Tacrolimus is one of the most commonly used basic immunosuppressants nowadays, but the high variability of tacrolimus blood concentration often leads to kidney transplant recipients frequently experiencing drug concentrations above or below the target concentration, resulting in renal toxicity or rejection of the transplanted kidney. The aim of this study is to explore the correlation of renal function with intra-patient variability (IPV) of tacrolimus blood concentration among recipients of renal transplants at 1-, 3-, 5-, and 10-year post-transplantation.

Recipients of renal transplants who were treated with tacrolimus for immunosuppression at the Shanghai General Hospital between January 2001 and December 2009, and followed up until 2019 were included in this retrospective study. Demographic characteristics and laboratory investigation results at their 1-, 3-, 5-, and 10-year follow-up visits were collected from their hospital medical records. Patients were divided into a low or high IPV group based on the IPV of their tacrolimus concentrations.

A total of 167 kidney transplant recipients were included in the study. At the 3-year follow-up visit, patients in the low IPV group had significantly lower blood urea nitrogen (BUN) (6.3±1.8 vs. 8.2±6.2 µmol/L, P=0.04), serum creatinine (Scr) (88.8±23.6 vs. 104.8±39.6 µmol/L, P=0.009), and blood uric acid (UA) (329.1±80.2 vs. 375.9±95.1 µmol/L, P=0.004), as well as significantly higher estimated glomerular filtration rate (eGFR) values than patients in the high IPV group. Blood UA levels were significantly lower in patients in the low IPV group than the high IPV group at the 10-year follow-up (362.7±92.6 vs. 398.5±105.2 µmol/L, P=0.042). There was no significant difference between the low and high IPV groups with respect to BUN, Scr, UA, or eGFR at the 1- and 5-year follow-up.

Recipients of renal transplants with lower IPV in tacrolimus concentration appeared to have better renal function over time. Controlling IPV may contribute to improved renal outcomes post-transplantation.

This study aims to explore the factors influencing tacrolimus intrapatient variability (TAC-IPV) and its association with 1-year post-transplant outcomes in pediatric liver transplant recipients.

Clinical and biological data of pediatric patients after liver transplantation were collected. The patients were divided into high- and low-IPV groups according to the median TAC-IPV for statistical comparisons. Factors with p < 0.05 in univariate analysis were introduced into binomial logistic regression analysis. Correlation analysis was used to test the connections between the Tac-IPV and outcomes within 1 year after liver transplantation (LT), and Kaplan-Meier was used to draw the survival curves.

A total of 116 children underwent 746 measurements of TAC trough concentrations. The median TAC-IPV was 32.31% (20.81%, 46.77%). Hematocrit (p = 0.017) and concomitant medications (p = 0.001) were identified as independent influencing factors for TAC-IPV. The incidence of transplant rejection (p = 0.008), CMV infection (p < 0.001), and hospital admission due to infection (p = 0.003) were significantly higher in the high-IPV group than in the low-IPV group. Kaplan-Meier survival analysis suggests that after considering the time factor, high IPV (IPV > 32.31%) was still significantly associated with transplant rejection (HR = 3.17 and p = 0.005) and CMV infection (HR = 2.3 and p < 0.001) within 1 year after LT.

The study highlights the significant variation in TAC-IPV among children post-liver transplantation, emphasizing the impact of hematocrit levels and concomitant medications on TAC-IPV. Elevated TAC-IPV is associated with increased risks of transplant rejection, CMV infection, and readmission due to infection in the first year after liver transplantation. Close monitoring of patients with high TAC-IPV is recommended to promptly detect adverse reactions and provide timely intervention and treatment.

To investigate the impact of intrapatient variability (IPV) in the levels of immunosuppressant drugs on health outcomes after liver transplantation.

A comprehensive systematic review and meta-analysis were conducted, examining literature from MEDLINE/PubMed, Embase, Web of Science, Cochrane Reviews, and Cochrane CENTRAL.

The analysis focused on acute rejection, graft survival, acute kidney injury, and cancer risk as health outcomes. Of 2901 articles screened, 10 met the inclusion criteria. The results indicate a 19% reduction in the risk of acute rejection in patients with lower IPV (RR = 0.81; 95% confidence interval, 0.66-0.99), although 6 studies found no significant association between high IPV and acute rejection. Contrasting results were observed for graft survival, with 1 study indicating worse outcomes for high IPV, whereas another reported no significant difference. High IPV was consistently associated with acute kidney injury across 3 studies. One study suggested a link between high IPV and hepatocellular carcinoma, although a meta-analysis for these outcomes was not feasible.

These findings point to a marginal but statistically significant association between high IPV and an increased risk of acute rejection, highlighting the importance of precise management of immunosuppressive drugs in liver transplant recipients to enhance patient outcomes.

