Treatment Response In Individual Patients Research Articles

Electroencephalography and analgesics

To assess centrally mediated analgesic mechanisms in clinical trials with pain patients, objective standardized methods such as electroencephalography (EEG) has many advantages. The aim of this review is to provide the reader with an overview of present findings in analgesics assessed with spontaneous EEG and evoked brain potentials (EPs) in humans. Furthermore, EEG methodologies will be discussed with respect to translation from animals to humans and future perspectives in predicting analgesic efficacy. We searched PubMed with MeSH terms 'analgesics', 'electroencephalography' and 'evoked potentials' for relevant articles. Combined with a search in their reference lists 15 articles on spontaneous EEG and 55 papers on EPs were identified. Overall, opioids produced increased activity in the delta band in the spontaneous EEG, but increases in higher frequency bands were also seen. The EP amplitudes decreased in the majority of studies. Anticonvulsants used as analgesics showed inconsistent results. The N-methyl-D-aspartate receptor antagonist ketamine showed an increase in the theta band in spontaneous EEG and decreases in EP amplitudes. Tricyclic antidepressants increased the activity in the delta, theta and beta bands in the spontaneous EEG while EPs were inconsistently affected. Weak analgesics were mainly investigated with EPs and a decrease in amplitudes was generally observed. This review reveals that both spontaneous EEG and EPs are widely used as biomarkers for analgesic drug effects. Methodological differences are common and a more uniform approach will further enhance the value of such biomarkers for drug development and prediction of treatment response in individual patients.

Impact of Different Standardized Uptake Value Measures on PET-Based Quantification of Treatment Response

PET-based treatment response studies typically measure the change in the standardized uptake value (SUV) to quantify response. The relative changes of different SUV measures, such as maximum, peak, mean, or total SUVs (SUV(max), SUV(peak), SUV(mean), or SUV(total), respectively), are used across the literature to classify patients into response categories, with quantitative thresholds separating the different categories. We investigated the impact of different SUV measures on the quantification and classification of PET-based treatment response. Sixteen patients with solid malignancies were treated with a multitargeted receptor tyrosine kinase inhibitor, resulting in a variety of responses. Using the cellular proliferation marker 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT), we acquired whole-body PET/CT scans at baseline, during treatment, and after treatment. The highest (18)F-FLT uptake lesions (~2/patient) were segmented on PET images. Tumor PET response was assessed via the relative change in SUV(max), SUV(peak), SUV(mean), and SUV(total), thereby yielding 4 different responses for each tumor at mid- and posttreatment. For each SUV measure, a population average PET response was determined over all tumors. Standard deviation (SD) and range were used to quantify variation of PET response within individual tumors and population averages. Different SUV measures resulted in substantial variation of individual tumor PET response assessments (average SD, 20%; average range, 40%). The most extreme variation between 4 PET response measures was 90% in individual tumors. Classification of tumor PET response depended strongly on the SUV measure, because different SUV measures resulted in conflicting categorizations of PET response (ambiguous treatment response assessment) in more than 80% of tumors. Variation of the population average PET response was considerably smaller (average SD, 7%; average range, 16%), and this variation was not statistically significant. Differences in tumor PET response were greatest between SUV(mean) and SUV(total) and smallest between SUV(max) and SUV(peak). Variations of tumor PET response at midtreatment and posttreatment were similar. Quantification and classification of PET-based treatment response in individual patients were strongly affected by the SUV measure used to assess response. This substantial uncertainty in individual patient PET response was present despite the concurrent robustness of the population average PET response. Given the ambiguity of individual patient PET responses, selection of PET-based treatment response measures and their associated thresholds should be carefully optimized.

SU‐E‐J‐91: Patient‐Specific Virtual Radiation Oncology: Predicting and Quantifying Treatment Response in Individual Glioblastoma Patients

