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bioRxiv2022

Approaches to generating virtual patient cohorts with applications in oncology

Virtual clinical trials (VCTs) have gained popularity for their ability to rationalize the drug development process using mathematical and computational modelling, and to provide key insights into the mechanisms regulating patient responses to treatment. In this chapter, we cover approaches for generating virtual cohorts with applications in cancer biology and treatment. VCTs are an effective tool for predicting clinical responses to novel therapeutics and establishing effective treatment strategies. These VCTs allow us to capture inter-individual variability (IIV) which can lead to diversity in patient drug responses. Here we discuss three main methodologies for capturing IIV with a VCT. First, we highlight the use of population pharmacokinetic (PopPK) models, which extrapolate from empirical data population PK parameters that best fits the individual variability seen in drug disposition using non-linear mixed effects models. Next, we show how virtual patients may be sampled from a normal distribution with mean and standard deviation informed from experimental data to estimate parameters in a mechanistic model that regulates drug PKs. Lastly, we show how optimization techniques can be used to calibrate virtual patient parameter values and generate the VCT. Throughout, we compare and contrast these methods to provide a broader view of the generation of virtual patients, and to aid the decision-making process for those looking to leverage virtual clinical trials in their research.

cancer biology↗

bioRxiv2024

Real-time prediction of drug-induced proarrhythmic risk with sex-specific cardiac emulators

In silico trials for drug safety assessment require a large number of high-fidelity 3D cardiac electrophysiological simulations to predict drug-induced QT interval prolongation, making the process computationally expensive and time-consuming. These simulations, while necessary to accurately model the complex physiological conditions of the human heart, are often cost-prohibitive when scaled to large populations or diverse conditions. To overcome this challenge, we develop sex-specific emulators for the real-time prediction of QT interval prolongation, with separate models for each sex. Building an extensive dataset from 900 simulations allows us to show the superior sensitivity of 3D models over 0D single-cell models in detecting abnormal electrical propagation in response to drug effects as the risk level increases. The resulting emulators trained on this dataset showed high accuracy level, with an average relative error of 4% compared to simulation results. This enables global sensitivity analysis and the replication of in silico cardiac safety clinical trials with accuracy comparable to that of simulations when validated against in vivo data. With our emulators, we carry out in silico clinical trials in seconds on a standard laptop, drastically reducing computational time compared to traditional high-performance computing methods. This efficiency enables the rapid testing of drugs across multiple concentration ranges without additional computational cost. This approach directly addresses several key challenges faced by the biopharmaceutical industry: optimizing trial designs, accounting for variability in biological assays, and enabling rapid, cost-effective drug safety evaluations. By integrating these emulators into the drug development process, we can enhance the reliability of preclinical assessments, streamline regulatory submissions, and advance the practical application of digital twins in biomedicine. This work represents a significant step toward more efficient and personalized drug development, ultimately benefiting patient safety and accelerating the path to market.

pharmacology and toxicology↗

bioRxiv2024

Development of an improved blood-stage malaria vaccine targeting the essential RH5-CyRPA-RIPR invasion complex

In recent years, reticulocyte-binding protein homologue 5 (RH5) has emerged as a leading blood-stage Plasmodium falciparum malaria vaccine antigen. The most advanced blood-stage vaccine candidate in a Phase 2b clinical trial, RH5.1/Matrix-M, is based on a full-length soluble protein-with-adjuvant formulation. RH5 interacts with cysteine-rich protective antigen (CyRPA) and RH5- interacting protein (RIPR) to form an essential heterotrimeric "RCR-complex". Here, we investigated whether a vaccine candidate based on the ternary RCR-complex could substantially improve upon the leading clinical candidate RH5.1/Matrix-M in preclinical studies. Using a panel of monoclonal antibodies (mAbs) we confirm that parasite growth-inhibitory epitopes on each antigen are exposed on the surface of the RCR-complex and that mAb pairs binding to different antigens can function additively or synergistically to mediate parasite growth inhibition activity (GIA) in vitro. However, immunisation of rats with the RCR-complex consistently fails to outperform RH5.1 alone. We show this is due to immuno-dominance of RIPR coupled with the inferior potency of anti-full length RIPR polyclonal IgG antibodies as compared to the anti-RH5 and anti-CyRPA response. To address this, we identified the growth-inhibitory antibody epitopes of RIPR are clustered within C-terminal EGF-like domains of RIPR. A fusion of these EGF domains to CyRPA, called "R78C", combined with RH5.1, provided a new vaccination strategy that improves upon the levels of in vitro GIA seen with RH5.1 alone. Superiority of the combination antigen vaccine candidate was achieved by the induction of a quantitatively higher, but qualitatively similar, polyclonal antibody response that demonstrated additive GIA across the three antigen targets. These preclinical data justified the advancement of the RH5.1+R78C/Matrix-M combination vaccine to a Phase 1 clinical trial.

