Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources

Functional Genomics in Psoriasis

Psoriasis is an autoimmune cutaneous condition that significantly impacts quality of life and represents a burden on society due to its prevalence. Genome-wide association studies (GWASs) have pinpointed several psoriasis-related risk loci, underlining the disease's complexity. Functional genomics is paramount to unveiling the role of such loci in psoriasis and disentangling its complex nature. In this review, we aim to elucidate the main findings in this field and integrate our discussion with gold-standard techniques in molecular biology-i.e., Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-and high-throughput technologies. These tools are vital to understanding how disease risk loci affect gene expression in psoriasis, which is crucial in identifying new targets for personalized treatments in advanced precision medicine.

Proposing a framework to quantify the potential impact of pharmacokinetic drug-drug interactions caused by a new drug candidate by using real world data about the target patient population

Drug development teams must evaluate the risk/benefit profile of new drug candidates that perpetrate drug-drug interactions (DDIs). Real-world data (RWD) can inform this decision. The purpose of this study was to develop a predicted impact score for DDIs perpetrated by three hypothetical drug candidates via CYP3A, CYP2D6, or CYP2C9 in type 2 diabetes mellitus (T2DM), obesity, or migraine. Optum Market Clarity was analyzed to estimate use of CYP3A, CYP2D6, or CYP2C9 substrates classified in the University of Washington Drug Interaction Database as moderate sensitive, sensitive, narrow therapeutic index, or QT prolongation. Scoring was based on prevalence of exposure to victim substrates and characteristics (age, polypharmacy, duration of exposure, and number of prescribers) of those exposed. The study population of 14,163,271 adults included 1,579,054 with T2DM, 3,117,753 with obesity, and 410,436 with migraine. For T2DM, 71.3% used CYP3A substrates, 44.3% used CYP2D6 substrates, and 44.3% used CYP2C9 substrates. For obesity, 57.1% used CYP3A substrates, 34.6% used CYP2D6 substrates, and 31.0% used CYP2C9 substrates. For migraine, 64.1% used CYP3A substrates, 44.0% used CYP2D6 substrates, and 28.9% used CYP2C9 substrates. In our analyses, the predicted DDI impact scores were highest for DDIs involving CYP3A, followed by CYP2D6, and CYP2C9 substrates, and highest for T2DM, followed by migraine, and obesity. Insights from RWD can be used to estimate a predicted DDI impact score for pharmacokinetic DDIs perpetrated by new drug candidates currently in development. This score can inform the risk/benefit profile of new drug candidates in a target patient population.

Shared genetic risk factors and causal association between psoriasis and coronary artery disease

Psoriasis and coronary artery disease (CAD) are related comorbidities that are well established, but whether a genetic basis underlies this is not well studied. We apply trans-disease meta-analysis to 11,024 psoriasis and 60,801 CAD cases, along with their associated controls, identifying one opposing and three shared genetic loci, which are confirmed through colocalization analysis. Combining results from Bayesian credible interval analysis with independent information from genomic, epigenomic, and spatial chromatin organization, we prioritize genes (including IFIH1 and IL23A) that have implications for common molecular mechanisms involved in psoriasis and CAD inflammatory signaling. Chronic systemic inflammation has been associated with CAD and myocardial infarction, and Mendelian randomization analysis finds that CAD as an exposure can have a significant causal effect on psoriasis (OR = 1.11; p = 3×10^-6) following adjustment for BMI and waist-hip ratio. Together, these findings suggest that systemic inflammation which causes CAD can increase the risk of psoriasis.

On the road to explainable AI in drug-drug interactions prediction: A systematic review

Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.

Data Mining and Meta-Analysis of Psoriasis Based on Association Rules

Psoriasis is a common chronic and recurrent disease in dermatology, which has a great impact on the physical and mental health of patients. Meta-analysis can evaluate the effectiveness and safety of defubao in the treatment of psoriasis vulgaris. This article observes psoriasis skin lesions treated with topical defubao and the changes in blood vessels under dermoscopy. Considering that the Apriori algorithm and the existing improved algorithm have the problems of ignoring the weight and repeatedly scanning the database, this paper proposes a matrix association rule method based on random forest weighting. This method uses the random forest algorithm to assign weights to each item in the data set, and introduces matrix theory to convert the transaction data set into a matrix form and store it, thereby improving operating efficiency. This article included 11 studies, of which 7 studies used the indicator "Researcher's Overall Assessment" (IGA) to evaluate the efficacy, 5 studies used the "Patient Overall Assessment" (PGA) as the efficacy evaluation index, and Loss Area and Severity Index (PASI) was used as an observation index to evaluate the efficacy. Seven studies conducted safety comparisons. In this paper, IGA and PGA were used as evaluation indicators. The treatment effect of the defubao group was better than the calcipotriol group and the betamethasone group. The differences were statistically significant. The effect of the Fubao treatment for 8 weeks is significantly better than that of 4 weeks and 2 weeks, and the differences are statistically different. Using PASI as the evaluation index, a descriptive study was carried out, and it was found that after 4 weeks of treatment for psoriasis vulgaris, the average PASI reduction rate of patients was higher than that of the calcipotriol group and the betamethasone group. The safety evaluation found that after 8 weeks of treatment, the incidence of adverse events in the defubao group was significantly lower than that in the calcipotriol group.

Interaction Between Florfenicol and Doxycycline Involving Cytochrome P450 3A in Goats (Capra hricus)

When two drugs are combined, drug-drug interactions (DDI) often occur. Metabolic DDI usually occur due to inhibition of the metabolism of one drug by the other. This leads to an increase in the plasma concentration of the drug whose metabolism is inhibited. The objective of this research study was to verify the DDI risk of two antibacterial, florfenicol (FF) and doxycycline (DOX) due to metabolism. Because food containing residues of any pharmacologically active substance could potentially constitute a public health hazard, we selected a food producing animal, goat, goat liver microsomes and recombinant metabolic enzymes, for in vivo and in vitro metabolism studies. In vitro experiments showed that CYP3A was the key enzyme subfamily in FF metabolism, DOX slowed down FF metabolism and R440 was possibly the key amino acid in the metabolic interaction between FF and DOX. In vivo studies in the goats showed that DOX inhibited up-regulation of CYP3A24 gene expression produced by FF; in liver and kidney, DOX slightly slowed down FF metabolism. Quantitative prediction of DDI risk suggest that when DOX is used in combination with FF in veterinary medicine, may result in a clinical significant increase of FF plasma and tissue concentrations, resulting a prevalence of harmful tissue residues of medicinal products in the food chain. Through our experimentation, when DOX is used in combination with FF, the withdrawal period of FF in the kidney was extended by 1 day. Otherwise, an appropriate withdrawal period (20 days) of FF was established for FF and DOX combined use to ensure that the animal can be safely slaughtered for food.