Purpose: To quantify and predict response to radiation therapy in vivo prior to treatment on a patient‐specific basis using mathematical modeling.Methods: Glioblastoma multiforme (GBM) are highly invasive primary brain tumors that diffusely invade the surrounding normal appearing tissue and yields short life expectancies despite aggressive treatment. The differential motility of glioma cells throughout the brain characterize tumor invasion. We present and apply a patient‐specific, mathematical model for glioma growth that quantifies response to radiation therapy (RT) in vivo in the 3D anatomy of the human brain. The mathematical model uses net rates of proliferation and migration of tumor cells to characterize the tumorˈs growth and invasion along with the linear‐quadratic model for response to radiation therapy. Results: Patient individualized simulations of tumor growth and radiation response are performed in 9 GBM patients validating a predictive relationship between the net rate of proliferation rate and radiosensitivity. The simulations accurately predict the imageable tumor post‐treatment within 2.4 mm. Because the patient‐specific net proliferation rate of the tumor is calculated prior to treatment, this result provides quantitative predictions of radiosensitivity on a patient‐by‐patient basis. Conclusions: This work presents the first mathematical model to quantify patient‐specific radiosensitivity based on in vivo data. Our results indicate that, using our model, clinicians will be able to identify the degree to which any given glioma patient will respond to radiation therapy prior to treatment. That is, a virtual in silico tumor with the same growth kinetics as a particular patient combined with the complex anatomy of the brain can predict treatment response in individual patients in vivo. This work marks a paradigm shift in the quantification of treatment response and provides a basis for individualizing radiation treatment according to both the dynamics of each tumor but also their diffuse extent.

Feature selection and classification of imbalanced datasets

Learning with discriminative methods is generally based on minimizing the misclassification of training samples, which may be unsuitable for imbalanced datasets where the recognition might be biased in favor of the most numerous class. This problem can be addressed with a generative approach, which typically requires more parameters to be determined leading to reduced performances in high dimension. In such situations, dimension reduction becomes a crucial issue. We propose a feature selection/classification algorithm based on generative methods in order to predict the clinical status of a highly imbalanced dataset made of PET scans of forty-five low-functioning children with autism spectrum disorders (ASD) and thirteen non-ASD low functioning children. ASDs are typically characterized by impaired social interaction, narrow interests, and repetitive behaviors, with a high variability in expression and severity. The numerous findings revealed by brain imaging studies suggest that ASD is associated with a complex and distributed pattern of abnormalities that makes the identification of a shared and common neuroimaging profile a difficult task. In this context, our goal is to identify the rest functional brain imaging abnormalities pattern associated with ASD and to validate its efficiency in individual classification. The proposed feature selection algorithm detected a characteristic pattern in the ASD group that included a hypoperfusion in the right Superior Temporal Sulcus (STS) and a hyperperfusion in the contralateral postcentral area. Our algorithm allowed for a significantly accurate (88%), sensitive (91%) and specific (77%) prediction of clinical category. For this imbalanced dataset, with only 13 control scans, the proposed generative algorithm outperformed other state-of-the-art discriminant methods. The high predictive power of the characteristic pattern, which has been automatically identified on whole brains without any priors, confirms previous findings concerning the role of STS in ASD. This work offers exciting possibilities for early autism detection and/or the evaluation of treatment response in individual patients.

Bridging the gap between aggregate data and individual patient management: A Bayesian approach

The aim of this study was to explore whether Bayesian reasoning can be applied to therapeutic questions in a way that is similar to its application in diagnostics. A clinically relevant, therapeutic question was formulated in accordance with Bayesian reasoning for the clinical management of patients with newly diagnosed rheumatoid arthritis (RA). Prior probability estimates of response to drug treatment (methotrexate, MTX) were obtained from the literature. As a marker of treatment response, changes in the Health Assessment Questionnaire (HAQ) scores were assessed after three months of treatment. Likelihood ratios for this marker were calculated on the basis of data from a clinical registry, using changes in the Disease Activity Score (DAS) as gold standard. Using Bayes' theorem, prior probability and likelihood ratios were combined to estimate posterior probabilities of treatment response in individual patients. On the basis of the literature, the prior probability of response of RA patients to MTX was estimated 45 percent. At 3 months follow-up, this probability increased to 80 percent or decreased to 23 percent, depending on the changes that were observed in Health Assessment Questionnaire scores. Bayesian reasoning can be applied to therapeutic issues in a way that is conceptually fully compatible with its use in diagnostics. As such, it can be used to bridge the gap between aggregate data and individual patient management.

The Role of Cardiac MR in New-Onset Heart Failure

In patients with heart failure, cardiovascular magnetic resonance imaging (CMR) allows a multifaceted approach to cardiac evaluation by enabling an assessment of morphology, function, perfusion, viability, tissue characterization, and blood flow during a single comprehensive examination. Given its accuracy and reproducibility, many believe CMR is the reference standard for the noninvasive assessment of ventricular volumes, mass, and function, and offers an ideal means for the serial assessment of disease progression or treatment response in individual patients. Delayed-enhancement (DE)-CMR provides a direct assessment of myopathic processes. This permits a fundamentally different approach than that traditionally taken to ascertaining the etiology of cardiomyopathy, which is vital in patients with nonischemic cardiomyopathy and incidental coronary artery disease and patients with mixed, ischemic and nonischemic cardiomyopathy. Precise tissue characterization with DE-CMR also improves the diagnosis of left ventricular thrombus, for which it is the emerging clinical reference standard. There is a growing body of literature on the utility of CMR for patient risk stratification, and its potential role in important management decisions such as for cardiac resynchronization therapy and defibrillator placement.

