Model-informed drug repurposing: A pharmacometric approach to novel pathogen preparedness, response and retrospection.
Ontology highlight
ABSTRACT:
SUBMITTER: Dodds M
PROVIDER: S-EPMC8013376 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-EPMC8359226 | biostudies-literature
Project description:AimWe hypothesized that viral kinetic modelling could be helpful to prioritize rational drug combinations for COVID-19. The aim of this research was to use a viral cell cycle model of SARS-CoV-2 to explore the potential impact drugs, or combinations of drugs, that act at different stages in the viral life cycle might have on various metrics of infection outcome relevant in the early stages of COVID-19 disease.MethodsUsing a target-cell limited model structure that has been used to characterize viral load dynamics from COVID-19 patients, we performed simulations to inform on the combinations of therapeutics targeting specific rate constants. The endpoints and metrics included viral load area under the curve (AUC), duration of viral shedding and epithelial cells infected. Based on the known kinetics of the SARS-CoV-2 life cycle, we rank ordered potential targeted approaches involving repurposed, low-potency agents.ResultsOur simulations suggest that targeting multiple points central to viral replication within infected host cells or release from those cells is a viable strategy for reducing both viral load and host cell infection. In addition, we observed that the time-window opportunity for a therapeutic intervention to effect duration of viral shedding exceeds the effect on sparing epithelial cells from infection or impact on viral load AUC. Furthermore, the impact on reduction on duration of shedding may extend further in patients who exhibit a prolonged shedder phenotype.ConclusionsOur work highlights the use of model-informed drug repurposing approaches to better rationalize effective treatments for COVID-19.
| S-EPMC8451752 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. Furthermore, the majority of patients become resistant to sorafenib. Recently, computational methods for drug repurposing have shown great promise to accelerate the discovery of new uses for existing drugs. In order to identify novel drugs for use against sorafenib resistant (SR)-HCC, we employed a transcriptomics-based drug repurposing method termed connectivity mapping. We conducted a comprehensive analysis of available in vitro and in vivo gene signatures of (SR)-HCC, and generated our own in vitro model using the Huh7 HCC cell line. We compared coverage of SR-HCC gene signatures across seven patient-derived HCC gene expression datasets, and observed that patients harboring the Huh7 SR-HCC gene signature had significantly reduced survival. Utilizing the Huh7 SR-HCC gene signature, we applied connectivity mapping to drug-induced gene expression profiles (n= 3,740 drugs) in the HepG2 HCC cell line from the LINCS database in order to find drugs that could oppose sorafenib resistance. We validated the use of two non-receptor tyrosine kinase inhibitors, dasatinib and fostamatinib, to reduce viability of sorafenib-resistant HCC cells and confirmed up-regulated activity of Src family kinases, the targets of dasatinib, in our SR-HCC models. We prospectively validated predicted gene expression changes in fostamatinib treated Huh7-SR via RNA-seq analysis.
2019-09-01 | GSE113005 | GEO
Project description:Motivation:Identification of novel therapeutic effects for existing US Food and Drug Administration (FDA)-approved drugs, drug repurposing, is an approach aimed to dramatically shorten the drug discovery process, which is costly, slow and risky. Several computational approaches use transcriptional data to find potential repurposing candidates. The main hypothesis of such approaches is that if gene expression signature of a particular drug is opposite to the gene expression signature of a disease, that drug may have a potential therapeutic effect on the disease. However, this may not be optimal since it fails to consider the different roles of genes and their dependencies at the system level. Results:We propose a systems biology approach to discover novel therapeutic roles for established drugs that addresses some of the issues in the current approaches. To do so, we use publicly available drug and disease data to build a drug-disease network by considering all interactions between drug targets and disease-related genes in the context of all known signaling pathways. This network is integrated with gene-expression measurements to identify drugs with new desired therapeutic effects based on a system-level analysis method. We compare the proposed approach with the drug repurposing approach proposed by Sirota et al. on four human diseases: idiopathic pulmonary fibrosis, non-small cell lung cancer, prostate cancer and breast cancer. We evaluate the proposed approach based on its ability to re-discover drugs that are already FDA-approved for a given disease. Availability and implementation:The R package DrugDiseaseNet is under review for publication in Bioconductor and is available at https://github.com/azampvd/DrugDiseaseNet. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC6084573 | biostudies-literature
