Small-Angle X-ray Scattering as a Powerful Tool for Phase and Crystallinity Assessment of Monoclonal Antibody Crystallites in Support of Batch Crystallization.

Crystalline suspensions of monoclonal antibodies (mAbs) have great potential to improve drug substance isolation and purification on a large scale and to be used for drug delivery via high-concentration formulations. Crystalline mAb suspensions are expected to have enhanced chemical and physical properties relative to mAb solutions delivered intravenously, making them attractive candidates for subcutaneous delivery. In contrast to small molecules, the development of protein crystalline suspensions is not a widely used approach in the pharmaceutical industry. This is mainly due to the challenges in finding crystalline hits and the suboptimal physical properties of the resulting crystallites when hits are found. Modern advances in instrumentation and increased knowledge of mAb crystallization have, however, resulted in higher probabilities of discovering crystal forms and improving their particle properties and characterization. In this regard, physical, analytical characterization plays a central role in the initial steps of understanding and later optimizing the crystallization of mAbs and requires careful selection of the appropriate tools. This contribution describes a novel crystal structure of the antibody pembrolizumab and demonstrates the usefulness of small-angle X-ray scattering (SAXS) for characterizing its crystalline suspensions. It illustrates the advantages of SAXS when used to (i) confirm crystallinity and crystal phase of crystallites produced in batch mode; (ii) confirm crystallinity under various conditions and detect variations in crystal phases, enabling fine-tuning of the crystallizations for phase control across multiple batches; (iii) monitor the physical response and stability of the crystallites in suspension with regard to filtration and washing; and (iv) monitor the physical stability of the crystallites upon drying. Overall, this work highlights how SAXS is an essential tool for mAb crystallization characterization.

Development of an application programming interface to automate downloading and processing of precision livestock data.

Advancements in technology have ushered in a new era of sensor-based measurement and management of livestock production systems. These sensor-based technologies have the ability to automatically monitor feeding, growth, and enteric emissions for individual animals across confined and extensive production systems. One challenge with sensor-based technologies is the large amount of data generated, which can be difficult to access, process, visualize, and monitor information in real time to ensure equipment is working properly and animals are utilizing it correctly. A solution to this problem is the development of application programming interfaces (APIs) to automate downloading, visualizing, and summarizing datasets generated from precision livestock technology (PLT). For this methods paper, we develop three APIs and accompanying processes for rapid data acquisition, visualization, systems tracking, and summary statistics for three technologies (SmartScale, SmartFeed, and GreenFeed) manufactured by C-Lock Inc (Rapid City, SD). Program R markdown documents and example datasets are provided to facilitate greater adoption of these techniques and to further advance PLT. The methodology presented successfully downloaded data from the cloud and generated a series of visualizations to conduct systems checks, animal usage rates, and calculate summary statistics. These tools will be essential for further adoption of precision technology. There is huge potential to further leverage APIs to incorporate a wide range of datasets such as weather data, animal locations, and sensor data to facilitate decision-making on time scales relevant to researchers and livestock managers.

Secondary production of the central rangeland region of the United States.

Rangelands are the dominant land use across a broad swath of central North America where they span a wide gradient, from <350 to >900 mm, in mean annual precipitation. Substantial efforts have examined temporal and spatial variation in aboveground net primary production (ANPP) to precipitation (PPT) across this gradient. In contrast, net secondary productivity (NSP, e.g., primary consumer production) has not been evaluated analogously. However, livestock production, which is a form of NSP or primary consumer production supported by primary production, is the dominant non-cultivated land use and an integral economic driver in these regions. Here, we used long-term (mean length = 19 years) ANPP and NSP data from six research sites across the Central Great Plains with a history of a conservative stocking to determine resource (i.e., PPT)-productivity relationships, NSP sensitivities to dry-year precipitation, and regional trophic efficiencies (e.g., NSP:ANPP ratio). PPT-ANPP relationships were linear for both temporal (site-based) and spatial (among site) gradients. The spatial PPT-NSP model revealed that PPT mediated a saturating relationship for NSP as sites became more mesic, a finding that contrasts with many plant-based PPT-ANPP relationships. A saturating response to high growing-season precipitation suggests biogeochemical rather than vegetation growth constraints may govern NSP (i.e., large herbivore production). Differential sensitivity in NSP to dry years demonstrated that the primary consumer production response heightened as sites became more xeric. Although sensitivity generally decreased with increasing precipitation as predicted from known PPT-ANPP relationships, evidence suggests that the dominant species' identity and traits influenced secondary production efficiency. Non-native northern mixed-grass prairie was outperformed by native Central Great Plains rangeland in sensitivity to dry years and efficiency in converting ANPP to NSP. A more comprehensive understanding of the mechanisms leading to differences in producer and consumer responses will require multisite experiments to assess biotic and abiotic determinants of multi-trophic level efficiency and sensitivity.