immunology↗

bioRxiv2024

Serotype and distribution of adhesion genes in Streptococcus mutans clinical isolates

The delicate balance of the oral cavity is disrupted by hygiene and diet changes, leading to caries. The virulence of Streptococcus mutans, a key caries pathogen, is partly explained by its rhamnose-glucose polysaccharide (RGP) and its sucrose-dependent and -independent adhesion mechanisms. This study investigates the diversity of S. mutans through analysis of its RGP and the distribution of sucrose-independent adhesion proteins (SpaP, WapA, Cnm, Cbm) in Parkinsons disease patients. In the PARKIDENT clinical trial, strains were isolated from saliva samples of Parkinsons patients before and after oral hygiene procedures. Strains were preanalyzed by multiplex PCR for serotype and collagen-binding proteins, followed by high-throughput sequencing and bioinformatics analysis using RAST(R) and BLAST(R). Phylogenetic analyses and protein modeling were also conducted. Of 40 patients in the Parkident clinical trial, only 24 had Streptococcus mutans strains isolable from salivary swabs. Serotyping revealed that over 80% of the 44 strains isolated were serotype c, in line with prevalence data in the literature. Furthermore, only SpaP types A and B were identified in the strains. The low variability observed for the WapA protein underlines its functional importance. Finally, the cbm gene was not found in any strain, and only 5 strains possessed the cnm gene, all of them serotype c. The study of these 44 Streptococcus mutans strains showed characteristics similar to other populations. Serotype c predominates, with minimal peptide variability in adhesion proteins (SpaP, WapA). Collagen-binding proteins Cnm and Cbm are rare or absent. Further research is essential for detailed strain distribution in Parkinsons-affected and general French populations due to S. mutans growing role in extra-oral pathologies.

microbiology↗

bioRxiv2024

The relationship between sleep and cognitive performance on tests of pattern separation

Study objectivesSleep disturbances are considered both a risk factor and symptom of dementia. The present research aimed to identify cognitive tests in which performance is associated with objective sleep quality or quantity, focusing on cognitive tests designed to evaluate the earliest cognitive changes in dementia. MethodsWe recruited older adults (50 years of age or older) and remotely monitored their sleep patterns for 7 consecutive days using wrist actigraphy and sleep diaries. On day 7, participants completed a battery of cognitive tests, which included the Psychomotor Vigilance Task (PVT), the Prodromal Alzheimers and Mild Cognitive Impairment battery from the Cambridge Neuropsychological Test Automated Battery (CANTAB), and the Mnemonic Similarity Task (MST), designed to tax pattern separation. The participants were also assessed with the Montreal Cognitive Assessment (MoCA). ResultsThe final sample included 34 participants (mean age: 65.56, SD: 9.57). There were significant correlations between objective total sleep time and PVT and MST performance. MoCA scores were correlated with performance on CANTAB and MST. Objective total sleep time also predicted MST performance when controlling for age and gender. ConclusionsPerformance on cognitive tests designed to assess pattern separation are sensitive to older adults objective sleep duration and the early cognitive changes associated with dementia. MST should be evaluated for potential use as a clinical trial outcome measure for sleep-promoting treatments in older adults. Statement of SignificanceThere is growing emphasis on the importance of sleep as a potential therapeutic target for neurodegenerative diseases (e.g., Alzheimers disease). Identifying cognitive measures that are sensitive to both sleep and the earliest cognitive changes associated with dementia are needed for use as outcome measures in clinical trials evaluating the effectiveness of sleep-promoting interventions. Our research addresses this need by investigating the relationship between sleep patterns and performance on cognitive tests designed to assess the earliest cognitive changes in dementia. Our results suggest that cognitive tests designed to assess pattern separation are uniquely sensitive to sleep quantity in older adults.

