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bioRxiv2018

Computational Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

BackgroundAlzheimers disease (AD) is a major public health concern and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients though validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential, but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials.\n\nMethodsWe implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers and cognitive impairment as non-linear first order ordinary differential equations (ODEs) to include amyloid-dependent and non-dependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD, and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of anti-amyloid therapy in late-onset AD.\n\nResultsThe computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration, followed by onset of cognitive decline based on cognitive reserve, as predicted by prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or non-amyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by prior literature. Forward simulation of anti-amyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients.\n\nConclusionWe have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies, and may in the future play a key role in personalizing approaches to treatment.

neuroscience↗

bioRxiv2017

Effect of vitamin D supplementation on biomarkers of inflammation and immune function: functional genomics analysis of the BEST-D trial

Vitamin D deficiency has been associated with multiple diseases, but the causal relevance and underlying processes are not fully understood. Elucidating the mechanisms of action of drug treatments in humans is challenging, but application of functional genomic approaches in randomised trials may afford an opportunity to systematically assess molecular responses to treatments. In the Biochemical Efficacy and Safety Trial of Vitamin D (BEST-D), 305 community-dwelling individuals aged over 65 years were randomly allocated to treatment with vitamin D34000 IU, 2000 IU or placebo daily for 12 months. Genome-wide genotypes at baseline, and transcriptome and plasma levels of cytokines (IFN-{gamma}, IL-10, IL-8, IL-6 and TNF-) at baseline and after 12 months, were measured. The trial had >90% power to detect a 2-fold change in gene expression. Allocation to vitamin D for 12-months was associated with 2-fold higher plasma levels of 25-hydroxy-vitamin D (25[OH]D), but had no significant effect on whole-blood gene expression (FDR <5%) or on plasma levels of cytokines compared with placebo. In pre-specified analysis, rs7041 (intron variant, GC) had a significant effect on circulating levels of 25(OH)D in the low dose but not on the placebo or high dose vitamin D regimen. A gene expression quantitative trait locus analysis (eQTL) demonstrated evidence of 31,568 cis-eQTLs (unique SNP-probe pairs) among individuals at baseline and 34,254 after supplementation for 12 months (any dose), but had no significant effect on cis-eQTLs specific to vitamin D supplementation. The trial demonstrates the feasibility of application of functional genomics approaches in randomised trials to assess the effects of vitamin D on immune function.\n\nOne sentence summarySupplementation with high-dose vitamin D in older people for 12 months in a randomised, placebo-controlled trial had no significant effect on gene expression or on plasma concentrations of cytokines.\n\nTrial registrationSRCTN registry (Number 07034656) and the European Clinical Trials Database (EudraCT Number 2011-005763-24).\n\nFundingMedical Research Council, British Heart Foundation, Wellcome Trust, European Research Council and Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom\n\nCopyrightOpen access article under the terms of CC BY.

clinical trials↗

bioRxiv2018

Rapid Therapeutic Recommendations in the Context of a Global Public Health Crisis using Translational Bioinformatics Approaches: A proof-of-concept study using Nipah Virus Infection

We live in a world of emerging new diseases and old diseases resurging in more aggressive forms. Drug development by pharmaceutical companies is a market-driven and costly endeavor, and thus it is often a challenge when drugs are needed for diseases endemic only to certain regions or which affect only a few patients. However, biomedical open data is accessible and reusable for reanalysis and generation of a new hypotheses and discovery. In this study, we leverage biomedical data and tools to analyze available data on Nipah Virus (NiV) infection. NiV infection is an emerging zoonosis that is transmissible to humans and is associated with high mortality rates. In this study, explored the application of computational drug repositioning and chemogenomic enrichment analyses using host transcriptome data to match drugs that could reverse the virus-induced gene signature. We performed analyses using two gene signatures: i) A previously published gene signature (n=34), and ii) a gene signature generated using the characteristic direction method (n= 5,533). Our predictive framework suggests that several drugs including FDA approved therapies like beclometasone, trihexyphenidyl, S-propranolol etc. could modulate the NiV infection induced gene signatures in endothelial cells. A target specific analysis of CXCL10 also suggests the potential application of Eldelumab, an investigative therapy for Crohns disease and ulcerative colitis, as a putative candidate for drug repositioning. To conclude, we also discuss challenges and opportunities in clinical trials (n-of-1 and adaptive trials) for repositioned drugs. Further follow-up studies including biochemical assays and clinical trials are required to identify effective therapies for clinical use. Our proof-of-concept study highlights that translational bioinformatics methods including gene expression analyses and computational drug repositioning could augment epidemiological investigations in the context of an emerging disease with no effective treatment.