Identification of Surfactant Impact on a Monoclonal Antibody Characterization via HPLC-Separation Based and Biophysical Methods.

PURPOSE OR OBJECTIVE: Surfactants, including polysorbates and poloxamers, play a crucial role in the formulation of therapeutic proteins by acting as solubilizing and stabilizing agents. They help prevent protein aggregation and adsorption, thereby enhancing the stability of drug substance and products., However, it is important to note that utilizing high concentrations of surfactants in protein formulations can present significant analytical challenges, which can ultimately affect the product characterization. METHODS: In our study, we specifically investigated the impact of elevated surfactant concentrations on the characterization of monoclonal antibodies. We employed various analytical techniques including size-exclusion chromatography (SEC), capillary electrophoresis (CE-SDS), a cell based functional assay, and biophysical characterization. RESULTS: The findings of our study indicate that higher levels of Polysorbate 80 (PS-80) have adverse effects on the measured purity, biological activity, and biophysical characterization of biologic samples. Specifically, the elevated levels of PS-80 cause analytical interferences, which can significantly impact the accuracy and reliability of analytical studies. CONCLUSIONS: Our study results highlight a significant risk in analytical investigations, especially in studies involving the isolation and characterization of impurities. It is important to be cautious of surfactant concentrations, as they can become more concentrated during common sample manipulations like buffer exchange. Indeed, the research presented in this work emphasizes the necessity to evaluate the impact on analytical assays when there are substantial alternations in the matrix composition. By doing so, valuable insights can be gained regarding potential challenges associated with assay development and characterization of biologics with complex formulations.

Lignin derived hydrophobic deep eutectic solvents for the extraction of nanoplastics from water.

As a growing concern in aqueous systems, micro- and nano-plastics, especially nanoplastics (NPs), have been widely detected in the environment and organisms, posing a potential threat to ecosystems and human health. Hydrophobic deep eutectic solvents (HDESs) have emerged as environmentally friendly solvents that have shown promise for extracting pollutants from water, either for detection or removal purposes. Herein, we investigated the extraction of polystyrene (PS) and polyethylene terephthalate (PET) NPs from aqueous solution using lignin based HDESs as sustainable solvents. Rapid extraction of both PET and PS NPs was observed with the high extraction efficiency achieved (> 95%). The extraction capacities for PET and PS could reach up to 525.877 mg/mL and 183.520 mg/mL, respectively, by the Thymol-2,6-dimethoxyphenol 1:2 HDES. Moreover, the extraction mechanism was studied using various techniques including Fourier-transform infrared analysis, contact angle measurements, molecular dynamics simulation, kinetics, and isotherm studies. This work lays a foundational basis for the future development of innovative HDES-based technologies in the detection and remediation of NPs as part of the grand challenge of plastic pollution.

Novel insights into the role of translesion synthesis polymerase in DNA incorporation and bypass of 5-fluorouracil in colorectal cancer.