neuroscience↗

bioRxiv2024

Video-based biomechanical analysis captures disease-specific movement signatures of different neuromuscular diseases

BackgroundAssessing human movement is essential for diagnosing and monitoring movement-related conditions like neuromuscular disorders. Timed function tests (TFTs) are among the most widespread assessments due to their speed and simplicity, but they cannot capture disease-specific movement patterns. Conversely, biomechanical analysis can produce sensitive disease-specific biomarkers but is traditionally confined to laboratory settings. Recent advances in smartphone video-based biomechanical analysis enable quantification of 3D movement with the ease and speed required for clinical settings. However, the potential of this technology to offer more sensitive assessments of human function than TFTs remains untested. MethodsTo compare video-based analysis against TFTs, we collected an observational dataset from 129 individuals: 28 with facioscapulohumeral muscular dystrophy, 58 with myotonic dystrophy, and 43 controls with no diagnosed neuromuscular condition. We used OpenCap, a free open-source software tool, to capture smartphone video-based biomechanics of nine different movements in a median time of 16 minutes per participant. From these recordings we extracted 34 interpretable movement features. Using these features, we evaluated the ability of video-based biomechanics to reproduce four TFTs (10-meter walk, 10-meter run, timed up-and-go, and 5-time sit-to-stand) while capturing additional disease-specific signatures of movement. ResultsVideo-based biomechanical analysis reproduced all four TFTs (r > 0.98) with similar test-retest reliability. In addition, video metrics outperformed TFTs at disease classification (p = 0.021). Unlike TFTs, video-based biomechanical analysis identified disease-specific signatures of movement such as differences in gait kinematics that are not evident in TFTs. ConclusionVideo-based biomechanical analysis can complement existing functional movement assessments by capturing more sensitive, disease-specific outcomes from human movement. This technology enables digital health solutions for assessing and monitoring motor function, complementing traditional clinical outcome measures to enhance care, management, and clinical trial design for movement-related conditions. DescriptionThis study demonstrates that smartphone video-based biomechanical analysis can accurately replicate traditional timed function tests (TFTs) -- commonly used to diagnose and assess movement-related conditions like neuromuscular disorders -- while also capturing disease-specific movement patterns that TFTs fail to detect. By enabling sensitive and interpretable assessments in clinical settings, this approach offers a scalable and objective tool to enhance diagnosis, monitoring, and clinical trial design for neuromuscular disorders.

bioengineering↗

bioRxiv2025

Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden

Pulmonary infection after inhalation of Mycobacterium tuberculosis (Mtb) causes tuberculosis (TB). TB presents with lung granulomas - complex spheroidal structures composed of immune cells and bacteria. Granulomas often have centralized caseum (necrotic tissue) where mycobacteria are quarantined, complicating and prolonging multi-antibiotic regimens. Determining which antibiotic regimens are optimal for reducing treatment time and toxicity is a goal of recent TB eradication campaigns. Clinical trials are expensive and challenging, making it difficult to untangle which host-pathogen interactions drive the heterogeneous infection and treatment outcomes observed at between-host and within-host scales. To determine responses to antibiotic regimens, we simulate treatments in HostSim, our whole-host mechanistic, multi-scale computational model of Mtb-infection. HostSim tracks dynamics of pulmonary Mtb-infection over molecular, cellular, tissue, organ, and whole-host scales. We create a heterogenous virtual cohort, comprising distinct hosts, for virtual clinical trials. We represent drug treatments by newly-integrating pharmacokinetics / pharmacodynamics into HostSim, simulating treatment with commonly-prescribed TB antibiotic regimens (e.g., HRZE or BPaL). Our approach allows us to identify both (1) which hosts/granulomas most improve with treatment, and (2) which mechanisms influence outcome heterogeneity. By tracking experimental and clinical measurements, we virtually recreate several drug rankings from literature. We find that many methods of ranking treatment efficacy are strongly influenced by the definition of improvement used and, in some cases, the detection threshold of CFU. Other rankings depend on initial bacterial burden of hosts/granulomas. Our work suggests that metrics for regimen optimality may be orthogonal, which could explain seemingly-contradictory findings from prior studies.

