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bioRxiv2016

Microarray analysis identifies malignant field signatures in biopsy samples at diagnosis predicting the likelihood of lethal disease in patients with localized Gleason 6 and 7 prostate cancer.

Overtreatment of early-stage low-risk prostate cancer patients represents a significant problem in disease management and has significant socio-economic implications. Development of genetic and molecular markers of clinically significant disease in patients diagnosed with low grade localized prostate cancer would have a major impact in disease management. A gene expression signature (GES) is reported for lethal prostate cancer in biopsy specimens obtained at the time of diagnosis from patients with Gleason 6 and Gleason 7 tumors in a Swedish watchful waiting cohort with up to 30 years follow-up. A 98-genes GES identified 89% and 100% of all death events 4 years after diagnosis in Gleason 7 and Gleason 6 patients, respectively; at 6 years follow-up, 83% and 100% of all deaths events were captured in Gleason 7 and Gleason 6 patients, respectively. Remarkably, the 98-genes GES appears to perform successfully in patients stratification with as little as 2% of cancer cells in a specimen, strongly indicating that it captures a malignant field effect in human prostates harboring cancer cells of different degrees of aggressiveness. In Gleason 6 and Gleason 7 tumors from prostate cancer patients of age 65 or younger, GES identified 86% of all death events during the entire follow-up period. In Gleason 6 and Gleason 7 tumors from prostate cancer patients of age 70 or younger, GES identified 90% of all death events 6 years after diagnosis. Classification performance of the reported in this study 98-genes GES of lethal prostate cancer appeared suitable to meet design and feasibility requirements of a prospective 4 to 6 years clinical trial, which is essential for regulatory approval of diagnostic and prognostic tests in clinical setting. Prospectively validated GES of lethal PC in biopsy specimens of Gleason 6 and Gleason 7 tumors will help physicians to identify, at the time of diagnosis, patients who should be considered for exclusion from active surveillance programs and who would most likely benefit from immediate curative interventions.

Genomics↗

bioRxiv2017

Development of a targeted sequencing approach to identify prognostic, predictive and diagnostic markers in paediatric solid tumours

The implementation of personalised medicine in childhood cancers has been limited by a lack of clinically validated multi-target sequencing approaches specific for paediatric solid tumours. In order to support innovative clinical trials in high-risk patients with unmet need, we have developed a clinically relevant targeted sequencing panel spanning 311 kb and comprising 78 genes involved in childhood cancers. A total of 132 samples were used for the validation of the panel, including Horizon Discovery cell blends (n=4), cell lines (n=15), formalin-fixed paraffin embedded (FFPE, n=83) and fresh frozen tissue (FF, n=30) patient samples. Cell blends containing known single nucleotide variants (SNVs, n=528) and small insertion-deletions (indels n=108) were used to define panel sensitivities of [≥]98% for SNVs and [≥]83% for indels [95% CI] and panel specificity of [≥]98% [95% CI] for SNVs. FFPE samples performed comparably to FF samples (n=15 paired). Of 95 well-characterised genetic abnormalities in 33 clinical specimens and 13 cell lines (including SNVs, indels, amplifications, rearrangements and chromosome losses), 94 (98.9%) were detected by our approach. We have validated a robust and practical methodology to guide clinical management of children with solid tumours based on their molecular profiles. Our work demonstrates the value of targeted gene sequencing in the development of precision medicine strategies in paediatric oncology.

