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.

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 Plant-Based COVID-19 Vaccines: Current Status, Design, and Development Strategies of Candidate Vaccines

Vaccines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 992
Author(s):  
Puna Maya Maharjan ◽  
Sunghwa Choe
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Vaccine Development ◽  
Production Systems ◽  
Transmission Rate ◽  
Neutralizing Antibody ◽  
Current Status ◽  
Economic Losses ◽  
Phase 2 Clinical Trial ◽  
Clinical Trial Results

The prevalence of the coronavirus disease 2019 (COVID-19) pandemic in its second year has led to massive global human and economic losses. The high transmission rate and the emergence of diverse SARS-CoV-2 variants demand rapid and effective approaches to preventing the spread, diagnosing on time, and treating affected people. Several COVID-19 vaccines are being developed using different production systems, including plants, which promises the production of cheap, safe, stable, and effective vaccines. The potential of a plant-based system for rapid production at a commercial scale and for a quick response to an infectious disease outbreak has been demonstrated by the marketing of carrot-cell-produced taliglucerase alfa (Elelyso) for Gaucher disease and tobacco-produced monoclonal antibodies (ZMapp) for the 2014 Ebola outbreak. Currently, two plant-based COVID-19 vaccine candidates, coronavirus virus-like particle (CoVLP) and Kentucky Bioprocessing (KBP)-201, are in clinical trials, and many more are in the preclinical stage. Interim phase 2 clinical trial results have revealed the high safety and efficacy of the CoVLP vaccine, with 10 times more neutralizing antibody responses compared to those present in a convalescent patient’s plasma. The clinical trial of the CoVLP vaccine could be concluded by the end of 2021, and the vaccine could be available for public immunization thereafter. This review encapsulates the efforts made in plant-based COVID-19 vaccine development, the strategies and technologies implemented, and the progress accomplished in clinical trials and preclinical studies so far.

scholarly journals Vaccines And Advanced Vaccines: -A landscape for advanced vaccine technology against infectious disease ,COVID-19 and tumor

2020 ◽  
Author(s):  
Xiaolong Cai ◽  
Huajun Bai ◽  
Xiaoyan Zhang
Keyword(s):  
Infectious Disease ◽  
Clinical Trial ◽  
Immune System ◽  
Disease Prevention ◽  
Vaccine Development ◽  
Tumor Vaccine ◽  
Clinical Stage ◽  
Current Status ◽  
Basic Information ◽  
Prevention And Treatment

The vaccine helps to provoke the immune system and is an efficacious means for disease prevention and treatment. At this particular time of the COVID-19 outbreak, the vaccine for COVID-19 is urgently needed to save tens of thousands of people’s lives. Here we give some basic information on vaccine classification, generation, and application, and make a brief review on the current status of COVID-19 vaccine and tumor vaccine development both in the clinical trial stage and pre-clinical stage. vaccine

Clinical Trial Design and Statistics

2016 ◽  
Author(s):  
Julie Ann Sosa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Statistical Tests ◽  
Critical Role ◽  
Clinical Trial Design ◽  
Type I ◽  
Comparable Group ◽  
Power Strategies ◽  
Clinical Trial Results

Clinical trials are planned experiments that measure the effectiveness of an intervention by comparing outcomes in a group of subjects treated with the test intervention with those observed in a comparable group of subjects receiving another intervention. As a result, clinical trials require investigators to assume significant responsibility and often consume significant human and financial resources. This chapter describes implementation of the study, including formulating the protocol from the study question; inclusion and exclusion criteria; pretesting and quality control; missing data; standardization, blinding, and randomization; planning for data management, monitoring, and audits; interim analyses; and the components of the research team. Because biostatistics plays a critical role in the interpretation of clinical trial results, this chapter discusses hypotheses and underlying principles, Type I and II errors, estimating sample size and power, strategies for minimizing sample size, statistical tests of significant, and relative risk. This review contains 32 references.

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