Top Three Pharmacogenomics and Personalized Medicine Applications at the Nexus of Renal Pathophysiology and Cardiovascular Medicine.

Pharmacogenomics is a field with origins in the study of monogenic variations in drug metabolism in the 1950s. Perhaps because of these historical underpinnings, there has been an intensive investigation of 'hepatic pharmacogenes' such as CYP450s and liver drug metabolism using pharmacogenomics approaches over the past five decades. Surprisingly, kidney pathophysiology, attendant diseases and treatment outcomes have been vastly under-studied and under-theorized despite their central importance in maintenance of health, susceptibility to disease and rational personalized therapeutics. Indeed, chronic kidney disease (CKD) represents an increasing public health burden worldwide, both in developed and developing countries. Patients with CKD suffer from high cardiovascular morbidity and mortality, which is mainly attributable to cardiovascular events before reaching end-stage renal disease. In this paper, we focus our analyses on renal function before end-stage renal disease, as seen through the lens of pharmacogenomics and human genomic variation. We herein synthesize the recent evidence linking selected Very Important Pharmacogenes (VIP) to renal function, blood pressure and salt-sensitivity in humans, and ways in which these insights might inform rational personalized therapeutics. Notably, we highlight and present the rationale for three applications that we consider as important and actionable therapeutic and preventive focus areas in renal pharmacogenomics: 1) ACE inhibitors, as a confirmed application, 2) VDR agonists, as a promising application, and 3) moderate dietary salt intake, as a suggested novel application. Additionally, we emphasize the putative contributions of gene-environment interactions, discuss the implications of these findings to treat and prevent hypertension and CKD. Finally, we conclude with a strategic agenda and vision required to accelerate advances in this under-studied field of renal pharmacogenomics with vast significance for global public health.

Genomik und personalisierte Medizin [Genomics and personalized medicine].

Personalized medicine has a substantial potential to transform the way diseases will be predicted, prevented and treated. The field will greatly benefit from novel DNA sequencing technologies, in particular commoditization of individual whole genome sequencing. This evolution cannot be stopped, and the medical and scientific community, as well as the society at large, have the responsibility to anticipate the expected benefits from this revolution, but also the potential risks associated with it. Massive investments will be needed for the potential of personalized medicine to be realized, and for the field to come to maturity. In particular, a paradigm change in the way clinical research is done is needed. Switzerland and its Western part pro-actively anticipate these changes.

In-depth analysis of hyaline fibromatosis syndrome frameshift mutations at the same site reveal the necessity of personalized therapy.

Hyaline fibromatosis syndrome is an autosomal recessive disease caused by mutations in ANTXR2, a gene involved in extracellular matrix homeostasis. Sixty percent of patients carry frameshift mutations at a mutational hotspot in exon 13. We show in patient cells that these mutations lead to low ANTXR2 mRNA and undetectable protein levels. Ectopic expression of the proteins encoded by the mutated genes reveals that a two base insertion leads to the synthesis of a protein that is rapidly targeted to the ER-associated degradation pathway due to the modified structure of the cytosolic tail, which instead of being hydrophilic and highly disordered as in wild type ANTXR2, is folded and exposes hydrophobic patches. In contrast, one base insertion leads to a truncated protein that properly localizes to the plasma membrane and retains partial function. We next show that targeting the nonsense mediated mRNA decay pathway in patient cells leads to a rescue of ANTXR2 protein in patients carrying one base insertion but not in those carrying two base insertions. This study highlights the importance of in-depth analysis of the molecular consequences of specific patient mutations, which even when they occur at the same site can have drastically different consequences.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

MGMT promoter methylation in malignant gliomas: ready for personalized medicine?

The DNA repair enzyme O(6)-methylguanine-DNA methyltransferase (MGMT) antagonizes the genotoxic effects of alkylating agents. MGMT promoter methylation is the key mechanism of MGMT gene silencing and predicts a favorable outcome in patients with glioblastoma who are exposed to alkylating agent chemotherapy. This biomarker is on the verge of entering clinical decision-making and is currently used to stratify or even select glioblastoma patients for clinical trials. In other subtypes of glioma, such as anaplastic gliomas, the relevance of MGMT promoter methylation might extend beyond the prediction of chemosensitivity, and could reflect a distinct molecular profile. Here, we review the most commonly used assays for evaluation of MGMT status, outline the prerequisites for standardized tests, and evaluate reasons for difficulties in reproducibility. We critically discuss the prognostic and predictive value of MGMT silencing, reviewing trials in which patients with different types of glioma were treated with various chemotherapy schedules, either up-front or at recurrence. Standardization of MGMT testing requires comparison of different technologies across laboratories and prospectively validated cut-off values for prognostic or predictive effects. Moreover, future clinical trials will need to determine, for each subtype of glioma, the degree to which MGMT promoter methylation is predictive or prognostic, and whether testing should become routine clinical practice.

