Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

The prevention of muscle atrophy carries with it clinical significance for the control of increased morbidity and mortality following physical inactivity. While major transcriptional events associated with muscle atrophy-recovery processes are the subject of active research on the gene level, the contribution of non-coding regulatory elements and alternative promoter usage is a major source for both the production of alternative protein products and new insights into the activity of transcription factors. We used the cap-analysis of gene expression (CAGE) to create a genome-wide atlas of promoter-level transcription in fast (m. EDL) and slow (m. soleus) muscles in rats that were subjected to hindlimb unloading and subsequent recovery. We found that the genetic regulation of the atrophy-recovery cycle in two types of muscle is mediated by different pathways, including a unique set of non-coding transcribed regulatory elements. We showed that the activation of “shadow” enhancers is tightly linked to specific stages of atrophy and recovery dynamics, with the largest number of specific regulatory elements being transcriptionally active in the muscles on the first day of recovery after a week of disuse. The developed comprehensive database of transcription of regulatory elements will further stimulate research on the gene regulation of muscle homeostasis in mammals.

Enhancer RNAs (eRNAs) play an important role in transcriptional regulation and serve as key intermediates linking genomic enhancers to their target genes. Although ongoing efforts aim to annotate enhancers in the chicken genome, the current understanding of avian enhancers remains less developed compared to that of mammals. We utilized CAGE-seq data from chicken tissues obtained through the “Genetic Technologies in Poultry” project to predict enhancers in the chicken genome. Preliminary predictions focused on non-coding regions exhibiting bidirectional transcription, which were subsequently validated using explicit Markov models and refined with hidden Markov models. To assess sequence family homogeneity, we developed a method based on Euclidean distances between explicit Markov model matrices. Our analysis revealed that the proportion of enhancer-associated DNA in chicken is approximately similar to that observed in mammals, encompassing 9.29% of the entire chicken genome, roughly similar to the estimate made by the ChickenGTEx project. A relatively small number of them (12,242 enhancers) were significantly expressed among all tissues. Notably, more than half of the enhancer DNA overlapped intronic regions. Additionally, based on the bimodal distribution of enhancer lengths combined with the homogeneity of their Markov models, we identified a class of long enhancer elements which we hypothesize to be absent in mammals.