Personalized therapy in pain management: where do we stand?

Genomic variations influencing response to pharmacotherapy of pain are currently under investigation. Drug-metabolizing enzymes represent a major target of ongoing research in order to identify associations between an individual's drug response and genetic profile. Polymorphisms of the cytochrome P450 enzymes (CYP2D6) influence metabolism of codeine, tramadol, hydrocodone, oxycodone and tricyclic antidepressants. Blood concentrations of some NSAIDs depend on CYP2C9 and/or CYP2C8 activity. Genomic variants of these genes associate well with NSAIDs' side effect profile. Other candidate genes, such as those encoding (opioid) receptors, transporters and other molecules important for pharmacotherapy in pain management, are discussed; however, study results are often equivocal. Besides genetic variants, further variables, for example, age, disease, comorbidity, concomitant medication, organ function as well as patients' compliance, may have an impact on pharmacotherapy and need to be addressed when pain therapists prescribe medication. Although pharmacogenetics as a diagnostic tool has the potential to improve patient therapy, well-designed studies are needed to demonstrate superiority to conventional dosing regimes.

Biological determinants of responsiveness to growth hormone: pharmacogenomics and personalized medicine

It is becoming most clear that many genes are involved in controlling the regulation of growth. Ultimately however, at the level of growth hormone (GH), the relevant question may be not whether a patient is GH-deficient, but whether he is GH-responsive. As these disturbances can be divided into two gross categories, namely alterations causing subnormal GH secretion and/or those presenting with subnormal GH sensitivity/responsiveness, the main aim of this review is to focus on genes involved in growth regulation leading to short stature caused by an alteration of GH insensitivity/GH responsiveness; in other words, clinical circumstances where individually adapted GH replacement therapy may help to increase height velocity and eventually final height.

E-Health towards ecumenical framework for personalized medicine via Decision Support System

The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment.

Personalized X-Ray Reconstruction of the Proximal Femur via Intensity-Based Non-rigid 2D-3D Registration

This paper presents a new approach for reconstructing a patient-specific shape model and internal relative intensity distribution of the proximal femur from a limited number (e.g., 2) of calibrated C-arm images or X-ray radiographs. Our approach uses independent shape and appearance models that are learned from a set of training data to encode the a priori information about the proximal femur. An intensity-based non-rigid 2D-3D registration algorithm is then proposed to deformably fit the learned models to the input images. The fitting is conducted iteratively by minimizing the dissimilarity between the input images and the associated digitally reconstructed radiographs of the learned models together with regularization terms encoding the strain energy of the forward deformation and the smoothness of the inverse deformation. Comprehensive experiments conducted on images of cadaveric femurs and on clinical datasets demonstrate the efficacy of the present approach.

Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients

Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented.

Do we need new personalized emergency telehealth solutions? A survey of 100 emergency department patients and a first report of the swiss limmex emergency wristwatch: an original study

Development of new personal mobile and wireless devices for healthcare has become essential due to our aging population characterized by constant rise in chronic diseases that consequently require a complex treatment and close monitoring. Personal telehealth devices allow patients to adequately receive their appropriate treatment, followup with their doctors, and report any emergency without the need of the presence of any caregivers with them thus increasing their quality of life in a cost-effective fashion. This paper includes a brief overview of personal telehealth systems, a survey of 100 consecutive ED patients aged >65 years, and introduces "Limmex" a new GSM based technology packaged in a wristwatch. Limmex can by a push of a button initiate multiple emergency call and establish mobile communication between the patient and a preselected person, institution, or a search and rescue service. To the best of our knowledge, Limmex is the first of its kind worldwide.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

Personalized Tuning of a Reinforcement Learning Control Algorithm for Glucose Regulation

Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach. The control algorithm provides daily updates of the basal rate and insulin-to-carbohydrate (IC) ratio in order to optimize glucose regulation. A method for the automatic and personalized initialization of the control algorithm is designed based on the estimation of the transfer entropy (TE) between insulin and glucose signals. The algorithm has been evaluated in silico in adults, adolescents and children for 10 days. Three scenarios of initialization to i) zero values, ii) random values and iii) TE-based values have been comparatively assessed. The results have shown that when the TE-based initialization is used, the algorithm achieves faster learning with 98%, 90% and 73% in the A+B zones of the Control Variability Grid Analysis for adults, adolescents and children respectively after five days compared to 95%, 78%, 41% for random initialization and 93%, 88%, 41% for zero initial values. Furthermore, in the case of children, the daily Low Blood Glucose Index reduces much faster when the TE-based tuning is applied. The results imply that automatic and personalized tuning based on TE reduces the learning period and improves the overall performance of the AC algorithm.

Taking personalized medicine seriously: Biomarker approaches in phase IIb/III studies in major depression and schizophrenia

The success rate in the development of psychopharmacological compounds is insufficient. Two main reasons for failure have been frequently identified: 1) treating the wrong patients and 2) using the wrong dose. This is potentially based on the known heterogeneity among patients, both on a syndromal and a biological level. A focus on personalized medicine through better characterization with biomarkers has been successful in other therapeutic areas. Nevertheless, obstacles toward this goal that exist are 1) the perception of a lack of validation, 2) the perception of an expensive and complicated enterprise, and 3) the perception of regulatory hurdles. The authors tackle these concerns and focus on the utilization of biomarkers as predictive markers for treatment outcome. The authors primarily cover examples from the areas of major depression and schizophrenia. Methodologies covered include salivary and plasma collection of neuroendocrine, metabolic, and inflammatory markers, which identified subgroups of patients in the Netherlands Study of Depression and Anxiety. A battery of vegetative markers, including sleep-electroencephalography parameters, heart rate variability, and bedside functional tests, can be utilized to characterize the activity of a functional system that is related to treatment refractoriness in depression (e.g., the renin-angiotensin-aldosterone system). Actigraphy and skin conductance can be utilized to classify patients with schizophrenia and provide objective readouts for vegetative activation as a functional marker of target engagement. Genetic markers, related to folate metabolism, or folate itself, has prognostic value for the treatment response in patients with schizophrenia. Already, several biomarkers are routinely collected in standard clinical trials (e.g., blood pressure and plasma electrolytes), and appear to be differentiating factors for treatment outcome. Given the availability of a wide variety of markers, the further development and integration of such markers into clinical research is both required and feasible in order to meet the benefit of personalized medicine. This article is based on proceedings from the "Taking Personalized Medicine Seriously-Biomarker Approaches in Phase IIb/III Studies in Major Depression and Schizophrenia" session, which was held during the 10th Annual Scientific Meeting of the International Society for Clinical Trials Meeting (ISCTM) in Washington, DC, February 18 to 20, 2014.

Towards personalized medicine: Chemosensitivity assays of patient lung cancer cell spheroids in a perfused microfluidic platform

Cancer is responsible for millions of deaths worldwide and the variability in disease patterns calls for patient-specific treatment. Therefore, personalized treatment is expected to become a daily routine in prospective clinical tests. In addition to genetic mutation analysis, predictive chemosensitive assays using patient's cells will be carried out as a decision making tool. However, prior to their widespread application in clinics, several challenges linked to the establishment of such assays need to be addressed. To best predict the drug response in a patient, the cellular environment needs to resemble that of the tumor. Furthermore, the formation of homogeneous replicates from a scarce amount of patient's cells is essential to compare the responses under various conditions (compound and concentration). Here, we present a microfluidic device for homogeneous spheroid formation in eight replicates in a perfused microenvironment. Spheroid replicates from either a cell line or primary cells from adenocarcinoma patients were successfully created. To further mimic the tumor microenvironment, spheroid co-culture of primary lung cancer epithelial cells and primary pericytes were tested. A higher chemoresistance in primary co-culture spheroids compared to primary monoculture spheroids was found when both were constantly perfused with cisplatin. This result is thought to be due to the barrier created by the pericytes around the tumor spheroids. Thus, this device can be used for additional chemosensitivity assays (e.g. sequential treatment) of patient material to further approach the personalized oncology field.

Risk profiles and penetrance estimations in multiple endocrine neoplasia type 2A caused by germline RET mutations located in exon 10

Multiple endocrine neoplasia type 2 is characterized by germline mutations in RET. For exon 10, comprehensive molecular and corresponding phenotypic data are scarce. The International RET Exon 10 Consortium, comprising 27 centers from 15 countries, analyzed patients with RET exon 10 mutations for clinical-risk profiles. Presentation, age-dependent penetrance, and stage at presentation of medullary thyroid carcinoma (MTC), pheochromocytoma, and hyperparathyroidism were studied. A total of 340 subjects from 103 families, age 4-86, were registered. There were 21 distinct single nucleotide germline mutations located in codons 609 (45 subjects), 611 (50), 618 (94), and 620 (151). MTC was present in 263 registrants, pheochromocytoma in 54, and hyperparathyroidism in 8 subjects. Of the patients with MTC, 53% were detected when asymptomatic, and among those with pheochromocytoma, 54%. Penetrance for MTC was 4% by age 10, 25% by 25, and 80% by 50. Codon-associated penetrance by age 50 ranged from 60% (codon 611) to 86% (620). More advanced stage and increasing risk of metastases correlated with mutation in codon position (609?620) near the juxtamembrane domain. Our data provide rigorous bases for timing of premorbid diagnosis and personalized treatment/prophylactic procedure decisions depending on specific RET exon 10 codons affected.

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

Systems medicine and integrated care to combat chronic noncommunicable diseases

We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.