Immunological evaluation of rheumatoid arthritis patients treated with itolizumab

Rheumatoid arthritis is an autoimmune disease characterized by joint inflammation that affects approximately 1% of the general population. Itolizumab, a monoclonal antibody specific for the human CD6 molecule mainly expressed on T lymphocytes, has been shown to inhibit proliferation of T cells and proinflammatory cytokine production in psoriasis patients. We have now assessed the immunological effect of itolizumab in combination with methotrexate in rheumatoid arthritis by analyzing clinical samples taken from 30 patients enrolled in a clinical trial. T and B cell subpopulations were measured at different time points of the study. Plasma cytokine levels and anti-idiotypic antibody response to itolizumab were also evaluated. The combined treatment of itolizumab and methotrexate led to a reduction in the frequency of T cell subpopulations, and plasma levels of proinflammatory cytokines showed a significant decrease up to at least 12 weeks after treatment ended. No anti-idiotypic antibody response was detected. These results support the relevance of the CD6 molecule as a therapeutic target for the treatment of this disease.     

Efficacy of topical non-steroidal anti-inflammatory drugs in the treatment of osteoarthritis: meta-analysis of randomised controlled trials

Objective To assess the efficacy of topical non-steroidal anti-inflammatory drugs (NSAIDs) in the treatment of osteoarthritis.Data sources Medline, Embase, Scientific Citation Index, CINAHL, Cochrane Library, and abstracts from conferences.Review methods Inclusion criterion was randomised controlled trials comparing topical NSAIDs with placebo or oral NSAIDs in osteoarthritis. Effect size was calculated for pain, function, and stiffness. Rate ratio was calculated for dichotomous data such as clinical response rate and adverse event rate. Number needed to treat to obtain the clinical response was estimated. Quality of trial was assessed, and sensitivity analyses were undertaken.Results Topical NSAIDs were superior to placebo in relieving pain due to osteoarthritis only in the first two weeks of treatment. Effect sizes for weeks 1 and 2 were 0.41 (95% confidence interval, 0.16 to 0.66) and 0.40 (0.15 to 0.65), respectively. No benefit was observed over placebo in weeks 3 and 4. A similar pattern was observed for function, stiffness, and clinical response rate ratio and number needed to treat. Topical NSAIDs were inferior to oral NSAIDs in the first week of treatment and associated with more local side effects such as rash, itch, or burning (rate ratio 5.29, 1.14 to 24.51).Conclusion Randomised controlled trials of short duration only (less than four weeks) have assessed the efficacy of topical NSAIDs in osteoarthritis. After two weeks there was no evidence of efficacy superior to placebo. No trial data support the long term use of topical NSAIDs in osteoarthritis.     

Abandoning personalization to get to precision in the pharmacotherapy of depression

Effectiveness studies and analyses of naturalistic cohorts demonstrate that many patients with major depressive disorder do not experience symptomatic remission with antidepressant treatments. In an effort to better match patients with effective treatments, numerous investigations of predictors or moderators of treatment response have been reported over the past five decades, including clinical features as well as biological measures. However, none of these have entered routine clinical practice; instead, clinicians typically personalize treatment on the basis of patient preferences as well as their own. Here, we review the reasons why it has been challenging to identify and deploy treatment‐specific predictors of response, and suggest strategies that may be required to achieve true precision in the pharmacotherapy of depression. We emphasize the need for changes in how depression care is delivered, measured, and used to inform future practice.     

Gene expression monitoring accurately predicts medulloblastoma positive and negative clinical outcomes

Prediction of medulloblastoma clinical outcome is crucial to personalizing treatment, both to identify high-risk patients for aggressive or alternative therapy and to spare those at low risk from excessive treatment. The best predictors [Pomeroy et al. (2002) Nature 415, 436–442], based on gene expression monitoring at diagnosis, have shown much less accuracy in recognizing patients with eventual failed outcomes – <50% for the predictor making fewest total errors – than those who would survive, while a single gene predictor exhibited reverse asymmetry. Such inaccuracy in recognizing one of the outcomes is a problem for clinical use. We hypothesized that a non-linear model could be built to significantly improve prediction of medulloblastoma outcome, thereby promoting use of gene-expression-based predictors in a clinical setting. In fact, this approach resulted in fewer errors and much less asymmetry in prediction, and bidirectional accuracy of about 80% could be obtained via its combination with other methods. Indeed, three combinations of methods were identified that yielded significantly better predictions of clinical outcome than previously attained, making feasible predictors of medulloblastoma treatment response with greatly improved bidirectional accuracy essential for clinical use.     