5-Fluorouracil (5-FU) is the first-line chemotherapeutic agent in colorectal cancer, and resistance to 5-FU easily emerges. One of the mechanisms of drug action and resistance of 5-FU is through DNA incorporation. Our quantitative reverse-transcription PCR data showed that one of the translesion synthesis (TLS) DNA polymerases, DNA polymerase η (polη), was upregulated within 72 h upon 5-FU administration at 1 and 10 µM, indicating that polη is one of the first responding polymerases, and the only TLS polymerase, upon the 5-FU treatment to incorporate 5-FU into DNA. Our kinetic studies revealed that 5-fluoro-2'-deoxyuridine triphosphate (5FdUTP) was incorporated across dA 41 and 28 times more efficiently than across dG and across inosine, respectively, by polη indicating that the mutagenicity of 5-FU incorporation is higher in the presence of inosine and that DNA lesions could lead to more mutagenic incorporation of 5-FU. Our polη crystal structures complexed with DNA and 5FdUTP revealed that dA:5FdUTP base pair is like dA:dTTP in the active site of polη, while 5FdUTP adopted 4-enol tautomer in the base pairs with dG and HX increasing the insertion efficiency compared to dG:dTTP for the incorrect insertions. These studies confirm that polη engages in the DNA incorporation and bypass of 5-FU.

ASAS-NANP symposium: mathematical modeling in animal nutrition-Making sense of big data and machine learning: how open-source code can advance training of animal scientists.

Advancements in precision livestock technology have resulted in an unprecedented amount of data being collected on individual animals. Throughout the data analysis chain, many bottlenecks occur, including processing raw sensor data, integrating multiple streams of information, incorporating data into animal growth and nutrition models, developing decision support tools for producers, and training animal science students as data scientists. To realize the promise of precision livestock management technologies, open-source tools and tutorials must be developed to reduce these bottlenecks, which are a direct result of the tremendous time and effort required to create data pipelines from scratch. Open-source programming languages (e.g., R or Python) can provide users with tools to automate many data processing steps for cleaning, aggregating, and integrating data. However, the steps from data collection to training artificial intelligence models and integrating predictions into mathematical models can be tedious for those new to statistical programming, with few examples pertaining to animal science. To address this issue, we outline how open-source code can help overcome many of the bottlenecks that occur in the era of big data and precision livestock technology, with an emphasis on how routine use and publication of open-source code can help facilitate training the next generation of animal scientists. In addition, two case studies are presented with publicly available data and code to demonstrate how open-source tutorials can be utilized to streamline data processing, train machine learning models, integrate with animal nutrition models, and facilitate learning. The National Animal Nutrition Program focuses on providing research-based data on animal performance and feeding strategies. Open-source data and code repositories with examples specific to animal science can help create a reinforcing mechanism aimed at advancing animal science research.

Livestock production is undergoing a new revolution of incorporating advanced technology to inform animal management. As more and more technologies come to market, new challenges arise with developing a workforce trained to handle big datasets generated from these technologies and turning datasets into insight for livestock producers. This can be especially challenging as multiple data streams ranging from climate and weather information to real-time metrics on animal performance need to be efficiently processed and incorporated into animal production models. Open-source code is one possible solution to these challenges because it is designed to be made publicly available so any user can view, alter, and improve upon existing code. This paper aims to highlight how open-source code can help address many of the challenges of precision livestock technology, including efficient data processing, data integration, development of decision tools, and training of future animal scientists. In addition, the need for open-source tutorials and datasets specific to animal science are included to help facilitate greater adoption of open science.

Green solvent mediated extraction of micro- and nano-plastic particles from water.

The production of plastic and the amount of waste plastic that enters the ecosystem increases every year. Synthetic plastics gradually break down into particles on the micro- and nano-scale in the environment. The micro- and nano-plastics pose a significant ecological threat by transporting toxic chemicals and causing inflammation and cellular damage when ingested; however, removal of those particles from water is challenging using conventional separation methods. Deep eutectic solvents (DES), a new class of solvents composed of hydrogen bond donors and acceptors, have been proposed as a cheaper alternative to ionic liquids. Hydrophobic DES derived from natural compounds (NADES) show promise as extractants in liquid-liquid extractions. This study investigated the extraction efficiency of micro- and nano-plastics including polyethylene terephthalate, polystyrene, and a bioplastic polylactic acid from fresh water and saltwater using three hydrophobic NADES. The extraction efficiencies fall in a range of 50-93% (maximum % extraction) while the extraction rates fall between 0.2 and 1.3 h (as indicated by the time to extract half the theoretical maximum). Molecular simulations show a correlation between the extraction efficiency and the association between the plastics and NADES molecules. This study demonstrates the potential of hydrophobic NADES as extractants for removal of different micro- and nano-plastic particles from aqueous solutions.

