scholarly journals In Silico Model Estimates the Clinical Trial Outcome of Cancer Vaccines

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3048
Author(s):  
Orsolya Lőrincz ◽  
József Tóth ◽  
Levente Molnár ◽  
István Miklós ◽  
Kata Pántya ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Cancer Vaccine ◽  
In Silico ◽  
Vaccine Development ◽  
Response Rates ◽  
Therapeutic Vaccines ◽  
Cell Responses ◽  
In Silico Model ◽  
Clinical Responses

Over 30 years after the first cancer vaccine clinical trial (CT), scientists still search the missing link between immunogenicity and clinical responses. A predictor able to estimate the outcome of cancer vaccine CTs would greatly benefit vaccine development. Published results of 94 CTs with 64 therapeutic vaccines were collected. We found that preselection of CT subjects based on a single matching HLA allele does not increase immune response rates (IRR) compared with non-preselected CTs (median 60% vs. 57%, p = 0.4490). A representative in silico model population (MP) comprising HLA-genotyped subjects was used to retrospectively calculate in silico IRRs of CTs based on the percentage of MP-subjects having epitope(s) predicted to bind ≥1–4 autologous HLA allele(s). We found that in vitro measured IRRs correlated with the frequency of predicted multiple autologous allele-binding epitopes (AUC 0.63–0.79). Subgroup analysis of multi-antigen targeting vaccine CTs revealed correlation between clinical response rates (CRRs) and predicted multi-epitope IRRs when HLA threshold was ≥3 (r = 0.7463, p = 0.0004) but not for single HLA allele-binding epitopes (r = 0.2865, p = 0.2491). Our results suggest that CRR depends on the induction of broad T-cell responses and both IRR and CRR can be predicted when epitopes binding to multiple autologous HLAs are considered.

scholarly journals Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB

2020 ◽  
Vol 21 (S17) ◽  
Author(s):  
Giulia Russo ◽  
Giuseppe Sgroi ◽  
Giuseppe Alessandro Parasiliti Palumbo ◽  
Marzio Pennisi ◽  
Miguel A. Juarez ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Immune System ◽  
In Silico ◽  
Vaccine Development ◽  
Clinical Trial Design ◽  
Therapeutic Vaccines ◽  
Clinical Phase ◽  
Resistant Tuberculosis ◽  
Multi Drug Resistant Tuberculosis ◽  
Clinical Trial Results

Abstract Background In 2018, about 10 million people were found infected by tuberculosis, with approximately 1.2 million deaths worldwide. Despite these numbers have been relatively stable in recent years, tuberculosis is still considered one of the top 10 deadliest diseases worldwide. Over the years, Mycobacterium tuberculosis has developed a form of resistance to first-line tuberculosis treatments, specifically to isoniazid, leading to multi-drug-resistant tuberculosis. In this context, the EU and Indian DBT funded project STriTuVaD—In Silico Trial for Tuberculosis Vaccine Development—is supporting the identification of new interventional strategies against tuberculosis thanks to the use of Universal Immune System Simulator (UISS), a computational framework capable of predicting the immunity induced by specific drugs such as therapeutic vaccines and antibiotics. Results Here, we present how UISS accurately simulates tuberculosis dynamics and its interaction within the immune system, and how it predicts the efficacy of the combined action of isoniazid and RUTI vaccine in a specific digital population cohort. Specifically, we simulated two groups of 100 digital patients. The first group was treated with isoniazid only, while the second one was treated with the combination of RUTI vaccine and isoniazid, according to the dosage strategy described in the clinical trial design. UISS-TB shows to be in good agreement with clinical trial results suggesting that RUTI vaccine may favor a partial recover of infected lung tissue. Conclusions In silico trials innovations represent a powerful pipeline for the prediction of the effects of specific therapeutic strategies and related clinical outcomes. Here, we present a further step in UISS framework implementation. Specifically, we found that the simulated mechanism of action of RUTI and INH are in good alignment with the results coming from past clinical phase IIa trials.

