(3). These conditions are formulated on the treatment parameters when considering the single and combined application of immunotherapy and chemotherapy. Hence, conditions for CLL cell eradication are given in the next result.

Result 2.

Minimum concentrations of therapies to ensure a continuous decrease in tumor burden. If only CAR-T cells are applied, the concentration of this therapy must satisfy the following condition:

$$\psi >{\displaystyle \frac{{\kappa }_1{\psi }_1}{{\kappa }_3}}.$$

On the other hand, if only the chlorambucil drug is applied $\left(z_0=ϵ\right)$ and the inequality ${\kappa }_4>{\kappa }_1$ holds, then the chemotherapy concentration must fulfill the following condition:

$$ϵ>{\displaystyle \frac{{ϵ}_1{\kappa }_1{\kappa }_5}{{\kappa }_4-{\kappa }_1}}.$$

When combined therapy is applied and the concentration of chlorambucil is fixed, the number of CAR-T cells that need to be administered into the CLL system in (1)–(3) to ensure leukemia cell eradication is given by

$$\psi >{\displaystyle \frac{\left[{\kappa }_1{\kappa }_5+\left({\kappa }_1-{\kappa }_4\right)z_{inf}\right]\left({\psi }_1+{\psi }_3\right)}{{\kappa }_3\left({\kappa }_5+z_{inf}\right)}}.$$

Proof. 

Let us apply the next Lyapunov’s candidate function

$$V\left(x\right)=x,$$

where it is necessary to highlight that we can explore this function because the lower and upper bounds have already been calculated for both CAR-T cells and the chlorambucil drug in the localizing domain $K_{xyz}$ shown in Result 1. Thus, inequalities can be constructed on the time-derivative of $V\left(x\right)$, which is given as follows:

$$\dot{V}\left(x\right)={\kappa }_{1}x\left(1-{\displaystyle \frac{x}{{\kappa }_2}}\right)-{\kappa }_{3}xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}}.$$

Therefore, the following three cases can be formulated based on the application of a single therapy or combined therapy.

Case 1:

Immunotherapy. When only the CAR-T cells are applied for cancer treatment, the time-derivative can be bounded from above as follows:

$$\dot{V}\left(x\right)={\kappa }_{1}x\left(1-{\displaystyle \frac{x}{{\kappa }_2}}\right)-{\kappa }_{3}xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}}<\left({\kappa }_1-{\displaystyle \frac{{\kappa }_1}{{\kappa }_2}}x-{\kappa }_3{y}_{inf}\right)x<0,$$

where $y_{inf}=\psi /{\psi }_1$, and the next constraint can be formulated on the immunotherapy concentration:

$$\psi >{\displaystyle \frac{{\kappa }_1{\psi }_1}{{\kappa }_3}}.$$

(11)

Case 2:

Chemotherapy. When only the chlorambucil drug is considered from the beginning of the treatment, the time-derivative is bounded from above, as indicated below:

$$\dot{V}\left(x\right)={\kappa }_{1}x\left(1-{\displaystyle \frac{x}{{\kappa }_2}}\right)-{\kappa }_{3}xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}}<\left({\kappa }_1-{\displaystyle \frac{{\kappa }_1}{{\kappa }_2}}x-{\displaystyle \frac{{\kappa }_4{z}_{inf}}{{\kappa }_5+z_{inf}}}\right)x<0,$$

where $z_{inf}=ϵ/{ϵ}_1$ as $z_0=ϵ$, and the next constraint is formulated on the chemotherapy concentration:

$$ϵ>{\displaystyle \frac{{ϵ}_1{\kappa }_1{\kappa }_5}{{\kappa }_4-{\kappa }_1}},$$

(12)

however, it is evident from the denominator that the next condition relating the rates of CLL cell growth and the cytotoxicity of the drug should be fulfilled:

$${\kappa }_4-{\kappa }_1>0,$$

(13)

or else Condition (12) will not have a biologically feasible solution.