Mutagenic incorporation of inosine into DNA via T:I mismatch formation by human DNA polymerase eta (polη).

Inosine is a key intermediate in de novo purine nucleotide biosynthesis in cells. Inosine is known to be mutagenic when it is present in DNA, in place of adenine via deamination, by facilitating the incorporation of dCTP exclusively, resulting in A:T to G:C mutation. The structural basis for the mutagenicity of inosine bypass has been reported in some DNA polymerases including human DNA polymerase eta (polη). However, the structural and biochemical basis for the mutagenic potential of the incorporation of deoxyinosine triphosphate (dITP) into DNA remains poorly understood. To gain insights into the mutagenic potential of the incorporation of inosine into DNA, we conducted structural and kinetic studies of human polη incorporating dITP across undamaged DNA template containing dC or dT. Polη incorporated dITP opposite dC 14-fold more efficiently than opposite dT, indicating that dITP incorporation by polη can be mutagenic unlike the bypass of inosine by polη, which incorporated dCTP almost exclusively opposite the templating inosine over dTTP (70:1). Polη-dC:dITP crystal structure showed that the incoming dITP formed Watson-Crick base pair along with wobble base pair via 4-imino-2-keto tautomer of cytosine diminishing the catalytic efficiency compared to dGTP incorporation across dC. In addition, the crystal structure of polη-dT:dITP revealed that dT and dITP formed Watson-Crick like base pair via 4-enol-2-keto tautomer of thymine, reinforced by wobble base pair via 4-keto-2-keto tautomer of thymine resulting in the increased mutagenicity of dITP incorporation (14:1 across dC and dT), which is 14-fold higher than dGTP incorporation by polη (190:1 across dC and dT).

Learning Relationships Between Chemical and Physical Stability for Peptide Drug Development.

PURPOSE OR OBJECTIVE: Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. Moreover, fluorescent reporter Thioflavin-T is commonly used to measure physical stability. Executing stability studies is a lengthy process and requires extensive resources. To reduce the resources and shorten the process for stability studies during the development of a drug product, we introduce a machine learning-based model for predicting the chemical stability over time using both formulation conditions as well as aggregation curves. METHODS: In this work, we develop the relationships between the formulation, stability timepoint, and the chemical stability measurements and evaluated the performance on a random test set. We have developed a multilayer perceptron (MLP) for total degradation prediction and a random forest (RF) model for potency. RESULTS: The coefficient of determination (R2) of 0.945 and a mean absolute error (MAE) of 0.421 were achieved on the test set when using MLP for total degradation. Similarly, we achieved a R2 of 0.908 and MAE of 1.435 when predicting potency using the RF model. When physical stability measurements are included into the MLP model, the MAE of predicting TD decreases to 0.148. Using a similar strategy for potency prediction, the MAE decreases to 0.705 for the RF model. CONCLUSIONS: We conclude two important points: first, chemical stability can be modeled using machine learning techniques and second there is a relationship between the physical stability of a peptide and its chemical stability.

Discovery and Control of Succinimide Formation and Accumulation at Aspartic Acid Residues in The Complementarity-Determining Region of a Therapeutic Monoclonal Antibody.