Personalized care in neuro-oncology coming of age: why we need MGMT and 1p/19q testing for malignant glioma patients in clinical practice.

Histological subtyping and grading by malignancy are the cornerstones of the World Health Organization (WHO) classification of tumors of the central nervous system. They shall provide clinicians with guidance as to the course of disease to be expected and the choices of treatment to be made. Nonetheless, patients with histologically identical tumors may have very different outcomes, notably in patients with astrocytic and oligodendroglial gliomas of WHO grades II and III. In gliomas of adulthood, 3 molecular markers have undergone extensive studies in recent years: 1p/19q chromosomal codeletion, O(6)-methylguanine methyltransferase (MGMT) promoter methylation, and mutations of isocitrate dehydrogenase (IDH) 1 and 2. However, the assessment of these molecular markers has so far not been implemented in clinical routine because of the lack of therapeutic implications. In fact, these markers were considered to be prognostic irrespective of whether patients were receiving radiotherapy (RT), chemotherapy, or both (1p/19q, IDH1/2), or of limited value because testing is too complex and no chemotherapy alternative to temozolomide was available (MGMT). In 2012, this situation has changed: long-term follow-up of the Radiation Therapy Oncology Group 9402 and European Organisation for Research and Treatment of Cancer 26951 trials demonstrated an overall survival benefit from the addition to RT of chemotherapy with procarbazine/CCNU/vincristine confined to patients with anaplastic oligodendroglial tumors with (vs without) 1p/19q codeletion. Furthermore, in elderly glioblastoma patients, the NOA-08 and the Nordic trial of RT alone versus temozolomide alone demonstrated a profound impact of MGMT promoter methylation on outcome by therapy and thus established MGMT as a predictive biomarker in this patient population. These recent results call for the routine implementation of 1p/19q and MGMT testing at least in subpopulations of malignant glioma patients and represent an encouraging step toward the development of personalized therapeutic approaches in neuro-oncology.

Improvement of therapeutic drug monitoring of imatinib by Bayesian prediction of trough levels. Personalized drug dosage

Aims: Plasma concentrations of imatinib differ largely between patients despite same dosage, owing to large inter-individual variability in pharmacokinetic (PK) parameters. As the drug concentration at the end of the dosage interval (Cmin) correlates with treatment response and tolerability, monitoring of Cmin is suggested for therapeutic drug monitoring (TDM) of imatinib. Due to logistic difficulties, random sampling during the dosage interval is however often performed in clinical practice, thus rendering the respective results not informative regarding Cmin values.Objectives: (I) To extrapolate randomly measured imatinib concentrations to more informative Cmin using classical Bayesian forecasting. (II) To extend the classical Bayesian method to account for correlation between PK parameters. (III) To evaluate the predictive performance of both methods.Methods: 31 paired blood samples (random and trough levels) were obtained from 19 cancer patients under imatinib. Two Bayesian maximum a posteriori (MAP) methods were implemented: (A) a classical method ignoring correlation between PK parameters, and (B) an extended one accounting for correlation. Both methods were applied to estimate individual PK parameters, conditional on random observations and covariate-adjusted priors from a population PK model. The PK parameter estimates were used to calculate trough levels. Relative prediction errors (PE) were analyzed to evaluate accuracy (one-sample t-test) and to compare precision between the methods (F-test to compare variances).Results: Both Bayesian MAP methods allowed non-biased predictions of individual Cmin compared to observations: (A) - 7% mean PE (CI95% - 18 to 4 %, p = 0.15) and (B) - 4% mean PE (CI95% - 18 to 10 %, p = 0.69). Relative standard deviations of actual observations from predictions were 22% (A) and 30% (B), i.e. comparable to the intraindividual variability reported. Precision was not improved by taking into account correlation between PK parameters (p = 0.22).Conclusion: Clinical interpretation of randomly measured imatinib concentrations can be assisted by Bayesian extrapolation to maximum likelihood Cmin. Classical Bayesian estimation can be applied for TDM without the need to include correlation between PK parameters. Both methods could be adapted in the future to evaluate other individual pharmacokinetic measures correlated to clinical outcomes, such as area under the curve(AUC).

Late side-effects after curative intent radiotherapy: Identification of hypersensitive patients for personalized strategy.

Radiation therapy undeniably enhances local control and thus improves overall survival in cancer patients. However, some long-term cancer survivors (less than 10%) develop severe late radio-induced toxicities altering their quality of life. Therefore, there is a need to identify patients who are sensitive to those toxicities and who could benefit from adapted care. In this review, we address all available techniques aiming to detect patients' hyper-radiosensitivity and present the scientific rationales these techniques are based on.

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).