Athanassios Iliadis

Cytidine Deaminase Residual Activity in Serum Is a Predictive Marker of Early Severe Toxicities in Adults After Gemcitabine-Based Chemotherapies

PURPOSE Anticipating toxicities with gemcitabine is an ongoing story, and deregulation in cytidine deaminase (CDA) could be associated with increased risk of developing early severe toxicities on drug exposure. PATIENTS AND METHODS A simple test to evaluate CDA phenotypic status was first validated in an animal model investigating relationships between CDA activity and gemcitabine-related toxicities. Next, relevance of this test as a marker for toxicities was retrospectively tested in a first subset of 64 adult patients treated with gemcitabine alone, then it was tested in a larger group of 130 patients who received gemcitabine either alone or combined with other drugs and in 20 children. Additionally, search for the 435 T>C, 208 G>A and 79 A>C mutations on the CDA gene was performed. Results In mice, CDA deficiency impacted on gemcitabine pharmacokinetics and had subsequent lethal toxicities. In human, 12% of adult patients experienced early severe toxicities after gemcitabine administration. A significant difference in CDA activities was observed between patients with and without toxicities (1.2 +/- 0.8 U/mg v 4 +/- 2.6 U/mg; P < .01). Conversely, no genotype-to-phenotype relationships were found. Of note, the patients who displayed particularly reduced CDA activity all experienced strong toxicities. Gemcitabine was well tolerated in children, and no CDA deficiency was evidenced. CONCLUSION Our data suggest that CDA functional testing could be a simple and easy marker to discriminate adult patients at risk of developing severe toxicities with gemcitabine. Particularly, this study demonstrates that CDA deficiency, found in 7% of adult patients, is associated with a maximum risk of developing early severe toxicities with gemcitabine.

Optimizing drug regimens in cancer chemotherapy by an efficacy—toxicity mathematical model

In cancer chemotherapy, it is important to design treatment strategies that ensure a desired rate of tumor cell kill without unacceptable toxicity. To optimize treatment, we used a mathematical model describing the pharmacokinetics of anticancer drugs, antitumor efficacy, and drug toxicity. This model was associated with constraints on the allowed plasma concentrations, drug exposure, and leukopenia. Given a schedule of drug administrations, the mathematical model optimized the drug doses that can minimize the tumor burden while limiting toxicity at the level of the white blood cells. The main result is that the optimal drug administration is an initial high-dose chemotherapy up to saturation of constraints associated with normal cell toxicity and a maintenance continuous infusion at a moderate rate. Data related to etoposide investigations were used in a feasibility study. Simulations with the optimized protocol showed better performances than usual clinical protocols. Model-based optimal drug doses provide for greater cytoreduction, while limiting the risk of unacceptable toxicity.

Optimizing drug regimens in cancer chemotherapy: a simulation study using a PK–PD model

In cancer chemotherapy, it is important to design treatment strategies for drug protocols that ensure a desired rate of tumor cell kill without overdosing the host. Mathematical modeling was used for optimization in which we minimize the end value of the tumor cells while limiting toxicity by always maintaining the white blood cell count beyond a limit. The optimal solution for this is a mixture of an initial bolus application of drug followed by no drug and then continuous infusion that keeps the normal cell population at its lower limit while decreasing the tumor cell population.

Mathematical Modeling of Tumor Growth and Metastatic Spreading: Validation in Tumor-Bearing Mice