Case 3:

Chemoimmunotherapy. When the combined therapy is applied, the time-derivative is bounded as follows:

$$\dot{V}\left(x\right)={\kappa }_{1}x\left(1-{\displaystyle \frac{x}{{\kappa }_2}}\right)-{\kappa }_{3}xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}}<\left({\kappa }_1-{\displaystyle \frac{{\kappa }_1}{{\kappa }_2}}x-{\kappa }_3{y}_{inf}-{\displaystyle \frac{{\kappa }_4{z}_{inf}}{{\kappa }_5+z_{inf}}}\right)x<0,$$

where $y_{inf}=\psi /\left({\psi }_1+{\psi }_3\right)$. In order to properly solve this condition, we set the CAR-T cells as the control therapy and come to the following result:

$$\psi >{\displaystyle \frac{\left[{\kappa }_1{\kappa }_5+\left({\kappa }_1-{\kappa }_4\right)z_{inf}\right]\left({\psi }_1+{\psi }_3\right)}{{\kappa }_3\left({\kappa }_5+z_{inf}\right)}},$$

(14)

in this case, it is important to consider two paths. If Condition (13) is satisfied, chemotherapy alone could eradicate the leukemia cell population described by the CLL mechanistic model in (1)–(3). However, both therapies may be combined in varying proportions to design a successful protocol that ultimately achieves cancer eradication. Conversely, if Condition (13) is not fulfilled, then a biologically feasible solution can be computed to estimate the concentration of CAR-T cells required to ensure the complete eradication of cancer cells in the corresponding scenarios.

Therefore, the proof for Result 2 is now complete. □

3.3. Persistence Conditions

In order to derive results regarding the so-called persistence conditions for both CLL cells and CAR-T cells, we compute the core equilibrium points in System (1)–(3) by excluding the therapy parameters and setting the equations to zero as follows:

$$\begin{array}{ccc}\hfill {\kappa }_{1}x-{\displaystyle \frac{{\kappa }_1{x}^2}{{\kappa }_2}}-{\kappa }_{3}xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}}& =& 0,\hfill \\ \hfill -{\psi }_{1}y+{\psi }_{2}xy-{\displaystyle \frac{{\psi }_{3}yz}{{\psi }_4+z}}& =& 0,\hfill \\ \hfill -{ϵ}_{1}z& =& 0.\hfill \end{array}$$

It should be noted that treatments are not considered because, in real-life clinical scenarios, anti-cancer therapies are administered in short cycles over limited periods of time. The latter, within the scope of nonlinear system theory, are referred to as perturbations and their primary characteristic is their ability to alter both the short- and long-term dynamics of a system. These changes may be minor, disrupting only short-term behavior, or major, affecting long-term dynamics and transitioning the system through completely different states. Hence, by performing the corresponding arithmetical operations, the following three sets of equilibrium points are identified:

$$\begin{array}{ccc}\hfill \left(x_0^{*},y_0^{*},z_0^{*}\right)& =& \left(0,0,0\right),\hfill \end{array}$$

(15)

$$\begin{array}{ccc}\hfill \left(x_1^{*},y_1^{*},z_0^{*}\right)& =& \left({\displaystyle \frac{{\psi }_1}{{\psi }_2}},{\displaystyle \frac{{\kappa }_1\left({\kappa }_2{\psi }_2-{\psi }_1\right)}{{\kappa }_2{\kappa }_3{\psi }_2}},0\right),\hfill \end{array}$$

(16)

$$\begin{array}{ccc}\hfill \left(x_2^{*},y_0^{*},z_0^{*}\right)& =& \left({\kappa }_2,0,0\right).\hfill \end{array}$$

(17)

Now, let us discuss the biological interpretation of each equilibrium point. Equilibrium (15) corresponds to the tumor-free state, representing the desired outcome after a successful treatment strategy that eradicates the CLL cell population. Concerning Equilibrium (16), we can see that it is biologically feasible if the persistence condition in (4) from Remark 3 is satisfied. Furthermore, positive values are required to describe real-life cell concentrations. This equilibrium represents a state where CAR-T cells persist in the long-term, enabling them to mount a consistent immune response against cancer cells, thereby reducing the tumor burden in the system to a value directly related to the ratio ${\psi }_1/{\psi }_2$. Equilibrium (17) represents a scenario in which leukemia cells reach their maximum carrying capacity. Within the framework of System (1)–(3), this implies the death of the subject, as the tumor burden becomes unsustainable for survival. Now, let us present the following result regarding the long-term concentration of cells populations.

Result 3.

