Monitoring Hydroxyurea Treatment Of Sickle Cell Anemia

Sickle cell is a hereditary disease affecting more than 100,000 people in the United States alone that causes hemoglobin in red blood cells to polymerize and turn the cell into a sickle shape, resulting in severe vaso-occlusive crises and ischemic attacks. Sickle cell patients often suffer from pain crises, with the number of pain crises linked to their prognosis, especially at a younger age. Currently, the drug hydroxyurea (HU) is used to treat the disease, with a measure of red blood cell volume (RBC MCV) as monitor for treatment progression. However, physicians have to wait atleast 120 days to identify treatment efficacy due to the amount of time it takes RBCs to reach a steady state volume after starting treatment. Therefore we propose measuring the volume of reticulocytes (MCVr), immature RBCs in the blood, as a marker for treatment efficacy, as the faster dynamics should allow for measurement of treatment efficacy after only 10 days. Data from 127 patients with various diagnoses (sickle cell, thalassemia, various forms of anaemia) and treatments (hydroxyurea, transfusions, no treatment) were analysed to establish relationships between MCVr and HU treatment, MCVr and RBC MCV, and other factors such as gender and time. The results suggest that there may be a correlation between MCV and MCVr for sickle cell patients treated with hydroxyurea versus other forms of treatment. Therefore, a prospective study should be planned to expand on the findings of this study

Biomarkers for Vincristine-induced Neuropathy

Vincristine is a vinca alkaloid, a commonly used chemotherapy drug for treating leukemia, lymphoma, multiple myeloma and some pediatric cancers. Its major dose-limiting side effect is peripheral neuropathy. The current dosing of “standard-dose-for-all” ignores the genetic and phenotypic variations among different patients, and causes severe neuropathy in some patients while ineffectively treats the others. In the present study, we aim to discover novel biomarkers involved in vincristine-induced neuropathy and identify patients with varied metabolic characteristics. Thus treatment can be tailored accordingly to improve outcomes of vincristine treatment. Pre-dose and post-dose serum samples were collected from two groups of patients (low and high toxicity groups) at the beginning of treatment and at the end of treatments. Liquid chromatography–mass spectrometry (LC-MS) was used to identify and quantify metabolites in the samples. Metabolomics data analysis tools were utilized to analyze the raw spectrum obtained from LC-MS. From statistical analysis and modeling, we identified 27 compounds that showed a difference in intensity between low toxicity and high toxicity patients at the beginning of the treatment. Further verification against database and validation are needed to confirm the biomarkers to be able to be useful in clinics. . Successful validation of the biomarkers will enable the clinicians to treat the patients according to their characteristics which will ultimately improve the survival and quality-of-life of cancer patients

A Metabolomics Approach for Early Prediction of Vincristine-Induced Peripheral Neuropathy

Vincristine is a core chemotherapeutic drug administered to pediatric acute lymphoblastic leukemia patients. Despite its efficacy in treating leukemia, it can lead to severe peripheral neuropathy in a subgroup of the patients. Peripheral neuropathy is a debilitating and painful side-effect that can severely impact an individual’s quality of life. Currently, there are no established predictors of peripheral neuropathy incidence during the early stage of chemotherapeutic treatment. As a result, patients who are not susceptible to peripheral neuropathy may receive sub-therapeutic treatment due to an empirical upper cap on the dose, while others may experience severe neuropathy at the same dose. Contrary to previous genomics based approaches, we employed a metabolomics approach to identify small sets of metabolites that can be used to predict a patient’s susceptibility to peripheral neuropathy at different time points during the treatment. Using those identified metabolites, we developed a novel strategy to predict peripheral neuropathy and subsequently adjust the vincristine dose accordingly. In accordance with this novel strategy, we created a free user-friendly tool, VIPNp, for physicians to easily implement our prediction strategy. Our results showed that focusing on metabolites, which encompasses both genotypic and phenotypic variations, can enable early prediction of peripheral neuropathy in pediatric leukemia patients

On an expanded framework for personalized cancer treatment: Beyond pharmacogenomics