genomics↗

bioRxiv2020

Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning

BackgroundA personalized prediction of upper limb neurorehabilitation outcomes in persons with multiple sclerosis (pwMS) promises to optimize the allocation of therapy and to stratify individuals for resource-demanding clinical trials. Previous research identified predictors on a population level through linear models and clinical data, including conventional assessments describing sensorimotor impairments. The objective of this work was to explore the feasibility of providing an individualized and more accurate prediction of rehabilitation outcomes in pwMS by leveraging non-linear machine learning models, clinical data, and digital health metrics characterizing sensorimotor impairments. MethodsClinical data and digital health metrics were recorded from eleven pwMS undergoing neurorehabilitation. Machine learning models were trained on data recorded pre-intervention. The dependent variables indicated whether a considerable improvement on the activity level was observed across the intervention or not (binary classification), as defined by the Action Research Arm Test (ARAT), Box and Block Test (BBT), or Nine Hole Peg Test (NHPT). ResultsIn a cross-validation, considerable improvements in ARAT or BBT could be accurately predicted (94% balanced accuracy) by only relying on patient master data. Considerable improvements in NHPT could be accurately predicted (89% balanced accuracy), but required knowledge about sensorimotor impairments. Assessing these with digital health metrics instead of conventional scales allowed increasing the balanced accuracy by +17% . Non-linear machine-learning models improved the predictive accuracy for the NHPT by +25% compared to linear models. ConclusionsThis work demonstrates the feasibility of a personalized prediction of upper limb neurorehabilitation outcomes in pwMS using multi-modal data collected before neurorehabilitation and machine learning. Information from digital health metrics about sensorimotor impairment was necessary to predict changes in dexterous hand control, thereby underlining their potential to provide a more sensitive and fine-grained assessment than conventional scales. Non-linear models outperformed ones, suggesting that the commonly assumed linearity of neurorehabilitation is oversimplified. clinicaltrials.gov registration number: NCT02688231

bioengineering↗

bioRxiv2021

Genomic variation across a clinical Cryptococcus population linked to disease outcome

Cryptococcus neoformans is the causative agent of cryptococcosis, a disease with poor patient outcomes, accounting for approximately 180,000 deaths each year. Patient outcomes may be impacted by the underlying genetics of the infecting isolate; however, our current understanding of how genetic diversity contributes to clinical outcomes is limited. Here, we leverage clinical, in vitro growth and genomic data for 284 C. neoformans isolates to identify clinically relevant pathogen variants within a population of clinical isolates from patients with HIV-associated cryptococcosis in Malawi. Through a genome-wide association study (GWAS) approach, we identify variants associated with fungal burden and growth rate. We also find both small and large-scale variation, including aneuploidy, associated with alternate growth phenotypes, which may impact the course of infection. Genes impacted by these variants are involved in transcriptional regulation, signal transduction, glycosylation, sugar transport, and glycolysis. We show that growth within the CNS is reliant upon glycolysis in an animal model, and likely impacts patient mortality, as CNS yeast burden likely modulates patient outcome. Additionally, we find genes with roles in sugar transport are enriched in regions under selection in specific lineages of this clinical population. Further, we demonstrate that genomic variants in two genes identified by GWAS impact virulence in animal models. Our approach identifies links between genetic variation in C. neoformans and clinically relevant phenotypes and animal model pathogenesis; shedding light on specific survival mechanisms within the CNS and identifying pathways involved in yeast persistence. ImportanceInfection outcomes for cryptococcosis, most commonly caused by C. neoformans, are influenced by host immune responses, as well as host and pathogen genetics. Infecting yeast isolates are genetically diverse; however, we lack a deep understanding of how this diversity impacts patient outcomes. To better understand both clinical isolate diversity and how diversity contributes to infection outcome, we utilize a large collection of clinical C. neoformans samples, isolated from patients enrolled in a clinical trial across 3 hospitals in Malawi. By combining whole-genome sequence data, clinical data, and in vitro growth data, we utilize genome-wide association approaches to examine the genetic basis of virulence. Genes with significant associations display virulence attributes in both murine and rabbit models, demonstrating that our approach can identify potential links between genetic variants and patho-biologically significant phenotypes.

genomics↗

bioRxiv2024

Genome-wide CRISPRi-seq identified ferredoxin-NADP reductase FprB as a synergistic target for gallium therapy in Pseudomonas aeruginosa