Defining tumor stage at diagnosis is a pivotal point for clinical decisions about patient treatment strategies. In this respect, early detection of occult metastasis invisible to current imaging methods would have a major impact on best care and long-term survival. Mathematical models that describe metastatic spreading might estimate the risk of metastasis when no clinical evidence is available. In this study, we adapted a top-down model to make such estimates. The model was constituted by a transport equation describing metastatic growth and endowed with a boundary condition for metastatic emission. Model predictions were compared with experimental results from orthotopic breast tumor xenograft experiments conducted in Nod/Scidγ mice. Primary tumor growth, metastatic spread and growth were monitored by 3D bioluminescence tomography. A tailored computational approach allowed the use of Monolix software for mixed-effects modeling with a partial differential equation model. Primary tumor growth was described best by Bertalanffy, West, and Gompertz models, which involve an initial exponential growth phase. All other tested models were rejected. The best metastatic model involved two parameters describing metastatic spreading and growth, respectively. Visual predictive check, analysis of residuals, and a bootstrap study validated the model. Coefficients of determination were [Formula: see text] for primary tumor growth and [Formula: see text] for metastatic growth. The data-based model development revealed several biologically significant findings. First, information on both growth and spreading can be obtained from measures of total metastatic burden. Second, the postulated link between primary tumor size and emission rate is validated. Finally, fast growing peritoneal metastases can only be described by such a complex partial differential equation model and not by ordinary differential equation models. This work advances efforts to predict metastatic spreading during the earliest stages of cancer.

Time dependency of adriamycin and adriamycinol kinetics

SummaryAdriamycin was administered by IV injection to seven patients with various solid tumors at a dose of 30 mg/m2 during successive courses. Extraction was carried out by the SEP-PAK method for plasma and by solvents for urine. Plasma and urinary levels of adriamycin and adriamycinol were determined by high-performance liquid chromatography over 72-h period after injection. Pharmacokinetic parameters for adriamycin and adriamycinol were calculated for each course of treatment. The results show significant inter- and intra-individual variations in the kinetics and elimination of both compounds. The analysis of pharmacokinetic data reveals a wide variability in the fluctuations observed during the successive courses in different patients. This study confirms the time-dependency of ADR kinetics.

Early severe toxicities after capecitabine intake: possible implication of a cytidine deaminase extensive metabolizer profile

We report here the case of a 19-year-old female patient who suffered from extremely severe toxicities (G4 mucitis, fever, diarrhea, alteration of general state) while undergoing low-dose capecitabine treatment for her metastatic corticosurrenaloma. The severe toxicities stopped as soon as treatment was suspended. Interestingly, this patient was not deficient in DPD, a pharmacogenetic syndrome usually associated with increased risk of developing severe/lethal toxicities in patients undergoing fluoropyrimidine therapy, and she had been treated previously with 5-FU with a good tolerance. We then hypothesized that cytidine deaminase (CDA) extensive phenotype could be responsible for the severe toxicities observed with capecitabine. CDA is affected by genetic polymorphism, with subsequent acquisition of either deficient or extensive metabolizer profile. Phenotypic investigations confirmed that CDA activity in this patient was +180% higher than the ones usually recorded in the general population. This strongly suggests that the extensive activation of triple-prodrug capecitabine could have occurred in this patient, resulting in overexposure to 5-FU and its cytotoxic metabolites eventually. This case report suggest for the first time that severe toxicities with a capecitabine-containing protocol could be, at least in part, linked with an extensive-CDA syndrome. The case reported here suggests therefore that besides DPD, screening for CDA activity could be of interest to ensure a better safety in the handling of oral capecitabine at the bedside.

Nonlinear dynamics and chaos theory: concepts and applications relevant to pharmacodynamics.

The theory of nonlinear dynamical systems (chaos theory), which deals with deterministic systems that exhibit a complicated, apparently random-looking behavior, has formed an interdisciplinary area of research and has affected almost every field of science in the last 20 years. Life sciences are one of the most applicable areas for the ideas of chaos because of the complexity of biological systems. It is widely appreciated that chaotic behavior dominates physiological systems. This is suggested by experimental studies and has also been encouraged by very successful modeling. Pharmacodynamics are very tightly associated with complex physiological processes, and the implications of this relation demand that the new approach of nonlinear dynamics should be adopted in greater extent in pharmacodynamic studies. This is necessary not only for the sake of more detailed study, but mainly because nonlinear dynamics suggest a whole new rationale, fundamentally different from the classic approach. In this work the basic principles of dynamical systems are presented and applications of nonlinear dynamics in topics relevant to drug research and especially to pharmacodynamics are reviewed. Special attention is focused on three major fields of physiological systems with great importance in pharmacotherapy, namely cardiovascular, central nervous, and endocrine systems, where tools and concepts from nonlinear dynamics have been applied.

