Prion removal capacity of plasma protein manufacturing processes

BACKGROUND The variant Creutzfeldt-Jakob disease incidence peaked a decade ago and has since declined. Based on epidemiologic evidence, the causative agent, pathogenic prion, has not constituted a tangible contamination threat to large-scale manufacturing of human plasma-derived proteins. Nonetheless, manufacturers have studied the prion removal capabilities of various manufacturing steps to better understand product safety. Collectively analyzing the results could reveal experimental reproducibility and detect trends and mechanisms driving prion removal. STUDY DESIGN AND METHODS Plasma Protein Therapeutics Association member companies collected more than 200 prion removal studies on plasma protein manufacturing steps, including precipitation, adsorption, chromatography, and filtration, as well as combined steps. The studies used a range of model spiking agents and bench-scale process replicas. The results were grouped based on key manufacturing variables to identify factors impacting removal. The log reduction values of a group are presented for comparison. RESULTS Overall prion removal capacities evaluated by independent groups were in good agreement. The removal capacity evaluated using biochemical assays was consistent with prion infectivity removal measured by animal bioassays. Similar reduction values were observed for a given step using various spiking agents, except highly purified prion protein in some circumstances. Comparison between combined and single-step studies revealed complementary or overlapping removal mechanisms. Steps with high removal capacities represent the conditions where the physiochemical differences between prions and therapeutic proteins are most significant. CONCLUSION The results support the intrinsic ability of certain plasma protein manufacturing steps to remove prions in case of an unlikely contamination, providing a safeguard to products.

Chlorine isotope composition in chlorofluorocarbons CFC-11, CFC-12 and CFC-113 in firn, stratospheric and tropospheric air

The stratospheric degradation of chlorofluorocarbons (CFCs) releases chlorine, which is a major contributor to the destruction of stratospheric ozone (O3). A recent study reported strong chlorine isotope fractionation during the breakdown of the most abundant CFC (CFC-12, CCl2F2, Laube et al., 2010a), similar to effects seen in nitrous oxide (N2O). Using air archives to obtain a long-term record of chlorine isotope ratios in CFCs could help to identify and quantify their sources and sinks. We analyse the three most abundant CFCs and show that CFC-11 (CCl3F) and CFC-113 (CClF2CCl2F) exhibit significant stratospheric chlorine isotope fractionation, in common with CFC-12. The apparent isotope fractionation (ϵapp) for mid- and high-latitude stratospheric samples are respectively −2.4 (0.5) and −2.3 (0.4) ‰ for CFC-11, −12.2 (1.6) and −6.8 (0.8) ‰ for CFC-12 and −3.5 (1.5) and −3.3 (1.2) ‰ for CFC-113, where the number in parentheses is the numerical value of the standard uncertainty expressed in per mil. Assuming a constant isotope composition of emissions, we calculate the expected trends in the tropospheric isotope signature of these gases based on their stratospheric 37Cl enrichment and stratosphere–troposphere exchange. We compare these projections to the long-term δ (37Cl) trends of all three CFCs, measured on background tropospheric samples from the Cape Grim air archive (Tasmania, 1978–2010) and tropospheric firn air samples from Greenland (North Greenland Eemian Ice Drilling (NEEM) site) and Antarctica (Fletcher Promontory site). From 1970 to the present day, projected trends agree with tropospheric measurements, suggesting that within analytical uncertainties, a constant average emission isotope delta (δ) is a compatible scenario. The measurement uncertainty is too high to determine whether the average emission isotope δ has been affected by changes in CFC manufacturing processes or not. Our study increases the suite of trace gases amenable to direct isotope ratio measurements in small air volumes (approximately 200 mL), using a single-detector gas chromatography–mass spectrometry (GC–MS) system.