Persistence and depletion of CAR-T cells. If Condition (4) from Remark 3 is satisfied, i.e.,

$${\psi }_2>{\displaystyle \frac{{\psi }_1}{{\kappa }_2}},$$

CAR-T cells will persist in the long-term after a single application of the therapy. Under this condition, cell populations will reach the following steady-state concentrations where both coexist:

$$\underset{t\to \infty }{lim}x\left(t\right)={\displaystyle \frac{{\psi }_1}{{\psi }_2}},\underset{t\to \infty }{lim}y\left(t\right)={\displaystyle \frac{{\kappa }_1\left({\kappa }_2{\psi }_2-{\psi }_1\right)}{{\kappa }_2{\kappa }_3{\psi }_2}}.$$

However, if Condition (4) is not satisfied, CAR-T cells will eventually be depleted, and CLL cells will grow to their maximum carrying capacity, i.e.,

$$\underset{t\to \infty }{lim}x\left(t\right)={\kappa }_2,\underset{t\to \infty }{lim}y\left(t\right)=0.$$

Proof. 

Local dynamics of the CLL mechanistic model can be explored by applying the Indirect Lyapunov’s Method, which requires the Jacobian matrix of the system, which is computed as follows:

$$J=\left[\begin{array}{ccc}{\kappa }_1-{\displaystyle \frac{2{\kappa }_{1}x}{{\kappa }_2}}-{\kappa }_{3}y-{\displaystyle \frac{{\kappa }_{4}z}{{\kappa }_5+z}}& -{\kappa }_{3}x& -{\displaystyle \frac{{\kappa }_4{\kappa }_{5}x}{{\left({\kappa }_5+z\right)}^2}}\\ {\psi }_{2}y& -{\psi }_1+{\psi }_{2}x-{\displaystyle \frac{{\psi }_{3}z}{{\psi }_4+z}}& -{\displaystyle \frac{{\psi }_3{\psi }_{4}y}{{\left({\psi }_4+z\right)}^2}}\\ 0& 0& -{ϵ}_1\end{array}\right].$$

(18)

From the latter, one can determine the corresponding eigenvalues to each equilibrium point, as indicated in the cases below.

Case 1:

Equilibrium $\left(x_0^{*},y_0^{*},z_0^{*}\right).$ The Jacobian matrix has the following form:

$${\left.J\right|}_{\left(x_0^{*},y_0^{*},z_0^{*}\right)}=\left[\begin{array}{ccc}{\kappa }_1& 0& 0\\ 0& -{\psi }_1& 0\\ 0& 0& -{ϵ}_1\end{array}\right].$$

Hence, it is evident that this equilibrium is unstable, as one of the eigenvalues is positive:

$$\begin{array}{ccc}{\lambda }_1={\kappa }_1,& {\lambda }_2=-{\psi }_1,& {\lambda }_3=-{ϵ}_1.\end{array}$$

This outcome is to be expected, as, once cancer cells evade the immune response, either by suppressing the immune system or by remaining undetected by effector cells, they will grow to their maximum carrying capacity or to the extent that the subject can endure. This scenario also indicates that treatment strategies were either unsuccessful or not administered.

Case 2:

Equilibrium $\left(x_1^{*},y_1^{*},z_0^{*}\right).$ The Jacobian matrix evaluated at this equilibrium point is given by

$${\left.J\right|}_{\left(x_1^{*},y_1^{*},z_0^{*}\right)}=\left[\begin{array}{ccc}-{\kappa }_1{\displaystyle \frac{{\psi }_1}{{\kappa }_2{\psi }_2}}& -{\kappa }_3{\displaystyle \frac{{\psi }_1}{{\psi }_2}}& -{\displaystyle \frac{{\kappa }_4}{{\kappa }_5}}{\displaystyle \frac{{\psi }_1}{{\psi }_2}}\\ -{\displaystyle \frac{{\kappa }_1}{{\kappa }_2{\kappa }_3}}\left({\psi }_1-{\kappa }_2{\psi }_2\right)& 0& {\kappa }_1{\psi }_3{\displaystyle \frac{{\psi }_1-{\kappa }_2{\psi }_2}{{\kappa }_2{\kappa }_3{\psi }_2{\psi }_4}}\\ 0& 0& -{ϵ}_1\end{array}\right],$$

with the next eigenvalues:

$$\begin{array}{ccc}\hfill {\lambda }_1& =& -{\displaystyle \frac{1}{2{\kappa }_2{\psi }_2}}\left({\kappa }_1{\psi }_1-\sqrt{{\kappa }_1^2{\psi }_1^2-4{\kappa }_1{\kappa }_2{\psi }_1{\psi }_2\left({\kappa }_2{\psi }_2-{\psi }_1\right)}\right),\hfill \\ \hfill {\lambda }_2& =& -{\displaystyle \frac{1}{2{\kappa }_2{\psi }_2}}\left({\kappa }_1{\psi }_1+\sqrt{{\kappa }_1^2{\psi }_1^2-4{\kappa }_1{\kappa }_2{\psi }_1{\psi }_2\left({\kappa }_2{\psi }_2-{\psi }_1\right)}\right),\hfill \\ \hfill {\lambda }_3& =& -{ϵ}_1.\hfill \end{array}$$