PURPOSE: Succinimide formation and isomerization alter the chemical and physical properties of aspartic acid residues in a protein. Modification of aspartic acid residues within complementarity-determining regions (CDRs) of therapeutic monoclonal antibodies (mAbs) can be particularly detrimental to the efficacy of the molecule. The goal of this study was to characterize the site of succinimide accumulation in the CDR of a therapeutic mAb and understand its effects on potency. Furthermore, we aimed to mitigate succinimide accumulation through changes in formulation. METHODS: Accumulation of succinimide was identified through intact and reduced LC-MS mass measurements. A low pH peptide mapping method was used for relative quantitation and localization of succinimide formation in the CDR. Statistical modeling was used to correlate levels of succinimide with basic variants and potency measurements. RESULTS: Succinimide accumulation in Formulation A was accelerated when stored at elevated temperatures. A strong correlation between succinimide accumulation in the CDR, an increase in basic charge variants, and a decrease in potency was observed. Statistical modeling suggest that a combination of ion exchange chromatography and potency measurements can be used to predict succinimide levels in a given sample. Reformulation of the mAb to Formulation B mitigates succinimide accumulation even after extended storage at elevated temperatures. CONCLUSION: Succinimide formation in the CDR of a therapeutic mAb can have a strong negative impact on potency of the molecule. We demonstrate that thorough characterization of the molecule by LC-MS, ion exchange chromatography, and potency measurements can facilitate changes in formulation that mitigate succinimide formation and the corresponding detrimental changes in potency.

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Opportunities and challenges of confined and extensive precision livestock production.

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

Interest and investment in precision technologies are growing within the livestock sector. Though these technologies offer many promises of increased efficiency and reduced inputs, there is a need to assess the opportunities and challenges associated with precision technology implementation in livestock production systems. In this review, precision livestock measurement and management tools are explained in the context of a logical and iterative five-step process that highlights the need for systems computer modeling to realize anticipated benefits from these technologies and avoid unintended consequences. This review includes key case studies to highlight past challenges and current opportunities within operations that house animals in a central area or building with sufficient infrastructure (confined livestock production systems) and other operation settings that utilize large grasslands that contain far less infrastructure (extensive livestock production systems). The key to precision livestock management success is training the next generation of animal scientists in computer modeling, precision technologies, computer programming, and data science while still being grounded in traditional animal science principles.

Protein Supplementation and Grazing Behavior for Cows on Differing Late-Season Rangeland Grazing Systems.

The objective was to determine if low- or high-residual feed intake (LRFI or HRFI, n = 24 for each) Hereford × Angus cows on continuously or rotationally grazed rangeland altered their grazing behavior when provided a protein supplement in late autumn. Treatments included continuously grazed, control (CCON, n = 12); continuously grazed, supplemented (CTRT, n = 12); rotationally grazed, control (RCON, n = 12); and rotationally grazed, supplemented pastures (RTRT, n = 12). Cows in each treatment had grazing time (GT), resting time (RT), and walking time (WLK) measured for 2 years with accelerometers. Bite rate (BR) was also measured. Time distributions of GT and RT differed by year (p < 0.05), being influenced by colder temperatures in 2016. Cattle in 2016 spent more time grazing during early morning and late evening (p < 0.05) and rested more during the day (p < 0.05). In 2017, cattle in the CCON treatment walked more (p < 0.05) during early morning time periods than did the CTRT cattle, indicative of search grazing. All supplemented cattle had greater BR (p < 0.05) than control cattle in 2017. Cattle with increased nutritional demands alter grazing behavior in a compensatory fashion when grazing late-season rangelands.

Grazing behavior and production for lactating cows differing in residual feed intake while grazing spring and summer rangeland.

