Mechanistic mathematical modelling of mercaptopurine effects on cell cycle of human acute lymphoblastic leukaemia cells

Mercaptopurine-Loaded Sandwiched Tri-Layered Composed of Electrospun Polycaprolactone/Poly(Methyl Methacrylate) Nanofibrous Scaffolds as Anticancer Carrier with Antimicrobial and Antibiotic Features: Sandwich Configuration Nanofibers, Release Study and in vitro Bioevaluation Tests

BACKGROUND

6-Mercaptopurine (6-MP) is a potential anti-cancer agent which its therapeutic and limitation applicability due to its high toxicity.

OBJECTIVE

Herein, 6-MP was loaded into tri-layered sandwich nanofibrous scaffold (the top layer composed of poly methyl methacrylate/polycaprolactone (PMMA/PCL), the middle layer was PCL/PMMA/6-MP, and the bottom layer was PCL/PMMA to improve its bioactivity, adjusting the release-sustainability and reduce its toxicity.

METHODS

Electrospun tri-layered nanofibers composed of PCL/PMMA were utilized as nano-mats for controlling sustained drug release. Four groups of sandwich scaffold configurations were investigated with alteration of (PMMA: PCL) composition.

RESULTS

The sandwich scaffold composed of 2%PCL/4%PMMA/1%6-MP showed the best miscibility, good homogeneity and produced the smoothest nanofibers and low crystallinity. All fabricated 6-MP-loaded-PCL/PMMA scaffolds exhibited antimicrobial properties on the bacterial and fungal organisms, where the cytotoxicity evaluation proved the safety of scaffolds on normal cells, even at high concentration. Scaffolds provided a sustained-drug release profile that was strongly dependent on (PCL: PMMA). As (PCL: PMMA) decreased, the sustained 6-MP release from PCL/PMMA scaffolds increased. Results established that ~18% and 20% of 6-MP were released after 23h from (4%PCL/4%PMMA/1%6-MP) and (2%PCL/4%PMMA/1%6-MP), respectively, where this release was maintained for more than 20 days. The anti-cancer activity of all fabricated scaffolds was also investigated using different cancerous cell lines (e.g., Caco-2, MDA, and HepG-2) results showed that 6-MP-loaded-nanofibrous mats have an anti-cancer effect, with a high selective index for breast cancer. We observed that viability of a cancer cell was dropped to about 10%, using nanofibers containing 2%PCL/4%PMMA/1%6-MP.

CONCLUSION

Overall, the PCL: PMMA ratio and sandwich configuration imparts a tight control on long-term release profile and initial burst of 6-MP for anticancer treatment purposes.

Hybrid modelling of biological systems using fuzzy continuous Petri nets

Integrated modelling of biological systems is challenged by composing components with sufficient kinetic data and components with insufficient kinetic data or components built only using experts’ experience and knowledge. Fuzzy continuous Petri nets (FCPNs) combine continuous Petri nets with fuzzy inference systems, and thus offer an hybrid uncertain/certain approach to integrated modelling of such biological systems with uncertainties. In this paper, we give a formal definition and a corresponding simulation algorithm of FCPNs, and briefly introduce the FCPN tool that we have developed for implementing FCPNs. We then present a methodology and workflow utilizing FCPNs to achieve hybrid (uncertain/certain) modelling of biological systems illustrated with a case study of the Mercaptopurine metabolic pathway. We hope this research will promote the wider application of FCPNs and address the uncertain/certain integrated modelling challenge in the systems biology area.

ARID5B Influences Antimetabolite Drug Sensitivity and Prognosis of Acute Lymphoblastic Leukemia

PURPOSE

Treatment outcomes for childhood acute lymphoblastic leukemia (ALL) have improved steadily, but a significant proportion of patients still experience relapse due to drug resistance, which is partly explained by inherited and/or somatic genetic alternations. Recently, we and others have identified genetic variants in the ARID5B gene associated with susceptibility to ALL and also with relapse. In this study, we sought to characterize the molecular pathway by which ARID5B affects antileukemic drug response in patients with ALL.

EXPERIMENTAL DESIGN

We analyzed association of ARID5B expression in primary human ALL blasts with molecular subtypes and treatment outcome. Subsequent mechanistic studies were performed in ALL cell lines by manipulating ARID5B expression isogenically, in which we evaluated drug sensitivity, metabolism, and molecular signaling events.