It is evident that the radicands in ${\lambda }_1$ and ${\lambda }_2$ will be positive and real if the persistence condition in (4) from Remark 3 holds, i.e., ${\psi }_2>{\psi }_1/{\kappa }_2$, which allows us to conclude that Equilibrium (16) is locally asymptotically stable. Conversely, if Condition (4) is not satisfied, Equilibrium (16) becomes unstable and biologically infeasible, as $y_1^{*}<0$. In this case, if therapies are unsuccessful, cancer cells will grow to their maximum carrying capacity ${\kappa }_2$, making (17) the only locally asymptotically stable equilibrium point in the system, as is shown in the next case.

Case 3:

Equilibrium $\left(x_2^{*},y_0^{*},z_0^{*}\right).$ The Jacobian matrix is upper triangular, as shown below:

$${\left.J\right|}_{\left(x_2^{*},y_0^{*},z_0^{*}\right)}=\left[\begin{array}{ccc}-{\kappa }_1& -{\kappa }_2{\kappa }_3& -{\displaystyle \frac{{\kappa }_2{\kappa }_4}{{\kappa }_5}}\\ 0& -\left({\psi }_1-{\kappa }_2{\psi }_2\right)& 0\\ 0& 0& -{ϵ}_1\end{array}\right].$$

Therefore, the eigenvalues correspond to the diagonal elements of the matrix:

$$\begin{array}{ccc}{\lambda }_1=-{\kappa }_1,& {\lambda }_2=-\left({\psi }_1-{\kappa }_2{\psi }_2\right),& {\lambda }_3=-{ϵ}_1.\end{array}$$

From these results, two scenarios are identified. First, Equilibrium Point (17) is locally asymptotically stable if

$${\psi }_2<{\displaystyle \frac{{\psi }_1}{{\kappa }_2}}.$$

This indicates that the persistence condition in (4) from Remark 3 is not satisfied. In this case, the long-term persistence of CAR-T cells cannot be expected, as Equilibrium (16) does not exist within the positive and biologically feasible domain of the system. Nonetheless, if the persistence condition in (4) is satisfied, Equilibrium (17) becomes unstable, and the only locally asymptotically stable equilibrium point is (16). This implies that both cell populations described by the CLL mechanistic model in (1)–(3) coexist. However, the in silico experimentation will reveal whether this sustained immune response by the CAR-T cells is sufficient to reduce the tumor burden to a level that is manageable for the subject’s health. Results from these three cases allow us to conclude that, if the chemoimmunotherapy treatment strategy is not successful, leukemia cells will always persist.

Therefore, the proof for Result 3 is now complete. □

3.4. Existence and Uniqueness

A system of first-order nonlinear ODEs may exhibit multiple solutions for a given set of initial conditions. This ambiguity poses significant challenges when modeling specific scenarios in tumor-immune dynamics, as these dynamics are intrinsically linked to particular conditions and parameter values. However, based on the results presented in this section, we establish the following conclusions regarding the existence and uniqueness of solutions for CLL System (1)–(3) proposed in this work.

Result 4.

Ensuring a reliable in silico experimentation for tumor-immune dynamics. Let vector fields $f_i\left(t,\rho \right)$, where $\rho ={\left[x,y,z\right]}^T$, be piecewise continuous in t and locally Lipschitz in ρ for all $t\ge t_0$ and all ρ in $K_{xyz}\subset {\mathbf{R}}_{+,0}^3$. Since every solution of System (1)–(3), with initial conditions $\rho \left(t_0\right)={\rho }_0\in K_{xyz}$, remains entirely within $K_{xyz}$, there exists a unique solution defined for all $t\ge t_0$.

Proof. 

