Chronic myelomonocytic leukaemia (CMML) is a disease that borders between a myelodyplastic syndrome (MDS) and myeloproliferative neoplasia (MPN). It is a progressive condition: about 20% of CMML patients progress to secondary acute myeloid leukaemia (sAML), which has a very poor prognosis. In all cases, normal bone marrow function is compromised with resulting depletion of circulating erythrocytes and platelets. We modelled the dynamics of CMML at two levels: a cytokinetic model was developed that describes bone marrow cell population dynamics for multiple cell lineages, and changes in DNA mutational status that occur in MDS and CMML. This model, in conjunction with pharmacokinetic (PK) models for cytotoxic drugs and hypomethylating agents, was used to study the pharmacodynamics (PD) of these drugs. For the subset of CMML with loss of activity of TET2, our models suggest that TET2 activators may restore normal DNA methylation with a resulting antileukaemic effect. Mouse and human data were used to place bounds on the parameters of our model. Our modelling suggests that both hypomethylation and cytotoxic effects contribute to the antileukaemic activity of decitabine.

A mechanism is proposed by which speciation may occur without the need to postulate geographical isolation of the diverging populations. Closely related species that occupy overlapping or adjacent ecological niches often have an almost identical genome but differ by chromosomal rearrangements that result in reproductive isolation. The mitotic spindle assembly checkpoint normally functions to prevent gametes with non-identical karyotypes from forming viable zygotes. Unless gametes from two individuals happen to undergo the same chromosomal rearrangement at the same place and time, a most improbable situation, there has been no satisfactory explanation of how such rearrangements can propagate. Consideration of the dynamics of the spindle assembly checkpoint suggest that chromosomal fission or fusion events may occur that allow formation of viable heterozygotes between the rearranged and parental karyotypes, albeit with decreased fertility. Evolutionary dynamics calculations suggest that if the resulting heterozygous organisms have a selective advantage in an adjoining or overlapping ecological niche from that of the parental strain, despite the reproductive disadvantage of the population carrying the altered karyotype, it may accumulate sufficiently that homozygotes begin to emerge. At this point the reproductive disadvantage of the rearranged karyotype disappears, and a single population has been replaced by two populations that are partially reproductively isolated. This definition of species as populations that differ from other, closely related, species by karyotypic changes is consistent with the classical definition of a species as a population that is capable of interbreeding to produce fertile progeny. Even modest degrees of reproductive impairment of heterozygotes between two related populations may lead to speciation by this mechanism, and geographical isolation is not necessary for the process.

The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed "cyclotherapy". Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies. We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint (SAC), the EGF signalling pathway and apoptosis. We incorporated sites of action of four drugs (palbociclib, gemcitabine, paclitaxel and actinomycin D) to illustrate potential applications of this approach. We show how drug effects on multiple cell populations can be simulated, facilitating simultaneous prediction of effects on normal and transformed cells. The consequences of aberrant signalling pathways or of altered expression of pro-or anti-apoptotic proteins can thus be compared. We suggest that this approach, particularly if used in conjunction with pharmacokinetic modelling, could be used to predict effects of specific oncogene expression patterns on drug response. The strategy could be used to search for synthetic lethality and optimise combination protocol designs.

The t(9;22) translocation that causes chronic myeloid leukemia (CML) drives both transformation and the progression process that eventually results in the disease changing to acute leukemia. Constitutively activated Bcr-Abl signaling in CML creates high levels of reactive oxygen species (ROS) that produce 8-oxo-guanine in DNA; this is mutagenic and causes chronic phase (CP) progression to blast phase (BP). We modeled three types of mutations involved in this progression: mutations that result in myeloid progenitor cells proliferating independently of external growth factors; mutations causing failure of myeloid progenitor cells to differentiate; and mutations that enable these cells to survive independently of attachment to marrow stroma. We further modeled tyrosine kinase inhibitors (TKI) as restoring myeloid cell apoptosis and preventing ROS-driven mutagenesis, and mutations that cause TKI resistance. We suggest that the unusually low rate of resistance to TKI arises because these drugs deplete ROS, which in turn decrease mutation rates.

