Laquinimod arrests experimental autoimmune encephalomyelitis by activating the aryl hydrocarbon receptor.

Laquinimod is an oral drug currently being evaluated for the treatment of relapsing, remitting, and primary progressive multiple sclerosis and Huntington's disease. Laquinimod exerts beneficial activities on both the peripheral immune system and the CNS with distinctive changes in CNS resident cell populations, especially astrocytes and microglia. Analysis of genome-wide expression data revealed activation of the aryl hydrocarbon receptor (AhR) pathway in laquinimod-treated mice. The AhR pathway modulates the differentiation and function of several cell populations, many of which play an important role in neuroinflammation. We therefore tested the consequences of AhR activation in myelin oligodendrocyte glycoprotein (MOG)-induced experimental autoimmune encephalomyelitis (EAE) using AhR knockout mice. We demonstrate that the pronounced effect of laquinimod on clinical score, CNS inflammation, and demyelination in EAE was abolished in AhR-/- mice. Furthermore, using bone marrow chimeras we show that deletion of AhR in the immune system fully abrogates, whereas deletion within the CNS partially abrogates the effect of laquinimod in EAE. These data strongly support the idea that AhR is necessary for the efficacy of laquinimod in EAE and that laquinimod may represent a first-in-class drug targeting AhR for the treatment of multiple sclerosis and other neurodegenerative diseases.

Biobanking in Israel 2016-17; expressed perceptions versus real life enrollment.

BACKGROUND: As part of the preparations to establish a population-based biobank in a large Israeli health organization, we aimed to investigate through focus groups the knowledge, perceptions and attitudes of insured Israelis, toward biobanking, and then, after input from focus groups' participants, to empirically assess the impact of a revised recruitment process on recruitment rates. METHODS: 1) Six Focus group discussions were conducted (n = 10 per group) with individuals who had routine blood laboratory tests taken in the last 2 years. 2) After addressing the issues raised in the focus groups and revising the recruitment process, individuals undergoing routine blood tests in phlebotomy clinics (N = 10,262) were invited to participate in the future biobank. RESULTS: At the outset of the focus groups there was an overall positive response to the prospect of a population-based biobank. Concerns revolved around infringement on privacy, fears of the "big brother"(e.g. insurance companies), and anxiety about inequality. Reaction to the language of the informed consent document revolved around concerns over ability to maintain anonymity, to withdraw consent, involvement of commercial entities, and the general tenor of the informed consent, which was perceived as legalistic and unilateral. In general, the longer participants were exposed to discussion about the biobank, the less likely they were to consent to sign in. Overall, only 20% (12) of the 60 participants stated they would agree to sign in by the end of the 2 hour group session. The feedback obtained from the focus groups was used in the second stage ("real life") of the study. A team of recruiters received extensive training to enable fruitful discussion and a detailed explanation to questions and concerns raised during the recruitment process. During the second stage of the study, after revising the consent form and training recruiters, a 53% consent rate was observed among 10,262 participants, more than 4 fold higher than estimated at the focus group stage. CONCLUSIONS: The qualitative focus group research helped identify important perceptions and concerns, which were subsequently addressed in the revised consent form and in the discussion the recruiters had with potential biobank donors.

Early patient stratification and predictive biomarkers in drug discovery and development: a case study of ulcerative colitis anti-TNF therapy.

The current drug discovery paradigm is long, costly, and prone to failure. For projects in early development, lack of efficacy in Phase II is a major contributor to the overall failure rate. Efficacy failures often occur from one of two major reasons: either the investigational agent did not achieve the required pharmacology or the mechanism targeted by the investigational agent did not significantly contribute to the disease in the tested patient population. The latter scenario can arise due to insufficient study power stemming from patient heterogeneity. If the subset of disease patients driven by the mechanism that is likely to respond to the drug can be identified and selected before enrollment begins, efficacy and response rates should improve. This will not only augment drug approval percentages, but will also minimize the number of patients at risk of side effects in the face of a suboptimal response to treatment. Here we describe a systems biology approach using molecular profiling data from patients at baseline for the development of predictive biomarker content to identify potential responders to a molecular targeted therapy before the drug is tested in humans. A case study is presented where a classifier to predict response to a TNF targeted therapy for ulcerative colitis is developed a priori and verified against a test set of patients where clinical outcomes are known. This approach will promote the tandem development of drugs with predictive response, patient selection biomarkers.