IL-4 augments anisomycin-induced p38 activation via Akt pathway in a follicular dendritic cell (FDC)-like line

Prediction of medulloblastoma clinical outcome is crucial to personalizing treatment, both to identify high-risk patients for aggressive or alternative therapy and to spare those at low risk from excessive treatment. The best predictors [Pomeroy et al. (2002) Nature 415, 436-442], based on gene expression monitoring at diagnosis, have shown much less accuracy in recognizing patients with eventual failed outcomes - <50% for the predictor making fewest total errors - than those who would survive, while a single gene predictor exhibited reverse asymmetry. Such inaccuracy in recognizing one of the outcomes is a problem for clinical use. We hypothesized that a non-linear model could be built to significantly improve prediction of medulloblastoma outcome, thereby promoting use of gene-expression-based predictors in a clinical setting. In fact, this approach resulted in fewer errors and much less asymmetry in prediction, and bidirectional accuracy of about 80% could be obtained via its combination with other methods. Indeed, three combinations of methods were identified that yielded significantly better predictions of clinical outcome than previously attained, making feasible predictors of medulloblastoma treatment response with greatly improved bidirectional accuracy essential for clinical use.     

Gene selection in arthritis classification with large-scale microarray expression profiles

The use of large-scale microarray expression profiling to identify predictors of disease class has become of major interest. Beyond their impact in the clinical setting (i.e. improving diagnosis and treatment), these markers are also likely to provide clues on the molecular mechanisms underlining the diseases. In this paper we describe a new method for the identification of multiple gene predictors of disease class. The method is applied to the classification of two forms of arthritis that have a similar clinical endpoint but different underlying molecular mechanisms: rheumatoid arthritis (RA) and osteoarthritis (OA). We aim at both the classification of samples and the location of genes characterizing the different classes. We achieve both goals simultaneously by combining a binary probit model for classification with Bayesian variable selection methods to identify important genes.We find very small sets of genes that lead to good classification results. Some of the selected genes are clearly correlated with known aspects of the biology of arthritis and, in some cases, reflect already known differences between RA and OA.     

Predictors of response to aromatase inhibitors

Aromatase inhibitors are now considered to be part of the endocrine treatment for most hormone receptor-positive breast cancer in post-menopausal women for both early and advanced disease. Despite the impressive efficacy of these agents, up to 50% of treated patients exhibit de novo or intrinsic resistance to aromatase inhibitors and hence identification of response predictors is essential to allow treatment to be directed towards responsive populations and for alternative or additional therapies to be offered to resistant patients. Emerging data seem to suggest a role for the conventional tumour markers of oestrogen receptor and progesterone receptor as possible predictors of response but, particularly in the adjuvant setting, the extent to which these are useful has not been fully elucidated. Data from both the neo-adjuvant and advanced disease settings suggest that response to aromatase inhibitors does not appear to be adversely affected by HER-2 overexpression. Within neo-adjuvant aromatase inhibitor studies, the proliferation marker Ki67 has shown a significant correlation with relapse-free survival, suggesting a role in prediction for measurement of Ki67 and other dynamic markers of response. Analysis of multiple gene expression changes over a short treatment period may also have potential clinical utility for prediction of response.     

Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT): Study Protocol for a Randomized Controlled Trial