First, note that vector fields $f_i\left(t,\rho \right)$ of System (1)–(3) are given by

$$\begin{array}{ccc}\hfill f_1\left(t,\rho \right)& =& {\kappa }_{1}x\left(1-{\displaystyle \frac{x}{{\kappa }_2}}\right)-{\kappa }_{3}xy-{\displaystyle \frac{{\kappa }_{4}xz}{{\kappa }_5+z}},\hfill \\ \hfill f_2\left(t,\rho \right)& =& \psi -{\psi }_{1}y+{\psi }_{2}xy-{\displaystyle \frac{{\psi }_{3}yz}{{\psi }_4+z}},\hfill \\ \hfill f_3\left(t,\rho \right)& =& ϵ-{ϵ}_{1}z,\hfill \end{array}$$

where all elements are continuous in $\rho ={\left[x,y,z\right]}^T$ by Remark 1, i.e., ${\mathbf{R}}_{+,0}^3=$$\left\{x\left(t\right),y\left(t\right),z\left(t\right)\ge 0\right\}$, and piecewise continuous in t. This is crucial because therapies are considered perturbations, applied as impulse changes in short cycles over limited periods of time. Furthermore, one can see that all components of the Jacobian Matrix $\left[\partial f/\partial \rho \right]\left(t,x\right)$ in (18) are also piecewise continuous in t and bounded within the domain $K_{xyz}\subset {\mathbf{R}}_{+,0}^3$, as established by Results 1 and 2. Consequently, $f_i\left(t,\rho \right)$ is locally Lipschitz in $\rho$ in the domain $\Omega =\left[t,t_1\right]\times K_{xyz}$, where $\left[t_0,t_1\right]\subset \left[t_0,\infty \right)$, as there exists a Lipschitz constant $\ell \ge 0$ such that

$$∥{\displaystyle \frac{\partial f}{\partial \rho }}\left(t,x\right)∥\le \ell$$

on $\Omega$, and $f_i\left(t,\rho \right)$ satisfies the Lipschitz condition:

$$∥f\left(t,{\rho }_1\right)-f\left(t,{\rho }_2\right)∥\le \ell ∥{\rho }_1-{\rho }_2∥.$$

Therefore, the proof for Result 4 is now complete. □

4. Discussion: In Silico Experimentation

In this section, we explore the global dynamics of our mechanistic model (Equations (1)–(3)) as well as its capabilities to investigate different scenarios of tumor-immune dynamics through in silico experimentation. First, Figure 2 illustrates outcomes related to Condition (4) from Result 3 and Eradication Condition (11) from Result 2 through the following three cases:

Case 1:

Depletion of CAR-T cells. The depletion of CAR-T cells in the short-term happens when Condition (4) is not satisfied. As expected, the concentration of CLL cells $\left[x\left(t\right)\right]$ grows to their maximum carrying capacity, while CAR-T cells $\left[y\left(t\right)\right]$ eventually fall below the threshold of clinically significant biological behavior, defined as fewer than one cell. Hence, solutions progress to Equilibrium Point (17), i.e, $\left(x_2^{*},y_0^{*},z_0^{*}\right)=\left({\kappa }_2,0,0\right)$. For the sake of numerical simulations, dynamics are simulated following a single application of the therapy under the next set of initial conditions: $x\left(0\right)=1\times {10}^{10}$ CLL cells, $y\left(0\right)=1\times {10}^7$ CAR-T cells (unique dose), and $z\left(0\right)=0$ mg of chlorambucil. Parameter values are as follows: ${\kappa }_1=1.680$, ${\kappa }_2=3\times {10}^{11}$, ${\kappa }_3=1.256\times {10}^{-7}$, ${\psi }_1=2.236\times {10}^{-2}$, ${\psi }_2=5.217\times {10}^{-14}$. Other parameters are set to zero, as chemotherapy is not considered in this scenario. Results for this in silico experimentation are shown in the top panels of Figure 2.

Case 2:

Persistence of CAR-T cells. The long-term persistence of CAR-T cells $\left[y\left(t\right)\right]$ is observed when Condition (4) is fulfilled. In this case, cell concentrations stabilize at a steady-state defined by Equilibrium Point (16) $\left(x_1^{*},y_1^{*},z_0^{*}\right)=\left({\displaystyle \frac{{\psi }_1}{{\psi }_2}},{\displaystyle \frac{{\kappa }_1\left({\kappa }_2{\psi }_2-{\psi }_1\right)}{{\kappa }_2{\kappa }_3{\psi }_2}},0\right)$. However, this outcome does not represent a viable treatment strategy for real-world clinical scenarios because the tumor burden $\left[x\left(t\right)\right]$ remains near the maximum carrying capacity. For the sake of numerical simulations, dynamics are simulated following a single application of the therapy under the next set of initial conditions: $x\left(0\right)=1\times {10}^{10}$ CLL cells, $y\left(0\right)=1\times {10}^7$ CAR-T cells (unique dose), and $z\left(0\right)=0$ mg of chlorambucil. Parameter values are as follows: ${\kappa }_1=1.680$, ${\kappa }_2=3\times {10}^{11}$, ${\kappa }_3=1.256\times {10}^{-7}$, ${\psi }_1=2.236\times {10}^{-2}$, ${\psi }_2=8.199\times {10}^{-14}$. Other parameters are set to zero, as chemotherapy is not considered in this scenario. Results for this in silico experimentation are shown in the middle panels of Figure 2.

Case 3:

Eradication of CLL cells. Within the scope of System (1)–(3), the eradication of CLL cells is ensured when the concentration of CAR-T cells meets Condition (11). Hence, the immunotherapy control parameter $\psi$ is set with a constant value of $1.1\times {\kappa }_1{\psi }_1/{\kappa }_3$, which equals 328,980 cells. Dynamics are simulated following a constant application of the therapy under the next set of initial conditions: $x\left(0\right)=1\times {10}^{10}$ CLL cells, $y\left(0\right)=$ 328,980 CAR-T cells (constant dose), and $z\left(0\right)=0$ mg of chlorambucil. Parameter values are as follows: ${\kappa }_1=1.680$, ${\kappa }_2=3\times {10}^{11}$, ${\kappa }_3=1.256\times {10}^{-7}$, ${\psi }_1=2.236\times {10}^{-2}$, ${\psi }_2=8.199\times {10}^{-14}$. Other parameters are set to zero, as chemotherapy is not considered in this scenario. Results for this in silico experimentation are shown in the bottom panels of Figure 2.

All chemoimmunotherapy protocols explored in this work are designed under the next assumptions: CAR-T cells will persist in the long-term after infusion into the subject, as was described in Case 2 in Figure 2, i.e, ${\psi }_2=8.199\times {10}^{-14}$${\left(\mathrm{cells}\times \mathrm{days}\right)}^{-1}$; CLL cells display the highest growth rate, i.e, ${\kappa }_1=1.680{\mathrm{days}}^{-1}$ and are resistant to the chemotherapy drug, i.e., ${\kappa }_5=92.785$ mg.

Hence, let us explore the dose-escalation scheme often employed in phase I clinical trials for cancer patients treated with T-cell therapies [38,39,40,41,42]. In such protocols, dose levels are typically defined as an absolute number of cells, ranging from $2\times {10}^7$ to $8\times {10}^7$ cells, or as dose normalized to the patient’s body weight, which may range from $5\times {10}^4$ cells/kg up to a maximum dose of $1\times {10}^6$ cells/kg. It is important to note that dosing regimens can vary between studies, with the optimal dose typically determined through careful escalation in clinical trials to balance efficacy and safety. However, this falls outside the scope of the present research, as the focus is on in silico experimentation rather than in vivo studies. Hence, after extensive iterations of numerical simulations, the scheme in

Table 3. Dose-escalation protocol for the combined application of CAR-T cells and chlorambucil.

First dose:	4,486,086 CAR-T cells at day 0, 32.40 mg of chlorambucil at day 5.
Second dose:	8,972,172 CAR-T cells at day 10, 48.60 mg of chlorambucil at day 15.
Third dose:	13,458,258 CAR-T cells at day 20, 64.80 mg of chlorambucil at day 25.

was designed using an algorithm developed in MATLAB.

This protocol aims to eliminate CLL cell concentrations under varying initial tumor burden scenarios, defined as $x\left(0\right)={10}^i$ leukemia cells, for $i=1,2,$...$,11$. For the purposes of the in silico experimentation, the maximum initial tumor burden considered represents approximately $33\%$ of the system’s estimated carrying capacity. Results are illustrated in Figure 3; the top panel corresponds to the CLL cells $\left[x\left(t\right)\right]$, demonstrating the combined effects of the CAR-T cells $\left[y\left(t\right)\right]$ and chlorambucil $\left[z\left(t\right)\right]$ doses over time. It is evident that, under the same protocol scheme, a lower initial concentration of leukemia cells is eradicated in a shorter period of time. Panels in the middle row depict the behavior of CAR-T cells and the chlorambucil drug in the bloodstream compartment, whereas panels in the bottom row illustrate the dose-escalation protocol. It should be noted that each dose of CAR-T cells satisfies conditions outlined in Result 2, particularly Condition (11), as the therapies are administered sequentially without overlapping. Chlorambucil doses were calculated based on the median weight of an adult CLL patient, estimated to be 81 kg [43,44], and drug cytotoxicity on both CLL and CAR-T cells changes for each dose concentration as indicated in

Table 4. Chlorambucil cytotoxicity on CLL and CAR-T cells for the selected doses of the drug.

