Results of ASERTAA, a randomized prospective crossover pharmacogenetic study of immediate-release versus extended-release tacrolimus in African American kidney transplant recipients

BACKGROUND: Differences in tacrolimus dosing across ancestries is partly attributable to polymorphisms in CYP3A5 genes that encode tacrolimus-metabolizing cytochrome P450 3A5 enzymes. The CYP3A5*1 allele, preponderant in African Americans, is associated with rapid metabolism, subtherapeutic concentrations, and higher dose requirements for tacrolimus, all contributing to worse outcomes. Little is known about the relationship between CYP3A5 genotype and the tacrolimus pharmacokinetic area under the curve (AUC) profile in African Americans or whether pharmacogenetic differences exist between conventional twice-daily, rapidly absorbed, immediate-release tacrolimus (IR-Tac) and once-daily extended-release tacrolimus (LifeCycle Pharma Tac [LCPT]) with a delayed absorption profile. STUDY DESIGN: Randomized prospective crossover study. SETTING & PARTICIPANTS: 50 African American maintenance kidney recipients on stable IR-Tac dosing. INTERVENTION: Recipients were randomly assigned to continue IR-Tac on days 1 to 7 and then switch to LCPT on day 8 or receive LCPT on days 1 to 7 and then switch to IR-Tac on day 8. The LCPT dose was 85% of the IR-Tac total daily dose. OUTCOMES: Tacrolimus 24-hour AUC (AUC MEASUREMENTS: CYP3A5 genotype, 24-hour tacrolimus pharmacokinetic profiles. RESULTS: ∼80% of participants carried the CYP3A5*1 allele (CYP3A5 expressers). There were no significant differences in AUC LIMITATIONS: This was primarily a pharmacogenetic study rather than an efficacy study; the follow-up period was too short to capture clinical outcomes. CONCLUSIONS: Achieving therapeutic tacrolimus trough concentrations with IR-Tac in most African Americans results in significantly higher peak concentrations, potentially magnifying the risk for toxicity and adverse outcomes. This pharmacogenetic effect is attenuated by delayed tacrolimus absorption with LCPT. TRIAL REGISTRATION: Registered at ClinicalTrials.gov, with study number NCT01962922

Prototype of an evidence-based tool to aid individualized treatment for type 2 diabetes

Data-driven tools are needed to inform individualized treatment decisions for people with type 2 diabetes (T2D). To show how treatment might be individualized, an interactive outline tool was developed to predict treatment outcomes. Individualized predictions were generated for change in HbA1c and body weight after initiation of newer antidiabetes drugs recommended by current guidelines. These predictions were based on data from randomized controlled trials of glucose-lowering drugs. The data included patient demographics and clinical characteristics (sex, age, body mass index, weight, diabetes duration, HbA1c level, current diabetes treatment and renal function). Predicted outcomes were determined using prespecified statistical models from original trial protocols and estimated coefficients for selected baseline characteristics. This prototype illustrates how evidence-based individualized treatment might be facilitated in the clinic for people with T2D. Further and ongoing development is required to improve the tool's prognostic value, including the addition of disease co-morbidities and patient-orientated outcomes. Patient engagement and data-sharing by sponsors of clinical trials, as well as real-world evidence, are needed to provide reliable predicted outcomes to inform shared patient–physician decision-making

Plasma biomarkers of depressive symptoms in older adults

The pathophysiology of negative affect states in older adults is complex, and a host of central nervous system and peripheral systemic mechanisms may play primary or contributing roles. We conducted an unbiased analysis of 146 plasma analytes in a multiplex biochemical biomarker study in relation to number of depressive symptoms endorsed by 566 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) at their baseline and 1-year assessments. Analytes that were most highly associated with depressive symptoms included hepatocyte growth factor, insulin polypeptides, pregnancy-associated plasma protein-A and vascular endothelial growth factor. Separate regression models assessed contributions of past history of psychiatric illness, antidepressant or other psychotropic medicine, apolipoprotein E genotype, body mass index, serum glucose and cerebrospinal fluid (CSF) τ and amyloid levels, and none of these values significantly attenuated the main effects of the candidate analyte levels for depressive symptoms score. Ensemble machine learning with Random Forests found good accuracy (∼80%) in classifying groups with and without depressive symptoms. These data begin to identify biochemical biomarkers of depressive symptoms in older adults that may be useful in investigations of pathophysiological mechanisms of depression in aging and neurodegenerative dementias and as targets of novel treatment approaches