A method and an apparatus for characterising a sample comprising particles is disclosed. The method comprises performing a first measurement on the sample using a first particle characterisation technique; flowing the sample from the first particle characterisation technique to a particle separating device; separating the sample with the particle separating device; and performing a second measurement on the separated sample. The apparatus is configured to perform the method, and comprises a measurement system for performing measurements according to a first particle characterisation technique and a particle separating device for separating samples comprising particles.

Compositions and methods for isolating L-glufosinate from a composition comprising L-glufosinate and glutamate are provided. The method comprises converting the glutamate to pyroglutamate followed by the isolation of L-glufosinate from the pyroglutamate and other components of the composition to obtain substantially purified L-glufosinate. The composition comprising L-glufosinate and glutamate is subjected to an elevated temperature for a sufficient time to allow for the conversion of glutamate to pyroglutamate, followed by the isolation of L-glufosinate from the pyroglutamate and other components of the composition to obtain substantially purified L-glufosinate. The glutamate alternatively may be converted to pyroglutamate by enzymatic conversion. The purified L-glufosinate is present in a final composition at a concentration of 90% or greater of the sum of L-glufosinate, glutamate, and pyroglutamate. In some embodiments, a portion of the glutamate in the starting composition may be separated from the L-glufosinate using a crystallization step. Solid forms of L-glufosinate materials, including crystalline L-glufosinate ammonium, are also described.

Methods and system for protein characterization using online chromatography and electrospray ionization mass spectrometry are provided.

Systems are described in which a primary detergent or surfactant in an aqueous solution is removed by the addition of a secondary detergent or surfactant in concentrations that exceed the critical micellar concentration (CMC) of the secondary detergent or surfactant using a size separation device. These systems are particularly applicable to protein-containing solutions. Typical primary detergents/surfactants include polysorbate 20, polysorbate 80, and Triton X-100. Suitable secondary detergents or surfactants can be ionic, nonionic, or zwitterionic. Typical secondary detergents/surfactants include, but are not limited to, galactoside detergents (e.g. octyl-β-galactoside), glucamide detergents (e.g. MEGA 8, MEGA 9, MEGA 10), cholamide detergents (e.g. CHAPS, CHAPSO, BIGCHAPS), and sulfobetaine detergents (such as sulfobetaine 3-10).

Systems are described in which a primary detergent or surfactant in an aqueous solution is removed by the addition of a secondary detergent or surfactant in concentrations that exceed the critical micellar concentration (CMC) of the secondary detergent or surfactant using a size separation device. These systems are particularly applicable to protein-containing solutions. Typical primary detergents/surfactants include polysorbate 20, polysorbate 80, and Triton X-100. Suitable secondary detergents or surfactants can be ionic, nonionic, or zwitterionic. Typical secondary detergents/surfactants include, but are not limited to, galactoside detergents (e.g. octyl-β-galactoside), glucamide detergents (e.g. MEGA 8, MEGA 9, MEGA 10), cholamide detergents (e.g. CHAPS, CHAPSO, BIGCHAPS), and sulfobetaine detergents (such as sulfobetaine 3-10).

In a method for facilitating selection of chromatography parameters for manufacturing a therapeutic protein, one or more process parameter values associated with a hypothetical chromatography process, and one or more molecular descriptors descriptive of the therapeutic protein, are received. The method also includes predicting a performance indicator for the hypothetical chromatography process at least by analyzing the one or more process parameters and the one or more molecular descriptors using a machine learning model. The machine learning model is a regression tree model, an extreme gradient boost model, or an elastic net model. The method also includes causing the predicted performance indicator, and/or an indication of whether the predicted performance indicator satisfies one or more acceptability criteria, to be presented to a user via a user interface.

In a method for facilitating selection of chromatography parameters for manufacturing a therapeutic protein, one or more process parameter values associated with a hypothetical chromatography process, and one or more molecular descriptors descriptive of the therapeutic protein, are received. The method also includes predicting a performance indicator for the hypothetical chromatography process at least by analyzing the one or more process parameters and the one or more molecular descriptors using a machine learning model. The machine learning model is a regression tree model, an extreme gradient boost model, or an elastic net model. The method also includes causing the predicted performance indicator, and/or an indication of whether the predicted performance indicator satisfies one or more acceptability criteria, to be presented to a user via a user interface.

The disclosure discloses a genetically engineered strain for producing porcine myoglobin and fermentation and purification thereof, and belongs to the technical field of genetic engineering. The disclosure realizes efficient secretion and expression of porcine myoglobin by integrating the gene of porcine myoglobin in P. pastoris. On this basis, optimization of the medium and culture conditions of recombinant P. pastoris can significantly increase the titer of porcine myoglobin, so that the titer can reach 285.42 mg/L under fermenter conditions. In addition, by creatively adding different concentrations of ammonium sulfate to fermentation broth step by step, the purity of myoglobin obtained by final concentration is up to 88.0%, and the purification rate is up to 66.1%. The disclosure realizes efficient expression and high purification of porcine myoglobin from various steps such as synthesis, fermentation and purification of porcine myoglobin, and provides broad prospects for industrial production of porcine myoglobin.

The disclosure discloses a genetically engineered strain for producing porcine myoglobin and fermentation and purification thereof, and belongs to the technical field of genetic engineering. The disclosure realizes efficient secretion and expression of porcine myoglobin by integrating the gene of porcine myoglobin in P. pastoris. On this basis, optimization of the medium and culture conditions of recombinant P. pastoris can significantly increase the titer of porcine myoglobin, so that the titer can reach 285.42 mg/L under fermenter conditions. In addition, by creatively adding different concentrations of ammonium sulfate to fermentation broth step by step, the purity of myoglobin obtained by final concentration is up to 88.0%, and the purification rate is up to 66.1%. The disclosure realizes efficient expression and high purification of porcine myoglobin from various steps such as synthesis, fermentation and purification of porcine myoglobin, and provides broad prospects for industrial production of porcine myoglobin.

Methods and system for protein characterization using online chromatography and electrospray ionization mass spectrometry are provided.