Genetika+: precision medicine solutions for mental health.

Genetika+ is developing a precision medicine tool to optimize the treatment of depression by helping physicians find the best drug therapy for their patients. The tool builds on traditional pharmacogenetics, introducing a 'brain-in-a-dish' screening platform for each patient that will overcome the challenge of limited pharmacodynamic knowledge of pharmacogenetics (PGx). In addition to PGx, our platform integrates patient data with innovative blood-derived patient neurons to test all categories of antidepressants and predict the best drug for each patient. This offers patients optimal drug treatment, allowing a faster response, fewer side effects and lower dosing.

Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies.

Breast cancer is the most common malignant disease worldwide, with over 2.26 million new cases in 2020. Its diagnosis is determined by a histological review of breast biopsy specimens, which can be labor-intensive, subjective, and error-prone. Artificial Intelligence (AI)-based tools can support cancer detection and classification in breast biopsies ensuring rapid, accurate, and objective diagnosis. We present here the development, external clinical validation, and deployment in routine use of an AI-based quality control solution for breast biopsy review. The underlying AI algorithm is trained to identify 51 different types of clinical and morphological features, and it achieves very high accuracy in a large, multi-site validation study. Specifically, the area under the receiver operating characteristic curves (AUC) for the detection of invasive carcinoma and of ductal carcinoma in situ (DCIS) are 0.99 (specificity and sensitivity of 93.57 and 95.51%, respectively) and 0.98 (specificity and sensitivity of 93.79 and 93.20% respectively), respectively. The AI algorithm differentiates well between subtypes of invasive and different grades of in situ carcinomas with an AUC of 0.97 for invasive ductal carcinoma (IDC) vs. invasive lobular carcinoma (ILC) and AUC of 0.92 for DCIS high grade vs. low grade/atypical ductal hyperplasia, respectively, as well as accurately identifies stromal tumor-infiltrating lymphocytes (TILs) with an AUC of 0.965. Deployment of this AI solution as a real-time quality control solution in clinical routine leads to the identification of cancers initially missed by the reviewing pathologist, demonstrating both clinical utility and accuracy in real-world clinical application.

Depression patient-derived cortical neurons reveal potential biomarkers for antidepressant response.

Major depressive disorder is highly prevalent worldwide and has been affecting an increasing number of people each year. Current first line antidepressants show merely 37% remission, and physicians are forced to use a trial-and-error approach when choosing a single antidepressant out of dozens of available medications. We sought to identify a method of testing that would provide patient-specific information on whether a patient will respond to a medication using in vitro modeling. Patient-derived lymphoblastoid cell lines from the Sequenced Treatment Alternatives to Relieve Depression study were used to rapidly generate cortical neurons and screen them for bupropion effects, for which the donor patients showed remission or non-remission. We provide evidence for biomarkers specific for bupropion response, including synaptic connectivity and morphology changes as well as specific gene expression alterations. These biomarkers support the concept of personalized antidepressant treatment based on in vitro platforms and could be utilized as predictors to patient response in the clinic.

Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson's Disease.

Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson's disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N = 88,867) and IBM MarketScan Research Databases (N = 106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, beneficial against common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study.