Chronic myeloid leukaemia (CML) is an unusual malignancy in which myeloid progenitor cells are transformed by a single chromosomal translocation where the Bcr domain of chromosome 22 is placed adjacent to the proto-oncogene c-Abl of chromosome 9, resulting in constitutive Abl tyrosine kinase activity. This has a twofold effect: it causes increased numbers of myeloid progenitor cells and circulating myeloid cells, and it causes leakage of reactive oxygen species from mitochondria. We describe a kinetic and pharmacodynamic (PD) model of Bcr–Abl signalling in myeloid cells that is used to simulate effects of four classes of drugs: Bcr–Abl signalling inhibitors, such as imatinib, cyclin-dependent kinase inhibitors, and pro- and anti-oxidants. The model also has the potential to describe the PD effects of agents acting on other sites in the Bcr–Abl signalling pathway. Having calibrated the model against dose–response curves of these drugs acting as single agents on Bcr–Abl-transformed cells in vitro, the model was used to predict effects of the agents in combination. Used in conjunction with pharmacokinetic models, our PD model enables an approach to protocol optimization: large numbers of doses and timings and (in the case of combination treatments) relative dose ratios can be simulated in silico. Predicted selectivity, as well as efficacy, can be extracted from the model. An understanding of the Bcr–Abl signalling pathway has implications for strategies to prevent acquired drug resistance, and for preventing or delaying CML progression to its blast phase.

The incidence of chronic myeloid leukemia (CML), which is caused by BCR/ABL chimeric oncogene formation in a pluripotent hematopoietic stem cell (HSC), increases with age and exposure to ionizing radiation. CML is a comparatively well-characterized neoplasm, important for its own sake and useful for insights into other neoplasms. Here, Surveillance, Epidemiology and End Results (SEER) CML data are analyzed after considering possible misclassification of chronic myelo-monocytic leukemia as CML. For people older than 25 years, plots of male and female CML log incidences versus age at diagnosis are approximately parallel straight lines with males either above or to the left of females. This is consistent with males having a higher risk of developing CML or a shorter latency from initiation to diagnosis of CML. These distinct mechanisms cannot be distinguished using SEER data alone. Therefore, CML risks among male and female Japanese A-bomb survivors are also analyzed. The present analyses suggest that sex differences in CML incidence more likely result from differences in risk than in latency. The simplest but not the sole interpretation of this is that males have more target cells at risk to develop CML. Comprehensive mathematical models of CML could lead to a better understanding of the role of HSCs in CML and other preleukemias that can progress to acute leukemia.

Abstract Physiomics and Pharmacometrics have collaborated to design a new database of anti-cancer drugs and therapeutic treatment information. The objective is to provide a database of anti-neoplastic agents, regimens and combinations for use by clinicians and researchers in oncology. The drugCARD database, accessible through the web, offers data on more than 130 anti-cancer drugs (small molecules and biologics) used in research and in the clinic. It contains information on drug combinations as well as several hundreds of cancer chemotherapy regimens used routinely in the clinic. The data are classified according to tumour type, species and experimental system (in vitro or in vivo). This database will be regularly expanded and curated with the most current information. Individual drug information contained within the database comprises pharmacokinetic profiles, mechanisms of action and resistance, dose-response effect, dosing limits, therapeutic index and immunosuppression data. Drug combinations are also referenced. The database covers synergy or antagonism, and includes the combination therapeutic index and cross-resistance information. Drug combinations where the level of synergy is dependent upon the drug schedule, drug sequence or administration timing are also referenced and thoroughly discussed. The user can browse and compare chemotherapeutic regimens, and analyse the overall drug dose over a course of treatment, by tumour type, in animal and clinical models. Moreover, the database enables users to design new combinations and regimens that obey dosing constraints (such as MLD and MTD), and can be used to determine drug candidates that could be combined with a new chemical or biological entity, given the respective mechanisms of action and other PK/PD data. Data can be exported for analysis in spreadsheets, modelling software or simulation packages. Advanced functions will include the ability to carry out statistical analysis on drug usage and dosing in various contexts. Finally, the database allows the expression and nomenclature of chemotherapy regimens to be standardized, which is of paramount importance in improving efficacy, as well as reducing medication errors (Kohler et al 1998). Citation Format: Eric Fernandez, Jianxiong Pang, Chris Snell, Cathy Derow, Frances Brightman, Christophe Chassagnole, Robert Jackson. drugCARD: a database of anticancer treatment regimens and drug combinations. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5147. doi:10.1158/1538-7445.AM2013-5147