RESULTS

ARID5B expression varied substantially by ALL subtype, with the highest level being observed in hyperdiploid ALL. Lower ARID5B expression at diagnosis was associated with the risk of ALL relapse, and further reduction was noted at ALL relapse. In isogenic ALL cell models in vitro, ARID5B knockdown led to resistance specific to antimetabolite drugs (i.e., 6-mercaptopurine and methotrexate), without significantly affecting sensitivity to other antileukemic agents. ARID5B downregulation significantly inhibited ALL cell proliferation and caused partial cell-cycle arrest. At the molecular level, the cell-cycle checkpoint regulator p21 (encoded by CDKN1A) was most consistently modulated by ARID5B, plausibly as its direct transcription regulation target.

CONCLUSIONS

Our data indicate that ARID5B is an important molecular determinant of antimetabolite drug sensitivity in ALL, in part, through p21-mediated effects on cell-cycle progression.

Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism

We consider two approaches to modelling the cell metabolism of 6-mercaptopurine, one of the important chemotherapy drugs used for treating acute lymphocytic leukaemia: kinetic ordinary differential equations, and Boolean networks supplied with one controlling node, which takes continual values. We analyse their interplay with respect to taking into account ATP concentration as a key parameter of switching between different pathways. It is shown that the Boolean networks, which allow avoiding the complexity of general kinetic modelling, preserve the possibility of reproducing the principal switching mechanism.

Pharmacodynamic Modeling of Cell Cycle Effects for Gemcitabine and Trabectedin Combinations in Pancreatic Cancer Cells

Combinations of gemcitabine and trabectedin exert modest synergistic cytotoxic effects on two pancreatic cancer cell lines. Here, systems pharmacodynamic (PD) models that integrate cellular response data and extend a prototype model framework were developed to characterize dynamic changes in cell cycle phases of cancer cell subpopulations in response to gemcitabine and trabectedin as single agents and in combination. Extensive experimental data were obtained for two pancreatic cancer cell lines (MiaPaCa-2 and BxPC-3), including cell proliferation rates over 0-120 h of drug exposure, and the fraction of cells in different cell cycle phases or apoptosis. Cell cycle analysis demonstrated that gemcitabine induced cell cycle arrest in S phase, and trabectedin induced transient cell cycle arrest in S phase that progressed to G₂/M phase. Over time, cells in the control group accumulated in G₀/G₁ phase. Systems cell cycle models were developed based on observed mechanisms and were used to characterize both cell proliferation and cell numbers in the sub G₁, G₀/G₁, S, and G₂/M phases in the control and drug-treated groups. The proposed mathematical models captured well both single and joint effects of gemcitabine and trabectedin. Interaction parameters were applied to quantify unexplainable drug-drug interaction effects on cell cycle arrest in S phase and in inducing apoptosis. The developed models were able to identify and quantify the different underlying interactions between gemcitabine and trabectedin, and captured well our large datasets in the dimensions of time, drug concentrations, and cellular subpopulations.

Genetics and nonmelanoma skin cancer in kidney transplant recipients

Kidney transplant recipients (KTRs) have a 65- to 250-fold greater risk than the general population of developing nonmelanoma skin cancer. Immunosuppressive drugs combined with traditional risk factors such as UV radiation exposure are the main modifiable risk factors for skin cancer development in transplant recipients. Genetic variation affecting immunosuppressive drug pharmacokinetics and pharmacodynamics has been associated with other transplant complications and may contribute to differences in skin cancer rates between KTRs. Genetic polymorphisms in genes encoding the prednisolone receptor, GST enzyme, MC1R, MTHFR enzyme and COX-2 enzyme have been shown to increase the risk of nonmelanoma skin cancer in KTRs. Genetic association studies may improve our understanding of how genetic variation affects skin cancer risk and potentially guide immunosuppressive treatment and skin cancer screening in at risk individuals.

Molecular insight into thiopurine resistance: transcriptomic signature in lymphoblastoid cell lines

Background

There has been considerable progress in the management of acute lymphoblastic leukemia (ALL) but further improvement is needed to increase long-term survival. The thiopurine agent 6-mercaptopurine (6-MP) used for ALL maintenance therapy has a key influence on clinical outcomes and relapse prevention. Genetic inheritance in thiopurine metabolism plays a major role in interindividual clinical response variability to thiopurines; however, most cases of thiopurine resistance remain unexplained.