With the rise of antibiotic-resistant bacteria, non-antibiotic therapies like gallium are increasingly gaining attention. Gallium ions exhibit potent activity against multidrug-resistant bacteria and intravenous gallium nitrite is under phase 2 clinical trials to treat chronic Pseudomonas aeruginosa infections in cystic fibrosis patients. However, its clinical efficacy is constrained by the achievable peak concentration in human tissue. To address this limitation, we applied a genome-wide CRISPR interference approach (CRISPRi-seq), to identify potential synergistic targets with gallium. Through the systemic screening, we classified the essential genes by response time and growth reduction, pinpointing the most vulnerable therapeutic targets in this species. In addition, we identified a highly conserved gene fprB, encoding a ferredoxin-NADP+ reductase, the downregulation of which dramatically sensitized the cells to gallium. Using a null mutant, we confirmed the loss of fprB lowers the minimum inhibitory concentration of gallium from 320 {micro}M to 10 {micro}M and shifted galliums mode of action from bacteriostatic to bactericidal. Further investigation revealed that FprB plays a critical role in modulating oxidative stress induced by gallium, via control of the iron homeostasis and reactive oxygen species accumulation. Deleting fprB also enhanced galliums efficacy against biofilms formation and improved outcomes in murine lung infection model of P. aeruginosa, suggesting FprB as a promising drug target in combination with gallium. Overall, our data showed CRISPRi-seq as a powerful tool for systematic genetic analysis of P. aeruginosa, advancing identification of novel therapeutic targets.

microbiology↗

bioRxiv2025

Spatially resolved mapping of monoacylglycerol lipase activity in the brain

Visualizing signaling systems in the brain with high spatial resolution is critical to understand brain function and to develop therapeutics. Especially enzymes are often regulated on the post-translational level, resulting in a disconnect between protein levels and activity. Conventional antibody-based methods have limitations, including potential cross reactivity and the inability of antibodies to discriminate between active and inactive enzyme states. Monoacylglycerol lipase (MAGL), an enzyme degrading the neuroprotective endocannabinoid 2-arachidonoylglycerol, is the target of inhibitors currently in clinical trials for the treatment of several neurological disorders. To support translational and (pre)clinical studies and fully realize the therapeutic opportunities of MAGL inhibitors, it is essential to map the spatial distribution of MAGL activity throughout the brain in both health and disease. Here, we introduce selective fluorescent activity-based probes for MAGL enabling direct visualization of its enzymatic activity in lysates, cultured cells and tissue sections. We show that oxidative stress, which inactivates MAGL through the oxidation of regulatory cysteines, reduces probe labeling, thereby validating the probes activity-dependence. Extending this approach, we developed an activity-based histology protocol to visualize MAGL activity in fresh-frozen mouse and human brain tissues. This approach revealed robust MAGL activity in astrocytes and presynaptic terminals within the mouse hippocampus, and further allows detection of MAGL activity in the human cerebral cortex. Collectively, these findings establish selective activity-based probes as powerful tools mapping MAGL activity with high spatial resolution across mammalian brain tissue.

neuroscience↗

bioRxiv2019

Modulating Sphingosine-1-Phosphate receptors to improve chemotherapy delivery to Ewing sarcoma

Tumor vasculature is innately dysfunctional. Poorly functional tumor vessels inefficiently deliver chemotherapy to tumor cells; vessel hyper-permeability promotes chemotherapy delivery primarily to a tumors periphery. Here we identify a method for enhancing chemotherapy delivery and efficacy in Ewing sarcoma (ES) in mice by modulating tumor vessel permeability. Vessel permeability is partially controlled by the G protein-coupled Sphinosine-1-phosphate receptors 1 and 2 (S1PR1 and S1PR2) on endothelial cells. S1PR1 promotes endothelial cell junction integrity while S1PR2 destabilizes it. We hypothesize that an imbalance of S1PR1:S1PR2 is partially responsible for the dysfunctional vascular phenotype characteristic of ES and that by altering the balance in favor of S1PR1, ES vessel hyper-permeability can be reversed. In this study, we demonstrate that pharmacologic activation of S1PR1 by SEW2871 or inhibition of S1PR2 by JTE-013 caused more organized, mature, and functional tumor vessels. Importantly, S1PR1 activation or S1PR2 inhibition improved chemotherapy delivery to the tumor and anti-tumor efficacy. Our data suggests that pharmacologic targeting of S1PR1 and S1PR2 may be a useful adjuvant to standard chemotherapy for ES patients.\n\nNOVELTY AND IMPACTThis study demonstrates that Sphingosine-1-Phosphate (S1P) receptors are potential novel targets for tumor vasculature remodeling and adjuvant therapy for the treatment of Ewing Sarcoma. Unlike receptor tyrosine kinases that have already been extensively evaluated for use as vascular normalizing agents in oncology, S1P receptors are G protein-coupled receptors, which have not been well studied in tumor endothelium. Pharmacologic activators and inhibitors of S1P receptors are currently in clinical trials for treatment of auto-immune and cardiovascular diseases, indicating potential for clinical translation of this work.

