Polymorphic cytochrome P450 enzymes (CYPs) and their role in personalized therapy

The cytochrome P450 (CYP) enzymes are major players in drug metabolism. More than 2,000 mutations have been described, and certain single nucleotide polymorphisms (SNPs) have been shown to have a large impact on CYP activity. Therefore, CYPs play an important role in inter-individual drug response and their genetic variability should be factored into personalized medicine. To identify the most relevant polymorphisms in human CYPs, a text mining approach was used. We investigated their frequencies in different ethnic groups, the number of drugs that are metabolized by each CYP, the impact of CYP SNPs, as well as CYP expression patterns in different tissues. The most important polymorphic CYPs were found to be 1A2, 2D6, 2C9 and 2C19. Thirty-four common allele variants in Caucasians led to altered enzyme activity. To compare the relevant Caucasian SNPs with those of other ethnicities a search in 1,000 individual genomes was undertaken. We found 199 non-synonymous SNPs with frequencies over one percent in the 1,000 genomes, many of them not described so far. With knowledge of frequent mutations and their impact on CYP activities, it may be possible to predict patient response to certain drugs, as well as adverse side effects. With improved availability of genotyping, our data may provide a resource for an understanding of the effects of specific SNPs in CYPs, enabling the selection of a more personalized treatment regimen

The Transformer database: biotransformation of xenobiotics

As the number of prescribed drugs is constantly rising, drug-drug interactions are an important issue. The simultaneous administration of several drugs can cause severe adverse effects based on interactions with the same metabolizing enzyme(s). The Transformer database (http://bioinformatics.charite.de/transformer) contains integrated information on the three phases of biotransformation (modification, conjugation and excretion) of 3000 drugs and >350 relevant food ingredients (e.g. grapefruit juice) and herbs, which are catalyzed by 400 proteins. A total of 100 000 interactions were found through text mining and manual validation. The 3D structures of 200 relevant proteins are included. The database enables users to search for drugs with a visual display of known interactions with phase I (Cytochrome P450) and phase II enzymes, transporters, food and herbs. For each interaction, PubMed references are given. To detect mutual impairments of drugs, the drug-cocktail tool displays interactions between selected drugs. By choosing the indication for a drug, the tool offers suggestions for alternative medications to avoid metabolic conflicts. Drug interactions can also be visualized in an interactive network view. Additionally, prodrugs, including their mechanisms of activation, and further information on enzymes of biotransformation, including 3D models, can be viewed

A Birds-Eye (Re)View of Acid-Suppression Drugs, COVID-19, and the Highly Variable Literature

This Perspective examines a recent surge of information regarding the potential benefits of acid-suppression drugs in the context of COVID-19, with a particular eye on the great variability (and, thus, confusion) that has arisen across the reported findings, at least as regards the popular antacid famotidine. The degree of inconsistency and discordance reflects contradictory conclusions from independent, clinical-based studies that took roughly similar approaches, in terms of both experimental design (retrospective, observational, cohort-based, etc.) and statistical analysis workflows (propensity-score matching and stratification into sub-cohorts, etc.). The contradictions and potential confusion have ramifications for clinicians faced with choosing therapeutically optimal courses of intervention: e.g., do any potential benefits of famotidine suggest its use in a particular COVID-19 case? (If so, what administration route, dosage regimen, duration, etc. are likely optimal?) As succinctly put this March in Freedberg et al. (2021), "…several retrospective studies show relationships between famotidine and outcomes in COVID-19 and several do not." Beyond the pressing issue of possible therapeutic indications, the conflicting data and conclusions related to famotidine must be resolved before its inclusion/integration in ontological and knowledge graph (KG)-based frameworks, which in turn are useful for drug discovery and repurposing. As a broader methodological issue, note that reconciling inconsistencies would bolster the validity of meta-analyses which draw upon the relevant data-sources. And, perhaps most broadly, developing a system for treating inconsistencies would stand to improve the qualities of both 1) real world evidence-based studies (retrospective), on the one hand, and 2) placebo-controlled, randomized multi-center clinical trials (prospective), on the other hand. In other words, a systematic approach to reconciling the two types of studies would inherently improve the quality and utility of each type of study individually

Man vs. machine: comparison of pharmacogenetic expert counselling with a clinical medication support system in a study with 200 genotyped patients