Chlorambucil Dosing	$\mathit{ϵ}$ $\left(\mathbf{mg}\right)$	${\mathit{\kappa }}_4$ $\left({\mathbf{days}}^{-1}\right)$	${\mathit{\psi }}_3$ $\left({\mathbf{days}}^{-1}\right)$
$0.4\mathrm{mg}\mathrm{per}1\mathrm{kg}$	$32.40$	$69.459\times {10}^{-3}$	$3.751\times {10}^{-3}$
$0.6\mathrm{mg}\mathrm{per}1\mathrm{kg}$	$48.60$	$98.211\times {10}^{-3}$	$5.303\times {10}^{-3}$
$0.8\mathrm{mg}\mathrm{per}1\mathrm{kg}$	$64.80$	$124.062\times {10}^{-3}$	$6.699\times {10}^{-3}$

.

Now, following nonlinear dynamical systems theory, we can expect that an initial higher dose of CAR-T cells will have a more significant and long-term effect on the overall behavior of the system. Using our algorithm, a dose-de-escalation protocol was designed with the characteristics shown in

Table 5. Dose-de-escalation protocol for the combined application of CAR-T cells and chlorambucil.

First dose:	10,048,833 CAR-T cells at day 0, 64.80 mg of chlorambucil at day 5.
Second dose:	6,699,222 CAR-T cells at day 10, 48.60 mg of chlorambucil at day 15.
Third dose:	3,349,611 CAR-T cells at day 20, 32.40 mg of chlorambucil at day 25.

.

Results are illustrated in Figure 4, where the arrangement of solutions, i.e., cell and drug concentration dynamics as well as protocols, is consistent with that in Figure 3. Nonetheless, there are some key differences that should be discussed. First, the dose-escalation protocol required a total of 26,916,516 CAR-T cells to completely eradicate the highest concentration of CLL cells, i.e., $x\left(0\right)={10}^{11}$ cells. In contrast, the dose-de-escalation scheme achieved leukemia eradication with a total of 20,097,666 CAR-T cells. Numerical simulations indicate that, in the dose-escalation protocol, CLL cell growth still occurs after the first dose of immunotherapy, as the initial lower concentration of CAR-T cells is insufficient to mount a successful immune response against cancer, even at lower tumor burdens. However, the dose-de-escalation protocol demonstrates a consistent reduction in CLL cell concentrations starting from the first dose. Furthermore, the highest dose in the protocol falls within the previously stated range, with ~${10}^7$ CAR-T cells administered in the first dose. However, assessing the maximum tolerated dose lies beyond the scope of this in silico experimentation. Additionally, it should be noted that the effects of the chemotherapy drug on CLL cell concentrations are not perceptible in Figure 3 and Figure 4 because numerical simulations are performed under two key assumptions. First, the leukemia cells are assumed to exhibit drug resistance, and, second, Condition (13) is not satisfied, as the growth rate of leukemia cells is deliberately set to its highest value.

While protocol failure is undesirable in cancer treatment, a significant proportion of phase I clinical trials encounter such outcomes [42,45,46]. Therefore, we illustrate in Figure 5 and Figure 6 tumor escape in both dose-escalation and dose-de-escalation schemes, respectively, emphasizing that any protocol may fail if doses are not accurately calculated and cancer cell concentrations are not consistently monitored. Parameter values are the same as the successful protocols, i.e., ${\kappa }_1=1.680$, ${\kappa }_2=3\times {10}^{11}$, ${\kappa }_3=1.256\times {10}^{-7}$, ${\kappa }_5=92.785$, ${\psi }_1=2.236\times {10}^{-2}$, ${\psi }_2=8.199\times {10}^{-14}$, ${\psi }_4=231.962$, ${ϵ}_1=11.090$. It is important to note that the CAR-T doses were determined heuristically, aiming for a solution $x\left(t\right)$ that approaches the threshold of one cancer cell, as established in Remark 2 for clinically significant biological behavior.

