Recently, it is suggested that brain insulin resistance may contribute to the development of Alzheimer’s disease; therefore, there is a high interest in its investigation. Streptozotocin (STZ) is often used to induce dysregulation of glucose and insulin metabolism in animal and cell culture models. Alteration in insulin sensitivity however, has not yet been assessed in neuronal cells after STZ treatment. We aimed at studying the concentration dependence of the protective effect of insulin on STZ-induced damage using SH-SY5Y cell line. Cells were treated with STZ and cell viability was assessed by resazurin reduction and lactate dehydrogenase release assays. Low serum (LS) medium was used as control damage. The effect of various concentrations (30, 100, 300, 1000 nM) of insulin was studied on cell viability and glycogen synthase kinase-3 (GSK-3) phosphorylation, an indicator of insulin signaling. STZ induced dose- and time-dependent cytotoxicity, its 1 mM concentration exerted a low, gradually developing damage. The cytoprotective effect of insulin was demonstrated in both STZ and LS groups. Its maximal effect was lower in the STZ-treated cells; however, its effective concentration remained largely unaltered. Insulin-induced GSK-3 phosphorylation was similar in the STZ- and LS-treated cells suggesting unchanged insulin signaling. Our present results indicate that STZ does not induce significant impairment in insulin sensitivity in SH-SY5Y cells, thus in this cell line it is not a good tool for studying the role of insulin resistance in neurodegeneration and to examine protective agents acting by improving insulin signaling.

Recently neuronal insulin resistance was suggested playing a role in Alzheimer’s disease. Streptozotocin (STZ) is commonly used to induce impairment in insulin metabolism. In our previous work on undifferentiated SH-SY5Y cells the compound exerted cytotoxicity without altering insulin sensitivity. Nevertheless, differentiation of the cells to a more mature neuron-like phenotype may considerably affect the significance of insulin signaling and its sensitivity to STZ. We aimed at studying the influence of STZ treatment on insulin signaling in SH-SY5Y cells differentiated by retinoic acid (RA). Cytotoxicity of STZ or low serum (LS) condition and protective effect of insulin were compared in RA differentiated SH-SY5Y cells. The effect of insulin and an incretin analogue, exendin-4 on insulin signaling was also examined by assessing glycogen synthase kinase-3 (GSK-3) phosphorylation. STZ was found less cytotoxic in the differentiated cells compared to our previous results in undifferentiated SH-SY5Y cells. The cytoprotective concentration of insulin was similar in the STZ and LS groups. However, the right-shifted concentration–response curve of insulin induced GSK-3 phosphorylation in STZ-treated differentiated cells is suggestive of the development of insulin resistance that was further confirmed by the insulin potentiating effect of exendin-4. Differentiation reduced the sensitivity of SH-SY5Y cells for the non-specific cytotoxicity of STZ and enhanced the relative significance of development of insulin resistance. The differentiated cells thus serve as a better model for studying the role of insulin signaling in neuronal survival. However, direct cytotoxicity of STZ also contributes to the cell death.

The clinical value of therapeutic drug monitoring can be increased most significantly by integrating assay results into clinical pharmacokinetic models for optimal dosing. The correct weighting in the modeling process is 1/variance, therefore, knowledge of the standard deviations (SD) of each measured concentration is important. Because bioanalytical methods are heteroscedastic, the concentration-SD relationship must be modeled using assay error equations (AEE). We describe a methodology of establishing AEE’s for liquid chromatography-tandem mass spectrometry (LC-MS/MS) drug assays using carbamazepine, fluconazole, lamotrigine and levetiracetam as model analytes. Following method validation, three independent experiments were conducted to develop AEE’s using various least squares linear or nonlinear, and median-based linear regression techniques. SD’s were determined from zero concentration to the high end of the assayed range. In each experiment, precision profiles of 6 (“small” sample sets) or 20 (“large” sample sets) out of 24 independent, spiked specimens were evaluated. Combinatorial calculations were performed to attain the most suitable regression approach. The final AEE’s were developed by combining the SD’s of the assay results, established in 24 specimens/spiking level and using all spiking levels, into a single precision profile. The effects of gross hyperbilirubinemia, hemolysis and lipemia as laboratory interferences were investigated. Precision profiles were best characterized by linear regression when 20 spiking levels, each having 24 specimens and obtained by performing 3 independent experiments, were combined. Theil’s regression with the Siegel estimator was the most consistent and robust in providing acceptable agreement between measured and predicted SD’s, including SD’s below the lower limit of quantification. In the framework of precision pharmacotherapy, establishing the AEE of assayed drugs is the responsibility of the therapeutic drug monitoring service. This permits optimal dosages by providing the correct weighting factor of assay results in the development of population and individual pharmacokinetic models.

The antihyerlipidemic drug atorvastatin (ATR) is used worldwide as part of the strategy to prevent cardiovascular events. The high prevalence of patient nonadherence remains an important challenge which could be addressed efficiently by precision pharmacotherapy based on therapeutic drug monitoring (TDM). ATR is metabolized to pharmacologically active metabolites, and evidence shows that the sums of ATR acid and lactone form concentrations (ATR + ATRL), or of ATR and hydroxylated metabolites (ATR + MET) should be assayed. A method is presented for the analysis of these substances in serum. Method validation included the estimation of the quantitative relationship between the concentrations and the standard deviations (SD), which supports the optimal incorporation of TDM results into nonparametric pharmacokinetic models. The concentrations of the analytes were evaluated in human subjects receiving ATR. The method’s performance improved by taking the sums of acid and lactone concentrations into account. The concentration–SD relationship was linear, and we recommend applying Theil’s regression for estimating the assay error. All analytes could be detected by 2 h post dose in the samples of human subjects. The changes in metabolite/parent drug concentration ratios in time depended on the dose. The method is suitable for the TDM of ATR with a focus on precision pharmacotherapy.

Ibrutinib (IBR) is an oral anticancer medication that inhibits Bruton tyrosine kinase irreversibly. Due to the high risk of adverse effects and its pharmacokinetic variability, the safe and effective use of IBR is expected to be facilitated by precision dosing. Delivering suitable clinical laboratory information on IBR is a prerequisite of constructing fit-for-purpose population and individual pharmacokinetic models. The validation of a dedicated high-throughput method using liquid chromatography–mass spectrometry is presented for the simultaneous analysis of IBR and its pharmacologically active metabolite dihydrodiol ibrutinib (DIB) in human plasma. The 6 h benchtop stability of IBR, DIB, and the active moiety (IBR + DIB) was assessed in whole blood and in plasma to identify any risk of degradation before samples reach the laboratory. In addition, four regression algorithms were tested to determine the optimal assay error equations of IBR, DIB, and the active moiety, which are essential for the correct estimation of the error of their future nonparametric pharmacokinetic models. The noncompartmental pharmacokinetic properties of IBR and the active moiety were evaluated in three patients diagnosed with chronic lymphocytic leukemia to provide a proof of concept. The presented methodology allows clinical laboratories to efficiently support pharmacokinetics-based precision pharmacotherapy with IBR.