An evaluation of the completeness of drug-drug interaction-related information in package inserts

Artificial intelligence-driven prediction of multiple drug interactions

When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug-drug, drug-food (excipients) and drug-microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug-drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.

Provision and Need for Medicine Information in Asia and Africa: A Scoping Review of the Literature

Published reviews of written medicine information (WMI) have mainly drawn on studies published in high-income countries, including very few Asian or African studies. We therefore set out to scope the research literature to determine the extent and type of studies concerning WMI for patients/consumers across these two continents. We sought empirical studies published between January 2004 and December 2019, conducted in any Asian or African country, as defined by the United Nations, in English or with an English abstract. The majority of the 923 papers identified were from high-income countries. We retained 26 papers from Africa and 99 from Asia. Most African studies (n = 20) involved patients in the development of PILs, in the assessment of the effectiveness of PILs or in surveys. In contrast, the highest proportion of Asian studies concerned the content of WMI (n = 42). WMI is desired, but needs to be in local languages, and there needs to be more use made of pre-tested pictograms. Existing WMI frequently does not meet local regulatory requirements, particularly locally manufactured products. A number of studies reported potentially positive impacts of providing WMI on knowledge and medicine use behaviours. Provision of medicine information is essential for safe use of medicines in all countries. Internationally agreed guidelines, incorporating good design principles, are needed to ensure the optimal content and design of WMI. The World Health Organization should support African and Asian regulatory bodies to share best practice in relation to WMI for patients/consumers and to develop and implement pan-continental guidelines that take into account consumer needs and preferences.

CYP2D6 genotype can help to predict effectiveness and safety during opioid treatment for chronic low back pain: results from a retrospective study in an Italian cohort

Background

Opioids are widely used for chronic low back pain (CLBP); however, it is still unclear how to predict their effectiveness and safety. Codeine, tramadol and oxycodone are metabolized by CYP/CYP450 2D6 (CYP2D6), a highly polymorphic enzyme linked to allele-specific related differences in metabolic activity.

Purpose

CYP2D6 genetic polymorphisms could potentially help to predict the effectiveness and safety of opioid-based drugs in clinical practice, especially in the treatment of CLBP.

Patients and methods

A cohort of 224 Italian patients with CLBP treated with codeine or oxycodone was retrospectively evaluated to determine whether adverse reactions and effectiveness were related to CYP2D6 single-nucleotide polymorphisms. CYP2D6 genotyping was performed using the xTAG^® CYP2D6 Kit v3 (Luminex) to determine CYP2D6 metabolizer phenotype (poor, intermediate, rapid and ultrarapid). Subjects from the cohort were categorized into two groups according to the occurrence of side effects (Case) or benefit (Control) after chronic analgesic treatment. The impact of CYP2D6 polymorphism on treatment outcome was tested at the metabolizer phenotype, diplotype and haplotype levels.

Results

CYP2D6 polymorphism was significantly associated with opioid treatment outcome (Omnibus P=0.018, for both global haplotype and diplotype distribution test). CYP2D6*6 and *9 carriers, alleles characterized by a reduced (*9) or absent (*6) enzymatic activity, were significantly (P<0.05) associated with therapeutic failure. CYP2D6 ultrarapid metabolizers (CYP2D6*2N patients) showed an increased risk of side effects, as would be predicted. Despite their low frequency, CYP2D6 *1/*11, *4/*6 and *41/* 2N diplotypes showed significant (P<0.05) associations of efficacy and side effects with chronic opioid treatment.

Conclusion

Our results showed that reduced CYP2D6 activity is correlated with lack of therapeutic effect. We found that the pharmacogenetic analysis of CYP2D6 could be helpful in foreseeing the safety and effectiveness of codeine or oxycodone treatment in CLBP.