# Bacteria-Mediated Modulatory Strategies for Colorectal Cancer Treatment
## 3 Microbiota in CRC  
### Influence of Microbiota on Drug Metabolism  
With a bacteria-to-cell ratio of roughly 1:1 in the human body, microbes encode for 150 times more genes than the human genome [19]. The discovery of microbiota-specific metabolic signatures contributes to a better knowledge of the relation between bacteria and human cells and several studies have demonstrated that microbiota-dependent metabolites have a great impact on the immune function, therefore better understanding could aid in the prediction of drug effects and outcomes in their application. [PERSON] and colleagues used a library of 833 metabolites to describe the metabolic identities of 178 gut bacteria with mass spectrometry and a machine learning workflow by using murine serum, urine, feces and caecal contents [136; 137]. In this study, they could precisely map genes according to bacteria's metabolism and their phenotypic variation as well as associate metabolites with microbial strains. For example, _Firmicutes_ and _Actinobacteria_, which are two phylogenetically distant strains were found to produce high levels of ornithine, which is important for the regulation of several metabolic processes, whereas _Enterococcus faecalis_ and _Enterococcus facicum_ were demonstrated to accumulate high levels of tyramine that is known to modulate neurological functions. On the other hand, _C. cadaveris_ has been shown to act as a consumer instead of a producer and to consume high levels of vitamin B5 that is linked to inflammatory bowel diseases [136; 138; 139]. These observations highlight the great potential of better knowledge about microbiota-dependent metabolites in drug therapy, because orally delivered chemicals are mainly absorbed in the gut and therefore represents the site where the majority of metabolic changes of medication takes place [137]. Because medications have a significant impact on microbiota composition and balance, it is critical to bring up the interacting relationship between drug components and the microbiome [140]. Anti-diabetics, proton pump inhibitors [140] and nonsteroidal anti-inflammatory medications are all representations of drug-induced toxicity on microorganisms [33]. However, bacteria have also been discovered to have the ability to digest medicines. In a previous study, [PERSON] et al. applied 1197 medicines from various therapeutic classes to 40 distinct bacteria species, excluding antibiotics, in an attempt to widely and thoroughly address these effects [140]. The researchers found that almost 30% of the substances examined hindered the proliferation of at least one bacterial species, therefore they hypothesized that antibiotic resistance may also arise as a result of changes in the microbiota caused by non-antibiotic exposure [140]. Genetic screens, and enzymatic analysis to find enzymes promoting specific drug conversions, have been used to investigate the reasons and effects of drug-microbiota interactions [141]. Recently, the metabolism of gut microbiota has gained more attention since it may explain why individuals suffering from the same disease and undergo the same treatment, show different therapeutical outcomes. Moreover, it shows the complex and challenging task to find an efficient treatment strategy for every individual. In order to find appropriate drugs for every patient, machine-learning frameworks using network-based analyses and data to identify drug biomarkers predicting drug responses increasingly take place [142]. With machine learning models and artificial intelligence, individual-specific cancer therapy can be developed to help improve therapeutic outcomes [142; 143]. Furthermore, identifying hazardous by-products of bacterial medication aids in the prediction of potential adverse effects in patients undergoing therapy.