microbiology↗

bioRxiv2017

SemEHR: A General-purpose Semantic Search System to Surface Semantic Data from Clinical Notes for Tailored Care, Trial Recruitment and Clinical Research

ObjectiveUnlocking the data contained within both structured and unstructured components of Electronic Health Records (EHRs) has the potential to provide a step change in data available forsecondary research use, generation of actionable medical insights, hospital management and trial recruitment. To achieve this, we implemented SemEHR - a semantic search and analytics, open source tool for EHRs.\n\nMethodsSemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualised mentions of a wide range of biomedical concepts within EHRs. Natural Language Processing (NLP) annotations are further assembled at patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data is serviced via ontology-based search and analytics interfaces.\n\nResultsSemEHR has been deployed to a number of UK hospitals including the Clinical Record Interactive Search (CRIS), an anonymised replica of the EHR of the UK South London and Maudsley (SLaM) NHS Foundation Trust, one of Europes largest providers of mental health services. In two CRIS-based studies, SemEHR achieved 93% (Hepatitis C case) and 99% (HIV case) F-Measure results in identifying true positive patients. At Kings College Hospital in London, as part of the CogStack programme (github.com/cogstack), SemEHR is being used to recruit patients into the UK Dept of Health 100k Genome Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast in searching phenotypes - time for recruitment criteria checking reduced from days to minutes. Validated on an open intensive care EHR data - MIMICIII, the vital signs extracted by SemEHR can achieve around 97% accuracy.\n\nConclusionResults from the multiple case studies demonstrate SemEHRs efficiency - weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of a patient, bringing in more and unexpected insight compared to study-oriented bespoke information extraction systems.\n\nSemEHR is open source available at https://github.com/CogStack/SemEHR.

bioinformatics↗

bioRxiv2018

Use of an individual-based model of pneumococcal carriage for planning a randomized trial of a vaccine

For encapsulated bacteria such as Streptococcus pneumoniae, asymptomatic carriage is more common and longer in duration than disease, and hence is often a more convenient endpoint for clinical trials of vaccines against these bacteria. However, using a carriage endpoint entails specific challenges. Carriage is almost always measured as prevalence, whereas the vaccine may act by reducing incidence or duration. Thus, to determine sample size requirements, its impact on prevalence must first be estimated. The relationship between incidence and prevalence (or duration and prevalence) is convex, saturating at 100% prevalence. For this reason, the proportional effect of a vaccine on prevalence is typically less than its proportional effect on incidence or duration. This relationship is further complicated in the presence of multiple pathogen strains. In addition, host immunity to carriage accumulates rapidly with frequent exposures in early years of life, creating potentially complex interactions with the vaccines effect. We conducted a simulation study to predict the impact of an inactivated whole cell pneumococcal vaccine--believed to reduce carriage duration--on carriage prevalence in different age groups and trial settings. We used an individual-based model of pneumococcal carriage that incorporates relevant immunological processes, both vaccine-induced and naturally acquired. Our simulations showed that for a wide range of vaccine efficacies, sampling time and age at vaccination are important determinants of sample size. There is a window of favorable sampling times during which the required sample size is relatively low, and this window is prolonged with a younger age at vaccination, and in a trial setting with lower transmission intensity. These results illustrate the ability of simulation studies to inform the planning of vaccine trials with carriage endpoints, and the methods we present here can be applied to trials evaluating other pneumococcal vaccine candidates or comparing alternative dosing schedules for the existing conjugate vaccines.\n\nAuthor SummaryStreptococcus pneumoniae, a bacterium carried in the nasopharynx of many healthy people, is also a leading cause of bacterial pneumonia, sepsis, and ear infections in children aged five years and younger. Vaccines targeting select strains of S. pneumoniae have been effective, and the development of new vaccines, particularly those that target all strains, can further lower disease burden. For clinical trials of these vaccines, the number of study participants needed depends on the expected effect of the vaccine on a conveniently measured outcome: asymptomatic carriage. The most economical way to test a vaccine for its effect on carriage is by measuring prevalence at a specific time, and comparing vaccinated to unvaccinated participants. The relationship between incidence (or duration) and prevalence is complex, and changes with time as children develop natural immunity. We explored this relationship using a mathematical model. Given a vaccine efficacy, our computer simulations predict that fewer study participants are needed if they are vaccinated at a younger age, taken from a population with intermediate levels of transmission, and sampled for carriage at a certain time window: 9 to 18 months after vaccination. Our study illustrates how simulation studies can help plan more efficient vaccine trials.

epidemiology↗

bioRxiv2018

Subcutaneous neurotrophin-3 infusion induces corticospinal neuroplasticity and improvements in dexterity and walking in elderly rats after large cortical stroke

There is an urgent need for a therapy which reverses disability after stroke when initiated in a time frame suitable for the majority of new victims. Neurotrophin-3 (NT3) is a growth factor made by muscle spindles and skin which is required for the survival, development and function of locomotor circuits involving afferents from muscle and skin that mediate proprioception and tactile sensation. Its level declines in muscle and other tissues postnatally. We show that levels of NT-3 in the bloodstream were low in humans with ischemia stroke relative to young healthy controls. Accordingly, we set out to determine whether subcutaneous delivery of NT3 improves sensorimotor recovery after stroke in elderly rats. We show that one-month-long subcutaneous infusion of NT3 protein induces sensorimotor recovery after cortical stroke in elderly rats. Specifically, in a randomised, blinded pre-clinical trial, we show improved dexterity, walking and sensory function in rats following cortical ischemic stroke when treatment with NT3 is initiated 24 hours after stroke. Importantly, NT-3 was given in a clinically feasible time frame via this straightforward route. MRI and histology showed that recovery was not due to neuroprotection, as expected given the delayed treatment. Rather, anterograde tracing showed that corticospinal axons from the less-affected hemisphere sprouted in the spinal cord from cervical levels 2 to 8. Importantly, Phase I and II clinical trials by others show that repeated, subcutaneously administered high doses of recombinant NT-3 are safe and well tolerated in humans with other conditions. This paves the way for NT-3 as a therapy for stroke.

neuroscience↗

bioRxiv2026

Ex vivo human tumor slices more accurately predict patient responses to an oncolytic virus than in vivo mouse models

Immunotherapies, including oncolytic viruses (OV), are promising therapies that can enhance anti-tumor immune responses. However, preclinical success of immunotherapies in mouse models has not always translated to clinical benefit in cancer patients. This study compared preclinical efficacy and mechanism of action for ASP9801, a vaccinia virus expressing IL-7 and IL-12, using mouse models of colorectal cancer (CRC) in vivo and in human organotypic tumor slice models ex vivo. The murine surrogate for ASP9801 significantly reduced tumor volumes in treated and abscopal tumors in two different CRC models in vivo (MC38 and RO100). Treatment efficacy was accentuated when combined with anti-PD1 treatment, and single-cell RNA sequencing analysis revealed depletion of tumor cells and increased T cell infiltration and activation in both treated and abscopal tumors. However, human tissue analysis ex vivo (E-slices) using PDX models and patient samples showed that ASP9801 is not effective in CRC, consistent with clinical trial results. On the other hand, ASP9801 was highly effective in GBM, indicating indication-specific efficacy of ASP9801, and how E-slice assays can be used to identify treatment-sensitive indications. This study demonstrates the superiority of E-slices over mouse models for predicting clinical response and its utility in planning clinical trials.

