A strategy for clone selection under different production conditions.

Top performing clones have failed at the manufacturing scale while the true best performer may have been rejected early in the screening process. Therefore, the ability to screen multiple clones in complex fed-batch processes using multiple process variations can be used to assess robustness and to identify critical factors. This dynamic ranking of clones' strategy requires the execution of many parallel experiments than traditional approaches. Therefore, this approach is best suited for micro-bioreactor models which can perform hundreds of experiments quickly and efficiently. In this study, a fully monitored and controlled small scale platform was used to screen eight CHO clones producing a recombinant monoclonal antibody across several process variations, including different feeding strategies, temperature shifts and pH control profiles. The first screen utilized 240 micro-bioreactors were run for two weeks for this assessment of the scale-down model as a high-throughput tool for clone evaluation. The richness of the outcome data enable to clearly identify the best and worst clone as well as process in term of maximum monoclonal antibody titer. The follow-up comparison study utilized 180 micro-bioreactors in a full factorial design and a subset of 12 clone/process combinations was selected to be run parallel in duplicate shake flasks. Good correlation between the micro-bioreactor predictions and those made in shake flasks with a Pearson correlation value of 0.94. The results also demonstrate that this micro-scale system can perform clone screening and process optimization for gaining significant titer improvements simultaneously. This dynamic ranking strategy can support better choices of production clones.

Novel metabolic bioactivation mechanism for a series of anti-inflammatory agents (2,5-diaminothiophene derivatives) mediated by cytochrome p450 enzymes.

The thiophene moiety is considered a structural alert in molecular design in drug discovery, largely because several thiophene-containing drugs, including tienilic acid and suprofen, have been withdrawn from the market because of toxicities. Reactive thiophene intermediates, activated via sulfur oxidation or ring epoxidation, are possible culprits for these adverse side effects. In this work, the metabolic activation of an anti-inflammatory agent, 1-(3-carbamoyl-5-(2,3,5-trichlorobenzamido)thiophen-2-yl)urea), containing a 2,5-diaminothiophene structure, was studied in liver microsomes in the presence of glutathione or N-acetylcysteine as trapping agents. In addition, the glutathione conjugate was detected in bile from a bile duct-cannulated rat study. The structure of the glutathione conjugate was identified by mass spectrometry and (1)H NMR. The glutathione molecule was attached to the thiophene ring, replacing the existing proton. Metabolic phenotyping experiments, using chemical inhibitors or recombinant cytochromes P450 (P450), demonstrated that CYP3A4 was the major P450 enzyme responsible for the metabolic activation, followed by CYP1A2, 2Cs, and 2D6. A novel metabolic activation mechanism is proposed whereby the 2,5-diaminothiophene moiety undergoes oxidation to a 2,5-diimine thiophene reactive intermediate. This mechanism was used to support efforts to eliminate reactive metabolite generation via structural modification of ring substituents using structure-activity relationships. The disruption of formation of the 2,5-diimine reactive intermediate resulted in the elimination of glutathione conjugate formation both in vitro and in vivo and provided a rational approach to mitigating potential safety risks associated with this class of thiophenes in drug research and development.

A hierarchical screening methodology for physicochemical/ADME/Tox profiling.

Full integration of pharmaceutical profiling into pharmaceutical lead selection and optimisation requires that complete sets of unequivocal data be available at the time compound design or advancement decisions are made. As the productivity of chemical synthesis expands, and the breadth of profiling assays grow in scope, physicochemical/ADME/Tox laboratories are being challenged to produce ever more data to support an accelerating decision cycle. This article focuses on the challenges of increasing preclinical profiling productivity while managing lower accuracy higher throughput data streams to preserve confidence in decision making. The authors propose a hierarchical screening strategy and describe the implementation of an automated system designed to support that strategy efficiently.