cancer biology↗

bioRxiv2020

Integrative Molecular Characterization of Sarcomatoid and Rhabdoid Renal Cell Carcinoma Reveals Determinants of Poor Prognosis and Response to Immune Checkpoint Inhibitors

Sarcomatoid and rhabdoid (S/R) renal cell carcinoma (RCC) are highly aggressive tumors with limited molecular and clinical characterization. Emerging evidence suggests immune checkpoint inhibitors (ICI) are particularly effective for these tumors1-3, although the biological basis for this property is largely unknown. Here, we evaluate multiple clinical trial and real-world cohorts of S/R RCC to characterize their molecular features, clinical outcomes, and immunologic characteristics. We find that S/R RCC tumors harbor distinctive molecular features that may account for their aggressive behavior, including BAP1 mutations, CDKN2A deletions, and increased expression of MYC transcriptional programs. We show that these tumors are highly responsive to ICI and that they exhibit an immune-inflamed phenotype characterized by immune activation, increased cytotoxic immune infiltration, upregulation of antigen presentation machinery genes, and PD-L1 expression. Our findings shed light on the molecular drivers of aggressivity and responsiveness to immune checkpoint inhibitors of S/R RCC tumors.

cancer biology↗

bioRxiv2023

Predicting gene expression changes upon epigenomic drug treatment

BackgroundTumors are characterized by global changes in epigenetic changes such as DNA methylation and histone modifications that are functionally linked to tumor progression. Accordingly, several drugs targeting the epigenome have been proposed for cancer therapy, notably, histone deacetylase inhibitors (HDACi) such as Vorinostatis and DNA methyltransferase inhibitors (DNMTi) such as Zebularine. However, a fundamental challenge with such approaches is the lack of genomic specificity, i.e., the transcriptional changes at different genomic loci can be highly variable thus making it difficult to predict the consequences on the global transcriptome and drug response. For instance, treatment with DNMTi may upregulate the expression of not only a tumor suppressor but also an oncogene leading to unintended adverse effect. MethodsGiven the pre-treatment transcriptome and epigenomic profile of a sample, we assessed the extent of predictability of locus-specific changes in gene expression upon treatment with HDACi using machine learning. ResultsWe found that in two cell lines (HCT116 treated with Largazole at 8 doses and RH4 treated with Entinostat at 1{micro}M) where the appropriate data (pre-treatment transcriptome and epigenome as well as post-treatment transcriptome) is available, our model distinguished the post-treatment up versus downregulated genes with high accuracy (up to ROC of 0.89). Furthermore, a model trained on one cell line is applicable to another cell line suggesting generalizability of the model. ConclusionsHere we present a first assessment of the predictability of genome-wide transcriptomic changes upon treatment with HDACi. Lack of appropriate omics data from clinical trials of epigenetic drugs currently hampers the assessment of applicability of our approach in clinical setting.

bioinformatics↗

bioRxiv2023

Bio-Mol:Pretraining Multimodality Bioactivity Profile for Enhancing Small Molecule Property Prediction