BACKGROUND: There is high demand to develop computer-assisted diagnostic tools to evaluate prostate core needle biopsies (CNBs), but little clinical validation and a lack of clinical deployment of such tools. We report here on a blinded clinical validation study and deployment of an artificial intelligence (AI)-based algorithm in a pathology laboratory for routine clinical use to aid prostate diagnosis. METHODS: An AI-based algorithm was developed using haematoxylin and eosin (H&E)-stained slides of prostate CNBs digitised with a Philips scanner, which were divided into training (1 357 480 image patches from 549 H&E-stained slides) and internal test (2501 H&E-stained slides) datasets. The algorithm provided slide-level scores for probability of cancer, Gleason score 7-10 (vs Gleason score 6 or atypical small acinar proliferation [ASAP]), Gleason pattern 5, and perineural invasion and calculation of cancer percentage present in CNB material. The algorithm was subsequently validated on an external dataset of 100 consecutive cases (1627 H&E-stained slides) digitised on an Aperio AT2 scanner. In addition, the AI tool was implemented in a pathology laboratory within routine clinical workflow as a second read system to review all prostate CNBs. Algorithm performance was assessed with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity, as well as Pearson's correlation coefficient (Pearson's r) for cancer percentage. FINDINGS: The algorithm achieved an AUC of 0·997 (95% CI 0·995 to 0·998) for cancer detection in the internal test set and 0·991 (0·979 to 1·00) in the external validation set. The AUC for distinguishing between a low-grade (Gleason score 6 or ASAP) and high-grade (Gleason score 7-10) cancer diagnosis was 0·941 (0·905 to 0·977) and the AUC for detecting Gleason pattern 5 was 0·971 (0·943 to 0·998) in the external validation set. Cancer percentage calculated by pathologists and the algorithm showed good agreement (r=0·882, 95% CI 0·834 to 0·915; p<0·0001) with a mean bias of -4·14% (-6·36 to -1·91). The algorithm achieved an AUC of 0·957 (0·930 to 0·985) for perineural invasion. In routine practice, the algorithm was used to assess 11 429 H&E-stained slides pertaining to 941 cases leading to 90 Gleason score 7-10 alerts and 560 cancer alerts. 51 (9%) cancer alerts led to additional cuts or stains being ordered, two (4%) of which led to a third opinion request. We report on the first case of missed cancer that was detected by the algorithm. INTERPRETATION: This study reports the successful development, external clinical validation, and deployment in clinical practice of an AI-based algorithm to accurately detect, grade, and evaluate clinically relevant findings in digitised slides of prostate CNBs. FUNDING: Ibex Medical Analytics.

Rab5 mediates an amyloid precursor protein signaling pathway that leads to apoptosis.

Alzheimer's disease (AD) involves activation of apoptotic pathways that may be regulated through signaling cascades initiated by the amyloid precursor protein (APP). Enlarged endosomes have been observed in postmortem AD brains at very early stages of the disease. We show here that exogenous expression of a familial AD (FAD) mutant of APP or of the APP binding protein APP-BP1 in neurons causes enlargement of early endosomes, increased receptor-mediated endocytosis via a pathway dependent on APP-BP1 binding to APP, and apoptosis. Levels of both APP-BP1 and Rab5 are elevated in early endosomes in cortical embryonic neurons expressing APP(V642I) or APP-BP1, in cultured skin fibroblast cells from Down syndrome subjects, and in postmortem hippocampal tissue of individuals with AD. Indeed, Rab5 was found to bind specifically to APP-BP1, between amino acids 443 and 479. Inhibition of Rab5 or dynamin activity, but not of Eps15 (epidermal growth factor receptor pathway substrate 15) activity, rescued neurons from apoptosis induced by either APP(V642I) or APP-BP1, without affecting levels of intracellular or secreted amyloid-beta (Abeta). Induction of Rab5 activity via expression of a constitutively active mutant led to an increase in neuronal apoptosis more than twice that attributable to induction of endosome enlargement via a Rab5-independent mechanism, regardless of Abeta production. Together, these findings suggest that Rab5 activation via an APP/APP-BP1-initiated signaling pathway mediates neuronal apoptosis caused by FAD mutants of APP and that, within this pathway, Rab5 has a specific role in signaling that is distinct from, although not independent of, its role in trafficking.

Large-scale transcriptomic analysis reveals that pridopidine reverses aberrant gene expression and activates neuroprotective pathways in the YAC128 HD mouse.

BACKGROUND: Huntington Disease (HD) is an incurable autosomal dominant neurodegenerative disorder driven by an expansion repeat giving rise to the mutant huntingtin protein (mHtt), which is known to disrupt a multitude of transcriptional pathways. Pridopidine, a small molecule in development for treatment of HD, has been shown to improve motor symptoms in HD patients. In HD animal models, pridopidine exerts neuroprotective effects and improves behavioral and motor functions. Pridopidine binds primarily to the sigma-1 receptor, (IC50 ~ 100 nM), which mediates its neuroprotective properties, such as rescue of spine density and aberrant calcium signaling in HD neuronal cultures. Pridopidine enhances brain-derived neurotrophic factor (BDNF) secretion, which is blocked by putative sigma-1 receptor antagonist NE-100, and was shown to upregulate transcription of genes in the BDNF, glucocorticoid receptor (GR), and dopamine D1 receptor (D1R) pathways in the rat striatum. The impact of different doses of pridopidine on gene expression and transcript splicing in HD across relevant brain regions was explored, utilizing the YAC128 HD mouse model, which carries the entire human mHtt gene containing 128 CAG repeats. METHODS: RNAseq was analyzed from striatum, cortex, and hippocampus of wild-type and YAC128 mice treated with vehicle, 10 mg/kg or 30 mg/kg pridopidine from the presymptomatic stage (1.5 months of age) until 11.5 months of age in which mice exhibit progressive disease phenotypes. RESULTS: The most pronounced transcriptional effect of pridopidine at both doses was observed in the striatum with minimal effects in other regions. In addition, for the first time pridopidine was found to have a dose-dependent impact on alternative exon and junction usage, a regulatory mechanism known to be impaired in HD. In the striatum of YAC128 HD mice, pridopidine treatment initiation prior to symptomatic manifestation rescues the impaired expression of the BDNF, GR, D1R and cAMP pathways. CONCLUSIONS: Pridopidine has broad effects on restoring transcriptomic disturbances in the striatum, particularly involving synaptic transmission and activating neuroprotective pathways that are disturbed in HD. Benefits of treatment initiation at early disease stages track with trends observed in the clinic.