Usefulness of collaboration between mathematical models and cell engineering for elucidating complex disease mechanisms and discover effective drugs

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
H Kohjitani ◽  
A Kashiwa ◽  
T Makiyama ◽  
F Toyoda ◽  
Y Yamamoto ◽  
...  
Keyword(s):  
Mathematical Model ◽  
In Silico ◽  
Complex Disease ◽  
Ion Selectivity ◽  
Public Institution ◽  
Cell Engineering ◽  
Funding Source ◽  
Patch Clamp Technique ◽  
In Silico Model

Abstract Background A missense mutation, CACNA1C-E1115K, located in the cardiac L-type calcium channel (LTCC), was recently reported to be associated with diverse arrhythmias. Several studies reported in-vivo and in-vitro modeling of this mutation, but actual mechanism and target drug of this disease has not been clarified due to its complex ion-mechanisms. Objective To reveal the mechanism of this diverse arrhythmogenic phenotype using combination of in-vitro and in-silico model. Methods and results Cell-Engineering Phase: We generated human induced pluripotent stem cell (hiPSC) from a patient carrying heterozygous CACNA1C-E1115K and differentiated into cardiomyocytes. Spontaneous APs were recorded from spontaneously beating single cardiomyocytes by using the perforated patch-clamp technique. Mathematical-Modeling Phase: We newly developed ICaL-mutation mathematical model, fitted into experimental data, including its impaired ion selectivity. Furthermore, we installed this mathematical model into hiPSC-CM simulation model. Collaboration Phase: Mutant in-silico model showed APD prolongation and frequent early afterdepolarization (EAD), which are same as in-vitro model. In-silico model revealed this EAD was mostly related to robust late-mode of sodium current occurred by Na+ overload and suggested that mexiletine is capable of reducing arrhythmia. Afterward, we applicated mexiletine onto hiPSC-CMs mutant model and found mexiletine suppress EADs. Conclusions Precise in-silico disease model can elucidate complicated ion currents and contribute predicting result of drug-testing. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Japan Society for the Promotion of Science, Grant-in-Aid for Young Scientists

scholarly journals 708 Application of a novel mSENS drug delivery technology for mRNA therapeutics

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A750-A750
Author(s):  
Sojin Lee ◽  
Joon Young Park ◽  
Goo-Young Kim ◽  
Sang Woo Jo ◽  
Minhyuk Yun ◽  
...  
Keyword(s):  
Particle Size ◽  
Protein Expression ◽  
Cancer Vaccine ◽  
Encapsulation Efficiency ◽  
Tumor Model ◽  
Cell Responses ◽  
Delivery Platform ◽  
Mrna Therapeutics

BackgroundSuccessful clinical translation of mRNA therapeutics requires an appropriate delivery strategy to overcome instability of mRNA and facilitate cellular uptake into the cells.1 Several lipid based nanoparticle approaches that encapsulate mRNA, notably lipid nanoparticle (LNP), have been developed, but their efficiency for delivery to certain target tissues and toxicity profiles still have room for improvement. The application of a novel polymer based nanoparticle technology platform, so called Stability Enhanced Nano Shells (SENS) for mRNA (mSENS) as a mRNA delivery platform for a cancer vaccine was demonstrated.MethodsThe physicochemical properties of mSENS formulation, particle size and encapsulation efficiency, were characterized using dynamic light scattering (DLS) and gel retardation assay. Using luciferase-encoding mRNA, the protein expression levels in vitro and in vivo were evaluated by luciferase assay or bioluminescence imaging (BLI), respectively. For cancer vaccine studies, antigen (tyrosinase-related protein 2 (Trp-2))-specific T cell responses were assessed by immunophenotyping mouse splenocytes using flow cytometry and by the enzyme-linked immunosorbent spot (ELISPOT) assay. The anti-tumor efficacy was studied in B16F10 lung tumor model in C57BL/6 mice. Liver and systemic toxicity of mSENS treated mice was evaluated through blood chemistry and complete blood count (CBC) tests.ResultsA library of mSENS formulations complexed with luciferase-encoding mRNA, were characterized for their particle size, surface charge, encapsulation efficiency, colloidal stability, and in vitro and in vivo luciferase protein expression level. Upon systemic administration in mice, varying biodistribution profiles were observed, implicating the potential for tailored delivery to target tissues. Particularly, cancer vaccine application was further developed leveraging the formulation with preferential spleen delivery. Following vaccination with Trp-2 mRNA encapsulated with mSENS (Trp-2 mRNA-mSENS) in B16F10 tumor bearing mice, strong Trp-2 antigen-specific IFN-γ T-cell responses were observed. Generated anti-tumor immunity also marked suppression of B16F10 lung tumors were observed in Trp-2-mSENS immunized mice compared to non-immunized controls, demonstrating the potential of mSENS as a mRNA delivery platform for the application for vaccine.ConclusionsProprietary biodegradable polymer based-mSENS platform offers an attractive delivery strategy for mRNA by tailoring to specific therapeutic applications. Depending on the application, whether it’s a vaccine or protein replacement, a rationally designed mSENS formulation can efficiently distribute mRNA to specific tissues. In particular, application of a splenic mSENS formulation for a cancer vaccine has been demonstrated in murine tumor model. In summary, mRNA delivery through mSENS platform is expected to provide significant opportunities in clinical development for mRNA therapeutics.Ethics ApprovalThe study was approved by Samyang Biopharmaceuticals’ IACUC (Institutional Animal Care and Use Committee), approval number SYAU-2027.ReferencePiotr S. Kowalski, Arnab Rudra, Lei Miao, and Daniel G. Anderson, delivering the messenger: advances in technologies for therapeutic mRNA delivery. Molecular Therapy Vol. 27 No 4 April 2019.

