Behavioral testing of antidepressant compounds: an analysis of crossover design for correlated binary data

The differential reinforcement of low-rate 72 seconds schedule (DRL-72) is a standard behavioral test procedure for screening potential antidepressant compounds. The protocol for the DRL-72 experiment, proposed by Evenden et al. (1993), consists of using a crossover design for the experiment and one-way ANOVA for the statistical analysis. In this paper we discuss the choice of several crossover designs for the DRL-72 experiment and propose to estimate the treatment effects using either generalized linear mixed models (GLMM) or generalized estimating equation (GEE) models for clustered binary data.     

The Error Rate of Tests for Regression Coincidence and Parallelism Under Unknown Multiplicative Heteroscedasticity

The usual F test of regression coincidence, which is appropriate under a homoscedastic model, is examined under a multiplicatively heteroscedastic model. The departure of the test from its nominal level is slight when the sample of explanatory variables is symmetric, but may be substantially inflated when the sample has positive skew. Conversely, the nominal level may be slightly depressed when the sample has negative skew. The size of the perturbation from the nominal level depends on the degree of heteroscedasticity, however its effect is more pronounced with positively skewed samples. Similar trends are evident for the usual F test of regression parallelism. There is no apparent pattern to the discrepancy of the level of the test with regard to the data which would permit empirical researchers to adjust their results.     

Fingerprint detection and process prediction by multivariate analysis of fed‐batch monoclonal antibody cell culture data

This work presents a sequential data analysis path, which was successfully applied to identify important patterns (fingerprints) in mammalian cell culture process data regarding process variables, time evolution and process response. The data set incorporates 116 fed‐batch cultivation experiments for the production of a Fc‐Fusion protein. Having precharacterized the evolutions of the investigated variables and manipulated parameters with univariate analysis, principal component analysis (PCA) and partial least squares regression (PLSR) are used for further investigation. The first major objective is to capture and understand the interaction structure and dynamic behavior of the process variables and the titer (process response) using different models. The second major objective is to evaluate those models regarding their capability to characterize and predict the titer production. Moreover, the effects of data unfolding, imputation of missing data, phase separation, and variable transformation on the performance of the models are evaluated. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1633–1644, 2015     

In silico design of potent EGFR kinase inhibitors using combinatorial libraries

This paper is an attempt to design 4-anilinoquinazoline compounds having promising anticancer activities against epidermal growth factor (EGFR) kinase inhibition, using virtual combinatorial library approach. Partial least squares method has been applied for the development of a quantitative structure–activity relationship (QSAR) model based on training and test set approaches. The partial least squares model showed some interesting results in terms of internal and external predictability against EGFR kinase inhibition for such type of anilinoquinazoline derivatives. In virtual screening study, out of 4860 compounds in chemical library, 158 compounds were screened and finally, 10 compounds were selected as promising EGFR kinase inhibitors based on their predicted activities from the QSAR model. These derivatives were subjected to molecular docking study to investigate the mode of binding with the EGFR kinase, and the two compounds (ID 3639 and 3399) showing similar type of docking score and binding patterns with that of the existing drug molecules like erlotinib were finally reported.     

Optimization of cyanophycin production in recombinant strains of Pseudomonas putida and Ralstonia eutropha employing elementary mode analysis and statistical experimental design

