Maximizing the native concentration and shelf life of protein: a multiobjective optimization to reduce aggregation

Artificial intelligence derived categorizations significantly improve HOMA IR/β indicators: Combating diabetes through cross-interacting drugs

Improvements in the homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of beta-cell function (HOMA-β) significantly reduce the risk of disabling diabetic pathies. Nanoparticle (AuNP-AgNP)-metformin are concentration dependent cross-interacting drugs as they may have a synergistic as well as antagonistic effect(s) on HOMA indicators when administered concurrently. We have employed a blend of machine learning: Artificial Neural Network (ANN), and evolutionary optimization: multiobjective Genetic Algorithms (GA) to discover the optimum regime of the nanoparticle-metformin combination. We demonstrated how to successfully employ a tested and validated ANN to classify the exposed drug regimen into categories of interest based on gradient information. This study also prescribed standard categories of interest for the exposure of multiple diabetic drug regimen. The application of categorization greatly reduces the time and effort involved in reaching the optimum combination of multiple drug regimen based on the category of interest. Exposure of optimum AuNP, AgNP and Metformin to Diabetic rats significantly improved HOMA β functionality (∼63 %), Insulin resistance (HOMA IR) of Diabetic animals was also reduced significantly (∼54 %). The methods explained in the study are versatile and are not limited to only diabetic drugs.

Stability of Protein Pharmaceuticals: Recent Advances

There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'

NADP/H binding nearly doubles the stability of a Mycobacterium drug target: an unfolding study

Mycobacterium Aspartate beta semialdehyde dehydrogenase (ASADH) was studied using various spectroscopic techniques and size exclusion chromatography to examine the unfolding of free (apo) and NADP/H-bound (holo) forms of ASADH. Non-cooperative guanidinium chloride (GdnHCl)-induced unfolding of the apo ASADH was discovered, and no partially folded intermediate structures were stabilized. On the other hand, it was observed that GdnHCl's unfolding of holoenzyme was a cooperative process without any stable intermediate structure. The native form of holoenzyme is found to be stable against the lower concentration of GdnHCl only (namely up to 1.25 M GdnHCl). The tryptophan environment appears to unfold cooperatively in case of the holoenzyme and is in well coordination with the overall unfolding of the holoenzyme. The presence of NADP/H shows a stabilizing effect on the tryptophan environment as well as on the native NADP/H-bound enzyme. Δ G Solvent o values reveal nearly two-fold (∼1.9) conformationally more stable folded holoenzyme compared to its native apo state. The Cm for the apo and holo forms of ASADH are 1.3 and 1.9 M, respectively. Novel drug leads targeting the NADP/H binding domain of ASADH could offer promising drugs against extremely infective Mycobacterium tuberculosis.Communicated by Ramaswamy H. Sarma.

Sustainable green approach to synthesize Fe3O4/α-Fe2O3 nanocomposite using waste pulp of Syzygium cumini and its application in functional stability of microbial cellulases

Synthesis of nanomaterials following green routes have drawn much attention in recent years due to the low cost, easy and eco-friendly approaches involved therein. Therefore, the current study is focused towards the synthesis of Fe3O4/α-Fe2O3 nanocomposite using waste pulp of Jamun (Syzygium cumini) and iron nitrate as the precursor of iron in an eco-friendly way. The synthesized Fe3O4/α-Fe2O3 nanocomposite has been extensively characterized through numerous techniques to explore the physicochemical properties, including X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectroscopy, Ultraviolet-Vis spectroscopy, field emission scanning electron microscope, high resolution transmission electron microscope and vibrating sample magnetometer. Further, efficiency of the Fe3O4/α-Fe2O3 nanocomposite has been evaluated to improve the incubation temperature, thermal/pH stability of the crude cellulase enzymes obtained from the lab isolate fungal strain Cladosporium cladosporioides NS2 via solid state fermentation. It is found that the presence of 0.5% Fe3O4/α-Fe2O3 nanocomposite showed optimum incubation temperature and thermal stability in the long temperature range of 50-60 °C for 15 h along with improved pH stability in the range of pH 3.5-6.0. The presented study may have potential application in bioconversion of waste biomass at high temperature and broad pH range.

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

Strategies for Fermentation Medium Optimization: An In-Depth Review

Optimization of production medium is required to maximize the metabolite yield. This can be achieved by using a wide range of techniques from classical “one-factor-at-a-time” to modern statistical and mathematical techniques, viz. artificial neural network (ANN), genetic algorithm (GA) etc. Every technique comes with its own advantages and disadvantages, and despite drawbacks some techniques are applied to obtain best results. Use of various optimization techniques in combination also provides the desirable results. In this article an attempt has been made to review the currently used media optimization techniques applied during fermentation process of metabolite production. Comparative analysis of the merits and demerits of various conventional as well as modern optimization techniques have been done and logical selection basis for the designing of fermentation medium has been given in the present review. Overall, this review will provide the rationale for the selection of suitable optimization technique for media designing employed during the fermentation process of metabolite production.

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 OD600nm 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 (OD600nm): 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.

Constrained azeotropic optimization of extraction system components for the safe and efficient recovery of a desired metabolite (e.g., 3-demethylated colchicine)

Constrained azeotropic optimization of extraction system components for the safe and efficient recovery of a desired metabolite (e.g., 3-DMC) using artificial learning and evolutionary optimization techniques.

Optimization of Extraction Parameters for Enhanced Production of Ovotransferrin from Egg White for Antimicrobial Applications

Ovotransferrin is the second most abundant protein (~12-13% of the total egg protein) in egg white after ovalbumin. Ovotransferrin is a potent natural antimicrobial agent as it possesses antibacterial, antifungal, and antiviral properties and is also the major metal binding protein found in egg, which makes it an industrially important protein. Ovotransferrin was extracted from egg white using its metal (iron) binding properties. In the present study, eggs from two different sources were used (fresh local eggs from domestic household source and poultry eggs from shops) to compare the results and Response Surface Methodology was used for the experiment design and data analysis. The following extraction conditions were optimized so as to maximize the yield of ovotransferrin from egg white: ethanol % (v/v) and pH and volume (mL) of 25 mM FeCl₃/50 mL of egg white. A maximum yield of ~85 ± 2.5% was obtained near the optimum extraction conditions. The yield was calculated based on the theoretical value (934 mg) of ovotransferrin in 100 mL of 1.5x diluted egg white solution. Our results suggest that efficient downstream processing may reduce the cost of overall production process of this promising enzyme, making it a natural and cost-effective alternative to the existing chemically synthesized antimicrobial agents.