Non-optimized pharmacokinetic parameters serve as the primary cause of failure in clinical trials of drugs. Therefore, the successful prediction of pharmacokinetic parameters during the pre-clinical stage is crucial for the success of drug candidates. Conventional methods primarily rely on 2D structural information, while advanced models extend the features to other structural-related information or use advanced computational models to improve prediction accuracy. However, to gain a comprehensive understanding of small molecules, integrating bioactivity profiles with chemical structural information is essential. One significant challenge in this integration is the high proportion of missing values within experimentally validated bioactivity profiles for most small molecules. To address this challenge, we introduce Bio-Mol, an artificial intelligence model designed to effectively handle this issue. Bio-Mol utilizes a pretrain and finetune strategy, enabling the incorporation of a large proportion of missing bioactivity profiles during the small molecule representation learning process. Comprehensive evaluations of Bio-Mol demonstrate a notable improvement in predicting molecule properties. The integration of missing bioactivity profiles enhances the AUROC of average 5.2% compared to the previous state-of-the-art models predictions. Furthermore, we explore the potential of Bio-Mol in predicting synergistic drug combinations, highlighting its versatility and broader applications in the field of drug discovery. The successful implementation of Bio-Mol showcases its efficacy in over-coming the challenges posed by missing bioactivity profile data. This model paves the way for optimizing small molecule pharmacokinetics prediction, providing valuable insights for drug development and discovery processes.

bioinformatics↗

bioRxiv2018

A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events

One of the main causes for failure in the drug development pipeline or withdrawal post approval is the unexpected occurrence of severe drug adverse events. Even though such events should be detected by in vitro, in vivo, and human trials, they continue to unexpectedly arise at different stages of drug development causing costly clinical trial failures and market withdrawal. Inspired by the \"moneyball\" approach used in baseball to integrate diverse features to predict player success, we hypothesized that a similar approach could leverage existing adverse event and tissue-specific toxicity data to learn how to predict adverse events. We introduce MAESTER, a data-driven machine learning approach that integrates information on a compounds structure, targets, and phenotypic effects with tissue-wide genomic profiling and our toxic target database to predict the probability of a compound presenting with different types of tissue-specific adverse events. When tested on 6 different types of adverse events MAESTER maintains a high accuracy, sensitivity, and specificity across both the training data and new test sets. Additionally, MAESTER scores could flag a number of drugs that were approved, but later withdrawn due to unknown adverse events - highlighting its potential to identify events missed by traditional methods. MAESTER can also be used to identify toxic targets for each tissue type. Overall MAESTER provides a broadly applicable framework to identify toxic targets and predict specific adverse events and can accelerate the drug development pipeline and drive the design of new safer compounds.

pharmacology and toxicology↗

bioRxiv2025

Armoring STEAP1 CAR T cells with IL-18 potentiates antitumor activity in Ewing sarcoma

BackgroundEwing sarcoma (EwS) is a highly aggressive cancer driven by the EWS::FLI1 fusion oncoprotein affecting children, adolescents, and young adults. Six transmembrane epithelial antigen 1 (STEAP1) is a cell surface antigen transcriptionally controlled by EWS::FLI1 that is broadly expressed in EwS, positioning it as a rational immunotherapy target. However, translating CAR T therapy to solid tumors requires overcoming barriers to potency while maintaining safety. MethodsAnalyses of transcriptome and proteome data were performed to evaluate the effects of EWS::FLI1 perturbation on STEAP1 expression at the transcript and protein levels in EwS models. STEAP1 expression was validated in EwS patient tissues by immunohistochemistry. Second-generation STEAP1-BB{zeta} CAR T cells were tested in orthotopic and disseminated EwS xenograft models. To enhance antitumor activity, an IL-18-armored STEAP1 CAR was engineered. Dose-dependent therapeutic efficacy and safety were evaluated through measurement of tumor burden, survival, and observation for gross toxicities. ResultsSTEAP1 was expressed in [~]97% of primary EwS tumors and directly associated with EWS::FLI1 fusion protein expression in EwS cell lines. In orthotopic EwS models, STEAP1 CAR T cells induced complete tumor regression at 5 x 106 cells. In disseminated disease models, responses were dose-dependent with no evidence of antigen loss. Notably, IL-18 armored STEAP1 CAR T cells achieved complete responses in [~]80% of mice at a reduced dose of 106 cells without overt toxicity. ConclusionsThese data establish STEAP1 as a clinically relevant and highly expressed target in EwS and demonstrate that IL-18 armoring significantly improves CAR T cell efficacy by enhancing potency evident through antitumor activity at reduced cell dose. STEAP1 CAR T cells are currently under evaluation in a first-in-human phase 1/2 dose-escalation clinical trial for metastatic castration-resistant prostate cancer (NCT06236139) and these studies support future clinical translation of STEAP1 CAR T cell therapy for relapsed/refractory EwS.