ABSTRACT: BACKGROUND: Limited controlled data exist to guide treatment choices for clinicians caring for patients with major depressive disorder (MDD). Although many putative predictors of treatment response have been reported, most were identified through retrospective analyses of existing datasets and very few have been replicated in a manner that can impact clinical practice. One major confound in previous studies examining predictors of treatment response is the patient's treatment history, which may affect both the predictor of interest and treatment outcomes. Moreover, prior treatment history provides an important source of selection bias, thereby limiting generalizability. Consequently, we initiated a randomized clinical trial designed to identify factors that moderate response to three treatments for MDD among patients never treated previously for the condition. METHODS: Treatment-naive adults aged 18-65 years with moderate-to-severe, non-psychotic MDD are randomized equally to one of three 12-week treatment arms: 1) cognitive behavior therapy (CBT, 16 sessions), 2) duloxetine (30-60 mg/d), or 3) escitalopram (10-20 mg/d). Prior to randomization, patients undergo multiple assessments, including resting state functional magnetic resonance imaging (fMRI), immune markers, DNA and gene expression products, and dexamethasone-corticotropin releasing hormone (Dex/CRH) testing. Prior to or shortly after randomization, patients also complete a comprehensive personality assessment. Repeat assessment of the biological measures (fMRI, immune markers, and gene expression products) occur at an early time-point in treatment, and upon completion of 12-week treatment, when a a second Dex/CRH test is also conducted, Patients remitting by the end of this acute treatment phase are then eligible to enter a 21-month follow-up phase, with quarterly visits to monitor for recurrence. Non-remitters are offered augmentation treatment for a second 12-week course of treatment, during which they receive a combination of CBT and antidepressant medication. Predictors of the primary outcome, remission, will be identified for overall and treatment-specific effects, and a statistical model incorporating multiple predictors will be developed to predict outcomes. DISCUSSION: The PReDICT study's evaluation of biological, psychological, and clinical factors that may differentially impact treatment outcomes represents a sizeable step toward developing personalized treatments for MDD. Identified predictors should help guide the selection of initial treatments, and identify those patients most vulnerable to recurrence, who thus warrant maintenance or combination treatments to achieve and maintain wellness.     

Gait Biomechanics and Patient-Reported Function as Predictors of Response to a Hip Strengthening Exercise Intervention in Patients with Knee Osteoarthritis

ObjectiveMuscle strengthening exercises have been shown to improve pain and function in adults with mild-to-moderate knee osteoarthritis, but individual response rates can vary greatly. Predicting individuals who respond and those who do not is important in developing a more efficient and effective model of care for knee osteoarthritis (OA). Therefore, the purpose of this study was to use pre-intervention gait kinematics and patient-reported outcome measures to predict post-intervention response to a 6-week hip strengthening exercise intervention in patients with mild-to-moderate knee OA.MethodsThirty-nine patients with mild-to-moderate knee osteoarthritis completed a 6-week hip-strengthening program and were subgrouped as Non-Responders, Low-Responders, or High-Responders following the intervention based on their change in Knee injury Osteoarthritis Outcome Score (KOOS). Predictors of responder subgroups were retrospectively determined from baseline patient-reported outcome measures and kinematic gait parameters in a discriminant analysis of principal components. A 3–4 year follow-up on 16 of the patients with knee OA was also done to examine long-term changes in these parameters.ResultsA unique combination of patient-reported outcome measures and kinematic factors was able to successfully subgroup patients with knee osteoarthritis with a cross-validated classification accuracy of 85.4%. Lower patient-reported function in daily living (ADL) scores and hip frontal plane kinematics during the loading response were most important in classifying High-Responders from other sub-groups, while a combination of hip, knee, ankle kinematics were used to classify Non-Responders from Low-Responders.ConclusionPatient-reported outcome measures and objective biomechanical gait data can be an effective method of predicting individual treatment success to an exercise intervention. Measuring gait kinematics, along with patient-reported outcome measures in a clinical setting can be useful in helping make evidence-based decisions regarding optimal treatment for patients with knee OA.     

神经根型颈椎病和脊髓型颈椎病手术治疗的研究进展

颈椎病是一种临床发病率较高的退行性病变,发病原因主要是颈椎长期劳损、骨质增生或椎间盘脱出、韧带增厚等导致颈椎脊髓、神经根或椎动脉受压,进而引起一系列功能性障碍。临床普遍认为,对于已经得到明确诊断,且神经根压迫症状严重的患者,以及经保守治疗后症状无明显好转的患者应及时采取手术治疗,解除压迫状态,从而获得良好的预后。随着融合技术的进步及合成材料的改进,颈椎病的手术治疗效果也不断提高。本文对近年来国内外神经根型颈椎病和脊髓型颈椎病手术治疗的相关文献进行综述。     

Predictors and Moderators of Internet- and Group-Based Cognitive Behaviour Therapy for Panic Disorder