When mammalian tissues are infected by bacteria or fungi, inflammatory cytokines are released that cause circulating neutrophils to invade the infected tissue. The cytosolic tyrosine kinase, c-Abl, in these tissue neutrophils is activated by TNFα. c-Abl then phosphorylates STAT transcription factors, which results in production of the antiapoptotic protein Mcl-1. The normally short-lived tissue neutrophils are then unable to enter apoptosis. c-Abl also causes release of reactive oxygen species (ROS) from the mitochondria of the activated neutrophils. These ROS, and ROS generated by NADPH oxidase, are bactericidal agents of the innate immune system. In some inflammatory diseases, such as chronic obstructive pulmonary disease (COPD), the invading neutrophils become permanently activated, and the resulting ROS overproduction causes severe tissue damage. The cyclin-dependent kinase inhibitor, seliciclib, blocks transcription through inhibition of cdk9. This results in a relatively rapid decline of antiapoptotic Mcl-1 transcripts in activated neutrophils, an increase in neutrophil apoptosis, and less ROS leakage and oxidative damage. We present here a model of neutrophil kinetics that simulates the principal pathways of c-Abl signalling and use it to explore possible treatment options for inflammatory lung disease.

The development of pharmacodynamic (PD) biomarkers in oncology has implications for design of clinical protocols from preclinical data and for predicting clinical outcomes from early clinical data. Two classes of biomarkers have received particular attention. Phosphoproteins in biopsy samples are markers of inhibition of signalling pathways, target sites for many novel agents. Biomarkers of apoptosis in plasma can measure tumour cell killing by drugs in phase I clinical trials. The predictive power of PD biomarkers is enhanced by data modelling. With pharmacokinetic models, PD models form PK/PD models that predict the time course both of drug concentration and drug effects. If biomarkers of drug toxicity are also measured, the models can predict drug selectivity as well as efficacy. PK/PD models, in conjunction with disease models, make possible virtual clinical trials, in which multiple trial designs are assessed in silico, so the optimal trial design can be selected for experimental evaluation.

We postulate the two checkpoints theory of cancer, a model of cancer development suggesting that malignant transformation of cells requires loss of function of both the G1 checkpoint and the mitotic spindle checkpoint. Malignant progression can be described as a process analogous to a genetic algorithm, which we term the malignant progression algorithm. There are two prerequisites for this process: first, there must be competition for reproductive resources, and this is driven by loss of the G1 checkpoint; second, there must be a source of genetic variation, and this is provided by loss of the mitotic spindle checkpoint, resulting in aneuploidy. These two factors then trigger a process of Darwinian selection, driving the emergence of cells with the various abnormalities that have been termed the “hallmarks of cancer”. Malignant progression is iterative, autocatalytic, and irreversible. The process can be modelled mathematically by describing the system as a finite state machine. The model indicates that loss of the two checkpoints is necessary and sufficient for tumour progression. The order of loss of the two checkpoints appears to be important: loss of the G1 checkpoint results in premalignant cells that replicate independently of physiological growth signals, but which remain diploid. Loss of the mitotic spindle checkpoint then results in aneuploid, malignant cells with highly error-prone replication, which rapidly progress to invasive, metastatic, hypoxia-tolerant, immortalised cells. This model of malignant progression has implications for the selection of anticancer drug targets and for tumour prevention strategies.

Abstract Physiomics and pharmacometrics have collaborated to design a new database of anti-cancer drugs and therapeutic treatment information aimed at researchers in oncology and clinicians. This database, accessible through the web, offers data on more than 130 anti-cancer drugs (small molecules and biologics) used in research and in the clinic. It contains information on drug combination as well as a several hundreds of cancer chemotherapy regimens routinely used in the clinic. These data are classified according to tumor type, species, or source (in vitro, in vivo or simulated). It will be constantly expanded and curated with the most recent information. Furthermore, it provides ways to standardize the expression and nomenclature of chemotherapy regimens unambiguously and uniformly is of paramount importance to improve efficacy, as well as to reduce medication errors. Individual drug information covers pharmacokinetic profiles, mechanism of action and of resistance, dose-response effect, dosing limits, therapeutic index, and immunosuppression data. Drug combinations are also referenced. The database covers synergy or antagonism, as well as a combination therapeutic index and cross-resistance information. Some drug combination having level of synergy depending on the drug schedule, drug sequence and administration timings are also referenced and thoroughly discussed. The user can also browse and compare chemotherapeutic regimens, analyze the overall drug dose over a course of treatment, by tumor type, in animal and clinical models. Advanced functions include the ability to do statistical analysis on drug usage and dosing in various contexts. It can also help determine which drug candidates are likely to be used in combination with a new chemical or biological entity, given the mechanism of action and other PK/PD data. Also, it will allow users to design new combinations and regimens, which obey dosing constraints, such as MLD and MTD. Finally, data can be exported and used in spread sheets, modeling software or simulation packages. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A36.