Methods

We used lymphoblastoid cell lines (LCLs) from healthy donors, selected for their extreme thiopurine susceptibility. Thiopurine metabolism was characterized by the determination of TPMT and HPRT activity. We performed genome-wide expression profiling in resistant and sensitive cell lines with the goal of elucidating the mechanisms of thiopurine resistance.

Results

We determined a higher TPMT activity (+44%; P = 0.024) in resistant compared to sensitive cell lines, although there was no difference in HPRT activity. We identified a 32-gene transcriptomic signature that predicts thiopurine resistance. This signature includes the GTPBP4 gene coding for a GTP-binding protein that interacts with p53. A comprehensive pathway analysis of the genes differentially expressed between resistant and sensitive cell lines indicated a role for cell cycle and DNA mismatch repair system in thiopurine resistance. It also revealed overexpression of the ATM/p53/p21 pathway, which is activated in response to DNA damage and induces cell cycle arrest in thiopurine resistant LCLs. Furthermore, overexpression of the p53 target gene TNFRSF10D or the negative cell cycle regulator CCNG2 induces cell cycle arrest and may also contribute to thiopurine resistance. ARHGDIA under-expression in resistant cell lines may constitute a novel molecular mechanism contributing to thiopurine resistance based on Rac1 inhibition induced apoptosis and in relation with thiopurine pharmacodynamics.

Conclusion

Our study provides new insights into the molecular mechanisms underlying thiopurine resistance and suggests a potential research focus for developing tailored medicine.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0150-6) contains supplementary material, which is available to authorized users.

Assessment of mercaptopurine (6MP) metabolites and 6MP metabolic key-enzymes in childhood acute lymphoblastic leukemia

Pediatric acute lymphoblastic leukemia (ALL) is treated with combination chemotherapy including mercaptopurine (6MP) as an important component. Upon its uptake, 6MP undergoes a complex metabolism involving many enzymes and active products. The prognostic value of all the factors engaged in this pathway still remains unclear. This study attempted to determine which components of 6MP metabolism in leukemic blasts and red blood cells are important for 6MP's sensitivity and toxicity. In addition, changes in the enzymatic activities and metabolite levels during the treatment were analyzed. In a cohort (N=236) of pediatric ALL patients enrolled in the Dutch ALL-9 protocol, we studied the enzymes inosine-5'-monophosphate dehydrogenase (IMPDH), thiopurine S-methyltransferase (TPMT), hypoxanthine guanine phosphoribosyl transferase (HGPRT), and purine nucleoside phosphorylase (PNP) as well as thioguanine nucleotides (TGN) and methylthioinosine nucleotides (meTINs). Activities of selected enzymes and levels of 6MP derivatives were measured at various time points during the course of therapy. The data obtained and the toxicity related parameters available for these patients were correlated with each other. We found several interesting relations, including high concentrations of two active forms of 6MP--TGN and meTIN--showing a trend toward association with better in vitro antileukemic effect of 6MP. High concentrations of TGN and elevated activity of HGPRT were found to be significantly associated with grade III/IV leucopenia. However, a lot of data of enzymatic activities and metabolite concentrations as well as clinical toxicity were missing, thereby limiting the number of assessed relations. Therefore, although a complex study of 6MP metabolism in ALL patients is feasible, it warrants more robust and strict data collection in order to be able to draw more reliable conclusions.

Modeling intrinsic heterogeneity and growth of cancer cells

Intratumoral heterogeneity has been found to be a major cause of drug resistance. Cell-to-cell variation increases as a result of cancer-related alterations, which are acquired by stochastic events and further induced by environmental signals. However, most cellular mechanisms include natural fluctuations that are closely regulated, and thus lead to asynchronization of the cells, which causes intrinsic heterogeneity in a given population. Here, we derive two novel mathematical models, a stochastic agent-based model and an integro-differential equation model, each of which describes the growth of cancer cells as a dynamic transition between proliferative and quiescent states. These models are designed to predict variations in growth as a function of the intrinsic heterogeneity emerging from the durations of the cell-cycle and apoptosis, and also include cellular density dependencies. By examining the role all parameters play in the evolution of intrinsic tumor heterogeneity, and the sensitivity of the population growth to parameter values, we show that the cell-cycle length has the most significant effect on the growth dynamics. In addition, we demonstrate that the agent-based model can be approximated well by the more computationally efficient integro-differential equations when the number of cells is large. This essential step in cancer growth modeling will allow us to revisit the mechanisms of multidrug resistance by examining spatiotemporal differences of cell growth while administering a drug among the different sub-populations in a single tumor, as well as the evolution of those mechanisms as a function of the resistance level.