Tacrolimus is the primary calcineurin inhibitor used in immunosuppressive regimens to prevent allograft rejection (AR) after organ transplantation. Recent studies have linked intrapatient variability (IPV) of tacrolimus with AR occurrence and reduced survival, especially in kidney transplant recipients. However, limited data are available on the impact of tacrolimus IPV on adverse outcomes after liver transplantation (LT).

The aim of this study was to assess the association between tacrolimus IPV using various methodologies with acute AR and long-term patient survival after LT.

All patients who underwent LT from January 2010 to July 2021 were retrospectively evaluated. Tacrolimus IPV was calculated for each patient using the mean and SD, mean absolute deviation (MAD), coefficient of variation (CV), and time in therapeutic range (TTR). These measures were then compared with AR within the first 24 months after LT and to long-term survival.

Out of 234 patients, 32 (13.7%) developed AR and 183 (78.2%) survived, with a mean follow-up of 101 ± 43 months. Tacrolimus IPV, assessed by mean, SD, MAD, and CV, was 8.3 ± 2.1, 2.7 ± 1.3, 32.0% ± 11.7%, and 39.4% ± 15.4%, respectively. There was no statistically significant correlation between Tacrolimus IPV and AR or survival post-LT.

In a large cohort of patients from diverse racial backgrounds, tacrolimus IPV was not associated with clinically relevant outcomes such as AR and survival after LT.

In current clinical practice, radiotherapy (RT) is prescribed as a pre-determined total dose divided over daily doses (fractions) given over several weeks. The treatment response is typically assessed months after the end of RT. However, the conventional one-dose-fits-all strategy may not achieve the desired outcome, owing to patient and tumor heterogeneity. Therefore, a treatment strategy that allows for RT dose personalization based on each individual response is preferred. Multiple strategies have been adopted to address this challenge. As an alternative to current known strategies, artificial intelligence (AI)-derived mechanism-independent small data phenotypic medicine (PM) platforms may be utilized for N-of-1 RT personalization. Unlike existing big data approaches, PM does not engage in model refining, training, and validation, and guides treatment by utilizing prospectively collected patient's own small datasets. With PM, clinicians may guide patients' RT dose recommendations using their responses in real-time and potentially avoid over-treatment in good responders and under-treatment in poor responders. In this paper, we discuss the potential of engaging PM to guide clinicians on upfront dose selections and ongoing adaptations during RT, as well as considerations and limitations for implementation. For practicing oncologists, clinical trialists, and researchers, PM can either be implemented as a standalone strategy or in complement with other existing RT personalizations. In addition, PM can either be used for monotherapeutic RT personalization, or in combination with other therapeutics (e.g. chemotherapy, targeted therapy). The potential of N-of-1 RT personalization with drugs will also be presented.

Background: Liver cancer is a common malignant tumor with an increasing incidence in recent years. We aimed to develop a model by integrating clinical information and multi-omics profiles of genes to predict survival of patients with liver cancer. Methods: The multi-omics data were integrated to identify liver cancer survival-associated signal pathways. Then, a prognostic risk score model was established based on key genes in a specific pathway, followed by the analysis of the relationship between the risk score and clinical features as well as molecular and immunologic characterization of the key genes included in the prediction model. The function experiments were performed to further elucidate the undergoing molecular mechanism. Results: Totally, 4 pathways associated with liver cancer patients' survival were identified. In the pathway of integrin cell surface interactions, low expression of COMP and SPP1, and low CNVs level of COL4A2 and ITGAV were significantly related to prognosis. Based on above 4 genes, the risk score model for prognosis was established. Risk score, ITGAV and SPP1 were the most significantly positively related to activated dendritic cell. COL4A2 and COMP were the most significantly positively associated with Type 1 T helper cell and regulatory T cell, respectively. The nomogram (involved T stage and risk score) may better predict short-term survival. The cell assay showed that overexpression of ITGAV promoted tumorigenesis. Conclusion: The risk score model constructed with four genes (COMP, SPP1, COL4A2, and ITGAV) may be used to predict survival in liver cancer patients.

Tacrolimus (Tac) is currently the most common calcineurin-inhibitor employed in solid organ transplantation. High intra-patient variability (IPV) of Tac (Tac IPV) has been associated with an increased risk of immune-mediated rejection and poor outcomes after kidney transplantation. Few data are available concerning the impact of high Tac IPV in non-kidney transplants. However, even in kidney transplantation, there is still a controversy whether high Tac IPV is indeed detrimental in respect to graft and/or patient survival. This may be due to different methods employed to evaluate IPV and distinct time frames adopted to assess graft and patient survival in those reports published up to now in the literature. Little is also known about the influence of high Tac IPV in the development of other untoward adverse events, update of the current knowledge regarding the impact of Tac IPV in different outcomes following kidney, liver, heart, lung, and pancreas tran splantation to better evaluate its use in clinical practice.