Optimizing Druggability through Liposomal Formulations: New Approaches to an Old Concept.

Developing innovative delivery strategies remains an ongoing task to improve both efficacy and safety of drug-based therapy. Nanomedicine is now a promising field of investigation, rising high expectancies for treating various diseases such as malignancies. Putting drugs into liposome is an old story that started in the late 1960s. Because of the near-total biocompatibility of their lipidic bilayer, liposomes are less concerned with the safety issue related to the possible long-term accumulation in the body of most nanoobjects currently developed in nanomedicine. Additionally, novel techniques and recent efforts to achieve better stability (e.g., through sheddable coating), combined with a higher selectivity towards target cells (e.g., by anchoring monoclonal antibodies or incorporating phage fusion protein), make new liposomal drugs an attractive and challenging opportunity to improve clinical outcome in a variety of disease. This review covers the physicochemistry of liposomes and the recent technical improvements in the preparation of liposome-encapsulated drugs in regard to the scientific and medical stakes.

A limited-sampling strategy for estimation of etoposide pharmacokinetics in cancer patients.

Abstract Etoposide (VP16), a widely used anticancer drug, is a topoisomerase II inhibitor. A number of studies have highlighted a correlation between hematologic toxicity and pharmacokinetic or physiological parameters. Other studies have also suggested that the anti-tumor response could be related to the plasma etoposide concentration. Therefore, it would seem of interest to individualize VP16 dose regimens on the basis of pharmacokinetic parameters. The aim of this study was to develop and validate a limited-sampling strategy allowing VP16 pharmacokinetic evaluation with minimal disturbance to the patient. A total of 34 patients (54 kinetics) received VP16 at various dose regimens, with doses ranging between 30 and 200 mg and infusion times varying between 0.5 and 2 h. The statistical characteristics of the pharmacokinetic parameters were assessed from the first courses of treatment performed in 23/34 patients; then the following three-sample protocol was designed: the end of the infusion and 5 and 24 h after the start of the infusion. For validation of the model the main pharmacokinetic parameters (clearance, half-lives, volume of distribution) were estimated in the 11 remaining patients by maximum-likelihood estimation (ML) and by Bayesian estimation (BE) using the three sampling times designed. Statistical comparison showed a good concordance between ML and BE estimates (the bias for clearance was –1.72%). The limited-sampling strategy presented herein can thus be used for accurate estimation of VP16 pharmacokinetic parameters.

Tumor growth modeling from clinical trials reveals synergistic anticancer effect of the capecitabine and docetaxel combination in metastatic breast cancer

PurposeMost of the cancer chemotherapy treatments employ drugs in combination. For combination treatments, it is relevant to assess interaction between two or more anticancer agents used in clinics. Based on clinical data and using modeling techniques, the work analyzes the pharmacodynamic interaction between capecitabine and docetaxel used in combination in metastatic breast cancer.MethodsWe developed mathematical models to describe tumor growth inhibition profile under treatment based on Phase II and Phase III clinical data of capecitabine and docetaxel in metastatic breast cancer. Model parameters were estimated by population approach with NONMEM® on single-agent and combination data. Simulations were performed using MATLAB.ResultsCapecitabine and docetaxel combination in metastatic breast cancer results in a synergistic effect as compared with the simple additive effects of single-agent treatments. Docetaxel is more efficient than capecitabine at the start of treatment but develops resistance faster. Modeling revealed no resistance of capecitabine for the combination data.ConclusionsModeling could be a powerful tool to design the most advantageous combination regimen for capecitabine and docetaxel in metastatic breast cancer in order to increase the time before regrowth and decrease the tumor size at regrowth.