Background: Medication problems such as strong side effects or inefficacy occur frequently. At our university hospital, a consultation group of specialists takes care of patients suffering from medication problems. Nevertheless, the counselling of poly-treated patients is complex, as it requires the consideration of a large network of interactions between drugs and their targets, their metabolizing enzymes, and their transporters, etc. Purpose This study aims to check whether a score-based decision-support system (1) reduces the time and effort and (2) suggests solutions at the same quality level. Patients and methods: A total of 200 multimorbid, poly-treated patients with medication problems were included. All patients were considered twice: manually, as clinically established, and using the Drug-PIN decision-support system. Besides diagnoses, lab data (kidney, liver), phenotype (age, gender, BMI, habits), and genotype (genetic variants with actionable clinical evidence I or IIa) were considered, to eliminate potentially inappropriate medications and to select individually favourable drugs from existing medication classes. The algorithm is connected to automatically updated knowledge resources to provide reproducible up-to-date decision support. Results: The average turnaround time for manual poly-therapy counselling per patient ranges from 3 to 6 working hours, while it can be reduced to ten minutes using Drug-PIN. At the same time, the results of the novel computerized approach coincide with the manual approach at a level of > 90%. The holistic medication score can be used to find favourable drugs within a class of drugs and also to judge the severity of medication problems, to identify critical cases early and automatically. Conclusion: With the computerized version of this approach, it became possible to score all combinations of all alternative drugs from each class of drugs administered ("personalized medication landscape ") and to identify critical patients even before problems are reported ("medication alert"). Careful comparison of manual and score-based results shows that the incomplete manual consideration of genetic specialties and pharmacokinetic conflicts is responsible for most of the (minor) deviations between the two approaches. The meaning of the reduction of working time for experts by about 2 orders of magnitude should not be underestimated, as it enables practical application of personalized medicine in clinical routine

Antibiotics Significantly Decrease the Survival of Head and Neck Carcinoma Patients with Immunotherapy: A Real-World Analysis of More Than 3000 Cases

Simple Summary: It is well known that antibiotics alter the gut microbiome, and because it plays a role in drug metabolism, alterations to the microbiome may lead to ineffective immunotherapy in cancer patients. We investigated a real-world cohort of oral cancer patients who received immunotherapy. Patients were matched for age, sex, BMI, metastases, alcohol and nicotine dependence and sepsis to create two comparable groups. Patients who received antibiotics had a significantly decreased survival compared to those who did not. We believe that this finding is associated with less effective immunotherapy due to antibiotic-related changes in the gut microbiome. Objective: The human gut microbiome is strongly influenced by the administration of drugs, namely antibiotics. We hypothesized that the effectiveness of immunotherapy with pembrolizumab in oral squamous cell carcinoma patients is decreased by the administration of antibiotics three months before and after immunotherapy. Methods: We retrieved data from patients diagnosed with head and neck squamous cell carcinoma (HNSCC) (International Classification of Diseases [ICD]-10 codes C00-C14) and receiving immunotherapy with pembrolizumab from the TriNetX network. Two cohorts were built: patients in cohort I did not receive any antibiotics within three months before or up to three months after immunotherapy, while patients in cohort II were administered antibiotics at least once within three months before or after immunotherapy. To exclude confounders, we matched cohorts 1:1 for age, sex, secondary lymph node metastases, nicotine dependence, the insertion of feeding devices, body mass index (BMI) and severe sepsis. After defining the primary outcome as "death", a Kaplan-Meier analysis was performed, and the risk ratio (RR), odds ratio (OR) and hazard ratio (HR) were calculated. Results: A total of 3651 patients were enrolled, and after matching, each cohort consisted of 1362 patients. Among cohorts I and II, 346 and 511 patients were deceased within one year (risk of death = 25.5 and 38.3%, respectively), whereby the risk difference was significant (p = 0.000; log-rank test). The RR was 0.68 (95% confidence interval: 0.60-0.76), OR was 0.57 (0.48-0.67) and HR was 0.58 (0.51-0.67). Conclusions: Our hypothesis was confirmed: administering antibiotics significantly decreases the drug effectiveness of immunotherapy. We hypothesize that this finding is associated with antibiotic-related changes in the gut microbiome. Prospective clinical studies on the gut microbiome in cancer patients are necessary to understand the complex ecosystem of microbiota during immunotherapy. Trial Registration: Due to the retrospective nature of the study, no registration was necessary