Pharmacogenomics strategies to optimize treatments for multiple sclerosis: Insights from clinical research.

Multiple sclerosis (MS) is a chronic, progressive, disabling disorder characterized by immune-mediated demyelination, inflammation, and neurodegenerative tissue damage in the central nervous system (CNS), associated with frequent exacerbations and remissions of neurologic symptoms and eventual permanent neurologic disability. While there are several MS therapies that are successful in reducing MS relapses, none have been effective in treating all patients. The specific response of an individual patient to any one of the MS therapies remains largely unpredictable, and physicians and patients are forced to use a trial and error approach when deciding on treatment regimens. A priori markers to predict the optimal benefit-to-risk profile of an individual MS patient would greatly facilitate the decision-making process, thereby helping the patient receive the most optimal treatment early on in the disease process. Pharmacogenomic methods evaluate how a person's genetic and genomic makeup affects their response to therapeutics. This review focuses on how pharmacogenomics studies are being used to identify biologically relevant differences in MS treatments and provide characterization of the predictive clinical response patterns. As pharmacogenomics research is dependent on the availability of longitudinal clinical research, studies concerning glatiramer acetate and the interferon beta products which have the majority of published long term data to date are described in detail. These studies have provided considerable insight in the prognostic markers associated with MS disease and potential predictive markers of safety and beneficial response.

A pharmacogenetic signature of high response to Copaxone in late-phase clinical-trial cohorts of multiple sclerosis.

BACKGROUND: Copaxone is an efficacious and safe therapy that has demonstrated clinical benefit for over two decades in patients with relapsing forms of multiple sclerosis (MS). On an individual level, patients show variability in their response to Copaxone, with some achieving significantly higher response levels. The involvement of genes (e.g., HLA-DRB1*1501) with high inter-individual variability in Copaxone's mechanism of action (MoA) suggests the potential contribution of genetics to treatment response. This study aimed to identify genetic variants associated with Copaxone response in patient cohorts from late-phase clinical trials. METHODS: Single nucleotide polymorphisms (SNPs) associated with high and low levels of response to Copaxone were identified using genome-wide SNP data in a discovery cohort of 580 patients from two phase III clinical trials of Copaxone. Multivariable Bayesian modeling on the resulting SNPs in an expanded discovery cohort with 1171 patients identified a multi-SNP signature of Copaxone response. This signature was examined in 941 Copaxone-treated MS patients from seven independent late-phase trials of Copaxone and assessed for specificity to Copaxone in 310 Avonex-treated and 311 placebo-treated patients, also from late-phase trials. RESULTS: A four-SNP signature consisting of rs80191572 (in UVRAG), rs28724893 (in HLA-DQB2), rs1789084 (in MBP), and rs139890339 (in ZAK(CDCA7)) was identified as significantly associated with Copaxone response. Copaxone-treated signature-positive patients had a greater reduction in annualized relapse rate (ARR) compared to signature-negative patients in both discovery and independent cohorts, an effect not observed in Avonex-treated patients. Additionally, signature-positive placebo-treated cohorts did not show a reduction in ARR, demonstrating the predictive as opposed to prognostic nature of the signature. A 10% subset of patients, delineated by the signature, showed marked improvements across multiple clinical parameters, including ARR, MRI measures, and higher proportion with no evidence of disease activity (NEDA). CONCLUSIONS: This study is the largest pharmacogenetic study in MS reported to date. Gene regions underlying the four-SNP signature have been linked with pathways associated with either Copaxone's MoA or the pathophysiology of MS. The pronounced association of the four-SNP signature with clinical improvements in a ~10% subset of the MS patient population demonstrates the complex interplay of immune mechanisms and the individualized nature of response to Copaxone.

Compositional differences between Copaxone and Glatopa are reflected in altered immunomodulation ex vivo in a mouse model.

Copaxone (glatiramer acetate, GA), a structurally and compositionally complex polypeptide nonbiological drug, is an effective treatment for multiple sclerosis, with a well-established favorable safety profile. The short antigenic polypeptide sequences comprising therapeutically active epitopes in GA cannot be deciphered with state-of-the-art methods; and GA has no measurable pharmacokinetic profile and no validated pharmacodynamic markers. The study reported herein describes the use of orthogonal standard and high-resolution physicochemical and biological tests to characterize GA and a U.S. Food and Drug Administration-approved generic version of GA, Glatopa (USA-FoGA). While similarities were observed with low-resolution or destructive tests, differences between GA and USA-FoGA were measured with high-resolution methods applied to an intact mixture, including variations in surface charge and a unique, high-molecular-weight, hydrophobic polypeptide population observed only in some USA-FoGA lots. Consistent with published reports that modifications in physicochemical attributes alter immune-related processes, genome-wide expression profiles of ex vivo activated splenocytes from mice immunized with either GA or USA-FoGA showed that 7-11% of modulated genes were differentially expressed and enriched for immune-related pathways. Thus, differences between USA-FoGA and GA may include variations in antigenic epitopes that differentially activate immune responses. We propose that the assays reported herein should be considered during the regulatory assessment process for nonbiological complex drugs such as GA.

Targeting Tumor Cells with Anti-CD44 Antibody Triggers Macrophage-Mediated Immune Modulatory Effects in a Cancer Xenograft Model.

CD44, a transmembrane receptor reported to be involved in various cellular functions, is overexpressed in several cancer types and supposed to be involved in the initiation, progression and prognosis of these cancers. Since the sequence of events following the blockage of the CD44-HA interaction has not yet been studied in detail, we profiled xenograft tumors by RNA Sequencing to elucidate the mode of action of the anti-CD44 antibody RG7356. Analysis of tumor and host gene-expression profiles led us to the hypothesis that treatment with RG7356 antibody leads to an activation of the immune system. Using cytokine measurements we further show that this activation involves the secretion of chemo-attractants necessary for the recruitment of immune cells (i.e. macrophages) to the tumor site. We finally provide evidence for antibody-dependent cellular phagocytosis (ADCP) of the malignant cells by macrophages.

Functional effects of the antigen glatiramer acetate are complex and tightly associated with its composition.

Glatiramer acetate (Copaxone®; GA) is a non-biological complex drug for multiple sclerosis. GA modulated thousands of genes in genome-wide expression studies conducted in THP-1 cells and mouse splenocytes. Comparing GA with differently-manufactured glatiramoid Polimunol (Synthon) in mice yielded hundreds of differentially expressed probesets, including biologically-relevant genes (e.g. Il18, adj p<9e-6) and pathways. In human monocytes, 700+ probesets differed between Polimunol and GA, enriching for 130+ pathways including response to lipopolysaccharide (adj. p<0.006). Key differences were confirmed by qRT-PCR (splenocytes) or proteomics (THP-1). These studies demonstrate the complexity of GA's mechanisms of action, and may help inform therapeutic equivalence assessment.

Alterations in cell adhesion molecule L1 and functionally related genes in major depression: a postmortem study.

BACKGROUND: Current research in depression aims to delineate genes involved in neuronal plasticity that are altered in the disease or its treatment. We have shown antidepressant induced increases in three interrelated genes, cell adhesion molecule L1 (CAM-L1), laminin, and cAMP response element binding protein (CREB), and a reciprocal decrease in these genes consequent to stress. Presently we hypothesized that CAM-L1, CREB, and laminin may be altered in post mortem brains of depressed subjects. METHODS: Studies were performed in the prefrontal and in the ventral parieto-occipital cortices, of 59 brains from depressed, bipolar, and schizophrenic subjects, and normal controls, obtained from the Stanley Foundation Brain Collection. mRNA and protein levels were determined by RT-PCR and Western blot analysis, respectively. RESULTS: Levels of CAM-L1 and of phosphorylated CREB (pCREB) were increased in the prefrontal cortex of the depressed group, while CAM-L1, laminin and pCREB were decreased in the parieto-occipital cortex. Depressed subjects receiving antidepressants differed from subjects not receiving antidepressants in the expression of CAM-L1 and laminin in the parieto-occipital cortex, and in the expression of pCREB in the prefrontal cortex. CONCLUSIONS: The present findings of specific alterations in depression and antidepressant treatment particularly in CAM-L1 suggest that this gene may play an important role in the pathophysiology and treatment of depression.

Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis.

BACKGROUND: Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease. METHODS: We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n = 116 = 44 + 15 + 30 + 27) were treated with infliximab, and their response assessed 14-16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression. RESULTS: The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment. CONCLUSIONS: We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.

Gene expression studies of a human monocyte cell line identify dissimilarities between differently manufactured glatiramoids.

Glatiramer Acetate (GA) has provided safe and effective treatment for multiple sclerosis (MS) patients for two decades. It acts as an antigen, yet the precise mechanism of action remains to be fully elucidated, and no validated pharmacokinetic or pharmacodynamic biomarkers exist. In order to better characterize GA's biological impact, genome-wide expression studies were conducted with a human monocyte (THP-1) cell line. Consistent with previous literature, branded GA upregulated anti-inflammatory markers (e.g. IL10), and modulated multiple immune-related pathways. Despite some similarities, significant differences were observed between expression profiles induced by branded GA and Probioglat, a differently-manufactured glatiramoid purported to be a generic GA. Key results were verified using qRT-PCR. Genes (e.g. CCL5, adj. p < 4.1 × 10(-5)) critically involved in pro-inflammatory pathways (e.g. response to lipopolysaccharide, adj. p = 8.7 × 10(-4)) were significantly induced by Probioglat compared with branded GA. Key genes were also tested and confirmed at the protein level, and in primary human monocytes. These observations suggest differential biological impact by the two glatiramoids and warrant further investigation.

Systems diagnostics: anticipating the next generation of diagnostic tests based on mechanistic insight into disease.

Societal demand for faster and more accurate assignment of treatments is based in both patient care needs and in health economics. From a patient care standpoint, there needs to be a transformation from the empiric method of therapeutic decision making to avoid unwanted side effects from inefficacious treatments. For health economics, the delay in effective therapy and expenditures for ineffective therapies add to the burden of care. To accomplish this transformation, we need to modify our current method of classifying disease from a phenotypic description to one that incorporates the different molecular drivers that created the observed phenotype. To do so, a deeper, systems-based understanding of these disease drivers is required, which will yield a new generation of diagnostic tests, or systems diagnostics.

Utilization of causal reasoning of hepatic gene expression in rats to identify molecular pathways of idiosyncratic drug-induced liver injury.

Drug-induced liver injury (DILI) represents a leading cause of acute liver failure. Although DILI can be discovered in preclinical animal toxicology studies and/or early clinical trials, some human DILI reactions, termed idiosyncratic DILI (IDILI), are less predictable, occur in a small number of individuals, and do not follow a clear dose-response relationship. The emergence of IDILI poses a critical health challenge for patients and a financial challenge for the pharmaceutical industry. Understanding the cellular and molecular mechanisms underlying IDILI is key to the development of models that can assess potential IDILI risk. This study used Reverse Causal Reasoning (RCR), a method to assess activation of molecular signaling pathways, on gene expression data from rats treated with IDILI or pharmacologic/chemical comparators (NON-DILI) at the maximum tolerated dose to identify mechanistic pathways underlying IDILI. Detailed molecular networks involved in mitochondrial injury, inflammation, and endoplasmic reticulum (ER) stress were found in response to IDILI drugs but not negative controls (NON-DILI). In vitro assays assessing mitochondrial or ER function confirmed the effect of IDILI compounds on these systems. Together our work suggests that using gene expression data can aid in understanding mechanisms underlying IDILI and can guide in vitro screening for IDILI. Specifically, RCR should be considered for compounds that do not show evidence of DILI in preclinical animal studies positive for mitochondrial dysfunction and ER stress assays, especially when the therapeutic index toward projected human maximum drug plasma concentration is low.