Elementary mode analysis was applied to simulate conditions for cyanophycin (CGP) biosynthesis and to optimize its production in bacteria. The conclusions from these simulations were confirmed by experiments with recombinant strains of the wild types and polyhydroxyalkanoate (PHA)-negative mutants of Ralstonia eutropha and Pseudomonas putida expressing CGP synthetase genes (cphA) of Synechocystis sp. strain PCC6308 or Anabaena sp. strain PCC7120. In particular, the effects of suitable precursor substrates and of oxygen supply as well as of the capability to accumulate PHA in addition to CGP biosynthesis were investigated. Since CGP consists of the amino acids aspartate and arginine, the tricarboxylic acid cycle (TCC), which provides intermediates for biosynthesis of these amino acids, seems to be important. Excretion of intermediates of the TCC upon cultivation at restricted oxygen supply and conversion of fumarate mainly to malate and to only little succinate in the absence of oxygen indicated that TCC intermediates for arginine and aspartate biosynthesis were provided by the oxidative or reductive parts of the TCC, respectively. The following important conclusions were made from the experiments and the simulations: (i) external arginine additionally supplied to the medium, (ii) oxygen limitation, and (iii) absence of PHA accumulation exerted positive effects on CGP accumulation. These conclusions were utilized to obtain CGP contents in the cells of as high as 17.9% (w x w(-1)) during cultivation of the investigated bacteria at the 30-L scale using mineral salts medium. Such high CGP contents were previously not obtained with these bacteria at a 30-L scale, even if complex media were used.     

Proteomic analysis of rabbit tear fluid: Defensin levels after an experimental corneal wound are correlated to wound closure

The cornea is the major refracting optical element of the eye and therefore critical for forming a retinal image. The exposed surface of the eye is protected from pathogens by the innate immune system whose components include defensins, naturally occurring peptides with antimicrobial properties, and the physical barrier formed by the outer epithelial layer of the cornea. The proteomic approach has revealed that tear levels of defensins are correlated with the course of healing of an experimental corneal wound. Tears were collected from New Zealand White rabbits prior to (day 0) and daily for 5 days (days 1-5) following a standard unilateral 6 mm diameter corneal epithelial abrasion. Tear protein profiles obtained from wounded and contra-lateral control eyes were compared using SELDI ProteinChip technology. Peptides and proteins of interest were purified by RP-HPLC and characterized by nanoESI-MS/MS. Mass spectra of tears on post-wound day 1, revealed 13 peaks whose level decreased and five that increased. During wound healing the tear protein profile correlated with wound closure. An important finding was that the levels of rabbit defensins (NP-1 and NP-2), which were elevated after wounding returned to normal levels by the time the corneal abrasion healed. Relative quantification of NP-2 in tear fluid prior to (day 0) and after corneal wounding (days 1- 3) was determined using iTRAQ technology. A corneal wound eliminates the barrier function of innate immunity and puts the cornea at risk from microbial attack until the epithelial cells restore the surface barrier. The increased availability of defensins in the tears during healing suggests that these peptides could protect the cornea from microbial attack during a period of increased vulnerability.     

Real‐time monitoring and control of the load phase of a protein A capture step

The load phase in preparative Protein A capture steps is commonly not controlled in real‐time. The load volume is generally based on an offline quantification of the monoclonal antibody (mAb) prior to loading and on a conservative column capacity determined by resin‐life time studies. While this results in a reduced productivity in batch mode, the bottleneck of suitable real‐time analytics has to be overcome in order to enable continuous mAb purification. In this study, Partial Least Squares Regression (PLS) modeling on UV/Vis absorption spectra was applied to quantify mAb in the effluent of a Protein A capture step during the load phase. A PLS model based on several breakthrough curves with variable mAb titers in the HCCF was successfully calibrated. The PLS model predicted the mAb concentrations in the effluent of a validation experiment with a root mean square error (RMSE) of 0.06 mg/mL. The information was applied to automatically terminate the load phase, when a product breakthrough of 1.5 mg/mL was reached. In a second part of the study, the sensitivity of the method was further increased by only considering small mAb concentrations in the calibration and by subtracting an impurity background signal. The resulting PLS model exhibited a RMSE of prediction of 0.01 mg/mL and was successfully applied to terminate the load phase, when a product breakthrough of 0.15 mg/mL was achieved. The proposed method has hence potential for the real‐time monitoring and control of capture steps at large scale production. This might enhance the resin capacity utilization, eliminate time‐consuming offline analytics, and contribute to the realization of continuous processing. Biotechnol. Bioeng. 2017;114: 368–373. © 2016 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals, Inc.     