The objectives were to determine if previously classified, efficient (LRFI, low-residual-feed intake, n = 12 × 2 yr) vs. inefficient (HRFI, high-residual-feed intake, n = 12 × 2 yr) lactating 2-yr-old Hereford × Angus cows differed in grazing behavior, body weight (BW), body condition score (BCS), and calf weaning weight while grazing rugged rangeland pastures. Cows were fitted with grazing halters containing both an accelerometer and a global positioning system (GPS) data logger during June 14 to July 4, 2016, August 2 to 25, 2016, May 23 to June 12, 2017, and August 5 to 28, 2017. GPS data were recorded at 7-min intervals in 2016 and 4-min intervals in 2017 and accelerometer data recorded at 25 times/s. Grazing time (GT), resting, walking, bite rate (BR), daily travel distance (DTD), elevation, and slope were analyzed with a mixed model that included fixed effects of RFI group, day, and RFI group × day and cow within treatment as the random effect. Cow BW, BCS, and calf weaning weight were analyzed by analysis of variance with treatment as the main effect. There were no differences (P > 0.10) due to RFI detected for BW, BCS, or calf weaning weights. During periods of mild heat load (MHL), HRFI cows spent more (P < 0.05) time resting during the day at lower elevations (P < 0.05) than LRFI cows. During a 6-d period in spring with only 2 h MHL, HRFI cows grazed 1.7 h/d longer than LRFI cows (P < 0.05); commencing grazing earlier in the morning and extending the grazing bout later. During the summer with > MHL, LRFI cows grazed more than HRFI cows 18% of the time (P < 0.10). The HRFI cows had greater GT than LRFI cows only 3% of the time (P < 0.10) during summer. There was no difference (P > 0.10) in BR between HRFI and LRFI cattle. The DTD tended (P < 0.10) to be greater for LRFI cattle during summer 2017. Over all sample periods, HRFI had greater walking than LRFI 15% of the time and LRFI exceeded HRFI cattle for walking 3% of the time (P < 0.10). The greater walking for HRFI was assumed to be associated with more search grazing. Metabolic heat load on hot summer days for HRFI cattle is presumed to have contributed to differences observed in grazing behavior. These results suggest that lactating cows with low-RFI phenotypes appear to be better adapted to grazing rugged rangelands in late summer during periods of MHL.

Coated microneedles for transdermal delivery of a potent pharmaceutical peptide.

Minimally invasive delivery of peptide and protein molecules represents a significant opportunity for product differentiation and value creation versus standard injectable routes of administration. One such technology utilizes microneedle (MN) patches and it has made considerable clinical advances in systemic delivery of potent macromolecules and vaccines. A sub-class of this technology has focused on preparation of solid dense MN arrays followed by precision formulation coating on the tips of the MN. The objective of this study was to develop a drug product using the MN technology that has similar bioperformance when compared to subcutaneous route of delivery and can provide improved stability under storage. Therapeutic peptide (Peptide A, Merck & Co., Inc., Kenilworth, NJ, USA) is being developed as a subcutaneous injection for chronic dosing with a submilligram estimated therapeutic dose. Peptide A has chemical and physical stability challenges in solution and this led to exploration of a viable drug product which could provide therapeutic dosages while overcoming the stability issues seen with the compound. This work focused on developing a coated solid microstructure transdermal system (sMTS) for Peptide A followed by detailed in vitro and preclinical evaluation for two different coating formulations. Based on initial assessment, ~250 µg of Peptide A could be coated with precision on a 1.27cm2 patch which contained 316 MN's. The delivery from these systems was achieved with absolute bioavailability being similar to the subcutaneous delivery (88% and 74% for coated sMTS 1 & 2 and 75% for subcutaneous delivery). Stability of Peptide A was also found to be significantly improved when coated on the sMTS system with minimal degradation recorded at room temperature storage as compared to the subcutaneous liquid formulation. Additionally, skin irritation (on pig skin) was also measured in this study and it was found to be minimal and self-resolving. This evaluation provided a viable option for developing a drug product with improved stability and successful delivery of the investigated molecule. Graphical abstractSchematic showing uncoated sMTS, resulting product with coated peptide, successful skin penetration with high delivery efficiency and bioavailability.

Peptide Oligomerization Memory Effects and Their Impact on the Physical Stability of the GLP-1 Agonist Liraglutide.

Peptides and proteins commonly have complex structural landscapes allowing for transformation into a wide array of species including oligomers, aggregates, and fibrils. The formation of undesirable forms including aggregates and fibrils poses serious risks from the perspective of drug development and disease. Liraglutide, a GLP-1 agonist for the treatment of diabetes, is a conjugated peptide that forms oligomers that can be stabilized by pH and organic solvents. We have developed an analytical toolkit to overcome challenges inherent to Liraglutide's conjugated acyl chain and probed the impact its oligomers have on its physical stability. Our studies show that Liraglutide's oligomer states have significant and potentially detrimental impacts on its propensity to aggregate and form fibrils as well as its potency. Liraglutide delivered as a synthetic peptide is able to maintain its oligomerization state in dried lyophilized powders, acting as a memory effect from its synthetic process and purification. Through Liraglutide's oligomer memory effect, we demonstrate the importance and impact the process for synthetic peptides can have on drug development spanning from discovery to formulation development.