Cancer has been a perennial challenge to clinicians and researchers for more than a century. The existing treatment modalities are effective only in the subsets of patient population. The current clinical approach of \u27standard-dose-for-all\u27 and subsequent titration through trial-and-error causes severe toxicity in some patients while proving insufficient to others as patients vary genetically as well as phenotypically. The much anticipated pharmacogenomics has been losing its sheen as a sole predictor of clinical outcome. The manifestation of gene (upstream causal variable) to clinical outcome (downstream response variable) proceeds through various stages; several biochemical processes interfere and manipulate the overall outcome. Given the complexity of biological processes and amount of available information, prediction of clinical response for a given treatment through simple deductive reasoning or through pharmacogenomics is infeasible. In order to meet this challenge, we developed a multidisciplinary quantitative approach, empowered by systems theoretic methodology, to serve as a decision-support mechanism for physicians to quantitatively predict the response and adjust dosage for each individual patient. The first step in treatment planning is the classification of patients into subgroups that are susceptible to extreme responses using biomarkers. To address this, we designed a metabolomics-based approach to study the global metabolic fingerprint in pre-dose samples and characteristic changes due to drug dosing in post-dose samples. The full set of metabolites was correlated to the observed clinical response using data analysis and modeling to identify differentially expressed metabolites in various response groups. The discovered biomarkers, together with our model, aid in identifying patients\u27 risk profiles well in advance, even before commencing their treatment. Once patients are divided into subgroups, the next crucial step is to predict optimal dosage for individual patients. Several classes of models, including kinetic, pharmacological and population balance models, were developed for the dynamic prediction of drug distribution, reaction and cellular interaction. Unlike physical sciences, the variability in these systems is extremely high (c.v. as high as 100%). Hence, we identified parameters using population approaches such as non-linear mixed effect modeling and Bayesian hierarchical modeling. To circumvent the scarcity of clinical data, we employed a global sensitivity analysis based model-reduction technique and improved the information content by optimal DoE techniques. The identified individual patient model was used to determine optimal dosage through robust model predictive control. To enable the translation of our framework into clinical practice, we developed a one-of-its-kind software, christened nEqualsOne. It shows a great potential to serve as a decision-support tool to enhance the decision-making capabilities of practicing clinicians and improve the survival and quality-of-life among cancer patients. The generic nature of the framework assures a broader impact in other areas of healthcare and beyond, and consequently bears wide socioeconomic implications

Optimal chemotherapy for leukemia: a model-based strategy for individualized treatment.

Acute Lymphoblastic Leukemia, commonly known as ALL, is a predominant form of cancer during childhood. With the advent of modern healthcare support, the 5-year survival rate has been impressive in the recent past. However, long-term ALL survivors embattle several treatment-related medical and socio-economic complications due to excessive and inordinate chemotherapy doses received during treatment. In this work, we present a model-based approach to personalize 6-Mercaptopurine (6-MP) treatment for childhood ALL with a provision for incorporating the pharmacogenomic variations among patients. Semi-mechanistic mathematical models were developed and validated for i) 6-MP metabolism, ii) red blood cell mean corpuscular volume (MCV) dynamics, a surrogate marker for treatment efficacy, and iii) leukopenia, a major side-effect. With the constraint of getting limited data from clinics, a global sensitivity analysis based model reduction technique was employed to reduce the parameter space arising from semi-mechanistic models. The reduced, sensitive parameters were used to individualize the average patient model to a specific patient so as to minimize the model uncertainty. Models fit the data well and mimic diverse behavior observed among patients with minimum parameters. The model was validated with real patient data obtained from literature and Riley Hospital for Children in Indianapolis. Patient models were used to optimize the dose for an individual patient through nonlinear model predictive control. The implementation of our approach in clinical practice is realizable with routinely measured complete blood counts (CBC) and a few additional metabolite measurements. The proposed approach promises to achieve model-based individualized treatment to a specific patient, as opposed to a standard-dose-for-all, and to prescribe an optimal dose for a desired outcome with minimum side-effects

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization.

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach

Schematic representation of 6-MP metabolism.

<p>Following oral intake to the gut, 6-MP is absorbed into the plasma from where it is eliminated through various routes. From plasma, 6-MP diffuses into the cells and enzymatically converted to 6-TGN and MeMP, which in turn are eliminated from the cells at a constant rate.</p

Glossary of state variables and parameters for MCV model.

<p>Glossary of state variables and parameters for MCV model.</p

Glossary of state variables and parameters for 6-MP model.

<p>Glossary of state variables and parameters for 6-MP model.</p