Project description:Nephrology has conducted few high-quality clinical trials, and the trials that have been conducted have not resulted in the approval of new treatments for primary or inflammatory glomerular diseases. There are overarching process issues that affect the conduct of all clinical trials, but there are also some specialty-specific issues. Within nephrology, primary glomerular diseases are rare, making adequate recruitment for meaningful trials difficult. Nephrologists need better ways, beyond histopathology, to phenotype patients with glomerular diseases and stratify the risk for progression to ESRD. Rigorous trial design is needed for the testing of new therapies, where most patients with glomerular diseases are offered the opportunity to enroll in a clinical trial if standard therapies have failed or are lacking. Training programs to develop a core group of kidney specialists with expertise in the design and implementation of clinical trials are also needed. Registries of patients with glomerular disease and observational studies can aid in the ability to determine realistic estimates of disease prevalence and inform trial design through a better understanding of the natural history of disease. Some proposed changes to the Common Rule, the federal regulations governing the ethical conduct of research involving humans, and the emerging use of electronic health records may facilitate the efficiency of initiating multicenter clinical trials. Collaborations among academia, government scientific and regulatory agencies, industry, foundations, and patient advocacy groups can accelerate therapeutic development for these complex diseases.
Project description:Primary mitochondrial diseases represent some of the most common and severe inherited metabolic disorders, affecting ~1 in 4,300 live births. The clinical and molecular diversity typified by mitochondrial diseases has contributed to the lack of licensed disease-modifying therapies available. Management for the majority of patients is primarily supportive. The failure of clinical trials in mitochondrial diseases partly relates to the inefficacy of the compounds studied. However, it is also likely to be a consequence of the significant challenges faced by clinicians and researchers when designing trials for these disorders, which have historically been hampered by a lack of natural history data, biomarkers and outcome measures to detect a treatment effect. Encouragingly, over the past decade there have been significant advances in therapy development for mitochondrial diseases, with many small molecules now transitioning from preclinical to early phase human interventional studies. In this review, we present the treatments and management strategies currently available to people with mitochondrial disease. We evaluate the challenges and potential solutions to trial design and highlight the emerging pharmacological and genetic strategies that are moving from the laboratory to clinical trials for this group of disorders.
Project description:This paper proposes nonparametric two-sample tests for the direct comparison of the probabilities of a particular transition between states of a continuous time non-homogeneous Markov process with a finite state space. The proposed tests are a linear nonparametric test, an L 2-norm-based test and a Kolmogorov-Smirnov-type test. Significance level assessment is based on rigorous procedures, which are justified through the use of modern empirical process theory. Moreover, the L 2-norm and the Kolmogorov-Smirnov-type tests are shown to be consistent for every fixed alternative hypothesis. The proposed tests are also extended to more complex situations such as cases with incompletely observed absorbing states and non-Markov processes. Simulation studies show that the test statistics perform well even with small sample sizes. Finally, the proposed tests are applied to data on the treatment of early breast cancer from the European Organization for Research and Treatment of Cancer (EORTC) trial 10854, under an illness-death model.
Project description:Unravelling the structure of genotype-phenotype (GP) maps is an important problem in biology. Recently, arguments inspired by algorithmic information theory (AIT) and Kolmogorov complexity have been invoked to uncover simplicity bias in GP maps, an exponentially decaying upper bound in phenotype probability with the increasing phenotype descriptional complexity. This means that phenotypes with many genotypes assigned via the GP map must be simple, while complex phenotypes must have few genotypes assigned. Here, we use similar arguments to bound the probability P(x → y) that phenotype x, upon random genetic mutation, transitions to phenotype y. The bound is [Formula: see text], where [Formula: see text] is the estimated conditional complexity of y given x, quantifying how much extra information is required to make y given access to x. This upper bound is related to the conditional form of algorithmic probability from AIT. We demonstrate the practical applicability of our derived bound by predicting phenotype transition probabilities (and other related quantities) in simulations of RNA and protein secondary structures. Our work contributes to a general mathematical understanding of GP maps and may facilitate the prediction of transition probabilities directly from examining phenotype themselves, without utilizing detailed knowledge of the GP map.