scholarly journals The Pseudomonas Quorum-Sensing Regulator RsaL Belongs to the Tetrahelical Superclass of H-T-H Proteins

2006 ◽  
Vol 189 (5) ◽  
pp. 1922-1930 ◽  
Author(s):  
Giordano Rampioni ◽  
Fabio Polticelli ◽  
Iris Bertani ◽  
Karima Righetti ◽  
Vittorio Venturi ◽  
...  
Keyword(s):  
Dna Binding ◽  
Quorum Sensing ◽  
In Silico ◽  
Mobility Shift ◽  
Specific Domain ◽  
Signal Molecule ◽  
In Silico Model ◽  
Opportunistic Human Pathogen

ABSTRACT In the opportunistic human pathogen Pseudomonas aeruginosa, quorum sensing (QS) is crucial for virulence. The RsaL protein directly represses the transcription of lasI, the synthase gene of the main QS signal molecule. On the basis of sequence homology, RsaL cannot be predicted to belong to any class of characterized DNA-binding proteins. In this study, an in silico model of the RsaL structure was inferred showing that RsaL belongs to the tetrahelical superclass of helix-turn-helix proteins. The overall structure of RsaL is very similar to the N-terminal domain of the lambda cI repressor and to the POU-specific domain of the mammalian transcription factor Oct-1 (Oct-1 POUs). Moreover, residues of Oct-1 POUs important for structural stability and/or DNA binding are conserved in the same positions in RsaL and in its homologs found in GenBank. These residues were independently replaced with Ala, and the activities of the mutated variants of RsaL were compared to that of the wild-type counterpart in vivo by complementation assays and in vitro by electrophoretic mobility shift assays. The results validated the RsaL in silico model and showed that residues Arg 20, Gln 38, Ser 42, Arg 43, and Glu 45 are important for RsaL function. Our data indicate that RsaL could be the founding member of a new protein family within the tetrahelical superclass of helix-turn-helix proteins. Finally, the minimum DNA sequence required for RsaL binding on the lasI promoter was determined, and our data support the hypothesis that RsaL binds DNA as a dimer.

scholarly journals MUC1-Specific Cytotoxic T Lymphocytes in Cancer Therapy: Induction and Challenge

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
David Roulois ◽  
Marc Grégoire ◽  
Jean-François Fonteneau
Keyword(s):  
Clinical Trials ◽  
T Cell ◽  
T Cell Response ◽  
Cytotoxic T Cell ◽  
Cell Vaccine ◽  
Therapeutic Vaccines ◽  
T Cell Vaccine ◽  
Clinical Responses

MUC1 glycoprotein is often found overexpressed and hypoglycosylated in tumor cells from numerous cancer types. Since its discovery MUC1 has been an attractive target for antitumor immunotherapy. Indeed,in vitroandin vivoexperiments have shown T-cell-specific responses against MUC1 in an HLA-restricted and HLA-unrestricted manner, although some animal models have highlighted the possible development of tolerogenic responses against this antigen. These observations permit the development of new T-cell vaccine strategies capable of inducing an MUC1-specific cytotoxic T cell response in cancer patients. Some of these strategies are now being tested in clinical trials against different types of cancer. To date, encouraging clinical responses have been observed with some MUC1 vaccines in phase II/III clinical trials. This paper compiles knowledge regarding MUC1 as a promising tumor antigen for antitumor therapeutic vaccines applicable to numerous cancers. We also summarize the results of MUC1-vaccine-based clinical trials.

