ABSTRACT
Tacrolimus is an immunosuppressant with a narrow therapeutic index and pharmacokinetic variability. This variability may be attributed to genetic variants in gene CYP3A5 associated with Tacrolimus metabolism. Studies focusing on genetic variants in the CYP3A5 gene associated with Tacrolimus metabolism have been published, a meta-analysis of these published articles may provide a direction that can change the future research and clinical management of renal transplant patients. In this systematic review and meta-analysis, we have reviewed and analyzed the studies and clinical trials conducted to determine the association between genetic variants of CYP3A5 and Tacrolimus metabolism from the PubMed database and clinical trials (www.clinicaltrials.gov). This meta-analysis also assessed the correlation of CYP3A5 genotype (rs776746) with concentration/dose (Co/D) of Tacrolimus in renal transplant patients. The 59 published articles on genetic association of the CYP3A5 on Tacrolimus doses were reviewed for this systematic review. Meta-analysis showed that the Tacrolimus Co/D ratio is significantly lower in the CYP3A5 expressor group as compared with non-expressor in Asian, European as well as in mixed populations at any post-transplant period (P<0.0001). Our study further confirmed that the CYP3A5 variant (rs776746) is clinically relevant for the dose determination of Tacrolimus. Variations in Tacrolimus Co/D have been found to be significantly linked to the patient's CYP3A5 genetic variant (rs776746). The addition of other genetic variants involved in the pharmacokinetic of Tacrolimus may determine efficient regimen for drug dose. Our meta-analysis confirmed that the CYP3A5 genetic variant (rs776746) analysis is relevant in personalizing the Tacrolimus dose determination in renal transplant patients.
ABSTRACT
OBJECTIVE: The aim of the present study was to develop a nomogram to accurately predict the need for intervention in patients suffering from LUTS due to benign prostatic hyperplasia (BPH) and internally validate it. MATERIAL AND METHODS: The data was collected from the community subjects from the state of Gujarat in western India. All the demographic data, physical examination, PSA, uroflowmetry and prostatic ultrasound was collected in 92 subjects and were followed up after 2 years. The data was analyzed and logistic regression model was used to build a predictive model. A nomogram was build using R software. Nomogram was internally validated using 50 subjects. RESULTS: 92 subjects were analyzed for developing the nomogram. Out of these, 17 patients needed intervention. 8 patients were started on medical therapy and 9 patients were taken up for surgical intervention. Of all the statistically significant predictors, peak flow rate was the most significant and was followed by median lobe enlargement, PSA, prostate volume and IPSS. These variables were used to develop a prediction model for the intervention required using reduced logistic regression model. The predictive accuracy of the model was 95.65% with a sensitivity of 88.28%, a specificity of 97.33%, a positive predictive value (PPV) of 88.24%, and a negative predictive value (NPV) of 97.33%. The AUC of the model was 0.799. Internal validation was done on 50 subjects which had sensitivity, specificity and AUC of the model at 89.66%, 90.48% and 0.968 respectively. CONCLUSION: The study demonstrates the clinical application of nomogram which uses IPSS, PSA, peak flow rate, prostate volume and median lobe enlargement (intravesical prostatic volume). It has a sensitivity of 88.24%, specificity of 97.33%. It predicts the need for intervention in BPH patients with accuracy of 95.65% which was internally validated with an accuracy of 90%.