Project description:ObjectivesTo assess whether obesity may affect response to infliximab, we conducted an individual participant data pooled analysis using data from clinical trials of infliximab in inflammatory bowel diseases (IBD), using the Yale Open Data Access (YODA) Project.MethodsWe analyzed individual participant data from four clinical trials of infliximab in adults with IBD (ACCENT-I, SONIC, ACT-1, and -2). Patients were categorized as obese (body mass index [BMI] ≥ 30 kg/m2) vs. non-obese, and by quartiles based on BMI or weight at time of trial entry. Primary outcome was clinical remission (Crohn's disease activity index [CDAI] < 150 or pediatric CDAI <10, Mayo Clinic Score <3); secondary outcomes were clinical response and mucosal healing. Multivariable logistic regression analysis was performed, after adjusting for sex, smoking, disease activity, and concomitant prednisone and/or immunomodulators.ResultsWe included 1205 infliximab-treated patients (mean age 37 years, 51.6% males, 14% obese). Obesity was not associated with odds of achieving clinical remission (obese vs. non-obese: adjusted OR, 0.93 [95% CI, 0.47-1.46]; Q4 vs. Q1: aOR, 0.94 [0.61-1.47], p-value for trend = 0.97), clinical response (Q4 vs. Q1: aOR, 0.84 [0.52-1.35], p = 0.45) or mucosal healing (Q4 vs. Q1: aOR, 1.13 [0.55-2.34], p = 0.95). These results were consistent across strata based on disease type (Crohn's disease and ulcerative colitis) and trial design (induction and maintenance therapy).ConclusionsBased on individual participant data pooled analysis, obesity is not associated with inferior response to infliximab in patients with IBD. Future studies examining the association between obesity and fixed-dose therapies are warranted.
Project description:Defective complement action is a cause of several human glomerular diseases including atypical hemolytic uremic syndrome (aHUS), anti-neutrophil cytoplasmic antibody mediated vasculitis (ANCA), C3 glomerulopathy, IgA nephropathy, immune complex membranoproliferative glomerulonephritis, ischemic reperfusion injury, lupus nephritis, membranous nephropathy, and chronic transplant mediated glomerulopathy. Here we summarize ongoing clinical trials of complement inhibitors in nine glomerular diseases and show which inhibitors are used in trials for these renal disorders (http://clinicaltrials.gov).
Project description:Neurodegenerative diseases (ND) are an entire spectrum of clinical conditions that affect the central and peripheral nervous system. There is no cure currently, with treatment focusing mainly on slowing down progression or symptomatic relief. Cellular therapies with various cell types from different sources are being conducted as clinical trials for several ND diseases. They include neural, mesenchymal and hemopoietic stem cells, and neural cells derived from embryonic stem cells and induced pluripotent stem cells. In this review, we present the list of cellular therapies for ND comprising 33 trials that used neural stem progenitors, 8 that used differentiated neural cells ,and 109 trials that involved non-neural cells in the 7 ND. Encouraging results have been shown in a few early-phase clinical trials that require further investigations in a randomized setting. However, such definitive trials may not be possible given the relative cost of the trials, and in the setting of rare diseases.
Project description:To date poor treatment options are available for patients with congenital pseudarthrosis of the tibia (CPT), a pediatric orphan disease. In this study we have performed an in silico clinical trial on 200 virtual subjects, generated from a previously established model of murine bone regeneration, to tackle the challenges associated with the small, pediatric patient population. Each virtual subject was simulated to receive no treatment and bone morphogenetic protein (BMP) treatment. We have shown that the degree of severity of CPT is significantly reduced with BMP treatment, although the effect is highly subject-specific. Using machine learning techniques we were also able to stratify the virtual subject population in adverse responders, non-responders, responders and asymptomatic. In summary, this study shows the potential of in silico medicine technologies as well as their implications for other orphan diseases.
Project description:Mitochondrial diseases are a clinically and genetically heterogeneous group of disorders that result from dysfunction of the mitochondrial oxidative phosphorylation due to molecular defects in genes encoding mitochondrial proteins. Despite the advances in molecular and biochemical methodologies leading to better understanding of the etiology and mechanism of these diseases, there are still no satisfactory therapies available for mitochondrial disorders. Treatment for mitochondrial diseases remains largely symptomatic and does not significantly alter the course of the disease. Based on limited number of clinical trials, several agents aiming at enhancing mitochondrial function or treating the consequences of mitochondrial dysfunction have been used. Several agents are currently being evaluated for mitochondrial diseases. Therapeutic strategies for mitochondrial diseases include the use of agents enhancing electron transfer chain function (coenzyme Q10, idebenone, riboflavin, dichloroacetate, and thiamine), agents acting as energy buffer (creatine), antioxidants (vitamin C, vitamin E, lipoic acid, cysteine donors, and EPI-743), amino acids restoring nitric oxide production (arginine and citrulline), cardiolipin protector (elamipretide), agents enhancing mitochondrial biogenesis (bezafibrate, epicatechin, and RTA 408), nucleotide bypass therapy, liver transplantation, and gene therapy. Although, there is a lack of curative therapies for mitochondrial disorders at the current time, the increased number of clinical research evaluating agents that target different aspects of mitochondrial dysfunction is promising and is expected to generate more therapeutic options for these diseases in the future.
Project description:Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment.