MicroDiscovery (Germany)

Plasma phosphorylated Tau217 (pTau217) represents a sensitive blood-based biomarker for Alzheimer’s disease (AD). This study investigated the performance of plasma pTau217 alone and as a composite score (Aβ1-40/Aβ1-42*pTau217) for detecting low Aβ1-42/1-40 in cerebrospinal fluid (CSF) as a surrogate marker of brain β-amyloid pathological changes. We analysed plasma samples from 82 pre-selected participants who were dichotomized according to their CSF Aβ42/40 ratio after data-driven cutoff determination by Gaussian mixture modelling. The study cohort included patients in very early disease stages with mild cognitive impairment (MCI) due to AD and mild dementia due to AD in the Aβ-positive group and MCI due to other causes and mild dementia due to other causes in the Aβ-negative group. Concentrations of plasma pTau181 and pTau217 were determined on the fully automated LUMIPULSE platform. Additionally, plasma Aβ1-42/1-40 and pTau217 were assessed after consecutive Aβ- and Tau-immunoprecipitations (IPs). Corrections for age and sex effects in primary variables were done using a multivariate linear model on a logarithmic scale. Results are reported using both adjusted and unadjusted values. After adjustment for age and sex, median plasma pTau217 was increased by 203% in participants with low CSF Aβ1-42/1-40 (i.e. Aβ-positive), while pTau181 was increased by only 61%. PTau217 showed a larger area under the receiver operating characteristics curve (AUC) of 0.88 in identifying Aβ-positive individuals compared to plasma pTau181 (AUC: 0.78). Plasma Aβ1-42/1-40 ratio in Aβ-IP eluates (adjusted for age and sex) was decreased by only 10%, and the standardized effect size (Cohen´s d) was smaller than that of pTau217. Tau-IP did not improve the performance of the pTau217 assay (p = 0.568, DeLong test). However, the Aβ1-40/Aβ1-42*pTau217 ratio following immunoprecipitations appeared to outperform the single blood biomarkers. PTau217 as measured with the fully automated Lumipulse G pTau217 Plasma < RUO > assay demonstrated high accuracy for detecting low CSF Aβ1-42/1-40 and appears to represent a particularly attractive biomarker for future implementation in clinical practice. Furthermore, the composite score Aβ1-40/Aβ1-42*pTau217 showed promise for enhancing the blood-based early and differential diagnosis of AD.

Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor gene (EGFR), the emergence of tumor resistance eventually occurs in all patients and this is driving the development of new therapies. In this paper, we present the In Silico EGFR-mutant LUAD (ISELA) model that links LUAD patients’ individual characteristics, including tumor genetic heterogeneity, to tumor size evolution and tumor progression over time under first generation EGFR tyrosine kinase inhibitor gefitinib. This translational mechanistic model gathers extensive knowledge on LUAD and was calibrated on multiple scales, including in vitro, human tumor xenograft mouse and human, reproducing more than 90% of the experimental data identified. Moreover, with 98.5% coverage and 99.4% negative logrank tests, the model accurately reproduced the time to progression from the Lux-Lung 7 clinical trial, which was unused in calibration, thus supporting the model high predictive value. This knowledge-based mechanistic model could be a valuable tool in the development of new therapies targeting EGFR-mutant LUAD as a foundation for the generation of synthetic control arms.

Abstract Introduction The in silico epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma (ISELA) model predicts tumor progression in patients with advanced EGFR-mutated lung adenocarcinoma [1]. To investigate model credibility, we dug into a simplifying assumption made in the ISELA model and in other models [2][3], namely the assumption that tumors have a spherical shape. The validity of the spherical assumption (SA) is assessed by analyzing two lung cancer datasets, and its impact on the model is assessed by comparing predictions of the ISELA model with different shape hypotheses. Material and Methods To evaluate the impact of tumor shape assumptions on the estimated tumor volume with respect to real-world data, two lung cancer datasets [4][5] were analyzed to appraise the sphericity of lung tumors. As individual longest tumor radii were available for each tumor, the estimated spherical volume was computed under the SA and compared to the reported tumor volume. The ellipsoid assumption (EA) was also explored as an alternative -less simplifying- shape assumption. As the three tumor axes are rarely reported and were unavailable in these datasets, the three tumor axes in the EA were defined with proportionality relationships to the longest available radius. The estimated elliptical volume was then compared to the measured volume. To quantify the impact of the SA on the model’s primary output, time to progression (TTP), an alternative ISELA model assuming ellipsoid tumors was implemented. Two clinical trial simulations -one under the SA and the other under the EA- were performed on the same virtual patients with only the sphericity parameter differing, thus allowing a patient per patient comparison. Results The datasets analysis revealed that under the SA the tumor volume was overestimated in most cases, whereas under the EA the tumor volume was better approximated. On average, tumors had their shortest axis equal to 0.7 times their longest radius. Comparison of predictions between simulations showed that only 5% of the virtual patients changed their treatment response status (non-responder/responder). In patients classified as responding to the treatment in both simulations, the median TTP difference was 14 days. Conclusion Both real data and in silico simulations enabled us to better understand to which extent the SA is a simplifying but yet credible assumption in modeling lung cancer progression. Considering ellipsoid tumors is nevertheless a promising alternative as it better predicts tumor volume. Additional studies are needed to further explore its use as support to clinical decision making.

The data currently described was generated within the EU/FP7 HeCaToS project (Hepatic and Cardiac Toxicity Systems modeling). The project aimed to develop an in silico prediction system to contribute to drug safety assessment for humans. For this purpose, multi-omics data of repeated dose toxicity were obtained for 10 hepatotoxic and 10 cardiotoxic compounds. Most data were gained from in vitro experiments in which 3D microtissues (either hepatic or cardiac) were exposed to a therapeutic (physiologically relevant concentrations calculated through PBPK-modeling) or a toxic dosing profile (IC20 after 7 days). Exposures lasted for 14 days and samples were obtained at 7 time points (therapeutic doses: 2-8-24-72-168-240-336 h; toxic doses 0-2-8-24-72-168-240 h). Transcriptomics (RNA sequencing & microRNA sequencing), proteomics (LC-MS), epigenomics (MeDIP sequencing) and metabolomics (LC-MS & NMR) data were obtained from these samples. Furthermore, functional endpoints (ATP content, Caspase3/7 and O2 consumption) were measured in exposed microtissues. Additionally, multi-omics data from human biopsies from patients are available. This data is now being released to the scientific community through the BioStudies data repository (https://www.ebi.ac.uk/biostudies/).

