Click here to read some interesting recently published papers from our community. If you have published an interesting article in the field of in silico medicine, send it to us: we will insert it in this section of the newsletter!
In silico design of recombinant multi-epitope vaccine against influenza A virus
Authors: Avisa Maleki et al
Background: Influenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine and has eradicated numerous infectious diseases. Recently, multi-epitope vaccines, which are based on the selection of epitopes, have been increasingly investigated.
Results: This study utilized an immunoinformatic approach to design a recombinant multi-epitope vaccine based on a highly conserved epitope of hemagglutinin, neuraminidase, and membrane matrix proteins with fewer changes or mutate over time. The potential B cells, cytotoxic T lymphocytes (CTL), and CD4 T cell epitopes were identified. The recombinant multi-epitope vaccine was designed using specific linkers and a proper adjuvant. Moreover, some bioinformatics online servers and datasets were used to evaluate the immunogenicity and chemical properties of selected epitopes. In addition, Universal Immune System Simulator (UISS) in silico trial computational framework was run after influenza exposure and recombinant multi-epitope vaccine administration, showing a good immune response in terms of immunoglobulins of class G (IgG), T Helper 1 cells (TH1), epithelial cells (EP) and interferon gamma (IFN-g) levels. Furthermore, after a reverse translation (i.e., convertion of amino acid sequence to nucleotide one) and codon optimization phase, the optimized sequence was placed between the two EcoRV/MscI restriction sites in the PET32a+ vector.
Conclusions: The proposed "Recombinant multi-epitope vaccine" was predicted with unique and acceptable immunological properties. This recombinant multi-epitope vaccine can be successfully expressed in the prokaryotic system and accepted for immunogenicity studies against the influenza virus at the in silico level. The multi-epitope vaccine was then tested with the Universal Immune System Simulator (UISS) in silico trial platform. It revealed slight immune protection against the influenza virus, shedding the light that a multistep bioinformatics approach including molecular and cellular level is mandatory to avoid inappropriate vaccine efficacy predictions.
Read the full paper: https://pubmed.ncbi.nlm.nih.gov/35109785/
We propose two articles that have a strong correlation between them, and for this reason, we have decided to present them together. The first one is a short article that discusses the economic impact of in silico trials using the use case, depicted in the second link, as an example to evaluate the added value of an in silico trial.
Simulated versus physical bench tests: The economic evaluation of the InSilc platform for designing, developing, and assessing vascular scaffoldsAuthors: Pierpaolo Mincarone et al
Background: In silico medicine allows for pre-clinical and clinical simulated assessment of medical technologies and the building of patient-specific models to support medical decisions and forecast personal health status. While there is increasing trust in the potential central role of in silico medicine, there is a need to recognize its degree of reliability and evaluate its economic impact. An in silico platform has been developed within a Horizon 2020-funded project (In-Silc) for simulations functional to designing, developing, and assessing drug-eluting bioresorbable vascular scaffolds.The main purpose of this study was to compare the costs of 2 alternative strategies: the adoption of In-Silc platform versus the performance of only physical bench tests.
Methods: A case study was provided by a medical device company. The values of the model parameters were principally set by the project partners, with use of interviews and semi-structured questionnaires, and, when not available, through literature searches or derived by statistical techniques. An economic model was built to represent the 2 scenarios.
Results: The InSilc strategy is superior to the adoption of physical bench tests only. Ceteris paribus, the costs are 424,355€ for the former versus 857,811€ for the latter.
Conclusions: In silico medicine tools can decrease the cost of the research and development of medical devices such as bioresorbable vascular scaffolds. Further studies are needed to explore the impact of such solutions on the innovation capacity of companies and the consequent potential advantages for target patients and the healthcare system.
Read the full paper: https://pubmed.ncbi.nlm.nih.gov/34160384/
Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer’s disease
Authors: Zhaoyi Chen et al
In this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimer’s disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trial’s study protocol to identify the study population, treatment regimen assignments and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care (SOC) arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher serious adverse event (SAE) rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23 mg arm than in the 10 mg SOC arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials’ design. Nevertheless, such an approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted.
Read the full paper: https://www.nature.com/articles/s41746-021-00452-1
Human in silico trials for parametric computational fluid dynamics investigation of cerebrospinal fluid drug delivery: impact of injection location, injection protocol, and physiology
Background: Intrathecal drug delivery has a significant role in pain management and central nervous system (CNS) disease therapeutics. A fluid-physics based tool to assist clinicians in choosing specific drug doses to the spine or brain may help improve treatment schedules.
Methods: This study applied computational fluid dynamics (CFD) and in vitro model verification to assess intrathecal drug delivery in an anatomically idealized model of the human CSF system with key anatomic features of the CNS. Key parameters analyzed included the role of (a) injection location including lumbar puncture (LP), cisterna magna (CM) and intracerebroventricular (ICV), (b) LP injection rate, injection volume, and flush volume, (c) physiologic factors including cardiac-induced and deep respiration-induced CSF stroke volume increase. Simulations were conducted for 3-h post-injection and used to quantify spatial-temporal tracer concentration, regional area under the curve (AUC), time to maximum concentration (Tmax), and maximum concentration (Cmax), for each case.
Results: CM and ICV increased AUC to brain regions by ~ 2 logs compared to all other simulations. A 3X increase in bolus volume and addition of a 5 mL flush both increased intracranial AUC to the brain up to 2X compared to a baseline 5 mL LP injection. In contrast, a 5X increase in bolus rate (25 mL/min) did not improve tracer exposure to the brain. An increase in cardiac and respiratory CSF movement improved tracer spread to the brain, basal cistern, and cerebellum up to ~ 2 logs compared to the baseline LP injection.
Conclusion: The computational modeling approach provides ability to conduct in silico trials representative of CSF injection protocols. Taken together, the findings indicate a strong potential for delivery protocols to be optimized to reach a target region(s) of the spine and/or brain with a needed therapeutic dose. Parametric modification of bolus rate/volume and flush volume was found to have impact on tracer distribution; albeit to a smaller degree than injection location, with CM and ICV injections resulting in greater therapeutic dose to brain regions compared to LP. CSF stroke volume and frequency both played an important role and may potentially have a greater impact than the modest changes in LP injection protocols analyzed such as bolus rate, volume, and flush.
Keywords: Biofluid mechanics; Biomechanics; Central nervous system; Cerebrospinal fluid; Cisterna magna drug delivery; Computational fluid dynamics; In vitro model; Intrathecal drug delivery; Magnetic resonance imaging; Multiphase solute transport; Ventricular drug delivery.
Read the full paper: https://pubmed.ncbi.nlm.nih.gov/35090516/