In vitro machine learning-based CAR T immunological synapse quality measurements correlate with patient clinical outcomes

The human immune system consists of a highly intelligent network of billions of independent, self-organized cells that interact with each other. Machine learning (ML) is an artificial intelligence (AI) tool that automatically processes huge amounts of image data. Immunotherapies have revolutionized the treatment of blood cancer. Specifically, one such therapy involves engineering immune cells to express chimeric antigen receptors (CAR), which combine tumor antigen specificity with immune cell activation in a single receptor. To improve their efficacy and expand their applicability to solid tumors, scientists optimize different CARs with different modifications. However, predicting and ranking the efficacy of different "off-the-shelf" immune products (e.g., CAR or Bispecific T-cell Engager [BiTE]) and selection of clinical responders are challenging in clinical practice. Meanwhile, identifying the optimal CAR construct for a researcher to further develop a potential clinical application is limited by the current, time-consuming, costly, and labor-intensive conventional tools used to evaluate efficacy. Particularly, more than 30 years of immunological synapse (IS) research data demonstrate that T cell efficacy is not only controlled by the specificity and avidity of the tumor antigen and T cell interaction, but also it depends on a collective process, involving multiple adhesion and regulatory molecules, as well as tumor microenvironment, spatially and temporally organized at the IS formed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. The optimal function of cytotoxic lymphocytes (including CTL and NK) depends on IS quality. Recognizing the inadequacy of conventional tools and the importance of IS in immune cell functions, we investigate a new strategy for assessing CAR-T efficacy by quantifying CAR IS quality using the glass-support planar lipid bilayer system combined with ML-based data analysis. Previous studies in our group show that CAR-T IS quality correlates with antitumor activities in vitro and in vivo. However, current manually quantified IS quality data analysis is time-consuming and labor-intensive with low accuracy, reproducibility, and repeatability. In this study, we develop a novel ML-based method to quantify thousands of CAR cell IS images with enhanced accuracy and speed. Specifically, we used artificial neural networks (ANN) to incorporate object detection into segmentation. The proposed ANN model extracts the most useful information to differentiate different IS datasets. The network output is flexible and produces bounding boxes, instance segmentation, contour outlines (borders), intensities of the borders, and segmentations without borders. Based on requirements, one or a combination of this information is used in statistical analysis. The ML-based automated algorithm quantified CAR-T IS data correlates with the clinical responder and non-responder treated with Kappa-CAR-T cells directly from patients. The results suggest that CAR cell IS quality can be used as a potential composite biomarker and correlates with antitumor activities in patients, which is sufficiently discriminative to further test the CAR IS quality as a clinical biomarker to predict response to CAR immunotherapy in cancer. For translational research, the method developed here can also provide guidelines for designing and optimizing numerous CAR constructs for potential clinical development.Trial Registration: ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00881920","term_id":"NCT00881920"}}NCT00881920.     

Chemical complementation: small-molecule-based genetic selection in yeast

Protein and metabolic engineering would greatly benefit from a general system linking the presence of a small molecule to the power of genetic selection. We use nuclear receptors to link the survival of Saccharomyces cerevisiae to the presence of small molecules through genetic selection, extending classical genetic complementation to a new "chemical complementation." In this system the Gal4 DNA-binding domain is fused to ligand-binding domains from two nuclear receptors, expressed in the strain PJ69-4A, and grown on plates containing known ligands for the receptors. Yeast survive on selective plates only in the presence of a nuclear receptor and the corresponding ligand. Mutagenesis can increase the sensitivity of chemical complementation. This system may be extended to engineer nuclear receptors for practically any small molecule through directed evolution coupled to genetic selection, and for performing metabolic engineering in yeast.     

