Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform

Postoperative liver transplant immunosuppression was personalized using a phenotypic, disease mechanism–independent and indication-agnostic approach. Personalizing drug dosing After organ transplant, patients are on a merry-go-round of medicines and procedures to make sure that the graft is not rejected. Currently, physicians use dosing guidelines for drugs meant to suppress the immune system, but also use educated guesses in choosing dose, to account for variability in patient response to the drugs and drug-drug interactions. Now, Zarrinpar and colleagues have come up with a mathematical approach to remove the guesswork. Their approach, called parabolic personalized dosing (PPD), relies on algebraic equations to relate phenotype (in this case, trough level of an immunosuppressant, tacrolimus) to input (tacrolimus concentration). By mapping patient response over the course of treatment, the equation produces a two-dimensional (2D) parabola that indicates the next dose that the patient should receive. The parabola shifts as drugs are added or taken away, or as the patient undergoes additional clinical procedures, such as hemodialysis, which can interfere with drug distribution within the body. The PPD approach was tested in four patients and compared to the standard of care, physician guidance. The PPD patients were out of trough range less frequently and for shorter periods of time than controls, suggesting that the equation was predicting next doses accurately. Future studies will involve more patients and will expand the PPD equation to represent a 3D parabolic surface, which will factor in drug combinations. The PPD approach will have broad applicability beyond transplant medicine, because it is independent of disease mechanism or drug of choice and could thus personalize regimens for many types of patients. Posttransplant immunosuppressive drugs such as tacrolimus have narrow therapeutic ranges. Inter- and intraindividual variability in dosing requirements conventionally use physician-guided titrated drug administration, which results in frequent deviations from the target trough ranges, particularly during the critical postoperative phase. There is a clear need for personalized management of posttransplant regimens to prevent adverse events and allow the patient to be discharged sooner. We have developed the parabolic personalized dosing (PPD) platform, which is a surface represented by a second-order algebraic equation with experimentally determined coefficients of the equation being unique to each patient. PPD uses clinical data, including blood concentrations of tacrolimus—the primary phenotypic readout for immunosuppression efficacy—to calibrate these coefficients and pinpoint the optimal doses that result in the desired patient-specific response. In this pilot randomized controlled trial, we compared four transplant patients prospectively treated with tacrolimus using PPD with four control patients treated according to the standard of care (physician guidance). Using phenotype to personalize tacrolimus dosing, PPD effectively managed patients by keeping tacrolimus blood trough levels within the target ranges. In a retrospective analysis of the control patients, PPD-optimized prednisone and tacrolimus dosing improved tacrolimus trough-level management and minimized the need to recalibrate dosing after regimen changes. PPD is independent of disease mechanism and is agnostic of indication and could therefore apply beyond transplant medicine to dosing for cancer, infectious diseases, and cardiovascular medicine, where patient response is variable and requires careful adjustments through optimized inputs.

[1]  Shankaracharya,et al.  Relationship Estimation from Whole-Genome Sequence Data , 2014, PLoS genetics.

[2]  J. Poulsen,et al.  Comparison of the pharmacokinetics of tacrolimus and cyclosporine at equivalent molecular doses. , 2003, Transplantation proceedings.

[3]  G. Malat,et al.  African American Kidney Transplantation Survival , 2009, Drugs.

[4]  J. Oh,et al.  Population Pharmacokinetic–Pharmacogenetic Model of Tacrolimus in the Early Period after Kidney Transplantation , 2014, Basic & clinical pharmacology & toxicology.

[5]  A. Åsberg,et al.  Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling , 2014, British journal of clinical pharmacology.

[6]  W. S. Ring,et al.  Utility of the Cylex assay in cardiac transplant recipients. , 2008, The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation.

[7]  G. Klintmalm,et al.  High Mean Fluorescence Intensity Donor‐Specific Anti‐HLA Antibodies Associated With Chronic Rejection Postliver Transplant , 2011, American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons.

[8]  Haiyang Xie,et al.  Influence of CYP3A5 Gene Polymorphisms of Donor Rather than Recipient to Tacrolimus Individual Dose Requirement in Liver Transplantation , 2006, Transplantation.

[9]  S. Loewe The problem of synergism and antagonism of combined drugs. , 1953, Arzneimittel-Forschung.

[10]  Peter Bächtold,et al.  Bottom-Up Approach , 2013 .