Detecting tumor responses to treatment using hyperpolarized 13C magnetic resonance spectroscopic imaging

Patients with similar tumor types can have markedly different responses to the same therapy. The development of new treatments would benefit significantly, therefore, from the introduction of imaging methods that allow an early assessment of treatment response in individual patients, allowing rapid selection of the most effective treatment [1]. We have been developing methods for detecting the early responses of tumors to therapy. This has included a targeted MRI contrast agent for detecting tumour cell death [2] and MR imaging of tumor cell metabolism using hyperpolarized 13C-labelled cellular metabolites. Nuclear spin hyperpolarization techniques can increase sensitivity in the MR experiment by >10,000x. This has allowed us to image the location of labeled cell substrates and, more importantly, their metabolic conversion into other metabolites. We showed that exchange of hyperpolarized 13C label between lactate and pyruvate, in the reaction catalyzed by the enzyme lactate dehydrogenase, could be imaged in tumors and that this flux was decreased in treated tumors undergoing drug-induced cell death [3]. We compared this method for detecting treatment response with measurements of fluorodeoxyglucose uptake [4]. We have shown, more recently, that hyperpolarized [1,4-13C]fumarate can be used to detect tumor cell necrosis post treatment [5]. We have also shown that tissue pH can be imaged from the ratio of the signal intensities of hyperpolarized H13CO3- and 13CO2 following intravenous injection of hyperpolarized H13CO3-. The technique was demonstrated with a study on a mouse tumor model, which showed that the average tumor pH was significantly lower than the surrounding tissue. Since bicarbonate is already used intravenously in humans, we propose that this technique could be used clinically to image disease and response to treatment [6].

Imaging tumour cell metabolism using hyperpolarized 13C magnetic resonance spectroscopy

Patients with similar tumour types frequently show different responses to the same therapy. The development of new treatments would benefit, therefore, from imaging methods that allow an early assessment of treatment response in individual patients, allowing rapid selection of the most effective treatment. We have been using (13)C MRSI (magnetic resonance spectroscopic imaging) of tumour cell metabolism, using hyperpolarized (13)C-labelled cellular metabolites, to detect treatment response. Nuclear spin hyperpolarization can increase sensitivity in the magnetic resonance experiment >10,000 times, allowing us to image labelled cell substrates in vivo and their subsequent metabolism. We showed that exchange of hyperpolarized (13)C label between lactate and pyruvate, catalysed by lactate dehydrogenase, was decreased in treated tumours undergoing drug-induced cell death, and that tissue pH could be imaged from the ratio of the signal intensities of hyperpolarized H(13)CO(3)(-) and (13)CO(2) following intravenous injection of hyperpolarized H(13)CO(3). Tumour cell glutaminase activity, a potential measure of cell proliferation, can be determined using hyperpolarized [5-(13)C]glutamine, and treatment-induced tumour cell necrosis can be imaged in vivo from measurements of the conversion of hyperpolarized [1,4-(13)C(2)]fumarate into malate. Since these substrates are endogenous and, in some cases, have already been safely infused into patients, these techniques have the potential to translate to the clinic.

Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach

Glioblastoma multiforme (GBM) is the most malignant form of primary brain tumors known as gliomas. They proliferate and invade extensively and yield short life expectancies despite aggressive treatment. Response to treatment is usually measured in terms of the survival of groups of patients treated similarly, but this statistical approach misses the subgroups that may have responded to or may have been injured by treatment. Such statistics offer scant reassurance to individual patients who have suffered through these treatments. Furthermore, current imaging-based treatment response metrics in individual patients ignore patient-specific differences in tumor growth kinetics, which have been shown to vary widely across patients even within the same histological diagnosis and, unfortunately, these metrics have shown only minimal success in predicting patient outcome. We consider nine newly diagnosed GBM patients receiving diagnostic biopsy followed by standard-of-care external beam radiation therapy (XRT). We present and apply a patient-specific, biologically based mathematical model for glioma growth that quantifies response to XRT in individual patients in vivo. The mathematical model uses net rates of proliferation and migration of malignant tumor cells to characterize the tumor's growth and invasion along with the linear-quadratic model for the response to radiation therapy. Using only routinely available pre-treatment MRIs to inform the patient-specific bio-mathematical model simulations, we find that radiation response in these patients, quantified by both clinical and model-generated measures, could have been predicted prior to treatment with high accuracy. Specifically, we find that the net proliferation rate is correlated with the radiation response parameter (r = 0.89, p = 0.0007), resulting in a predictive relationship that is tested with a leave-one-out cross-validation technique. This relationship predicts the tumor size post-therapy to within inter-observer tumor volume uncertainty. The results of this study suggest that a mathematical model can create a virtual in silico tumor with the same growth kinetics as a particular patient and can not only predict treatment response in individual patients in vivo but also provide a basis for evaluation of response in each patient to any given therapy.