Project description:Novel coronavirus first appeared in Wuhan, China, in December 2019, and it speedily expanded globally. Some medications which are used to treat other diseases seem to be effective in treating COVID-19 even without explicit support. The existing drugs that are summarized in this review primarily focused on therapeutic agents that possessed activity against other RNA viruses such as MERS-CoV and SARS-CoV. Drug repurposing or repositioning is a promising field in drug discovery that identifies new therapeutic opportunities for existing drugs such as corticosteroids, RNA-dependent RNA polymerase inhibitors, interferons, protease inhibitors, ivermectin, melatonin, teicoplanin, and some others. A search for new drug/drug targets is underway. Thus, blocking coronavirus structural protein, targeting viral enzyme, dipeptidyl peptidase 4, and membrane fusion blocker (angiotensin-converting enzyme 2 and CD147 inhibitor) are major sites based on molecular targets for the management of COVID-19 infection. The possible impact of biologics for the management of COVID19 is promising and includes a wide variety of options such as cytokines, nucleic acid-based therapies targeting virus gene expression, bioengineered and vectored antibodies, and various types of vaccines. This review demonstrates that the available data are not sufficient to suggest any treatment for the eradication of COVID-19 to be used at the clinical level. This article aims to review the roles of existing drugs and drug targets for COVID-19 treatment.
| S-EPMC8063850 | biostudies-literature
Project description:Introduction: Flat dosing regimen has recently been approved for programmed death receptor-1 (PD-1) inhibitors including toripalimab, nivolumab and pembrolizumab. The objective of this study is to provide pharmacological evidence for a flat dosing regimen of toripalimab by assessing the efficacy and safety profile of a 240 mg Q3W flat dose relative to the currently approved 3 mg/kg Q2W. Methods: A population pharmacokinetic (PopPK) model was established based on 1,014 evaluable patients in 13 clinical studies. The exposure-objective response rate (ORR, n = 234) and exposure-safety (n = 152) analyses were performed by logistic regression. Three safety endpoints including grade ≥ 3 adverse events (AEs), treatment-related grade ≥ 3 AEs, and AEs leading to study drug discontinuation were evaluated. Progression-free survival (PFS, n = 234) was evaluated using a Cox proportional hazard model with the Kaplan-Meier survival curve. Results: The PK profiles of toripalimab are best described by a two-compartment model with time-varying clearance characterized by a sigmoidal maximum effect (Emax) function. Simulations for the first dose and steady-state exposures for the 240 mg Q3W dosing regimen were comparable to those for the 3 mg/kg Q2W dosing regimen with 95% exposure coverage ranging from 88% to 96%. The exposure-safety analysis showed that the probability of an adverse event occurring did not increase with increases in toripalimab exposure. A flat exposure-response relationship for ORR was identified. The Kaplan-Meier survival curve showed that exposure was a predictor for PFS; however, no difference in treatment benefit was demonstrated across exposure quantiles using a Cox proportional hazard model. Discussion: This study revealed that toripalimab exposure of 240 mg Q3W dosing regimen was comparable to 3 mg/kg Q2W dosing regimen. The safety and efficacy E-R results of 240 mg Q3W is flat. Hence, the 240 mg Q3W dosing regimen is determined to be a preferred therapeutic dosage for toripalimab due to the convenience of flat dose.
| S-EPMC9880172 | biostudies-literature
Project description:Several repurposed drugs are currently under investigation in the fight against coronavirus disease 2019 (COVID-19). Candidates are often selected solely by their effective concentrations in vitro, an approach that has largely not lived up to expectations in COVID-19. Cell lines used in in vitro experiments are not necessarily representative of lung tissue. Yet, even if the proposed mode of action is indeed true, viral dynamics in vivo, host response, and concentration-time profiles must also be considered. Here we address the latter issue and describe a model of human SARS-CoV-2 viral kinetics with acquired immune response to investigate the dynamic impact of timing and dosing regimens of hydroxychloroquine, lopinavir/ritonavir, ivermectin, artemisinin, and nitazoxanide. We observed greatest benefits when treatments were given immediately at the time of diagnosis. Even interventions with minor antiviral effect may reduce host exposure if timed correctly. Ivermectin seems to be at least partially effective: given on positivity, peak viral load dropped by 0.3-0.6 log units and exposure by 8.8-22.3%. The other drugs had little to no appreciable effect. Given how well previous clinical trial results for hydroxychloroquine and lopinavir/ritonavir are explained by the models presented here, similar strategies should be considered in future drug candidate prioritization efforts.
