Diversity is an omnipresent element in clinical practice: in the genome, in the environment, in patients’ lifestyles and habits. Precision medicine addresses the variability of the individual to improve diagnosis and treatment. It is increasingly used in specialties such as oncology, neurology, and cardiology. A personalized approach has many objectives, including to optimize treatment, minimize the risk of adverse effects, facilitate early diagnosis, and determine predisposition to disease. Genomic technologies, such as massive sequencing techniques, and tools such as CRISPR-Cas9, are key to the future of personalized medicine.
Jesús Oteo Iglesias, MD, PhD, a specialist in microbiology and director of Spain’s National Center for Microbiology, spoke at the Spanish Association of Infectious Diseases and Clinical Microbiology’s recent conference. He discussed various precision medicine projects aimed at reinforcing the fight against antibiotic resistance.
Infectious diseases are complex, because the diversity of the pathogenic microorganism combines with the patient’s own diversity, which influences the interaction between the two, said Oteo. Thus, the antibiogram and targeted antibiotic treatments (which are chosen according to the species, sensitivity to antimicrobials, type of infection, and patient characteristics) have been established applications of precision medicine for decades. However, multiple tools could further strengthen personalized medicine against multiresistant pathogens.
Therapeutic drug monitoring, in which multiple pharmacokinetic and pharmacodynamic factors are considered, is a strategy with great potential to increase the effectiveness of antibiotics and minimize toxicity. Owing to its costs and the need for trained staff, this tool would be especially indicated in the treatment of patients with more complex conditions, such as those suffering from obesity, complex infections, or infections with multiresistant bacteria, as well as those in critical condition. Multiple computer programs are available to help determine the dosage of antibiotics by estimating drug exposure and to provide recommendations. However, clinical trials are needed to assess the pros and cons of applying therapeutic monitoring for types of antibiotics other than those for which a given type is already used (eg, aminoglycosides and glycopeptides).
One technology that could help in antibiotic use optimization programs is microneedle-based biosensors, which could be implanted in the skin for real-time antibiotic monitoring. This tool “could be the first step in establishing automated antibiotic administration systems, with infusion pumps and feedback systems, like those already used in diabetes for insulin administration,” said Oteo.
Artificial intelligence could also be a valuable technology for optimization programs. “We should go a step further in the implementation of artificial intelligence through clinical decision support systems,” said Oteo. This technology would guide the administration of antimicrobials using data extracted from the electronic medical record. However, there are great challenges to overcome in creating these tools, such as the risk of entering erroneous data, the difficulty in entering complex data, such as data relevant to antibiotic resistance, and the variability at the geographic and institutional levels.
Genomics is also a tool with great potential for identifying bacteria’s degree of resistance to antibiotics by studying mutations in chromosomal and acquired genes. A proof-of-concept study evaluated the sensitivity of different Pseudomonas aeruginosa strains to several antibiotics by analyzing genome sequences associated with resistance, said Otero. The researchers found that this system was effective at predicting the sensitivity of bacteria from genomic data.
In the United States, the PATRIC bioinformatics center, which is financed by the National Institute of Allergy and Infectious Diseases, works with automated learning models to predict the antimicrobial resistance of different species of bacteria, including Staphylococcus aureus, Streptococcus pneumoniae, and Mycobacterium tuberculosis. These models, which work with genomic data associated with antibiotic resistance phenotypes, are able to dentify resistance without prior knowledge of the underlying mechanisms.
Another factor to consider with regard to the use of precision medicine for infectious diseases is the microbiota. Oteo explained that the pathogenic microorganism interacts not only with the host but also with its microbiota, “which can be diverse, is manifold, and can be very different, depending on the circumstances. These interactions can be translated into ecological and evolutionary pressures that may have clinical significance.” One of the best-known examples is the possibility that a beta-lactamase-producing bacterium benefits other bacteria around it by secreting these enzymes. Furthermore, some known forms of bacterial interaction (such as plasmid transfer) are directly related to antibiotic resistance. Metagenomics, which involves the genetic study of communities of microbes, could provide more information for predicting and avoiding infections by multiresistant pathogens by monitoring the microbiome.
The CRISPR-Cas9 gene editing tool could also be an ally in the fight against antibiotic resistance by eliminating resistance genes and thus making bacteria sensitive to certain antibiotics. Several published preliminary studies indicate that this is possible in vitro. The main challenge for the clinical application of CRISPR is in introducing it into the target microbial population. Use of conjugative plasmids and bacteriophages could perhaps be an option for overcoming this obstacle in the future.
Exploiting the possibilities of precision medicine through use of the most innovative tools in addressing antibiotic resistance is a great challenge, said Oteo, but the situation demands it, and it is necessary to take small steps to achieve this goal.
This article was translated from Univadis Spain.