Cancer incidence and mortality risks in a large US Barrett's oesophagus cohort.

OBJECTIVE: Barrett's oesophagus (BE) increases the risk of oesophageal adenocarcinoma by 10-55 times that of the general population, but no community-based cancer-specific incidence and cause-specific mortality risk estimates exist for large cohorts in the USA. DESIGN: Within Kaiser Permanente Northern California (KPNC), we identified patients with BE diagnosed during 1995-2012. KPNC cancer registry and mortality files were used to estimate standardised incidence ratios (SIR), standardised mortality ratios (SMR) and excess absolute risks. RESULTS: There were 8929 patients with BE providing 50 147 person-years of follow-up. Compared with the greater KPNC population, patients with BE had increased risks of any cancer (SIR=1.40, 95% CI 1.31 to 1.49), which slightly decreased after excluding oesophageal cancer. Oesophageal adenocarcinoma risk was increased 24 times, which translated into an excess absolute risk of 24 cases per 10 000 person-years. Although oesophageal adenocarcinoma risk decreased with time since BE diagnosis, oesophageal cancer mortality did not, indicating that the true risk is stable and persistent with time. Relative risks of cardia and stomach cancers were increased, but excess absolute risks were modest. Risks of colorectal, lung and prostate cancers were unaltered. All-cause mortality was slightly increased after excluding oesophageal cancer (SMR=1.24, 95% CI 1.18 to 1.31), but time-stratified analyses indicated that this was likely attributable to diagnostic bias. Cause-specific SMRs were elevated for ischaemic heart disease (SMR=1.39, 95% CI 1.18 to 1.63), respiratory system diseases (SMR=1.51, 95% CI 1.29 to 1.75) and digestive system diseases (SMR=2.20 95% CI 1.75 to 2.75). CONCLUSIONS: Patients with BE had a persistent excess risk of oesophageal adenocarcinoma over time, although their absolute excess risks for this cancer, any cancer and overall mortality were modest.

Proton Pump Inhibitor and Histamine-2 Receptor Antagonist Use and Iron Deficiency.

BACKGROUND & AIMS: Proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs) suppress gastric acid production, which can inhibit iron absorption. However, few data exist regarding whether these medications increase the risk of clinical iron deficiency. METHODS: A community-based case-control study evaluated the association between acid-suppressing medication use and the subsequent risk of iron deficiency. It contrasted 77,046 patients with new iron deficiency diagnoses (January 1999-December 2013), with 389,314 controls. Medication exposures, outcomes, and potential confounders used electronic databases. We excluded patients with pre-existing risk factors for iron deficiency. Associations were estimated using conditional logistic regression. RESULTS: Among cases, 2343 (3.0%) received a prior ≥2-year supply of PPIs and 1063 (1.4%) received H2RAs (without PPI use). Among controls, 3354 (0.9%) received a prior ≥2-year supply of PPIs and 2247 (0.6%) H2RAs. Both ≥2 years of PPIs (adjusted odds ratio, 2.49; 95% confidence interval, 2.35-2.64) and ≥2 years of H2RAs (odds ratio, 1.58; 95% CI, 1.46-1.71) were associated with an increased subsequent risk for iron deficiency. Among PPI users, the associations were stronger for higher daily doses (>1.5 vs <0.75 PPI pills/d; P value interaction = .004) and decreased after medication discontinuation (P-trend < .001). Some of the strongest associations were among persons taking >1.5 pills per day for at least 10 years (odds ratio, 4.27; 95% CI, 2.53-7.21). No similar strong associations were found for other commonly used prescription medications. CONCLUSIONS: Among patients without known risk factors for iron deficiency, gastric acid inhibitor use for ≥2 years was associated with an increased subsequent risk of iron deficiency. The risk increased with increasing potency of acid inhibition and decreased after medication discontinuation.