Inhibition of oxidative stress-induced amyloid β formation in NT2 neurons by culture filtrate of a strain of Streptomyces antibioticus

Actinomycetes isolated from Iran soil habitats were tested for the capacity to produce compounds which can protect neurons from cell death generated by oxidative stress in NT2 neurons. Confirmation of our initial hit was accomplished via the determination of amyloid β level using the enzyme-linked immunosorbent assay test. The most interesting amyloid β formation inhibitor discovered in our study was a secondary metabolite which was produced by strain HM45. This bioactive strain was identified as a strain of Streptomyces antibioticus DSM 40234 using polyphasic approach. The strain HM45 was deposited in Deutsche Sammlung von Mikroorganismen und Zellkulturen as S. antibioticus DSM 41955 and University of Tehran Microorganisms Sollection as S. antibioticus UTMC 00105. This work is the first report on efficiency of an actinomycete metabolite in prohibition of neurons death caused by amyloid β formation.     

Application of one-step liquid chromatography–electrospray tandem MS/MS and collision-induced dissociation to quantification of ezetimibe and identification of its glucuronated metabolite in human serum: A pharmacokinetic study

A new one-step liquid chromatography–electrospray tandem MS/MS method is described to quantify ezetimibe (EZM) a novel lipid lowering drug in human serum. Also using collision-induced dissociation (CID) of the analyte, identification and chromatographic separation of its major metabolite, ezetimibe glucuronide (EZM-G) is achieved in this study. A thawed serum aliquot of 100 μL was deproteinated by addition of 500 μL methanol containing omeprazole as internal standard (I.S.). Separation of the drug, its metabolite and the I.S. were achieved using acetonitrile–water (70:30, v/v) as mobile phase at flow rate of 0.5 mL/min on a MZ PerfectSil target C18 column. Multiple reaction monitoring (MRM) mode of precursor–product ion transition (408.7 → 272.0 for EZM and 345 → 194.5 for the I.S.) was applied for detection and quantification of the drug while, EZM-G was chromatographically separated and identified using CID. The analytical method was linear over the concentration range of 1–32 ng/mL of EZM in human serum with a limit of quantification of 1 ng/mL. The coefficient variation values of both inter- and intra-day analysis were less than 8% whereas the percentage error was less than 3.7. The validated method was applied in a randomized cross-over bioequivalence study of two different EZM preparations in 24 healthy volunteers.     

Nanosized paclitaxel-loaded niosomes: formulation,in vitro cytotoxicity,and apoptosis gene expression in breast cancer cell lines

Molecular Biology Reports - In this study, the optimized niosomal formulation containing paclitaxel using non-ionic surfactants and cholesterol was designed and its cytotoxic effects against...     

The HHEX rs1111875A/G gene polymorphism is associated with susceptibility to type 2 diabetes in the iranian population

The illuminating picture of genetic mechanisms underlying the development of type 2 diabetes (T2DM) includes differently accumulated genetic polymorphisms that increase the risk along with environmental factors. A number of single nucleotide polymorphisms (SNPs) are indicated to be linked with T2DM, but also conflicting results have been found. To examine the contribution of these polymorphisms in conferring susceptibility to T2DM, the association of HHEX rs1111875A/G and CDKN2A/B rs10811661C/T common gene polymorphisms with the risk of T2DM in an Iranian population was evaluated. In this study participated 140 patients and 140 controls. Genomic DNA was extracted from samples and genotyping of the polymorphisms was performed by the Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) technique. A significant association was found with the G allele (OR = 1.729, CI = 1.184–2.523, P = 0.004) and GG genotype (OR = 2.921, 95% CI = 1.789–4.771, P< 0.001) of the rs1111875A/G SNP for susceptibility to T2DM in the recessive model. Furthermore, compared with the GG genotype, individuals with the GA genotype had a lower risk to develop T2DM (OR = 0.237, 95% CI = 0.137–0.408, P< 0.001) in the additive model. In addition, an association between the polymorphism and BMI in regard to the risk of T2DM was identified. The genotype and allele frequencies of the rs10811661C/T polymorphism did not show a statistically significant association with T2DM in any genetic model. Our results show that the rs1111875A/G polymorphism is an important susceptibility polymorphism for the development of T2DM in the Iranian population. Also, these findings support that this polymorphism is a key genetic risk factor for the development of T2DM in multiple ethnic populations.