Robust design and the use of auxiliary variables in estimating population size for an open population

We consider the problem of estimating the population size for an open population where the data are collected over secondary periods within primary periods according to a robust design suggested by Pollock (1982, Journal of Wildlife Management 46, 757-760). A conditional likelihood is used to estimate the parameters associated with a generalized linear model in which the capture probability is assumed to have a logistic form depending on individual covariates. A Horvitz-Thompson-type estimator is used to estimate the population size for each primary period and the survival probabilities between primary periods. The asymptotic properties of the proposed estimators are investigated through simulation and are found to perform well. A data set for such a robust design of a small-mammal capture-recapture study conducted at Dummy Bottom within Browns Park National Wildlife Refuge is analyzed.     

Multiway analysis applied to time‐resolved chemiluminescence for simultaneous determination of paracetamol and codeine in pharmaceuticals

This method is based on the enhancing effect of codeine (COD) and paracetamol (PAR) on the chemiluminescence (CL) reaction of Ru(phen)₃²⁺ with Ce(IV). In the batch mode, COD gives a relatively sharp peak with the highest CL intensity at 4.0 s, whereas the maximum CL intensity of the PAR appears at ~60 s after injection of Ce(IV) solution. Whole CL time profiles allowed use of the time‐resolved CL data in combination with multiway calibration techniques, as multiway partial least squares (N‐PLS), for the quantitative determination of both COD and PAR in binary mixtures. In this work, we found that the impact of Ce(IV) concentration on the CL intensity was different for COD and PAR. Therefore, a Ce(IV) concentration mode was added to the time and sample modes to obtain 3D data. The percent relative standard deviation (%RSD) values for 10 determinations of 1.0 × 10^?5 mol/L of COD and 1.0 × 10^?4 mol/L of PAR were 6.1% and 8.7%, respectively. The limit of detection (LOD) values (S/N = 3) were 0.9 × 10^?8 mol/L and 1.0 × 10^?6 mol/L for COD and PAR, respectively. The proposed method was successfully applied to the determination of PAR and COD in commercial pharmaceutical formulations. Acceptable recoveries (90–110%) were obtained for the quantification of these drugs in the real samples. Copyright © 2016 John Wiley & Sons, Ltd.     

Computational design and experimental study of tighter binding peptides to an inactivated mutant of HIV-1 protease

Drug resistance in HIV-1 protease, a barrier to effective treatment, is generally caused by mutations in the enzyme that disrupt inhibitor binding but still allow for substrate processing. Structural studies with mutant, inactive enzyme, have provided detailed information regarding how the substrates bind to the protease yet avoid resistance mutations; insights obtained inform the development of next generation therapeutics. Although structures have been obtained of complexes between substrate peptide and inactivated (D25N) protease, thermodynamic studies of peptide binding have been challenging due to low affinity. Peptides that bind tighter to the inactivated protease than the natural substrates would be valuable for thermodynamic studies as well as to explore whether the structural envelope observed for substrate peptides is a function of weak binding. Here, two computational methods-namely, charge optimization and protein design-were applied to identify peptide sequences predicted to have higher binding affinity to the inactivated protease, starting from an RT-RH derived substrate peptide. Of the candidate designed peptides, three were tested for binding with isothermal titration calorimetry, with one, containing a single threonine to valine substitution, measured to have more than a 10-fold improvement over the tightest binding natural substrate. Crystal structures were also obtained for the same three designed peptide complexes; they show good agreement with computational prediction. Thermodynamic studies show that binding is entropically driven, more so for designed affinity enhanced variants than for the starting substrate. Structural studies show strong similarities between natural and tighter-binding designed peptide complexes, which may have implications in understanding the molecular mechanisms of drug resistance in HIV-1 protease.