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Reducing protein quantification bottlenecks by automating the Octet® HTX with a Hudson PlateCrane
No full textThe Hudson Robotics PlateCrane EX microplate handler and the Octet HTX system are well-suited to work together for hands-off protein quantification and reaction kinetics in high-throughput laboratories. The system combination is used by Novartis for antibody quantification and kinetics and the use of the system is reviewed in this Application Note
Modulating captive mammalian social behavior: a scoping review on olfactory treatments
No full textMany species use olfaction as a primary form of communication. Because of this, odor signals could be a useful tool to improve captive animal welfare by reducing aggression and promoting socio-positive behavior. However, to fully gauge the potential benefits of odor manipulations, the quality of existing literature must first be evaluated. Therefore, a systematic search and scoping review was conducted to summarize prevalent methods, treatment outcomes, and modulating factors in existing literature on the effect of mammalian, intraspecies odors on non-reproductive social behavior. Results from a systematic search of three databases were included if they were published in a peer reviewed journal, used a terrestrial mammalian species, and contained original data evaluating how an odor signal from the subject species directly affected non-reproductive social behavior. All articles were screened by two researchers, data were extracted by one, and reporting quality was assessed by both using the SYRCLE risk of bias tool. Sixty-three articles were included based on this criteria. Most subjects were sexually mature, male rodents. The most common odor treatment originated from urine and aggressive behavior was measured most often. Overall, urine and saliva treatments had a variable effect on aggression, while urine most often increased scent marking and social investigation behavior. Concerningly, most articles showed unclear or high risk of bias. Data from this review highlights a need for additional research on how odor signals from sources other than urine affect behavior and how socio-positive behaviors are affected in general. Further, it emphasizes the need for more transparent reporting as the current body of literature makes it difficult to determine each experiment’s quality and how much weight it should be given when interpreting outcomes pertaining to our overall understanding of olfactory communication
ZNRF3 and RNF43 cooperate to safeguard metabolic liver zonation and hepatocyte proliferation.
No full textAXIN2 and LGR5 mark intestinal stem cells (ISCs) that require WNT/β-Catenin signaling for constant homeostatic proliferation. In contrast, AXIN2/LGR5+ pericentral hepatocytes show low proliferation rates despite a WNT/β-Catenin activity gradient required for metabolic liver zonation. The mechanisms restricting proliferation in AXIN2+ hepatocytes and metabolic gene expression in AXIN2+ ISCs remained elusive. We now show that restricted chromatin accessibility in ISCs prevents the expression of β-Catenin-regulated metabolic enzymes, whereas fine-tuning of WNT/β-Catenin activity by ZNRF3 and RNF43 restricts proliferation in chromatin-permissive AXIN2+ hepatocytes, while preserving metabolic function. ZNRF3 deletion promotes hepatocyte proliferation, which in turn becomes limited by RNF43 upregulation. Concomitant deletion of RNF43 in ZNRF3 mutant mice results in metabolic reprogramming of periportal hepatocytes and induces clonal expansion in a subset of hepatocytes, ultimately promoting liver tumors. Together, ZNRF3 and RNF43 cooperate to safeguard liver homeostasis by spatially and temporally restricting WNT/β-Catenin activity, balancing metabolic function and hepatocyte proliferation
Open innovation: the many shades of a promising road to scientific discoveries
No full textInterview of Dr. Hans Widmer (Novartis) for the NCCR TransCure newsletter of Dec. 2021.
Open access publishing, sharing of data, permeable research environments: these and many more traits characterize “open innovation”, a trend that is gaining importance both in the academic and private sector. In this interview, Hans Widmer from the Novartis Institutes for Biomedical Research illustrates the multifaceted meaning of openness in science, and how researchers can move within its boundaries
Establishing the Bioequivalence Safe Space for Immediate-Release Oral Dosage Forms using Physiologically Based Biopharmaceutics Modeling (PBBM): Case Studies.
No full textFor oral drug products, in vitro dissolution is the most used surrogate of in vivo dissolution and absorption. In the context of drug product quality, safe space is defined as the boundaries of in vitro dissolution, and relevant quality attributes, within which drug product variants are expected to be bioequivalent to each other. It would be highly desirable if the safe space could be established via a direct link between available in vitro data and in vivo pharmacokinetics. In response to the challenges with establishing in vitro-in vivo correlations (IVIVC) with traditional modeling approaches, physiologically based biopharmaceutics modeling (PBBM) has been gaining increased attention. In this manuscript we report five case studies on using PBBM to establish a safe space for BCS Class 2 and 4 across different companies, including applications in an industrial setting for both internal decision making or regulatory applications. The case studies provide an opportunity to reflect on practical vs. ideal datasets for safe space development, the methodologies for incorporating dissolution data in the model and the criteria used for model validation and application. PBBM and safe space, still represent an evolving field and more examples are needed to drive development of best practices
Don’t overweight weights: Evaluation of weighting strategies for multi-task bioactivity classification models
No full textMachine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow privacy-preserving usage of large amount of data from diverse sources, which is crucial for achieving good generalization and high-performance results. Using large, real world data sets from six pharmaceutical companies, here we investigate different strategies for averaging weighted task loss functions to train multi-task bioactivity classification models. The weighting strategies shall be suitable for federated learning and ensure that learning efforts are well distributed even if data are diverse. Comparing several approaches using weights that depend on the number of sub-tasks per assay, task size, and class balance, respectively, we find that a simple sub-task weighting approach leads to robust model performance for all investigated data sets and is especially suited for federated learning
Therapeutic Assessment of Targeting ASNS Combined with l-Asparaginase Treatment in Solid Tumors and Investigation of Resistance Mechanisms
No full textAsparagine deprivation by L-Asparaginase (L-ASNase) is an effective therapeutic strategy in Acute Lymphoblastic Leukemia, with resistance occurring due to upregulation of ASNS,the only human enzyme synthetizing Asparagine1. L-Asparaginase efficacy in solid tumors is limited by dose-related toxicities 2. Large-scale loss of function genetic in vitro screens identified ASNSas a cancer dependency in several solid malignancies 3,4. Here we evaluate the therapeutic potential of targeting ASNS in melanoma cells. While we confirm in-vitro dependency on ASNS silencing, this is largely dispensable for in vivo tumor growth, even in face of asparagine deprivation, suggesting the need to further characterize such pathway to devise novel therapeutic strategies. Using ex vivo quantitative proteome and transcriptome profiling, we characterize the compensatory mechanism elicited by ASNS knockout melanoma cells allowing their survival. Additionally, genome-wide CRISPR screens upon manipulation of aminoacids levels identify BCLXL, MAPK and GCN2 as critical nodes mediating the observed resistance mechanism. Importantly, pharmacological inhibition of such hits synergizes with L-Asparaginase-mediated Asparagine deprivation in ASNS deficient cells suggesting novel potential therapeutic combinations in melanoma
An Improved Method for the Simultaneous Determination of Water Uptake and Swelling of Tablets
No full textWater uptake and swelling are processes occurring during active pharmaceutical ingredient (API) dissolution from tablets. Thereby, disintegration is promoted and the enhanced exposure of API surface area to the dissolution medium facilitates API dissolution. A method for the simultaneous determination of time-resolved water uptake and swelling of round flat-faced tablets was introduced, where the water penetrated the tablets through their front face. The water uptake was measured gravimetrically and swelling was detected by a digital camera. An algorithm for the symmetry-based 3D volume reconstruction (SVR) was applied to obtain volumes of the tablets from 2D images. Inert porous ceramic test specimens were employed to repeatedly perform real-time water uptake measurements for the validation of the gravimetrical analysis. Using the inert test specimens a precision within 6 % towards the end of the analysis and an accuracy within 6.3 % deviation were identified. X-ray micro-CT technique (µ-CT) was used to validate the accuracy of the SVR, where the determined volumes were in good accordance within 6.1 % deviation. A case study with binary formulations of microcrystalline cellulose (MCC, filler), and croscarmellose sodium (CCS, disintegrant) or sodium starch glycolate (SSG, disintegrant), proved reproducibility, as well as the ability to discriminate formulation characteristics, such as disintegrant type, composition and porosity for water uptake and swelling with the necessary time resolution
Mini Review: The Last Mile—Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology
No full textThe 2019 manuscript by the Special Interest Group on Digital Pathology and Image Analysis of the Society of Toxicologic pathology suggested that a synergism between artificial intelligence (AI) and machine learning (ML) technologies and digital toxicologic pathology would improve the daily workflow and future impact of toxicologic pathologists globally. Now 2 years later, the authors of this review consider whether, in their opinion, there is any evidence that supports that thesis. Specifically, we consider the opportunities and challenges for applying ML (the study of computer algorithms that are able to learn from example data and extrapolate the learned information to unseen data) algorithms in toxicologic pathology and how regulatory bodies are navigating this rapidly evolving field. Although we see similarities with the “Last Mile” metaphor, the weight of evidence suggests that toxicologic pathologists should approach ML with an equal dose of skepticism and enthusiasm. There are increasing opportunities for impact in our field that leave the authors cautiously excited and optimistic. Toxicologic pathologists have the opportunity to critically evaluate ML applications with a “call-to-arms” mentality. Why should we be late adopters? There is ample evidence to encourage engagement, growth, and leadership in this field