Internet-based cognitive behaviour therapy (ICBT) can be equally effective as traditional face-to-face cognitive behaviour therapy (CBT) for treating panic disorder (PD). However, little is known about the predictors and moderators of outcome of ICBT when delivered in psychiatric outpatient settings. This study investigated a selection of outcome predictors and moderators of ICBT for panic disorder based on data from a randomised controlled trial where therapist-guided ICBT was compared with group CBT (GCBT) for panic disorder. Participants (N = 104) received 10 weeks of ICBT or GCBT and were assessed before and after treatment, and after six months. Multiple regression analyses were used to test for significant predictors of treatment outcome. Predictors of positive treatment response for both modalities were having low levels of symptom severity and work impairment. In addition, anxiety sensitivity was found to have a small negative relationship with treatment outcome, suggesting that anxiety sensitivity may slightly enhance treatment response. Treatment modality had a moderating effect on the relationship between domestic impairment and outcome and on the relationship between initial age of onset of panic symptoms and treatment outcome, favouring ICBT for patients having had an early onset of PD symptoms and for patients having a high domestic functional impairment. These results suggest that both ICBT and GCBT are effective treatment modalities for PD and that it is possible to predict a significant proportion of the long-term outcome variance based on clinical variables.     

A novel role for macrophage: antigen discrimination of distinct carbohydrate bonds.

Velocity sedimentation-derived subpopulations of peritoneal exudate macrophages have been pulsed with a variety of TNP-coupled carbohydrate/protein antigens and investigated for their ability to present those antigens in immunogenic form to whole spleen cell populations. The data presented indicate that while no difference is seen in the ability of the various subpopulations examined to present protein antigens for an antibody response, different cells are certainly involved in the antigen handling of carbohydrate antigens for an antibody response. Moreover, it seems that individual macrophage subpopulations can only interact with certain subpopulations of B lymphocytes to induce antibody responses to the same immunogenic determinants.     

Identification of subpopulations with distinct treatment benefit rate using the Bayesian tree

Characterization of a subpopulation by the difference in marginal means of the outcome under the intervention and control may not be sufficient to provide informative guidance for individual decision and public policy making. Specifically, often we are interested in the treatment benefit rate (TBR), that is, the probability of benefitting an intervention in a meaningful way. For binary outcomes, TBR is the proportion that has “unfavorable” outcome under the control and “favorable” outcome under the intervention. Identification of subpopulations with distinct TBR by baseline characteristics will have significant implications in clinical setting where a medical intervention with potential negative health impact is under consideration for a given patient. In addition, these subpopulations with unique TBR set the basis for guidance in implementing the intervention toward a more personalized scheme of treatment. In this article, we propose a Bayesian tree based latent variable model to seek subpopulations with distinct TBR. Our method offers a nonparametric Bayesian framework that accounts for the uncertainty in estimating potential outcomes and allows more exhaustive search of the partitions of the baseline covariates space. The method is evaluated through a simulation study and applied to a randomized clinical trial of implantable cardioverter defibrillators to reduce mortality.     

Molecular markers in malignant cutaneous melanoma: Gift horse or one‐trick pony?

The management of malignant cutaneous melanoma is problematic. Current clinical prognostic factors do not adequately predict disease recurrence and overall survival in a significant subset of patients. Adjuvant therapies for melanoma are notoriously toxic and associated with significant morbidity. Furthermore, it has been difficult to predict which patients will respond best to these treatments, if at all. DNA and RNA biomarkers have been developed to help overcome these problems. Biomarkers have been shown to upstage patients with melanoma, but are the assays sensitive and specific enough for clinical use as predictors of disease outcome or treatment response? We review our experience with DNA and RNA biomarkers in terms of their prognostic and predictive capabilities in malignant melanoma and outline their likely role in the future of melanoma staging, surveillance, and treatment. © 2005 Wiley‐Liss, Inc.     

Baseline Predictors of Success When Comparing Two Treatments

Since few medications are equally effective in all patients, physicians can maximize the risk/benefit ratio of therapy for their patients by limiting exposure based on baseline predictors of success. Traditional procedures typically evaluate the response of patients receiving the same treatment regimen without evaluating a comparator. However, when treatments are compared, such as in clinical trials, traditional procedures of identifying predictors must be modified to analyze the treatment effect on the primary outcome variable. We focus on clinical and statistical considerations that arise when developing baseline predictors through models which consider treatment differences. To illustrate an application of this method, we used data from 1,026 patients completing at least 6 months of double-blind therapy in clinical trials comparing fluoxetine (N=522) with placebo (N=504) for weight loss. Stepwise regression procedures were used to identify baseline variables which were predictive of a beneficial fluoxetine treatment effect on last-visit-carried-forward (LVCF) weight change. In this example, age, smoking activity, and uric acid concentration were the best baseline predictors of long-term treatment effect relative to LVCF weight change. Patients were more likely to achieve long-term benefit with fluoxetine if they were older, and/or were nonsmokers, and/or had high concentrations of uric acid at baseline. These predictors, developed through models keying on treatment effect, can be used to identify patients who are more likely to accrue benefits with active therapy beyond those expected with placebo therapy, thus enriching the treatment population so that a higher proportion of treated patients are successful.     