systems biology↗

bioRxiv2026

AptCancerDB: A Curated Knowledgebase and Translational Discovery Platform for Anticancer Aptamers

Aptamers are emerging as important molecular recognition ligands in oncology, playing significant roles in cancer diagnostics, targeted therapies, drug delivery systems, and molecular imaging. Numerous aptamers have advanced to clinical trials, indicating their potential for real-world applications; however, existing databases fail to capture that. To bridge this critical gap, we developed AptCancerDB (https://webs.iiitd.edu.in/raghava/aptcancerdb/), a comprehensive, manually curated database of experimentally verified anticancer aptamers. The current release contains 1,941 entries collected from studies published between 2000 and 2025, covering 29 cancer types, approximately 200 cancer cell lines, and direct links to 22 clinical trials. Each entry is annotated with sequence information, target details, cancer type, cell line, SELEX methodology, affinity determination data, chemical modifications, and biological activities. The dataset is dominated by 82.7% ssDNA, reflecting its superior stability and ease of synthesis, while only 16.6% is ssRNA and appears primarily in studies targeting complex intracellular or protein-protein interactions. To facilitate structural analysis, predicted secondary structures, dot-bracket notations, specific structural elements, and minimum free energy values were also included. AptCancerDB integrates a MySQL backend with an ArcadeDB/OpenCypher-based Knowledge Graph, enabling exploration of relationships among aptamers, targets, cancer types, cell lines, and functional applications. The platform provides advanced search and browsing facilities, BLASTn-based similarity searching, and GC Calculator. Built on a modern, responsive frontend (React/TypeScript/Tailwind CSS), the platform includes a REST API for data retrieval. By integrating fragmented experimental data into a unified cancer-focused resource, AptCancerDB serves as a valuable resource for comparative analysis, aptamer discovery, and the development of next-generation aptamer-based diagnostics and therapeutics. HighlightsO_LICurated knowledge base of experimentally validated anticancer aptamers. C_LIO_LIAptCancerDB contain therapeutic, tumor-homing and cell-penetrating aptamers. C_LIO_LISummarizes clinical progress and translational trends in anticancer aptamer research. C_LIO_LISupports rational aptamer design using molecular, functional, and clinical annotations C_LIO_LIDisease-focused resource for cancer diagnosis, therapy, and drug delivery C_LI TeaserAptCancerDB maintains experimentally validated anticancer aptamers relevant to diagnosis, drug delivery, and therapy.

cancer biology↗

bioRxiv2020

Favipiravir and severe acute respiratory syndrome coronavirus 2 in hamster model

Despite no or limited pre-clinical evidence, repurposed drugs are massively evaluated in clinical trials to palliate the lack of antiviral molecules against SARS-CoV-2. Here we used a Syrian hamster model to assess the antiviral efficacy of favipiravir, understand its mechanism of action and determine its pharmacokinetics. When treatment was initiated before or simultaneously to infection, favipiravir had a strong dose effect, leading to dramatic reduction of infectious titers in lungs and clinical alleviation of the disease. Antiviral effect of favipiravir correlated with incorporation of a large number of mutations into viral genomes and decrease of viral infectivity. The antiviral efficacy observed in this study was achieved with plasma drug exposure comparable with those previously found during human clinical trials and was associated with weight losses in animals. Thereby, pharmacokinetic and tolerance studies are required to determine whether similar effects can be safely achieved in humans.