A Review of Mathematical Models for Leukemia and Lymphoma

Recently, there has been significant activity in the mathematical community, aimed at developing quantitative tools for studying leukemia and lymphoma. Mathematical models have been applied to evaluate existing therapies and to suggest novel therapies. This article reviews the recent contributions of mathematical modeling to leukemia and lymphoma research. These developments suggest that mathematical modeling has great potential in this field. Collaboration between mathematicians, clinicians, and experimentalists can significantly improve leukemia and lymphoma therapy.

Lifespan based pharmacokinetic-pharmacodynamic model of tumor growth inhibition by anticancer therapeutics

Accurate prediction of tumor growth is critical in modeling the effects of anti-tumor agents. Popular models of tumor growth inhibition (TGI) generally offer empirical description of tumor growth. We propose a lifespan-based tumor growth inhibition (LS TGI) model that describes tumor growth in a xenograft mouse model, on the basis of cellular lifespan T. At the end of the lifespan, cells divide, and to account for tumor burden on growth, we introduce a cell division efficiency function that is negatively affected by tumor size. The LS TGI model capability to describe dynamic growth characteristics is similar to many empirical TGI models. Our model describes anti-cancer drug effect as a dose-dependent shift of proliferating tumor cells into a non-proliferating population that die after an altered lifespan T_A. Sensitivity analysis indicated that all model parameters are identifiable. The model was validated through case studies of xenograft mouse tumor growth. Data from paclitaxel mediated tumor inhibition was well described by the LS TGI model, and model parameters were estimated with high precision. A study involving a protein casein kinase 2 inhibitor, AZ968, contained tumor growth data that only exhibited linear growth kinetics. The LS TGI model accurately described the linear growth data and estimated the potency of AZ968 that was very similar to the estimate from an established TGI model. In the case study of AZD1208, a pan-Pim inhibitor, the doubling time was not estimable from the control data. By fixing the parameter to the reported in vitro value of the tumor cell doubling time, the model was still able to fit the data well and estimated the remaining parameters with high precision. We have developed a mechanistic model that describes tumor growth based on cell division and has the flexibility to describe tumor data with diverse growth kinetics.

Exposure time independent summary statistics for assessment of drug dependent cell line growth inhibition

Background

In vitro generated dose-response curves of human cancer cell lines are widely used to develop new therapeutics. The curves are summarised by simplified statistics that ignore the conventionally used dose-response curves’ dependency on drug exposure time and growth kinetics. This may lead to suboptimal exploitation of data and biased conclusions on the potential of the drug in question. Therefore we set out to improve the dose-response assessments by eliminating the impact of time dependency.

Results

First, a mathematical model for drug induced cell growth inhibition was formulated and used to derive novel dose-response curves and improved summary statistics that are independent of time under the proposed model. Next, a statistical analysis workflow for estimating the improved statistics was suggested consisting of 1) nonlinear regression models for estimation of cell counts and doubling times, 2) isotonic regression for modelling the suggested dose-response curves, and 3) resampling based method for assessing variation of the novel summary statistics. We document that conventionally used summary statistics for dose-response experiments depend on time so that fast growing cell lines compared to slowly growing ones are considered overly sensitive. The adequacy of the mathematical model is tested for doxorubicin and found to fit real data to an acceptable degree. Dose-response data from the NCI60 drug screen were used to illustrate the time dependency and demonstrate an adjustment correcting for it. The applicability of the workflow was illustrated by simulation and application on a doxorubicin growth inhibition screen. The simulations show that under the proposed mathematical model the suggested statistical workflow results in unbiased estimates of the time independent summary statistics. Variance estimates of the novel summary statistics are used to conclude that the doxorubicin screen covers a significant diverse range of responses ensuring it is useful for biological interpretations.

Conclusion

Time independent summary statistics may aid the understanding of drugs’ action mechanism on tumour cells and potentially renew previous drug sensitivity evaluation studies.