| S-EPMC7988345 | biostudies-literature
Project description:Identification of unintended drug effects, specifically drug repurposing opportunities and adverse drug events, maximizes the benefit of a drug and protects the health of patients. However, current observational research methods are subject to several biases. These include confounding by indication, reverse causality and missing data. We propose that Mendelian randomization (MR) offers a novel approach for the prediction of unintended drug effects. In particular, we advocate the synthesis of evidence from this method and other approaches, in the spirit of triangulation, to improve causal inferences concerning drug effects. MR addresses some of the limitations associated with the existing methods in this field. Furthermore, it can be applied either before or after approval of the drug, and could therefore prevent the potentially harmful exposure of patients in clinical trials and beyond. The potential of MR as a pharmacovigilance and drug repurposing tool is yet to be realized, and could both help prevent adverse drug events and identify novel indications for existing drugs in the future.
| S-EPMC5837479 | biostudies-literature
Project description:Objective: Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. The majority of patients become resistant to sorafenib, the only approved first-line therapy for advanced cancer, underscoring the need for newer, more effective drugs. The purpose of this study is to expedite identification of novel drugs against sorafenib resistant (SR)-HCC. Methods: We employed a transcriptomics-based drug repurposing method termed connectivity mapping using gene signatures from in vitro-derived SR Huh7 HCC cells. For proof of concept validation, we focused on drugs that were FDA-approved or under clinical investigation and prioritized two anti-neoplastic agents (dasatinib and fostamatinib) with targets associated with HCC. We also prospectively validated predicted gene expression changes in drug-treated SR Huh7 cells as well as identified and validated the targets of Fostamatinib in HCC. Results: Dasatinib specifically reduced the viability of SR-HCC cells that correlated with up-regulated activity of SRC family kinases, its targets, in our SR-HCC model. However, fostamatinib was able to inhibit both parental and SR HCC cells in vitro and in xenograft models. Ingenuity pathway analysis of fostamatinib gene expression signature from LINCS predicted JAK/STAT, PI3K/AKT, ERK/MAPK pathways as potential targets of fostamatinib that were validated by Western blot analysis. Fostamatinib treatment reversed the expression of genes that were deregulated in SR HCC. Conclusion: We provide proof of concept evidence for the validity of this drug repurposing approach for SR-HCC with implications for personalized medicine.
| S-EPMC7598246 | biostudies-literature
Project description:Exploiting drug polypharmacology to identify novel modes of actions for drug repurposing has gained significant attentions in the current era of weak drug pipelines. From a serendipitous to systematic or rational ways, a variety of unimodal computational approaches have been developed but the complexity of the problem clearly needs multi-modal approaches for better solutions. In this study, we propose an integrative computational framework based on classical structure-based drug design and chemical-genomic similarity methods, combined with molecular graph theories for this task. Briefly, a pharmacophore modeling method was employed to guide the selection of docked poses resulting from our high-throughput virtual screening. We then evaluated if complementary results (hits missed by docking) can be obtained by using a novel chemo-genomic similarity approach based on chemical/sequence information. Finally, we developed a bipartite-graph based on the extensive data curation of DrugBank, PDB, and UniProt. This drug-target bipartite graph was used to assess similarity of different inhibitors based on their connections to other compounds and targets. The approaches were applied to the repurposing of existing drugs against ACK1, a novel cancer target significantly overexpressed in breast and prostate cancers during their progression. Upon screening of ?1,447 marketed drugs, a final set of 10 hits were selected for experimental testing. Among them, four drugs were identified as potent ACK1 inhibitors. Especially the inhibition of ACK1 by Dasatinib was as strong as IC(50)=1nM. We anticipate that our novel, integrative strategy can be easily extended to other biological targets with a more comprehensive coverage of known bio-chemical space for repurposing studies.
| S-EPMC3864554 | biostudies-literature