scholarly journals In-silico clinical trial using high performance computational modeling of a virtual human cardiac population to assess drug-induced arrhythmic risk

2021 ◽  
Author(s):  
Jazmin Aguado-Sierra ◽  
Constantine Butakoff ◽  
Renee Brigham ◽  
Apollo Baron ◽  
Guillaume Houzeaux ◽  
...  
Keyword(s):  
Clinical Trial ◽  
In Silico ◽  
High Performance ◽  
Drug Induced ◽  
Computational Framework ◽  
Major Health ◽  
Action Potential Prolongation ◽  
Performance Computing ◽  
Major Health Issue

AbstractDrug-induced arrhythmia continues to be a major health issue worldwide. The need for reliable pro-arrhythmic predictors became relevant during early phases of the SarsCoV2 pandemic, when it was uncertain whether the use of hydroxychloroquine (HCQ) and azithromycin (AZM) could be more harmful than beneficial due to their reported pro-arrhythmic effects.In this work we describe a computational framework that employs a gender-specific, in-silico cardiac population to assess cardiac drug-induced QT-prolongation after the administration of a single or a combination of potentially cardiotoxic drugs as HCQ and AZM. This novel computational methodology is capable of reproducing the complex behavior of the clinical electrocardiographic response to drug-induced arrhythmic risk, in-silico. Using high performance computing, the computational framework allows the estimation of the arrhythmic risk in a population, given a variety of doses of one or more drugs in a timely manner and providing markers that can be directly related to the clinical scenario. The pro-arrhythmic behavior observed in subjects within the in-silico trial, was also compared to supplemental in-vitro experiments on a reanimated swine hearts. Evidence of transmurally heterogeneous action potential prolongation after the administration of a large dose of HCQ was an observed mechanism of arrhythmia, both in the in-vitro and the in-silico model. The virtual clinical trial also provided remarkably similar results to recent published clinical data. In conclusion, the in-silico clinical trial on the cardiac population is capable of reproducing and providing evidence of the normal phenotype variants that produce distinct arrhythmogenic outcomes after the administration of one or various drugs.

scholarly journals An in vitro, in utero and in silico framework of oxygen diffusion in intricate vascular networks of the placenta

2021 ◽  
Author(s):  
Nikhilesh Bappoo ◽  
Lachlan J Kelsey ◽  
Yutthapong Tongpob ◽  
Kirk W Feindel ◽  
Harrison Caddy ◽  
...  
Keyword(s):  
In Silico ◽  
Oxygen Diffusion ◽  
In Utero ◽  
Growth Restriction ◽  
Vascular Networks ◽  
Placental Perfusion ◽  
In Silico Model ◽  
Flow Deceleration ◽  
Function Relationship

The placenta is a temporary and complex organ critical for fetal development through its subtle but convoluted harmonization of endocrine, vascular, haemodynamic and exchange adaptations. Yet, due to experimental, technological and ethical constraints, this unique organ remains poorly understood. In silico tools are emerging as a powerful means to overcome these challenges and have the potential to actualize novel breakthroughs. Here, we present an interdisciplinary framework combining in vitro experiments used to develop an elegant and scalable in silico model of oxygen diffusion. We then use in utero imaging of placental perfusion and oxygenation in both control and growth-restricted rodent placentas for validation of our in silico model. Our framework revealed the structure-function relationship in the feto-placental vasculature; oxygen diffusion is impaired in growth-restricted placentas, due to the diminished arborization of growth-restricted feto-placental vasculature and the lack of decelerated flow for adequate oxygen diffusion and exchange. We highlight the mechanisms of impairment in a rat model of growth restriction, underpinned by placental vascular impairment. Our framework reports and validates the prediction of blood flow deceleration impairment in growth restricted placentas with the placenta's oxygen transfer capability being significantly impaired, both globally and locally. Key words: Placenta; fetal growth restriction; oxygen diffusion; computational fluid dynamics; MRI

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