Dynamic rendering of the heterogeneous cell response to anticancer treatments

The antiproliferative response to anticancer treatment is the result of concurrent responses in all cell cycle phases, extending over several cell generations, whose complexity is not captured by current methods. In the proposed experimental/computational approach, the contemporary use of time-lapse live cell microscopy and flow cytometric data supported the computer rendering of the proliferative process through the cell cycle and subsequent generations during/after treatment. The effects of treatments were modelled with modules describing the functional activity of the main pathways causing arrest, repair and cell death in each phase. A framework modelling environment was created, enabling us to apply different types of modules in each phase and test models at the complexity level justified by the available data. We challenged the method with time-course measures taken in parallel with flow cytometry and time-lapse live cell microscopy in X-ray-treated human ovarian cancer cells, spanning a wide range of doses. The most suitable model of the treatment, including the dose-response of each effect, was progressively built, combining modules with a rational strategy and fitting simultaneously all data of different doses and platforms. The final model gave for the first time the complete rendering in silico of the cycling process following X-ray exposure, providing separate and quantitative measures of the dose-dependence of G₁, S and G₂M checkpoint activities in subsequent generations, reconciling known effects of ionizing radiations and new insights in a unique scenario.

The antiproliferative response to anticancer treatment is the result of concurrent effects in all cell cycle phases, where molecular control pathways (checkpoints) are activated and cells may be arrested to repair DNA damage or killed if not able to succeed in the repair process. The complexity and inter-cell variability of these phenomena are not captured by the available methods, and the origin of the dose-dependence of the response remains elusive. In this work, we present an experimental-computational method that discloses and measures the individual responses of cell cycle controls in each phase and generation. We demonstrate that the method, exploiting jointly data sets obtained by flow cytometry and time-lapse in vivo imaging with a suitable experimental design, is able to achieve a full reconstruction in silico of the actual movement of cell cohorts following X-ray exposure, providing separate and quantitative measures of the dose-dependence of G₁, S and G₂M checkpoint activities in subsequent generations. Best fit parameters values are actual measures of the probability of activation of the specific pathways of arrest, repair or death within the cell population, linking the molecular scale to the “macroscopic” response, with full appreciation of its dynamics and inter-cell heterogeneity.

Pharmacodynamic modeling of cell cycle and apoptotic effects of gemcitabine on pancreatic adenocarcinoma cells

PURPOSE

The standard of care for treating patients with pancreatic adenocarcinomas includes gemcitabine (2',2'-difluorodeoxycytidine). Gemcitabine primarily elicits its response by stalling the DNA replication forks of cells in the S phase of the cell cycle. To provide a quantitative framework for characterizing the cell cycle and apoptotic effects of gemcitabine, we developed a pharmacodynamic model in which the activation of cell cycle checkpoints or cell death is dependent on gemcitabine exposure.

METHODS

Three pancreatic adenocarcinoma cell lines (AsPC-1, BxPC-3, and MiaPaca-2) were exposed to varying concentrations (0-100,000 ng/mL) of gemcitabine over a period of 96 h in order to quantify proliferation kinetics and cell distributions among the cell cycle phases. The model assumes that the drug can inhibit cycle-phase transitioning in each of the 3 phases (G1, S, and G2/M) and can cause apoptosis of cells in G1 and G2/M phases. Fitting was performed using the ADAPT5 program.

RESULTS

The time course of gemcitabine effects was well described by the model, and parameters were estimated with good precision. Model predictions and experimental data show that gemcitabine induces cell cycle arrest in the S phase at low concentrations, whereas higher concentrations induce arrest in all cell cycle phases. Furthermore, apoptotic effects of gemcitabine appear to be minimal and take place at later time points.

CONCLUSION

The pharmacodynamic model developed provides a quantitative, mechanistic interpretation of gemcitabine efficacy in 3 pancreatic cancer cell lines, and provides useful insights for rational selection of chemotherapeutic agents for combination therapy.

Is it time for a new paradigm for systemic cancer treatment? Lessons from a century of cancer chemotherapy

U.S. SEER (Surveillance Epidemiology and End Results) data for age-adjusted mortality rates for all cancers combined for all races show only a modest overall 13% decline over the past 35 years. Moreover, the greatest contributor to cancer mortality is treatment-resistant metastatic disease. The accepted therapeutic paradigm for the past half-century for the treatment of advanced cancers has involved the use of systemic chemotherapy drugs cytotoxic for cycling cells (both normal and malignant) during DNA synthesis and/or mitosis. The failure of this therapeutic modality to achieve high-level, consistent rates of disease-free survival for some of the most common cancers, including tumors of the lung, colon breast, brain, melanoma, and others is the focus of this paper. A retrospective assessment of critical milestones in cancer chemotherapy indicates that most successful therapeutic regimens use cytotoxic cell cycle inhibitors in combined, maximum tolerated, dose-dense acute treatment regimens originally developed to treat acute lymphoblastic leukemia and some lymphomas. Early clinical successes in this area led to their wholesale application to the treatment of solid tumor malignancies that, unfortunately, has not produced consistent, long-term high cure rates for many common cancers. Important differences in therapeutic sensitivity of leukemias/lymphomas versus solid tumors can be explained by key biological differences that define the treatment-resistant solid tumor phenotype. A review of these clinical outcome data in the context of recent developments in our understanding of drug resistance mechanisms characteristic of solid tumors suggests the need for a new paradigm for the treatment of chemotherapy-resistant cancers. In contrast to reductionist approaches, the systemic approach targets both microenvironmental and systemic factors that drive and sustain tumor progression. These systemic factors include dysregulated inflammatory and oxidation pathways shown to be directly implicated in the development and maintenance of the cancer phenotype. The paradigm stresses the importance of a combined preventive/therapeutic approach involving adjuvant chemotherapies that incorporate anti-inflammatory and anti-oxidant therapeutics.