cancer biology↗

bioRxiv2018

Accurate Prediction of Antibody Resistance in Clinical HIV-1 Isolates

Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection with several undergoing clinical trials. Due to high sequence diversity and mutation rate of HIV-1, viral isolates are often resistant to particular bNAbs. Resistant strains are commonly identified by time-consuming and expensive in vitro neutralization experiments. Here, we developed machine learning-based classifiers that accurately predict resistance of HIV-1 strains to 33 neutralizing antibodies. Notably, our classifiers achieved an overall prediction accuracy of 96% for 212 clinical isolates from patients enrolled in four different clinical trials. Moreover, use of the tree-based machine learning method gradient boosting machine enabled us to identify critical epitope features that distinguish between antibody resistance and sensitivity. The availability of an in silico antibody resistance predictor will facilitate informed decisions of antibody usage in clinical settings.

bioinformatics↗

bioRxiv2018

Association Between Rare Copy Number Variation and Response to Social Skills Training in Autism Spectrum Disorder

Challenges in social communication and interaction are core symptoms in autism spectrum disorder (ASD) for which social skills group training (SSGT) is a commonly used intervention. SSGT has shown modest but heterogeneous effects in clinical trials, and therefore identification of effect moderators could enable more precise intervention decisions. One of the major genetic risk factors in ASD are rare copy number variation (CNV). However, limited information exists whether rare CNVs profiles can be used to aid in intervention decisions. Therefore, we conducted the first study to date analyzing rare CNVs as genetic moderators in the outcome of SSGT in ASD. For this, we analyzed rare genic CNV carrier status of 207 children of which 105 received SSGT and 102 standard care as part of a recent randomized clinical trial for 12-weeks SSGT. We used mixed linear models to assess the association of being a CNV carrier, grouped by the effect and size of the CNVs and the primary response to SSGT, the parent-report Social Responsiveness Scale (SRS) measured at post-intervention and 3-months follow-up. Additionally, we analyzed the secondary outcome assessments included parent-rated adaptive behaviors (ABAS-II) and trainer-rated clinical global impression (CGI). We show that being a carrier of any size rare genic CNV did not impact on the SSGT outcome. However, when stratifying the groups by size of the CNVs, we identified that carriers of large CNVs (>500 kb) showed inferior SRS outcomes at post-intervention ({beta} = 15.35, 95% CI 2.86-27.84, P=0.017) and follow-up ({beta} = 14.19, 95% CI 1.68-26.70, P=0.028). Similar results were shown for the parent-rated secondary outcome. In contrast, the carriers of small CNVs had better outcome at post-intervention ({beta} = -1.20, 95 % CI - 2.0 - -0.4 P = 0.003) but not at follow-up for the trainer-rated secondary outcome CGI. These results remained when we tested the specificity of the effect by including the standard care group and adjusting for IQ levels. While our study suggests that being a carrier of any size rare genic CNV did not impact the outcome, it provides preliminary evidence that carriers of high-risk CNVs might not benefit on SSGT as much as non-carriers. Our results indicate that genetic information eventually might help guide personalized intervention planning in ASD. We additionally highlight that more research is needed to understand the intervention needs of autistic individuals with specified molecular alterations.

genetics↗

bioRxiv2017

Pair Matcher (PaM): Fast Model-Based Optimisation Of Treatment/Case-Control Matches Using Demographic And Genetic Data

