Cancer remains a public health problem worldwide. Although conventional therapies have led to some excellent outcomes, some patients fail to respond to treatment, they have few therapeutic alternatives and a poor survival prognosis. Several strategies have been proposed to overcome this issue. The most recent approach is immunotherapy, particularly the use of recombinant antibodies and their derivatives, such as the single-chain fragment variable (scFv) containing the complete antigen-binding domains of a whole antibody that successfully targets tumor cells. This review describes the recent progress made with scFvs as a cancer diagnostic and therapeutic tool, with an emphasis on preclinical approaches and their potential use in clinical trials.

Vaccines are the pharmaceutical products that offer the best cost‐benefit ratio in the prevention or treatment of diseases. In that a vaccine is a pharmaceutical product, vaccine development and production are costly and it takes years for this to be accomplished. Several approaches have been applied to reduce the times and costs of vaccine development, mainly focusing on the selection of appropriate antigens or antigenic structures, carriers, and adjuvants. One of these approaches is the incorporation of bioinformatics methods and analyses into vaccine development. This chapter provides an overview of the application of bioinformatics strategies in vaccine design and development, supplying some successful examples of vaccines in which bioinformatics has furnished a cutting edge in their development. Reverse vaccinology, immunoinformatics, and structural vaccinology are described and addressed in the design and development of specific vaccines against infectious diseases caused by bacteria, viruses, and parasites. These include some emerging or re‐emerging infectious diseases, as well as therapeutic vaccines to fight cancer, allergies, and substance abuse, which have been facilitated and improved by using bioinformatics tools or which are under development based on bioinformatics strategies.

Tuberculosis (TB) is a chronic infectious disease, considered as the second leading cause of death worldwide, caused byMycobacterium tuberculosis. The limited efficacy of the bacillus Calmette-Guérin (BCG) vaccine against pulmonary TB and the emergence of multidrug-resistant TB warrants the need for more efficacious vaccines. Reverse vaccinology uses the entire proteome of a pathogen to select the best vaccine antigens byin silicoapproaches.M. tuberculosisH37Rv proteome was analyzed with NERVE (New Enhanced Reverse Vaccinology Environment) prediction software to identify potential vaccine targets; these 331 proteins were further analyzed with VaxiJen for the determination of their antigenicity value. Only candidates with values ≥0.5 of antigenicity and 50% of adhesin probability and without homology with human proteins or transmembrane regions were selected, resulting in 73 antigens. These proteins were grouped by families in seven groups and analyzed by amino acid sequence alignments, selecting 16 representative proteins. For each candidate, a search of the literature and protein analysis with different bioinformatics tools, as well as a simulation of the immune response, was conducted. Finally, we selected six novel vaccine candidates, EsxL, PE26, PPE65, PE_PGRS49, PBP1, and Erp, fromM. tuberculosisthat can be used to improve or design new TB vaccines.

Tuberculosis is a disease caused by Mycobacterium tuberculosis, representing the second leading cause of death by an infectious agent worldwide. The available vaccine against this disease has insufficient coverage and variable efficacy, accounting for a high number of cases worldwide. In fact, an estimated third of the world’s population has a latent infection. Therefore, developing new vaccines is crucial to preventing it. In this study, the highly antigenic PE_PGRS49 and PE_PGRS56 proteins were analyzed. These proteins were used for predicting T- and B-cell epitopes and for human leukocyte antigen (HLA) protein binding efficiency. Epitopes GGAGGNGSLSS, FAGAGGQGGLGG, GIGGGTQSATGLG (PE_PGRS49), and GTGWNGGKGDTG (PE_PGRS56) were selected based on their best physicochemical, antigenic, non-allergenic, and non-toxic properties and coupled to HLA I and HLA II structures for in silico assays. A construct with an adjuvant (RS09) plus each epitope joined by GPGPG linkers was designed, and the stability of the HLA-coupled construct was further evaluated by molecular dynamics simulations. Although experimental and in vivo studies are still necessary to ensure its protective effect against the disease, this study shows that the vaccine construct is dynamically stable and potentially effective against tuberculosis.

The last influenza A pandemic provided an excellent opportunity to study the adaptation of the influenza A(H1N1)pdm09 virus to the human host. Particularly, due to the availability of sequences taken from isolates since the beginning of the pandemic until date, we could monitor amino acid changes that occurred in the hemagglutinin (HA) as the virus spread worldwide and became the dominant H1N1 strain. HA is crucial to viral infection because it binds to sialidated cell-receptors and mediates fusion of cell and viral membranes; because antibodies that bind to HA may block virus entry to the cell, this protein is subjected to high selective pressure. Multiple alignment analysis of sequences of the HA from isolates taken since 2009 to date allowed us to find amino acid changes that were positively selected as the pandemic progressed. We found nine changes that became prevalent: HA1 subunits D104N, K166Q, S188T, S206T, A259T, and K285E; and HA2 subunits E47K, S124N, and E172K. Most of these changes were located in areas involved in inter- and intrachain interactions, while only two (K166Q and S188T) were located in known antigenic sites. We conclude that selective pressure on HA was aimed to improve its functionality and hence virus fitness, rather than at avoidance of immune recognition.