## 5 Precision Medicine in Soriasis Management  
One of the main drawbacks of current management of psoriasis is the inability to predict treatment responses of psoriasis biologics in individual patients. An \"assessment gap\" is often observed between the moment the response to a treatment is assessed biologically and when it is determined clinically [87]. An \"assessment gap\" is an important part in the drug administration which predicts the time taken for the drug to reach its target and act through the signalling pathways eliciting its effect or failing to alter the pathogenic state. A personalized/precise approach, considering an individual's genetic, cellular, and molecular variability, is essential for psoriasis disease management. The vast amount of transcriptomic information available for psoriasis makes it an excellent example of precision medicine, elucidating treatment outcome from molecular data. By combining the data and information generated by transcriptomic and other analyses, it is now possible to predict the effects and responses of drugs to provide treatments and therapies with high efficacy, thereby reducing the \"assessment gap\". Such a study, performed by [PERSON] et al., showed that gene expression data obtained from lesional skin in the first 4 weeks of treatment with four specific drugs--Etancercept, Ustekinumab, Adalimumab, and Methotrexate--could predict clinical outcome by week 12. Through this, the clinical assessment gap could be reduced at least by two months [87]. One of the first GWAS studies evaluating treatment response variation (baseline and at weeks 4 and 12) in 65 psoriasis patients after anti-TNF\(\alpha\) therapy found strong association of single nucleotide polymorphisms (SNPs) in JAG2 and ADRA2A, which were associated with treatment responses to anti-TNF-\(\alpha\) agents [88]. A recent study by [PERSON] et al. demonstrated that, by integrating transcriptomic data from RNA-seq and genomic data of in vitro cytokine responses to a cohort of etanercept treated psoriatic patients, effective drug response could be assessed [89]. Molecular profiles were assessed in more than 200 RNA-seq samples, which showed a correlation between drug response and molecular changes during the treatment. Additionally, the study also showed that non-lesional gene expression data was a better predictor of clinical response to etanercept compared to lesional psoriatic skin. Another study on the clinical response to treatment with etanercept showed that signals of response in patients could be detected at baseline (in lesional, non-lesional skin, and blood) even before commencing the therapy. Gene expression profiling, through RNA-seq and serum proteome analysis from blood, showed that there was a strong association between clinical response and TNF-regulated genes in skin and blood [90]. The use of a machine-learning algorithm to predict patients' response to three of the psoriasis biologics was studied in a recent study [91]. In this large cohort study, involving 242 psoriasis patients, dermal patch biomarker patches were applied before and after 12 weeks of drug treatment with IL-23, IL-17, and TNF-\(\alpha\). By combining the dermal transcriptome with machine learning algorithm, it was found that the patient response rate was 64% for the entire cohort and 47.6-72.5% for different biologics. Only one patient did not respond to any of the treatment. Such studies effectively reduce the trial-and-error approach to the biologic treatment of psoriasis and emphasises the role pf precision medicine in the current management of psoriasis. Another similar study was performed using the blood samples of 266 moderate to severe psoriasis patients, at baseline and 4 weeks after treatment with Tofacitinib and Etanercept. Statistical and machine learning techniques were used to analyse this data to predict the treatment response after 12 weeks. Many relevant psoriasis markers including IL-17A and IL-17C were enriched in this blood transcriptome study.