microbiology↗

bioRxiv2026

Forecasting novel therapeutic development in biomedical research

Early identification of promising drug research topics is challenging yet crucial for the scientific community to accelerate the development of novel therapeutics. In this work, we leverage large-scale public data from the biomedical literature to extract predictive features to identify promising therapeutic research topics at an early stage. We divide the global citation graph of biomedical literature into a time series of research topics and extract topic features based on citation activity, publication content, and measurable flocking of scientists into novel research topics. Based on these features, our machine learning model identifies research topics that in the future yield Food and Drug Administration (FDA)-approved drugs years before approval (F1-score of 0.84). 80% of target drugs are predicted in advance, with 65% predicted 8 or more years before approval. This predates the start of phase 2 clinical trials in the vast majority of positive predictions. These results show this approach can efficiently flag research topics generating approved drugs several years prior to approval using public data that would have been contemporaneous at the time of prediction. Thus, reliable forecasting can be accomplished with a high-level view of the publication and citation behavior of scientists, without depending on clinical trial data that may only be deposited with a significant lag. This demonstrates that it is possible to detect early signals of future FDA approved therapies even without any specialized information about these applied research efforts. TeaserLarge-scale data analysis can use the full set of scientific citations to predict which areas of research will yield new FDA approved drugs, years in advance.

scientific communication and education↗

bioRxiv2019

Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomas

The use of mathematical models for personalization of cancer therapies and raising hypothesis of potential clinical impact is an emerging topic in the interface between mathematics and oncology. Here we put forward a mathematical model describing the response of low-grade (WHO grade II) oligodendrogliomas (LGO) to temozolomide (TMZ). The model described the longitudinal volumetric dynamics of tumor response to TMZ of a cohort of 11 LGO patients treated with TMZ. After finding patient-specific parameters, different therapeutical strategies were tried computationally on the in-silico twins of those patients. Chemotherapy schedules with larger-than-standard rest periods between consecutive cycles had either the same or better long-term efficacy than the standard 28-day cycles. The results were confirmed in a large virtual clinical trial including 2000 patients. These long-cycle schemes would also have reduced toxicity and defer the appearance of resistances.\n\nOn the basis of those results, a combination scheme consisting of five induction TMZ cycles given monthly plus 12 maintenance cycles given every three months was found to provide substantial survival benefits for the in-silico twins of the 11 LGO patients (median 5.69 years, range: 0.67 to 68.45 years) and in a large virtual trial including 2000 patients. This scheme could be useful for defining a standardized TMZ treatment for LGO patients with survival benefits.\n\nAuthor summaryA mathematical model described the longitudinal volumetric growth data of grade II oligodendrogliomas patients and their response to temozolomide. The model was used to explore alternative therapeutical protocols for the in-silico twins of the patients and in virtual clinical trials. The simulations show that enlarging the time interval between chemotherapy cycles would maintain the therapeutical efficacy, while limiting toxicity and defering the development of resistances. This may allow for improved drug-exposure by administering a larger number of cycles for longer treatment periods. A scheme based on this idea consisting of an induction phase (5 consecutive cycles, 1 per month) and a maintenance phase (12 cycles given in three-months intervals) led to substantial survival benefits in-silico.

cancer biology↗

bioRxiv2022

Development of a Novel SARS-CoV-2 Immune Complex Vaccine Candidate (CRCx) with Broad Immune Responses: A Preclinical Trial in Animal Model

BackgroundThe ongoing pandemic of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a serious threat to global public health and imposes a severe burden on the entire human population. Faced with a virus that can mutate its structure while immunity is incapacitated, a need to develop a universal vaccine that can boost immunity to coronaviruses is highly needed. DesignFive formulations of two types (CRCx2 and CRCx3) of immune complexes with an immunogen adjuvant were evaluated in a mouse model as candidate SARS CoV-2 vaccines in a pretrial prior to clinical trials in humans. CRCx3 comprises 3 different formulas and CRCx2 comprises 2. Balb/c mice were vaccinated intraperitoneally on days 0/7 with a high or low dose of CRCx2 or on days 0/7/14 with a high, medium, or low dose of CRCx3 series, and their blood was sampled for serum antibody measurements. Mice were challenged with live virus after immunization with either vaccine to evaluate prophylaxis ability or treated with them after challenge to evaluate therapeutic ability on day 15. Immunological markers and histopathological studies as well as titration of neutralizing antibodies to the vaccines were evaluated and analyzed. ResultsCRCx 3 and CRCx 2 vaccine candidates induced elevated levels of positive neutralizing antibodies as well as a cellular immune response with safety, efficient productivity, and good genetic stability for vaccine manufacturing to provide protection against SARS-CoV-2 with relatively higher levels with the high dose CRCx2 candidate combination. ConclusionsHighly efficient protection and therapeutic effect against SARS-CoV-2 were obtained with a double-dose immunization schedule spaced at 7-day intervals using injections 0.25 of or 0.40 ml of CRCx2 vaccine formulations with a 25-mm needle. These results support further evaluation of CRCx in a clinical trial on humans.