Abstract CN01-02: Detecting tumor responses to treatment using magnetic resonance imaging and spectroscopy

Abstract CN01-02: Detecting tumor responses to treatment using magnetic resonance imaging and spectroscopy

Biomarkers for interferon response in MS

Randomized, placebo-controlled studies have firmly established interferon beta (IFNβ) as standard therapy for relapsing remitting multiple sclerosis (RRMS).1–3 Many MS experts believe that early and continuous treatment with IFNβ reduces brain inflammation, irreversible CNS injury, and brain atrophy in RRMS, and reduces permanent neurologic disability. The use of IFNβ preparations has expanded to worldwide annual sales exceeding $4 billion USD. However, IFNβ is only partially effective (e.g., relapse rates are reduced by about 30%), so many patients have relapses, new MRI lesions while on treatment, and patients may become increasingly disabled with time. There are no sensitive, validated clinical markers of treatment response for individual patients. How is the neurologist to decide whether a patient is responding to treatment? Objective, practical markers of IFNβ pharmacologic response would be extremely helpful to guide therapy. In this issue of Neurology ®, Malucchi et al.4 describe a novel approach to the problem. Pharmacologic response was defined as increased levels of peripheral blood mononuclear cell myxovirus-resistance protein A (MxA) mRNA, 12 hours after an IFNβ injection. IFNβ exerts biologic effects by inducing expression of several hundred interferon-responsive genes (ISGs). This large number of ISGs is necessary …

Pharmacologic Treatment of Obsessive-compulsive Disorder

<P>The main goal of treatment for obsessive-compulsive disorder (OCD) is to reduce the severity and the intrusiveness of obsessions and compulsions so as to improve the patient’s quality of life. This article reviews current pharmacologic treatments for OCD, including a comprehensive review of the literature regarding the effectiveness of various agents, as well as their side effects and factors affecting treatment response in individual patients.</P> <H4>ABOUT THE AUTHORS</H4> <P>Dr. Marazziti is professor of psychiatry, Dipartimento di Psichiatria, Neurobiologia, Farmacologia e Biotecnologie, University of Pisa, Pisa, Italy. Dr. Catena is resident, Università degli Studi Firenze, [LOCATION]. Dr. Pallanti is professor of psychiatry, University of Florence, Florence, Italy, and Mount Sinai School of Medicine, New York, NY. </P> <P>Address reprint requests to: Dr. Donatella Marazziti, Dipartimento di Psichiatria, Neurobiologia, Farmacologia e Biotecnologie, University of Pisa, via Roma, 67, I-56100 Pisa, Italy; or e-mail <a href="mailto:dmarazzi@psico.med.unipi.it">dmarazzi@psico.med.unipi.it</a>. </P> <P>The authors disclosed no relevant financial relationships.</P>

Development of biomarkers in multiple sclerosis.

Multiple sclerosis is a complex disease, as several pathophysiological processes (including inflammation, demyelination, axonal damage and repair mechanisms) participate in the disease process. Furthermore, as new pathological evidence reveals, these processes are not uniformly represented across patient populations but can selectively predominate in individual patients, thus contributing to the heterogeneity in phenotypic expression of the disease, its prognosis and response to therapies. While the armamentarium of available therapies for multiple sclerosis broadens, little is known about factors that predict treatment response in individual patients to a specific drug. More importantly, we are beginning to understand that, analogous to cancer therapy, the successful therapeutic strategy in multiple sclerosis might ultimately involve the combination of different therapeutics targeting several dominant pathophysiological processes. The development of these process-specific therapies will be impossible without the use of biomarkers that reflect the targeted process, can select patient population in which the targeted process is prevailing and can aid during the more rapid screening of therapeutic agents in the early phase of their development. This review summarizes the general concepts of biomarkers and their potential use as surrogate endpoints and tailors these concepts to specific applications in multiple sclerosis research.