Post-translational stabilization of thiopurine S-methyltransferase by S-adenosyl-L-methionine reveals regulation of TPMT*1 and *3C allozymes

Thiopurine S-methyltransferase (TPMT; EC 2.1.1.67) plays a pivotal role in thiopurine treatment outcomes. However, little has been known about its intracellular regulation. Here, we describe the effect of fluctuations in physiological levels of S-adenosyl-L-methionine (SAM) and related metabolites on TPMT activity levels in cell lines and erythrocytes from healthy donors. We determined higher TPMT activity in wild-type TPMT*1/*1 individuals with high SAM concentrations (n=96) compared to the low SAM level group (n=19; P<0.001). These findings confirm the results of our in vitro studies, which demonstrated that the restriction of L-methionine (Met) in cell growth media reversibly decreased TPMT activity and protein levels. Selective inhibition of distinct components of Met metabolism was used to demonstrate that SAM is implicitly responsible for direct post-translational TPMT stabilization. The greatest effect of SAM-mediated TPMT stabilization was observed in the case of wild-type TPMT*1 and variant *3C allozymes. In addition to TPMT genotyping, SAM may serve as an important biochemical marker in individualization of thiopurine therapy.

Polymeric nanohybrids and functionalized carbon nanotubes as drug delivery carriers for cancer therapy

The scope of nanotechnology to develop target specific carriers to achieve higher therapeutic efficacy is gaining importance in the pharmaceutical and other industries. Specifically, the emergence of nanohybrid materials is posed to edge over chemotherapy and radiation therapy as cancer therapeutics. This is primarily because nanohybrid materials engage controlled production parameters in the making of engineered particles with specific size, shape, and other essential properties. It is widely expressed that these materials will significantly contribute to the next generation of medical care technology and pharmaceuticals in areas of disease diagnosis, disease prevention and many other treatment procedures. This review focuses on the currently used nanohybrid materials, polymeric nanoparticles and nanotubes, which show great potential as effective drug delivery systems for cancer therapy, as they can be grafted with cell-specific receptors and intracellular targeting molecules for the targeted delivery of therapeutics. Specifically, this article focuses on the current status, recent advancements, potentials and limitations of polymeric nanohybrids and functionalized carbon nanotubes as drug delivery carriers.

Azathioprine-related myelosuppression in a patient homozygous for TPMT*3A

BACKGROUND

A 50-year-old man who had received a simultaneous pancreas and kidney transplant 9 years earlier developed pancytopenia 3 weeks after starting azathioprine therapy to treat worsening proteinuria suspected to be caused by sirolimus.

INVESTIGATIONS

Laboratory tests, including complete blood counts, measurement of serum levels of vitamin B(12) and folate, liver function tests, virological assays, and thiopurine S-methyltransferase (TPMT) genotyping.

DIAGNOSIS

Severe myelosuppression as a consequence of azathioprine therapy in a patient homozygous for the TPMT*3A allele.

MANAGEMENT

Discontinuation of azathioprine, treatment with an erythropoiesis-stimulating agent, red blood cell transfusions, filgrastim (a granulocyte colony-stimulating factor analogue) and folic acid.

Optimizing cancer pharmacotherapeutics using mathematical modeling and a systems biology approach

Research in mathematics and in mathematical biology on cancer and its treatments has been soaring in the past 10 years at an unprecedented speed. Such thriving is likely due as much to new findings in fundamental biology as to an emerging general interest from mathematicians and engineers towards applications in biology and medicine and to their subsequently designed representations and predictions of tumor processes that are now allowed by modern means of computation and simulation. This article, which does not claim the status of an extended review paper on mathematical models of cancer and its treatment, is focused on modeling in a systems biology perspective. I will list here the most necessary mathematical methods, in my opinion, which, while enforcing already existing methods, should be further developed towards designing and applying optimized individualized treatments of cancer in the clinic.