Our model captures key interactions between CLL cells, CAR-T cells, and chlorambucil, providing a mechanistic framework to explore short-term chemoimmunotherapy effects and inform dose-finding strategies. We describe leukemia growth with a sigmoidal logistic law, commonly used to approximate tumor expansion in solid cancers [47]. Hence, it is important to recognize the need for long-term clinical data to further refine this assumption. Additionally, while our model considers CLL cells in the bloodstream, a large fraction of malignant cells may reside in bone marrow and metastatic tissues, which could introduce artifacts or simplifications in the system dynamics. Nonetheless, we believe this work represents a significant step toward a more comprehensive mathematical characterization of CLL under combined chemoimmunotherapy, offering a valuable foundation for future model refinements and clinical applications.

In silico experiments presented in this section were performed on a computer with the following specifications: Ryzen 9 7950X CPU, 192 GB DDR5 CL40 RAM, and a 1 TB Samsung 990 Pro Gen 4 NVMe M.2 drive. The algorithm used for these experiments, designed in MATLAB R2024b Update 3 from The MathWorks Inc., is available as Supplementary Material in the GitHub repository https://github.com/DrPaulValle/CLL-system-Hemato-, created by the first author on 9 January 2025, and certified by México’s National Copyright Institute (INDAUTOR) under the registration number 03-2025-011313211200-01.

5. Conclusions

In this work, we proposed a mechanistic nonlinear model to study the dynamics of CLL under combined chemoimmunotherapy. The model consists of a system of three first-order ODEs that describe the effects of CAR-T cells and chlorambucil on the leukemia cell population over both short- and long-term timescales. Using a model-based design approach and extensive in silico experimentation, we determined sufficient dosing strategies for each therapeutic application. The local and global dynamics of Model (1)–(3) were thoroughly analyzed using various nonlinear systems theories. Consequently, we derived conditions to ensure the eradication of cancer cells, with numerical simulations illustrating that the population falls below the threshold for clinically significant biological behavior, defined as one cell.

Furthermore, our algorithm enables the exploration of different scenarios. As illustrated, we compared two chemoimmunotherapy application protocols: dose escalation and dose de-escalation. While dose-escalation protocols are commonly used in Phase I trials, numerical simulations suggest that the dose-de-escalation protocol may require a lower total dose to achieve effective tumor-immune dynamics, as a higher initial dose tends to have a greater impact. However, in silico experiments also show that, even with complete remission (peripheral blood lymphocyte count $<4\times {10}^9$ cells/L) or partial remission (a decrease in the number of blood lymphocytes to $50\%$ or less from the value before therapy) [48], relapse can occur if the dosing is insufficient or if therapy is administered without ensuring the eradication of minimal residual disease.

Nonetheless, for any model-based design approach to be successful in dose-finding for anti-cancer therapies, one open problem remains: all parameters of a mathematical model, such as the one proposed in this work, must be carefully estimated for each patient. Therefore, clinical data on cancer cell growth rates, CAR-T cell activation rates, and the cytotoxicity rates of therapies are crucial values for protocol dosing design from the perspective of nonlinear dynamical systems and in silico experimentation. Thus, our algorithm could be useful for estimating key parameters through nonlinear regression functions in the MATLAB environment when tumor-immune dynamics data, in the form of cell counts over time, are available for a given patient.

The mathematical modelling of cancer dynamics in Quantitative Systems Pharmacology is emerging as a powerful tool for gaining insights into switching dosing schedules. Recently, QSP has demonstrated its effectiveness in providing essential mechanistic rationale to support recommended dosing regimens. The division of Pharmacometrics & Systems Pharmacology from Pfizer applied a system of more than 30 ODEs to describe the dynamics of biomarkers, to support a specific dosing regime, and to indicate that a change of dosing interval from QW to Q2W is not likely to result in decreased efficacy. All these statements were reviewed by the Department of Health and Human Services from the FDA [49]. However, as highlighted by Shtylla [50], despite using both preclinical and clinical data for model calibration and validation, many model parameters remain non-identifiable. We expect that this work will serve as a foundation for researchers seeking to bridge mathematical modeling and real-world cancer treatment strategies. By linking mechanistic insights with in silico experiments, our approach aims to support the design of more effective and personalized chemoimmunotherapy dosing schedules.