Dileep Tiwari

NOTA: a potent metallo-β-lactamase inhibitor

Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa; Antimicrobial Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban, South Africa; Biomedical Resource Unit, Westville Campus, University of KwaZulu-Natal, Durban, South Africa; Department of Virology, National Health Laboratory Service/University of KwaZulu-Natal, c/o Inkosi Albert Luthuli Central Hospital, Durban, South Africa; Science for Life Laboratory, Drug Discovery and Development Platform, and Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden

Modeling and optimization of a continuous bead milling process for bacterial cell lysis using response surface methodology

Efficient cell lysis for intracellular protein recovery is a major bottleneck in the economics and commercial feasibility of any biotechnological process. Grinding of cells with abrasive beads, also known as bead milling remains a method of choice, as it can handle a large volume of cells. Bead mills when operated in a continuous mode substantiate to be economical, and more productive as compared to a batch mode process. In this study, the recovery of recombinant cholesterol oxidase (COD) was investigated and optimized using response surface methodology (RSM) based on Central Composite Design (CCD) in a continuous bead milling process. Process parameters, viz. slurry feed rate (A), bead loading (B), cell loading (C) and process time (D) were found to be significant during the continuous bead milling process. A polynomial model was developed to correlate the participating factors for efficient cell disruption. Optimized conditions yielded 3.20 g L−1 (∼90%) of COD with A = 300.6 mL h−1, B = 77.5% (v/v), C = 69.9 (OD600 nm) and D = 29.7 (min), when compared to existing batch mode operations (3.56 g L−1). This is the very first study that attempts to optimize a continuous bead milling process using RSM to maximize the intracellular protein (COD in this case) recovery with minimum inputs to make the process economical and scalable to industrial levels. The developed model in this study can be scaled-up to large-scale for efficient recovery of intracellular proteins in similar expression systems.

Artificial Intelligence vs. Statistical Modeling and Optimization of Continuous Bead Milling Process for Bacterial Cell Lysis

For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery. RSM predicted an optimum of feed rate of 310.73 mL/h, bead loading of 79.9% (v/v), cell loading OD600 nm of 74, and run time of 29.9 min with a recovery of ~3.2 g/L. ANN-GA predicted a maximum COD recovery of ~3.5 g/L at an optimum feed rate (mL/h): 258.08, bead loading (%, v/v): 80%, cell loading (OD600 nm): 73.99, and run time of 32 min. An overall 3.7-fold increase in productivity is obtained when compared to a batch process. Optimization and comparison of statistical vs. artificial intelligence techniques in continuous bead milling process has been attempted for the very first time in our study. We were able to successfully represent the complex non-linear multivariable dependence of enzyme recovery on bead milling parameters. The quadratic second order response functions are not flexible enough to represent such complex non-linear dependence. ANN being a summation function of multiple layers are capable to represent complex non-linear dependence of variables in this case; enzyme recovery as a function of bead milling parameters. Since GA can even optimize discontinuous functions present study cites a perfect example of using machine learning (ANN) in combination with evolutionary optimization (GA) for representing undefined biological functions which is the case for common industrial processes involving biological moieties.

A sensitive WST-8-based bioassay for PEGylated granulocyte colony stimulating factor using the NFS-60 cell line

Abstract Context: Granulocyte colony stimulating factor (G-CSF) has been commonly used to treat neutropenia caused by chemotherapy, radiotherapy, and organ transplants. Improved in vitro efficacy of G-CSF has already been observed by conjugating it to polyethylene glycol (PEG). Objective: The in vivo bioassay using tetrazolium dye with the NFS-60 cell line has been recommended for G-CSF but no such monographs are available for PEGylated G-CSF in pharmacopeias. In the present study, the assay recommended for G-CSF was evaluated for its suitability to PEGylated G-CSF. Materials and methods: The generally used MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium]-based assay was compared with a bioassay employing a water-soluble tetrazolium dye, WST-8 [2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium], using NFS-60 cells at a concentration of 7 × 105 cells/ml against 800 IU/ml of PEGylated G-CSF at 24, 48, 72, and 72 h time points to determine the efficacy of PEGylated G-CSF. Further, the optimized WST-8 dye-based assay was used to test the potency of various commercially available PEGylated G-CSF preparations. Results: The results demonstrated enhanced sensitivity of the WST-8-based assay over the conventional MTS-based assay for determining the potency of PEGylated G-CSF using the NFS-60 cell line. Conclusion: Our study demonstrates the potential application of WST-8-based bioassays for other biotherapeutic proteins of human and veterinary interest.

Evaluation of MALDI Biotyping for Rapid Subspecies Identification of Carbapenemase-Producing Bacteria via Protein Profiling

The method of direct mass spectrometry profiling is reliable and reproducible for the rapid identification of clinical isolates of bacteria and fungi. This is the first study evaluating the approach of MALDI-TOF mass spectrometry profiling for rapid identification of carbapenemase-resistant enterobacteriaceae (CRE). Proof of concept was achieved by the discrimination of CRE using MALDI Biotyper MS based on the protein. This profiling appears promising by the visual observation of consist- ent unique peaks, albeit low intensity, that could be picked up from the mean spectra (MSP) method. The Biotyper MSP creation and identification methods needed to be optimized to provide significantly improved differences in scores to allow for subspe- cies identification with and without carbapenemases. These spectra were subjected to visual peak picking and in all cases; there were pertinent differences in the presence or absence of potential biomarker peaks to differentiate isolates. We also evaluated this method for potential discrimination between different carbapenemases bacteria, utilizing the same strategy. Based on our data and pending further investigation in other CREs, MALDI-TOF MS has potential as a diagnostic tool for the rapid identification of even closely related carbapenemases but would require a paradigm shift in which Biotyper suppliers enable more flexible software control of mass spectral profiling methods.