In clinical trials, individuals are matched for demographic criteria, paired, and then randomly assigned to treatment and control groups to determine a drugs efficacy. The successful completion of pilot trials is a prerequisite to larger and more expensive Phase III trials. One of the chief causes for the irreproducibility of results across pilot to Phase III trials is population stratification bias caused by the uneven distribution of ancestries in the treatment and control groups. Pair Matcher (PaM) addresses stratification bias by optimising pairing assignments a priori- and\\or posteriori to the trial using both genetic and demographic criteria. Using simulated and real datasets, we show that PaM identifies ideal and near-ideal pairs that are more genetically homogeneous than those identified based on racial criteria or Principal Component Analysis (PCA) alone. Homogenising the treatment (or case) and control groups can be expected to improve the accuracy and reproducibility of the study. PaMs ability to infer the ancestry of the participants further allows identifying subgroup of responders and developing a precision medicine approach to treatment. PaM is simple to execute, fast, and can be used for clinical trials and association studies. PaM is freely available via R scripts and a web interface.

bioinformatics↗

bioRxiv2015

Using Cell line and Patient samples to improve Drug Response Prediction

BackgroundRecent advances in high-throughput technologies have facilitated the profiling of large panels of cancer cell lines with responses measured for thousands of drugs. The computational challenge is now to realize the potential of these data in predicting patients responses to these drugs in the clinic.\n\nMethodsWe address this issue by examining the spectrum of prediction models of patient response: models predicting directly from cell lines, those predicting directly from patients, and those trained on cell lines and patients at the same time. We tested 21 classification models on four drugs, that are bortezomib, erlotinib, docetaxel and epirubicin, for which clinical trial data were available.\n\nResultsOur integrative models consistently outperform cell line-based predictors, indicating that there are limitations to the predictive potential of in vitro data alone. Furthermore, these integrative models achieve better predictive accuracy and require substantially fewer patients than would be the case if only patient data were available.\n\nConclusionsThe integration of in vitro and ex vivo genomic data results in more accurate predictors using only a fraction of the patient information, which can help optimize the development of personalized predictors of therapy response. Altogether our results support the relevance of preclinical data for therapy prediction in clinical trials, enabling more efficient and cost-effective trial design.

Bioinformatics↗

bioRxiv2018

The QNOAEL vs. BMD for Point of Departure

Quantile bootstrap (QB) methods can be applied to the problem of estimating the No Observed Adverse Effect Level (NOAEL) of a New Molecular Entity (NME) to anticipate a safe starting dose for beginning clinical trials. An estimate of the NOAEL from the extended QB method (called the QNOAEL) can be calculated using multiple disparate studies in the literature and/or from laboratory experiments. The QNOAEL is similar in some ways to the Benchmark Dose (BMD) and is superior to the BMD in others. The Benchmark Dose method is currently widely used in toxicological research.\n\nResults are used in a simulation based on nonparametric cluster analysis methods to calculate confidence levels on the difference between the Effect and the No Effect studies. The QNOAEL simulation generates an intuitive curve that is comparable to the dose-response curve.\n\nThe QNOAEL of ellagic acid (EA) will be calculated for clinical trials of its use as a component therapeutic agent (in BSN476) for treating Chikungunya infections. This will be the first application of QB to the problem of NOAEL estimation for a drug. The specific aims of the proposed study are to evaluate the accuracy and precision of the QB Simulation and QNOAEL compared to the Benchmark Dose Method, and to calculate the QNOAEL of EA for BSN476 Drug Development.

pharmacology and toxicology↗

bioRxiv2017

Lack of Evidence that Ursodeoxycholic Acid’s Effects on the Gut Microbiome Influence Colorectal Adenoma Risk

ObjectiveWe previously reported that Ursodeoxycholic acid (UDCA), a therapeutic bile acid, reduces risk for advanced colorectal adenoma in men but not women. Interactions between the gut microbiome and fecal bile acid composition as a factor in colon cancer neoplasia have been postulated but evidence is limited to small cohorts and animal studies.\n\nDesignUsing banked stool samples collected as part of a phase III randomized clinical trial of UDCA for the prevention of colorectal neoplasia, we compared change in the microbiome composition after 3 years intervention in a subset of participants randomized to 8-10 mg/kg of body weight UDCA (n=198) to placebo (n=203). UDCA effects on the microbiome, sex and adenoma outcome were investigated.\n\nResultsStudy participants randomized to UDCA experienced compositional changes in their microbiome that were statistically more similar to other individuals in the UDCA arm than to those in the placebo arm. This change reflected an UDCA-associated shift in microbial community distance metrics (P <0.001), independent of sex, with no evidence of UDCA effect on microbial richness (P > 0.05). These UDCA-associated shifts in microbial community distance metrics from baseline to end-of-study were not associated with risk of any or advanced adenoma (all P> 0.05) in men or women.\n\nConclusionDespite a large sampling of randomized clinical trial participants, daily UDCA use only modestly influenced the relative abundance of microbial species in stool with no evidence for effects of UDCA on stool microbial community composition as a modifier of colorectal adenoma risk.\n\nSUMMARYO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIUrsodeoxycholic acid (UDCA) is a therapeutic bile acid used in the treatment of primary biliary cirrhosis (PBC) and investigated for anti-cancer activity in the colon\nC_LIO_LIIn humans, UDCA is produced in the colon from the conjugation of primary bile acids by intestinal bacteria\nC_LIO_LIIntestinal bacteria play a critical role in human intestinal health and disease including a hypothesized role in the development of colorectal cancer.\nC_LIO_LIUDCA was found to reduce the risk of more advanced colorectal adenoma with effects present in men but not women.\nC_LIO_LITherapeutic UDCA was recently shown to reduce the extent of bacterial dysbiosis in patients with PBC\nC_LI\n\nWhat are the new findings?O_LIAmong a population of patients with colorectal adenoma, low dose oral UDCA taken daily produced modest changes in fecal bacterial composition\nC_LIO_LIUDCA associated changes in the gut microbiome were similar in men and women.\nC_LIO_LIUDCA associated changes in the gut micobiome were not associated with risk of any or advanced colorectal adenoma in the patient population.\nC_LI\n\nHow might it impact on clinical practice in the foreseeable future?O_LIThese findings confirm effects of oral UDCA on the microbiome that may be beneficial for patients with PBC.\nC_LIO_LIThese findings suggest that the anti-cancer effects of UDCA for colorectal adenoma prevention are not due to major effects of UDCA on the gut microbiome.\nC_LI

cancer biology↗

bioRxiv2017

Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer

Immune therapies have shown promise in a number of cancers, and clinical trials using the anti-PD-L1/PD-1 checkpoint inhibitor in lung cancer have been successful for a number of patients. However, some patients either do not respond to the treatment or have cancer recurrence after an initial response. It is not clear which patients might fall into these categories or what mechanisms are responsible for treatment failure. To explore the different underlying biological mechanisms of resistance, we created a spatially explicit mathematical model with a modular framework. This construction enables different potential mechanisms to be turned on and off in order to adjust specific tumor and tissue interactions to match a specific patient's disease. In parallel, we developed a software suite to identify significant computed tomography (CT) imaging features correlated with outcome using data from an anti-PDL-1 checkpoint inhibitor clinical trial for lung cancer and a tool that extracts these features from both patient CT images and \"virtual CT\" images created from the cellular density profile of the model. The combination of our two toolkits provides a framework that feeds patient data through an iterative pipeline to identify predictive imaging features associated with outcome, whilst at the same time proposing hypotheses about the underlying resistance mechanisms.

cancer biology↗

bioRxiv2018

A simian-adenovirus-vectored rabies vaccine suitable for thermostabilisation and clinical development for low-cost single-dose pre-exposure prophylaxis

BackgroundEstimates of current global rabies mortality range from 26,000 to 59,000 deaths per annum. Although pre-exposure prophylaxis using inactivated rabies virus vaccines (IRVs) is effective, it requires two to three doses and is regarded as being too expensive and impractical for inclusion in routine childhood immunization programmes.\n\nMethodology/ Principal FindingsHere we report the development of a simian-adenovirus-vectored rabies vaccine intended to enable cost-effective population-wide pre-exposure prophylaxis against rabies. ChAdOx2 RabG uses the chimpanzee adenovirus serotype 68 (AdC68) backbone previously shown to achieve pre-exposure protection against rabies in non-human primates. ChAdOx2 differs from AdC68 in that it contains the human adenovirus serotype 5 (AdHu5) E4 orf6/7 region in place of the AdC68 equivalents, enhancing ease of manufacturing in cell lines which provide AdHu5 E1 proteins in trans.\n\nWe show that immunogenicity of ChAdOx2 RabG in mice is comparable to that of AdC68 RabG and other adenovirus serotypes expressing rabies virus glycoprotein. High titers of rabies virus neutralizing antibody (VNA) are elicited after a single dose. The relationship between levels of VNA activity and rabies glycoprotein monomer-binding antibody differs after immunization with adenovirus-vectored vaccines and IRV vaccines, suggesting routes to further enhancement of the efficacy of the adenovirus-vectored candidates. We also demonstrate that ChAdOx2 RabG can be thermostabilised using a low-cost method suitable for clinical bio-manufacture and ambient-temperature distribution in tropical climates. Finally, we show that a dose-sparing effect can be achieved by formulating ChAdOx2 RabG with a simple chemical adjuvant. This approach could lower the cost of ChAdOx2 RabG and other adenovirus-vectored vaccines.\n\nConclusions/ SignificanceChAdOx2 RabG may prove to be a useful tool to reduce the human rabies death toll. We have secured funding for Good Manufacturing Practice-compliant bio-manufacture and Phase I clinical trial of this candidate.\n\nAuthor summaryRabies was, after smallpox, the second human disease for which an efficacious vaccine was developed, by Pasteur in 1885. Although it is eminently preventable, with highly efficacious vaccines available for both humans and animals, it still causes considerable mortality in low and middle-income countries. It is a particular problem in areas with the weakest healthcare and veterinary infrastructure, where achieving prompt post-exposure vaccination or high-coverage dog vaccination are challenging.\n\nHere, we report the development of a new candidate rabies vaccine, designed to enable low-cost single-dose pre-exposure human rabies prophylaxis in such settings. ChAdOx2 RabG is based upon a simian adenovirus-vectored candidate previously shown to achieve protection after a single dose in non-human primates, now modified to allow clinical-grade bio-manufacture. We show that it induces a potent immune response in mice, that this response can be further enhanced by clinically-relevant adjuvant, and that we can stabilise it such that it can withstand temperatures of up to 45 {degrees}C for a month. We will be performing a clinical trial of this candidate in the near future.

immunology↗

bioRxiv2016

An Empirical Biomarker-based Calculator for Autosomal Recessive Polycystic Kidney Disease The Nieto-Narayan Formula