immunology↗

bioRxiv2023

Exploiting the therapeutic vulnerability of IDH-mutant gliomas with zotiraciclib

Isocitrate dehydrogenase (IDH)-mutant gliomas have distinctive metabolic and biological traits that may render them susceptible to targeted treatments. Here, by conducting a high-throughput drug screen, we pinpointed a specific susceptibility of IDH-mutant gliomas to zotiraciclib (ZTR). ZTR exhibited selective growth inhibition across multiple IDH-mutant glioma in vitro and in vivo models. Mechanistically, ZTR at low doses suppressed CDK9 and RNA Pol II phosphorylation in IDH-mutant cells, disrupting mitochondrial function and NAD+ production, causing oxidative stress. Integrated biochemical profiling of ZTR kinase targets and transcriptomics unveiled that ZTR-induced bioenergetic failure was linked to the suppression of PIM kinase activity. We posit that the combination of mitochondrial dysfunction and an inability to adapt to oxidative stress resulted in significant cell death upon ZTR treatment, ultimately increasing the therapeutic vulnerability of IDH-mutant gliomas. These findings prompted a clinical trial evaluating ZTR in IDH-mutant gliomas towards precision medicine (NCT05588141). HighlightsO_LIZotiraciclib (ZTR), a CDK9 inhibitor, hinders IDH-mutant glioma growth in vitro and in vivo. C_LIO_LIZTR halts cell cycle, disrupts respiration, and induces oxidative stress in IDH-mutant cells. C_LIO_LIZTR unexpectedly inhibits PIM kinases, impacting mitochondria and causing bioenergetic failure. C_LIO_LIThese findings led to the clinical trial NCT 05588141, evaluating ZTR for IDH-mutant gliomas. C_LI

cancer biology↗

bioRxiv2022

Genomic alterations drive brain metastases formation in colorectal cancer: The role of IRS2

Colorectal cancer (CRC) is currently the fourth leading etiology of brain metastasis (BM). Yet, mechanisms supporting the formation of CRC BM are mostly unknown. In order to identify drivers that lead to tropism and adaptation of CRC cells to the brain environment, we analyzed an extensive genomic database, consisting of over 36,000 human CRC primary and metastasis samples. Several genomic alterations specific for BM were noted, among them increased prevalence of insulin receptor substrate 2 (IRS2) amplification, observed in 7.6% of BM, compared to only 2.9% of primary tumors or other metastatic sites (p<7E-05). This observation was validated by Immunohistochemistry studies of human clinical samples, showing increased expression of IRS2 protein in BM. IRS2 is a cytoplasmic adaptor mediating effects of insulin and IGF-1 receptors and is involved in more aggressive behavior of different cancer types. In order to study the ability of IRS2 to promote growth of CRC cells under brain microenvironment conditions, we employed an in vitro system consisting of cultured human astrocytes or their conditioned media. Indeed, IRS2-overexpressed CRC cells survived better and formed larger 3D spheres when grown in brain-mimicking conditions, while IRS2-silenced CRC cells showed a mirror image. Similarly, In vivo studies, using intracranial CRC BM mouse model, demonstrated that IRS2-overexpressed cells generated larger brain lesions, while silencing IRS2 dramatically decreased tumor outgrowth and extended survival. Transcriptomic analysis revealed enrichment of oxidative phosphorylation (OXPHOS) and Wnt/{beta}-catenin pathways by IRS2. Indeed, IRS2-expressing cells showed increased mitochondrial activity and glycolysis-independent viability. Furthermore, IRS2-expressing cells had increased {beta}-catenin transcriptional activity, and either {beta}-catenin inhibition or IRS2 inhibition in IRS2-expressing cells decreased their viability, {beta}-catenin transcriptional activity, and mitochondrial activity. These data suggest involvement of IRS2 in modulating OXPHOS through {beta}-catenin. Exploiting this mechanism as a potential vulnerability allowed us to develop novel treatment strategies against CRC BM. NT219 is a novel IRS1/2 inhibitor already being tested in clinical trials. Treatment of mice harboring CRC BM with NT219 and 5-flourouracil reduced tumor growth and prolonged mice survival. These data reveal, for the first time, the unique genomic profile of CRC BM and imply the IRS2 role in promoting CRC BM. These effects may be mediated, at least in part, by modulation of the {beta}-catenin and OXPHOS pathway. These findings may pave the way for clinical trials evaluation this novel IRS2-based strategy for the treatment of CRC BM. Accession no GSE203017 and here is the link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE203017