     

Pentacycloundecane lactam vs lactone norstatine type protease HIV inhibitors: binding energy calculations and DFT study

Background

Novel pentacycloundecane (PCU)-lactone-CO-EAIS peptide inhibitors were designed, synthesized, and evaluated against wild-type C-South African (C-SA) HIV-1 protease. Three compounds are reported herein, two of which displayed IC₅₀ values of less than 1.00 μM. A comparative MM-PB(GB)SA binding free energy of solvation values of PCU-lactam and lactone models and their enantiomers as well as the PCU-lactam-NH-EAIS and lactone-CO-EAIS peptide inhibitors and their corresponding diastereomers complexed with South African HIV protease (C-SA) was performed. This will enable us to rationalize the considerable difference between inhibitory concentration (IC₅₀) of PCU-lactam-NH-EAIS and PCU-lactone-CO-EAIS peptides.

Results

The PCU-lactam model exhibited more negative calculated binding free energies of solvation than the PCU-lactone model. The same trend was observed for the PCU-peptide inhibitors, which correspond to the experimental activities for the PCU-lactam-NH-EAIS peptide (IC₅₀ = 0.076 μM) and the PCU-lactone-CO-EAIS peptide inhibitors (IC₅₀ = 0.850 μM). Furthermore, a density functional theory (DFT) study on the natural atomic charges of the nitrogen and oxygen atoms of the three PCU-lactam, PCU-lactim and PCU-lactone models were performed using natural bond orbital (NBO) analysis. Electrostatic potential maps were also used to visualize the electron density around electron-rich regions. The asymmetry parameter (η) and quadrupole coupling constant (χ) values of the nitrogen and oxygen nuclei of the model compounds were calculated at the same level of theory. Electronic molecular properties including polarizability and electric dipole moments were also calculated and compared. The Gibbs theoretical free solvation energies of solvation (∆G_solv) were also considered.

Conclusions

A general trend is observed that the lactam species appears to have a larger negative charge distribution around the heteroatoms, larger quadrupole constant, dipole moment and better solvation energy, in comparison to the PCU-lactone model. It can be argued that these characteristics will ensure better eletronic interaction between the lactam and the receptor, corresponding to the observed HIV protease activities in terms of experimental IC₅₀ data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12929-015-0115-5) contains supplementary material, which is available to authorized users.     

High-performance liquid chromatographic determination of lamivudine in human serum using liquid-liquid extraction; application to pharmacokinetic studies

A simple, fast, and sensitive high performance liquid chromatographic (HPLC) assay was developed for quantitation of lamivudine in human serum. Lamivudine is polar compound and its extraction from the human serum in previously published HPLC methods involved either protein precipitation or solid phase extraction techniques. However, existence of endogenous peaks which interfere with the drug or appeared as late eluting peaks and lead to long run time of analysis has been reported. Application of either an ion pairing agent in the mobile phase or time consuming column purge has been used in the published methods. Present paper describes liquid - liquid extraction of lamivudine and internal standard (famotidine) using dichloromethane-isopropyl alcohol (1:1, v/v) as an extracting solvent and salting out approach. The mobile phase was a mixture of phosphate buffer (0.05 M) containing triethylamine (1 mL/L, v/v; pH 3.5) and methanol (91:9, v/v) at a flow rate of 2.2 mL/min. The analysis was performed on a column (150 mm x 6 mm i.d.) which was packed with 5 microm particles of ODS packing material. Under these conditions no interference in the assay from any endogenous substance was observed. The limit of quantification was evaluated to be 5 ng/mL. Accuracy and precision of the method were also studied and the technique was shown to be selective and linear into the concentration range of 5-2500 ng/mL. This method has been used in two randomized crossover bioequivalence studies of 100 and 150 mg lamivudine preparations in 12 and 24 healthy volunteers, respectively.