Arguably the most dramatic phase in the cell cycle is mitosis, during which replicated chromosomes are sorted into two distinct sets. Aurora kinases are central to the accurate segregation of chromosomes during mitosis. Consequently, they have been selected as possible targets for cancer therapy. Anti-cancer drugs that target Aurora kinases are normally designed to inhibit their function. The complexity of the roles of Aurora kinases and their interaction with respective inhibitors means that it is often very difficult to obtain meaningful links between inhibitor concentration and efficacy using standard methods. To overcome these difficulties, we propose a novel mathematical modelling approach. We present a pharmacodynamic model that is able to encapsulate the key roles of two kinases, Aurora A and B, in the spindle assembly checkpoint. Moreover, the model is capable of qualitatively differentiating between the effects of inhibiting Aurora A, Aurora B and A plus B, respectively, by predicting cell behaviour. Consequently, predictions regarding the qualitative relationship between inhibitors, measurable biomarkers and cell damage can be obtained using this powerful modelling approach.

The main difficulty in the development of ATP antagonist kinase inhibitors is target specificity, since the ATP-binding motif is present in many proteins. We introduce a strategy that has allowed us to identify compounds from a kinase inhibitor library that block the cyclin-dependent kinases responsible for regulating transcription, i.e., CDK7 and especially CDK9. The screening cascade employs cellular phenotypic assays based on mitotic index and nuclear p53 protein accumulation. This permitted us to classify compounds into transcriptional, cell cycle, and mitotic inhibitor groups. We describe the characterization of the transcriptional inhibitor class in terms of kinase inhibition profile, cellular mode of action, and selectivity for transformed cells. A structural selectivity rationale was used to optimize potency and biopharmaceutical properties and led to the development of a transcriptional inhibitor, 3,4-dimethyl-5-[2-(4-piperazin-1-yl-phenylamino)-pyrimidin-4-yl]-3H-thiazol-2-one, with anticancer activity in animal models.

Through cell-based screening of our kinase-directed compound collection, we discovered that a subset of N-phenyl-4-(thiazol-5-yl)pyrimidin-2-amines were potent cytotoxic agents against cancer cell lines, suppressed mitotic histone H3 phosphorylation, and caused aberrant mitotic phenotypes. It was subsequently established that these compounds were in fact potent inhibitors of aurora A and B kinases. It was shown that potency and selectivity of aurora kinase inhibition correlated with the presence of a substituent at the aniline para-position in these compounds. The anticancer effects of lead compound 4-methyl-5-(2-(4-morpholinophenylamino)pyrimidin-4-yl)thiazol-2-amine (18; K(i) values of 8.0 and 9.2 nM for aurora A and B, respectively) were shown to emanate from cell death following mitotic failure and increased polyploidy as a consequence of cellular inhibition of aurora A and B kinases. Preliminary in vivo assessment showed that compound 18 was orally bioavailable and possessed anticancer activity. Compound 18 (CYC116) is currently undergoing phase I clinical evaluation in cancer patients.

The spindle assembly checkpoint is a cell cycle surveillance mechanism that ensures the proper separation of chromosomes prior to cell division at mitosis. Aurora kinases play critical roles in mitotic progression and hence small-molecule inhibitors of Aurora kinases have been developed as a new class of potential anti-cancer drugs. In this paper we present for the first time an integrated pharmacokinetic–pharmacodynamic model of the functional effects of CYC116 (a known inhibitor of Aurora kinases A and B) on the spindle assembly checkpoint. We use the model to simulate two common experimental systems: cell culture and p.o. dosing of mice and present predictions of the effects of CYC116 for a range of doses and drug scheduling regimes. The model reveals that a critical peak drug concentration is required to cause aberrant kinetochore-microtubule attachments. The model also predicts that provided this threshold concentration is exceeded, a high total oral dose causes a high number of aberrant attachments within any given damaged cell. However, the proportion of cells which enter anaphase with aberrant attachments is associated with the total length of time for which the plasma concentration is maintained above the threshold. Moreover, our model reveals that the length of prometaphase/metaphase is a nonlinear function of drug dose and this time period can be extended or shortened. Finally, a strong saturation effect on CYC116 efficacy is predicted by the model. We discuss how these predictions may have implications for further drug trials using CYC116 and other similar AK inhibitors.