Using adaptive model predictive control to customize maintenance therapy chemotherapeutic dosing for childhood acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a common childhood cancer in which nearly one-quarter of patients experience a disease relapse. However, it has been shown that individualizing therapy for childhood ALL patients by adjusting doses based on the blood concentration of active drug metabolite could significantly improve treatment outcome. An adaptive model predictive control (MPC) strategy is presented in which maintenance therapy for childhood ALL is personalized using routine patient measurements of red blood cell mean corpuscular volume as a surrogate for the active drug metabolite concentration. A clinically relevant mathematical model is developed and used to describe the patient response to the chemotherapeutic drug 6-mercaptopurine, with some model parameters being patient-specific. During the course of treatment, the patient-specific parameters are adaptively identified using recurrent complete blood count measurements, which sufficiently constrain the patient parameter uncertainty to support customized adjustments of the drug dose. While this work represents only a first step toward a quantitative tool for clinical use, the simulated treatment results indicate that the proposed mathematical model and adaptive MPC approach could serve as valuable resources to the oncologist toward creating a personalized treatment strategy that is both safe and effective.

S-adenosylmethionine regulates thiopurine methyltransferase activity and decreases 6-mercaptopurine cytotoxicity in MOLT lymphoblasts

Six-mercaptopurine (6-MP) is a pro-drug widely used in treatment of various diseases, including acute lymphoblastic leukaemia (ALL). Side-effects of thiopurine therapy have been correlated with thiopurine methyltransferase (TPMT) activity. We propose a novel TPMT-mediated mechanism of S-adenosylmethionine (SAM)-specific effects on 6-mercaptopurine (6-MP) induced cytotoxicity in a model cell line for acute lymphoblastic leukemia (MOLT). Our results show that exogenous SAM (10-50microM) rescues cells from the toxic effects of 6-MP (5microM) by delaying the onset of apoptosis. We prove that the extent of methylthioinosine monophosphate (MeTIMP) induced inhibition of de novo purine synthesis (DNPS) determines the concentrations of intracellular ATP, and consequently SAM, which acts as a positive modulator of TPMT activity. This leads to a greater conversion of 6-MP to inactive 6-methylmercaptopurine, and thus lower availability of thioinosine monophosphate for the biotransformation to cytotoxic thioguanine nucleotides (TGNs) and MeTIMP. We further show that the addition of exogenous SAM to 6-MP treated cells maintains intracellular SAM levels, TPMT activity and protein levels, all of which are diminished in cells incubated with 6-MP. Since TPMT mRNA levels remained unaltered, the effect of SAM appears to be restricted to protein stabilisation rather than an increase of TPMT expression. We thus propose that SAM reverses the extent of 6-MP cytotoxicity, by acting as a TPMT-stabilizing factor. This study provides new insights into the pharmacogenetics of thiopurine drugs. Identification of SAM as critical modulator of TPMT activity and consequently thiopurine toxicity may set novel grounds for the rationalization of thiopurine therapy.

Cell killing and resistance in pre-operative breast cancer chemotherapy

Background

Despite the recent development of technologies giving detailed images of tumours in vivo, direct or indirect ways to measure how many cells are actually killed by a treatment or are resistant to it are still beyond our reach.

Methods

We designed a simple model of tumour progression during treatment, based on descriptions of the key phenomena of proliferation, quiescence, cell killing and resistance, and giving as output the macroscopically measurable tumour volume and growth fraction. The model was applied to a database of the time course of volumes of breast cancer in patients undergoing pre-operative chemotherapy, for which the initial estimate of proliferating cells by the measure of the percentage of Ki67-positive cells was available.

Results

The analysis recognises different patterns of response to treatment. In one subgroup of patients the fitting implied drug resistance. In another subgroup there was a shift to higher sensitivity during the therapy. In the subgroup of patients where killing of cycling cells had the highest score, the drugs showed variable efficacy against quiescent cells.

Conclusion

The approach was feasible, providing items of information not otherwise available. Additional data, particularly sequential Ki67 measures, could be added to the system, potentially reducing uncertainty in estimates of parameter values.