cancer biology↗

bioRxiv2019

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

ObjectiveSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.\n\nMethodsWe applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.\n\nResultsExamination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.\n\nConclusionGiven that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

bioinformatics↗

bioRxiv2021

PU.1 and MYC transcriptional network defines synergistic drug responses to KIT and LSD1 inhibition in acute myeloid leukemia

Activating mutations in the KIT tyrosine receptor kinase confer an adverse prognosis for patients with acute myeloid leukemia (AML). Outside of bone marrow transplantation, treatment options are limited. Here we demonstrate combined KIT and LSD1 inhibition produces synergistic cell death against KIT mutant AML cells. This combination suppresses MYC expression to drive cell cycle exit and apoptosis. This decreased MYC expression results from a loss of PU.1 binding at downstream MYC enhancers. The drug combination also inactivates PI3K/AKT/GSK3a/b signaling to decrease MYC protein abundance. KIT-mutant AML cells rapidly adapt to KIT inhibitor monotherapy by restoring PI3K/AKT activity, but cannot when treated with combined KIT and LSD1 inhibitor. In addition, we validate MYC suppression as a mechanism of synergy in KIT-mutant AML patient samples. Collectively, this work provides rational for a clinical trial to assess the efficacy of KIT and LSD1 inhibition in patients with KIT-mutant AML. Statement of significanceEffective treatment options for AML are limited. We describe the synergistic response to combined KIT and LSD1 inhibition in KIT-mutant AML and identify key biomarkers of drug response. The specificity and efficacy of this combination in cell lines and patient samples provides rationale for investigation in early phase clinical trials.

cancer biology↗

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↗

bioRxiv2021

Development of a novel, pan-variant aerosol intervention for COVID-19

To develop a universal strategy to block SARS-CoV-2 cellular entry and infection represents a central aim for effective COVID-19 therapy. The growing impact of emerging variants of concern increases the urgency for development of effective interventions. Since ACE2 is the critical SARS-CoV-2 receptor and all tested variants bind to ACE2, some even at much increased affinity (see accompanying paper), we hypothesized that aerosol administration of clinical grade soluble human recombinant ACE2 (APN01) will neutralize SARS-CoV-2 in the airways, limit spread of infection in the lung and mitigate lung damage caused by deregulated signaling in the renin-angiotensin (RAS) and Kinin pathways. Here we show that intranasal administration of APN01 in a mouse model of SARS-CoV-2 infection dramatically reduced weight loss and prevented animal death. As a prerequisite to a clinical trial, we evaluated both virus binding activity and enzymatic activity for cleavage of Ang II following aerosolization. We report successful aerosolization for APN01, retaining viral binding as well as catalytic RAS activity. Dose range-finding and IND-enabling repeat-dose aerosol toxicology testing were conducted in dogs. Twice daily aerosol administration for two weeks at the maximum feasible concentration revealed no notable toxicities. Based on these results, a Phase I clinical trial in healthy volunteers can now be initiated, with subsequent Phase II testing in individuals with SARS-CoV-2 infection. This strategy could be used to develop a viable and rapidly actionable therapy to prevent and treat COVID-19, against all current and future SARS-CoV-2 variants. One Sentence SummaryPreclinical development and evaluation of aerosolized soluble recombinant human ACE2 (APN01) administered as a COVID-19 intervention is reported.

pharmacology and toxicology↗