Proposal on How To Conduct a Biopharmaceutical Process Failure Mode and Effect Analysis (FMEA) as a Risk Assessment Tool

With the publication of the quality guideline ICH Q9 "Quality Risk Management" by the International Conference on Harmonization, risk management has already become a standard requirement during the life cycle of a pharmaceutical product. Failure mode and effect analysis (FMEA) is a powerful risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to biopharmaceutical processes brings about some difficulties. The proposal presented here is intended to serve as a brief but nevertheless comprehensive and detailed guideline on how to conduct a biopharmaceutical process FMEA. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. The application for such a biopharmaceutical process FMEA is widespread. It can be useful whenever a biopharmaceutical manufacturing process is developed or scaled-up, or when it is transferred to a different manufacturing site. It may also be conducted during substantial optimization of an existing process or the development of a second-generation process. According to their resulting risk ratings, process parameters can be ranked for importance and important variables for process development, characterization, or validation can be identified. LAY ABSTRACT: Health authorities around the world ask pharmaceutical companies to manage risk during development and manufacturing of pharmaceuticals. The so-called failure mode and effect analysis (FMEA) is an established risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to pharmaceutical processes that use modern biotechnology (biopharmaceutical processes) brings about some difficulties, because those biopharmaceutical processes differ from processes in mechanical and electrical industries. The proposal presented here explains how a biopharmaceutical process FMEA can be conducted. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. With the help of this guideline, different details of the manufacturing process can be ranked according to their potential risks, and this can help pharmaceutical companies to identify aspects with high potential risks and to react accordingly to improve the safety of medicines.

Sedation and weaning from mechanical ventilation: effects of process optimization outside a clinical trial

PURPOSE: We studied the effects of reorganization and changes in the care process, including use of protocols for sedation and weaning from mechanical ventilation, on the use of sedative and analgesic drugs and on length of respiratory support and stay in the intensive care unit (ICU). MATERIALS AND METHODS: Three cohorts of 100 mechanically ventilated ICU patients, admitted in 1999 (baseline), 2000 (implementation I, after a change in ICU organization and in diagnostic and therapeutic approaches), and 2001 (implementation II, after introduction of protocols for weaning from mechanical ventilation and sedation), were studied retrospectively. RESULTS: Simplified Acute Physiology Score II (SAPS II), diagnostic groups, and number of organ failures were similar in all groups. Data are reported as median (interquartile range).Time on mechanical ventilation decreased from 18 (7-41) (baseline) to 12 (7-27) hours (implementation II) (P = .046), an effect which was entirely attributable to noninvasive ventilation, and length of ICU stay decreased in survivors from 37 (21-71) to 25 (19-63) hours (P = .049). The amount of morphine (P = .001) and midazolam (P = .050) decreased, whereas the amount of propofol (P = .052) and fentanyl increased (P = .001). Total Therapeutic Intervention Scoring System-28 (TISS-28) per patient decreased from 137 (99-272) to 113 (87-256) points (P = .009). Intensive care unit mortality was 19% (baseline), 8% (implementation I), and 7% (implementation II) (P = .020). CONCLUSIONS: Changes in organizational and care processes were associated with an altered pattern of sedative and analgesic drug prescription, a decrease in length of (noninvasive) respiratory support and length of stay in survivors, and decreases in resource use as measured by TISS-28 and mortality.

A continuous-time MILP model for short-term scheduling of make-and-pack production processes

In process industries, make-and-pack production is used to produce food and beverages, chemicals, and metal products, among others. This type of production process allows the fabrication of a wide range of products in relatively small amounts using the same equipment. In this article, we consider a real-world production process (cf. Honkomp et al. 2000. The curse of reality – why process scheduling optimization problems are diffcult in practice. Computers & Chemical Engineering, 24, 323–328.) comprising sequence-dependent changeover times, multipurpose storage units with limited capacities, quarantine times, batch splitting, partial equipment connectivity, and transfer times. The planning problem consists of computing a production schedule such that a given demand of packed products is fulfilled, all technological constraints are satisfied, and the production makespan is minimised. None of the models in the literature covers all of the technological constraints that occur in such make-and-pack production processes. To close this gap, we develop an efficient mixed-integer linear programming model that is based on a continuous time domain and general-precedence variables. We propose novel types of symmetry-breaking constraints and a preprocessing procedure to improve the model performance. In an experimental analysis, we show that small- and moderate-sized instances can be solved to optimality within short CPU times.