Autosomal polycystic kidney disease (ARPKD) is associated with progressive enlargement of the kidneys fuelled by the formation and expansion of fluid-filled cysts. The disease is congenital and children that do not succumb to it during the neonatal period will, by age 10 years, more often than not, require nephrectomy+renal replacement therapy for management of both pain and renal insufficiency. Since increasing cystic index (CI; percent of kidney occupied by cysts) drives both renal expansion and organ dysfunction, management of these patients, including decisions such as elective nephrectomy and prioritization on the transplant waitlist, could clearly benefit from serial determination of CI. So also, clinical trials in ARPKD evaluating the efficacy of novel drug candidates could benefit from serial determination of CI. Although ultrasound is currently the imaging modality of choice for diagnosis of ARPKD, its utilization for assessing disease progression is highly limited. Magnetic resonance imaging or computed tomography, although more reliable for determination of CI, are expensive, time-consuming and somewhat impractical in the pediatric population. Using a well-established mammalian model of ARPKD, we undertook a big data-like analysis of minimally- or non-invasive serum and urine biomarkers of renal injury/dysfunction to derive a family of equations for estimating CI. We then applied a signal averaging protocol to distill these equations to a single empirical formula for calculation of CI. Such a formula will eventually find use in identifying and monitoring patients at high risk for progressing to end-stage renal disease and aid in the conduct of clinical trials.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC=\"FIGDIR/small/064139_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (31K):\norg.highwire.dtl.DTLVardef@6fb814org.highwire.dtl.DTLVardef@9e597borg.highwire.dtl.DTLVardef@4d81aorg.highwire.dtl.DTLVardef@7c642_HPS_FORMAT_FIGEXP M_FIG C_FIG

Bioinformatics↗

bioRxiv2018

Repurposing of drugs as novel influenza inhibitors from clinical gene expression infection signatures

BackgroundInfluenza virus infections remain a major and recurrent public health burden. The intrinsic ever-evolving nature of this virus, the suboptimal efficacy of current influenza inactivated vaccines, as well as the emergence of resistance against a limited antiviral arsenal, highlight the critical need for novel therapeutic approaches. In this context, the aim of this study was to develop and validate an innovative strategy for drug repurposing as host-targeted inhibitors of influenza viruses and the rapid evaluation of the most promising candidates in Phase II clinical trials.\n\nMethodsWe exploited in vivo global transcriptomic signatures of infection directly obtained from a patient cohort to determine a shortlist of already marketed drugs with newly identified, host-targeted inhibitory properties against influenza virus. The antiviral potential of selected repurposing candidates was further evaluated in vitro, in vivo and ex vivo.\n\nResultsOur strategy allowed the selection of a shortlist of 35 high potential candidates out of a rationalized computational screening of 1,309 FDA-approved bioactive molecules, 31 of which were validated for their significant in vitro antiviral activity. Our in vivo and ex vivo results highlight diltiazem, a calcium channel blocker currently used in the treatment of hypertension, as a promising option for the treatment of influenza infections. Additionally, transcriptomic signature analysis further revealed the so far undescribed capacity of diltiazem to modulate the expression of specific genes related to the host antiviral response and cholesterol metabolism. Finally, combination treatment with diltiazem and virus-targeted oseltamivir neuraminidase inhibitor further increased antiviral efficacy, prompting rapid authorization for the initiation of a Phase II clinical trial.\n\nConclusionsThis original, host-targeted, drug repurposing strategy constitutes an effective and highly reactive process for the rapid identification of novel anti-infectious drugs, with potential major implications for the management of antimicrobial resistance and the rapid response to future epidemic or pandemic (re)emerging diseases for which we are still disarmed.

microbiology↗

bioRxiv2018

Identifying individual risk rare variants using protein structure-guided local tests (POINT)

Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9 associated with low-density lipoprotein in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.\n\nAuthor summaryWhile it is known that rare variants play an important role in understanding associations between genotype and complex diseases, pinpointing individual rare variants likely to be responsible for association is still a daunting task. Due to their low frequency in the population and reduced signal, localizing causal rare variants often requires additional information, such as type of DNA change or location of variant along the sequence, to be incorporated in a biologically meaningful fashion that does not overpower the genotype data. In this paper, we use the observation that important variants tend to cluster together on functional domains to propose a new approach for prioritizing rare variants: the protein structure guided local test (POINT). POINT uses a genes 3-dimensional protein folding structure to guide aggregation of information from neighboring variants in the protein in a robust manner. We show how POINT improves selection performance over single variant tests and sliding window approaches. We further illustrate how it can be used to prioritize individual rare variants using the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data, finding five promising variants within PCSK9 in association with low-density lipoprotein, including three new mutations near the PCSK9-LDLR binding domain.

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