Probabilistic modeling of DNA mismatch repair effects on cell cycle dynamics and iododeoxyuridine-DNA incorporation

Previous studies in our laboratory have described increased and preferential radiosensitization of mismatch repair-deficient (MMR(-)) HCT116 colon cancer cells with 5-iododeoxyuridine (IUdR). Indeed, our studies showed that MMR is involved in the repair (removal) of IUdR-DNA, principally the G:IU mispair. Consequently, we have shown that MMR(-) cells incorporate 25% to 42% more IUdR than MMR(+) cells, and that IUdR and ionizing radiation (IR) interact to produce up to 3-fold greater cytotoxicity in MMR(-) cells. The present study uses the integration of probabilistic mathematical models and experimental data on MMR(-) versus MMR(+) cells to describe the effects of IUdR incorporation upon the cell cycle for the purpose of increasing IUdR-mediated radiosensitivity in MMR(-) cells. Two computational models have been developed. The first is a stochastic model of the progression of cell cycle states, which is applied to experimental data for two synchronized isogenic MMR(+) and MMR(-) colon cancer cell lines treated with and without IUdR. The second model defines the relation between the percentage of cells in the different cell cycle states and the corresponding IUdR-DNA incorporation at a particular time point. These models can be combined to predict IUdR-DNA incorporation at any time in the cell cycle. These mathematical models will be modified and used to maximize therapeutic gain in MMR(-) tumors versus MMR(+) normal tissues by predicting the optimal dose of IUdR and optimal timing for IR treatment to increase the synergistic action using xenograft models and, later, in clinical trials.

BRCA1 activates a G2-M cell cycle checkpoint following 6-thioguanine-induced DNA mismatch damage

Human DNA mismatch repair (MMR) is involved in the response to certain chemotherapy drugs, including 6-thioguanine (6-TG). Consistently, MMR-deficient human tumor cells show resistance to 6-TG damage as manifested by a reduced G(2)-M arrest and decreased apoptosis. In this study, we investigate the role of the BRCA1 protein in modulating a 6-TG-induced MMR damage response, using an isogenic human breast cancer cell line model, including a BRCA1 mutated cell line (HCC1937) and its transfectant with a wild-type BRCA1 cDNA. The MMR proteins MSH2, MSH6, MLH1, and PMS2 are similarly detected in both cell lines. BRCA1-mutant cells are more resistant to 6-TG than BRCA1-positive cells in a clonogenic survival assay and show reduced apoptosis. Additionally, the mutated BRCA1 results in an almost complete loss of a G(2)-M cell cycle checkpoint response induced by 6-TG. Transfection of single specific small interfering RNAs (siRNA) against MSH2, MLH1, ATR, and Chk1 in BRCA1-positive cells markedly reduces the BRCA1-dependent G(2)-M checkpoint response. Interestingly, ATR and Chk1 siRNA transfection in BRCA1-positive cells shows similar levels of 6-TG cytotoxicity as the control transfectant, whereas MSH2 and MLH1 siRNA transfectants show 6-TG resistance as expected. DNA MMR processing, as measured by the number of 6-TG-induced DNA strand breaks using an alkaline comet assay (+/-z-VAD-fmk cotreatment) and by levels of iododeoxyuridine-DNA incorporation, is independent of BRCA1, suggesting the involvement of BRCA1 in the G(2)-M checkpoint response to 6-TG but not in the subsequent excision processing of 6-TG mispairs by MMR.

Modeling suppression of cell death by Bcl-2 over-expression in myeloma NS0 6A1 cells

A novel population-balance model was employed to evaluate the suppression of cell death in myeloma NS0 6A1 cells metabolically engineered to over-express the apoptotic suppressor Bcl-2. The model is robust in its ability to simulate cell population dynamics in batch suspension culture and in response to thymidine-induced growth inhibition: 89% of simulated cell concentrations are within two standard deviations of experimental data. Kinetic rate constants in model equations suggest that Bcl-2 over-expression extends culture longevity from 6 days to at least 15 days by suppressing the specific rate of early apoptotic cell formation by more than 6-fold and necrotic cell formation by at least 3-fold, despite nearly a 3-fold decrease in initial cell growth rate and no significant change in the specific rate of late apoptotic cell formation. This computational analysis supports a mechanism in which Bcl-2 is a common mediator of early apoptotic and necrotic events occurring at rates that are dependent on cellular factors accumulating over time. The model has current application to the rational design of cell cultures through metabolic engineering for the industrial production of biopharmaceuticals.