[11]  C. I. Bliss The calculation of microbial assays. , 1956, Bacteriological reviews.

[12]  Aris Persidis,et al.  Cancer multidrug resistance , 1999, Nature Biotechnology.

[13]  Morton B. Brown,et al.  Role of intestinal P‐glycoprotein (mdr1) in interpatient variation in the oral bioavailability of cyclosporine , 1997, Clinical pharmacology and therapeutics.

[14]  Wojciech Piekoszewski,et al.  Pharmacokinetics of tacrolimus in liver transplant patients , 1995, Clinical pharmacology and therapeutics.

[15]  T. Starzl,et al.  Immunosuppression and Other Nonsurgical Factors in the Improved Results of Liver Transplantation , 1985, Seminars in liver disease.

[16]  S. Masuda,et al.  Effect of intestinal CYP3A5 on postoperative tacrolimus trough levels in living-donor liver transplant recipients , 2006, Pharmacogenetics and genomics.

[17]  U. Christians,et al.  Metabolism of the immunosuppressant tacrolimus in the small intestine: cytochrome P450, drug interactions, and interindividual variability. , 1995, Drug metabolism and disposition: the biological fate of chemicals.

[18]  A. Zarrinpar,et al.  Liver transplantation: past, present and future , 2013, Nature Reviews Gastroenterology &Hepatology.

[19]  D. Lauffenburger,et al.  Integration of multiple signaling pathway activities resolves K-RAS/N-RAS mutation paradox in colon epithelial cell response to inflammatory cytokine stimulation. , 2010, Integrative biology : quantitative biosciences from nano to macro.

[20]  Chih-Ming Ho,et al.  Rapid optimization of drug combinations for the optimal angiostatic treatment of cancer , 2015, Angiogenesis.

[21]  Benoit Blanchet,et al.  Determination of the Most Influential Sources of Variability in Tacrolimus Trough Blood Concentrations in Adult Liver Transplant Recipients: A Bottom-Up Approach , 2014, The AAPS Journal.

[22]  Jeff S. Shamma,et al.  Systematic quantitative characterization of cellular responses induced by multiple signals , 2011, BMC Systems Biology.

[23]  A. Israni,et al.  Dosing equation for tacrolimus using genetic variants and clinical factors. , 2011, British journal of clinical pharmacology.

[24]  T. Chou,et al.  Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. , 1984, Advances in enzyme regulation.

[25]  Jun Yu Li,et al.  Individualization of tacrolimus dosage basing on cytochrome P450 3A5 polymorphism – a prospective, randomized, controlled study , 2013, Clinical transplantation.

[26]  Chih-Ming Ho,et al.  An optimized small molecule inhibitor cocktail supports long-term maintenance of human embryonic stem cells. , 2011, Nature communications.

[27]  C. Staatz,et al.  Bayesian Forecasting and Prediction of Tacrolimus Concentrations in Pediatric Liver and Adult Renal Transplant Recipients , 2003, Therapeutic drug monitoring.

[28]  D. Holdstock Past, present--and future? , 2005, Medicine, conflict, and survival.

[29]  R. Sun,et al.  Closed-loop control of cellular functions using combinatory drugs guided by a stochastic search algorithm , 2008, Proceedings of the National Academy of Sciences.

[30]  L. Shaw,et al.  Cyclosporine bioavailability in heart-lung transplant candidates with cystic fibrosis. , 1990, Transplantation.

[31]  A. Tosti,et al.  Role of natural killer cell alloreactivity in HLA-mismatched hematopoietic stem cell transplantation. , 1999, Blood.

[32]  C. O’Seaghdha,et al.  Tacrolimus trough-level variability predicts long-term allograft survival following kidney transplantation , 2016, Journal of Nephrology.

[33]  Chih-Ming Ho,et al.  Identification and Optimization of Combinatorial Glucose Metabolism Inhibitors in Hepatocellular Carcinomas , 2015, Journal of laboratory automation.

[34]  Dong-Keun Lee,et al.  Mechanism-independent optimization of combinatorial nanodiamond and unmodified drug delivery using a phenotypically driven platform technology. , 2015, ACS nano.

[35]  A. Prémaud,et al.  Tacrolimus Population Pharmacokinetic-Pharmacogenetic Analysis and Bayesian Estimation in Renal Transplant Recipients , 2009, Clinical pharmacokinetics.

[36]  D. F. Roberts,et al.  Age at menarche , 1994, The Lancet.