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Improved iGAL 2.0 Metric Empowers Pharmaceutical Scientists to Make Meaningful Contributions to U.N. Sustainable Development Goal 12
No full textThe large and steadily growing demand for medicines combined with their inherent resource-intense manufacturing necessitates their sustainable production. However, the absence of clearly defined standards impedes pharmaceutical companies from performing reliable Life Cycle Assessments of their medicines and assessing the true value of their sustainable development achievements. Guided by the unambivalent purpose of the UN Sustainable Development Goal 12, which aims at substantially reducing production waste by 2030, and driven by a vision to catalyze greener API manufacturing around the globe, we herein introduce iGAL 2.0 alongside the new Key Sustainability Indicators Convergence, including a new formula for Convergence and its potential utility in CASP algorithms. We demonstrate that iGAL 2.0 is based upon an improved statistical model that enables determination of meaningful API waste figures and early identification of potentially underperforming and environmentally more concerning processes, thereby delivering both environmental and economic value. We further illustrate how the metric is complemented by its scorecard companion to highlight the value-added of process scientists to their peers, managers and the general public with respect to API waste reduction. Taking everything into account, we believe that iGAL 2.0 can readily be adopted by pharmaceutical firms around the globe and thereby empower and inspires their scientists to make meaningful and significant contributions to UN SDG 12
Phase II, randomized study of spartalizumab (PDR001), an anti-PD-1 antibody, vs chemotherapy in patients with recurrent/metastatic nasopharyngeal cancer
No full textBackground: No standard treatment exists for platinum refractory, recurrent/metastatic nasopharyngeal cancer (NPC). This phase II study (NCT02605967) evaluated progression-free survival (PFS) of spartalizumab, an anti-programmed cell death protein-1 (PD-1) monoclonal antibody, versus chemotherapy, in NPC.
Patients and Methods: Patients with non-keratinizing recurrent/metastatic NPC who progressed on/after platinum-based chemotherapy were enrolled. Spartalizumab was dosed 400 mg once every 4 weeks and chemotherapy was received per investigator’s choice.
Results: Patients were randomized to receive either spartalizumab (82 patients) or chemotherapy (40 patients). The most common spartalizumab treatment-related adverse events were fatigue (10.3%) and pruritus (9.3%). Median PFS in the spartalizumab arm was 1.9 months versus 6.6 months in the chemotherapy arm (P = 0.915). The overall response rate in the spartalizumab arm was 17.1% versus 35.0% in the chemotherapy arm. Median duration of response (DOR) was 10.2 versus 5.7 months in spartalizumab versus chemotherapy arms, respectively. Median overall survival (OS) was 25.2 and 15.5 months in spartalizumab and chemotherapy arms, respectively. Tumor RNA sequencing showed a correlation between response to spartalizumab and IFN-γ, LAG-3, and TIM-3 gene expression. Conclusion: Spartalizumab demonstrated a safety profile consistent with other anti-PD-1 antibodies. The primary endpoint of median PFS was not met, however , median OS and median DOR were longer with spartalizumab compared with chemotherapy
GAS MEASUREMENTS AT LEA Performance and analysis of labelled biomedical compounds
No full textFew data regarding performance and analysis of labelled biomedical compounds for the internal journal of the ETH Zurich university capturing the collaboration between ETH and Novartis
Acute Cell Stress Screen with Supervised Machine Learning in Association with in vitro Pharmacological Profiling Predicts Cytotoxicity of Excipients
No full textExcipients serve as vehicles, preservatives, solubilizers, and colorants for drugs, food, and cosmetics. They are considered to be inert at biological targets; however, several reports suggest that some could interact with human targets and cause unwanted effects. We investigated 40 commonly used drug excipients for cellular stress in the AsedaSciences SYSTEMETRIC Cell Health Screen®, which was developed to estimate toxicity risk of small molecular entities (SMEs). The screen uses supervised machine learning (ML) to classify test compound cell stress phenotypes against a training set of on-market and withdrawn drugs. While 80% (n=32) of the excipients did not show elevated risk in a broad, but pharmacologically relevant, concentration range (5nM to 100µM), we identified 20% (n=8) with elevated risk. This group included two mercury containing preservatives, propyl gallate, methylene blue, benzethonium chloride, and cetylpyridinium chloride, all known for previously reported safety issues. All compounds were tested in parallel in an in vitro assay panel regularly used to investigate off-target effects of drug candidates. Target engagement in this assay panel confirmed risk-indicative biological activity for the same excipients, except propyl gallate, which may have a separate mechanism. In conclusion, the SYSTEMETRIC Cell Health Screen® in conjunction with in vitro pharmacological profiling can provide a fast and cost effective methodology for first line testing of small molecular entities, including excipients to avoid cellular damage, particularly in the GI, where they are represented in high concentrations
A Strategy to Assess the Cellular Activity of E3 Ligase Components against Neo-Substrates using Electrophilic Probes
No full textTargeted protein degradation is a rapidly developing therapeutic modality that promises lower dosing and enhanced selectivity as compared to traditional occupancy-driven inhibitors, and the potential to modulate historically intractable targets. While well-characterized E3 ligases such as CRBN and VHL have been successfully redirected to degrade numerous proteins, there are approximately 600 predicted additional E3 family members that may offer improved activity, substrate selectivity, and/or tissue distribution. Characterizing the potential applications of these many ligases for targeted protein degradation has proven challenging. Here, we report the development of an approach to evaluate the ability of recombinant E3 ligase components to support neo-substrate degradation. Bypassing the need for hit finding to identify specific E3 ligase binders, this approach makes use of simple maleimide-thiol chemistry for Covalent Functionalization Followed by E3 Electroporation into live cells (COFFEE). We demonstrate this method by electroporating recombinant VHL, covalently functionalized with JQ1 or dasatinib, to induce degradation of BRD4 or tyrosine kinase targets, respectively. Furthermore, by applying COFFEE to SPSB2, a Cullin-RING ligase 5 receptor, as well as to SKP1, the adaptor protein for Cullin-RING ligase 1 F-box (SCF) complexes, we validate this method as a powerful approach to define the activity of previously uncharacterized ubiquitin ligase components, and provide further evidence that not only ligase receptors but also adaptors can be directly hi-jacked for neo-substrate degradation
Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics
No full textMachine learning is widely applied in drug discovery research to predict molecular properties and aid in the identification of new active compounds. Herein, we introduce a new approach that uses model-internal information from compound activity predictions to uncover relationships between target proteins. On the basis of a large-scale analysis comparing machine learning models for more than 200 proteins, feature importance correlation analysis is shown to detect similar compound binding characteristics. Furthermore, rather unexpectedly, the analysis also reveals functional relationships between proteins that are independent of binding characteristics. The underlying concept does not depend on specific representations, algorithms, or metrics and is generally applicable as long as predictive models can be derived. On the basis of our findings, feature importance correlation represents a new facet of machine learning in drug discovery with potential for practical applications
Nanomicelle-enhanced, asymmetric ERED-catalyzed reductions of activated olefins. Applications to 1-pot chemo- and bio-catalysis sequences in water
No full textBio-catalytic reactions involving ene-reductases (EREDs) can be aided by the presence of the surfactant TPGS-750-M, which form nanomicelles in water and act as a “reservoir” for both starting materials and importantly, resulting products, thereby minimizing enzymatic inhibition. These nanomicelles also function as nanoreactors in which chemo-catalysis occurs, thus enabling both reaction types to carried out in various combinations, and in tandem within the same reaction vessel. Transformations that illustrate the variety of sequences that are now possible, including use of EREDs, and that feature the
interchange of reagent- and enzyme-based processes in a 1-pot operation, are highlighted as representative examples of this
environmentally attractive approach to organic synthesis in water
A roadmap towards sustainable chemical products and processes for Switzerland
No full textPollution of the environment with man-made chemicals is an issue of increasing concern with scientists, society and legislators. Environmental chemistry and (eco-)toxicology research provide evidence for the disruptive effects that many chemicals have on ecosystem and human health. Involved scientists also point out that problems recognized some 30 years ago (e.g., with persistent organic pollutants) continue, while the market is flooded with new chemicals that exhibit similar properties or even raise new concerns [1-3]. In light of this, one may question the impact the “12 principles of green chemistry” have had, which John Warner and Paul Anastas had formulated in 1998 to guide chemical research and industry towards greener chemical processes and products [4]
De novo Design of SARS-CoV-2 Main Protease Inhibitors
No full textThe COVID-19 pandemic prompted many scientists to investigate remedies against SARS-CoV-2 and related viruses that are likely to appear in the future. As the main protease of the virus, MPro, is highly conserved among coronaviruses, it has emerged as a prime target for developing inhibitors. Using a combination of virtual screening and molecular modeling, we identified small molecules that were easily accessible and could be quickly diversified. Biochemical assays confirmed a class of pyridones as low micromolar non-covalent inhibitors of the viral main proteas
BATF and IRF4 cooperate to counter exhaustion in tumor-infiltrating CAR T cells.
No full textThe transcription factors nuclear factor of activated T cells (NFAT) and activator protein 1 (AP-1; Fos-Jun) cooperate to promote the effector functions of T cells, but NFAT in the absence of AP-1 imposes a negative feedback program of T cell hyporesponsiveness (exhaustion). Here, we show that basic leucine zipper ATF-like transcription factor (BATF) and interferon regulatory factor 4 (IRF4) cooperate to counter T cell exhaustion in mouse tumor models. Overexpression of BATF in CD8 T cells expressing a chimeric antigen receptor (CAR) promoted the survival and expansion of tumor-infiltrating CAR T cells, increased the production of effector cytokines, decreased the expression of inhibitory receptors and the exhaustion-associated transcription factor TOX and supported the generation of long-lived memory T cells that controlled tumor recurrence. These responses were dependent on BATF-IRF interaction, since cells expressing a BATF variant unable to interact with IRF4 did not survive in tumors and did not effectively delay tumor growth. BATF may improve the antitumor responses of CAR T cells by skewing their phenotypes and transcriptional profiles away from exhaustion and towards increased effector function