Risk Of Severe Myelosuppression Research Articles

Abstract CN04-02: Interrogating the heritable genome to achieve the goal of personalized cancer therapeutics.

Abstract CN04-02: Interrogating the heritable genome to achieve the goal of personalized cancer therapeutics.

Covariate-based dose individualization of the cytotoxic drug indisulam to reduce the risk of severe myelosuppression

Chemotherapy with indisulam causes myelosuppression. This study aimed to evaluate the influence of patient-related covariates on pharmacokinetics and pharmacodynamics, to identify patients at risk for severe myelosuppression and to develop a dosing algorithm for treatment optimization. Pharmacokinetic and pharmacodynamic data of 412 patients were available. Non-linear mixed effects modeling was used to determine the relative risk of dose-limiting myelosuppression for various covariates (demographics, physical condition, prior treatment, comedication, CYP2C genotype and biochemistry). Body surface area (BSA), race and CYP2C genotype had a significant impact on indisulam elimination (P < 0.001). Low BSA, Japanese race, variant CYP2C genotype, low baseline neutrophil and thrombocyte counts and female sex were clinically relevant risk factors of dose-limiting myelosuppression (RR > 1.1). A dosing strategy was developed to optimize treatment for patient subgroups. This study has identified covariates related to an increased risk of myelosuppression after indisulam therapy. Dose individualization may contribute to treatment optimization.

Use of fast orthogonal search to predict chemotherapy-induced leukopenia

706 Background: Myelosuppression is the predominant DLT of chemotherapy (CT). Growth factors (GF) can reduce the incidence and severity of myelosuppression, but are neither necessary nor cost-effective for all patients on CT. Attempts at developing a predictive model to identify patients at risk for severe myelosuppression have been unsuccessful to date. A reliable model to identify at risk patients early would allow closer monitoring and timely introduction of GF support. We propose using a nonlinear mathematical model, Fast Orthogonal Search (FOS), to achieve this goal. Methods: Women receiving adjuvant CMF (n=15), CEF (n=14), or CAF (n=6) for early breast cancer were selected from a single clinical practice. All were managed in a homogeneous fashion and GF support was introduced only as secondary prophylaxis. Using data from the electronic patient record, patients (pts) were retrospectively classified into high and low risk (HR/LR) categories. HR if: any hospitalization, ≥3 delays, any delay >40 days, delay at cycle 2, day 8 of any cycle not given, or any dose reduction in the first 3 cycles; LR if: no event, or delay beyond cycle 3. Pts meeting neither HR nor LR criteria were deemed medium risk and excluded from the analysis. Using complete blood count values from cycle 1 (baseline, day 8, day 28), the FOS model was trained on 14 randomly selected pts evenly split between HR and LR groups, and validated on a separate set of 14, together with an independent set of 7. Results: The model correctly classified 19 of the 21 pts. None of the LR and only 2 of the HR pts were misclassified, Fisher’s exact test p<0.00023 (2-tailed) and Matthews’ correlation coefficient φ = +0.83. The model was re-built switching the initial testing and training sets, leaving the independent 7 pts as part of the testing procedure. The model correctly classified 17 out of 21 pts. Four of the 10 LR pts were misclassified and none of the 11 HR were misclassified, Fisher’s exact test p<0.0039 (2-tailed) and Matthews’ correlation coefficient φ = +0.66. Conclusions: FOS might be applied to prospectively identify pts at high-risk for neutropenia. Further studies are needed to replicate this work on a separate larger data set, and define its reproducibility in the setting of other chemotherapy regimens. No significant financial relationships to disclose.

Monitoring of Cyclosporin and Azathioprine in Organ Transplantation

Cyclosporin A (CsA) is a cyclic undecapeptide that was first isolated from the fungus Tricoderma polysporum. The introduction of this potent immunosuppressive compound significantly improved the outcome of solid organ and bone marrow allograft transplantation. There is now a large body of accumulated experience in the monitoring of this drug, and several consensus conferences have addressed this issue /1-3/. Azathioprine in CsA-based triple therapy with steroids is still widely used in organ transplantation even though it is known to carry the risk of severe myelosuppression. Measurement of white blood cell (WBC) and platelet counts is generally used to recognize azathioprine toxicity. However, recent reports /4-6/ have indicated that patients with a genetically determined deficiency in thiopurine methyltransferase (TPMT), an important enzyme in azathioprine metabolism, are at high risk for myelosuppression. Monitoring of active azathioprine metabolites, which are responsible both for the immunosuppressive action and for the toxicity of azathioprine, may be necessary in such cases. It should also be noted that the incidence of posttransplant lymphoproliferative disorder (PTLD) is a function of the intensity of immunosuppression /7,8/. Thus the combination of CsA, azathioprine and steroids is associated with an approximately three-fold higher incidence of PTLD than low dose CsA guided by therapeutic drug monitoring /8/.

Modeling Interpatient Pharmacodynamic Variability of Etoposide

This report describes approaches to modeling interpatient pharmacodynamic variability of etoposide effect as measured by white blood cell count nadir. Such models may be utilized in adaptive control dosing to individualize the dose administered to a patient with the aim of lessening the risk of severe myelosuppression. We have successfully employed adaptive control dosing of etoposide administered by 72-hour continuous infusion based on our prior pharmacodynamic model for white blood cell count nadir. Data from our most recent series of 41 patients were used to investigate new linear and nonlinear pharmacodynamic models. We then cross-validated our best models on data from an independent series of 27 similarly treated patients. We identified an unbiased model that exhibited high precision in both data sets (root mean square errors of 1.11 x 10(3) and 1.20 x 10(3) cells/microL, respectively). The optimal model was a nonlinear Hill model defined by 24-hour etoposide concentration, pretreatment white blood cell count, and pretreatment serum albumin level. The level of albumin was found to be both a component of kinetic (protein binding) and dynamic (patient health) variability. Patients with lower pretreatment albumin levels are at higher risk of severe myelosuppression during etoposide therapy.