Persisters and beyond: Mechanisms of phenotypic drug resistance and drug tolerance in bacteria

Abstract

One of the challenges in clinical infectious diseases is the problem of chronic infections, which can require long durations of antibiotic treatment and often recur. An emerging explanation for the refractoriness of some infections to treatment is the existence of subpopulations of drug tolerant cells. While typically discussed as “persister” cells, it is becoming increasingly clear that there is significant heterogeneity in drug responses within a bacterial population and that multiple mechanisms underlie the emergence of drug tolerant and drug-resistant subpopulations. Many of these parallel mechanisms have been shown to affect drug susceptibility at the level of a whole population. Here we review mechanisms of phenotypic drug tolerance and resistance in bacteria with the goal of providing a framework for understanding the similarities and differences in these cells.     

Radiation therapy after radical prostatectomy: why patience is a virtue! The case for salvage radiation therapy

Without reliable clinical or pathologic predictors of local recurrence, selection of patients for adjuvant radiotherapy based on any combination of clinical or pathological parameters is bound to lead to the unnecessary treatment of significant numbers of patients whose disease might not have ultimately recurred or who might have been destined to have recurrence with extrapelvic metastatic disease, for which pelvic radiation would be ineffective. Furthermore, new ultrasensitive prostate-specific antigen (PSA) assays can identify patients actually failing surgery with a detectable and rising PSA earlier than ever, when disease volume is low and still amenable to salvage radiation therapy, and can allow the calculation of the PSA doubling time, which is gaining widespread acceptance as a proven predictor of response to salvage radiation therapy in this setting. Therefore, the rationale for preemptive adjuvant radiation therapy after radical prostatectomy is weaker than ever.     

Early diagnosis to enable early treatment of pre-osteoarthritis

Osteoarthritis is a prevalent and disabling disease affecting an increasingly large swathe of the world population. While clinical osteoarthritis is a late-stage condition for which disease-modifying opportunities are limited, osteoarthritis typically develops over decades, offering a long window of time to potentially alter its course. The etiology of osteoarthritis is multifactorial, showing strong associations with highly modifiable risk factors of mechanical overload, obesity and joint injury. As such, characterization of pre-osteoarthritic disease states will be critical to support a paradigm shift from palliation of late disease towards prevention, through early diagnosis and early treatment of joint injury and degeneration to reduce osteoarthritis risk. Joint trauma accelerates development of osteoarthritis from a known point in time. Human joint injury cohorts therefore provide a unique opportunity for evaluation of pre-osteoarthritic conditions and potential interventions from the earliest stages of degeneration. This review focuses on recent advances in imaging and biochemical biomarkers suitable for characterization of the pre-osteoarthritic joint as well as implications for development of effective early treatment strategies.     

On the predictive utility of animal models of osteoarthritis

Animal models of osteoarthritis are extensively used for investigating disease pathways and for preclinical testing of novel therapies. Their predictive utility, however, has often been questioned, mainly because preclinical efficacy of novel therapeutics is poorly translated in clinical trials. In the current narrative review, we consider the preclinical models that were used to support undertaking clinical trials for disease-modifying osteoarthritis drugs, and compare outcomes between clinical and preclinical studies. We discuss this in light of the 1999 Food and Drug Administration draft guidelines for industry for use in the development of drugs, devices, and biological products intended for the treatment of osteoarthritis, which raised five considerations on the usefulness of osteoarthritis models. We systematically discuss what has been learnt regarding these five points since 1999, with emphasis on replicating distinct risk factors and subtypes of human osteoarthritis, and on comprehensive evaluation of the disease in animals, including pathology of all joint tissues, biomarker analysis, and assessment of pain and joint function. Finally, we discuss lessons learnt and propose some recommendations for how the evidence from preclinical research might be strengthened with a view to improving success in clinical translation.