cancer biology↗

bioRxiv2026

KRAS inhibition drives adaptive surfaceome remodeling and reveals ALPP/ALPPL2 as a targetable vulnerability in pancreatic cancer

KRAS is mutated in over 90% of pancreatic ductal adenocarcinoma (PDAC) and is required for tumor initiation and maintenance. While clinical trials testing mutant-selective and pan-RAS inhibitors (KRASi) have demonstrated significant clinical benefit, genetic and non-genetic mechanisms of resistance limit the long-term efficacy of monotherapy strategies. Here, we used quantitative temporal analysis of the global, phosphorylation, and surface proteome to comprehensively identify non-genetic adaptive pathways and cell surface biomarkers in response to acute and long-term KRAS inhibition in PDAC. Through integrative machine learning analysis of the KRASi-proteome and publicly available datasets, we discovered top priority cell surface targets for combination therapy with KRASi. We identified Placental Alkaline Phosphatase (ALPP) as upregulated in response to KRASi through compensatory JAK2/STAT3 signaling. We developed a novel clinical grade ALPP/ALPPL2 (ALPG)-targeting antibody drug conjugate (ADC) with site-specific MMAE conjugation and demonstrated single agent efficacy in PDAC models, as well as enhanced internalization and anti-tumor efficacy in combination with KRASi. Together, these studies comprehensively define the KRASi-induced proteome and identify ALPP/ALPPL2 as a promising cell surface target for combination with KRASi to enhance response and forestall KRASi resistance.

cancer biology↗

bioRxiv2025

Prompt-to-Pill: Multi-Agent Drug Discovery and Clinical Simulation Pipeline

This study presents a comprehensive, modular framework for AI-driven drug discovery (DD) and clinical trial simulation, spanning from target identification to virtual patient recruitment. Synthesized from a systematic analysis of 51 LLM-based systems, the proposed Prompt-to-Pill *architecture and corresponding implementation leverages a multi-agent system (MAS) divided into DD, preclinical and clinical phases, coordinated by a central Orchestrator. Each phase comprises specialized large language model (LLM) for molecular generation, toxicity screening, docking, trial design, and patient matching. To demonstrate the full pipeline in practice, the well-characterized target Dipeptidyl Peptidase 4 (DPP4) was selected as a representative use case. The process begins with generative molecule creation and proceeds through ADMET evaluation, structure-based docking, and lead optimization. Clinical-phase agents then simulate trial generation, patient eligibility screening using EHRs, and predict trial outcomes. By tightly integrating generative, predictive, and retrieval-based LLM components, this architecture bridges drug discovery and preclinical phase with virtual clinical development, offering a demonstration of how LLM-based agents can operationalize the drug development workflow in silico.

bioinformatics↗

bioRxiv2020

Combined prophylactic and therapeutic use maximizes hydroxychloroquine anti-SARS-CoV-2 effects in vitro

While the SARS-CoV-2 pandemic is hardly hitting the world, it is of extreme importance that significant in vitro observations guide the quick set up of clinical trials. In this study, we evidence that the anti-SARS-CoV2 activity of a clinically achievable hydroxychloroquine concentration is maximized only when administered before and after the infection of Vero E6 cells. This strongly suggests that only a combined prophylactic and therapeutic use of hydroxychloroquine may be effective in limiting viral replication in patients.

microbiology↗

bioRxiv2021

An integral genomic signature approach for tailored cancer targeted therapy using genome-wide sequencing data