Abstract Background and Aims Chronic kidney disease (CKD) is a major global health problem, affecting an estimated 850 million people worldwide. Non-diabetic CKD accounts for up to 70% of the CKD burden, which includes morbidity and mortality due to CKD progression to kidney failure and due to cardiovascular disease. This analysis uses real-world evidence to provide insights into the baseline clinical characteristics of individuals with non-diabetic CKD treated in routine clinical practice. Method The Optum Clinformatics Data Mart was used to identify individuals with non-diabetic CKD (enrolled in the database between January 1, 2008 and December 31, 2018), based on common diagnosis, procedure and laboratory codes. To be eligible for inclusion, individuals were required to have CKD stage 3 or 4, as identified by estimated glomerular filtration rate (eGFR) 15–59 ml/min/1.73 m2 and/or by an International Classification of Diseases (ICD) code, and confirmed by a second eGFR value or ICD code 90–365 days apart (index date). Individuals had to be ≥18 years old at index and have 365 days of continuous insurance coverage prior to the index event (baseline period). Those with diabetes mellitus, CKD stage 5 or end-stage kidney disease prior to the index date, or who experienced kidney failure (acute or unspecified), kidney transplant or dialysis at baseline, were excluded from the analysis. Patient demographics, clinical characteristics, comorbidities and medications were assessed at baseline. Results Of the 64 million individuals in the Optum Clinformatics Data Mart during the analysed time period, 504,924 satisfied the selection criteria. Median (interquartile range) age was 75 (68–81) years, 60% were female, 63% were white and 10% were black. The proportions of individuals with CKD stage 3 and 4 at index were 95% and 5%, respectively. At baseline, eGFR values were available for 62% of individuals; median (interquartile range) eGFR was 53 (47–57) ml/min/1.73 m2. A urine albumin-to-creatinine ratio was recorded in 6% of individuals, of whom 73%, 21% and 6% had normal-to-mildly increased (&amp;lt;30 mg/g), moderately increased (≥30 to ≤300 mg/g) and severely increased (&amp;gt;300 mg/g) albuminuria, respectively. The most common baseline comorbidities were hypertension (85% of individuals), hyperlipidaemia (68%), hypothyroidism (26%), anaemia (25%), pulmonary disease (24%) and coronary artery disease (24%). Heart failure, atrial fibrillation and peripheral artery disease were recorded in 16%, 15% and 14% of individuals, respectively. The most frequently used medication classes at baseline were statins (47% of individuals), beta blockers (44%), nonsteroidal anti-inflammatory drugs (36%) and angiotensin-converting enzyme inhibitors (34%). Angiotensin receptor blockers and mineralocorticoid receptor antagonists were used by 21% and 4% of individuals, respectively. The speciality of the diagnosing provider was reported on 26% of claims for the index event, the most common being family or internal medicine, followed by nephrology. Conclusion This analysis contributes to the characterisation of a real-world population with non-diabetic CKD treated in routine clinical practice in the US. A large cohort of individuals with moderate-to-severe CKD was identified. The majority were elderly with multiple serious cardiovascular and pulmonary comorbidities and frequent use of nonsteroidal anti-inflammatory drugs. Overall, the analysis highlights the urgent need for improving early diagnosis, prevention and effective treatment of CKD.

Mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs), which cover both microvesicles (MV) and exosomes, are increasingly in focus for immune-modulatory application as an alternative or substitute in MSC cellular therapy. Experimentally it has been documented that EVs take part in cell-to-cell communication, cell signaling, and changing cell or tissue physiological functions over short or long distances in the organism. It has been observed that MVs and exosomes influence the tissue responses to injury, infection, and disease. MSC-derived EVs contain cytokines, growth factors, signaling lipids, mRNAs, and regulatory miRNAs being able to modify the innate and adaptive immune response. The MSC EVs materials are not static but rather a product of the MSC tissue conditions and can be primed to an anti-inflammatory state.

BACKGROUND: In several randomized controlled trials (RCT) acetylcholinesterase-inhibitors (AChE-I) were tested in patients with mild cognitive impairment (MCI) but were ineffective in delaying disease progression as determined by neuropsychological testing only. Here we present data from an open label observational extension of a multicenter RCT in order to assess if biomarkers are providing useful additional information about a drug’s efficacy. We followed 83 amnestic MCI patients and performed correlational analyses of Aβ 1–42 and total-Tau in the cerebrospinal fluid (CSF), hippocampal and amygdala volume at baseline, the total duration of blinded and open label AChE-I treatment and the outcome 24 months after inclusion into the RCT. Twelve out of 83 amnestic MCI (14%) had progressed to Alzheimer’s disease (AD). Overall, worsening and disease progression as measured by the Alzheimer’s Disease Assessment Scale - cognitive subscale (ADAS-cog), Alzheimer’s Disease Cooperative Study - Activities of Daily Living (ADCS-ADL) and Clinical Dementia Rating (CDR) did not correlate with the duration of AChE-I treatment. However, a specific multidimensional biomarker profile at baseline indicated more reliably than cognitive testing alone progression to AD. We conclude that pharmacological RCTs testing symptomatic treatment effects in MCI should include biomarker assessment.