This review examines various classes of drugs, focusing on their therapeutic and adverse effects, particularly in relation to immunostimulation. We emphasize the potential of new approach methodologies (NAMs) to study both expected and unexpected immunostimulatory effects. By evaluating the modes of action of different immunostimulatory drugs, we aim to provide insights into effectively assessing unwanted immunostimulatory responses. The review begins by exploring drugs that stimulate the immune system-including immunostimulants, monoclonal antibodies, chemotherapeutics, and nucleic acid-based drugs-to outline NAMs that could be employed to evaluate immunostimulation.

This editorial, inspired by a recent study published in the World Journal of Gastrointestinal Oncology, covers the research findings on microbiota changes in various diseases. In recurrent colorectal polyps, the abundances of Klebsiella, Parvimonas, and Clostridium increase, while those of Bifidobacterium and Lactobacillus decrease. This dysbiosis may promote the formation and recurrence of polyps. Similar microbial changes have also been observed in colorectal cancer, inflammatory bowel disease, autism spectrum disorder, and metabolic syndrome, indicating the role of increased pathogens and decreased probiotics in these conditions. Regulating the gut microbiota, particularly by increasing probiotic levels, may help prevent polyp recurrence and promote gut health. This microbial intervention strategy holds promise as an adjunctive treatment for patients with colorectal polyps.

Babesiosis is a significant haemoparasitic infection caused by apicomplexan parasites of the genus Babesia. This infection has continuously threatened cattle farmers owing to its devastating effects on productivity and severe economic implications. Failure to curb the increase of the infection has been attributed to largely ineffective vaccines. This study was designed to develop a potential vaccine candidate.

Rhoptry-associated protein-1 (RAP-1) was used to identify and design a potential multi-epitope vaccine candidate due to its immunogenic properties through an immunoinformatics approach.

A multi-epitope vaccine comprising 11 CD8+, 17 CD4+, and 3 B-cell epitopes was constructed using the AAY, GPGPG, and KK linkers. Beta-defensin-3 was added as an adjuvant to potentiate the immune response using the EAAK linker. The designed vaccine was computationally predicted to be antigenic (antigenicity scores: 0.6), soluble (solubility index: 0.730), and non-allergenic. The vaccine construct comprises 595 amino acids with a molecular weight of 64 152 kDa, an instability and aliphatic index of 13.89 and 65.82, which confers stability with a Grand average of hydropathicity (GRAVY) value of 0.122, indicating the hydrophobicity of the construct. Europe has the highest combined class population coverage, with a percentage of 96.07%, while Central America has the lowest population coverage, with a value of 22.94%. The DNA sequence of the vaccine construct was optimized and successfully cloned into a pET-28a (+) plasmid vector. Analysis of binding interactions indicated the stability of the complex when docked with Toll-like receptor-2 (TLR-2). The subunit vaccine construct was predicted to induce and boost sufficient host cellular and humoral responses in silico. However, further experimental research and analysis is required to validate the findings.

This study is purely computational, and further experimental validation of these findings through in vivo and in vitro conditions is required.

Nowadays, the inception of computer modeling and simulation in life science is a matter of fact. This is one of the reasons why regulatory authorities are open in considering in silico trials evidence for the assessment of safeness and efficacy of medicinal products. In this context, mechanistic Agent-Based Models are increasingly used. Unfortunately, there is still a lack of consensus in the verification assessment of Agent-Based Models for regulatory approval needs. VV&UQ is an ASME standard specifically suited for the verification, validation, and uncertainty quantification of medical devices. However, it can also be adapted for the verification assessment of in silico trials for medicinal products.

Here, we propose a set of automatic tools for the mechanistic Agent-Based Model verification assessment. As a working example, we applied the verification framework to an Agent-Based Model in silico trial used in the COVID-19 context.

Using the described verification computational workflow allows researchers and practitioners to easily perform verification steps to prove Agent-Based Models robustness and correctness that provide strong evidence for further regulatory requirements.

Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as In Silico Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research communities might not be appropriate. In this paper we propose a theoretical framing for assessing the credibility of a predictive models for In Silico Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models.

Adjuvants are substances used to enhance the efficacy of vaccines. They influence the magnitude and alter the quality of the adaptive immune response to vaccine antigens by amplifying or modulating different signals involved in the innate immune response. The majority of known adjuvants have been empirically identified. The limited immunogenicity of new vaccine antigens and the need for safer vaccines have increased the importance of identifying single, well-defined adjuvants with known cellular and molecular mechanisms for rational vaccine design. Depletion or functional inhibition of CD4⁺CD25⁺FoxP3⁺ regulatory T cells (Tregs) by molecular adjuvants has become an emergent approach in this field. Different successful results have been obtained for specific vaccines, but there are still unresolved issues such as the risk of autoimmune disease induction, the involvement of cells other than Tregs and optimization for different conditions. This work provides a comprehensive analysis of current approaches to inhibit Tregs with molecular adjuvants for vaccine improvement, highlights the progress being made, and describes ongoing challenges.