With the advent of low-cost sequencing, transcriptome and genome sequencing is expected to become clinical routine and transform precision oncology within next decade. However, viable genome-wide modeling methods that can facilitate rational selection of patients for tailored intervention while tolerating sequencing biases are far lacking. Here we propose an integral genomic signature (iGenSig) analysis as a new class of transparent, interpretable, and resilient methods for precision oncology based on multiple types of genome-wide sequencing data. We postulate that the redundant high-dimensional genomic features, which are typically eliminated during multi-omics modeling, may help overcome the sequencing biases. We thus conceive a novel method that models the therapeutic response using the high-dimensional transcriptional and mutational features predictive of tumor response, which we termed as an integral genomic signature (iGenSig), and then algorithmically resolve the feature redundancy tailored for each patient subject. Using genomic dataset of chemical perturbations, we developed the iGenSig models for predicting targeted therapy responses, and applied selected models to independent datasets for cancer cell lines, patient-derived xenografts, and patient subjects. iGenSig models exhibit outstanding cross-dataset performance compared to artificial intelligence methods, with exceptional resilience against simulated errors in genomic features. In particular, the iGenSig model for the EGFR inhibitor Erlotinib significantly predicted the responses of patient-derived xenografts and patients from a clinical trial, biological interpretation of which led to new insights into the predictive signature pathways with clinical relevance. Together, iGenSig will provide a computational infrastructure to empower tailored cancer intervention based on genome-wide sequencing data.

bioinformatics↗

bioRxiv2023

Repurposing Remdesivir for COVID-19: Computational Drug Design Targeting SARS-CoV-2 RNA Polymerase and Main Protease using Molecular Dynamics Approach

The coronavirus disease of 2019 (COVID-19) is a highly contagious respiratory illness that has become a global health crisis with new variants, an unprecedented number of infections, and deaths and demands urgent manufacturing of potent therapeutics. Despite the success of vaccination campaigns around the globe, there is no particular therapeutics approved to date for efficiently treating infected individuals. Repositioning or repurposing previously effective antivirals against RNA viruses to treat COVID-19 patients is a feasible option. Remdesivir is a broad-spectrum antiviral drug that the Food and Drug Administration (FDA) licenses for treating COVID-19 patients who are critically ill patients. Remdesivirs low efficacy, which has been shown in some clinical trials, possible adverse effects, and dose-related toxicities are issues with its use in clinical use. Our study aimed to design potent derivatives of remdesivir through the functional group modification of the parent drug targeting RNA-dependent RNA polymerase (RdRp) and main protease (MPro) of SARS-CoV-2. The efficacy and stability of the proposed derivatives were assessed by molecular docking and extended molecular dynamics simulation analyses. Furthermore, the pharmacokinetic activity was measured to ensure the safety and drug potential of the designed derivatives. The derivatives were non-carcinogenic, chemically reactive, highly interactive, and stable with the target proteins. D-CF3 is one of the designed derivatives that finally showed stronger interaction than the parent drug, according to the docking and dynamics simulation analyses, with both target proteins. However, in vitro and in vivo investigations are guaranteed to validate the findings in the future.

bioinformatics↗

bioRxiv2020

Neuronal Population Models Reveal Specific Linear Conductance Controllers Sufficient to Rescue Preclinical Disease Phenotypes

Objective During the preclinical phase of drug development, potential drug candidates are often screened for their ability to alleviate certain in vitro electrophysiological features among neurons. This ability is assessed by measuring treatment outcomes using the population mean, both across different cells and different animals. The go/no-go decision for progressing a drug to a clinical trial is then based on ‘average effects’, yet these measures may not be sufficient to mitigate clinical end point risk. Population-based modeling is widely used to represent the intrinsic variability of electrophysiological features among healthy, disease and drug treated neuronal phenotypes. We pursued a method for optimizing therapeutic target design by identifying a single coherent set of ion channel targets for recovery of the healthy (Wild type) cellular phenotype simultaneously across multiple measures. Specifically, we aimed to determine the set of target modulations that best recover a heterogeneous Huntington’s disease (HD) population of model neurons into a multivariate region of phenotypic measurements corresponding to the healthy excitability profile of a heterogenous Wild type (WT) population of model neurons.Methods Our approach combines mechanistic simulations with populations modeling of striatal neurons using evolutionary algorithms for population optimization to design ‘virtual drugs’. We introduce efficacy metrics to score population of model outcomes and use these to rank our virtual candidates.Results We found that virtual drugs identified using heuristic approaches performed better than single target modulators and those derived from standard classification methods. We compare a real drug to the virtual candidates and demonstrate a novel in silico triaging method.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