DBEndo: a web-based endodontic case management tool

BACKGROUND: The success of endodontic treatment depends-among many other factors-on good documentation. Paper-based records are often difficult to read or incomplete and commercially available tools focus on billing. An electronic record captures the state of treatment at all times. Databases are a common tool in everyday life. RESULTS: Here, we present a database created for the Charite-Universitatsmedizin Berlin, Germany. Through consistent digital documentation, data analytics of patients, root canal anatomies, instrumentation techniques, efficacy of chemical disinfection, root filling techniques, and corresponding recall success rates, which needed extensive research before, are now easy to perform. Tables and even graphics and data analystics are only one click away and can be exported to other programs. CONCLUSIONS: DBEndo is a database to store and visualise internally, as well as to share endodontic cases online. For academic use we provide the database including all forms and some anonymous data for free at: http://dbendo.charite.de . Through easy import and export of the data, the system is open and flexible

mVOC 2.0: a database of microbial volatiles

Metabolic capabilities of microorganisms include the production of secondary metabolites (e.g. antibiotics). The analysis of microbial volatile organic compounds (mVOCs) is an emerging research field with huge impact on medical, agricultural and biotechnical applied and basic science. The mVOC database (v1) has grown with microbiome research and integrated species information with data on emitted volatiles. Here, we present the mVOC 2.0 database with about 2000 compounds from almost 1000 species and new features to work with the database. The extended collection of compounds was augmented with data regarding mVOC-mediated effects on plants, fungi, bacteria and (in-)vertebrates. The mVOC database 2.0 now features a mass spectrum finder, which allows a quick mass spectrum comparison for compound identification and the generation of species-specific VOC signatures. Automatic updates, useful links and search for mVOC literature are also included. The mVOC database aggregates and refines available information regarding microbial volatiles, with the ultimate aim to provide a comprehensive and informative platform for scientists working in this research field. To address this need, we maintain a publicly available mVOC database at: http://bioinformatics.charite.de/mvoc

A retrospective case–control study for the comparison of 5-year survival rates: the role of adjuvant and neoadjuvant chemotherapy in craniofacial bone sarcoma in adults

Background: The impact of adjuvant or neoadjuvant chemotherapy in the treatment of craniofacial bone sarcomas has not been clarified. This study aimed to assess whether survival outcomes differed between patients who underwent adjuvant or neoadjuvant chemotherapy. Methods: A retrospective search for adult patients diagnosed with malignant neoplasms of the craniofacial bones (International Classification of Diseases 10 codes C41.0-C41.1), within the past 20 years from the access date 28 April 2022, was conducted using the TriNetX network (TriNetX, Cambridge, MA, USA). Cohort I included patients who underwent adjuvant chemotherapy and cohort II included patients with neoadjuvant chemotherapy. A refined search for individuals that received common chemotherapeutic agents, such as methotrexate, doxorubicin, cisplatin, and/or ifosfamide, was conducted and patients were assigned to cohort A (adjuvant chemotherapy) and cohort B (neoadjuvant chemotherapy). Following matching for age and sex, Kaplan-Meier analysis was performed, and risk ratio, odds ratio (OR), and hazard ratio were calculated. Results: Patients were assigned to two cohorts, with 181 patients each after matching. In cohorts I and II, 55 and 41 patients died, respectively. No significant differences were found between the two cohorts regarding the 5-year survival probability (I: 59.87% versus II: 68.45%; p = 0.076; log-rank test), or the risk of dying (I: 0.304 versus II: 0.227; risk difference: 0.077; p = 0.096). The risk analysis before matching for age and sex showed a significant survival benefit in cohort II (OR: 1.586; p = 0.0295; risk difference: 0.093). After a refined query to identify patients treated with methotrexate, doxorubicin, cisplatin, and/or ifosfamide, the two cohorts included 47 patients, respectively. In cohort A (adjuvant chemotherapy), 19 patients died, whereas 12 patients died in cohort B (neoadjuvant chemotherapy) within 5 years after diagnosis. Further analysis indicated a greater survival in cohort B, but the survival probability between the cohorts did not differ significantly (A: 43.55% versus B: 54.49%; p = 0.171). Conclusion: The use of neoadjuvant chemotherapy may improve survival rates in patients with surgically treated craniofacial bone sarcomas. Due to the retrospective nature of this study, randomized controlled studies are required to derive treatment recommendations

Living and Learning with Chronic Disease: Toward Better Methods of Education

Learning to live with a chronic disease is a lifelong endeavor. Literature concerning learning, identity, and education as it relates to chronic illness are reviewed. Session participants will discuss best practices regarding learning and health education for the chronically ill

Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study

Background: The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC). Methods: Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning. Results: Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86-90%. Conclusions: Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles