Alessandra Vitaliti

Long‐Acting Octreotide and Prolonged‐Release Lanreotide Formulations Have Different Pharmacokinetic Profiles

Single‐dose pharmacokinetic (PK) profiles and multiple‐dose PK modeling were compared for long‐acting octreotide (20 or 60 mg) and prolonged‐release lanreotide (90 or 120 mg) over 91 days; steady‐state profiles were simulated. All treatments were well tolerated. Octreotide 20‐mg profile showed increased concentration on day 1, lag from days 2 to 6, then prolonged plateau phase (days 11–41); 60‐mg PK was dose proportional. Lanreotide 90‐mg profile showed Cmax on day 1 then elimination (apparent t1/2 25.5 days); 120‐mg profile was underproportional. Steady‐state PK of octreotide 20 mg/28 d suggested a Cmean of 1216 pg/mL (range, 1065–1585) with low fluctuation index (43%). Steady‐state PK of lanreotide 90 mg/28 d suggested a Cmean of 4455 ρg/mL (range, 2499–9279) with high fluctuation index (152%). Long‐acting octreotide had more predictable PK than prolonged‐release lanreotide. Simulated steady‐state profiles suggest long‐acting octreotide could be optimized to meet individual patient needs. In contrast, prolonged‐release lanreotide requires exposure constantly above the therapeutic target to enable monthly longterm therapy.

Sotrastaurin and cyclosporine drug interaction study in healthy subjects.

Introduction. Sotrastaurin is an immunosuppressant that inhibits protein kinase C and blocks T‐lymphocyte activation. The authors determined the effect of combining sotrastaurin with the calcineurin inhibitor cyclosporine on the pharmacokinetics and biomarker responses to both drugs. Methods. This was a randomized, 4‐period, crossover study in 20 healthy subjects who received single oral doses of (1) sotrastaurin 100 mg, (2) cyclosporine 400 mg, (3) 100 mg sotrastaurin with 100 mg cyclosporine and (4) 100 mg sotrastaurin with 400 mg cyclosporine. Blood samples were collected to measure drug levels and biomarkers of T‐lymphocyte activation (interleukin‐2 and tumor necrosis factor producing T‐cells and interleukin‐2 messenger RNA levels) and of T‐lymphocyte proliferation (thymidine uptake). Results. Sotrastaurin did not alter cyclosporine AUC; however, low‐dose and high‐dose cyclosporine increased sotrastaurin AUC by 1.2‐fold [90% confidence interval, 1.1–1.4] and 1.8‐fold [1.6–2.1], respectively. Adding high‐dose cyclosporine to a low‐therapeutic dose of sotrastaurin significantly enhanced the inhibition of cytokine production by 31% [95% confidence interval, 25–36%], of interleukin‐2 messenger RNA levels by 13% [7–19%], and of thymidine uptake by 37% [32–42%] compared with sotrastaurin alone. Addition of low‐dose cyclosporine elicited slightly lower enhancements in inhibition by 21% [14–28%], 6% [−4–16%], and 26% [21–30%], respectively, compared with sotrastaurin alone. Conclusions. Sotrastaurin did not alter the pharmacokinetics of cyclosporine, but cyclosporine increased sotrastaurin AUC up to 1.8‐fold. The combined drugs elicited a significantly greater inhibition of T‐cell activation and proliferation than sotrastaurin alone. Copyright

Gene expression profiling of immunomagnetically separated cells directly from stabilized whole blood for multicenter clinical trials

BackgroundClinically useful biomarkers for patient stratification and monitoring of disease progression and drug response are in big demand in drug development and for addressing potential safety concerns. Many diseases influence the frequency and phenotype of cells found in the peripheral blood and the transcriptome of blood cells. Changes in cell type composition influence whole blood gene expression analysis results and thus the discovery of true transcript level changes remains a challenge. We propose a robust and reproducible procedure, which includes whole transcriptome gene expression profiling of major subsets of immune cell cells directly sorted from whole blood.MethodsTarget cells were enriched using magnetic microbeads and an autoMACS® Pro Separator (Miltenyi Biotec). Flow cytometric analysis for purity was performed before and after magnetic cell sorting. Total RNA was hybridized on HGU133 Plus 2.0 expression microarrays (Affymetrix, USA). CEL files signal intensity values were condensed using RMA and a custom CDF file (EntrezGene-based).ResultsPositive selection by use of MACS® Technology coupled to transcriptomics was assessed for eight different peripheral blood cell types, CD14+ monocytes, CD3+, CD4+, or CD8+ T cells, CD15+ granulocytes, CD19+ B cells, CD56+ NK cells, and CD45+ pan leukocytes. RNA quality from enriched cells was above a RIN of eight. GeneChip analysis confirmed cell type specific transcriptome profiles. Storing whole blood collected in an EDTA Vacutainer® tube at 4°C followed by MACS does not activate sorted cells. Gene expression analysis supports cell enrichment measurements by MACS.ConclusionsThe proposed workflow generates reproducible cell-type specific transcriptome data which can be translated to clinical settings and used to identify clinically relevant gene expression biomarkers from whole blood samples. This procedure enables the integration of transcriptomics of relevant immune cell subsets sorted directly from whole blood in clinical trial protocols.

Validation—The Key to Translatable Cytometry in the 21st Century

With the primary goal of translating scientific advances and innovative technologies to applications that directly benefit patient care and treatment, Translational Science has justifiably generated considerable enthusiasm. Unfortunately, this enthusiasm has been somewhat tampered by the reality that the outcomes have not fully met the expectations of accelerating the transition of new discoveries from the bench to the bedside. This failure to deliver can be attributed, in part, to the lack of reproducible standards and procedures. Thus it stands to reason that suggestions for increasing the success rate of the Translation Science initiative include the implementation of more robust methods, standardization and validation. This chapter will explore opportunities in the translational science space and key concepts of analytical method validation as applied to cytometry.